The Survey-Based Strategies of Adopting Artificial Intelligence in the Construction Industry
Abstract
Artificial intelligence (AI) holds transformative potential for the construction industry, offering solutions to persistent challenges such as low productivity, labour shortages, and high accident rates. Despite global advancements, the Malaysian construction sector has been slow to embrace AI, hindered by financial constraints, cultural resistance, and a lack of expertise. This study explores strategies to accelerate AI adoption in Malaysia’s construction industry by examining its potential benefits, identifying barriers, and determining critical success factors. A quantitative approach was employed, using a structured, closed-ended questionnaire distributed to 186 G7 contractor companies across Selangor. Data were analysed using SPSS and Microsoft Excel, focusing on reliability, frequency, and average index methods. The findings reveal that while AI offers substantial benefits, including improved project scheduling, cost reductions, and enhanced risk management, key challenges persist, such as the high cost of implementation, lack of skilled personnel, and reluctance to adopt new technologies. This study provides a framework for overcoming these barriers through targeted strategies, including workforce upskilling, management support, and improved collaboration among stakeholders. These insights offer practical solutions to facilitate the integration of AI in the Malaysian construction sector and contribute to its alignment with the Fourth Industrial Revolution.
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Singh, A., Dwivedi, A., Agrawal, D., & Singh, D. (2023). Identifying issues in adoption of AI practices in construction supply chains: towards managing sustainability. Operations Management Research, 16(4), 1667–1683. https://doi.org/10.1007/s12063-022-00344-x
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Adeloye, A., Diekola, O., Delvin, K., & Gbenga, C. (2023). Applications of Artificial Intelligence (AI) in the construction industry: A review of Observational Studies. Trends in Applied Sciences Research. 42-52.
Ahmad, M., Husin, N., Ahmad, A., Abdullah, H., Wei, C., & Nawi, M. (2022). Digital transformation: an exploring barriers and challenges practice of artificial intelligence in manufacturing firms in malaysia. Journal of Advanced Research in Applied Sciences and Engineering Technology, 29(1), 110-117. https://doi.org/10.37934/araset.29.1.110117
Aiyetan, O. and Dillip, D. (2018). System dynamics approach to mitigating skilled labour shortages in the construction industry: a south africa context. Construction Economics and Building, 18(4), 45-63. https://doi.org/10.5130/ajceb.v18i4.6041
Alaloul, W. S., Musarat, M. A., Rabbani, M. B. A., Iqbal, Q., Maqsoom, A., & Farooq, W. (2021). Construction Sector Contribution to Economic Stability: Malaysian GDP Distribution. Sustainability, 13(9), 5012. https://doi.org/10.3390/su13095012
Aldosari, S. (2020). The future of higher education in the light of artificial intelligence transformations. International Journal of Higher Education, 9(3), 145. https://doi.org/10.5430/ijhe.v9n3p145
Bartneck, C., Belpaeme, T., Eyssel, F., Kanda, T., Keijsers, M., & Šabanović, S. (2020). Human-Robot interaction. https://doi.org/10.1017/9781108676649
Bhadoriya, A. (2024). A review on possibilities of artificial intelligence in construction industry. International Journal for Research in Applied Science and Engineering Technology, 12(3), 511-513. https://doi.org/10.22214/ijraset.2024.58858
Feng, Y., Qiu, L., & Sun, B. (2021). A measurement framework of crowd intelligence. International Journal of Crowd Science, 5(1), 81-91. https://doi.org/10.1108/ijcs-09-2020-0015
Fernando, Y., Wahyuni-TD, I. S., Gui, A., Ikhsan, R. B., Mergeresa, F., & Ganesan, Y. (2022). A mixed-method study on the barriers of industry 4.0 adoption in the Indonesian SMEs manufacturing supply chains. Journal of Science and Technology Policy Management, 14(4), 678–695. https://doi.org/10.1108/jstpm-10-2021-0155
Göde, A. (2023). What is artificial intelligence?.. https://doi.org/10.58830/ozgur.pub392.c1548
