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                          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.

4.0 SITUATIONS, CONTEXTS and PROCESSES of COMPUTERIZATION

Before embarking on a substantive analysis of computing
developments, we need to lay our conceptual groundwork.  A major
difference between discrete-entity and web models hinges on the
concept of "context." An operational definition of context can
help us.  We have used the lay definition of "context" as
"something larger" that gives meaning to the topic in focus.
This is a rather vague conception for empirical analysis.  Our
operational definition will hinge on another common and vague
term - "situation," which we will also sharpen.  We will also
explain how our analysis rests on two different perspectives on
social action in organizational life: organizational process
(Cyert and March, 1963) and negotiated order (Strauss, 1978).

4.1 Situations of Computing Development and Use

     Our primary unit of analysis is the situation  in which
individuals or larger collectivities carry out their going
concerns while they engage with computer-based technologies.
Any account of computing development or use implicitly defines
situations by identifying participants, equipment, location, and
time scales.  Not all situational definitions are equally useful
for understanding the actual consequences of computerization.
Situations should be defined by set of participants, equipment,
spatial and temporal relations, and social processes which shape
participants actions.

Situations can vary considerably in:

     1. the number of participants;

     2. the set of artifacts involved;

     3. the spatial scale and arrangements of activity;

     4. the time periods of key social activities; and

     5. the primary social processes that shape critical
behavior.

     Particular situations may be located along the first two
dimensions based on the number of participants or artifacts
within them.  Situations may be located along the spatial
dimension by the amount of space their equipment occupies and by
the amount of space their participants take up when engaging with
computing and related activities.  Situations may be located on
the time scale based on the periods of time over which key events
or social relations developed.  At the smaller end of these
dimensions is the fleeting encounter between a person and a
discrete computing resource - e.g., an airline passenger and a
video screen listing flight departure times in an urban airport.
Somewhat larger on these dimensions is the use of an automated
class assignment and scheduling system to match students,
classes, and times in a university.  Even larger still is the
monthly preparation, dispersal, and reception of 40 million
monthly individual payments by the Social Security
Administration.

     Situations are somewhat open-ended, and these structural
dimensions and their characteristics are simply useful
constructs, rather than eternal verities.  It is not sufficient
simply to identify a set of participants.  Critical social
relationships between them must also be identified.  At the very
least, the bases and kinds of cooperation or conflict between
various groups is often critical.  Computer systems will usually
"work" more smoothly if all parties cooperate in sharing relevant
data, cooperate in providing infrastructural support, etc.  When
a behavior setting that surrounds computer use is populated by
groups who are in conflict, computing resources easily become
bound up in the larger conflicts.

     The kinds of social relations that bind participants
together are particularly  critical: their division of labor,
relative status and power, etc.  Processual elements of a
situation will also be important: the beliefs of participants,
their resources, common practices and procedures, and the rates
of change of key social relationships.  We will discuss both
social relations and key social processes in a following section.

4.2 Context of Computing Development and Use

     Discrete-entity and web analysts differ considerably in
drawing boundaries which define situations for explaining the
payoffs, problems, and dilemmas of computing. This section
examines ways of drawing social boundaries for web analyses and
contrasts them with the strategies of discrete-entity analysts.

     Discrete-entity analyses use formal criteria for bounding
the development and use of computer-based systems.  These formal
criteria -- such as official jobs and roles typically lead to
compactly defined situations.  For example, a dominant image in
the computing literature emphasizes one person employing a
particular piece of equipment as a tool in some well defined
task.  Some examples are the programmer working at a terminal
developing a new application, the manager using a routine
computer-based report to make a specific decision, the bank
customer extracting funds from an automated teller machine are
common examples, and the student using a special instructional
computer package.  Description of situations that are larger than
one person and one machine are also common:

a.   Larger population scale (e.g., The students and faculty at
     XYZ University (XYZ-U) have access to a network of 50
     powerful workstations and 20 minicomputers for text
     processing and scientific computations to support
     scholarship and instruction.

b.   Larger equipment scale (e.g., the computing equipment at
     XYZ-U supports several text editors (EMACS, VI), formatters
     (Nroff, Troff, LaTex), programming languages (C, Prolog,
     Fortran-77, and Ada), a database (Ingress), a statistical
     package (SPSS-X) and all the machines run Berkeley Unix 4.3.

c.   Larger time scale (e.g., XYZ-U has owned this equipment for
     two years).

