We often hear that companies must learn to embrace change. This is particularly true of companies that are applying advanced technologies to improve their competitive position. The full advantages of such technologies cannot simply be purchased off the shelf; they are won by patiently and carefully tailoring the technology to fit a given firm’s organizational and strategic context. At the same time, organizational skills, procedures, and assumptions within the firm need to be adapted to fit the new technology.1
Little is known, however, about how organizations actually go about modifying new process technologies, or how they adapt their own practices in response to technological change. Most of the research on this topic has assumed that users learn about and modify new technologies gradually. These assumptions have been built into our theories and images about technological adaptation — such as the familiar learning curve, which implies a highly regular accretion of improvements over time. The same assumptions are built into the prescriptions many researchers offer to management. These researchers exhort managers to “allow plenty of time” to digest new process technologies and to strive for “continuous improvement” (see Figure 1).
Yet most of the research on which these assumptions are based was performed at the aggregate level. Certainly, an entire firm or factory must strive for continuous improvement. But, at the level of a particular new technology, the process of learning about and modifying a new process may not be continuous at all. Indeed, our research suggests that the pattern of adaptation for an individual new technology is often a decidedly “lumpy” or episodic one (see Figure 2). In general, it appears that the introduction of a new technology into an operating environment triggers an initial burst of adaptive activity, as users explore the new technology and resolve unexpected problems. However, this activity is often short-lived, with effort and attention declining dramatically after the first few months of use. In effect, the technology, as well as the habits and assumptions surrounding it, tend to become “taken for granted” and built into standard operating procedures. This initiates a period of stability in which users focus attention more on regular production tasks than on further adaptation. Later on, users often refocus their attention on unresolved problems or new challenges, creating additional spurts of adaptive activity.
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A.H. Van de Ven, “Central Problems in the Management of Innovation,” Management Science 32 (1986): 590–607.
2. M. Tyre and W. Orlikowski, “Windows of Opportunity: Temporal Patterns of Technological Adaptation in Organizations,” Organization Science 5 (forthcoming, 1994).
3. S.C. Wheelwright and K.B. Clark, Revolutionizing Product Development: Quantum Leaps in Speed, Efficiency, and Quality (New York: Free Press, 1992).
4. R.W. Hall, Zero Inventories (Homewood, Illinois: Dow Jones-Irwin, 1983), p. 197.
5. Ibid., p. 199.
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8. Hall (1983), p. 60.
10. M. Imai, Kaizen: The Key to Japan’s Competitive Success (New York: McGraw-Hill Publishing Company, 1986), p. 63.
11. Ibid., p. 74. See, also:
P.S. Adler, “Time-and-Motion Regained,” Harvard Business Review, January–February 1993, pp. 97–108.
12. Imai (1986), p. 75.
13. R. Jaikumar, “Postindustrial Manufacturing,” Harvard Business Review, November–December 1986, p. 76.
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16. Hall (1983); and
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19. Hall (1983), p. 200.
20. Cusumano (1992), p. 468.
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26. Hall (1983).
27. Imai (1986).
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29. Weick (1990); and
A. Etzioni, “Humble Decision Making,” Harvard Business Review, July–August 1989, pp. 122–126.
30. E. von Hippel and M.J. Tyre, “How Learning by Doing Is Done: Problem Identification in Novel Process Equipment,” Research Policy (forthcoming).
31. Imai (1986), p. 62.
32. Hall (1983); and
Clark and Fujimoto (1991).
33. Von Hippel and Tyre (forthcoming).
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37. Hall (1983); and
38. Meyer (1982).
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47. Ogawa (1991).
48. Jaikumar (1986).