The analytics paradigm in business research

被引:105
作者
Delen, Dursun [1 ]
Zolbanin, Hamed M. [2 ]
机构
[1] Oklahoma State Univ, Spears Sch Business, Dept Management Sci & Informat Syst, Tulsa, OK 74106 USA
[2] Ball State Univ, Dept Informat Syst & Operat Management, Miller Coll Business, Muncie, IN 47306 USA
关键词
Business analytics; Causal-explanatory modeling; Predictive modeling; Big data; Business disciplines; Business research; PARTIAL LEAST-SQUARES; SEQUENTIAL INFORMATION; KNOWLEDGE DISCOVERY; CLUSTER-ANALYSIS; BIG DATA; CLASSIFICATION; PREDICTION; SUPPORT; SCIENCE; SYSTEM;
D O I
10.1016/j.jbusres.2018.05.013
中图分类号
F [经济];
学科分类号
02 ;
摘要
The availability of data in massive collections in recent past not only has enabled data-driven decision-making, but also has created new questions that cannot be addressed effectively with the traditional statistical analysis methods. The traditional scientific research not only has prevented business scholars from working on emerging problems with big and rich data-sets, but also has resulted in irrelevant theory and questionable conclusions; mostly because the traditional method has mainly focused on modeling and analysis/explanation than on the real/practical problem and the data. We believe the lack of due attention to the analytics paradigm can to some extent be attributed to the business scholars' unfamiliarity with the analytics methods/methodologies and the type of questions it can answer. Therefore, our purpose in this paper is to illustrate how analytics, as a complement, rather than a successor, to the traditional research paradigm, can be used to address interesting emerging business research questions.
引用
收藏
页码:186 / 195
页数:10
相关论文
共 99 条
  • [1] Abdi H., 2003, PARTIAL LEAST SQUARE
  • [2] Scale Coarseness as a Methodological Artifact Correcting Correlation Coefficients Attenuated From Using Coarse Scales
    Aguinis, Herman
    Pierce, Charles A.
    Culpepper, Steven A.
    [J]. ORGANIZATIONAL RESEARCH METHODS, 2009, 12 (04) : 623 - 652
  • [3] Anand A., 2013, AUSTR C INF SYST, P1
  • [4] Anderson C., 2008, Wired, DOI DOI 10.1180/MINMAG.2008.072.1.7
  • [5] Anderson C., 2015, CREATING DATA DRIVEN
  • [6] [Anonymous], 2013, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
  • [7] [Anonymous], 2017, Competing on Analytics the New Science of Winning
  • [8] [Anonymous], 2016, Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner
  • [9] Is theory king?: questioning the theory fetish in information systems
    Avison, David
    Malaurent, Julien
    [J]. JOURNAL OF INFORMATION TECHNOLOGY, 2014, 29 (04) : 327 - 336
  • [10] Banerjee A., 2013, Vikalpa, V38, P1, DOI DOI 10.1177/0256090920130401