lynx   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i5p1873-d1597354.html
   My bibliography  Save this article

Investigating AI Adoption, Knowledge Absorptive Capacity, and Open Innovation in Chinese Apparel MSMEs: An Extended TAM-TOE Model with PLS-SEM Analysis

Author

Listed:
  • Chen Qu

    (Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Nomi 9231292, Japan
    Southampton International College, Dalian Polytechnic University, Dalian 116034, China)

  • Eunyoung Kim

    (Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Nomi 9231292, Japan)

Abstract
The rapid evolution of artificial intelligence (AI) has significantly transformed industries, positioning the fashion sector as a critical area of study due to its mass production and pressing sustainability challenges. As the world’s largest apparel producer, China faces unique hurdles in terms of integrating AI technologies, highlighting the intersection of technological innovation and sustainability within this industry. In this context, this study aims to provide the initial exploratory correlations between AI adoption and open innovation from apparel manufacturing micro-, small-, and medium-size enterprises (MSMEs) managers’ perspectives, identifying knowledge absorptive capacity (KACAP)’s significant impacts through an integrated and extended TAM-TOE model. We conducted PLS-SEM to empirically validate the antecedents of AI adoption and its consequential effects on KACAP and open innovation by collecting information from 269 of the apparel manufacturing MSMEs’ top managers. The results show that the TAM-TOE structural model explains 60.7% of the variance in AI adoption, 47.4% in KACAP, and 55.4% in open innovation, which suggests that the model has good explanatory capacity, and that all these Q 2 values indicate a sizeable predictive accuracy threshold. Drawing on the proposed model, the study has identified technological (e.g., perceived usefulness) and environmental factors (e.g., competitive pressure, market uncertainty, and government support and policy) that significantly impact AI adoption, while organizational factors (e.g., organizational readiness) directly impact KACAP, and environmental factors (e.g., competitive pressure, supplier involvement, and market uncertainty) directly impact open innovation. Subsequently, the AI construct is having a significant influence on MSMEs’ open innovation through KACAP. This fills existing theoretical gaps by linking AI technology to organizational innovation processes and demonstrates the mediating influence of KACAP. Also, the proposed model provides a foundation for future research by exploring the intersection of AI and innovation in similar industries.

