Building information modelling (BIM) has been considered an effective and efficient tool among stakeholders of the construction sector as it delivers prominent results. Hence, this work aimed to determine the intangible benefits of BIM in construction projects. Various statistical analyses were performed to identify the most critical intangible benefits of BIM required for justification. To rank the intangible benefits of BIM, the relative importance index was adopted. Then, the rotated component matrix was conducted to correlate each intangible benefit to its estimated components. In the last phase of the research, interviews were conducted to validate and confirm the critical intangible benefits. Five critical intangible benefits were identified: better understanding of design, better information received and given, improved communication through visualization, reduce design error and improved accuracy of drawings. By knowing these five critical benefits, clients should prioritise these benefits in their justification of BIM implementation.

As technologies change rapidly, improved information technology (IT) products and services are released every day throughout the world. This phenomenon gives tremendous pressure on construction managers to plan, implement and adopt new technology solution in accommodating such changes. However, looking at the tools that can be used to help in this situation, the understanding of the trends of IT change and its impact to the construction industry is essential. Even though most studies in IT change trend have been carried out across industries; however far too little attention has been paid to measure the trend of IT change in construction industry and how fast it has been invented. The purpose of this paper is to explain the implications of IT change and consequently provides an understanding on how IT is changing in construction industry, its current trends, and what are the predictions of its scenario in future construction industry. The study is based on textual reading ranging from 1996 to the first part of 2011, which was gathered from journals, literature books, specialized databases and other information sources available on the Internet. A principal finding is that even though the introduction of new technology in construction is slower than other industries, the implications towards construction industry have significant impacts. This finding provides useful information in order to develop a tool for construction organizations handling the rapid changes by tackling the issues affected by the IT change.Keywords: IT changes, IT trends, IT investment, construction industry, information technology.

Corporate social responsibility (CSR) has become a vital component in addressing environmental, social, and economic challenges. In the context of the construction industry, due to the widespread presence of irresponsible practices, CSR implementation studies have gained significant attention, particularly in developing countries. This study consists of a systematic literature review of 58 articles published between 2018 and 2024, seeking to explore the internal and external factors affecting CSR implementation in emerging construction nations. The analysis employs the PESTEL framework, which refers to external impact factors, including political, economic, social, technological, environmental, and legal aspects. Additionally, the SWOT framework is utilized to identify the strength, weaknesses, opportunities, and threats. The Analytic Hierarchy Process (AHP) is then applied to prioritize the identified factors. The results demonstrate that institutional pressures and economic progress are the most influential external opportunities. Internal weaknesses, including subcontracting and quality challenges and financial constraints of SMEs, are significant barriers. This study contributes to the CSR literature by integrating external macro-environmental factors and internal organizational dynamics, offering a strategic and actionable framework for the advancement of CSR implementation in the construction industries of developing countries.

The Construction Industry faces a significant gap in the managerial competencies of Construction Project Managers (CPM) towards project performance. The identification of a competent project manager is essential as it contributes towards economic growth and the achievement of the current Sustainable Development Goals (SDGs). This research aims to determine the core managerial competencies from the CPM’s perspective. Specifically, to achieve the aim of this research it comes with two (2) objectives, which are to identify the managerial competencies and to determine the core competencies required by CPM. The research method adopted was a quantitative approach via structured questionnaire survey. Based on research findings, it establishes the core managerial competencies which are knowledge, skills, and attitude as a crucial barometer for CPM led to a positive impact on the help of Machine Learning (ML) toward project performance. The contribution of this research is imperative and can serve as reference benchmarking for CPM.

Ensuring the identification of building attributes is the primary task in developing a machine learning cost estimation model. However, the existing research on building attributes has the following shortcomings: it struggles to categorize building characteristics according to various cost types, and the suggested sets of attributes do not clearly establish measurement standards for these qualities. To address these issues, this study aims to select a set of building attributes suitable for conceptual cost estimation and establishment of measurement standards. Through a two-round process of focused group discussions, this research ultimately identified 13 building attributes that can be collected before the completion of building design. These attributes serve as a basis for assessing completed building projects during the model development phase and for evaluating new projects during the model application phase. This study provides a foundational framework for the development of conceptual cost estimation models, ultimately enhancing the accuracy of machine learning cost estimation models.