     In these situational definitions, groups are usually
identified by their formal relationships and behavior is often
defined by official roles.

     In contrast, web analysts use fundamentally social criteria
for defining situations.  They ask: how do participants
conceptualize their actions, what resources they have available,
what are their common practices and procedures, what coalitions
they participate in, what options they have, what constraints
they face etc.?  There is considerable evidence that social
elements such as these profoundly influence the kind and quality
of computer-based products that organizations produce, adopt, and
use (Kling, 1980; Kling, 1987; Kraemer, Dickhoven,
Fallows-Tierney, and King, 1985).  Resources, relationships
between interest groups, social meanings, procedural constraints,
are often defined in another situation that is yet "larger" on
some of the four dimensions of population size, equipment, space,
and time.  Any large organization that employs computer-based
technologies is rich in examples of computing developments that
are shaped by larger situational definitions that include the
purposes, common practices, resources, procedural constraints,
options, etc. that shape smaller scale developments.   But one
cannot easily and accurately infer these inherited meanings,
social relationships, social structures and resources from the
smaller definitions.

     We use the term "context" to refer to these social relations
and structures in a larger scale situation. We often say that
one situation (Sc) provides a context for understanding a second
situation (Sf).  The term context indicates a relationship
between the two situations, Sc and Sf: Situation Sc is the level
of analysis at which intentions which are observed in Sf are
enacted and constraints made that influence other behavior
observed in Sf. For example, the computing milieu described above
at XYZ university would typically be more attractive to computer
scientists than to social or physical scientists.  Some faculty,
such as economists, sociologists, physicists, etc. are likely to
want software developed at other universities and which runs on
IBM mainframes or on Vaxes under VMS, but not on the
minicomputers at XYZ University under Berkeley Unix.  The
elements (a,b,c 2 paragraphs above) defines a situation Sf.

     The relationships between economists at XYZ-U and their
colleagues at other universities who expect certain kinds of time
series analyses which are not supported by the software described
in (b) defines part of a larger situation Sc.  Since academic
scholarship is evaluated in a national community, often funded by
Federal agencies, and rewarded by promotions committees on
particular campuses, these groups are likely to participate in
the situation (Sc) which a scholar, like our economist, uses in
defining the appropriateness of the computing equipment at XYZ-U.
Sc is the context which makes the attractiveness of the equipment
described in (a,b) above to an economist who is a participant in
Sf.

     It is always possible to enlarge the definition of a
situation to larger and larger populations, sets of equipment,
space, and time.  One could examine  computerization at XYZ
University in the context of industrial development in late
twentieth century United States. We need operational criteria
which lead to useful and manageable boundaries.  Below we propose
three criteria which restrain any easy expansionist sentiment
that aim for the most holistic defining situations; they require
the analyst to make explicit causal ties between key elements of
the defining situation and variations in the development of use
of the focal computing resource.

     Where and how should one draw useful boundaries?  It is
possible to set up boundaries a-priori.  Turner (1981), for
example, suggests that the whole organization that develops or
uses computing provides useful situational boundaries.  This
criterion is a useful heuristic and expands the range of
participants, equipment, space, resources, and procedures to a
larger set than are usually found in most discrete-entity
analyses of computing developments.  Unfortunately, it does not
help explain patterns of computer use that are influenced by
social or economic patterns in still larger situations like Sc
above (Kling and Gerson, 1977; Kling, 1978a-c; Goodman, 1979;
Dutton, 1981; Danziger, Dutton, Kling, and Kraemer, 1982; Dutton
and Kraemer, 1984; Kraemer, Dickhoven, Fallows-Tierney, and King,
1985;McHenry & Goodman, 1986; Jewett and Kling, 1991).  In these
studies, organizational participants select computing
arrangements to leverage their negotiations in a larger social
order.  They also find that participants are constrained by
resources and organizational routines that are defined outside
the formal boundaries of their organizations.

