IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v11y2024i1d10.1057_s41599-024-04018-w.html
   My bibliography  Save this article

The mental health implications of artificial intelligence adoption: the crucial role of self-efficacy

Author

Listed:
  • Byung-Jik Kim

    (University of Ulsan)

  • Julak Lee

    (Chung-Ang University)

Abstract

The rapid adoption of artificial intelligence (AI) in organizations has transformed the nature of work, presenting both opportunities and challenges for employees. This study utilizes several theories to investigate the relationships between AI adoption, job stress, burnout, and self-efficacy in AI learning. A three-wave time-lagged research design was used to collect data from 416 professionals in South Korea. Structural equation modeling was used to test the proposed mediation and moderation hypotheses. The results reveal that AI adoption does not directly influence employee burnout but exerts its impact through the mediating role of job stress. The results also show that AI adoption significantly increases job stress, thus increasing burnout. Furthermore, self-efficacy in AI learning was found to moderate the relationship between AI adoption and job stress, with higher self-efficacy weakening the positive relationship. These findings highlight the importance of considering the mediating and moderating mechanisms that shape employee experiences in the context of AI adoption. The results also suggest that organizations should proactively address the potential negative impact of AI adoption on employee well-being by implementing strategies to manage job stress and foster self-efficacy in AI learning. This study underscores the need for a human-centric approach to AI adoption that prioritizes employee well-being alongside technological advancement. Future research should explore additional factors that may influence the relationships between AI adoption, job stress, burnout, and self-efficacy across diverse contexts to inform the development of evidence-based strategies for supporting employees in AI-driven workplaces.

