Search results

In the apparel industry, suppliers play a significant role, directly affecting customer service levels and business profits. Integrating sustainable requirements into supplier selection not only aligns with global environmental goals but also enhances business performance, social responsibility and overall industry well-being. This study aims to design a multi-criteria model to evaluate and select the most sustainable suppliers in the fashion industry, trying to balance the conflicts in the set of sustainable development criteria.

The integration of analytic hierarchy process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) offers advantages in sustainable supplier selection within the apparel industry. The AHP plays a crucial role in engaging multiple decision-makers with conflicting criteria to reach a consensus during the decision-making process. Conversely, the TOPSIS is used to compute alternative ratings. By simultaneously determining criteria weights and incorporating stakeholder preferences, hybrid models enhance decision-making strength and overcome limitations observed in classical multi-criteria decision-making techniques.

This research identified and classified 16 critical criteria impacting the selection of apparel industry suppliers, focusing on sustainable development. The criteria were weighted, providing a robust statistical foundation for the selection model. The results indicated that the most influential criteria were staff training, production capability, flexibility and practice of recycling. The proposed sustainable supplier selection model explains to decision-makers how criteria influence supplier ranking results compared to traditional models, supporting managers in making informed and sustainable supply chain decisions through continuous updates and enhancements.

This research provides new insight into the weighted impact of factors related to sustainable supplier selection in the apparel industry. The combination of a precise process and scientific knowledge will improve the quality of sustainable supplier selection.

This chapter provides information on the development of Vietnamese education under the influence of global forces based on the analysis of relevant education research and policies using Wolhuter’s frameworks. In the process of coming up with ways to develop education in the face of different influences of globalization, besides having reactions with patterns commonly found in countries around the world, Vietnam also has responses that reflect its own political, sociocultural and economic characteristics. The state still plays a controlling role in education at all levels and many culture-related features that have existed throughout the country’s history have hardly changed, namely aspects related to teachers, learners and teaching and learning methods. To sustain its education in the globalized era, Vietnam must make more efforts in various aspects such as the link between education and employment, the logic of education objectives, the feasibility and appropriateness of curricula, quality of education, especially of higher education and equality in education for underprivileged groups.

Innovation allows firms to gain or maintain their competitive advantages in both domestic and international markets. However, the findings on the association between innovation and export vary depending on countries, samples, time, variables and methods used for empirical analysis. The purpose of this study is to examine the influence of different types of innovation on export in small- and medium-sized enterprises (SMEs) in a developing economy in the context of global integration.

For empirical analysis, the authors use a sample of manufacturing SMEs in Vietnam in 2013 and 2015. Because the firms do not engage in innovation at random in the research sample, the authors use propensity score matching to account for self-selection bias. The authors also use different matching estimators to ensure robust results.

Innovation activities are found to be positively associated with the probability of engaging in export activities. The effects are mainly contributed by the adoption of new technology or process.

This research underscores the importance of public interventions and policies designed to promote innovation and export in the SMEs.

To the best of the authors’ knowledge, this research is one of the early attempts to examine the association between different types of innovation and export in SMEs in a developing economy.

The COVID-19 lockdown has forced many organizations and employees to work from home. In such uncertain and unprecedented context, it is crucial for organizations to stimulate their employees’ creativity to adapt to new working environment and thus to sustain and improve organizational performance. This paper aims to examine how to stimulate employees’ creativity by focusing on their online knowledge sharing (OKS) behaviors, their use of online platforms and their organizations’ innovation climate in a working from home (WFH) context because of the lockdown. For empirical analysis, this research uses data from Vietnam – a developing country in the Southeast Asia.

Data are collected from employees working in Ho Chi Minh City but WFH during the COVID-19 lockdown. Structural equation models are used for analyzing the data.

Online platform use and organizational innovation climate are positively associated with creativity directly and indirectly via the mediating roles of internal and external OKS.

This research provides policymakers, organizational leaders and managers with an important evidence on how to stimulate creativity by emphasizing the roles of knowledge sharing, online platforms and innovation climate. Accordingly, relevant practical implications are also drawn to sustain or improve organizational performance in the context of WFH context because of COVID-19 lockdown. This research also contributes to knowledge management literature by providing an evidence on the relationships between online platform use, organizational innovation climate, OKS and creativity.

This research is among the early attempts that explore the associations between employees’ use of online platforms, their organizations’ innovation climate, their internal and external OKS behaviors and their creativity in the context of WFH because of a lockdown.

This study aims to develop a strategic framework for the success of coastal urban projects in Vietnam, which is one of the Asia Pacific countries significantly affected by climate change.

