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Organizational Analysis in Computer Science
Rob Kling
Department of Information & Computer Science
and
Center for Research on Infromation Technology and Organizations
University of California at Irvine,
Irvine, CA 92717, USA
kling@ics.uci.edu (714-856-5955)
June 1993 (v. 13.2)
Apears in The Information Society, 9(2) (Mar-Jun, 1993):71-87.
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 Computer
Science 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 terms "groupware" and
"coordination theory" 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 it [1]. But even if computerization proves to be a
productive investment, in the net, in most economic sectors,
there is good reason to believe that many organizations get much
less value from their computing investments than they could and
should.
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[2].
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;
* the ways that computerized systems simplify or
complicate coordination within and between
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 U.S.
Executive branch's Office of Management and Budget, one must
appreciate the institutional relations between them. 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.
Computing in a Web of Technological and Social Dependencies:
The Role of Infrastructure
Another key feature of computerized systems is the technological
and organizational infrastructure required to support their
effective use (Kling and Scacchi, 1982; Kling, 1987; Kling,
1992). The information processing models of computerized systems
focus on the "surface structures," such as information flows
within a system. For example, one can compare the information
processing capabilities of computerized modelling systems in
terms of the complexity and variety of computations that they
support, the richness of their graphical displays, and so on.
Text processing systems can be similarly compared by contrasting
their capabilities for handling footnotes, graphics, fine grained
text placement, custom dictionaries and so on. From an
information processing point of view, system A is usually better
than system B if it offers many more capabilities than system B.
Information processing conceptions have also fueled much of the
talk about high performance computing. It is common to talk about
massively parallel computing in terms of the scale and unit cost
of computation (Kling, Scherson, and Allen 1992), and the
discussions of networking in terms of the wide data bandwidths
that new technologies offer.
If we ask how these technologies improve organizational
performance, then we have to ask how they can be made usable to
diverse groups. The most powerful modelling system may be of
limited utility if it requires sophisticated programming skills
to create and modify every data transformation. Alternatively,
such a package can be made more widely useful by having the
modelling efforts managed by a programming group whihc provides
added value for added cost.
Few people are capable or interested in primarily using "raw
computing" for their work. The diverse array of "productivity
software" -- such as text processing, presentation graphics,
spreadsheets, databases and so on gain their value when they can
be provided and maintained in a way that matches the skills and
available time of people who will use them. Both skill and time
are scarce resources in most organizations. Skilled time is
especially expensive.
Similarly, the organizational value of digital libraries can't be
adequately conceptualized in terms of simple data-centric
measures, like the number of gigabytes of available files. The
ease of people accessing useful documents is much more pertinent,
although much less frequently discussed today.
In each of these cases, the support systems for the focal
computing system is integral to the effective operation of the
technology. Infrastructure refers to the set of human and
organizational resources that help make it simpler and faster for
skilled people to use computerized systems. Infrastructure should
be part of the conceptualization. Often the support systems for a
computing can involve several different organizations, including
hardware and software vendors, telecommunication support groups,
divisional systems groups, and local experts (Kling, 1992). It
can be organizationally very complex and unresponsive in some
cases and organizationally simpler and more effective in others.
In any case, the infrastructure for systems support can't be
ignored when one is interested in improving organizational
performance.
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
modeules 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). It is particularly helpful to organize
system designs that help minimize the complexity and cost of its
infrastructure (Kling, 1992).
Fish 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.
System Security and Reliability
In a simplified engineering model of computing, the reliability
of products is assured through extensive testing in a development
lab. The social world of technology use not perceived as shaping
the reliability of systems, except through irascible human
factors, such as "operator errors." An interesting and tragic
illustration of the limitations of this view can be found in some
recent studies of the causes of death and maiming by an electron
accelerator which was designed to help cure cancer, the Therac-25
(Jacky, 1991, Leveson and Turner, 1993).
