Knowledge Workers and Radically New Technology
Information technology implementation in organizations has gone from automating back-office clerks to supporting the complex tasks of autonomous knowledge workers. The research I report here is not about new organizations or those transcending a deep crisis; rather, it concerns the push and pull of managers attempting to implement a new technology. It tells the story of companies trying to change the behavior of employed but fiercely independent “revenue producers” while, at the same time, trying not to drive away the best performers. The research investigates how different executives try to make the same people in the same roles work with a new tool.
My research was inspired by the notion that the work of salespeople (or lawyers or doctors) might be as radically transformed by technology as was the work of tinsmiths, hoopers, and portrait painters when technology impinged on their lives and livelihood. Traditionally, the knowledge worker has had more autonomy than the laborer, thus challenging the manager who is attempting to “automate” knowledge work. However, as technology infringes on the domain of symbolic, abstract work, the interaction between user and tool becomes more complex. Although automating knowledge tasks has proved a noxious process, organization after organization has tried to gain more influence over knowledge workers. The promise of productivity in this domain is a powerful force, drawing entrepreneur and bureaucrat alike into new, more comprehensive attempts.
The increasing sophistication of the type and degree of information technology has heightened this tension. More often, computer technologies are not passive but active tools that manage the process of work. A doctor’s notes on a patient, once a document of record, now are an interactive “protocol-driven data-capture device”—both supporting and constraining the doctor’s activities. The salesperson’s “pitchbook” is replaced by a multimedia offering, and the old order form is replaced by a configuration system based on laptop computers and their software. An aggressive form of software is expert systems, which specifically aim at codifying knowledge and creating a specific method to do a task. The challenge of getting people to use expert systems is an interesting example of the general problem of influencing knowledge workers’ behavior with computer-based tools.
Creating a model that will predict the successful implementation of any new technology is almost as challenging as creating a general-purpose thinking machine. Neither has come to fruition.
References
1. For the pioneering work of Fritz Machlup in categorizing and articulating the nature of the knowledge economy, see:
F. Machlup, The Branches of Learning (Princeton, New Jersey: Princeton University Press, 1982).
Daniel Bell’s work set the path for many to follow. See:
D Bell, The Coming of Post-Industrial Society: A Venture in Social Forecasting (New York: Basic Books, 1976).
2. B.T. Pentland, personal conversation.
3. M.L. Markus and D. Robey, “Information Technology and Organizational Change: Causal Structure in Theory and Research,” Management Science, volume 34, May 1988, pp. 583–598.
4. See M.L. Markus, and M. Keil, “If We Build It, They Will Come: Designing Information Systems That People Want to Use,” Sloan Management Review, volume 35, Summer 1994, pp. 11–25.
5. H.C. Lucas, M.J. Ginzberg, and R.L. Schultz, Information Systems Implementation: Testing a Structural Model (Norwood, New Jersey: Ablex Publishing Corporation, 1990).
6. R. Kling and S. Iacono, “The Control of Information Systems Development after Implementation,” Communications of the ACM, volume 27, 1984, pp. 1218–1226.
7. T.V. Bonoma, “Case Research in Marketing: Opportunities, Problems, and a Process,” Journal of Marketing Research, volume 22, May 1985, p. 199–208; and
T.D. Cook and D.T. Campbell, “Validity,” in Quasi-Experimentation (Chicago: Rand-McNally, 1977), pp. 37–94.
8. See J. Sviokla, “PlanPower: The Financial Planning Expert System” (Boston: Harvard Business School, Case 186–293, 1986); and
J. Sviokla, “Expert Systems and Their Impact on the Firm: The Effects of PlanPower Use on the Information Processing Capacity of the Financial Collaborative,” Journal of Management Information Systems, volume 6, Winter 1989–1990, pp. 65–84.
9. J.E. Etillie, “A Note on the Relationship between Managerial Change Sector Firms,” R&D Management, volume 13, October 1983, pp. 231–244.
10. See J. McKenney, Waves of Change: Business Evolution through Information Technology (Boston: Harvard Business School Press, 1995).
11. See L. Applegate, J. McKenney, and F.W. McFarlan, Corporate Information Systems Management (Homewood, Illinois: Irwin Publishers, 1996).
12. For a comprehensive model of implementation factors, see:
Lucas et al. (1990).
13. In a seminal book on case research, Yin suggests that when trying to isolate specific causative variables, researchers should choose sites with control variables as similar as possible and independent variables with as much variance as possible, which he terms “case replication logic”; each case is a “test.” See:
R.K. Yin, Case Study Research: Design and Methods (Newbury Park, California: Sage Publications, 1989).
14. For example, I had the opportunity to include a Japanese site in the study, but I refrained from including this site because the distribution of life insurance in Japan is much different from that in England, Australia, or the United States. In Japan, the tradition of door-to-door selling of life insurance — with frequent collection of premiums (mostly by women) still is a thriving distribution mechanism. I also had the opportunity to, but consciously avoided looking at, this same technology in the context of a direct sales insurance provider or in a large financial services firm that also rolled out the technology. I wanted to keep the basic industry, organizational, and individual context as comparable as possible.
