IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v133y2021icp34-50.html
   My bibliography  Save this article

Big data and human resource management research: An integrative review and new directions for future research

Author

Listed:
  • Zhang, Yucheng
  • Xu, Shan
  • Zhang, Long
  • Yang, Mengxi

Abstract

The lack of sufficient big data-based approaches impedes the development of human resource management (HRM) research and practices. Although scholars have realized the importance of applying a big data approach to HRM research, clear guidance is lacking regarding how to integrate the two. Using a clustering algorithm based on the big data research paradigm, we first conduct a bibliometric review to quantitatively assess and scientifically map the evolution of the current big data HRM literature. Based on this systematic review, we propose a general theoretical framework described as “Inductive (Prediction paradigm: Data mining/Theory building) vs. Deductive (Explanation paradigm: Theory testing)”. In this framework, we discuss potential research questions, their corresponding levels of analysis, relevant methods, data sources and software. We then summarize the general procedures for conducting big data research within HRM research. Finally, we propose a future agenda for applying big data approaches to HRM research and identify five promising HRM research topics at the micro, meso and macro levels along with three challenges and limitations that HRM scholars may face in the era of big data.

Suggested Citation

  • Zhang, Yucheng & Xu, Shan & Zhang, Long & Yang, Mengxi, 2021. "Big data and human resource management research: An integrative review and new directions for future research," Journal of Business Research, Elsevier, vol. 133(C), pages 34-50.
    • Handle: RePEc:eee:jbrese:v:133:y:2021:i:c:p:34-50
    DOI: 10.1016/j.jbusres.2021.04.019
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296321002563
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbusres.2021.04.019?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. Letina, Srebrenka, 2016. "Network and actor attribute effects on the performance of researchers in two fields of social science in a small peripheral community," Journal of Informetrics, Elsevier, vol. 10(2), pages 571-595.
    2. Ghaddar, Bissan & Naoum-Sawaya, Joe, 2018. "High dimensional data classification and feature selection using support vector machines," European Journal of Operational Research, Elsevier, vol. 265(3), pages 993-1004.
    3. Chaomei Chen, 2006. "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 359-377, February.
    4. Cipriano Santos & Tere Gonzalez & Haitao Li & Kay-Yut Chen & Dirk Beyer & Sundaresh Biligi & Qi Feng & Ravindra Kumar & Shelen Jain & Ranga Ramanujam & Alex Zhang, 2013. "HP Enterprise Services Uses Optimization for Resource Planning," Interfaces, INFORMS, vol. 43(2), pages 152-169, April.
    5. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    6. Erevelles, Sunil & Fukawa, Nobuyuki & Swayne, Linda, 2016. "Big Data consumer analytics and the transformation of marketing," Journal of Business Research, Elsevier, vol. 69(2), pages 897-904.
    7. Wang, Yichuan & Hajli, Nick, 2017. "Exploring the path to big data analytics success in healthcare," Journal of Business Research, Elsevier, vol. 70(C), pages 287-299.
    8. Yucheng Zhang & Long Zhang & Hui Lei & Yumeng Yue & Jingtao Zhu, 2016. "Lagged effect of daily surface acting on subsequent day’s fatigue," The Service Industries Journal, Taylor & Francis Journals, vol. 36(15-16), pages 809-826, December.
    9. Constantiou, Ioanna D & Kallinikos, Jannis, 2015. "New games, new rules: big data and the changing context of strategy," LSE Research Online Documents on Economics 63017, London School of Economics and Political Science, LSE Library.
    10. Shah, Naimatullah & Irani, Zahir & Sharif, Amir M., 2017. "Big data in an HR context: Exploring organizational change readiness, employee attitudes and behaviors," Journal of Business Research, Elsevier, vol. 70(C), pages 366-378.
    11. Rohrer, Julia M. & Brümmer, Martin & Schmukle, Stefan C. & Goebel, Jan & Wagner, Gert G., 2017. ""What else are you worried about?" – Integrating textual responses into quantitative social science research," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 12(7), pages 1-34.
    12. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    13. Wickham, Hadley, 2011. "The Split-Apply-Combine Strategy for Data Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i01).
    14. Berk, Lauren & Bertsimas, Dimitris & Weinstein, Alexander M. & Yan, Julia, 2019. "Prescriptive analytics for human resource planning in the professional services industry," European Journal of Operational Research, Elsevier, vol. 272(2), pages 636-641.
    15. Tanujit Chakraborty & Swarup Chattopadhyay & Ashis Kumar Chakraborty, 2018. "A novel hybridization of classification trees and artificial neural networks for selection of students in a business school," OPSEARCH, Springer;Operational Research Society of India, vol. 55(2), pages 434-446, June.
    16. Atanu Sengupta & Sanjoy De, 2020. "Review of Literature," India Studies in Business and Economics, in: Assessing Performance of Banks in India Fifty Years After Nationalization, chapter 0, pages 15-30, Springer.
    17. Blazquez, Desamparados & Domenech, Josep, 2018. "Big Data sources and methods for social and economic analyses," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 99-113.
    18. Benjamin Edelman, 2012. "Using Internet Data for Economic Research," Journal of Economic Perspectives, American Economic Association, vol. 26(2), pages 189-206, Spring.
    19. Shin, Dong-Hee, 2016. "Demystifying big data: Anatomy of big data developmental process," Telecommunications Policy, Elsevier, vol. 40(9), pages 837-854.
    20. Dimitris Bertsimas & Nathan Kallus, 2020. "From Predictive to Prescriptive Analytics," Management Science, INFORMS, vol. 66(3), pages 1025-1044, March.
    21. Tu, Chunming & He, Xi & Shuai, Zhikang & Jiang, Fei, 2017. "Big data issues in smart grid – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1099-1107.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Stefania Capogna, 2023. "Sociology between big data and research frontiers, a challenge for educational policies and skills," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 193-212, February.
    2. Sergi, Bruno S. & Ključnikov, Aleksandr & Popkova, Elena G. & Bogoviz, Aleksei V. & Lobova, Svetlana V., 2022. "Creative abilities and digital competencies to transitioning to Business 4.0," Journal of Business Research, Elsevier, vol. 153(C), pages 401-411.
    3. Zhang, Wenyao & Zhang, Wei & Daim, Tugrul U, 2023. "The voluntary green behavior in green technology innovation: The dual effects of green human resource management system and leader green traits," Journal of Business Research, Elsevier, vol. 165(C).

