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This paper adds to the ongoing debate on judgements, opportunity evaluation and founder identity theory and shows that founders vary in their prioritisation and combination of judgement criteria, linked to their respective social founder identity. It further reveals how this variation among founder identity types shapes their perception of distinct entrepreneurial opportunities and the forming of first-person opportunity beliefs.

This study uses a qualitative approach by presenting three business scenarios to a sample of 34 first-time founders. It adopts a first-person perspective on their cognitive processes during the evaluation of entrepreneurial opportunities using verbal protocol and content analysis techniques.

The theorised model highlights the use of similar categories of judgement criteria by individual founders during opportunity evaluation that followed two distinct stages, namely search and validation. Yet, founders individualised their judgement process through the prioritisation of different judgement criteria.

The authors provide new insights into how individuals individuate entrepreneurial opportunities through the choice of different judgement criteria that enable them to develop opportunity confidence during opportunity evaluation. The study also shows that first-time founders depict variations in their cognitive frames that are based on their social identity types as they assess opportunity-related information and elicit variations in reciprocal relationships emerging between emotion and cognition. Exposing these subjective cognitive evaluative processes provides theoretical and practical implications that are discussed as well.

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.