IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v222y2026ics0040162525004184.html

How does artificial intelligence adoption shape employee performance? A novel exploration of mimetic artificial intelligence performance through a hybrid approach based on PLS-SEM and ANN

Author

Listed:
  • Liu, Shengmin
  • Mei, Yu

Abstract

Within the context of Industry 5.0 and digital transformation, the rapid advancement of information technology has rendered artificial intelligence ubiquitous, presenting significant challenges and opportunities for the workforce. This study employs a hybrid Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) approach to investigate employee performance within artificial intelligence-adoption work environments. Utilizing a three-wave experience sampling methodology, we collected 308 valid questionnaires from employees in Chinese internet companies. Our findings demonstrate that artificial intelligence adoption at work positively associated with employees' problem-solving efficacy, which in turn influences adaptive performance. Furthermore, stronger artificial intelligence adoption at work is associated with an increased employees' mimetic artificial intelligence performance through employee learning from artificial intelligence. Task-oriented leadership amplifies the effects of artificial intelligence adoption at work on both problem-solving efficacy and adaptive performance. Conversely, knowledge-oriented leadership strengthens the relationship between artificial intelligence adoption at work and employee learning from artificial intelligence, thereby developping mimetic artificial intelligence performance. This research contributes by introducing and measuring the novel concept of “mimetic artificial intelligence performance,” extending the application of social comparison, learning and influence theories. The study offers valuable theoretical insights and practical implications for understanding and optimizing employee performance in artificial intelligence-driven workplaces.

