A process model of voluntary travel behavior modification and effects of Travel Feedback
Programs (TFPs)

byAyako Taniguchi, Ph.D. Eng.
Assistant Professor
Department of Risk Engineering, University of Tsukuba
1-1-1, Ten-noh-dai, Tsukuba, Ibaraki 305-8573, Japan
Tel: +81-(0)29-853-5754
Fax: +81-(0) 29-853-5754
E-mail: taniguchi@risk.tsukuba.ac.jp
andSatoshi Fujii, Ph.D. Eng.
Professor
Department of Civil Engineering, Tokyo Institute of Technology
2-12-1, Ookayama, Meguro, Tokyo 152-8552, Japan
Tel: +81-(0)3-5734-2590
Fax: +81-(0)3-5734-2590
E-mail: fujii@plan.cv.titech.ac.jp

Total number of words: 4,574 + 250*7 items = 6,324 words

1

ABSTRACTThis study tested an integrated process model of travel behavior modification. We used a modelthat combined the theory of planned behavior (TPB), norm activation theory (NAT), a theory of
implementation intention, and theories of habit. To test the integrated model, we used panel data(n = 208) obtained before and after travel feedback programs (TFPs); the TFP is acommunication program aimed at voluntary travel behavior modification, from automobile use
to non-auto means of travel such as public transit and bicycle use. A structural equations modelusing the data collected tested the integrated model. The results showed that habit prevents
people from forming behavioral intentions for behavior change; behavior change is induced by
implementation intentions, which are, in turn, influenced by behavior intentions; and behavioral
intentions are influenced by altruistic determinants rather than by non-altruistic determinants.
KEYWORDS: Habit, implementation intention, norm activation theory, theory of planned
behavior, TFP, travel feedback program, voluntary travel behavior modification2

INTRODUCTION
To ease traffic congestion in urban areas, and to reduce environmental problems
resulting from automobile emissions, transport policy makers have begun to implement mobility
management measures1) 2). Mobility management is intended to influence individual awarenessand certain psychological factors in order to encourage voluntary behavior change. Mobility
management includes the provision of specific information on public transit, as well as thedevelopment of travel campaigns, and travel education. A typical program might involve
participants reporting their travel behavior or requesting necessary information for travel
behavior change; they might also receive feedback that would include information on the CO2
emissions their car produces, advice on how to reduce car use, and individualized information
on public transit that could be used as an alternative. Examples of such programs include
Individualized Marketing3), Travel Smart4), Travel Blending5) 6), and the Wise Way to Use a Car
Program7) 8). We have called these behavior modification programs ‘travel feedback programs’(TFP) 8).
Travel feedback programs have been widely implemented in EU countries, inAustralia, and in Japan, and have reduced car use by about 7% to 15% among thoseparticipating in EU countries and in Australia1) and by 19% (the average of 10 TFP cases)
among those participating in Japan9). However, it is not well understood how travel feedback
programs actually modify travel behavior. If we could understand how travel behavior changes
as a result of travel feedback programs, the development of more effective and efficient
programs for travel behavior modification, from car use to pro-environmental means oftransport, would be facilitated.
In order to understand the process of voluntary travel behavior modification, from car
use to public transit use, we examined various theories about behavior and attitude, including
Ajzen’s theory of planned behavior10), norm activation theory11), a theory of implementation
intention12) 13), and theories of habit14) 15) 16).
It should be noted that to our knowledge, these theories have not previously been
applied together to describe the process of voluntary travel behavior change, although eachtheory has been used to provide a partial explanation of the process of voluntary travel behavior
modification8) 14) 16) 17). With this in mind, we proposed and investigated an integrated processmodel of voluntary travel behavior change, as shown in Figure 1, which incorporated the theoryof planned behavior, norm activation theory, a theory of implementation intention and theoriesof habit. By evaluating this integrated model using empirical data, we hoped to gain anunderstanding of which determinants were more important for voluntary travel behavior change.
Statistical analysis based on such an integrated process model might indicate which
determinants are more important for activating behavioral intention for travel behavior change,
and which less so, for example, non-altruistic determinants assumed in theory of planned
behavior or altruistic determinants assumed in norm activation theory. In addition, such analysis
might indicate which aspects of habitual car use have negative impacts on behavioral intention,
implementation intention, and actual behavior. Such findings might be helpful in designing
more effective travel feedback programs in the future.
Dynamic data about travel behavior and psychological variables obtained before andafter travel feedback programs are more helpful in this respect than the cross sectional data used
for testing theoretical models or behavioral hypotheses about travel mode choice, because they
are more suitable for testing causality. Analysis of the process of voluntary travel behavior
modification resulting from TFP is useful for a theoretical understanding of travel behavior, andfor developing better travel feedback programs for practical mobility management. Therefore,
we used panel data obtained from two surveys, one implemented before and one after theintervention that was conducted to test the model.

