Study 1 elucidated the intellectual structure of the field, and Study 2 adds to this by providing an overview of its historical evolution. Citation relations are classified as essential if there are no other pathways (i.e. Methodologies and algorithms for group‐rankings decision, Match demands of professional rugby football codes: a review from 2008 to 2015, Efficient, high‐quality force‐directed graph drawing. Their responses were internally consistent and had high face validity (e.g. 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). Nevertheless, there is a lot of potential impact for predictive analytics and data‐driven strategies in these settings (cf. 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. The quality of the partitioning can thus be quantified via the modularity of the network – a value that represents the density of links within communities as compared with links between communities. The 1252 documents in the co‐citation network stabilized into ten clusters. Wamba et al., 2017). This study faces several limitations, of which we discuss three below. 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). 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. wearables, sensors), algorithms (e.g. 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. Third, it provides insights into the field's potential evolution via bibliographic coupling. Moreover, a review could stimulate cross‐fertilization of best practices, research designs and theoretical frameworks by unveiling discrepancies in the maturity of BDA of different functional management domains and their research streams. Moreover, in contrast earlier studies (Fosso Wamba et al., 2015; Grover and Kar, 2017; Sheng, Amankwah‐Amoah and Wang, 2017), we did not encounter studies exploring BDA applications in the public sector specifically. This is a worrying development as it suggests that a vast amount of information and knowledge is not diffused in the greater scientific community, meaning scholars and practitioners could overlook best practices or novel algorithms. Data analytics and performance: The moderating role of intuition-based HR management in major league baseball. Particularly, the resource‐based view is often cited in relation to the BDA–performance linkage, postulating that resources (such as capital or information) can provide organizations with the competitive advantage and greater performance (Barney, 1991). Cluster five (N = 10) examined corporate social responsibility with the ratings of Kinder, Lyndenberg, Domini Research and Analytics (e.g. Big data and analytics (BDA) continue to spark interest among scholars and practitioners. This produces a spatial representation analogous to a geographic map that can demonstrate how knowledge domains and individual studies relate to one another. In terms of important publications in the future of the debate, Figure 4 puts forward Ji‐fan Ren et al. It’s a relatively new term that was only coined during the latter part of the last decade. A correlated‐adjusted decision forest proposal, Evaluating structural equation models with unobservable variables and measurement error, How “big data” can make big impact: findings from a systematic review and a longitudinal case study, Physical demands of professional rugby league training and competition using microtechnology, Effects of physical, technical, and tactical factors on final ladder position in semiprofessional rugby league, Organization design: An information processing view. long‐short‐term memory networks) or sectors (e.g. On the one hand, this could mean that some functions (e.g. We now have Cloud providers such as Amazon, Microsoft, and Google enabling capabilities that could only be dreamed about before. It explores how far along companies are on their data journey and how they can best exploit the massive amounts of data they are collecting. 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. 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. The most important cluster involves the main debate on the implications of BDA for organizational performance and seems closely knit with a cluster on BDA from IT and Supply Chain perspectives. Overall, BDA can be considered both a resource and a capability that can enable efficient and effective business operations, if leveraged appropriately considering the internal and external organizational context. 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. Antecedents to firm performance and competitiveness using the lens of big data analytics: a cross-cultural study. Additionally, research suggests that the combination of resources and capabilities matters. (2017), related to algorithms, organizational capabilities, innovation and strategy, and corporate social responsibility. First, it elucidates its intellectual foundations via co‐citation analysis. Uncover key events and milestones that helped to shape big data from the origins of 19th century census data processing and gathering to the early developments and advancements of today’s computerized data gathering and processing. 