1. The classical risk-return doctrine has its origins in microeconomics, portfolio theory, and capital allocation models, with the capital asset pricing model being a prime example. This model states that the expected risk premium of an investment is proportional to its (undiversifiable) systemic risk, meaning that increased risk should be rewarded with commensurately higher returns.
2. See V. Bush, “Science: The Endless Frontier” (Washington, D.C.: U.S. Government Printing Office, 1945); D.E. Stokes, “Pasteur’s Quadrant: Basic Science and Technological Innovation” (Washington, D.C.: Brookings Institution Press, 1997); and J.M. Dudley, “Defending Basic Research,” Nature Photonics 7, no. 5 (May 2013): 338-339.
3. See F.G. Solis, “Characterizing Enabling Innovations and Enabling Thinking” (Ph.D. dissertation, Purdue University, School of Civil Engineering, 2015); F. Solis and J. Sinfield, “Rethinking Innovation: Characterizing Dimensions of Impact” (paper presented at the ASEE Annual Conference and Exposition: 360 Degrees of Engineering Education, Indianapolis, Indiana, June 15-18, 2014); and F. Solis and J.V. Sinfield, “Rethinking Innovation: Characterizing Dimensions of Impact,” Journal of Engineering Entrepreneurship 6, no. 2 (June 2015): 83-96.
4. See B. Godin, “Innovation Studies: The Invention of a Specialty (Part I),” (Montreal, Quebec: Project on the Intellectual History of Innovation, working paper 7, 2010); and B. Godin, “Innovation Studies: The Invention of a Specialty (Part II),” (Montreal, Quebec: Project on the Intellectual History of Innovation, working paper 8, 2010).
5. Solis and Sinfield, “Rethinking Innovation”: 83.
7. Until relatively recently, most definitions of innovation were focused on solutions — in particular, technical solutions. Solutions were characterized by their novelty and the way they differed from prior solutions. Product and process innovations focused on improvements to what was already available. Solutions that have broad applicability (such as the steam engine, electrification, and the Internet) have often been called general purpose technologies, or GPTs. Beginning in the 1990s, researchers also began paying close attention to service innovations and business model innovations. These involve changes in how solutions are designed and implemented, but they don’t always involve new technology. See J.M. Utterback and W.J. Abernathy, “A Dynamic Model of Process and Product Innovation,” Omega, The International Journal of Management Science 3, no. 6 (December 1975): 639-656; J.E. Ettlie, W.P. Bridges, and R.D. O’Keefe, “Organization Strategy and Structural Differences For Radical Versus Incremental Innovation,” Management Science 30, no. 6 (June 1984): 682-695; and T.F. Bresnahan and M. Trajtenberg, “General Purpose Technologies ‘Engines of Growth?’” Journal of Econometrics 65, no. 1 (January 1995): 83-108.
8. Also in the 1990s, a new perspective on innovation began to focus on end-users. Rather than looking at developments in comparison to prior solutions, researchers examined what users found important. In his work on disruptive innovation, Harvard Business School professor Clayton M. Christensen found that some users were willing to make significant trade-offs in performance and forgo traditional benefits based on what they sought to accomplish in their own circumstances, particularly when they were constrained by wealth, time, access, or expertise. An extension of this work has focused on market-creating innovations, as exemplified by Ford Motor Co.’s Model T and the personal computer. Such innovations democratize complicated or costly products for markets that previously didn’t exist. See C.M. Christensen, “The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail” (Boston: Harvard Business Press, 1997); C.M. Christensen and D. van Bever, “The Capitalist’s Dilemma,” Harvard Business Review 92, no. 6 (June 2014): 60-68; and S.D. Anthony, M.W. Johnson, J.V. Sinfield, and E.J. Altman, “The Innovator’s Guide to Growth: Putting Disruptive Innovation to Work” (Boston: Harvard Business Press, 2008).
9. Solis and Sinfield, “Rethinking Innovation”: 88-89.
10. Ibid.; and Solis, “Characterizing Enabling Innovations.”
11. Our research suggests that this pattern is equally applicable to large-scale socioeconomic and systems-level challenges often encountered in nonprofit and government contexts.
