Large language models as enablers of industrial information integration in construction: an adoption mechanism study
DOI: https://doi.org/10.3846/jcem.2026.28725Abstract
Large language models (LLMs) are emerging enablers of industrial information integration, yet their adoption mechanisms in construction remain unclear. This study develops an integrated framework that combines the Technology Acceptance Model and the Information Systems Success model, extended with trust, prior LLM experience, perceived substitution crisis, and job specialization, to explain individual adoption of LLMs for information integration in construction. Data from a cross-sectional survey of 82 Chinese construction professionals are analyzed using partial least squares structural equation modeling and bootstrapping. Results show that perceived ease of use robustly strengthens attitude and provides directional evidence for strengthening perceived usefulness, while perceived usefulness strengthens attitude and attitude drives intention to use. Trust, reinforced by perceived information quality, shows a positive association with intention under the primary one-tailed criterion, but this relationship requires further validation under the two-tailed robustness check. System quality and information quality indirectly foster adoption by improving perceived ease of use, although the relationship between system quality and trust is supported only as directional evidence, while prior LLM experience enhances ease of use but not perceived usefulness or trust. Perceived substitution crisis and job specialization exhibit no significant effects. The findings highlight that successful LLM-enabled information integration depends on both technical quality and psychological appraisals, and they offer implications for designing trustworthy, humancentered LLM applications that advance intelligent, interconnected construction ecosystems. The study links micro-level user acceptance with macro-level digital transformation and integration strategic objectives.
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large language models, industrial information integration, construction industry, technology acceptance model, information systems success modelHow to Cite
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Abioye, S. O., Oyedele, L. O., Akanbi, L., Ajayi, A., Davila Delgado, J. M., Bilal, M., Akinade, O. O., & Ahmed, A. (2021). Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. Journal of Building Engineering, 44, Article 103299. https://doi.org/10.1016/j.jobe.2021.103299
Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences, 30(2), 361–391. https://doi.org/10.1111/j.1540-5915.1999.tb01614.x
Aktar, A., & Pangil, F. (2017). The relationship between employee engagement, HRM practices and perceived organizational support: Evidence from banking employees. International Journal of Human Resource Studies, 7(3), 1–22. https://doi.org/10.5296/ijhrs.v7i3.11353
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411
Bagozzi, R. P., & Yi, Y. (1989). The degree of intention formation as a moderator of the attitude-behavior relationship. Social Psychology Quarterly, 52(4), 266–279. https://doi.org/10.2307/2786991
Bicocchi, N., Cabri, G., Mandreoli, F., & Mecella, M. (2019). Dynamic digital factories for agile supply chains: An architectural approach. Journal of Industrial Information Integration, 15, 111–121. https://doi.org/10.1016/j.jii.2019.02.001
Bilquise, G., Ibrahim, S., & Salhieh, S. M. (2024). Investigating student acceptance of an academic advising chatbot in higher education institutions. Education and Information Technologies, 29(5), 6357–6382. https://doi.org/10.1007/s10639-023-12076-x
Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management & Organization, 24(2), 239–257. https://doi.org/10.1017/jmo.2016.55
Chang, W., & Park, J. (2024). A comparative study on the effect of ChatGPT recommendation and AI recommender systems on the formation of a consideration set. Journal of Retailing and Consumer Services, 78, Article 103743. https://doi.org/10.1016/j.jretconser.2024.103743
Cho, H.-C., & Abe, S. (2013). Is two-tailed testing for directional research hypotheses tests legitimate? Journal of Business Research, 66(9), 1261–1266. https://doi.org/10.1016/j.jbusres.2012.02.023