Figure 1
Determinant of behavioral intention

3

Figure 1 shows the integrated model for car use reduction that was tested in this study.
The model shows the assumption that the behavior of car use reduction is influenced by the
behavioral intention to reduce automobile use, and that behavioral intention is, in turn,influenced by psychological factors, which include attitude and perceived behavioral control.
These factors are considered in the theory of planned behavior, one of the most widely usedbehavioral theories18).
Although the theory of planned behavior refers to non-altruistic behavior, voluntary
travel behavior modification, from car use to public transit use, may be encouraged by altruistic
motivations, as is hypothesized in norm activation theory. According to norm activation theory,
behavior is influenced by an awareness of the negative consequences of car use. The modelshows three types of awareness of consequences (social, biosphere, and general), and a generalenvironmental concern that may be related to an awareness of consequences19); these concerns,
as motivators, could be determinants of behavioral intention.
Other determinants of behavioral intention include an injunctive subjective norm, adescriptive subjective norm, the perceived beliefs of family members regarding the necessity of
car use, and an individual’s beliefs regarding the necessity of car use. The first three are all
related to social pressure20) and may have a similar effect to the subjective norm assumed in thetheory of planned behavior. The injunctive subjective norm refers to the belief that others, suchas family members, have a moral obligation to reduce car use, whereas the descriptive
subjective norm refers to the belief that others, such as family members, agree with the target
individual's car use reduction.
Implementation Intention
Behavioral intention is necessary for sustained car use reduction, but it is not sufficient.
In the real world, travel behavior is not always modified, in spite of an intention to do so. In an
attempt to determine how intentions are implemented in behavior, Heckhausen and Gollwitzer21)
and Gollwitzer12) 13) made a distinction between ‘goal intention’ and ‘implementation intention’.
While goal intention can be regarded as a behavioral intention22) 23), implementation intention
entails a plan for when, where, and how to implement the target behavior. Gärling and Fujii22)
hypothesized that the effect of behavioral intention on behavior is mediated by implementation
intention. They used data on causal relations between behavioral intention, implementation
intention or planning, and actual behavior to support this assumption.
Habit
In order to understand travel behavior modification, it is necessary to consider habit.
Studies have suggested that the habit of using a car prevents travel behavior modification, fromcar use to alternative travel modes14) 15) 16). The authors hypothesized that behavioral intention,
implementation intention, and behavior are weakened by a habit of car use.

Figure 2METHOD
In order to test our model of travel behavior modification, we used data obtained from a twowave panel survey, conducted before and after a travel feedback program implemented in anelementary school in Sapporo, Japan. Sapporo city is a government-ordinance-designated city,with a population of approximately 1.8 million. There are three subway lines and a full bus
network. The vehicle trip share is about 40 to 50%. The target elementary school was located ina suburban area, with buses arriving every five minutes at peak times, and a subway station
fifteen minutes away. Students commuted on foot, but in case of rain, their parents would oftentransport them by car. Almost all students came from car-owning households. The participantsin the travel feedback program included fifth grade students (10–11 year olds) and their family
members (age of participants were 28% on twenties, 63% on thirties, and 9% on fifties; andratio of female was almost half). The survey included 208 family members out of 398 (response
rate was 52%), and the data collected only family members were used to test the model.

