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This study aims to identify key knowledge enabling factors (KEFs) which can enable construction companies to improve various organizational effectiveness outcomes (OEOs).

Using a questionnaire, data are collected from construction companies in Vietnam. Mean score method is used to calculate the mean values of KEFs. In addition, regression analysis is used to identify KEFs which significantly affect OEOs.

A list of 32 KEFs, whose ranking orders of importance are provided according to different types of construction companies, is presented. In addition, different lists of specific KEFs which could significantly affect different OEOs are identified. Furthermore, seven key KEFs which could have a significant impact on many OEOs are highlighted.

The findings of this study could help construction companies to know the controllable KEFs, on which they should focus more. Hence, they could perform these KEFs properly to improve various aspects of organizational effectiveness.

This study identifies 32 KEFs and 10 OEOs specifically for knowledge management in construction companies. This study also provides construction companies with a better understanding of the impact of KEFs on various aspects of organizational effectiveness. Hence, they could develop effective KEFs-based management strategies to enhance various aspects of organizational effectiveness.

This study aims to focus on exploring the construction productivity of building projects under the influence of potential factors. The three primary purposes are (1) determining critical factors affecting construction productivity; (2) identifying causal relationship and occurrence probability of these factors to develop a Bayesian network (BN) model; and (3) validating the accuracy of predictions from the proposed BN model via a case study.

A conceptual framework that includes three performance stages was used. Twenty-two possible factors were screened from a comprehensive literature review and evaluated through expert opinions. Data were collected using a structured questionnaire-based survey and case-study-based survey. The sampling methods were based on non-probability sampling.

Worker characteristic-related factors significantly affect labour productivity for a construction task. Construction productivity is dominated by the working frequency of workers (overtime), complexity of the task, level of technology application and accidents. Labour productivity is defined as nearly 50% of the baseline productivity using the BN model created by the caut 2sal relationship and probability of factors. The prediction error of the BN model was 6.6%, 10.0% and 9.3% for formwork (m²/h), reinforcing steel (ton/h) and concrete (m³/h), respectively.

The evaluation or prediction of productivity performance has become a necessary topic for research and practice.

Managers and practitioners in the construction sector can utilise the outcome of this study to create good productivity management policies for their prospective projects.

Worker-related characteristics are dominant among critical factors affecting labour productivity for a construction task; the proposed BN-based predictive model is built based on these critical factors. The BN approach is highly accurate for construction productivity prediction. The findings of this study can fill gaps in the construction management body of knowledge when modelling construction productivity under the effects of multiple factors and using a simple probabilistic graphic tool.

This paper aims to propose a comprehensive framework for prioritizing complexity criteria. The framework was validated by applying in infrastructure international development (ID) project as a case study.

A literature review highlighted the limitations of existing complexity prioritization methods. Then, a combination of the fuzzy decision-making trial and evaluation laboratory (DEMATEL) and fuzzy analytic network process (ANP) was employed as a foundation to develop a three-stage complexity prioritization framework. Focus group discussion and questionnaire surveys were used to practically test the framework in the infrastructure ID projects.

The three-stage complexity prioritization framework was validated to be reliable and feasible. The findings showed ability of consultants, scope uncertainties, site compensation and clearance, communication between stakeholders, administrative procedure and project duration were the most significant complexity criteria of ID projects in the Vietnamese context.

The framework is a robust tool that enables the researchers to grasp the interaction of complexity criteria for complexity prioritization. Later studies can apply the proposed framework, with some minor revisions, to assess the interaction of criteria in other research topics in, and beyond, project complexity. Results of the case study suggest project stakeholders focusing on complex interactions among criteria to reduce project complexity.

This study contributes to the body of knowledge by providing a comprehensive complexity prioritization framework that grasps the interrelationship of complexity criteria. For stakeholders of ID projects, the findings provide insightful perspectives to understand complexity, which can help to enhance project performance.

The purpose of this study is to identify driving factors and a quantitative model for implementing public-private partnership (PPP) projects in Ethiopia as a case study in emerging economies.

A review of the literature and semi-structured interviews were carried out to identify driving factors affecting the implementation of PPP projects in the Ethiopian context. Data were collected through a questionnaire survey within three months, with 59 validated responses; mean score technique and factor analysis were conducted. The fuzzy synthetic evaluation (FSE) method was applied to develop a driving index (DI) for implementing infrastructure PPP projects. Finally, a comparative analysis of top-five drivers was conducted between four emerging economies.

Mean values show that all driving variables are important. Through factor analysis, 22 identified driving variables were grouped into six factors, namely, benefit for public and private sectors, attention of private sector, social development, cost reduction, management ability of public sector and ability of private sector. The FSE method constructs a DI and shows that benefit for public and private sectors is the most crucial factor for PPP implementation in the context of Ethiopia. Apart from this, most driving forces for adopting PPP projects in these countries related to financial problems.

This study is one of the first integrate driving factors for PPP implementation. The index provides the decision-makers with a comprehensive tool to assess the needs of PPP implementation.

The purpose of this paper is to identify risk patterns of road and bridge projects in Vietnam, where the construction market is emerging but attractive to construction organizations, especially foreign companies.

