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Organizational Analysis in Computer Science
Rob Kling
Department of Information & Computer Science
University of California at Irvine,
Irvine, CA 92717, USA
kling@ics.uci.edu (714-856-5955)
January 10, 1993 [Working Draft 11b]
Note: To appear: The Information Society, 9(2) (Mar-Jun, 1993). A
much shorter version of this paper will appear as "Computing
for Our Future in a Social World" in Communications of the
ACM, February 1993, in a Forum that discusses Computing the
Future: A Broader Agenda for Computer Science and
Engineering. Hartmanis, Juris and Herbert Lin (Eds).
Washington, DC: National Academy Press, 1992.
ABSTRACT
Computer Science is hard pressed in the US to show broad utility to help
justify billion dollar research programs and the value of educating well over
40,000 Bachelor of Science and Master of Science specialists annually in the
U.S. The Computer Science and Telecommunications Board of the U.S. National
Research Council has recently issued a report, "Computing the Future
(Hartmanis and Lin, 1992)" which sets a new agenda for Computer Science. The
report recommends that Computer Scientists broaden their conceptions of the
discipline to include computing applications and domains to help understand
them. This short paper argues that many Computer Science graduates need some
skills in analyzing human organizations to help develop appropriate systems
requirements since they are trying to develop high performance computing
applications that effectively support higher performance human organizations.
It is time for academic Computer Science to embrace organizational analysis
(the field of Organizational Informatics) as a key area of research and
instruction.
INTRODUCTION
Computer Science is being pressed on two sides to show broad utility for
substantial research and educational support. For example, the High
Performance Computing Act will provide almost two billion dollars for research
and advanced development. Its advocates justified it with arguments that
specific technologies, such as parallel computing and wideband nets, are
necessary for social and economic development. In the US, Computer Science
academic programs award well over 30,000 Bachelor of Science (BS) and almost
10,000 Master of Science (MS) degrees annually. Some of these students enter
PhD programs and many work on projects which emphasize mathematical Computer
Science. But many of these graduates also take computing jobs for which they
are inadequately educated, such as helping to develop high performance
computing applications to improve the performance of human organizations.
These dual pressures challenge leading Computer Scientists to broaden their
conceptions of the discipline to include an understanding of key application
domains, including computational science and commercial information systems.
An important report that develops this line of analysis, "Computing the
Future" (CTF) (Hartmanis and Lin, 1992), was recently issued by the National
Computing and Telecommunications Board of the U.S. National Research Council.
CTF is a welcome report that argues that academic Computer Scientists must
acknowledge the driving forces behind the substantial Federal research support
for the discipline. The explosive growth of computing and demand for CS in the
last decade has been driven by a diverse array of applications and new modes
of computing in diverse social settings. CTF takes a strong and useful
position in encouraging all Computer Scientists to broaden our conceptions of
the discipline and to examine computing in the context of interesting
applications.
CTF's authors encourage Computer Scientists to envision new technologies in
the social contexts in which they will be used. They identify numerous
examples of computer applications in earth science, computational biology,
medical care, electronic libraries and commercial computing that can provide
significant value to people and their organizations. These assessments rest on
concise and tacit analyses of the likely design, implementation within
organizations, and uses of these technologies. For example, CTF's stories of
improved computational support for modelling are based on rational models of
organizational behavior. They assume that professionals, scientists, and
policy-makers use models to help improve their decisions. But what if
organizations behave differently when they use models? For example suppose
policy makers use models to help rationalize and legitimize decisions which
are made without actual reference to the models?
One cannot discriminate between these divergent roles of modelling in human
organizations based upon the intentions of researchers and system designers.
The report tacitly requires that the CS community develop reliable knowledge,
based on systematic research, to support effective analysis of the likely
designs and uses of computerized systems. CTF tacitly requires an ability to
teach such skills to CS practitioners and students. Without a disciplined
skill in analyzing human organizations, Computer Scientists' claims about the
usability and social value of specific technologies is mere opinion, and bears
a significant risk of being misleading. Further, Computer Scientists who do
not have refined social analytical skills sometimes conceive and promote
technologies that are far less useful or more costly than they claim.
Effective CS practitioners who "compute for the future" in organizations need
some refined skills in organizational analysis to understand appropriate
systems requirements and the conditions that transform high performance
computing into high performance human organizations. Since CTF does not spell
out these tacit implications, I'd like to explain them here.