4In the experiment, four classes in the elementary school participated in two different
types of travel feedback program. Students in two classes, as well as members of their families,
received individualized information and advice on how to reduce their car use. This group isreferred to as the ‘advice group’. Students in the other two classes, as well as members of theirfamilies, were asked to make behavioral plans that would reduce their car use. This group isreferred to as the ‘planning group’ (see Figure 2). Details of analysis comparing those twogroups were reported at Fujii & Taniguchi 24).
Six weeks prior to the experimental intervention, the students and their families
answered a questionnaire in which the model’s psychological factors were measured. Data for
the factors, except habit, were obtained using five-point scales, in which the most proenvironmental end was calibrated as five and the other end was calibrated as one. Exact
wordings and scale antipodes used for measuring factors are shown in Table 1. Verplanken et al.
developed a way to measure habit 25) 26), a method we used in the questionnaire.

Table 1
Four weeks before the intervention, all the students were given lessons as lecture 1 on
the issue of global warming, the role of CO2 in this problem, and levels of CO2 emissions
resulting from car use.
One week after the lecture 1, all the families in the advice group filled in a three-day
activity-travel diary. Participants logged start times, end times, types and locations of all theiractivities, as well as the mode of transportation used to travel to and from these activities. After
receiving their diaries, we made diagnostic checklists7) 8) for each family, in which their threeday activity-travel patterns were graphically presented, and the number and length of theirjourneys by each mode of travel were recorded, along with comments that included suggestions
on how the participants’ activity-travel patterns might be modified to reduce CO2 emissions.
Diagnostic checklists were then given to each family in the second lesson as lecture 2, toencourage the reduction of car use. Three weeks after the diary survey, in the lecture 2, students
participating in the advice group received diagnostic checklists for themselves and their families.
Using the diagnostic checklists, students calculated their own and their families’ CO2 emissions.
The CO2 emissions for the whole class were also calculated and confirmed. Finally, theylearned the easy ways to reduce CO2 emissions caused by car-use, such as “Change travelmode”, “Change destination”, “Combine errands, and reduce the number of times the car isused”.
In the planning group’s second lesson as lecture 2, students developed behavioral
plans to modify their families’ car use and increase more sustainable travel modes. In theirlesson, bus and train maps and timetables were distributed and students considered concrete
ways to modify their travel behavior. Following the lecture 2, students brought their behavioral
plan sheets home, and each family was asked to develop behavioral plans to modify home-based
car trip chains with the goal of reducing CO2 emissions. They were instructed to develop
behavioral plans to modify up to three of their car trip chains. Families filled out questionnaires
describing planned departure times, arrival times, and travel modes for each trip, as well as thelocations of each stop in their modified car trip chains. This intervention took place on the same
day as the intervention for the advice group.
Two weeks after the second lesson, participants filled out an exact duplicate of theoriginal questionnaire they had filled out six weeks before the intervention.
The third lesson as lecture 3 was held one month after the second. In the lecture 3,
students received their results comparing the before and after questionnaires, discussed theirefforts to reduce car use, and considered what more could be done to reduce CO2 emissions
further in the future.
Students’ families did not attend the lessons, but did commit to answering thequestionnaires presented before and after the intervention, to filling in a travel diary for threedays, and to making a behavioral planning sheet with their children. Through these activities,

5the program aimed to have an effect not only on the participating children but also on theirfamilies.
RESULTS
Table 2 shows mean (M) and standard deviations (STD) for factors in both surveys,
and t statistics for differences between the pre- and post-travel feedback program. This tableshows that awareness of consequences (general), awareness of consequences (social), and thesubjective norm (injunctive) significantly increased. It also shows that behavioral intentions,
implementation intentions, and pro-environmental behavior increased significantly. These
results suggest that the travel feedback program encouraged attitude and behavior modifications
in a pro-environmental direction.

