Other publications cover more methodological topics, such as structural equation modelling and partial least squares regression (Fornell and Larcker, 1981; Hair, Ringle and Sarstedt, 2011; Wetzels, Odekerken‐Schröder and van Oppen, 2009), mediation (Baron and Kenny, 1986; Devaraj and Kohli, 2003; Tippins and Sohi, 2003), or measurement issues (Podsakoff et al., 2003; Santhanam and Hartono, 2003). This … Here, the program distinguishes essential from non‐essential citation relations in the network, and only the essential relations are retained (van Eck and Waltman, 2014a). Additionally, co‐citations can reveal the intellectual roots of a scientific domain through the identification of its core, most cited works. Paul van der Laken is a data scientist and applied management researcher. Big data and analytics needs to be in line not only with the organizations’ strategy, but also with its culture (Gupta and George, 2016). A first limitation involves our search strategy. 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. For a fifth insight, we refer to the existence of cluster eight (N = 55) on the relationship between ethics, corporate social responsibility and firm performance. Therefore, it can reflect both the state of a certain intellectual field as well as the shifts in schools of thought (Pasadeos, Phelps and Kim, 1998). Other papers consider the strengths and weaknesses of measuring corporate social responsibility with the social ratings of Kinder, Lydenberg, Domini Research & Analytics (e.g. Moreover, it includes research on genetic algorithms predicting stock markets (Esfahanipour and Mousavi, 2011) and optimizing production lines (Balakrishnan, Gupta and Jacob, 2006). and you may need to create a new Wiley Online Library account. Document co‐citation analysis and algorithmic historiography were applied to the sample of secondary papers whereas bibliographic coupling was applied to the sample of primary papers. The lack of such studies in this review is striking and worrying, and we urge scholars to pay more focused attention to this topic. Authors have argued that BDA can now be considered ‘a major differentiator between high performing and low‐performing organizations’ (Liu, 2014), allowing organizations to become more proactive and future‐oriented, while decreasing customer acquisition costs and increasing revenue. A History of Big Data: Management and Systems Coined as early as 1941, Big Data made the transition from being a term used in specialist technology circles into the mainstream as recently as 2012, in part due to being featured in a report by the World Economic Forum titled “Big Data, Big Impact”. Statistical innovations – such as the bagging of multiple predictors (Breiman, 1996) or decision tree and random forest algorithms (Breiman, 2001; Breiman et al., 1984) – have only been fully leveraged by the customer analytics cluster (N = 124). The review concludes with a discussion of the implications for different functional management domains and the gaps for both research and practice. The practice of data analysis has gradually developed over time, gaining huge benefits from evolution in computing. Cluster five (N = 10) examined corporate social responsibility with the ratings of Kinder, Lyndenberg, Domini Research and Analytics (e.g. Because iterative clustering algorithms use a random starting point, we confirmed the robustness of solution by running the algorithm 50 times (using Gephi's default resolution settings; i.e. However, the application of big data and the quest to understand the available data is something that has been in existence for a long time. For instance, the General Data Protection Regulation (GDPR, 2016) in Europe makes the gathering and use of personal data of individuals significantly more challenging for organizations. Similar to the co‐citation analysis (Figure 2), clusters relating to new technological and methodological advances (e.g. Some empirical evidence, Empirical research on the resource‐based view of the firm: an assessment and suggestions for future research, Fast algorithm for detecting community structure in networks, Sentiment analysis on social media for stock movement prediction, The Knowledge Creating Company: How Japanese Companies Create the Dynamics of Innovation, Exploratory Social Network Analysis with Pajek. These experts were asked to elicit ten keywords describing the relationship between BDA and performance at various levels (i.e. The style of writing the papers may differ from function to function, which can, for example, suggest that certain writing styles are more frequent in one function over the other (e.g. social media, wearables, sensors, video, audio; Angrave et al., 2016; van der Laken et al., 2018; Yarkoni and Westfall, 2017). Organizations are increasingly aware that they may process and analyse their large data volumes to capture value for their businesses and employees (George, Haas and Pentland, 2014). For example, temporal networks can be applied on the secondary papers, and the temporal closeness centrality (Pan and Saramäki, 2011) – which measure how quickly all other nodes can be reached from a given node – can be used to show the intellectual evolution and possible future trends. general linear models), thereby not realizing the full potential of the ‘big’ data collected through modern technology (e.g. Subsequently, the normalized data were loaded into Gephi (Bastian, Heymann and Jacomy, 2009), a leading open‐source visualization and exploration software for graphs and networks, which allows for more flexibility in refinement and visualization. Additionally, meta‐analytical review approaches could help future researchers to quantify the added value of BDA for organizational performance, to test the effectiveness of different BDA applications and strategies, or to compare the potential geographical, sectoral or functional differences in BDA impact. Finally, we suggest that scholars exploring the BDA–performance relationship should explore a more diverse range of theoretical perspectives. In applied BDA research, ethical considerations are essential (Boyd and Crawford, 2012; Herschel and Miori, 2017). Data mining is the process of analyzing