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August 12, 2026Open Access

Predicting Employee Turnover for Effective Retention Using Hr Analytics

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Authors

MMM. MaheswariVSV. T. ShailashriPRP. Radha

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Overview

Randomized trial explores HR analytics effects on employee retention, highlighting key predictors and strategies.

Key Points

  • This project aims to develop and validate a framework for predicting employee turnover using HR analytics.
  • Quantitative cross-sectional design with data from 200 individuals in various sectors.
  • Structured questionnaire with 52 measurement items covering 13 latent components.
  • Utilized statistical methods including CFA and SEM for data analysis.
  • Employee engagement (β=0.45, p<0.001) and job satisfaction (β=0.40, p<0.001) significantly enhance retention.
  • Leadership support and career development also positively influence retention outcomes.
  • Burnout negatively impacts retention with a strong correlation to increased intention to leave.

Cite This Study

Maheswari et al. (2026) studied this question.

synapsesocial.com/papers/6a7c209506a85aed514b776chttps://doi.org/10.5281/zenodo.21871313
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1PREDICTING EMPLOYEE TURNOVER FOR EFFECTIVE RETENTION USING HR ANALYTICS2026
  2. 2SUSTAINABLE WORKFORCE RETENTION THROUGH HR ANALYTICS AND PREDICTIVE MODELLING2026
  3. 3Predicting Employee Turnover Through Advanced Hr Analytics: Implications For Engagement Strategies2024 · 2 citations
  4. 4Predictive HR Analytics to Optimize Decision-Making Processes and Enhance Workforce Performance2024 · 6 citations
  5. 5AN INTEGRATED HR ANALYTICS MODEL FOR EMPLOYEE MENTAL HEALTH PREDICTION AND RETENTION2026