Social learning dynamics influence performance and career self-efficacy in career-oriented educational virtual environments.

Educational virtual environments (EVEs) are defined by their features of immersion (degree of sensory engagement) and fidelity (degree of realism). Increasingly, EVEs are being used for career development and training purposes, which we refer to as career-oriented EVEs. However, little research has examined the effects of immersion and fidelity on career-related outcomes, like self-efficacy and interests, and the learning dynamics that may influence these outcomes. We address these research needs across two studies using an inductive approach. Study 1 compares welding career exploration in EVEs to traditional career exploration and finds that individuals using EVEs report more positive career self-efficacy. Study 2 examines the influence of social learning dynamics, or how individuals learn from each other through behavioral modeling, on performance and career-related self-efficacy and interest. Groups were assigned to use either a high or low immersion and fidelity EVE. Findings indicate strong social learning dynamics in both EVEs, but the effects were stronger for groups using the higher immersion and fidelity EVE. Specifically, groups converged on two performance measures, and the performance of individuals who were situated as behavioral models significantly predicted the performance of other group members. Performance at the individual level, in turn, predicted career self-efficacy and interest for men but not women, and only for those using the higher immersion and fidelity EVE. Based on these findings, we conclude with practical recommendations for and implications of implementing career-oriented EVEs for career exploration and skills training.

Moderating effects of person and job characteristics on digital monitoring outcomes.

Many individuals perceive digital monitoring to be an inherently negative practice that invades privacy, but recent research suggests that it has positive effects for workers under certain circumstances. This review expands upon existing digital monitoring frameworks by adopting a psychological perspective to explain individual and contextual variation in monitoring reactions. To do so, we identify person characteristics (e.g. trait reactance, self-efficacy, ethical orientation, goal orientation) and job characteristics (e.g. manual versus nonmanual labor, autonomy, task significance) that moderate workers' reactions and performance outcomes while being digitally monitored. Future research on moderators such as these will remain important as organizations continue to collect big data using digital monitoring.