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). Dynamic capabilities: current debates and future directions, Measuring the effects of business intelligence systems: the relationship between business process and organizational performance, A strategic approach to knowledge development and protection, A genetic programming model to generate risk‐adjusted technical trading rules in stock markets, Information processing design choices, strategy, and risk management performance, Talking off the top of your head: toward a mental prosthesis utilizing event‐related brain potentials, Enhancing accuracy and interpretability of ensemble strategies in credit risk assessment. A third insight is the cluster on the corporate social responsibility that arose in both the co‐citation and bibliographic coupling networks. 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. [Color figure can be viewed at wileyonlinelibrary.com]. Three bibliometric analyses were conducted. For instance, organizations might design their HR systems (e.g. The most frequently cited theoretical perspective in our sample was the resource‐based view. Learn more. 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. Second, this first cluster is closely connected to several other clusters, which cover more specialized topics related to BDA. Additionally, research suggests that the combination of resources and capabilities matters. [Color figure can be viewed at wileyonlinelibrary.com]. Complex and inaccurate data or predictions can create a false sense of authority, whereby organizational decisions based on them appear objective and indisputable. Information Technology (IT) & Supply Chain (SC) (106), 8. Dashboards as a service: why, what, how, and what research is needed? Chen, Cheng and Hsu, 2013; Song et al., 2013). document) has to other nodes, weighted for the edges’ importance. The underlying assumption is that secondary papers that are co‐cited (i.e. This seems particularly useful for BDA research, which may span different research domains (Günther et al., 2017). (2010) for a more technical explanation). 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. CitNetExplorer reduces this full citation network in two ways. big data, machine learning, deep learning, data science, analytics, artificial intelligence), we obtained 54 keyword combinations (e.g. Other contemporary papers build mostly on the statistical perspective and cover predictive analytics focused on customer behaviour (e.g. business studies, human resource management). Ten experts (21.3%) responded and, based on the most frequently proposed keywords (e.g. Customer event history for churn prediction: how long is long enough? Blog Upgraded agility for the modern enterprise with IBM Cloud Pak for Data. This could (have) cause(d) differences in the speed of development of BDA applications in Europe compared with, for instance, the Americas or Asia. First, a bibliometric approach is more macro‐oriented, because it allows the analysis of a comprehensive field of research. For instance, HR data may be used to predict the hiring success of applications, the effectiveness of training courses, or the number of workplaces needed (Marler and Boudreau, 2017). 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. All other large clusters seemed to draw on the algorithms cluster to a lesser extent. He received his PhD from the University of Reading. Here, strongly connected nodes are grouped and assumed to represent an evolutionary stream over time (Waltman and van Eck, 2012). Reinmoeller and Ansari, 2016; Sheng, Amankwah‐Amoah and Wang, 2017). In order to synthesize past research and advance knowledge of the potential organizational value of BDA, the authors obtained a data set of 327 primary studies and 1252 secondary cited papers. 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. Via document co‐citation analysis, we aimed to explore the intellectual structure/foundations of the BDA–performance debate. : Past, Present, and Future CitNetExplorer is a software tool for visualizing and analysing citation networks of scientific publications. How the use of big data analytics affects value creation in supply chain management, A new approach to the group ranking problem: finding consensus ordered segments from users’ preference data, Business intelligence and analytics: from big data to big impact, Modeling and optimizing a vendor managed replenishment system using machine learning and genetic algorithms, Absorptive capacity: a new perspective on learning and innovation, Customer relationship management and firm performance, Using social network analysis to improve communities of practice, Working knowledge: How organizations manage what they know, Competing on Analytics: The New Science of Winning, The financial performance effects of IT‐based supply chain management systems in manufacturing firms, Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Sixth and final, two small clusters were found: one on big data analytics in sport (N = 28) and one on brain–computer interfaces (N = 11). 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. 