The Therac-25 was designed and marketed in the mid 1980s by a
Canadian firm, Atomic Energy of Canada Limited (AECL), as an
advanced medical technology. It featured complete software
control over all major functions (supported by a DEC PDP-11),
among other innovations. Previous machines included electro-
mechanical interlocks to raise and lower radiation shields.
Several thousand people were effectively treated with the Therac-
25 each year. However, between 1985 and 1987 there were six known
accidents in which several people died in the US. Other were
seriously maimed or injured [3].
Both studies concur that there were subtle but important flaws in
the design of the Therac-25's software and hardware. AECL's
engineers tried to patch the existing hardware and (finally)
software when they learned of some of the mishaps. But they
treated each fix as the final repair.
Both studies show how the continuing series of mishaps was
exacerbated by diverse organizational arrangements. Jacky claims
that pressures for speedy work by radiological technicians
coupled with an interface design that did not enhance important
error messages was one of many causes of the accidents. Leveson
and Turner differ in downplaying the working conditions of the
Therac-25's operators and emphasize the flawed social system for
communicating the seriousness of problems to Federal regulators
and other hospitals. Both studies observe that it is unlikely for
the best of companies to develop perfect error-free systems
without high quality feedback from users. Their recommendations
differ: Jacky discusses the licensing of system developers and
the regulation of computerized medical systems to improve minimal
standards of saftey. Leveson and Turner propose extensive
education and training of software engineers and more effective
communication between manufacturers and their customers.
However, both studies indicate that an understanding of the
safety of computer systems must go beyond the laboratory and
extend into the organizational settings where it is used. In the
case of the Therac-25, it required understanding a complex web of
interorganizational relationships, as well as the technical
design and operation of the equipment. Nancy Leveson (1992)
points out that most major disasters technological disasters in
the last 20 years "involved serious organizational and management
deficiencies." Hughes, Randall and Shapiro (1992:119) observe
that British no civil collision in UK air space has been
attributed to air traffic control failures. But their Mediator
control system was failing regularly and had no backup during the
period that they studied it. They observe that the reliability of
the British air traffic control system resides in totality of the
relevant social and technical systems, rather than in a single
component.
The need for this kind of organizational understanding is
unfortunately slighted in the CS academic world today. CTF
discusses only those aspects of computer system reliability which
are amenable to understanding through laboratory-like studies
(Hartmanis and Lin, 1992:110-111). But cases of safety critical
systems, like the Therac-25 and British Air Traffic Control,
indicate why some Computer Scientists must be willing to
undertake (and teach) organizational analysis.
Worldviews and Surprises about Computerization
These few paragraphs barely sketch the highlights of a fertile
and significant body of research about computer systems in use.
Perhaps the most important simplification for traditional
computer scientists is to appreciate how people and their
organizations are situated in a social world and consequently
compute within a social world. People act in relationship to
others in various ways and concerns of belonging, status,
resources, and power are often central. The web of people's
relationships extend beyond various formally defined group and
organizational boundaries (Kling and Scacchi, 1982; Kling, 1987;
Kling, 1992). People construct their worlds, including the
meanings and uses of information technologies, through their
social interactions.
This view is, of course, not new to social scientists. On the
other hand, there is no specific body of social theory which can
easily be specialized for "the case of computing," and swiftly
produce good theories for Organizational Informatics as trivial
deductions. The best research in Organizational Informatics draws
upon diverse theoretical and methodological approaches within the
social sciences with a strong effort to select those which best
explain diverse aspects of computerization.
ORGANIZATIONAL INFORMATICS WITHIN COMPUTER SCIENCE
CTF places dual responsibilities on Computer Scientists. One
responsibility is to produce a significant body of applicable
research. The other responsibility is to educate a significant
fraction of CS students to be more effective in conceiving and
implementing systems that will enhance organizational
performance. It may be possible to organize research and
instruction so as to decouple these responsibilities. For
example, molecular biologists play only a small role in training
doctors. However, CS departments act like an integrated Medical
school and Biology department. They are the primary academic
locations for training degreed computing specialists, and they
conduct a diverse array of less applicable and more applicable
research. In practice, the research interests of CS faculty shape
the range of topics taught in CS departments, especially the 150
PhD granting departments. CS curricula mirror major areas of CS
research and the topics which CS faculty understand through their
own educations and subsequent research. As a consequence, CS
courses are likely to avoid important CS topics which appear a
bit foreign to the instructor.