Choi, J. K., & Ji, Y. G. (2015). Investigating the importance of trust on adopting an autonomous vehicle. International Journal of Human–Computer Interaction, 31(10), 692–702. https://doi.org/10.1080/10447318.2015.1070549
Cuesta-Valiño, P., Gutiérrez-Rodríguez, P., Núñez-Barriopedro, E., & García-Henche, B. (2023). Strategic orientation towards digitization to improve supermarket loyalty in an omnichannel context. Journal of Business Research, 156, Article 113475. https://doi.org/10.1016/j.jbusres.2022.113475
Czvetkó, T., & Abonyi, J. (2023). Data sharing in Industry 4.0 – AutomationML, B2MML and International Data Spaces-based solutions. Journal of Industrial Information Integration, 33, Article 100438. https://doi.org/10.1016/j.jii.2023.100438
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
de Winter, J. C. F., Dodou, D., & Wieringa, P. A. (2009). Exploratory factor analysis with small sample sizes. Multivariate Behavioral Research, 44(2), 147–181. https://doi.org/10.1080/00273170902794206
DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60–95. https://doi.org/10.1287/isre.3.1.60
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748
Diamantopoulos, A., & Siguaw, J. A. (2006). Formative versus reflective indicators in organizational measure development: A comparison and empirical illustration. British Journal of Management, 17(4), 263–282. https://doi.org/10.1111/j.1467-8551.2006.00500.x
Ding, Y.-M., Yan, Y.-T., Liu, F., & Chen, Z.-S. (2025). Enhancing digital transformation in construction: Strategic leadership, collaboration, and machine learning insights. Enterprise Information Systems, 19(12), Article 2560342. https://doi.org/10.1080/17517575.2025.2560342
Diro, A., Kaisar, S., Saini, A., Fatima, S., Hiep, P. C., & Erba, F. (2025). Workplace security and privacy implications in the GenAI age: A survey. Journal of Information Security and Applications, 89, Article 103960. https://doi.org/10.1016/j.jisa.2024.103960
Du, C., Esser, S., Nousias, S., & Borrmann, A. (2026). Text2BIM: Generating building models using a large language model-based multiagent framework. Journal of Computing in Civil Engineering, 40(2), Article 04025142. https://doi.org/10.1061/JCCEE5.CPENG-6386
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. https://doi.org/10.1037/1082-989X.4.3.272
Fan, W., Liu, J., Zhu, S., & Pardalos, P. M. (2020). Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS). Annals of Operations Research, 294(1–2), 567–592. https://doi.org/10.1007/s10479-018-2818-y
Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474. https://doi.org/10.1016/S1071-5819(03)00111-3
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280. https://doi.org/10.1016/j.techfore.2016.08.019
Fuller, C. M., Simmering, M. J., Atinc, G., Atinc, Y., & Babin, B. J. (2016). Common methods variance detection in business research. Journal of Business Research, 69(8), 3192–3198. https://doi.org/10.1016/j.jbusres.2015.12.008
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519
Ghimire, P., Kim, K., & Acharya, M. (2024). Opportunities and challenges of generative AI in construction industry: Focusing on adoption of text-based models. Buildings, 14(1), Article 220. https://doi.org/10.3390/buildings14010220
Goktas, P., Karakaya, G., Kalyoncu, A. F., & Damadoglu, E. (2023). Artificial intelligence chatbots in allergy and immunology practice: Where have we been and where are we going? The Journal of Allergy and Clinical Immunology: In Practice, 11(9), 2697–2700. https://doi.org/10.1016/j.jaip.2023.05.042
Guo, Z., Xu, X., Gao, X., Xu, Y., Liu, J., Zhang, M., & Li, F. (2025). Enhanced risk-based quality control for hydraulic engineering construction projects considering the risk-influencing mechanism. Journal of Construction Engineering and Management, 151(2), Article 04024204. https://doi.org/10.1061/JCEMD4.COENG-15579
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson Prentice Hall.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
Hair, J. F., Jr., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121. https://doi.org/10.1108/EBR-10-2013-0128
