United States 

Department of 

Agriculture 

Forest Service 

lndermountain 

en, UT 84401 

General Technical 

Report INT-184 

May 1985 
FOREWORD 

Gaining a better understanding of how recreationists make decisions about the activities in which 

they engage and the places they go could greatly 

aid recreation managers. It would improve the 

effectiveness of communications with recreationists by better focusing on the type of information 

provided as well as the level of detail needed at 

the time it is provided. It would provide managers with a clearer understanding of how their 

actions might affect recreationists, changing the 

activities in which they engage, the places they 

go, or the experiences they enjoy. And it would 

improve the effectiveness with which management 

can deliver recreation services. 

But our understanding of the choice process is 

poor. Looking at what people do now, or what they 

did in the past, sheds only partial light on the 

complex issue. The choices made by recreationists 

are subject to many influences--knowledge, experience, social pressures, personal preferences, and 

others. Also, the images of the world held by 

recreationists likely are different from those 

held by managers; consequently, managers' efforts 

to facilitate the choice process by the kind of 

information they provide as well as the kinds of 

settings they offer might be hindered. 

The papers in this proceedings examine the issue 

of recreation choice behavior. Six of them were 

presented at a technical session of the Northwest 

Science Association meeting held at the University 

of Montana in March 1984. The remainder were presented at a meeting of the Western Association of 

Recreation Professionals, held at the same time. 

The papers examine the choice behavior process 

from both a theoretical and empirical perspective. 

Methodological concerns are also examined. 
Proceedings-Symposium on 

Recreation Choice Behavior 

Missoula, Montana, March 22-23, 1984 

Compilers: 

GEORGE H. STANKEY, Research Social Scientist, lntermountain 

Research Station, Missoula, Montana 

STEPHEN F. McCOOL, Professor, School of Forestry, University of 

Montana, Missoula, Montana 

Proceedings of a symposium sponsored by: 

Northwest Science Association 

lntermountain Research Station, Forest Service, U.S. Department of 

Agriculture 
CONTENTS 

Page 

Choosing Recreation Settings: Processes, 

Findings, and Research Directions 

Stephen F. McCool, George H. Stankey, 

and Roger N. Clark ........... 1 

Reconceptualizing the Motive/Environment 

Link in Recreation Choice Behavior 

Richard Schreyer, Richard C. Knopf, 

and Daniel R. Williams ......... 9 

Substitution in Recreation Choice Behavior 

George L. Peterson, Daniel J. Stynes, 

Donald H. Rosenthal, and John F. Dwyer . 19 

A Developmental Model of Recreation Choice 

Behavior 

Daniel R. Williams ........... 31 

Choices of Wilderness Environments-- 

Differences Between Real and Hypothetical 

Choice Situations 

Jean T. Beaulieu and Richard Schreyer . 38 

Do Choices of Sport Fisheries Reflect 

Angler Preferences for Site Attributes? 

Charles C. Harris, B. L. Driver, 

and E. P. Bergersen .......... 46 

Approximating Kecreation Site Choice: The 

Predictive Capability of a Lexicographic 

Semi-order Model 

Alan E. Watson and Joseph W. 

Roggenbuck .............. 55 

Why Here and Not There: The Conditional 

Nature of Recreation Choice 

Roger N. Clark and Kent B. Downing ... 61 

Evaluating User Impacts and Management 

Controls: Implications for Recreation 

Choice Behavior 

Harriet H. Christensen and 

Nanette J. Davis ............ 71 

Resource and Activity Substitutes for 

Recreational Salmon Fishing in New Zealand 

Bo Shelby ............... 79 

Predicting the Impacts of a High-Voltage 

Transmission Line on Big Game Hunting 

Opportunities in Western Montana 

Stewart Allen ............. 86 

Methodology for Studying Recreation Choice 

Behavior with Emphasis on Grounded Inquiry 

Kent B. Downing and Roger N. Clark . . 101 
CHOOSING RECREATION SETTINGS: PROCESSES, FINDINGS, AND RESEARCH DIRECTIONS 

Stephen F. McCool, George H. Stankey, and Roger N. Clark 

ABSTRACT: This paper reviews the discussion contained in the 11 presentations given at a symposium on recreation choice behavior. It addresses 

major points of commonality in the papers, as well 

as areas where differences exist. It also suggests a number of areas in which additional 

research on recreation choice behavior is needed. 

INTRODUCTION 

Many issues confront us as we begin to try to 

understand recreation choice behavior. Choice is 

obviously not a random process, nor is it a process that is unique to each individual. Some areas 

and recreation activities are enormously popular, 

others are not. However, is the popularity an 

area or activity enjoys (or lacks) a function of 

the characteristics it possesses (or lacks), or is 

it a reflection of other factors such as relative 

availability, intervening opportunities, or the 

knowledge or skill held by the participant? Do 

people choose activities or places because of what 

they are familiar with, or because they want to 

"go along with the crowd"? 

RECREATION CHOICE BEHAVIOR--SO WHAT? 

How do recreationists choose the settings in which 

they participate? What are the factors that 

facilitate and constrain the choice process? Are 

some factors essential to choice, with others 

playing only a supplementary role, or do recreationists compensate for the unavailability of one 

factor by substituting another? Answering the 

questions is an important step in gaining a better 

understanding of what people seek from recreation 

and in determining how management can better provide desired recreation services. 

One might ask, "So what? So we have a better 

understanding of choice and the role of attributes; in the final analysis people will use what 

is available, regardless of what a manager does." 

However, if we assume that the goal of recreation 

management is to provide benefits to the public, 

it becomes important that we understand how best 

to do this. Because people use what is currently 

provided does little other than tell us that they 

use what is there; it in no way ensures that an 

optimal level of benefits is being provided, nor 

does it ensure that the best use of land is occurring. Recreation managers need to address such 

Stephen F. McCool is professor, School of 

Forestry, University of Montana, Missoula, MT; 

George H. Stankey is research social scientist, 

USDA Forest Service, Intermountain Research 

Station, Missoula, MT; Roger N. Clark is project 

leader, USDA Forest Service, Pacific Northwest 

Forest and Range Experiment Station, Seattle, WA. 

concerns as choice behavior and site attributes, 

just as timber managers identify appropriate silvicultural systems for each timber type. Further, 

identifying desired attributes might reduce conflict, particularly between renewable resource 

management activities and recreational values. 

A recreation setting may be defined as a place 

where the combination of physical-biological, 

social, and managerial characteristics or attributes gives that place value as a locale for 

leisure behavior (Clark and Stankey 1979). Physical-biological attributes, such as topography, 

vegetation, or water, provide the basic raw material for many recreational activities. They are 

also facets of the setting that management can 

influence, either directly or indirectly, through 

actions taken to achieve other management objectives. The importance associated with the physical-biological attributes of a setting varies 

among recreationists, between recreationists and 

managers, and, likely, with the timing and extensiveness of manipulations of such attributes. 

This latter characteristic makes it difficult to 

inventory physical-biological attributes in a 

fashion that can be commonly interpreted by all 

users or for all uses. 

The following 11 papers in this proceedings 

explore the issue of recreation choice behavior at 

both theoretical and empirical levels. They were 

presented at the 1984 annual meeting of the Northwest Science Association held in Missoula, MT. 

The theme of choice behavior was selected as a 

result of the realization by many investigators 

that existing models of recreation behavior did 

not adequately explain how recreationists came to 

use particular settings in the first place, nor 

did they adequately cope with the linkage between 

the factors or attributes that comprise recreation 

settings (and which management is able to influence) and the kinds of experiences recreationists 

derive from participating in these settings. 

AM ATTRIBUTE APPROACH 

The setting--the place where recreation occurs--is 

clearly a major component of the process within 

which recreation services are demanded (Driver and 

Brown 1978; Clark and Stankey 1979). The activities in which individuals and groups participate 

and the experiences or social-psychological outcomes they derive from such participation are also 

important. Yet, the setting plays an especially 

important role in this scheme. It is where activity participation occurs and its attributes and 

condition can facilitate (or hinder) not only the 

activity engagement but the satisfactions obtained 

as well. Ultimately, it is the setting and the 

specific attributes that comprise it that is the 

focus of planning and management. Having a better 
understanding of how recreationists choose settings and how they evaluate them can give managers 

a better grasp of how their decisions affect user 

judgments of a site's ability to accommodate particular activities as well as provide different 

experiences. 

Social attributes are those elements of the setting that are a function of the recreation use it 

receives and would include the amount, timing, and 

type of use. Social attributes are very important 

to many users and, as with the physical-biological 

attributes, the importance attached to them will 

vary among users, between managers and users, and 

with time, group affiliation, and other variables. 

Variable interpretations of social attributes can 

lead to vastly different conditions (for example, 

the number of other persons in sight or sound) 

being defined commonly (for example, solitude), or 

vice-versa. 

The type, amount, and obtrusiveness of managerial 

activity represents the third class of attribute 

that shapes the nature of recreational settings. 

This attribute is a subtle influence because many 

actions are undertaken for the explicit purpose of 

protecting a particular setting, yet the very 

presence of the management activity can lead to a 

change in the kind of place it is. Also, actions 

instituted by managers to protect resource quality 

can thwart inadvertently the objectives of the 

recreationist, leading to sharp conflicts between 

the two groups. As Christensen and Davis (this 

proceedings) note, there are often sharp differences not only between the perception of impacts 

by managers and users, but also between their 

views as to the most effective methods of contending with such problems. 

The relative importance of each type of attribute 

will vary among recreationists depending upon a 

variety of factors, including activity interests, 

experience, and expectations. Regardless of the 

specific significance associated with an attribute, however, an understanding of the role attributes play in the choice process employed by 

recreationists is critical to management. It is 

the setting (described by its attributes) that 

recreationists seek, use, and impact; it is the 

setting that managers manipulate, modify, or 

influence; it is the setting that is allocated to 

one dominant use or another. In this paper, we 

briefly review the themes underlying the papers in 

this volume that deal with how recreationists 

select settings and activities, and suggest 

research directions that hold promise for addressing unresolved questions. Understanding choice 

behavior is also fundamental to management because 

recreationists will use information about setting 

attributes in making choices; thus, managers play 

an important and active role in choice processes 

through provision of information about attributes. 

A BROADENED RESEARCH FOCUS 

Over the past decade, our understanding of what 

people seek from recreational engagements--indeed, 

our understanding of the nature of the recreation 

process--has increased greatly. Much of this 

increased understanding can be attributed to the 

work of Brown, Driver, and their associates. 

Their work, built upon the expectancy-valence 

theories of Ajzen and Fishbein (1980), conceives 

of recreational engagements as being a behaviorally founded production process. In this conception, visitors are seen as coming to sites with 

expectations and desires for specific types of 

satisfaction. They engage in activities at sites 

where the combination of physical, social, and 

managerial conditions helps them achieve their 

desired satisfactions. And, upon leaving the 

site, the achievement of desired satisfactions is 

seen as leading to subsequent personal, and perhaps societal, benefits (Brown 1979). 

Expectancy-Valence Model Shortcomings 

This conception of the recreation production process rests upon a model of human behavior in which 

behavior is rational. It also suggests a reductionist approach to the study of behavior. Nevertheless, the model has served well in identifying 

systematically critical elements of recreation 

behavior. It has, perhaps most importantly, 

highlighted the human experiences obtained from 

recreational engagements as the key product of 

recreation management efforts rather than the traditional measures of acres designated, facilities 

built, or participation recorded. 

This focus on the experiences or outcomes obtained 

by recreationists led to necessary concerns with 

the recreational setting. It is the setting to 

which recreationists come; where they carry out 

activities, which they impact; and from where, in 

association with the activity, they derive their 

experience. The setting is also the element of 

the recreation production model that managers can 

influence, either directly or indirectly, through 

their actions. Finally, it logically follows that 

the characteristics or attributes of the setting 

should at least influence, if not control, the 

kinds of experiences that recreationists are able 

to obtain. For example, if solitude is the 

desired outcome, then a setting with few others 

would seem necessary. Conversely, if challenge 

and risk are desired, a setting laden with signs, 

barriers, and rangers hardly would be conducive to 

achieving those outcomes. 

The linkage between setting and outcome, however, 

has proven to be a complex one, lacking any direct 

or easily predictable relationship. No deterministic tie between setting and outcome exists (nor 

has such a relationship ever been implied). At 

best, the link between a particular combination of 

attributes within a setting and the realization of 

some outcomes can only be expressed in probabilistic terms, or even more realistically, in broad, 

ordinally measured statements of likelihood. Some 

experiences appear to be essentially independent 

of the setting; for example, physical exercise or 

family kinship can be achieved in many settings 

and are not distinguished by varying setting characteristics (Driver and others, in press). 
If we look at the setting-outcome linkage from the 

other direction (Can we identify desired settings 

if we know the desired outcomes?), there is little 

change in the situation. For example, Harris and 

others (this proceedings) report "the few efforts 

made to predict from perceived recreation outcomes 

or experience to use of different settings have 

had mixed results . . . . The more complex and 

mentally taxing compensatory processes like those 

operationalized with attitude and motivation 

theories do not seem to provide adequate representations of recreationists' decisionmaking processes." Schreyer and others (this proceedings) 

reinforce this position when they state that "the 

capacity to predict either behavioral or environmental choice through knowledge of motive scores 

has yet to be demonstrated." However, such 

expressed dissatisfaction does not constitute so 

much a rejection of the expectancy-valence model 

of recreation behavior as it reflects a recognition that this model does not adequately cope with 

the complex process underlying how recreationists 

choose settings. This conclusion is a recurrent 

theme throughout the papers in this proceedings. 

Cognitive Development Models 

Following along these lines, several of the papers 

give particular attention to the role of cognitive 

development in the recreation choice process. 

Williams notes that the cognitive development 

level of an individual refers to the amount and 

type of information a person has and that this is 

thought to influence the frame of reference within 

which that individual makes decisions about recreation choices. Harris and others attribute the 

growth of interest in the cognitive development 

model to a number of factors, including the availability of increasingly sophisticated statistical 

techniques, the need for better definitions of the 

returns on recreation investments, and, as noted 

earlier, the general dissatisfaction with the 

performance of the expectancy-valence model in 

explaining recreationists' choice behavior. 

Because the cognitive development model focuses 

attention on the information an individual possesses and how that information is used, a number 

of important questions must be addressed: What 

types of information has the individual acquired? 

Through what sources? Through what experiences is 

this information base updated? How does the 

acquisition of new information lead to changes in 

choice behavior? Williams (this proceedings) 

notes that to distinguish cognitive development 

from mere change, there must be some underlying 

progress, order, and direction. Thus, with 

expanded cognitive development comes increasing 

differentiation (an increasing recognition of 

conceptual complexity), as well as increasing 

specialization (Bryan 1979) or an increasingly 

refined set of preferences and behaviors. With 

both comes a more complex manner in which the 

individual processes information regarding recreation choice. 

Compensatory Versus Noncompensatory Models 

A major issue discussed in several papers is the 

concept of compensatory versus noncompensatory 

choice models. Briefly, the compensatory approach 

suggests that recreationists evaluate setting 

attributes using a subjectively determined weighting system. A cumulative potential site utility 

score is derived for each alternative site based 

on the weight assigned each attribute. The site 

with the highest potential utility is chosen by 

the recreationist. The noncompensatory approach 

views recreationists as ranking the importance of 

attributes, then evaluating alternative sites on 

an attribute-by-attribute basis. For each attribute, the least satisfactory site(s) is eliminated, until only one alternative remains. 

The compensatory choice model has its origins in 

the expectancy-valency framework described earlier. By coupling information on the value of specific outcomes with the perceived probability of 

achieving that outcome in particular settings, 

compensatory theorists argue that behavioral 

intentions can be predicted. But the compensatory 

model is complex and might represent an overrationalized view of recreationist decision behavior. The noncompensatory model is often supported 

as a simpler and more realistic approximation of 

how people make decisions, processing information 

in a sequential fashion from most important to 

least important. 

Harris and others (this proceedings) suggest that 

respondents use noncompensatory choice processes 

rather than subjective utility approaches. Watson 

and Roggenbuck (this proceedings) expand on this 

assertion by suggesting that a lexicographic 

decision model is employed by recreationists in 

site selection. The lexicographic semi-order 

choice model used in their study assumes that recreationists order the relevant attributes and compare alternatives on the basis of acceptability; 

alternatives are systematically eliminated until 

there is only one satisfactory alternative left. 

Such a model predicted over one-half of the first 

choices of settings by the subjects in their 

study, and in nearly three-fourths of the cases, 

eliminated 80 percent of the alternatives. 

Peterson and others (this proceedings) also discuss the importance of examining alternative, 

process-oriented choice models. In their discussion, they hypothesize that the relationship among 

some attributes might be lexicographic, while for 

others it is compensatory. Their research using a 

nested choice model was only partially supported, 

and as they conclude, "a good start but far from 

complete." 

What is refreshing in these papers is the willingness of researchers to develop, test, and apply 

other approaches to choice behavior. These 

approaches seem to have an implicit recognition of 

the dynamic nature of choice behavior, and, to 

some extent, are more comprehensive than earlier 

attempts. 
Psychological Versus Sociological Perspectives 

Nearly all the papers in this volume focus on 

psychological or individual characteristics of the 

person in the choice situation. Only a few 

explicitly address the importance of group processes, normative standards, or other social influences in choice behavior. Although most researchers refer to choices satisfying the needs of 

individuals, Clark and Downing continually refer 

to the importance of "group needs," resolving 

intragroup conflict about alternatives, and the 

desires of the group in choice behavior situations. Schreyer and others also recognize the 

importance of accounting for this dimension when 

they state that "we cannot understand the link 

between motive and choice unless we know the 

social context. I' 

One suspects that the social context is a much 

more significant factor in the choice process than 

currently recognized and understood. Recreation 

is almost exclusively a social behavior; decisions 

about what to do and where to go likely are made 

within the context of a group rather than by a 

single individual. Little is known about how 

groups obtain and interpret information or how 

conflicting ideas, motives, and desires are 

resolved. The dominance of psychological models 

of behavior probably accounts for the lack of 

recognition of the potentially significant role of 

group influences on choice behavior. It is important to build this element into models of choice 

behavior now, rather than "retrofitting" them 

later when the full importance is identified. 

Choice Behavior and Substitutability 

If we assume that recreationists follow some sort 

of systematic process in their evaluation of setting attributes, how do they accommodate, and 

respond to, situations where their choice is constrained? What happens, for example, if a key 

attribute is not satisfactory or missing altogether, but the setting is the only feasible 

alternative? Is it still an acceptable alternative, and, if so, is it capable of providing the 

same satisfaction to the recreationist? 

Such questions raise the critical issue of substitutability. In the most general terms, substitutability refers to the extent to which one setting 

could effectively replace another in terms of its 

ability to accommodate particular activities and 

to produce particular outcomes. Substitutability 

is a concept with important implications to managers and planners who are faced with identifying 

and interpreting the consequences of actions that 

eliminate or alter recreation settings. Will providing a new setting to replace one eliminated by 

a timber xarvest constitute an adequate and 

acceptable replacement for recreationists? Are 

settings just the sum of a set of attributes, or 

are there antecedent or situational factors critical to the area's definition as a substitute? 

The question of what constitutes a substitute goes 

beyond the onsite characteristics. For an area to 

serve as a substitute it must constitute a real 

choice for the recreationist. The ideally endowed 

site cannot be considered a substitute if it is 

inaccessible, or if recreationists are unaware of 

it. 

One can raise the issue of substitutability in 

terms of activities or the social context within 

which participation occurs. For example, an individual faced with the loss of certain setting 

attributes might substitute one activity for 

another or might substitute a particular activity 

style for another style in an effort to maintain 

participation at a favorite recreation site. In 

this sense, changes in the setting have induced 

shifts in demand for particular activities. Clark 

and Downing (this proceedings) also argue that the 

concept of substitutability can encompass substitutions in social group context. 

The extent of the linkage between the concept of 

substitutability and choice behavior is reflected 

in its occurrence in the papers in this proceedings. In particular, the paper by Peterson and 

others (this proceedings) highlights the various 

disciplinary perspectives and models of substitutability, suggesting that the general behavior 

involved in the substitution process occurs in a 

wide variety of life settings. It is therefore 

likely that in investigating its nature and role 

in recreation settings we will discover important 

insights by carefully examining how substitution 

works elsewhere. 

Shelby (this proceedings) details an interesting 

investigation of the perceived substitutability 

between two salmon fishing streams in New Zealand. 

His study provides clear evidence that recreationists perceive important differences between apparently similar recreation resources and illustrates 

how the foreclosure of one opportunity cannot be 

presumed to be offset by the physical presence of 

another in some simple one-for-one fashion. 

Shelby's results provide support for noncompensatory choice processes. 

Allen (this proceedings) specifically addresses 

the problem of changes in site attributes because 

of the construction of a major powerline and its 

influence on big game hunters. His study found 

that new roads constructed for access to the powerline right-of-way and towers would negatively 

impact the experience of those hunters seeking 

remote-type opportunities. For this group of recreationists, a legitimate question to ask concerns 

the availability of substitute settings, their 

relative locations, and accessibility--What alternative real choices do they have? 

Williams (this proceedings) identifies the difficulties that will arise as increasingly specialized recreationists, with highly specific demands 

for settings, find an increasingly narrower range 

of choice in terms of settings that meet their 

requirements. In the most extreme case, highly 

specialized recreationists (for example, advanced 

white water kayakers) might have only one area 

that offers the combination of attributes they 

desire. Because of the scarcity of such settings, 
their vulnerability to change and the likelihood 

that users will be displaced are high, a situation 

not unlike that described for many threatened and 

endangered species. 

FUTURE RESEARCH DIRECTIONS 

The study of recreation choice behavior has only 

recently started. However, the selection of 

papers included in this volume suggests the range 

of perspectives and research methodologies 

employed in pursuit of greater understanding of 

this topic. Much research remains to be conducted. Rased upon the discussions in the symposium papers and among participants, the following 

issues appear to warrant further research attention if we are to achieve a better understanding 

of recreation choice behavior. 

Compensatory and Noncompensatory Models 

The papers in this volume focus on examining the 

differences in compensatory versus noncompensatory 

choice models and thereby represent the beginning 

of new research models. It might very well be 

that for some attributes decision processes are 

compensatory, while for other attributes such 

processes are noncompensatory. Watson and 

Roggenbuck argue that researchers must consider 

the latter model for it holds great promise for 

manager-developed information systems, and, consequently, visitors. Clarification of the choice 

process and its variability across the various 

contexts of decisionmaking is critical. For 

example, does the process vary for different setting opportunities, at different levels of resolution in the choice process (for example, macroversus microscale decisions), or for different 

levels of specialization or differentiation? 

Alternative Models 

Attribute Roles and Measurement 

Here we suggest development of alternative choice 

models, such as those in the disciplines indicated 

by Peterson and others (this proceedings), and 

testing those models in a wide variety of decision 

situations. Such models need evaluation in terms 

of their ability to guide future research, and 

also in terms of their usefulness to management. 

We are encouraged by researchers1 willingness to 

develop alternative models, particularly in the 

noncompensatory domain. We suggest continued 

examination of models developed in other disciplines for their adaptability to recreational 

choice. 

One area of needed investigation only briefly 

discussed in the papers is that of routinized or 

habitual choice situations. Given the role of 

such choices in other spheres of everyday life, it 

is likely that much recreation choice behavior 

might also be in this category. We need to know 

not only how this behavior develops, but with what 

frequency, and how to model it. 

The development of alternative choice behavior 

models implies a need to examine ways in which 

these alternatives might be integrated. Some 

approaches to choice might be founded solely in 

psychology, others in sociology, still others in 

economics and management science. Our perspective 

is that choice behavior is actually a set of 

behaviors and processes, some of which are more 

effectively modeled by one discipline, others by 

other fields of endeavor. It is important that 

researchers begin to think of linking their 

efforts with the aim of developing more holistic 

models of choice behavior. 

Holistic model building will require collaborative 

efforts, leading to stronger models that more completely describe and explain the processes involved. The linkage from one discipline to the 

next will be difficult to establish but should be 

attempted nevertheless. We feel that such integrative model construction will lead to far greater understanding than continuing to pursue models 

within one's own discipline. 

What role do attributes play in the definition of 

recreation opportunities? Peterson and others 

(this proceedings) cite consumer theory that 

posits that the object of choice is not the good 

itself but rather the attributes possessed by that 

good. Thus, from both a theoretical and management perspective, it is critical that we be able 

to identify the composition of recreation opportunities in terms of their attributes. 

Within this broad research realm, a variety of 

specific endeavors are needed. Initially, it 

seems important to develop a framework within 

which attributes can be defined and related to one 

another as well as to other decision factors. 

Conceptualizations developed in the various disciplines that concern themselves with recreation 

behavior need to be carefully reviewed for their 

potential applicability or adaptation. Following 

this, there is a need to identify the attribute 

composition of different recreation opportunities. 

What patterns of variability or stability can be 

discerned across settings? Are some attributes 

deemed essential to the production of certain outcomes, or can different attribute combinations 

produce those same outcomes (this bears on the 

basic issue of compensatory versus noncompensatory 

decision processes)? What variability in attribute condition can occur before recreationists 

define it as unacceptable? 

A better understanding of the relevant attributes 

that define recreation opportunities has many 

implications. For example, Beaulieu and Schreyer 

(this proceedings) express concern regarding the 

current USDA Forest Service ROS Users Guide (n.d.) 

because of its heavy emphasis on physical-biological attributes in defining recreation opportunities and the relatively minor role accorded social 

and managerial attributes. They argue that the 

focus on physical-biological attributes does 

little to provide managers with information about 

how other attributes contribute to opportunities 

or experiences. Additionally, we must acknowledge 

that if the ROS (or any other recreation planning 
system) is founded upon a conception of recreation 

opportunities comprised of attributes not relevant 

to users1 decisions, its utility is jeopardized. 

Christensen and Davis (this proceedings) provide 

clear evidence that user perspectives as to what 

constitutes the relevant aspects of desirable 

recreation opportunities are not shared with 

managers, leading to the imposition of controls 

that not only fail to address user concerns but 

that can actually increase problems of deviance 

and depreciative behavior. 

Finally, identification of attributes must be 

coupled with a knowledge of the relevant units of 

analysis. For example, we might find that the 

level of use at sites is deemed an important setting attribute; however, how sh,ould that attribute 

be best expressed? Is it total number of individuals, number of groups, or is it some behavioral 

measure of who they are, what they are doing and 

where, and so forth? Recreationist advice on this 

matter is essential, as it is likely there are 

differences between managers and users as to how 

attributes should be measured. 

Attribute Resolution Levels 

A closely related issue is the level of resolution 

of attributes perceived by recreationists and managers. Schreyer and others (this proceedings) 

recognize the importance of this direction when 

they comment that "it is possible to construe the 

character of an environment as ranging along a 

continuum from macroscopic to microscopic." Clark 

and Downing (this proceedings) reinforce this 

position by stating that understanding the appropriate sequence of choices "may determine which 

macro and micro site ROS factors are likely to be 

salient in final place selection." Allen (this 

proceedings) recognizes the significance of 

detailed perception of attributes in his study of 

how big game hunters are affected by a powerline 

right-of-way and associated roads. 

How do recreationists perceive settings? Are the 

general outlines of settings perceived, or are the 

details included in the perception? Here again, 

cognitive processes might be important. As 

Williams (this proceedings) suggests, increased 

differentiation and specialization can lead to 

increasingly specific settings; one would suspect 

that experienced recreationists perceive attributes at a different level than inexperienced 

ones. More cognitively developed recreationists 

probably also rely on different attributes than 

those less developed, even though the same activity is involved. 

The Role of Experience 

Recognition of the role of cognitive development 

suggests the importance of examining the influence 

of prior experience in an activity on one's perception of attributes. Outside of a few papers 

(for example, Schreyer 1982), researchers have all 

but ignored how experience influences recreation 

behavior, including choice behavior. Bryan's 

(1979) monograph concerning specialization certainly points to evolutionary processes in development of experience, changes in activity styles, 

and shifts in setting dependency. Whether one is 

speaking of images or cognitive sets, establishing 

linkages between experience and attribute perception and preference is an important task in a 

society that is becoming increasingly attached in 

an emotional way to resources. 

Understanding how such images are formed is also 

important in interpreting the dynamics of recreation demand. For example, LaPage and Ragain 

(1974) note that declining per capita participation in camping might be related to the incongruency between the images held by individuals and 

created either by previous experience or through 

various marketing strategies and the conditions 

encountered onsite. 

What is Being Chosen? 

A fundamental research question involves resolution of this issue: Are recreationists choosing 

locales for their ability to produce certain 

social-psychological outcomes? Are they selecting 

a locale because of its attributes? Do they view 

recreation sites as places for activities? Or is 

the site incidental to group processes? 

Clark and Downing (this proceedings) argue for the 

need to develop a "framework for relating people's 

decisions to choose specific places for particular 

activities . . . .I' However, there exists a substantial literature based on what Schreyer and 

others (this proceedings) term the "belief . . . that particular patterns of behavior in given 

environmental settings would yield specific, identifiable kinds of products." Although this seems 

reductionistic or even deterministic, there is a 

considerable literature based on this perspective. 

What is needed is a constructive debate on the 

quest ion. 

The Role of Substitution 

When decisions regarding recreation settings and 

activities are constrained, recreationists must 

consider alternative uses of their time and 

resources. The issue of what constitutes substitutability in recreation behavior and how recreationists process information to make decisions as 

to whether a setting constitutes an acceptable 

substitute for another is one of the most perplexing questions before us. Yet it is one with substantial implications for planners and managers 

and deserves expanded investigation. 

The issue of substitution relates closely to many 

of the other research topics we have discussed. 

For example, knowing more about what it is that 

people choose when they make decisions is critical 

in the evaluation as to whether one setting is an 

acceptable substitute for another. Similarly, an 

improved understanding of the decision itself in 

terms of the compensatory versus noncompensatory 

nature of the process needs to be obtained so that 
the extent of substitutability between settings 

can be understood. 

Psychological and Sociological Linkages 

We discussed earlier the emphasis the papers in 

this volume have on psychological factors over 

sociological ones in conceptualization about 

choice processes. Perhaps this reflects the dominant background of researchers today. Perhaps it 

is suggestive of the ease of measurement. And 

perhaps there is a presumption that group decisions are simply the sum of individual decisions. 

Whatever the reason, we feel that sociological 

factors (group processes, development of internalized norms, and so forth) are critically important 

in choice behavior. Most recreation behavior in 

wildland settings occurs in group situations. The 

dynamics of group interaction, the effects of differing leadership styles, the balancing of conflicting interests among others, must all play a 

significant, if not dominant, role in choice 

behavior. It would be a mistake to develop models 

of choice behavior that somehow did not account 

for or include these factors. 

Choice Behavior Disruption 

We have recognized the implication that a shift in 

research focus from outcomes to process results in 

a much more dynamic view of the world. But even 

with the focus on a dynamic model of choice behavior there is a temptation to overlook disrupting 

factors such as changes in life cycles or site 

management. Somehow, research should also account 

for these disrupting situations: What are they? 

Under what conditions do they occur? How frequently do they happen? What impacts on the 

choice process do they produce? It could be that 

a model of choice behavior as eventually settled 

upon might be a model of behavior that is, statistically speaking, "average," but that actually 

occurs only infrequently. Given the natural variation in human behavior, disruptions likely are 

the rule rather than the exception. 

We noted earlier that a great deal of choice 

behavior might be fairly well routinized. There 

is great security in maintaining routine behavior 

and it is likely this behavioral mode carries over 

into recreation behavior. However, what happens 

when this routinized behavior is disrupted? What 

coping strategies do recreationists employ to deal 

with the disruption? Is the disruption followed 

by a resumption of the original behavior or does 

it lead to altered behaviors? If the latter is 

the case, does the alteration collectively involve 

activities, settings, and outcomes, or are one or 

more of these elements of behavior maintained? 

Experimental Design Needs 

We are not the first to note the near total 

dependency on the ex post facto design in recreation research. Clark (1977), among others, has 

commented on the intrinsic limitations and utility 

of this design in answering questions of interest 

to researchers. In the field of choice behavior, 

we suggest more creativity in designing experiments, with alternative treatments, control 

groups, and so on to test hypotheses. In this 

respect the Williams paper here represents a 

desirable research direction. 

Progress in understanding recreation choice behavior and the associated issues we have discussed 

will necessitate innovative methodological approaches and, likely, a reduced dependence upon 

cross-sectional surveys as a primary data source. 

In a paper not presented at the symposium, but 

included here because of its significance to the 

study of choice process, Clark and Downing develop 

a strong rationale for a qualitative, grounded 

approach to the investigation of choice behavior. 

CONCLUSIONS 

Intrinsic in the notion of recreation choice 

behavior is the recognition of the dynamic nature 

of human activity. Choice behavior, what it is, 

and how it occurs, is inextricably related to setting attributes. We suggest that a greater understanding of how people choose among alternatives 

will lead to a greater awareness of what settings 

are, and how managerial activity influences those 

settings. Greater understanding of the choice 

process will also allow managers to develop more 

sophisticated techniques to softly, lighthandedly, 

manage both sites and visitors. 

Although the choice process is complex, it seems 

imperative that we gain a better understanding of 

it. For example, if we better understood the 

kinds of information recreationists consider 

essential to make decisions, we could better 

tailor information programs for people to use. 

Understanding more about the decision process 

could influence how information is made available 

as well as its timing and location. Knowing the 

importance of decision factors could also aid prediction of the kinds of impacts stemming from 

alterations in settings and the implications and 

consequences of such changes. And an understanding of the choice process is necessary to many of 

the education-oriented management programs in 

which there is increasing interest. 

REFERENCES 

Ajzen, Icek; Fishbein, Martin. Understanding attitudes and predicting behavior. Englewood Cliffs, 

NJ: Prentice-Hall, Inc.; 1980. 278 p. 

Brown, Perry J. The opportunity spectrum: techniques and implications for resource planning 

and coordination. In: Shaw, Joan, ed. Dispersed 

recreation and natural resource management: a 

focus on issues, opportunities, and priorities. 

Logan, UT: Utah State University; 1979: 82-87. 

Bryan, Hobson. Conflict in the great outdoors. 

University, AL: University of Alabama; 1979. 

98 p. 
Clark, Roger N. Alternative strategies for studying river recreationists. In: Proceedings, river 

recreation management and research. General 

Technical Report NC-28. St. Paul, MN: U.S. 

Department of Agriculture, Forest Service, North 

Central Forest Experiment Station; 1977: 91-100. 

Clark, Roger N.; Stankey, George H. The recreation 

opportunity spectrum: a framework for planning, 

management, and research. General Technical 

Report PNW-98. Portland, OK: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest 

Forest and Range Experiment Station; 1979. 32 p. 

Driver, B. L.; Brown, Perry J. The opportunity 

spectrum concept and behavioral information in 

outdoor recreation resource supply inventories: 

a rationale. In: Lund, Gyde H.; LaBau, 

Vernon J.; Ffolliott, Peter F.; Robinson, 

David W., tech. coords. Integrated inventories 

of renewable natural resources: proceedings of 

the workshop. General Technical Report RM-55. 

Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and 

Range Experiment Station; 1978: 24-31. 

Driver, B. L.; Brown, Perry J.; Stankey, George 

H.; Gregoire, Timothy G. The ROS planning 

system: evolution, basic concepts, and research 

needed. Leisure Sciences. (in press). 

LaPage, W. F.; Ragain, D. P. Family camping trends 

--an eight-year pane2 study. Journal of Leisure 

Research. 6: 101-112; 1974. 

Schreyer, Richard. Experience level affects expectations for recreation participation. In: Lime, 

David W., tech. coord. Forest and river recreation: research update. Miscellaneous Publication 

18. St. Paul, MN: University of Minnesota, Agricultural Experiment Station; 1982: 154-159. 

U.S. Department of Agriculture, Forest Service. 

ROS users guide. Washington, DC: U.S. Department 

of Agriculture, Forest Service; n.d. 38 p. 
RECONCEPTUALIZING THE MOTZVE/ENVIRONMENT LTNK 

IN RECREATION CHOICE BEHAVIOR 

Richard Schreyer, Richard C. Knopf and Daniel R. Williams 

ABSTRACT: Effective inventory, management, and 

planning for recreation resources depend upon knowing why persons choose certain environments to 

attain specific goals, and what elements in those 

environments facilitate goal-attainment. Research 

focused on relating motives for participation to 

specific components in the environment has had only 

limited success. This paper proposes an alternative 

organization of the motive-behavior/environment 

relationship intended to improve the predictive 

power of this line of research. The model is 

founded on an attempt to separate the process of 

recreation behavior from the content (i.e., the 

specific overt manifestations of behavior). The 

process of recreation behavior is seen as the 

attempt to attain a desirable state of consciousness which is experienced as satisfaction in 

recreation. 

INTRODUCTION 

Perhaps the most significant contribution of 

social scientists conducting recreation research 

during the last decade has been to focus attention on the goal-directed nature of recreation 

participation. We have come to dismiss recreation as being random or spontaneous behavior, 

conceiving it as directed, purposeful behavior 

intended to realize specific outcomes for the 

individual. Further, research has supported the 

notion that these outcomes, rather than the 

activity pursued per se, motivate participation. 

These new perspectives appeared to have provided 

a model of the recreation resource management 

process that allowed direct comparison to other 

resource production efforts. These psychological "outcomes" became const rued as products 

delivered by the resource management process. 

Ostensibly, they could be measured and 

evaluated, allowing them to be compared to 

other products, both recreational and nonrecreational, which might be produced by the 

Paper presented at the Recreation Choice Behavior 

Symposium, Missoula, MT, March 22-23, 1984. 

Richard Schreyer is Associate Professor of Recreation Resource Behavior, Utah State University, 

Logan, UT; Richard C. Knopf is Principal Research 

Forester, North Central Forest Experiment Station, 

USDA Forest Service, St. Paul, MN; Daniel R. 

Williams is Assistant Professor of Recreation and 

Leisure, University of Utah, Salt Lake City, UT. 

resource. Lack of such comparability has 

troubled recreation resource specialists for 

years. 

Further, there was the possibility of defining 

direct links between given products and 

environmental conditions leading to their 

production. Particular patterns of behavior 

(e.g., different recreational activities) in 

given environmental settings would yield 

specific, identifiable kinds of products. If 

these could be cataloged, then planners could 

develop a means for inventorying outdoor 

resources according to the products they yield, 

and specify the conditions necessary in those 

settings for the production process to occur. 

This would be analogous to inventorying and 

managing a forest environment in order to 

maximize the production of certain classes of 

timber products. In fact, a major resource 

management planning system, the Recreation 

Opportunity Spectrum (ROS), is being implemented 

based on these assumptions. 

While insights from social science have resulted 

in a marked sophistication in understanding the 

nature of the recreation resource in general, 

the dreams of defining the production process 

in operational terms have been slow in coming to 

fruition. Studies have reported differences on 

scales developed to measure motivation for 

participation across groups engaged in different 

outdoor activities (Knopf 1972), among different 

persons engaged in the same activity in the same 

setting (Schreyer and others 1976; Schreyer and 

Nielson 1978) and in different outdoor settings 

(Knopf and others 1983). But while studies 

yield differences on motive scales attaining 

statistical significance, the studies generally 

fail to register substantial differences in 

absolute scale values. In other words, these 

studies have not provided compelling evidence 

for variations in motivation which can be 

attributed to either activity or setting. 

After more than a decade of intensive research 

on the dynamics of recreation choice, capacity 

to predict either behavioral or environmental 

choice through knowledge of motive scores has 

yet to be demonstrated. It is possible to argue 

that such scales are not intended to predict at 

high levels of specificity. This leads to 

broader questions, however, concerning how the 

recreation product output model is in fact 

expected to work, and which variables are 

necessary to provide what degree of predi~tive 
power. Such questions have theoretical as well as pragmatic implications. 

Our purpose in this paper is to take a closer 

look at the conceptual bases for understanding 

the link between motivation and the choice of 

recreation behaviors and environmental settings. 

We will begin by providing a brief statement of 

the model of choice behavior as it currently 

exists. We will then examine a number of issues 

concerning application of the model. Finally, 

we will show how a different organization of the 

same variables might increase the strength of 

the link between psychological variables and 

environmental behavior. It is not our purpose 

to provide a new model of human behavior. 

Rather, we wish to provide an alternative 

perspective as to how the model might be 

elaborated through a more useful specification 

of the various concepts and their 

interrelationships. 

MOTIVATION FOR PARTICIPATION AND ENVIRONMENTAL 

CHOICE 

The prevailing view of recreation participation 

suggests that a person selects a given behavior 

in a specific environment to attain desired 

psychological outcomes. In essence, the person 

is motivated to pursue certain behaviors to gain 

psychological rewards. Thus, outcomes are 

operationalized in terms of identified 

psychological rewards. 

The individual is assumed to be "rational ," 

selecting from a known repertoire of responses 

those behaviors which, based on past learning 

and reinforcement, are judged most effective in 

obtaining the targeted rewards. By definition 

certain behaviors are consequences of 

motivations to obtain specific desired 

psychological outcomes. Figure 1 illustrates 

the components of this model of recreation behavior, which is in reality a specific 

application of a general model of human 

behavior. 

The environment plays a major role in this 

model. Different environments provide varying 

opportunities to engage in certain behaviors. 

This affects the repertoire of responses 

available to the individual. Conversely, 

certain environments may be sought precisely 

because they allow behaviors which will yield 

the desired outcomes. Thus, the link between 

motivation and behavior cannot be understood in 

itself, but must be seen as motivation X leading 

to behavior A in environment B. It is the 

combination of behavior and environment that 

defines the "experience opportunity setting'' 

(Driver and Brown 1978), the fundamental unit 

for identifying the products which may be 

produced from a recreation resource. 

If these assumptions are valid, then it is 

necessary to be able to predict what outcomes 

are produced by different combinations of 

behaviors and environments. If this is not the 

ANTECEDENT 

CONDITIONS 

case, then there is no basis for 

operationalizing the concept of desired outcome 

as a planninglmanagement tool, except as an 

anecdotal heuristic that helps managers better 

appreciate that "people are different." The 

following section will describe problems which 

have been encountered in attempting to establish 

these relationships. 

DIFFERENTIAL 

MOTIVE 

INTENSITY 

CHOICE 

PROCESS 

Issues in Understanding Motivation-- 

Behavior/Environment Linkages 

- 

While the model shown in figure 1 suffices as a 

general explanation of human behavior, we must 

be careful about applying it to any specific 

circumstance. It is one thing to assert that a 

given motive will result in a person acting on 

it, and quite another to say that an observed 

behavior explained ex post facto was the direct 

inevitable consequence of a certain motivation. 

While it is possible to argue that the effort to 

satisfy certain needs involves specific 

behaviors in a given environment, that does not 

satisfy the conditions for determining which 

aspects of either the behavior or the 

environment will allow for goal attainment. 

PARTICIPATION PSYCHOLOGICAL 

Figure 1.--Basic model of recreation behavior. 
The Directness of the Link 

A key consideration in the attempt to classify 

recreation behavior is the extent of the 

directness of the link between behavior and 

motivation. Keep in mind that there are many 

ways in which behavior may be initiated. Models 

presented in recreation motive research imply a 

more direct correspondence than is likely 

present. Three aspects of this problem will be 

discussed in this section. The first has to do 

with the level of generality at which the 

various concepts are organized. The second 

involves the problem of motive intensity. The 

last concerns definitions of the concepts being 

used. 

Levels of organization.--We assume that there 

are elements of an environment which facilitate 

goal attaiment. If that were not true, it would 

make no difference where a person was at any 

particular point in time. We are perplexed, 

however, by the seemingly insurmountable 

challenge of not being able to identify 

deterministically the elements of an environment 

which actually serve as facilitators. 

The major issue we are dealing with here is how 

to organize the concepts of motive, behavior, and 

environment in such a way that we know what is 

to be predicted. For instance, the term 

"behavior" can mean "to recreate," or it can 

mean "to go for a two-day backpacking trip with 

a close friend." It can also mean spending the 

greater part of the day while on that trip 

sitting in a comfortable place reading and 

engaging in casual fishing. Environment can 

mean "the outdoors" or "a wilderness" or "the 

Bridger Wilderness" or "the southern tip of 

Green River Lake at the north end of the Bridger 

Wilderness." At what level is motive to predict 

either behavior or environment? 

While a set of scales can be devised to measure 

"motives," there is no guarantee that these do 

not relate to behaviors or environments at 

differing levels of organization. One motive 

may relate to the broadest of behavioral 

categories, while another may be able to predict 

a much narrower set of behaviors. This can also 

be applicable for the range of environmental 

conditions in which such behaviors may be 

pursued. Part of this problem may be due to the 

fact that we may not be controlling for the 

level of organization of motives as well. We 

could start at the most general level of 

"approach positive outcomes," move down to the 

desire for "affiliation," then to the need for 

"doing things with the family" and finally to 

"kinship bonding." Each will be more specific 

in its implications for behavior. Unless the 

level of focus is made a direct element of 

research, these relationships will continue to 

be masked by spurious influences. 

Motive intensity.--Motive scales request a 

rating by the individual of the relative 

influence on the specific behavior of the 

reasons listed. The assumption is, the more 

important the rating, the more direct causal 

link between that motive and the behavior being 

studied. This is not necessarily the case. 

General motives scales may not be able to 

capture the overall importance of behavior to 

need-fulfillment. 

For instance, two persons receive a questionnaire upon completion of a river trip, and 

are asked, among other motive questions, how 

important was the opportunity for escape. Both 

mark "of utmost importance," and both are 

correct. One was there, however, without strong 

allegiance to the particular activity. He had a 

strong desire to get out of town for the 

weekend, and river-running seemed like a good 

thing to do at the time. Going for a ride in 

the hills would have done just as well, as would 

going to the movies. The other was a person who 

carefully evaluated every plausible alternative 

for meeting her needs at the time, and concluded 

that only a float trip on that particular river 

in that particular setting would yield the exact 

requirements she needed to gain a sensation of 

escape. Both persons have very different 

resource requirements, exhibit very different 

behaviors on the river, demand different support 

facilities, have differing opinions about 

management and conflict when they interact. 

