Original Research

Employee well-being in robot, artificial intelligence and service automation-integrated workplace: A scoping review

Kiky D.H Saraswati, Fajrianthi Fajrianthi, Sami’an Sami’an
SA Journal of Industrial Psychology | Vol 51 | a2323 | DOI: https://doi.org/10.4102/sajip.v51i0.2323 | © 2025 Kiky D.H. Saraswati, Fajrianthi Fajrianthi, Sami’an Sami’an | This work is licensed under CC Attribution 4.0
Submitted: 14 May 2025 | Published: 25 November 2025

About the author(s)

Kiky D.H Saraswati, Doctoral Psychology Program, Faculty of Psychology, Airlangga University, Surabaya, Indonesia; and Department of Psychology, Faculty of Psychology, Universitas Tarumanagara, Jakarta, Indonesia
Fajrianthi Fajrianthi, Doctoral Psychology Program, Faculty of Psychology, Airlangga University, Surabaya, Indonesia
Sami’an Sami’an, Doctoral Psychology Program, Faculty of Psychology, Airlangga University, Surabaya, Indonesia

Abstract

Orientation: The integration of Robot, Artificial Intelligence and Service Automation (RAISA) has transformed the workplace, reshaping work processes and efficiency. However, RAISA also brings complex implications for employee well-being.
Research purpose: This study aimed to explore determinants, intervening variables and theoretical frameworks associated with employee well-being in RAISA-integrated workplaces through a scoping review.
Motivation for the study: Despite the rapid expansion of RAISA, research on its impact on well-being remains fragmented. A scoping review provides a structured synthesis to guide future research and inform practical strategies, including human resource management (HRM) initiatives that foster well-being.
Research approach/design and method: Peer-reviewed studies were sourced from Scopus, Web of Science, EBSCOhost, PubMed and Taylor & Francis. Following predefined inclusion and exclusion criteria, 35 studies were charted to answer the research questions.
Main findings: Employee well-being in RAISA contexts is affected by personal factors (e.g. awareness, efficacy, job insecurity) and organisational factors (e.g. leadership, job design, support systems). Mediators and moderators such as self-efficacy, emotional responses and leadership styles influence how these factors relate to well-being. Theoretical frameworks, like Job Demands-Resources (JD-R) Model and Self-Determination Theory (SDT), offer robust explanations of the dynamic interactions between studied variables.
Practical/managerial implications: The review highlights the need for HRM to balance RAISA’s efficiency gains with strategies that support employee health, development and resilience.
Contribution/value-add: By synthesising fragmented literature, this study provides a comprehensive foundation for future research and offers valuable insights for organisational strategies to manage human impacts of RAISA, particularly regarding well-being.


Keywords

well-being; employee; smart technology; artificial intelligence; robot; automation; workplace; scoping review

JEL Codes

J24: Human Capital • Skills • Occupational Choice • Labor Productivity; J63: Turnover • Vacancies • Layoffs; J81: Working Conditions

Sustainable Development Goal

Goal 3: Good health and well-being

Metrics

Total abstract views: 983
Total article views: 2072


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