Suggested Citation

  • Liu, Shengmin & Mei, Yu, 2026. "How does artificial intelligence adoption shape employee performance? A novel exploration of mimetic artificial intelligence performance through a hybrid approach based on PLS-SEM and ANN," Technological Forecasting and Social Change, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:tefoso:v:222:y:2026:i:c:s0040162525004184
    DOI: 10.1016/j.techfore.2025.124387
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162525004184
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2025.124387?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Varela-Neira, Concepción & Araujo, Marisa del Río & Sanmartín, Emilio Ruzo, 2018. "How and when a salesperson's perception of organizational politics relates to proactive performance," European Management Journal, Elsevier, vol. 36(5), pages 660-670.
    2. Donate, Mario J. & González-Mohíno, Miguel & Paolo Appio, Francesco & Bernhard, Fabian, 2022. "Dealing with knowledge hiding to improve innovation capabilities in the hotel industry: The unconventional role of knowledge-oriented leadership," Journal of Business Research, Elsevier, vol. 144(C), pages 572-586.
    3. Jarrahi, Mohammad Hossein, 2018. "Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making," Business Horizons, Elsevier, vol. 61(4), pages 577-586.
    4. Donate, Mario J. & Sánchez de Pablo, Jesús D., 2015. "The role of knowledge-oriented leadership in knowledge management practices and innovation," Journal of Business Research, Elsevier, vol. 68(2), pages 360-370.
    5. Lai-Wan Wong & Garry Wei-Han Tan & Keng-Boon Ooi & Binshan Lin & Yogesh K. Dwivedi, 2024. "Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis," International Journal of Production Research, Taylor & Francis Journals, vol. 62(15), pages 5535-5555, August.
    6. Bindl, Uta K. & Unsworth, Kerrie L. & Gibson, Cristina B. & Stride, Christopher B., 2019. "Job crafting revisited: implications of an extended framework for active changes at work," LSE Research Online Documents on Economics 90175, London School of Economics and Political Science, LSE Library.
    7. Li, Yunjian & Song, Yixiao & Sun, Yanming & Zeng, Mingzhuo, 2024. "When do employees learn from artificial intelligence? The moderating effects of perceived enjoyment and task-related complexity," Technology in Society, Elsevier, vol. 77(C).
    8. Simona Sternad Zabukovšek & Zoran Kalinic & Samo Bobek & Polona Tominc, 2019. "SEM–ANN based research of factors’ impact on extended use of ERP systems," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 27(3), pages 703-735, September.
    9. Onder, Irem & McCabe, Scott, 2025. "How AI hallucinations threaten research integrity in tourism," Annals of Tourism Research, Elsevier, vol. 111(C).
    10. Wang, Phyllis Xue & Kim, Sara & Kim, Minki, 2023. "Robot anthropomorphism and job insecurity: The role of social comparison," Journal of Business Research, Elsevier, vol. 164(C).
    11. Dewie Tri Wijayati & Zainur Rahman & A’rasy Fahrullah & Muhammad Fajar Wahyudi Rahman & Ika Diyah Candra Arifah & Achmad Kautsar, 2022. "A study of artificial intelligence on employee performance and work engagement: the moderating role of change leadership," International Journal of Manpower, Emerald Group Publishing Limited, vol. 43(2), pages 486-512, January.
    12. Liébana-Cabanillas, Francisco & Marinković, Veljko & Kalinić, Zoran, 2017. "A SEM-neural network approach for predicting antecedents of m-commerce acceptance," International Journal of Information Management, Elsevier, vol. 37(2), pages 14-24.
    13. Baabdullah, Abdullah M. & Alalwan, Ali Abdallah & Algharabat, Raed S. & Metri, Bhimaraya & Rana, Nripendra P., 2022. "Virtual agents and flow experience: An empirical examination of AI-powered chatbots," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    14. Miguel González-Mohíno & Mario J. Donate & Fátima Guadamillas & L. Javier Cabeza-Ramírez, 2024. "Knowledge-oriented leadership for improved coordination as a solution to relationship conflict: effects on innovation capabilities," Knowledge Management Research & Practice, Taylor & Francis Journals, vol. 22(4), pages 388-403, July.
    15. Chaturvedi, Rijul & Verma, Sanjeev & Das, Ronnie & Dwivedi, Yogesh K., 2023. "Social companionship with artificial intelligence: Recent trends and future avenues," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    16. 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.
    17. Suhail, Faisal & Adel, Mouhand & Al-Emran, Mostafa & AlQudah, Adi Ahmad, 2024. "Are students ready for robots in higher education? Examining the adoption of robots by integrating UTAUT2 and TTF using a hybrid SEM-ANN approach," Technology in Society, Elsevier, vol. 77(C).
    18. Arpaci, Ibrahim & Karatas, Kasim & Kusci, Ismail & Al-Emran, Mostafa, 2022. "Understanding the social sustainability of the Metaverse by integrating UTAUT2 and big five personality traits: A hybrid SEM-ANN approach," Technology in Society, Elsevier, vol. 71(C).
    19. Abbas, Jawad & Dabić, Marina & Stojčić, Nebojša, 2026. "Digital divide in industry 5.0: Role of generative AI knowledge bases and intellectual capital in organizational resilience performance under territorial proximity," Technovation, Elsevier, vol. 149(C).
    20. Hillol Bala & Viswanath Venkatesh, 2016. "Adaptation to Information Technology: A Holistic Nomological Network from Implementation to Job Outcomes," Management Science, INFORMS, vol. 62(1), pages 156-179, January.
    21. Zhou, Qiwei & Chen, Keyu & Cheng, Shuang, 2024. "Bringing employee learning to AI stress research: A moderated mediation model," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xu, Yong & Xie, Peijun & Naeem, Rana Muhammad & Almugren, Intesar & Hameed, Zahid & Agarwal, Shivani, 2026. "Responsible AI and employee service innovation behavior: A sequential mediation model of AI self-efficacy and AI crafting," Technological Forecasting and Social Change, Elsevier, vol. 224(C).

