Zheng, Y; Tang, L and Chau, K W (2025) BIM investment decision model (BIDM): Evaluation of features and proposal of a regression model based on the LASSO method. Journal of Financial Management of Property and Construction, 30(2), pp. 232-255. ISSN 1366-4387
Abstract
Purpose: This paper aims to develop the building information modeling (BIM) investment decision model (BIDM) for Hong Kong architecture, engineering, construction and operation (AECO) industry utilization in early BIM investment decision-making. The developed BIDM is designed to assist company leaders in measuring and amending their investment decisions and BIM strategy by considering estimators [features and net positivity (NP)] and results based on BIDM. Design/methodology/approach: This research is conducted using a mixed methodology of qualitative and quantitative analysis. The necessary indicators were collected from literature and interviews with relevant researchers, where 545 semistructured questionnaires were distributed to selected AECO company leaders and collected by the authors. The least absolute contraction and selection operator (LASSO)-based result was conducted to help company leaders. The results of the validation test validated the model based on the LASSO method and the outcomes of the p-value test also supported the significance of BIDM. Findings: More than 80 determinators were processed to conduct 19 main indicators for generating BIDM, and 6 significant main indicators on final BIDM. The data set of this research included 483 samples, which are categorized into 7 groups according to their role in an infrastructure project. Originality/value: To the best of the authors’ knowledge, this is the first LASSO-used investment decision-making model integrated with the proposal of NP in the AECO industry. The value of current knowledge is the development of BIDM, which benefits company leaders in BIM investment decision-making and commercially benefits consulting cooperators as an investment forecasting tool. BIDM will help future users make better, more dynamic investment strategies.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | building information modeling; construction/project management; investment analysis and portfolio management; machine learning |
| Index terms: | strategy, investment strategy, interview, proposal, quantitative analysis, machine learning, methodology, estimator, questionnaire, validation, investment analysis, investment decision-making, portfolio management, building information modelling, regression model, Hong Kong, forecasting, infrastructure project, project management, investment decision |
| Subjects: | research methods, Geography, cognitive psychology, financial analysis, project planning, economic analysis, management, professional development, project management theory and practice, infrastructure and transport systems, information systems, statistical analysis, data collection methods, profession, artificial intelligence, data analysis and analytics, prediction and forecasting |
| Topics: | Geographical Context, Project Management, Engineering Principles, Cost Management, Business Strategy, Research Practice, Information Management, Roles and Professions, Digital Applications |
| Descriptive scope: | 5 PCTEA |
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