Yet both elicited identical responses for the 

motive scale. Thus, the score of intensity for 

a motive scale may not be an accurate predictor 

of behavior, because relative intensity is not 

accounted for. 

Conceptual semantics--A major part of this 

problem may result from problems with the 

semantics of recreation. Few concepts have been 

defined with precision, resulting in unclear 

conceptions of how people are likely to behave, 

or to choose recreation environments. For 

instance, the Recreation Opportunity Spectrum is 

founded upon a continuum of primitive to urban 

experience (Buist and Hoots 1982). Is it really 

the "degree of primitiveness" that constitutes 

the specific aspect of outdoor environments 

which dictate the character of outcomes 

generated? We agree that primitive environments 

provide opportunties for self-reliance and 

solitude, for spiritual experience and challenge 

(Brown and others 1978). But can we conclude 

that urban environments do not? Ask any street 

gang member about self-reliance and challenge. 

Ask devout believers of any faith where they get 

their spiritual experience. As for solitude, 

why do we have locks on bathroom doors? 

This underscores the problem of a possibly 

misplaced focus on what aspects of the 

environment are important to people in the 

outdoors. Even if the "primitive-urban" 

continuum as posed by ROS is proved to be 

statistically associated with variation in the 

delivery of outcomes, we cannot necessarily 

conclude that we have construed the correct 

aspect of environmental variation. How much of 

what we describe as "primitive" really implies 

other environmental attributes that covary with 
primitiveness, but are not really the same? For 

instance, research in environmental psychology 

has generated findings that one of the most 

fundamental dimensions along which people 

differentiate environments is natural/manmade 

(Ward and Russell 1981). This may not be 

equivalent, however, to the primitivelurban 

dimension. The former may have to do with the 

presence or absence of certain patterns of 

stimulation which may be more or less preferred 

in processing information about the environment 

(Kaplan and Kaplan 1978). The latter may havee 

more to do with the presence or absence of 

facilities which may be more or less preferred 

in accommodating levels of physical comfort in 

the environment (Schreyer and Roggenbuck 1978). 

The Nature of Decisions 

The third box in figure 1 shows that the link 

between motivation and environment/behavior is 

through some sort of choice process. Decisionmaking is extremely complex; recreational 

choices are influenced by certain aspects of 

decision-making which tend to confuse our 

analysis of the motive-behavior/environment 

link. Two of these will be described below. 

One has to do with the way motives are involved 

in decisions; the second deals with the 

regularity of decision-making. 

Layers of decision-making.--When we ask a person 

what reasons were important for engaging in a 

particular activity at a specific point in time, 

we assume the answers they give are directly 

relevant to that participation. There are 

several layers, however, of decision-making 

underlying the choice to engage, Consider the 

case of a person choosing to go canoeing on 

Jackson Lake next Saturday. At one level, there 

are a set of reasons why the person elected to 

engage in recreation as opposed to nonrecreation alternatives, such as doing chores or 

fulfilling social obligations. At a second 

level, there are a set of reasons why canoeing 

was chosen for recreation over many other 

possible alternatives. Why does the person want 

to canoe rather than hike, assuming he/she does 

both? At the third level, there are a set of 

reasons why Saturday at a certain time was 

appropriate for this behavior, as opposed to 

other possible times. At the fourth level, 

there are reasons why Jackson Lake was chosen 

instead of the Snake River. 

All of the reasons involved in all of the levels 

of decision-making will be included in the 

person's self-report of reasons for engaging in 

that specific behavior. From one perspective, 

they are indeed all relevant to the situation, 

and it is appropriate to consider them to gain 

an &era11 profile of the activity. Motives, 

however, precipitating the general decision to 

recreate will not discriminate among specific 

recreation activities. Motives precipitating 

the decision to engage in canoeing as opposed to 

hiking will not be able to distinguish between 

different types of canoeing opportunities for 

planning/inventory purposes. In other words, 

the reported motivations for making a choice to 

participate may not be sufficiently detailed to 

allow for the definition of an experience 

opportunity setting. 

Routinized decision-making.--Rational models of 

decision-making imply a direct correspondence 

between the need state at a given point in time 

and choice of behavior. Humans, however, have 

the capacity neither to exhaustively analyze the 

requirements for an optimal existence nor to 

analyze the consequences of every behavioral 

alternative. Indeed, humans might be more 

characterized by the regularity of behavior 

patterns. 

The patterns of behavior one adopts may most 

likely be continuations of appropriate forms of 

expression one has learned over time rather than 

specific and rational attempts to fulfill 

existing needs of the present. One's desire to 

go canoeing, for example, may really reflect the 

fact that canoeing is something that person does 

every August. Over time the behavior has 

satisfied needs which have surfaced in the past. 

There may be a package of outcomes that a person 

will gain through such participation. The 

specific behavior, however, may have little to 

do with the need state of the individual at the 

time of decision. 

We must understand that the more routinized a 

behavior, the more likely that motives will be 

general and not specific to the behavior itself. 

The study of motives, however, usually ties them 

to the specific behavior and specific 

environmental features. An alternative behavior 

and/or environment may have been more 

functional, given one's motives at the point of 

decision. But the individual continues engaging 

in what has historically been beneficial. As 

long as some motives are serviced, the cost of 

searching for new behavioral options is avoided, 

The behaviors and the need state are only 

incompletely linked. 

Antecedent Conditions 

It is also .possible to assert that the link of 

motive to behavior and environment cannot 

explain the situation unless more information is 

available. Such information may have to do with 

what are often referred to as "antecedent 

conditions" (see figure 1). These may have to 

do with the nature of individual differences 

that will determine which motives are likely to 

initiate behavior, and what behaviors and 

environments are seen as being best suited to 

attain the desired outcomes. Such forces may 

also influence the nature of the choice itself. 

Beyond mentioning that they exist, there has 

been little in the way of systematic research 

inquiry into what facets of individual diversity 

may be most important to targeting motives to 

specific behaviors and environments. 
One of the most important of these factors is 

the social milieu. It has often been suggested 

that social motives themselves may be among the 

most important reasons for recreation participation. Social dynamics may do much to mask the 

link between motive and environment/behavior. 

Given that many environments and behaviors may 

be selected to attain various outcomes, the 

specific behaviors and environments observed may 

be more indicative of socio-cultural values 

about participation than of the inherent 

characteristics of either. 

We often recognize the importance of obligate 

functions, or the tendency to go along with 

certain types of behavior because friends or 

family are so engaged. Such behavior removes 

direct need fulfillment from the choice process, 

except for the social needs. As such, a 

person's answers on a motive scale may reflect 

nothing more than a social mythology, a shared 

belief about what one is supposed to be getting 

from the participation. People tend to play out 

social scripts, which makes the process of the 

behavior more significant than the outcomes. 

Thus, we cannot understand the link between 

motive and choice unless we know the social 

context. 

The amount of information available to the 

individual may also affect the link between 

motive and choice of a specific recreation 

setting. Persons may select generic types of 

environments in which to recreate based on the 

assumption that the attributes desired will be 

present. There may be little correspondence, 

however, between what is encountered and what 

one desires. Decisions can often be made based 

on images which are not entirely correct. For 

instance, a person may choose to visit a 

specific national park assuming it will have 

visitor services generally associated with such 

environments, though this may not actually be 

the case. 

Thus, if research measures motives which 

resulted in the choice of a particular 

environment, but the environment does not 

actually conform to the image that resulted in 

the choice, then the link between the two will 

be assumed to be stronger than it actually is. 

The likelihood of this occurring increases to 

the extent that conditions in environments are 

changing. Rapid shifts in the nature of social 

interaction, managerial regimentation, and 

facility development can increase the likelihood 

that reported motives represent conditions which 

may not actually be in the environment under 

analysis. 

RECREATION STATES AND GOAL-ATTAINMENT 

These issues, whi-le troubling, should not be 

insurmountable. Rather, we believe there is a 

need for us to examine the motive link to 

environment and behavior in light of these 

concerns, and to try to begin identifying 

approaches to the problems that will move in the 

direction of greater predictive power. 

We wish to propose an elaboration of the 

motivation-based recreation behavior model. It 

is founded not only on the addition of key 

concepts required to predict specific behaviors 

and environmental characteristics sought, but 

also on a conceptual clarification of the 

meaning of motivation. The approach is founded 

on a systematic separation of elements of the 

model which describe the process of recreation 

behavior and those which characterize the 

content or specific manifestation of that 

behavior. It also seeks to describe the 

increased significance of antecedent conditions 

influencing the working of the model. 

Figure 2 presents the components of this model. 

In the following sections we will explain the 

nature and significance of the 

EXPERIENCE 

SATISFACTION 

Figure 2.--Elaborated model of recreation behavior. 

r 

LIFESTYLE DOMAIN OF ACTIVITIES/ GROUP - BEHAVIORS USED TO 

FULFILL RECREATIOW RELATED MOTIVES 

i 

STANDING 

PATTERN OF RECREATION 

BEHAVIOR 

----- 

I CONTENT: I 17 Learned Modes I 

, of Expression I 

L---- I 

' 

RECREATION 

ENVIRONMENT 

RECREATION . - -Ib PARTICIPATION 

L 

EXPERIENCE LEVEL OF 

DEVELOPMENT + 

i I Attention I 

I ik A I-- - -- 

MOTIVATION 

t "'- 
interrelationships among these components. 

First, we will examine the process of recreation 

behavior from a cognitive perspective. Next we 

will show why it is useful to consider 

"recreation experience preferences" (or what we 

measure to represent motivation) as the content 

of recreation participation. We will then show 

how the recreation environment fits within this 

model. Finally, we will discuss some issues 

related to the measurement of these concepts. 

Recreation as On-going Process 

State vs. outcome.--In resource management-based 

research, we have tended to emphasize 

identifying and inventorying the outcomes of 

various choice processes (which are measured as 

"recreation experience preferences"). 

Motivation is assumed to be part of the process 

by which outcomes are attained. We believe it 

is more useful to consider the various motives 

as the content of recreation behavior, and that 

the process is more correctly expressed through 

the dynamics of the states of attention applied 

to such behavior. 

An increasing amount of research is being 

pointed toward understanding the subjective 

state of the experience of leisure (Mannell 

1982; Dirkin 1983). The focus is less on 

documenting the "products" of leisure and more 

on understanding the processes or psychological 

states experienced during leisure involvement. 

This research has developed along two fronts. 

One seeks to define what are the essential 

components of what people describe as leisure 

(Unger and Kernan 1983). That is, what is 

leisure itself; how do people know leisure when 

they experience it? The second is more 

cognitive in nature, concentrating on the more 

extreme states of consciousness afforded by 

human behavior, commonly described as flow or 

peak experiences (Csikszentmihalyi 1975; Murphy 

1977). This research explores the requisites 

for bringing these states into existence. 

Process-oriented research reports what the 

person experiences while recreating. This may 

be what we describe as "satisfaction" in 

progress. There is the tendency for people to 

describe the subjective feeling as "fun." The 

rationale for engaging in recreation behavior is 

often described as the desire to "have fun." 

This is usually dismissed by motive theorists as 

an inaccurate expression of the "real" reasons 

why one recreates, which are to attain rather 

specific products that are delivered by 

conditions present in environments. 

We believe however that there is more than meets 

the eye to the concept of "fun." Fun--the 

enjoyment of a process--may in itself be a 

motivator for engagement in outdoor recreation, 

a notion which may have little to do with 

meeting needs created in non-recreation 

environments. To the extent that recreationists 

make decisions based on "having fun," the predictive utility of traditional motivational 

models may break down. Our desire now is to 

describe the concept of "state" as a human 

motivator, and to suggest how that concept can 

help us better organize our conceptions of the 

relationships between recreational motives and 

behavior/environment choices. 

The meaning of "fun1'.--The dynamics of leisure 

involvement are in fact more complex than 

previously described, as there are many possible 

states which are subsumed under the notion of 

"fun." The varied state could hypothetically be 

arrayed along a developmental continuum from 

least to most complex in terms of the degree of 

involvement of the cognitive arousal process. 

At the most fundamental level are states 

involving involuntary attention (James 1892), 

where consciousness is free to attend to 

whatever is inherently gripping. By definition, 

there are no distractions, as there is no need 

to suppress unwanted stimuli (Kaplan 1978). 

Such processes may be most frequently associated 

with casual removal from one's more mundane surroundings, perhaps described best as "letting 

the mind wander." The mind is free to follow 

its will; there is little in the way of 

concentrated involvement. 

As we move up the continuum, we find states that 

could be described as sensory arousal. These 

states would involve momentary and pleasurable 

stimulation of sensory equipment beyond its 

rested homeostatic level. At the higher end of 

the continuum are states involving 

flow experiences (Csikszentmihalyi 1975), 

evoking sensations of self-environment fusion 

and loss of time-consciousness. States at the 

top of the continuum might best be epitomized by 

what Maslow (1971) described as 

peak experiences, perhaps the ultimate display 

of human cognitive capacity. 

Why are these states relevant to recreation? 

From a cognitive perspective, there is value in 

being able to pay attention only to those 

stimuli which one desires to attend. Taken as a 

process, we can say that satisfaction represents 

the ability of the person to engage in desired 

behaviors, paying attention to whatever will 

attain a desired state. When constraints 

offered by the environment are not sufficiently 

critical to cause the person's attention to 

deviate from the tasks at hand, that person can 

be described as satisfied. The feeling the 

person has is one of having fun. 

The capacity to engage in a desired degree of 

attention is experienced as positive by the 

individual. We believe the primary value of 

recreation lies in its ability to deliver these 

kinds of states. We posit that people are 

oriented to, and make choices based upon, .the 

prospect of attaining the state itself, rather 

than the various externally defined products 

that may be manifested through those states. 

Since the subjective feeling associated with any 

of these states, regardless of location on the 

continuum, can be experienced as "fun," 
recreationists are being accurate in their 

stated intent "to have fun" as a major 

motivation for recreation participation. 

If there is a desire to be able to control the 

circumstances of one's attention, and if the 

opportunities to do so are more strongly limited 

in day to day situations, then more emphasis may 

be placed upon leisure situations as vehicles to 

attain such states. The process of the state is 

what people are attracted to. 

In figure 2 we represent the process of 

attaining a certain state as being part of the 

standing pattern of behavior, the routine 

pattern of participation which characterizes the 

way a person goes about engaging in recreation. 

The cognitive process, however, is only one 

component of that standing pattern. In order to 

understand the behavior, we must also know what 

characterizes the content of that behavior. 

This is where motivation comes in. 

Linking Motives to States 

Where do our traditional conceptions of 

recreation motives in general and motive scales 

in particular fit withn the context of this 

state orientation? People do identify 

motivations which they say precipitate leisure 

behavior beyond mere fun. Our model still deals 

with goal-directed behavior, but it 

simultaneously calls for a much more integrated 

or global concept of goals. We believe that 

motives should be construed in terms of a 

relatively limited set of cognitive states that 

people pursue during leisure. 

We recognize, of course, that it has been 

possible to develop a number of complex motive 

scales which people, through their responses, 

seem to affirm. What is being responded to 

however? Are these scales a measure of what 

people are searching for, or of learned modes 

of expression which yield access to the desired 

cognitive states? We believe that our current 

use of motive scales tends to intermingle goaldirected behaviors with modes of expression. 

The concept "experience nature" could be a means 

by which people engage in behaviors designed to 

get into cognitive states at all points along 

the continuum. As such, we would not expect it 

to be very predictive of behavior. Similarly, 

persons may have learned to attain a level of 

sensory arousal through different paths, one 

doing it by way of achievement, another through 

leadership/autonomy. 

If the states people pursue are few and the 

routes to them are many, this implies a great 

deal of potential functional equivalency across 

a wide range of activities and settings. We 

believe that people tend to choose particular 

activities in specific environments for such 

innocuous reasons as convenience, access, time 

available, money, and familiarity. The specific 

domain of activities people select from is 

circumscribed by their socio-cultural value 

system, which in turn is strongly influenced by 

the opportunities that are immediately 

available. 

While most discussions of recreation behavior 

acknowledge these facts, they tend not to 

recognize that these very variables limit the 

capacity to predict preferred behaviors and 

environmental arrays. From the perspective of 

ultimate function, these behaviors are highly 

interchangeable. Directedness toward particular 

activities and settings thus becomes a matter of 

opportunistic convenience rather than a matter 

of rational examination of the differential 

rewards which might accrue from a host of 

alternatives. 

The focus for research should not be on motives 

as predictors of behaviors and environments. 

Rather, it should be on those elements in the 

individual's environment that determine which 

modes of expression are linked to which 

cognitive states. These variables will allow 

for a more useful prediction of behavior and 

environmental choice. We are thus proposing 

that the notion of motive must be considered as 

part of three-part construct including 

antecedent conditions, motive (learned pattern 

of behavior) and cognitive state. 

How can the indicators of cognitive state best 

be represented? This remains to be determined. 

In figure 2 are shown two factors we feel, 

however, are of major potential importance: 

experience use history and lifestyle. 

Experience use history relates to the nature and 

extent of information available to persons in 

making recreational choices. It is also likely 

related to the extent of progression along the 

developmental continuum of cognitive states 

applied to recreation. 

The social milieu, the values expressed by the 

lifestyle with which the individual identifies, 

will provide directed information concerning 

both behaviors and environments seen as 

desirable to attain various cognitive states. 

Cultures will likely also provide input into the 

extent to which certain states have status value 

or are seen as being "unhealthy." Thus they 

will also influence the level of development 

sought through recreation. 

To the extent that these lifestyles are shared 

across wide segments of a culture, there will be 

a consistency in patterns of recreation 

participation. Within this model, recreation 

planning involves not so much providing the 

opportunity to engage in behaviors facilitating 

certain outcomes as it involves facilitating 

certain generally accepted modes of expression 

which allow persons to attain cognitive states. 

The Role of the Environment 

We have already stated that environment features 

do make a difference to persons in terms of 

their desired experience. There continues to be 
the question of how the environment should best 

be represented in light of this model. A basic 

concern is how to define the appropriate level 

of specificity at which attributes should be 

represented in predictive models. How stringent 

are people's requirements for the attainment of 

desired states? 

It is possible to construe the character of an 

environment as ranging along a continuum from 

macroscopic to microscopic. At the macroscopic 

level, we see environments basically as 

settings, as holistic environments which affect 

behaviors by very broad and generalized features 

perceived as a whole by the individual. At the 

microscopic level of analysis, we see 

environments as an array of attributes, as 

collections of individual entities, each of 

which may have the power to impact certain 

aspects of human behavior. 

We believe that the most useful organization of 

environment for the study of behavioral choice 

is at the holistic level rather than at the 

attribute level. This presumption seems more in 

line with emerging conceptions of the 

organization and operation of human cognitive 

processes, and the attendant limitations in our 

capacities to evaluate a broad range of features 

as we make discriminations among environments. 

Rather, persons tend to match environments (as 

well as other objects or aspects of life) to 

broadly defined representatives of categories of 

similar environments, or "prototypes" (Rosch 

1978; Mervis and Rosch 1981). 

We recognize, of course, that perceptions will 

vary from most specific to most general. We 

feel, however, that the major dimensions of 

recreation behavior are aligned with more 

general organizations of environment than 

traditional paradigms in outdoor recreation 

research acknowledge. We believe people do not 

search for specific elements of the environment 

as much as they search for settings which will 

allow them to behave in the ways they desire-- 

for settings which will give them sufficient 

leeway to attend to that which will allow for 

the attainment of the desired cognitive state. 

This line of thought may initially sound 

consistent with the notion of the "experience 

opportunity setting'' as described in the 

literature (Brown and others 1978). But here we 

are implying much more looseness of structure on 

what needs to be present in the environment. As 

remarkably resourceful and adaptive organisms, 

humans have considerable capacity to engage in 

desired behaviors under a broad range of 

environmental situations (Stokols 1978). Much 

of this position is founded in our belief that 

outdoor -recreation behavior is primarily used by 

people to help them maintain certain states of 

consciousness. We feel that people can be 

extremely adaptive in attaining these states, 

and should not be construed as being passive 

victims of whatever external forces may be 

present (Knopf 1983). As long as outdoor 

settings meet minimum criteria for behavioral 

flexibility to allow one to pay attention to 

desired stimuli, then the larger portion of the 

collection of environmental attributes becomes 

virtually irrelevant. 

This approach raises a new wave of unanswered 

questions. At what level are environmental 

features interpreted by recreationists acting in 

those environments? At what level can the 

character of the environmental setting be 

defined? At what level are constraints on free 

initiative perceived? 

While the answers to such questions must await 

future research, we believe there are few 

elements in outdoor settings which functionally 

serve to constrain free initiative beyond 

providing the basic capacity to engage in an 

activity (one does need water for canoeing). We 

feel that the most important factors influencing 

free initiative lie not in the external 

environmental backdrop, but in the social milieu 

in which the recreation activity takes place, 

and in the managerial policies controlling that 

social milieu. 

As social interaction is very fluid, it becomes 

difficult to associate particular patterns of 

interaction with specific forms of environmental 

setting (Lee 1972). Physical setting is seen 

not so much as a facilitator or controller of 

behavior, as it is a repository for the social 

definitions of the appropriate behaviors. 

Resource inventories focus on physical 

characteristics because they are easy to 

measure. We focus on them, however, for the 

wrong reasons. The presence of water obviously 

allows the pursuit of canoeing. However, it is 

the social definition of the setting that will 

determine what behaviors will take place. Thus, 

we believe that those elements which most likely 

constrain free initiative (and therefore thwart 

the attainment of the desired states) are least 

likely to be captured in traditional motiveenvironment choice models. 

Measurement Considerations 

If the concept of motivation to participate is 

ever to be successfully captured for recreation 

planninglmanagement purposes, it must be 

carefully separated from the modes of 

expression. A key is to make the distinction 

between the initiation of behaviors (motivation) 

and the expression of that behavior (learned 

mode). 

Such a process might occur in a two-step 

fashion. First, it would be necessary to 

identify critical outcomes. Recognizing the 

generality of the different levels of decisions 

about participation described previously, it 

would be necessary to identify which would 

discriminate individual choices for engaging in 

certain activities in specific settings. The 

combination of such critical outcomes with key 

antecedent variables (experience, lifestyle) 

should predict a given mode of expression. 
The mode of expression would then be tied to 

states that people are trying to attain in order 

to describe the standing pattern of behavior. 

These variables represent the experience 

opportunty setting characteristics of recreation 

by representing social forces in the physical 

setting. The question then is how to describe 

such a concept. 

We believe that one way to help the process is 

to develop an expanded conception of the 

dimensions required to inventory learned modes 

of expression. Since we wish to discriminate 

this concept from the traditional motive 

measure, we need to focus more directly on the 

link between behavior and environment. Our 

earlier arguments that people respond to 

environments at the broad level of organization 

as settings as opposed to the specific 

attributes present suggests that such indicators 

should accordingly be more holistically 

descriptive. 

In this sense, the primitive-urban continuum 

proposed in ROS is a useful dimension. It is 

but one dimension, however, and the 

identification of alternate predictors of 

important differences in mode of expression is a 

challenge yet to be unravelled by research. 

Nevertheless, we might find that the processoriented inquiry examining the subjective states 

of leisure is already offering some clues. The 

key is to identify dimensions of human activity 

that represent contrasting modes of expression, 

which ultimately would affect the capacity to 

attain a desired state. Continuums such as 

active/passive, social/solitary, experiential1 

informational, arousal-seeking/arousal-reducing, 

and comfort/asceticism are potential candidates 

for research. 

CONCLUSIONS 

What we have presented here is inherently 

frustrating because it adds another layer of 

complexity to an already complex situation. 

Further, it has disturbing implications for the 

applied efforts to inventory recreation 

resources for planning and management purposes. 

We are talking about defining recreation 

settings in terms of appropriate modes of 

behavior and social settings rather than in 

terms of physical features. This does not lead 

to easy application, and renders physical inventory of extremely limited utility. 

We are essentially offering another means of 

organizing a body of concepts about outdoor 

recreation behavior. We do not intend to 

invalidate the traditional conceptions, as much 

as to put them in a different context. We 

recognize that virtually everything we have 

offered is conjecture. Our motives are to 

stimulate more systematic and directed debate 

concerning which organization of reality is the 

most useful for the purposes to which these 

inquiries are ultimately to be put. In so 

doing, we hope that this mode of expression of 

those motives will allow us to attain the desired level of consciousness! 

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information in outdoor recreation resource 

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Rocky Mtn. For. and Range Expt. Stn.; 1978: 

73-84. 

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J. For. 80:84-86; 1982. 

Csikszentmihalyi, M. Beyond boredom and 

anxiety. San Francisco: Jossey-Bass; 1975. 

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James, William. Psychology: the briefer 

course. New York: Collier Books; 1892. 

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Kaplan and R. Kaplan eds., Humanscape: 

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Wohlwill, eds. Behavior in the natural 

environment. N.Y. : Plenum; 1983: 205-240. 

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Motives for recreational river floating-- 

Relative consistency across settings. 

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recreation places. In: W. R. Burch, N. H. 

Cheek and L. Taylor, eds. Social behavior, 

natural resources, and the environment. 

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natural objects. Ann. Rev. of Psychology; 

32: 89-115; 1981. 

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Rosch and B. Lloyd, eds. Cognition and 

categorization. Hillsdale, N.J.: Earlbaum 

Assoc., Inc.; 1978: 27-48. 

Schreyer, R.; Nielson, M. Westwater and 

Desolation Canyons--Whi tewater River 

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196 p. 

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experience expectations on crowding 

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SUBSTITUTION IN RECREATION CHOICE BEHAVIOR 

George L. Peterson 

Daniel J. Stynes 

Donald H. Rosenthal 

John 

ABSTRACT: This review discusses concepts and 

theories of substitution in recreation choice. It 

brings together the literature of recreation 

research, psychology, geography, economics, and 

transportation. Parallel and complementary 

developments need integration into an improved 

theory of substitution. Recreation decision 

behavior is characterized as a nested or 

sequential choice process. Examples are included 

from urban and dispersed recreation research. 

INTRODUCTION 

Recreation resource management is dependent 

on understanding recreation choice behavior. Many 

management actions directly affect the attributes 

of recreation sites, e.g., changes in services and 

facilities, modification of site quality or 

capacity, changes in user fees, and congestion 

management. Such changes may strongly modify the 

quantity and types of recreation participation in 

general as well as at specific sites. If 

management actions are to be efficient and 

effective, the resulting effects on recreation 

choice need to be predictable. 

How recreationists respond to management 

actions depends largely on two factors: (1) the 

underlying preferences of consumers as constrained 

by their resources, and (2) the available 

alternatives. If a new campground is established 

in an area where there are several similar 

campgrounds, the changes in behavior will be 

driven largely by reductions in travel cost. The 

new campground can be expected to intercept those 

campers for whom it has a lower travel. cost, 

thereby reducing visits at more distant sites. 

Paper presented at the Recreation Choice Behavior 

Symposium, Missoula, MT, March 22-23, 1984. 

George L. Peterson is Project Leader, Rocky 

Mountain Forest and Range Experiment Station, 

USDA Forest Service, Fort Collins, CO; Daniel J. 

Stynes is Associate Professor, Dept. of Park 

and Recreation Resources, Michigan State 

University, East Lansing, MI. Donald H. 

Rosenthal is an Economist, Rocky Mountain Forest 

and Range Experiment Station, USDA Forest Service, 

Fort Collins, CO. John F. Dwyer is Project 

Leader, North Central Forest Experiment Station, 

USDA Forest Service, Chicago, IL. 

F. Dwyer 

And, because of the lower cost, it may generate 

increased camping activity. This increase in 

camping may draw people away from other 

activities, such as gardening or television 

watching. 

If the new campground also differs from 

all the others by providing hot showers, still 

more behavioral change can be expected. The 

location factor is a price change while the hot 

showers are a site quality change. There may 

be people who now drive farther than before in 

order to have a camping experience that 

includes a hot shower. And, the availability 

of showers may attract new campers. Unless we 

understand how individuals evaluate and choose 

among recreation opportunities, serious errors 

may be made in predicting how they will respond 

to management actions. 

The theoretical framework for making such 

predictions has not been adequately developed 

and applied. One of the most difficult and 

least understood parts of this problem is the 

question of how people tradeoff activities, 

sites, and site attributes when change is 

encountered. This paper discusses this concept 

of "substitution" rin recreation choice behavior 

and exposes some common problems. Theories of 

choice behavior from several disciplines are 

reviewed and their implications discussed. 

Some recent research results from urban and 

dispersed recreation provide examples. 

The concern here is with substitution as 

it pertains to modeling choice behavior. 

People make choices in decision situations, and 

the purpose of a choice model is to allow such 

choices to be predicted and analyzed. A choice 

model might be formulated at any of several 

levels of the choice process. People make 

choices among recreation sites, among 

attributes of those sites, among activities 

that might take place at the sites, and among 

the consequences or outcomes of performing 

alternative activities at alternative sites. 

These consequences include the satisfaction of 

motives or expectations that may or may not be 

in the recipient's domain of awareness. 

While there are many important reasons for 

explaining recreation choices at all of these 

levels, our purpose is to explore substitution 

among sites and site attributes in models based 

on observations of overt choice behavior. What 

goes on inside the mind and body of the 

consumer is left for others to explore. 
Webster defines "substitution" as, "to put in 

the place of." Within this concept, substitution 

can be defined at different levels. At the 

simplest level, two objects that are physically 

identical in every respect are likely to be 

substitutable for each other. Thus, physical 

similarity is one way to attack the question. At 

a more general level, substitution may derive from 

functional similarity. Two physically dissimilar 

objects may be capable of performing the same 

function. One object can be put in the place of 

another and perform similar functions, e.g., pen 

for pencil. 

Whether or not two objects are substitutes 

can be learned by observing behavioral response to 

changing opportunities. A simplistic approach 

would hold that, other things being equal, if the 

consumption of A goes up when the price of B goes 

up, the two are substitutes; if the reverse is 

true, they are complements. If nothing happens to 

A when the price of B changes, they are 

independent. 

The trouble with this approach is that it 

reveals only what is known already. It is not 

possible to predict choices under new circumstances, (i.e., to predict behavioral response to 

innovation). Neither is it possible to recognize 

when apparent substitutability is simply the 

product of spurious correlation. What is needed 

is a theory of choice behavior that explains 

choices in terms of those variables to which they 

are sensitive. Lancaster (1966) and Becker (1965) 

responded with a new consumer theory based on the 

concept that the objects of choice are not the 

goods themselves, but the attributes possessed by 

those goods. Consumers use the attributes as 

input factors for a consumption technology that 

produces utility. For example, they combine time, 

travel, equipment, facilities, and the features of 

natural environments to produce outdoor 

experiences such as camping. 

The attributes may be thought of as parameters describing the physical state of a thing. 

Thus, an automobile can be described in terms of 

color, make, model, rate of fuel consumption, 

braking distance, luxury options, etc. In this 

sense, substitution again reduces to physical 

similarity, except that in a given decision 

context only selected physical attributes may be 

relevant. Substitution is thus physical 

similarity vis-a-vis only those variables relevant 

to the deci-sion context. On the other hand, as 

expressed by Morishima (1959), demand is created 

by human objectives. Goods are demanded because 

of their ability to serve human purposes. Certain 

attributes or context variables may modify the 

effectiveness of a thing in a given role. 

~ub&itution can occur among purposes, among 

attributes, or among goods, and this complicates 

matters. 

It becomes still more complicated when we 

allow decision rules to vary. Some goods, 

attributes, or purposes may be compensatory, 

others noncompensatory. Some noncompensatory 

decisions may involve lexicographic decision 

rules. For example, for aerobic conditioning, 

jogging and bicycling are generally regarded as 

compensatory activities. More bicycling can 

compensate for less jogging in the maintenance of 

aerobic capacity when a minor injury interrupts a 

runner's training schedule. However, more 

carbohydrate in the diet cannot compensate for 

iron deficiency. The two nutrients have independent functions and are noncompensatory. A 

lexicographic decision rule requires criteria to 

be satisfied in sequential or hierarchical order. 

In shopping for formal wear, a conventional male 

goes first to the men's department and then 

compares alternatives. He does not compare 

evening gowns and tuxedoes. There may also be 

threshold decision rules such that in a given 

range the relationship among attributes is 

lexicographic, while in another range the attributes are compensatory (Peterson and Worrall 

1970, de Bettencourt and Peterson 1981, Krumpe and 

McLaughlin 1982). 

Whatever the decision rules, it is clear that 

they must be known and understood if prediction of 

substitution in recreation choice behavior is to 

be effective. A given model structure may be 

entirely inappropriate in a given decision 

context. What is needed is a theory of recreation 

choice behavior and interpretation of real choice 

situations in terms of that theory. Therefore, 

the next section reviews the approaches of several 

different disciplines. 

REVIEW OF SUBSTITUTION RESEARCH 

While there is a great deal of research 

related to substitution, only a small portion of 

this research confronts the substitution issue 

directly. First, the treatment of substitution in 

empirical studies within recreation will be 

reviewed, and then, briefly, more basic research 

from geography, psychology, transportation, and 

economics. The latter provide some of the pieces 

for a much needed conceptual and theoretical basis 

for substitution research within recreation and 

leisure. We will not at this stage attempt to 

assemble the pieces, but hope to begin to identify 

the components of a more comprehensive model in 

which substitution may be addressed. 

Recreation Research on Substitution 

There are two major bodies of substitution 

research within recreation: (1) recreation/ 

leisure activity substitution, and (2) recreation 

site substitution. The former has largely been 

the domain of sociologists and psychologists while 

the latter has been addressed by geographers and 

economists. A few studies examine substitution 

between resource settings (O'Leary and Dottavio 

1981), geographic areas (Ditton and others 1975, 

Vaske and Donnelly 1982), and providers (Cordell 

1976). 
Hendee and Burdge (1974) raised the importance of the substitution concept for recreation 

planning and management in the context of 

recreation activity substitution. Growing out of 

the leisure typology research was an attempt by a 

number of investigators to shed light on substitution relationships by factor analyzing 

recreation participation data. Recreation 

activities were grouped into activity types with 

the idea that activities within the same group are 

in some sense "s~bstitutable.~' Planners could 

then meet the recreation needs of clientele groups 

by providing at least several activities from each 

group, rather than attempting to provide all 

activities. Beaman (1975) pointed out the 

inappropriateness of factor analysis for deriving 

activity packages, recommending cluster analvsis 

as the more suitable tool. Because the activity 

packages derived through factor analytic methods 

are based upon correlations between participation 

in different activities, activities falling in the 

same cluster are just as likely to be complements 

as substitutes. Christiansen and Yoesting (1977) 

tested whether recreationists would derive similar 

'lsatisfactions" from activities within the same 

type category, concluding that the approach of 

deriving activity packages empirically is too 

simplistic to yield much insight into substitution 

behavior. 

A more productive approach to activity 

substitution is the development of recreation 

activity packages based upon similarity judgments 

of real or perceived characteristics of recreation 

activities. Ritchie (1975) and Becker (1976) have 

used multidimensional scaling techniques (MDS) for 

this purpose. Holbrook (1980) recommends the 

method of multidimensionally scaled correlations 

(MSC) as preferable to both factor analysis and 

MDS in representing association among recreation 

activities. MSC performs an MDS procedure on the 

correlation matrix of variables rather than the 

variables themselves. A limitation of MDS, MSC, 

and related attitude scaling methods for 

substitution research is the problem of linking 

attitudes with behavior. Becker (1976) notes that 

situational variables must be studied in 

conjunction with attitudinal data in order to 

predict actual behavior. 

The problem of how to incorporate the effects 

of substitute sites is common to both economic 

demand models and geographic models of spatial 

choice. Investigators from both disciplines tend 

to favor revealed preference approaches in which a 

variety of situational factors must be controlled. 

Motivational and attitudinal variables that are 

difficult to observe are downplayed, and 

observable characteristics of recreationists, 

recreation sites, and selected environmental 

variables are studied. 

Measures of recreation site substitution have 

been incorporated into aggregate recreation demand 

and trip distribution models. The travel cost 

model (Dwyer and others 1977, Rosenthal and others 

1984) and the gravity model (Ewing 1980) have been 

the primary vehicles for examining substitution in 

recreation economics and geography, respectively. 

To predict the number of trips or visits to a set 

of destinations, those gravity models that include 

a substitute component typically capture it by 

means of a single variable measuring the quantity 

and/or quality of alternative recreation 

opportunities available from each origin. The 

selection of sites to be included as potential 

substitutes and the measurement of their 

characteristics is most often done by the 

investigator, rather than the consumers whose 

choices are to be predicted. Many of these 

studies limit the range of substitutes to sites 

managed by the same authority or sites that are 

similar according to some criteria defined by the 

investigator. Thus, television viewing is never 

considered a substitute to visiting a given state 

park, and in many cases neither is visiting a 

county or urban park. When studying state parks, 

only other state parks are likely to be 

considered. Park visitation models that include a 

substitution component are those of Grubb and 

Goodwin (1968), Cheung (1972), Cesario (1973), and 

Smith (1980). 

The substitution issue has also been prominent in the development of travel cost models, 

which are widely used to estimate the demand for 

and value of recreation sites. Such models can 

yield very misleading results if substitute sites 

are not properly specified. This is well illustrated by Knetsch (1977). Good examples of 

alternative ways of incorporating substitutes into 

recreation demand models are the studies of Burt 

and Brewer (1971) and Cesario and Knetsch (1976). 

Both studies estimate the demand for individual 

parks within a system of equations, taking into 

account the presence and attributes of alternative 

sites in estimating the demand for any particular 

site. 

Because both the gravity and travel cost 

models have traditionally been applied to 

aggregate data, they can only reveal aggregate 

patterns of substitution. In order to shed more 

light on individual differences in recreation 

choices, recent work in geography, economics, and 

transportation has explored disaggregate modeling 

of choices. These models can be applied to both 

activity and site choice problems, provide a 

stronger theoretical base for aggregate models, 

and can more fully incorporate a variety of 

assumptions about substitution. Since these new 

directions in choice research are not well known 

within recreation, we first provide a brief 

background on the development of choice models in 

psychology and then discuss some recent development in geography, economics, and transportation. 

In summary, while sociologists and psychologists have concentrated on motivational and 

attitudinal determinants of choice, geographers 

and economists have focused more upon situational 

factors. However, their studies have examined 

substitution in the context of site choice, not 

activity choice, and no one has really attempted 

to link these two somewhat independent bodies of 

research. 
Psychology is the Tversky (1972a, 1972b) elimination by 

aspects (EBA) model. 

Psychological choice research focuses upon 

individual choice and relies heavily upon experimental methods. A strength of the psychological 

research is the reduction of choice to fundamental 

concepts and relationships, resulting in tight 

controls in research designs. A corresponding 

weakness is that to achieve rigid experimental 

controls the conditions and settings are often 

oversimplified and sometimes artificial. This 

makes it difficult to generalize the results to 

highly complex real-world settings. 

The relevant psychological research may be 

divided into two classes: (1) mathematical models 

of choice that establish set-theoretic foundations 

for applied work, and (2) more applied research, 

which explores choice in terms of individual 

perceptions, preferences, and information 

processing. The latter category overlaps 

considerably with the microlevel geographic 

research, but tends to have a stronger 

methodological orientation. 

Along with psychophysics and learning, choice 

is one of the three primary subject areas within 

mathematical psychology. The models of Luce 

(1959) and Tversky (1972a, 1972b) have guided much 

of the theoretical choice research over the past 

two decades and provide the foundations for the 

majority of applied studies of choice. Both are 

highly abstract theories couched in purely 

set-theoretic terms. 

Luce's model formalizes Arrow's (1951) 

''independence of irrelevant alternatives" (IIA) 

assumption into a choice axiom. Because it 

relates to recreation choice modeling, this axiom 

is discussed and illustrated in a subsequent 

section. This powerful and simplifying assumption 

makes the Luce model easy to apply, although it is 

known to fail in many simple kinds of choices 

(DeBreu 1960) because of interrelationships among 

choice alternatives that are not captured in the 

highly abstract model. Nevertheless, the Luce 

model provides a reasonable approximation for many 

kinds of choices (Luce 1977) and, perhaps more 

importantly, provides a starting point for choice 

research including substitution phenomena. The 

Luce model has been shown to be essentially 

equivalent to Thurstone's (1927) Law of 

Comparative Judgement (Case V) if Thurstone's 

assumption of independent, normally distributed 

random variables is replaced by double 

exponential, random disturbances (Yellott 1977, 

McFadden 1973). The difference distribution of 

two independent double exponential random 

variables is the logistic distribution, which is 

the basis for the multinomial logit model (MNL) 

(Stynes and Peterson 1984). Thus, there is a 

direct link between the widely applied MNL model 

and basic theories of choice studied by Luce, 

Thurstone, and others. The simple counterexamples 

to the Luce model (DeBreu 1960, Luce 1977, Tversky 

1972a) are also cases where the MNL model will 

fail. These cases have provided the stimulus and 

direction for revised choice theories that more 

fully capture interrelationships among the choice 

objects. Most notable of these within psychology 

Although the EBA model, like Luce's, can be 

formulated in purely abstract set-theoretic terms, 

the natural interpretation of the model involves 

the identification of the choice alternatives in 

terms of a set of attributes or characteristics. 

Luce's model is a special case in which the 

alternatives do not have any characteristics in 

common. The EBA model is lexicographic. A choice 

is made from a set of alternatives by successively 

eliminating those that do not possess desired 

attributes. There is an assumption that 

individuals have a ranking of desired attributes 

and eliminate alternatives beginning with the most 

desired attribute, then the second most desired 

attribute, etc., until only a single alternative 

(the choice) remains. The model is noncompensatory because a lack of one desired attribute 

cannot be compensated by another. Any alternative 

lacking the most desired attribute is eliminated 

in the first step regardless of what other 

desirable attributes it may possess. The EBA 

model results in nested or sequential decision 

structures that can become combinatorially complex 

when the alternatives possess many different 

attributes (Krumpe and Mclaughlin 1982). Indow 

(1975) questions whether any mathematical model 

can fully capture the complexity of individual 

choice processes, suggesting computer simulation 

as an alternative approach. Grether and Plott 

(1979) have found that individual behavior is 

often at odds with what is implied by the formal, 

and fairly mathematical, theory of consumer demand 

in economics. 

The strength of the formal mathematical 

treatment of choice lies in the precision with 

which concepts can be defined and the manner in 

which properties may be deduced and tested (See 

Luce and Suppes 1965, Tversky and Russo 1969). In 

the absence of such approaches, the essential 

differences and similarities between alternative 

choice models, like the Thurstone and Luce models, 

would remain obscure. By employing a common 

mathematical language, the theories of choice 

behavior from psychology and economics can be 

shown to share common foundations. This helps a 

great deal to clarify the properties and limits of 

these models, including the implied assumptions 

about substitution. 

Applied studies of preference, utility, and 

choice within psychology look more closely at the 

choice set, choice setting, and attributes of the 

choice alternatives in order to explore individual 

differences and common patterns of choice 

behavior. Many of these studies attempt to 

identify a utility function, and model choices as 

a utility maximizing process. The methods are 

variously known as information integration, 

functional measurement, conjoint analysis, and 

policy capturing. These kinds of methods are 

widely applied in geography, marketing, economics, 

transportation, and related fields. The 

psychological contributions to this area have 

identified a number of methodological issues that 

must be considered in the design of choice 

experiments. For example, Tversky's (1977) review 
of the concept of similarity presents both 

theoretical and empirical evidence against 

representing similarities between objects or 

choice alternatives geometrically. This is 

precisely what the MDS and related methods do. 

Tversky suggests that similarity judgments are 

the result of a feature matching process and may 

not satisfy the metric assumptions of symmetry and 

transitivity. Curry and others (1983), in an 

application to recreation, conclude that a number 

of contextual and measurement issues can 

significantly influence the results of choice 

experiments, making it difficult to generalize 

from one choice setting to another. These 

problems may explain many of the apparently 

conflicting results of choice experiments as well 

as the difficulty of generalizing from highly 

artificial settings to real-world choices. 

Geography 

Geographic choice research focuses upon 

spatial choice and is therefore relevant to the 

recreation site choice problem. Geographic 

measures of substitute and intervening opportunities provided the basis for numerous 

recreation applications of the gravity and related 

trip distribution models. At the macrolevel, 

geographers have become increasingly concerned 

with the effects of spatial structure on the 

parameter estimates of gravity models 

(Fotheringham 1981). Spatial structure of 

alternative sites cannot be adequately represented 

by a single variable, and models incorporating 

spatial structure more fully are being sought. At 

the microlevel, there have been numerous efforts 

within geography to better explain spatial choice 

by examining data for individuals. These 

investigations have added cognitive dimensions to 

gravity models (Cadwallader 1981) and explored 

individual variations in perceptions of distance 

and alternatives. Much of this work overlaps with 

work in marketing and psychology on functional 

measurement, information integration, and the like 

(see Golledge and Rushton 1976 and Burnett 1981, 

for examples). 

Economics 

Economics is the study of the employment and 

consumption of scarce resources that may have 

alternative uses (Samuelson 1976). Two questions 

addressed are (1) how factors of production (e.g., 

land, labor, capital) should be allocated to 

firms, and (2) how the goods produced by firms 

should be distributed to consumers. Substitution 

in recreation choice is a matter of consumer 

behavior, so the relevant contribution of 

economics is the theory of consumer demand, which 

also has a similar development in the theory of 

the firm. The economic point of view assumes that 

prices are the key mechanism regulating the 

consumer's and firm's allocation of scarce 

resources. 

The theory is well known and is only summarized here. A full exposition is available in, 

for example, Henderson and Quandt (1980). The 

amount of a given good consumed by a person is a 

function of tastes and preferences, prices of that 

good and of other goods, and income. The 

consumer's preferences among choice options are 

assumed to be describable by an ordinal utility 

function that has certain restrictive properties. 

