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The history, evolution, and future of big data and analytics 39 Journal of Business Research respectively. Both these studies examined the effect of BDA on organizational performance in light of dynamic organizational capabilities. 2.6 Discussion This paper reviewed literature on the relationship between big data, analytics and the performance in and of organizations. Three bibliometric method (co-citation analysis, algorithmic historiography, and bibliographic coupling) were applied to a dataset of 324 primary papers and 1252 secondary, cited papers collection via the ISI Web of Knowledge Database. VOSviewer, CitNetExplorer, and Gephi were used to process and visualize the bibliometric networks. The results provided insight into the intellectual foundation and structure, the historic evolution, and the future evolution of research on BDA and organizational performance. Most saliently, clusters of research on predictive analytics were found related to financial risk management, customer relationship management, and, to some extent, marketing. Research using BDA within the functional management domains of supply chain and information technology was also identified but discourse here focused on business intelligence and relationships at an organizational level. Other functional management domains, such as human resource management or legal, seem to be trailing behind, at least in terms of scientific output. The following section discusses the findings in more detail, comparing them to prior review findings, discussing some of our limitations and providing suggestions for future research. 2.6.1 Main Findings & Theoretical Contribution Our bibliometric review is among the first to provide a comprehensive overview of the different perspectives that have been used to explore the implications and applications of BDA for organizational performance at various levels. Here, we discuss the four main insights. First, we found significant overlap and several gaps when comparing our results to those of earlier reviews. Similar to earlier work, we found that BDA is already implemented in the management of customers, information, innovation, technology, and supply chain (Fosso Wamba et al., 2015; Grover & Kar, 2017), and that key topics include machine learning, business intelligence, text analytics and social media data (Grover & Kar, 2017). Moreover, our results cover four out of the six BDA debates found by Günther et al. (2017): our clusters deal with algorithms, the organizational capabilities BDA provide, BDA innovation and strategies, and corporate social responsibility. The inductive-deductive debate and the modes of big data access were not covered in our review. While the number of scientific publication in our reviewed sample was considerably larger than prior reviews, our focus was narrower (i.e. performance in organizations). Potentially, as a result, our review did not replicate the big data research streams in healthcare, education, and public management/government (Grover & Kar, 2017; Fosso Wamba et al., 2015; Sheng et al., 2017). Second, our review provided several new insights. For example, all three analyses display that there are, at best, weak linkages between the strategic management of organizations in the era of BDA, and the actual implementation and operationalization of