Studying ComputerAided Software Engineering Diffusion in Organizations: Complementing Classical Diffusion Theory With Organizational Learning Perspective

Computer-aided software engineering (CASE), a relatively recent technological innovation, is viewed by both researchers and practitioners as a potential means to increase the productivity (Banker and Kauffman, 1991; Norman and Nunamaker, 1988; Stamps, 1987; Swanson, et al., 1991) and quality (Howard, 1990) of information systems development activities, reduce costs and time spent in systems development (Feuche, 1989; Martin, 1989), and ease the software development and maintenance burden threatening to overwhelm information systems departments (Bachman, 1988; Banker and Kauffman, 1991; Swanson, et al., 1991). Actual experiences with CASE tools, however, have been mixed. While some studies have reported productivity gains (or perception of such gains) from the use of CASE tools (Banker and Kauffman, 1991; Necco, et al., 1989; Norman and Nunamaker, 1988; Swanson, et al., 1991), many others have found that the expected productivity gains are elusive (Card, et al., 1987; Yellen, 1990), or hampered by inadequate training and experience, developer resistance, and increased design and testing time (Norman, et al., 1989; Orlikowski, 1988, 1989, 1993; Vessey, et al., 1992). These contradictory experiences withCASE tools have been difficult to interpret and have puzzled both practitioners and researchers. The inadequacy of conceptual and theoretical foundation of organizational innovation diffusion, primarily based on the classical diffusion theory first espoused by Rogers (1962), have been cited as a prime reason for the contradictory empirical findings (Fichman, 1992). The classical diffusion theory, used in most studies of IT diffusion in general and CASE diffusion in organizations in particular, has many shortcomings. First, the theory operates under the assumption of an unchanging innovation (Brown, 1981). In reality, innovation is a continual process whereby the form and function of the innovation are modified throughout its life (LeonardBaron, 1988; Walton, 1989). Second, the theory emphasizes the demand aspect of diffusion, assuming that everyone has an equal opportunity to adopt; the supply side of the innovation is almost ignored (Brown, 1981). In fact, institutions that supply and market innovations determine to a certain extent who adopts them and when. Third, the classical diffusion theory considers the technological adoption decisions of individuals or organizations without taking into account community issues, assuming that individuals adopt innovations for their own independent use (Fichman, 1992). However, there is evidence that the technology can be subject to network externalities (Katz and Shapiro, 1986; Markus, 1987), which means that the value of use to any single adopter will depend on the size of network of other users. Fourth, the classical theory fails to distinguish between two types of communication involved in the diffusion process: signaling versus knowhow or technical knowledge (Attewell, 1991). It assumes that signaling information takes different lengths of time to get to different potential adopters (according to their centrality to communications networks and links to prior adopters), resulting in the early, middle, and late Scurve adopters, and is therefore viewed as central in explaining the diffusion process. However, one may question whether signaling information is a limiting factor in situations where information about the existence of new technologies and their benefits is widely broadcast by manufacturers\u27 advertisements, by specialized business journals, and by trade associations (Burt 1987). The technical knowledge required to use a complex innovation successfully places far greater demands on potential users and on supplyside organizations than does signaling (Attewell, 1992). If obtaining technical knowledge is slower and more problematic, it can be posited that it plays a more important role in the diffusion of complex technologies than does signaling. Finally, most of the studies of supply-side institutions in innovation conceptualize the diffusion process in terms of knowledge transfer. Attewell (1992) argues that such studies treat the movement of complex technical knowledge under a model of communication most appropriate for signaling. Studies have, however, shown that although one can readily buy the machinery that embodies an innovation, the knowledge needed to use modern production innovations is acquired much more slowly and with considerably more difficulty (Arrow, 1962; Dutton and Thomas, 1985, Ray, 1969; Pavitt, 1985; von Hippel, 1988). Absorbing a new complex technology not only requires modification and mastery of the technology, but it also often requires (frequently unanticipated) modifications in organizational practices and procedures (Stasz, Bikson, and Shapiro, 1986; Johnson and Rice, 1987). Thus, implementing a complex technology requires both individual and organizational learning. Not surprisingly, the findings of past studies of IT diffusion show inconclusive support for the classical diffusion theory in the case of diffusion of complex information technologies (such as CASE) which exhibit user interdependency and impose knowledge burden on users (Fichman, 1992). (When the adoption decision of individuals or organizations depends on the dynamics of community-wide levels of adoption because of network externalities, innovation diffusion is characterized as exhibiting user interdependencies. Similarly, when technologies cannot be adopted as a black box solution but rather impose a substantial knowledge burden on potential adopters, innovation diffusion is characterized to exhibit high knowledge burden.) One interpretation of these findings is that classical diffusion variables by themselves may not be strong predictors of adoption and diffusion of complex technologies at the organizational level (Fichman, 1992). Fichman (1992) recommends that future research on IT diffusion at the organizational level consider other than classical or communications perspective, such as market and infrastructure, economic, and organizational learning perspectives, to account for these inconsistencies. In this study we complement the classical diffusion theory with an organizational learning perspective

