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The history, evolution, and future of big data and analytics 29 citations (Batistič, Černe, & Vogel, 2017). Therefore, it can reflect both the state of a certain intellectual field as well as the shifts in schools of thought (Pasadeos, Phelps, & Kim, 1998). Additionally, co-citations can reveal the intellectual roots of a scientific domain through the identification of its core, most cited works. Via document co-citation analysis, we aimed to explore the historical overview of the big data – performance debate. The previously described database of secondary articles was normalized for association strength in VOSviewer (Van Eck & Waltman, 2014b), thereby acknowledging that certain nodes (secondary papers) are more important to the network because they have more connections. Subsequently, the normalized data was loaded into Gephi (Bastian, Heymann, & Jacomy, 2009) – the leading open-source visualization and exploration software for graphs and networks, which includes a broad statistics and metrics framework for network analysis, and allows flexibility in network refinement and visualization. Using a forced-directed network layout (Hu, 2005), we displayed nodes (i.e., papers) in a two-dimensional space in such a way that more related nodes are co-located whereas weakly related nodes are distant from each other. 2.3.1 Results The 1252 documents in the co-citation network stabilized into ten clusters. The content of these clusters was assessed by examining the full texts of the most important articles by weighted degree. Consequently, the clusters were named (1) BDA Foundation, (2) Statistical Algorithms, (3) Marketing Analytics, (4) Customer Analytics, (5) Knowledge and Innovation, (6) Information Technology (IT) and Supply Chain (SC), (7) Adoption and Integration, (8) Corporate Social Responsibility, (9) Sports Analytics, and (10) Brain- Computer Interfaces (BCI). Table 2.3 provides an overview of these clusters and their articles. The structure of the co-citation network (Figure 2.1) provided several insights. First, a large cluster of papers (N = 324), very central to the network, covers a variety topics that are seemingly the foundation for research linking BDA to performance in organizations. Popular publications explain how BDA and data-driven strategies provide organizations a competitive advantage (Barton & Court, 2012; Davenport, 2006; Davenport, Barth, & Bean, 2012; Davenport & Harris, 2007; Fosso Wamba et al., 2015; LaValle et al., 2011) whereas other publications focus on the impact of IT for organizational performance (Melville, Kraemer, & Gurbaxani, 2004; Devaraj & Kohli, 2003; Mithas, Ramasubbu, & Sambamurthy, 2011; Santhanam & Hartono, 2003; Tippins & Sohi, 2003). In either case, the resource-based view is theory that explains the impact (Barney, 1991; Bharadwaj, 2000). Others publications cover more methodological topics, such as structural equation modelling and partial least square (Fornell & Larcker, 1981; Hair, Ringle, & Sarstedt, 2011; Wetzels, Odekerken-Schröder, & Van Oppen, 2009), mediation (Baron & Kenny, 1986; Devaraj & Kohli, 2003; Tippins & Sohi, 2003), or measurement issues (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003; Santhanam & Hartono, 2003). Second, this BDA foundation cluster is closely connected to several others clusters, which cover more specialized topics related to BDA. For instance, there is a separate