Income and prices are generally assumed to be 

fixed. Faced with a choice among several 

competing alternatives, the consumer is assumed to 

pick that option which maximizes utility, subject 

to a budget constraint. The result of this 

utility maximization is the individual demand 

function. This function describes for that person 

the relationship between the quantities of goods 

consumed and their prices. The aggregate or 

market demand function is the sum of individual 

demand functions. To attain more effective 

treatment of substitution and complementarity, 

consumer theory has been extended by Morishima 

(1959) and Lancaster (1966). 

The theory of consumer demand provides a 

disciplined framework for the treatment of 

substitution. The price elasticity of demand 

gives the percent change in demand for a good (or 

attribute) resulting from (1) a 1% change in .the 

price of that good (attribute), or (2) a 1% change 

in the price of some other good (attribute). The 

price elasticity of demand is dependent on prices 

and marginal rates of substitution. The marginal 

rate of substitution (MRS) is defined by the 

utility function. It is the amount of good Y the 

consumer is willing to give up to attain one more 

unit of good X, Two goods are perfect substitutes 

if the MRS is constant, no matter how much X or Y 

is owned. The price of a good is determined by 

the quantity available and the quantity demanded. 

When trying to predict demand or estimate 

value for a recreation activity or site, it is 

important to assess the availability of substitutes. The demand function is the basis for 

predicting participation and estimating benefits 

under alternative management options. Failure to 

incorporate the effect of substitutes in the 

demand function can produce incorrect results. 

For example, assume the managers of a certain 

recreation site want to raise entrance fees, but 

are unaware that a perfect substitute with excess 

capacity is available a short distance away. 

Raising the fee will simply divert to the alternative site those users whose added travel cost is 

less than the price increase. 

A decision not to close the site may be 

similarly misguided. Estimates of the net 

benefits to the public of a government-operated 

site will be too high if an uncongested nearby 

substitute is ignored. Assume that closing the 

site will require all users to travel one mile 

farther up the canyon. The loss of benefit caused 

by the closure is only the cost of the added one 

mile of travel. Of course, if the second site has 

insufficient capacity, there will be a loss of 

quality and/or an increase in price that will 

affect the outcome of the closure. 

Thus, the demand for a recreation site or 

activity is a function of its price plus the 

prices of all other goods and services in the 
economy. If demand and substitution are to be 

tractable in this overwhelmingly complex framework, some simplification is required. Complex 

demand functions can be accurately represented 

through the use of "flexible functional forms" 

(Simmons and Weiserbs 1979). The demand functions 

themselves can be simplified through assumptions 

about separability in the utility function (Barten 

1977, Powell 1974). If the utility function is 

weakly separable, all goods can be partitioned 

into a set of mutually exclusive subgroups such 

that the MRS between two goods within a group is 

independent of the quantity consumed (and price) 

of any good in another subgroup, holding utility 

constant. This allows consumer decisions to be 

structured in terms of a nested utility tree 

(Deaton and Muellbauer 1980), which in turn 

enables the estimation of conditional demand 

functions. 

What this means is that consumer decisions 

can be viewed as sequential (Katzner 1970). At 

one level, the consumer decides how much income to 

allocate to each of several groups of goods. 

Given the allocation of income, say, to a group of 

recreation sites, choices among those sites are 

independent of choices among goods in other 

groups. If the partial utility function 

describing how preferences are ordered within the 

group is homothetic (Silberberg 1978, p. 254), 

then proportional allocations of income among 

goods within the group are also independent of the 

amount of income allocated to the group. This 

framework leads to a nested conditional choice 

structure that includes as special cases the EBA 

model and the sequential model of Krumpe and 

McLaughlin (1982). 

Such assumptions and conditions greatly 

simplify the analysis of recreation choice 

behavior. Unfortunately, these assumptions are 

too often hidden and unrecognized. These hidden 

assumptions describe the world to which the model 

applies. The important question is how far that 

world is from the real one. This line of economic 

reasoning reveals the kinds of questions that 

should be asked when various types of models are 

used. It also provides a framework for defining 

and answering questions about substitution. When 

we decide to consider only a finite set of 

substitutes in a model, say a group of state 

parks, we have made a strong implicit assumption 

about the underlying choice process. Would it not 

be better, through research, to discover the 

structure of the choice process, and then to ask 

what kinds of models and sets of substitutes are 

implied thereby? This discovery of the underlying 

choice processes is the objective of relevant 

research in psychology, geography, and recreation. 

Integration of this work within the organized 

framework of economics would be a major 

contribution. Progress is hampered by a lack of 

the data needed to verify and interrelate the 

theoretical propositions. 

Transportation 

Transportation researchers appear to be at 

the forefront of applied choice modelling. Most 

of the work has centered around mode choice and 

the multinomial logit model or variations thereof. 

Transportation researchers seem to have been 

successful in integrating economic, geographic, 

and psychological variables into formal choice 

models that can be empirically estimated and 

tested. Domencich and McFadden (1975) and Stopher 

and Meyburg (1975) derive the MNL model in the 

transportation context based upon utility 

maximization. Much of the recent work in 

transportation addresses some of the undesirable 

properties of this model, including assumptions 

about substitution. This has led to a variety of 

models with different assumptions about substitution, including nested logit models, probit 

models, and generalized extreme value models (Chu 

1981, Amemiya 1981). 

A recent book by Anas (1982) provides a 

framework for the estimation of conditional demand 

functions, based on a nested or sequential 

decision process, as deri.ved from assumptions of 

separability in the utility function. His 

approach provides the analytical framework for 

treatment of substitution in conditional and 

nested choice models. These models have the 

advantage of beginning with assumptions about 

individuals and then generating aggregate results 

by combining the individual choice models under 

various distributional and separability assumptions. In this way they can capture the common 

features of individual decisionmaking processes 

without attempting to "predict" individual 

behavior. Recent research has explored interpersonal variations in the choice set (Richardson 

1982), and the effects of a number of possible 

misspecification problems on travel demand 

forecasting (Williams and Ortuzar 1982). The MNL 

model has been applied to recreation by Stopher 

and Ergun (1979, 1982), Peterson and others 

(1982), and Peterson and others (1983). Stynes 

and Peterson (1984) provide a general review of 

logit models, drawing conclusions for applications 

to recreation choices. They, as does Vickerman 

(1978), specifically discuss the independence of 

irrelevant alternatives (IIA) property of the MNL 

model in the recreation context. 

CHOICE MODELING AND NESTED CHOICE STRUCTURE 

The demand models typically used in 

recreation are general share models. They exhibit 

the IIA property and imply strict assumptions 

about separability in the utility function. This 

means, in general, that adding or deleting an 

option will not change the proportional shares of 

demand to the other alternatives. For example, 

let two existing parks, A and By each receive 50 

percent of park use. The IIA property implies that 

adding a third park, C, might reduce total participation at A and By but the proportional allocation between A and B will not change. Both will 

continue to receive half of the participation 

allocated to A and B. Clearly, this is reasonable 

in some situations and not in others. The key to 

proper choice modeling is an understanding of 

choice structure, including the separability and 

form of the utility function and the implications 

for substitution. 
The typical example used to illustrate the 

IIA problem comes from mode choice analysis in 

transportation planning. The object is to predict 

how travelers between two locations allocate 

themselves among automobile, bus, and rapid 

transit as a function of the characteristics of 

the three modes. Assume an appropriate general 

share model (such as a logit model) has been 

developed, based on observed choice behavior. 

Assume also that all the buses are red. NOW, the 

planning authority decides to paint half the buses 

blue and wants to predict the new distribution of 

riders. The analyst assumes that red buses 

compete with blue buses in the same way that buses 

compete with autos and rapid transit. If, before 

the painting, the three modes each received 

one-third of the use, the model will allocate 

one-fourth of the use to each mode after the 

painting. If the riders are indifferent to color, 

or less concerned about color than about other 

things, they will continue to allocate one-third 

to buses. The new situation will be 113 to autos, 

113 to rapid transit, 116 to red buses and 116 to 

blue buses, while the model predicts 114 to each. 

The mistake arises because the analyst fails 

to understand the role of color in mode substitution. Red buses and blue buses were seen by the 

riders as perfect substitutes but were assumed by 

the analyst to be imperfect substitutes at the 

same level as buses versus autos. Technically 

speaking, this problem is caused by lack of 

independence among the random elements of utility. 

When some alternatives are more similar than 

others, the random elements will be correlated and 

the IIA assumption will be violated. The trouble 

is, it is practically impossible in recreation 

applications to correctly identify sets of 

balanced substitutes without much more theory and 

information than now available. 

Other conditions also cause problems. 

Different sites may attract different mixes of 

activity and different mixes of preference for 

site attributes. Mixing these processes violates 

the IIA assumption, because the parameters and 

perhaps even the specification in a model will 

change for different subsets of a given set of 

alternative sites. The solution is to isolate the 

separate components of the mixed process. 

In the mode choice example the analyst saw 

the situation as figure 1, in which riders face 

four substitutable alternatives. However, the 

riders saw it as figure 2, in which they face, at 

one level of choice, three competing alternatives. 

Given that bus is chosen, a new level of choice is 

confronted: red or blue. The preferences to which 

the red versus blue choice relates are different 

than the preferences that govern, in this case, 

choice of travel mode. The two sets of 

preferences may or may not differ in relative 

importance. In the example, color was assumed to 

be unimportant to mode choice. In any case, a 

different utility process applies to the choice of 

mode than applies to the choice of color. The 

decisions need to be separated, because while bus 

substitutes for auto and transit, blue does not. 

n Choice 

Transit Red Bus Blue Bus 

Figure 1.--The Red Bus - Blue Bus problem as a 

simultaneous choice. 

Figure 2.--The Red Bus - Blue Bus problem 

as a nested choice. 

In the extreme case, preferences might be 

hierarchical. Choices, choice sets, and substitution would have to be nested in terms of the 

hierarchy of objectives. For example, people have 

objectives that relate to comfort, convenience, 

and esthetics, in choice of transportation mode. 

They also have objectives that relate to cost, 

safety, and access to desired destination. A mode 

that does not serve the destination is not 

acceptable, no matter how cheap or pretty it may 

be. Given two modes that serve the destination, 

choice will probably be influenced by significant 

differences in safety. An abnormally high risk of 

being killed or injured cannot be compensated by 

differences in cost, comfort, convenience, 

esthetics, etc. This approach leads to a nested 

conditional model of recreation choice behavior 

that might take the sequential form proposed by 

Krumpe and McLaughlin ( 1982) . 

Figure 3 illustrates a hypothetical (and 

highly simplified) recreation choice process. The 

objective of the analyst is to predict 

participation in fishing at five different sites. 

Two of the sites (3 and 4) are restricted to fly 

fishing only, while the other three (1, 2 and 5) 

are unrestricted. Use of an IIA general share 

model, which treats all five sites as substitutes, 

suffers from the "red bus-blue bus" disease. 
0-0 Choice Death & Life 

Leisure 

I Chotce Other. Chotce * Ftshing 

c Choice WarrnWater Choice 

L I 

Site 1 Site 1 

Site 2 Site 2 

Site 5 Site 5 

A Choice 

(choice) 

Figure 3.--A nested fishing site choice example. 

Different fishing methods (e.g. fly fishing) 

involve different site choice rules, because the 

requirements of the methods and the objectives of 

the fishermen differ. Effective modeling requires 

exposure of the nested conditional structure in 

the choice process. It is then necessary to 

stratify models and population subgroups in terms 

of that structure. On face value it is the same 

old problem of "interaction" commonly encountered 

in social and behavioral research (Sonquist 1970). 

In terms of economic theory, it arises in 

recreation choice behavior because of 1) 

conditions of separability in the individual 

utility function, 2) differences among individual 

utility functions, and 3) similarities and 

differences among the choice options. 

Using a general share model to predict demand 

for a proposed new fly-only site (site 6) would 

violate the IIA assumption because of mixed 

processes and mixed levels. Site 6 competes with 

sites 1, 2, and 5 only for people who have decided 

on fly fishing. Sites 3, 4 and 6 compete, given 

that the choice is to go to a fly-only site. Fly 

fishing competes with bait and spin fishing at a 

different level. The problem is corrected in part 

by stratifying by fly fishermen, but fly fishermen 

who select a fly-only site may follow a different 

utility process, given this choice, than those who 

go to a general site. The problem is ultimately 

solved only by modeling each decision level 

conditional on the outcome of the higher level. 

It is noteworthy that an early concern with this 

type of problem is implicit in the Burt and Brewer 

Model (1971). They classified their sites into 

subgroups of perfect substitutes with imperfect 

substitution among subgroups. 

Direct Evidence From Site Choice Modeling in 

Urban Forest Recreation 

In a recent study, Peterson and others (1983) 

found the IIA property to be violated in an 

application of an MNL model to urban forest 

recreation site choice. The choice set contained 

21 sites in the Chicago metropolitan area, 

including two indoor conservatories, a large 

arboretum, a large botanical garden, and 17 forest 

preserve areas. Variables used in the model were 

travel distance (price) and four perceived site 

attributes: stimulating-educational, quiet, safe, 

and forested. When the model was estimated for 

different subsets of the 21 sites, the distance 

coefficient was found to be quite stable. 

Coefficients for the perceived site attributes 

fluctuated considerably, however, with those for 

"quiet" and "forested" actually varying in sign. 

This is prima facia evidence of violation of the 

IIA property, and plausible causes were discussed. 

Apparently, the specified choice set contained 

sites that represented different levels in a 

nested choice process. Consequently, different 

sites tended to attract people with different 

purposes. While the model presumed these sites to 

obey specific substitution rules, they clearly did 

not. 

Subsequently Lin (1983) used the same data to 

estimate a nested choice model. Based on the 

perceived attribute scores, Lin used cluster 

analysis to partition the 21 sites into three 

groups. Within a group the sites were perceived 

as more similar than among the groups. It is not 

surprising that the arboretum and botanical garden 

formed one group, the conservatories formed a 

second group, and the forest preserves made up the 

third group. These groups were then used to set 

up a nested choice process as in figure 4. At the 

first decision level, the consumer faces a choice 

among conservatory, botanical garden, or forest 

preserve. These represent different activity 

types, perhaps selected to satisfy different 

objectives. At the second level the alternatives 

depend on the outcome of the first. 

In the earlier study, Peterson and others 

used a simultaneous choice model that forced 

substitution across those hypothetical decision 

levels. When the mix was changed, the model had 

to change. Lin used tests-developed by McFadden 

and others (1976) to reject the hypothesis that 
DIRECTIONS FOR FUTURE RESEARCH 

I,,;,,,,,,,,, 1 Botancial Garden 

(choice) 

Figure 4.--Lin's nested urban recreation 

site choice model. 

the 21 alternatives obey the IIA property required 

by the simultaneous choice model. He then 

estimated the nested logit choice model with the 

structure illustrated in figure 4. The 

explanatory power was improved significantly and 

the meaning of the model was improved. Within the 

conditional groups, the IIA property was obeyed. 

Between the groups, different but meaningful 

choice processes were seen to be operating. 

Indirect Evidence From a Wilderness Site Demand 

Model 

In an earlier study, Peterson and others 

(1982) developed a site-demand model for the 

Boundary Waters Canoe Area Wilderness (BWCAW). 

The model was stratified by 12 types of use 

occurring in the same wilderness. The data 

consisted of a census of trips for th'e period 1974 

through 1977. No attempt was made to include 

substitute sites for any of the 12 types of use. 

The model simply distributes trips among origins 

under the assumption of no substitutes. 

The distance elasticity of demand (percent 

change in demand percent change in distance) was 

found to differ by type of use. These differences 

demonstrate that different demand processes are at 

work among the different types of use. The 

different user groups have different objectives, 

their on-site activities have different input 

requirements, and they face different sets of 

substitute sites. Therefore, they respond 

differently to travel cost and, by implication, to 

on-site attributes. 

In this paper we have attempted to expose and 

motivate interest in a major problem in recreation 

choice, that of substitution in recreation choice 

behavior. It is an important question in many 

aspects of recreation resource management and 

research, yet it is poorly understood. Toward 

correction of this inadequacy, we have tried to 

define the question and review various lines of 

research that seem to be converging on it, albeit 

from different points of view and with different 

languages. Finally, drawing on this research, we 

have framed the substitution problems in terms of 

nested choice structure, which we illustrate with 

hypothetical and empirical examples. 

The problem of substitution is still far from 

resolved. Questions arise which point directions 

for much-needed future research. One important 

need is for researchers and managers to recognize 

that the decisions they make about model 

specifications and substitute sets in demand and 

participation analyses carry some important 

implied assumptions about choice behavior. These 

assumptions should be exposed and challenged. 

A second important need is for better 

integration of the related concepts of several 

disciplines into a theory of recreation choice. 

Well-developed bodies of literature in psychology, 

geography, economics, and transportation, for 

example, provide the basis for a powerful 

theoretical framework. Once integrated, such a 

framework needs to be made operational in terms of 

practical models and research methods. Further, 

the concepts need to be turned into facts about 

recreation choice behavior. What are the nesting 

structures that describe the actual choice 

processes of different types of people in 

recreation choice situations? What decision rules 

do they follow? What are the practical choice 

sets that are considered when decisions are made? 

Are these ad hoc questions that must be 

encountered in every practical or research problem 

or can empirical research be generalized into 

useful rules and principles? 

Depending on the objectives, still more work 

may be needed in explaining the decision processes 

thus revealed. Are we satisfied with the 

descriptive approach of revealed preference, or is 

there a need to delve into motivational factors 

that lie behind and are the object of recreation 

choice? The work of Morishima (1959), Lancaster 

(1966), and Becker (1965) presents an interesting 

extension of the powerful and well-developed 

theory of economics, which seems to be reaching 

toward behavioral. research in the psychology of 

recreation choice. 

At first glance there appears to be lack of 

consensus among researchers from different 

disciplines on the merits of further emphasis on 

these behavioral aspects. For estimation of 

demand, the benefits at least include exposure of 

the assumptions implied by various demand specifications. In general, though, the quest ion should 

be asked in the context of the purpose to be 

served. Different purposes require different 
kinds of information. The information needed to 

answer questions asked by economists, for example, 

may be different than is needed to satisfy 

designers or planners. Indeed, this may be an 

example of the most serious obstacles to 

multi-disciplinary integration of a theory of 

substitution in recreation choice. Different 

disciplines, different studies, and different 

applied contexts seem to be answering mixtures of 

different questions which have not been asked 

clearly or adequately separated. One of the most 

needed contributions may simply be to identify the 

different questions being stirred in the research 

pot, the applied contexts from which they derive, 

and the kinds of answers they require. 

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A DEVELOPMENTAL MODEL OF RECREATION CHOICE BEHAVIOR 

Daniel R. Williams 

ABSTRACT: Recreation choices are viewed as 

including, at least implicitly, a selection of 

an activity, a setting, and a set of companions. 

With development these three elements become 

increasingly differentiated from one another. 

The model is tested by examining the perceived 

similarities among a set of 15 recreation choices 

depicted in color slides. 

DEVELOPMENTAL PERSPECTIVES 

The subject of how recreationists choose leisure 

settings or activities has received increasing 

attention among researchers and managers as made 

obvious by these proceedings. Drawing on behavioral decision theories, much of this work has 

been directed at describing how a specific 

recreation choice is made using various cognitive 

models. The developmental perspective on choice 

taken in this paper turns the focus away from 

describing how individual decisions are made to 

searching out patterns of choice and the process 

by which people develop and become specialized 

in their leisure pursuits. 

Calling for a cognitive developmental theory of 

leisure and work, Blocher and Siegal (1981:42) 

describe the dynamics of leisure development: 

"As individuals move into higher cognitive levels, 

the meaning of leisure activities may change .... as people grow cognitively they are attracted to 

stimuli (recreation settings and activities) that 

involve higher and higher degrees of novelty, 

complexity, ambiguity, and abstraction." Thus, 

with cognitive development comes a gradual change 

in the expression of choice. This paper explores 

how such growth comes about and what influence it 

has on subsequent recreation choices and preferences. A developmental model of recreation choice 

will be described. 

The concept of leisure or recreation development 

has been explored in various contexts including 

studies of socialization (Kelly 1977), comparisons of pre- and post-retirement leisure patterns 

(Yuskaitis 1981), discussions of leisure and 

family life cycles (Parker 1979), and analysis of 

play behavior (Levy 1978). In outdoor recreation 

research, developmental perspectives have taken 

three forms. One emphasizes the influence of 

previous use or participation on perceptions, 

preferences, and attitudes (Schreyer and others 

Paper presented at the Recreation Choice Behavior 

Symposium, Missoula, MT, March 22-23, 1984. 

Daniel R. Williams is Assistant Professor, Department of Recreation and Leisure, University of 

Utah, Salt Lake City, UT 84112. The research 

was supported by North Central Forest Experiment 

Station, USDA Forest Service, St. Paul, MN. 

1984, Hammitt and McDonald 1983). The second, 

recreation specialization, assigns developmental 

levels based on the participant's involvement in 

and commitment to a leisure pursuit as indicated 

by participation, monetary investment, and impact 

of the activity on the participant's lifestyle 

(Bryan 1977; Wellman and others 1982). The third 

form examines socialization as a shaper of preference (Burch 1969). Studies may focus on one's 

membership in social circles of leisure or social 

reference groups, for example, socialization in 

the surfing community (Devall 1973), or the 

influence of childhood recreation experiences on 

adult leisure patterns (Yoesting and Burkhead 

1973; Sofranko and Nolan 1972). Common to all 

three perspectives is that amount and timing of 

experience as a participant is thought to be an 

important determinant of setting preferences, 

activity styles (trolling for bass versus fly 

casting for trout), and the social context of the 

outing (Bryan 1977) . 

The cognitive developmental level of an individual 

is by definition the amount and type of information 

a person has, and is thought to influence the 

frame of reference by which a participant evaluates 

recreation choices. In forming cognitive developmental models some indicator of the recreationist's 

accumulated experience is used to evaluate preference for recreation experiences or participation. 

Variables that indicate skill, knowledge, or 

familiarity may act as indicators. Among people 

with different amounts of recreation experience 

(and presumably development), for example, perceptions of crowding vary (Schreyer and Roggenbuck 

1978). 

Such learning or developmentally based approaches 

may better represent the mental process through 

which a person interprets life (and recreation 

opportunities) than static antecedent variables, 

which consider only past history. Traditional 

socio-demographic and personality approaches 

assume shared similarities in life history and 

home environment that help to define the mental 

processes through which recreation choices are 

made. The developmental approach attempts to 

describe the process by more directly quantifying 

the amount of information a person has relevant 

to the activity being considered. Thus, locating 

a person on a leisure development continuum provides 

a summary description, a frame of reference from 

which a person makes decisions about recreation. 

Further, a developmental continuum may be useful 

in analysis of change in decision-making styles. 

Research on recreation decision-making has focused 

on identification of the attributes of decision 

alternatives, the order in which they are considered, and the importance assigned to then. Both 

the attributes considered and the way they are combined and weighted in forming a decision are likely 
to vary with the developmental level of the 

decision-maker. For example, some participants 

may become highly involved in an activity. If 

development were related to commitment we might 

expect more advanced participants to base decisions 

on attributes that facilitate an enjoyable experience and less on constraints (time or money) in 

their choices (Krumpe and McLaughlin 1982). 

COGNITIVE DEVELOPMENT 

Usually development is thought of in terms of 

growth in ability to acquire, organize, and use 

information--as a cognitive skill acquired with 

time and experience. To distinguish development 

from simple change, definitions of development 

usually imply progress, direction, and order along 

some continuum. In applying developmental theories 

to research on large-scale environment-behavior 

interactions, Moore (1976, p. 145) defines development as: 

qualitive changes, differences, or variation 

in the organization of behavior such that 

what are called developmentally "more advanced" behaviors are more differentiated 

than what come to be called "lower" stages 

of behavior, and logically include and 

heirarchically integrate the "lower" stages... 

Moore leaves open the question of whether a particular developmental stage is correlated with 

age or time. Differences in the organization of 

behavior or information may be described developmentally in the context of life cycle changes 

(ontogentic development; along an age continuum). 

However, development may also be described as 

changes occurring at different periods of time 

(microgenetic development; for example, learning 

about a new city or achieving new levels of 

leisure satisfaction with increasing participation). 

In studies of cognitive complexity (Bieri 1966), 

categorization and judgment (Rosch and others 1976), 

and environmental cognition (Moore 1976; Williams 

1980), persons more advanced in their interest and 

thinking within a domain (developmentally specialized persons), may be expected to perceive and 

structure elements within that domain in more 

complex ways and make finer discriminations among 

the elements. Thus, developmentally advanced 

recreationists are expected to process decision 

attributes in more complex ways. 

A DEVELOPMENTAL MODEL OF RECREATION CHOICE 

According to Flavell (1972) the three requirements 

for a developmental analysis of any behavior include: (1) specifying a set of acquisitions 

(what it is that develops), (2) identifying the 

order and processes involved, and (3) defining 

the time dimension of the sequence. 

What is it that develops? For Flavell (1972) the 

items in a developmental sequence may refer to a 

structure, skill, concept, belief, attitude, bit 

of knowledge, or any other type of cognitive unit 

that a developmental psychologist might define 

and study. A logical starting place for examining 

developmental sequences of recreation participation 

is to focus on what it is that recreationists 

choose among. A recreation activity often is considered the basic element of choice. Though such 

a definition is widely used, it is widely viewed 

as inadequate (Driver and Brown 1983). The activity concept perpetuates the belief that recreation 

is a behavior rather than an end state, which is 

equivalent to viewing education and health as 

behaviors rather than end states. Still, choices 

result in alternative behaviors and what is lacking is an adequate procedure for describing what 

has been chosen when we talk about recreation 

decisions. The set of acquisitions for development 

may be defined as the amount, type, and organization 

of information about recreation choices. 

Cognitive Structure and the Elements of Choice 

A consumer behavior analogy is behind many choice 

theories applied to recreation (Krumpe 1979). 

Recreation activities have been viewed as a product line in decision-making research. People 

participate in, make decisions about, and hold 

preferences for recreation activities--hunting, 

camping, and hiking, for example. Unlike most 

consumer products, however, participants can exercise some control in the design of their desired 

products and thus the experiences they derive from 

participation. To shape an experience, recreation 

consumers have three basic choice elements: 

activities, settings, and companions. Every decision to participate, consciously or unconsciously, 

includes a decision about what to do, where to do 

it, and whom to do it with. The research task is 

to describe the psychological structure of recreation choices (the underlying decision elements) 

and the dynamics of how these structures evolve 

and change with experience. 

Past approaches to describing recreation choice 

have been too limited. For most research, an 

activity definition of recreation has been used in 

attempts to unravel the mechanisms of leisure participation. The nature of the activity, however, 

may not be the basis of participation. Activities 

may be subordinate to the social meaning of the 

participation (with whom we participate may be more 

important in the decision to recreate than what 

activity we select; O'Leary and others 1974). Or 

the nature of the activity may be subordinate to 

where recreation takes place (Ditton and others 

1975). The basis for participation in any recreational activity also may be linked to some symbolic 

or emotional attachment to specific places or types 

of settings (for example, "Granite Peak" Wilderness 

or any national park; Williams 1980; Tuan 1977). 

Take the social basis of participation as an 

example. For a particular group of people who have 

gathered together to enjoy the outdoors, several 

activities and a variety of leisure settings may 

present equally satisfying recreation opportunities 

for the group. Therefore, what people choose are 

not activities as the term is commonly used, but 

what Driver and Tocher (1970) describe as recreation 

engagements highlighting the multi-element nature 

of recreation participation. 

Three components, then, are needed to completely 

specify any form of outdoor recreation 
participation--activities, settings, and companions 

Such a model is not derived from empirical data; 

it is based on a survey of the literature. The 

three components, which appear scattered and unconnected in the outdoor recreation literature, 

may be combined to varying degrees to define what 

was chosen. The three components describe how 

recreation choices may be classified: recreation 

choices may be differentiated by activity categories, by setting categories, and by categories 

of social organization or interaction. Any one 

component is not considered in general to be more 

important, superordinate, or dominant; although 

the relative make-up, importance, or salience of 

any one component may vary across individuals and 

for any specific choice. 

The developmental model begins with a multielement choice structure as the set of developmental acquisitions. For any individual decision, 

the choice structure may be specified as a 

weighted combination of the three components described above: activity, setting, and social. All 

three are necessary to uniquely categorize any 

recreation choice. But not all possible combinations of activities, settings, and social groups 

are equally likely to occur. Some forms of 

activity and some settings naturally co-vary; 

canoeing requires a body of water. However, this 

should not beconstruedas a reason to ignore one 

component when describing a recreation choice. 

An activity may necessarily take place in a certain setting (fishing near water), but specifying 

the setting (water) is not sufficient to indicate 

the activity (fishing) . 

The Developmental Sequence 

How does the cognitive structure evolve and change 

with development? Within the recreation literature, the specialization concept has attracted 

some attention (Bryan 1977) and appears particularly germaine. Specialization in recreation 

behavior indicates an evolution of preference and 

style of participation in an activity from the 

general to the particular and may reflect changes 

in the role or importance of one or more of the 

decision elements. However, with Bryan's concept 

of specialization as a sequence of acquired behaviors a different set of acquisitions must be 

identified for each activity. An alternative 

approach, which may apply across activity types, 

is to expand the notion of activity specialization 

to include all of the recreation choice elements. 

Specialization is one of two processes Flavell 

(1972) identifies for development that have significance for a developmental analysis of recreation 

choices. The other, differentiation, refers to 

the degree of structural complexity of a concept 

as it is organized in the brain. Differentiation 

as a developmental process involves making conceptual distinction where none had previously been 

made. Thus, what was initially a single, 

"undifferentiated" response becomes with development two "differentiated responses each restricted 

to one region of the original domain" (in this 

case two elements within the domain of a recreation 

choice). 

Specialization in the cognitive literature is 

related to differentiation but is linked more to 

the content of cognitions than their structure. 

Differentiation of responses to some event results 

in each being a "specialization or delimitation of 

function which represents some sort of constraint 

or restriction on the way an individual will respond to an event" (Flavell 1972:300). Specialization represents a process of focusing attention 

on some subset of the differentiated structures. 

Specialization follows differentiation in that the 

brain must be able to differentiate two items 

before it can selectively attend to one over the 

other. 

Building further a model of recreation development, 

components of a recreation choice become increasingly differentiated from each other (structurally 

more separate and complex, see fig. 1). As each 

domain becomes differentiated, specializations 

develop (a tendency to focus differentiation in 

some domains more than others). The limitation of 

Bryan's (1977) concept of recreation specialization 

is overcome because specialization is no longer 

tied to changes in content of a specific choice 

element (activity) to describe the specialization 

process. 

ACTIVITIES 

SETTINGS 

COMPANIONS 

J, 

RECREATION 

ENGAGEMENT 

44 

DIFFERENTIATION 

Figure 1.--As the three components of recreation 

choice become increasingly differentiated, 

specializations develop. 

A tendency to focus attention, learning, or the 

acquisition of knowledge in one domain over another 

may be defined as specialization. Thus, for any 

choice, specialization is the relative cognitive 

differentiation within and perceptual orientation 

toward each domain; social character, activity type, 

and setting type. Thus, differentiation is the 

making of conceptual distinctions and specialization denoting the content domain where the 

differences are made. If, for example, a participant were to distinguish one choice from another 

with exclusive regard to the primitiveness of the 

surroundings he/she would be a setting specialist. 
In a developmental sequence a decision-maker first 

perceives recreation choices as different to some 

degree (differentiation). The form that differentiation takes may be based on any of a number of 

components of recreation choices; the most prominent in the choice model are hypothesized to be 

activity, setting, and social group. Next in the 

sequence is specialization which refers to the 

contents or results of the differentiation process. 

Specialization may be described by specifying the 

domain in which differentiation is concentrated. 

An attempt is made to identify the type or nature 

of the conceptual differentiations of a decisionmaker. To the extent that a decision-maker uses 

one or a subset of the possible components to 

differentiate recreation choices he/she may be 

described as a specialist. To the extent that a 

decision-maker uses all or a large subset of 

possible components he/she may be described as a 

generalist. Note, however, that "generalist" is 

not meant to imply one who is casually interested 

in many things, but one who differentiates recreation choices on many components. The generalist 

is perhaps the closest to what Bryan (1977) described as highly specialized; a participant who 

demonstrates an advanced technique and displays welldeveloped preferences for settings and companions. 

The Developmental Continuum 

As in any developmental study; subjects, responses, 

characteristics, skills, or preferences must be 

compared across some time continuum if we are to 

identify an ordered sequence. For most explanations of development the continuum is age of the 

subject. Moore (1976) argues, however, that 

experience in or familiarity with a situation may 

also be considered a developmental continuum. 

For studying recreation development, experience 

with the objects of recreation choice is the 

developmental continuum of interest; that is, the 

role of experience in how recreation choices are 

structured. 

Wellman and others (1982) present the most complete 

attempt to measure amount of experience. They 

scaled canoeing experience along two dimensions, 

participation and commitment, using a series of 

questionnaire items. A single index was constructed by standardizing selected items and 

summing the score across all items. The approach 

would appear to be applicable to scaling experience in other activities. 

ANALYSIS OF THE MODEL 

The model was tested by examining the perceived 

similarities among a set of 15 recreation choices 

as depicted in color slides. From research on 

cognitive differentiation (Bieri 1966) it was 

hypothesized that increasing participation, involvement, and commitment to a recreation 

activity would result in increasing differentiation of similarity judgments. Further, 

differentiation would be concentrated along three 

dimensions--activity, setting, and companions. 

Individual subjects would display a specialization in the use of one or more of these dimensions 

in their similarity judgments. 

A series of laboratory studies was conducted to 

evaluate the developmental model just outlined; 

two will be described here. The first study was 

used to scale a set of photographs in terms of 

the structure of recreation choices proposed in 

the developmental model (the three elements of 

choice--activities, settings, and companions). 

In the second study, subjects' judgments of recreation choices were compared at three levels of 

activity experience. Common to both studies was 

the use of photographs depicting alternative outdoor recreation choices. Fifteen color photographs, 

carefully selected through a series of pilot 

studies, varying in activity, setting, and social 

content, were used to elicit subjects' cognitive 

structure of recreation choices. These structures 

can then be compared across experience levels to 

identify how the perceived structure of recreation 

choices changes with development. 

The first study served as a stimulus scaling 

procedure designed to assign scale values to each 

of the 15 photographs for each element of recreation choice. Ten judges were asked to sort 

the 15 photographs (in the form of 3% by 5-inch 

prints) into categories according to three rules 

(the rules corresponding to each element in a 

recreation choice). In random order, judges were 

asked to sort the pictures into categories according to the recreation activity portrayed, the 

social group present, and the setting of the recreation outing. For each sorting rule, judges 

first sorted the pictures into as many categories 

as desired and then into three, five, and seven 

categories. An index of similarity between each 

pair of pictures for each sorting rule was derived 

by a method described by Ward (1977). This method 

assumes that the more categories available when 

two photographs are placed in the same category, 

the more similar are the two photographs. For 

each sorting rule, a two-dimensional matrix of 

similarities (photos X photos) was derived by 

averaging across the 10 subjects. The three 

resulting matrices were analyzed using SINDSCAL 

algorithm (Prusansky 1975) for individual differences multidimensional scaling. 

Though a three-dimensional solution was hypothesized, a four-dimensional solution derived from 

the analysis provided the best fit to the data. 

The dimensions were labeled activity (activepassive), setting (primitive-urban), social 

(intimatelfamily-social/group), and land-water 

based on the pattern of dimension weights derived 

from the three sorting rules. The fourth dimension (land-water) that emerged in the judges' 

ratings was related both to the activities and 

settings portrayed in the photographs, indicating 

some covariance between activities and settings 

as represented in the photographs (fishing 

activities go with water and hiking with land). 

The results, consistent with the model of 

recreation choice, were used in a second study that 

solicited photograph ratings from 60 subjects of 

varying levels of participation and involvement. 

Subjects judged the perceived similarity among all 

105 possible pairs of photographic slides. Using 

individual differences mulitdimensional scaling 

these individual judgments were then fit to the 
four-dimensional configuration of the photographs 

derived from the first scaling of photos. 

Indexes for amount of participation in each of the 

activities shown in the photographs (fishing, 

canoeing, and backpacking) were constructed. 

Further, an index was constructed for nonparticipatory commitment and involvement in the subject's 

most preferred of the three activities following 

the method of Wellman and others (1982). This 

latter index included items concerning the importance of the activity to their lifestyle, menibership 

in organizations, and expenditures on equipment. 

Differentiation is indicated by the degree to which 

a subject's similarity ratings fit the fourdimensional scaling of the photographs; the greater 

the fit the more differentiated are the judgments 

along the four dimensions. In table 1 the correlations between developmental indexes and fit are 

listed. Only in the case of fishing experience 

was a significant correlation observed. Those 

higher on the fishing participation index showed 

greater differentiation of recreation judgments as 

indicated by fit. 

It was further hypothesized that increasing 

participation and involvement would result in 

specialization in the use of the various dimensions of similarity judgment. Specialization, 

the importance of a particular element of 

recreation choice, is indicated by the relative 

importance of each of the four dimensions in 

describing (in terms of fit) a subject's similarity ratings. Correlations between developmental 

indexes and dimensional salience (importance) 

weights are given in table 2. Only two relationships were found significant. Greater backpacking 

experience was positively associated with the 

social dimension and involvement/comrnitment was 

positively associated with the activity dimension. 

For both differentiation and specialization the 

results seem to be activity specific. No general 

trend of differentiation or specialization was 

identified for the various indexes. Therefore, 

relationships between the preferred activity and 

fit and dimensional salience weights were examined 

(table 3). A significant difference was found 

for the social dimension with fishermen using the 

social dimension in their judgments less than 

Table 1.--Correlations of experience indices with individual fit scores 

Developmental index Correlation with fit index 

Fishing experience 0.30" 

Canoeing experience - .10 

Backpacking experience - .05 

"Significant at 0.01. 

Table 2.--Correlations between experience indices and dimensional weights 

-- - -- 

Developmental index Dimensional weight correlation 

~andlwater Social Activity Setting 

Fishing experience 0.15 

Canoe experience - .15 

Backpacking experience - .14 

Involvement/commitment - .10 

"Significant at 0.05. 
Table 3.--Analysis of variance and group means for activity preference differences on fit and dimensional 

salience weight scores using Student-Newman-Kuels difference of means 

Activity Preference 

Criterion Fishing Backpacking Canoeing F-value SNK 

Fit 0.74 

Social .07 

Activity .25 

Setting .39 

Land/water .39 

- - 

0.47 

3.12* F-B 

6.26Ji F-B , C 

1.76 

.58 

backpackers. Further, those who preferred fishing used the activity dimension less than both 

canoeists and backpackers. Though not significant, the fishing group relied more on the setting 

and land-water dimensions than the canoeists or 

the backpackers. 

CONCLUSIONS 

Viewed in the context of the tentative and 

exploratory nature of the metl~ods employed, finding partial support for the developmental model 

suggests that it has promise and warrants further 

investigation. The data provide the beginnings 

from which developmental patterns of recreation 

choice behavior may be studied. However, measurement of recreation participation levels (amount of 

experience and involvement) appears to be a major 

methodogical constraint in the further exploration 

of developmental models. The lack of variance on 

the items used to build experience indexes and 

the low accuracy inherent in such self-report 

measures tend to moderate any real correlation 

between the perceived structure of recreation 

choices and amount of experience. An alternative 

would be to study groups of participants who have 

demonstrated different levels of skill and involvement (for example, participants in beginning versus 

advanced backpacking classes). 

The model has important implications for 

substitutability research (Williams 1983). The 

elements of recreation choice represent the basic 

dimensions along which substitutes may be conceptualized. What constitutes a substitute 

depends on the developmental level upon which the 

decision-maker approaches the choice. An activity 

specialist, for example, is not likely to exchange 

the activity component of participation, but may 

be willing to participate in a different setting 

or w&h a different set of companions. Others 

may be more sensitive to setting or companion 

components of the choice, substituting activities 

to maintain satisfaction in a specific setting 

or with a specific group. 

Future research might evaluate some of the implications of differentiation and specialization in 

a more direct decision-making context. For example, 

following Krumpe's (1979) study on influencing 

recreation decision-making, one might hypothesize 

that specialists (more experienced subjects) would 

be less likely to change choices when provided 

with information (consistent with their specialty) 

and that persons who differentiate recreation 

choices along activity dimensions might be more 

likely to make use of new information than those 

differentiating in setting terms. Further, 

different decision models might be tested with the 

hypothesis that specialists in one form or another 

might show different decision-making styles than 

non-specialists. 

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Symposium on Leisure Research: abstracts; 1983 

October 2-5; Alexandria, VA: National Recreation 

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of New York; 1981. Unpublished M.A. Thesis. 
CHOICES OF WILDERNESS ENVIRONMENTS-- 

DIFFERENCES BETWEEN REAL AND HYPOTHETICAL CHOICE SITUATIONS 

Jean T. Beaulieu and Richard Schreyer 

ABSTRACT: Factors considered most critical in 

selecting a specific wilderness environment in 

which to recreate are likely to vary depending 

upon the type of environment involved, the 

range and extent of experience of the recreationists, and the situational context of the 

decision (whether a real or a hypothetical 

decision was made). This study examines differences in factors considered in choosing a wilderness environment based upon whether the choice 

was an actual report of behavior or a hypothetical 

decision. A questionnaire requesting information 

concerning choices of wilderness areas in which 

to participate was administered to two groups. 

A sample of 325 recreationists were contacted 

on-site in wilderness areas in Utah and Wyoming 

and asked to report on why they had actually 

chosen that area. The second sample was a mail 

survey of Utah Wilderness Association members, 

and requested a hypothetical decision concerning 

a place in which to recreate and the factors 

used to make that decision. Responses were 

analyzed for the two sets in terms of the most 

important factor cited in making the choice, the 

degree of specificity of -the most important 

factor, the total number of factors considered 

important in making the choice, and the combination of attributes considered important. In 

all cases, significant differences were noted. 

THE USER/ENVIRONMENT INTERFACE 

As the focus on wilderness moves from designation to management, there is a growing need 

for more accurate information concerning user 

relationships with the resource. One important 

concern is identifying which aspects of wilderness environments have the greatest influence on 

recreation choice behavior. This topic focuses 

on why certain areas are chosen, which trails 

are selected, what areas are perceived as desirable for camping, and what behaviors are seen 

as appropriate. 

Actual attempts to carry out research on the 

topic have been fraught with difficulties 

concerning the measurement of attributes and the 

Paper presented at the Recreation Choice Behavior 

Symposium, Missoula, MT, March 22-23, 1984. 

Jean T. Beaulieu is a Doctoral Candidate, 

Department of Forest Resources, Utah State 

University, Logan, UT. Richard Schreyer is 

Associate Professor of Recreation Resource 

Behavior, Utah State University, Logan, UT. 

link of reported significance to actual behavior. This paper reports one approach to 

assessing the environmental attributes which 

affect choice behavior, comparing potential 

differences between persons sampled in the 

wilderness environment itself and those who are 

sampled at home. 

Attributes and Wilderness Management 

Rather than being a trivial academic 

pursuit, this line of research has distinct 

implications for recreation resource management 

and planning. Knowledge of such relationships 

can assist management through identifying which 

criteria are most relevant for inventorying the 

resource. Further, it would be possible to 

target which types of information are most 

important in affecting recreationists' 

behaviors. To the extent that management may 

desire to affect that behavior, either to 

minimize impact on the resource or to enhance 

the experience for the visitor, such inputs will 

be critical to designing effective information 

systems. 

The need for this type of information is 

augmented by the fact that the Recreation 

Opportunity Spectrum (Driver and Brown 1979; 

Clark 2nd Stankey 1978) is currently the 

primary recreation planning tool used by the 

Forest Service and the Bureau of Land 

Management. It is based on the assumption that 

recreation experiences are dependent upon 

specific attributes of the recreation 

environment chosen by the recreat ionist. 

Therefore, environments are to be inventoried 

according to the types of experiences they can 

provide. A goal of recreation resource 

management using this model is to help 

recreationists match desired outcomes with 

settings that will maximize the probability of 

their attainment. 

The ROS catalogs all lands into six different 

classes of recreation opportunity, from primitive 

wildlands to organized urban recreation. 

Designation is based on a complex list of 

attributes and weighting factors. A map of the 

entire management unit can be created showing 

the classification of each area, allowing the 

recreationist to pick his/her own desired mix of 

opportunities. 

This classification scheme has been based almost 

entirely on measurement of physical characteristics, though many of the physical criteria do 
have obvious social or psychological aspects 

(e.g. remoteness). The Forest Service's ROS 

manual contains almost 15 pages delineating the 

physical requirements for classification, and 

less than one page apiece for social and managerial factors. This causes ROS to be primarily a 

physical inventory procedure that provides practitioners with little direct information concerning the recreationists' perceptions of the 

ability of the land to provide desired experiences. 

In order for such a planning system to operate 

effectively, it must be able to determine: 1) 

which attributes of the environment are important for the production of a quality recreation 

experience, 2) what specific aspects of the 

environment affect recreation behavior, 3) what 

aspects of the same environment are sought by 

recreationists desiring different experiences, 

and 4) how the diversity among users can be more 

effectively characterized. 

With this information the manager may be able to 

formulate the most practical method for providing recreation opportunities that realistically allow hislher clientele to increase the 

benefits obtained from the recreation experience 

while protecting the character of the resource. 

One approach to gathering this information and 

understanding the influence of environments on 

behavior is to study the way people cognitively 

organize information about environments. This 

suggests an examination of the types of information used to classify and understand natural 

environments, and the way in which that information varies across different types of users. 

In other words, we believe the best way to 

approach the link between environment and 

behavior is to study the way that link is made 

in the mind of the user. We have approached 

this through the analysis of environmental 

images. 

The Image of the Environment 

Psychologists studying cognition do not know 

what specific processes are involved in image 

formation, though it is a simple activity for 

most persons. For example, a subject can be 

asked to imagine a wildland campsite, and the 

task is readily accomplished. Images consist of 

many constituent fragments that combine to form 

the individual's conception of a given environment. There is an interactive process between 

mind and environment which suggests that 

"images are not static pictures but are processes that begin and end with varying existence" (Bugelski 1971:35). There is an element 

of creative construction involved in that attributes presently recorded by the senses often 

stimulate the mind to remember other attributes 

that past experience indicates should be correlated. This creates a composite image uniting 

observed and imputed characteristics (Bunting 

and Guelke 1979). 

