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A General framework for studying the evolution of the digital innovation ecosystem: The case of big data

机译:研究数字创新生态系统演变的通用框架:大数据案例

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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.
机译:本文提出了研究数字创新生态系统的总体框架。复杂网络的概念为分析数字创新生态系统的出现和发展提供了一个概念镜头。该框架使用数字数据和进化社区检测分析对数字创新格局进行实证研究。所提出的框架适用于大数据生态系统。来自Twitter的三年数据将进行处理和分析。这项研究揭示了形式和能力各异的大量元素,包括组织,概念(例如,#analytics,#iot),技术(例如,#hadoop),应用程序(例如,#healthcare),基础架构(例如, #cloud),法规,专业会议和协会,工具和知识。这些元素及其社区在大数据生态系统中得到了发展。研究结果通过两个机制(变异和选择性保留)强调了数字创新的发展,这两个机制是非线性的,通常是不可预测的。提出了影响,并讨论了改进建议框架的潜在方法。该研究旨在为数字创新研究做出概念上和方法上的贡献。

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