15. See D. Leonard-Barton, “Implementation as Mutual Adaptation of Technology and Organization,” Research Policy, volume 17, October 1988, pp. 251–267;
J.F. Rockart, “The Line Takes the Leadership — IS Management in a Wired Society,” Sloan Management Review, volume 29, Summer 1988, pp. 57–64; and
E. von Hippel, The Sources of Innovation (New York: Oxford University Press, 1988).
16. J.L. King, V. Gurbarani, K. Kraemer, F.W. McFarlan et al., “Institutional Factors in Information Technology Innovation,” Information Systems Research, volume 5, June 1994, pp. 139–169.
17. R. Walton, Up and Running: Integrating Information Technology and the Organization (Boston: Harvard Business School Press, 1989).
18. W. Orlikowski, “CASE Tools as Organizational Change: Investigating Incremental and Radical Changes in Systems Development,” MIS Quarterly, volume 17, September 1993, pp. 309–340.
19. I encountered a number of significant methodological challenges when trying to assess the introduction of Profiling technology and its subsequent use and effect on sales. Perceived usage and end-user satisfaction, two often-used measures, have been severely criticized for their lack of reliability. See:
N.P. Melone, “Theoretical Assessment of the User-Satisfaction Construct in Information Systems Research,” Management Science, volume 36, January 1990, pp. 76–91.
My study utilizes actual use statistics gathered from the information systems, down to each individual transaction as the measure of use.
Many studies stop at satisfaction or have only a tenuous link to performance; this study, by contrast, uses product sales and commission data as sources of actual performance of individuals: the organizational metrics are the aggregation of the individual measurers.
20. I conducted structured interviews with more than 100 salespeople, sales managers, and senior managers both before and after Profiling’s implementation on the selling approach, the types of technology in current use, and Profiling’s fit with current methods. I compiled a year-by-year comparative history of each firm’s environmental situation and tracked the major internal organizational changes and business initiatives. In addition, I created a technology time line that documented the entire implementation effort at each organization in terms of important meetings, dates, and personnel changes. I constructed a comparative time line across each organization. And I regularly met with senior managers to review impressions and verify findings.
21. See F.F. Reichheld and W.E. Sasser, “Zero Defections: Quality Comes to Services,” Harvard Business Review, volume 68, September–October 1990, pp. 105–112. This article articulates the significant financial implications of keeping retailing customers. See also:
J. Sviokla and B. Shapiro, Keeping Customers (Boston: Harvard Business School Press, 1994).
22. J. Sviokla, “Lutheran Brotherhood and the FSNAR+ Pilot” (Boston: Harvard Business School, Case 190–163, 1990).
23. J. Sviokla, “Profiling at National Mutual (A), (B), and (C)” (Boston: Harvard Business School, Cases 191-078, 191-101, and 191-102, 1991).
24. J. Sviokla, “Sun Alliance Insurance Group, PLC” (Boston: Harvard Business School, Case 192-073, 1992).
25. J. Sviokla, “Client Profiling: The Prudential Insurance Company of America” (Boston: Harvard Business School, Case 193-084, 1993).
26. Estimate by senior sales managers at Lutheran Brotherhood.
27. J. Sviokla, “Managing a Transformational Technology: A Field Study of the Introduction of Profiling” (Boston: Harvard Business School, Working Paper 93-059, 1993).
28. In 1987, the passage of the Financial Services Act in Great Britain required any insurance agent to show “knowledge of the customer” and “best advice.” For Sun Alliance, this meant filling out a fact-finding questionnaire for the client to sign or not. (The client also had the option of not sharing data; that exercised right would be noted on the form.) Most insurance organizations dealt with the regulation by having individual agents fill out a generic questionnaire that was then filed at the local sales office.
29. An Australian dollar equals approximately US$ 0.80.
30. Sviokla, “Profiling at National Mutual (B),” p. 2.
31. Jeanette Lawrence documents how judges consider the goals, means, and feasibility of decision options simultaneously. This is similar to the behavior we saw in the managers concerning implementation. See:
J. Lawrence, “Expertise on the Bench: Modeling Magistrates Judicial Decision Making,” in The Nature of Expertise, ed. M. Chi, R. Glaser, and M. Farr (Hillsdale, New Jersey: Lawrence Erlbaum Associates, Inc., 1988), pp. 229–259.
32. M.J. Tyre and W. Orlikowski, “Windows of Opportunity: Temporal Patterns of Technological Adaptation in Organizations,” Organization Science, volume 5, February 1994, pp. 98–118.
33. For an interesting exception and specific discussion of the mechanisms of momentum, see:
G. Moore, Crossing the Chasm: Marketing and Selling Technology Products (New York: Harper Business, 1991).
34. R.E. Walton, “The Diffusion of New Work Structures: Explaining Why Success Didn’t Take,” Organizational Dynamics, Winter 1975, pp. 3–22.