Trends and perspectives in enhancing the competitiveness of Slovak businesses through predictive HR analytics

• Autorzy: Stacho Zdenko , Stachová Katarína , Barok Alexandra , Olexová Cecília

Identyfikatory

DOI

10.30657/pea.2024.30.33

• Języki publikacji: EN

Abstrakty

EN

The use of HR analytics has been on the rise in recent years, with organizations increasingly recognizing its potential to improve HR processes, increase employee productivity and engagement, and reduce costs. The research presented in this paper extends the knowledge base, especially the characteristics of the degree of implementation of HR analysis into working systems of human resources management utilized within businesses operating within the Slovakian context, emphasizing their role in bolstering and enhancing competitiveness within the European economic arena. Although there was a clear interest in the use of predictive analytics in Slovak companies, there was still a lot of room for improvement and adoption of this approach in HR practice. The authors' findings also suggest that companies in Slovakia are increasingly aware of the value of data-driven decision-making in HR and are willing to invest in these technologies to gain a competitive advantage. The objective of this study is to ascertain contemporary human resource management instruments utilized within businesses operating within the Slovakian context. The authors assumed that the perceived importance of the data approach in HR among Slovak companies is strong, and companies are open to learning more about this approach. A sample of 841 respondents was collected throughout 2020, sample included enter prises from the Slovak Republic. The interviews were conducted via phone in November 2022. The interview respondents are 7 HR representatives. The authors' findings suggest that companies in Slovakia are increasingly aware of the value of data-driven decision-making in HR and are willing to invest in these technologies to gain a competitive advantage.

Słowa kluczowe

EN

HR analytics; a predictive analytics process

PL

Analityka HR; proces analizy predykcyjnej

• Wydawca: Oficyna Wydawnicza Stowarzyszenia Menedżerów Jakości i Produkcji
• Czasopismo: Production Engineering Archives
• Rocznik: 2024
• Tom: Vol. 30, Iss. 3
• Strony: 333--343
• Opis fizyczny: Bibliogr. 68 poz,. rys., tab.

Twórcy

autor

Stacho Zdenko

• University of Ss. Cyril and Methodius, Institut of Management, Hajdóczyho 1, 91701 Trnava, Slovakia

• https://orcid.org/0000-0001-5588-8340

autor

Stachová Katarína

• University of Ss. Cyril and Methodius, Institut of Management, Hajdóczyho 1, 91701 Trnava, Slovakia

• https://orcid.org/0000-0003-4761-2509

autor

Barok Alexandra

• University of Ss. Cyril and Methodius, Institut of Management, Hajdóczyho 1, 91701 Trnava, Slovakia

• https://orcid.org/0009-0005-8883-8565

autor

Olexová Cecília

• University of Ss. Cyril and Methodius, Institut of Management, Hajdóczyho 1, 91701 Trnava, Slovakia