     Organizations that have critical on-going negotiations with
outsiders -- clients, auditors, regulators, vendors, competitors
-- will sometimes develop computing arrangements to enhance their
bargaining positions.  To understand these choices, a larger
situational boundary that includes this expanded organizational
set must be drawn.  These boundaries cannot be completely defined
a-priori.  Nor can they be defined before the participants of
these larger negotiating contexts are identified.  Also, the
choices that organizations can make relative to computing are
limited by actions taken by agents outside their boundaries
(e.g., clients, equipment vendors, auditors, competitors, labor
unions, regulators) or by the ecologies from which they draw
resources (e.g., local labor markets, vendor supply practices).
These critical exogenous elements will vary from situation to
situation, and fixed boundary criteria will be far too rigid for
explanatory purposes.  Useful boundaries depend upon the behavior
being explained and the lines of explanation adopted.

     We propose three criteria for drawing the boundaries of
larger scale situations Sc which serve as useful contexts for a
given focal situation Sf (Kling, 1987). The defining situation
Sc is bounded by the populations, equipment, spatial and temporal
(PEST) elements that meet either of these criteria:

C1.  the actors in Sf depend upon the PEST elements of Sc when
     acting in the focal situation Sf; Or

C2.  the actors in Sf take account of the PEST elements or time
     of Sc when constructing their actions in the focal situation
     Sf; Or

C3.  the PESTs of Sc constrains the action of the actors in the
     focal situation, Sf.  (The focal actors need not be aware of
     their social relationships with the PEST of Sc and their
     influence on their behaviors.)

     The defining situation Sc also includes those social
processes that meet criteria C1, C2, and C3.  These three
criteria are essentially social.  In contrast, discrete entity
analysts usually draw formal boundaries and stay within one level
of analysis.  Web analysts almost by definition work with
multiple units of analysis, both Sf and Sc at minimum.  Web
analysts choose boundaries for the defining situation Sc
depending upon the questions they ask. It takes care to draw
boundaries for a defining situation Sc that are neither too
limited nor so encompassing as to explain nothing.

     Suppose one wishes to answer the question: how useful will a
particular text processing be in helping the Desktop Computing
Research Project (DCRP) Staff?  One way of answering the question
is to examine the mix of letters, memos, and papers produced by
the DCRP staff and examine the ease of producing them given the
features of a given word processor. A web analysis goes beyond
this kind of  task assessment by examining the kinds of resources
available to participants. In the DCRP case, criterion C2 asks,
who do the project staff take account of when they are writing
and disseminating documents (e.g., recipients of DCRP
correspondence, respondents to questionnaires, readers of
technical reports)?

     Laser printers were a key resource which the DCRP staff did
not own before mid-1989. But they could use laser printers
located at ICS or at PPRO. (Each printer was available on a
different schedule and under different conditions). Criterion C1
identifies the printing resources and people who control access
to them in both ICS and PPRO as part of the situation of text
processing use on the DCRP. Criteria C1, C2, and C3 should be
applied to develop a suitably "large" defining situation, while
avoiding unworkably "vast" situations.  There would be no
explanatory gain in enlarging the defining situation to include
"advanced industrial societies since 1900." Fortunately, criteria
C1, C2, and C3 will lead to a focus upon the kinds of
participants and social relationships examined in the last
paragraph.  C1, C2, and C3 will exclude improbably wide
situational boundaries like "advanced industrial societies since
1900" since people who are long dead are very unlikely to have
much direct influence upon an RA in the DCRP  (C2) or be taken
into account by her (C1). Such a situational definition helps
focus on the information processing leverage provided by the text
processor. The temporal scale should be closer to the life-cycle
for the software rather than the time frame for the RA to write
on memo.

4.3 The Political and Interpretive Dimensions of Organizational
Action

     So far, we have emphasized relatively static dimensions of
situations: populations, equipment, space, and time.  But to
breathe real life into situations that develop in and around
computing, we need a model of the social terrain on which people
act (with or without computers).  Modern complex organizations
have a special kind of terrain: they produce most of their goods
or services for some external clientele, and organize their work
through a variety of specialized groups and explicit divisions of
labor.

