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Chapter 2 30 cluster focusing on how information technology and business intelligence and analytics add value to organizations (Elbashir, Collier, & Davern, 2008; Fairbank, Labianca, Steensma, & Metters, 2006; Kohli & Grover, 2008) particularly in improving supply chain management (Dehning, Richardson, & Zmud, 2007; Hendricks, Singhal, & Stratman, 2007; Kannan & Tan, 2005; Trkman, McCormack, de Oliveira, & Ladeira, 2010; Stadtler, 2005). Here too, the resource-based view seems a central theory (Newbert, 2007; Wade & Hulland, 2004). Another example is cluster five (N = 116), which we dubbed Knowledge and Innovation. It includes several seminal publications in the general BDA debate (e.g. Hsinchun, Chiang, & Storey, 2012; Manyika et al., 2011; McAfee & Brynjolfsson, 2012) – evidenced by their high weighted degree and closeness centrality in the network (Table 2.3) – but the majority of its publications is specifically focused on how organizations create, transfer, andmanage knowledge, innovation, and learning (e.g., Cohen & Levinthal, 1990; Grant, 1996; Kogut & Zander, 1992; Nonaka & Takeuchi, 1995; Zander & Kogut, 1995). For more details, for instance, regarding the Marketing Analytics and Adoption and Integration clusters, we refer to Table 2.3 and the online appendix. 2 Third, the cluster containing publications on statistics and machine learning algorithms was far removed from the above central clusters. Statistical innovations – such as the bagging of multiple predictors (Breiman, 1996) or decision tree and random forest algorithms (Breiman, 2001; Breiman, Friedman, Stone, & Olshen, 1984) – have only been fully leveraged by the customer analytics cluster (N = 124). Here, scholars have used advanced algorithms and predictive designs to try and predict customers’ loyalty, retention and purchasing behaviors (e.g., Buckinx & Van den Poel, 2005; Larivière & Van den Poel, 2005; Verbeke, Martens, Mues, & Baesens, 2011). All the other large clusters seemed to draw on the algorithms cluster to a lesser extent. For a fourth insight, we refer to the existence of cluster eight (N = 55) on the relationship between ethics, corporate social responsibility and firm performance. Most of its core publications (e.g., Berman, Wicks, Kotha, & Jones, 1999; Graves & Waddock, 1994; Russo & Fouts, 1997) show the (mutually) positive relationships between ethical and green business policies and their performance (for an exception, Hillman & Keim, 2001), as reverberated by the meta-analysis in this cluster (see Orlitzky, Schmidt, & Rynes, 2003). Other papers consider strengths and weaknesses of measuring corporate social responsibility with the social ratings of Kinder, Lydenberg, Domini Research & Analytics (e.g., Berman et al., 1999; Chatterji, Levine, & Toffel, 2009; Sharfman, 1996). Nevertheless, this CSR cluster remains somewhat dislocated from the main network. Fifth and final, two small clusters were found: one on big data analytics in sport (N = 28) and one on brain-computer interfaces (N = 11). The publication dates of their main papers suggest that they are relatively emerging fields (see Table 2.3) and these clusters too appeared only marginally connected to the rest of the network. Overall, Study 1 provided insights into the intellectual structure and foundation of the BDA-performance debate. The largest cluster included both the most renowned management literature on BDA as well as the scientific theories used to link BDA and 2 Datasets available via https://bit.ly/2pHSb57