• https://orcid.org/0000-0003-2154-9564

Bibliografia

• 1. Albert, J.,Bennett, G., 2018. The value of analytics in sports: An overview. Journal of Sports Analytics, 4(1), 43-59.
• 2. Al-Tit, A. A.,Eleyan, D., 2018. Predictive analytics in supply chain management: A review. International Journal of Production Economics, 203, 70-87.
• 3. Anandarajan, M., Khan, A., Tandon, A., 2019. Predictive Analytics in insurance fraud detection: A conceptual framework. Journal of Financial Crime, 26(2), 361-377.
• 4. Arora, A., Goyal, N., Mahajan, V., 2022. Big Data in HR Analytics: A Com prehensive Overview. Springer.
• 5. Bender, J. K., Fish, A., 2021. Exploring the development of HR analytics in organizations: A systematic review. International Journal of Human Resource Management, 32(2), 298-333.
• 6.Berry, M. J. A., Linoff, G., 2019. Predictive Analytics for Business: Algorithms, Tools, and Statistical Methods. Wiley.
• 7.Bersin, J., 2013. HR technology disruptions for 2014: A ten-year view. Deloitte Development LLC.
• 8. Bondarouk, T., Ruël, H., 2019. Electronic HRM in Theory and Practice. Emerald Publishing Limited.
• 9. Bondarouk, T., Parry, E., Furtmueller, E., 2017. Electronic HRM: Four decades of research on adoption and consequences. The International Journal of Human Resource Management, 28(1), 98-131.
• 10. Boudreau, J. W., 2018. Re-examining the potential of HR analytics. Human Resource Management Review, 28(3), 310-320.
• 11. Boudreau, J. W., Cascio, W. F., 2017. Investing in people: Financial impact of human resource initiatives. Routledge.
• 12. Braganza, A., Bharati, P., 2021. Employee turnover prediction using predictive analytics: An empirical study. International Journal of Information Management, 56, 102200.
• 13. Cappelli, P., Meister, J. C., 2018. Big data in human resources and talent management: Emerging practices. Routledge
• 14. Chen, C. Y., Wu, J., Lin, J., 2019. Machine learning for credit scoring: A review. Expert Systems with Applications, 118, 104-112.
• 15. Chen, Z., Yu, H., Ma, L., Chen, X., 2020. Predicting stock prices using a com bination of machine learning algorithms and technical indicators. PloS One, 15(12)
• 16. Conte, F., Siano, A., 2023. Data-driven human resource and data-driven talent management in internal and recruitment communication strategies: an empirical survey on Italian firms and insights for European context. Cor porate Communications: An International Journal, 28(4), 618-637.
• 17. Copuš, L., Wojčák, L., Majtánová, M., Šajgalíková, H., 2019. Industry 4.0 and its Impact on Organizational Systems and Human Resources. The Journal of Culture, 9 (2), 3-8.
• 18. Dhar, S., 2021. HR Analytics: The What, Why, and How. Springer. Diez, F., Bussin, M. Lee, V., 2019. Tools for HR Analytics. 10.1108/978-1 78973-961-920191002.
• 19. Fajčíková, A., Urbancová, H., 2019. Factors influencing students’ motivation to seek higher education - A case study at a State University in the Czech Republic. Sustainability, 11(17), 4699.
• 20. Fernandez, V. and Gallardo-Gallardo, E., 2021. Tackling the HR digitalization challenge: key factors and barriers to HR analytics adoption. Competitiveness Review, 31(1), 162-187
• 21. Gupta, A., Sagar, M., 2022. Predictive analytics for employee turnover: A case study in the IT industry. Journal of Business Research, 141, 87-95.
• 22. Gupta, M., 2020. HR Analytics: A Tool for Talent Management. International Journal of Psychosocial Rehabilitation. 24. 2667-2673
• 23. Hitka, M., Lorincová, S., Bartáková, G. P., Ližbetinová, L., Štarchoň, P., Li, C., ... Mura, L., 2018. Strategic tool of human resource management for operation of SMEs in the wood-processing industry. BioResources, 13(2), 2759-2774.
• 24. Hitka, M., Lorincová, S., Ližbetinová, L., Bartáková, G. P., Merková, M., 2017. Cluster analysis used as the strategic advantage of human resource management in small and medium-sized enterprises in the wood-processing industry. BioResources, 12(4), 7884-7897.