     We will treat organizations as naturalistic open systems
(Scott, 1987:20-24,100-101), and will explain these terms below.
There are numerous sociological models of the ways that
organizations behave. But the information systems literature is
dominated by "rational systems" models. In the extreme rational
models, writers characterize organizations as unified task
systems which their uppermost managers guide subordinates towards
clear goals through explicit strategies. These models do not
suggest that there is much ambiguity in the kinds of problems
facing organizational participants, their goals, lines of action,
available means, etc.  Charles Perrow has written eloquently
about the way in which these accounts reflect a managerial
ideology which attributes significant insight, foresight and
power to managers (Perrow, 1986).

     But strong assumptions of rationality are also contained in
other, more behaviorally descriptive models of organizational
behavior. One of the more influential of such models is the
organizational process model (Cyert and March, 1963; Allison,
1971; Steinbruner, 1974). In this model, organizations are noisy
production systems composed of a network of work groups which
exchange and produce goods and services according to a host of
relatively standardized procedures (SOPs), formal and informal.
Information, actions, and material flow imperfectly from node to
node along standard channels.  The characteristics of channels
and nodes are also subject to routinized temporal patterns: staff
can change at specific nodes because of shift work each day,
regular vacations, or rotating assignments.  Communication and
transportation through channels is also subject to temporal
regulation -- material may be transported on fixed schedules,
information passed at regular intervals, etc.  Certain key events
and resources may occur on regular schedules: systems are often
audited, data archived, and budgets renewed on regular cycles.
Channels and nodes are imperfect: delays and losses commonly
occur.

     In this model, organizations and their subunits are
characterized by their routine outputs and their average behavior
in providing these goods or services.  This model emphasizes the
production of typical outputs and the character of typical
inputs.  Clients make typical demands and organizational
participants develop routines (formal or informal) to get on with
their work.  Resources come from relatively standardized sources
(e.g., standard vendors, standard labor pools, etc.).  Outputs go
to standard markets, etc.

     The organizational process model emphasizes the purposive
side of organizations, since it focusses on tasks and goals,
rather than the construction of meaning and symbolic action.  It
departs from the most formal rational approaches in many critical
matters: "organizational goals" are redefined by subunits that
enact them, organizational participants have limited capacities
to process information; participants' pursuit of their interests
is tempered by their propensity to avoid uncertainty;
participants have limited capacities to process information;
behavior is characterized by many common routines and procedures,
even if these don't match formal or public rules and practices;
organizations adapt slowly as a byproduct of solving particular
problems (Cyert and March, 1963).  Groups neither share common
values nor do they agree on common goals, but jointly cooperative
outcomes are common.

     In the organizational process model, larger organizational
arrangements, the values of participants, the distribution of
rewards and constraints, and the set of procedures which describe
ongoing behavior are largely taken for granted.  We require a
different model to ask how these elements of organizational life
develop.

      In the rational models of organizations as collectivities
devoted to specific purposes, organizations should gracefully
dissolve when they meet their goals. There is an interesting body
of sociological research which shows that organizational members
often develop new purposes to keep an organization going, even
when original goals were met or at least resolved. Rather, many
key members may have a key stake in keeping the organization
stable or growing. The label "naturalistic" refers to the
practices of organizational members in keeping their organization
"alive" -- like an organism in nature.

     An interesting example, today, is the U.S. military, in the
wake of the dissolution of the Soviet bloc. Since the late 1940s,
the US military justified its huge budgets in terms of fighting
communism, or being able to fight a war with the Soviet Union or
China, if necessary. With these military threats significantly
reduced, one might expect the US military to shrink substantially
in size and cost. However, if the US military acts as a "natural
system," its officers will try to maintain its size and influence
as best they can.