An interesting example of this coupling can be illustrated by
CTF, in a brief description of public-key encryption systems and
digital signatures (Hartmanis and Lin, 1992:27). In the simple
example, Bob and Alice can send messages reliably if each
maintains a secret key. Nothing is said about the social
complications of actually keeping keys secret. The practical
problems are similar to those of managing passwords, although
some operational details differ because the 100 digit keys may be
stored on media like magstripe cards rather than paper. In real
organizations, people lose or forget their password and can lose
the media which store their keys. Also, some passwords can be
shared by a group of with shifting membership, and the "secret
key" can readily become semi-public. The main point is that the
management of keys is a critical element of cryptographic
security in practice. But Computer Scientists are prone to teach
courses on cryptography as exercises in applied mathematics, such
as number theory and Galois theory, and to skirt the vexing
practical problems of making encryption a practical
organizational activity.
Today, most of the 40,000 people who obtain BS and MS degrees in
CS each year in the U.S. have no opportunities for systematic
exposure to reliable knowledge about the best design strategies,
common uses, effective implementation, and assessments of value
of computing in a social world (Lewis, 1989). Yet a substantial
fraction of these students go on to work for organizations
attempting to produce or maintain systems that improve
organizational performance without a good conceptual basis for
their work. Consequently, many of them develop systems that
underperform in organizational terms even when they are
technically refined. They also recommend ineffective
implementation procedures and are sometimes even
counterproductive.
One defensible alternative to my position is that CS departments
should not take on any form of organizational analysis. They
should aggressively take a role akin to Biology departments
rather than taking on any instructional or research roles like
Medical schools. To be sincere, this position requires a high
level of restraint by academic Computer Scientists. First and
foremost, they should cease from talking about the uses, value or
even problems of computerized systems that would be used in any
organizational setting. Research proposals would be mute about
any conceivable application of research results. Further, they
should make effective efforts to insure that anyone who employs
their graduates should be aware that they may have no special
skills in understanding organizational computing. It would take
an aggressive "truth in advertising" campaign to help make it
clear that Computer Scientists have no effective methods for
understanding computerization in the social world. Further,
Computer Scientists would forsake their commitments to subfields
like software engineering which tacitly deals with ways to
support teams of systems developers to work effectively (Curtis,
et. al. 1988). Computer Scientists, in this view, would remove
themselves from addressing organizational and human behavior, in
the same way that molecular biologists are removed from
professionally commenting on the practices of cardiologists and
obstetricians. CTF argues that this view would be self-defeating.
But it would be internally consistent and have a distinctive
integrity.
In contrast, CS faculty are often reluctant to wholly embrace
Organizational Informatics. But some CS subfields, such as
software engineering, depend upon organizational analysis
(Curtis, et. al., 1988). Further, CS faculty do little to
advertise the distinctive limitations in the analytical skills of
our programs' graduates. Part of the dilemma develops because
many CS faculty are ambivalent about systematic studies of human
behavior. Applied mathematics and other modes of inquiry which
seem to yield concise, crisp and concrete results are often the
most cherished. As a consequence, those who conduct behaviorally
oriented research in CS departments are often inappropriately
marginalized. Their students and the discipline suffers as a
result.
Between 1986 and 1989, the total number of BS and MS CS degrees
awarded annually in the US declined from about 50,000 to
approximately 40,000. The number of students majoring in CS
rapidly declined at a time when computerization was becoming
widespread in many fields. A significant fraction of the decline
can be attributed to many students finding CS programs insular
and indifferent to many exciting forms of computerization. The
decline of military R&D in the U.S. can amplify these trends or
stimulate a more cosmopolitan view in CS departments. The decline
in military R&D is shifting the job market for new CS graduates
towards a markedly more civilian orientation. This shift, along
with the trend towards computing distributed into diverse work
groups, is leading to more job opportunities for people with CS
education who know Organizational Informatics.
