From: Rob Kling <kling@ics.uci.edu>
Subject: Behind the Terminal, File #2 of 6
Newsgroups: comp.society
Date: 14 Aug 92 00:11:10 GMT




                              File 2 of  6
                          Behind the Terminal:
             The Critical Role of Computing Infrastructure
         In Effective Information Systems' Development and Use


                               Rob Kling
             Department of Information and Computer Science
                  Public Policy Research Organization
                        University of California
                            Irvine, Ca 92717
                              714-856-5955
                        email:kling@ics.uci.edu

                               Draft 4.02
                      July 17, 1991 (Aug 4, 1991)

To appear in: Challenges and Strategies for Research in Systems
Development. William Cotterman and James Senn (Ed.). John Wiley,
London.

1.0 INTRODUCTION

In this paper I discuss the development and use of computerized
systems in organizations in ways that can help systems analysts
make more effective recommendations.  The key organizing ideas of
this paper are based on almost 20 years of empirical studies of
the ways that people and organizations adopt, develop and use
computerized systems. It is based on an understanding of how
people and organizations actually behave rather than upon an
abstract model of how they should behave (Kling and Scacchi,
1982; Kling, 1987; Dunlop and Kling, 1991).

Computerizing an organizational activity involves much more than
mapping information flows, finding equipment to automate work and
decisions, installing the equipment, and training users. Many
computerization projects lead to new work practices and new
divisions of labor. They may raise (or at least alter) standards
for acceptable goods and services. They may lead people to expect
themselves and their coworkers to work differently and often
faster. Many analysts assume that the "technologically best
equipment" will lead to the most significant improvements, even
though professionals may differ over which features of work and
organizations should be viewed as worst and best. The "surface
features" of computerization are the most visible and the primary
subject of debates and systems analysis. But they are only one
part of computerization projects. Many key parts of information
systems are neither immediately visible or interesting in their
novelty.

An analogy with urban architecture is illustrative. We often
think of the architecture of cities like New York in terms of
skylines and the design of major buildings. For me, the phrase
"New York City architecture" evokes images of the imperial
skyscrapers like the Empire State Building, the World Trade
Center, the AT&T building, and the Guggenheim Museum, as well as
residential streets with brownstones. While the variety of
business life in New York City depends upon skyscrapers, it also
depends upon numerous services that are invisible from the street
-- including electric lines, telephone cables, and sewer lines.
It depends upon "transportation systems" including cabs, buses,
and subways. This urban infrastructure facilitates business life
in the office buildings. Without electricity and communication
lines, business in the most imperial of skyscrapers would grind
to a halt as elevators and computers stopped, and the only way to
contact someone would be by walking up and down 10, 20 or even 50
flights of stairs. If the cabbies, subway engineers, and bus
drivers all quit work simultaneously, the streets would be
clogged with cars. Despite the city's dependence on a complex and
refined urban infrastructure, much of it is out of sight, taken
for granted, treated as dirty, and not a primary part of our
image of "business support in New York City."

Another analogy comes from theater and film. We normally focus on
the visible elements of a plays and films: the cast, director,
writers (script) and sets. The actual production of a play or
film involves numerous other people to design costumes, coach
actors, locate sets, design lighting, and so on. In the 1980s,
films began to give credit to a much larger group of these
essential "backstage participants." One can now see film credits
for hair designers, voice coaches, and grips, as well as the
traditional cast, writers, and directors.

Workable computing arrangements also depend upon a set of
supporting resources, which can be physical, technological or
social. Physical resources include the space to place equipment;
technological resources includes electricity and communication
lines. The social resources include people skilled in using and
repairing equipment and practices for allocating resources. I
call this collection of resources computing infrastructure.

Computing infrastructure is a key element in any computerization
effort (Kling and Scacchi, 1982;  Gasser, 1986; King and Kraemer,
1986; Kling, 1987; Clement, 1990; Clement and Parsons, 1990;
Jewett and Kling, 1990; George, Iacono, and Kling, 1991; Jewett
and Kling, 1991). People report having better quality worklives
in workgroups where the computing infrastructure for supporting
training and consulting is best developed (Lepore, Kling, Iacono,
and George, 1989). Further, poorer organizations, like many
public schools, sometimes have trouble effectively using gifts of
advanced computer systems because of weakness in their computing
infrastructure. Training is one aspect of computing
infrastructure which receives some attention. But computing
infrastructure refers to a much richer array of resources and
practices. This chapter examines the role of infrastructure in
computer use and suggests how systems analysts might include an
analysis of computing infrastructure in their recommendations
about appropriate computing arrangements.

