IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v90y2018icp186-195.html
   My bibliography  Save this article

The analytics paradigm in business research

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296318302480
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbusres.2018.05.013?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Benjamin T. Hazen & Joseph B. Skipper & Christopher A. Boone & Raymond R. Hill, 2018. "Back in business: operations research in support of big data analytics for operations and supply chain management," Annals of Operations Research, Springer, vol. 270(1), pages 201-211, November.
    2. Bechler, Georg & Steinhardt, Claudius & Mackert, Jochen & Klein, Robert, 2021. "Product line optimization in the presence of preferences for compromise alternatives," European Journal of Operational Research, Elsevier, vol. 288(3), pages 902-917.
    3. Cleridy E. Lennert‐Cody & Richard A. Berk, 2007. "Statistical learning procedures for monitoring regulatory compliance: an application to fisheries data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(3), pages 671-689, July.
    4. Tom Pape, 2020. "Prioritising data items for business analytics: Framework and application to human resources," Papers 2012.13813, arXiv.org.
    5. Conboy, Kieran & Mikalef, Patrick & Dennehy, Denis & Krogstie, John, 2020. "Using business analytics to enhance dynamic capabilities in operations research: A case analysis and research agenda," European Journal of Operational Research, Elsevier, vol. 281(3), pages 656-672.
    6. Ranyard, J.C. & Fildes, R. & Hu, Tun-I, 2015. "Reassessing the scope of OR practice: The Influences of Problem Structuring Methods and the Analytics Movement," European Journal of Operational Research, Elsevier, vol. 245(1), pages 1-13.
    7. Mantin, Benny & Rubin, Eran, 2018. "Price volatility and market performance measures: The case of revenue managed goods," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 120(C), pages 35-50.
    8. Richard Berk & Lawrence Sherman & Geoffrey Barnes & Ellen Kurtz & Lindsay Ahlman, 2009. "Forecasting murder within a population of probationers and parolees: a high stakes application of statistical learning," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 191-211, January.
    9. Isabelle Piot-Lepetit & Joseph Nzongang, 2021. "Business analytics for managing performance of microfinance Institutions: A flexible management of the implementation process," Post-Print hal-03209188, HAL.
    10. Isabelle Piot-Lepetit & Joseph Nzongang, 2021. "Business Analytics for Managing Performance of Microfinance Institutions: A Flexible Management of the Implementation Process," Sustainability, MDPI, vol. 13(9), pages 1-22, April.
    11. Duan, Yanqing & Cao, Guangming & Edwards, John S., 2020. "Understanding the impact of business analytics on innovation," European Journal of Operational Research, Elsevier, vol. 281(3), pages 673-686.
    12. Hindle, Giles A. & Vidgen, Richard, 2018. "Developing a business analytics methodology: A case study in the foodbank sector," European Journal of Operational Research, Elsevier, vol. 268(3), pages 836-851.
    13. Käki, Anssi & Kemppainen, Katariina & Liesiö, Juuso, 2019. "What to do when decision-makers deviate from model recommendations? Empirical evidence from hydropower industry," European Journal of Operational Research, Elsevier, vol. 278(3), pages 869-882.
    14. Hendrik Hilpert & Johann Kranz & Matthias Schumann, 2013. "Leveraging Green IS in Logistics," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 5(5), pages 315-325, October.
    15. Bissan Ghaddar & Ignacio Gómez-Casares & Julio González-Díaz & Brais González-Rodríguez & Beatriz Pateiro-López & Sofía Rodríguez-Ballesteros, 2023. "Learning for Spatial Branching: An Algorithm Selection Approach," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1024-1043, September.
    16. Michelsen, Carl Christian & Madlener, Reinhard, 2016. "Switching from fossil fuel to renewables in residential heating systems: An empirical study of homeowners' decisions in Germany," Energy Policy, Elsevier, vol. 89(C), pages 95-105.
    17. Daysi Sanmartín-Durango & Maria Alejandra Henao-Bedoya & Yair Tadeo Valencia-Estupiñan & Jairo Humberto Restrepo-Zea, 2019. "Efficiency of health expenditure in the OECD and LAC: a data envelopment analysis," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 91, pages 41-78, Julio - D.
    18. Nahushananda Chakravarthy H G & Karthik M Seenappa & Sujay Raghavendra Naganna & Dayananda Pruthviraja, 2023. "Machine Learning Models for the Prediction of the Compressive Strength of Self-Compacting Concrete Incorporating Incinerated Bio-Medical Waste Ash," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
    19. Wen, Shaoting & Buyukada, Musa & Evrendilek, Fatih & Liu, Jingyong, 2020. "Uncertainty and sensitivity analyses of co-combustion/pyrolysis of textile dyeing sludge and incense sticks: Regression and machine-learning models," Renewable Energy, Elsevier, vol. 151(C), pages 463-474.
    20. Alessandra Cepparulo & Gilles Mourre, 2020. "How and How Much? The Growth-Friendliness of Public Spending through the Lens," European Economy - Discussion Papers 132, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jbrese:v:90:y:2018:i:c:p:186-195. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbusres .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.