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The relationship between a firm's experience with knowledge recombination and the value of its innovation output, using a longitudinal dataset of patents in the photographic imaging industry. The study distinguishes between specific and other types of experience and tests the hypothesis that the stock of specific recombination experience has an inverted U-shaped relationship with innovation value, and that recombining across knowledge domains has a larger positive effect than recombining within a domain.
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Anindya Ghosh, Univeristy of Pennsylvania Xavier Martin, Tilburg University Johannes M Pennings, University of Pennsylvania & Tilburg University and Filippo C Wezel, University of Lugano
SMS 29th Annual Conference Submission February 9, 2009
This paper develops an evolutionary theory of the innovation process through the lens of organizational learning. We consider the firm’s experience with recombining within and across disparate domains of technological knowledge and attempt to determine the impact of its innovative output as revealed by the acknowledgement of peer firms. We distinguish between recombinations taking place within versus across knowledge domains to describe how complex the search for new knowledge is. We also distinguish among specific and other types of experience, depending on whether past experience is directly related to the focal recombination. Using a longitudinal dataset of patents in the photographic imaging industry, we test the following: a) the stock of specific recombination experience has an inverted U-shaped relationship with the value of an innovation as measured by forward citations of its intellectual property; and b) recombining across knowledge domains has a larger positive effect on that value than recombining within a domain.
Keywords: Recombination, Innovation, Knowledge, Imaging, Patents, Organizational Learning
boundaries of technological niches (Podolny & Stuart, 1995) and to investigate how a structural position in an industry network influences firm survival (Podolny, Stuart, & Hannan, 1996). Direct and indirect ties with incumbent firms as well as structural holes in a firm’s network have also been explored as conditions which are conducive to innovative output (Ahuja, 2000; Burt, 1992, 2004); other studies dwelled on explorative search by the bricolage of knowledge dispersed among various actors (Garud & Karnoe, 2003). Knowledge integration more than knowledge itself has been claimed as the critical source of competitive advantage (Grant, 1996) and a critical source of novelty and success of innovations (Hargadon & Sutton, 1997; Hsu & Lim, 2008). Recent innovation research has exploited publicly available patent data as revealing innovative outcroppings of firms and sectors. A wide body of research is now available about diverse mechanisms of recombination, pointing to the intensity and to the rareness of the coupled knowledge (Fleming & Sorenson, 2001), to the usage of scientific evidence (Fleming & Sorenson, 2004; Sorenson & Fleming, 2004), and to scientists’ mobility across regions or firm alliances (Rosenkopf & Almeida, 2003) as key determinants of the focal innovation’s impact – and thereby of firms in their competitive domains (Rosenkopf & Nerkar, 2001). Most of the literature examines the link between the success of an innovation (e.g., the number of forward citations received) to either (1) the characteristics of the focal patent (e.g., number of technological classes, inter-personal ties among inventors) or (2) the characteristics of the focal firm (e.g., structural position in the industry, alliances) at the time that the innovation occurs. Few studies have explicitly dealt with the accumulation of recombination experience as a factor in successful recombination, and none to our knowledge addresses our specific research question.
Knowledge Recombination - Concepts
We define Knowledge Recombination as the bundling of disparate domains of knowledge. We conceptualize knowledge to be comprised of broad domains with smaller sub-domains. For example, the broad domain of chemical knowledge is further subdivided into sub-domains of Coating Chemical knowledge and Resin Chemical knowledge. We further refine knowledge recombination in two categories. First, Specific Knowledge Recombination refers to the bundling of a given set of knowledge domains or sub-domains that are likely to be essential to both derive competitive advantage and survive in a given sector. Second, we define Other Knowledge Recombination as various combinations of knowledge domains or sub-domains other than the focal set. Further, we distinguish between across and within-domain recombination to identify the breadth of recombination of knowledge. Across-Domain Recombination refers to recombination across knowledge domains. An example would be a patent combining optical technologies and chemical technologies. Within-Domain Recombination refers to any combination of which all the components are within a single knowledge domain but that mix knowledge from two or more sub-domains within it.
Knowledge Recombination Experience and Impact of Innovation
Experience with Specific Recombination As outlined above, the literature argues that recombining knowledge is associated with more substantive innovation, but has largely ignored differences among innovating firms, as well as differences in whether the recombination spans multiple knowledge domains. From an organizational learning perspective, experience (Argote, 1999) and routine development (Nelson et al., 1982) are essential to successfully mastering a given knowledge recombination.