Suggested Citation

  • Byung-Jik Kim & Julak Lee, 2024. "The mental health implications of artificial intelligence adoption: the crucial role of self-efficacy," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
  • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-04018-w
    DOI: 10.1057/s41599-024-04018-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-024-04018-w
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1057/s41599-024-04018-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Landers, Richard N. & Behrend, Tara S., 2015. "An Inconvenient Truth: Arbitrary Distinctions Between Organizational, Mechanical Turk, and Other Convenience Samples," Industrial and Organizational Psychology, Cambridge University Press, vol. 8(2), pages 142-164, June.
    2. Shrestha, Yash Raj & Krishna, Vaibhav & von Krogh, Georg, 2021. "Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges," Journal of Business Research, Elsevier, vol. 123(C), pages 588-603.
    3. Dwivedi, Yogesh K. & Hughes, Laurie & Ismagilova, Elvira & Aarts, Gert & Coombs, Crispin & Crick, Tom & Duan, Yanqing & Dwivedi, Rohita & Edwards, John & Eirug, Aled & Galanos, Vassilis & Ilavarasan, , 2021. "Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy," International Journal of Information Management, Elsevier, vol. 57(C).
    4. Brougham, David & Haar, Jarrod, 2018. "Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA): Employees’ perceptions of our future workplace," Journal of Management & Organization, Cambridge University Press, vol. 24(2), pages 239-257, March.
    5. Pan, Wenrong & Xie, Tao & Wang, Zhuwang & Ma, Lisha, 2022. "Digital economy: An innovation driver for total factor productivity," Journal of Business Research, Elsevier, vol. 139(C), pages 303-311.
    6. Magistretti, Stefano & Dell’Era, Claudio & Messeni Petruzzelli, Antonio, 2019. "How intelligent is Watson? Enabling digital transformation through artificial intelligence," Business Horizons, Elsevier, vol. 62(6), pages 819-829.
    7. Yogesh K. Dwivedi & Nripendra P. Rana & Anand Jeyaraj & Marc Clement & Michael D. Williams, 2019. "Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model," Information Systems Frontiers, Springer, vol. 21(3), pages 719-734, June.
    8. Denise Albieri Jodas Salvagioni & Francine Nesello Melanda & Arthur Eumann Mesas & Alberto Durán González & Flávia Lopes Gabani & Selma Maffei de Andrade, 2017. "Physical, psychological and occupational consequences of job burnout: A systematic review of prospective studies," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-29, October.
    9. Ding, Wenzhi & Levine, Ross & Lin, Chen & Xie, Wensi, 2021. "Corporate immunity to the COVID-19 pandemic," Journal of Financial Economics, Elsevier, vol. 141(2), pages 802-830.
    10. Zirar, Araz & Ali, Syed Imran & Islam, Nazrul, 2023. "Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda," Technovation, Elsevier, vol. 124(C).
    11. Sophie-Charlotte Meyer & Lena Hünefeld, 2018. "Challenging Cognitive Demands at Work, Related Working Conditions, and Employee Well-Being," IJERPH, MDPI, vol. 15(12), pages 1-14, December.
    12. Kaplan, Andreas & Haenlein, Michael, 2020. "Rulers of the world, unite! The challenges and opportunities of artificial intelligence," Business Horizons, Elsevier, vol. 63(1), pages 37-50.
    13. Innocenti, Stefania & Golin, Marta, 2022. "Human capital investment and perceived automation risks: Evidence from 16 countries," Journal of Economic Behavior & Organization, Elsevier, vol. 195(C), pages 27-41.
    14. Siliang Tong & Nan Jia & Xueming Luo & Zheng Fang, 2021. "The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance," Strategic Management Journal, Wiley Blackwell, vol. 42(9), pages 1600-1631, September.
    15. Cheng, Bao & Lin, Hongxia & Kong, Yurou, 2023. "Challenge or hindrance? How and when organizational artificial intelligence adoption influences employee job crafting," Journal of Business Research, Elsevier, vol. 164(C).
    16. Beatrice Van der Heijden & Christine Brown Mahoney & Yingzi Xu, 2019. "Impact of Job Demands and Resources on Nurses’ Burnout and Occupational Turnover Intention Towards an Age-Moderated Mediation Model for the Nursing Profession," IJERPH, MDPI, vol. 16(11), pages 1-22, June.
    17. Makarius, Erin E. & Mukherjee, Debmalya & Fox, Joseph D. & Fox, Alexa K., 2020. "Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization," Journal of Business Research, Elsevier, vol. 120(C), pages 262-273.
    18. Prem Borle & Kathrin Reichel & Fiona Niebuhr & Susanne Voelter-Mahlknecht, 2021. "How Are Techno-Stressors Associated with Mental Health and Work Outcomes? A Systematic Review of Occupational Exposure to Information and Communication Technologies within the Technostress Model," IJERPH, MDPI, vol. 18(16), pages 1-19, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Olimpia Ban & Irina Maiorescu & Mihaela Bucur & Gabriel Cristian Sabou & Betty Cohen Tzedec, 2024. "AI between Threat and Benefactor for the Competences of the Human Working Force," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 26(67), pages 762-762, August.
    2. Byung-Jik Kim & Min-Jik Kim & Julak Lee, 2024. "Code green: ethical leadership’s role in reconciling AI-induced job insecurity with pro-environmental behavior in the digital workplace," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-16, December.
    3. Chowdhury, Soumyadeb & Budhwar, Pawan & Dey, Prasanta Kumar & Joel-Edgar, Sian & Abadie, Amelie, 2022. "AI-employee collaboration and business performance: Integrating knowledge-based view, socio-technical systems and organisational socialisation framework," Journal of Business Research, Elsevier, vol. 144(C), pages 31-49.
    4. O. C. Ferrell & Dana E. Harrison & Linda K. Ferrell & Haya Ajjan & Bryan W. Hochstein, 2024. "A theoretical framework to guide AI ethical decision making," AMS Review, Springer;Academy of Marketing Science, vol. 14(1), pages 53-67, June.
    5. Kamoonpuri, Sana Zehra & Sengar, Anita, 2023. "Hi, May AI help you? An analysis of the barriers impeding the implementation and use of artificial intelligence-enabled virtual assistants in retail," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    6. Chenfeng Yan & Quan Chen & Xinyue Zhou & Xin Dai & Zhilin Yang, 2024. "When the Automated fire Backfires: The Adoption of Algorithm-based HR Decision-making Could Induce Consumer’s Unfavorable Ethicality Inferences of the Company," Journal of Business Ethics, Springer, vol. 190(4), pages 841-859, April.
    7. Zhang, Chao & Zhu, Weidong & Dai, Jun & Wu, Yong & Chen, Xulong, 2023. "Ethical impact of artificial intelligence in managerial accounting," International Journal of Accounting Information Systems, Elsevier, vol. 49(C).
    8. Makarius, Erin E. & Mukherjee, Debmalya & Fox, Joseph D. & Fox, Alexa K., 2020. "Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization," Journal of Business Research, Elsevier, vol. 120(C), pages 262-273.
    9. Feng, Cai (Mitsu) & Botha, Elsamari & Pitt, Leyland, 2024. "From HAL to GenAI: Optimizing chatbot impacts with CARE," Business Horizons, Elsevier, vol. 67(5), pages 537-548.
    10. Chiara Panari & Giorgio Lorenzi & Marco Giovanni Mariani, 2021. "The Predictive Factors of New Technology Adoption, Workers’ Well-Being and Absenteeism: The Case of a Public Maritime Company in Venice," IJERPH, MDPI, vol. 18(23), pages 1-14, November.
    11. De Obesso Arias, María de las Mercedes & Pérez Rivero, Carlos Alberto & Carrero Márquez, Oliver, 2023. "Artificial intelligence to manage workplace bullying," Journal of Business Research, Elsevier, vol. 160(C).
    12. Fernandes, Teresa & Oliveira, Elisabete, 2021. "Understanding consumers’ acceptance of automated technologies in service encounters: Drivers of digital voice assistants adoption," Journal of Business Research, Elsevier, vol. 122(C), pages 180-191.
    13. Hoffmann, Stefan & Lasarov, Wassili & Dwivedi, Yogesh K., 2024. "AI-empowered scale development: Testing the potential of ChatGPT," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    14. Volkmar, Gioia & Fischer, Peter M. & Reinecke, Sven, 2022. "Artificial Intelligence and Machine Learning: Exploring drivers, barriers, and future developments in marketing management," Journal of Business Research, Elsevier, vol. 149(C), pages 599-614.
    15. Zirar, Araz & Ali, Syed Imran & Islam, Nazrul, 2023. "Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda," Technovation, Elsevier, vol. 124(C).
    16. Keding, Christoph & Meissner, Philip, 2021. "Managerial overreliance on AI-augmented decision-making processes: How the use of AI-based advisory systems shapes choice behavior in R&D investment decisions," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    17. Liu, Qian & Gao, Jian & Li, Shijie, 2024. "The innovation model and upgrade path of digitalization driven tourism industry: Longitudinal case study of OCT," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    18. Jing Wang & Zeyu Xing & Rui Zhang, 2023. "AI technology application and employee responsibility," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.
    19. Denicolai, Stefano & Zucchella, Antonella & Magnani, Giovanna, 2021. "Internationalization, digitalization, and sustainability: Are SMEs ready? A survey on synergies and substituting effects among growth paths," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    20. Zhao, Jingyou & Hu, Enhua & Han, Mingyan & Jiang, Keshen & Shan, Hongmei, 2023. "That honey, my arsenic: The influence of advanced technologies on service employees’ organizational deviance," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-04018-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.