According to the happy-productive worker thesis (HPWT), “happy” workers perform better than “less happy” ones. This study aimed to explore the different patterns of relationships between performance and wellbeing, synergistic (i.e., unhappy-unproductive and happy-productive) and antagonistic (i.e., happy-unproductive and unhappy-productive), taking into account different operationalizations of wellbeing (i.e., hedonic vs. eudaimonic) and performance (i.e., self-rated vs. supervisors’ ratings). It also explored different demographic variables as antecedents of these patterns. We applied two-step cluster analysis to the data of 1647 employees. The results indicate four different patterns—happy-productive, unhappy-unproductive, happy-unproductive, and unhappy-productive—when performance is self-assessed, and three when it is assessed by supervisors. On average, over half of the respondents are unhappy-productive or happy-unproductive. We used multidimensional logistic regression to explain cluster membership based on demographic covariates. This study addresses the limitations of the HPWT by including both the hedonic and eudaimonic aspects of wellbeing and considering different dimensions and sources of evaluation. The “antagonistic” patterns identify employees with profiles not explicitly considered by the HPWT.

The happy-productive worker thesis (HPWT) assumes that happy employees perform better. Given the relevance of teams and work-units in organizations, our aim is to analyze the state of the art on happy-productive work-units (HPWU) through a systematic review and integrate existing research on different collective well-being constructs and collective performance. Research on HPWU (30 studies, 2001–2018) has developed through different constructs of well-being (hedonic: team satisfaction, group affect; and eudaimonic: team engagement) and diverse operationalizations of performance (self-rated team performance, leader-rated team performance, customers’ satisfaction, and objective indicators), thus creating a disintegrated body of knowledge about HPWU. The theoretical frameworks to explain the HPWU relationship are attitude–behavior models, broaden-and-build theory, and the job-demands-resources model. Research models include a variety of antecedents, mediators, and moderating third variables. Most studies are cross-sectional, all propose a causal happy–productive relationship (not the reverse), and generally find positive significant relationships. Scarce but interesting time-lagged evidence supports a causal chain in which collective well-being leads to team performance (organizational citizenship behavior or team creativity), which then leads to objective work-unit performance. To conclude, we identify common issues and challenges across the studies on HPWU, and set out an agenda for future research.

No abstract

The current article presents the development and validation of the Valencia Eustress-Distress Appraisal Scale (VEDAS), carried out in two studies. In the first study, we subjected data from 603 Spanish social service professionals to principal axis factoring analysis, yielding four related factors (Relationships, Personal Accountability, Home-Work Balance, and Workload) for both the eustress and distress scales. In the second study, we employed confirmatory factor analysis to test data from 431 Spanish social service professionals. Results yielded a four-factor structure for the distress (root mean square error of approximation [RMSEA] ϭ .07; comparative fit index [CFI] ϭ .98; non-normed fit index [NNFI] ϭ .96; standardized root mean square residual ]SRMR[ ϭ .06) and eustress (RMSEA ϭ .07; CFI ϭ .97; NNFI ϭ .97; SRMR ϭ .08) scales. The results suggest essential unidimensionality of the VEDAS, with one dominating dimension (Relationships) and three secondary dimensions (Personal Accountability, Home-Work Balance, and Workload) for both the eustress and distress scales. The results provide evidence of the VEDAS's internal consistency reliability, criterionrelated validity, and test-retest reliability. The VEDAS addresses a gap in

No abstract