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The literature currently offers only fragmentary insights into the research on the relationship between employee emotions and human resource management (HRM). Therefore, further research is essential to bridge this knowledge gap. Our study aims to identify the mediating effects of positive employee emotions and exhaustion in the relationship between HRM and employee engagement.

Drawing on the literature review findings, a conceptual model was formulated to illustrate the relationship between HRM, employee emotions and engagement. A confirmatory analysis was conducted using structural equation modelling (SEM CFA) on a sample of 1,000 employees to validate the proposed model. The data were collected in 2021, with a particular emphasis on exploring the indirect influence of HRM on engagement through positive employee emotions and exhaustion.

The quantitative research aimed to test a model depicting the relationship between HRM and employee emotions. The findings indicate the robust effect of HRM on positive employee emotions and exhaustion. The authors observed significant variation in the level of impact depending on the size of the organisation (stronger in large firms) and the sector (stronger in the public sector).

The study bridges the gap in our understanding of the link between HRM and employee emotions. It would be advisable to further explore the specific impact of individual HRM practices on both positive and negative employee emotions. It is worth extending the scope of future research to explore components of the investigated constructs as well as mediators and moderators of the relationship between HRM and employee emotions.

Existing literature on algorithmic management practices – defined as autonomous data-driven decision making in people's management by adoption of self-learning algorithms and artificial intelligence – suggests complex relationships with employees' well-being in the workplace. While the use of algorithms can have positive impacts on people-related decisions, they may also adversely influence job autonomy, perceived justice and – as a result – workplace well-being. Literature review revealed a significant gap in empirical research on the nature and direction of these relationships. Therefore the purpose of this paper is to analyse how algorithmic management practices directly influence workplace well-being, as well as investigating its relationships with job autonomy and total rewards practices.

Conceptual model of relationships between algorithmic management practices, job autonomy, total rewards and workplace well-being has been formulated on the basis of literature review. Proposed model has been empirically verified through confirmatory analysis by means of structural equation modelling (SEM CFA) on a sample of 21,869 European organisations, using data collected by Eurofound and Cedefop in 2019, with the focus of investigating the direct and indirect influence of algorithmic management practices on workplace well-being.

This research confirmed a moderate, direct impact of application of algorithmic management practices on workplace well-being. More importantly the authors found out that this approach has an indirect influence, through negative impact on job autonomy and total rewards practices. The authors observed significant variation in the level of influence depending on the size of the organisation, with the decreasing impacts of algorithmic management on well-being and job autonomy for larger entities.

While the influence of algorithmic management on various workplace practices and effects is now widely discussed, the empirical evidence – especially for traditional work contexts, not only gig economy – is highly limited. The study fills this gap and suggests that algorithmic management – understood as an automated decision-making vehicle – might not always lead to better, well-being focused, people management in organisations. Academic studies and practical applications need to account for possible negative consequences of algorithmic management for the workplace well-being, by better reflecting complex nature of relationships between these variables.