Maaz, Z N; Adediran, A O; Mohamad Ramly, Z and Darmansah, N F (2026) Artificial intelligence monetization model preference: Supply and demand insights from the built environment sector. Built Environment Project and Asset Management, 16(2), pp. 396-414. ISSN 2044-124X
Abstract
Purpose – This study applies supply and demand theory to examine how the Built Environment (BE) sector can implement Artificial Intelligence (AI) monetization. Although AI potential is gaining traction, limited attention has been given to how BE stakeholders can engage with monetization mechanisms to capture value from data. The study examines the preferences of BE stakeholders for AI monetization models and identifies key factors that influence the adoption of these models. Design/methodology/approach – The study employed self-administered surveys completed by 67 BE stakeholders. The questionnaire assessed preferences across eight AI monetization models and examined factors of adoption. Expert evaluation and reliability testing established validity. Data analysis includes descriptive analysis, T-test and Ordinary Least Squares regression. Findings – Results reveal significant differences between data providers and data buyers, with both groups showing consistent preference for subscription, free data and freemium models. Data providers preferred profit sharing model. The study also found technology maturity and financial capacity are key factors in the adoption of AI monetization, while organizational experience showed influence primarily among data buyers. Practical implications – The findings provide actionable insights for policymakers, technology providers and BE organization seeking to accelerate AI adoption. Understanding stakeholder preferences and factors enables the design of equitable monetization strategies, fosters trust in data sharing and supports Malaysia's digital transformation agenda. Originality/value – This study offers the first empirical exploration of AI monetization models within the Malaysian BE sector, filling a significant literature gap. By applying supply and demand theory, the study uniquely illuminates the structural tensions between data supply and data demand, providing a theoretical lens to understand how stakeholder roles, capacities and needs shape monetization preferences.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial intelligence; big data analytics; built environment; construction 4.0; data monetization strategies; digital transformation; Malaysia; supply and demand theory |
| Index terms: | transformation, validity, construction 4.0, Malaysia, supply and demand, artificial intelligence, testing, built environment, profit, survey, big data, strategy, data analysis, evaluation, preference, questionnaire, traction, exploration, methodology |
| Subjects: | professional practice, economic analysis, environmental resource management, evaluation and assessment methods, digital engineering, transportation engineering, market analysis, Geography, research methods, decision-making and reasoning, data analysis and analytics, artificial intelligence, data collection methods, business, infrastructure and transport systems, information systems, management |
| Topics: | Research Practice, Business Strategy, Digital Applications, Urban Studies, Engineering Principles, Geographical Context, Sustainability |
| Descriptive scope: | 5 PCTEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here