    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. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    2. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    3. Brewis, Claire & Dibb, Sally & Meadows, Maureen, 2023. "Leveraging big data for strategic marketing: A dynamic capabilities model for incumbent firms," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    4. Purva Grover & Arpan Kumar Kar, 2017. "Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 203-229, September.
    5. Liedong, Tahiru Azaaviele & Rajwani, Tazeeb & Lawton, Thomas C., 2020. "Information and nonmarket strategy: Conceptualizing the interrelationship between big data and corporate political activity," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    6. El-Haddadeh, Ramzi & Osmani, Mohamad & Hindi, Nitham & Fadlalla, Adam, 2021. "Value creation for realising the sustainable development goals: Fostering organisational adoption of big data analytics," Journal of Business Research, Elsevier, vol. 131(C), pages 402-410.
    7. Shet, Sateesh.V. & Poddar, Tanuj & Wamba Samuel, Fosso & Dwivedi, Yogesh K., 2021. "Examining the determinants of successful adoption of data analytics in human resource management – A framework for implications," Journal of Business Research, Elsevier, vol. 131(C), pages 311-326.
    8. J. Piet Hausberg & Kirsten Liere-Netheler & Sven Packmohr & Stefanie Pakura & Kristin Vogelsang, 2019. "Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis," Journal of Business Economics, Springer, vol. 89(8), pages 931-963, December.
    9. Ghasemaghaei, Maryam & Calic, Goran, 2019. "Does big data enhance firm innovation competency? The mediating role of data-driven insights," Journal of Business Research, Elsevier, vol. 104(C), pages 69-84.
    10. Candice WALLS & Brian BARNARD, 2020. "Success Factors of Big Data to Achieve Organisational Performance: Theoretical Perspectives," Expert Journal of Business and Management, Sprint Investify, vol. 8(1), pages 1-16.
    11. Ghasemaghaei, Maryam & Calic, Goran, 2020. "Assessing the impact of big data on firm innovation performance: Big data is not always better data," Journal of Business Research, Elsevier, vol. 108(C), pages 147-162.
    12. Leogrande, Angelo, 2021. "The Destruction of Price-Representativeness," MPRA Paper 111239, University Library of Munich, Germany.
    13. Boccali, Filippo & Mariani, Marcello M. & Visani, Franco & Mora-Cruz, Alexandra, 2022. "Innovative value-based price assessment in data-rich environments: Leveraging online review analytics through Data Envelopment Analysis to empower managers and entrepreneurs," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    14. Blazquez, Desamparados & Domenech, Josep, 2018. "Big Data sources and methods for social and economic analyses," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 99-113.
    15. Miikka Blomster & Timo Koivumäki, 2022. "Exploring the resources, competencies, and capabilities needed for successful machine learning projects in digital marketing," Information Systems and e-Business Management, Springer, vol. 20(1), pages 123-169, March.
    16. Carlianne Patrick & Amanda Ross & Heather Stephens, 2016. "Designing Policies to Spur Economic Growth: How Regional Scientists Can Contribute to Future Policy Development and Evaluation," Working Papers 16-04, Department of Economics, West Virginia University.
    17. Loutfi, Ahmad Amine, 2022. "A framework for evaluating the business deployability of digital footprint based models for consumer credit," Journal of Business Research, Elsevier, vol. 152(C), pages 473-486.
    18. Patrick Mikalef & Ilias O. Pappas & John Krogstie & Michail Giannakos, 2018. "Big data analytics capabilities: a systematic literature review and research agenda," Information Systems and e-Business Management, Springer, vol. 16(3), pages 547-578, August.
    19. Matheus Pereira Libório & Petr Iakovlevitch Ekel & Carlos Augusto Paiva Martins, 2023. "Economic analysis through alternative data and big data techniques: what do they tell about Brazil?," SN Business & Economics, Springer, vol. 3(1), pages 1-16, January.
    20. Castellani, Marco, 2019. "Does culture matter for the economic performance of countries? An overview of the literature," Journal of Policy Modeling, Elsevier, vol. 41(4), pages 700-717.

    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:133:y:2021:i:c:p:34-50. 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.