BROADENING COMPUTER SCIENCE:
FROM COMPUTABILITY TO USABILITY
The usability of systems and software is a key theme in the history of CS. We
must develop theoretical foundations for the discipline that give the deepest
insights in to what makes systems usable for various people, groups and
organizations. Traditional computer scientists commonly refer to mathematics
as the theoretical foundations of CS. However, mathematical formulations give
us limited insights into understanding why and when some computer systems are
more usable than others.
Certain applications, such as supercomputing and computational science are
evolutionary extensions of traditional scientific computation, despite their
new direction with rich graphical front ends for visualizing enormous mounds
of data. But other, newer modes of computing, such as networking and
microcomputing, change the distribution of applications. While they support
traditional numerical computation, albeit in newer formats such as
spreadsheets, they have also expanded the diversity of non-numerical
computations. They make digitally represented text and graphics accessible to
tens of millions of people.
These technological advances are not inconsistent with mathematical
foundations in CS, such as Turing machine formulations. But the value of these
formats for computation is not well conceptualized by the foundational
mathematical models of computation. For example, text editing could be
conceptualized as a mathematical function that transforms an initial text and
a vector of incremental alterations into a revised text. Text formatting can
be conceptualized as a complex function mapping text strings into spatial
arrays. These kinds of formulations don't help us grasp why many people find
"what you see is what you get" editors as much more intuitively appealing than
a system that links line editors, command-driven formatting languages, and
text compilers in series.
Nor do our foundational mathematical models provide useful ways of
conceptualizing some key advances in even more traditional elements of
computer systems such as operating systems and database systems. For example,
certain mathematical models underlie the major families of database systems.
But one can't rely on mathematics alone to assess how well networks,
relations, or object-entities serve as representations for the data stored in
an airline reservation system. While mathematical analysis can help optimize
the efficiency of disk space in storing the data, they can't do much to help
airlines understand the kinds of services that will make such systems most
useful for reservationists, travel agents and even individual travellers. An
airline reservation system in use is not simply a closed technical system. It
is an open socio-technical system (Hewitt, 1986; Kling, 1992). Mathematical
analysis can play a central role in some areas of CS, and an important role in
many areas. But we cannot understand important aspects of usability if we
limit ourselves to mathematical theories.
The growing emphasis of usability is one of the most dominant of the diverse
trends in computing. The usability tradition has deep roots in CS, and has
influenced the design of programming languages and operating systems for over
25 years. Specific topics in each of these areas also rest on mathematical
analysis which Computer Scientists could point to as "the foundations" of the
respective subdisciplines. But Computer Scientists envision many key advances
as design conceptions rather than as mathematical theories. For example,
integrated programming environments ease software development. But their
conception and popularity is not been based on deeper formal foundations for
programming languages. However, the growth of non-numerical applications for
diverse professionals, including text processing, electronic mail, graphics,
and multimedia should place a premium on making computer systems relatively
simple to use. Human Computer Interaction (HCI) is now considered a core
subdiscipline of CS.
The integration of HCI into the core of CS requires us to expand our
conception of the theoretical foundations of the discipline. While every
computational interface is reducible to a Turing computation, the foundational
mathematical models of CS do not (and could not) provide a sound theoretical
basis for understanding why some interfaces are more effective for some groups
of people than others. The theoretical foundations of effective computer
interfaces must rest on sound theories of human behavior and their empirical
manifestations (cf. Ehn, 1991, Grudin, 1989).
Interfaces also involve capabilities beyond the primary information processing
features of a technology. They entail ways in which people learn about systems
and ways to manage the diverse data sets that routinely arise in using many
computerized systems (Kling, 1992). Understanding the diversity and character
of these interfaces, that are required to make many systems usable, rests in
an understanding the way that people and groups organize their work and
expertise with computing. Appropriate theories of the diverse interfaces that
render many computer systems truly useful must rest, in part, on theories of
work and organization. There is a growing realization, as networks tie users
together at a rapidly rising rate, that usability cannot generally be
determined without our considering how computer systems are shaped by and also
alter interdependencies in groups and organizations. The newly-formed
subdiscipline of Computer Supported Cooperative Work and newly-coined term
"groupware" are responses to this realization (Greif, 1988; Galegher, Kraut
and Egido, 1990).
