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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.

OBIBHUNUN LUCKY, PEACE DEINMA IFIDON GABRIEL

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OBIBHUNUN LUCKY Corresponding Author

Department of Employment Relations and Human Resource Management, Faculty of Administration and Management

ORCID: 0009-0009-4368-1727

Correspondence: lucky.obibhunun@ust.edu.ng

Received
12 Oct 2026
Published
10 Nov 2026

Abstract

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.

Keywords: Predictive HR Analytics, Employee Turnover, Turnover Intention, Voluntary Turnover, Actual Turnover, Employee Retention.
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How to Cite

APA

LUCKY, O., & GABRIEL, P. D. I. (2026). Predictive HR Analytics and Employee Turnover: Developing an Early-Warning Model for Workforce Retention in the Oil and Gas Industry in Rivers State.. British Journal of Advanced Research, 1(5), Article 005. https://doi.org/10.68263/BJAR-MTBKFDWY

MLA

LUCKY, OBIBHUNUN, and PEACE DEINMA IFIDON GABRIEL. "Predictive HR Analytics and Employee Turnover: Developing an Early-Warning Model for Workforce Retention in the Oil and Gas Industry in Rivers State.." British Journal of Advanced Research, vol. 1, no. 5, 2026, article 005. DOI: https://doi.org/10.68263/BJAR-MTBKFDWY

Chicago

LUCKY, OBIBHUNUN, and PEACE DEINMA IFIDON GABRIEL. "Predictive HR Analytics and Employee Turnover: Developing an Early-Warning Model for Workforce Retention in the Oil and Gas Industry in Rivers State.." British Journal of Advanced Research 1, no. 5 (2026): 005. https://doi.org/10.68263/BJAR-MTBKFDWY

References

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Metadata
ISSN 3153-709X
Tracking ID BJAR_OCT_26_023
Article No. 005
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Article Info
Journal BJAR
Volume Vol 1, No 5
Year 2026
Type Original Research Article
Licence CC BY-NC-SA 4.0
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