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The emergence of new generations of artificial intelligence (AI), such as ChatGPT or Copilot has brought about a wave of innovation in the service workplace. These robotic agents can serve as companions, helping employees cope with work-related stress. This research introduces the concept of “artificial companionship,” which explains how robotic agents can function as partners in assisting service employees to fulfill their job responsibilities and maintain their mental well-being.

This research uses a mixed methods approach grounded in social support theory from psychology and management to develop a conceptual framework for the stress-alleviating implications of artificial companionship. A qualitative employee survey is conducted to justify the relevance of the propositions.

This research delineates the concept of artificial companionship. It highlights four distinct roles that AI can play in companionship – instrumental, informative, caring, and intimate. Building on this foundation, the research presents a series of propositions that elucidate the potential of artificial companionship in mitigating stress among employees.

Firms should consider aligning the types of artificial companionship with the demands inherent in employees’ job responsibilities to better reinforce their resilience and sustainment in overcoming work-related challenges.

This research introduces a new perspective on artificial companionship through the lens of social support theory. It extends the current understanding of human-robot collaboration in service workspaces and derives a set of propositions to guide future investigations.

This paper aims to respond to questions being raised about the challenges, risks and impacts of Human Resource Analytics (HRA). Based on a study of 40 companies, it discusses the implications of HRA for practitioners, praxis and practices in HRM and adds to this a concern with whether HRA is enhancing mutuality in employment relationships.

Using an abductive approach, the authors analyzed data from semistructured interviews with an HRA leader or specialist in 40 large organizations.

While wrestling with the challenge of data quality and integration, the practice of descriptive analytics is widely adopted in these companies and the majority can demonstrate improvement in the efficiency and effectiveness of HR activities through predictive analytics. The analytical competence of HR specialists is an ongoing issue. While much more research is needed, the companies in the authors’ sample demonstrated some valuable examples of mutual gains from HRA.

Education in HRA must not only help to raise quantitative competencies among HR specialists but should also help them to ask critical questions about the theoretical propositions and subjective data points being built into HRA. Boundary spanning is important to enable effective HRA and processes for employee voice to be improved. Arguably, the time has come for a more formalized data analytics' strategy in large organizations.

This paper provides evidence on how HRA is being implemented in large companies, including how HRA leaders are managing its challenges and risks and the impacts it is having on business and employee outcomes.