The full text of this article hosted at iucr.org is unavailable due to technical difficulties. We call for future research examining to what extent the above issues occur in organizations, how they are currently handled, and what best practices can be implemented to prevent them from happening. The responsible papers cover customer event history (Ballings and Poel, 2012) and the ways in which big data may form a competitive advantage for organizations (Manyika et al., 2011). Hence, it remains unclear whether and how BDA applications in the different domains overlap, how these domains perceive BDA, and what theories have been used to ground potential BDA–performance linkages (Sheng, Amankwah‐Amoah and Wang, 2017; Sivarajah et al., 2017). Potentially, IT scholars could draw on research on marketing, organizational behaviour or human resource management for such insights. 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. Big data systems: knowledge transfer or intelligence insights? For example, in the inbound logistics part of the framework, BDA can analyse historical data to provide support for a just‐in‐time approach to receiving, storing and distributing inputs internally. From an IT perspective, three main organizational resources are considered: (1) the tangible resources related to the physical IT infrastructure; (2) the human IT resources (e.g. In 2002, Doug Cutting and Mike Cafarella were working on Apache Nutch Project that aimed at building a web search engine that would crawl and index websites. However, the direction rather than the weight of this relationship is of importance as relationships are binary – a primary paper either does or does not cite a second primary paper. Here, we followed the established guidelines (Eck and Waltman, 2014a; Garfield, Pudovkin and Istomin, 2003), and we compared different settings in order to test the robustness of analyses. Number of times cited according to CrossRef: Big Data Analytics in Building the Competitive Intelligence of Organizations. LaValle et al., 2011), little research has been done. Particularly when it comes to predictive analytics, scholars and practitioners should take additional care in preventing the creation of self‐fulfilling prophecies or the incorporation of human bias into decision‐making algorithms (Herschel and Miori, 2017). While research seems to have evolved following two main, isolated streams, the past decade has witnessed more cross‐disciplinary collaborations. Table 2 provides an overview of these clusters and their papers. Ostroff and Bowen, 2016) or to increase their employees’ human capital, which, in turn, might make them more proficient with the BDA tools (Mikalef et al., 2018; Rasmussen and Ulrich, 2015). Big data evolution: Forging new corporate capabilities for the long term Big data evolution: forging new corporate capabilities for the long term is an Economist Intelligence Unit report, sponsored by SAS. Data analytics and performance: The moderating role of intuition-based HR management in major league baseball. The extent to which an organization is able to develop, mobilize and exploit resources are called their organizational capabilities (Russo and Fouts, 1997). Blockchain in the operations and supply chain management: Benefits, challenges and future research opportunities. Finally, betweenness centrality represents a node's uniqueness in connecting other unconnected nodes. Here, mainstream clusters such as Strategic Big Data and Analytics could learn from collaborations with scholars in the peripheral clusters. Reflections on the 2014 Decade Award: is there strength in the construct of HR system strength? It could be that our research setup (e.g. Removing all non‐essential relations minimizes the edges in the network while ensuring that all previously connected publications still have a pathway connecting them. Moreover, because it relies on the references within documents, the results of bibliographic coupling are more stable over time because reference lists do not change over time (in contrast to citation counts and relations). Potentially, as a result, our review does not replicate the big data research streams in healthcare, education and public management/government included in previous work (Fosso Wamba et al., 2015; Grover and Kar, 2017; Sheng, Amankwah‐Amoah and Wang, 2017), or the other two BDA debates found by Günther et al. Second, CitNetExplorer performed a so‐called transitive reduction of the citation network. 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. Yet, this stream does not include advanced analytical applications or empirical investigations. First, a bibliometric approach is more macro‐oriented, because it allows the analysis of a comprehensive field of research. Although some attempts have been made to review and theorize how organizational value can be derived from BDA, these attempts have mostly taken on a narrow information systems and technology perspective (for some exceptions, see Grover and Kar, 2017; Günther et al., 2017; Fosso Wamba et al., 2015). Wenzel and Van Quaquebeke, 2018). On the one hand, there is a general discussion regarding how BDA influences organizational performance, and specifically the performance of several management functions (e.g. While BDA may be implemented to stimulate a data‐driven culture, managerial decisions on various hierarchical levels will often still be based mainly on the experience and intuition of decision‐makers (McAfee et al., 2012). Yet, this stream does not include advanced analytical applications or empirical investigations. A closely connected third cluster (N = 40) focused on how knowledge and information can be strategically developed, managed and leveraged in organizations (e.g. In general, BDA will add business value, as it stimulates data‐driven decision‐making capabilities, in which case judgements are often more precise than when they are based solely on intuition or experience (McAfee et al., 2012). Although it includes some seminal publications in the general BDA debate (e.g. 1.0) for Study 1 and 3 and taking the number of clusters closest to the average optimal number (respectively, 10.38 and 8.02). First, a bibliometric approach is more macro‐oriented, because it allows the analysis of a comprehensive field of research. relations) connecting two publications. 