IDEAS home Printed from https://ideas.repec.org/a/ers/journl/vxxviiy2024i2p203-215.html
   My bibliography  Save this article

The Use of Data Analytics in Human Resource Management

Author

Listed:
  • Julia Nowicka
  • Yury Pauliuchuk
  • Zbigniew Ciekanowski
  • Beata Falda
  • Karol Sikora

Abstract

Purpose: The objective of the article is to investigate the role of data analysis and Big Data in managing human resources (HRM). The authors focus on identifying the benefits resulting from the use of data analysis in personnel management processes, understanding the threats and challenges associated with this practice, and presenting perspectives on the future of this field. Design/Methodology/Approach: Perspectives on the development of data analysis and Big Data utilization in human resource management are presented, along with potential directions for further research in this area. The authors summarize the main conclusions and recommendations derived from the study. The research problem is formulated as follows: How does the use of data analysis and Big Data affect human resource management? Our research aims to explore the role and potential of data analysis in the context of human resource management, understand how organizations employ data analysis in recruitment, selection, training, performance assessment, and talent management processes. Another objective is to identify the primary benefits that organizations can attain through the use of data analysis in personnel management, such as enhanced decision-making, improved efficiency of personnel processes, and optimized utilization of human resources. The study does not confine itself solely to potential benefits. The authors endeavour to identify the principal challenges and risks associated with employing data analysis in human resource management. The study drew upon the latest research presented in documents and reports published by international organizations, as well as literature analysis based on scientific articles from recent years and credible online sources, which facilitated the discovery of new trends in human resource management. Findings: Utilizing data analysis in human resource management yields numerous benefits (enhanced decision-making in personnel matters, optimization of recruitment and employee development processes, increased efficiency in performance evaluation, talent identification, and trend prediction, which aligns with organizational strategic goals), but it also presents challenges (personal data protection, risk of discrimination, the imperative of ensuring data security) and responsibility. Practical implications: The article focuses on identifying threats and challenges linked with employing data analysis and Big Data in human resource management. Discussed are issues about personal data protection and data security, along with an analysis of challenges connected with data interpretation and ensuring adequate technological resources, analytical competencies, and ethical awareness. Responsible application of data analysis and Big Data in human resource management, in line with best practices, can yield significant benefits for organizations, enhancing both business outcomes and employee experiences. Originality/Value: Global Big Data statistics indicate that data serves as the linchpin for transforming any company. However, numerous organizations still do not sufficiently invest in analytical solutions. The authors endeavour to provide concrete recommendations for organizations aiming to effectively utilize data analysis in personnel management while ensuring compliance with relevant legal regulations and respect for employees' rights.

Suggested Citation

  • Julia Nowicka & Yury Pauliuchuk & Zbigniew Ciekanowski & Beata Falda & Karol Sikora, 2024. "The Use of Data Analytics in Human Resource Management," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 203-215.
  • Handle: RePEc:ers:journl:v:xxvii:y:2024:i:2:p:203-215
    as

    Download full text from publisher

    File URL: https://ersj.eu/journal/3380/download
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Akter, Shahriar & Wamba, Samuel Fosso & Gunasekaran, Angappa & Dubey, Rameshwar & Childe, Stephen J., 2016. "How to improve firm performance using big data analytics capability and business strategy alignment?," International Journal of Production Economics, Elsevier, vol. 182(C), pages 113-131.
    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. Agnieszka Krol & Jolanta Zygadlo & Katarzyna Ochyra-Zurawska & Aneta Chrzaszcz & Julia Nowicka, 2025. "Enhancing Workplace Safety: Addressing Psychosocial Hazards in Modern Organizations," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 696-706.

