Management theory and big data literature: From a review to a research agenda

de Camargo Fiorini, P., Roman Pais Seles, B.M., Chiappetta Jabbour, C.J. , Barberio Mariano, E. and Lopes de Sousa Jabbour, A.B. (2018) Management theory and big data literature: From a review to a research agenda. International Journal of Information Management, 43 . pp. 112-129. ISSN 0268-4012

Full content URL: http://doi.org/10.1016/j.ijinfomgt.2018.07.005

Full text not available from this repository.

Item Type:Article
Item Status:Live Archive

Abstract

• 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. © 2018 Elsevier Ltd

Additional Information:cited By 16
Keywords:Information management, Big Data Analytics, Business growth, Empirical research, Management theory, Organizational performance, Organizational theory, Research agenda, State of the art, Big data
Divisions:Lincoln International Business School
ID Code:39692
Deposited On:20 Jan 2020 11:00

Repository Staff Only: item control page