Holzmann, V., & Lechiara, M. (2022). Artificial Intelligence in Construction Projects: An Explorative Study of Professionals’ Expectations. European Journal of Business and Management Research, 7(3), 151–162. https://doi.org/10.24018/ejbmr.2022.7.3.1432
Jadallah, H., Friedland, C., Nahmens, I., Pecquet, C., Berryman, C., & Zhu, Y. (2022). Instructional design framework for construction materials training. Frontiers in Built Environment, 7. https://doi.org/10.3389/fbuil.2021.798843
Korke, P., Gobinath, R., Shewale, M., & Khartode, B. (2023). Role of artificial intelligence in construction project management. E3S Web of Conferences, 405, 04012. https://doi.org/10.1051/e3sconf/202340504012
Laurie A. Harris (2023). Artificial Intelligence: Overview, Recent Advances, and Considerations for the 118 th Congress. https://sgp.fas.org/crs/misc/R47644.pdf
Lee, K. and Kim, E. S. (2022). Explainable artificial intelligence in the early diagnosis of gastrointestinal disease. Diagnostics, 12(11), 2740. https://doi.org/10.3390/diagnostics12112740
Mahmoudi, A., Sadeghi, M., & Deng, X. (2022). Performance measurement of construction suppliers under localization, agility, and digitalization criteria: fuzzy ordinal priority approach. Environment Development and Sustainability. https://doi.org/10.1007/s10668-022-02301-x
Mironova, L. I., Fomin, N. A., & Vilisova, A. D. (2021). Issues of invention in the construction sector in conditions of its digitalization. Russian Journal of Construction Science and Technology, 7(2), 47–52. https://doi.org/10.15826/rjcst.2021.2.005
Nurlia, N. (2023). Ai implementation impact on workforce productivity : the role of ai training and organizational adaptation. Escalate, 1(01), 01-13. https://doi.org/10.61536/escalate.v1i01.6
Phaladi, M., Mashwama, X., Thwala, W., & Aigbavboa, C. (2022). A theoretical assessment on the implementation of artificial intelligence (ai) for an improved learning curve on construction in south africa. Iop Conference Series Materials Science and Engineering, 1218(1), 012003. https://doi.org/10.1088/1757-899x/1218/1/012003
Rahim, F., Yusoff, N., Chen, W., Zainon, N., Yusoff, S., & Deraman, R. (2016). The challenge of labour shortage for sustainable construction. Planning Malaysia, 14(5). https://doi.org/10.21837/pmjournal.v14.i5.194
Ramachandran, K. K., Srivastava, A., Panjwani, V., Kumar, D., Cheepurupalli, N. R., & Mohan, C. R. (2024). Developing AI-powered Training Programs for Employee Upskilling and Reskilling. Journal of Informatics Education and Research, 4(2). https://doi.org/10.52783/jier.v4i2.903
Rane, N., Choudhary, S., & Rane, J. (2024). Artificial intelligence acceptance and implementation in construction industry: factors, current trends, and challenges. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4841619
Roslan, N., Nurul Azreen Izzatie Roslan, Zainal, R., Izwan Abd Rahim, M., & Kassim, N. (2024). Artificial intelligence (AI) implementation in improving construction site workflow performance. Research in Management of Technology and Business, Vol. 5No. 1(2024) 1275-1301, e-ISSN: 2773-5044. https://doi.org/10.30880/rmtb.2024.05.01.086
Singh, A., Dwivedi, A., Agrawal, D., & Singh, D. (2023). Identifying issues in adoption of AI practices in construction supply chains: towards managing sustainability. Operations Management Research, 16(4), 1667–1683. https://doi.org/10.1007/s12063-022-00344-x
Tjebane, M., Musonda, I., & Okoro, C. (2022). A systematic literature review of influencing factors and strategies of artificial intelligence adoption in the construction industry. Iop Conference Series Materials Science and Engineering, 1218(1), 012001. https://doi.org/10.1088/1757-899x/1218/1/012001
Wu, M.-J., Zhao, K., & Fils-Aime, F. (2022). Response rates of online surveys in published research: A meta-analysis. Computers in Human Behavior Reports, 7(2), 1–11. https://doi.org/10.1016/j.chbr.2022.1002061
Published
2024-11-01
How to Cite
HALID, Mohamad Amir et al.
The Survey-Based Strategies of Adopting Artificial Intelligence in the Construction Industry.
International Journal of Business and Technology Management, [S.l.], v. 6, n. S2, p. 363-376, nov. 2024.
ISSN 2682-7646.
Available at: <https://myjms.mohe.gov.my/index.php/ijbtm/article/view/27944>. Date accessed: 05 dec. 2024.
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Section
English Section
