• Priyansh Gupta, Manoj Hudnurkar, Suhas Ambekar


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.


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How to Cite

Priyansh Gupta, Manoj Hudnurkar, Suhas Ambekar. (2020). HR- ANALYTICS FOR EFFECTIVE PERFORMANCE APPRAISAL. PalArch’s Journal of Archaeology of Egypt / Egyptology, 17(6), 4288 - 4308. Retrieved from