Llewellyn C. M. Tang’s research while affiliated with The University of Hong Kong and other places

Purpose-This study evaluates the drivers that stimulate the adoption of integrated digital delivery (IDD) for sustainable construction. The study provides a comprehensive understanding of the complexities of the drivers of IDD and the means by which they contribute to the broader scheme of sustainable development in the construction industry. Design/methodology/approach-This study conducted a mixed-method systematic review in two phases to identify and examine how the drivers of IDD contribute to sustainable development goals (SDGs) in literature. A total of 386 articles were retrieved for bibliometric analysis with the R-programming language, and 57 articles were selected for further systematic analysis. Findings-Conclusively, 30 drivers were identified and categorized into six classes based on their practical commonalities. The study identified "resource development drivers" as the most prominent drivers. In addition, the study identified eight value domains stimulated by the drivers of IDD to achieve three SDGs in the construction industry. The study significantly contributes to knowledge by developing a framework of drivers of IDD to achieve SDG 9 (innovation, innovation and infrastructure), SDG 11 (sustainable cities and communities) and SDG 13 (climate change). Originality/value-This study makes a significant contribution to knowledge by being the foremost on the drivers of IDD. Findings from this study provide a grounded understanding of the intricacies of the drivers that encourage the adoption of IDD for sustainable construction. For policy and corporate decision-making, findings of the sustainability value domains would be valuable for informed policy drafting and corporate evaluations of IDD adoption to achieve sustainable developments goals 9, 11 and 13. Lastly, the study proposes a viable framework to achieve sustainable construction, which serves as a guide for further deliberation on IDD adoption in the construction industry.

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.

Purpose- As the stock price’s fluctuation is nonlinear and greatly affected by environmental and industrial factors, stock prediction is extremely complicated and has various methods. This paper aims to find the optimal prediction model for predicting the Hang Seng stock market data from six main forecasting methods: ARIMA (Auto Regressive Integrated Moving Average), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Long short-term memory (LSTM), Gated Circulation Unit (GRU) and Conv1D-LSTM. Design/methodology/approach- In this project, the authors apply the open data of Hang Seng stock’s hour-by-hour closing prices to the above methods to establish forecasting models. Among them, hourly technical indicators (such as MA, EMA, and Momentum) are used in training neural network models as features. MSE and MAE methods are utilized to evaluate the discrepancy between the predicted and actual values. Finally, the accuracy of the buying decision (purchase or sell) is tested by the yield calculated by the actual closing price and the predicted closing price, respectively. Findings- The data collected from open data of the Hang Seng dataset from September 2019 to September 2020 contained hourly stock indexes, including closing price, opening price, high price, low price, volume, and other fundamental indicators. The total amount of data was 4608 (hours)× 26 (indexes) samples. The contribution of this paper to the existing knowledge is the evidence that the LSTM model has more advantages in forecasting and purchasing decisions compared with other models limited to the Hang Seng stock market. On this basis, the in-depth analysis demonstrates that the datasets multi-dimensionality and timeliness should be considered in the prediction model design. And the proposed architecture of deep learning models in this study could be modified to an expert system coupled with several models to explore the best approach to predicting a stock market's trading trend. Originality/value- The contribution of this paper to the existing knowledge is the evidence that the LSTM model has more advantages in forecasting and purchasing decisions compared with other models limited to the Hang Seng stock market. On this basis, the in-depth analysis demonstrates that the datasets multi-dimensionality and timeliness should be considered in the prediction model design. And the proposed architecture of deep learning models in this study could be modified to an expert system coupled with several models to explore the best approach to predicting a stock market's trading trend.

Digitalization is considered a dynamic change process powered by the rapid development of innovative concepts (e.g., building information modeling) that brings substantial potential benefits to the construction industry. However, previous studies of the benefits of digitalization (BIM) were mainly based on laboratory data rather than actual practices, which compromised the reliability of the results for construction practitioners. This study investigated the impact of digitalization in EPC construction projects by integrating descriptive statistics and survey-based exploratory factor analysis (SEFA). After a detailed review of related studies and meetings with experts, ten main variables and 14 unified variables were identified. The data on these variables were collected by questionnaire surveys and the extraction of information from project documentation. A total of 62 participants from 33 EPC construction projects responded to the questionnaire survey. The SEFA results indicated that digitalization can improve both the cost and time performance, with greater potential for improvement in time performance for EPC projects. In a scenario with limited digitalization implementation, this research could motivate future researchers to develop more applied research and guidelines to achieve best practices in the domain of the digitalization of construction projects.

Purpose This paper aims to propose a space-oriented solution as an interface enabling the knowledge transfer between the building and the facilities management (FM) industry. Design/methodology/approach The research gap is explored based on practical investigations in six large-scale hospitals in China. The theory of engineering systems integration inspires the proposed solution. A practical scenario is demonstrated to explain the workflow of this solution. Findings It is founded that lagging information updates of FM systems in hospital project are one of the main reasons for inefficient and costly FM workflow. Building information modelling (BIM) model could provide accurate building information to the FM systems at the building handover stage. However, few researchers focus on the continuous information transfer solution from the BIM model to FM systems during the building in-use phase. An interface should be established for the “conversation” between the frequent changes of building and the FM systems in the post-construction period. Practical implications The information updates in three FM systems due to space changes in a hospital project is considered as a practical scenario in this paper. It is presented with the workflow and the data logic chain. Originality/value The originality of this paper is to propose a solution to integrate the space information provided by the BIM model with the parameters of particular FM systems. This solution deploys a BIM model for the FM industry. The solution could allow the FM personnel to ease operations and maintenance workflow by updating the space information in the BIM model.