     Another kind of example are the central information systems
departments of large organizations. Through the 1970s,
centralized data processing departments often maintained a
relative monopoly over the development and operation of
organizational information systems. In the 1970s, the  lower cost
of minicomputers began to make departmental centers financially
feasible. In the 1980s, the rise of desktop computing has moved
significant computing into workgroups, and further eroded the
demand for centralized data processing departments. The natural
systems models view the members of organizations at all levels as
acting on behalf of their self-interest, not just simply trying
to generously solve organizational problems. This does not mean
that the staff of centralized data processing will embrace all
possible sources of future services. Rather, it means that they
will try to assess possibilities for service in terms of what
they think that it means for the growth of their department, as
well as what it means for other departments in the organization.
For example, a centralized IS department may try to insist that
they will build a significant new information system rather than
recommending that a department be able to promptly purchase a
better one from an outside vendor (see, for example, Jewett and
Kling, 1990).

     We also view organizations as open systems. In closed system
models, organizations are not much influenced by their
environment. They are a coherent social unit oriented toward the
pursuit of specific goals. In open system models, organizations
are "coalitions of shifting interest groups that develop goals by
negotiation; the structure of the coalition, its activities and
its outcomes are strongly influenced by environmental factors
(Scott, 1987:23)." In the DCRP case, the research strategies and
preferences for laser printing were influenced by practices in
the teams' research community outside their own university.
Similarly, the computer scientists preference for Unix and
related software was based on emerging standards in academic
computer science, rather than only on a selection of the most
efficient equipment to carry out their work.

     We view organizations as work systems in which participants
make decisions about their work rather than as decision systems
in which work is incidentally done. For example, writing papers
entails physical work, and is not just a physical byproduct of a
stream of choices. This distinction is of far reaching
importance.  While many people make decisions in the course of
their work, most jobs result in a set of actions based on
whatever decisions are made by the actor or others. In
organizational life, the physical work with and around computers
influences what is done and what is used.  We take seriously the
lines of action in which people come to engage with computing,
the going concerns of the organizations they inhabit, and the
patterns of incentives and constraints which influence them
(Kling and Scacchi, 1982). In this chapter I will focus on two
naturalistic, open system models of organizational behavior,
negotiated order models (Strauss, 1978) and institutional
models.

4.3.1 Negotiated Order Models of Organizational Action

     Negotiated order analyses focus on the ways that
organizational practices, such as systems standards, are worked
out between groups with differing interests and orientations.
Institutional analyses focus on the ways that certain practices,
such as commitments to specific operating systems or programming
languages, are hard to change, even when they are not very
effective. These two naturalistic open systems models complement
each other.

     The negotiated order model views the actions of
organizations as the byproducts of ongoing negotiations.
Analysts who use the negotiated order model have often taken the
coordination of complex organizations as a central issue, and one
which is problematic for participants.  Analysts who adopt an
organizational process analysis usually assume work is factored
into tasks for specialized subunits, and that dominant coalitions
play a strong role in assuring that organizational outcomes do
not undermine their interests.  Negotiated order analysts do not
take the influence of a dominant coalition for granted.  They pay
special attention to who is bargaining over what, and with what
options in different negotiating contexts.  Both models presume
that participants' definitions of situations are influenced by
their socialization, training, group allegiances, and the social
worlds in which they participate.

     The organizational process model identifies the key concept
of standardized operating procedures (SOPs). But it takes these
routines for granted and simply assumes that they were the
outcome of some bargaining between coalitions; but the bargaining
takes place offstage.  The negotiated order model focuses on the
creation of procedures and how variations are negotiated.  While
both models accept conflicts as an ordinary element of the
ongoing interplay of groups and organizations, the details and
tapestry of conflict are more center stage in the negotiated
order model.  Participants' strategies, the stakes they contend
for, their options and relative power, and the bargains they
strike in a negotiating context are all central.  Moreover,
participants in some complex work organizations  engage in
multiple and overlapping negotiating contexts in which their
stakes and option provide cross subsidies and constraints
(Becker, 1960).  (For example, in the case of the DCRP, the
faculty PI and the RAs participated both in the research
institute, PPRO, and in academic departments.)

