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The history, evolution, and future of big data and analytics 41 accurately assess whether these data are truly “big” or “smart”, or how analytics is involved in the process. On the other hand, the studies in these CSR clusters did not employ the more advanced, predictive algorithms but rather relied on traditional linear and logistic regression methods. We had hoped to find studies demonstrating how organizations may deal with ethics and privacy concerns when deriving business value through BDA, or how organizations may use BDA to solve costly environmental issues, such as pollution or energy waste. In our eyes, both of these themes would be interesting avenues for future research connecting BDA, CSR, and performance. 2.6.2 Limitations This study faces several limitations, of which we discuss three below. A first limitation involves our search strategy. Although we reached out to nearly fifty experts in the field, only ten responded with keywords for our search. Their responses were internally consistent and had high face validity (e.g., big data, machine learning, deep learning, data science, analytics, artificial intelligence ), but may have had a strong influence on our results. For instance, one could question whether the more distant clusters (e.g., brain-computer interfaces) belong in a review on BDA and performance in organizations. Alternatively, our search strategy may have caused an underrepresentation of specific data types (e.g., wearables, sensors), algorithms (e.g., long-short-term memory networks), or sectors (e.g., healthcare, governments). Second, the interpretation of the results – the networks and the clusters – was limited to our human capabilities in terms of text and information processing. In line with the topic of big data and analytics, future studies could extend our current analysis with a more data-driven approach. For instance, text mining algorithms such as latent Dirichlet allocation (Blei, Ng, & Jordan, 2003) could be used to identify the state of the art topics in big data research. Unfortunately, we were unable to perform such analysis due to the nature of the data extracted from VOSViewer. A third and final limitation is that we had to apply certain thresholds in order to process the data. Here, we followed the established guidelines (i.e., Eck & Waltman, 2014a; Garfield et al., 2003) and we compared different settings in order to test the robustness of analyses. Nevertheless, we acknowledge that these thresholds may have introduced bias in the otherwise relatively objective bibliometric methods. 2.6.3 Future Research Directions Despite these limitations, the current review extends our knowledge of how BDA influences the management and performance of organizations. Based on the results, we have two main directions for future research, related to cross-functional collaborations and to ethics. First, we demonstrated that the cross-functional adoption and application of BDA is scarce, but imminent. While scholars have noted that management researchers within certain streams are too strongly reliant on traditional methodology (e.g., general linear models) and therefore unable to realize the full potential of the “big” data collected through novel technologies (e.g., social media, wearables, sensors, video, audio) (Angrave et al., 2016; Chapter 3), the first bridges have been made. Future scholars and