Software development, so critical to the effective use of information technology, is poorly understood and managed. Numerous software engineering process innovations have been proposed to improve software development, the latest of which is object orientation. How can information systems managers decide whether to invest in such technologies? This paper proposes a general two-dimensional framework based on theories about organizational and communitywide innovations, and it accurately describes their adoption trajectories. Then they apply it to object orientation and take the controversial position that this new technology is not likely to be quickly adopted by large in-house business information systems groups.
1. M.L. Carnevale, “DSC Says Software Change Led to Phone Outages,” Wall Street Journal, 10 July 1991, p. 5.
2. The innovation literature distinguishes process technology innovations from product innovations. A process innovation is one that changes the production process, that is, the way a product is produced. Most industrial tools, when first introduced, are simultaneously process and product innovations — a product innovation for the tool producer and a process innovation for the tool consumer. For example, for a DBMS vendor, a new RDB offering is a product innovation; for a systems developer, RDB technology is a process innovation that, among other things, involves the purchase of a product. As this article is targeted at consumers rather than producers of software technologies, we define them as process innovations. For further reading on the topic of process and product innovations, see:
W.J. Abernathy and J.M. Utterback, “Patterns of Industrial Innovation,” Technology Review, June–July 1978, pp. 40–47.
3. L. Thurow, “Who Owns the Twenty-First Century?” Sloan Management Review, Spring 1992, pp. 5–17.
4. F.P. Brooks, “No Silver Bullet: Essence and Accidents of Software Engineering,” IEEE Computer20 (1987): 10–19.
5. W.M. Bulkeley, “Bright Outlook for Artificial Intelligence Yields to Slow Growth and Big Cutbacks,” Wall Street Journal, 5 July 1990, pp. B1, B3.
6. “Software Made Simple,” Business Week, 30 September 1991, pp. 92–100.
7. E.M. Rogers, Diffusion of Innovations (New York: Free Press, 1983).
8. Ibid., ch. 6.
9. See, for example:
J.E. Eveland and L.G. Tornatzky, “The Deployment of Technology,” in The Processes of Technological Innovation, eds. L.G. Tornatzky and M. Fleischer (Lexington, Massachusetts: Lexington Books, 1990), pp. 117–148;
T.H. Kwon and R.W. Zmud, “Unifying the Fragmented Models of Information Systems Implementation” in Critical Issues in Information Systems Research, eds. J.R. Boland and R. Hirshheim (New York: John Wiley & Sons, 1987);
D. Leonard-Barton, “Implementation Characteristics of Organizational Innovations,” Communication Research 15 (1988): 603–631;
G.C. Moore, “End-User Computing and Office Automation: A Diffusion of Innovations Perspective,” Infor25 (1987): 214–235;
J.M. Pennings, “Technological Innovations in Manufacturing,” in New Technology as Organizational Change, eds. J.M. Pennings and A. Buitendam (Cambridge, Massachusetts: Ballinger, 1987), pp. 197–216; and
A.H. Van de Ven, “Managing the Process of Organizational Innovation” in Changing and Redesigning Organizations, ed. G.P. Huber (New York: Oxford University Press, 1991).
10. We use Rogers’s five innovation attributes mainly because they are familiar in the DOI field. Van de Ven, Moore, and Kwon and Zmud also use Rogers’s definitions, although others have provided alternative taxonomies of the salient attributes of complex organizational technologies. Leonard-Barton identifies transferability, organizational complexity, and divisibility. Pennings identifies concreteness, divisibility, and cost. Eveland and Tornatzky identify trialability, lumpiness, adaptability, degree of packaging, and the “hardness” of the underlying science. In most cases, these attributes can be mapped to one or more of Rogers’s original five attributes, at least as they are used here.
11. W.B. Arthur, “Competing Technologies: An Overview,” in Technical Change and Economic Theory, ed. G. Dosi (New York: Columbia University Press, 1987).
J. Farrell and G. Saloner, “Competition, Compatibility, and Standards: The Economics of Horses, Penguins, and Lemmings,” in Product Standardization and Competitive Strategy, ed. H.L. Gabel (Amsterdam: North-Holland, Elsevier Science, 1987);
M.L. Katz and C. Shapiro, “Technology Adoption in the Presence of Network Externalities,” Journal of Political Economy 94 (1986): 822–841.
13. Farrell and Saloner (1987).
15. N. Rosenberg, “On Technological Expectations,” in Inside the Black Box: Technology and Economics (New York: Cambridge University Press, 1982).
16. G. Rifkin and M. Betts, “Strategic Systems Plans Gone Awry,”Computerworld, 14 March 1988, pp. 1, 104–105.
17. R. Fichman and C. Kemerer, “Object-Oriented and Conventional Analysis and Design Methodologies: Comparison and Critique,” IEEE Computer 25 (1992): 20–39.
18. See S. Atre, “The Scoop on OOPS,” Computerworld, 17 September 1990, pp. 1115–1116;
J. Moad, “Cultural Barriers Slow Reusability,” Datamation, November 1989, pp. 87–92; and
M. Stewart, “Object Projects: What Can Go Wrong,” Hotline on Object-Oriented Technology 2 (1991): 15–17.
19. Moad (1989).
20. Stewart (1991).
21. Atre (1990).
22. Rogers (1983) notes that unfulfilled expectations about an innovation’s benefits are a primary cause of subsequent discontinuance. Leonard-Barton has argued that discontinuers can become influential “negative” opinion leaders, and that entrenched opinions about an early technology generation are hard to overturn, even when later and more viable technology generations become available. See:
D. Leonard-Barton, “Experts as Negative Opinion Leaders in the Diffusion of a Technological Innovation,” Journal of Consumer Research, 11 March 1985, pp. 914–926.
23. As mentioned previously, adoption context is important to the ratings. In some segments, such as CAD/CAM, CASE, operating systems, and simulation-oriented applications more obviously suited to OO’s strengths, a different classification may result.
We received helpful comments on earlier versions of this paper from S. Brobst, E. Brynjolfsson, J. Quillard, W. Orlikowski, J. Rockart, W. Stevens, L. Votta, and participants at the UCLA Information Systems Colloquium. We gratefully acknowledge the funding provided for this work by Credit Suisse and the MIT Center for Information Systems Research.