The image of wilderness is not created simply by 

observation of the physical environment, since 

it is also contained within a psychological 

realm, symbolizing many internal meanings and 

needs. When discussing the image of an environment it must be remembered that it will be 

experienced as a unified field, no matter how 

many attributes must be mentally added to complete the image. This internal representation 

will not be an exact depiction of what others 

perceive to be out there. The image will be 

distorted by the perceiver's own set of wants, 

purposes, preferred attributes, and symbolic 

values. These will be strongly influenced by 

the definitions of place meaning and value 

espoused within the individual's culture. This 

should particularly encourage researchers 

looking for attributelbehavior links to broaden 

their studies to include other dimensions of 

attributes besides the physical (Moore 1979). 

It is important to note that the concept of 

environmental image carries with it some 

conception of appropriate behaviors. "Designers 

and architects have long known that the form and 

appearance of a building influences certain 

behaviors that take place within it" (Ittelson 

and others 1974:l). This aspect of environment, 

its influence over behavior, is of great 

importance when wildland recreation places are 

considered, as it suggests that the meanings 

assigned to such places are likely keyed to the 

uses we make of them. Thus, the attributes we 

use to construct our mental images should be 

tied to behavior. 

We assume recreationists use some form of 

categorization process for keeping images 

organized when they are involved in recreation 

planning. For example, if a recreationist feels 

like going fishing, helshe mentally runs through 

a list of places where fishing is allowed and 

then selects a particular place. Each recreation activity may have many subcategories (e.g. 

fly fishing vs. bass fishing, places where the 

rest of the family can have fun while I fish). 

The question remains as to how images of 

environments may best be measured. We have 

taken the approach of using open-ended responses 

from recreationists. Bugelski noted (1971) that 

reports of images are still only reports, not 

images. Since no one has discovered where 

images reside, or any physical trace of an 

image, researchers dealing with images must be 

prepared to have them shift and change quite 

readily. This changing of cognitive representations probably is not a serious problem, as 

many attributes of a desired recreation 

experience will likely remain constant. Once 

again, there are only so many elements which 

will contribute to participation in certain 

activities; however, the relative emphasis on 

these may change. 

Cognitive Sets and Behavior 

A helpful way to organize the variability in 

images is to think in terms of a "cognitive 

set." A cognitive set is "a plan to select 
specific types of data for processing or to 

perform specific mental operations on information being processed" (Leff and others 

1974:396). Cognitive sets are useful in that 

they 1) cause the respondent to give priority to 

specific types of attributes, and 2) assume that 

the various constructs are interrelated with 

associated meanings. Thus, when a person thinks 

of an area, a generalized category for that type 

of area is accessed in the mind, which should 

result in the generation of specific types of 

attributes. 

When a person visits a wilderness area the 

observed behaviors are a physical manifestation 

of the outcome of an internal cognitive process. 

The recreationist made a choice and the choice 

was dependent on that person's past reinforcement history, or experience. This suggests a 

more or less consistent pattern of learned 

behaviors in particular types of environments, 

or "activity style." Such a pattern would 

likely generate a fairly consistent cognitive 

representation of any type of recreation engagement. The image generated would be continuously 

modified by information concerning new recreation 

places and activities, and by constraints on 

potential behaviors present in the environment. 

While images are dynamic, they are nevertheless 

likely stable enough to be measured. Mack 

(1971) spoke of the basic conservatism that 

people display in regards to past successful 

decisions. It is psychologically easier to 

repeat an action that was previously successful 

than to attempt an untried action. As the 

pattern of behavior becomes more ingrained the 

range of choices becomes more restrictive, but 

as experiences are gained the number of known 

recreation places (that satisfy the basic 

requirements) should also increase. 

Thus, one of the most important factors 

affecting choice behavior should be the amount 

and type of experience a person has had with the 

activity. The image of a recreation place would 

be much richer and more complex for someone who 

has experienced many different types of places. 

This richness suggests that while a person with 

more experience may be looking at the same 

environment, he/she.may be seeing more detail. 

Thus, the attributes responded to may exist at a 

more detailed or specific level of organization. 

If recreationists respond to attributes at 

differing levels of specificity, they may not 

react similarly to pre-set lists of 

attributes. This suggests that amount of 

experience should be used as a control variable 

in establishing relationships between environment 

and choice behavior. Further, the specificity 

of attributes considered (the level of 

organization most directly related to choice) 

should be examined as well as the type of 

attribute. 

Assessing Choice Behavior 

When rec'reationists begin to choose where they 

will engage in their next recreation experience 

they must begin by delineating the problem space 

and the task structure. Both of these are 

difficult for the researcher to discern, since 

these activities may be occurring subconsciously 

and all that can readily be observed are the 

outcomes (where the recreationist went), which 

are visible and unambiguous. 

We believe the most accurate way to ascertain 

information on this step is through selfreporting by the user. The content of this 

self-report should be greatly influenced by the 

recreationist's activity style, which will 

specify the place and activity that the respondent will discuss and by the level of experience, 

which will affect the number and kind of attributes that will be readily accessible by the 

respondent. 

One of the problems in decision research is how 

to present the problem in a totally naturalistic 

manner so that the problem has not already been 

decomposed into only a few dimensions. This 

decomposition might make the research design 

easier since it will help control the number of 

variables, but it may make the validity suspect. 

Most present research has the decision-maker 

being given a list of potential attributes, with 

the task of choosing among the,m (Phelps and 

Shantreau 1978; Slovic 1975). It is likely, 

however, that real world decisions are not made 

in this disjointed manner. In the real world 

the decision-maker has a great deal more influence over what type of decisions will be made 

and what type of information will be examined 

before the final choice. Thus, in order to 

gather realistic information, the subjects must 

be presented with a holistic representation of 

the pre-decision situation and be allowed to 

access whatever information is deemed relevant. 

There are two ways such an analysis can proceed. 

First, persons actually visiting wilderness 

environments could be sampled. Such persons 

will have already screened information to make a 

choice, and the behavior will be real. A selfreport in this situation would be most directly 

tied to the environment/behavior link. Such a 

methodology, however, is time consuming and 

expensive. As wilderness users tend to disperse 

themselves, the generation of a sufficient 

sample size to provide reliable results could be 

very costly. 

A second approach would be to contact persons at 

home, through a mail questionnaire for instance, 

in order to generate large numbers of responses. 

Persons could be requested to make a hypothetical 

decision, based upon their previous experience, 

and the results would thus be tied to potential 

recreation choice behavior. The question, of 

course, is: How representative of real choices 

in the field are hypothetical choices of similar 

environments? 

This paper reports a study that seeks to obtain 

open-ended responses concerning the attributes 

considered important in making choices for 

wildland recreation environments. Attributes 

are analyzed in terms of the most important 
factor used to make a given choice, the degree 

of specificity of that factor, the total number 

of factors considered important to the choice, 

and the mix of attributes considered. Further, 

we make a comparison of two methodologies used 

to collect these data: real choices reported by 

recreationists in the field and hypothetical 

choices made by respondents answering a 

questionnaire at home. 

METHODOLOGY 

Study Instrument 

The questionnaire used in the study was the end 

result of four pretests involving over 200 

subjects. Two forms were used: one designed to 

be a mail-out questionnaire, and one designed to 

be administered on-site. In the mailed 

version, the wording reflected the fact that the 

choice of an environment being made was to be 

hypothetical. It requested an initial choice of 

a preference between a mountain or desert 

environment. To respondents residing in the 

Intermountain West opportunities for both are 

equally attainable. The questionnaire then 

asked respondents to choose where they would go 

for a three-day weekend. Potential limitations 

of money, time available, and access to 

transportation were removed from the decision 

process, so that preference for the environment 

would be the major consideration. In the case 

of the on-site questionnaire, respondents were 

asked why they had chosen the particular 

environment in which they were contacted. 

Sample Populations 

Two sets of respondents were used for the actual 

and hypothetical choice situations. The actual 

choice situation involved an on-site administration of the questionnaire to 324 wildland 

users. Areas sampled were both mountain and 

desert wildland environments in the Intermountain West. Persons on hikinglbackpacking 

trips were approached at trailheads or campsites 

and asked to complete the questionnaire. All 

members of a given party were requested to 

complete the questionnaire. Success rate in 

questionnaire completion was 95 percent. 

Areas sampled included both. National Park Service 

and National Forest backcountry areas. For mountain environment, the High Uintas Primitive Area, 

the Bridger Wilderness Area (both uSFS) and the 

backcountry of Grand Teton National Park were 

used. For the desert environment, Canyonlands 

National Park was used. Approximately two-thirds 

of the sample involved mountain environments. 

The hypothetical choice situation required a 

sampling technique that was targeted toward 

known users of wildland environments. Even in 

the Intermountain West, the proportion of persons engaging in such recreational activities is 

relatively low. A general population sample 

would likely yield a very small response rate of 

actual participants. 

In order to maximize the probability that the 

persons contacted would be participants, members 

of the Utah Wilderness Association were chosen 

as respondents. That organization made its 

mailing list available for the purpose of the 

study. While restriction of a sample to membership in one group could introduce bias into the 

survey, it was also a realistic aggregation of 

persons most likely to have a large amount of 

information concerning the actual use of these 

areas. Response rate to the questionnaire was 

255, or 68.4 percent. 

Independent Variables 

Choice context and environmental context.--The 

two samples represented the real or the 

hypothetical choice situations. The environment 

chosen was also used as a control variable, as 

there might be significant differences in the 

attributes chosen depending upon whether a 

mountain or desert environment were selected. 

For the hypothetical group, their stated 

preference for mountain or desert was used. For 

the on-site group, the actual location of 

sampling was used. 

Experience use history.--The amount of 

experience in this type of recreation was 

measured through a series of questions on 

previous participation. The dimensions used 

were length of time participating in wildland 

recreation, the average number of visits per 

year to such areas, and the total number of 

areas visited. Responses to each variable were 

standardized, and a composite value was assigned 

to each person. This overall value was divided 

by thirds, so that the group represented low, 

medium, and high experience values. 

Dependent Variables 

Most important factor.--Respondents were 

requested, after having made their choice, to 

list the most important factor in making that 

choice. The range of response was reduced to a 

more manageable list by a coding process developed through a pretest of a similar question. 

Fifty-four students in three classes at Utah 

State University generated 163 different attributes in making a similar choice. These attributes were sorted in a panel procedure into 

twelve different classification groups. 

In order to allow for multivariate analysis, 

these twelve were ultimately reduced to four 

general categories. Attributes related to 

specific activity participation were labeled 

Activity Attributes. Physical Attributes were 

developed from categories previously labeled as 

Landscape Characteristics and Landscape 

Character. Psychological Benefits and Social 

Benefits were combined into a category called 

Social/~sychological Attributes. Finally, a 

category called Managerial Attributes was 

created out of those labeled Restrictions, 

Developed Areas, Access, Size and Management 

Setting. 
Specificity.--The rating of specificity was also 

developed in the pretest. Once the four categories had been established, the attributes 

listed under each category were rated by a panel 

as to the degree of specificity of the label. 

The rating was made in reference to all other 

attributes, so the frame of reference was comparative as opposed to absolute. Four categories ranging from most to least specific were 

pre-determined, again allowing a basis for 

comparison. 

Number of attributes.--After the most important 

attribute in the decision had been identified, 

respondents were asked to list any other factors 

which were important in the choice of the target 

environment. This was presented in an open-ended 

format so that the limit was set by the 

respondent. The total number of attributes 

listed was registered. 

Mix of attributes.--Recognizing that the total 

set of attributes listed would range across 

several or all of the categories, a variable was 

created to represent the overall pattern of 

response. A decision rule was established that 

one of the four categories of attributes would 

only be considered if the number of attributes 

in that category mentioned made 15 percent of the 

total number of responses. Otherwise, the amount 

of detail would be virtually meaningless. 

This process led to the development of fifteen 

different combinations of attributes. In order 

to reduce this number to a manageable level for 

purposes of comparison, groups were clustered 

based on similarity. It was noted that each 

group not containing a Physical Attribute 

comprised less than 4 percent of the total responses. Only three gorups containing a Physical 

Attribute comprised less than 4 percent of the 

total. Thus, all groups not containing a Physical 

Attribute were consolidated, and the three 

groups including the physical component with less 

than 4 percent response were eliminated. This 

left a total of seven combinations. The most 

common grouping was of Physical and SocialPsychological attributes, which represented 

19 percent of the total sample. Next was the 

non-Physical class, which was a combination of 

all attribute classes naming no specific physical 

attributes, representing 17 percent of the 

responses. Next was the response mix of Physical/ 

Managerial/Social-Psychological attributes 

(14 percent). The category containing only 

Physical Attributes comprised 13 percent of 

responses, followed by the Physical/Activity/ 

Social-Psychological mix (12 percent) and the 

PhysicallManagerial mix (10 percent). 

RESULTS 

Most important factor.--The most important 

factor was categorized according to the four 

major classes of attributes. A contingency 

table was generated for the comparison of real 

vs. hypothetical choice on the attribute 

selected as most important. Two analyses were 

performed, controlling for mountain vs. desert 

environment. No control was made for amount of 

experience, as a previous analysis showed that 

there were no significant relationships between 

experience and the dependent variable. 

Tables 1 and 2 show the results of these 

analyses. For the mountain environment, the x2 

Table 1.--Results of the crosstabulation of kind of attribute considered 

the most important factor in the current decision on place to 

recreate. This contingency table was generated for respondents 

in the mountain environment 

Decision context 

Kind of Actual Hypothetical Total 

attribute No. (%) No. (%) No. (%) 

Physical 

Managerial 

Activity 

S-psych 

Chi square = 20.47 3 degrees of freedom Significance = .0001 

N = 356 

Table 2.--Results of the crosstabulation of kind of attribute considered 

the most important factor in the current decision on place to 

recreate. This contingency table was generated for respondents 

in the desert environment 

Decision context 

Kind of Actual Hypothetical Total 

attribute No. (%) No. (%) No. (%) 

Physical 

Managerial 

Activity 

S-psych 

Chi square = 13.57 3 degrees of freedom Significance = .004 

N = 230 
was 20.47 with p<.O 01 while in the desert 

environment, the X 9 was 13.57 with p<.004. In 

the mountain environment, major differences 

appear to be a tendency on the part of persons 

in the hypothetical context to mention managerial attributes more frequently, with a slight 

tendency to mention social-psychological attributes more frequently. Persons in the actual 

decision context mention activity attributes 

much more frequently, while physical attributes 

are mentioned about evenly. 

In the desert environment, the equality in the 

tendency to mention physical attributes is 

maintained, as is the tendency for persons in 

the actual decision context to mention activity 

attributes. The situation, however, with 

managerial attributes is reversed, as persons in 

the actual decision context mention these much 

more frequently, while the tendency for persons 

in the hypothetical mode to mention socialpsychological attributes is increased. 

Specificity of attribute.--The level of 

specificity was analyzed using a three-factor 

analysis of variance. The factors used were 

level of experience, environment (mountain vs. 

desert), and decison context. Table 3 shows the 

results of this analysis. While difference in 

environment failed to register a significant 

difference, both amount of experience (F=3.4, 

p<0.04) and decision context (~=7.1, p<.01) 

attained an acceptable level. In terms of 

actual numbers, persons in the hypothetical 

context had an average specificity of 2.3, while 

those in the actual context averaged 2.5, 

indicating a lower level of specificity. 

Further, in both analyses, there was a two-way 

interaction between the decision context and the 

type of environment. Analysis of this 

interaction showed that persons in the desert 

context in the hypothetical situation picked 

attributes that were rated much more specific 

(2.1) than those in the real decision context 

(2.7). In the mountain environment, however, the 

difference was virtually non-existent. 

Total number of attributes.--A three-factor 

analysis of variance was also performed on the 

total number of attributes mentioned as being 

important to the choice of a recreation 

environment. Results were significant (F=71.7; 

p<.001), as persons in the hypothetical decision 

context named more attributes (5.0) than those 

in the real context (3.4). 

Attribute mix.--Tables 4 and 5 show the results 

for the analyses involving the mix of attributes 

compared by decision context, controlling for 

mountain and desert environments. In both 

cases, results were significant (mountain 

x2=30.1, p<.0001; desert x2=30.0, p<.0001). For 

both environments, there was a tendency for 

persons in the actual decision context to mention 

physical-managerial and non-physical attributes 

more frequently, and physical/socialpsychological attributes less frequently. 

Table 3.--Results of the hierarchically arranged analysis of variance 

examining the specificity of the most important factor in the 

current choice of a place to recreate 

Sum of Mean Signif 

Source of variation squares DF square F of F 

Main effects 10.115 

Experience 4.944 

Decis. context (D.C.) 5.072 

Envir. context (E.C.) 0.099 

2-way interactions 18.405 

Experi X D.C. 1.201 

Experi X E.C. 1.621 

D.C. X E.C. 12.378 

Explained 30.565 

Residual 414.313 

Total 444.878 

Table 4.--Results of the crosstabulation of the attribute mix generated in 

the current choice of a place to recreate. This contingency table 

was generated for respondents who had chosen mountain environments 

Attribute mix 

Actual 

No. (%) 

Decision context 

Hypothetical 

No. (%) 

Total 

No. (%) 

~hys 14(8) 

~hys/~-psych 23(13) 

Phys/Mana/S-psych 18 (10) 

Phys/Act/S-psych 24 (13) 

PhysIManagerial 27(15) 

Non-physical 75 (41) 

Chi square = 30.14 5 degrees of freedom Significance = .0000 

N = 312 
Table 5.--Results of the crosstabulation of the attribute mix generated in 

the current choice of a place to recreate. This contingency table 

was generated for respondents who had chosen desert environments 

Attribute mix 

Decision context 

Actual Hypo thetical Total 

No. (%) No. (%) No. (%) 

Phys 2(2) 

~hys/~-psych 21(24) 

Phys/Mana/S-psych 8(9) 

P~~s/Ac~/s-~s~c~ 7(9) 

PhysIManagerial 16(18) 

Non-physical 34(38) 

Chi square = 29.95 5 degrees of freedom Significance = .0000 

N = 208 

DISCUSSION 

Sample Differences 

There were differences across all dependent 

variables, depending upon whether the response 

format was a self-report of a real choice in the 

field or a hypothetical choice made at home. 

Several reasons may account for the differences. 

In the case of the total number of attributes 

listed, the fact that persons at home would list 

a greater number is certainly understandable, as 

they would have more time to reflect on possible 

choices. 

The other differences are less straightforward. 

It is likely that many of the differences are a 

function of the nature of respondents in the two 

samples. The field survey interviewed persons 

in the backcountry, regardless of their 

philosophy or commitment to the resource. On 

the other hand, the mail survey contacted 

persons who were members of the Utah Wilderness 

Association, which implies both a philosophical 

and emotional commitment to such environments. 

Further, such persons may not actually be users 

of the resource, but may only support it for 

the knowledge value of its existence. 

There were significant differences in the actual 

amount experience use history of the two groups. 

As might be expected, members of the Utah 

Wilderness Association were on average more 

experienced than persons contacted in the field. 

Forty-one percent of the hypothetical group were 

rated high in experience as opposed to 27 percent 

of the actual group. This increase, however, in 

experience was accounted for through hierarchical extraction in the analyses of variance. In 

other words, variance explainable by differences 

in amount of experience use history had been 

removed before the analysis on decision context 

was performed. In the case of the two Chisquare analyses, both were run only after it had 

been determined that experience did not have a 

signiyicant influence on the results. 

Beyond experience, however, is the intangible 

value/commitment factor. A person who is 

sufficiently interested in wilderness to join the 

Utah Wilderness Association may be prone to have 

read more material on such environments, and 

could likely pay attention to different types of 

environmental features. This could also imply a 

different pattern of motivation for 

participation, leading to differing demands 

upon the environment. It is, not, however, 

entirely clear just how these differences 

specifically influence the results obtained. 

Patterns of Response 

Respondents in the hypothetical context in both 

environmental types were less likely to name 

activity attributes. It is possible such 

respondents would be less concerned about what 

they would specifically be doing than on-site 

respondents, for whom the actual activity was 

much more immediate. In contrast, respondents 

in the hypothetical mode tended to name more 

social-psychological attributes. Further, hypothetical respondents choosing mountains tended 

to name managerial attributes more often than 

those in real choice situations, while those 

choosing desert environments tended to name such 

attributes less often. 

Deserts in the Intermountain West are primarily 

administered by the Bureau of Land Management, 

an agency not having many regulations concerning 

behavior. It is possible the hypothetical 

choices were geared more toward such 

environments, while respondents in the real 

choice situation were interviewed in a National 

Park, an environment with much more regulation. 

In the case of mountain environments, the implied 

presence of either Forest Service or Park 

Service administration might result in a higher 

level of concern for managerial control from the 

hypothetical choice population, while persons 

contacted in such environments may have 

experienced a successful trip without being 

hindered by regulations. 

The disproportionate emphasis on socialpsychological attributes was present in the 

analyses on attribute mix. It is possible that 

persons imagining such trips might tend to place 

greater emphasis on potential psychological 

benefits of participation such as escaping the 

routine and having solitude, while persons on-site 
might be more concerned with the immediate 

activity. This was supported by the higher 

frequency of response for on-site respondents 

in the activity categories. 

Persons in the hypothetical mode were 

considerably more specific in identifying 

attributes for desert environments than persons 

contacted on-site. This difference did not show 

up in relation to mountain environments. It is 

possible that the image of the desert is a much 

more variable construct than that of mountains. 

While one may assume that mountain ecosystems 

are more diverse than arid ones, the features of 

such environments may be more widely known, and 

persons from varying backgrounds may tend to 

report such images similarly. 

Deserts, however, are much less known as 

environments, and there may be a greater range 

in the ways such environments are known. It is 

conceivable that Utah Wilderness Association 

members have a higher interest in this type of 

environment, and as mentioned, may have seen 

more literature concerning it. As a result, 

their images might contain more specific types 

of information. Whatever the explanation, these 

differences suggest that an exploration of the 

potential differences in image composition 

between different types of environments would be 

fruitful. 

CONCLUSIONS 

The differences in the response patterns for the 

two types of survey suggest that results from 

studies using either approach be limited in 

their generalization. Of most importance, mailback studies completed by persons at home may 

not be very accurate indicators of preferences 

for real choices of recreation environments. 

This is of particular concern as recreation 

planners seek to gather public input for 

resource inventory purposes. While it may be 

easy to dismiss the differences reported here as 

being solely due to the different types of 

respondents, such an effect is not in and of 

itself proven. 

There are pragmatic concerns which must also be 

addressed. A general survey of the public would 

have required a much more costly mailing to find 

an adequate sample of backcountry 

recreationists. mailing list of identified 

users would potentially introduce bias. For 

instance, it could have been possible to use 

addresses from backcountry permits issued by 

either the Forest Service or Park Service. This 

would, however, select for persons willing to 

use areas where permits are required assuming 

they were mandatory, or for persons willing to 

fill them out in places where they were 

voluntary. 

While it is always easy to call for "further 

research," the results of this study provide 

support for the need to establish more 

systematically the nature and extent of these 

differences before the use of survey data can be 

generalized sufficiently to be incorporated into 

planninglmanagement systems. 

ACKNOWLEDGMENT 

Support for this research was provided in part 

by the Wilderness Management Research Unit, 

Intermountain Forest and Range Experiment 

Station, USDA Forest Service, Missoula, Mont. 

REFERENCES 

Bugelski, B. R. The definition of the image in 

imagery: current cognitive approaches. 

New York: Academic Press; 1971. 

Bunting, T. E.; Guelke, L. Behavioral and 

perception geography: A critical appraisal. 

Annals of the Assn. of Am. Geog. 69:448- 

462; 1979. 

Driver, B. L.; Brown, P. J. The opportunity 

spectrum concept and behavioral information 

in outdoor recreation supply inventories: A 

rationale. In: Integrated Inventories of 

Renewable Natural Resources. Ft. Collins, 

Colo.: Rocky Mountain Forest and Range 

Expt. Stn., USDA Forest Service; 1978: 24- 

31. 

Hendee, J. C. A scientist's views on some 

current wilderness management issues. 

Western Wildlands. 1: 27-32; 1974. 

Ittelson, W. H., Proshansky, H. M.; Rivlin, L. 

G.; Winkel, G. H. An introduction to 

environmental psychology. N. Y.: Holt, 

Rinehart and Winston, Inc; 1974. 

Leff, H. L.; Ferguson, J. G. Cognitive set and 

environmental awareness. Environment and 

Behavior. 6: 395-447; 1974. 

Mack, R. P. Planning on uncertainty. N.Y.: 

John Wiley, Inc. 1971. 

Moore, G. T. Knowing about environmental 

knowing: The current state of theory and 

research on environmental cognition. 

Environment and Behavior. 11 : 33-70; 

1979. 

Phelps, R. H.; Shantreau, J. Livestock judges: 

How much information can an expert use. 

Organization Behavior and Human Performance. 

21: 209-219; 1978. 

Slovic, P. Choice between equally valued 

alternatives. Journal of Experimental 

Psychology, 1: 280-287; 1975. 

Wagar, J. A.; Folkman, W. S. Public 

participation in forest management 

decisions. Journal of Forestry. 72: 405- 

407; 1974. 
DO CPOICES OF SPORT FISHERIES REFLECT AKCLER PREFERENCES 

FOR S ITE ATTRIBUTES ? 

Charles C. Harris 

B. L. Driver 

ABSTRACT: A revised recreation choice model is 

proposed and partially tested using results of 

a 1980 survey of Colorado anglers. Results of 

discriminant analyses show modest and useful 

prediction from preference for trout fishery 

site attributes to choice of type of fishery 

used. 

PURPOSES 

During the past 20 years, considerable 

research has studied the preferences and 

behaviors of outdoor recreationists. Those 

studies have had economic (e.g., Dwyer and 

others l977), sociologi.ca1 (e. g., Cheek and 

Burch 1976) , psychological (e.g. , Knopf 1983) 

and other disciplinary orientations. Topics 

examined have been varied and include psychological need assessment (Tinslev and Kass 

1980), visual perception and landscape assessment (Elsner and Smardon 1979), recreation 

experience preference (Driver and Brown 1978), 

con£ lict resolution (Jacob and Schreyer 1980), 

user satisfaction (Dorfman 1978), and trend 

prediction (Stynes and others 1980). Most of 

the studies conducted within each of these areas 

have attempted to understand and explain 

recreation behavior. Only a few studies have 

focused on predicting the users' choice of 

recreation activities, settings or areas from a 

variety of independent variables. 

Two situations help explain why greater 

predictability has not been attained between 

area, site, or activity choice as dependent 

variables and area attributes, experience 

preferences, and other independent variables. 

One has been the general tendency of recreation 

behavioral scientists to report results of 

Paper presented at the Recreation Choice 

Behavior Symposium, Missoula, MT, March 22-23, 

1984. 

Harris is an Assistant Professor, with the 

Department of Wildland Recreation Management, 

University of Idaho. Driver is a Research 

Forester with the USDA Forest Service, Rocky 

Mountain Forest and Range Experiment Station. 

Bergersen is Unit Leader of the Colorado 

Cooperative Fishery Research Unit. 

Bergersen 

statistical tests of differences between classes 

and subclasses of independent variables, but not 

report amounts of variance explained. The other 

has been the infrequent use of theoretical 

models to suggest probable paths of statistical 

association (Driver and Knopf 1881, Tinsley 

1984) . 

Because efforts are needed to both improve 

explanation and prediction of choice as a 

critical component of recreation behavior, this 

paper presents and partially tests a revised 

cognitive model of recreation site choice. The 

model builds on other models presented in the 

literature (Harris 1983, Krumpe and YcLaughlin 

1982, Haas and others 1981, Driver and Brown 

1975). Its new dimensions are that it emphasizes the concept of constrained demand (or 

preferences), and it details more explicitly the 

conceptual link between preferences for specific 

setting attributes of a site and preferences for 

specific types of desired experiences. 

The mode] applies to all recreation area/ 

site choices. After it is described, the model 

is partially applied to test two hvpotheses 

about choice of sport fisheries in Colorado: 

1. Preferences for the attributes of 

specific types of Colorado sport 

fisheries will be significantly related 

to anglers' choices of fisheries. 

2. Choices of Colorado sport fisheries 

actually used will be influenced by 

constraining and facilitating 

attributes of those fisheries. 

A REVISED MODEL 

The revised model of recreation area and 

site choice (figure 1) draws heavilv on our 

previous research (Harris 1983, Driver and Brown 

1.975). 

One feature of the model js its elaboration 

of the concept of constrained demand. Constrained demand is emphasized, because the model 

recognizes that most human decj-sions are 

strongly infl.uenced by limited monev, time, and 

other personal resources and capabilities. 

Demands, and behavior, are constrained psychologically, too, because most individuals are 

limited in the amount and complexity of information they can process while making decisions. 
Characteristics 

-Past experience 3 Choice: Search for and Examination of 

, 

Alternative Areas. 

Recreationist's 

Social-Psychological Field 

-Experience Preferences 

-Attitudes 

I Unconstrained 1 i Constrained 

l Preferences I ' -1 Preferences 

I for Area h 7 for Area 

L~ttributes ------ 

Engagement 

4 ?- Perceptions 

I of Area 

Attributes I 5 Recreationist's 

Constraints 

-Money 

-Time 

-Energy 

-Social Demands 

-Skills/abilities 

4 I #\ 

I 

'igure 1.--A behavioral model of recreation choice. 

An example of a current recreation planning 

problem, the allocation of fishery management 

resources to different types of Colorado 

fisheries, is used to clarify the model 

components and their relationships. To simplify 

the discussion, the model depicts the process 

with which a given angler chooses to visit a 

particular type of sport fishery. 

The model begins with a potential recreationist with distinguishing characteristics 

(Box 1). That recreationist possesses a "social 

psychological. field" (Box 2), which consists of 

mental constructs including motives and 

attitudes. One distinction made in this 

conceptualization is that angler attitudes 

include preferences for particular site 

attributes that can be used to distinguish among 

different types of settings, while angler 

experience preferences are antecedent to 

attitudes toward site attributes, may be less 

salient, and may or may not be attributedependent. Thus, the experience preferences 

explain the "why" behind the setting attribute 

preferences which relate to "what for whom." 

The extent to which these attributes are 

expected and valued depends on the extent to 

which an angler has specialized in the activity 

of fishing. Attributes theorized to be relevant 

here include fish-related attributes (e.g., 

species of fish, rate of catch, etc.) and other 

setting attributes (i.e., biophysical, social, 

and managerial site attributes). 

Recreation 

Experience 

-Response 

to Area 

Attributes 

-Perceived 

outcomes 

derived from 

I 

engagement 

8 d Evaluation 

of Experience 

These preferences for setting attributes, 

however, are unconstrained preferences (Box 3A); 

an individual's schedule of unconstrained 

preferences is represented in microeconomic 

theory by the concept of a utility function. In 

realitv, limits exist on the use of any site 

that stem from the interaction of the recreationist's perception of site attributes (Box 4) 

and actual constraints on the recreationist 

(e.~., money, time, skill level, group norms, 

etc; Box 5). We theorize that factors such as 

the distance traveled to a site are perceived to 

be constraining attributes of that site, and 

that the recreationist's experience preferences 

and attitudes can significantly influence 

his/her perceptions of a site and its constraining attributes. 

In the face of these constraints, the 

indivi-dual chooses among the attributes of 

available alternative sites. The site finally 

chosen represents the decisionmaker's 

constrained set of preferences for site 

attributes (Box 3B). This concept of constrained preferences parallels the concept of 

demand as formulated in m~croeconomic theory; 

demand curves are, in theory, derived by tracing 

out these price-quantity points where each 

individual's indifference curve (which reflects 

that individual's utility, or unconstrained 

preferences) intersects hislher budget constraint function. Implicit in this decision 
process is a consideration of the values and 

expected levels of the salient attributes of the 

various alternative sites. Only minimal. levels 

of availability may suffice for some site 

attributes, and the decision process in which 

site attributes are considered may be more 

appropriately modelled with a lexicographic 

choice rule than with the mental calculus 

implicit in subjective expected utility models. 

If the individual recreates at the site 

chosen (Box 6), that recreationist will 

experience the site attributes and perceive that 

certain outcomes have been derived from that 

engagement. This response to the engagement is 

the recreation experience (Box 7). The 

recreationist evaluates the site and the 

experience by comparing the attributes and 

outcomes expected and desired from the enga-gement with those actually experienced (Box 8). 

It is on the basis of t.his comparison that the 

individual assesses his/her satisfaction with 

the site (Box 9). 

This conceptualization provides an extended 

framework that integrates both site choice and 

recreation experience evaluation in a model of 

recreationist decisionmaking. Perhaps the most 

basic relationship suggested by our model is 

that between the recreationist's constrained 

preferences for site attributes and his/her 

choice of a type of site to visit. Although 

this conceptual link underlies both theory of 

recreation choice and consumer demand theory 

(Lancaster 1971), little research to date has 

attempted to assess the correlation between 

actual choice behaviors and stated preferences 

for particular site attributes (both constraining and facilitating). 

and actively participating anglers. Given that 

these are the anglers whose desires and 

preferences are likely to be of greatest concern 

in fishery management planning, this sample was 

judged to be adequate. 

Data Preparation 

The responding anglers were asked to record 

the number of fishing trips they had taken to 

each of four types of fishing sites: (1) wild, 

or trout fisheries in mountainous areas that are 

not readily accessible by vehicle, (2) basicyield, or trout fisheries in mountainous areas 

that can be easily reached by vehicle, (3) 

rural, or mixed species waters, typically cool 

or warm, in nonurban areas of Colorado, and (4) 

urban, or warm-water fisheries bordering or 

within the cities of Colorado. In addition, 

respondents were asked to indicate the type for 

which they had recorded the highest number (i.e, 

the most frequented fishery type). As a 

validity check, the consistency of the responses 

to these two methods of measuring the most 

frequented fishery type was investigated and the 

case involved was dropped from the analysis when 

an inconsistency was found. 

The anglers also responded to 32 preference 

items in terms of the last (most recent) trip 

they had taken to a fishery of their most 

frequented type. For these items, the respondent was asked to rate the importance of biophysical, facility, social, and fish-related 

attributes for choosing the fishhg site visited 

on that trip, using a six-point Likert-type 

scale with response options ranging from Not At 

All Important (1) to Extremely Important (6). 

Analysis 

METHODS 

Subjects 

The data were collected in 1.981 as part of 

a survey of angler demands for Colorado sport 

fisheries. A random sample of persons who had 

acquired licenses for fishing in Colorado in 

1980 was drawn. Questionnaires were mailed to 

these individuals in two waves, followed by a 

postcard reminder. Of the total sample of 1502 

licensees, 484 (or 32 percent) returned usable 

questionnaires. 

Although this rate of response was 

relatively low, it was of the same order of 

magnitude as that obtained by the Colorado 

Division of Wildlife on its annual angl-er 

surveys. A telephone survey of a sample of 

nonrespondents indicated that most nonrespondents were minimally interested anglers and that 

they were not users of any particular type of 

fishery. Thus, the survey responses obtained 

were probably from the more serious, concerned, 

The 32 preference items were clustered hto 

groups on the basis of correlations among the 

responses to those items using the ICLUST VI 

clustering program. ICLUST VI first searches 

the. initial item pool for that pair of items 

which is most highly correlated, and these items 

are combined to form a cluster. This procedure 

is repea-ted and the remaining items and 

clusters, are formed into higher-order clusters 

until Cronbach's alpha or an estimate of 

"coefficient beta" (the worst split-half 

reliabilitv! of the combined cluster would be 

less than that of sub-clusters. This clustering 

procedure and subjective judgment were used to 

decide the content of the final scales. To 

obtain scale scores, an average was taken of 

respondent's ratings on all of the items comprising that scale. 

Next, discriminant analysis was used to 

determine significant differences in the 

attribute preference scale scores of four groups 
identified on the basis of the fisherv type they 

reported they visited most frequently. The 

groups identified by choice of a type of fishery 

to frequent were the dependent variable, and the 

attribute preference scales were the discriminating or independent variables. Given that 

the weighting coefficients in the discriminant 

function are a measure of the correlation 

between the independent variables and the 

discriminant function (and are thus roughly 

analogous in interpretation to the beta weights 

in regression analysis), this analysis suggests 

the relative importance of the preference scales 

for discriminating among the groups. 

Table 1.--Items for attribute preference scales, 

with scale reliahj.!.ity coefficients. 

Scale items 

Familiar waters 

Reliabj.1-ity 

Coeffiqient 

NA 

RESULTS 

Cluster analysis of the attribute preference items resulted in six multiple-item scales 

and four single-item scales (see table 1). 

Reliability coefficients for the six multipleitem scales ranged from 0.75 to 0.88. 

The numbers of respondents reporting a 

choice of each of the four fishery types are 

listed in table 2. A total of 260 fishermen 

(70%) preferred the basic-yield type of fishery, 

77 (21%) chose the wild type of fishery, 25 (72) 

the rural type of fishery, and 9 (22) the urban 

type of fishery. 

Also included in tab3.e 2 are the group 

memberships of the respondents predicted on the 

basis of their discriminant function scores. As 

that table %-shows, 43 percent of the individuals 

who were predicted to be in the basic-yield 

group were in fact in that group, 77 percent of 

those predicted to be in the wild group were in 

that group, 40 percent of those predicted to be in 

the rural group were actually in that group, and 

89 percent of those predicted to be in the urban 

group were in that group. Overall, 51 percent 

of the cases were correctly classified. 

The results of the discriminant analysis 

suggested that significant differences did exist 

in the attribute preferences of anglers choosing 

different types of recreation areas to visit 

most frequently. As table 3 shows, the square 

of the canonical correlation for the flrst and 

most important discriminant function was .33, 

the proportion of the variance in this function 

associated with the dependent variable. This 

function accounted for 48.6 percent of total 

variance in the discriminating variables. (See 

Brown and Tinsley 1983 for a discussion of 

evaluating discriminant functions.) In 

addition, 7 percent of the variance in the 

second function was associated with the variance 

in the dependent variable; that second function 

accounted for an additional 7.8 percent of the 

total variance in the discriminating variables. 

Table 3 also lists all the attribute 

preference scales that were included as independent variables in the discriminant analysis, 

along with the mean importance ratings of the 

four choice-based groups. The first function is 

New waters N A 

Waters close to home or work NA 

Fast-moving waters N A 

Trophy fish .90 

-- Catching as large a fish 

as you can 

-- Catching trophy fish 

-- Catching big fish 

Unique fish 

-- Catching kinds of fish not 

often caught in Colorado 

-- Catching kinds of fish not 

common in Colorado 

-- Catching fish not raised 

in hatcheries 

Smart fish 

-- Outsmarting more experienced 

fish 

-- Fishing for smarter fish 

-- Pitting your wits against 

the fish 

-- Fishing where you have to be 

skilled to catch fish 

Wild setting .88 

-- Seeing few man-made structures 

-- Fishing an area of high scenic 

beauty 

-- Fishing where you are likely to 

see only a small number of 

other fishermen 

-- Fishing where you feel far away 

from people and cities 

-- Fishing in remote areas 

-- Fishing where you cannot see or 

hear busy traffic 

-- Fishing where other people are 

not engaged in other recreational 

activities 

Fish yield 

-- Catching your limit 

-- Catching fish to eat 

-- Fishing where fish are biting 

regardless of what else you find 

at the area 

-- Catching as many fish as you can 

-- Catching enough fish for a good 

meal 

-- Catching fish to store for future 

needs 

Facilities .75 

-- Fishing at areas where the whole 

family enjoys recreating 

-- Fishing where boat launches, 

parking lots, boat rentals, etc., 

are available 

-- Fishing where you don't have to 

walk more than 15 minutes 

-- Fishing where campsites, picnic 

tables, restrooms, etc., are 

available 

A reliability coefficient is not applicable 

(NA) for a single-item scale. 
Table 2.--Numbers of anglers in actual and 

predicted fishery choice groups. 

Predicted Choice G~OUDS 

BasicActual Choice N yield Wild Rural Urban 

Wild 

Rural 2 5 6 1 10 8 

(24) (4) (40) (32) 

Urban 

Totals 371 131 119 63 5 8 

Figures in parentheses represent row 

percentages. 

defined by the "wild setting" and "facilities" 

scales, which were found to be equally important 

as discriminators and provided most of the 

explanation of variance in area choice. The 

fisherman group that actually chose the wild 

type of area to fish placed the greatest 

importance of any group on the "wild setting" 

attribute dimension and the least on the 

"facilities" dimension, while the basic-yield, 

rural, and urban groups reported progressively 

lower means on the "wild setting" dimension and 

progressively higher means on the "facilities" 

dimension. 

The second function included "waters close 

to work or home" and "novel waters" as attribute 

preference scales that were nearly equally 

important as useful discriminators among type 

choices. The convenience of waters close to 

work or home was most important to anglers 

choosing the urban type of recreation area most 

frequently; it was of decreasing importance to 

rural and basic-yield user groups, while the 

wild choice group placed only slight importance 

on that attribute. No clear trend is reflected 

by the means obtained with the "novel waters" 

scale, although fishermen choosing the wild type 

of area placed a somewhat greater importance on 

that attribute in comparison with groups 

choosing other types of areas. "Fast-moving 

waters" was also an attribute important for 

discriminating among area choices: a clear trend 

was reflected in mean scores for this attribute 

preference scale, with the wild user group 

placing the greatest importance on it of any 

group and the basic-yield, rural, and urban 

choice groups reporting progressivelp lower 

means for this attribute. 

These results are presented graphically in 

figure 2, which shows the relative positions of 

the four predicted choice groups vis-a-vis the 

two discriminant functions and the preference 

scores on which the functions are based. Each 

point represents a given group's position as 

reflected by that group's centroid, which was 

derived by evaluating the discriminant functions 

at the group means. 

DISCUSSION 

Given the complexity and uncertainty of 

predicting human behavior, the results of the 

discriminant function analysis are interpreted 

as disclosing a moderatelv high degree of 

consistency between fishermen's choice of type 

of fishery and their preferences for attributes 

characterizing each type. With an adjustment 

for the size of each of the four groups, 25 

percent of the members of each group would be 

correctly classified as actual members of the 

group by chance alone. (We disregarded any 

prior probabilities of classifying anglers based 

on group sizes, which would have masked the 

association between our independent and 

dependent variables). In contrast, the 

predictive model based on the two discriminant 

functions generally did much better than this 

random allocation; it correctly classified 77 

percent of the anglers in the wild group, 43 

percent of the anglers in the basic-yield group, 

40 percent of the anglers in the rural group, 

and 89 percent of the anglers in the urban 

group. These proportions indicate that the 

model was most effective in identifying anglers 

in the wild and urban groups. This result is to 

be expected, given that these two groups 

differed the most in their attribute preferences 

(see table 3, figure 2). The model was less 

effective in identifying members of the 

basic-vield and rural groups, although it still 

significantly jncreased the accuracy of classification in the case of these groups over that 

achieved with a random process. 

These results provide support for our first 

hypothesis, suggesting that, when anglers' 

preferences for specific fishery attributes that 

are important in a particular choice situation 

are considered, actual choice behaviors of the 

anglers do reflect the importance. placed on 

those attributes. Nonetheless, usjng the square 

of the canonical correlation coefficient as a 

rough indicator, at least 60 percent of the 

variance in area type choice was unassociated 

with the variance in attribute preferences. 

This finding is not unexpected, given the 

possible sources of variance for which we did 

not account. A number of these sources point to 

possible limitations of the study. For 

instance, there may have been critical decision 

criteria underlying the choice process (e.g., 

knowledge of available sites) that were not 

included in the analysis but that would have 

explained some of the error it revealed. In 

addition, the methods used to obtain attribute 

preference ratings were somewhat circuitous and 

could have caused some instrument bias. A 

respondent had to first recall numbers of annual 

trips taken to specific types of sport fisheries 
Table 3.--Discriminant analysis of choices of types of fisheries. 

Mean Scores on Preference Scales by Discriminant Function 

Choice ~r0u~s-l Coefficients 

Attribute 

Preference Basic- Discriminant Discriminant 

Scales Wild yield Rural Urban Function 1 Function 2 

Wild Setting - 5.15 4.17 3.35 3.27 -0.58 

Facilities 

Waters close to work 

or home 

Novel Waters 

Fast-moving waters 

Smart Fish 

Familiar waters 

Unique fish 

Trophy fish 

Fish-yield 

Percent of variance explained in area choice4 

Percent of variance explained in important ratings 

Chi-square value 

'choice groups consist of respondents choosing a type of fishery to fish most frequently; mean 

preference scores are based on a scale ranging from 1 ("Not At All Important") to 6 ("Extremely 

21mportant"). 

30nly moderate to large coefficients (20.40) are listed. 

4Numbers in parentheses are standard deviations. 

5Square of the canonical correlation coefficient for that function. 

Statistically significant, ~~0.05. 

then record that type he/she had visited most 

frequently, then think specifically of the last 

trip taken to an area of that type, and finally 

rate the importance of 32 attribute-related items 

for choosing to visit that specific area. 

Choice of a general area type would be only 

indirectly related to the importance of site 

attributes for choosing the last area visited, 

and this link was further attenuated through the 

structure of the questionnaire. So we might 

expect some error. If anything, our results 

suggest that people were reasonably capable in 

following the instructions of the questionnaire 

and conscientious in answering the questions it 

contained. 

It is also important to recognize that 

complete homogeneity of preferences should not 

be expected for a group of anglers using a given 

type of fishery. To the contrary, it follows 

from recent theory on recreation behavior (e.g., 

Driver and Brown 1978) that, because even 

recreationists using the same site differ in 

terms of their experience preferences, 

differences in preferences for site attributes 

should also be found. Therefore, although 

differences in attribute importance ratings 

should be greater among the groups frequenting 

different types of fisheries than they are among 

anglers within any one group, heterogeneity in 

the preferences of anglers using a particular 

type of fishery is to be expected. 