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017). Mirror, mirror on the wall: A comparative evaluation of composite-based structural equation modeling methods. Journal of the Academy of Marketing Science, 45(5), 616–632. https://doi.org/10.1007/s11747-017-0517-x
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Hair, J. F., Jr., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021a). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE Publications. https://doi.org/10.1007/978-3-030-80519-7
Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021b). Evaluation of reflective measurement models. In Partial least squares structural equation modeling (PLS-SEM) using R: A workbook (pp. 75–90). Springer, Cham. https://doi.org/10.1007/978-3-030-80519-7_4
Heersmink, R., de Rooij, B., Clavel Vázquez, M. J., & Colombo, M. (2024). A phenomenology and epistemology of large language models: Transparency, trust, and trustworthiness. Ethics and Information Technology, 26(3), Article 41. https://doi.org/10.1007/s10676-024-09777-3
Herrenkind, B., Brendel, A. B., Nastjuk, I., Greve, M., & Kolbe, L. M. (2019). Investigating end-user acceptance of autonomous electric buses to accelerate diffusion. Transportation Research Part D: Transport and Environment, 74, 255–276. https://doi.org/10.1016/j.trd.2019.08.003
Hsu, C.-L., Lu, H.-P., & Hsu, H.-H. (2007). Adoption of the mobile Internet: An empirical study of multimedia message service (MMS). Omega, 35(6), 715–726. https://doi.org/10.1016/j.omega.2006.03.005
Hsu, P.-F., Yen, H. R., & Chung, J.-C. (2015). Assessing ERP post-implementation success at the individual level: Revisiting the role of service quality. Information & Management, 52(8), 925–942. https://doi.org/10.1016/j.im.2015.06.009
Huang, A. H., Wang, H., & Yang, Y. (2023). FinBERT: A large language model for extracting information from financial text. Contemporary Accounting Research, 40(2), 806–841. https://doi.org/10.1111/1911-3846.12832
Jiang, M., Guo, Y., Huang, S., & Pu, J. (2024). Generating the assembly instructions of helicopter subassemblies using the hierarchical pruning strategy and large language model. Journal of Industrial Information Integration, 42, Article 100723. https://doi.org/10.1016/j.jii.2024.100723
Kamalov, F., Santandreu Calonge, D., & Gurrib, I. (2023). New era of artificial intelligence in education: Towards a sustainable multifaceted revolution. Sustainability, 15(16), Article 12451. https://doi.org/10.3390/su151612451
Kirov, V., & Malamin, B. (2022). Are translators afraid of artificial intelligence? Societies, 12(2), Article 70. https://doi.org/10.3390/soc12020070
Kock, N. (2015a). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
Kock, N. (2015b). One-tailed or two-tailed P values in PLS-SEM?. International Journal of e-Collaboration, 11(2), 1–7. https://doi.org/10.4018/IJEC.2015040101
Lee, S., & Yu, J. (2016). Comparative study of BIM acceptance between Korea and the United States. Journal of Construction Engineering and Management, 142(3), Article 05015016. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001076
Li, F., Laili, Y., Chen, X., Lou, Y., Wang, C., Yang, H., Gao, X., & Han, H. (2023). Towards big data driven construction industry. Journal of Industrial Information Integration, 35, Article 100483. https://doi.org/10.1016/j.jii.2023.100483
Li, C., Lu, S., Wang, M., & Hu, Y. (2025a). Overview of large language models in construction industry applications. In Proceedings of the 2025 International Conference on Artificial Intelligence and Computational Intelligence (pp. 298–302). Association for Computing Machinery. https://doi.org/10.1145/3730436.3730486
Li, J., Li, J., Yang, G., Yang, L., Chi, H., & Yang, L. (2025b). Applications of large language models and multimodal large models in autonomous driving: A comprehensive review. Drones, 9(4), Article 238. https://doi.org/10.3390/drones9040238
Lin, T.-L., Wang, H., Chen, Z.-S., & Guo, J. (2025). Navigating the road to BIMS-GPT adoption: Perceptions of construction professionals on drivers and barriers. Journal of Building Design and Environment, 3(3), Article 202549. https://doi.org/10.70401/jbde.2025.0015
Liu, D., Lu, W., & Niu, Y. (2018). Extended technology-acceptance model to make smart construction systems successful. Journal of Construction Engineering and Management, 144(6), Article 04018035. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001487
