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No systematic review has previously been dedicated to comprehensively investigate predictors of well-being and ill-being in working contexts. Empirical studies have vastly associated well-being as the result of autonomous motivation and basic psychological needs satisfaction, while frustration results in ill-being. The purpose of this study is to integrate the variables identified in empirical studies associated with the occurrence of the phenomena, individual/organizational features and consequences associated with workers' well-being/ill-being.

This systematic review includes 44 empirical studies published up to February 2021. Findings are summarized based on quantitative analysis of the evidence.

Results reinforce the role of self-determined motivation and needs satisfaction in promoting well-being, while amotivation and needs frustration led to ill-being. Besides, they indicate that ill-being can both lead to negative consequences and diminish positive work outcomes. Findings also revealed that: integrated motivation does not seem to be empirically distinct from intrinsic and identified motivation in promoting well-being; introjected motivated behaviors may be less harmful to psychological health than externally oriented ones; the relationship between external motivation and well-being/ill-being requires prospective investigations; and amotivation seems to have a detrimental effect in workers' psychological health.

Results provide practical information for HRM practitioners to design work environments and practices that promote employees' psychological health.

An unprecedented framework that aggregates empirical findings regarding the antecedents, predictors and consequences of ill-being/well-being in working contexts is presented.

This study is dedicated to critically analysing research addressing human resource management (HRM) and the adoption of artificial intelligence (AI) with the purpose of driving development in the field of human resources (HR) at the strategic and managerial level.

A systematic literature review (SLR) was conducted using the Scopus database, which gathered 61 articles between 2002 and 2022. The SLR process has the potential to, in addition to generating knowledge and theories, support and guide policy development and practice in many disciplines.

The results of this study allowed the author to identify three main conclusions: (a) there are four thematic clusters – (i) Strategic HR and AI, (ii) Recruitment and AI, (iii) Training and AI and (iv) Future of work; (b) there is a growing academic interest in studying the implementation of AI to develop the HR sector and (c) the application of AI stands out in the strategic HR and AI cluster as a means of achieving profit maximisation and the overall development of the organisation.

This study is the first SLR to present a strategic and managerial view on AI applications associated with specific HRM dimensions. The study is also the first SLR to identify key trends in the literature, drivers and obstacles to the development of AI in HRM and then place them within the landscape of positive and negative approaches in a framework. Also, as a contribution, the study has practical implications for HR managers and practitioners in adopting AI as a decision support in the area's processes.