FACTORS AFFECTING CODE REUSE: lMPLICATIONS FOR A MODEL OF COMPUTER AIDED SOFTWARE ENGINEERING DEVELOPMENT PERFORMANCE

An examination of code reuse at a large financial institution yields insights into the process of code reuse. The software development environment -- based on an integrated CASE system -- was designed to support code reuse, but at the end of its first two years we find that programmers are not taking full advantage of the reuse opportunities which the CASE environment provides, The organization has provided technical support for code reuse, but has not made organizational adjustments, and the technical solution alone does not suffice. We also review an existing economic model of CASE development performance that incorporates code reuse, suggesting refinements that are based upon our observations. Finally, we draw some conclusions about steps that managers can take to promote code reuse.Information Systems Working Papers Serie

OUTPUT MEASUREMENT METRICS IN AN OBJECT-ORIENTED COMPUTER AIDED SOFTWARE ENGINEERING (CASE) ENVIRONMENT: CRITIQUE, EVALUATION AND PROPOSAL

Output measurement metrics for the software development process need to be re-examined to determine their performance in the new, radically changed CASE development environment. This paper critiques and empirically evaluates several approaches to the measurement of outputs from the CASE process. The primary metric evaluated is the function points method developed by Albrecht. A second metric tested is a short-form variation of function points that is easier and quicker to calculate. We also propose a new output metric called object points and a related short-form, which are specialized for output measurement in object-oriented CASE environments that include a central object repository. These metrics are proposed as more intuitive and lower cost approaches to measuring the CASE outputs. Our preliminary results show that these metrics have the potential to yield as accurate, if not better, estimates than function points-based measures.Information Systems Working Papers Serie

MONITORING THE SOFTWARE ASSET: REPOSITORY EVALUATION OF SOFTWARE REUSE

Traditionally, software management has focused primarily upon cost control. Today, with the emerging capabilities of computer aided software engineering (CASE) and corresponding changes in the development process, the opportunity exists to view software development as an activity that creates reusable software assets, rather than just expenses, for the corporation. With this opportunity comes the need to monitor software at the corporate level, as well as at that of the individual software development project. Integrated CASE environments can support such monitoring. In this paper we propose the use of a new approach called repository evaluation, and illustrate it in an analysis of the evolving repository-based software assets of two large firms that have implemented integrated CASE development tools. The analysis shows that these tools have supported high levels of software reuse, but it also suggests that there remains considerable unexploited reuse potential. Our findings indicate that organizational changes will be required before the full potential of the new technology can be realized.Information Systems Working Papers Serie

TRACKING THE 'LIFE CYCLE TRAJECTORY': METRICS AND MEASURES FOR CONTROLLING PRODUCTIVITY OF COMPUTER AIDED SOFTWARE ENGINEERING (CASE) DEVELOPMENT

This paper proposes a new vision for the measurement and management of development productivity related to computer aided software engineering (CASE) technology. We propose that productivity be monitored and controlled in each phase of software development life cycle, a measurement approach we have termed life cycle trajectory measurement. Recent advances in CASE technology that make low cost automated measurement possible have made it feasible to collect life cycle trajectory measures. We suggest that current approaches for productivity management involve the use of static metrics that are available only at the beginning and end of the project. Yet the depth of the insights needed to make proactive adjustments in the software development process requires monitoring the range of activities across the entire software development life cycle. This can only be accomplished with metrics that can measure performance parameters in each phase of the life cycle. We develop metrics that have the ability to measure and estimate software outputs from each intermediate phase of the development life cycle. These metrics are based on a count of the objects and modules that are used as building blocks for application development in repository object-based CASE environments. The viability of such object-based metrics for life cycle trajectory measurement has been empirically tested for the software construction phase using project data generated in Integrated CASE development environments.Information Systems Working Papers Serie

CASE TECHNOLOGY AS A MEDIATING FACTOR IN ANALYST AND PROGRAMMER JOB OUTCOMES

Information Systems Working Papers Serie

MANAGING DEVELOPMENT PRODUCTIVITY OF THE COMPUTER AIDED SOFTWARE ENGINEERING (CASE) PROCESS WITH DYNAMIC LIFE CYCLE TRAJECTORY METRICS

This paper proposes a new vision for the measurement and management of development productivity related to computer aided software engineering (CASE) technology. We propose that they be monitored and controlled via the application of dynamic software development "life cycle trajectory metrics." This view develops out of management accounting approaches for process control and recent advances in CASE technology that make automated measurement possible. We suggest that current approaches involve the use of "static metricsâ for estimation and evaluation, with the result that the depth of the insights they can provide to management is necessarily limited. They only provide "point estimatesâ of output or productivity at the beginning and end of the project. Yet to manage software development proactively for improved efficiency and effectiveness, management needs to track the range of activities and effort across the entire software development life cycle. This can only be accomplished when timely and relevant information is obtained about the software size output, as well as costs, via âdynamic metrics,â which provide a richer phase-by-phase view.Information Systems Working Papers Serie