Suggested Citation

  • Chen Qu & Eunyoung Kim, 2025. "Investigating AI Adoption, Knowledge Absorptive Capacity, and Open Innovation in Chinese Apparel MSMEs: An Extended TAM-TOE Model with PLS-SEM Analysis," Sustainability, MDPI, vol. 17(5), pages 1-31, February.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:5:p:1873-:d:1597354
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/5/1873/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/5/1873/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cao, Guangming & Duan, Yanqing & Edwards, John S. & Dwivedi, Yogesh K., 2021. "Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making," Technovation, Elsevier, vol. 106(C).
    2. Arunava Narayan Mukherjee, 2022. "Application of artificial intelligence: benefits and limitations for human potential and labor-intensive economy – an empirical investigation into pandemic ridden Indian industry," Management Matters, Emerald Group Publishing Limited, vol. 19(2), pages 149-166, June.
    3. Sushil Gupta & Hossein Rikhtehgar Berenji & Manish Shukla & Nagesh N. Murthy, 2023. "Opportunities in farming research from an operations management perspective," Production and Operations Management, Production and Operations Management Society, vol. 32(6), pages 1577-1596, June.
    4. Bahoo, Salman & Cucculelli, Marco & Qamar, Dawood, 2023. "Artificial intelligence and corporate innovation: A review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    5. Ulrich Lichtenthaler & Eckhard Lichtenthaler, 2009. "A Capability‐Based Framework for Open Innovation: Complementing Absorptive Capacity," Journal of Management Studies, Wiley Blackwell, vol. 46(8), pages 1315-1338, December.
    6. Yang, Miying & Fu, Mingtao & Zhang, Zihan, 2021. "The adoption of digital technologies in supply chains: Drivers, process and impact," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    7. Saeed Badghish & Yasir Ali Soomro, 2024. "Artificial Intelligence Adoption by SMEs to Achieve Sustainable Business Performance: Application of Technology–Organization–Environment Framework," Sustainability, MDPI, vol. 16(5), pages 1-24, February.
    8. Jiang Min & Shuja Iqbal & Muhammad Aamir Shafique Khan & Shamim Akhtar & Farooq Anwar & Sikandar Ali Qalati, 2020. "Impact of supervisory behavior on sustainable employee performance: Mediation of conflict management strategies using PLS-SEM," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-20, September.
    9. Broekhuizen, Thijs & Dekker, Henri & de Faria, Pedro & Firk, Sebastian & Nguyen, Dinh Khoi & Sofka, Wolfgang, 2023. "AI for managing open innovation: Opportunities, challenges, and a research agenda," Journal of Business Research, Elsevier, vol. 167(C).
    10. Wei Guan & Wenhong Ding & Bobo Zhang & Jerome Verny & Rubin Hao, 2023. "Do supply chain related factors enhance the prediction accuracy of blockchain adoption? A machine learning approach," Post-Print hal-04063438, HAL.
    11. Bodendorf, Frank & Xie, Qiao & Merkl, Philipp & Franke, Jörg, 2022. "A multi-perspective approach to support collaborative cost management in supplier-buyer dyads," International Journal of Production Economics, Elsevier, vol. 245(C).
    12. Sachin Kamble & Angappa Gunasekaran & Vikas Kumar & Amine Belhadi & Cyril Foropon, 2021. "A machine learning based approach for predicting blockchain adoption in supply chain," Post-Print hal-03539287, HAL.
    13. Guan, Wei & Ding, Wenhong & Zhang, Bobo & Verny, Jerome & Hao, Rubin, 2023. "Do supply chain related factors enhance the prediction accuracy of blockchain adoption? A machine learning approach," Technological Forecasting and Social Change, Elsevier, vol. 192(C).
    14. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
    15. Saurabh Sharma & Vijay Kumar Gahlawat & Kumar Rahul & Rahul S Mor & Mohit Malik, 2021. "Sustainable Innovations in the Food Industry through Artificial Intelligence and Big Data Analytics," Logistics, MDPI, vol. 5(4), pages 1-16, September.
    16. Liu, Jun & Chang, Huihong & Forrest, Jeffrey Yi-Lin & Yang, Baohua, 2020. "Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    17. Chen Qu & Eunyoung Kim, 2024. "Reviewing the Roles of AI-Integrated Technologies in Sustainable Supply Chain Management: Research Propositions and a Framework for Future Directions," Sustainability, MDPI, vol. 16(14), pages 1-27, July.
    18. Gianmario Verona & Davide Ravasi, 2003. "Unbundling dynamic capabilities: an exploratory study of continuous product innovation," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 12(3), pages 577-606, June.
    19. Kamble, Sachin S. & Gunasekaran, Angappa & Kumar, Vikas & Belhadi, Amine & Foropon, Cyril, 2021. "A machine learning based approach for predicting blockchain adoption in supply Chain," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    20. Jolanta Dziembowska-Kowalska & Rolf H. Funck, 2000. "Cultural activities as a location factor in European competition between regions: Concepts and some evidence," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 34(1), pages 1-12.
    21. Mariani, Marcello M. & Machado, Isa & Nambisan, Satish, 2023. "Types of innovation and artificial intelligence: A systematic quantitative literature review and research agenda," Journal of Business Research, Elsevier, vol. 155(PB).
    22. Lima-Junior, Francisco Rodrigues & Carpinetti, Luiz Cesar Ribeiro, 2019. "Predicting supply chain performance based on SCOR® metrics and multilayer perceptron neural networks," International Journal of Production Economics, Elsevier, vol. 212(C), pages 19-38.
    23. Kinkel, Steffen & Baumgartner, Marco & Cherubini, Enrica, 2022. "Prerequisites for the adoption of AI technologies in manufacturing – Evidence from a worldwide sample of manufacturing companies," Technovation, Elsevier, vol. 110(C).