4.3.2 Institutional Models of Organizational Action

     Institutional analyses hold that organizations may not
change, even when they would improve their resources, power, etc.
in response to shifting dependencies with other participants or
their "environment."  Interdependent computerized systems have
important "institutional" dimensions. "Institutions" are
organized social arrangements which persist and are
taken-for-granted by participants. The organized technical and
social elements of a computerized systems have important
institutional dimensions when they are not all fluid and easily
subject to change by specific coalitions or interest groups.  The
inflexibility of some organized computing arrangements may impede
those who desire large-scale changes. When routines become
outmoded and no longer useful to the organization, many managers
and users may push for large-scale changes in their computing
arrangements.

     The organizational routines which facilitated efficient
activities and stable environments in the past may prevent the
changes which key participants may believe are critical to the
organization's  continuing survival.  We call this kind of
organized rigidity, "institutional inflexibility."  Rather than
focusing blame for failed implementations on resistant end-users
or on "insensitive" system designers, the focus is on the social
arrangements and practices which support the development and
operation of the computerized systems.

     The payments system of the United States federal Social
Security Administration (SSA) illustrates institutional
inflexibility. It produces over 40 million checks per month and
was developed in the 1950s in a crude programming language for
specific computers (Autocoder). And many of the old programs
remained relatively intact through the early 1980s. The SSA has
tried to overhaul the payments system at least three times in the
last 15 years without success, although a new Systems
Modernization Plan that may be completed in the 1990s is in
progress (Laudon and Laudon, 1991). Even though some new laws
have been passed during the late 1970s (e.g, eligibility
requirements) which change the way in which payments are  legally
distributed, none of these laws had actually been implemented in
software for at least five years. We do not have adequately
detailed accounts of the earlier efforts to change the SSA's
payments systems to understand where the difficulties lay. But
the persistence of the SSA's outdated payments system
architecture from the 1950s indicates that large scale
computerized systems can be exceptionally difficult to replace.

     Examining the institutional dimensions of computing
infrastructure  is important because it helps an analysts
identify which technologies and practices are most difficulty to
change. The actual usability of computerized systems in specific
social settings is the critical factor in assessing social
benefits, not the potential capabilities of the technology as it
may be used by special individuals or groups. Ironically,
computerized systems that are well-used and have stable
infrastructures for supporting and using them will be usually
much more difficult to change or replace than those with less
associated social structure and fewer participants.

     When analysts emphasize the social and political choices
that organizational actors have made over time, they are placing
its institutional character in the foreground. Many of these
institutional dimensions are intangible and taken-for-granted.
Long after some interest groups have lost power or influence in
the organization, their interests and visions may still be
embedded commitments to equipment, SOPs and organizational
arrangements.

     "Institutionalized behaviors" can vary between departments
in the same organization. For example, accounting departments are
much more likely to value accurate data, while production
departments in manufacturing are more likely to value meeting
quotas by regular shipping dates. Academic computer scientists
are more likely to prefer programmable, powerful, and complex
text formatters while people who do not have technologically
sophisticated backgrounds may prefer simpler menu driven systems.
These groups participate in social worlds which have different
systems of meaning through which they value different kinds of
technologies.

     From an institutional perspective the ways in which work is
organized across work groups in an organization influences the
SOPs of participant groups.  Different user groups often share
some interdependent work schedules and routines. In manufacturing
work environments, products are shipped as the result of the
collective activities of many diverse departments and divisions.
The interdependence of work routines implies that the
contribution of some component, policy or practice hinges on its
dependency upon other components, policies and work-group
practices.  For example, when a fast machine replaces a much
slower machine, one expects speedier production.  However,
increased demand on the new machine may result in some groups of
users waiting in line for access.  Their lost waiting time may
exceed the gains of speedier technology. Further, when the
speedier equipment does expand productivity in one work group, it
can lead to log-jams elsewhere. Thus, improvements in some
components of an ensemble of computing technologies do not
necessarily improve overall performance.

