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Year 2025, Volume: 12 Issue: 3, 925 - 949, 30.09.2025

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References

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  • De Oliveira, L. B., Cavazotte, F., & Dunzer, R. A. (2019). The interactive effects of organizational and leadership career management support on job satisfaction and turnover intention. International Journal of Human Resource Management, 30(10), 1583–1603. https://doi.org/10.1080/09585192.2017.1298650
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  • 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.
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Understanding the Employee: A Qualitative Study on Human Resources Analytics Practices

Year 2025, Volume: 12 Issue: 3, 925 - 949, 30.09.2025

Abstract

In this study, it is aimed to examine the HR analytics practices applied in different businesses by utilizing the views and experiences of HR professionals. Within the framework of this aim, the research was constructed with qualitative method. The participants of the study consist of human resources professionals who work/are working in private sector organizations in Turkey. In this research, people from different ages, genders, sectors and positions were reached through purposive sampling method. Within the scope of the research, semi-structured interviews were conducted with 11 people from different positions and sectors. The data analyzed by content analysis method are presented as themes. Maxqda Analytics Pro program was used to visualize and present the data. Retention, turnover, exit interviews, recruitment, training, performance and compensation management, employee engagement and satisfaction were identified as themes. According to the results of the study, it is seen that the main aim of HR analytics practices is to obtain data about the employee, to retain and understand the employee. It has been revealed that more descriptive HR analytics are used. It can be stated that there are many metrics developed for recruitment and different employee retention practices. It is hoped that this study will expand the discussions on HR analytics and shed light on practitioners.

Ethical Statement

Ethics committee approval for the study was obtained from the Tarsus University Ethics Committee on May 16, 2024, with decision number 2024/46. The author declares that the study was conducted in accordance with research and publication ethics. The author confirms that AI tools has been employed only to enhance spelling and grammar, as well as augment the overall readability of the article. The author declares that there are no financial conflicts of interest involving any institution, organization, or individual associated with this article. The author declares that the whole research processes was carried out by the sole declared author.