12. See H. Bray, “You Are Here: From the Compass to GPS, the History and Future of How We Find Ourselves” (New York: Basic Books, 2014); and W.H. Guier and G.C. Weiffenbach, “Genesis of Satellite Navigation,” Johns Hopkins Technical Digest 19, no. 1 (January-March 1998): 14-17.
13. M. Yunus, “Banker to the Poor: Micro-Lending and the Battle Against World Poverty” (New York: Public Affairs, 1999).
14. See D. Dougherty and C. Hardy, “Sustained Product Innovation in Large, Mature Organizations: Overcoming Innovation-to-Organization Problems,” Academy of Management Journal 39, no. 5 (October 1996): 1120-1153; E. Maine and E. Garnsey, “Commercializing Generic Technology: The Case of Advanced Materials Ventures,” Research Policy 35, no. 3 (April 2006): 375-393; N. Matta and R. Ashkenas, “Why Good Projects Fail Anyway,” Harvard Business Review 81, no. 9 (December 2003): 109-116; B.J. Sauser, R.R. Reilly, and A.J. Shenhar, “Why Projects Fail? How Contingency Theory Can Provide New Insights — A Comparative Analysis of NASA’s Mars Climate Orbiter Loss,” International Journal of Project Management 27, no. 7 (October 2009): 665-679; and L.R. Cohen and R.G. Noll, “Government R&D Programs For Commercializing Space,” American Economic Review 76, no. 2 (May 1986): 269-273.
15. See B.H. Kevles, “Naked to the Bone: Medical Imaging in the Twentieth Century” (New Brunswick, N.J.: Rutgers University Press, 1997); and R.B. Gunderman, “X-Ray Vision: The Evolution of Medical Imaging and Its Human Significance” (New York: Oxford University Press, 2012).
16. In fact, the enabling-progressive pattern suggests that a handful of solutions underpin a tremendous amount of impact that can be exploited by businesses, agencies, foundations, and societies alike.
17. See A. Gawande, “Two Hundred Years of Surgery,” New England Journal of Medicine 366, no. 18 (May 3, 2012): 1716-1723; and A. Gawande, “Slow Ideas,” The New Yorker, July 29, 2013.
18. Bray, “You Are Here.”
19. J. Hecht, “City of Light: The Story of Fiber Optics” (New York: Oxford University Press, 2004).
20. In what could be considered lily pads to mobile robotics, iRobot developed capabilities in spatial exploration through the Genghis robot, in real-time data gathering through MicroRig, in real-time interactions through the toy My Real Baby and the robot IT, in navigation and mobility through the Gecko and Holon robots, and in floor cleaning through the NexGen Floor Care Solution. See iRobot, “Cool Stuff: The Story of Our Robots,” www.irobot.com.
21. W. Isaacson, “The Innovators: How a Group of Hackers, Geniuses, and Geeks Created the Digital Revolution” (New York: Simon & Schuster, 2014).
22. For example, while attempting to synthesize an antimalarial agent, two scientists unexpectedly produced a purple compound that turned out to be a synthetic dye. Recognizing the potential to exploit this synthetic chemistry capability, many Swiss and German companies, including IG Farben (part of which was later split off as Bayer), led the creation of the synthetic dye industry. Eventually, the capabilities developed by these companies in the manufacture of synthetic dyes helped them transform into modern pharmaceutical companies and led to important successes in human health applications such as aspirin, antibacterials, and chemotherapy. See B.J. Yeh and W.A. Lim, “Synthetic Biology: Lessons From the History of Synthetic Organic Chemistry,” Nature Chemical Biology 3, no. 9 (September 2007): 521-525.
23. Gawande, “Two Hundred Years”; and Gawande, “Slow Ideas.”
24. iRobot, “2014 Annual Report,” http://investor.irobot.com.
26. J. Ginsberg, “Development of Deep-Tank Fermentation, Pfizer Inc.” (Washington, D.C.: American Chemical Society, June 12, 2008).
27. J.V. Sinfield, E. Calder, B. McConnell, and S. Colson, “How to Identify New Business Models,” MIT Sloan Management Review 53, no. 2 (winter 2012): 85-90.
i. Solis, “Characterizing Enabling Innovations.”