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Journals / JOEEP: Journal of Emerging Economies and Policy / 2026 / Cilt: 9 Sayı: Special Issue

The Broken Paradigm: A Study of Human Resource Analytics Practices

Kırılan Paradigma: İnsan Kaynakları Analitiği Uygulamalarına İlişkin Bir Çalışma

Fatma Zehra Yıldız(Tarsus Üniversitesi)0000-0002-0631-6589
Pages
1–10
Publication Date
2024-12-30
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As a result, AI applications provide time and cost savings by alleviating the workload of HR professionals in HR analytics processes and support these employees to move towards more meaningful work in accordance with the society 5.0. paradigm.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The Society 5.0. paradigm has led businesses to more applications related to HR analytics.
📊
Key Findings & Data:The purpose of this study is to examine the transformation in HR analytics applications with the society 5.0 paradigm.
💡
Conclusion & Impact:As a result, AI applications provide time and cost savings by alleviating the workload of HR professionals in HR analytics processes and support these employees to move towards more meaningful work in accordance with the society 5.0. paradigm.
🔬Design:Derleme
📍Scope:Comprehensive Sample
⚙️Variables:İnsan Kaynakları Analitiği · Toplum 5.0. · Yönetimsel Paradigma

Abstract

The Society 5.0. paradigm has led businesses to more applications related to HR analytics. This situation has also affected HR analytics in enterprises, resulting in the development of human resources analytics applications based on artificial intelligence. The purpose of this study is to examine the transformation in HR analytics applications with the society 5.0 paradigm. Advanced HR analytics and artificial intelligence applications are not only limited to analysing, predicting and diagnosing situations, but can also support HR departments in many areas such as talent recruitment, training and development, retention and employee engagement. As a result, AI applications provide time and cost savings by alleviating the workload of HR professionals in HR analytics processes and support these employees to move towards more meaningful work in accordance with the society 5.0. paradigm.

Özet

Toplum 5.0. paradigması, işletmeleri İK analitiği ile ilgili uygulamalara daha fazla yöneltmiştir. Bu durum, işletmelerdeki İK analitiğini de etkileyerek yapay zekaya dayalı insan kaynakları analitiği uygulamalarının geliştirilmesiyle sonuçlanmıştır. Bu çalışmanın amacı, toplum 5.0. paradigmasıyla birlikte İK analitiği uygulamalarında yaşanan dönüşümün incelenmesidir. Gelişmiş İK analitiği ve yapay zeka uygulamaları, sadece durumların analiz edilmesi, öngörülmesi ve teşhis edilmesi ile sınırlı kalmamakta aynı zamanda yetenekleri işe alma, eğitim ve geliştirme, elde tutma ve çalışan bağlılığı gibi pek çok konuda İK departmanlarına destek olabilmektedir. Sonuç olarak, yapay zeka uygulamaları, İK analitiği süreçlerinde İK profesyonellerinin iş yükünü hafifleterek zaman ve maliyet tasarrufu sağlamaktadır ve toplum 5.0. paradigmasına uygun şekilde bu çalışanların daha anlamlı işlere yönelmesini desteklemektedir.

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Cite This Article

Yıldız, F. Z. (2024). The Broken Paradigm: A Study of Human Resource Analytics Practices. JOEEP: Journal of Emerging Economies and Policy, Cilt: 9 Sayı: Special Issue, 1–10. https://ajindex.com/articles/1504245

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References (82)

