The Quality at Work Tool (AQ@workT_S) to assess the quality of life at work in university staff

Theoretical background. The rapid changes that academics have experienced over recent years are resulting in several challenges for those who work in the University. These changes, which transversely affect all university workers, result in more significant work intensification, which may have adverse outcomes. On the topic, the JD-R model provides a simple but complete heuristic framework linking a wide variety of characteristics related to job content and a wide range of job results. This model was developed to provide a comprehensive framework to understand the factors that may challenge or enhance workers’ physical, mental, and psychological well-being. The present study provides evidence for a valid and reliable tool, the Quality at Work Tool (Brondino et al., 2022) staff version (AQ@workT_S), grounded in the job demands-resources model. The tool aims to investigate the quality of life at work in technical and administrative staff within the university sector. Methodology. The AQ@workT_S was developed by the QoL@Work research team, namely a group of expert academics in the field of work and organizational psychology affiliated with the Italian Association of Psychologists. The psychometric properties were assessed in a study comprising a wide sample of Italian university staff, and analyses were carried out on a sample of 1820 technical-administrative staff workers. After appropriately calculating asymmetry and kurtosis indices, aggregate confirmatory analyses were proposed for demands, resources, mediators/moderators and output of the questionnaire constructs, according to the distinction arising from the Job Demands-Job Resources theoretical model. Missing data less than 10% were estimated by means of the FIML algorithm, while scales with missings greater than that percentage were processed keeping only the present ones. Results. Reliability and content, and construct validity were supported, as well as measurement invariance across gender and seniority of service. The results suggest that job resources (superiors and colleagues support, job autonomy, organisational identification, environment quality, distributive justice, organisational work-family support, meaning of work), demands (work intensity, workload, Conflict, work-family balance, out-of-hours demands), mediators/moderators (workaholism) and outcomes (work engagement, burnout, technostress) present acceptable fit indices, according to the criteria proposed by Kline (2016, CFI > .90, RMSEA < .08, SRMR <.08). Construct validity was finally measured by means of correlation analysis between the variables and revealed polarities and intensities consistent with the literature. Invariance was then subsequently tested for university, seniority, and gender. Future studies, such as longitudinal tests of the AQ@workT_S, should test predictive validity among the variables in the tool. Limitations. Self-report questionnaire, convenience sampling strategy, and social desirability bias. Practical Implications. Evidence from the present study shows that the AQ@workT_S represents a useful and reliable tool to assist university management in enhancing the quality of life, managing work-related stress, and mitigating the potential for harm to technical and administrative staff, which also takes into account techno-stress and post-pandemic re-organization. Recommendations are offered for management on how academic staff can be maintained and supported to achieve well-being at work.