Big data analytics and firm performance: Findings from a mixed-method approach

doi:10.1016/j.jbusres.2019.01.044

Journal of Business Research

Volume 98, May 2019, Pages 261-276

https://doi.org/10.1016/j.jbusres.2019.01.044 Get rights and content

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Abstract

Big data analytics has been widely regarded as a breakthrough technological development in academic and business communities. Despite the growing number of firms that are launching big data initiatives, there is still limited understanding on how firms translate the potential of such technologies into business value. The literature argues that to leverage big data analytics and realize performance gains, firms must develop strong big data analytics capabilities. Nevertheless, most studies operate under the assumption that there is limited heterogeneity in the way firms build their big data analytics capabilities and that related resources are of similar importance regardless of context. This paper draws on complexity theory and investigates the configurations of resources and contextual factors that lead to performance gains from big data analytics investments. Our empirical investigation followed a mixed methods approach using survey data from 175 chief information officers and IT managers working in Greek firms, and three case studies to show that depending on the context, big data analytics resources differ in significance when considering performance gains. Applying a fuzzy-set qualitative comparative analysis (fsQCA) method on the quantitative data, we show that there are four different patterns of elements surrounding big data analytics that lead to high performance. Outcomes of the three case studies highlight the inter-relationships between these elements and outline challenges that organizations face when orchestrating big data analytics resources.

Keywords

Big data analytics

Complexity theory

fsQCA

Business value

Mixed-method

Environmental uncertainty

Cited by (0)

Patrick Mikalef is a Marie Skłodowska-Curie post-doctoral research fellow in the area of Information Systems Strategy. He received his B.Sc. in Informatics from the Ionian University, his M.Sc. in Business Informatics for Utrecht University, and his Ph.D. in IT Strategy from the Ionian University. His research interests are on strategic use of information systems and IT-business value in turbulent environments. He has published work in international conferences and peer-reviewed journal including the Journal of Business Research, Industrial Management & Data Systems, Journal of Theoretical and Applied Electronic Commerce Research, and Health Information and Libraries Journal.

Maria Boura is post-doctoral researcher at the Department of Management Science and Technology of the Athens University of Economics and Business. Her research interests focus on Business Strategy, Corporate Social Responsibility, Business Ethics and Corruption, and Big data and management. Her research papers have been published in international academic conferences (Academy of Management, European Academy of Management, British Academy of Management, European Group for Organizational Studies, European Conference on Information Systems). She holds a PhD and an MSc from the Athens University of Economics and Business (Greece).

George Lekakos is Associate Professor at the Department of Management Science and Technology, Athens University of Economics and Business, Greece. He is also Director of the MSc in Management Science and Technology. He leads the Intelligent Media Lab (IML), which is a research group within the ELTRUN Research Center (http://www.eltrun.gr) at the Athens University of Economics and Business (AUEB), Greece. His research focuses on Machine learning, Recommender Systems, Big Data and Strategy, and e-business. He has published more than sixty papers in international journals and conferences (e.g. EJOR, International Journal of Electronic Commerce, User Modelling and User adapted interaction etc.), and he is co-editor of books, conference proceedings, journals' special issues and serves as editorial board member of international journals.

John Krogstie holds a PhD (1995) and a MSc (1991) in information systems from the Norwegian University of Science and Technology (NTNU), where he is currently a full professor in information systems at the IDI-department. At IDI he is Department Head. John Krogstie is the Norwegian representative and Vice-Chair for IFIP TC8 and was chair of IFIP WG 8.1 on information system design and evaluations (2010–2015). His research interests are information systems modelling, quality of models and modelling languages, eGovernment and mobile information systems. He has published around 250 refereed papers in journals, books and archival proceedings since 1991. H-index as of July 2018 is 40, G-index 58 according to Google Scholar.