Supporting Institution

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Thanks

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References

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  • Aral, A., Brynjolfsson, E., & Wu, L. (2012). Three-way complementarities: Performance pay, HR analytics and information technology. Management Science, 58(5), 913–931.
  • Armstrong, M. (2017). Armstrong’un stratejik insan kaynakları yönetimi el kitabı (Y. D. Gürol & E. Gemici, Transl.). Nobel Kitabevi.
  • Arora, M., Prakash, A., Mittal, A., & Singh, S. (2021 December 7-8). HR analytics and artificial intelligence: Transforming human resource management. (Conference presentation) International Conference on Decision Aid Sciences and Application, Sakheer, Bahrain. https://doi.org/10.1109/DASA53625.2021.9682325
  • 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
  • Bar-Gil, O., Ron, T., & Czerniak, O. (2024). AI for the people? Embedding AI ethics in HR and people analytics projects. Technology in Society, 77, 1-12. https://doi.org/10.1016/j.techsoc.2024.102527
  • Berg, B. L., & Lune, H. (2015). Sosyal bilimlerde nitel araştırma yöntemleri (H. Aydın, Transl.). Eğitim Kitabevi.
  • Black, J.S. & van Esch, P. (2020), AI-enabled recruiting: what is it and how should a manager use it?. Business Horizons, 63(2), 215-226.
  • Bondarouk, T., & Brewster, C. (2016). Conceptualising the future of HRM and technology research. The International Journal of Human Resource Management, 27(21), 2652–2671.
  • Boudreau, J. & Cascio, W. (2017). Human capital analytics: why are we not there?. Journal of Organizational Effectiveness: People and Performance, 4(2), 119-126.
  • Cayrat, C., & Boxall, P. (2022). Exploring the phenomenon of HR analytics: A study of challenges, risks, and impacts in 40 large companies. Journal of Organizational Effectiveness: People and Performance, 9(4), 572-590. https://doi.org/10.1108/JOEPP-08-2021-0238
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  • Chalutz, B.-G. H. (2019). An ROI-based review of HR analytics: Practical implementation tools. Personnel Review, 48(6), 1429–1448. https://doi.org/10.1108/PR-11-2017-0362
  • Creswell, J. W. (2018). Nitel araştırma yöntemleri: Beş yaklaşıma göre nitel araştırma ve araştırma deseni (M. Bütün & S. B. Demir, Transl.). Siyasal Kitabevi.
  • Das, A. K., & Malik, P. (2024). Ascertaining factors inducing engagement and stay intention among Gen Z: A qualitative study in the Indian context. International Journal of Organizational Analysis. https://doi.org/10.1108/IJOA-09-2023-3994
  • DeCenzo, D. A., Robbins, S. P., & Verhulst, S. L. (2017). İnsan kaynakları yönetiminin temelleri (C. Çetin & M. L. Arslan, Transl.). Nobel Kitapevi.
  • 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
  • De Oliveira, L. B., Cavazotte, F., & Dunzer, R. A. (2019). The interactive effects of organizational and leadership career management support on job satisfaction and turnover intention. International Journal of Human Resource Management, 30(10), 1583–1603. https://doi.org/10.1080/09585192.2017.1298650
  • Dulebohn, J. H., & Johnson, R. D. (2013). Human resource metrics and decision support: A classification framework. Human Resource Management Review, 23, 71–83.
  • Davenport, T. (2019, April 18). Is HR the most analytics driven function? Harvard Business Review. https://hbr.org/2019/04/is-hr-the-most-analytics-driven-function
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  • 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.
  • 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.
  • Fernandez, V. & Gallardo-Gallardo, E. (2021), Tackling the HR digitalization challenge: key factors and barriers to HR analytics adoption. Competitiveness Review, 31(1), 162-187. https://doi.org/10.1108/CR-12-2019-0163
  • Ferraris, A., Mazzoleni, A., Devalle, A. & Couturier, J. (2019). Big data analytics capabilities and knowledge management: impact on firm performance. Management Decision, 57(8), 1923-1936.
  • Fitz-enz, J., & Mattox, J. II. (2014). Predictive analytics for human resources. Wiley and Sons.
  • Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Aldine.
  • Green, D. (2017). The best practices to excel at people Analytics. Journal of Organizational Effectiveness: People and Performance, 4(2), 137-144.
  • Guenole, N., Ferrar, J., & Feinzig, S. (2017). The power of people: Learn how successful organizations use workforce analytics to improve business performance, Cisco Press.
  • Guest, G., Bunce, A.E., & Johnson, L. (2006). How many interviews are enough? Field Methods, (18), 59-82.
  • Harris, J. G., Craig, E., & Light, D. A. (2011). Talent and analytics: New approaches, higher ROI. Journal of Business Strategy, 32(6), 4–13. https://doi.org/10.1108/02756661111180087
  • Hom, P. W., Lee, T. W., Shaw, J. D., & Hausknecht, J. P. (2017). One hundred years of employee turnover theory and research. Journal of Applied Psychology, 102(3), 530–545. https://doi.org/10.1037/apl0000103
  • Isson, J. P., & Harriott, J. S. (2016). People analytics in the era of big data: Changing the way you attract, acquire, develop, and retain talent. John Wiley & Sons.
  • Jeske, D. & Calvard, T. (2020). Big data: Lessons for employers and employees. Employee Relations, 42(1), 248-261.
  • Jiang, Y., & Akdere, M. (2022). An operational conceptualization of human resource analytics: Implications for human resource development. Industrial and Commercial Training, 54(1), 183–200. https://doi.org/10.1108/ICT-04-2021-0028
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Details

Primary Language English
Subjects Human Resources Management, Workforce Planning
Journal Section Research Articles
Authors

Fatma Zehra Yıldız 0000-0002-0631-6589

Publication Date September 30, 2025
Submission Date October 6, 2024
Acceptance Date August 20, 2025
Published in Issue Year 2025 Volume: 12 Issue: 3

Cite

APA Yıldız, F. Z. (2025). Understanding the Employee: A Qualitative Study on Human Resources Analytics Practices. Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 12(3), 925-949. https://doi.org/10.30798/makuiibf.1562349

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