Artificial intelligence monetization model preference: supply and demand insights from the built environment sector
Zafira Nadia Maaz et al.
What the paper says
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.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.