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Research and development projects fail more often than they succeed. In fact, out of every 10 R&D projects, five are flops, three are abandoned and only two ultimately become commercial successes.1 These statistics are certainly daunting for any organization making substantial investments in R&D.
A principal problem in managing innovation is that many companies don’t know how best to organize their labs to succeed. A classic hierarchical structure, for instance, tends to impede the rapid spread of knowledge. Its inefficiencies can be absorbed, to a degree, by allowing informal structures, such as social networks, to compensate. An alternative structure is the matrix organization, but it too has its shortcomings. Matrix organizations can suffer from information logjams, confusion and conflict, with the overlap of responsibilities resulting in “turf battles and a loss of accountability.”2 These sentiments have been echoed in a recent survey of new organizational forms by The Economist magazine.3 The conundrum remains: What type of organizational design will create and sustain a learning organization in which people share knowledge quickly and willingly, a design that will successfully address the tension between too little versus too much structure?
To answer this question, I conducted an in-depth study of six R&D projects at the laboratory of a Fortune 500 corporation (henceforth referred to as “Global East”). Among other factors, I investigated the social networks at the facility. (See “About the Research.”) Employees tend to form different informal networks depending on the types of relationships they maintain and the content of the information they exchange. These include friendship networks, professional-advice relationships, gossip-exchange circles and so on. In my research, I was concerned with the effect of multiple social networks on R&D projects and with the content of the information and communication flow that is specific to a technical environment. I was especially interested in the relation between the informal social networks and the formal organizational structures in place.
About the Research
This research was conducted at a U.S.-based Fortune 500 corporation that has been a leader in its core businesses for the past century. The company (henceforth referred to as “Global East”) employs more than 100,000 employees in nearly 30 countries, and it has a strong commitment to technological innovation, operating more than 20 R&D facilities with more than 2,000 researchers and engineers.
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1. See H.J. Braun, “Symposium on ‘Failed Innovations’: Introduction,” Social Studies of Science 22, no. 2 (1992): 213–230; and R. Balachandra and J.H. Friar, “Factors for Success in R&D Projects and New Product Innovation: A Contextual Framework,” IEEE Transactions on Engineering Management 44, no. 3 (1997): 276–287.
2. C.A. Bartlett and S. Ghoshal, “Matrix Management: Not a Structure, a Frame of Mind,” Harvard Business Review 68 (July–August 1990): 138–145.
3. “The New Organisation: A Survey of the Company,” Economist, Jan. 21–27, 2006, 5.
4. H. Ibarra, “Personal Networks of Women and Minorities in Management: A Conceptual Framework,” Academy of Management Review 18, no. 1 (1993): 56–87.
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8. This result has been derived from the application of the methodology of Qualitative Comparative Analysis. QCA is appropriate for small-N studies and is “especially well-suited for addressing questions about outcomes resulting from multiple and conjectural causes — where different conditions combine in different and sometimes contradictory ways to produce the same or similar results.” See C. Ragin, “Constructing Social Research” (Thousand Oaks, California: Pine Forge Press, 1994), 16. The findings from the QCA minimization procedure show that the necessary and sufficient condition for the projects’ “high success” outcome is the combination of the four factors acting in conjunction, as opposed to the same four factors acting independently and still being able to produce the outcome. See C. Ragin, “Fuzzy-Set Social Science” (Chicago: The University of Chicago Press, 2000); and C. Ragin, “The Comparative Method: Moving Beyond Qualitative and Quantitative Strategies” (Berkeley, California: University of California Press, 1987).
9. For an in-depth discussion of the critical importance of technical communication to R&D effectiveness and the vital role of the informal channels in the information-exchange process, see T.J. Allen, “Managing the Flow of Technology” (Cambridge, Massachusetts: MIT Press, 1977); and M. Tushman, “Technical Communication in R&D Laboratories: The Impact of Project Work Characteristics,” Academy of Management Journal 21, no. 4 (1978): 624–645.
10. A. Saxenian, “The Cheshire Cat’s Grin: Innovation and Regional Development in England,” Technology Review 91 (February–March 1988): 67–75.
11. For specific details on how to map informal networks in an organization and how to read social-networks diagrams, see D. Krackhardt and J.R. Hanson, “Informal Networks: The Company Behind the Chart,” Harvard Business Review 71 (July–August 1993): 104–111; and R. Cross, N. Nohria and A. Parker, “Six Myths About Informal Networks,” MIT Sloan Management Review 43, no. 3 (spring 2002): 67–75.
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18. J. Podolny and K. Page, “Network Forms of Organization,” 57–76.
19. R.S. Burt, “The Network Structure of Social Capital,” in “Research in Organizational Behavior,” 390.
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23. See D. Ancona, H. Bresman and K. Kaeufer, “The Comparative Advantage of X-Teams,” MIT Sloan Management Review 43, no. 3 (spring 2002): 33–39.
24. For alternative insightful accounts of the effect of formal and informal structural positions on behavior, see D.J. Brass and E. Burkhardt, “Potential Power and Power Use: An Investigation of Structure and Behavior,” Academy of Management Journal 36, no. 3 (1993): 441–470; and W. Stevenson and M. Gilly, “Information Processing and Problem Solving: The Migration of Problems Through Formal Positions and Networks of Ties,” Academy of Management Journal 34, no. 4 (1991): 918–928.