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This paper aims to explore factors influencing apartment price volatility in the two biggest cities in Vietnam, Hanoi and Ho Chi Minh City.

The study uses the supply and demand approach and provides a literature review of previous studies to develop four main hypotheses using four determinants of apartment price volatility in Vietnam: gross domestic product (GDP), inflation rate, lending interest rate and construction cost. Subsequently, the Vector Error Correction Model (VECM) is used to analyze a monthly data sample of 117.

The research highlights the important role of construction costs in apartment price volatility in the two largest cities. Moreover, there are significant differences in how all four determinants affect apartment price volatility in the two cities. In addition, there is a long-run relationship between the determinants and apartment price volatility in both Hanoi and Ho Chi Minh City.

Limitations related to data transparency of the real estate industry in Vietnam lead to three main limitations of this paper, including: this paper only collects a sample of 117 valid monthly observations; apartment price volatility is calculated by changes in the apartment price index instead of apartment price standard deviation; and this paper is limited by only four determinants, those being GDP, inflation rate, lending interest rate and construction cost.

The study provides evidence of differences in how the above determinants affect apartment price volatility in Hanoi and Ho Chi Minh City, which helps investors and policymakers to make informed decisions relating to the real estate market in the two biggest cities in Vietnam.

This paper makes several recommendations to policymakers and investors in Vietnam to ensure a stable real estate market, contributing to the stability of the national economy.

This paper provides a new approach using VECM to analyze both long-run and short-run relationships between macroeconomic and sectoral independent variables and apartment price volatility in the two biggest cities in Vietnam.

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.

Based on the model of Big-Five personality traits and theories of person–environment interaction, this study aims to investigate the moderating effects of personality traits on innovativeness through knowledge sharing (KS).

A sample of 318 Vietnamese employees was collected. The hypothesized model was tested by using partial least squares structural equation modelling.

The results indicate that extraversion, agreeableness, conscientiousness and openness to experience have relationships with innovativeness through the mediating effect of KS. Furthermore, transformational leadership (TL) mitigates the positive relationship between agreeableness and openness to experience and innovativeness.

Based on the research results, the authors suggest several practical implications for enhancing employees' innovative organizational behaviours. Transformational leaders should be aware of and control the relationships with employees high in agreeableness and open to experience to ensure that employees' innovativeness can be freely developed.

This research systematically investigates the effect of each personality on employees' innovativeness. Furthermore, this study contributes to the leadership literature by suggesting the dark side of TL that can negatively influence the innovative ability of employees with certain personality traits.

The reverse logistics supply chain (RLSC) for demolition waste management (DWM) is a complex process that inherits significant interdependent risks. However, studies on the RLSC have not explicitly identified the risks of its inter-relationships by disentangling their effects on operational performance. Accordingly, this paper aims to identify and assess the inter-dependencies of the risks in the RLSC to improve quality-related operational performance.

A sequential exploratory mixed-method research approach, consisting of qualitative and quantitative methods, was employed. The qualitative approach involved 25 semi-structured interviews, whereas the 18 subsequent structured interviews were conducted with stakeholders in the entire RLSC as part of the quantitative method. These were used to identify the cause and effect relationships of the identified risks. The data were analysed using thematic analysis, and the Bayesian belief network (BBN) technique was used to develop a conceptual risk model.

In total 20 risks in four RLSC sub-processes, namely, dismantling and on-site process, off-site resource recovery process, marketing of secondary products and residue disposal, emerged. Among cause and effect relationships of identified risk factors, inferior quality of secondary products was found to have the strongest relationship with customer satisfaction. Under-pricing of dismantling job, improper landfill operations and inadequacy of landfill levy are independent risks that initiate other risks down the supply chain. The aggregate effects of these risks affect customer dissatisfaction of the end-product, as well as health and safety risks in on-site, off-site and residue disposal.

This study only identify the cause and effect relationships of the identified risks within the RLSC for DWM operations. It has not targeted a specific construction material or any secondary production, which could be practiced through a case study in future research.

The results encourage the investigation of RLSC process quality by maintaining the relationship between recycler and customer to enable a safe workplace environment. Hence, the role of relevant practitioners and government is inseparable in supporting decision-making. Future research could discuss the impact of those inter-related risks in relation to time- or cost-related operational performance criteria.

This study contributes to the field through presenting the first major study on the identification and assessment of the inter-dependencies of the risks in the RLSC in South Australia. The RLSC process mapping had been identified as a tactical and operational management approach. However, the risk management process is a strategic management approach. Therefore, the integration of both process mapping and the risk management approaches in one platform is germane to construction management research.

This study aims to determine barriers to innovation and to develop a quantitative model for the barrier to innovation in Vietnamese construction organizations of different sizes.

A literature review and discussions with experienced practitioners were implemented to determine barriers to innovation in construction organizations. The rank-based non-parametric test analyzed collected data from a questionnaire survey to examine if there were significant differences between the three groups of organizations, including small, medium and large construction organizations. The fuzzy synthetic evaluation (FSE) technique was employed to develop barrier indexes (BIs) for organizations of different sizes in Vietnam.

The findings showed 17 barriers to innovation which were categorized into four groups, including organizational, human resources, economic and market barriers. Statistical analysis revealed significant differences regarding barriers to innovation between small, medium and large construction organizations in Vietnam. The post hoc test highlighted barriers to innovation differently separated into two groups: SMEs and large construction organizations. The FSE analysis integrated the identified barriers into the comprehensive BIs for SMEs and large construction organizations. The FSE analysis illustrated that the organizational barrier is the most critical barrier for SMEs. On the other hand, the market barrier received the most significant attention in large construction organizations.

This research is one of the first integrated barriers to innovation into a comprehensive formulation. The indexes provide the decision-makers with a practical and reliable tool to evaluate barriers to innovation in construction organizations of different sizes.