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.                            ABSTRACTComputer Science is hard pressed in the US to show broad utilityto help justify billion dollar research programs and the value ofeducating well over 40,000 Bachelor of Science  and Master ofScience specialists annually in the U.S. The Computer Science andTelecommunications Board of the U.S. National Research Councilhas recently issued a report, "Computing the Future (Hartmanisand Lin, 1992)" which sets a new agenda for Computer Science. Thereport recommends that Computer Scientists broaden theirconceptions of the discipline to include computing applicationsand domains to help understand them. This short paper argues thatmany Computer Science graduates need some skills in analyzinghuman organizations to help develop appropriate systemsrequirements since they are trying to develop high performancecomputing applications that effectively support higherperformance human organizations. It is time for academic ComputerScience to embrace organizational analysis (the field ofOrganizational Informatics) as a key area of research andinstruction.                          INTRODUCTIONComputer Science is being pressed on two sides to show broadutility for substantial research and educational support. Forexample, the High Performance Computing Act will provide almosttwo billion dollars for research and advanced development. Itsadvocates justified it with arguments that specific technologies,such as parallel computing and wideband nets,  are necessary forsocial and economic development. In the US, Computer Scienceacademic programs award well over 30,000 Bachelor of Science (BS)and almost 10,000 Master of Science (MS) degrees annually. Someof these students enter PhD programs and many work on projectswhich emphasize mathematical Computer Science. But many of thesegraduates also take computing jobs for which they areinadequately educated, such as helping to develop highperformance computing applications to improve the performance ofhuman organizations.These dual pressures challenge leading Computer Scientists tobroaden their conceptions of the discipline to include anunderstanding of key application domains, including computationalscience and commercial information systems. An important reportthat develops this line of analysis, "Computing the Future" (CTF)(Hartmanis and Lin, 1992), was recently issued by the ComputerScience and Telecommunications Board of the U.S. NationalResearch Council.CTF is a welcome report that argues that academic ComputerScientists must acknowledge the driving forces behind thesubstantial Federal research support for the discipline. Theexplosive growth of computing and demand for CS in the lastdecade has been driven by a diverse array of applications and newmodes of computing in diverse social settings.  CTF takes astrong and useful position in encouraging all Computer Scientiststo broaden our conceptions of the discipline and to examinecomputing in the context of interesting applications.CTF's authors encourage Computer Scientists to envision newtechnologies in the social contexts in which they will be used.They identify numerous examples of computer applications in earthscience, computational biology, medical care, electroniclibraries and commercial computing that can provide significantvalue to people and their organizations. These assessments reston concise and tacit analyses of the likely design,implementation within organizations, and uses of thesetechnologies. For example, CTF's stories of improvedcomputational support for modelling are based on rational modelsof organizational behavior. They assume that professionals,scientists, and policy-makers use models to help improve theirdecisions. But what if organizations behave differently when theyuse models? For example  suppose policy makers use models to helprationalize and legitimize decisions which are made withoutactual reference to the models?One cannot discriminate between these divergent roles ofmodelling in human organizations based upon the intentions ofresearchers and system designers. The report tacitly requiresthat the CS community develop reliable knowledge, based onsystematic research, to support effective analysis of the likelydesigns and uses of computerized systems. CTF tacitly requires anability 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 valueof specific technologies is mere opinion, and bears a significantrisk of being misleading. Further, Computer Scientists who do nothave refined social analytical skills sometimes conceive andpromote technologies that are far less useful or more costly thanthey claim. Effective CS practitioners who "compute for thefuture" in organizations need some refined skills inorganizational analysis to understand appropriate systemsrequirements and the conditions that transform high performancecomputing into high performance human organizations. Since CTFdoes not spell out these tacit implications, I'd like to explainthem here.                  