BROADENING COMPUTER SCIENCE:
FROM HIGH PERFORMANCE COMPUTING
TO HIGH PERFORMANCE ORGANIZATIONS
The arguments of CTF go beyond a focus on usable interface designs to claims
that computerized systems will improve the performance of organizations. The
report argues that the US should invest close to a billion dollars a year in
CS research because of the resulting economic and social gains. These are
important claims, to which critics can seek systematic evidence. For example,
one can investigate the claim that 20 years of major computing R&D and
corporate investment in the US has helped provide proportionate economic and
social value.
CTF is filled with numerous examples where computer-based systems provided
value to people and organizations. The tough question is whether the overall
productive value of these investments is worth the overall acquisition and
operation costs. While it is conventional wisdom that computerization must
improve productivity, a few researchers began to see systemic possibilities of
counter-productive computerization in the early 1980s (King and Kraemer,
1981). In the last few years economists have found it hard to give
unambiguously affirmative answers to this question. The issue has been termed
"The Productivity Paradox," based on a comment attributed to Nobel laureate
Robert Solow who remarked that "computers are showing up everywhere except in
the [productivity] statistics (Dunlop and Kling, 1991a)."
Economists are still studying the conditions under which computerization
contributes to organizational productivity, and how to measure iteasy. But
there is no automatic link between computerization and improved productivity.
While many computer systems have been usable and useful, productivity gains
require that their value exceed all of their costs.
There are numerous potential slips in translating high performance computing
into cost-effective improvements in organizational performance. Some
technologies are superb for well-trained experts, but are difficult for less
experienced people or "casual users." Many technologies, such as networks and
mail systems, often require extensive technical support, thus adding hidden
costs (Kling, 1992).
Further, a significant body of empirical research shows that the social
processes by which computer systems are introduced and organized makes a
substantial difference in their value to people, groups and organizations
(Lucas, 1981; Kraemer, et. al. 1985; Orlikowski, 1992). Most seriously, not
all presumably appropriate computer applications fit a person or group's work
practices. While they may make sense in a simplified world, they can actually
complicate or misdirect real work.
Group calendars are but one example of systems that can sound useful, but are
often useless because they impose burdensome record keeping demands (Grudin,
1989). In contrast, electronic mail is one of the most popular applications in
office support systems, even when other capabilities, like group calendars,
are ignored (Bullen and Bennett, 1991). However, senders are most likely to
share information with others when the system helps provide social feedback
about the value of their efforts or they have special incentives (Sproull and
Kiesler, 1991; Orlikowski, 1992). Careful attention to the social arrangements
or work can help Computer Scientists improve some systems designs, or also
appreciate which applications may not be effective unless work arrangements
are changed when the system is introduced.
The uses and social value of most computerized systems can not be effectively
ascertained from precise statements of their basic design principles and
social purposes. They must be analyzed within the social contexts in which
they will be used. Effective social analyses go beyond accounting for formal
tasks and purposes to include informal social behavior, available resources,
and the interdependencies between key groups (Cotterman and Senn, 1992).
Many of the BS and MS graduates of CS departments find employment on projects
where improved computing should enhance the performance of specific
organizations or industries. Unfortunately, few of these CS graduates have
developed an adequate conceptual basis for understanding when information
systems will actually improve organizational performance. Consequently, many
of them are prone to recommend systems-based solutions whose structure or
implementation within organizations would be problematic.
ORGANIZATIONAL INFORMATICS
Organizational Informatics denotes a field which studies the development and
use of computerized information systems and communication systems in
organizations. It includes studies of their conception, design, effective
implementation within organizations, maintenance, use, organizational value,
conditions that foster risks of failures, and their effects for people and an
organization's clients. It is an intellectually rich and practical research
area.
Organizational Informatics is a relatively new label. In Europe, the term
Informatics is the name of many academic departments which combine both CS and
Information Systems. In North America, Business Schools are the primary
institutional home of Information Systems research and teaching. But this
location is a mixed blessing. It brings IS research closer to organizational
studies. But the institutional imperatives of business schools lead IS
researchers to emphasize the development and use of systems in a narrow range
of organizations — businesses generally, and often service industry firms. It
excludes information systems in important social sectors such as health care,
military operations, air-traffic control, libraries, home uses, and so on. And
Information Systems research tries to avoid messy issues which many practicing
Computer Scientists encounter: developing requirements for effective systems
and mitigating the major risks to people and organizations who depend upon
them.
