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Chapter 2 26 domains combined. Science mapping may provide the bigger picture of the state of the art of these domains combined. Third, we use three different and complementary bibliometric methods. Via document co-citation analysis and algorithmic historiography, we explore respectively the past intellectual structure/foundations and the evolution of the BDA-performance debate whereas bibliographic coupling facilitates an objective exploration of the possible future state of research. A bibliometric review of the relationship between BDA and organizational performance contributes to the literature in two ways. First, the bibliographic methods complement other qualitative and quantitative reviews. For instance, they allow a more comprehensive and objective exploration of the history and the evolution of the BDA – performance debate. Compared to previous reviews (see Günther et al., 2017 for some exceptions; Grover & Kar, 2017; FossoWamba et al., 2015), we are not limited to a specific focus, are able to include a larger sample of documents, and thus able to cover and discover more marginal topics within the debate. Second, the bibliometric approach may provide an objective speculation of the potential future of BDA research. Via bibliographic coupling, we hope to shift attention from traditions to future trends, proposing development areas for the future evolution of the debate. We aim to demonstrate how distant or disconnected topics may be linked through theory or empirical applications whereas emerging research fields may derive learning from those related and more established. This paper will continue with a description of the sample, after which each of the three methods and their results will be discussed in separate sections. We conclude with an overall discussion, in light of previous reviews and the future trends resulting from our analyses. 2.2 General Methods 2.2.1 Sample To identify the primary research papers on BDA and performance, we contacted 47 prominent scholars and practitioners who either published on BDA in general or on BDA in management fields (e.g., business studies, human resource management). These experts were asked to elicit ten keywords describing the relationship between BDA and performance at various levels (i.e., organizational, business unit, team, individual). Ten experts responded (21.3%), providing 90 keyword sentences which included 160 single keywords. The most frequently occurring keyword sentences were “ machine learning ” (n = 5), “ data science ” (3), " analytics ” (2), “ deep learning ” (2), “ future ” (2), “ Hadoop ” (2), “ HR analytics ” (2), and “ sensors ” (2) whereas the single words “ data ” (13), “ learning ” (7), “ analytics ” (6), “ machine ” (5), and “ work ” (5) occurred most often. These keywords were used to build our comprehensive search strategy of 54 keyword combinations displayed in Table 2.1. Documents were thus included if they mentioned a BDA keyword as well as “performance” at a certain organizational level.