Socio-Behavioural Determinants of AI Adoption for Economic Sustainability in Pakistan’s Built Environment Sector
DOI:
https://doi.org/10.70670/sra.v4i2.2000Keywords:
Artificial Intelligence (AI) Adoption, Socio-Behavioural Factors, Economic Sustainability, Built Environment Sector, SmartPLS (PLS-SEM)Abstract
The rapid adoption of Artificial Intelligence (AI) in the built environment industry has tremendous potential to improve economic sustainability, especially in developing economies like Pakistan. The adoption of AI is, however, not evenly spread as socio-behavioural factors affect the decision-making of industry professionals. This paper will analyse the socio-behavioural factors surrounding AI adoption in the economic sustainability of the built environment sector in the Pakistani context and the architect, interior designer and construction professional professions. Based on the known theories of technology adoption, the research will examine the influence of perceived usefulness, social influence, and environmental awareness on behavioural intention, and the resultant influence of behavioural intention on actual AI adoption. Quantitative research design was used where data was collected using a structured questionnaire among professionals in the major urban centres in Pakistan. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to analyse the data through SmartPLS. Cronbach alpha, composite reliability, average variance extracted (AVE), and discriminant validity were used to measure the measurement model whereas bootstrapping evaluated structural model by estimating the path coefficients, t-values, and levels of significance. The findings show that the three variables (perceived usefulness, social influence, and environmental awareness) have significant and positive effects on behavioural intention, with perceived usefulness being the most significant predictor. Also, behavioural intention is mediating as it has a strong and significant influence on AI adoption in economic sustainability. In general, the model has a high explanatory and predictive power, which implies that socio-behavioural factors play a significant role in explaining the adoption behaviour of AI. The current research adds to the literature by combining socio-behavioural determinants with AI adoption and economic sustainability in the context of a developing economy. These results can help policymakers, industry stakeholders, and technology developers design more targeted and practical policies that encourage the adoption of AI. In turn, this can support more sustainable economic development within the built environment sector by aligning technological innovation with real-world industry needs.
References
Benitez, J., Henseler, J., Castillo, A., & Schuberth, F. (2020). How to perform and report PLS analyses. Industrial Management & Data Systems, 120(2), 192–210.
Cavalcante, I. M., Frazzon, E. M., Forcellini, F. A., & Ivanov, D. (2019). A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management, 49, 86-97.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In Modern methods for business research (pp. 295-336). Psychology Press.
Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319-340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003.
Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American journal of theoretical and applied statistics, 5(1), 1-4.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
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 marketing science, 43(1), 115-135.
Jamil, K., Zhang, W., Anwar, A., & Mustafa, S. (2025). Exploring the influence of AI adoption and technological readiness on sustainable performance in Pakistani export sector manufacturing small and medium-sized enterprises. Sustainability, 17(8), 3599.
Khan, M. A. (2023). Role of Artificial Intelligence in Construction Industry of Pakistan. International Journal of Advanced Engineering, Management and Science, 9, 12.
Kumar, M., Sharma, R., Sharma, A., Kumar, R., & Sharma, N. (2025). AI for Sustainable Design and Construction Practices. In Intelligent Systems for Sustainable Infrastructure: AI Solutions Shaping a Green Future: Leveraging AI Innovations for Eco Friendly Infrastructure and Environmental Resilience (pp. 237-247). Cham: Springer Nature Switzerland.
Na, S., Heo, S., Choi, W., Kim, C., & Whang, S. W. (2023). Artificial intelligence (AI)-Based technology adoption in the construction industry: a cross national perspective using the technology acceptance model. Buildings, 13(10), 2518.
Ringle, C. M., Wende, S., & Becker, J. M. (2015). SmartPLS 3. SmartPLS GmbH. https://www.smartpls.com
Sabri, M. F., Law, S. H., Fei, C. K., Nang, A. A. S., Rahim, H. A., & Anthony, M. (2025). Factors influencing Fintech adoption among Malaysian consumers: the moderating role of Fintech literacy. International Journal of Monetary Economics and Finance, 18(5), 349-375.
Sarstedt, M., & Cheah, J. H. (2019). Partial least squares structural equation modeling using SmartPLS: A software review. Journal of Marketing Analytics, 7(3), 196–202.
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial least squares structural equation modeling. In Handbook of market research (pp. 587-632). Cham: Springer International Publishing.
Ugural, M. N., Aghili, S., & Burgan, H. I. (2024). Adoption of lean construction and AI/IoT technologies in iran’s public construction sector: a mixed-methods approach using fuzzy logic. Buildings, 14(10), 3317.
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 information technology: Toward a unified view1. MIS quarterly, 27(3), 425-478.
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the Unified Theory of Acceptance and Use of Technology1. MIS quarterly, 36(1), 157-178.