The emerging field of Organizational Informatics builds upon research
conducted under rubrics like Information Systems and Information Engineering.
But it is more wide ranging than either of these fields are in practice.
Organizational Informatics Research
In the last 20 years a loosely organized community of some dozens
of researchers have produced a notable body of systematic
scientific research in Organizational Informatics. These studies
examine a variety of topics, including:
* how system designers translate people's preferences
into requirements;
* the functioning of software development teams in
practice;
* the conditions that foster and impede the
implementation of computerized systems within
organizations;
* how people and organizations use systems in practice;
* the roles of computerized systems in altering work,
group communication, power relationships, and
organizational practices.
Researchers have extensively studied some of these topics, such as
computerization and changing work, appear in synoptic review articles (Kling
and Dunlop, in press). In contrast, researchers have recently begun to examine
other topics, such software design (Winograd and Flores, 1986; Kyng and
Greenbaum, 1991), and have recently begun to use careful empirical methods
(e.g. Suchman, 1983; Bentley, et. al, 1992; Fish, et. al., 1993). I cannot
summarize the key theories and rich findings of these diverse topics in a few
paragraphs. But I would like to comment upon a few key aspects of this body of
research.
Computer Systems Use in Social Worlds
Many studies contrast actual patterns of systems design, implementation, use
or impacts with predictions made by Computer Scientists and professional
commentators. A remarkable fraction of these accounts are infused with a
hyper-rational and under-socialized view of people, computer systems,
organizations and social life in general. Computer Scientists found that rule
driven conceptions to be powerful ways to abstract domains like compilers. But
many Computer Scientists extend them to be a tacit organizing frame for
understanding whole computer systems, their developers, their users and others
who live and work with them. Organizations are portrayed as generally
cooperative systems with relatively simple and clear goals. Computer systems
are portrayed as generally coherent and adequate for the tasks for which
people use them. People are portrayed as generally obedient and cooperative
participants in a highly structured system with numerous tacit rules to be
obeyed, such as doing their jobs as they are formally described. Using data
that is contained in computer systems, and treating it as information or
knowledge, is a key element of these accounts. Further, computer systems are
portrayed as powerful, and often central, agents of organizational change.
This Systems Rationalist perspective infuses many accounts of computer systems
design, development, and use in diverse application domains, including CASE
tools, instructional computing, models in support of public policy
assessments, expert systems, groupware, supercomputing, and network
communications (Kling, 1980; Kling, Scherson and Allen, 1992).
All conceptual perspectives are limited and distort "reality." When
Organizational Informatics researchers systematically examine the design
practices in particular organizations, how specific groups develop computer
systems, or how various people and groups use computerized systems, they find
an enormous range of fascinating and important human behavior which lies
outside the predictive frame of Systems Rationalism. Sometimes these behaviors
are relatively minor in overall importance. But in many cases they are so
significant as to lead Organizational Informatics researchers to radically
reconceptualize the processes which shape and are shaped by computerization.
There are several alternative frames for reconceptualizing computerization as
alternatives to Systems Rationalism. The alternatives reflect, in part, the
paradigmatic diversity of the social sciences. But all of these reconceptions
situate computer systems and organizations in richer social contexts and with
more complex and multivalent social relations than does systems rationalism.
Two different kinds of observations help anchor these abstractions.
Those who wish to understand the dynamics of model usage in public agencies
must appreciate the institutional relationships which influence the
organization's behavior. For example, to understand economic forecasting by
the US Congress and the Executive branch's Office of Management and Budget,
one must appreciate the institutional relations between Congress and the
Executive branch. They are not well described by Systems Rationalist
conceptions because they were designed to continually differ with each other
in their perspectives and preferred policies. That is one meaning of "checks
and balances" in the fundamental design of the US Federal Government. My
colleagues, Ken Kraemer and John King, titled their book about Federal
economic modelling, DataWars (Kraemer, et. al., 1985). Even this title
doesn't make much sense within a Systems Rationalist framework.