Today, there are two dominant approaches to systems analysis:

1.   Formal information processing approaches which emphasize the
     information processing capabilities of computer systems as
     instruments for manipulating data or making decisions. These
     approaches (such as those which map information flow with
     charts and flow diagrams) focus on formal tasks and
     generally ignore the social relationships between people and
     organizations who develop, maintain and use computerized
     systems. They also assume that people should follow certain
     narrow rationalistic norms, and tacitly assume that people
     and organizations will devote whatever resources are
     necessary to make computing systems effective. These
     approaches can be useful under special conditions, but they
     do not help analysts adequately understand the conditions
     under which people are likely to use systems and the ways
     that likely patterns of use should influence the social
     designs of information systems. Further, these approaches
     focus on the most visible aspects of information systems --
     and ignore the resources required to build and operate them
     effectively.

2.   Social process approaches, such as socio-technical systems,
     Christiansen's (1990) activity approach, Checkland's soft
     systems (Checkland, in press), help people and organizations
     better identify their preferences (so called requirements).
     Process analysts focus on ways of defining problems
     involving a wide variety of participants in the conception
     and design of systems. Their strength is to build commitment
     from people who are likely users of computerized systems, to
     better identify participant's preferences, and to help
     developers and users learn about each other's work and the
     systems which facilitate them. However, they rarely bring
     key organizing concepts about computer systems, such as
     infrastructure, to these design meetings.

In this chapter I advocate the use of a specific set of ideas
about computing infrastructure in organizations which could help
expend the intellectual repertoire of rationalistic designers and
enrich the design discussions of social processes designers. Any
systems analysis must characterize the computer-based
technologies, the social settings where they are used, and the
social forces which shape their use.  Systems analyses differ in
four ways which have a tremendous influence on their
recommendations:

1.   the scope of the boundaries they draw around a
     computer-based system and the key actors identified --
     developers, users, regulators, maintainers, promoters,
     system saboteurs, clients of computer-using work groups,
     etc.;

2.   the extent to which they examine social relations between
     key participants, such as the kinds of interdependence,
     control, cooperation and conflict;

3.   the extent to which they examine resources that key
     participants have to carry out important lines of action;
     and

4.   the extent to which they examine actual social meanings
     which participants bring to their encounters with
     computer-based technologies.

Unfortunately, systems analyses for computing rarely have
explicit characterizations of technologies other than information
processing models.  This chapter examines how a sociological
characterization of the computing infrastructure and
organizations which develop, adopt, and use them can improve the
quality of systems analysis.

This chapter examines two explicit models which underlie much of
the thinking about computer systems in organizations.  These
models help identify some of the key choices that systems
analysts often make along the four dimensions identified above.
One class of models (mentioned above) focuses on relatively
formal-rational conceptions of the capabilities of information
technologies and the social settings in which they are developed
and used. These conceptions, which we label discrete-entity
models, focus on explicit economic, physical, or information
processing features of the technology. The social context in
which the technology is developed and used, and the history of
the participating organizations, are limited to a few formal
relationships or are ignored.  Systems are assumed to be loosely
aggregated collections of equipment, people, procedures, and
beliefs, which may easily be broken down into separate elements,
and costed and evaluated independently.  Social relations are
assumed to be cooperative and resources are available as needed.
Discrete-entity analysts also assume that "good" social actors
will value "better" technologies. These conceptions are most
common in professional and academic publications which emphasize
positive management.

