Chapter 18: Applying data science in organizational change research
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An often-sought goal in organizational change research is to make sense of complex systems. Once understood, change practitioners can effectively intervene, produce change, and monitor the progress of change in a system. To develop understanding, we must extract and refine factors that hold large influence (i.e., processes and variance; Van de Ven & Poole, 2005) among the myriad factors that compose a system. Extraction and refinement (i.e., data gathering and data analysis) take a variety of different forms (e.g., gathering interview data and performing content analysis or gathering survey data and performing statistical analysis) and are informed by change theory, which guides inquiry and informs practice. Thus, tools well suited to facilitate the activity of developing and maintaining a coherent understanding of complex systems are welcome additions to the toolbox of any change practitioner. Fortunately, recent advances in the multidisciplinary field of data science have produced a vast set of open-source and readily accessible tools for researchers seeking to do just that. The introduction of data science into various fields has brought both challenges and opportunities for researchers as they seek to integrate new capabilities into existing frameworks (e.g., Schuwirth et al., 2019). This chapter serves as an attempt to integrate data science into organizational change research. In this chapter, we will review the existing general framework for research in organizational change, introduce the field of data science, develop an integrative framework for data science in organizational change research, and discuss opportunities for applying the framework.

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