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). Arguably, this is undesirable: HR missing the big data bandwagon may imply a loss for organizations and cause harm for employees, whose interests could consequently be overlooked in BDA initiatives (Angrave et al., 2016; Liang and Liu, 2018). The relationship between stakeholder management models and firm financial performance, A resource‐based perspective on information technology capability and firm performance: an empirical investigation, The Berlin brain–computer interface: non‐medical uses of BCI technology, Fast unfolding of communities in large networks, Descriptive, instrumental and strategic approaches to corporate social responsibility: Do they drive the financial performance of companies differently, How the resource‐based and the dynamic capability views of the firm inform corporate‐level strategy, Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon, A contingent resource‐based perspective of supply chain resilience and robustness, ‘Strength in numbers: how does data‐driven decision‐making affect firm peformance’, Customer base analysis: partial defection of behaviourally loyal clients in a non‐contractual FMCG retail setting, Strategy, human resource management and performance: sharpening line of sight, Linking business analytics to decision making effectiveness: a path model analysis, The impact of supply chain analytics on operational performance: a resource‐based view, The impact of advanced analytics and data accuracy on operational performance: a contingent resource based theory (RBT) perspective, Shaping up for e‐commerce: institutional enablers of the organizational assimilation of web technologies. Data modeling and databases evolved together, and their history dates back to the 1960’s. More research attention is needed on the primary causes of such differences and their implications. In general, BDA will add business value, as it stimulates data‐driven decision‐making capabilities, in which case judgements are often more precise than when they are based solely on intuition or experience (McAfee et al., 2012). Hence, it remains unclear whether and how BDA applications in the different domains overlap, how these domains perceive BDA, and what theories have been used to ground potential BDA–performance linkages (Sheng, Amankwah‐Amoah and Wang, 2017; Sivarajah et al., 2017). (2017) – the inductive–deductive debate and the modes of big data access. Related Content. Some findings of this second study align with those of the first: the large gap between the methodological and theoretical discussions surrounding BDA is visible in both Figures 1 and 2. Moreover, the study identified several research topics undergoing focused development, including financial and customer risk management, text mining and evolutionary algorithms. After a lot of research, Mike Cafarella and Doug Cutting estimated that it would cost around $500,000 in hardware with a monthly running cost of $30,000 for a system supporting a one-billion-page index. finance, supply chain, IT). Yet, seeing BDA as an organizational resource or capability leading to improved performance seems quite fitting from an IT or general management perspective (Mikalef et al., 2018), but potentially less relevant when considering other functional management perspectives. Hence, we were surprised that no cluster or studies in our results specifically focused on ethical perspectives related to BDA or the ethical issues related to predictive analytics particularly. Figure 4 again centres the Customer Analytics cluster – which also proved to be an important bridge in the networks of Figure 2 and 2. IJAA Big data systems: knowledge transfer or intelligence insights? The Evolution of Data “Big Data” is a technology buzzword that comes up quite often. The higher the closeness, the more central a document's location in the network. Chatterji, Levine and Toffel, 2009; Lucas and Noordewier, 2016; Waddock and Graves, 1997). Managing potential and realized absorptive capacity: how do organizational antecedents matter? CitNetExplorer is a software tool for visualizing and analysing citation networks of scientific publications. This produces a spatial representation analogous to a geographic map that can demonstrate how knowledge domains and individual studies relate to one another. Another example is cluster five (N = 116), which we dubbed Knowledge and Innovation. The weighted degree centrality represents the number of edges (i.e. Wamba et al., 2017). This shifts the attention from the organization itself to the external organizational environment and the actions required to reshape and align business operations in light of constantly changing global demands (Easterby‐Smith, Lyles and Peteraf, 2009; Gunasekaran et al., 2017). Learn more. First, we demonstrated that the cross‐functional adoption and application of BDA is scarce, but imminent. However, now let’s look at some (relatively) more recent retail history, how it impacts what we buy and sell, and how we behave today. finance, supply chain, IT). Strategic Big Data and Analytics (56), 50. Hence, a change in individual mindsets and organizational culture is necessary to achieve a more data‐driven, objective and impactful decision‐making. Other papers consider the strengths and weaknesses of measuring corporate social responsibility with the social ratings of Kinder, Lydenberg, Domini Research & Analytics (e.g. and you may need to create a new Wiley Online Library account. 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