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This paper presents a general framework for studying the digital innovation ecosystem. The notion of complex networks offers a conceptual lens to analyze the emergence and evolution of a digital innovation ecosystem. The framework uses digital data and evolutionary community detection analysis for the empirical inquiry of the digital innovation landscape. The proposed framework is applied to the big data ecosystem. Data from Twitter, for a three year period, is processed and analyzed. This study reveals a large number of elements that are diverse in form and capacity, including organizations, concepts (e.g., #analytics, #iot), technologies (e.g., #hadoop), applications (e.g., #healthcare), infrastructures (e.g., #cloud), regulations, professional meetings and associations, tools, and knowledge. These elements and their communities have evolved in the big data ecosystem. The findings highlight the evolution of digital innovation by two mechanisms, variation and selective retention, which are nonlinear and often unpredictable. Implications are presented and potential ways to improve the proposed framework are discussed. The study aims to make both conceptual and methodological contributions to digital innovation research. |
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