Search results

This study aims to identify the enabling factors for Building Information Modeling (BIM) adoption in Vietnamese construction enterprises and uncover their interrelationships. This will help stakeholders focus on controlling and allocating resources (time, personnel, and costs) appropriately to adopt BIM and differentiate themselves from fierce competition in the architectural, engineering, construction and operations (AECO) industry.

This study first identifies and evaluates 32 enabling factors for applying BIM in the Vietnamese construction industry according to the TOE extended framework. Afterwards, a hybrid questionnaire survey using a convenient sampling method is conducted to capture stakeholders' views. The exploratory factor analysis (EFA) and the partial least squares structural equation modelling (PLS-SEM) technique are then applied to identify the constructs of the enabling factors and their interrelationships.

The study extracts six constructs that could have a significant impact on the adoption of BIM in construction enterprises, namely: technical feasibility (TF), human resources and management (HRM), company business vision (CBV), political environment (PE), economic viability (EV), and legal aspects (LA). Based on eleven proposed hypotheses, the analysis results confirm nine hypotheses and show that the HRM, TF, and CBV have the strongest effects on managers in evaluating the factors for BIM.

The results of the study fill the gap in knowledge by discovering the interrelationships among the enabling factors for BIM adoption in construction enterprises. The results might support the construction enterprises and their stakeholders in increasing the application of BIM, and digital transformation in construction industry.

This paper presents a review of the existing state-of-the-art literature on machine learning (ML) in logistics and supply chain management (LSCM) by analyzing the current literature, contemporary concepts, data and gaps and suggesting potential topics for future research.

A systematic/structured literature review in the subject discipline and a bibliometric analysis were organized. Information regarding industry involvement, geographic location, research design and methods, data analysis techniques, university, affiliation, publishers, authors, year of publications is documented. A wide collection of eight databases from 1994 to 2019 were explored using the keywords “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract. A total of 110 articles were found, and information on a chain of variables was gathered.

Over the last few decades, the application of emerging technologies has attracted significant interest all around the world. Analysis of the collected data shows that only nine literature reviews have been published in this area. Further, key findings show that 53.8 per cent of publications were closely clustered on transportation and manufacturing industries and 54.7 per cent were centred on mathematical models and simulations. Neural network is applied in 22 papers as their exclusive algorithms. Finally, the main focuses of the current literature are on prediction and optimization, where detection is contributed by only seven articles.

This review is limited to examining only academic sources available from Scopus, Elsevier, Web of Science, Emerald, JSTOR, SAGE, Springer, Taylor and Francis and Wiley which contain the words “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract.

This paper provides a systematic insight into research trends in ML in both logistics and the supply chain.

This paper aims to examine the management status quo of archaeological heritage in Vietnam seen in the case of Vuon Chuoi, a complex of Bronze Age sites located in Central Hanoi, which has been believed to be Hanoi’s first human settlement. Like other archaeological sites located in urban areas, this site has been under threat of destruction caused by land encroachment pressure. Although researchers have long waged a campaign for preservation, the dissensus among key stakeholders and the dispute over responsibility have left this site at the heart of an interminable polemic over legislation.

This research utilises a qualitative approach, and the primary data were collected throughout multiple field trips in 2019 and 2020. Several open-ended interviews were conducted with various state and nonstate actors involved in the Vuon Chuoi Complex’s management process. The discussion was also supported by analysing related legal documents retrieved from national archives and official online directories.

This paper dissects the current legislative and administrative framework applied in governing heritage in general and archaeological sites in Vietnam, in particular. The results indicate that existing flaws in Vietnam’s legal system are detectable, and the unsystematic organisation has led to deferment of the decision-making processes. Also, there is an apparent difference found in the attitude of the bodies in charge toward the treatment of listed and unlisted sites.

This research outlines that in the wake of urbanisation and industrialisation in Vietnam, a consensus among key stakeholders and an inclusive legal system are required to help preserve archaeological sites in urgent need of attention. Although several Vietnamese laws and regulations have been put into practice, they have shown critical barriers and gaps in conserving Vietnamese cultural heritage.

Terrestrial laser scanning (TLS) point clouds have been widely used in deformation measurement for structures. However, reliability and accuracy of resulting deformation estimation strongly depends on quality of each step of a workflow, which are not fully addressed. This study aims to give insight error of these steps, and results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. Thus, the main contributions of the paper are investigating point cloud registration error affecting resulting deformation estimation, identifying an appropriate segmentation method used to extract data points of a deformed surface, investigating a methodology to determine an un-deformed or a reference surface for estimating deformation, and proposing a methodology to minimize the impact of outlier, noisy data and/or mixed pixels on deformation estimation.

In practice, the quality of data point clouds and of surface extraction strongly impacts on resulting deformation estimation based on laser scanning point clouds, which can cause an incorrect decision on the state of the structure if uncertainty is available. In an effort to have more comprehensive insight into those impacts, this study addresses four issues: data errors due to data registration from multiple scanning stations (Issue 1), methods used to extract point clouds of structure surfaces (Issue 2), selection of the reference surface S_ref to measure deformation (Issue 3), and available outlier and/or mixed pixels (Issue 4). This investigation demonstrates through estimating deformation of the bridge abutment, building and an oil storage tank.

