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The history, evolution, and future of big data and analytics 35 stream discussed the resource-based view (Barney, 1991), the dynamic capabilities of organizations (Wernerfelt, 1984), and a knowledge-based theory of organizations (Barney, 1991; Grant, 1996; Wernerfelt, 1984). This foundation has resulted in two main themes in contemporary papers within the stream. On the one hand, there is a general discussion regarding how big data and analytics affect organizational performance and specifically the performance of several business functions (e.g., supply chain, human resource management) (LaValle et al., 2011; Trkman et al., 2010). On the other hand, there are papers discussing the general topics of business intelligence and analytics in this second stream (Fosso Wamba et al., 2015; Hsinchun et al., 2012). These publications mostly explored the theory behind and evidence for impact of business intelligence, analytics and big data on organizational performance, but lack rigorous advanced analytical methods. An interesting final deduction we can make from Figure 2.2 is that the above two evolutionary streams have only recently been connected. The responsible papers cover customer event history (Ballings & Poel, 2012) and the ways in which BDA may form a competitive advantage for organizations (Manyika et al., 2011). Overall, where Study 1 elucidated the intellectual foundation and structure of the field, Study 2 added to this by providing an overview of its historical evolution. Some findings of the two studies overlap. For instance, the large gap between the methodological and theoretical discussions surrounding BDA is visible in both Figures 2.1 and 2.2. Moreover, the paper linking the two evolutionary streams in Figure 2.2 studied customer event history (Ballings & Poel, 2012) whereas the Customer Analytics cluster bridged the algorithms with the rest of the network in Study 1. 2.5 Study 3: Bibliographic Coupling Bibliographic coupling examines the extent to which documents cite the same secondary documents. This implies that the primary, citing document is the focus of analysis rather than the cited, secondary documents (Vogel & Güttel, 2013). The general assumption is that the more the bibliographies of two documents overlap, the stronger their connection is. Bibliographic coupling is different from other bibliometric methods as it does not derive the importance of papers within a scholarly community from their citation count or relations (Verbeek, Debackere, Luwel, & Zimmermann, 2002). This prevents an (over)emphasis on mainstream documents that may be popular but insignificant to a fields’ intellectual development. Moreover, because it relies on the references within documents, the results of bibliographic coupling are more stable over time because reference lists do not change over time (in contrast to citation counts and relations). All this makes coupling particularly suitable for detecting current trends and future priorities as these are commonly covered in the more recent publications, which inherently are not the most cited. Although we intended to use the retrieved dataset of 324 primary papers in the bibliographic coupling, only 211 of these primary documents (65.12%) were interconnected in the same network. The other papers had completely unconnected reference lists and were thus automatically removed by VOSviewer (Van Eck & Waltman,