The Factors Influencing Users’ Adoption of Industry 4.0 in China’s Manufacturing Industry: A Conceptual Paper
DOI:
https://doi.org/10.24203/anfqb464Keywords:
Industry 4.0 (I4.0), Users' Adoption, UTAUT, TTF, Government InterventionsAbstract
This conceptual paper explores the factors influencing the users’ adoption of Industry 4.0 technologies in the manufacturing industry in Jiangsu Province, China, with a particular focus on government intervention as a mediating factor. Using the UTAUT and TTF models, the research examines key internal factors—performance expectancy, effort expectancy, social influence and facilitating conditions—alongside external factor such as technology compatibility. It would highlight gaps between policy initiatives and practical adoption challenges, investigating the role of financial incentives, regulations, and training programs in accelerating technological transformation. By offering insights for policymakers and industry stakeholders, it contributes to the development of targeted strategies that facilitate Industry 4.0 adoption and enhance China’s manufacturing competitiveness.
References
Agarwal, R., Sambamurthy, V., & Stair, R. M. (2000). Research Report: The Evolving Relationship between General and Specific Computer Self-Efficacy? An Empirical Assessment. Information Systems Research, 11(4), 418-430. http://dx.doi.org/10.1287/isre.11.4.418.11876
Aiken, M., Gu, L., & Wang, J. (2013). Task Knowledge and Task Technology Fit in a Virtual Team. International Journal of Management, 30(1)
Alfaro‐Serrano, D., Balantrapu, T., Chaurey, R., Goicoechea, A., & Verhoogen, E. (2021). Interventions to promote technology adoption in firms: A systematic review. Campbell Systematic Reviews, 17(4), e1181. https://doi.org/10.1002/cl2.1181
Almaiah, M. A., Alamri, M. M., & Al-Rahmi, W. (2019). Applying the UTAUT model to explain the students’ acceptance of mobile learning system in higher education. IEEE Access, 7, 174673-174686. https://doi.org/10.1109/ACCESS.2019.2957206
Bandyopadhyay, K., & Fraccastoro, K. (2007). The Effect of Culture on User Acceptance of Information Technology. Communications of the Association for Information Systems, 19, pp-pp. https://doi.org/10.17705/1CAIS.01923
Bangemann, T., Riedl, M., Thron, M., & Diedrich, C. (2016). Integration of classical components into industrial cyber-physical systems. Proceedings of the IEEE, 104(5), 947-959.
Bianchini, M., & Kwon, I. (2021). Enhancing SMEs’ resilience through digitalisation: The case of Korea. OECD SME and Entrepreneurship Papers, No. 27. OECD Publishing. https://doi.org/10.1787/23bd7a26-en
Brettel, M., Friederichsen, N., Keller, M., & Rosenberg, M. (2014). How virtualization, decentralization and network building change the manufacturing landscape: An Industry 4.0 perspective. International Journal of Science, Engineering and Technology, 8(1), 37-44.
Chatterjee, S., Rana, N. P., Khorana, S., Mikalef, P., & Sharma, A. (2021). Assessing organizational users’ intentions and behavior to AI integrated CRM systems: A Meta-UTAUT approach. Information Systems Frontiers, 1-15. https://doi.org/10.1007/s10796-021-10181-1
D'Ambra, J., Wilson, C., & Akter, S. (2013). Application of the task-technology fit model to structure and evaluate the adoption of E-books by academics. Journal of the American Society for Information Science and Technology, 64(1), 48-64.
Faaeq, M. K., Ismail, N. A., Osman, W. R., Al‐Swidi, A. K., & Faieq, A. K. (2013). A meta-analysis of the unified theory of acceptance and use of technology studies among several countries. Electronic Government, An International Journal, 10, 343-360
Farmer, R. (2025). China's economy rallies to reach growth target; 2025 outlook remains uncertain. US-China Business Council. Retrieved from https://www.uschina.org/articles/chinas-economy-rallies-to-reach-growth-target-2025-outlook-remains-uncertain/
Frank, A.G., Dalenogare, L.S., & Ayala, N.F. (2019). Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Economics. 210,15–26.
Frank, A. G., Mendes, G. H. S., Ayala, N. F., & Ghezzi, A. (2019). Servitization and industry 4.0 convergence in the digital transformation of product firms: A business model innovation perspective. Technological Forecasting and Social Change, 141, 341-351. https://doi.org/10.1016/j.techfore.2019.01.014
Gadenne, D. L., Kennedy, J., & McKeiver, C. (2009). An Empirical Study of Environmental Awareness and Practices in SMEs. Journal of Business Ethics, 84, 45-63. https://doi.org/10.1007/s10551-008-9672-9
Gao, L., & Bai, X. (2014). A unified perspective on the factors influencing consumer acceptance of internet of things technology. Asia Pacific Journal of Marketing and Logistics, 26(2), 211-231. https://doi.org/10.1108/APJML-06-2013-0061
Gebauer, J., & Ginsburg, M. (2009). Exploring the black box of task-technology fit. Communications of the ACM, 52(1), 130–135. https://doi.org/10.1145/1435417.1435447
Goodhue, D. L. (2006). Task-technology fit. In P. Zhang & D. F. Galletta (Eds.), Human-Computer Interaction and Management Information Systems: Foundations, 184-204.