BROADENING COMPUTER SCIENCE:                 FROM COMPUTABILITY TO USABILITYThe usability of systems and software is a key theme in thehistory of CS. We must develop theoretical foundations for thediscipline that give the deepest insights in to what makessystems usable for various people, groups and organizations.Traditional computer scientists commonly refer to mathematics asthe theoretical foundations of CS. However, mathematicalformulations give us limited insights into understanding why andwhen some computer systems are more usable than others.Certain applications, such as supercomputing and computationalscience are evolutionary extensions of traditional scientificcomputation, despite their new direction with rich graphicalfront 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 supporttraditional numerical computation, albeit in newer formats suchas spreadsheets, they have also expanded the diversity ofnon-numerical computations. They make digitally represented textand graphics accessible to tens of millions of people.These technological advances are not inconsistent withmathematical foundations in CS, such as Turing machineformulations. But the value of these formats for computation isnot well conceptualized by the foundational mathematical modelsof computation. For example, text editing could be conceptualizedas a mathematical function that transforms an initial text and avector of incremental alterations into a revised text. Textformatting can be conceptualized as a complex function mappingtext strings into spatial arrays. These kinds of formulationsdon't help us grasp why many people find "what you see is whatyou get" editors as much more intuitively appealing than a systemthat links line editors, command-driven formatting languages, andtext compilers in series.Nor do our foundational mathematical models provide useful waysof conceptualizing some key advances in even more traditionalelements of computer systems such as operating systems anddatabase systems. For example, certain mathematical modelsunderlie the major families of database systems. But one can'trely on mathematics alone to assess how well networks, relations,or object-entities serve as representations for the data storedin an airline reservation system. While mathematical analysis canhelp optimize the efficiency of disk space in storing the data,they can't do much to help airlines understand the kinds ofservices that will make such systems most useful forreservationists, travel agents and even individual travellers. Anairline reservation system in use is not simply a closedtechnical system. It is an open socio-technical system (Hewitt,1986; Kling, 1992). Mathematical analysis can play a central rolein some areas of CS, and an important role in many areas. But wecannot understand important aspects of usability if we limitourselves to mathematical theories.The growing emphasis of usability is one of the most dominant ofthe diverse trends in computing. The usability tradition has deeproots in CS, and has influenced the design of programminglanguages and operating systems for over 25 years. Specifictopics in each of these areas also rest on mathematical analysiswhich Computer Scientists could point to as "the foundations" ofthe respective subdisciplines. But Computer Scientists envisionmany key advances as design conceptions rather than asmathematical theories. For example, integrated programmingenvironments ease software development. But their conception andpopularity is not been based on deeper formal foundations forprogramming languages. However, the growth of non-numericalapplications for diverse professionals, including textprocessing, electronic mail, graphics, and multimedia shouldplace a premium on making computer systems relatively simple touse. Human Computer Interaction (HCI) is now considered a coresubdiscipline of CS.The integration of HCI into the core of CS requires us to expandour conception of the theoretical foundations of the discipline.While every computational interface is reducible to a Turingcomputation, the foundational mathematical models of CS do not(and could not) provide a sound theoretical basis forunderstanding why some interfaces are more effective for somegroups of people than others. The theoretical foundations ofeffective computer interfaces must rest on sound theories ofhuman behavior and their empirical manifestations (cf. Ehn, 1991,Grudin, 1989).Interfaces also involve capabilities beyond the primaryinformation processing features of a technology. They entail waysin which people learn about systems and ways to manage thediverse data sets that routinely arise in using many computerizedsystems (Kling, 1992). Understanding the diversity and characterof these interfaces, that are required to make many systemsusable, rests in an understanding the way that people and groupsorganize their work and expertise with computing. Appropriatetheories of the diverse interfaces that render many computersystems truly useful must rest, in part, on theories of work andorganization. There is a growing realization, as networks tieusers together at a rapidly rising rate, that usability cannotgenerally be determined without our considering how computersystems are shaped by and also alter interdependencies in groupsand organizations. The newly-formed subdiscipline of ComputerSupported 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 ORGANIZATIONSThe arguments of CTF go beyond a focus on usable interfacedesigns to claims that computerized systems will improve theperformance of organizations.  The report argues that the USshould invest close to a billion dollars a year in CS researchbecause of the resulting economic and social gains. These areimportant claims, to which critics can seek systematic evidence.For example, one can investigate the claim that 20 years of majorcomputing R&D and corporate investment in the US has helpedprovide proportionate economic and social value.CTF is filled with numerous examples where computer-based systemsprovided value to people and organizations. The tough question iswhether the overall productive value of these investments isworth the overall acquisition and operation costs. While it isconventional wisdom that computerization must improveproductivity, a few researchers began to see systemicpossibilities of counter-productive computerization in the early1980s (King and Kraemer, 1981). In the last few years economistshave found it hard to give unambiguously affirmative answers tothis question. The issue has been termed "The ProductivityParadox," based on a comment attributed to Nobel laureate RobertSolow who remarked that "computers are showing up everywhereexcept in the [productivity] statistics (Dunlop and Kling,1991a)."