Identifying talent: public organisation with skewed performance scores

Abstract

PurposeThis study aims to draw lessons on how talent identification becomes a critical factor in the field of talent management (TM).Design/methodology/approachA simulation approach with three developed scenarios is used in the paper. The first utilised the standard deviation of skewed performance scores, the second applied the standard deviation of normalised data and the third practised a percentile approach. Concerning the normalisation process of employee performance data, the paper proposed a weighted function to address skewness.FindingsThe results indicate that the process of identifying talent using a nine-grid box is sensitive to changes in the classification criteria used, indicating a bias in identifying talent. In sum, using a standard deviation approach using transformation data is the most appropriate choice for use in performance data with a skewed distribution.Practical implicationsThe Government of West Java Province, Indonesia, can use the simulation results to objectively identify excellent civil servants and develop an appropriate TM strategy. A similar process treatment can be implemented in other organisations that have skew distribution issues.Originality/valueThis paper introduces a weighted function approach to address practical problems in the unsymmetrical distribution of employee performance scores when identifying talent using a TM framework. It shows the application of a unique mathematical technique to solve issues found in the field of human resources management systems.