Table 2
We previously reported statistical tests of the effectiveness of the travel feedback
programs in this experiment, and performed a comparative analysis of the effectiveness ofdifferent types of travel feedback program24) 27). The effectiveness of travel feedback programson psychological variables such as behavioral intention and awareness of consequences was not
significantly different for the planning and advice groups, but car use reduction, as measured byfrequency of car use and range of travel time over three days, was significantly larger for theplanning group than for the advice group.
The primary objective of the present study was to test the structural relationships
shown in Figure 1, rather than to test the effectiveness of travel feedback programs or make acomparative analysis between planning and advice groups as done previously24) 27). However,one might expect structural relationships to differ between the advice and planning groups.
Therefore, before we tested the structural relationships shown in Figure 1 using data from thisexperiment, we estimated structural equations models separately for the advice and planninggroups. However, we did not find any significantly different coefficients across the two models.
Therefore, we decided to use only one model. However, the model included a dummy variable
to indicate the difference between two types of travel feedback program, since we had alreadyfound that car use reduction differed across the two models, as shown in Fujii and Taniguchi24)
and Taniguchi et al. 27).
In order to test structural relationships, we determined change variables for pre- andpost-travel feedback program interventions. The change variables were then used to estimate astructural equations model, shown in Figure 1. The results from the structural equations modelusing these change variables are more suitable than are results from cross-sectional data forassessing causal relations between variables.
In the structural equations analysis, paths from dummy coded variables indicating thetype of travel feedback program for all the variables were also estimated in addition to paths
shown in Figure 1. We also estimated constant terms for all the variables. Since constant terms
for change variables indicate the changes occurring from pre- to post-travel feedback program
periods, these can be regarded as direct effects of the travel feedback program’s implementation.
The result of structural equations analysis can be seen in Table 3 and Table 4. Table 3
shows estimated path coefficients and their t-statistics, and Table 4 shows estimated direct
effects of the travel feedback program’s implementation. Figure 3
gives a graphic
representation of the significant paths reported in Table 3 and Table 4. With respect to total
goodness of fit to the data, NFI is 0.749, CFI is 0.837, and RMSEA is 0.0731. Although theseindexes may not be satisfactory, χ2[n=208; degree of freedom = 75] is 157.6, which indicatedthat the goodness of fit of the model is acceptable when judged by conventional criteria28) 29).
1

NFI represent Normed Fit Index, CFI represent Comparative Fit Index, RMSEA represent Root
Mean Square Error of Approximation. See Jöreskog, K. G., & Sörbom, D. (1993) 30) about details ofthese indexes.

6This implies that the data lend a support of the proposed model show in Figure 1 that integrated
theory of planned behavior, norm activation theory and theories for implementation intention
and habit.

Table 3

Table 4

Figure 3
According to Table 3 and Figure 3, behavior change for car use reduction was
significantly induced by implementation intention, which was caused, in turn, by behavioral
intention. This supports the hypothesis of a mediating effect for implementation intention,
between behavioral intention and behavior.
The results show that behavioral intention is significantly influenced by awareness ofconsequence (biosphere), awareness of consequence (general), and general environmental
concern. Attitude, perceived behavioral change, subjective norms, and necessities did not
significantly influence behavioral intention, so norm activation theory may be more suitable fordescribing voluntary travel behavior change than behavioral hypotheses, assuming non-altruistic
motivations, as posited by the theory of planned behavior.
The results also show that habits have a negative effect on behavioral intention. This
supports the hypothesis that habits impede behavioral change. This travel feedback program
seemed to significantly and directly activate both behavioral intention and implementation
intention. This program seemed to have a positive effect on subjective norms, but subjective
norms had no effect on any factors assumed in the model.
Finally, according to Table 4 and Figure 3, a dummy coded variable on the type oftravel feedback program had a significant effect on the subjective norm (descriptive), but had noeffect on any other variables. So, travel feedback programs in which participants receive advice
on how to reduce car use seem to be as effective, with respect to behavior and attitude
modification, as programs in which participants are requested to make a behavioral plan on how
to reduce their car use.
DISCUSSION
Behavior, Intentions and Habit
The dynamic data obtained from the panel survey before and after the travel feedback program
confirmed the hypothesis that car use reduction is caused by an implementation intention to
reduce car use, and that the implementation intention is, in turn, caused by a behavioral
intention to reduce car use. These results support those from previous studies22) 23).
It appears that habitual car use has a negative effect on behavioral intention. This
supports studies that found habit impeded behavioral modification14) 15) 16). However, thesestudies discussed other consequences of habit; for example, they showed that habit reduces theconsistency between behavioral intention and behavior. Although this study did not focus on
intention-behavior consistency, but was rather an investigation of direct effects between
variables, an analysis on intention-behavior consistency is necessary for a more comprehensive
understanding of the effects of habit on behavior modification.
Determinants of Behavioral Intention
The analysis indicated that increased awareness of consequences for the biosphere, a generalawareness of the consequences of car use, and a general environmental concern all had a7

