Mining High Performance Managers Based on the Results of Psychological Tests

Abstract

Selecting high performance managers represents a risky task mainly due to the costs involved in a wrong choice. This fact led to the development of many approaches to select the candidates that best fit into the requirements of a certain position. However, defining what are the most important features that condition a good personnel performance is still a problem. In this paper, we discuss an approach, based on data mining techniques, to help managers in this process. We built a classifier, based in the Combinatorial Neural Model (CNM), taking as dependent variable the performance of managers as observed along their careers. As independent variables, we considered the results of wellknown psychological tests (MBTI and DISC). The rules generated by CNM enabled the arising of interesting relations between the psychological profile of managers in their start point in the company and the quality of their work after some years in action. These rules are expected to support the improvement of the selection process by driving the choice of candidates to those with a best prospective. Also, the adequate allocation of people - the right professional in the right place - shall be improved.