  1. Abellán-Sevilla, A.-J., & Ortiz-de-U. C. M. (2023), Smart human resource analytics for happiness management. Journal of Management Development, 42(6), 514-525. https://doi.org/10.1108/JMD-03-2023-0064
  2. Acar A. C. (2018). “İnsan kaynakları planlaması ve işgören seçimi”, İnsan Kaynakları Yönetimi, Cavide Uyargil, Ömer Sadullah, Ahmet C. Acar, Gönen İ. Dündar, İsmail D. Atay, Zeki Adal & Vala L. Tüzüner, (8. Baskı). İstanbul. Beta Basım.
  3. Allied Market Research (2022). HR Analytics Market Statistics, 2031, (Erişim: 10.08.2024). https://www.alliedmarketresearch.com/hr-analytics-market-A31486
  4. Álvarez-Gutiérrez, F. J., Stone, D. L., Castaño, A. M., & García-Izquierdo, A. L. (2022). Human resources analytics: A systematic review from a sustainable management approach. Journal of Work and Organizational Psychology, 38(3), 129–147. https://doi.org/10.5093/jwop2022a18
  5. Arı, E. S. (2021). Süper Akıllı Toplum: Toplum 5.0., Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 23(1):455-479. https://doi.org/10.16953/deusosbil.808359
  6. Armstrong, M. (2017). Armstrong’un stratejik insan kaynakları yönetimi el kitabı. Yonca Deniz Gürol, Evrim Gemici. (Çev.) İstanbul: Nobel Kitabevi.
  7. Arora, M., Prakash, A., Mittal, A., & Singh, S. (2021). HR analytics and artificial intelligence: Transforming human resource management. In 2021 International Conference on Decision Aid Sciences and Application (DASA 2021) (pp. 288-293).
  8. Arora, M., Prakash, A., Dixit, S., Mittal, A., & Singh, S. (2023). A critical review of HR analytics: Visualization and bibliometric analysis approach. Information Discovery and Delivery, 51(3), 267-282. https://doi.org/10.1108/IDD-05-2022-0038
  9. Asadullah, M.A. & Ullah, A. Z. (2018). Social-economic contribution of vocational education and training: an evidence from OECD countries. Industrial and Commercial Training, 50(4), 172-184.
  10. Ataay, İ. D., & Acar, A. C. (2018). Ücret yönetimi. İçinde C. Uyargil, Ö. Sadullah, A. C. Acar, G. İ. Dündar, İ. D. Atay, Z. Adal, & V. L. Tüzüner (Eds.), İnsan kaynakları yönetimi (ss. 123-145). İstanbul: Beta Basım.
  11. Bahuguna, P.C., Srivastava, R. & Tiwari, S. (2024). Human resources analytics: where do we go from here?, Benchmarking: An International Journal, 31(2), 640-668. https://doi.org/10.1108/BIJ-06-2022-0401
  12. Banger, G. (2016). Endüstri 4.0 ve akıllı işletme. Ankara. Dorlion Yayınları.
  13. Cevizci, A. (2005). Paradigma Felsefe Sözlüğü. İstanbul. Paradigma Yayıncılık.
  14. Chatterjee, S., Chaudhuri, R., Vrontis, D. & Siachou, E. (2022). Examining the dark side of human resource analytics: an empirical investigation using the privacy calculus approach, International Journal of Manpower, 43(1), 52-74. https://doi.org/10.1108/IJM-02-2021-0087
  15. Cherif, O. A., Aregena, A. Y., & Sanchez, R. C. (2021). Recruitment: How to identify, select, and retain talents from around the world using artificial intelligence. Technological Forecasting & Social Change, 169, 120822. https://doi.org/10.1016/j.techfore.2021.120822
  16. Cote, C. (2021). 4 Types of Data Analytics to Improve Decision-Making. (Erişim:10.08.2024), https://online.hbs.edu/blog/post/types-of-data-analysis
  17. Dahlbom, P., Siikanen, N., Sajasalo, P. & Jarvenpää, M. (2020). Big data and HR analytics in the digital era. Baltic Journal of Management, 15(1), 120-138. https://doi.org/10.1108/BJM-11-2018-0393
  18. Debolina Dutta, Sushanta Kumar Mishra & Divya Tyagi (2023). Augmented employee voice and employee engagement using artificial intelligence-enabled chatbots: a field study. The International Journal of Human Resource Management, 34 (12), 2451-2480, doi: 10.1080/09585192.2022.2085525
  19. DeCenzo, D. A., Robbins, S. P., & Verhulst, S. L. (2017). İnsan kaynakları yönetiminin temelleri (C. Çetin & M. L. Arslan, Çev. Ed.). Ankara: Nobel Akademik Yayıncılık
  20. Diana Yan, W. & Katok, E. (2006). Learning, communication, and the bullwhip effect. Journal of Operations Management, 24(6), 839-850.
