Howdy, Robo-Partner: exploring artificial companionship and its stress-alleviating potential for service employees

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Practical implications

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.

Originality/value

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.

Citation

Le, K.B.Q. and Cayrat, C. (2024), "Howdy, Robo-Partner: exploring artificial companionship and its stress-alleviating potential for service employees", Journal of Service Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JOSM-09-2023-0408