The construction industry is a significant contributor towards global environmental degradation and resource depletion, with developing economies facing unique challenges in adopting sustainable construction practices. This systematic review aims to investigate the gap in sustainable construction implementation among global counterparts. The study utilizes the P5 (People, Planet, Prosperity, Process, Products) Standard as a framework for evaluating sustainable construction project management based on environmental, social, and economic targets. A Systematic Literature Review from a pool of 994 Sustainable Construction Project Management (SCPM) papers is conducted utilizing the PRISMA methodology. Through rigorous Identification, Screening, and Eligibility Verification, an analysis is synthesized from 44 relevant literature discussing SCPM Implementations worldwide. The results highlight significant challenges in three main categories: environmental, social, and economic impacts. Social impacts are found as the most extensively researched, while environmental and economic impacts are less studied. Further analysis reveals that social impacts are a major concern in sustainable construction, with numerous studies addressing labor practices and societal well-being. However, there is a notable gap in research on human rights within the construction industry. Environmental impacts, such as resource utilization, energy consumption, and pollution, are less frequently addressed, indicating a need for more focused studies in these areas. Economic impacts, including local economic impact and business agility, are further substantially underrepresented in the literature, suggesting that economic viability is a critical yet underexplored aspect of sustainable construction. The findings underscore the need for further research in these areas to address the implementation challenges of sustainable project management effectively. This research contributes towards the overall research of global sustainable construction through the utilization of the P5 Standards as a new lens of determining sustainability performance for construction projects worldwide.

This study investigates the escalating complexity and unpredictability of global supply chains, with a particular emphasis on resilience in the agricultural sector of Antioquia, Colombia. The aim of the study is to identify and analyze the dynamic capabilities, specifically flexibility and adaptability that significantly enhance resilience within agri-food supply chains. Given the sector’s vulnerability to external disruptions, such as climate change and economic volatility, a thorough understanding of these capabilities is imperative for the formulation of effective risk management strategies. This research is essential to provide empirical insights that can inform stakeholders on fortifying their supply chains, thereby contributing to enhanced competitiveness and sustainability. By presenting a comprehensive framework for evaluating dynamic capabilities, this study not only addresses existing gaps in the literature but also offers practical recommendations aimed at bolstering resilience in the agricultural sector.

This study aims to identify the causes of delays in public construction projects in Thailand, a developing country. Increasing construction durations lead to higher costs, making it essential to pinpoint the causes of these delays. The research analyzed 30 public construction projects that encountered delays. Delay causes were categorized into four groups: contractor-related, client-related, supervisor-related, and external factors. A questionnaire was used to survey these causes, and the Relative Importance Index (RII) method was employed to prioritize them. The findings revealed that the primary cause of delays was contractor-related financial issues, such as cash flow problems, with an RII of 0.777 and a weighted value of 84.44%. The second most significant cause was labor issues, such as a shortage of workers during the harvest season or festivals, with an RII of 0.773. Additionally, various algorithms were used to compare the Relative Importance Index (RII) and four machine learning methods: Decision Tree (DT), Deep Learning, Neural Network, and Naïve Bayes. The Deep Learning model proved to be the most effective baseline model, achieving a 90.79% accuracy rate in identifying contractor-related financial issues as a cause of construction delays. This was followed by the Neural Network model, which had an accuracy rate of 90.26%. The Decision Tree model had an accuracy rate of 85.26%. The RII values ranged from 68.68% for the Naïve Bayes model to 77.70% for the highest RII model. The research results indicate that contractor financial liquidity and costs significantly impact construction operations, which public agencies must consider. Additionally, the availability of contractor labor is crucial for the continuity of projects. The accuracy and reliability of the data obtained using advanced data mining techniques demonstrate the effectiveness of these results. This can be efficiently utilized by stakeholders involved in construction projects in Thailand to enhance construction project management.

This study investigates the impact of the Belt and Road Initiative (BRI) on the construction sector in Southeast Asia, focusing on Thailand, Malaysia, and Cambodia. Qualitative research approach is used to analyze the implications of Chinese investments in these countries, exploring both the opportunities and challenges faced by Chinese investors. Key research questions address the resilience of the construction sector, the obstacles encountered by investors, and the influence of policy on the construction business. Through interviews with CEOs and senior managers of major construction companies and a review of relevant documents, the study uncovers the economic and geopolitical motivations behind China’s BRI strategy. The findings reveal significant insights into the benefits and drawbacks of BRI financing, providing recommendations for overcoming challenges and leveraging future opportunities in Southeast Asian construction sectors.

The aim was to examine the relationships between selected demographic and psychographic factors and consumers' willingness to accept content generated by advanced technological innovations (AIGC) in social infrastructure. The sample consisted of 1,308 respondents. Spearman's correlation coefficient was used to examine the relationships between ordinal variables. To assess the differences between groups of respondents, a one-way analysis of variance was used, during which multiple linear regression analysis was used to confirm the predictive power of awareness and experience in relation to AI-generated content in relation to the tendency to accept such content. The study confirmed a statistically significant but weak negative relationship between the age of respondents and their willingness to accept AIGC, with younger age groups showing a slightly higher rate of acceptance. Respondents' attitudes toward the use of personal data through AI and their overall awareness of technological trends had a more significant impact on acceptance. The findings show that respondents who are open to data collection through AI technologies show a significantly higher level of acceptance of automatically generated content. Similarly, respondents who positively evaluate the current quality of AIGC have higher expectations for the future transformation of marketing strategies and media practices. The decisive factors in the social infrastructure for the acceptance of AIGC are not so much the age of the respondents, but rather their awareness, technological literacy, and level of trust in the technology itself. The study therefore recommends increasing transparency and public awareness about the use of AI in marketing and media practices in order to strengthen consumer confidence in automated content.

LEED (Leadership in Energy and Environmental Design) is a certification program for quantitatively assessing the qualifications of homes, non-residential buildings, or neighborhoods in terms of sustainability. LEED is supported by the U.S. Green Building Council (USGBC), a nonprofit membership-based organization. Worldwide, thousands of projects received one of the four levels of LEED certification. One of the five rating systems (or specialties) covered by LEED is the Building Design and Construction (BD + C), representing non-residential buildings. This rating system is further divided into eight adaptations. The adaptation (New Construction and Major Renovation) or NC applies to newly constructed projects as well as those going through a major renovation. The NC adaptation has six major credit categories, in addition to three minor ones. The nine credit categories together have a total of 110 attainable points. The Energy and Atmosphere (EA) credit category is the dominant one in the NC adaptation, with 33 attainable points under it. This important credit category addresses the topics of commissioning, energy consumption records, energy efficiency, use of refrigerants, utilization of onsite or offsite renewable energy, and real-time electric load management. This study aims to highlight some differences in the EA credit category for LEED BD + C:NC rating system as it evolved from version 4 (LEED v4, 2013) to version 4.1 (LEED v4.1, 2019). For example, the updated version 4.1 includes a metric for greenhouse gas reduction. Also, the updated version 4.1 no longer permits hydrochlorofluorocarbon (HFC) refrigerants in new heating, ventilating, air-conditioning, and refrigeration systems (HVAC & R). In addition, the updated version 4.1 classifies renewable energy into three tiers, differentiating between onsite, new-asset offsite, and old-asset offsite types.