positive effect on behavioral intention to reduce car use. These results support the causal
relations hypothesized in norm activation theory. On the other hand, changes in attitude toward
car use and perceived behavioral control had no effect on behavioral intentions. In addition,changes in beliefs about the necessity of car use and subjective norms had no effect on
behavioral intentions. These results do not support the causal relations hypothesized in thetheory of planned behavior. Thus, norm activation theory can better explain the motivation forvoluntary travel behavior change than the theory of planned behavior. Voluntary travel
behavioral modification might be encouraged by altruistic motivation, such as awareness ofconsequences, as opposed to non-altruistic motivation.
Similar results were reported in Gärling et al. (2003)31), who found that proenvironmental behavioral intentions (e.g., contribution of money to an environmentalorganization, or signing a petition in support of environmental laws) were significantly
influenced by factors hypothesized in norm activation theory, but were not influenced byawareness of egoistic consequences. They pointed out that since the theory of planned behavior(assuming that concerns of self-interest are the determinants of intention) had received
substantial empirical support18), it has been suggested that pro-environmental behavior might
differ from other behavior that has been studied. Our results also suggest that individuals who
voluntarily modify their travel behavior may not be displaying self-interest, but may bedisplaying altruistic behavior, as well as other types of pro-environmental behavior, such as
those investigated in Gärling et al. (2003)31) and in other, similar studies32) 33) 34) 35).
Policy Implications
Participants’ behavioral intentions and implementation intentions appeared to increase
after participation in the travel feedback program. The increase did not differ according to thedifferent types of travel feedback program, i.e., the program that involved participants making abehavioral plan on how to reduce car use, and the program that provided individual advice on
how to reduce car use. These results clarify why travel feedback programs have been so
effective in reducing car use1)-8). Travel feedback programs have positive direct effects on bothbehavioral intentions and on implementation intentions and these changes in these forms ofintention are expected to reduce car use. The direct effect on behavioral intentions may be dueto the fact that travel feedback programs always provide an opportunity to choose between
different means of travel. The direct effect on implementation intentions may be due to the factthat travel feedback programs provide specific information on how to reduce car use or on how
to use alternative means of transport, such as trains and buses.
Travel feedback programs consist of many different types of intervention, including
the provision of information on how to use alternative means of travel; the provision of
information intended to persuade participants not to use their car, and the request thatparticipants make behavioral plans on how to reduce car use. Although this study indicates thatthe kind of intervention used in this travel feedback program made no difference to the travel
behavior modification that occurred, other types of interventions may have other effects. In
order to develop more effective and efficient programs, further empirical studies are needed to
identify those methods that are most effective for modifying specific psychological variables.
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10

Figure 1. Hypothesis of the integrated process model of travel behavior modification.
Figure 2. Procedure of Experiment "TFP".
Figure 3. Significant effects shown in the results of the structural equations model.

Table 1. Measurements of the participants’ psychological factors and the endpoints of scales.
Table 2. Mean (M) and standard deviation (STD) for each psychological factor, and p values forone-way analysis of variance (before vs. after survey) of psychological factors
assumed in the integrated process model.
Table 3. Estimated results of path coefficients.
Table 4. Estimated direct effects of the effect of the travel feedback program on each factor.

11

awareness ofconsequences(social)awareness ofconsequences(biosphere)awareness ofconsequences(general)subjective norm
(injunctive)

generalenvironmental
concernsubjective norm
(descriptive)

attitude toward
car usebelief of necessity(others)

perceived
behavioral control

habit of car use

behavioral
intention
implementation
intention

belief of necessity(self)

Variables defined intheory of planned behavior (),
norm activation theory (),
theory of implementation intention (theory of Habit ().

behavior
car use reduction

Figure 1. Hypothesis of the integrated process model of travel behavior modification.

