Generative AI Adoption Among System Integration Professionals in the China's Greater Bay Area: A UTAUT Perspective
DOI:
https://doi.org/10.70301/Keywords:
technology adoption, generative AI, UTAUTAbstract
Generative artificial intelligence introduces an adoption-obsolescence paradox, a condition where technology simultaneously enhances efficiency yet threatens professionals' core skills. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT), this study evaluated how performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC) predict behavioral intention (BI) and use behavior (UB) among system integration professionals in China’s Greater Bay Area (GBA). Utilizing a mixed-methods design, the research analysed survey data from 319 professionals alongside qualitative insights from 50 focus group participants. The model accounted for 58.8% of BI variance and 50.9% of UB variance. High utility coexists with adoption anxiety, and a governance gap inhibits actual use. Professional capacity determines adoption intensity: role, education, and autonomy yielded significant variances, whereas age, gender, experience and company size were non-significant. Mainland professionals reported significantly higher BI (M = 5.54) than Hong Kong counterparts (M = 5.12), t(317) = −2.53, p = .012. Consequently, leaders should prioritize data privacy and human-in-the-loop policies over top-down mandates to foster sustainable innovation.
References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
Amankwah-Amoah, J., & Appiah, G. (2025). Unmasking the silent threat: AI-induced human capital obsolescence and business failure. Technological Forecasting and Social Change, 205, Article 123456.
Ambrozio, S., Lindeque, J. P., & Peter, M. K. (2025). Navigating uncertainty: Isomorphic pressures in cloud computing adoption. In M. Moussa & A. McMurray (Eds.), The Palgrave handbook of breakthrough technologies in contemporary organisations (pp. 221-234). Springer Nature Singapore.
Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396–402.
Arrieta, A. B., Diaz-Rodriguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., & Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.
Blut, M., Chong, A. Y. L., Tsiga, Z., & Venkatesh, V. (2022). Meta-analysis of the unified theory of acceptance and use of technology (UTAUT): Challenging its validity and charting a re-search agenda in the red ocean. Journal of the Academy of Marketing Science, 50(4), 643–672.
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., ... & Liang, P. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
Bonin, A. L., Smolinski, P. R., & Winiarski, J. (2025). Exploring the impact of generative ar-tificial intelligence on software development in the IT sector: Preliminary findings on productivity, efficiency and job security. Journal of Computer Science and Technology, 40(2), 112–128.
Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1(3), 185–216. https://doi.org/10.1177/135910457000100301
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., ... & Zhang, Y. (2023). Sparks of artificial general intelligence: Early experiments with GPT-4. arXiv pre-print arXiv:2303.12712.
Burton-Jones, A., & Hubona, G. S. (2005). Individual differences and usage behavior: Revis-iting a technology acceptance model assumption. The DATA BASE for Advances in In-formation Systems, 36(2), 58–77.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211.
Costa, C. J., Aparicio, M., Aparicio, S., & Aparicio, J. T. (2024). The democratization of arti-ficial intelligence: Theoretical framework. Applied Sciences, 14(18), 8236.
Coutinho, M., Marques, L., Santos, A., Dahia, M., Franca, C., & de Souza Santos, R. (2024). The role of generative AI in software development productivity: A pilot case study. In Pro-ceedings of the 1st ACM International Conference on AI-Powered Software (pp. 131–138). ACM.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of in-formation technology. MIS Quarterly, 13(3), 319–340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132.
Deloitte AI Institute. (2025). State of generative AI in the enterprise: Now decides next (Q4 report). Deloitte Development LLC.
DeSimone, J. A., Harms, P. D., & DeSimone, A. J. (2015). Best practice recommendations for data screening. Journal of Organizational Behavior, 36(2), 171–181.
Faulconbridge, J., Sarwar, A., & Spring, M. (2025). How professionals adapt to artificial in-telligence: The role of intertwined boundary work. Journal of Management Studies, 62(5), 1991–2024.
Fetters, M. D., Curry, L. A., & Creswell, J. W. (2013). Achieving integration in mixed methods design: Principles and practices. Health Services Research, 48(6pt2), 2134–2156.
Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024). Generative AI. Business & Information Systems Engineering, 66(1), 111–126.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59–82.
Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE Publications.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Mar-keting Science, 43(1), 115–135.
Ho, C. W. L. (2025). Governance of medical AI in the Greater Bay Area in Southern China: Regulatory rule of law and AI sovereignty. Policy Studies, 1–32.
Hobday, M., Davies, A., & Prencipe, A. (2005). Systems integration: A core capability of the modern corporation. Industrial and Corporate Change, 14(6), 1109–1143.
