Based on a review of the history of the employee engagement construct and its measurement, we define workforce engagement as the aggregate of the work engagement experiences of individual employees in an organization. In contrast to most research on employee engagement, we study companies rather than individuals and the companies represent a diverse set of industries. We hypothesize and demonstrate on a sample of (up to) 102 publicly traded companies that workforce engagement significantly predicts organizational financial (adjusting for industry:Return on Assets, Net Margin but not Tobin's q) and customer metrics (the American Customer Satisfaction Index and the Harris Reputation Quotient) 1 and 2 years after the workforce engagement data were collected. In addition, using a split-sample approach to avoid method bias, we hypothesize and show that (a) company organizational practices (the strongest correlate), supervisory support, and work attributes are significant correlates of workforce engagement and (b) that workforce engagement mediates the relationship between these correlates of engagement and the organizational performance metrics. Implications of the findings for research and practice are discussed.
This study employed a multifaceted model assessment approach to investigate the dimensionality and nomological network of a popular measure of trait reactance, the Hong Psychological Reactance Scale (HPRS; Hong & Page, 1989 ). To address confusion regarding the scoring and modeling of the HPRS as well as its limited external validity evidence, we tested competing factor models, diagnosed model-data misfit, examined relationships between competing factor models and key personality traits, and cross-validated the results. Confirmatory factor analytic results supported modeling the HPRS via a bifactor model and, when this model was applied, trait reactance was negatively related to agreeableness, conscientiousness, and conformity, and positively related to entitlement, as expected. However, we also demonstrated the consequences of championing a 1-factor model by highlighting differences in relationships with external variables. Specifically, although modeling the HPRS scores with the bifactor model resulted in greater model-data fit than the 1-factor model, relationships with external variables based on the 2 models differed negligibly. Moreover, bifactor statistical indexes indicted that scores were essentially unidimensional, providing some support that HPRS scores can be treated as unidimensional in structure. Implications for using and scoring the HPRS are discussed.
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