    24. Chatterjee, Sheshadri & Rana, Nripendra P. & Dwivedi, Yogesh K. & Baabdullah, Abdullah M., 2021. "Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    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. Badghish, Saeed & Shaik, Aqueeb Sohail & Sahore, Nidhi & Srivastava, Shalini & Masood, Ayesha, 2024. "Can transactional use of AI-controlled voice assistants for service delivery pickup pace in the near future? A social learning theory (SLT) perspective," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    2. Shahzad, Khuram & Zhang, Qingyu & Ashfaq, Muhammad & Zafar, Abaid Ullah & Ahmad, Bilal, 2024. "Pre- to post-adoption of blockchain technology in supply chain management: Influencing factors and the role of firm size," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    3. Zhang, Ying & Tavalaei, M. Mahdi & Parry, Glenn & Zhou, Peng, 2024. "Evolution or involution? A systematic literature review of organisations' blockchain adoption factors," Technological Forecasting and Social Change, Elsevier, vol. 208(C).
    4. Capestro, Mauro & Rizzo, Cristian & Kliestik, Tomas & Peluso, Alessandro M. & Pino, Giovanni, 2024. "Enabling digital technologies adoption in industrial districts: The key role of trust and knowledge sharing," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    5. Chen Qu & Eunyoung Kim, 2024. "Reviewing the Roles of AI-Integrated Technologies in Sustainable Supply Chain Management: Research Propositions and a Framework for Future Directions," Sustainability, MDPI, vol. 16(14), pages 1-27, July.
    6. Gupta, Somya & Ghardallou, Wafa & Pandey, Dharen Kumar & Sahu, Ganesh P., 2022. "Artificial intelligence adoption in the insurance industry: Evidence using the technology–organization–environment framework," Research in International Business and Finance, Elsevier, vol. 63(C).
    7. Rodríguez-Espíndola, Oscar & Chowdhury, Soumyadeb & Dey, Prasanta Kumar & Albores, Pavel & Emrouznejad, Ali, 2022. "Analysis of the adoption of emergent technologies for risk management in the era of digital manufacturing," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    8. Lili Geng & Huixian Hui & Xiaomeng Liang & Shaocong Yan & Yongji Xue, 2023. "Factors Affecting Intention Toward ICT Adoption in Rural Entrepreneurship: Understanding the Differences Between Business Types of Organizations and Previous Experience of Entrepreneurs," SAGE Open, , vol. 13(3), pages 21582440231, September.
    9. An, Meng & Lin, Jiabao & Luo, Xin (Robert), 2024. "The impact of human AI skills on organizational innovation: The moderating role of digital organizational culture," Journal of Business Research, Elsevier, vol. 182(C).
    10. Ritala, Paavo & Aaltonen, Päivi & Ruokonen, Mika & Nemeh, Andre, 2024. "Developing industrial AI capabilities: An organisational learning perspective," Technovation, Elsevier, vol. 138(C).
    11. Lin, Boqiang & Xu, Chongchong, 2024. "The effects of industrial robots on firm energy intensity: From the perspective of technological innovation and electrification," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    12. Wang, Guoqiang & Tan, Garry Wei-Han & Yuan, Yunpeng & Ooi, Keng-Boon & Dwivedi, Yogesh K., 2022. "Revisiting TAM2 in behavioral targeting advertising: A deep learning-based dual-stage SEM-ANN analysis," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    13. Dehghani, Milad & William Kennedy, Ryan & Mashatan, Atefeh & Rese, Alexandra & Karavidas, Dionysios, 2022. "High interest, low adoption. A mixed-method investigation into the factors influencing organisational adoption of blockchain technology," Journal of Business Research, Elsevier, vol. 149(C), pages 393-411.
    14. Zhang, Xi & Cheng, Yihang & Chen, Aoshuang & Lytras, Miltiadis & de Pablos, Patricia Ordóñez & Zhang, Renyu, 2022. "How rumors diffuse in the infodemic: Evidence from the healthy online social change in China," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    15. Jung-Chieh Lee & Yuyin Tang & SiQi Jiang, 2023. "Understanding continuance intention of artificial intelligence (AI)-enabled mobile banking applications: an extension of AI characteristics to an expectation confirmation model," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
    16. Lijie Feng & Zhenzhen Zhao & Jinfeng Wang & Ke Zhang, 2022. "The Impact of Knowledge Management Capabilities on Innovation Performance from Dynamic Capabilities Perspective: Moderating the Role of Environmental Dynamism," Sustainability, MDPI, vol. 14(8), pages 1-19, April.
    17. Pramanik, Paritosh & Jana, Rabin K. & Ghosh, Indranil, 2024. "AI readiness enablers in developed and developing economies: Findings from the XGBoost regression and explainable AI framework," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    18. Zhiyuan Yu & Doudou Jin, 2021. "Determinants of Users’ Attitude and Intention to Intelligent Connected Vehicle Infotainment in the 5G-V2X Mobile Ecosystem," IJERPH, MDPI, vol. 18(19), pages 1-19, September.
    19. Latan, Hengky & Lopes de Sousa Jabbour, Ana Beatriz & Sarkis, Joseph & Chiappetta Jabbour, Charbel Jose & Ali, Murad, 2024. "The nexus of supply chain performance and blockchain technology in the digitalization era: Insights from a fast-growing economy," Journal of Business Research, Elsevier, vol. 172(C).
    20. Gerli, Paolo & Clement, Jessica & Esposito, Giovanni & Mora, Luca & Crutzen, Nathalie, 2022. "The hidden power of emotions: How psychological factors influence skill development in smart technology adoption," Technological Forecasting and Social Change, Elsevier, vol. 180(C).

    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:gam:jsusta:v:17:y:2025:i:5:p:1873-:d:1597354. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.
    Лучший частный хостинг