1. The Happy-Productive Worker Model and Beyond: Patterns of Wellbeing and Performance at Work
- Author
-
Núria Tordera, José M. Peiró, Isabel Rodríguez-Molina, and Malgorzata W. Kozusznik
- Subjects
Adult ,Employment ,Male ,IMPACT ,Health, Toxicology and Mutagenesis ,Happiness ,lcsh:Medicine ,050109 social psychology ,Environmental Sciences & Ecology ,Efficiency ,Disease cluster ,Logistic regression ,GOALS ,Eudaimonia ,ORGANIZATIONAL CITIZENSHIP ,Article ,Job Satisfaction ,RATINGS ,happy-productive worker ,0502 economics and business ,Covariate ,Humans ,0501 psychology and cognitive sciences ,occupational wellbeing ,Work Performance ,Public, Environmental & Occupational Health ,HAPPINESS ,Science & Technology ,05 social sciences ,lcsh:R ,Public Health, Environmental and Occupational Health ,Middle Aged ,JOB-SATISFACTION ,Work (electrical) ,Spain ,YOUNG ,Female ,EMPLOYEES ,HEALTH ,Psychology ,Social psychology ,Life Sciences & Biomedicine ,050203 business & management ,Environmental Sciences ,performance - Abstract
According to the happy-productive worker thesis (HPWT), &ldquo, happy&rdquo, workers perform better than &ldquo, less happy&rdquo, 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&rsquo, 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&mdash, happy-productive, unhappy-unproductive, happy-unproductive, and unhappy-productive&mdash, 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 &ldquo, antagonistic&rdquo, patterns identify employees with profiles not explicitly considered by the HPWT.
- Published
- 2019