• 25. Huang, J. L., Niu, X., 2019. A review of human resource analytics: Evolution, applications, and future directions. Human Resource Management Re view, 29(3), 347-357.
• 26. Ibrahim, H., Mohd Zin, M. L., Aman-Ullah, A., Mohd Ghazi, M. R., 2023. Impact of technostress and information technology support on HRIS user satisfaction: a moderation study through technology self-efficacy. Kybernetes.
• 27. Jaffara Z., Noorb W., Kanwalc Z.(2019): Predictive Human Resource Analytics Using Data Mining Classification Techniques, International Jour nal of Computer Jain et al., 2020. Understanding the concept of HR analytics, International Journal on Emerging Technologies, 11(2), p. 644 – 652
• 28. Jankelová, N., Joniaková, Z., Procházková, K., Blštáková, J., 2020. Diversity Management as a Tool for Sustainable Development of Health Care Facilities. Sustainability, 12 (13), 5226. DOI: 10.3390/su12135226
• 29. Kapler, M., 2021. Barriers to the implementation of innovations in information systems in SMEs. Production Engineering Archives,27(2) 156 162. DOI: 10.30657/pea.2021.27.20
• 30. Kavanagh, M. J., Johnson, R. D., 2017. The human resources scorecard: Measuring the return on investment. Routledge.
• 31. Kavanagh, M. J., Thite, M., 2019. Human resource information systems: Basics, applications, and future directions (3rd ed.. Sage Publications)
• 32. Kelleher, J. D., Tierney, B., Tierney, B., 2018. Data science fundamentals. Chapman and Hall/CRC.
• 33. Kruger. N.A., 2023. Entrepreneurial Ecosystems in Technology Transfer: A Case Study on Successful Innovation Commercialization. Polish Journal of Management Studies, 2023, 27(1)
• 34.Lawler, E. E., Levenson, A. R., Boudreau, J. W., 2021. Human resource management and analytics: An era of change. Emerald Publishing Limited.
• 35. Lawler, J. J., Levenson, A., 2019. Talent management: A focus on big data and analytics. Organizational Dynamics, 48(1), 1-7.
• 36. Liao, S. H., Chu, P. H., Hsiao, P. Y., 2017. Data mining techniques and ap plications–A decade review from 2007 to 2016. Expert Systems with Applications, 83, 298-321.
• 37. London, M., Volmer, J., Zyberaj, J., Kluger, A. N., 2023. Attachment style and quality listening: Keys to meaningful feedback and stronger leader member connections. Organizational Dynamics, 100977.
• 38. Lorincová, S., Hitka, M., Bajzíková, Ľ., Weberová, D., 2019. Are the motivational preferences of employees working in small enterprises in Slovakia changing in time? Entrepreneurship and sustainability issues, 6(4), 1618-1635. DOI: 10.9770/jesi.2019.6.4(5)
• 39. Margherita A., 2021. Human resources analytics: A systematization of re search topics and directions for future research, Human Resource Management Review, ISSN 1053-4822
• 40. Marr, B., 2018. Data-driven HR: How to use analytics and metrics to drive performance. Kogan Page Publishers
• 41. Marr, B., 2021. HR Analytics: The What, Why, and How. Kogan Page Publishers.
• 42. Marr, B., 2023. Data-driven HR: How to Use AI, Analytics and Data to Drive Performance. Kogan Page Publishers.
• 43. Mayo, A., 2018. Applying HR analytics to talent management. Strategic HR Review. 17. 10.1108/SHR-08-2018-0072
• 44. Nascimento, A. L., Rocha, T. V., Serra, R. M., Soares, C. D., 2019. Evolution of HR Analytics: A Systematic Literature Review. In Proceedings of the 14th International Conference on Software Technologies (ICSOFT 2019) (pp. 152-15)
• 45. Nedeliaková, E., Štefancová, V. Hranický, M., 2019. Implementation of Six Sigma methodology using DMAIC to achieve processes improvement in railway transport. Production Engineering Archives, 23 (23), 18-21. DOI: 10.30657/pea.2019.23.03
• 46. Nguyen, H. T., Nguyen, V. T., Nguyen, V. P., Duong, T. H., 2021. Predictive analytics in healthcare: A systematic review. International Journal of Medical Informatics, 148, 104414.
• 47. Olexová, C., Gajdoš, J., 2016. Logistics Simulation Game Proposal–a Tool for Employees’ Induction. Quality Innovation Prosperity, 20 (2), 53-68. DOI: 10.12776/qip.v20i2.753
