The structure and dynamics of generative innovations: An organizational genetics approach

本研究提出组织遗传学作为一种新的理论和实证方法来理解生成性创新的结构和动力。生成性是指由大量、多样化和不协调的受众驱动的自发变化的整体能力，如Apple、Youtube、Android和WordPress等数字生态系统。在这样的生态系统中，大多数创新都是由第三方个人完成的，他们往往超越了原始创新者的设计意图。利用来自高度生成性创新环境的数据，研究人员试图了解作为遗传元素的现有技术组件的组合（即，基因型）可以在产品水平上产生令人难以置信的丰富和动态的品种（即，表型）。每一项创新都被描述为一组现有组件（称为“技术基因”）的共同表达，其方式与系统生物学家通过基因的共同表达网络描述细胞行为的结构和动力学类似。此外，技术基因的突变可能会导致共表达网络的变化，从而导致数字产品行为的变化。这种对生成性创新的演化分析最终使我们能够理解分布式组织如何在没有集中管理和协调创新活动的传统组织结构的情况下产生生成性创新。更具体地说，该研究有助于组织文献开发新的进化模型的组织设计的创新和开放式创新文献的开放式创新的结构和动态模式，通过第三方开发人员之间的互动。其他重要的方法论贡献是在分析大数据的分析方法的进步，以了解复杂的和多层次的，社会技术现象。虽然来自开放生态系统的数字文物已成为经济的重要组成部分，很少有人知道这些文物的生成性。理解生成性的机制可以在几个方面增强组织的能力：首先，他们可以通过使用现有的人工制品来设计新的数字人工制品，从而变得更具创新性;其次，它可以帮助组织在有效利用数字平台的基础上创造创新机会，从而促进异构和分布式创新者之间的不协调互动。最后，理解生成性不仅可以帮助组织进行产品设计和创新，还可以帮助组织进行病毒式营销等相关工作。这些发现不仅会引起企业和创新者的兴趣，也会引起政策制定者和媒体的兴趣。

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.