     When computerized systems are characterized as institutions,
organizational politics play an important role. Powerful groups
can attempt to influence the developments of a computerized
systems even when they can not control the outcomes they desire
(Kling and Iacono 1984b). Data Processing (DP) departments
specialize their work over time as they routinely fulfill the
computing preferences of particular groups over the preferences
of other groups. They are likely to hire new staff who primarily
share their world views -- in preferring particular application
domains (e.g., finance), languages (e.g., COBOL), equipment
vendors (e.g, IBM), etc.  However, specialization and
routinization reduce the ability of DP departments to engage in
work activities which depart from their skills and SOPs at a
later time.  Consequently, an engineering department which seeks
computing support for computational programs written in Pascal or
C which run under Unix on a DEC-Vax will usually find systemic
difficulties in obtaining meaningful service from a DP shop which
develops financial applications on large IBM 308x mainframes
under VM in COBOL.  (The reverse is also likely -- that finance
staff would have difficulty in obtaining high quality data
processing service from an engineering oriented computing staff
who prefer different kinds of applications, programming
languages, and machines.)

     Institutions develop a character based on the interests they
have served in the past, and the world-views which bind their
participants together. Incremental changes in computing
arrangements usually improve the fit between a computerized
systems and its organization. As a computerized systems fits an
organization better, it is much harder for powerful actors to
easily replace it. Participants organize their work lives around
the belief that activities which have become routinized will
persist. They come to depend on existing social and technical
arrangements for working and for achieving personal goals. For
these users, and for other important actors in the organization,
the particular social organization of a computerized systems can
become indispensable.

     Staff stabilize and routinize their work environments and
create highly organized computing milieus in the process. There
is an important paradox: a useful and stable computerized systems
can be problematic when key organizational actors try to change
it in adapting to new conditions (Kling and Iacono, 1989). Even
when computerized systems are fairly primitive and social
relations are informal, elements of the computing environment can
became highly organized and taken-for-granted. Critical social
dimensions that can been taken into account by organizational
actors include: the skills and work expectations of computing
staff; the work prioritization schemes that had developed between
computer staff and end-users; who controls new developments in
the computing environment and the deployment of resources;
organizational practices about training users and computer staff;
the degree of specialization in terms of computing software and
hardware; and past commitments to the specific work groups which
effectively exclude other groups from decision-making.

     Computing arrangements that are highly institutionalized
will arise when there is shared control and competing interest
groups vying for the resources of a computerized systems.  An
institutional analysis is critical for understanding how
computerized systems are likely to be used in more complex work
settings where constraints and limitations are part of the
everyday work world. It is a paradox that the more stable and
usable a current social organization of computing is, the more
difficult it will be to make substantial changes.  This does not
mean that implementations of new computerized systems are
impossible. Rather, the social organization of computing must be
taken into account prior to the setting of realistic expectations
of performance and schedules. In practice, we observe that new
generations of computerized systems and other replacement
computer technologies are often implemented and effectively used
on a much slower time scale than many key participants expect.

     Institutionalization has many ironies. Innovators like to
see their innovations widely adopted for indefinite periods.
Moreover, there are many economies of scale when innovations are
institutionalized (Kraemer, Perry, King, and Dunkle, 1988). But
the technologies and innovative practices of one era have often
acted as a brake on subsequent innovation. For example, once an
organization develops technical standards, they often remain in
place for decades, despite the availability of technically better
alternatives. COBOL is still a mainstay business programming
language after 30 years, despite the wide availability of fourth
generation database languages. MS-DOS and IBM-PC compatible
micros remain the business standard, despite the availability of
MacIntoshs with easier to learn interfaces and IBM's promotion of
a more complex OS/2 operating  system. In cases like these, the
immense installed base of earlier technologies means that
substantial conversions are also very costly, in changing
equipment, skills, visions of "appropriate computerization," and
organizational practices. We would be surprised to see COBOL and
MS-DOS comparably entrenched in 30 years, since organizations do
shift their technical commitments.

     Similarly, the social organization of computing shifts over
time. The widespread use of microcomputers has moved the locus of
control of some systems from central information systems
departments to end users. These shifts of control are coupled
with shifting costs for computing and also changing professional
ideologies about what computers are good for and how
computerization projects should develop. But such changes are
relatively slow in many organizations. Social theories of
computerization have to accommodate social processes of change
and of stasis.