Title: Predictive analytics for efficient decision making in personnel selection

Authors: Joonghak Lee; Steven B. Kim; Youngsang Kim; Sungjun Kim

Addresses: College of Business, Gachon University, Seongnam, South Korea ' Department of Mathematics and Statistics, California State University, Monterey Bay, Seaside, CA, USA ' Department of Management, SKK Business School, Sungkyunkwan (SKK) University, Room #33414, Business Building, 25-2 Sungkyunkwanro Jongnogu, Seoul, South Korea ' Department of Business Administration, Kookmin University, Seoul, South Korea

Abstract: This study investigates the predictive modelling in personnel selection. In particular, we focus on the prediction of interview performance using combinations of variables which assess personality and cognitive ability. Based on a dataset of 1,989 subjects, we generate 1,024 possible models with ten predictors including six personalities and four cognitive factors and apply the mixed-effect logistic regression to account for the random effect. The predictive performance of each model is evaluated by the area under receiver operating characteristic curve. The results show that the model with a combination of ambition and agreeableness as well as verbal and reasoning can predict the interview performance at 68% accuracy and this predictive power is not substantially different from the predictive performance of more complicated models. Our results suggest that personnel selection with fewer factors can be as efficient as all factors in the prediction. This study contributes to the selection literature by emphasising and justifying efficient decision making with predictive models, and it demonstrates that the personnel selection procedure can be simplified in an organisation and can save the organisation resources.

Keywords: predictive modelling; personnel selection; personality; cognitive ability; interview performance.

DOI: 10.1504/IJMDM.2023.127688

International Journal of Management and Decision Making, 2023 Vol.22 No.1, pp.106 - 122

Received: 16 Aug 2021
Accepted: 02 Mar 2022
Published online: 14 Dec 2022 *