The study shows that both random sample consensus (RANSAC) and region growing–based methods [a cell-based/voxel-based region growing (CRG/VRG)] can be extracted data points of surfaces, but RANSAC is only applicable for a primary primitive surface (e.g. a plane in this study) subjected to a small deformation (case study 2 and 3) and cannot eliminate mixed pixels. On another hand, CRG and VRG impose a suitable method applied for deformed, free-form surfaces. In addition, in practice, a reference surface of a structure is mostly not available. The use of a fitting plane based on a point cloud of a current surface would cause unrealistic and inaccurate deformation because outlier data points and data points of damaged areas affect an accuracy of the fitting plane. This study would recommend the use of a reference surface determined based on a design concept/specification. A smoothing method with a spatial interval can be effectively minimize, negative impact of outlier, noisy data and/or mixed pixels on deformation estimation.

Due to difficulty in logistics, an independent measurement cannot be established to assess the deformation accuracy based on TLS data point cloud in the case studies of this research. However, common laser scanners using the time-of-flight or phase-shift principle provide point clouds with accuracy in the order of 1–6 mm, while the point clouds of triangulation scanners have sub-millimetre accuracy.

This study aims to give insight error of these steps, and the results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds.

The results of this study would provide guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. A low-cost method can be applied for deformation analysis of the structure.

Although a large amount of the studies used laser scanning to measure structure deformation in the last two decades, the methods mainly applied were to measure change between two states (or epochs) of the structure surface and focused on quantifying deformation-based TLS point clouds. Those studies proved that a laser scanner could be an alternative unit to acquire spatial information for deformation monitoring. However, there are still challenges in establishing an appropriate procedure to collect a high quality of point clouds and develop methods to interpret the point clouds to obtain reliable and accurate deformation, when uncertainty, including data quality and reference information, is available. Therefore, this study demonstrates the impact of data quality in a term of point cloud registration error, selected methods for extracting point clouds of surfaces, identifying reference information, and available outlier, noisy data and/or mixed pixels on deformation estimation.

Customer reviews on online platforms after their service experience not only provide useful information to help other customers make reasonable decisions about the hotel, but also provide a great opportunity for scholars to refresh the research directions on customer experience and satisfaction in tourism services. This paper aims to discover the key driver of international tourist satisfaction in the hospitality service and the way to effectively improve this factor, starting with the abundant online customer reviews.

A multi-perspective approach was used to compare the differences in service providers and service user’s interpretation of the factors that shape customer satisfaction. In addition to content analysis of nearly 2,500 online reviews of international tourists for 21 high-class hotels in Vietnam on Booking.com in 2019, the authors conducted in-depth interviews with the managers of 5-star hotels to show a comprehensive picture of customer satisfaction drivers, especially the staff factor.

The research results have clarified the main aspects of the staff – the most important service factor among the seven hotel service factors investigated from the customer’s perspective. On the other hand, the study also emphasized the gap between customers’ perception of the staff and the enterprise’s efforts and implementation of this factor. These findings allowed to identify aspects of staff that could be further improved to more effectively satisfy customers and provide a superior experience.

The study suggested a potential research direction using a multi-perspective approach to better understand trends and shifts in customer experience and satisfaction. Further studies could overcome the limitation of the research scope of this paper by examining different destinations or expanding the exploration of both mid-range and budget hotels.

The findings offer implications for managers to improve tourist satisfaction by developing organizational culture and mindfulness-related training programs for employees.

This study has enriched the literature of tourism and hospitality services by providing empirical evidence on the exploitation of big data sources and deepening the insights into international tourist satisfaction with hotel services.

This study aims to investigate the effect of supply chain quality management on operational performance in Vietnamese manufacturing companies.

Questionnaire survey is conducted to collect data from Vietnamese manufacturing companies in the electronic/electric industry. Collected data were analyzed by using correlation analysis and hierarchical regression analysis.

This study points out the current situation of implementing supply chain quality management practices in Vietnamese manufacturing companies which are more focused on internal quality management practices than upstream and downstream quality management practices. Correlation and regression analyses that demonstrate the significant linkage between supply chain quality management practices and operational performance are confirmed in this study. Upstream and downstream quality management are found to be the significant predictor for high performance on quality, cost and delivery.

This study has some limitations that can be addressed in future studies. The first limitation is the relatively small sample size due to lack of time and resources. These constraint can be overcome in future works by collecting more data in diverse countries and industries to re-examine the framework. Furthermore, larger sample will allow researchers to apply other statistical methods such as path analysis and structural equation modelling, which would provide interesting results. Another limitation is that the collected data in this paper mainly rely on perceptions of the respondents, which may cause individual bias in analysis. To reduce this type of bias, both subjective and objective measurement of scales, especially performance measures, can be used in future studies.