Goodhue, D. L., & Thompson, R. L. (1995). Task-Technology Fit and Individual Performance. MIS Quarterly, 19(2), 213–236. https://doi.org/10.2307/249689
Haddara, M., & Elragal, A. (2015). The readiness of ERP systems for the factory of the future. Procedia Computer Science, 64, 721-728. https://doi.org/10.1016/j.procs.2015.08.598
Handoko, B. L., & Liusman, S. (2021). Analysis of external auditor intentions in adopting artificial intelligence as fraud detection with the unified theory of acceptance and use of technology (UTAUT) approach. In The 2021 12th International Conference on E-business, Management and Economics (pp. 96-103). https://doi.org/10.1145/3481127.34811
Hashim, H. S., & Al-Sulami, Z. A. (2020). A Model Of Factors Influencing Users’ Adoption Of Internet Of Things Services: A Case Study Of Iraqi Educational Institutions. IOP Conference Series: Materials Science and Engineering, 769, 012006. https://doi.org/10.1088/1757-899X/769/1/012006
He, L. (2024). China's economic growth rate skepticism. CNN. Retrieved from https://edition.cnn.com/2024/03/08/business/china-economic-growth-rate-skepticism-hnk-intl/index.html
Janak, L., & Hadas, Z. (2015). Machine tool health and usage monitoring system: An initial analyses. MM Science Journal, 2015(04), 794-798. https://doi.org/10.17973/MMSJ.2015_12_201564
Jennings, R. (2025). IMF raises China's 2025 GDP forecast by a hair, cites stimulus effects. South China Morning Post. Retrieved from https://www.scmp.com/economy/china-economy/article/3295215/imf-raises-chinas-2025-gdp-forecast-hair-cites-stimulus-effects
Keynes, J. (1936). The General Theory of Employment, Interest and Money. London: Macmillan Cambridge University Press, for Royal Economic Society.
Li, D., Li, X., & Wan, J. (2017). A cloud-assisted handover optimization strategy for mobile nodes in industrial wireless networks. Computer Networks, 128, 133-141. https://doi.org/10.1016/j.comnet.2017.05.026
Lin, B., Wu, W., & Song, M. (2023). Industry 4.0: driving factors and impacts on firm’s performance: an empirical study on China’s manufacturing industry. Annals of Operations Research, 329(1), 47-67. https://doi.org/10.1007/s10479-019-03433-6
Lin, H. C., Ho, C. F., & Yang, H. (2022). Understanding adoption of artificial intelligence-enabled language e-learning system: An empirical study of UTAUT model. International Journal of Mobile Learning and Organization, 16(1), 74-94. https://doi.org/10.1504/IJMLO.2022.119966
Phang, T. C. H., Chen, C., & Tiong, R. L. K. (2020). New model for identifying critical success factors influencing BIM adoption from precast concrete manufacturers’ view. Journal of Construction Engineering and Management, 146(4). https://doi.org/10.1061/(ASCE)CO.1943-7862.0001773
Qiu, M., & Xu, Y. (2010). Information technology, distance education, and task-technology fit model. Review of Business Research, 10(5), 240-246
Rahim, N. I. M., Iahad, N. A., Yusof, A. F., & Al-Sharafi, M. A. (2022). AI-Based chatbots adoption model for higher-education institutions: A hybrid pls-sem-neural network modelling approach. Sustainability, 14(19), 726. https://doi.org/10.3390/su141912726
Ras, E., Wild, F., Stahl, C., & Baudet, A. (2017). Bridging the skills gap of workers in Industry 4.0 by human performance augmentation tools: Challenges and roadmap. Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments, June 21-23, pp. 428-432.
Rennung, F., Luminosu, C. T., & Draghici, A. (2016). Service provision in the framework of Industry 4.0. Procedia - Social and Behavioral Sciences, 221, 372-377. https://doi.org/10.1016/j.sbspro.2016.05.127
Robles-Flores, J. A., & Roussinov, D. (2012). Examining Question-Answering Technology from the Task Technology Fit Perspective. Communications of the Association for Information Systems, 30(1), 26
Scupola, A. (2009). SMEs' E-commerce Adoption: Perspectives from Denmark and Australia. Journal of Enterprise Information Management, 22(1-2), 152-166. https://doi.org/10.1108/17410390910932803
Seeman, E. D., O'Hara, M. T., Holloway, J., & Forst, A. (2007). The impact of government intervention on technology adoption and diffusion: The example of wireless location technology. Electronic Government, an International Journal, 4(1), 1-19. https://doi.org/10.1504/EG.2007.012176
Selim, H. M., Eid, R., & Agag, G. (2020). Understanding the role of technological factors and external pressures in smart classroom adoption. Education + Training, 62(6), 631-644. https://doi.org/10.1108/ET-03-2020-0049
Shafiq, S. I., Sanin, C., Szczerbicki, E., & Toro, C. (2015). Virtual Engineering Object/Virtual Engineering Process: A specialized form of Cyber Physical System for Industrie 4.0. Procedia Computer Science, 60, 1146-1155. https://doi.org/10.1016/j.procs.2015.08.166
Shariff, M. N. M., Peou, C., & Ali, J. (2010). Moderating effect of government policy on entrepreneurship and growth performance of small-medium enterprises in Cambodia. International Journal of Business and Management Science, 3(1), 57.
