Maaz, Z. N.; Jamaludin, A. F.; Mohd Rahim, F. A.; Rashidi, A. and Darmansah, N. F. (2026) Artificial intelligence capabilities in project cost management in the Malaysian construction industry: An exploratory study. Engineering, Construction and Architectural Management, pp. 1-22. ISSN 0969-9988
Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial intelligence; competitive advantage; cost management; factor analysis |
| Index terms: | Malaysia, cost management, accuracy, construction industry, forecasting, validation, exploratory study, monitoring, project cost, integration, decision support, exploratory factor analysis, competitiveness, capacity development, factor analysis, competitive advantage, government agency, resource-based view, survey, questionnaire, relative importance index, artificial intelligence, construction sector, cost data, management decision, coordination, preference, automation, dimension, methodology |
| Subjects: | economics, research methods, industry analysis, management, control systems, administrative law, decision-making and reasoning, organizational analysis, statistical analysis, health monitoring assessment and metrics, Geography, accounting and finance, artificial intelligence, automation and robotics, professional development, social and economic development, prediction and forecasting, decision analysis, risk assessment, data collection methods, market analysis |
| Topics: | Digital Applications, Health and Safety, Business Strategy, Cost Management, Geographical Context, Human Resources, Organizational Design, Research Practice, Site Management, Information Management, Legal Issues, Risk Management |
| 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