Management theory and big data literature: From a review to a research agenda - Fiorini, Paula de Camargo [UNESP]; Roman Pais Seles, Bruno Michel [UNESP]; Jabbour, Charbel Jose Chiappetta; Mariano, Enzo Barberio [UNESP]; Jabbour, Ana Beatriz Lopes de Sousa (Elsevier B.V., 2018)
The purpose of this study is to enrich the existing state-of-the-art literature on the impact of big data on business growth by examining how dozens of organizational theories can be applied to enhance the understanding of the effects of big data on organizational performance. While the majority of management disciplines have had research dedicated to the conceptual discussion of how to link a variety of organizational theories to empirically quantified research topics, the body of research into big data so far lacks an academic work capable of systematising the organizational theories supporting big data domain. The three main contributions of this work are: (a) it addresses the application of dozens of organizational theories to big data research; (b) it offers a research agenda on how to link organizational theories to empirical research in big data; and (c) it foresees promising linkages between organizational theories and the effects of big data on organizational performance, with the aim of contributing to further research in this field. This work concludes by presenting implications for researchers and managers, and by highlighting intrinsic limitations of the research.
Keywords: Big data; Big data analytics; Organizational theory; Firms' performance; Research agenda