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As innovation becomes more democratic, many of the best ideas for new products and services no longer originate in well-financed corporate and government laboratories. Instead, they come from almost anywhere and anyone.1 How can companies tap into this distributed knowledge and these diverse skills? Increasingly, organizations are considering using an open-innovation process, but many are finding that making open innovation work can be more complicated than it looks. PepsiCo, the food and beverage giant, for example, created controversy in 2011 when an open-sourced entry into its Super Bowl ad contest that was posted online featured Doritos tortilla chips being used in place of sacramental wafers during Holy Communion. Similarly, Kraft Foods Australia ran into challenges when it launched a new Vegemite-based cheese snack in conjunction with a public naming contest. The name Kraft initially chose from the submissions, iSnack 2.0, encountered widespread ridicule, and Kraft abandoned it. (The company instead asked consumers to choose among six other names. The company ultimately picked the most popular choice among those six, Vegemite Cheesybite.)
Reports of such problems have fed uncertainty among managers about how and when to open their innovation processes. Managers tell us that they need a means of categorizing different types of open innovation and a list of key success factors and common problems for each type. Over the last decade, we have worked to create such a guide by studying and researching the emergence of open-innovation systems in numerous sectors of the economy, by working closely with many organizations that have launched open-innovation programs and by running our own experiments.2 This research has allowed us to gain a unique perspective on the opportunities and problems of implementing open-innovation programs. (See “About the Research.”) In every organization and industry, executives were faced with the same decisions. Specifically, they had to determine (1) whether to open the idea-generation process; (2) whether to open the idea-selection process; or (3) whether to open both. These choices led to a number of managerial challenges, and the practices the companies implemented were a major factor in whether the innovation efforts succeeded or failed.
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1. Eric von Hippel has written extensively about the democratization of the innovation process, starting with users and now encompassing open communities. See E. von Hippel, “Democratizing Innovation” (Cambridge, Massachusetts: MIT Press, 2005).
2. “Open innovation” has come to imply two distinct models for organizing innovation. The first perspective considers markets for intellectual property, in which companies trade patents and other assets in a bilateral fashion. The second perspective is focused on the rise of distributed innovation systems that allow individuals from around the world to participate in innovation processes through voluntary self-selection and decentralized knowledge flows. In this paper, we refer to the second perspective. For the first perspective, see H. Chesbrough, “Open Innovation: The New Imperative for Creating and Profiting From Technology” (Boston: Harvard Business Review Press, 2003); for a NASA example, see K.J. Boudreau and K.R. Lakhani, “The Confederacy of Heterogeneous Software Organizations and Heterogeneous Developers: Field Experimental Evidence on Sorting and Worker Effort” in “The Rate and Direction of Inventive Activity Revisited,” ed. J. Lerner and S. Stern (Chicago: University of Chicago Press, 2012): 483-505; and for a medical example, see E. Guinan, K.J. Boudreau and K.R. Lakhani, “Experiments in Open Innovation at Harvard Medical School,” MIT Sloan Management Review 54, no. 3 (spring 2013): 45-52.
3. For an evolutionary perspective on organizational change involving the generation and selection of concepts, see D.C. Campbell, “Variation and Selective Retention in Socio-Cultural Evolution,’’ in “Social Change in Developing Areas: A Reinterpretation of Evolutionary Theory,” ed. H.R. Barringer, G.I. Blanksten and R.W. Mack (Cambridge, Massachusetts: Schenkman Publishing, 1965).
4. For a statistical view of innovation based on finding extreme-value outcomes (innovations with very high payoffs) through a process that generates lots of varying ideas, see E. Dahan and H. Mendelson, “An Extreme-Value Model of Concept Testing,” Management Science 47, no. 1 (January 2001):102-116.
5. For a compelling analytical approach and case study of users as innovators, including generation and selection of ideas, see C.Y. Baldwin, C. Hienerth and E. von Hippel, “How User Innovations Become Commercial Products: A Theoretical Investigation and a Case Study,” Research Policy 35, no. 9 (December 2006).
6. K.J. Boudreau, N. Lacetera and K.R. Lakhani, “Incentives and Problem Uncertainty in Innovation Contests: An Empirical Analysis,” Management Science 57, no. 5 (May 2011): 843-863; L.B. Jeppesen and K.R. Lakhani, “Marginality and Problem-Solving Effectiveness in Broadcast Search,” Organization Science 21, no. 5 (September 2010): 1016-1033; and Guinan et al., “Experiments in Open Innovation.”
7. Please see A. Winston, “GE’s Eco-Innovation Platform,” October 26, 2011, http://blogs.hbr.org.
8. K.J. Arrow, “Essays in the Theory of Risk-Bearing” (Amsterdam, The Netherlands: North-Holland Publishing Company, 1971), 152.
9. Eric von Hippel and colleagues have discussed tool kits for innovation. E. von Hippel and R. Katz, “Shifting Innovation to Users Via Toolkits,” Management Science 48, no. 7 (July 2002): 821-833; and N. Franke and E. von Hippel, “Satisfying Heterogeneous User Needs via Innovation Toolkits: The Case of Apache Security Software,” Research Policy 32, no. 7 (July 2003): 1199-1215.
10. K.R. Lakhani, H. Lifshitz-Assaf and M. Tushman, “Open Innovation and Organizational Boundaries: Task Decomposition, Knowledge Distribution and the Locus of Innovation,” in “Handbook of Economic Organization: Integrating Economic and Organization Theory,” ed. A. Grandori (Northampton, Massachusetts: Edward Elgar Publishing, 2013), 355-382.
11. The term “spillover” is used in the social sciences to denote that some of the benefits of an activity may accrue to additional actors beyond those pursuing the activity. For example, one company’s R&D investment may help other organizations as well.