Mapping and Synthesizing the Landscape of Artificial Intelligence and Technostress: A Hybrid Bibliometric and Systematic Review Approach

Abstract

The objective of this study is to map and synthesize the extant scientific literature concerning the relationship between Artificial Intelligence (AI) and technostress. To this end, a hybrid approach will be employed, integrating bibliometric analysis and a systematic literature review (SLR). As the adoption of artificial intelligence (AI) accelerates on a global scale, novel psychological stressors have emerged, extending beyond the conventional frameworks of information and communication technology (ICT). A comprehensive analysis of 34 empirical studies published between 2020 and 2025 was conducted to identify publication trends, conceptual developments, and the key mechanisms underlying AI-induced technostress. Bibliometric mapping reveals a significant surge in research output since 2023, with dominant contributions from China, India, Germany, and Spain. A thematic synthesis of the extant literature reveals that AI-induced technostress primarily emerges from three sources: cognitive overload, algorithmic complexity, and perceived loss of control. These mechanisms correspond to but also expand upon classical technostress creators. The findings further indicate that psychological outcomes such as burnout, anxiety, and reduced wellbeing frequently accompany AI integration. However, coping strategies, digital literacy, and organizational support serve as mitigating factors. The study's findings culminate in the proposal of an integrated conceptual model that incorporates the Technostress Model, Job Demands Resources (JD R) framework, technology acceptance theories, and Task Technology Fit (TTF). This model aims to enhance the explanatory power of the dual role of AI as both a demand-enhancing and resource-enhancing technological agent. Furthermore, it advocates for the utilization of longitudinal and cross-cultural research methodologies in future studies.