This project puts forth organizational genetics as a novel theoretical and empirical approach to understand the structure and dynamics of generative innovations. Generativity refers to an overall capacity to produce unprompted change driven by a large, varied, and uncoordinated audience, as seen in digital ecosystems such as Apple, Youtube, Android and Wordpress. In such ecosystems, most innovations are accomplished by third-party individuals who often go beyond the design intent of the original innovators. Using data from highly generative innovation contexts, the researchers seek to understand how combinations of existing technological components that act as genetic elements (i.e., genotypes) can give birth to the incredibly rich and dynamic varieties at the product level (i.e., phenotypes). Each individual innovation is characterized as a co-expression of a certain set of existing components (referred to as "technology genes") in the similar way system biologists characterize the structure and dynamics of cell behaviors though a co-expression network of genes. Furthermore, the ways in which mutations in technology genes can cause changes in the co-expression network, which in turn causes changes in the behavior of the digital product is explored.

Such evolutionary analysis of generative innovations ultimately allows an understanding of how distributed organizations can produce generative innovations without a traditional organizational structure that centrally governs and coordinates innovation activities. More specifically, the research contributes to the organization literature by developing new evolutionary model of organization design for innovations and to the open innovation literature by showing the structural and dynamic patterns of open innovations through interactions among third-party developers. Additional important methodological contributions are in the advancement of an analytical approach to analyze big data to understand complex and multi-level, socio-technical phenomenon.

Although digital artifacts from open ecosystems have become an important part of the economy, very little is known about the generativity of such artifacts. Understanding the mechanisms of generativity can empower organizations in several ways: first, they can become more innovative by using existing artifacts to design new digital artifacts; second, it can help organizations create innovation opportunities based on effective exploitation of digital platform that prompts uncoordinated interactions among heterogeneous and distributed innovators; and finally, understanding generativity can not only help organizations in product design and innovation, but also in related contexts such as viral marketing. These findings will be of interest not only to firms and innovators, but also to policy makers and the media as well.

National Science Foundation (NSF)
Division of Social and Economic Sciences (SES)
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Chu-Hsiang Chang
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Temple University
United States
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