    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. 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.
    2. Mohammad Ali Yamin, 2021. "Investigating the Drivers of Supply Chain Resilience in the Wake of the COVID-19 Pandemic: Empirical Evidence from an Emerging Economy," Sustainability, MDPI, vol. 13(21), pages 1-16, October.
    3. Rosa Lombardi & Raffaele Trequattrini & Federico Schimperna & Myriam Cano-Rubio, 2021. "The Impact of Smart Technologies on theManagement and Strategic Control: A Structured Literature Review," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2021(suppl. 1), pages 11-30.
    4. Luther Yuong Qai Chong & Thien Sang Lim, 2022. "Pull and Push Factors of Data Analytics Adoption and Its Mediating Role on Operational Performance," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    5. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2025. "Critical analysis of the impact of artificial intelligence integration with cutting-edge technologies for production systems," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 61-93, January.
    6. Li, Ying & Dai, Jing & Cui, Li, 2020. "The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model," International Journal of Production Economics, Elsevier, vol. 229(C).
    7. Queiroz, Maciel M. & Fosso Wamba, Samuel, 2019. "Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA," International Journal of Information Management, Elsevier, vol. 46(C), pages 70-82.
    8. 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).
    9. Pan Liu & Shu-ping Yi, 2018. "Investment decision-making and coordination of a three-stage supply chain considering Data Company in the Big Data era," Annals of Operations Research, Springer, vol. 270(1), pages 255-271, November.
    10. Ron Berman & Ayelet Israeli, 2022. "The Value of Descriptive Analytics: Evidence from Online Retailers," Marketing Science, INFORMS, vol. 41(6), pages 1074-1096, November.
    11. Acharya, Abhilash & Singh, Sanjay Kumar & Pereira, Vijay & Singh, Poonam, 2018. "Big data, knowledge co-creation and decision making in fashion industry," International Journal of Information Management, Elsevier, vol. 42(C), pages 90-101.
    12. Bargoni, Augusto & Santoro, Gabriele & Messeni Petruzzelli, Antonio & Ferraris, Alberto, 2024. "Growth hacking: A critical review to clarify its meaning and guide its practical application," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    13. Oesterreich, Thuy Duong & Anton, Eduard & Teuteberg, Frank & Dwivedi, Yogesh K, 2022. "The role of the social and technical factors in creating business value from big data analytics: A meta-analysis," Journal of Business Research, Elsevier, vol. 153(C), pages 128-149.
    14. Xiaochen Zhou & Jijiao Jiang & Cong Zhou & Xiang Li & Ming Yin, 2024. "Circular supply chain management: Antecedent effect of social capital and big data analysis capability and their impact on sustainable performance," Sustainable Development, John Wiley & Sons, Ltd., vol. 32(5), pages 5311-5330, October.
    15. Teng, Yuanyang & Zheng, Jianzhuang & Li, Yicun & Wu, Dong, 2024. "Optimizing digital transformation paths for industrial clusters: Insights from a simulation," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    16. 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).
    17. Roßmann, Bernhard & Canzaniello, Angelo & von der Gracht, Heiko & Hartmann, Evi, 2018. "The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 135-149.
    18. Shah, Tushar R., 2022. "Can big data analytics help organisations achieve sustainable competitive advantage? A developmental enquiry," Technology in Society, Elsevier, vol. 68(C).
    19. Sabeen Hussain Bhatti & Wan Mohd Hirwani Wan Hussain & Jabran Khan & Shahbaz Sultan & Alberto Ferraris, 2024. "Exploring data-driven innovation: What’s missing in the relationship between big data analytics capabilities and supply chain innovation?," Annals of Operations Research, Springer, vol. 333(2), pages 799-824, February.
    20. Li, Pengcheng & Guo, Xiaochuan & Wang, Fengzheng & Zhang, Qifeng, 2025. "Digital transformation and corporate innovation boundaries: Role of supply chain concentration and transparency," International Review of Financial Analysis, Elsevier, vol. 98(C).

    More about this item

    Keywords

    Data analysis; Big Data; management; human resources; organization.;
    All these keywords.

    JEL classification:

    • M12 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Personnel Management; Executives; Executive Compensation
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics
    • M50 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - General

    Statistics

    Access and download statistics

    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:ers:journl:v:xxvii:y:2024:i:2:p:203-215. 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: Marios Agiomavritis (email available below). General contact details of provider: https://ersj.eu/ .

    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.