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An integrated model to evaluate the transparency in predicting employee churn using explainable artificial intelligence
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References

  • 2016Understanding The Small Business Sector413 cited
  • 2019Job satisfaction and employee turnover determinants in high contact services: Insights from Employees’Online reviews360 cited
  • 2020Predicting Employee Attrition Using Machine Learning Techniques326 cited
  • 2015Understanding the Millennial Generation324 cited
  • 2020Overcoming Barriers to Technology Adoption When Fostering Entrepreneurship Among the Poor: The Role of Technology and Digital Literacy302 cited
  • 2016Prediction of Employee Turnover in Organizations using Machine Learning Algorithms243 cited
  • 2013The Hidden Biases in Big Data235 cited
  • 2007Developing In-House Careers and Retaining Management Talent219 cited
  • 2010Employee churn prediction188 cited
  • 2015Quality of work life and job satisfaction among frontline hotel employees184 cited
  • 2019An ROI-based review of HR analytics: practical implementation tools174 cited
  • 2022Bridging the gap: why, how and when HR analytics can impact organizational performance173 cited
  • 2012There Are Significant Business Costs to Replacing Employees167 cited
  • 2021The impact of artificial intelligence on economic growth and welfare145 cited
  • 2019An Improved Random Forest Algorithm for Predicting Employee Turnover

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  • 2021Introducing a multi-stakeholder perspective on opacity, transparency and strategies to reduce opacity in algorithm-based human resource management136 cited
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