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This study examines at how HR analytics may be used to forecast employee turnover in the IT sector. It focuses on five important factors: leadership support, HRM competency, data availability, technological infrastructure, and change management. Data was gathered from 255 HR professionals and IT company workers using a standardized questionnaire using a 5-point Likert scale format. AMOS was used to analyze the data in order to evaluate the predicted correlations among the determinants and employee turnover as well as test the model fit. According to the results, all five criteria have a considerable impact on turnover, but the effects of Leadership Support and Data Availability are more noticeable. With indices like CMIN/df, GFI, AGFI, CFI, and RMSEA falling within suggested bounds, the model showed a satisfactory match. The findings highlight how crucial it is to include data-driven tactics into HR procedures in order to accurately predict and lower employee turnover. For IT companies looking to improve employee retention through the strategic use of HR analytics, the report provides useful takeaways.

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HR Analytics in Predicting Employee Turnover in IT Industry: A Structural Equation Modeling Approach

  • 会议论文

  • 发表日期：

    2026年

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  • G. Ramanjaneyulu,
  • Somasekhar Donthu,
  • B. Venkata Lakshmi,
  • V. Mouneswari,
  • Y. Mallikarjuna Achari,
  • S. Md. Ershad

摘要

This study examines at how HR analytics may be used to forecast employee turnover in the IT sector. It focuses on five important factors: leadership support, HRM competency, data availability, technological infrastructure, and change management. Data was gathered from 255 HR professionals and IT company workers using a standardized questionnaire using a 5-point Likert scale format. AMOS was used to analyze the data in order to evaluate the predicted correlations among the determinants and employee turnover as well as test the model fit. According to the results, all five criteria have a considerable impact on turnover, but the effects of Leadership Support and Data Availability are more noticeable. With indices like CMIN/df, GFI, AGFI, CFI, and RMSEA falling within suggested bounds, the model showed a satisfactory match. The findings highlight how crucial it is to include data-driven tactics into HR procedures in order to accurately predict and lower employee turnover. For IT companies looking to improve employee retention through the strategic use of HR analytics, the report provides useful takeaways.