This study provides empirical evidences that practitioners can use to improve a specific type of performance, especially in the context of emerging economies. In a turbulent and unpredictable business environment, it is emphasized that quality management should be extended at supply chain level to utilize the capabilities of external partners. In Vietnamese manufacturing firms, implementation of such practices as information technology links, information sharing, supplier involvement and customer involvement would lead to high level of operational performance.

Supply chain management and quality management are two fields of study that have gained much attention of researchers. However, the concept of supply chain quality management as well as its impact on business performance have not been fully studied, especially in context of a developing country. Through extensive literature review, this study proposes and tests an analytical framework that characterizes supply chain quality management into three elements: internal quality management, upstream quality management and downstream quality management. Subsequently, their impact on quality, cost and delivery are analyzed to provide better understanding of supply chain quality management in Vietnamese context.

COVID-19 hits every country’s health-care system and economy. There is a trend toward using automation technology in response to the COVID-19 crisis not only in developed countries but also in those with lower levels of technology development. However, current studies mainly focus on the world level, and only a few ones report deployments at the country level. The purpose of this paper is to investigate the use of automation solutions in Vietnam with locally available materials mainly in the first wave from January to July 2020.

The authors collected COVID-related automation solutions during the first wave of COVID-19 in Vietnam from January to July 2020 through a search process. The analysis and insights of a panel consisting of various disciplines (i.e. academia, health care, government, entrepreneur and media) aim at providing a clear picture of how and to what extent these solutions have been deployed.

The authors found seven groups of solutions from low to high research and development (R&D) levels deployed across the country with various funding sources. Low R&D solutions were widely spread owing to simplicity and affordability. High R&D solutions were mainly deployed in big cities. Most of the solutions were deployed during the first phases when international supply chains were limited with a significant contribution of the media. Higher R&D solutions have opportunities to be deployed in the reopening phase. However, challenges can be listed as limited interdisciplinary research teams, market demand, the local supporting industry, end-user validation and social-ethical issues.

To the authors’ best knowledge, this is the first study analyzing the use of automation technology in response to COVID-19 in Vietnam and also in a country in Southeast Asia. Lessons learned from these current deployments are useful for future emerging infectious diseases. The reality of Vietnam’s automation solutions in response to COVID-19 might be a reference for other developing countries with similar social-economic circumstances and contributes to the global picture of how different countries adopt technology to combat COVID-19.

This study aims to explore the mediating roles of self-efficacy and learning orientation in the relationship between entrepreneurship education and entrepreneurial intentions of university students in Vietnam.

The data were collected from an online survey of 1,021 university students in Vietnam. The authors conducted a hierarchical regression analysis to test the hypotheses.

The results of hierarchical regression analysis reveal that entrepreneurship education positively affects entrepreneurial intentions, and this relationship is mediated by both learning orientation and self-efficacy.

This study confirms the importance of entrepreneurship education in encouraging university students' entrepreneurial intentions.

This study offers practical implications for universities and policy makers.

This study is one of the first to empirically examine the concept of entrepreneurship education and entrepreneurial intentions in an Asia-Pacific context.

This study emphasises the significance of entrepreneurship education and its effects on university students' entrepreneurial intentions. Furthermore, the findings confirm that self-efficacy and learning orientation play an important part in explaining how entrepreneurship education relates to entrepreneurial intentions.

This study aims to point out the importance and the relevance of sustainability issues and the need for their enhancement in the contemporary model of corporate social responsibility (CSR) as the top priority for business entities. Furthermore, it also highlights ways to enhance sustainability in the contemporary CSR model in the fast fashion industry of developing countries.

Literature review has been extensively done through available databases, including ProQuest and ScienceDirect.

This study delivers a full insight into the current state of research on the CSR model and sustainability in developing countries. The prevalent corporate environmental sustainability practices of global apparel brands are identified to propose ways to enhance sustainability in the contemporary CSR model.

The growth of fast fashion industry brings the risks of unsustainability to developing countries. Thus, it is a must for the garment and textile companies to enhance a sustainable CSR model through promoting sustainable leadership and integrating corporate culture.

To the best of author’s knowledge, this study is the first to emphasise the role of sustainable leadership in promoting a sustainable CSR model in the fast fashion industry of developing countries.

This study simultaneously explores the nexus among formal, semiformal and informal credit markets and farm households' credit demand determinants in Vietnam.

This study uses a multistage stratified random sampling process for a survey of 648 smallholder farmers in the Red River Delta (RRD), Vietnam. The trivariate probit model (TVPM) is used to address the interdependence of farm households' credit demands in different credit markets.

The results reveal complementary relationships among two pairs of credit markets (formal versus informal and semiformal versus informal). There are dissimilarities among the determinants (household characteristics, household head's characteristics, credit history and geographic factors) of farm households' credit demands in different markets, reflecting segmentation of Vietnam credit markets.

The study's empirical findings are important for policymakers and credit providers to enhance farm households' access to credit for agriculture and to improve the operations of the three credit markets.

This is the first empirical study in Vietnam and one of few in other developing countries simultaneously exploring the determinants of credit demand in and interrelationships among all three credit markets to provide more comprehensive and accurate results.