Love, P. E. D., Matthews, J., Simpson, I., Hill, A., & Olatunji, O. A. (2014). A benefits realization management building information modeling framework for asset owners. Automation in Construction, 37, 1–10. https://doi.org/10.1016/j.autcon.2013.09.007
Ma, Z., Skibniewski, M. J., & Chen, Z.-S. (2026). Toward agent-native infrastructure in AEC: From fragmented knowledge to executable workflows. Journal of Construction Engineering, Management & Innovation, 9(2), Article 721. https://doi.org/10.31462/jcemi.2026.721
Maheshwari, G. (2024). Factors influencing students’ intention to adopt and use ChatGPT in higher education: A study in the Vietnamese context. Education and Information Technologies, 29(10), 12167–12195. https://doi.org/10.1007/s10639-023-12333-z
Martilla, J. A., & James, J. C. (1977). Importance-performance analysis. Journal of Marketing, 41(1), 77–79. https://doi.org/10.1177/002224297704100112
Massa, N., Santarpia, F. P., & Consiglio, C. (2023). Work characteristics as determinants of remote working acceptance: Integrating UTAUT and JD-R models. In M. Kurosu, & A. Hashizume (Eds.), Lecture notes in computer science: Vol. 14011. Human-computer interaction. HCII 2023 (pp. 163–180). Springer, Cham. https://doi.org/10.1007/978-3-031-35596-7_12
Menard, S. W. (2002). Longitudinal research (2nd ed.). SAGE Publications. https://doi.org/10.4135/9781412984867
Moshood, T. D., Rotimi, J. O. B., Shahzad, W., & Bamgbade, J. A. (2024). Infrastructure digital twin technology: A new paradigm for future construction industry. Technology in Society, 77, Article 102519. https://doi.org/10.1016/j.techsoc.2024.102519
Mustapha, K. B. (2025). A survey of emerging applications of large language models for problems in mechanics, product design, and manufacturing. Advanced Engineering Informatics, 64, Article 103066. https://doi.org/10.1016/j.aei.2024.103066
Neyman, J., & Pearson, E. S. (1992). On the problem of the most efficient tests of statistical hypotheses. In S. Kotz & N. L. Johnson (Eds.), Breakthroughs in statistics: Foundations and basic theory (pp. 73–108). Springer, Cham. https://doi.org/10.1007/978-1-4612-0919-5_6
Panagiotopoulos, I., & Dimitrakopoulos, G. (2018). An empirical investigation on consumers’ intentions towards autonomous driving. Transportation Research Part C: Emerging Technologies, 95, 773–784. https://doi.org/10.1016/j.trc.2018.08.013
Peng, J., & Liu, X. (2023). Automated code compliance checking research based on BIM and knowledge graph. Scientific Reports, 13(1), Article 7065. https://doi.org/10.1038/s41598-023-34342-1
Pillai, R., Sivathanu, B., Metri, B., & Kaushik, N. (2024). Students’ adoption of AI-based teacher-bots (T-bots) for learning in higher education. Information Technology & People, 37(1), 328–355. https://doi.org/10.1108/ITP-02-2021-0152
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Postle, B., & Salingaros, N. A. (2025). LLM and pattern language synthesis: A hybrid tool for human-centered architectural design. Buildings, 15(14), Article 2400. https://doi.org/10.3390/buildings15142400
Prakash, A. V., & Das, S. (2021). Medical practitioner’s adoption of intelligent clinical diagnostic decision support systems: A mixed-methods study. Information & Management, 58(7), Article 103524. https://doi.org/10.1016/j.im.2021.103524
Richter, N. F., Sinkovics, R. R., Ringle, C. M., & Schlägel, C. (2016). A critical look at the use of SEM in international business research. International Marketing Review, 33(3), 376–404. https://doi.org/10.1108/IMR-04-2014-0148
Rindfleisch, A., Malter, A. J., Ganesan, S., & Moorman, C. (2008). Cross-sectional versus longitudinal survey research: Concepts, findings, and guidelines. Journal of Marketing Research, 45(3), 261–279. https://doi.org/10.1509/jmkr.45.3.261
Saqib, G., Hassan, M. U., Zubair, M. U., & Choudhry, R. M. (2023). Investigating the acceptance of an electronic incident reporting system in the construction industry: An application of the technology acceptance model. Journal of Construction Engineering and Management, 149(5), Article 04023021. https://doi.org/10.1061/JCEMD4.COENG-12583
Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach (7th ed.). John Wiley & Sons.