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. Reinmoeller and Ansari, 2016; Sheng, Amankwah‐Amoah and Wang, 2017). All this makes coupling particularly suitable for detecting current trends and future priorities, as these are commonly covered in the more recent publications, which inherently are not the most cited. On the other hand, it could be that researchers in some fields (e.g. A more comprehensive review of the implications of BDA for the management of performance in and of organizations seems warranted. Co‐citation is a dynamic measure, because it changes over time as documents accumulate citations (Batistič, Černe and Vogel, 2017). Using a force‐directed network layout (Hu, 2005), the program displays nodes (i.e. History and evolution of big data analytics The concept of big data has been around for years; most organizations now understand that if they capture all the data that streams into their businesses, they can apply analytics and get significant value from it. Apart from its limitations, this current review extends our knowledge of how BDA influence the management and performance in and of organizations. Although the core publications in these clusters did consider the effect of (perceived) corporate social responsibility on organizational performance, they had little to do with BDA (e.g. The co‐citation network with 1252 secondary papers and ten clusters, Note: Different shades are used to indicate the cluster to which a secondary paper has been assigned. 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). 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. Different shades represent the cluster to which primary papers have been assigned. 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. Big Data and Performance: What Can Management Research Tell us?. 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. Our second suggestion is to use temporal networks that can inform the evolution and the future trends at the same time. Although we reached out to nearly fifty experts in the field, only ten responded with keywords for our search. Common method biases in behavioral research: a critical review of the literature and recommended remedies, Competitive Strategy: Techniques for Analyzing Industries and Competitors, Random forests for multiclass classification: Random multinomial logit, Learning from practice: how HR analytics avoids being a management fad, The persistence of a stigmatized practice: a study of competitive intelligence. The lack of such studies in this review is striking and worrying, and we urge scholars to pay more focused attention to this topic. Similar to the co‐citation analysis (Figure 2), clusters relating to new technological and methodological advances (e.g. Paul's research has been published in journals such as Human Resource Management and Human Resource Management Review. and you may need to create a new Wiley Online Library account. Removing all non‐essential relations minimizes the edges in the network while ensuring that all previously connected publications still have a pathway connecting them. 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. User Experience (UX) in Business, Management, and Psychology: A Bibliometric Mapping of the Current State of Research. Second, science mapping consists of a classification and visualization of previous research (Small, 1999). For example, text mining can be used to explore abstracts or whole papers to reveal new facts, trends or constructs deriving from patterns and relationship in the text. citation relationships) a node (i.e. Wenzel and Van Quaquebeke, 2018). This prevents an (over)emphasis on mainstream documents that may be popular but insignificant to a fields’ intellectual development. 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). Inevitably, the term ‘Big data’ was coined to distinguish from small data, which is generated purely by a firm’s internal transaction systems. Generally speaking, the left side of Figure 3 relates to the development of new statistical methods and applications within the fields of financial and customer analytics. Several network statistics were calculated during the analyses. For instance, scholars in the Sports Analytics domain already leverage data from wearables and sensors for scientific and practical purposes. Nowadays it is blended with many techniques such as artificial intelligence, statistics, data science, database theory and machine learning. The related academic communities and their discourse are quite dispersed. This produces a spatial representation analogous to a geographic map that can demonstrate how knowledge domains and individual studies relate to one another. organizational, business unit, team, individual). The methodological cluster dealing with big data algorithms is, surprisingly, situated in the periphery (Figure 2) and linked to the rest of the network predominantly through Customer Analytics research. Moreover, we found similar key topics, including machine learning, business intelligence, text analytics and social media data (Grover and Kar, 2017). Ballings and Poel, 2012). Big Data and Performance: What Can Management Research Tell us?. 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. The 211 primary documents in the bibliographic coupling network formed eight clusters. Figures 2 and 4 suggest that developments within marketing, supply chain and IT are on their way as well. We provide a first and novel review approach for the BDA–performance debate. The methodological cluster dealing with big data algorithms is, surprisingly, situated in the periphery (Figure 2) and linked to the rest of the network predominantly through Customer Analytics research. 