The situation of CS departments has some parallels with
Statistics departments. Statistics are widely used and taught in
many academic disciplines. But Statistics departments have often
maintained a monkish isolation from "applications." Consequently,
the application of statistics thrives while Statistics
departments have few students and modest resources. Might the
status of Statistics indicate a future possibility for an insular
approach to CS?
The best Organizational Informatics research in North America is
conducted by faculty in the Information Systems departments in
business schools and by scattered social scientists (cf. Boland
and Hirschheim, 1987; Galegher, Kraut and Egido, 1990; Cotterman
and Senn, 1992; Sproull and Kiesler, 1991). But Computer
Scientists cannot effectively delegate the research and teaching
of Organizational Informatics to business Schools or social
science departments.
Like Computer Scientists, faculty in these other disciplines
prefer to focus on their own self-defined issues. Computer
Scientists are much more likely to ask questions with attention
to fine grained technological nuances that influence designs. For
example, the professional discussions of computer risks have been
best developed through activities sponsored by the ACM's Special
Interest Group on Software (SIGSOFT). They are outside the
purview of business school faculty and, at best, only a few
social scientists are interested in them. Generally, technology
plays a minor role in social science theorizing. And when social
scientists study technologies, they see a world of possibilities:
energy technologies, transportation technologies, communication
technologies (including television), medicinal drugs and devices,
and so on. They see little reason to give computer-related
information technologies a privileged role within this
cornucopia. As a consequence, the few social scientists who take
a keen interest in studying computerization are unfortunately
placed in marginal positions within their own disciplines. Often
they must link their studies to mainstream concerns as defined by
the tastemakers of their own fields, and the resulting
publications appear irrelevant to Computer Scientists.
Further, faculty in these other disciplines are not organized to
effectively teach tens of thousands of CS students, students who
are steeped in technology and usually very naive about
organizations, about systems development and use in
organizations. In North America there is no well developed
institutional arrangement for educating students who can
effectively take leadership roles in conceptualizing and
developing complex organizational computing projects (Lewis,
1989).
CTF is permeated with interesting claims about the social value
of recent and emerging computer-based technologies. While many of
these observations should rest on an empirically grounded
scientific footing, Computer Scientists have deprived themselves
of access to such research. For example, the discussion of
systems risks in the ACM rests on a large and varied collection
of examples and anecdotes. But there is no significant research
program to help better understand the conditions under which
organizations are more likely to develop systems using the best
risk-reducing practices. There is an interesting body of
professional lore, but little scholarship to ground it (See
Appendix).
Computer Scientists have virtually no scholarship to utilize in
understanding when high performance networks, like the National
Research and Education Network, will catalyze social value
proportional to their costs. Consequently, many of the "obvious"
claims about the value of various computing technologies that we
Computer Scientists make are more akin to the lore of home
remedies for curing illness. Some are valid, others are unfounded
speculation. More seriously, the theoretical bases for
recommending home medical remedies and new computer technologies
can not advance without having sound research programs.
WHAT IS NEEDED
CTF sets the stage for developing Organizational Informatics as a
strong subfield within Computer Science. CTF bases the expansion
of the discipline on a rich array of applications in which many
of the effective technologies must be conceived in relationship
to plausible uses in order provide attractive social value for
multi-billion dollar public investments.
The CS community needs an institutionalized research capability
to produce a reliable body of knowledge about the usability and
value of computerized systems and the conditions under which
computer systems improve organizational performance. In Western
Europe there are research projects about Organizational
Informatics in a few Computer Science departments and research
funding through the EEC's Espirit program (Bubenko, 1992; Iivari,
1991; Kyng and Greenbaum, 1991). These new research and
instructional programs in Western Europe give Organizational
Informatics a significantly more effective place in CS education
and research than it now has in North America.