Modelling can be a form of intellectual exploration. It can also be a medium
of communication, negotiation, and persuasion. The social relationships
between modelers, people who use them and diverse actors in Federal
policymaking made these socially mediated roles of models sometimes most
important. In these situations, an alternative view of organizations as
coalitions of interest groups was a more appropriate conceptualization. And
within this coalitional view of organizations, a conception of econometric
models as persuasion support systems rather than as decision support systems
sometimes is most appropriate. Organizational Informatics researchers found
that political views of organizations and systems developments within them
apply to many private organizations as well as to explicitly political public
agencies.
Another major idea to emerge from the broad body of Organizational Informatics
research is that the social patterns which characterize the design,
development, uses and consequences of computerized systems are dependent on
the particular ecology of social relationships between participants. This idea
may be summarized by saying that the processes and consequences of
computerization are "context dependent." In practice, this means that the
analyst must be careful in generalizing from one organizational setting to
another. While data wars might characterize econometric modelling on Capitol
Hill, we do not conclude that all computer modelling should be interpreted as
persuasion support systems. In some settings, models are used to explore the
effects of policy alternatives without immediate regard for their support as
media for communication, negotiation or persuasion. At other times, the same
model might be used (or abused with cooked data) as a medium of persuasion.
The brief accounts of models for global warming in CTF fit a Systems
Rationalist account. Their uses might appear much less "scientific" if they
were studied within the actual policy processes within which they are
typically used.
Repercussions for Systems Design
Even when computerized systems are used as media of intellectual exploration,
Organizational Informatics researchers find that social relationships
influence the ways that people use computerized systems. Christine Bullen and
John Bennett (1991) studied 25 organizations that used groupware with diverse
modules such as databases, group calendars, text annotating facilities and
electronic mail. They found that the electronic mail modules were almost
universally valued, while other system facilities were often unused.
In a recent study, Sharyn Ladner and Hope Tillman examined the use of the
Internet by university and corporate librarians. While many of them found data
access through databases and file transfer to be important services, they also
reported that electronic mail was perhaps the most critical Internet feature
for them.
The participants in our study tell us something that we
may have forgotten in our infatuation with the new
forms of information made available through the
Internet. And that is their need for community. To be
sure, our respondents use the Internet to obtain
information not available in any other format, to
access databases ... that provide new efficiencies in
their work, new ways of working. But their primary use
is for communication. Special librarians tend to be
isolated in the workplace — the only one in their
subject specialty (in the case of academe), or the only
librarian in their organization (in the case of a
corporate library). Time and time again our
respondents expressed this need to talk to someone --
to learn what is going on in their profession, to
bounce ideas off others, to obtain information from
people, not machines.
There are tremendous implications from the Internet
technology in community formation — the Internet may
indeed provide a way to increase community among
scholars, including librarians. The danger we face at
this juncture in time, as we attach library resources
to the Internet, is to focus all of our energies on the
machine-based resources at the expense of our human-
based resources, i.e., ourselves (Ladner and Tillman,
1992).
In these studies, Organizational Informatics researchers have developed a
socially rich view of work with and around computing, of computing within a
social world.
These studies have strong repercussions for the design of software. A good
designer cannot assume that the majority of effort should go into the
"computational centerpiece" of a system, while devoting minor efforts to
supporting communication facilities. One of my colleagues designed a modelling
system for managers in a major telephone company, after completing an
extensive requirements analysis. However, as an afterthought, he added a
simple mail system in a few days work. He was surprised to find that the
people who used these systems regularly used his crude electronic mail system,
while they often ignored interesting modelling capabilities. Such balances of
attention also have significant repercussions. Many people need good mail
systems, not just crude ones: systems which include facile editors, ease in
exporting and importing files, and effective mail management (Kling and Covi,
1993).
Assessing people's preferences for systems' designs is an exercise in social
inquiry. While rapid prototyping may help improve designs for some systems, it
is less readily applicable to systems which are used by diverse groups at
numerous locations. Computer scientists are beginning to develop more reliable
methods of social inquiry to better understand which systems designs will be
most useful (Bentley, et. al. 1992; Kyng and Greenbaum, 1991). Root and his
colleagues (1993) recently reported the way that the explicit use of social
theory helped them design more effective group meeting systems. Unfortunately,
these newer methods are rarely taught to CS students. When computer
specialists build an imbalanced system, it should not be a surprise when the
resulting organizational value of their efforts is very suboptimal.
[CONTINUED in RISKS-14.25.]
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