History, Evolution and Future of Big Data and Analytics: A Bibliometric Analysis of Its Relationship to Performance in Organizations

被引:133
作者
Batistic, Sasa [1 ]
van der Laken, Paul [1 ]
机构
[1] Tilburg Univ, Sch Social & Behav Sci, Dept Human Resource Studies, Tilburg, Netherlands
关键词
RESOURCE-BASED PERSPECTIVE; SUPPLY CHAIN MANAGEMENT; INFORMATION-TECHNOLOGY CAPABILITY; CORPORATE SOCIAL-RESPONSIBILITY; FINANCIAL PERFORMANCE; FIRM PERFORMANCE; CHURN PREDICTION; OPERATIONAL PERFORMANCE; STAKEHOLDER MANAGEMENT; ABSORPTIVE-CAPACITY;
D O I
10.1111/1467-8551.12340
中图分类号
F [经济];
学科分类号
02 ;
摘要
Big data and analytics (BDA) are gaining momentum, particularly in the practitioner world. Research linking BDA to improved organizational performance seems scarce and widely dispersed though, with the majority focused on specific domains and/or macro-level relationships. 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. This paper reviews this body of research, using three bibliometric methods. First, it elucidates its intellectual foundations via co-citation analysis. Second, it visualizes the historical evolution of BDA and performance research and its substreams through algorithmic historiography. Third, it provides insights into the field's potential evolution via bibliographic coupling. The results reveal that the academic attention for the BDA-performance link has been increasing rapidly. The study uncovered ten research clusters that form the field's foundation. While research seems to have evolved following two main, isolated streams, the past decade has witnessed more cross-disciplinary collaborations. Moreover, the study identified several research topics undergoing focused development, including financial and customer risk management, text mining and evolutionary algorithms. The review concludes with a discussion of the implications for different functional management domains and the gaps for both research and practice.
引用
收藏
页码:229 / 251
页数:23
相关论文
共 190 条
[1]   A comparative study on base classifiers in ensemble methods for credit scoring [J].
Abelian, Joaquin ;
Castellano, Javier G. .
EXPERT SYSTEMS WITH APPLICATIONS, 2017, 73 :1-10
[2]   How to improve firm performance using big data analytics capability and business strategy alignment? [J].
Akter, Shahriar ;
Wamba, Samuel Fosso ;
Gunasekaran, Angappa ;
Dubey, Rameshwar ;
Childe, Stephen J. .
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2016, 182 :113-131
[3]   FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND PREDICTION OF CORPORATE BANKRUPTCY [J].
ALTMAN, EI .
JOURNAL OF FINANCE, 1968, 23 (04) :589-609
[4]   HR and analytics: why HR is set to fail the big data challenge [J].
Angrave, David ;
Charlwood, Andy ;
Kirkpatrick, Ian ;
Lawrence, Mark ;
Stuart, Mark .
HUMAN RESOURCE MANAGEMENT JOURNAL, 2016, 26 (01) :1-11
[5]  
[Anonymous], MACH LEARN MACH LEARN
[6]  
[Anonymous], 2016, **NON-TRADITIONAL**, V59, P1
[7]  
[Anonymous], WORKING KNOWLEDGE OR
[8]  
[Anonymous], 2004, PHYS REV
[9]  
Balakrishnan PV, 2006, COMPUT OPER RES, V33, P639, DOI [10.1016/j.cor.2004.07.011, 10.1016/j.cor.2004.07.01]
[10]   Evaluating multiple classifiers for stock price direction prediction [J].
Ballings, Michel ;
Van den Poel, Dirk ;
Hespeels, Nathalie ;
Gryp, Ruben .
EXPERT SYSTEMS WITH APPLICATIONS, 2015, 42 (20) :7046-7056