Industrial impact of Big Data in 2020: Machine Learning and Artificial Intelligence will proliferate. While research seems to have evolved following two main, isolated streams, the past decade has witnessed more cross‐disciplinary collaborations. Removing all non‐essential relations minimizes the edges in the network while ensuring that all previously connected publications still have a pathway connecting them. Overall, Study 1 provided insights into the intellectual structure of the BDA and performance debate. Here, mainstream clusters such as Strategic Big Data and Analytics could learn from collaborations with scholars in the peripheral clusters. The full text of this article hosted at iucr.org is unavailable due to technical difficulties. Clusters represent closely related papers, sharing thematic similarities. Journal of Product Innovation Management. The results provide insight into the intellectual structure and the past and future evolution of research linking BDA to organizational performance. While research seems to have evolved following two main, isolated streams, the past decade has witnessed more cross‐disciplinary collaborations. This … Yet, different review methods can be used to shed light further on the debate. Based on the full text of their most important papers, we named the clusters (1) Risk and Customer Predictions, (2) Strategic BDA, (3) Information and Knowledge Management, (4) Text and Genetic Algorithms, (5) CSR, (6) Clustering, (7) Sports Analytics, (8) BCI. Complex and inaccurate data or predictions can create a false sense of authority, whereby organizational decisions based on them appear objective and indisputable. Based on the content of strategic BDA cluster in Study 3, we suggest two ways for potential expansion. Their responses were internally consistent and had high face validity (e.g. The use of BDA seems established in relation to financial risk and customer relationship management, where predictive modelling and the more advanced statistical algorithms are already widely applied, researched and discussed. First, it identifies the core publications through the concept of k‐cores (Seidman, 1983), where publications are considered core when they have a certain minimum number of ingoing or outgoing citation relations with other core publications. While the number of scientific publications in our reviewed sample was considerably larger than prior reviews, our focus was narrower (i.e. Second, this first cluster is closely connected to several other clusters, which cover more specialized topics related to BDA. Although we reached out to nearly fifty experts in the field, only ten responded with keywords for our search. In management literature, at least, a cross‐disciplinary overview of the BDA discussion is lacking. The International Journal of Human Resource Management, https://doi.org/10.1016/S0305-0548(99)00149-5, International Journal of Sports Science & Coaching, Journal of Management Information Systems, International Journal of Production Research, International Journal of Production Economics, Journal of the Association for Information Systems, 1. Consequently, the clusters could be 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). Cluster six (N = 6) examined how clusters can be identified and ranked in order to improve recommendation engines and other business processes (e.g. If you do not receive an email within 10 minutes, your email address may not be registered, general linear models), thereby not realizing the full potential of the ‘big’ data collected through modern technology (e.g. Finally, we suggest that scholars exploring the BDA–performance relationship should explore a more diverse range of theoretical perspectives. 2005 - The tiny toy elephant Hadoop was developed by Doug Cutting and Mike Cafarella to handle the big data explosion from the web. Other geographical, sectoral or domain‐level differences in BDA development may include the technological capabilities of the workforce, or the perceived ethicality of using predictive profiling in specific settings. Nowadays it is blended with many techniques such as artificial intelligence, statistics, data science, database theory and machine learning. Scholars have argued that novel machine learning capabilities may realize the predictive value of big data, unleashing its strategic potential to transform business processes and providing the organizational capabilities to tackle key business challenges (Fosso Wamba et al., 2015). Big data and analytics (BDA) are gaining momentum, particularly in the practitioner world. His current research is focused on multi‐level issues between HR systems, climates and employees’ behaviours, leadership and organizational socialization. Similarly, the historiography (Figure 3) and the coupling network (Figure 4), underline the weak overlap in the shared knowledge and discourse between research covering strategical issues in BDA research (e.g. People analytics effectiveness: developing a framework. For instance, text‐mining algorithms such as latent Dirichlet allocation (Blei, Ng and Jordan, 2003) could be used to identify the state‐of‐the‐art topics in BDA research. 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