the search space; diminishing and even negative returns emerge beyond some point due to opportunity exhaustion. Thus we predict: H1: Experience with a specific across-domain knowledge recombination is inversely U- shaped related to the impact of that specific recombinant innovation. Other Recombination Experience The possibility also exists of substantive spillover effects across innovation projects. Again, the distinction between within-domain and between-domain recombination is relevant. Learning may result from practicing a different combination of knowledge sub-domains within the same domain, albeit with the aforementioned threat that exploitation displaces exploration (Levinthal & March, 1993; Benner & Tushman, 2002) and with the additional risk that the relevance of one sub-domain to another may be overestimated. The fuller power of organizational learning should manifest itself in the case of across- domain recombinations. The knowledge being drawn upon may be more diverse, yet applicable to the extent that some domains are explored in common between successive projects. Furthermore, reaching across multiple domains, by itself, stands to develop a discipline of bridging across scientific fields and managing the complex demands of broad search projectsthat encourages exploration and overcomes learning myopia (Levinthal & March, 1993). Though more weakly applicable than the benefits of direct experience with a specific recombination, other across-domain recombination experience may also be less prone to opportunity exhaustion as the search space is potentially much bigger. Thus, though still subject to declining returns, it is an open question whether this type of search exhibits negative returns. Thus we hypothesize that: H2: Experience with other across-domain knowledge recombination is positively related with the impact of an innovation
Empirical Setting and Initial Results and Discussion
We test our predictions in the photographic imaging sector from 1976-2002. This contains participants from multiple industries including many of its progenitors - the chemically based, silver halide photographic technologies with major players such as Kodak, Agfa, Polaroid and Fuji, for whom digital imaging technology represented a “competence destroying” discontinuity. By contrast some new entrants originated from the consumer electronics industry (e.g. Panasonic), and leveraged their experience with video. Another group originated from the graphic arts and printing industry and pioneered the use of electronic scanning. Finally, many entrants entered from computer hardware, software and semiconductor industries (e.g. Intel, Hewlett Packard, Adobe) as digital cameras began to be accepted as computer peripherals. Thus, photographic imaging today draws on technological competencies from the semiconductor and electronics industries, computer hardware and software industries, and conventional film based imaging industries. We use the 32,000 most significant imaging patents identified suggested by technology intelligence researchers at Kodak and select firms that have at least one patent per year on average over the window. We focus on recombinations of Chemical, Computer & Communication and Electrical & Electronics knowledge given their importance amidst the technological convergence between chemical and digital technologies. Following extant research, we measure innovation impact and value via forward citations in a multi-year window. Initial results show consistent support for H1: Experience with specific across-domain recombinations is associated with greater innovation impact up to a point, but with lower innovation impact subsequently. H2 receives mixed support, with some but not all across-domain recombinations being beneficial to innovations involving different domain recombinations. The results show several other interesting patterns, including additional effects of recombination
Ahuja, G. 2000. Collaboration Networks, Structural Holes, and Innovation: A Longitudinal Study. Administrative Science Quarterly , 45(3): 425-455. Argote, L. 1999. Micro underpinnings of organizational learning, Retaining, and Transferring Knowledge. Organizational Learning: Creating, Argyres, N. S., & Silverman, B. S. 2004. R&D, Organization Structure, and the Development of CorporateTechnological Knowledge. Strategic Management Journal , 25(8/9): 929. Burt, R. S. 1992. Structural Holes. Burt, R. S. 2004. Structural Holes and Good Ideas. Cambridge, MA. American Journal of Sociology , 110(2): 349-399. Fleming, L. 2001. Recombinant Uncertainty in Technological Search.132. Management Science , 47(1): 117- Fleming, L., & Sorenson, O. 2001. Technology as a complex adaptive system: evidence from patent data. Research Policy , 30(7): 1019-1039. Fleming, L., & Sorenson, O. 2004. Science as a map in technological search. Journal , 25(89): 909-928. Strategic Management Garud, R., & Karnoe, P. 2003. Bricolage versus breakthrough: distributed and embedded agency intechnology entrepreneurship. Research Policy , 32(2): 277-300. Grant, R. M. 1996. Toward a Knowledge-Based Theory of the Firm.109-122. Strategic Management Journal , 17: Griliches, Z. 1984. Hargadon, A., & Sutton, R. I. 1997. Technology Brokering and Innovation in a Product Development Firm. R & D, Patents, and Productivity : University of Chicago Press.
Henderson, R., & Clark, K. 1990. Architectural innovation.^ Administrative Science Quarterly , 42(4): 716-749. Administrative Science Quarterly , 35(1): 9- Hsu, D. H., & Lim, K. 2008. The Antecedents and Innovation Consequences of Organizational Knowledge30. Jaffe, A. B., Trajtenberg, M., & Henderson, R. 1993. Geographic Localization of Knowledge Spillovers asBrokering Capability.^ Working Paper. Munir, K. A. 2003.Evidenced by Patent Citations. Technological Evolution and the Construction of Dominant Designs in the^ The Quarterly Journal of Economics , 108(3): 577-598. Nelson, R. R., & Winter, S. G. 1982. Imaging Industry : McGill University Doctoral Dissertation. An Evolutionary Theory of Economic Change : Harvard University Podolny, J. M., & Stuart, T. E. 1995. A Role-Based Ecology of Technological Change.Press. The American Podolny, J. M., Stuart, T. E., & Hannan, M. T. 1996. Networks, Knowledge, and Niches: Competition in Journal of Sociology , 100(5): 1224-1260. the Worldwide Semiconductor Industry, 1984-1991.102(3): 659-689. The American Journal of Sociology , Rosenkopf, L., & Almeida, P. 2003. Overcoming Local Search Through Alliances and Mobility. Management Science , 49(6): 751-766. Rosenkopf, L., & Nerkar, A. 2001. Beyond Local Search: Boundary-Spanning, Exploration, and Impact inthe Optical Disk Industry. Strategic Management Journal , 22(4): 287-306. Schumpeter, J. A. 1942. Singh, J. 2007. Asymmetry of Knowledge Spillovers between MNCs and Host Country Firms. Capitalism, Socialism and Democracy. New York: Harper. Journal of
Sorenson, O., & Fleming, L. 2004. Science and the diffusion of knowledge. International Business Studies , 38(5): 764-786. Research Policy , 33(10): 1615-1634.
