This study investigates the relation between two distributive justice rules (equity and equality) and team effectiveness, and the relation between procedural justice (PJ) and team effectiveness. The extent to which cooperation mediates these relations is examined. Results from 132 teams working on an interdependent task indicate that teams operating under equality rules were more cooperative, which led to fewer errors and better quality. Results also indicate that teams exposed to higher PJ were more cooperative. The increased cooperation in teams exposed to higher PJ led to better quality and higher ratings of team viability; these effects occurred regardless of whether an equity rule or an equality rule was used to allocate rewards. Areas for future research and implications for team rewards are discussed.

Using game-based assessments (GBAs) to assess and select job applicants presents the dual challenges of measuring intended job-relevant constructs while analyzing GBA data that contain more predictors than observations. Exploring those challenges, we analyzed two GBAs that were designed to measure conscientiousness facets (i.e., achievement striving, self-discipline, and cautiousness). Scores on traditional measures of personality and cognitive ability were modeled using either a restricted set of GBA predictors using cross-validated ordinary least squares (OLS) regression or by the fuller set (p = 248) using random forests regression. Overall, the prediction of personality was near-zero; but the latter approach explained 14%-30% of the variance in predicting cognitive ability. Our findings warn of GBAs potentially measuring unintended constructs rather than their intended constructs.

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The present evaluation focused primarily on the usefulness of the 0*NET system for Army occupational analysis for selection and classification purposes. The evaluation focused on the appropriateness of 0*NET descriptors that would typically be used in an Army occupation analysis for selection and classification purposes; abilities, skills, generalized work activities [GWAs], and work context. Four civilian and four officer occupations were selected for this research. The objective was to produce data for the military occupations that could be compared to civilian 0*NET data. Therefore, it was important to follow processes currently used by 0*NET for data collection. In effect, this meant collecting information on occupational tasks, abilities skills, GWAs, and work context from Army Subject Matter Experts (SMEs) and collecting ability and skill ratings using trained analysts. The results showed that Army SMEs as well as other types of analysts could make reliable ratings on the 0*NET descriptors. Potential uses for the 0*NET descriptors are discussed.