    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. 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).
    3. Pinochet, Luis Hernan Contreras & de Gois, Fernanda Silva & Pardim, Vanessa Itacaramby & Onusic, Luciana Massaro, 2024. "Experimental study on the effect of adopting humanized and non-humanized chatbots on the factors measure the intensity of the user's perceived trust in the Yellow September campaign," Technological Forecasting and Social Change, Elsevier, vol. 204(C).
    4. Prentice, Catherine & Wong, IpKin Anthony & Lin, Zhiwei (CJ), 2023. "Artificial intelligence as a boundary-crossing object for employee engagement and performance," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    5. Jabeen, Manahil & Jafar, Rana Muhammad Sohail & Li, Zhenghui, 2026. "Leveraging metaverse technologies for a sustainable future: The role of knowledge management practices and technology readiness," Technology in Society, Elsevier, vol. 84(C).
    6. Azam Malik, 2024. "A Study on the Relationship of Artificial Intelligence Applications in HR Processes for Assessing Employee Engagement, Performance, and Job Security," International Review of Management and Marketing, Econjournals, vol. 14(5), pages 216-221, September.
    7. Siyal, Abdul Waheed & Chen, Hongzhuan & Jamal Shah, Syed & Shahzad, Fakhar & Bano, Shaher, 2024. "Customization at a glance: Investigating consumer experiences in mobile commerce applications," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).
    8. repec:bcp:journl:v:9:y:2025:i:11:p:4100-4113 is not listed on IDEAS
    9. Liwei Chen & J. J. Po-An Hsieh & Arun Rai, 2022. "How Does Intelligent System Knowledge Empowerment Yield Payoffs? Uncovering the Adaptation Mechanisms and Contingency Role of Work Experience," Information Systems Research, INFORMS, vol. 33(3), pages 1042-1071, September.
    10. Ma, Liang & Yu, Peng & Zhang, Xin & Wang, Gaoshan & Hao, Feifei, 2024. "How AI use in organizations contributes to employee competitive advantage: The moderating role of perceived organization support," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    11. Alcántara-Pilar, Juan Miguel & Rodriguez-López, María Eugenia & Kalinić, Zoran & Liébana-Cabanillas, Francisco, 2024. "From likes to loyalty: Exploring the impact of influencer credibility on purchase intentions in TikTok," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).
    12. Chakraborty, Debarun & Polisetty, Aruna & G, Sowmya & Rana, Nripendra P. & Khorana, Sangeeta, 2024. "Unlocking the potential of AI: Enhancing consumer engagement in the beauty and cosmetic product purchases," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    13. Hajdas, Monika & Radomska, Joanna & Szpulak, Aleksandra & Kawa, Arkadiusz, 2025. "The trade-offs of metaverse value creation. Real-Time-Delphi-based scenario analysis by 2040," Technology in Society, Elsevier, vol. 83(C).
    14. Xu, Xiao-Yu & Jia, Qing-Dan & Tayyab, Syed Muhammad Usman, 2024. "Exploring the stimulating role of augmented reality features in E-commerce: A three-staged hybrid approach," Journal of Retailing and Consumer Services, Elsevier, vol. 77(C).
    15. Liu, Xing (Stella) & Li, Xiaonan & Law, Rob, 2025. "Leveraging rituals to boost unity of employee-robot team," Annals of Tourism Research, Elsevier, vol. 115(C).
    16. Robertson, Jeandri & Ferreira, Caitlin & Botha, Elsamari & Oosthuizen, Kim, 2024. "Game changers: A generative AI prompt protocol to enhance human-AI knowledge co-construction," Business Horizons, Elsevier, vol. 67(5), pages 499-510.
    17. Simona VINEREAN & Carolina ȚÎMBALARI & Alin OPREANA, 2025. "Why Do Shoppers Prefer M-Commerce? Discovering Key Drivers Based On A Sem–Ann Approach," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 20(3), pages 322-344, December.
    18. Hongyi Mao & Zongjun Wang & Lin Yi, 2021. "Does Entrepreneurial Orientation Lead to Successful Sustainable Innovation? The Evidence from Chinese Environmentally Friendly Companies," Sustainability, MDPI, vol. 13(18), pages 1-19, September.
    19. 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.
    20. Christoph Riedl & Eric Bogert, 2024. "Who Benefits from AI? Self-Selection, Skill Gap, and the Hidden Costs of AI Feedback," Papers 2409.18660, arXiv.org, revised Apr 2026.
    21. Farooq, Ali & Laato, Samuli & Islam, A.K.M. Najmul & Isoaho, Jouni, 2021. "Understanding the impact of information sources on COVID-19 related preventive measures in Finland," Technology in Society, Elsevier, vol. 65(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    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:eee:tefoso:v:222:y:2026:i:c:s0040162525004184. 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: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    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.