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Labor process theory explains that employers prefer technology and systems over employees for efficiency gains. However, the mechanisms explaining the influence of technology and systems on different work-related employee outcomes are still a question mark. The purpose of this study is to test a mediation mechanism explaining how workforce analytics influence the work fulfillment experience of employees through work volition.

This study collected dyadic data from 55 HR managers and 350 employees serving in 55 different subsidiaries of Multinational Corporations in Pakistan.

The statistical results demonstrate that workforce analytics negatively affect fulfillment at work. However, work volition may reduce the negative relationship between workforce analytics and fulfillment at work. This study also found a significant but negative relationship between work volition and fulfillment at work.

This study found that integrating the use of workforce analytics with the work volition of employees is critical for positive employee outcomes.

Organizations are increasingly deploying algorithmic human resource management (HRM) for decision-making. Despite algorithms beginning to permeate HRM practices, our understanding of how to interpret and leverage the functions of algorithmic HRM remains limited. This study aims to review the stock of knowledge in this field of algorithmic HRM and introduce a theoretical perspective of functional affordance to enhance the understanding of the value of algorithmic HRM.

A systematic literature review was conducted in this study based on 283 articles. The articles are extracted from the Web of Science and Scopus. The content of the articles was then integrated to formulate the framework for this study.

Functional affordance highlights algorithmic HRM can be systematically embedded within the organizational environment, with its characteristics naturally suggesting the functionalities or actions available for HR managers to choose from. The findings of this study demonstrate five features of algorithmic HRM from the perspective of functional affordance: awareness of algorithmic HRM, alignment with business model design, action readiness, adaptation to business context and attribution to individuality.

This study provides a novel perspective for understanding the insufficiently theorized application of algorithmic HRM within organizations. It presents an integrated framework that elucidates the key features of algorithmic HRM and elaborates on how organizations can better develop algorithm-driven capabilities based on functional affordance.

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.