HR- ANALYTICS FOR EFFECTIVE PERFORMANCE APPRAISAL - Priyansh Gupta, Manoj Hudnurkar, Suhas Ambekar (PalArch's Journal of Archaeology of Egypt / Egyptology, 2020)
This paper aims to remove subjectivity from the performance appraisal process through data analytics. In doing so, the paper lists down all the biases that affects the ratings of an employee and develop methods that flag off those potential biases. This paper uses statistical parameters, statistical tools as well as parametric tests to devised methods to quantify the identified biases. We have considered weighted average ratings given by a manager to its subordinates over the period of years and utilized it to classify the consistent behaviour of the manager into several biases. The distribution pattern of ratings given to employees by most of the managers shows highly negative skewed normal distribution curve indicating the presence of leniency as well as central tendency bias. We have also found out that 52% of the employees exhibit subjective bias indicating the presence of biases in the performance appraisal system. This paper provides implications for all the people associated with the performance management area for reducing the subjectivity in the appraisal process using data analytics and for future researchers to test and analyse the suggested methods for different organizational settings. The paper offers insights about how employee performance data can be used to identify different existing biases in performance appraisal system by suitable analytical methods and thus contributing in making a more transparent, more objective and hence, a more effective Performance Appraisal System.