Data monetization is leveraging data to obtain economic benefits. In the context of Big Data Analytics (BDA), data serves as a fundamental asset, enabling the transformation into valuable insights throughout various construction phases. Unfortunately, there is lack of studies focusing on data monetization within the construction industry, given the unique supply and demand characteristics among construction stakeholders. This study aims to address the objective of identifying data monetization strategies applicable to the construction industry, particularly for data sellers and data buyers. Data was gathered through a quantitative survey among 100 construction practitioners in Malaysia, encompassing developers, contractors, consultants, government agencies, technology providers, and academia. Respondents were further categorized into data sellers and data buyers. The data were analyzed using mean analysis, t-test and Spearman's rank correlation coefficient. The study identified six data monetization strategies, comprising 19 determinants. The analysis revealed high preference on all data monetization strategies and moderate differences in the preferred strategies between data sellers and buyers. Significant differences were found in 3 determinants which are (1) trading data to facilitate decision-making, (2) trading data for construction reports, benchmarks, and indices, and (3) selling visualized data on real-time platforms. The t-test indicated that data sellers are more inclined towards the three strategies for effective monetization. Furthermore, Spearman’s correlation coefficient revealed the 3 determinants also positively influence another 3 determinants of (1) data wrapping to reflect better service from data provider, (2) buying raw data with its inherit information and (3) monetizing internal data to optimize organization’s work process. The insights enable stakeholders to implement mechanisms that foster data monetization within project cultures accelerating BDA undertakings. Future recommendations include using larger sample sizes to enhance generalizability and to explore more areas such as construction contracts, cost, health, and safety.

Artificial intelligence (AI) is increasingly being adopted for decision support in sustainable building risk management, yet the trustworthiness of AI-supported sustainability risk decisions depends as much on data quality as on analytical capability. Poor data conditions can amplify sustainability risks by producing unreliable decision support, yet existing studies provide limited insights into which data quality dimensions should be prioritized to enable trustworthy AI outcomes. This study identifies and prioritizes the critical data quality dimensions for trustworthy AI-supported decisions in sustainable building risk management. A questionnaire survey was conducted of accredited sustainable building professionals and their expert judgements were analyzed through an Analytic Hierarchy Process (AHP). The findings reveal that system-dependent dimensions, particularly traceability and interoperability, are prioritized over intrinsic dimensions like accuracy and consistency. The findings suggest that trustworthy AI-supported sustainability decisions depend strongly on a verifiable data provenance, cross-system integration and interpretable outputs rather than data correctness alone. This study reframes data quality from a general prerequisite to a prioritized, context-sensitive construct underpinning trustworthy AI applications, extending data-driven decision theory in the sustainable building domain. Ultimately, a phased data governance approach is recommended to prioritize traceability and interoperability as the foundational conditions for construction organizations implementing trustworthy AI in sustainable building risk management.

Purpose: This study investigates how artificial intelligence (AI) capabilities support perceived organisational competitiveness in project cost management among construction stakeholders in Malaysia, where AI adoption remains emerging and not yet well understood. Design/methodology/approach: A questionnaire survey was conducted among Malaysian construction stakeholders involved in project cost management decision making, including developers, contractors, consultants, government agencies and technology providers. Two hundred and seventy-eight questionnaires were distributed and 203 valid responses were obtained. Data were analysed using relative importance index to rank AI capabilities and exploratory factor analysis to identify underlying AI capability dimensions. Expert validation was conducted to confirm the interpretability and relevance of the results. Findings: Results show 29 AI capabilities supporting competitive advantage in project cost management cluster into four dimensions: predictive, diagnostic, descriptive and prescriptive. Predictive capabilities were ranked highest, followed by diagnostic capabilities, highlighting the importance of forecasting accuracy, cost monitoring and risk detection. Descriptive capabilities supporting integration of project, market and sustainability cost data were also valued, while prescriptive capabilities received comparatively lower rankings, indicating preference for AI as decision support over full decision automation. Research limitations/implications: Results show AI capabilities in project cost management cluster into four dimensions of predictive, diagnostic, descriptive and prescriptive. Predictive capabilities were ranked highest, followed by diagnostic capabilities, highlighting stakeholder's emphasis on forecasting accuracy, verification, discrepancy detection in project cost control. Descriptive capabilities supporting the integration of project, market and sustainability cost data were also valued for improving information visibility and decision coordination, while prescriptive capabilities received comparatively lower rankings, indicating continued preference for AI-driven decision support over full decision automation. Originality/value: This study contributes to construction AI research by empirically structuring AI decision capabilities in project cost management and providing contextual insight from the Malaysian construction industry, where AI adoption remains emerging. Extending to resource-based view, findings inform strategic AI investment, policy direction and professional capacity development in construction sector.

The construction sector faces increasing pressure to demonstrate sustainability, with Green Building Rating Systems (GBRSs) increasingly functioning as decision-support tools that influence design prioritization, investment decisions, and certification outcomes. Sustainability in the built environment is commonly understood as the balanced consideration of environmental protection, economic viability, and social equity. However, limited empirical attention has been given to whether credit weighting structures in the rating systems reflect the balance in shaping sustainability decision priorities. This study examines whether GBRSs embed imbalanced decision logics that privilege specific sustainability dimensions over others. Using content analysis of technical manuals and normalized scoring of credit weightings, Malaysian GBRSs of Green Building Index (GBI), GreenRE, and MyCREST are compared with the international Leadership in Energy and Environmental Design (LEED) tool. Credit allocations are mapped across environmental, economic, and social pillars to examine their relative emphasis. The results reveal a pronounced environmental criteria dominance (61–85%), alongside modest attention to social aspects (11–28%) and minimal representation of economic considerations (0–5%). The findings indicate that current GBRSs embed implicit decision biases that systematically prioritize environmental performance while underrepresenting economic viability and social outcomes. By revealing how sustainability assessment frameworks structure decision priorities through credit weighting mechanisms, this study contributes to a more critical understanding of how GBRSs influence sustainability decision-making.

Purpose: The built environment (BE) sector faces growing pressure to improve sustainability performance and artificial intelligence (AI) is increasingly used to support sustainability decisions. Beyond model performance, the decision-support capacity of AI also depends on underlying data characteristics. However, existing research mainly focused on AI techniques and model performance, with limited attention to the data characteristics underpinning AI applications. This study examines how data characteristics shape AI-driven decision-making in the BE sector. Design/methodology/approach: Following PRISMA methodology, this study systematically reviews 125 studies to analyse patterns of AI-driven sustainability decision-making and the underlying data characteristics in the BE sector. Findings: AI-driven sustainability decision-making contexts concentrate in energy management and multi-dimensional sustainability trade-offs. Prediction and optimisation are dominant decision focuses, while monitoring and control remain comparatively limited. Most AI workflows rely on secondary, structured and historical datasets, as unstructured and real-time data require more extensive preprocessing. Hybrid AI approaches and diverse preprocessing techniques are adopted to address fragmented data characteristics. Recurring data limitations highlight the importance of data governance in supporting AI-driven sustainability decision-making. Originality/value: The review advances current knowledge by synthesising AI-driven sustainability decision-making alongside the underlying data characteristics. Findings show that data characteristics and preprocessing requirements influence the decision-making contexts, decision focuses and AI techniques that can be supported. The study further indicates that AI maturity depends not only on AI technique advancement, but also effective data governance, AI skills and data literacy.