Exploiting Opportunities for Technological Improvement in Organizations
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.
References
1. D.A. Leonard-Barton, “Implementation as Mutual Adaptation of Technology and Organization,” Research Policy 17 (1988): 251–267; and
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.
6. N.S. Langowitz, “Plus Development Corp (A)” (Boston: Harvard Business School, Case No. 9-687–001, 1986).
7. K.B. Clark and T. Fujimoto, Product Development Performance (Boston: Harvard Business School Press, 1991), p. 202.
8. Hall (1983), p. 60.
9. Ibid.
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.
14. K.B. Clark, R. Henderson, and R. Jaikumar, “A Perspective on Computer-Integrated Manufacturing Tools” (Boston: Harvard Business School, Working Paper No. 88-048, 1989).
15. R.J. Schonberger, Japanese Manufacturing Practices (New York: Free Press, 1982).
16. Hall (1983); and
Schonberger (1982).
17. M.A. Cusumano, Japan’s Software Factories (New York: Oxford University Press, 1991).
18. M.A. Cusumano, “Shifting Economies: From Craft Production to Flexible Systems and Software Factories,” Research Policy 21 (1992): 468.
19. Hall (1983), p. 200.
20. Cusumano (1992), p. 468.
21. K. Weick, “Technology as Equivoque,” Technology and Organizations, ed. P.S. Goodman et al. (San Francisco: Jossey-Bass, 1990), pp. 21–22.
22. D. Newtson,“Attribution and the Unit of Perception of Ongoing Behavior,” Journal of Personality and Social Psychology 28 (1973): 1, 28–38; and
A.W. Kruglanski and T. Freund, “The Freezing and Unfreezing of Lay Interferences: Effects on Impressional Primacy, Ethnic Stereotyping, and Numerical Anchoring,” Journal of Experimental Social Psychology 19 (1983) : 448–468.
23. E.J. Langer and L. Imber, “When Practice Makes Imperfect: The Debilitating Effects of Overlearning,” Journal of Personality and Social Psychology 37 (1979): 2014–2025.
24. H. Ogawa, “Information Flow and Learning in New Process Development: Construction Project in the Steel Industry” (Cambridge, Massachusetts: MIT Sloan School of Management, unpublished thesis, 1991).
25. R.H. Hayes and K.B. Clark,“Exploring the Sources of Productivity Differences at the Factory Level,” The Uneasy Alliance, ed. Clark et al. (Boston: Harvard University Press, 1985).
26. Hall (1983).
27. Imai (1986).
28. D. Levinthal and J. March, “A Model of Adaptive Organizational Search,” Journal of Economic Behavior and Organization 2 (1981): 307–333.
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).
34. R. Katz, “The Effects of Group Longevity on Project Communication and Performance,” Administrative Science Quarterly 27 (1982): 81–104.
35. A.D. Meyer, “Adapting to Environmental Jolts,” Administrative Science Quarterly 27 (1982): 551–537;
J.E. Dutton and S.E. Jackson, “Categorizing Strategic Issues: Links to Organizational Action,” Academy of Management Review 12 (1987): 76–90; and
Weick (1990).
36. S.E. Jackson and J.E. Dutton, “Discerning Threats and Opportunities,” Administrative Science Quarterly 33 (1988): 370–387.
37. Hall (1983); and
Jaikumar (1986).
38. Meyer (1982).
39. R.E. Bohn, “Learning by Experimentation in Manufacturing” (Boston: Harvard Business School, Working Paper No. 88-001, 1988).
40. M.G. Bradac, D.E. Perry, and L.G. Votta, “Prototyping a Process Monitoring Experiment” (Baltimore, Maryland: Proceedings of the Fifteenth International Conference on Software Engineering, May 1993).
41. J.R. Hackman, Groups That Work (and Those That Don’t) (San Francisco: Jossey-Bass, 1990).
42. C.J. Gersick, “Time and Transition in Work Teams: Toward a New Model of Group Development,” Academy of Management Journal 31 (1988): 1, 9–31.
43. Hayes and Clark (1985).
44. M.J. Tyre and O. Hauptman, “Effectiveness of Organizational Response Mechanisms to Technological Change in the Production Process,” Organization Science 3 (1992): 301–320.
45. W.M. Cohen and D.A. Levinthal, “Absorptive Capacity: A New Perspective on Learning and Innovation,” Administrative Science Quarterly 35 (1990): 128–152.
46. W. Mackay, “Users and Customizable Software: A Co-adaptive Phenomenon” (Cambridge Massachusetts: MIT Sloan School of Management, unpublished Ph.D. thesis, 1990).
47. Ogawa (1991).
48. Jaikumar (1986).