References

BJAR

British Journal of Advanced Research

BJAR

3153-709X

Ivory and Finch Publishers

10.68263/BJAR-MTBKFDWY

BJAR_OCT_26_023

Original Research Article

Predictive HR Analytics and Employee Turnover: Developing an Early-Warning Model for Workforce Retention in the Oil and Gas Industry in Rivers State.

LUCKY

OBIBHUNUN

https://orcid.org/0009-0009-4368-1727

Department of Employment Relations and Human Resource Management, Faculty of Administration and Management lucky.obibhunun@ust.edu.ng DEINMA IFIDON GABRIEL

PEACE

Rivers State University

10 11 2026

1 5

This study examined the relationship between Predictive HR Analytics and Employee Turnover: Developing an Early-Warning Model for Workforce Retention in the Oil and Gas Industry in Rivers State. The study was guided by three research objectives and hypotheses and anchored on the Human Capital Theory and Resource-Based View Theory. The study adopted the positivist philosophical paradigm and employed correlational and cross-sectional survey research designs. The population comprised seventy-eight oil and gas companies in Rivers State. Due to the manageable population size, a census sampling technique was adopted, while purposive sampling was used to select 78 managerial respondents consisting of three managers each from the companies. Data were collected using a structured questionnaire, while validity was confirmed by academic supervisors; reliability was confirmed using Cronbach’s Alpha coefficients above 0.70. Out of 84 questionnaires administered, 75 usable copies were retrieved and used for the analysis with the aid of SPSS version 27.0. Spearman Rank Order Correlation Coefficient and partial correlation analysis were used to test the hypotheses. Findings revealed that there is a positive relationship between Predictive HR Analytics and Employee Turnover in oil and gas companies in Rivers State. The study concluded that predictive HR Analytics significantly improve the organizational ability to identify those who want to leave the organization. The study recommended that oil and gas companies should adopt a proactive employee-retention strategy that addresses turnover intention before it develops into voluntary resignation and ultimately contributes to actual employee turnover.

Predictive HR Analytics Employee Turnover Turnover Intention Voluntary Turnover Actual Turnover Employee Retention.

12 10 2026 16 10 2026

This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

References

Abubakar, J. N., & Don-Baridam, L. Q. (2024). Prescriptive human resource analytics and employee efficiency of multinational oil and gas companies in Nigeria. Global Academic and Scientific Journal of Multidisciplinary Studies, 2(9).

Al-Ali, M., Alwateer, M., Alsaedi, S. A., Balaha, H. M., Badawy, M., & Elhosseini, M. A. (2026). Integrating machine learning and explainable AI for employee attrition prediction in HR analytics. Scientific Reports, 16, 6344.

Avrahami, D., Pessach, D., Singer, G., & Ben-Gal, H. C. (2022). A human resources analytics and machine-learning examination of turnover: Implications for theory and practice. International Journal of Manpower, 43(6), 1405–1424.

Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108

Becker, G. S. (1964). Human capital: A theoretical and empirical analysis, with special reference to education. University of Chicago Press.

Charity, E. (2017). Human resource management practices and retention of core employees in the oil and gas industry in Nigeria. International Journal of African and Asian Studies, 36.

Duchek, S. (2020). Organizational resilience: A capability-based conceptualization. Business Research, 13, 215–246. https://doi.org/10.1007/s40685-019-0085-7

Ekweozor, U. C., & Omah, O. (2025). Strategic talent management and employee retention of oil and gas servicing firms in Rivers State. International Journal of Multidisciplinary Research and Growth Evaluation, 6(5), 353–360.

Etebu, C. (2017). Human resource management practices and retention of core employees in the oil and gas industry in Nigeria. International Journal of African and Asian Studies.

Fadare, A. A. (2024). Prediction of HR employee attrition with machine learning: Bagging and random forest application. International Journal of Research and Scientific Innovation, 11(8), 410–419.

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

Izen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Lazzari, M., Alvarez, J. M., & Ruggieri, S. (2022). Predicting and explaining employee turnover intention. International Journal of Data Science and Analytics, 14, 279–292.

Odigie, A. O. (2025). Employee turnover intentions in Nigeria's oil and gas service industry: The role of retention strategies [Doctoral dissertation, Liberty University].

Singh, P., Shokeen, S., Raghava, V., & Garg, S. (2023). A case study on HR analytics employee attrition using predictive analytics. European Economic Letters, 13(5), 789–796.

Talebi, S., et al. (2025). Machine learning approaches for predicting employee turnover: A systematic review. Engineering Reports.

Tett, R. P., & Meyer, J. P. (1993). Job satisfaction, organisational commitment, turnover intention, and turnover: Path analyses based on meta-analytic findings. Personnel Psychology, 46(2), 259–293. https://doi.org/10.1111/j.1744-6570.1993.tb00874.x