The second class of models, web models, are a form of
naturalistic, open systems models (Scott, 1987a). They make
explicit connections between a focal technology, its immediate
users, and a rich ecology of social relationships with other
social groups and organizations in which the technology is
developed, adopted and used (Kling and Scacchi, 1982; Kling,
1987; Wood-Harper and Corder, 1988). Computer systems, in this
conception, are developed, operated, and used by an
interdependent network of producers and consumers and cannot be
adequately analyzed solely according to their discrete features
and components.  Web models treat computerized systems as a form
of social organization with important information processing,
social, and institutional properties: they are not only flexible
information processing tools.  Their "shape", the way they are
used, the leverage they provide, and the interests they serve
depend upon the interplay of stakeholders, resources, and social
games within which they are deployed. Their capabilities and fit
within organizations depend upon these social relationships as
well as upon their information processing characteristics.

Web models of computing emphasize the history of commitments made
in the course of computing deployments and the infrastructure
that supports the deployments of computer-based technologies.
They draw "larger boundaries" around the focal computing
resources than do discrete entity models, by examining how their
development and use depend upon a social context of complex
social actions in which the focal computing resources is
developed or used.  Web models define a social context for a
computer-based technologies by taking into account:

     the social relations between a set of participants who can
     influence the adoption, development, or use of the focal
     computing technologies;

     the infrastructure available for their support;

     the history of commitments made in developing and operating
     related computer-based technologies.

These three elements will serve as key organizing concepts for
web analyses.  In addition, they point to useful methodologies
for doing web analyses.  We discuss appropriate methods briefly
near the end of the chapter.

Discrete-entity models and web models represent ideal constructs.
By making them explicit and sharpening their differences we can
contrast the analytic power of each model and ask about the
insights each model provides into the sagas of computing in
organizational life.  Discrete entity models have dominated
systems analysis to date. We argue that web models better account
for critical aspects of computing developments: their costs and
effectiveness, their speed of planned change, and their
integration into organizational life. In the following sections
we describe these two models in more detail, report a case study
of computerization which helps illustrate key concepts, and
examine key aspects of computing infrastructure with examples
from the case.


2.0 MODELS OF COMPUTERIZATION: DISCRETE-ENTITY AND WEB

In contrasting discrete-entity and web models, it is first useful
to examine their underlying assumptions in more detail, and to
identify their implications.  Table #1 lists five key assumptions
of discrete entity models.

======================================

                             Table 1

              Assumptions of Discrete-Entity Models

D1.  A computing resource is best conceptualized as a particular
     piece of equipment, application, or technique which provides
     specifiable information processing capabilities.
     a.   Each computing resource has costs and skill
          requirements which are largely identifiable.
     b.   Computer-based technologies are tools, and are socially
          neutral.

D2. Role of Infrastructure:

     a.   The infrastructure for supporting the focal computing
          resource and the organizational procedures by which it
          is organized and sustained are critical elements.

     b.   Each computer-based service is provided through a set
          of structured computing resources and organized
          infrastructure.  Deploying, managing, and setting
          procedures for these infrastructural resources is
          separable from deployment of the focal computer-based
          technology.  Infrastructure, either technical or
          administrative, is a neutral resource.

     c.   "Human factors" must be taken into account to ensure
          that people are well trained and motivated to do what
          is required.  But "human factors" are "organizational
          problems" which are separable from "technical
          problems."

D3.  Control over Infrastructure: Organizations have ample
     resources to support all of their computing developments and
     uses simultaneously.  Elements of infrastructure are
     necessary for making the equipment or technique available to
     developers or users, and they can be counted on to be of
     adequate quality and available as necessary.

D4.  The focal computing resource and any element of
     infrastructure can be analyzed independently of:
     a.   its interactions with other computing resources;
     b.   the social or organizational arrangements within which
          computer-based services are developed and provided
          (infrastructure and macrostructures).

D5.  Social Action:

     a.   Organizational behavior is best described by the formal
          goals, procedures, and administrative arrangements of
          the acting units.

     b.   The use of a computing resource is best described by
          its formal purposes and features.
======================================

The basic units of analysis of discrete-entity models are
computing resources (e.g., an Apple MacIntosh computer, a
specific computer program) and the formal tasks to which they are
applied.  Analysts who employ discrete-entity models assume
instrumental rationality, and base their predictions and
diagnoses on two logics of technical development: piecemeal
aggregation and direct substitution.  A simple example of these
attractive assumptions can be illustrated when participants
describe their computing environments by a listing of equipment.