Sommer, L. (2015). Industrial Revolution—Industry 4.0: Are German manufacturing SMEs the first victims of this revolution? Journal of Industrial Engineering and Management, 8(5), 1512-1532.
Srite, M. (2006). Culture as an Explanation of Technology Acceptance Differences: An Empirical Investigation of Chinese and US Users. Australasian Journal of Information Systems, 14(1). https://doi.org/10.3127/ajis.v14i1.4
Staples, D. S., Hulland, J. S., & Higgins, C. A. (1999). A Self-Efficacy Theory Explanation for the Management of Remote Workers in Virtual Organizations. Organization Science, 10(6), 758-776
Thoben, K. D., Pöppelbuß, J., Wellsandt, S., Teucke, M., & Werthmann, D. (2014). Considerations on a lifecycle model for cyber-physical system platforms. In Grabot, B., Vallespir, B., Gomes, S., Bouras, A., & Kiritsis, D. (Eds.), Advances in Production Management Systems. Innovative and Knowledge-Based Production Management in a Global-Local World. APMS 2014. IFIP Advances in Information and Communication Technology (Vol. 438). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44739-0_11
van Zeebroeck, N., Kretschmer, T., & Bughin, J. (2021). Digital “is” strategy: The role of digital technology adoption in strategy renewal. IEEE Transactions on Engineering Management, PP(99), 1-15. https://doi.org/10.1109/TEM.2021.3079347
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2016). Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead. Journal of the Association for Information Systems, 17(5). https://doi.org/10.17705/1jais.00428
Veugelers, R. (2012). Which policy instruments to induce clean innovating? Research Policy, 41(10), 1770-1778. https://doi.org/10.1016/j.respol.2012.06.012
Wahl, M. (2015). Strategic factor analysis for Industry 4.0. Journal of Security and Sustainability Issues, 5(2), 241-247.
Wang, K., Guo, F., Zhang, C. and Schaefer, D. (2024), From Industry 4.0 to Construction 4.0: barriers to the digital transformation of engineering and construction sectors, Engineering, Construction and Architectural Management, 31(1), 136-158. https://doi.org/10.1108/ECAM-05-2022-0383
Xiao, L., & Ma, X. (2014). The influence of government intervention on logistics enterprise's adoption of information technology. Journal of Digital Information Management, 12(1), 8-17.
Xie, J., Ye, L., Huang, W., & Ye, M. (2021). Understanding FinTech Platform Adoption: Impacts of Perceived Value and Perceived Risk. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1893-1911. https://doi.org/10.3390/jtaer16050106
Xie, M., Ding, L., Xia, Y., Guo, J., Pan, J., & Wang, H. (2021). Does artificial intelligence affect the pattern of skill demand? Evidence from Chinese manufacturing firms. Economic Modelling, 96(C), 295-309.
Yu, Y., Zhang, J. Z., Cao, Y., & Kazancoglu, Y. (2021). Intelligent transformation of the manufacturing industry for Industry 4.0: Seizing financial benefits from supply chain relationship capital through enterprise green management. Technological Forecasting and Social Change, 172(C).
Zhai, H., Yang, X., Xue, J., Lavender, C., Ye, T., Li, J., Xu, L., Lin, L., Cao, W., & Sun, Y. (2021). Radiation oncologists’ perceptions of adopting an artificial intelligence–assisted contouring technology: Model development and questionnaire study. Journal of Medical Internet Research, 23(9), e27122. https://doi.org/10.2196/27122
Zhan, Z.-H., Liu, X.-F., Gong, Y.-J., Zhang, J., Chung, H. S.-H., & Li, Y. (2015). Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Computing Surveys, 47(4), Article 63, 33 pages. https://doi.org/10.1145/2788397
Zhang, N., Ye, J., Zhong, Y., & Chen, Z. (2023). Digital transformation in the Chinese construction industry: Status, barriers, and impact. Buildings, 13(4), 1092. https://doi.org/10.3390/buildings13041092
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Asian Journal of Business and Management

This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
- Papers must be submitted on the understanding that they have not been published elsewhere (except in the form of an abstract or as part of a published lecture, review, or thesis) and are not currently under consideration by another journal published by any other publisher.
- It is also the authors responsibility to ensure that the articles emanating from a particular source are submitted with the necessary approval.
- The authors warrant that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required.
- The authors ensure that all the references carefully and they are accurate in the text as well as in the list of references (and vice versa).
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
- The journal/publisher is not responsible for subsequent uses of the work. It is the author's responsibility to bring an infringement action if so desired by the author.