organizational analysis in computer science.txt

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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
                    [e-mail] (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)."