Suppose that the staff of a university department describe their
computing environment by listing this set of equipment:

Microcomputers: 40 diverse IBM PC's, 5 diverse Apple
MacIntoshes;

Word processors: Microsoft Word 5, WordPerfect 5.1.;

Dot matrix printers: 1 Apple Imagewriter, 20 Epson FX86
(9 pin);

Laser printers: 2 Apple LaserWriters;

Databases: Paradox 3.5 and Dbase-IV; and

Spreadsheets: Microsoft Excel

The first logic of technical development, piecemeal aggregation,
assumes that new components add new capabilities whose best
features are available for all participants.  For example, by
aggregation, one could assume that any microcomputer user in this
department could use a laser printer.  In practice, diverse sets
of equipment like those on this list are usually organized into
several collections that work well together.  But the ensembles
may not work well together.  For example, suppose that the
faculty use the IBM PC's and have the dot matrix printers.
Suppose that the departmental secretaries and office staff use
the Macs and share the laser printers.  The Apple laser printer
may work well with Microsoft Word on the Macs, but not with
applications run on IBM PC's.  Documents which the office staff
prepare may routinely appear around the university with
distinctive laser printed fonts.  University administrators might
assume that the faculty also can use the LaserWriter.  But some
of the PC software may not have drivers for the LaserWriters
varied fonts.  On the other hand, the faculty may be able to have
staff support with budgeting on spreadsheets since they all use
Microsoft's Excel.  This department may look computationally
richer than one which has no laser printers.  But the factoring
of the milieu into somewhat distinct computing worlds means that
only one subworld is "rich" in laser printers. Conversely, only
the users of the IBM-PC's may be able to use the databases.

The second logic, direct substitution, assumes that the gains
realized by replacing one computing device by a more powerful are
the same for all participants in all settings -- from a testing
laboratory to an office. For example, suppose that the department
chair considers two proposals:

a.   Replace three of the 9 pin Epson dot matrix printers with an
     equal number of 24 pin Epson dot matrix printers;

b.   Replace the slowest IBM PC's with the fastest IBM PC clones
     available.

These two proposals illustrate the archetypical case of direct
substitution; a better component replaces a lesser quality
component and users of the components can exploit the information
processing capabilities that the new component offers.  A third
proposal, to replace two Epson dot matrix printers with two Apple
LaserWriter, may also appear as direct substitution.  At one
level of abstraction this substitution on the department's
computer inventory list may make it appear to be computer richer.
However, in terms of the day to day work of faculty and staff,
this third proposal is redistributive. Some faculty would lose
printing capabilities because they would relinquish their
dot-matrix printers and have to share the remaining printers
while the departmental staff would gain access to additional
laser printers.  A fourth proposal, to replace the IBM PC's with
more powerful engineering workstations based which run Unix
System V would also appear as enriching on paper.  But if the
faculty who used the PC's lost access to important software (like
Paradox 3.5 and a strong word processor) in the shift from the
PC's to the workstations, they could end up computationally
poorer (e.g., "hardware rich and software poor").

These technological illustrations suggest the difficulties of
examining the value of computing resources as isolated entities,
rather than as parts of larger systems and specific
organizational arrangements. In the case of this hypothetical
university department, we have examined the interplay of
technical features.  However, we should examine the interplay of
both technical and social features a key feature of web models.

======================================

Table 2 -- Assumptions of Web Models

W1.  A computer system is best conceptualized as an ensemble of
     equipment, applications, and techniques with identifiable
     information processing capabilities.
     a.   Each computing resource has costs and skill
          requirements which are only partially identifiable.
     b.   In addition to its functional capabilities as an
          information processing tool, computer-based
          technologies are also social objects which may be
          highly charged with meaning.

W2.  Role of Infrastructure:
     a.   The infrastructure for supporting the focal computing
          resource and the organizational procedures by which it
          is organized and sustained are critical elements.
     b.   Each computer-based service is provided through a set
          of structured computing resources and organized
          infrastructure. This organization of essential
          resources makes computer-based systems into a form of
          social organization. Like any organization or
          institution, it is not necessarily neutral.
     c.   There is no "human factor" which is specially separable
          from the delivery of computer-based information
          services. Much of the development and routine
          operations of computer-based technologies hinge on many
          human judgments and actions carried out within complex,
          organized social settings.