  21. Diken, Ö.F., Almatarı, M.H. & Diken, A., Yönetim, organizasyon ve strateji üzerine araştırmalar. İçinde Yönetimde Paradigma Değişikliğinin Örgütsel Yapılara Etkileri. Özgür Yayın.
  22. Dulebohn, J. H., & Johnson, R. D. (2013). Human resource metrics and decision support: A classification framework. Human Resource Management Review, 23, 71–83.
  23. Duman, Ç. M. (2022). Toplum 5.0: İnsan odaklı dijital dönüşüm. Journal of Social Policy Conferences, 82, 309-336. https://doi.org/10.26650/jspc.2022.82.1008072
  24. Ekuma, K. (2024). Artificial Intelligence and Automation in Human Resource Development: A Systematic Review. Human Resource Development Review, 23(2), 199-229. https://doi.org/10.1177/15344843231224009
  25. Escolar-Jimenez, C. C., Matsuzaki, K., Okada, K., & Gustilo, R. C. (2019). Data-driven decisions in employee compensation utilizing a neuro-fuzzy inference system. International Journal of Emerging Trends in Engineering Research, 7(8), 163-169.
  26. Falletta, S.V. & Combs, W.L. (2021). The HR analytics cycle: a seven-step process for building evidence-based and ethical HR analytics capabilities. Journal of Work-Applied Management, 13(1), 51-68. doi: 10.1108/JWAM-03-2020-0020.
  27. Feldman, P.H., Ryvicker, M., Evans, L.M. & Barron, Y. (2019). The homecare aide workforce initiative: implementation and outcomes. Journal of Applied Gerontology, 38(2), 253-276.
  28. Fukuda, K. (2020). Science, technology and innovation ecosystem transformation toward society 5.0. International Journal of Production Economics, 220, 107-117. https://doi.org/10.1016/j.ijpe.2019.07.033
  29. Gomez-Mejia, Luls, R., Balkin, David B. D., & Cardy, R. L. (2016). Managing Human Resources, London. Pearson.
  30. González-Romá, V., Schaufeli, W. B., Bakker, A. B., & Lloret, S. (2006). Burnout and work engagement: Independent factors or opposite poles? Journal of Vocational Behavior, 68(1), 165–174. https://doi.org/10.1016/j.jvb.2005.01.003
  31. Higashihara, T. (2018). A Search for Unicorns and the Building of “Society 5.0”. World Economic Forum, Davos.
  32. Huang, X., Yang, F., Zheng, J., Feng, C., & Zhang, L. (2023). Personalized human resource management via HR analytics and artificial intelligence: Theory and implications. Asia Pacific Management Review, 28(4), 598-610.
  33. Hughes, C., Robert, L., Frady, K., & Arroyos, A. (2019). Artificial intelligence, employee engagement, fairness, and job outcomes. In Managing Technology and Middle- and Low-skilled Employees (The Changing Context of Managing People) (pp. 61-68). Emerald Publishing Limited. https://doi.org/10.1108/978-1-78973-077-720191005
  34. Imershein, A. W. (1977). Organizational change as a paradigm shift. The Sociological Quarterly, 18(1), 33-43.
  35. Ivanov, S. H., Webster, C., & Berezina, K. (2017). Adoption of robots and service automation by tourism and hospitality companies. Revista Turismo & Desenvolvimento, 27(28), 1501-1517.
  36. Korkmaz, A. Ç., & Keçecioğlu, T. (2014). Yeni insan kaynakları vizyonu: Yetenek Yönetimi metrikleri. Uluslararası Yönetim İktisat ve İşletme Dergisi, 10(22), 155-171. https://doi.org/10.11122/ijmeb.2013.9.19.459
  37. Krishnan, LRK., Praveen, K., & Poorani, S. (2024). Artificial intelligence in human resource management: Enhancing efficiency & transforming employee experience. The Indian Journal of Industrial Relations, 59(4). 589-605.
  38. Levenson, A. (2011). Using targeted analytics to improve talent decisions. People and Strategy, 34(2).
  39. Madhani, P. M. (2023). Human Resources Analytics: Leveraging Human Resources for Enhancing Business Performance. Compensation & Benefits Review, 55(1), 31-45. https://doi.org/10.1177/08863687221131730
  40. Maity, S. (2019). Identifying opportunities for artificial intelligence in the evolution of training and development practices. Journal of Management Development, 38(8), 651–663. https://doi.org/10.1108/JMD-03-2019-0069
  41. Malik, A., Thevisuthan, P. & De Sliva, T. (2022). Artificial intelligence, employee engagement, experience, and HRM. In: Malik, A. (Ed.), Strategic Human Resource Management and Employment Relations: an International Perspective. Springer International Publishing, pp. 171–184. https://doi.org/10.1007/978-3-030-90955-0_16.