Despite the error factor in our results, we 

believe that they also confirmed our second 

hypothesis that preferences for both constraining and facilitating attributes influenced the 

anglers' choices. Perhaps the most interesting 

findings of the study are suggested by a consideration of the role of specific attribute 

preferences. For example, the most important 

discriminating preferences were not ones for 

attributes of the area that were directly 

fish-related (e.g., potential number or size of 

fish that might be caught). Rather, preferences 

for setting attributes unrelated to fishery 

characteristics were most important. In fact, 

when the importance means for fish-related 

attributes (e.g., trophy fish, fish yield, 

unique fish, etc.) were compared across choice 

groups, all of the groups placed similar and 

fairly high 1evel.s of importance on these 

features, as one might expect of serious 

fishermen in any setting. The greater 
FUNCTION 1 

Low Score on 

Natural Setting 

0 Urban 

Rural 

High Score 

on Novel 

Waters and 

Convenience 

I I 

- 2 - 1 

Low Score 

on Fastmoving 

Waters 

Wild 0 

High Score on 

Natural Setting 

2 

High Score on 

Facilities 

Low Score 

on Novel 

Waters and 

Convenience 

High Score ' I 

on Fast- 0 

moving N 

Waters 2 

Low Score on 

Facilities 

Figure 2.--Relative positions of four predicted fishing groups in relation to two discriminant 

functions and preference scores upon which the functions were based. 

importance of setting variables as discriminators of area choice indicates that site 

attributes defining the character of the overall 

recreation experience, and not just fish-related 

attributes, are important to fishermen. 

Further, as theorized in the revised model, 

the setting attributes considered in the choice 

process are both constraining and facilitating 

ones. For example, the most important 

at'tributes for choice discrimination included 

"wild setting" and "facilities". In the case of 

the group choosing the wild type of fishery to 

visit most frequently, the "wild setting" 

attribute was definitely an attractive or 

facilitating feature, while the presence of 

"facilities" might be interpreted (on the basis 

of its low importance) as a detracting feature 

of an area in the minds of these fishermen. The 

convenience of "waters close to work or home" 

was an important factor in the second function, 

especially for the urban group whose high 

importance score on this attribute reflects the 

constraint that distance from fisheries 

apparently represents to the group. Also useful 

as a discriminator was the attribute "novel 

waters," which was an important facilitating 

feature to the wild group. (It might be noted 

that "familiar waters" was more attractive to 

the groups using the more developed and con- 
venient fishery types.) This finding may have 

particular relevance for efforts to model 

recreation site demand and evaluate site benefits, which make the basic assumption that, all 

site attractiveness features being equal, 

recreationists will choose the site closest to 

them. The importance of novelty for members of 

the wild group suggests that they, in 

particular, might well visit a more distant site 

for the sake of its novelty, and failure to 

account for this influence could result in 

biased estimates of site demand and val-ue. 

A conclusion, then, that might be drawn 

from the study findings concerns efforts to 

understand the decisionmaking processes of 

recreationists making site choices. The results 

of the research suggest that only a small number 

of area attribute dimensions may be central 

determinants of site choice, and this finding 

lends some weight to those proposing that 

simpler choice rules are appropriate for 

modeling processes of recreationist decision 

making (e. g., Krumpe and McLaughlin 1982). 

In addition, the study findings indicate 

the value of measuring preferences for specific 

area attributes along with preferences for 

specific types of experiences. However, further 

research is needed to examine the relationship 

between recreation experience and site-attribute 

preferences as well as to more fully describe 

other elements and relationships influencing 

recreation decisionmaking processes. 

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Cheek, Neil H.; Burch, William R., Jr. The 

social organization of leisure in human 

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Dorfman, Peter W. Measurement and meaning of 

recreation satisfac-tion: A case study in 

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Driver, B. L.; Brown, Perry J. A 

socio-psychological definition of recreation 

demand, with implications for recreation 

resource planning. pp. 64-88. Assessing 

demand for outdoor recreation. National 

Academy of Sciences, Washington, D. C. 1975. 

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spectrum concept and behavioral information 

in outdoor recreation resource supply 

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H. Lund (tech. coords.). Integrated 

inventories and renewable natural resources: 

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Mountain Forest and Range Experiment Station, 

Fort Collins, CO. 1978. 482 p. 

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on the quality of outdoor recreation research 

and other constraints on its application. 

pp. 85-98. In Proceedings 1980 conference on 

social research in national parks and 

wildland areas. U. S. National Park Service, 

Asheville, North Carolina. 1981. 133 p. 

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Michael D. Improved procedures for valuation 

of the contribution of recreation to national 

economic development. Water Resources Center 

Report 128, Univ. of Illinois at 

Urbana-Champaign, Champaign, IL. 1977. 

218 p. 

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Proceedings of our national landscape: A 

conference on applied techniques for analysis 

and management of the visual resource. USDA 

Forest Service General Technical Report 

PSW-35, Pacific Southwest Forest and Range 

Experiment Station, Berkeley, CA. 1979. 

752 p. 

Haas, Glenn E.; Driver, B. L.; Brown, Perry J. 

Measuring wilderness recreation experiences. 

pp. 25-30. 2 Lance Cannon (ed.) 

Proceedings, wilderness psychology group's 

annual conference. Department of 

Psychology, University of New Hampshire, 

Durham, New Hampshire. 1981. 

Harris, Charles C. Assessing the validity of 

economic methods for evaluating sport fishery 

benefits: A behavioral approach. University 

Microfilms, University of Michigan, Ann 

Arbor, unpubl. Ph.D. dissertation. 1983. 

202 p. 

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in outdoor recreation: A theoretical 

perspective. Journal of Leisure Research. 

12 (4) : 368-380. 1980. 

Knopf, Richard C. Recreational needs and 

behavior in natural settings. p. 205-240. 

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(eds .) . Behavior and the natural 

environment: Advances in theory and research. 

Vol. 6, Behavior and the Natural. Environment. 

Plenum Press, NY. 1983. 346 p. 

Krumpe, Edward E.; McLaughlin, William J. A 

model of recreationists' decisionmaking 

process. pp. 94-99. , Jn- D. Lime (tech. 

coord.) Forest and river recreation: 

Research update. Miscellaneous Publication 

18-1982. The Agricultural Experiment 

Station, University of Minnesota, St. Paul. 

1982. 163 p. 

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approach. Columbia University Press, NY. 

1971. 
Stynes, Daniel J.; Bevins, M. I.; Brown, T. L. 

Trends or methodological differences? 

Proceedings, 1980 National Outdoor Recreation 

Trends Symposium. p. 223-232. Vol. I, USDA 

Forest Service General Technical Report 

NE-57, Northeastern Forest Experiment 

Station, Broomall, PA. 1980. 

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construct validity of the Leisure Activities 

Questionnaire and of the Paragraphs about 

Leisure. Educational and Psychological 

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aspirations: A confession of fallibility and 

a promise to strive for perfection. Journal 

of Leisure Research. 16(2): 93-98. 1984. 
APPROXIMATING RECREATION SITE CHOICE: THE PREDICTIVE CAPABILITY 

OF A LEXICOGRAPHIC SEMI-ORDER MODEL 

Alan E. Watson and Joseph W. Roggenbuck 

ABSTRACT: The relevancy of a lexicographic 

semi-order model, as a basis for development of a 

microcomputer-based decision aid for backcountry 

hikers, was investigated. In an interactive 

microcomputer exercise, it was found that a decision aid based upon this model may assist 

recreationists in reduction of an alternative 

set to a cognitively manageable number. 

offices daily to obtain information on primitive 

recreation opportunities in the immediate area. 

If information about the full spectrm of opportunities available in a particular area could 

be presented, recreationists may be able to choose 

sites that meet their needs outside heavily used 

wilderness. 

DECISION AIDS 

INTRODUCTION 

In the eastern United States, many Forest Service 

managers of wilderness areas are reporting heavy 

and increasing use levels (Roggenbuck and Watson 

1981). Managers generally believe that techniques 

to disperse users within an area or across areas 

could help reduce congestion, overuse, and perhaps 

physical impacts (Hendee and others 1978; 

Roggenbuck and Watson 1981). 

Not all Forest Service wilderness area managers 

in the East report overuse, however. Indeed, 

reports show several areas have very little use 

or use at such low levels that impact is negligible (Roggenbuck and Watson 1981) . 

Alternatives to visiting a National Forest wilderness area often exist. Within a given region, 

wilderness managed by the National Park Service, 

Fish and Wildlife Service, some states (for example, Tennessee Pocket Wilderness), and some privately owned and managed backcountry (for example, 

Grandfather Mountain in North Carolina), and many 

National Forest , National Park, and Sta te-managed 

backcountry opportunities are often available. 

The question becomes: How do we encourage people 

looking for a primitive outdoor recreation experience to avoid the areas that are currently 

overused? 

Often, decisions are made to visit a geographic 

region without an actual decision concerning a 

specific recreation activity site. This is evidenced by the number of visitors entering Forest 

Service visitor centers and District Ranger 

Paper presented at the Recreation Choice Behavior 

Symposium, Missoula, MT, March 22-23, 1984. 

Alan E. Watson is Assistant Professor, Recreation 

and Leisure Services, Georgia Southern College, 

S tatesboro, GA. Joseph W. Roggenbuck is Associate 

Professor of Forestry, Virginia Polytechnic Institute and State University, Blacksburg, VA. Support 

for this research was provided by the Intermountain 

Forest and Range Experiment Station, USDA Forest 

Service. 

Our interest in the research reported here was 

in providing a framework for development of decision aids for backcountry recreation site selection. These decision aids would most likely be 

used in a Forest Service district office or visitor center or in a corresponding contact situation 

of other land management agencies. Currently, 

brochures and some verbal message from an attendant in a visitor center are typically provided. 

The same written information is presented to all 

visitors regardless of reasons for visiting. This 

often leads to the visitor searching through these 

materials for personally relevant information. 

One improvement over the brochure presentation 

system within the Forest Service is the Recreation 

Opportunity Guide (ROG) (USDA Forest Service 1979). 

The ROG is available for the recreationist's use 

at the visitor center or for use by a receptionist 

in helping the recreationist decide upon a site 

for a chosen activity. The ROG for the Pisgah 

District of the Pisgah National Forest in North 

Carolina is divided into two loose-leaf notebooks: 

(1) trails; and (2) all other recreation resources 

in the district. Within the trails section of 

the ROG, names of trails are listed alphabetically. 

Various information is listed for each trail such 

as length, a mapped location, access points, attractions and considerations, recommended season, 

amount of use received, difficulty, and change 

in elevation by distance. 

The ROG for trails on the Pisgah district is 

indexed in four ways. Trails are listed by activity (for example, hiking, skiing, horseback 

riding); by name (alphabetically); by length (in 

miles); and by area (planning unit within the 

district). Using this index, some alternatives 

can be eliminated based upon location, length 

of trail, and activity interest. The recreationist 

must, however, search through an alphabetical 

listing to obtain additional information on the 

remaining alternatives . 

Another innovative decision aid, tested in a dispersed recreation setting, is the Backcountry 

Trail Selector (Krumpe and Brown 1 98 2) . The 

Backcountry Trail Selector leads a decision maker 
through a series of intermediate choices (for 

example, a trail along a stream vs. a trail over 

high passes) to a final choice between alternatives. 

This decision aid is described as a decision net, 

usually depicted as a branching tree of successive 

decision steps. The decision nodes, or branches, 

represent the individual, intermediate choices, 

where attributes of possible alternative sites 

are considered. The branch taken at a node depends 

on whether or not the level of the attribute under 

consideration is satisfactory or unsatisfactory. 

The need is to examine these two innovations to 

determine strengths and weaknesses. Decision 

models evolving from psychology and consumer behavior research offer some insight into how we might 

build upon these two decision aids. Additionally, 

microcomputer-visi tor interaction sys tems potentially open new possibilities in construction 

and application of decision aids. If a theoretically sound method of information presentation 

were provided, an individual could work interactively with the microcomputer to gain personally 

relevant informa tion efficiently . 

DECISION MODELS 

A large quantity of decision-related research 

in recreation is based upon the compensatory 

approach of the expectancy X valence theory framework (Haas and others 1981; Manfredo 1979; Cockrell 

1981). This framework suggests that decisions 

might be best predicted by combining information 

on attributes (specifically, information on the 

value of specific outcomes related to the attributes and probability of achieving the specific 

outcomes) to derive a single evaluative score 

for each alternative behavior (for example, visits 

to alternative recreation sites) being considered. 

Applications of the Fishbein model of expectancy 

X valence theory also often include a normative 

component as a further determinant of behavioral 

intentions (Cockrell 1981) . 

On the basis of their research, Cockrell (1981) 

and Krumpe (1 979) believe that noncompensatory 

models may be better at predicting choice in the 

recreation context (for whatever reason) than 

previously tested compensatory models. Tversky 

(1 972, p . 298) , one noncompensatory theorist, 

supports this belief by stating that the lexicographic noncompensatory model may be a "good 

approximation to much more complicated compensatory 

modelsff and, in fact, may be "a useful simplification procedure. " Compensatory models may be 

good predictors of choice, but in the recreation 

context a more simply operationalized approximation 

may provide better results than past efforts at 

a very complex task. Normative factors, weigh tings 

of importance of attributes, and calculations 

of value and expectancies are not involved in 

a noncompensatory model, though their effects 

may be approximated in a simplified manner. 

The noncompensatory models of choice look for 

alternatives in a decision situation that are 

satisfactory for all attributes or a particular 

set of attributes. Alternatives are usually compared on an attribute-by-attribute basis, rather 

than by comparing an evaluative score as in the 

compensatory models. In this type of decision 

model one attribute does not compensate for the 

lack of another. Rather, if the attribute is 

not present in sufficient quantity, the alternative 

is excluded from further consideration. 

The lexicographic model calls for a finite ordering 

of elements (Fishburn 1974). Most notably, the 

lexicographic semi-order choice model (Luce 1956; 

Coombs 1964 ; Tversky 1969; Fishburn 1974) assumes 

an ordering of the relevant attributes a priori. 

This ordering is based on importance of the attribute to the decision maker when choosing among 

alternatives. In this model all alternatives 

are first compared on the most important attribute. 

The a1 ternatives judged not satisfactory for this 

most important attribute are deleted from further 

consideration. This process continues with those 

attributes judged to be second, third, fourth, 

etc., in importance until only one acceptable 

alternative is left. 

Notice use of the term "not satisfactory" in determining which alternative to drop from further 

consideration. Fishburn (1 974) has recognized 

that in many cases it is not possible to discover 

a feasible alternative that is "acceptable" or 

"best" for all criteria or wants. With a lexicographic order in mind, Georgescu-Roegen (1 954, 

p. 518) asserts that "choice aims at satisfying 

the greatest number of wants starting with the 

most important and going down the hierarchy. 

Therefore, choice is determined by the least 

important want that could be reached." 

Fishburn (1974) refers to this process as "satisficing-plus." In this case, one achieves a satisfactory level on as many of the most important 

criteria as possible, then uses the next most 

important criteria (for which only one of the 

remaining alternatives is "satisfactory") to 

differentiate among the alternatives that are 

satisfactory for all preceding criteria. 

This semi-order model is characterized by Luce 

(1956) as having a just noticeable difference 

structure imposed on a lexicographic ordering. 

Inclusion of the semi-order factor results from 

research findings which indicate that preference 

for some values of some attributes is not always 

transitive (Tversky 1969). Those supporting a 

compensatory approach seem to believe that values 

of other attributes might compensate for low values 

of an important factor. Semi-order lexicographic 

proponents, however, believe that several values 

of one attribute may not be "noticeably different ," 

thus are intransitive. Selection may theref ore 

be based on less important attributes though the 

alternatives differ, but not to a noticeable 

degree, on a very important one. 
Tversky (1 969) used the lexicographic semi-order 

model to study intransitivities in preference. 

In a laboratory experiment he studied the decision 

process of subjects who were choosing from college 

applicants the one they thought should be accepted. 

Subjects also were asked to indicate preference 

between simple gambles. Tversky found that the 

semi-order choice model predicted subject choice 

better than a model assuming weak stochastic 

transitivity (the most general probabilistic 

version of transitivity) . Tversky (1 969) concluded 

that when faced with complex multidimensional 

alternatives, it is extremely difficult to utilize 

properly all of the available information. Instead, 

it appears that people may employ various approximation methods that enable them to process the 

relevant information in making a decision. The 

lexicographic semi-order choice model may be just 

such an approximation. It is easy to apply, 

without complex mathematical computations, and 

is fairly easily understandable by researchers 

and those who apply research findings. 

As for many decision models that have been proposed 

in the fields of economics and psychology, there 

have been relatively few empirical studies of 

the lexicographic semi-order model of choice 

(Azumi 1981). Tversky (1 972) pointed out that 

the primary reasons for the lack of such model 

testing are the difficulties involved in controlling 

the experiment appropriately and obtaining adequate 

estimates of choice probabilities. 

Selection of the lexicographic semi-order choice 

model seems, however, justified through a series 

of smaller, individual research and theory building 

accomplishments. Evidence supporting use of such 

a model deals with a subject's ability to make 

decisions from information prp,snted sequentially. 

In many cases, subjects have shown a tendency 

to seek and process information in that manner. 

The relationship between subject-expressed preferences for attributes and choices made between 

alternatives has also been explored in a variety 

of contexts. 

Studies that have successfully presented information in a sequential manner to decision makers, 

or found the subject to use available information 

in this manner, are varied in purpose and discipline 

(Olshavsky 1979; Powell 1979; Crow and others 

1980; Englander and Tyszka 1980; Herstein 1981; 

Lowery 1981; Busemeyer 1982). Taken together, 

although not specifically testing the lexicographic semi-order choice model, they do 

present data that support the concept of sequential 

information processing. 

The noncompensatory approach, and the lexicographic 

semi-order model in particular, appears to be 

a viable alternative to previously used compensatory models in predicting recreationist choice. 

In developing a decision aid for recreation site 

selection it is desirable to use a presentation 

format known to approximate the actual decision 

process and could, therefore, provide such a format 

for presentation of information. For these reasons, 

we selected this model for testing in our research. 

METHODS 

Our methodology included a laboratory exercise in 

which subjects interacted with a microcomputer 

to collect data. Overnight backpackers, as one 

of the most common user groups of backcountry, 

were chosen as the subject population. Fifty 

members of the local community were selected from 

response to local appeals for volunteers. Subjects 

selected had backpacking experience ranging from 

several years to none (those planning their first 

trip). 

In Task 1, subjects were asked to select from 

15 hypothetical backcountry areas (fig. 1) a first, 

second, and third choice as a place to visit. 

The purpose of this choice task was to provide 

a criterion variable on which to evaluate the 

predictive ability of the lexicographic semiorder choice model. Hypothetical areas were 

assigned numbers rather than names to avoid preference effects due to the attractiveness of the 

name. The 15 attributes listed in the same order 

for each area were those found to be most relevant 

to participants in a pilot study. The order of 

presentation of attribute information was randomly 

generated and assumed to have no significance. 

Values for the attributes were determined with 

concern for assuring some conflict. We sought 

to avoid making any area so attractive that every 

subject would choose it. Also, the 15 areas needed 

to be different enough for a single choice to 

emerge. 

Task 2 entailed collection of data via a microcomputer. In an interactive microcomputer exercise 

developed specifically for this study, each subject 

ranked the 15 attributes from Task 1 in terms 

of importance to choosing among a1 ternative backcountry recreation sites. Each subject was next 

asked to indicate a preference for the values 

of the attributes in the following manner: "2," 

category that was most desirable; "1," categories 

that were acceptable; or "0," categories that 

were definitely unacceptable. Minimum thresholds 

of acceptance were established for attributes 

with at least one value rated "0." Indications 

of preference of values were obtained for attributes containing a "2" designation. From this 

information alternatives were eliminated in a 

sequential application of the lexicographic semiorder model and a final choice predicted. 

In the elimination process the experimenter started 

with each subject' s most important attribute and 

proceeded through the attributes one at a time 

(fig. 2). All alternatives that exhibited unacceptable values for the most important attribute were 

eliminated. This process continued with the second, 

third, etc., most important attributes with the 

goal of reaching only one alternative. If more 

than one alternative remained, we then selected 

as the predicted choice the alternative that 

possessed the preferred category of the highest 

ranked attribute, for which no other alternative 

possessed the preferred category. 

RESULTS and knowledge of the purpose to which responses 

were to be applied. 

In 48 of the 50 cases, a clear choice was predicted 

from information collected through the microcomputer exercise. In only two cases could no 

clear "winner" be predicted. In 27 cases (54 

percent), the predicted choice matched the subject's 

actual choice. The predictive success of this 

noncompensatory model is difficult to compare 

directly with past tests of compensatory approaches 

in the recreation context. In one of the most 

recent applications of the Fishbein model, however, 

Cockrell (1981) reported nonsignificant R-squares 

in five of six regression equations used for 

prediction. Elements of the regression equations 

included attitudes and norms toward various alternative areas. The lack of significant R-squares 

was interpreted by Cockrell to be an indication 

of little predictive success. He concluded that 

a river runner's intentions to participate in 

a river trip are not determined by the person's 

attitudes toward the trip and his social norms 

about the trip. 

Although the choice prediction capability of the 

lexicographic semi-order model is substantial, 

the model's ability to predict elimination of 

alternatives is even more noteworthy. In 72 percent of the cases in our study, the prediction 

process successfully eliminated twelve of the 

15 alternatives, leaving a set of three alternatives, one of which was the subject's first 

choice. This figure increased to 86 percent for 

deriving a set of five areas that contained the 

subject' s first choice. 

The area selected as the first choice by the subject in Task 1 and which served as the criterion 

measure for evaluating the predictive capability 

of the lexicographic semi-order choice model may 

not have been the individual's "best" choice. 

Recall that Task 1 was a written exercise that 

required the subject to name a first, second, 

and third choice among 15 hypothetical backcountry 

areas. Information on 15 attributes for each 

of these 15 areas was presented .to the subject 

in tabular form. This task, designed as it was 

to assure some conflict in decision making, may 

have been so difficult that the "best" decision 

was not made. If this occurred, then the predictive capability of the model may be underestimated. 

The predictive capability may have been better 

had fewer areas, fewer attributes, or a different 

method of presenting the attribute information 

been used in determining the criterion measure. 

It is possible, however, that the procedure used 

to identify the criterion measure may have increased 

the likelihood of a valid selection of a "best" 

choice. The researcher communicated instructions 

to the subjects orally, only one subject at a 

time was processed, and the subject was aware 

that the general goal of the study was to find 

out how people make decisions about where to go 

on recreation visits. A subject making a decision 

in this context may have attempted to very carefully 

process every item of information available. Such 

processing may, in fact, be more complete than 

in a real situation without oral instructions 

CONCLUSIONS 

The lexicographic semi-order choice model used 

in this study did not approximate the backcountry 

site selection decision process closely enough 

to select a final choice for all users. Our findings do, however, suggest that a decision aid 

based upon this model could assist a recreationist 

in reducing a set of alternative site choices 

to a manageable number. Efforts to develop such 

an aid, which would assist the user in determining 

a viable alternative set containing three to five 

alternatives, would provide a great service to 

the user and be justified. 

One possible immediate application of these findings 

would be to develop a microcomputer-based decision 

aid built upon the principles of the lexicographic 

semi-order model for use on a Forest Service District to supplement current information and education efforts. This decision aid could improve 

information dissemination programs while it collected some very useful information for the manager. 

Information that could be recorded would include 

what attributes must meet minimum thresholds for 

acceptance, what these threshold levels are, and 

finally, given this information, what specific 

areas decision aid users decide to visit. 

Needed additional research would include the opportunity to compare the site chosen from the decision 

aid exercise to the actual site visited. These 

data could be collected observationally and would 

be a desirable follow-up test on the usefulness 

of such a decision aid. Another research need 

is to manipulate data considered in the decision 

process to test for resultant changes in choice 

behavior. Both of these suggestions arise from 

the need to demonstrate that recreationists can 

and will use such a decision aid, and that the 

content and manner of presentation of the information supplied produces enough confidence in 

the decision that the chosen area is actually 

visited. 

The empirical support of the lesicographic semiorder choice model provides a basis for analysis 

of the Recreation Opportunity Guide and the Backcountry Trail Selector as decision aids. An obvious 

weakness of the ROG is the lack of adaptability 

to the individual. The lack of desirability of 

a few fixed indexing items is reinforced by 

examination of the variations in the rankings 

of attributes in this study. Many different attributes and areas were indicated as top choices 

by study participants. It therefore seems erroneous 

to assume that miles of trail, name of area, and 

planning unit location are relevant or important 

attributes for every user of the ROG. Without 

indexing by relevant variables, the individual 

user of a ROG must leaf through every page to 

search out a destination. This is exactly the 

type of overwhelming information (often leading 

to unsatisfactory choices) that any decision aid 

should be trying to minimize. 
The method of presentation of information ,using 

the Backcountry Trail Selector has the same weakness--lack of adaptability to the needs of the 

individual user. By forcing every individual 

to consider the same attribute first, we are assuming that the attribute is not only a relevant 

one, but the most important one for all users. 

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WHY HERE AND NOT THERE: 

THE CONDITIONAL NATURE OF RECREATION CHOICE 

Roger N. Clark and Kent B. Downing 

ABSTRACT: This paper reports results of several 

studies to identify the state of the art and 

direction of research on how recreationists make 

choices. Findings from the studies have been 

combined into a list of propositions; the 

propositions can be considered hypotheses from 

which future studies can be developed or the 

effect of management activities on choices can 

be evaluated. Concepts that underlie recreation 

choices are illustrated. The complex and 

diverse nature of the recreationistsl decision 

making process supports a grounded approach in 

future investigations. 

INTRODUCTION 

How people make decisions about when and where 

to recreate has become a topic of increasing 

interest in the past few years (Brown and Ross 

1982; Knopp and Leatherberry 1982; Krunipe and 

McLaughlin 1982; McDonough 1982; Stynes 1982; 

Watson 1983). The new focus is evident in both 

recreation research and management. In research, 

studies have evolved from those that describe 

recreation events and users to those that focus 

on why the events observed occur. The focus on 

recreationist's decision making (a process 

orientation) has been a logical outcome of this 

progression. 

For managers, understanding the process of making 

recreation choices is extremely important for 

two reasons: (1) to recognize when and how 

integrated resource management activities (in 

recreation, timber, minerals, range, engineering, 

wildlife, hydrology, cultural resources, and so 

on) impact specific kinds of recreation 

opportunities, in positive and negative ways; 

and (2) to recognize when specific recreation 

goals and objectives can be achieved without 

unnecessarily constraining the management of other 

resources. 

Identification and evaluation of effects of 

resource management, as well as other, more 

general social changes, have become important 

areas of concern. Recreationists make their own 

decisions, managers do not make decisions for 

the recreationists. But managers1 actions 

affect the places recreationists go. An 

understanding of the process people go through, 

as well as the factors they consider, will 

facilitate the judgmental process managers must 

apply in matching supply with demand and 

protecting the integrity of quality recreation 

opportunities. 

The purpose of this paper is to report results of 

several recent studies in the West and Alaska to 

identify the state of the art and direction of 

research in the area of choice behavior. Concepts 

that underlie recreation choices, and how they 

can be related to management as well as research, 

are illustrated. 

VARIED RESEARCH PERSPECTIVES 

Investigators have followed divergent pathways 

in pursuit of knowledge about recreation behavior. 

Knopp and Leatherberry (1982) observe that in 

assessing "how individuals choose an activity and 

place, the primary emphasis has been on motivational factors often referred to as needs, desires, 

reasons or similar terms. Some researchers have 

included what can be called intervening factors, 

for example, cost, distance, access, knowledge. 

Unfortunately, in failing in our research to 

account for experience and the tendency for people 

to tire of an activity and drop it to take up 

new pastimes, we obtain a static view of 

decision making." Similarly, variation in the 

patterns of use associated with different places 

displays dynamic qualities of choice behavior 

as well--discovery of places, attachment to 

favorite ones, repeat visitation, relocation as 

sites change in undesirable ways, and so forth 

(Downing 1982). Only recently do we seem to be 

looking into the dynamic dimensions of recreation 

Paper presented at the Recreation Choice Behavior choice. But, how do we represent the dynamic Symposium, Missoula, MT, March 22-23, 1984. nature of recreational choice phenomena; in 

particular how do we express the balancing of 

Roger N. Clark is Project Leader, Wildland Recre- conflicting desires? ation Research, Forestry Sciences Laboratory, USDA 

Forest Service, ~acif ic- Northwest Forest and- Range Propst and Lime (1982) observe that recreation 

Experiment Station, Seattle, Washington; Kent B. researchers have most often looked to other 

Downing is Associate Professor, College of Natural disciplines for help in solving conceptual problems. Resources, Utah State University, Logan, Utah. They cite efforts at applying economic and job 

satisfaction models to recreation satisfaction, 
but suggest that the results have been mixed. 

Another investigator has recently proposed applying 

a variation of a noncompensatory model as a 

"simplified approximation of whatever the 

complex cognitive decision process might actually 

be" (Watson 1983). Indeed, what might it actually 

be? We persist in efforts to fit our data to 

existing theoretical perspectives, many of which 

are themselves in need of further elaboration and 

refinement in the substantive area from which 

they were initially derived. Most of these models 

have their critics. 

DEVELOPING A GROUNDED CONCEPTUAL APPROACH 

A review of the literature indicated that both 

the research methods currently being used and 

the various theoretical perspectives discussed 

have not fully explained the complex nature of 

the choices inherent in the data described in 

this paper. Most past work seemed to lack a 

behavioral basis. So we turned to Glaser and 

Strauss (1967) for developing a "grounded 

approacht' to explain the choices observed and 

reported in our investigations. Glaser and 

Strauss (1967) and Schatzman and Strauss (1973) 

state that a grounded approach may be of greater 

assistance, at least initially, in representing 

the theoretical complexity in a substantive area 

of inquiry. They advocate entering the field 

with minimal commitment to an existing 

theoretical model; a model can then be derived 

inductively to conform to the richness, diversity, 

discrepancies, and apparent inconsistencies in 

data encountered. 

In many respects, the grounded theory guided the 

design of studies discussed later in this paper. 

In general, our research progressed from using 

qualitative to quantitative methods then back 

again to qualitative where appropriate. Each 

approach offers a different level of resolution 

about how recreationists make decisions. This 

issue is discussed more fully by Downing and 

Clark in this proceedings. 

FACTORS OF RECREATION CHOICE 

Within the context of a given outing at a specific 

time, people frequently are faced with diverse and 

often conflicting choices, choices that require 

trade-offs among competing desires. When 

deciding whether to go on an outing, participants 

must make many decisions that are conditional on 

many, often interrelated factors. Types of 

decisions to be'made and factors to be considered 

are shown in table 1. Although many of the 

factors have been described in previous studies, 

the pattern of interrelationships between the 

choices and kinds of factors has not been 

described. The items shown are not necessarily 

exhaustive nor are they listed in any order of 

priority. Most of them are discussed later in 

this paper. 

Table 1.--Types of decisions to be made and factors that often weigh 

in recreation choices 

-- 

Decisions Factors 

Go versus do not go 

With whom: 

Primary group 

Alone 

Others 

Where: 

Specific geographic area 

Macrosite characteristics 

To do what: 

Single or multiple activities 

Desires of individual or group 

How: 

Mode of travel 

Style of activity 

When: 

Time of day, week 

Season 

Why: 

Satisfactions desired 

Needs, motives 

Preferences, etc. 

Informat ion and knowledge 

Individual considerations: 

Time/money available 

Desire for change 

Limitations/handicaps 

Group considerations: 

Primary group 

Secondary group 

Competition from others 

Activity considerations: 

Equipment available 

Season 

Place considerations: 

Weatherlseasons (may 

impede/f acilitate) 

Opportunities there 

Natural attractions 

Management considerations: 

Ruleslregulations 

Facilities provided 

Perceived safety 

When people reach the site of their choice, they 

may re-evaluate many of their prior decisions. 

As a consequence, the individual or group may 

adapt as necessary to match expectations with 

realizations encountered on site. Thus, the 

decisions and factors shown in table 1 must be 

viewed in a dynamic and probabilistic rather 

than deterministic way. 

The requirements for decisions--evidence of 

diverse mediating factors in decision making-- 

have been referenced by other authors (Anderson 

and Brown 1984; Harvey 1976; Krumpe and 

McLaughlin 1982; Peterson 1974; Young and Smith 

1979). This perspective gives rise to a number 

of questions about places, people, management 

activities, users' decision processes, and the 

structure of interrelationships among the 

decision factors shown in table 1. A variety 

of such questions is given in the paper by 

Downing and Clark. 

WHAT WAS FOUND 

Three studies provide the basis from which we 

derive a series of propositions related to the 

decision process. The studies were located in 

Alaska, Washington, Oregon, and Utah and used 

various methods for data collection. The study 

in the Pacific Northwest (Oregon and Washington) 

used on-site participant observation, a 

questionnaire, and interviews in roaded, dispersed 

areas (Clark and others 1984). The Alaska study 

used an in-home interview with residents and 

focused on their perceptions and use of natural 

resources. The study represented the views of 

non-users as well as users (Clark and others 1982). 

The Utah study was also conducted on-site but used 

naturalistic inquiry (ethnography) in developed 

and undeveloped areas (Downing 1982). 
Each of the studies focused in one way or another 

on aspects of recreational choice behavior. The 

studies in the Pacific Northwest and Utah intended 

to explain why people chose the areas in which 

they were found. The Alaska study determined 

specifically where people went from their residence 

and which of the places identified (on a map) were 

their favorite or most often visited, and what 

similar place they would use if the favorite was 

somehow changed in an undesirable way. In this 

study, respondents were also asked what would be 

the consequence of resource management activities, 

such as logging, on their favorite place, that is, 

would the changes make the place more or less 

desirable, and if less desirable, would they stop 

going there. 

A grounded approach, as described by Glaser and 

Strauss (1967), and an ecological perspective 

(McDonough 1982; Psopst and Lime 1982) have been 

adopted in an attempt to put into perspective past 

research findings. Findings and conclusions from 

the three studies were combined and are summarized 

below as propositions. They may be considered 

hypotheses from which management implications and 

research studies can be developed. Later we 

discuss the structure or pattern of relationships 

among the decision factors that affect how people 

make choices. 

Propositions About People 

Most visits are in groups (friends, family, 

multiple-families, peer groups, etc.) 

The group one recreates with plays a major role 

in choice behavior (Cheek and others 1976). Most 

studies document that few people recreate alone, 

even in Wilderness (Hendee and others 1978). 

So the process of negotiation between members of 

the primary group must be evaluated; yet most 

studies focus on individual desires and behavior, 

an orientation that does great injustice to the 

reality of decision making. The decision process 

includes individual and group past experiences 

that must somehow be melded into a satisfactory 

solution. Data from these studies did not document 

the differential "power" each person in the group 

possessed, but numerous responses, particularly 

in the Pacific Northwest and Utah studies, revealed 

that this phenomenon plays an important role in 

final decisions. Future studies should allow 

researchers to distinguish between "the 

recreationist as an individual'' and "the 

recreationist as a group member" (whether the 

party head, peer, or subordinate). Perhaps some 

of the methods and concepts of behavioral 

sociologists and psychologists might be utilized 

to evaluate this exchange process. 

User characteristics 

Group composition may be fluid with members 

arriving and departing at different times. 

Members may engage in the same activity as a 

group; at other times, individuals and subgroups 

engage in separate, and occasionally, widely 

disparate activities. 

It is common to observe variation in style of 

outing within a group (for example, some may 

camp in tents of different style, whereas others 

stay in pickup campers or recreation vehicles). 

Not all needs or preferences of individuals 

within the group may be met; trade-offs and 

accommodations must be made at times. The group 

may require compromise and consensus, or submission 

to some of the desires of its members. 

Attachment to place 

Recreationists form attachments to sites and 

return to favorite or preferred places again and 

again (Knopf 1983). 

They tend to use places similar to those used 

as children. 

They visit other "acceptable" places for similar 

outings as well. Occasionally visitors use sites 

with attributes they rate as "undesirable" or 

"unacceptable," although these conditions do not 

preclude engagement in the chosen activity with 

the chosen group. 

They tend to travel within a well-defined area 

("home range"), generally no more than 2-4 hours 

for people using roaded forest areas and boats 

in coastal areas. 

They may decide where to live based on the 

availability of desirable places and opportunities 

for activities. 

Preferences for setting 

Recreationists include individuals who prefer 

developed sites, those who prefer dispersed sites, 

and those who have no preference and use both types 

of setting. 

They will, under certain circumstances, switch 

from dispersed to developed settings and vice 

versa in apparent contradiction to stated 

preferences. 

Dispersed area users take advantage of 

opportunities in primitive and semi-primitive 

settings by bringing their own equipment to 

provide the creature comforts found in developed 

campgrounds. 
Developed sites are chosen Propositions About Places for convenience and 

safety, dispersed places for freedom and lack of 

regimentation. 

Both dispersed and developed sites offer 

opportunities for socializing with others not in 

the party or for privacy, depending on site 

selection and timing of visits. 

Water, whether in lakes, streams, or marine 

locations, is an important component in making 

choices (Clark and others 1984; Lime 1971; Lucas 

1964) . 

Adaptability 

Forest recreationists may be tempararily or 

permanently displaced from places that change 

in unacceptable ways. 

Displacement may be of a "conditional" nature; 

that is, recreationists may return under special 

circumstances. 

Forest recreationists appear to vary in their 

"threshold of disruption" and in their willingness 

to adapt to undesirable changes in a place. 

Expectations and the availability of alternatives 

are key elements in understanding how thresholds 

operate to change patterns of actual use. 

They vary in their willingness to explore new 

and unknown territory, particularly outside the 

bounds of the home range. 

Propositions About Activities 

In each of the studies, there were few activities 

in which there were no participants, even though 

the environmental conditions ranged from developed 

campgrounds to wilderness. 

A variety of activities occurs on each outing, 

which leads to diverse experiences and satisfact ions. 

Activities may include all or part of the primary 

group and any other people who join together at 

the site. Activities range from very active to 

very passive and span a wide geographical frontier 

in relation to the prime site that the group 

visits. 

The same activity may take on different styles. 

People can participate in the same activity in a 

variety of ways and in many different places 

ranging from city parks to wilderness. An 

activity such as hiking, for example, is basically 

the same in each area, but the motives for engaging 

in the activity, the style of participation, and 

the regulting experiences can vary dramatically 

from one area to another (Clark and Stankey 1979). 

Attributes of a place can attract or detract 

and can facilitate or constrain a specific 

activity. 

Place is a major element that composes a recreation 

outing and that must be evaluated in individual 

and group decisions. Several features of place 

have been identified in the Recreation Opportunity 

Spectrum (ROS) for example, including physical 

and biological factors, man-made factors, and 

social features (Brown and others 1978; Clark and 

Stankey 1979). Studies reported in this paper 

indicate that each of these features may or may 

not play a role in the decision making process. 

Place may be evaluated at macrosite or microsite 

levels. One level or the other may take precedence 

at different times and for different individuals 

in the primary group. One person's favorite may 

be another's least desirable place to go. 

Users of dispersed and developed sites are found 

in dispersed and developed setpings (different 

microsites) located in geographic proximity along 

paved highways and gravel roads in forests, near 

settled areas, and in more remote regions. 

At a macrosite level, the place may be acceptable 

to users of dispersed and developed sites, whereas 

preferences at the microsite level may differ 

substantially. 

Users of dispersed and developed sites frequently 

travel into dispersed areas for day use activities. 

Acceptability of macrosites and microsites varies 

by season (for example, a place may be acceptable 

for an individual or group for hunting in the fall 

and cross-country skiing inwinter but not as a 

dispersed or developed site for family camping in 

the summer). 

Propositions About Management 

* Few visitors to a roaded forest learn about the 

place through information supplied by the agency. 

In£ ormal personal contacts, most often family and 

friends, are the most important source of 

information about opportunities. 

Lack of developed campgrounds is infrequently a 

reason users give for choosing dispersed sites. 

Dispersed and developed settings are not 

substitutes one for the other. 

User acceptance of management practices varies 

(Downing and Clark 1979). 

Users of roaded recreation settings, in contrast 

to primitive settings, are less likely to be 

annoyed by common management activities such as 

logging and grazing. The size of clearcuts, and 

their location relative to campsites, however, 

is important. 
Users support road closures for some management 

purposes (to protect wildlife, for example), but 

have less support for others (to save limited 

maintenance money, for example). 

Many visitors in Pacific Northwest forests 

express strong support for agency presence, ranger 

patrols specifically. 

Visitors value unchanging settings. For example, 

visitors in the Pacific Northwest indicated that 

opening areas for dispersed recreation is aagood 

reason for building new roads, but not in the 

area which they were in at the time. 

Acceptability' of visitor impacts varies. Some 

recreationists express little concern about 

litter and garbage, fires, noise, vandalism and 

theft. Others, however, have relocated (have 

been displaced) from settings where these problems 

have been present. 

Propositions About Recreationists' Decisions 

General characteristics of choice 

Recreationists may choose a particular place 

for a given outing for a variety of reasons: 

(1) habit, tradition; 

(2) a desire to be in a place of expected stability, 

reliability, familiarity in a changing world 

(Iso-Ahola 1980); 

(3) a desire for psychological distance--to be 

far enough away to feel like they have gotten 

away ; 

(4) to explore for new and unknown territory; or 

(5) to accommodate to particular needs and 

constraints of a trip (mediating factors): 

special needs of the group, time constraints, 

and cost constraints (Cheek and Burch 1976; 

Kelly 1976; McDonough 1982) . 

S~ecific characteristics of choice 

Access is a key determinant influencing 

recreation choices and the location and amount of 

use. Of all the management factors identified by 

Clark and Stankey (1979) in the ROS, access 

probably plays the most critical role in either 

facilitating or constraining recreational use. 

Whether one chooses the term "facilitating" or 

"constraining" to describe access conditions 

depends on a personal point of view. 

* Favorite sites are frequently more remote and 

possess desired land characteristics. Conversely, 

most-visited places are usually more accessible, 

more convenient, and provide particular activities 

and facilities. 

Dispersed sites are often chosen over developed 

sites because they can better accommodate needs 

of large groups. 

Dispersed settings offer privacy, freedom to 

engage in activities not allowed in developed 

sites, freedom to adapt the place to the particular 

"style" of the group. 

Users of dispersed and developed sites have 

stopped visiting (have been displaced from) 

formerly used dispersed and developed sites 

because : 

(1) settings have changed in undesirable 

ways ; 

(2) too many people are there; 

(3) number of undesirable people (people 

who are not considerate of others and 

do not know how to behave) has increased; 

(4) resource management activities have 

increased ; 

(5) more preferred places have been found; or 

(6) life circumstances have changed (for 

example, places that supported the needs 

and desires of a family with children 

are not as desirable after the children 

grow older or leave the parents). 

At the macrosite level 

Once pretrip considerations are settled (such 

as group needs, trip constraints, desire for change 

of place), recreationists evaluate and make choices 

among options (Watson 1983) in terms of the 

following attributes of a setting: 

(1) variety of physiographic, topographic, 

and landscape features (Haas 1979); 

(2) season and elevation; 

(3) remoteness from home (psychological 

distance--far enough to feel like getting away) 

and from centers of population; 

(4) variety of resource-dependent opportunities available; and 

(5) ROS conditions (ranging from primitive 

conditions and settings to developed areas with 

roads and facilities). 

At the microsite level 

Many of the options applicable to the macrosite 

level also apply to the microsite level. 

Choices may be changed upon arrival depending 

on the availability of sites (favorite, preferred, 

or otherwise) as well as activities planned and 

special desires of the group. 

Both dispersed and developed sites may be 

selected for separation, remoteness, and isolation 

from others not in the group. 
Predictability of choices 

The process of making choices is inherently 

complex, and prediction is exceedingly difficult 

with present knowledge. 

(1) Choice of place (at both the microsite 

and macrosite level) is difficult to predict for 

a given type of outing without understanding 

circumstances about the trip (Knopp and 

Leatherberry 1982; Krumpe and McLaughlin 1982). 

(2) The likelihood of disruption at favorite 

sites (to determine the threshold of disruption) 

is difficult to predict from users1 statements 

of preference. 

(3) Although the process is difficult to 

predict, trade-offs people make may not be 

terribly stressful. 

Although favorite sites would be less attractive 

with certain changes (clearcuts, new houses or 

buildings, new logging, mine tailings, new roads, 

log storage), not everyone would stop going to 

their favorite place if such changes would occur. 

A variety of situational (mediating) factors 

limit freedom of choice (for example, distance to 

alternative sites, cost of fuel, travel time). 

Places subsequently chosen to accommodate the 

outing and regarded as "acceptable" may otherwise 

be rated as "unacceptable" (Schreyer and Roggenbuck 

1981). 

Expectations are important in recreationists' 

decision making. They serve as filters to narrow 

the range of activities and places that will be 

considered for a particular outing with a 

particular group. They are employed during 

pretrip planning for weighing anticipated tradeoffs among alternative possibilities of group 

composition, activity or experience, and place. 

How satisfied one is with a particular trip or 

activity depends in part on how well pretrip 

expectations match actual outcomes (Roggenbuck 

and Schreyer 1977). 

DISCUSSION 

It was difficult to determine the sequence in 

which the various decision factors shown in 

table 1 enter recreationistsl decision process. 

Obviously there is great variability between 

individuals and between one trip and another as 

to what circumstances are most important. 

A variety of factors that influence recreation 

choice behavior may operate independently at times 

and interact in complex ways at other times. 

The content (decision factors) and structure 

(relationships among factors) of the decision 

process are of primary interest and must be 

represented in a holistic way. Numerous studies 

on recreation behavior are not referenced in this 

paper; most did not deal directly with the 

behavioral choices of recreationists in real-life 

situations. Future work will be directed at 

integrating the results from these investigations. 

Grounded perspectives about recreational choice 

behavior are important theoretically and are 

relevant to managers. Some particularly important 

concepts follow. 