12), and8-10th,Sep.

answer questionnaire (before)

25th,
Sep.

Lecture 1: global warming, role of CO2, emission from car

28th,
Oct.

fill travel diaryplanning
group

Lecture 2:
receive advice

Lecture 2:
How to makebehavioral plan

advicegroup
10-12nd,
Nov.
25th,
Nov.

make behavioral plananswer questionnaire (after)

Lecture 3: discuss how to reduce car use
Figure 2. Procedure of Experiment "TFP".

13

Students
onlyStudents& FamilyTable 1. Measurements of the participants’ psychological factors and the endpoints of scales.
Variable

Measurements

habit

Which transportation do you use
to visit a friend?
to shop for clothes?
to go to the movies?
to go for lunch?
to go out for dinner?
to go swimming?
to go skiing?
to go to the hospital?
to go to the convenience store?
to go to the bookstore?
- Do you think you should think about
environmental problems?
- Are you concerned about environmental
problems?
- Do you think it is necessary for everyone to bethoughtful about environmental problems?
- Do you think that we must pay attention toenvironmental problems at the present time?
Do you think car use has general undesirable
consequences?

generalenvironmental
concernawareness ofconsequences(general)awareness ofconsequences(biosphere)awareness ofconsequences(social)attitude toward caruse
subjective norm
(injunctive)
subjective norm
(descriptive)
belief of necessity(others)
belief of necessity(self)

Do you think car use has undesirable
consequences on the biosphere?

implementation
intention (decisioncommitment†)

Cronbach´s α(before/after)

Family car,Train,
Bus, Bicycle,
Walking,
Motorcycle,
Other

-

No / Yes(five-point scale)

No / Yes(five-point scale)

No / Yes

Do you think car use has undesirable No / Yes
consequences with respect to society?
(five-point scale)- Do you enjoy car use?
- Do you feel comfortable using a car?
Do you think your family or friends feel a moral
obligation to reduce car use?
Do your family or friends agree with yourreduction in car use?
Do you think that your family or friends believethat a car is a necessity in daily life?
Do you believe that a car is a necessity in daily
life?

perceived behavioral - Do you think that it takes a lot of effort to reduce
car use?
controlbehavioral intention

Scale
antipodes- Do you think that it is difficult to reduce car use?
- Have you formulated the intention to reduce car
use as much as possible?
- Do you intend to reduce car use as much as
possible?
- Do you make an effort to refrain from car use as
much as possible?
- Do you do various things to refrain from car use
as much as possible?
- To what extent do you refrain from using a car?
- Do you actually refrain from using a car?

No / Yes(five-point scale)

No / Yes(five-point scale)

No / Yes(five-point scale)

No / Yes(five-point scale)

No / Yes(five-point
scale)
No / Yes(five-point scale)

No / Yes
No / Yes(five-point scale)(.78/.80).70/.70.70/.76.81/.84

No / Yes
No / Yes(five-point scale).72/.82

No / Yes

none/ a lot
behavior.86/.87
No / Yes(reported car use(five-point scale)
reduction)†
As suggested by Fujii (2005), implementation intentions were measured using a measurementfor decisional commitment, which is theoretically related to implementation intention.

14

Table 2. Mean (M) and standard deviation (STD) for each psychological factor, and pvalues for one-way analysis of variance (before vs. after survey) of psychologicalfactors assumed in the integrated process model.
beforeM(STD)
habit of car use
7.45 ( 2.44 )general environmental concern
4.03 ( 0.71 )awareness of consequences (general)
2.06 ( 0.99 )awareness of consequences (biosphere)
3.61 ( 1.07 )awareness of consequences (social)
2.79 ( 1.15 )attitude toward car use
4.00 ( 0.81 )subjective Norm (injunctive)
3.21 ( 0.82 )subjective Norm (descriptive)
2.64 ( 0.92 )belief of necessity (others)
4.44 ( 0.77 )belief of necessity (self)
4.48 ( 0.86 )perceived behavioral control
3.49 ( 1.17 )behavioral Intention
2.77 ( 1.09 )implementation intention (decision commitment)
2.30 ( 1.06 )behavior (self-reported car use reduction)
2.39 ( 1.23 )t : t-statistics