large data sets (Big Data) from different perspectives and uncovering correlations and patterns to summarize them into useful information. Humanitarian supply chain: a bibliometric analysis and future research directions. Wenzel and Van Quaquebeke, 2018). methodological advancements clusters vs. mainstream management clusters). We uncovered that, historically, BDA research has evolved in two large, but isolated research streams, but cross‐disciplinary bridges have formed during the past decade. Bibliographic coupling is different from other bibliometric methods as it does not derive the importance of papers within a scholarly community from their citation count or relations (Verbeek et al., 2002). Ancient History of Data C 18,000 BCE The earliest examples we have of humans storing and analyzing data are the tally sticks. For instance, although studies mention the rise of BDA and algorithmic intelligence in the HR field (e.g. We believe that improved cross‐disciplinary collaborations might improve the diversity of perspectives and ultimately lead to better theoretical understanding of the full BDA–performance link. For instance, organizations might design their HR systems (e.g. Hence, a change in individual mindsets and organizational culture is necessary to achieve a more data‐driven, objective and impactful decision‐making. Social media technology usage and customer relationship performance: a capabilities‐based examination of social CRM, The impact of business analytics on supply chain performance, Knowledge‐based extraction of intellectual capital‐related information from unstructured data, An empirical comparison of techniques for handling incomplete data using decision trees, Multiple classifier application to credit risk assessment, Fine‐grained analysis of explicit and implicit sentiment in financial news articles, Expanding the methodological toolbox of HRM researchers: the added value of latent bathtub models and optimal matching analysis, CitNetExplorer: a new software tool for analyzing and visualizing citation networks, A comparison of two techniques for bibliometric mapping: multidimensional scaling and VOS, Evolving to a new dominant logic for marketing, Measuring progress and evolution in science and technology – I: the multiple uses of bibliometric indicators, Building comprehensible customer churn prediction models with advanced rule induction techniques, The dynamic capability view in strategic management: a bibliometric review, The corporate social performance–financial performance link, Review: the resource‐based view and information systems research: review, extension, and suggestions for future research, A new methodology for constructing a publication‐level classification system of science, Big data analytics and firm performance: effects of dynamic capabilities, A comparative assessment of ensemble learning for credit scoring, ExpertRank: a topic‐aware expert finding algorithm for online knowledge communities, The decline and dispersion of marketing competence, The double‐edged sword of big data in organizational and management research: a review of opportunities and risks, Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration, Utilization of practice session average inertial load to quantify college football injury risk, Evolution of game‐play in the Australian Football League from 2001 to 2015, Choosing prediction over explanation in psychology: lessons from machine learning, Knowledge and the speed of the transfer and imitation of organizational capabilities: an empirical test, Bibliometric methods in management and organization. big data, machine learning, deep learning, data science, analytics, artificial intelligence), but may have had a strong influence on our results. Big data and analytics needs to be in line not only with the organizations’ strategy, but also with its culture (Gupta and George, 2016). A bibliometric review using science mapping could be particularly valuable, providing several advantages over classical qualitative and meta‐analytical methods. Similarly, we did not encounter studies on the use of BDA in legal, procurement, M&A, health and safety, public administration or facility management. Managing organizational culture: compliance or Genuine Change? Synthesizing past research findings is one of the most important tasks for advancing a field of research, particularly one characterized by an extensive growth of publications, such as BDA research (Garfield, 2004; Zupic and Čater, 2015). Please check your email for instructions on resetting your password. Next, five smaller clusters were identified. This foundation has resulted in two main themes in contemporary papers within the stream. The higher the betweenness, the more a document functions as an important pathway connecting other documents (for more information see Nooy, Mrvar and Batagelj, 2011). It all started in the year 2002 with the Apache Nutch project. 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). Scholars have noted that, for a long time, management researchers have been focused on traditional methodology (e.g. Third, multiple, complementary bibliometric methods can be easily combined in a single study. The cluster interpretation followed the suggestion of Zupic and Čater (2015). A third and final limitation is that we had to apply certain thresholds in order to process the data. CitNetExplorer reduces this full citation network in two ways. The Missing Link: “Big” Memory • Big Data solves the storage problem using data distribution on commodity hardware • Requires Big Algorithms using “in-database” strategies. In either case, the resource‐based view is a theory that explains the impact (Barney, 1991; Bharadwaj, 2000). Alternatively, our search strategy may have caused an underrepresentation of specific data sources (e.g. 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). Van Eck and Waltman (2014a) consider publications ‘core’ if they have citation relations with at least ten other core publications, whereas Garfield, Pudovkin and Istomin (2003) propose limiting the core publications to approximately 5% of the total number of publications. 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