Shah, R., & Goldstein, S. M. (2006). Use of structural equation modeling in operations management research: Looking back and forward. Journal of Operations Management, 24(2), 148–169. https://doi.org/10.1016/j.jom.2005.05.001
Shah, S. H., Rehman, S. U., Kim, I., & Hwang, K.-E. (2026). Transformer-based framework for mapping client requirements to BIM. Automation in Construction, 181, Article 106601. https://doi.org/10.1016/j.autcon.2025.106601
Shahzad, M. F., Xu, S., & Zahid, H. (2025). Exploring the impact of generative AI-based technologies on learning performance through self-efficacy, fairness & ethics, creativity, and trust in higher education. Education and Information Technologies, 30(3), 3691–3716. https://doi.org/10.1007/s10639-024-12949-9
Shamshiri, A., Ryu, K. R., & Park, J. Y. (2024). Text mining and natural language processing in construction. Automation in Construction, 158, Article 105200. https://doi.org/10.1016/j.autcon.2023.105200
Shmueli, G., Ray, S., Velasquez Estrada, J. M., & Chatla, S. B. (2016). The elephant in the room: Predictive performance of PLS models. Journal of Business Research, 69(10), 4552–4564. https://doi.org/10.1016/j.jbusres.2016.03.049
Shojaei, R. S., & Burgess, G. (2022). Non-technical inhibitors: Exploring the adoption of digital innovation in the UK construction industry. Technological Forecasting and Social Change, 185, Article 122036. https://doi.org/10.1016/j.techfore.2022.122036
Sun, X.-F. (2024). Developing and validating a model for assessing autonomous vehicles acceptance. International Journal of Human–Computer Interaction, 40(18), 5503–5516. https://doi.org/10.1080/10447318.2023.2238343
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd
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
Verma, S., & Singh, V. (2022). Impact of artificial intelligence-enabled job characteristics and perceived substitution crisis on innovative work behavior of employees from high-tech firms. Computers in Human Behavior, 131, Article 107215. https://doi.org/10.1016/j.chb.2022.107215
Wang, P., Li, Z., Yu, L., Jiang, J., & Ma, X. (2025). Factors affecting attitude and intention to adopt artificial intelligence for sustainable triage: An exploratory study of emergency department staff. Technology in Society, 82, Article 102912. https://doi.org/10.1016/j.techsoc.2025.102912
Wen, H., AbouRizk, S., & Mohamed, Y. (2025). Gap analysis of digitalization levels in construction and manufacturing: A comparative study of Construction 4.0 and Industry 4.0. Journal of Construction Engineering and Management, 151(3), Article 04025001. https://doi.org/10.1061/JCEMD4.COENG-14896
Willaby, H. W., Costa, D. S. J., Burns, B. D., MacCann, C., & Roberts, R. D. (2015). Testing complex models with small sample sizes: A historical overview and empirical demonstration of what partial least squares (PLS) can offer differential psychology. Personality and Individual Differences, 84, 73–78. https://doi.org/10.1016/j.paid.2014.09.008
Wu, C., Ding, W., Jin, Q., Jiang, J., Jiang, R., Xiao, Q., Liao, L., & Li, X. (2025). Retrieval augmented generation-driven information retrieval and question answering in construction management. Advanced Engineering Informatics, 65, Article 103158. https://doi.org/10.1016/j.aei.2025.103158
Wu, C., Li, X., Guo, Y., Wang, J., Ren, Z., Wang, M., & Yang, Z. (2022). Natural language processing for smart construction: Current status and future directions. Automation in Construction, 134, Article 104059. https://doi.org/10.1016/j.autcon.2021.104059
Xiao, B., Wang, Y., Zhang, Y., Chen, C., & Darko, A. (2024). Automated daily report generation from construction videos using ChatGPT and computer vision. Automation in Construction, 168, Article 105874. https://doi.org/10.1016/j.autcon.2024.105874
Yan, J., Zhou, Y., & Mei, M. Q. (2022). Why do you use a smartphone to pay? Determinants of mobile payment adoption in China. Strategic Change, 31(6), 603–611. https://doi.org/10.1002/jsc.2527
Yang, Z., Tang, C., Zhang, T., Zhang, Z., & Doan, D. T. (2024). Digital twins in construction: Architecture, applications, trends and challenges. Buildings, 14(9), Article 2616. https://doi.org/10.3390/buildings14092616
Zhang, C., & Zhang, H. (2025). The impact of generative AI on management innovation. Journal of Industrial Information Integration, 44, Article 100767. https://doi.org/10.1016/j.jii.2024.100767
Zhang, G., Lu, C., & Luo, Q. (2025). Application of large language models in the AECO industry: Core technologies, application scenarios, and research challenges. Buildings, 15(11), Article 1944. https://doi.org/10.3390/buildings15111944
Zhang, N., Ye, J., Zhong, Y., & Chen, Z. (2023). Digital transformation in the Chinese construction industry: Status, barriers, and impact. Buildings, 13(4), Article 1092. https://doi.org/10.3390/buildings13041092
Zhou, S., Liu, X., Li, D., Gu, T., Liu, K., Yang, Y., & Wong, M. O. (2025). Integrating domain-specific knowledge and fine-tuned general-purpose large language models for question-answering in construction engineering management. Automation in Construction, 175, Article 106206. https://doi.org/10.1016/j.autcon.2025.106206
Zirar, A., Ali, S. I., & Islam, N. (2023). Worker and workplace artificial intelligence (AI) coexistence: Emerging themes and research agenda. Technovation, 124, Article 102747. https://doi.org/10.1016/j.technovation.2023.102747
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