  42. Mamela, T. L., Sukdeo, N., & Mukwakungu, S. C. (2020). The integration of AI on workforce performance for a South African banking institution. In 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD) (pp. 1–8). IEEE. https://doi.org/10.1109/icABCD49160.2020.9183834
  43. Marler, J.H. & Boudreau, J.W. (2017). An evidence-based review of HR Analytics. The International Journal of Human Resource Management, 28(1), 3-26.
  44. McIver, D., Lengnick-Hall, M. L., & Lengnick-Hall, C. A. (2018). A strategic approach to workforce analytics: Integrating science and agility. Business Horizons, 61(3), 397-407.
  45. Mohammed, A. (2019). HR analytics: A modern tool in HR for predictive decision making. Journal of Management, 6(3), 51-63.
  46. Nahavandi, S. (2019). Industry 5.0- a human-centric solution. Sustainability, 11.
  47. Özalp, Ç., & Hatipoğlu, Z. (2021). İş Gücü Planlamasında Dengenin Anahtarı: İnsan Kaynakları Analitiği. İş’te Davranış Dergisi, 6(1), 40-51. https://doi.org/10.25203/idd.954212
  48. Özçelik, B. (2017). İK'nın Geleceği Büyük Veride. Hürriyet İK. https://www.hurriyet.com.tr/ik-yeni-ekonomi/iknin-gelecegi-buyuk-veride-40423034
  49. Palazzeschi, L., Bucci, O. & Di Fabio, A. (2018). Re-thinking innovation in organizations in the industry 4.0 scenario: new challenges in a primary prevention perspective. Front Psychol, 9, 1-30.
  50. Paleti Narendar, D., & Mishra, M. (2021). Impact of HR analytics on training and development in an organization. Psychology and Education, 58(1), 3606-3614.
  51. Pannu, A. (2015). Artificial intelligence and its application in different areas. Artificial Intelligence, 4(10), 79-84.
  52. Pillai, R., & Sivathanu, B. (2022). Measure what matters: descriptive and predictive metrics of HRM-pathway toward organizational performance. International Journal of Productivity and Performance Management, 71(7), 3009-3029. https://doi.org/10.1108/IJPPM-10-2020-0509
  53. Prentice, C., Wong, I. A. & Lin, Z. (2023). Artificial intelligence as a boundary-crossing object for employee engagement and performance. Journal of Retailing and Consumer Services, 73, 1-8. https://doi.org/10.1016/j.jretconser.2023.103376
  54. Potočan, V., Mulej, M., & Nedelko, Z. (2021), Society 5.0: balancing of Industry 4.0, economic advancement and social problems. Kybernetes, 50(3), 794-811. https://doi.org/10.1108/K-12-2019-0858
  55. Ramachandran, R., Babu, V., & Murugesan, V.P. (2023). Human resource analytics revisited: a systematic literature review of its adoption, global acceptance and implementation. Benchmarking: An International Journal, https://doi.org/10.1108/BIJ-04-2022-0272
  56. Raman, R., Venugopalan, M., & Kamal, A. (2024). Evaluating human resources management literacy: A performance analysis of ChatGPT and Bard. Heliyon, 10, 1-26.
  57. Rasmussen, T. & Ulrich, D. (2015). Learning from practice: how HR analytics avoids being a management fad. Organizational Dynamics, 44(3), 236-242.
  58. Lawler, E. E., Levenson, A., & Boudreau, J. W. (2004). HR metrics and analytics: Use and impact. Human Resource Planning, 27(3), 27–35.
  59. Lei, H., Khamkhoutlavong, M. & Le, P.B. (2021). Fostering exploitative and exploratory innovation through HRM practices and knowledge management capability: the moderating effect of knowledge-centered culture. Journal of Knowledge Management, 25(8), 1926-1946.
  60. Saracel, N. & Aksoy, I. (2020). Toplum 5.0: Süper Akıllı Toplum. Social Sciences Research Journal, 9 (2), 26-34.
  61. Savaget, P., Geissdoerfer, M., Kharrazi, A. & Evans, S. (2019). The theoretical foundations of sociotechnical systems change for sustainability: a systematic literature review. Journal of Cleaner Production, 206, 878-892.
  62. Saxena, M., & Mishra, D. K. (2023). Artificial intelligence: The way ahead for employee engagement in corporate India. Global Knowledge, Memory and Communication. https://doi.org/10.1108/GKMC-09-2022-0215