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). This review aims to demonstrate: what BDA applications have been, are being, and will be studied in relation to organizational performance; how distant, disconnected perspectives could be linked via theory or empirical application; how emerging research fields may learn from more established domains; what the current rate and topics of development of BDA are; and how these can be stimulated further into the 21st century. Preliminary empirical evidence from fields such as operations and IT management shows that a combination of management and statistical perspectives can add great value to firm performance (cf. CitNetExplorer draws the resulting network by, on the vertical axis, the publication year and, on the horizontal axis, the closeness between publications (see van Eck et al. Humanitarian supply chain: a bibliometric analysis and future research directions. A final deduction of Figure 4 is that the reference lists of the more strategic research streams were closely interrelated (Strategic BDA, Information and Knowledge and CSR) whereas the other, more technical and operational streams are dispersed across the network. 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. In general, the higher the weighted degree, the more important a document is to the network. 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. However, particularly in the latter two domains, research is focused mostly on the high‐level strategic impact of BDA (Chen, Preston and Swink, 2015; Germann, Lilien and Rangaswamy, 2013; Trainor et al., 2014; Trkman et al., 2010) rather than actual applications or individual‐level predictions within these functional domains (for some exceptions see Ballings et al., 2015; Chi et al., 2007; Esfahanipour and Mousavi, 2011). Performance impacts of information technology: is actual usage the missing link? People analytics effectiveness: developing a framework. Third, the cluster containing publications on statistics and machine learning algorithms was far removed from the above central clusters. Use the link below to share a full-text version of this article with your friends and colleagues. A few years ago, you rarely heard the words big data mentioned. 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. 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. both referred to in the same primary document) share content‐wise similarities and are thus semantically related. Second, this first cluster is closely connected to several other clusters, which cover more specialized topics related to BDA. Like other bibliometric methods, a historiography considers the relationships between various primary papers. Ten experts (21.3%) responded and, based on the most frequently proposed keywords (e.g. To reduce the complexity of this large data set of secondary documents, we determined a citation threshold – the minimum number of citations a secondary document had to have in order to be included. Another example is cluster five (N = 116), which we dubbed Knowledge and Innovation. Since 2014, he has managed and executed data science, analytics and machine learning initiatives, mostly within the HR domain, at several national and multinational organizations. Study 1 elucidated the intellectual structure of the field, and Study 2 adds to this by providing an overview of its historical evolution. His current research is focused on multi‐level issues between HR systems, climates and employees’ behaviours, leadership and organizational socialization. A bibliometric review of the leadership development field: How we got here, where we are, and where we are headed. Detecting clusters in a network requires the partitioning of a network into communities of densely connected nodes. A resource‐based perspective on corporate environmental performance and profitability, Issues in linking information technology capability to firm performance, The construct validity of the Kinder, Lydenberg & Domini social performance ratings data, A multidisciplinary perspective of big data in management research, Positional match demands of professional rugby league competition, Critical analysis of Big Data challenges and analytical methods, Co‐citation in the scientific literature: a new measure of the relationship between two documents, A comparative study of dimensionality reduction techniques to enhance trace clustering performances, Supply chain management and advanced planning – basics, overview and challenges, “Environment” submissions in the UK's research excellence framework 2014. Via bibliographic coupling, we hope to shift attention from traditions to future trends, highlighting the current and future development areas for continued evolution of the BDA debate. While it may still be ambiguous to many people, since it’s inception it’s become increasingly clear what big data is and why it’s important to so many different companies. First, a large cluster of papers (N = 324), very central to the network, covers various topics that are seemingly the foundation for research linking BDA to performance in organizations. Liang et al., 2007). Our historiography (Figure 3) demonstrates that the first bridges between the two main research streams have recently been established, building on Ballings and Poel (2012) and Manyika et al. 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. Based on the content of strategic BDA cluster in Study 3, we suggest two ways for potential expansion. The 211 primary documents in the bibliographic coupling network formed eight clusters. Data analysis is rooted in statistics, which has a pretty long history.
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