PSYCHOLOGICAL CAPITAL AND EMPLOYEE PERFORMANCE: A LATENT GROWTH MODELING APPROACH

The positive core construct of psychological capital (consisting of efficacy, hope, optimism, and resilience) has been conceptually and empirically demonstrated to be related to employee performance. However, much of this work has relied on cross‐sectional designs to examine these relationships. This study utilizes longitudinal data from a large financial service organization (N= 179 financial advisory‐type employees) to examine within‐individual change in psychological capital over time and if this change relates to their change in performance. Latent growth modeling analyses revealed statistically significant within‐individual change in psychological capital over time, and that this change in psychological capital was related to change in 2 types of performance outcomes (supervisor‐rated performance and financial performance, i.e., individual sales revenue). Moreover, results of an exploratory cross‐lagged panel analysis suggested a causal relationship such that prior psychological capital leads to subsequent performance rather than vice versa. Taken together, these results highlight the impact employees’ psychological capital may have on their subjectively and objectively measured performance over time and offer evidence‐based practical guidelines for human resource selection, development, and performance management.     

TOWARD BETTER META‐ANALYTIC MATRICES: HOW INPUT VALUES CAN AFFECT RESEARCH CONCLUSIONS IN HUMAN RESOURCE MANAGEMENT SIMULATIONS

Simulations and analyses based on meta‐analytic matrices are fairly common in human resource management and organizational behavior research, particularly in staffing research. Unfortunately, the meta‐analytic values estimates for validity and group differences (i.e., ρ and δ, respectively) used in such matrices often vary in the extent to which they are affected by artifacts and how accurately the values capture the underlying constructs and the appropriate population. We investigate how such concerns might influence conclusions concerning key issues such as prediction of job performance and adverse impact of selection procedures, as well as noting wider applications of these issues. We also start the process of building a better matrix upon which to base many such simulations and analyses in staffing research. Finally, we offer guidelines to help researchers/practitioners better model human resources processes, and we suggest ways that researchers in a variety of areas can better assemble meta‐analytic matrices.