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The analytics paradigm in business research

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  • Delen, Dursun
  • Zolbanin, Hamed M.

Abstract

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.

Suggested Citation

  • Delen, Dursun & Zolbanin, Hamed M., 2018. "The analytics paradigm in business research," Journal of Business Research, Elsevier, vol. 90(C), pages 186-195.
  • Handle: RePEc:eee:jbrese:v:90:y:2018:i:c:p:186-195
    DOI: 10.1016/j.jbusres.2018.05.013
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    1. Richard A. Berk, 2006. "An Introduction to Ensemble Methods for Data Analysis," Sociological Methods & Research, , vol. 34(3), pages 263-295, February.
    2. Christoph Flath & David Nicolay & Tobias Conte & Clemens Dinther & Lilia Filipova-Neumann, 2012. "Cluster Analysis of Smart Metering Data," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 4(1), pages 31-39, February.
    3. Jennifer Schroeder & Jennifer Xu & Hsinchun Chen & Michael Chau, 2007. "Automated criminal link analysis based on domain knowledge," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(6), pages 842-855, April.
    4. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    5. Matthew J. Liberatore & Wenhong Luo, 2010. "The Analytics Movement: Implications for Operations Research," Interfaces, INFORMS, vol. 40(4), pages 313-324, August.
    6. A. Prinzie & D. Van Den Poel, 2007. "Predicting home-appliance acquisition sequences: Markov/Markov for Discrimination and survival analysis for modeling sequential information in NPTB models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/442, Ghent University, Faculty of Economics and Business Administration.
    7. Yong-bae Ji & Choonjoo Lee, 2010. "Data envelopment analysis," Stata Journal, StataCorp LP, vol. 10(2), pages 267-280, June.
    8. Mortenson, Michael J. & Doherty, Neil F. & Robinson, Stewart, 2015. "Operational research from Taylorism to Terabytes: A research agenda for the analytics age," European Journal of Operational Research, Elsevier, vol. 241(3), pages 583-595.
    9. Vasant Dhar & Tomer Geva & Gal Oestreicher-Singer & Arun Sundararajan, 2014. "Prediction in Economic Networks," Information Systems Research, INFORMS, vol. 25(2), pages 264-284, June.
    Full references (including those not matched with items on IDEAS)

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