W3.  Control over Infrastructure:
     a.   Organizations have limited resources. Not all necessary
          infrastructural resources are available (in adequate
          quality) as needed.
     b.   Computer-using organizations rarely have complete
          administrative or political control over all their
          requisite infrastructure. Infrastructural resources may
          be spread across several organizational units or
          nominally independent organizations.

W4.  The information processing leverage provided by a focal
     computing resource, and its other costs and benefits, social
     and economic, are contingent upon:
     a.   its interactions with other computing resources;
     b.   the social or organizational arrangements within which
          computer-based services are developed and provided
          (infrastructure and macrostructures).

W5.  Social Action:
     Political interests, structural constraints, and
     participants definitions of their situations often influence
     organizational action.  Use naturalistic open systems
     organizational model, such as  a negotiated order or
     institutional model of social activities, to best describe
     the social relations among key participants.
======================================

Five critical assumptions of a web model are listed in Table 2.
Web analysts are concerned with the array of activities that
people actually engage in while pursuing some task, rather than
examining isolated components of a computer based system and
assuming that they will be smoothly grafted into a formal task
system.  Thus, a web analyst would not start by asking "what kind
of equipment is being used?" or "what are the formal goals of
this organization?," but "who are the key actors, what kinds of
things do they do here, what incentives influence their
activities, and what organizational routines constrain their
actions and choices?" The web analyst identifies the local
ecology of games (Long, 1958) and local behavioral constraints to
examine how they influence computerization.  Assumptions W2 and
W3 bear directly on the complexity of social arrangements into
which computing is embedded and the extent to which different
participants control key resources.  These analyses are explicit
rather than implicit.

The logical framework of web analyses rests on the assumption
that a computing resource is being used by an interdependent
network of producers and consumers.  We call this network a
production lattice.  The production lattice for a particular
computer-based system or service is a social organization which
is embedded in a larger matrix of social and economic relations
and is dependent on a local infrastructure.  According to web
models, broader social relations and the local infrastructure
shape the kind of computer-based service made available at each
node of the production lattice.  These "broader social relations"
specially include 1) the inter-dependencies between different
groups who develop and use a computing resource and 2) other
social agents who most depend upon the computing resource or upon
whom the resources' users most depend.

Workable computing arrangements depend upon the infrastructural
resources available.  Some of these infrastructural resources,
such as equipment and staff with specialized technical skills are
difficult to replace instantly.  They evolve over time.
Computing developments are shaped by a set of local commitments
to technologies and social arrangements to support them which
develop over time. In short, web models view computer-based
systems as complex social objects whose architecture and use are
shaped by the social relations between influential participants,
the infrastructure that supports them, and the history of
commitments.  Web analyses focus explicitly on each of these
influences.

This chapter examines the relationship between computing
infrastructure, computer use, and organizational behavior based
on web models.  Analysts who use discrete-entity models can argue
that computer-based systems are powerful media - for good or bad.
Analysts who use a discrete-entity model can emphasize new
capabilities and new opportunities by focusing on the
capabilities of computing resources while neglecting the
available infrastructure, the context of likely use, or the
history of computing commitments which may constrain a
computer-using organization.  Social boundaries that are drawn
tightly around a computer system and a single user can make it
appear that the user can gain all the information processing
benefits that the system can provide.  A boundary drawn tightly
around the administrative domain of a manager can make it appear
that he is in control of his organizational unit's destiny.

Focusing on the context of computer use, the infrastructure which
supports computer use, and the history of computing developments
usually adds constraints and conditions which weaken the direct
links between computer use and some social outcome -- whether it
is socially desirable or troublesome.  If new computing resources
or organizations which adopt them depend upon the resources or
cooperation of others, then it less certain that participants
will experience the most extreme capabilities of computing.
Examining the available resources further (e.g., assumption W3a)
can also mute enthusiasm or criticism.  Moreover, assumption W4b
focuses on social practices which can highlight conflict, as well
as cooperation.  Assumption W4b also points to activities which
can stigmatize organizations if they are evaluated in a
conventional moral perspective.  Overall, these foci make web
analyses less intensely exciting or frightening than the hopes of
liberation or fears of dehumanization which are often married to
new computing developments in discrete-entity analyses.
Newsgroups: comp.society.development