Home Range 

For a given outing, people choose among options 

about place or decide to discontinue visiting 

certain sites within a bounded geographic area or 

"home rangef'--an area judged appropriate to a 

particular group, activity, or experience. Home 

range for recreationists visiting roaded, forested 

areas of Oregon, Washington, and Utah for 2-3 

days dura-tion is within 2-3 hours drive of 

home. The same time frame tends to hold in 

southeast Alaska as well, but boats rather than 

wheeled vehicles are the main mode of travel. 

Some people are more "migratory" in nature than 

others (they act like tourists) and go beyond 

the bounds of the home range as defined by the 

majority of recreationists. But they appear to 

be more the exception than the rule. 

Home range seems to be an important concept. It 

suggests that there is a more-or-less geographically 

bounded and behaviorally relevant area within 

which most trade-offs among settings for a given 

type of activity or experience are made. The home 

range contains sites judged by people as "favorite," 

"most often visited," "acceptable" alternatives, 

and "unacceptable" places once visited but visited 

no longer. 

The size of home range may vary for different 

activities, different modes of travel, and 

different regions of the country. In southeast 

Alaska, for example, mode of travel and 

availability of a range of recreation opportunities 

closely related to local communities explains 

why most use is within 25 miles of home. For 

Anchorage, on the other hand, most recreation 

opportunities are further from town, and 

competition for them increases. The amount of 

time and distance traveled are greater, yet are 

still within generally predictable limits. The 

practical significance is that if people do 

associate a particular travel time and maximum 

distance with a particular type of activity or 

experience, "home range" should be incorporated 

into assessing the relative availability of and 

demand for different types of place-related 

opportunities. The activities and experiences 

possible become the "browse" within the home 

range. To the extent that resource managers 

control the basic factors that determine browse 

available to recreationists within their home 

ranges, understanding the relationship between 

available opportunities and use will be important 

in predicting consequences of resource management 

options for the public. 
Considerations for Modeling Choice Behavior 

We have not developed a detailed model of the 

decision process, but we can suggest some concepts 

that need to be integrated into a comprehensive 

model. 

The choice process seems to work under two general 

conditions: the "initial condition" and the 

"adaptive condition" (fig. 1). The initial 

condition is where the participants essentially 

are exploring "new ground" with regard to 

recreation events. The adaptive condition is 

where the participants have prior experience and 

are evaluating whether or not to repeat an event 

at a particular place with a specified group. 

It is within these two conditions that concepts 

such as substitutability and ultimate trip 

satisfaction for each member of the group begin 

to make sense. 

A Declslon level 

Oe~isi0n.9 Substitute (work or other lelsure) 

GO OR DON'T GO 

' \\ 

I I I I I (41 Group Actlvlty Place Actlv~ty Place Group 

5. Oeclslon level 

Decisions 

(1) GO AGAIN-NO - Substitute (work or 

ye[ \\ other lelsurel 

/ (not shown ~n thls diagram) (2) Same Place 

Yes , ' hop subst~tute place 

ye& N o --------- Substitute Actlvlty 

/ (4) Same Group 

Subst~tute Grow 

Figure 1.--Conditions in the recreation choice 

process: A, Initial condition; B, Adaptive 

condition. 

When making the initial decision, a recreationist 

must consider group, place, and activity. The 

first choice likely limits or constrains 

subsequent choices (a filtering process). What 

decision comes first--about place, people, or 

activity--likely varies from outing to outing 

and from individual to individual. Moreover, 

these early choices determine which macrosite 

and microsite factors in the ROS are likely to be 

salient in final selection of place. Other 

personal or group constraints may become 

considerations as well and may dominate from the 

beginning of the decision process. Thus, it is 

not always possible to select the macrosite and 

microsite based on individual preferences for 

attributes of a particular setting. 

When considering whether or not to repeat a trip, 

the recreationist can adapt as a result of prior 

experience or because of new decision factors. 

Substitution can then occur at any of the four 

levels shown in figure 1B. Not only can a 

recreationist substitute a place, but he or she 

can also substitute one activity or group for 

another. when the same combination of decisions 

is made time after time--that is, the same group 

goes to the same place to do the same sort of 

activity, often at the same time of year--the 

event becomes traditional. 

CONCLUSIONS 

The decision process for making recreation choices 

is complex from a research perspective: final 

decisions are conditional on interaction between 

people and places with a variety of mediating 

influences. The process is dynamic with new 

information continually affecting what people do 

as well as why they do so. 

Preferences are not necessarily equal to actual 

choices--at least not some of the time and not for 

some things. This is really not surprising. We 

must deal with group preferences versus individual 

preferences. "My favorite" and "our favorite" may 

not be the same. People in different groups may 

do the same or different things for the same or 

different reasons. What "appears to be" may be 

very different than what "really is." Understanding 

some of the "why" underlying recreationists' choices 

will facilitate programs that will provide the 

range of conditions acceptable to the public. 

The focus on place is fundamental, particularly 

when viewed within the context of home range; it 

both facilitates and constrains groups and 

activities. It provides the palette, or array of 

options, from which people create their own 

images. People have favorite places; they visit 

them and can identify attributes associated with 

those places. People also have favorite 

activities and people with whom they recreate; 

however, people often choose other than their 

favorites. Perhaps only seldom do all favorites 

(place, people, and activity) overlap (fig. 2). 

The studies discussed earlier indicate, however, 

that such apparent "inconsistencies" are rational. 

Whether they choose other-than-favorite places, 

activities, or people because of a desire for 

diversity or because of constraints, recreationists, nonetheless, may have a satisfying time. 

Choices about a setting might be viewed as 

"rational" for a given outing but vary from party 

to party and from trip to trip. Rationality is 

indicated by the evaluation and eventual selection 

of places expected to adequately accommodate the 

fluid nature of arrival and departure times of 

group members, group needs for activity and space, 

varying tastes of individuals within the group, 

and constraints of the particular situation. 

Evidence of such considerations is common ill 

users' explanations of why they are "here" 

rather than "there." 
Figure 2.--Relationships between favorite people, 

favorite place, and favorite activity. 

People generally compromise in making recreation 

choices. They weigh individual versus collective 

considerations, plus other constraints and 

opportunities, in reaching a decision. At the 

group decision making level, there may, in fact, 

be a close match between preference and behavior. 

Looking at preferences at the individual level 

may be appropriate to show the range of 

possibilities, but may not be a good predictor of 

actual behavior. 

We must conclude that recreationists (at least 

some members of a group) are often able to achieve 

satisfactory trip experiences in what they would 

otherwise regard as a marginally acceptable 

setting. Under such circumstances, it appears that 

attributes of a setting support, albeit minimally, 

the desires of the group regarding activity or 

experience. When users expect less than an ideal 

place, going there must be valued more than not 

going. Anticipated satisfactions of a trip are 

largely dependent on engaging in desired activities 

with a desired group. An "optimal" setting is not 

always required or, alternatively, the requirements 

of an optimal setting may be quite minimal. Places 

must then be judged for the degree to which they 

adequately support activity, experience, and group 

needs. It may not be possible to label a setting 

as "optimal" in abstract terms. This may help 

explain why favorite places are not necessarily 

the same as those used most frequently for a given 

kind of outing, and why people often use settings 

with attributes they regard as somewhat undesirable. 

The concepts of "home range" and "browse" seem to 

apply to recreation as well as to wildlife. People 

seek diversity and may go to a variety of places, 

assuming they have knowledge of opportunities 

available, but generally do so in a confined 

territory, at least at the aggregate level. They 

can chosse the same browse (experiences and 

satisfactions) in the same or different place, or 

go routinely to the same place for different browse. 

Past attempts to simplify the methodology and 

substance of the recreation decision making process 

have done injustice to the topic. Little is known 

at present about choice behavior that can be 

generalized with any degree of reliability. The 

fact that researchers from diverse disciplines 

have converged on the matter of recreation choice 

is comforting; the lack of an integrating 

perspective is not. The results from this 

symposium may lead us in a more productive direct ion. 

RESEARCH NEEDS 

Additional investigations based on a grounded, 

behavioral approach are needed to ensure that 

researchers are working with relevant elements 

and relationships. Verification through further 

data processing can then follow for data sets 

which clearly reflect the idiosyncrasies of the 

recreation choice process. Research has begun to 

demonstrate the complexity involved, but more 

studies should be conducted within a holistic, 

ecological framework. 

There is a need to integrate research methods, 

models, and concepts. Multidisciplinary studies 

are also required to understand the complex nature 

of the recreation choice process. The move from 

descriptive to explanative studies will necessitate 

using alternative research designs--longitudinal 

and experimental--and strategies to collect a mix 

of quantitative and qualitative data. 

There is need to refine the home range concept 

and to describe the nature and extent of home 

ranges for different kinds of groups, activities, 

and experiences. What are the constraints or 

factors that shape home ranges? Moreover, there 

is need to determine which of the interrelationships between place, activity, and group are most 

significant to recreationists' choices, and to 

determine which management activities are most 

likely to influence recreationists' choices. 

Research must focus more on the group, rather 

than the individual, largely because decisions 

are typically made within the group unit. 

And last, researchers need to know more about 

what really matters versus what is nice to know 

but irrelevant for effective management. Determining this balance requires on-going cooperation 

between researchers and managers. 

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143-154; 1979. 
EVALUATING USER IMPACTS AND MANAGEMENT CONTROLS: 

IMPLICATIONS FOR RECREATION CHOICE BEHAVIOR 

Harriet H. Christensen and Nanette J. Davis 

ABSTRACT: This paper describes potential factors 

a f f e c t i n g recreation chdice behavior. Freedom 

and lack of constraints are experiences frequently 

sought by recreationists. Data in the paper are 

based on questionnaires completed by agency 

managers and informal conversations with users in 

the Mount Rainier area of Washington State. 

Managers' and users' perceptions of impacts such 

as vandalism, theft and littering, and social 

control practices are described for developed and 

semi-developed campgrounds. A framework 

illustrates the recursive nature of deviance and 

social control. Implications of impacts and 

regimentation practices for influencing choice of 

recreation settings are discussed. 

INTRODUCTION 

Demand for recreation opportunities in natural 

settings continues to grow. Recreation use on 

National Forest lands during fiscal year 1983 

numbered 228 million recreation visitor days; 

78 percent of this use occurred in the western 

States and Alaska (U. S . Department of Agriculture, 

Forest Service 1984). Only one-third of this use 

is in improved locations; the remainder is found 

throughout undeveloped forest lands. At these 

areas, users can view wildlife, sightsee, camp, 

hike, picnic, hunt, fish, ride horseback, canoe, 

raft, ride off-road vehicles, and take part in 

many other outdoor activities. These opportunities 

are found throughout the 191 million acres of 

National Forest land, 87 percent of which is 

located in the West, and on other public lands, 

also located primarily in western States. 

Paper presented at the Recreation Choice Behavior 

Symposium, Missoula, MT, March 22-23, 1984. 

Harriet H. Christensen and Nanette J. Davis, 

respectively, Research Social Scientist, USDA 

Forest Service, Pacific Northwest Forest and 

Range Experiment Station, Seattle, WA., and 

Professor, Department of Sociology, Portland State 

University, Portland, OR., and Visiting Professor, 

Western Washington University, Bellingham, WA. 

Several studies have addressed the kinds of 

experiences people desire in natural environs-- 

wilderness use, Hendee and others 1978; snomobi1.e 

use, McLaughlin and Paradice 1980; river running, 

Schreyer and Roggenbuck 1978; off-road vehicle 

use, Watson and others 1980; developed camping, 

Clark and others 1971; and dispersed use, Clark 

and others 1984. Satisfactions expected by users 

include experiencing natural environs that are 

largely unaltered by human use, feeling free and 

independent, and escaping adverse conditions such 

as noise, unsafe areas, and pollution. (For an 

extensive discussion on motives and experiences 

desired, see Knopf 1983). 

Recreation choice behavior in this paper refers 

to factors that may influence recreationist's 

decision making and choice of recreation setting. 

The issue of recreation choices has become more 

important. We have reached a turning point in 

recreation management because of increased use 

and the need for more astute management practices 

to deal with the many problems of congestion, 

vandalism, and reduced budgets of forest 

management. What needs to be known is how people 

make choices and to what degree impacts and 

regimentation affect those choices. 

This paper has three objectives: (1) to identify 

user impacts, such as vandalism and theft, and 

to discuss possible influences they have on 

recreation choices users make, (2) to identify 

the possible negative effects of social control 

and regimentation on user choice behavior, and 

(3) to propose a theoretical model for future 

empirical research. Specifically, we will discuss 

managers' and users' perceptions of impacts; for 

example, illegal entry of personal vehicles, a 

stolen camp stove or sleeping bag, and other 

depreciative behaviors. Second, we will look at 

users' and managers' perceptions of regimentation, 

such as enforcement practices. Finally, we will 

present a control model that summarizes the 

recursive nature of depreciative behavior in 

some instances, and how impacts and control 

practices can produce unanticipated results in 

terms of possible effects on recreation choice 

behavior. 
Throughout the paper we draw on the Recreation 

Opportunity Spectrum model (ROS). Inherent in the 

ROS is the assumption that opportunities include 

activities such as camping and hiking; settings 

that encompass physical, social, and managerial 

environments; experiences such as freedom, solitude, 

adventure, and socializing with people; and 

benefits such as improved health. The ROS model 

posits that a wide range of choices is available 

to users with multiple types of activities and 

experiences possible. Two elements of the 

opportunity spectrum--level of visitor impacts and 

level of regimentation--will be used in this paper 

to speculate on how users define available 

recreation choices (Clark and Stankey 1979, Driver 

and Brown 1978). 

VALUES, IMPACTS, AND SOCIAL CONTROL 

Available findings on users' expectations reveal 

a variety of values. As regimentation increases, 

we, in research, increasingly need to consider 

freedom, quality experience, and security (or 

absence of victimization) as crucial values in 

the outdoor experience. Quality experiences are 

expected by users in the outdoor environs. 

Recreational enjoyment as an aspect of quality 

entails an absence of threats from vandalism, 

theft, and litter. Kinds of deviance that may be 

distracting include rest rooms with heavy graffiti, 

theft of a backpack or wallet, or breaking and 

entering a vehicle at a trailhead. Yet we know 

there is discrepancy between values and behavior 

t lark and others 1971). With litter, ,for example, 

we believe it is inappropriate to litter, yet we 

do it anyway (Clark and others 1972, Heberlein 

1971). Therefore, we need to monitor choices of 

behavior actually made rather than rely totally 

on what users say about their behavior. 

Freedom and security are two primary qualities of 

the recreation experience that users expect. In 

fact, the nature, extent, and level of control 

over use are factors that characterize various 

recreational opportunities. Control measures 

range from subtle techniques, such as barriers 

for traffic control (a form of site design) and 

providing information to users, to more 

authoritarian kinds of control, such as law 

enforcement. The state of the art on social 

control is not advanced: dichotomies such as 

direct versus indirect, manipulative versus 

regulatory, or coercive versus benign approaches 

have been addressed in the literature (Christensen 

and Davis 1984, Fish and Bury 1981, Hendee and 

others 1978). 

In dispersed areas, one of the values appealing 

to users is the lack of regimentation and control 

found with other kinds of recreation; this value 

was reported by nearly two-thirds of the campers 

in three dispersed areas in the Pacific Northwest 

(Clark and others 1984). Most of the dispersed 

2rea users who had been returning to the same 

location for about 6 years agreed that being 

free to alter campsites to meet their own needs-- 

by setting up tables or building fire rings, 

for example--was important. A sense of security 

is also important. Nearly 75 percent of campers 

and day users in this study reported feeling 

safe although they expressed a desire to be kept 

informed by recreation managers of potential 

dangers such as unsafe drinking water, poisonous 

snakes, and dangerous roads. 

Similar results are found in the Downing and 

Moutsinas (1978) study of dispersed, roaded 

forest land users in the Pacific Northwest. Users 

want the opportunity to "do their own thing." 

They prefer little development and value privacy, 

freedom, and peace and quiet (Downing and Clark 

1979). 

Certainly freedom and security are values also 

expected in wilderness areas; however, they may 

take on a different meaning by wilderness users 

than users of dispersed or developed recreation 

areas. Some studies on wilderness user attitudes 

have shown support for trailhead registering, 

wilderness rangers patrolling the backcountry, 

some regulation of areas, willingness to accept 

more controls, and so forth. Charging entrance 

fees and assigning visitors to campsites are not 

favored by wilderness users (Lucas 1980; Stankey 

1973, 1980). 

Researchers do not know much about users' 

interpretation of freedom and constraint in 

outdoor recreation settings. Some users may 

arrive at an area; expect rules, official contact, 

or visibility of the ranger; and feel the absence 

if the controls are not present. Dissatisfaction 

and reaction may occur. Inappropriate social 

control may thus lead to dissatisfaction with the 

recreation experience: some users may desire and 

expect fairly comprehensive and explicit 

regulations, some may prefer no regulations. We 

do not know how much is too much and how little 

is too little in different settings or what effects 

these have on recreation choice behavior. 

Intrinsically, wilderness implies fewer controls 

and controls that are more subtle and benign in 

nature compared to those for other areas. In 

fact, we do not know if there are fewer rules in 

wilderness than in dispersed areas. Many policies 

govern practices in wilderness areas--these 

include policies on minimum impact hiking and 

camping, fire rings, and wood fires. 

By contrast, developed settings for overnight or 

day use have many rules and regulations and 

expectations by the managing agency. Clark and 

others (1971) found that users of such areas 

expected to enjoy tranquility and solitude but 

did not seek exclusion from neighboring campers. 

Furthermore, campers reported that their 

recreational experience was not reduced by the 

presence of additional rules and regulations, 

sound of other campers talking and singing, or 

people bringing city conveniences to the 

campgrounds. 

Freedom and security to people in developed 

settings may mean something quite different than 

freedom and security in the wilderness, 

backcountry, or dispersed areas. Lee (1972) 
suggests that the everyday normative constraints 

are still present during some leisure behavior but 

perhaps operate at a low level of awareness. Users 

in developed settings still experience traffic 

constraints and prohibition of dogs off leash, 

for example, but not to the degree they do in the 

urban setting. 

People recreate in different developed settings 

depending on their social group, their experiences, 

and their preferences for specific activities. If 

regimentation and official intervention exceed 

their expectations of appropriate control, 

dissatisfaction and, in some instances, reaction 

will occur. Reaction may include displacement and 

selecting other locations and opportunities for 

recreation where regimentation is less and more 

appropriate "in the eyes of the beholder" (Clark 

and others 1971). Reaction may also entail 

deviance: rulebreaking, theft, and vandalism. The 

question we pose is, "What are users1 expectations 

of legitimate social control practices and policies 

over the use of space and social organization?" 

METHODS 

To study the magnitude and dynamics of vandalism 

and other types of depreciative behavior, we have 

drawn data from two sources: questionnaires 

completed by agency managers and informal 

conversations with users in the Mount Rainier 

area of Washington State. 

Managerial data are based on a comprehensive 

multi-agency survey conducted during autumn 1982. 

Agencies participating in the assessment included 

the U.S. Department of Agriculture, Forest Service; 

U.S. Department of the Interior, Fish and Wildlife 

Service, National Park Service, and Bureau of Land 

Management; and the U.S. Army Corps of Engineers. 

The census was conducted of managers working at 

various administrative levels in recreation or 

resource management, law enforcement, planning, 

maintenance, and cultural resource management in 

California, Oregon, Washington, and Alaska. Total 

questionnaires mailed numbered 667 with an overall 

return rate of 90 percent. For this paper, we 

extrapolated the responses of 36 managers in the 

Mount Rainier area to compare with values of 

users in the same region. 

Informal contacts were made with users in Mount 

Rainier National Park and Mount Baker-Snoqualmie 

National Forest. Specific sites chosen for study 

were a Park campground and a range of day use and 

overnight recreation sites along a 30-mile stretch 

of Washington State Highway 410 northeast of the 

park. Study sites included: White River 

Campground, developed campground managed by the 

National Park Service in Mount Rainier National 

Park; Dalles and Silver Springs, two semi-developed 

campgrounds; and Greenwater drainage, a dispersed 

recreation area in the Mount Baker-Snoqualmie 

National Forest. Each study area was visited during 

two or three weekends throughour the summer of 1980 

according to a predetermined systematic schedule. 

Forest Service researchers unobtrusively circulated 

throughout the recreation areas, reaching as many 

people as were available and engaging in informal 

and open conversation with users at their campsite. 

The resulting sample was non-random and did not 

represent all users at these specific sites. 

Given the exploratory and qualitative nature of 

the study, however, the sample was considered 

sufficient to identify the range of issues of 

concern to users and users1 responses. Contacts 

with users were unstructured, thus users had an 

opportunity to express their feelings and judgments 

in a manner that did not restrict responses yet 

allowed for comparability between them. 

A brief summary of users1 and managers1 

characteristics is given in table 1. Four 

Federal natural resource agencies are represented 

in the sample of managers from the Mount Rainier 

area. Duties were described earlier in the paper. 

Most managers were in their present job for six 

years and had management and administrative 

responsibilities. 

Table 1.--Selected characteristics of users and managers in the 

Mount Rainier Area 

Study 

Participant Percent (Number) 

Users : 

Location where contacted-- 

National Forest 

National Park 

Total 

Approximate age-- 

Under 18 

18-30 

31-50 

Over 50 

Total 

Sex of user group-- 

Male only 

Female only 

Male and female 

Total 

Number of visits to area-- 

First visit 

Once before 

2-3 times before 

4 times or more before 

Total 

Managers : 

Agency represented-- 

Forest Service 

National Park Service 

Fish and Wildlife Service 

Corps of Engineers 

Total 

Major duty of job-- 

Managementladministration 

Law enforcement 

Planningle~aluatin~ 

Combination of above 

0 t her 

Total 

Other characteristics-- 

Mean of 6 years at this position 

Mean of 6 years assigned to 

this recreation area 

Mean of 10 years working in any 

recreation area 
The 191 users rqpresented a mix of male and female 

visitors. The age distribution reflects the 

observer's estimate of the user's age and is 

accurate only as a general indicator. These users 

were primarily from western Washington; however, 

their history of previous visitation indicated a 

wide variation in the amount of past experience 

they had with the specific recreation location in 

which they were contacted. 

The perception data presented in this paper offer 

a beginning to understand choices users' make 

(Worchel and Cooper 1983, p. 524): different 

perspectives lead to different perceptions about 

what is available. This means that differences 

between managers1 and users' perspectives relate 

to choices users make regarding recreation behavior. 

But as yet, we do not know to what extent these 

differences lead to changes in expectations users 

have or in choices users make. 

Table 2 shows managers' and users' perceptions of 

problems in semi-developed and developed campgrounds 

in the Mount Rainier area. For a variety of 

reasons such as status, ideology, and function, 

there are major differences between users' and 

managers1 perceptions regarding the seriousness of 

Table 2.--Managers1 and users' perceptions of problems in semi-developed 

and developed campgrounds in the Mount Rainier area 

Problem Semi- Percentage Percentage 

and Perception Developed Point Developed Point 

~rou~l Campground Difference Campground Difference 

Percent (~unbkr)~ Percent (Number) 

Theft of users' equipment: 

Managers 

Users 

Vandalism to users' equipment: 

Managers 

Users 

Litter: 

Managers 

Users 

Rulebreaking: 

Managers 

Users 

Conflict between users: 

Managers 

Users 

The question in the instrument was "How important are each of the 

following problems in this recreation area? Possible responses for managers 

were "not at all," "somewhat," "very much," and "do not know." Values for 

managers reflect the sum of responses "somewhat" and "very much" of a 

problem. Users' responses were "a problem" and "not a problem." The 

managers' questionnaire was structured; informal conversations with users 

were unstructured. Because the nature of instrument construction was 

different in the studies, results are tentative and suggestive. 

the problems (Davis 1984). Taking semi-developed 

campgrounds, what is defined as "problems" for 

users--theft or vandalism of their equipment and 

conflict between users, is seen as even more 

problematical by managers, with a full 28 

percentage point difference on the theft measure, 

26 percentage point difference between managers 

and users on the vandalism issue, and a 41 

percent difference on the conflict between users 

measure. Even more dramatic differences in 

perception between managers and users occur on 

issues of litter (58 percentage point difference) 

and rulebreaking (47 percentage point difference) 

Turning to developed campgrounds, the data show 

similar disparities. Theft of users' equipment, 

an issue managers and users were divided on in 

semi-developed campgrounds is recognized as 

"somewhat" or "very much of a problem" by 90 

percent of the managers but only 35 percent of 

users (55 percentage point difference). Vandalism 

of users1 equipment, a problem articulated by 

only 39 percent of the users, was seen as 

"somewhat" or "very much of a problem" by 77 

percent of managers. On the other measures, 

differences between the two groups remain sharply 

divided, with users trailing behind managers 55 

percent on the litter variable, 44 percent on the 

rulebreaking measure, and 55 percent on the issue 

of conflict between users. Two conclusions have 

been drawn from these data. First, there are 

strong differences between users' and managers' 

perceptions regarding the seriousness of the 

problems. Managers indicate a far greater 

likelihood to perceive various impacts such as 

vandalism as a problem. For example, they have 

an entirely separate domain of concerns often 

unrelated to users' interests and needs. These 

differences suggest that rather than a presumed 

set of identical interests that are often said 

to prevail between managers and users, these two 

groups may have conflicting interests. Managers 

may set the general parameters for recreational 

opportunities, but to enable democratic choice 

by users, they must respond to users' specific 

interests and needs. 

Second, social control and regimentation are 

invariably related to impacts. Regimentation, 

defined as the nature, extent, and level of 

control over recreational use, historically has 

had two modes. The literature has tended to 

contrast social control as polar opposites: 

direct versus indirect or coercive versus benign 

approaches (Christensen 1984, Christensen and 

Davis 1984, Hendee and others 1978). These 

dichotomous values, however, oversimplify the 

complex reality of social control for different 

recreational opportunities. Control practices 

vary across the recreational spectrum with more 

prevalent and possibly more coercive control 

Numbers in parentheses signify the number of responses from which the 

percentages were derived. 
toward the developed end of the recreational 

spectrum. Recent research suggests that impacts 

also vary across the spectrum (Christensen and 

Davis 1984, Christiansen 1983). Thus, as use of 

various opportunities changes and as impacts 

increase, regimentation can also be expected to 

change. 

How likely are managers and users to agree on 

what constitutes effective prevention and 

control strategies? Table 3 provides some . 

tentative answers. At developed campgrounds, 

the differences in perspectives between the two 

groups are sometimes sharp. For example, 88 

percent of the managers believe that maintenance 

of facilities is effective as a strategy for 

preventing impacts, but only slightly more than 

half the users agree. Yet there is extensive 

literature on the positive effects of maintenance 

on reducing depreciative behavior (Boston Parks 

and Recreation Commission 1978; Christensen and 

Clark 1979; Samdahl and Christensen, in press). 

Although there are sharp differences in 

perceptions on site design, education, and 

incentives and rewards, the fact that users lag 

behind managers about 20 to 35 percentage points, 

suggests that visitors perceive the recreational 

experience far differently than managers. Only in 

the use of stricter enforcement do we begin to 

see the gap between users and managers closing, 

with users 18 percent behind managers. 

Table 3.--Managersq and users' perceptions about the effectiveness of 

various prevention and control strategies in the Mount Rainier area 

Management Practice Semi- Percentage Developed Percentage 

and Developed Point Campground Point 

Perception ~rou~l Campground Difference (Rural) Difference 

Percent umber)' Percent (Number) 

Stricter enforcement: 

Managers 

Users 

Education: 

Hanagers 

Users 

Incentives and rewards: 

Hanagers 

Users 

Site design: 

Managers 

Users 

Maintenance: 

Managers 

Users 

The question in the instrument was "In your judgment, do you believe the 

following strategies would be effective in reducing problems that are 

occurring at this recreation areal" Poeeible responses for managers were 

"not at all," "somewhat," "very much," and "do not know." Users' responses 

were "effective" and "not effective." The managers' questionnaire was 

structured; informal conversations with users were unstructured. Because 

the nature of instrument construction was different in the studies, results 

are tentative and suggestive. 

Numbere in parentheses signify the number of responses from which the 

percentages were derived. 

Shifting to the semi-developed campground, two 

variations from the developed campground emerge. 

First, there are substantial differences on all 

measures across the two opportunities. And, 

second, the directions of differences show two 

opposing patterns. On some measures, there is 

an even greater perceptual departure of users 

from managers. For instance, education is 

perceived by users as even less effective in 

semi-developed sites than in developed campgrounds; 

and users are half as likely as managers to 

assert the effectiveness of incentives and rewards. 

On other measures, users are less divided over 

the effectiveness of specific strategies in 

semi-developed areas, as compared with developed 

sites. Again, law enforcement as a control 

strategy apparently is held in relatively high 

agreement by both managers and users, with users 

showing only a 12 percentage point difference. 

In summary, these data point to the following 

conclusions: (1) there is greater disparity 

between managers and users on what constitutes 

a depreciative problem than on what to do about 

impacts; (2) the potential for conflicts between 

managers and users may be more pervasive regarding 

control practices than over users' potential or 

actual loss of quality recreation because of 

impacts; and (3) the failure of managers to 

communicate to users the rationale behind 

deploying strategies such as education, . incentives, design, and maintenance may contribute 

to the incipient and unanticipated expansion of 

stricter law enforcement, which as these data 

suggest, both managers and users are more likely 

to agree is an effective control strategy. What 

this means is that this disparity between the 

two groups may be influencing managers' interaction 

with users in such a way that managers perceive 

users in negative ways; for example, as a cause 

of the problem. Furthermore, because managers 

see and experience more problems, they may 

promote negative interaction. Users, in turn, 

may respond negatively by withdrawal and deviance, 

thereby lessening the recreational experience. 

Our recursive model considers possible 

implications of this negative interaction. 

RECURSIVE MODEL OF SOCIAL CONTROL 

The apparent contradiction between freedom, the 

desired staee for users, and constraint, the 

managers' mandate to protect property and 

resources, can be depicted using a recursive 

model. This model clarifies how mutually 

responsive elements in a system of interactions 

can produce unanticipated results. Such results 

occur when managers' perceptions of impacts are 

the sole or nearly exclusive source for defining 

a situation. In brief, the model shows the 

circular, or recursive effect of deviance: 

deviance may produce social control, the control 

may create negative reactions, reactions may 

increase the rate of deviance, and the deviance 

may contribute further to more control' (fig. 1). 
Differential perception Differential perception 

of impacts of constraints 

Figure 1.--Recursive model of social control. 

(M-1 = managers' perceptions of impacts, U-1 = 

users' perceptions of impacts, M-C = managers' 

perceptions of control, and U-C = users' 

perceptions of control.) 

The differential perception of impacts by managers 

and users occurs because of different interests 

and needs of the two groups, and because the lines 

of communication between groups are absent or 

inadequate. Management thus develops an unshared 

definition of a problem, which becomes the sole 

or near exclusive definition of reality. Thus, 

fairly trivial impacts (for example, minor 

rulebreaking, simple graffiti and litter) may be 

interpreted by managers as more significant signs 

of the general breakdown of all rules. Despite 

the selective (and often distorted) definition, 

management puts pressure on the control system 

and on users to accept this definition of reality 

by instituting more rules and regulations (see 

Lemert 1972 for a discussion on adaptive control). 

users' perceptions, where they disagree with 

managements' definitions, are either ignored or 

presumed to be identical with managersv views, 

and there is a general move toward the extension 

of control. Thus, external controls, such as law 

enforcement without users' consent have the 

paradoxical effect of creating social reactions, 

including a new round of rulebreaking by resentful, 

disgruntled, or excluded users. Displacement of 

users may also occur. 

In this way the stage is set for increasing the 

credibility gap between authority and users with 

predictable consequences: users' reactions 

further generate management restrictions. Given 

this context, the recursive effect is inevitable. 

Increased constraints curtail users' margin of 

freedom and a rise in deviance leads to 

managements' further dependence on restrictive 

approaches to tighten security. 

~~ecificall~ focusing on the model, the direction 

of the arrows indicates the direction of influence 

or dominance. Managers' perceptions of impacts 

(M-1) obviously influence controls (M-C) they put 

into place. A recent example in a major urban 

area in the West began with rowdyism and vandalism 

(knocking down a few signs). Users were displaced 

by a few controls: removal of the parking slips, 

placement of ''no parking" signs, and planters 

strategically placed around the area. Users' 

perceptions of appropriate controls (U-C) may be 

deemed irrelevant and may be ignored if they do 

not understand or agree with them. To avoid 

conflict, managers need to explain the rationale 

behind rules and practices. Legitimate feedback 

from users, rather than "catch as catch can," is 

also needed. Some users have more input than 

others in the establishment of policy and rules; 

for instance, wilderness users. The recursive 

model does not apply across the entire spectrum 

of opportunities; it is for developed and 

semi-developed campgrounds. 

Similarly, managers' perceptions of controls (M-C) 

are imposed on users' definition of (U-1) 

possible sanctions if they were to break the rule. 

For example, a sign in Yosemite may read: "Please 

do not feed the bears." A user may feed the 

bear anyway even though they are aware of a fine 

if caught. Users' perceptions are affected by 

controls: they may lead to a greater feeling of 

safety or to the belief that users1 freedom is 

curtailed. The model suggests users may not be 

heard regarding definitions of rulebreaking and 

appropriate controls. Users are guided by 

knowledge of sanctions and self management; 

constraints come from authority: "Can I park 

here?" "Can I have a fire here?" "Can I get 

firewood here?" Here, we are dividing social 

control between definitions and practices--the 

concepts, images, and language ("discourse"). 

Managers initiate a discourse based on the ideas 

and the images that users are rowdy, potential 

vandals, or deviants. This may lead to 

overreaction rather than to correct assessment of 

the recreational situation. 

CONCLUSIONS 

How can the recursive effect of the buildup of 

deviance and control in recreation settings be 

prevented? Given that this model points out 

the most negative potential outcomes between 

managers and users, how can freedom and constraint 

be balanced in this system? How can users' 

choices genuinely be respected in the recreational 

environment at the same time that managers must 

protect the public domain? The polar distinctions 

of freedom or constraint need to be amended to a 

unified conception of freedom and constraint, 

given the variety of different recreation 

opportunities. This requires alternative 

strategies of open communication, linkages between 

management and user groups, more dependence on 

informal and benign control practices, limited 

use of coercive controls, and other approaches 

that rely more explicitly on users1 expectations. 

Such adaptive control suggests a greater opening 

for volunteerism and other user-centered approaches. 

The flexible system would adopt a variety of 

interventions (social control) useful for different 
recreational contexts and would also integrate 

users at various points in the system. 

Future research should address the extent to which 

certain types of environments, impacts, or controls 

encourage or discourage visits. The research 

should entail the kinds of conditions users find 

acceptable, including the forms of vandalism and 

regimentation that deter users from enjoying the 

full range of recreational opportunities and 

returning to formerly visited areas. In this 

proposed research, it should be imperative to 

clarify the users' perspectives in terms of what 

freedom and constraints mean in various settings. 

ACKNOWLEDGMENT 

This work was based on a cooperative agreement 

between USDA Forest Service, Pacific Northwest 

Forest and Range Experiment Station, and the 

Department of Sociology, Western Washington 

University. 

Appreciation is extended to Diane Samdahl, Linda 

Sims, Kevin Burke, and Marti Henderson for their 

assistance with this study. 

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RESOURCE AND ACTIVITY SUBSTITUTES FOR 

RECREATIONAL SALMON FISHING IN NEW ZEALAND 

- Bo Shelby 

ABSTRACT: Substitutes become an issue when 

people are const-rained from participating in 

desired activities. This study investigates and 

compares activity and resource substitutes for 

recreat.iona1 salmon fishing in New Zealand. 

Results suggest that resource substitul-ion and 

inventories need more attention, user 

perceptions of substitutes are important, 

substitutes can be asymmetrical, activity 

substitutes may be hard to find, and 

substitutability studies need to be integrated 

with availability studies. 

INTRODUCTION 

Substitutes become an issue when individuals are 

constrained from participating in a desired 

activity. Constraints on participation 

(Baumgartner 1978) can be externally imposed 

(e.g. resource areas closed by non-recreational 

uses) or based on individual limitations (e.g. 

insufficient time or money to participate). In 

either case, the individual can compensate by: 

(1) selecting a different activity which meets 

particular needs; (2) choosing an alternative 

resource to continue participation in the 

original activity; or (3) deferring parricipalion to a more auspicious time (Vaske and 

Donnelly 1982). The aim of subst-itutability 

research is to understand the consl-raints people 

face and their subsequent compensatory 

processes. 

The most common approaches to recreation substitutability correspond to the first two compensatory processes. Accordingly, substitutes have 

been identified between recreat-ion activities 

and between recreation resources or sites. The 

former has received the most attention. 

Paper presented at the Recreation Choice Behavior 

Symposium, Missoula, MT, March 22-23, 1984. 

Bo Shelby is Associate Professor, Dept. of 

Resource Recreation Management, Oregon State 

University, Corval lis, OR. 

Activity Substitutes 

The most commonly accepted definition of activity 

substitutability is "the interchangeability of 

recreation activities in satisfying people's 

needs, motives, and preferences" (Hendee and 

Burdge 1974). Factor or cluster analyses have 

generally been used to construct activit-y 

groupings based on empirical intercorrelations. 

The measures employed in the analysis include 

participation rates (e.g. Moss and Lamphear 

1970), reported preferences (e.g. Chase and Cheek 

1979), and perceived similarities between pairs of 

activities (e.g. Becker 1976). 

Activity groupings have also been based on the 

kinds of satisfaction sought by participants 

(Tinsley and Kass 1978; Hawes 1978). Measures 

of "satisfaction" such as getting along with 

others or utilizing abilitites are factor or 

cluster analyzed to create groupings that are 

similar in terms of the satisfactions they 

provide. The assumption is that activities satisfying similar needs are substitutable. 

Activity groupings have also been formed on the 

basis of the social groups with which an individual participates (e.g. Field and O'Leary 1973). 

The assumption here is that the basis for participation may lie in the social group experience 

rather than the activity itself. Activities 

within groupings are considered interchangeable 

because they provide the same type of group 

experience. 

The assumption in all these approaches is that 

because activites are intercorrelated on one of 

these dimensions they must provide similar satisfations and are therefore substitutable. Although 

this makes sense at some levels, some researchers 

have argued that even similar act-ivities may not 

provide the same satisfactions, depending on 

factors such as the activities in question, the 

"style" in which they are performed, and/or the 

characteristics of the user. In addition, intercorrelated activities may be complementary rather 

than substitutable. 

For example, Christ-ensen and Yoesting (1977) used 

four activity types (games and sports, hunting and 

fishing, nature appreciation, and motorized activities) from an earlier study to see if respondents 

considered other activities within an activity 

type to be good substitutes. On the average, only 

60 percent of respondents could substitute within 

an activity type; activities in the games and 
sports type were most substitutable (almost 70 

percent), while those in the hunting and fishing 

type were least so (45 percent). 

In a study of two hunting activities similar in 

form, Baumgartner and Heberlein (1981) found that 

deer hunters perceived fewer substitutes than 

goose hunters, apparently because deer hunters 

placed more importance on the process of participation, the goal of the activity, and social 

interaction. It appears that activities will 

have fewer substitutes if numerous elements- of a 

specific nature are rated important by participants. The results suggest that research 

on activity substitutes must consider the experiential elements of activities. 

Vaske and Donnelly (1981) compare the activity 

type approach to a "direct question method" where 

respondents were simply asked to specify substitutes for a particular activity. Maryland turkey 

hunters who were displaced by season closure were 

asked to: (1) specify participation rates in a 

number of recreation activities (activity type 

method); and (2) indicate three.substitutes for 

turkey hunting (direct question method). The 

activities predicted as substitutes by the 

activity type method accounted for only 15 percent 

of the activities specified as substitutes by the 

direct question method. 

Resource Substitutes 

A problem with the activity substitutes approach 

is that it often overlooks the physical context 

in which activities take place. None of these 

approaches focus on the resource itself, which is 

what managers can often manipulate most- directly. 

Economists have studied substitutes and complement relationships for years (Clawson 1966:90), 

although there are few empirical studies in the 

recreation field. Cordell (1976) explored the 

substitutability of public and private open space 

in urban areas. Demand for private recreational 

open space was significantly related to price of 

private space, quantity of public space, income, 

and two measures of the quality of private outdoor space (the proportion of land with creeks 

and golf courses). Income was the variable with 

the greatest effect, although Cordell argues that 

willingness to substitute has an effect independent of ability,to substitute. 

In a study more closely related to rural areas, 

Kurtz and King (1979) explored substitute and 

complement relationships for motorboat use of 

reservoirs in Arizona. Relationships were shown 

as cross-elasticities of demand, based on 

equations predicting the number of trips to each 

area from the costs (on and off site) of participation at each area. Results were explained in 

terms of characteristics of the individual reservoirs such as proximity to urban centers, access, 

travel time, travel distance, size of reservoir, 

facilfties available, activities (fishing, water 

skiing, or cruising) and engine horsepower. 

Income was not a significant factor in explaining 

participation rates, presumably because-the proportion of income spent on boating at the reservoir was small. 

These studies suggest the kinds of factors that 

might affect the substitutability of resources. 

The specific variables would change from one area 

to another, but issues such as access, facilities 

or developments for recreation, other resource 

uses besides recreation, perceived sire impacts, 

use density, conflicting recreation uses, and the 

regimentation of rules and regulations, need to be 

considered from the resouce point of view. 

METHOD 

The Rakaia and'waimakariri Rivers are located on 

New Zealand's South Island, flowing sout-heasterly 

out of the Southern Alps across the Canterbury 

Plain to reach the sea near Christ-church. These 

rivers have many of the same physical attributes 

and appear to offer similar recreational 

amenities. Both rivers have water right applications pending for large-scale irrigation development, and both are likely to be nominated for 

preservation under the new "Wild and Scenic 

Rivers" amendment to the New Zealand Soil and 

Water Conservation Act. These alternative uses 

are incompatible, and the possibilil-y of a compromise solution depends partly on whether the two 

rivers are close substitutes and one could accomodate a shift of use from the other. 

In this study, resource substitutability was 

approached from two points of view (see Shelby 

[I9831 for a more complete description of the 

study). First is a review of existing information 

on resource characteristics describing the Rakaia 

and Waimakariri. These include river bed and 

catchment, geology, climat-e, proximit:y to population centers, t-ravel times, road accesses, 

current recreational activities, regul at ions 

affecting retreat-ion, agencies, river flow, 

fishery and fish habitat, and developments and 

facilities. The two rivers are compared in terms 

of these variables by organizing data already 

available. 

The second source of information about resource 

substitutability is a questionnaire distributed to 

fishermen on both rivers. In order to determine 

their first hand knowledge about substitutes, 

fishermen were asked which of the ten South Island 

salmon rivers they had fished before. Fishermen 

then indicated which rivers were acceptable substitutes. For those considered not acceptable, 

they indicated the reasons why not. Finally, 

fishermen were asked to specify the "best" subsiitute river and evaluate this river in relation to 

the Rakaia or Waimakariri. 

Activity substitutes were determined in two ways. 

Possible substitute fishing activities were 

presented in a list; respondents simply circled 

"yes'' or "no" to indicate whether each was a 

substitute for salmon fishing and then indicated 

the location where the activity would take place. 

Fishermen were also asked to list the non-fishing 

activities that gave them the same type of satisfaction or benefit they got from salmon fishing. 
Finally, level of commitment to an activity and 

knowledge of substitutes have been shown to 

affect substitutability. Commitment to salmon 

fishing was measured in terms of years spent 

salmon fishing, length of time spent on an 

average visit, where the respondent stayed on 

overnight trips, and a general item indicating 

the degree of personal involvement with salmon 

fishing. Knowledge of substitutes was measured 

by asking about fishing experience on other South 

Is1 and salmon rivers. 

Questionnaires were distributed on weekend days 

in February when the river was fishable and 

fishermen were present. An effort was made to 

contact all fishermen below the Gorge Bridge on 

sampling days, using vehicle access points, jet 

boats, and kayaks. The samples are thus drawn 

from fishermen present on fishable weekend days 

in February. 

Of the 367 questionnaires distributed on the 

Rakaia, 146 were completed and returned, a 

response rate of 40 percent. Of the 400 questionnaires distributed on the Waimakariri, 121 

were completed and returned, a response rate of 

30 percent. The ability to send follow-up 

reminders would certainly have increased 

response, but because of limited resources, names 

and addresses of respondents were not obtained. 

Alt-hough disappointing, these response rates are 

close to what can be expected from a one-shot 

distribution effort under these kinds of conditions. 

It is difficult to specify the extent to which 

the samples represent all Rakaia or Waimakariri 

fishermen because response rates are low and the 

samples represent only those present on fishable 

weekend days in February. We have no reason to 

conclude that these two factors bias the study 

findings, but that possibility does exist, and 

the results presented here should be viewed as 

suggestive rather than conclusive. 

Table 1.--Rakaia fishermen's evaluations of possible 

RESULTS 

Subs~itutability Based on Resource Characteristics 

The resource assessment revealed both similarities 

and differences. The rivers are similar in terms 

of length, channel, distance from the Gorge 

Bridges to rhe sea, geologic areas, and rainfall 

and climatic patterns. They serve much of the 

same populated area by the same highways, and are 

clear and fishable on roughly the same days. The 

rivers are different in that the Rakaia has a 

higher catchment, greater flow, wider braided 

channel, shorter gorge, greater distance from 

State Highway 1 lo the sea, and lacks the extensive stop banks and facility development found on 

the lower Waimakariri. The Rakaia is farther from 

the Christchurch metropolitan area and has less 

road access in the area below the Gorge Bridge. 

The salmon run in the Rakaia has at least twice as 

many fish as the Waimakariri. 

Fishermen's Perceptions of Substitutablity for the 

Rakaia 

Rakaia fishermen were given a list of South Island 

salmon rivers and asked to indicate which ones 

were acceptable substitutes for salmon fishing on 

the Rakaia (see table 1). Over 70 percent agreed 

that the Waiau, Hurunui, Opihi, Waitaki , Cl utha, 

and Rangitata were not acceptable substitutes. 