Jäkel, L., & Hummel, J. T. (2025). Strategic factors influencing AI adoption in organisations. In M. Moussa & A. McMurray (Eds.), The Palgrave handbook of breakthrough technologies in contemporary organisations. Springer Nature Singapore. https://doi.org/10.1007/978-981-96-2516-1_6
Jussupow, E., Benbasat, I., & Heinzl, A. (2022). Identity threats as a reason for resistance to artificial intelligence. Information Systems Research, 33(4), 1159–1176.
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10.
Kohtamäki, M., Parida, V., Oghazi, P., Gebauer, H., & Baines, T. (2019). Digital servitization business models in ecosystems: A theory of the firm. Journal of business research, 104, 380-392.
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174.
Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705–737.
Lin, Z.-S., Yang, Z., Lam, J. F. I., & Li, L. (2024). Greater Bay Area cooperation: Historical process and driving mechanisms. Advances in the Social Sciences, 13(6), 297–314.
Ma, A. (2024). Regulation in pursuit of artificial intelligence (AI) sovereignty: China's mix of restrictive and facilitative modalities. The African Journal of Information and Communica-tion, 34, 1–16.
Maddukuri, N. (2025). The transformative impact of AI on modern system integration. Inter-national Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(2), 229–236.
Maia, D., das Neves, J. V. P., Veloso, G., Guerra, G., Gomes, H., Oliveira, L. C., & dos Santos, S. C. (2025). The impact of generative AI on IT professionals’ work routines: A systematic literature review. In International Conference on Computer Supported Education (CSEDU) Proceedings (Vol. 2, pp. 163–173). SCITEPRESS.
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222.
Pingili, R. (2025). Generative AI unlocking adaptive workflow design. Journal of Next-Generation Research 5.0, 5(1), 45–60.
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210.
Ronge, R., Maier, M., & Rathgeber, B. (2025). Towards a definition of generative artificial intelligence. Philosophy & Technology, 38(1), 31.
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78.
Schmitt, A., Gajos, K. Z., & Mokryn, O. (2024). Generative AI in the software engineering domain: Tensions of occupational identity and patterns of identity protection. arXiv preprint arXiv:2410.03571.
Simaremare, M., & Edison, H. (2024). The state of generative AI adoption from software practitioners' perspective: An empirical study. Proceedings of the 31st Asia-Pacific Software Engineering Conference (APSEC).
Singh, N., Chaudhary, V., Singh, N., Soni, N., & Kapoor, A. (2024). Transforming business with generative AI: Research, innovation, market deployment and future shifts in business mod-els. arXiv preprint arXiv:2411.14437.
Stibe, A., & Dinh, T. H. (2024). Exploring human artificial intelligence using the knowledge behavior gap model. In International Conference on Mobile Web and Intelligent Information Systems (pp. 189–203). Springer Nature Switzerland.
Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of com-peting models. Information Systems Research, 6(2), 144–176.
Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 125–143.
Toniolo, K., & Sobrero, M. (2025). AI adoption challenges in private firms: Insights from vendors and corporate users. In M. Moussa & A. McMurray (Eds.), The Palgrave handbook of breakthrough technologies in contemporary organisations (pp. 41-52). Springer Nature Singapore.
Tunçalp, D. (2025). Navigating digital transformation: Integrating the breakthrough infor-mation systems and management in organisations. In M. Moussa & A. McMurray (Eds.), The Palgrave handbook of breakthrough technologies in con-temporary organisations (pp. 185–196). Springer Nature Singapore.
Venkatesh, V. (2022). Adoption and use of AI tools: A research agenda grounded in UTAUT. Annals of Operations Research, 308(1), 641–652.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of infor-mation technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
Venkatesh, V., & Zhang, X. (2010). Unified theory of acceptance and use of technology: US vs. China. Journal of Global Information Technology Management, 13(1), 5–27.
Wu, F., Li, M., & He, H. (2024). Innovation ecosystems and sustainable high innovation per-formance: Evidence from the Guangdong–Hong Kong–Macao Greater Bay Area. Sustain-ability, 16(21), 9487.
Xie, J., Li, X., & Chan, H. C. (2024). Data privacy governance and enterprise AI deployment in Hong Kong: Compliance frameworks and liability boundaries. Asia Pacific Journal of In-formation Systems, 34(2), 88–112.
Ye, W., Hu, Y., & Chen, L. (2021). Urban innovation efficiency improvement in the Guang-dong–Hong Kong–Macao Greater Bay Area from the perspective of innovation chains. Land. https://doi.org/10.3390/LAND10111164
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