  63. Sharma, A. & Sharma, T. (2017). HR analytics and performance appraisal system: a conceptual framework for employee performance improvement. Management Research Review, 40(6), 684-697, doi: 10.1108/MRR-04-2016-0084.
  64. Sharp, L. (2020). Society 5.0: A Brave New World, Impact, 2, 2-3.
  65. Shiroishi, Y., Uchiyama, K. & Suzuki, N. (2019). Better actions for society 5.0: using AI for evidence based policy making that keeps humans in the loop, The IEEE Computer Society, 52(11), 73-78.
  66. Shrivastava, S., Nagdev, K. and Rajesh, A. (2018). Redefining HR using people analytics: the case of Google. Human Resource Management International Digest, 26(2) 3-6. https://doi.org/10.1108/HRMID-06-2017-0112
  67. SHRM. (2022). The Use of People Analytics in HR. (Erişim: 11.08.2024) https://www.shrm.org/topics-tools/research/the-use-of-people-analytics-in-hr
  68. Stone, D. L., Lukaszewski, K. M., & Johnson, R. D. (2024). Will artificial intelligence radically change human resource management processes? Organizational Dynamics, 53(1). https://doi.org/10.1016/j.orgdyn.2024.101034
  69. Sweeney, J. (2010). UnitedHealth group leverages predictive analytics for enhanced staffing and retention”, in Fitz-enz, J. (Ed.), The New HR Analytics: Predicting the Economic Value of Your Company’s Human Capital Investments. AMACOM. 265-270.
  70. Şen, H. (2022). Çalışma hayatında paradigma kayması: Endüstri 4.0 ile geleceğin mesleklerine bakış. Ankara. İksad Yayınları.
  71. Troisi, O., Visvizi, A., & Grimaldi, M. (2024). Rethinking innovation through industry and society 5.0 paradigms: A multileveled approach for management and policy-making. European Journal of Innovation Management, 27(9).22-51.
  72. Tüzüner, L. (2014). İnsan Kaynakları Yönetiminde Ölçme ve Değerlendirme. İstanbul. Beta Yayınları.
  73. Ulrich, D., Allen, J., Brockbank, W., Younger, J. & Nyman, M. (2012). İK dönüşümü: İnsan kaynaklarını dışarıdan içeriye doğru inşa etmek. Çev. Emre Eren, Hümanist Kitap Yayıncılık. İstanbul.
  74. Ünlü, M., & Bayraktar, O. (2017). İnsan kaynakları işlevlerinin etkinliğinin değerlendirilmesi. Ekonomi İşletme ve Yönetim Dergisi, 1(2), 78-96.
  75. Veshne, N., & Jamnani, J. (2024). Enhancing employee engagement through artificial intelligence. In V. K. Shukla, P. Kulkarni, D. Gaur, P. N., J. P. G. Lacap, & A. Omrane (Eds.), Industry 4.0 and people analytics: A technical perspective of HRM (1st ed.). Apple Academic Press. https://doi.org/10.1201/9781003414193
  76. Van der Laken, P., Bakk, Z., Giagkoulas, V., van Leeuwen, L. & Bongenaar, E. (2018). Expanding the methodological toolbox of HRM researchers: the added value of latent bathtub models and optimal matching analysis. Human Resource Management, 57(3), 751-760, doi: 10.1002/hrm.21847
  77. Varma, A., Pereira, P. & Patel, P. (2024). Artificial intelligence and performance management, Organizational Dynamics. https://doi.org/10.1016/j.orgdyn.2024.101037
  78. Wang, W., Chen, L., Xiong, M. & Wang, Y. (2021). Accelerating AI adoption with responsible AI signals and employee engagement mechanisms in health care. Information Systems Frontiers, 1–18. https://doi.org/10.1007/s10796-021-10154-4.
  79. Wang, S., Wan, J., Zhang, D., Li, D., & Zhang, C. (2016). Towards smart factory for industry 4.0: A self-organized multi-agent system with big data based feedback and coordination. Computer Networks, 101, 158-168. https://doi.org/10.1016/j.comnet.2015.12.017
  80. Wexley, K. N., & Latham, G. P. (1991). Developing and training human resources in organizations (2nd ed.). New York: HarperCollins Publishers.
  81. Yoon, S. W., Han, S., & Chae, C. (2024). People Analytics and Human Resource Development – Research Landscape and Future Needs Based on Bibliometrics and Scoping Review. Human Resource Development Review, 23(1), 30-57. https://doi.org/10.1177/15344843231209362
  82. Zel, S., & Kongar, E. (2020). Transforming digital employee experience with artificial intelligence. In 2020 IEEE/ITU International Conference on Artificial Intelligence for Good (AI4G 2020) (pp. 176-179). Institute of Electrical and Electronics Engineers Inc.