• 48. Papula J., Kohnová L., Papulová Z. Suchoba M., 2019. Industry 4.0: Preparation of Slovak Companies, the Comparative Study. EAI/Springer Innovations in Communication and Computing. DOI: 10.1007/978-3-319 76998-1_8
• 49. Phillips, J. J., Connell, A. D., 2003. Costing human resources: The financial impact of behavior in organizations. Cengage Learning EMEA
• 50. Rosak Szyrocka, J., Żywiołek, J., Shengelia, N., Stverkova, H., Santo, P. Pilař, L., 2022. Employee perception of CSR and its effects on the company’s image. Production Engineering Archives,28(3) 210-216. DOI: 10.30657/pea.2022.28.25
• 51. Siegel, E., 2016. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. Wiley.
• 52. Singh, N., Singh, V., Singh, H., 2020. Performance evaluation of machine learning algorithms for heart disease prediction using UCI dataset. International Journal of Advanced Science and Technology, 29(4), 10754 10760.
• 53. Sirkova, M., Taha, V. A., Ferencova, M., 2016. Management of HR processes in the specific contexts of selected area. Polish journal of management studies, 13(2), 142-152.
• 54. Štaffenová, N., Kucharčíková, A., 2023. Digitalization in the Human Capital Management. Systems, 11(7), 337. Stanbery, K., Lindley, K., Huffman, C., 2023. The feasibility of using net pro moterscore to measure real-time employee engagement. JONA: The Journal of Nursing Administration, 53(1), 34-39.
• 55. Stareček, A., Babeľová, Z. G., Vraňaková, N., Jurík, L., 2023. The impact of Industry 4.0 implementation on required general competencies of employees in the automotive sector, Production Engineering Archives, 29(3), 3923, pp.254-262.
• 56. Starecek, A., Gyurak Babel’Ova, Z., Makysova, H., Caganova, D., 2021. Sus tainable human resource management and generations of employees in industrial enterprises. doi:10.22306/al.v8i1.201 Acta Logistica, 8(1), 45-53.
• 57. Tharwat, A., 2020. Classification assessment methods for machine learning based breast cancer prediction: A comprehensive evaluation. Computer Methods and Programs in Biomedicine, 188, 105314. Tseng, W. C., Chen, Y. S., 2020. Predictive analytics of NBA games: A ma chine learning approach. Journal of Sports Analytics, 6(3), 181-190.
• 58. Urbancová, H., Vnouckova, L., 2015. Investigating talent management philosophies. Journal of Competitiveness, 7(3), 3-18.
• 59. Van der Rijt, P., Bondarouk, T., Looise, J. K., 2019. HR analytics adoption: The influence of organizational factors. The International Journal of Hu man Resource Management, 30(15), 2123-2152
• 60. Verma, D., Singh, R., 2019. Predictive analytics in banking: A literature re view. International Journal of Bank Marketing, 37(1), 53-73.
• 61. Vetráková, M., Smerek, L., 2019. Competitiveness of Slovak enterprises in Central and Eastern European region. E+M Ekonomie a Management, 22(4), 36-51. DOI: 10.15240/tul/001/2019-4-003
• 62. Wang, S., Gupta, M., 2020. Big data analytics for quality control in manufacturing: A review. Journal of Manufacturing Systems, 54, 165-180.
• 63. Wexler, S., Shaffer, J., Cotgreave, A., 2017. The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios. Wiley. Winters, R., 2019. Practical Predictive Analytics: Models and Methods for the Business Problems of Today. Apress.
• 64. Wojčák, E., Copuš, L., Majtánová, M., 2018. Requirements on Human Re sources in Context of Industry 4.0. Grant Journal, 7 (2), 6-11.
• 65. Xue, Y., Sheng, W., 2019. Predictive analytics in banking: Trends, challenges, and opportunities. Journal of Financial Services Research, 55(3), 261-295.
• 66. Yahia, N. B., Hlel, J. and Colomo-Palacios, R., 2021. From Big Data to Deep Data to Support People Analytics for Employee Turnover Prediction, in IEEE Access, vol. 9, pp. 60447-60458
• 67. Zeng, S., Chen, X., Liu, H., 2019. Big data analytics for predictive maintenance: A review. IEEE Access, 7, 142006-142020.
• 68. Zhang, L., Zhao, Y., Cao, J., Mao, Y., 2019. Predicting stock prices using ensemble machine learning algorithms. Journal of Risk Financial Management, 12(4), 1-18.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).