15

after
M
(STD)
7.25 ( 2.57 )4.01 ( 0.74 )2.29 ( 0.97 )3.67 ( 0.82 )3.01 ( 1.00 )3.97 ( 0.79 )3.38 ( 0.83 )2.71 ( 0.85 )4.35 ( 0.76 )4.46 ( 0.83 )3.54 ( 1.13 )3.08 ( 1.09 )2.71 ( 1.07 )2.69 ( 1.17 )*p<.05,**

p<.01,

t1.56
0.42-2.86 ***
-0.82
-2.71 ***
0.62
-2.67 ***
-0.94
1.54
0.35
-0.70
-4.28 ***
-5.89 ***
-4.25 ******

p<.001

Table 3. Estimated results of path coefficients.
Path to←ｔPath from

Coefficientbelief of necessity (self) ← belief of necessity (others)

0.12

1.72 *← habit of car use-0.09-2.54 **← general environmental concern

0.25

2.37 **← awareness of consequences (general)

0.17

2.73 ***← awareness of consequences (biosphere)

0.11

1.69 *← awareness of consequences (social)

0.02

0.27

-0.15

-1.63

← subjective Norm (injunctive)

0.04

0.52

← subjective Norm (descriptive)

0.07

1.09

← belief of necessity (others)

-0.07

-0.82

← belief of necessity (self)

-0.09

-1.04

← perceived behavioral control

-0.07

-1.15

← habit of car use

0.01

0.23

← behavioral intention

0.55

9.75 ***

-0.01

-0.26

0.64

11.72 ***

habit of car use ←

-0.12

-0.46

general environmental concern ←

0.05

0.50

awareness of consequences (general) ←

0.16

1.01

awareness of consequences (biosphere) ←

0.04

0.29

behavioral intention ← attitude toward car use

implementation intention

behavior ← habit of car use(self-reported car use reduction) ← implementation intention

awareness of consequences (social) ←

0.14

0.88

attitude toward car use ←

-0.11

-0.95

-0.11

-0.86

0.27

1.83 *

-0.07

-0.65

0.11

0.94

perceived behavioral control ←

0.16

1.00

behavioral Intention ←implementation intention←(decision commitment)
behavior←(self-reported car use reduction)

0.01

0.10

-0.13

-1.14

0.14

1.28

p<.01,***

subjective Norm (injunctive) ← Dummy variables indicate the type ofsubjective Norm (descriptive) ← TFP experiment. If it is the planninggroup, then the dummy variable is 1;
belief of necessity (others) ← otherwise, it is 0.
belief of necessity (self) ←*16

p<.05,**

p<.001

Table 4. Estimated direct effects of the effect of the travel feedback program on each factor.
Coefficientthabit of car use

-0.15

-0.82

general environmental concern

-0.04

-0.65

awareness of consequences (general)

0.16

1.42

awareness of consequences (biosphere)

0.04

0.41

awareness of consequences (social)

0.15

1.36

attitude toward car use

0.01

0.18

subjective Norm (injunctive)

0.23

2.54 **

subjective Norm (descriptive)

-0.06

-0.57

belief of necessity (others)-0.14-1.76 *

belief of necessity (self)

0.03

0.33

perceived behavioral control

-0.02

-0.19

behavioral Intention

0.22

2.36 **

implementation intention (decision commitment)

0.31

3.79 ***

-0.03

-0.39

**

***

behavior (self-reported car use reduction)*

t: t-statistics

17

p<.05,

p<.01,

p<.001

awareness ofconsequences(social)awareness ofconsequences(biosphere)awareness ofconsequences(general)subjective norm
(injunctive)

Generalenvironmental
concernsubjective norm
(descriptive)

attitude toward
car usebelief of necessity(others)

perceived
behavioral control

behavioral
intention

habit of car use
Variables defined intheory of planned behavior (),
norm activation theory (),
theory of implementation intention (theory of Habit ().

implementation
intention

belief of necessity(self)
planninggroupχ2(75)=157.6
n=208
significant positive effect), andbehavior
car use reduction

significant negative effectvariables activatedsignificantly by TFPFigure 3. Significant effects shown in the results of the structural equations model.

18