Celina Druart

Symbolic Attributes and Organizational Attractiveness: The Moderating Effects of Applicant Personality

The present study examined the moderating influence of the Big Five personality factors in the relationship between five symbolic, trait-based inferences about organizations (Sincerity, Excitement, Competence, Prestige, and Ruggedness) and organizational attractiveness. Drawing on the similarity-attraction paradigm, six hypotheses were formulated, stating that the relationship between trait-based inferences and organizational attractiveness would be stronger for persons who perceive the organization as similar to them. Results of moderated regression analyses on data from a sample of 245 prospective applicants for the Belgian military revealed two significant two-way interactions, showing that Sincerity was positively related to organizational attractiveness only for individuals high on Conscientiousness, and that the relationship between Excitement and organizational attractiveness is more positive for individuals high on Openness to Experience. Practical implications, strengths and limitations, as well as directions for further research are presented.

Designing Pareto-Optimal Systems for Complex Selection Decisions:

The current practice of personnel selection faces the challenge of reconciling the often competing goals of obtaining a high-quality as well as a diverse workforce. To address this challenge for simple selections, De Corte, Sackett, and Lievens (2011) propose using Pareto-optimal predictor composites. These composites yield trade-offs between selection quality and selection diversity levels that cannot be improved simultaneously by any other composite. The current article describes how these Pareto-optimal composites and trade-offs can be developed in complex selection situations, which are characterized by vacancies for at least two different positions and applicants that apply either for one or several of these open positions simultaneously. An analytic method that estimates the selection quality and adverse impact of these complex selection decisions is presented and implemented in a multi-objective optimization program, so as to obtain Pareto-optimal predictor composites. The resulting decision aid is subsequently used to illustrate how it can lead to substantive contributions when considering the quality-diversity dilemma in a complex selection context.

Computing Pareto‐Optimal Predictor Composites for Complex Selection Decisions

A complex selection situation encompasses vacancies for several different positions and applicants that apply simultaneously for one or several of these positions. This article presents an analytic method for estimating the expected selection quality, as well as the adverse impact ratio of these complex selections, when the decisions are based on a single predictor composite score. In addition, the method is integrated within a broader decision-making framework for designing complex selection decisions that show a Pareto-optimal balance between the selection quality and diversity goals. Finally, the decision aid is used to demonstrate the importance of applying the appropriate selection format (either the simple or the complex format) when exploring the front of Pareto-optimal outcomes of planned selections.