For these rivers the most common reason (given by 

59-91 percent of respondents) was that the drive 

takes too long. Some fishermen (26-40 percent) 

also indicated that it is too expensive to fish on 

these rivers. A substantial number (36-46 percent) added low salmon numbers as the reason why 

the Waiau, Hurunui, and Opihi were not acceptable 

substitutes. 

The Ashley and Ashburton Rivers were not acceptable substitutes for 100 percent and 88 percent 

of the fishermen, respectively. However, the 

length of the drive was less likely to be a 

substitutions for the Rakaia 

Rivers 

Substitute 

for 

Rakaia 

Drive Scenery Poor 

(percent Takes Too Too not as Fewer fishing 

"no") too long expensive crowded good salmon conditions 

Waiau 

Hurunui 

Opihi 

Wait aki 

Clutha 

Rangi t ata 

Ashley 

Ashburt on 

Waimakariri 
problem (26-32 percent checked this reason). For 

these rivers, 45-55 percent cited fewer salmon and 

53-55 percent cited poor fishing conditions as 

reasons why they were not acceptable substitutes. 

It is often assumed that fishermen can substitute 

the Waimakariri for the Rakaia. Of the Rakaia 

fishermen surveyed, 50 percent said the 

Waimakariri is not an acceptable substitute. The 

most common reason (given by 40 percent of respondents) was that the river is too crowded, followed 

by fewer salmon (26 percent), and poor fishing 

conditions (19 percent). 

Rakaia fishermen were asked to choose the river 

which for them was the best substitute for the 

Rakaia. As the preceding data would suggest, 

almost half (46 percent) chose the Waimakariri, 

while 28 percent chose the Rangitata. None of the 

other rivers was chosen by more than 3 percent of 

the respondents, and 18 percent refused t.0 choose 

a "next-best substitute," saying there was no 

substitute for the Rakaia. 

In order to assess trade-offs between the Rakaia 

and Waimakariri, we asked Rakaia fishermen to 

indicate how many days of salmon fishing on their 

substitute river were required to give the same 

enjoyment as they got from one day on the Rakaia. 

Responses for those who chose the Waimakariri or 

Rangitata are shown in table 2. Only 12 percent 

said they were willing to give up one day on the 

Rakaia for one-half to one day on the Waimakarira; 

31 percent would need one and one-half to two days 

on the Waimakariri; 28 percent would need three to 

five days, and 30 percent would need six or more. 

For those who chose the Rangitata, 15 percent 

would need one-half to one day, 39 percent would 

need one and one-half to two days, 27 percent 

would need three to five days, and 18 percent 

would need six or more. These results indicate 

that in general the Waimakariri and the Rangitata 

are not "equal" in value to the Rakaia for those 

who consider them the best substitutes. 

Rakaia fishermen were also asked if any other 

fishing activities would give them the same satisfaction or benefit they got from salmon fishing 

(see table 3). Sea fishing was not a substitute 

for 84 percent, and lake salmon fishing was not a 

substitute for 85 percent. Flounder and perch 

fishing were unacceptable for 94 percent and 99 

percent, respectively. Trout fishing did provide 

the same type of benefit for 50 percent- of Rakaia 

fishermen, but in a separate item 62 percent indicated that "For me no other fishing is a substitute for salmon fishing." Trout fishing may 

provide similar benefits for some, but the 

majority still contend that other types of fishing 

are not substitutes for salmon fishing. 

Rakaia fishermen were asked if any non-fishing 

activities would give them the same satisfaction 

or benefit they receive from salmon fishing. 

Approximately 80 percent listed no substitute 

activities and indicated that "For me no nonfishing activity is a substitute for salmon 

fishing." Fourteen percent 1ist.ed one substitute 

activity, and 5 percent listed two. 

Fishermen's Perceptions of Substitutability for 

the Waimakariri 

Waimakariri fishermen were given the same list of 

South Island salmon rivers and asked to indicate 

which ones were acceptable substitutes for salmon 

fishing on the Waimakariri (see table 4). Over 75 

percent agreed that the Waiau, Hurunui, Opihi, 

Waitaki, Clutha, and Rangitata were not acceptable 

substitutes. For these rivers, the most common 

reason (given by 66-91 percent of respondents) was 

that the drive takes too long. Some fishermen 

(38-52 percent) also indicated that it is too 

expensive to fish on these rivers. A substantial 

number (25-39 percent.) added that there were too 

few salmon in the Waiau, Hurunui, and Opihi. 

The Ashley was not an acceptable substitute for 

88 percent of Waimakariri fishermen, primarily due to 

fewer salmon (61 percent) and poor fishing conditions 

(56 percent). The Ashburton was not a substitute 

for 85 percent of Waimakariri fishermen. The 

most common reason (52 percent) was the length of 

the drive, but expense (32 percent), fewer salmon 

(30 percent), and poor fishing conditions (38 

percent) were also cited as problems. 

The Rakaia was an acceptable substitute for 80 

percent of the Waimakariri fishermen. For those 

who said it was not, the major reason was the 

length of the drive. 

Table 2.--Trade-offs between the Rakaia, Waimakariri, and Rangitata 

Days on Waimak to 

equal one day on 

Rakaia 

Days on Rangitata to 

equal one day on 

Rakaia 

Days on Rakaia to 

equal one day on 

Waimak 

One-half to one 

One and one-half 

Three to five 

Six or more 

TOTAL 100% (58) 

Average 5.5 

(3-4 days) 

4.7 

(2-3 days) 

3.4 

(1-1/2 - 2 days) 
Table 3.--Substitute fishing activities for Rakaia and Waimakariri fishermen 

Fishing activities 

Substitute for salmon fishing? 

(percent "no") 

Rakaia Fishermen Waimak Fishermen 

Sea fishing 8 4 7 4 2.0* 

Lake salmon fishing 8 5 78 1,. 4 

Flounder fishing 9 4 9 2 .9 

Perch fishing 99 9 6 1.8 

Trout fishing 5 0 44 1 .O 

* means are significantly different, p 5 .05. 

Table 4.--Waimakariri fishermen's evaluations of possible substitutes for the Waimakariri 

Substitute ---------------------- If not, why?----------------------- 

for (percent checked) 

Rivers Rakai a 

Drive Scenery Poor 

(percent takes Too Too not as Fewer fishing 

"no" ) too long expensive crowded good salmon conditions 

Wai au 

Hurunui 

Opihi 

Waitaki 

Clutha 

Rangi tata 

Ashley 

Ashbur ton 

Rakai a 

In order to assess the trade-offs between the 

Waimakariri and the Rakaia, we asked Waimakariri 

fishermen to indicate how many days of salmon 

fishing on their substitute river were required 

to give the same enjoyment as they got from one 

day on the Waimakariri. Responses for those who 

chose the Rakaia are shown in table 2. The 

majority (56 percent) said they were willing to 

give up one day on the Waimakariri in return for 

one day or less on the Rakaia; 16 percent would 

need one and one-half to two days on the Rakaia, 

18 percent would need three to five days, and 10 

percent would need six or more. These results 

suggest that the Rakaia is more nearly equal in 

value for the Waimakariri fishermen than the 

Waimakariri is for the Rakaia fishermen. 

Waimakariri fishermen were also asked if any 

other fishing activity would give them the same 

satisfaction or benefit they receive from salmon 

fishing (table 3). Sea fishing was not a substitute for 74 percent, and lake salmon fishing was 

not a substitute for 78 percent. Flounder and 

perch fishing were not substitutes for 92 percent 

and 96 percent, respectively. Trout fishing did 

provide the same type of benefit for 56 percent of 

Waimakariri fishermen. In a separate item, 38 

percent indicated that "For me no ot-her fishing 

is a substitute for salmon fishing." 

Waimakariri fishermen were asked if any nonfishing activities would give them the same satisfaction or benefit they got from salmon fishing. 

About 65 percent listed no substitute activities 

and indicated that "For me no non-fishing activity 

is a substitute for salmon fishing." Twenty-six 

percent listed one substitute activity and 5 

percent listed two. 

In summary, most Rakaia and Waimakariri fishermen 

agreed that the Waiau, Hurunui, Opihi, Clutha, and 

Rangitata are not substitutes, primarily because 

the drive is too long. Both groups agree that the 

Ashley and Ashburton are not acceptable substitutes, primarily due to fewer fish and poor 

conditions, although more Waimakariri fishermen 

(probably those who live north of Chirstchurch) 

felt the Ashburton was too far to drive. Most 

Waimakariri fishermen felt- the Rakaia was an 

acceptable and nearly equal subsitute, but only 

half the Rakaia fishermen felt the Waimakariri was 

a substitute, and of lower value at that. Most 

objections to the Waimakariri centered around 

crowding and fewer fish. Waimakariri fishermen 

were a little more likely to view other 
activities, both fishing and non-fishing, as substitutes for salmon fishing. 

DISCUSSION 

Findings presented here suggest several interesting issues for substitutability research. 

First, what is the relative importance of 

resource subst-itutes versus activity substitutes? 

Activity substitutes have received the most 

attention, primarily at a theoretical level. But 

from a management point of view, resource substitutes may be more important-. If constraints on 

participation are externally imposed (as when, a 

resource area is closed by non-recreation uses), 

public agencies may be obligated to provide an 

alternative setting which offers the same 

activitv. It mav not be enough " to dis~lace L -- users -- - 

and simply say "they can participate in other 

recreation activities." It is also possible that 

what were previously assumed to be called activity 

substitutes or replacements are really activity 

complements or alternatives, as discussed below. 

This issue needs further attention. 

Second, studies of resource substitutes need to 

include careful inventories. Studies by economists (Cordell 1976; Kurtz and King 1979) have 

suggested the importance of variables describing 

important resource characteristics, and those 

assessed in the present study add to the list. 

Variables such as costs, proximity to population 

centers and user groups, access, travel times and 

distances, size of recreation area, climate, 

physical and geographic characteristics, activities possible, facilities available, and level of 

development should at least be considered. It 

may be that resources which appear similar at 

first glance are quite different when the details 

are known, as was found with the Rakaia and 

Waimakariri. 

Third, information about resource characteristics 

needs to be interpreted in light of corresponding 

information about user perceptions of those 

characteristics. In the present study, for 

example, information about fishermen's perceptions of substitutes showed that driving time was 

more often a problem than expense. Crowding and 

the quality of scenery were less often cited as 

problems, although it is not clear whether this 

is because fishermen don't care about these two 

fact~ors or because conditions are fairly comparable on most of the New Zealand rivers studied. 

(The latter explanation is suggested by finding 

that the most heavily used river, the Waimakariri, was the one where a large proportion of 

fishermen cited crowding as a problem.) The 

point is that perceptions provide the evaluative 

dimension needed to determine the importance of 

objective differences. 

Fourth, resource substitutes are not necessarily 

symmetrical. The majority of Waimakariri fishermen felt the Rakaia was an acceptable substitute, 

but,Rakaia fishermen did not feel that way about 

the Waimakariri. 

Fifth, findings presented here suggest that different (non-fishing) activities are not good substitutes. This casts doubts on some of the 

activity groupings reported in earlier studies and 

suggests that intercorrelated clusters may represent alternatives or complements rather than substitutes. In addition, the present study shows 

that some forms of the same activity are not good 

substitutes, reinforcing the findings of earlier 

studies (Christensen and Yoesting 1977; 

Baumgartner and Heberlein 1981). The definition 

of the substitutabilit-y concept is particularly 

important here. For example, Vaske and Donnelly 

(1981) asked displaced turkey hunters what they 

would do "instead," which could include complements or alternatives as well as true substitutes 

or replacement-s. In the present study, we asked 

if other fishing and non-fishing activities would 

give "the same type of satisfaction or benefit," a 

more restrictive definition of a true substitute. 

Finally, we need to integrate substitutability 

studies with "availability" studies of use levels 

and carrying capacities. Even if a substitute 

resource can be found and users perceive it as 

such, we need to know if the new area can accommodate a shift in use. This requires knowledge of 

the current use levels in both areas and the 

capacity of the new area. In the Rakaia and 

Waimakariri, for example, most fishing areas were 

already at or above capacity, and neither river 

could accommodate a major shift of use (Shelby 

1983). This adds another dimension to the problem 

of actually providing substitutes rather than just. 

talking about them at a theoretical level. 

ACKNOWLEDGMENT 

This paper is based on research funded jointly by 

the Canterbury United Council and North Canterbury 

Catchment Board, Christchurch, New Zealand, The 

Agricultural Economics Research Unit, Lincoln 

College, New Zealand, and a sabbatical leave from 

the School of Forestry, Oregon State University. 

The paper is drawn, in part, from an earlier 

project report (Shelby 1983). 

REFERENCES 

Baumgartner, R. Recreational subst:itutability: 

An empirical investigation of goose hunt-ing at 

the Horicon Marsh. M.S. Thesis. Univ. of 

Wisconsin, Madison. 1978. 

Baumgartner, R; Heberlein, T.A. Process, goal and 

social interaction differences in recreation: 

What makes an activity substitutable? Leisure 

Sciences. 4(4): 443-458. 1981. 

Becker, B.W. Perceived similarities among recreational activities. Journal of Leisure 

Research. 8(2): 112-122. 1976. 
Chase, D.R; Cheek, N.H., Jr. Activity preferences Vaske, J. J.; Donnelly, M.P. Substitutability 

and participation: Conclusions from a factor research: A review and synthesis. Interim 

analytic study. Journal of Leisure Research. Report to the USDA Forest Service, PNW Experill(2): 92-101. 1979. ment Station from Department of Recreation, 

University of Maryland, (mimeo). 1982. 

Christensen, J.E.; Yoesting, D.R. The substitutability concept: A need for further development. Journal of Leisure Research. 9(3): 188- 

207. 1977. 

Clawson, M.; Knetsch, J.L. Economics of outdoor 

recreation. Baltimore: John Hopkins Press. 

1966. 

Cordell, H.K. Substitution between privately and 

publicly supplied urban recreational open 

space. Journal of Leisure Research. 8(3): 

160-174. 1976. 

Field, D.R.; OILeary, J.T. Social groups as a 

basis for assessing participation in selected 

water activities. Journal of Leisure Research. 

5:16-25. 1973. 

Hawes, D.K. Satisfactions derived from leisure 

time pursuits: An exploratory nationwide 

survey. Journal of Leisure Research. lO(4): 

247-264. 1978. 

Hendee, J.C.; Burdge, R. J. The substitutability 

concept: Implications for recreation research 

and management. Journal of Leisure Research. 

6:157-162. 1974. 

Kurtz, W.B.; King, D.A. Evaluating substitution relationships between recreation areas. 

Proceedings of the International Union of 

Forestry Research Organizations Meeting, March 

1979. 

Moss, W.T.; Lamphear, S.C. Substitutability of 

recreational activities in meeting stated needs 

and drives of the visitor. Environmental 

Education. l(4): 129-131. 1970. 

Shelby, B. Recreational substitutability and 

carrying capacity for the Rakaia and 

Waimakariri Rivers. Discussion Paper No. 78, 

Agricultural Economics Research Unit, Lincoln 

College, New Zealand. 1983. 

Tinsley, H.E.A.; Kass, R.A. Leisure activities 

and need satisfaction: A replication and 

extension. Journal of Leisure Research. 

10(3):191-202. 1978. 

Vaske, J.J. An empirical comparison of 

methodological approaches to recreational 

substitutability. Ph.D. Dissertation. Univ. 

of Maryland, College Park. 1980. 

Vaske, J.J.; Donnelly, M.P. Two methodological 

approaches to recreational substitutability. 

Paper presented at the Annual Meeting of the 

Rural Sociological Society, Guelph, Ontario. 

1981. 
PREDICTING THE IMPACTS OF A HIGH-VOLTAGE TRANSMISSION LINE 

ON BIG GAME HUNTING OPPORTUNITIES IN WESTERN MONTANA 

Stewart A1 Len 

ABSTRACT: The State of Montana, in cooperation with the U.S. Forest Service and Bonnevi Lle Power Administration, is conducting a 

five-vear study on the impacts of a 500-kV 

transmission Line on elk habitat and hunter 

opportunity. In fa L L, 1983, baseline data 

on hunting patterns and qua li ty were 

collected from over 600 hunters interviewed 

in three western Montana study areas. A 

cluster analysis revealed seven types of 

hunters, each of which should be affected 

differently by the transmission Line 

impacts. Hypotheses developed wi 11 be 

tested during two subsequent hunter surveys 

in 1985 and 1986. 

INTRODUCTION 

In the spring of 1983, Bonnevi lLe Power 

Administration [BPAI began clearing the 

right-of-way for a 1581niLe-Long section of 

a 500-ki lovo Lt transmission line across 

western Montana. The Line, which begins at 

Garrison, wi L L transfer power generated at 

Colstrip to a substation at Taft, near the 

Idaho border. 

The final route for the transmission 

line was chosen from many alternatives, each 

having different effects on the Montana 

envi ronment . The State and Federal agencies 

locating the line gave considerab,Le weight 

to social concerns in the siting process, 

choosing a route that crossed very Little 

private land. In western Montana, this 

meant avoiding the val leys, instead crossing 

National Forest Lands that provide a wide 

spectrum of wi ldli fe habitat and dispersed 

recreation settings. 

Paper presented at the Recreation Choice 

Behavior Symposium, Missoula, MT, 

March 22-23, 1984. 

When this paper was prepared, Stewart Allen 

was a Recreation Special ist, Montana 

Department of Conservation, He1 ena , MT. 

He now is a private consultant in Helena. 

I n i t s final report on the project, the 

Montana Department of Natural Resources and 

Conservation (DNRCI said that the chosen 

route would affect fewer people and fewer 

homes than the other routes, but would have 

greater potenti a L to damage natural systems 

(DNRC 19831. 

Of particular concern was the potential 

for adverse effects on hunting npportunity; 

the Line would cross areas providing secure 

summer and fall habitat for elk and other 

big game species. Among these were three 

areas that had been eva Luated under the 

Forest Service's Road less Area Review and 

Eva Luation process [RARE I1 I. These areas 

were released and wi 1 L Likely be subject to 

multiple-use management. Habitat security 

in roadless areas is essential to at Low elk 

to hide from hunters (Lonner and Cada 

19821. 

Not only is hunting big business in 

western Montana, it is an integral part of 

many Montana residents' li festy les, and 

probably one of the reasons many people 

choose to live here. 

The line could have three main types of 

effects on hunting opportunities in western 

Montana : 

1. The many miles of new and upgraded 

roads needed to construct the line and 

maintain access to the towers would 

increase hunter access to these areas, 

potentially increasing elk vutnerabi Li ty 

and ki 11 rates. 

2. The increased access could make the 

areas Less remote, both physically and 

perceptua 1 Ly . Hunters desi ring a remote 

setting would be Less satisfied, whi le 

road hunters or others desi ring improved 

access wou Ld benef i t . 

3. The transmission Line itself, with 

its 175-foot-tal L steel lattice towers 

and 150-foot-wi de right-of-way clearing, 

could intrude on the naturalness of the 
area, decreasing the quality of the 

experience for hunters who value 

esthetics. 

The Montana Department of Fish, 

Wi ldlife, and Parks [DFWPI is especially 

concerned about the effects of increased 

access, which is already a problem in 

western Montana. A 1980 survey of hunters 

in western Montana found that If.. .Forest 

roads and the access they provide are, and 

wi Ll continue to be, a serious threat to the 

future of big game management in Region 2" 

[Thomas 1980, p. 1 1. Asked whether 

motorized travel should be reduced or 

increased in the hunting district, 24 

percent of the hunters interviewed at check 

stations indicated that access should be 

greatly reduced, and 23 percent felt it, 

should be moderately reduced. Thi rty-seven 

percent favored maintaining present access 

Levels, and only 12 percent wanted to see 

access moderately or greatly increased. It 

was concluded that hunters support existing 

road closure policies and favor further 

reductions in road access during hunting 

season. 

Big game species such as elk are also 

valued by people who don't necessari ly buy a 

hunting License. To many, wildlife 

symbolizes envi ronmental quality and the 

human relationship with nature [Shaw 19781. 

This study does not address nongame values 

of wi ldlife, but readers should keep in mind 

that decreases in elk habitat may be viewed 

as negative by hunters and nonhunters alike. 

THE ELK MONITORING STUDY 

The Department of Natural Resources and 

Conservation, in cooperation with BPA, the 

Forest Service, Bureau of Land Management, 

and Montana Department of Fish, Wi ldli fe, 

and Parks [DFWPI is monitoring transmission 

line impacts on elk habitat and hunting 

opportunity, and wi 11 explore the potential 

to reduce any significant impacts 

identified. The monitoring program has 

three main objectives: 

1. Determine the changes in elk use of 

summer/falL security area habitat due to 

the construction of access roads and 

operation of the transmission line. 

2. Determine the changes in hunting 

opportunity and quality caused by 

operation of the Line and access roads. 

3. Provide for integration of the elk 

monitoring study data into future 

logging plans for speci fi c projects and 

the forest planning process. 

The 5-year moni tori ng program has 

bio logi cal and soci a1 components. Current 

animal use of elk habitat, the extent and 

quality of elk habitat affected by 

construction, elk response to construction 

and operation disturbances, and elk 

population characteristics are being 

measured through foot surveys and radio 

telemetry [Elliott 19831. 

For the social portion, hunters are 

being interviewed in the field at three 

times: once before construction; once in 

1985 after the line is built and energized; 

and again in 1986 to see if any changes in 

hunting patterns remain over time. The 

baseline survey wi 11 also serve as a pi lot 

study, to help develop hypotheses and 

research designs for the subsequent phases. 

The preconstruction survey was designed 

to obtain baseline information on current 

hunter use of the study areas and predict 

how different types of hunters will be 

affected by the transmission Line. This 

required a description of current users of 

the study areas. We needed to know not only 

who they were, where they were from, and how 

often they hunted in the study areas, but 

why they chose the study areas as recreation 

settings, and how these areas compared to 

their other hunting grounds. This 

information should help assess the hunterst 

responses to changes in the hunting setting 

[Manfredo and others 19831. 

Our principal concern was how changes in 

the setting would alter the chance of 

hunters having desi rab le experi ences [Clark 

and Stankey 19791. 

If the setting attributes valued by 

hunters change, and settings simi larly 

valued are unavai lable [or farther away), 

then current hunters may have to use the 

altered area, perhaps decreasing hunting 

satisfaction, or find a substitute. Of 

course, new hunters could be attracted to 

the altered setting [Anderson 19811. 

This paper presents the results from the 

baseline hunter survey, conducted by DNRC in 

fa 11 1983. The fo 1 lowing sections describe 

the framework of the study, research 

hypotheses, results, and what the 

conclusions mean for the Phase I1 

[post-const ruction 1 survey and detai led 

impact assessment. 

The elk and hunter studies are taking 

place in three areas Located along the 

transmission line route: the 

Harvey/Eightmile; Middle Fork Rock Creek; 

and Packer Creek areas. These were chosen 

because they contain elk security habitat, 

support large elk populations, and represent 

the various climato logi ca 1 and vegetationa 1 

characteristics of lands along the route. 
In addition, the Forest Service is planning 

timber sales near each of the areas, which 

could add to impacts created by the 

transmission line and access roads. Another 

criterion was ease of measuring hunter use; 

seven check stations were adequate to cover 

primary access points into the three study 

areas. Car counters were instal led by DFWP 

at or near each check station to compare use 

leve 1s during genera 1 hunting season with 

use before and after. 

Use of the Harvey/Eightmile study area, 

about 13,000 acres in the John Long 

Mountains south of 1-90 and between Rock 

Creek and Hall, was measured by check 

stations at Harvey Creek and Beavertai 1. 

The Middle Fork Rock Creek study area, about 

6,000 acres north of 1-90 between Twelve 

Mi le road and DeBorgia, was monitored by 

three check stations. The Packer Creek 

study area, about 9,600 acres Located north 

of 1-90 between Haugen and Saltese, was 

monitored by two check stations. 

LITERATURE REVIEW 

It is helpful to begin by defining 

hunting as a recreational activitv in which 

people participate in a recreationa 1 settinq 

to attain certain types of experiences or 

satisfactions, and thereby achieve certain 

psychological outcomes or benefits [Driver 

and Brown 1975, 1978; Manfredo and others 

19831. By understanding what types of 

experiences people are seeking through 

hunting, we can Learn about their setting 

preferences--how likely a given recreational 

setting is to meet thei r needs [Clark and 

Stankey 19791. 

Hunting, like other recreational 

activities, is done for many reasons. 

Utilitarians may hunt primarily for the 

meat, and thei r satisfaction can be measured 

by the size or quantity of animals ki 1 led. 

Others may hunt to bag a trophy animal, 

gaining a sense of achievement from 

displaying the trophy. Others perhaps hunt 

to be outdoors, walking through the woods 

and enjoying natural surroundings. Sharing 

the experience with others, or even just 

getting some exercise, are also possible 

motivations for hunting. To other hunters, 

seeing wildlife i n i t s natural habitat or 

having a chance to test their tracking and 

shooting ski 11s may provide the greatest 

enjoyment . 

Hunters seek these and other types of 

experiences in different patterns and levels 

of intensity [Hautaluoma and Brown 1978; 

Brown and others 1977; Hautaluoma and others 

19821. Nearly every study of hunting has 

concluded that there is far more to hunting 

than ki 1 Ling an animal; hunting is enjoyable 

for many reasons. In a review of 56 

studies, Hendee and Bryan [I9781 found that 

nearly all of the reasons people gave for 

hunting could be categorized under 16 

general types of satisfaction people gain 

from outdoor recreation. This suggests that 

hunters share many goals with participants 

in other activites, such as backpacking. 

Ashor and McCooL [l983), for example, found 

that hunters and nonhunters visited the Bob 

Marshall Wilderness for many of the same 

reasons. 

Schole and others [I9731 identified 12 

sources of satisfaction among Colorado 

hunters, including being outdoors, 

soci a Li zing, and escaping from the dai ly 

routine. Potter and others [1973) reached a 

simi Lar conclusion: "Hunting satisfaction i s 

complex and consists of many elements or 

aspects of the hunting experience" [p. 

2201. Their study of Washington State 

hunters listed 11 dimensions of hunting 

satisfaction, that overlapped Scho le and 

others' categories. 

In a national poll, Kellert [I9781 found 

that ki lling an animal, hunting for sport, 

and enjoying nature were the broad reasons 

given for hunting. He believed that 

although people hunt for al 1 of these (and 

other) reasons, one reason should be the 

most important, and the others subordinate. 

Once researchers identified the various 

dimensions of satisfaction, the next obvious 

step was to see if hunters could be grouped 

into categories based on their patterns of 

satisfactions derived from hunting. This 

would let resource managers gain a better 

understanding of thei r "clients, " thus 

improving their ability to create and 

maintain satisfying recreational 

experiences. 

This has been accomplished in several 

studies using cluster analysis to group 

hunters based on types of satisfaction they 

va Lue. Such ana lyses fi rst requi re 

identifying the dimensions of satisfaction 

for a given group of hunters. After the 

hunters are scored on each dimension, their 

scores across all dimensions are compared. 

Hunters having simi lar patterns of responses 

are grouped together, forming a hunter 

"typeM having common reasons for hunting, 

at ti tudes toward possi b le management 

actions, and demographic characteristics. 

This method has also been used to type the 

atti tudes of resource managers toward 

recreation activities (Allen 1979). 

Hautaluoma and Brown (19781 used data 

collected in Washington State to identify 10 

types of hunters, each having a different 

pattern of satisfactions across five 

dimensions: nature; harvest; equipment; 

out-group contact; and skill. The types 
also differed on many other variables, 

including age when they began hunting, 

commitment to the sport, age, education, and 

income. Some types of satisfaction-- 

enjoying nature and applying hunting 

ski 1 ls--were important to near ly every 

hunter type. Other variab les--desi re for 

harvest and so li tude--effectively 

di sti ngui shed one type f ram another. 

Brown and others (19771 used the same 

method to define types of deer hunters ins 

Co lorado. They identi fied nine dimensions 

of hunting satisfaction, four of which were 

chosen for cluster analysis: easy hunt; 

harvest; out-group contact ; and nature. 

Based on how much each of the four 

dimensions added to or detracted from thei r 

hunting experiences, eight hunter types 

emerged. The types also differed on 

attitudes toward three of ten possible 

management actions, inc luding two dea ling 

with increased access to hunting areas. 

Elk hunting in Co lorado has been 

simi tar ly studied. Hauta luoma and others 

[I9821 found that six sources of hunting 

satisfaction [outgroup contact, harvest, 

easy hunt, frustration release, in-group 

contact, and a broad dimension containing 

ski 11, equipment, excitement, and nature1 

were stab le across four license groups 

[resident rif le hunters, nonresident rif le 

hunters, muzzle-loaders, and archers]. 

Depending on the license group, from 

five to seven types of hunters were found. 

Among resident rif le hunters, for instance, 

one type was Low on all dimensionspotential dropouts of the activity who did 

not seem enthusiastic about any aspect of 

hunting. A second group was characterized 

by a high need for ski 11 display, equipment 

use, excitement, and contact with nature, 

and by low needs for an easy hunt or 

releasing frustration. A thi rd group was 

high on all dimensions, enthusiastic hunters 

who enjoy many aspects of hunting. The 

fourth type was also high on most 

dimensions, but had a Low need for releasing 

frustration. The fifth group did not derive 

satisfaction from out-group or in-group 

social contact, and the sixth was not 

sufficiently unique to score one way or the 

other across the six dimensions. The 

authors used this information, plus the 

demographic correlates of the hunter types, 

to develop a brief case history for each 

group, outlining the simi lari ti es and 

di fferences among the various types. 

This research has a clear application 

for resource managers. If hunting is done 

for many reasons, then a 

mu lti ple-sati sfaction approach to game 

management is necessary [Hendee 19741. The 

tradi tiona 1 approaches of using game ki 1 led 

or hunter days expended as measures of 

r e c r e a t i o n a l . q u a l i ty ignore many of the 

other, sometimes more important, aspects of 

hunting. Like other recreational 

activities, hunting is done for different 

reasons, and i denti fyi ng these reasons-and 

for whom they are important--can help 

resource managers: 

Rather than managing as if al l users 

are alike, it is possible to conceive of 

the kinds of users according to the 

patterns of satisfaction they seek, the 

relative proportions of the kinds of 

users in the population, and the 

characteristics that distinguish the 

kinds of users f rom one another 

[Hautaluoma and others 1982, p. 791. 

METHODS 

ONRC assumed that some hunters--those 

who value aspects of the setting that could 

be altered by the transmission line--wi 11 be 

particularly vulnerable to the types of 

impacts expected to occur. The key 

vari ab les Li ke ly to predi ct vu lnerabi ti ty to 

impacts were isolated from the Literature 

based on the anticipated effects of the line 

on hunting areas, and on the hunting 

satisfactions that seem most dependent on 

the settings that wi 11 be affected. Hunters 

were also asked to indicate on a map where 

they had hunted. The following variables 

were measured: 

1. The role of nature, harvest, access, 

remoteness, convenience, and past 

experience in recreation setting choice; 

2. Hunting style and success; 

3. Hunters' evaluations of the study 

areas compared to other places they 

hunt; and 

4. Age, residence, previous big game 

hunting experience. 

DNRC asked hunters whether enjoying 

nature was extremely, very, moderately, 

slightly, or not at at l important in their 

decision to hunt where they did. [This same 

response format was used in subsequent 

questions.] The larger the proportion of 

hunters for whom enjoying nature is 

important, the greater the chance the line 

would disrupt thei r behavior. 

Two questions were used to tap the 

access-remoteness dimension : the importance 

of hunting in an area with few roads; and of 

hunting in an area with few other hunters. 

In phrasing the questions this way, DNRC 

assumed that the study areas were perceived 

as having few roads and other hunters; the 

study areas provide secure habitat because 

roads are sparse. Hunters saying these two 

reasons were not important in their setting 
choice could, however, actual ly be saying 

that they do not consider the study areas to 

have these attributes. 

The issue was split out because the 

impacts could be independent. For example, 

it might not be the roads themselves that 

people object to, but the likelihood that 

more hunters wi 11 use the area. Similarly, 

other hunters might not mind seeing more 

people, but object to being around vehicles, 

or simply being near a road. Each of these 

access items was scaled similarly to the 

question on enjoying nature. 

Another question was designed to measure 

the perceived adequacy of the existing road 

system; hunters indicated whether, for 

hunting purposes, they believed the study 

areas contained too few, too many, or about 

the right number of access roads. 

Because road hunters could have 

different views on appropriate levels of 

access than those hunting primari ly away 

from roads, DNRC categorized hunters as road 

hunters or non-road hunters based on their 

responses to a question asking where they 

hunted . 

Two Likert-scaled items simi lar to those 

described above esked hunters the importance 

of obtaining a trophy animal, and the 

importance of putting meat in the freezer. 

Although both involve ki lling an animal, 

hunters rating one of these as more 

important could be affected differently by 

the Line. Trophy hunters would presumably 

be more concerned over effects on 

branch-antlered bulls, and meat hunters 

concerned about effects on all elk. 

Although these two aspects of harvest have 

often been Lumped 'together in past studies, 

it was desirable to separate them to better 

define impact vu lnerabi li ty . 

Hunters were also asked how important 

hunting nearby was in recreation setting 

choice. If the most important reason 

someone hunts a given area is simply that it 

is close, then he or she may continue to 

hunt there, even if the setting is altered. 

However, if hunters value hunting nearby in 

combination with other experiences, such as 

being in an area having few roads, then any 

impacts on the study area gain significance. 

We also asked hunters how many years 

they had hunted in the specific area they 

indicated on the map. Hunters using the 

area for the fi rst year were formed into one 

group for analysis. The remaining hunters 

were formed into two groups, those who had 

hunted there less than or more than the 

median number of years hunted [not including 

fi rst-time visitors]. 

Hunters who return to the same settings 

year after year can develop a knowledge of 

and affinity for these areas. Viewing the 

area as their ''home range," they may respond 

with "territorial defense1! to alterations of 

thei r hunting spots [Thomas and others 

1973). They should therefore be more 

opposed to changes in the physical, 

biological, social, or managerial character 

of the resource than someone having little 

experience in the area [unless, of course, 

they find such changes beneficial). They 

should also be more apt to view the area as 

one of their favorite places to hunt, and to 

favor the number of existing roads in the 

area. Hunters return to an area, 

presumably, because they find aspects of it 

attractive. More experienced hunters should 

also be more specialized [Bryan 19791 and, 

therefore, more dependent on a specific area 

or type of area in which to hunt. 

How the study area fits into the range 

of places people hunt in western Montana 

should be crucial in predicting both the 

magnitude and significance of impacts from 

the transmission line. If a person's 

favorite hunting spot is disrupted, the 

impact would be perceived as greater than if 

the affected area is just one of many places 

a person hunts. Hunters were asked whether 

the specific area was thei r favorite place 

to hunt in western Montana, one of their 

favorite places, one of many places they 

hunt, or whether they prefer to hunt 

elsewhere. 

The survey was conducted during the 1983 

big game hunting season, October 25 to 

November 27. The seven check stations, 

staffed for 91 person-days with six 

interviewers hired by DNRC, consisted of a 

State vehicle and two signs [one 500 feet up 

the road and one at the vehicle) similar to 

those regularly used by DFWP at its 

enforcement check stations. [In Montana, 

hunters are required by law to stop at check 

stations, even if they have not harvested 

any game. l 

All hunters who stopped were asked to 

answer a few questions about their hunting 

trip. After identifying themselves. the 

interviewers administered the first half of 

the questionnaire, recording each hunter's 

responses on sepamte forms. If they were 

hunting in or adjacent one of the study 

areas, hunters were asked to complete the 

second half of the questionnaire themselves, 

and given a clipboard and penci 1. 

Hunters who had already completed a 

questionnaire on an earlier visit were not 

esked to complete another unless they were 

hunting in a different area or had killed an 

elk or deer. Repeat hunters [and 
nonhuntersl were noted to help interpret car 

counter data. [This report wi 11 not deal 

with that information. 1 

The first 2 days of the survey were 

treated as a pilot study, after which DNRC 

and the interviewers reviewed the 

questionnai re and check station methods, 

refining them as needed. Because no changes 

were made in questionnaire content, the data 

from these 2 days were combined with that - 

from the rest of the study. 

RESULTS AND DISCUSSION 

During hunting season, about 6,000 

hunters passed by the seven check stations. 

Questionnaire data were obtained from 664 

hunters. Respondents hunting elk in or 

within 1 mile of the study areas were also 

asked to complete the back side of the 

questionnaires, so some questions had a 

sample size of 525. Seven hunters went past 

the check stations without stopping, and 

seven more stopped but refused to complete 

the questionnaire, citing a variety of 

reasons [some of them quite colorfull. The 

final response rate was thus high--98 

percent . 

The check stations efficiently sampled 

study area users; 89 percent hunted inside 

or within 1 mile of the study areas. Only 

one check station, Cabin City, sampled a 

high rate of people hunting elsewhere (40 

percentl. Most were hunting for the day [83 

percentl, whi le 9 percent were on a 2-day 

trip. Eighty-six percent Lived in Montana, 

and 36 percent of the sample lived in 

Missoula. Thei r average age was 36, and 

they had been hunting big game in Montana 

for over a decade [mean = 13 years, median = 

101. The specific locations where they 

hunted were coded on a grid system for each 

study area. 

Two-thirds of the hunters (67 percentl 

said that enjoying nature was an extremely 

or very important reason they were hunting 

in the specific area indicated on the map, 

while 10 percent said that nature was of 

slight or no importance. Hunters rated this 

dimension of hunting as the most important 

in setting choice of any reason Listed, a 

finding consistent with past research. 

Hunters in the study areas were hunting 

close to the line's future path; 54 percent 

hunted to within one-half mile of the route, 

and 18 percent more were within one mile. 

Furthermore, 70 percent of the hunters would 

have to cross under the Line to reach their 

hunting areas. This close proximity 

indicates that any esthetic impacts would be 

very noti ceab Le, although some hunters may 

not mind the change. 

Because 74 percent of those sampled 

hunted primari ly away from roads, one would 

expect them to value remote hunting areas 

containing limited road access and not 

overpopulated by other hunters. This was 

the case, as over half (54 percentl of the 

hunters said that being in an area where 

there were few other hunters was extremely 

or very important in their setting choice 

[table 1 I. Only one-fifth said this was not 

much of a factor in their decisions. The 

Table 1 .-Hunters1 ratings of the importance of reasons for hunting in the area 

[total sample, n = 5281 

Reasons 

Ratinas 

Extremely Very Moderately Slightly Not at all 

important important important important important 

To enjoy nature 33 34 23 

To put some meat in 

the freezer 30 23 3 1 

To hunt in a remote 

area where road 

access is limited 

To get a trophy 

animal 

Because I [or 

others I know] 

have hunted here 

before 12 

Because I Live 

near here 

To hunt in an area 

where there are few 

other hunters 28 
other Likert-scaled question on remoteness 

found that 40 percent rated being in an area 

having limited road access as an extremely 

or very important reason in their setting 

choice [table 11. Twenty-nine percent said 

this was unimportant in their decision. 

These findings can be interpreted two 

ways. The preference for limited road 

access and few other hunters could indicate 

desire for solitude. It could also, 

however, indicate a concern over hunting 

success; increased roads and hunters could 

be viewed as more competition for game. 

The hunters did not favor increased road 

access; 62 percent thought that the existing 

number of roads was about right, 27 percent 

thought there were too many, and only 11 

percent felt the number of roads was too 

few. It is not surprising that the majority 

favored the status quo, for if they didn't 

Like the araa, they probably wouldnlt hunt 

there, assuming better-li ked settings ere 

avai table. If the number of roads is 

altered, however, most hunters would prefer 

to see it lessened, not increased. Many 

comments volunteered by hunters dealt with 

access roads, showing the concern over this 

i ssue. 

The hunters interviewed killed 20 elk, 

38 deer, and one bear. About 900 

hunter-days were expended to ki 11 the elk, 

with 45 hunter-days required for each e lk. 

This corresponds rough ly to historical 

figures [OFWP 19781. 

The primary aspects of hunting 

success--putting meat in the freezer and 

getting a trophy animal--were valued very 

differently by the hunters. As has been 

found in previous research, the prospect of 

bagging an animal for meat was a primary 

component of hunting. However, it was 

slight ly less important than enjoying nature 

[t = 3.62, p <0.011. Hunting for meat 

[along with hunting in an area where there 

are few other hunters1 was the second most 

important reason given for setting choice; 

53 percent said this was extremely or very 

important to them [table 11. Only 16 

percent said its importance was Low. 

Obtaining a trophy animal was the least 

valued reason of all Listed, extremely or 

very important to only 17 percent. An equal 

proportion said it was only of minor 

importance, and 34 percent said obtaining a 

trophy was not a factor in their setting 

choice at all. Other aspects of hunting 

played a greater role in choosing the erea 

in which to hunt. 

Some hunters said they chose a hunting 

Location in part because they Lived nearby; 

32 percent rated this as extremely or very 

important, compared to 47 percent who said 

it was of little or no importance. Overall, 

this was the second- lowest factor in setting 

choice. 

The other question. designed in part to 

tap a convenience/preference dimension, 

asked about the role of having hunted in an 

erea before. Thirty-six percent said this 

was an extremely or very important reason 

they chose to hunt where they did, while a 

nearly equal number, 39 percent. said it was 

of little or no importance. No differences 

among check stations were evident [F = 0.77, 

p = 0.601, 

One-third of the hunters (33 percent] 

were hunting in the area for the first year, 

while 13 percent were returning for the 

second year, and 10 percent for the third. 

The overall average number of years hunting 

was 6. Two-thirds of the hunters were 

returning to thei r hunting areas. presumably 

because they found some aspect(s1 of them 

attractive. For these returning hunters. 

the potential for impacts is high, 

particularly if the changes in the setting 

conf licted with peoples1 desi red 

experiences . 

Sixteen percent said they were hunting 

in their favorite spot in western Montana, 

and 25 percent said it was one of their 

favorite places. Another 45 percent said it 

was just one of many places they hunt, and 

14 percent said they prefer to hunt 

elsewhere. 

For a1 1 hunters, evaluation was c learly 

linked to previous experience in the area. 

Among f i rst-year vi si tors, only 3 percent 

were in thei r favorite area, 14 percent were 

in one of their favorites, and 29 percent 

said they prefer to hunt elsewhere. Among 

those having visited the area 6 or more 

years. however, 3U percent said it was their 

favorite, 32 percent said it was one of 

their favorites, and only 4 percent said 

they would prefer to hunt elsewhere. The 

longer people hunt in an area, the more 

likely they are to become attached to it and 

its many attributes. 

Evaluation also tended to be linked to 

success. Among hunters who killed an elk or 

deer, 60 percent said they were in their 

favorite or one of their favorite areas, 

compared to 40 percent of the unsuccessful 

hunters. Nearly 90 percent of the hunters 

who said they were in their favorite area. 

however, didn't kill anything, suggesting 

that an area can be viewed very favorably 

even if no game is harvested. 

Predictab ly, Montana residents d i f f e r e d 

from nonresidents, who were older [average 

age 42 vs. 35 for residents1 and had less 

big game hunting experience in Montana. ALL 

of the out-of-State hunters were after elk, 
and they killed one-third of the elk despite 

comprising only 12 percent of the sample. 

They were also more likely to see elk; 15 

percent of the residents saw one or more, 

compared to 26 percent of the nonresidents. 

The out-of-State hunters were thus more 

successful. Although many were not as 

familiar with the area. 44 percent were 

returning for at least the second time, and 

some may have been hunting with guides. 

Fewer of them were road hunters, 26 percent 

vs. 31 percent of the Montanans. They were 

also trying to harvest game for different 

reasons. Hunting for meat was less 

important that it was for residents [t = 

4.6, p <O .Dl I, and obtaining a trophy was 

more important [t = 3.9, p (0.01 1. 

Even though out-of-State hunters were 

only a small proportion of the sample [<4 

percent], they came to Montana in part to 

shoot a trophy animal, and seemed dedicated 

to this pursuit. They were also highly 

concerned with enjoying nature in the study 

a reas. 

Cluster Analysis 

A primary goal of the survey was to 

determine the distributive effects of the 

Line: who would view the impacts as 

positive; who would view them as negative; 

and who would be indifferent. We have 

hypothesized that the reasons hunters chose 

for hunting in the study areas wi 11 predict 

how they will react to the line-induced 

changes in the setting. To reach this goal, 

it would be valuable to group hunters 

together based on their reasons for hunting 

where they did; we could then predict how 

each group would respond to the physical, 

biological, social, and esthetic changes in 

the study areas. The cluster analysis 

technique used in previous studies of 

hunting seemed we1 1 suited for the task. 

Hunters' responses to the questionnai re 

items on reasons for choosing hunting 

locations were selected to perform a cluster 

analysis. Clustering the hunters on these 

questions identi fied subgroups of hunters 

[hunter types1 who had simi lar patterns of 

response. The cluster analysis was 

performed for the sample as a whole, rather 

than separately for each study area or check 

station. 

The BMDPKM program [Dixon 19831 was 

chosen to perform the cluster analysis. 

Known as a k-means procedure, this 

hierarchical clustering method fi rst assumes 

that all respondents form one cluster and at 

each step divides the cluster into two, 

based on how close each case is to the 

cluster center [measured by the Euclidean 

distance). Once the speci fi ed number of 

clusters is reached, the program iteratively 

reallocates cases into the cluster whose 

center is closest. Hunters within the final 

clusters should be simi tar, and differ from 

hunters in other clusters [Harti gan 19751. 

Because there was no predetermined basis 

for picking the final number of clusters, 

the program was run six times to obtain 

final cluster sizes of 4, 5, 6, 7, 8, and 

9. Examination of the results showed that 

seven clusters best described the hunters, 

based on comparison of the cluster means for 

each of the seven variables, cluster sizes, 

interpretability of the finaings, and the 

desire for a concise explanation. A cluster 

size of seven [which required 12 iterations 

to reallocate the cases into clusters1 

provided important information not present 

in smal ler cluster sizes. Increasing 

cluster size, however, added little to the 

analysis whi le making interpretation more 

comp lex. 

