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 reduc...
kopia23