Using a questionnaire, experienced practitioners of various contractors were interviewed to collect risk-related data in terms of actual likelihood and impact from road and bridge construction projects in Vietnam. Using the collected data of actual likelihood and impact, the specific probability and impact of risk factors were determined for different types of road and bridge projects, including small and medium type, big type, government-funding type, and other-funding type (e.g. official development assistance funds, public-private partnership).

The results of analysis indicate the specific probability and impact of risk factors in four risk themes, including contractor-related, project-related, owner-related, and external risks. Actual risk patterns for different types of road and bridge projects in Vietnam were identified.

The identification of actual risk patterns could help practitioners to know which risk factors are severe in frequency and/or impact. Hence, they could establish proper strategies to manage risk-related problems of road and bridge projects, in which they are directly involved.

The findings of this study could provide construction companies, especially foreign companies, with a better understanding of real risk panorama in Vietnamese road and bridge construction. Hence, they could make effective improvements on risk management of road and bridge projects in Vietnam.

The purpose of this paper is twofold: first, to develop a performance evaluation framework for construction and demolition waste management (CDWM); second, to investigate feasible and effective strategies to improve the CDWM performance.

A review of the literature highlighted a lack of comprehensive research to evaluate CDWM performance of key project stakeholders, like owners, contractors and consultants. After the identification of 22 performance variables through a pilot study, a first questionnaire survey was conducted to investigate the views of respondents toward CDWM performance. The 132 responses were analyzed using factor analysis to determine specific CDWM performance factors, which formed a conceptual performance evaluation framework of CDWM. Furthermore, a practical index (PI) was proposed to integrate the feasibility and effectiveness of CDWM strategies. The values of PI were employed to prioritize CDWM strategies from data collected in a second questionnaire survey.

The validated results from factor analysis revealed that the conceptual performance evaluation framework of CDWM consists of six factors; and the attitude toward CDWM emerged as the foremost critical factor. The prioritization of PI values indicated that raising CDWM awareness among construction stakeholders was the most feasible and effective strategy for CDWM.

This CDWM performance evaluation framework is one of the first to holistically evaluate CDWM from key stakeholder perspectives. In addition, the PI firstly enables quantitative integration of the feasibility and effectiveness of CDWM strategies.

The purpose of this paper is to evaluate the waste occurrence level in the construction industry. It includes: first, identifying the mean value of frequency of waste occurrence according to respondents’ characteristics; second, identifying the main predictive factors for waste occurrence based on latent relationships between initial waste factors; and third, identifying the waste occurrence-level indicator (WOLI) for the construction industry based on the main waste measurement factors.

A total of 19 waste factors were sorted from the literature review. A structured questionnaire was adopted to carry out the survey. The respondents are professionals who have much experience in construction and management of project. Shapiro-Wilk test of normality, Levene’s test, ANOVA test, and factor analysis technique were used to analyze the collected data.

Frequency of waste occurrence in construction projects is quite high. There was no statistically and practically significant difference in means for waste occurrence between selected population categories. Based on factor analysis technique, there were five principal components extracted with 56.7 percent of total variance. The WOLI in the construction industry was found as 61.55 per the scale of 100.

The non-probability sampling was applied to collect data because of several certain limitations and difficulties. The number of data sets is relatively small. This study has only examined the frequency of waste occurrence without quantitative information.

This is another study of waste factors in the construction industry, which is different from traditional waste studies.

The contribution of this study to the practical project management is that a proposed evaluation sheet for WOLI could be applied for any construction firm.

Global value changes continued to expand until the late 2000s. On the other hand, regional value chains have formed around major regional hubs due to the expansion of domestic demand in emerging economies, such as China, and strengthened trade protectionism since the global financial crisis. Such changes lead to the reorganisation of value chains, focusing on domestic markets (reshoring) or neighbouring countries (nearshoring). In particular, the importance of supply chain risk management has been highlighted following disruptions to the supply network due to the COVID-19 outbreak in December 2019. In this regard, major countries such as the USA and the EU are rapidly shifting to regional value chains for stable and sustainable production, rather than primarily aiming for production efficiency targeted at reducing costs. Industries in particular are more exposed to such supply chain risks under the existing structure and it now has become extremely important for businesses to take reaction to such risks. This is especially important for major industries in a country such as automobile or semiconductor manufacturing industries in South Korea. The aim of this study, therefore, is to establish the basis for the simultaneous growth of ports and linked industries by examining the existing structure of the global value chain for the automotive industry, which has a strong presence in South Korea’s domestic economy. In this regard, this research carries out a supply chain analysis focusing on the imports and exports of automotive parts. It also analyses the current structural risks and suggests risk management measures to secure a stable supply chain.

Increasing income inequality particularly since the economic crisis of 1997 has called attention to the issue of growing educational inequality in South Korea. Although much recent research has been directed at understanding the socioeconomic gap in academic achievement, few studies have empirically examined how this gap has changed over time during the past decade in South Korea. Using nationally representative data for the most recent three cohorts (1999, 2003, and 2007) of eighth-grade South Korean students from Trends in International Mathematics and Science Study (TIMSS), this study examines trends in the relationship between socioeconomic background and student achievement. The eighth-grade TIMSS data demonstrate that the influence of socioeconomic background on student achievement has increased over time during the past decade, offering evidence of growing educational inequality in South Korea. Various factors may contribute to higher educational inequality, including the widening income gap and recent educational transformations geared toward school choice and tracking.