The CS community in the U.S. has 30 years of experience in
institutionalizing research programs, especially through the
Defense Advanced Research Projects Agency and the National
Science Foundation (NSF). There are many approaches, including
establishing national centers, supporting individual investigator
research grants, supporting short institutes to help train new
investigators and supporting research workshops for ongoing
research. All such programs aim to develop and sustain research
fields with a combination of direct research funds, the education
of future researchers, and the development of research
infrastructure. They are all multimillion dollar efforts. Today,
NSF devotes about $125K annually to Organizational Informatics as
part of the Information Technology in Organizations program. This
start is far short of the level of funding required to develop
this field within CS.
The North American CS curricula must also include opportunities
for students to learn the most reliable knowledge about the
social dimensions of systems development and use (Denning, 1992).
These opportunities, formed as courses, can provide varied levels
of sophistication. The most elementary courses introduce students
to some of the key topics in Organizational Informatics and the
limitations of Systems Rationalism as an organizing frame (for
example, Dunlop and Kling, 1991a). More advanced courses focus on
specific topics, such as those I have listed above. They teach
about substantive problems and theoretical approaches for
analyzing them. While many of these approaches are anchored in
the sociological theory of organizations, CS students usually
won't grasp the importance of the theories without numerous
computing examples to work with [4]. They also have trouble
grasping the character of computing in organizations without
guided opportunities for observing and analyzing computerization
in practice. Consequently, some courses should offer
opportunities for studying issues of computerization in actual
organizations.
Fortunately, a few CS departments offer some courses in
Organizational Informatics. In addition, some CS faculty who
research and teach about human behavior in areas like Human-
Computer Interaction and Software Engineering can help expand the
range of research and instruction. Curricula would vary, but they
should include diverse courses for students who seek basic
exposure to Organizational Informatics and those seek more
thorough instruction. Unfortunately, only a fraction of the CS
departments in the US. have faculty who study and teach about
computing and human behavior.
While the study of Organizational Informatics builds upon both
the traditional technological foundations of CS and the social
sciences, the social sciences at most universities will not
develop it as an effective foundational topic for CS. On specific
campuses, CS faculty may be able to develop good instructional
programs along with colleagues in social sciences or Schools of
Management.
But delegating this inquiry to some other discipline does not
provide a national scale solution for CS. Other disciplines will
not do our important work for us. Mathematics departments may be
willing to teach graph theory for CS students, but the analysis
of algorithms would be a much weaker field if it could only be
carried out within Mathematics Departments. For similar reasons,
it is time for academic Computer Science to embrace
Organizational Informatics as a key area of research and
instruction.
NOTES
[1] See Dunlop and Kling, 1991a for an accessible introduction to
these debates. Economic statistics about national level
productivity are inexact, and sometimes weak. Baily and Gordon
(1988) examined the extent to which measurement problems account
for the difficulties of seeing the positive computerization show
up in the US national productivity statistics. They concluded
that measurements were inexact, and very poor in some sectors
like banking, measurement errors were not the primary cause of
difficulties.
[2] Organizational Informatics is a new term, and I have found
that some people instantly like it while others are put off. I've
experimented with alternative labels, like Organizational
Computing, which has also resulted in strong and mixed reactions.
Computing is a more common term than Informatics, but it's too
narrow for some researchers. Informatics also can connote
"information," which is an important part of this field.
Sociological Computer Science would have the virtues of being a
parallel construction of Mathematical Computer Science, but
doesn't connote information either. I have not yet found a short
distinctive label which characterizes the field and whose
connotations are rapidly grasped by both outsiders and insiders.
[3] Jacky's early study was based on published reports, while
Leveson and Turner's more thorough study was based upon a
significant body of original documents and interviews with some
participants.
[4] One hears similar concerns about teaching mathematics to CS
students. CS students are much more motivated to learn graph
theory, for example, when they learn those aspects which best
illuminate issues of computation and when their teaching includes
some good computing examples.
