Zobraziť v EDS

Impact of Technostress on the Adoption of Artificial Intelligence - A Bibliometric Analysis

Uložené v:
Podrobná bibliografia
Názov: Impact of Technostress on the Adoption of Artificial Intelligence - A Bibliometric Analysis
Autori: Zron, Anamarija
Zdroj: Proceedings of the 36th International Scientific Conference: Central European Conference on Information and Intelligent Systems: CECIIS 2025. :193-200
Informácie o vydavateľovi: 2025.
Rok vydania: 2025
Predmety: Technostress, Artificial Intelligence, VOSviewer, Bibliometric Analysis, AI Adoption
Popis: This study presents a bibliometric analysis of research on technostress and its impact on the adoption of Artificial Intelligence (AI) from 2022 to 2025, a period marked by the rapid rise of generative AI tools such as ChatGPT. Based on 19 carefully selected publications from the Web of Science database, including the top 10 most cited papers, the paper identifies key research themes, disciplinary intersections, and industry-specific insights. Grounded in the Technostress Model by Tarafdar et al. (2011), findings show that technostressors, particularly techno-insecurity, techno-complexity, and technouncertainty, are critical barriers to effective AI adoption across sectors such as IT, healthcare, finance, and tourism. The results highlight how technostress varies by industry, reflecting variations in digital maturity and employee readiness. Practical implications are outlined, with emphasis on strategies to reduce employee stress and resistance. Overall, the paper contributes to a deeper understanding of human factors in AI implementation and offers a foundation for future research on managing tech-driven organizational change.
Druh dokumentu: Conference object
ISSN: 1847-2001
Prístupové číslo: edsair.dris...01492..13fb162c432d23c5006e67c7785af3a7
Databáza: OpenAIRE
Popis
Abstrakt:This study presents a bibliometric analysis of research on technostress and its impact on the adoption of Artificial Intelligence (AI) from 2022 to 2025, a period marked by the rapid rise of generative AI tools such as ChatGPT. Based on 19 carefully selected publications from the Web of Science database, including the top 10 most cited papers, the paper identifies key research themes, disciplinary intersections, and industry-specific insights. Grounded in the Technostress Model by Tarafdar et al. (2011), findings show that technostressors, particularly techno-insecurity, techno-complexity, and technouncertainty, are critical barriers to effective AI adoption across sectors such as IT, healthcare, finance, and tourism. The results highlight how technostress varies by industry, reflecting variations in digital maturity and employee readiness. Practical implications are outlined, with emphasis on strategies to reduce employee stress and resistance. Overall, the paper contributes to a deeper understanding of human factors in AI implementation and offers a foundation for future research on managing tech-driven organizational change.
ISSN:18472001