Cluster One.-This group, composed of 64 

hunters [I2 percent of the total sample], 

was characterized by the hunters' low 

ratings of importance on nearly all of the 

variables; as shown in figure 1, they rated 

five of the seven possible reasons as having 

little importance. 

They did indicate that enjoying nature 

and hunting for meat were moderately to very 

important. These two reasons were rated as 

this important by all hunter types; everyone 

wanted to enjoy nature while hunting and 

also to put some meat in the freezer. As 

seen in figure 1, these two variables did 

not distinguish very well among the seven 

hunter types. Worthy of note, however, for 

this and the other hunter types, was whether 

nature or meat was rated as more important. 

Hunters in Cluster One rated hunting for 

meat as slightly more important than 

enjoying nature--one of only two types to do 

so. 

Twelve percent of this group was from 

out-ofstate, a proportion near the middle 

compared to other clusters. A full 65 

percent were under the median age for all 

hunters; this group had one of the two 

highest proportions of younger hunters out 

of the seven clusters. Eighty-six percent 

were on day trips, a figure near the middle 

compared to other groups. 

A relatively high proportion--35 

percent--were primari ly road hunters; it is 

therefore not surprising that they tended 

more than any other group to rate their 

hunting areas as having too few roads 

[although the majority still felt that the 

number of roads was about right]. This 

group had the lowest rate of seeing elk and 

ki lling an elk or deer of el l groups. 
Not at all 

important 

Slightly 

important 

Moderately 

important 

Very 2 important 

Extremely 1 

important 

I I I I I I 1 

Nature Meat Few Trophy Here Live Few 

roads before near others 

REASONS FOR CHOOSING HUNTING LOCATION 

Figure 1 .--Graph of cluster means. 

Along with one other cluster, this group 

had a very high proportion of hunters 

visiting the area for the first time-52 

percent, with only 22 percent having visited 

the area for 6 or more years. This group 

therefore did not rate the area in which 

they were hunting as one of their favorite 

places; only 6 percent said this was the 

case, the lowest proportion of any cluster. 

Similarly, 31 percent of this group said 

they would prefer to hunt elsewhere, again 

the highest percentage of the seven hunter 

types. 

This group is particularly concerned 

with many characteristics of the study 

areas, and though most are from Montana, 

they didn't choose the area because it was 

close to home. 

These hunters would either favor or not 

greatly object to the increased road access 

and would Likely not be as concerned as 

other hunters with the esthetic changes in 

the setting caused by the transmission 

line. This group would not Likely be 

displaced by the line, and they may even 

prefer the new setting to the oLd. 

Attitudes toward possible management 

actions or restriction were not measured in 

Phase I, so we cannot be sure how this group 

[or the other hunter types I would react. 

For a1 1 hunters, this is another possible 

source or impact that would probably be 

viewed by some as positive and by others as 

negative. 

uuster Ta.-This group of 80 hunters 

(15 percent of the total) was the only other 

cluster to rate hunting for meat as more 

important than enjoying nature in choosing a 

hunting location. Hunting in an area with 

few roads was of little or no importance to 

them, but they did rate as moderately 

important being in an area with few other 

hunters. Obtaining a trophy was of little 

importance, but hunting close to home was 

very important in their hunting area 

decision. Having hunted in the area before 

was moderately important, near the middle of 

the seven clusters. 

This group, interviewed mainly at 

Beavertail, Cabin City, and Haugen, 

contained only 5 percent out-of-State 

hunters. They tended to be older hunters, 

with 56 percent over the median age for a1 1 

hunters, and contained more day hunters than 

any other type. 

This group had the highest proportion of 

road hunters (43 percent), but also the 

highest proportion who said the number of 
roads was about right. As many felt there 

should be more roads as fewer. They saw an 

average number of elk [89 percent saw none1 

but killed fewer elk or deer than did any 

other hunter type. 

They had considerable experience hunting 

in the area; only 22 percent were hunting 

where interviewed for the first time, and 39 

percent had been hunting there 6 or more 

years. Forty percent said they were hunti-ng 

in their favorite spot or one of their 

favorite spots, and only 14 percent said 

they would prefer to hunt elsewhere. 

These hunters are also not likely to be 

affected by the line, although the effects 

could be more adverse than for the first 

type. Although many are road hunters, they 

do not want to see more roads. Because they 

are more concerned with the number of 

hunters than with the number of roads, 

successfu 1 closure of new roads would 

probably effectively mi tigate any adverse 

impacts, as long as large numbers of new 

hunters did not use the new access. 

Enjoying nature, although of moderate 

importance, was rated by this group as less 

important than by any other cluster, 

suggesting that any esthetic impacts would 

be moderate. 

This group of hunters, 15 percent of the 

total interviewed, would likely not be 

displaced by the Line; they would keep using 

the study areas as long as the game was 

still there; a decrease in the opportunity 

to shoot a trophy animal would not bother 

them greatly. However. those hunters who 

did not Like the changes could have a more 

di fficult time finding other attractive 

hunting locations; they have returned to 

these areas for many years. and a sizable 

minority are hunting in one of their 

favorite places. Even so, this group may 

not be strongly affected by the transmission 

li ne . 

cluster Three.-This was the smal lest 

group [491, comprising only 9 percent of the 

hunters interviewed. They were 

characterized by their lack of concern for 

hunting for a trophy or in areas where they 

have hunted before, and the importance they 

placed on the other variables. Unlike the 

previous two hunter types, this group rated 

enjoying nature as much more important than 

hunting for meat, and rated nature, few 

roads, and few hunters as very important. 

Hunting near home was also valued by this 

group. 

This group contained a low proportion of 

nonresidents (6 percentl. They were older 

than other hunters, with 65 percent above 

the median age, and hunted mostly on day 

trips. A relatively high number [37 

percentl hunted along and near roads, but 

this group, more than any other, preferred 

to see far fewer roads in their hunting 

areas. This was the only cluster in which 

more hunters rated the number of roads as 

too many than rated the number as about 

right. They saw an average number of elk 

but were more successful (12 percentl than 

other hunters at killing an elk or deer. 

These hunters were older and had more 

experience than other respondents in hunting 

in thei r areas ; a fu 11 47 percent had been 

returning for 6 or more years. They were 

average in the ratings of the area; 34 

percent said it was their favorite or one of 

thei r favorite places to hunt, and 16 

percent said they prefer other Locations. 

Because this group placed high 

importance on being in an area with few 

roads and few other hunters, the increased 

access accompanying the line wi 11 li ke ly be 

viewed negatively . The very strong emphasis 

on nature, especially when viewed relative 

to the lower emphasis on meat, suggests that 

esthetic aspects of hunting may also be 

negatively affected by the line. A majority 

of the hunters may have a great deal of 

trouble finding other places to hunt if 

indeed they respond negatively to the 

changes in the setting, but some of these 

hunters value the study area locations 

highly, in part because of the close 

proximity to their homes. However, they are 

not particularly attached to an area simply 

because they have been there before 

[although they have extensive experience 

hunting in the areal. 

This is one of the few groups for whom 

estheti c impacts wou ld Likely outweigh 

game-related impacts. They could likely be 

displaced from the study areas, and if other 

settings were not avai lable nearby, the 

impacts would gain significance. Of the 

first three clusters identified, this 

relatively small group [9 percent of the 

total sample] is the most vulnerable to the 

Line's effects. 

Cluster Four.-This group, the largest 

of the seven [IOOI, comprised 19 percent of 

the total sample. They attached relatively 

high levels of importance to all variables 

except hunting close to home, which was 

viewed as having little or no role in their 

hunting area decisions. Enjoying nature was 

more important than hunting for meat; in 

fact, this group attached Less importance 

than any other to hunting for meat. They 

were seeking areas having few roads and 

other hunters, and said that their previous 

hunting experience in the area was important 

to them. Obtaining a trophy was moderately 

important. 

This cluster had the lowest proportion 

of Beavertai 1 hunters of any subgroup [only 
17 percentl, with Larger ratios of hunters 

from Haugen and Saltese. Nearly one 

quarter, 24 percent, were from out-of-State. 

the largest percentage of any cluster. 

About half were above and half were below 

the median age for the whole sample, and 

more of this group that1 any other hunted for 

more than 1 day--not surprising given the 

higher number of nonresidents in the group. 

Few were road hunters [20 percentl, and only 

5 percent thought that thei r hunting areas 

should have more roads. They saw more elk 

than any other type of hunter but were not 

quite as successful as most in killing a 

deer or elk. 

One-third were hunting in the area for 

the first time, and about the same number 

had hunted there for 6 or more years. 

Forty-six percent were hunting in their 

favorite or one of their favorite areas, and 

only seven percent said they would rather 

hunt somewhere else. Like the other groups, 

most said the area where they were hunting 

that day was one of many places they hunt in 

western Montana. 

These hunters are nonresidents and 

residents who value the esthetics of hunting 

more than obtaining a trophy or meat, 

although all aspects of thei r hunting choice 

were important. The exception was hunting 

near home; these hunters may be willing to 

travel farther to find hunting areas meeting 

thei r expectations. This may be disturbed 

by the line's effects on esthetics and 

access, but are apt to be slightly Less 

concerned over any impacts on hunting 

success. It seems Likely that if acceptable 

substitute settings were available, they 

would be wi 1 ling to travel there, somewhat 

reducing the signi ficance of the impacts. 

Cluster Five.-This group of 79 hunters 

composed 15 percent of the hunters 

interviewed. Thei r main characteristic was 

a very high level of concern for all 

variab les except obtai ning a trophy. They 

attached as much importance as any hunter 

type to enjoying nature, getting meat, 

hunting where they've been before and near 

home, and to being in an area with few other 

hunters. No other group, however, placed 

Less importance on obtaining a trophy. 

Like most other groups, this contained a 

high proportion of hunters interviewed at 

Beavertai 1. Very few--only 3 percent--were 

from out-of-State, and their ages were 

mixed. Eighty-seven percent hunted for the 

day only; the rest stayed out for 2 or more 

days. A relatively low proportion [20 

percent I were hunting primari ly along roads, 

but about as many felt there should be more 

roads as felt there should be fewer. They 

saw and killed an average number of elk and 

deer, despite thei r extensive experience 

hunting in the area. Only 16 percent [the 

lowest of all types1 were fi rst-time 

visitors, and 44 percent had hunted there 

for more than 6 years. More hunters in this 

group than any other said they were hunting 

in thei r favorite place in western Montana, 

and only 3 percent [the lowest of all 

groups1 said they prefer to hunt elsewhere. 

To these hunters, everything was 

important except obtaining a trophy. The 

importance they attached to nature, few 

roads, and few other hunters highlights 

thei r vu lnerabi li ty to the new access roads 

and the line's esthetic impacts. They could 

also be concerned about decreased 

opportunity to obtain meat, though they 

probably would not object if their chances 

of obtaining a trophy decreased. For them, 

like the previous two groups, the potential 

for adverse impacts is high. However, the 

significance of those impacts is greater for 

this group, because they also place a high 

value on hunting near home and say that the 

affected area is one of their favorites. 

Impacts of the line may force this group 

to travel farther and hunt elsewhere, or 

stay in the same areas with a-decrease in 

perceived hunting quality. 

Cluster Six.-This group of 83 [I6 

percent of the total I placed relatively high 

levels of importance on enjoying nature, 

getting meat, and being in an area with few 

other hunters. Of very low importance were 

hunting near home or in a previously hunted 

area. The presence of few roads or chances 

of obtaining a trophy were moderately 

important. 

Thi rty-five percent of the group were 

interviewed at Beavertai 1, with Haugen and 

Sa ltese also represented. The second 

highest proportion were from out-of-State 

[20 percentl, part of the reason for their 

low value on hunting near home. They were 

younger than any other group except Cluster 

One; 64 percent were below the median age 

for all hunters. More than any other group, 

they stayed out for over a day. 

Only 23 percent were primarily road 

hunters, and the majority rated the number 

of roads as about right. Equal portions of 

the group felt there should be more or fewer 

roads. Twenty-one percent saw one or more 

elk, and 12 percent killed an elk or deer. 

Unlike the previous four groups, however, 

they were relatively inexperienced in the 

area; only 17 percent [the Lowest of any 

group1 had hunted there for 6 or more 

years. Like the members of CLuster One. 

most said the area was one of their many 

hunting grounds. and over twice as many said 

they prefer other places as said it was 

thei r favorite. 

In terms of likely response to the 

Line's effects predicted in Phase I, this 
group is harder to pin down than the 

others. They have not hunted where they 

were interviewed for long, are not 

particularly attached to those areas, and 

didn't choose them to hunt near home. 

Seeing few other hunters was more important 

than being away from roads in thei r 

decisions, suggesting that road closures may 

be effective mitigation for those who either 

want fewer roads or feel that the current 

number is about right. This group may not 

be greatly affected by the line, compared to 

Clusters Three, Four, and Five. Yet, if 

they are, they could probably find somewhere 

else to hunt without great difficulty. They 

could be more concerned than groups One. 

Two, and Three if the supply of trophy 

animals were to decrease, but remain about 

as concerned as other groups over enjoying 

nature and hunting for meat. 

This type of hunter may not be as 

vulnerable to the impacts as some of the 

other groups, but their behavioral responses 

are more difficult to predict. 

pluster Seven.-This group of 73 [I4 

percent of the tota ll has a profi le that is 

immediately distinguishable from all other 

types. They rated every one of the possible 

reasons as very important, and their ratings 

across the board were as high or higher than 

those of any other group. 

A low number. 7 percent, were 

nonresidents, but otherwise these hunters 

shared many characteristics of the 

nonresident hunters [except thei r concern 

for hunting near home]. They were of 

average age, and 22 percent hunted for more 

than one day on thei r trip. This group had 

the lowest proportion of road hunters [only 

12 percentl, and the Lowest number who said 

there were too few roads [3 percentl; 40 

percent wanted to see fewer roads in thei r 

hunting areas. A relatively Large 

proportion saw elk, and a greater number 

killed an elk or deer [I3 percentl than did 

any other hunter type. 

Given this profile, one would expect 

them to be experienced in their hunting 

areas, and they were; 41 percent were 

returning for at least the sixth year, and 

only 19 percent were first-time visitors. A 

full 63 percent said they were hunting intheir favorite or one of thei r favorite 

areas--far more than any other group. 

This seems the group of hunters most at 

risk from the transmission line. They value 

hunting in an area perceived to contain few 

roads and want to see even fewer. They 

place great emphasis on success, both for 

meat and trophy, and said that enjoying 

nature was also very important. They are 

skilled hunters who are used to the study 

areas and consider them one of thei r 

favorite places to hunt. They also value 

hunting near home. 

These hunters wou ld probably object to 

the line on many grounds, and thei r 

long-establi shed hunting patterns cou Ld be 

disrupted. They may have a difficult time 

finding acceptable substitute areas to hunt 

i f they are di sp laced. 

CONCLUSIONS 

The results of the baseline study show 

that about 59 percent of the hunters 

interviewed have characteristics that may 

sensitize them to the transmission line's 

effects. Soma hunters will view the changes 

as an improvement, and some will not care 

much one way or the other. The cluster 

analysis enabled DNRC to identi fy groups of 

hunters that wi 11 be affected differently by 

the line and to hypothesize how each wi 11 

react to the setting changes: We cannot yet 

say what will precisely happen, but the next 

phase of the study will assess the accuracy 

of our empi rically derived predictions and 

measure the type and Level of impacts to 

hunting opportunity in the study areas. 

The findings to date suggest that, for 

many, the impact magnitude will be high. Of 

the hunter types i denti fi ed , Clusters One 

and Two [27 percent of the total] are not as 

likely to be adversely affected, and the net 

effect may be positive. This, of course, 

depends in part on what happens to the elk 

populations, which wi 11 be studied by the 

biological portion of the monitoring effort. 

Clusters Three, Four, and Five (43 

percent of those sampled], will likely be 

adversely affected. The net impact wi 11 

depend on whether they have acceptable 

substitutes, but hunters in these groups 

chose the study areas in part for their 

perceived limited road access. 

For hunters in Cluster Seven [I6 percent 

of the total], both the magnitude and the 

significance of the impacts are likely to be 

high, and these users are the most prone to 

be displaced by the line. We may therefore 

anticipate finding fewer of these types of 

hunters in the sample during the next phase; 

this hypothesis wi 11 be tested after the 

line is built. If they continue to hunt in 

the study areas, it wi 11 likely be out of 

convenience and with decreased 

satisfaction. Again, this depends on the 

impacts to elk; if the hunting were to 

improve, for example, some of the negative 

impacts could be offset. 

The effects on Cluster Six [I6 percentl 

were difficult to predict. Hunters within 
this cluster may respond di fferent ly, 

depending on how they interpret the impacts. 

The magnitude of the impacts on the 

study areas, however, is only part of the 

picture; we must also examine the 

significance of these impacts-or how the 

study areas ere viewed in relation to other 

hunting grounds. Hunters who do not like 

the changes in the study areas may not 

object if they have many other places to 

hunt that also meet their criteria [or meat 

them better than the study areas]. The 

study area impacts must be placed in a 

greater context to interpret thei r 

significance. The next phase will 

accomp li sh thi s by asking hunters speci f i c 

questions about how the attributes of the 

study area are perceived--before the line, 

after the Line, and in relation to specific 

attributes of other places they hunt in 

western Montana. 

Most of the nonresident hunters fell 

into the groups that would likely be 

adversely affected. Possible economic 

impects to local communities could result 

unless these hunters find other hunting 

locations near the study areas. Assuming 

they continue to hunt big game in Montana, 

however, other communities could benefit. A 

complete economic impact study is beyond the 

scope of our research, and we can only 

speculate. 

So far, we have talked about impacts to 

the esthetic, physical, biological, and 

social characteristics of the study areas. 

Another important aspect of recreational 

settings is the managerial situation [Clark 

and Stankey 19791. 

If elk were adversely affected by the 

increased hunting pressure and loss o.f 

secure habitat, managers may have to 

impose stricter regulations. The questionnai re did not contain items on perceptions of hunting regulations, but the 

Large number of comments received on the 

issuerpoints to its notoriety as a topic 

among hunters. It is clear that different 

types of hunters would react differently to 

new management actions. Previous cluster 

analytic studies of hunters have found that 

response to management actions can be 

predicted from hunter types [Hautaluoma and 

others 19821. 

Reactions to management actions wi 11 be 

assessed during Phase 11, when hunters have 

something specific to respond to [such as 

access roads]. Hunters will be asked how 

they would prefer to see the new roads 

managed and will be asked other 

management-related questions. DNRC also 

plans to assess the effectiveness of visual 

mitigation and to assess the actual esthetic 

impacts. Planned use of the study areas 

will also be meesured, and it may be 

possible to study bow hunters, who were not 

studied during Phase I. 

Access roads were an important 

consideration to five of the seven clusters 

identified, or 73 percent of the hunters. 

It may be that hunters of all types will 

react negatively to access roads if they are 

gated. Hunters who prefer limited numbers 

of roads may not approve, but neither would 

those who want new access roads without 

locked gates. In any case, it wi 11 be 

important to Learn their reactions and see 

what proportion of hunters favors what 

management strategy. 

For Phase I1 studies, a potential 

problem exists if current users are 

displaced, some hunters would not be there 

to interview if they decided not to hunt 

there at all. It would be valuable to talk 

with this group, because they would be the 

most adversely affected by the line [their 

behavior would have been changed]. 

This problem wi 11 be dealt with in two 

ways: one based in a change in methodology; 

and one by the type of analysis that has 

been end will be conducted. The change in 

method will be to administer the entire 

questionnaire to every hunter 

interviewed--regardless of whether they 

hunted in or near the study areas. This 

will provide e control group that has not 

hunted near the line. In addition, some 

hunters who do not Like the changes in the 

settings may continue to hunt in the areas 

[out of convenience, for instance] and wi 11 

still be there to interview. The baseline 

study found people who preferred to hunt 

elsewhere, and the next phase wi 11 too. 

Second, the baseline data collected in 

1983 will be compared to that collected 

during Phase 11. Any substantial changes in 

hunter populations or types should be able 

to be detected. For example, another 

cluster analysis wi 11 explore simi lari ties 

to and differences from the types of hunters 

now using the study areas. 

Subtle changes may not be discernible or 

attributable to the line and its impacts. 

But this is compatible with the research 

goals, for it is likely that minor impacts 

would create less demand for additional 

mi ti gation. Substantial changes, however, 

would call for a closer look at the 

magnitude and significance of the impacts 

[Phase 1111 and at the need for further 

mi tigation. 
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METHODOLOGY FOR STUDYING RECREATION CHOICE BEHAVIOR 

WITH EMPHASIS ON GROUNDED INQUIRY 

Kent B. Downing and Roger N. Clark 

ABSTRACT: This paper reports methodology for. 

investigating recreation choice behavior using 

grounded, naturalistic research methods. How 

these techniques can be coordinated with other 

social research methods is described. Naturalistic 

methods are preferred over other approaches to 

uncover recreationists' decision making processes 

at work. 

INTRODUCTION 

This paper reports on procedures using naturalistic, grounded, research methods for investigating 

recreation choice behavior. It addresses how 

these techniques might be coordinated with other 

social research methods. Substantive findings are 

reported by Clark and Downing in a paper included 

in these proceedings. Results presented in that 

paper are based on an integrated analysis of data 

from several different studies that used traditional 

quantitative survey research methods as well as 

qualitative methods. 

The naturalistic model relies on field stud-y and 

emphasizes the discovery of information about human 

behavior as it is affected by the context within 

which it occurs. The techniques of naturalistic 

inquiry are generally qualitative (for reasons that 

will be explained later). Theory development is 

grounded in data generated from field studies 

(Guba and Lincoln 1981). Glaser and Strauss (1967) 

refer to this as "grounded theory. " 

Methodological Overview 

Clark and Downing attempted to develop the basis 

for "substantive" as opposed to "formal" theory, 

as defined by Glaser and Strauss (1967). Substantive theory is developed for a rather specific topical or empirical area of inquiry, such as wildland 

recreation choice behavior or patterns of high-risk 

recreational activities. Formal theory generally 

applies to broader areas of inquiry such as the role 

of leisure in various lifestyles. "Substantive and 

formal theories exist on distinguishable levels of 

generality, which differ only in terms of degree" 

(Glaser and Strauss 1967); Glaser and Strauss further describe how formal theory may later grow out 

Kent B. Downing is Associate Professor, College of 

Natural Resources, Utah State University, Logan, 

Utah; Roger N. Clark is Project Leader, Wildland 

Recreation Research, Pacific Northwest Forest and 

Range Experiment Station, USDA Forest Service, 

Seattle, Washington. 

of work begun initially in a narrower, substantive 

area. 

The approach for model or theory building can be 

described briefly as follows: Naturalistic inquiry 

requires the investigator to study a phenomenon in 

its ongoing, real-life context. Being grounded is 

accomplished by building and refining models inductively through close, day-to-day processing of field 

data; theory is thus derived directly from data. As 

the investigation progresses and new factors and relationships are uncovered that are relevant to the 

emerging model, the model is modified. The fluid nature of the process renders quantitative analysis 

very difficult if not impossible. Quantification is 

delayed until it is needed when, for example, efforts 

turn to testing and verification of hypotheses 

deductively derived from the model. 

Motivation for Initiating Qualitative Inquiry 

We explored the naturalistic, grounded, qualitative 

methods because of our uneasiness over the inability 

to explain or predict recreational behavior from 

studies relying on quantitative (primarily survey) 

methods alone. Most studies reported in wildland 

recreation research literature are descriptive; 

findings have not been generated that enable sociologists to predict specific behavioral outcomes. 

Knopf (1983) in discussing research on recreation 

preferences captured the essence of this concern: 

"...data abound, theory does not ... The normal 

process seems to be to collect as much descriptive data as possible, then search through it in 

hope of finding revelations about preference ... data collection has taken precedence over confirmation or disconfirmation of theoretical 

postulates ... As a discipline, we continue to 

suffer from an inability to predict in advance of 

data how and why recreationists are likely to relate to a setting. This is not a moot issue for 

recreation planners,, who frequently are forced to 

make decisions without the benefit of available 

data. " 

Although the authors agree that advancement of 

knowledge of recreation values suffers from the 

scarcity of systematic verification, this paper 

discusses another equally compelling explanation 

for the inability to predict responses or choices 

of recreationists. Most investigations have failed 

to examine actual recreation choice behavior or to 

construct models consistent with the observed data. 

It is difficult to find studies that, as the basic 

methodological strategy, seek to develop theory which 

begins with and builds upon participants' behaviors 

and their explanations of those behaviors. 
To apply qualitative methods for purposes of understanding wildland recreation choice behavior, models 

were developed that account for factors people consider and the processes they follow in making decisions. Previous studies have not recognized 

the range of choices people make from one trip to 

the next. If models are to aid in predicting 

actual behavior, they must reflect tradeoffs people 

invariably must make in choosing to recreate in a 

particular way. 

BASIC METHODOLOGICAL ISSUES 

The fundamental approach for constructing models 

using naturalistic, grounded, qualitative methods 

is induction. It is a process of discovery and 

creativity based on careful observation of individual events. This is followed by interpreting, 

structuring, and modifying the emerging skeleton 

of an idea into a fully fleshed out model of the 

phenomenon of concern. 

Can there be a logic to discovery? Some have argued 

"that there is no order or method in the process of 

discovery at all, 'the creative' side of science 

is wild and undisciplined" (Jarvie 1964). 

Conversely, other philosophers, defining logic more 

broadly, (Norwood Hansen in physics and Abraham 

Kaplan in the social sciences) have argued that 

there can be a logic of discovery--that the probability of producing new views of the world can be 

heightened through persistence, chance, and intense 

creative thought (Austin 1978). In particular, 

the more active the investigator in searching, exploring, and playing with a diverse array of ideas, 

the more likely that new and meaningful discovery 

will occur. The debate on the relationship between 

deductive and inductive approaches to knowledge 

has occupied scientific philosophers for centuries 

and will likely continue. 

The grounded, naturalistic approach contains the 

essential elements of the natural history approach, 

the origins of which are found in the work of 

Darwin, who is generally considered the originator 

of modern natural history method. Darwin's reasoning process combined both inductive and deductive 

logic. But Darwin expressed openly his suspicions 

of deduction. He always insisted upon careful and 

precise observation. Then, from the ordering of 

the facts, he constructed interpretations and 

theories (what we might refer to as working models 

or hypotheses) against which he tested new observations and facts. This view represents the core 

of the approach outlined in this paper. 

We believe that where an understanding of and an 

ability to predict behavior relating to recreation 

are sought in real-life situations, the contextual 

settings where diverse behavioral responses occur 

must be recognized and worked within. Our research 

must account for choices made within the context 

of social relationships, recreation setting 

requirements, preferences, and other supportive 

and constraining factors. 

Several assumptions underlie the approach: 

1. A grounded approach will generate models 

of substantive phenomena from which hypotheses can be deduced that will have a higher 

probability of withstanding rigorous "scientific" testing. 

2. A naturalistic, grounded approach will allow 

rapid development of models and hypotheses, which 

in turn, will allow faster verification. 

Platt in his 1964 article in Science, "Strong 

In£ erence," devotes considerable attention to why 

some fields seem to move ahead more rapidly than 

others. He attributes this to the systematic application of an inductive intellectual process which 

he terms "strong inference." In its separate elements, strong inference is the old-fashioned 

method of inductive inference that goes back to 

Francis Bacon, yet in reality it consists of applying 

formally, explicity, and regularly the following 

steps to every problem in science: (1) devising alternative hypotheses; (2) devising a crucial experiment or several of them with alternative possible 

outcomes, each of which will, as nearly as possible, 

exclude one or more of the hypotheses; (3) carrying 

out the experiment; and (4) recycling the procedure. 

A formal diagram reminds the investigator to move 

ahead with the recycling process "without dawdling 

or getting tied up in irrelevanciesV1 (Platt 1964). 

platt writes that for exploring the unknown, there 

is no faster method; this is the minimum sequence 

3f steps. As a result, strong inference offers 

a systematic method for reaching firm inductive conclusions one after the other, as rapidly as possible. 

;Natliralistic, grounded methods, because of their 

 responsiveness to new and discrepant data that 

point toward new phases of theoretical sampling, 

are particularly adept at facilitating the rapid 

Aevelopment and refinement of hypotheses--grounded 

hypotheses likely to stand the rigors of quantitative and qualitative verification. 

3. Model development and elaboration may proceed 

hand-in-hand with verification during some kinds of 

investigations (Deising 1971). Justification and 

verification need not be treated as a separate 

set of procedures occurring after "discovery," but 

can be included within the process of discovery. 

In some methods, verification is scattered throughout the process, and in others it occurs at one 

definite point. In some methods there are two or 

more kinds of verification, and in others there is 

only one. In any case, verification is always a 

subordinate part of a larger process of discovery; 

it constitutes the check point or points in the 

process (Deising 197 1). 

POTENTIAL SIGNIFICANCE OF A GROUNDED APPROACH 

Grounded investigation of a situation/contextual/ 

ecological nature is im~ortant from two perspectives : 

1. Theoretically, to uncover the nature and structure of complex patterns associated with the out- 
come of alternative choices. To understand a 

particular behavior, we must learn about the range 

of factors and their interrelationships relevant to 

the decision. 

2. Practically, to determine which contextual 

factors can be manipulated by resource managers 

and to predict the consequences of changes that are 

beyond the control of resource managers. It should 

be added with considerable emphasis, that the lack 

of an integrated, behaviorally based, grounded 

theoretical perspective seriously complicate-s our 

ability to predict the relationships between resource inputs for recreational management (money, 

staffing, land allocations, and the like) and recreational outputs (numbers, types, behaviors, 

satisfactions, experiences of people, return visits, 

as well as decisions to avoid areas in the future-- 

displacement and conditional displacement). 

Evidence from our investigations suggests that from 

an ecological, ongoing, real-life perspective, 

people have a lot of things to choose from that are 

often conflicting--and choosing requires trade-offs. 

This gives rise to questions about users' decision 

processes and the factors they consider such as 

people, places, and management activities, as well 

as the interrelationships among the decision factors. 

Naturalistic field inquiry uncovered instances of 

each of the factors (and their interactions) as 

expressed in the questions in the following list. 

"Answers" to the questions do not necessarily 

require quantifying. In fact, quantifying may not 

be possible when addressing such questions until 

the types of variables inherent in them are better 

understood. Only after the processes at work have 

been discovered is it appropriate to begin measuring 

the direction and magnitude of the relationships. 

Questions About People's Decisions, Choices: 

What role do prior experiences, preferences for, and 

expectations about setting attributes play in 

choosing the site? 

How does willingness to adapt to setting changes or 

the desire for and propensity to explore new and 

unknown territory influence choice? 

How does habit or tradition influence choice? 

How does knowledge of a range of options influence 

choice? 

What effect does being in a particular group have 

in choice? 

Why are "favorite" sites not necessarily those 

used "most frequently?" 

How frequently do constraining conditions which 

confound the relationship between preferences and 

actual choices dominate the choice of settings? 

Questions About Places: 

What are the relative influences on recreational 

choice of macrosite attributes (canyons, river 

valleys, mountains, lakes, deserts, seashores) as 

compared with microsite attributes (at a campsite 

or day-use area, for example)? 

What changes at the macrolevel and microlevel are 

likely to lead to temporary or permanent relocation 

(conditional or permanent displacement), change in 

timing of visit, or change of activity? 

What setting conditions are "necessary" and 

"sufficient , I' as opposed to "nice-to-have," to 

achieve overall trip satisfaction by the group or 

by each individual within the group? Does this 

vary depending on whether the outing is constrained 

by other decision factors or not? 

Questions About Management: 

Which macrolevel and microlevel characteristics 

are susceptible to management control or influence? 

How do management factors associated with other 

resource uses (timber management, visual management, 

etc.) interact to affect recreational choice? How 

do they interact with factors beyond management 

influence? 

Which managerial factors are most likely to change 

recreational choice? 

Which strategies can minimize disruption to 

established users, particularly those who rate a 

setting among their most favorite or those who 

know of no acceptable options? 

What role does information provided by managers 

about site options play in choice? 

Questions About Complex Interrelationships: 

What is the relationship between expectations of 

trip satisfaction and recreation choice when 

trade-offs are required among group, place, and 

activity? 

How are we to represent the interrelatedness of 

the various decision factors? How can we represent 

the pattern complexity in an ecological framework 

or model that is scientifically verifiable and 

reflective of user concerns and behaviors patterns? 

Are the concepts "necessary" and "sufficient" 

realistically applicable in understanding recreational choice? 

RESEARCH PROCEDURES WITH CASE ILLUSTRATIONS 

The procedures described below have evolved through 

application and field testing. The literature 

provided a basic approach for looking at the 

choice behavior process. This basic approach was 

modified as necessary to respond to the problems 

and opportunities encountered. 

Because for many researchers the approach described 

will be somewhat different from that ~reviously used, 
initial applications will be slow and awkward--this 

is not an easy approach, although it appears to be 

so on the surface. By using this approach, it was 

possible to identify factors and their interrelationships that account, in part, for people's choices 

of particular macrosites and microsites for recreation. Progress to date, in the derivation of a 

grounded model related to choice behavior, is discussed. 

A. Select and Define the Phenomenon of Interest. 

It May be Defined Quite Narrowly and Precisely or 

in More General, Open-ended Terms. 

The Utah study (see Clark and Downing in this 

proceedings) originally had the objective of describing seasonal variation in dispersed recreational 

use of the North Slope of the Uinta Mountains. The 

underlying concern was to determine the extent to 

which recreation opportunities varied for the 

same area because of seasonal changes, and whether 

the users were the same regardless of season. 

B. Survey Carefully the Literature in the Substantive Area of Interest. (Note, However That 

Some Theorists Argue That Literature Review in the 

Substantive Area of Inquiry Should at Times be 

Delayed Until Much Later in the-Investigation as 

a Protection Against Prejudging Which Data to 

Collect and Which Interrelationships to Test. This 

Clearly is a Departure from Traditional Research 

Practice). 

An exhaustive search of literature was not conducted 

in our study. The decision was to see what might 

result from taking the most extreme approach in 

applying the method. Only much later in our work 

were the results compared with other studies on 

dispersed recreation. 

A comprehensive study was conducted of the varied 

views and procedures relating to the broad area 

of qualitative sociological research including 

naturalistic inquiry, ethnographic research, 

qualitative sociology, grounded theory, and the 

like. The general results of that effort were 

summarized at the beginning of this paper. 

what changes they had noticed, and whether they used 

the area during other seasons. In addition, they 

were asked why they were in the area rather than at 

locations closer to home. Finally, they were asked 

if they ever used developed campgrounds in the area, 

why or why not, and the conditions under which they 

would choose developed sites over dispersed sites. 

The process is largely unstructured. A set of 

standardized questions is not asked; rather, the 

researcher probes in an open-ended way for a variety 

of issues related to the topic of interest. As new, 

relevant topics are encountered, they are also pursued. It is therefore imperative that the principal 

researcher be involved in the field; this work cannot be delegated to technical personnel, no matter 

how competent and qualified. 

D. Analyze the Findings. 

Following a period of interviewing (2-3 hours; 1-2 

days) the comments were reviewed to see if the pattern of responses suggested other lines of inquiry 

(such as whether each party was at their favorite 

kind of place or doing their favorite activity). 

"Scope" sampling (Willer 1967) or tttheoreticalff 

sampling (Deising 1971) was used, which means the 

investigator chose the next sample site(s) based on 

what was learned in previous interviews. For example, 

on the basis of widely scattered field contacts, it 

was decided that there was a need to talk to people 

in developed sites in the macrosite as well. It 

was found that some respondents in both kinds of 

settings in close geographic proximity were there 

because it was a preferred location; others were 

there for other reasons. 

Careful probing disclosed that some members of the 

same groups were pleased with the microsite chosen 

(dispersed campsite) while others in the party wished 

they were down the road in a developed site with 

water, toilets, etc. Given the opportunity, they 

gave rational reasons for preferring to be elsewhere for that kind of outing. It was evident, 

however, that going on the outing for these people 

was preferred over not going at all--thus, a tradeoff within the groups was apparent. It was decided 

to probe further for this phenomenon. 

C. Enter the Field for Data Collection. 

E. Reenter the Field. 

Dispersed recreation sites in two classes of the 

recreation opportunity spectrum were identified 

and mapped in the study area. This information 

was used in selecting locations in dispersed roaded 

areas in which to interview groups of recreationists. Contacts were made in locations that met the 

criteria for "roaded natural" and "semiprimitive 

motorized" as described by the USDA Forest Service. 

Campingqarties were approached and asked if they 

would be willing to discuss their recreational 

outing and if the comments could be tape-recorded. 

They were asked where they lived, what they were 

doing while in the area (asked to show on maps 

places they might hike or drive to to sightsee, 

fish, etc.), how long they had used the area, 

Based on the analysis, the selection of interview 

locations and some of the questions asked were 

changed. Interviews in developed sites were added 

and those people were asked about their use of dispersed areas. Questioning was expanded to determine 

what other places they used for a similar kind of 

outing; why they were here, rather than at other 

locations; the conditions under which they would 

use other areas, rather than the sample site; if 

there were areas they had used previously for this 

kind of outing, but do so no more, and why they 

stopped using those areas. (The other acceptable 

areas and the areas no longer used were mapped and 

became sample sites for a small pilot study the 

following year). 
The data were analyzed again and the process was 

finally ended when it was determined that no new 

factors and no new relationships among factors were 

uncovered to explain dispersed and developed site 

roaded recreation activities. At this point the 

model was "saturated" and ready for testing using 

other methods (quasi-experimental designs, survey 

research based on predictions of patterns of responses). 

F. Match the Emerging Grounded Model With 

Literature. 

culties with predicting recreational behavior, this 

may be a strong point. Initially, there is little 

emphasis on the question of "how much" of anything 

is present because the investigat.or is open to various qualities that may later prove to be worthy of 

attention. Of greatest importance is "what to look 

for," and until a worthwhile focus is formed, quantitative measurements are of no importance. Indeed, 

premature efforts to quantify are luxuries researchers cannot afford in tight financial times. 

Grounded, naturalistic strategies incorporate many 

standard social science tools; participant observation, in-depth interviews, and questionnaire surThe Clark and Downing paper (this proceedings) veys. Naturalistic strategies differ most strikinggives some initial interpretations of information ly from survey research in the degree to which the 

in which the findings from qualitative and quanresearch design and data collection procedures may 

titative studies are combined. Both kinds of in- be adjusted during the course of the investigation. 

formation were used to develop hypotheses for Grounded strategies must be adjusted if they are 

further field verification. to take advantage of newly emerging data. As a 

result, a significantly different approach to sampTo match the grounded model with the results of 

previous research, it is necessary to pay close attention to areas of agreement and points of discrepancy between the model and reported findings. 

Discrepancies in particular may require the investigator to: (1) reenter the field and further modify 

the model or (2) systematically subject the competing 

ideas to a critical test. 

G. Test the Model. 

The authors are testing the model now in the Utah 

study and have initiated some work that is consistent with predictions from the model. Insufficient 

qualitative data, and certainly not enough quantitative data, have been generated to support any 

firm conclusions. Hypotheses have been formulated 

concerning what should be expectefi in other roaded 

forest areas. Hypotheses can be formulated concerning the effects of management activities on altering 

user pat terns. 

To test a grounded model several options can be used. 

A first option is to enter new, but similar areas to 

collect data necessary to test hypothesized relation- 

- - 

ling and data analysis is required. Traditional 

concerns with validity and reliability must be 

conceptualized in other ways as well (Dobbert 1982; 

Glaser and Strauss 1967; Guba and Lincoln 1981). 

CONCLUSIONS 

When is a specific data collection strategy appropriate or inappropriate? In describing alternatives 

to the traditional cross-sectional survey, Clark 

(1977) provides a framework for making choices. Two 

fundamental categories of questions must be considered: First, questions that describe events (what 

is happening? when? where? how much?); descriptions 

of participants (who is involved?); and descriptions 

of preferences (what do people prefer?). Second, 

questions arise about the explanations of events 

(why is it happening?) and explanations of change 

(how can behaviors be modified or changed?). Clark 

notes that no single combination of research design 

and measurement strategy will provide data to answer 

the variety of questions about a particular research 

phenomenon that are raised by researchers and managers. Methods must be chosen with reference to 

the nature of questions in need of answers. 

ships either qualitatively or quantitatively. An The purpose of the efforts reported here has been to 

acceptable qualitative test occurs when a newly explore the contribution that qualitative, naturderived model does not conflict with the initial alistic, grounded inquiry can make toward gaining 

model. The test may, however, depart to the extent reliable knowledge about the sociology of natural 

that new relationships are uncovered. A quanti- resource use. It was decided to include this methotative test is judged in terms of statistical agree- dological approach in some of our studies in order 

ment between predicted and actual outcomes that to identify and understand processes by which people 

result from changes in the values of factors within make recreational choices, choices of setting in 

the model. 

A second option is to enter related but different 

areas to collect data neceassary to assess the 

"fit" of the model in other contexts (Guba and 

Lincoln 1981). Does it apply to choice among 

developed ski area alternatives, for example. This 

concept is similar to assessing how far the model 

can be generalized. 

In summary, the process described is highly iterative 

and heuristic. It places great value on the "discovery" of new perspectives and, given the diffiparticular. It was felt necessary to view this phenomenon from the point of view of the research 

subjects themselves--that is, to allow them the 

opportunity to describe,the kinds of decisions 

they have made from one trip to the next, and t,o 

explain factors that weighed in the decisions. The 

research question that guided our investigation 

was: "What decision factors and interrelationships 

are evident in respondents' explanations of their 

actions, choices, and behavior?" We wanted people 

to tell us in their own words how they arrived at a 

particular choice of group, activity, and recreation 

setting. Their answers provided the elements to 

be incorporated into the model. 
the authors anticipated that grounded, naturalistic Killer, D. Scientific sociology: theory and method. 

approaches might allow quicker discovery of the Englewood Cliffs, NJ: Prentice-Hall; 1967. 

range of factors involved in making choices, as well 131 p. 

as something of the structure of the interrelationships among factors. Because the findings were 

grounded in actual behavior, it was expected that 

they would be of immediate interest and relevance 

in resource management decision making. 

If the purpose of a specific investigation is to 

describe population characteristics, use patterns, 

etc., in statistically representative terms, survey 

sampling is an appropriate tool to use. Alternatively, grounded, naturalistic methods are highly preferred (and efficient) techniques for uncovering 

processes at work. They provide the means to 

learn more about the "why" of things (particularly 

when behaviors appear to be inconsistent with preferences); to model complicated interrelationships; 

and to identify the range of thought, emotion, and 

behavior occurring in real-life settings. If a 

grounded model has been properly derived, it offers 

a framework more likely to be capable of withstanding rigorous quantitative testing. 

REFERENCES 

Austin, J. Chase, chance, and creativity. New York: 

Columbia University Press; 1978. 237 p. 

Clark, R.N. Alternative strategies for studying 

river recreationists. In: Proceedings, River 

recreation management and research symposium; 

1977 January 24-27; Minneapolis, MN. Gen. Tech. 

Rept. NC-28. St. Paul, MN: U.S. Department of 

Agriculture, Forest Service. North Central Forest 

Experiment Station; 1977: 91-100. 

Clark, R.N.; Downing K.B. Why here and not there: 

The conditional nature of recreation choice. 

Elsewhere in these proceedings. 

Deising, P. Patterns of discovery in the social 

sciences. NEW York: Aldine Press; 1971. 

350 p. 

Dobbert, M.L. Ethnographic research. New York: 

Praeger; 1982. 391 p. 

Glaser, B.G.; Strauss, A. The discovery of grounded 

theory: strategies for qualitative research. 

New York: Aldine Press; 1967. 271 p. 

Guba, E.G.; Lincoln, Y.S. Effective evaluation. 

San Francisco, CA: Josse-Bass; 1981. 423 p. 

Jamie, I.C. The revolution in anthropology. 

London: Rutledge and Kegan; 1964. 

Knopf, R.C. Recreational needs and behavior in 

natural settings. In: Altman, I.; Wohlwill, 

eds. Behavior in the natural environment. 

New York: Plenum Press; 1983. 346 p. 

Platt, J. R. Strong Inf erence. Science. 

146(3246):347-353; 1964. 

~2 US. GOVERNMENT PRINTING OFFICE: 1985-576-040110,542 REGION NO. 8 
Stankey, George H.; McCool, Stephen F., compilers. 

Proceedings--symposium on recreation choice behavior; 1984 

March 22-23; Missoula, MT. General Technical Report 

INT-184. Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Intermountain Research Station; 1985. 106 p. 

Presents an overview paper and 11 contributed papers 

focused on the issue of the processes that underlie 

decision making by recreationists. The papers span a range 

of topics including the theory of choice behavior, empirical 

investigations of choice, and methodological approaches to 

the study of choice behavior. 

KEYWORDS: choice behavior, substitutability, recreation 

settings, recreation experiences, setting 

attributes 
The lntermountain Station, headquartered in Ogden, Utah, is one 

of eight regional experiment stations charged with providing scientific knowledge to help resource managers meet human needs and 

protect forest and range ecosystems. 

The lntermountain Station includes the States of Montana, 

Idaho, Utah, Nevada, and western Wyoming. About 231 million 

acres, or 85 percent, of the land area in the Station territory are 

classified as forest and rangeland. These lands include grasslands, deserts, shrublands, alpine areas, and well-stocked forests. 

They supply fiber for forest industries; minerals for energy and industrial development; and water for domestic and industrial consumption. They also provide recreation opportunities for millions 

of visitors each year. 

Field programs and research work units of the Station are maintained in: 

Boise, ldaho 

Bozeman, Montana (in cooperation with Montana State 

University) 

Logan, Utah (in cooperation with Utah State University) 

Mlssoula, Montana (in cooperation with the University 

of Montana) 

Moscow, ldaho (in cooperation with the University of 

Idaho) 

Provo, Utah (in cooperation with Brigham Young University) 

Reno, Nevada (in cooperation with the University of 

Nevada)