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ACKNOWLEDGEMENTS
This paper builds on ideas which I've developed over the last
decade. But they have been deepened by some recent events, such
as the CTF report. They were also sharpened through a lecture and
followon discussion with colleagues at the University of Toronto,
including Ron Baeker, Andy Clement, Kelley Gottlieb, and Marilyn
Mantei. Rick Weingarten suggested that I write a brief position
paper reflecting those ideas. At key points, Peter Denning and
Peter Neumann provided helpful encouragement and sage advice. I
also appreciate the efforts of numerous other friends and
colleagues to help strengthen this paper through their comments
and critical assistance. The paper is immeasurably stronger
because of the prompt questions and suggestions that I received
in response to an evolving manuscript from the following people:
Mark Ackerman, Jonathan P. Allen, Bob Anderson, Lisa Covi, Brad
Cox, Gordon Davis, Phillip Fites, Simson Garfinkel, Les Gasser,
Sy Goodman, Beki Grinter, Jonathan Grudin, Pertti Jarvinen, John
King, Heinz Klein, Trond Knudsen, Kenneth Kraemer, Sharyn Ladner,
Nancy Leveson, Lars Matthiesen, Colin Potts, Paul Resnick, Larry
Rosenberg, Tim Standish, John Tillquist, Carson Woo and Bill
Wulf.
APPENDIX
Published Materials about Computer Risks
Unfortunately, there is no single good book or comprehensive
review article about the diverse risks of computerized systems
to people and organizations, and ways to mitigate them. The
Internet board, comp.risks, is the richest archive of diverse
episodes and diverse discussions of their causes and cures. While
its moderator, Peter Neumann does a superb job of organizing
discussions of specific topics each year and also creates
periodic indices, there is no simple way to sift through the
megabytes of accumulated comp.risks files.
Computerization and Controversy edited by Charles Dunlop and Rob
Kling (1991) includes two major sections on "security and
reliability" and "privacy and social control" which identify many
key debates and reprint some key articles and book excerpts which
reflect different positions. Another major source is a series
of articles, "Inside Risks, which Peter Neumann edits for
Communications of the ACM.
This is a list of this series of articles, to date:
(All articles are by Peter Neumann unless otherwise indicated.)
Jul 90. 1. Some Reflections on a Telephone Switching Problem
Aug 90. 2. Insecurity About Security?
Sep 90. 3. A Few Old Coincidences
Oct 90. 4. Ghosts, Mysteries, and Risks of Uncertainty
Nov 90. 5. Risks in computerized elections
Dec 90. 6. Computerized medical devices, Jon Jacky
Jan 91. 7. The Clock Grows at Midnight
Feb 91. 8. Certifying Programmers and Programs
Mar 91. 9. Putting on Your Best Interface
Apr 91. 10. Interpreting (Mis)information
May 91. 11. Expecting the Unexpected Mayday!
Jun 91. 12. The Risks With Risk Analysis, Robert N. Charette
Jul 91. 13. Computers, Ethics, and Values
Aug 91. 14. Mixed Signals About Social Responsibility, Ronni
Rosenberg
Sep 91. 15. The Not-So-Accidental Holist
Oct 91. 16. A National Debate on Encryption Exportability,
Clark Weissman
Nov 91. 17. The Human Element
Dec 91. 18. Collaborative Efforts
Jan 92. 19. What's in a Name?
Feb 92. 20. Political Activity and International Computer
Networks, Sy Goodman
Mar 92. 21. Inside ``Risks of `Risks' ''
Apr 92. 22. Privacy Protection, Marc Rotenberg
May 92. 23. System Survivability
Jun 92. 24. Leaps and Bounds (Leap-year and distributed system
problems)
Jul 92. 25. Aggravation by Computer: Life, Death, and Taxes,
Aug 92. 26. Fraud by Computer
Sep 92. 27. Accidental Financial Losses
Oct 92. 28. Where to Place Trust
Nov 92. 29. Voting-Machine Risks, Rebecca Mercuri
Dec 92. 30. Avoiding Weak Links
Jan 93. 31. Risks Considered Global(ly)
Feb 93. 32. Is Dependability Attainable?
Mar 93. 33. Risks of Technology