A questionnaire was used to collect data from practitioners in Vietnam. Principal component analysis (PCA) technique was used to identify critical success factors (CSFs) of coastal urban projects. A strategy map for the success of coastal urban projects was also proposed using the balanced scorecard (BSC) method.

This study identified 41 project success factors that could contribute to project success, and thence, extracted 11 CSFs for coastal urban projects using the PCA technique. In addition, 11 key performance indicators (KPIs) for coastal urban projects were listed and their linking with project success factors and CSFs was explored. Furthermore, a strategy map for the success of coastal urban projects was proposed using the BSC method. The strategy map included five perspectives: learning and growth, internal processes, social and environmental performance, financial performance, and stakeholders' satisfaction.

This study identified 11 CSFs for coastal urban projects and proposed a strategy map for the success of coastal urban projects.

Reconstruction of old buildings is often necessary after prolonged usage, but these projects encounter various complexities, leading to their failure. However, there is a lack of studies that focus on investigating the complexity of old building reconstruction initiatives. Thus, this study aims to examine the complexity of reconstruction projects for old buildings in Vietnamese urban areas.

Through a literature review and discussions with experienced practitioners, 12 significant complexity factors were identified. The study then utilized the fuzzy decision-making trial and evaluation laboratory (DEMATEL) technique to analyze the interrelationships among these complexity factors.

The findings revealed intricate interdependencies among the complexity factors, visualized in a complexity map. The map highlighted the critical complexities of site compensation, clearance and the long project duration. Furthermore, a causal diagram categorized the complexity factors into net-effect and net-cause groups.

By examining the relationships and interactions among these factors, the study emphasizes the interconnected nature of complex systems and the influence of one factor on others. Therefore, decision-makers and stakeholders involved in reconstructing old buildings are suggested to possess a comprehensive understanding of these interdependencies to ensure effective decision-making.

The purpose of this paper is twofold: to explain access to formal and informal credit in agriculture of Vietnam; and to compare the effectiveness between regular econometrics and machine learning techniques.

The multinomial logit (MNL) regression model and the random forest (RF) technique are employed for comparison purposes. To avoid heteroskedasticity, the robust covariance matrix is computed to estimate the sandwich estimator which in turn provides an asymptotic covariance matrix for biased estimators. Additionally, multicollinearity is tested among independent variables with variance inflation factors less than 3. Adequacy approach and sensitivity analysis are used to determine relevant levels of predictors. For models comparison, statistical evaluation metrics including Cohen’s κ, mean absolute error, root mean squared error and relative absolute error are employed.

The discrepancy between sensitivity analysis and adequacy approach revealed that MNL is more compatible for explaining determinants of credit participation. Due to insignificant differences in the evaluation metrics between models, the winner of choice is undetermined. Among other determinants, collateral, farmsize, income, procedure, literacy and all risk variables stand out to be critical factors when deciding borrowing schemes. While financially literate farmers tend to acquire loans from both sources, borrowing decisions against different risk sources depend on risk type and famers’ own desire to borrow.

Results of the MNL model are more consistent with literatures, which reinforce the role of collateral in the local credit scheme. Besides, financial literacy and farmers’ perception on different risk sources also influence how farmers’ borrowing strategies vary among sources.

Vietnam is firmly committed to attaining the Sustainable Development Goals articulated in the United Nations 2030 Sustainable Development Agenda. Goal 4 concerns quality education, and target 4.3 refers to ensuring access by all men and women to quality and affordable technical, vocational and tertiary education, including university education. In 2017, the Prime Minister issued a directive that included five actions to be taken by Vietnam’s Ministry of Education and Training to achieve target 4.3 in the context of the higher education sector. This chapter provides an opportunity to review some challenges the Ministry faces in implementing the five actions specified.

In the early stage of the COVID-19 pandemic, Vietnam imposed many drastic restrictions to curb the outbreak of this virus. Such restrictions interrupted the normal functioning of various economic sectors, including agriculture. This research examined disruptions to agricultural activities, income loss and perceived food insecurity among farm households during the pandemic, and then explored the relationships among these economic factors.

Household data from Vietnam and Generalized Structural Equation Model (GSEM) were used for empirical analysis.

Descriptive analyses found that only a small proportion of farm households suffered from the COVID-19 disruptions to their agricultural activities, a large percentage experienced income loss, and a medium number were worried about their food insecurity. GSEM results also revealed that the COVID-19 disruptions to agricultural activities significantly increased the likelihood of worrying about food insecurity, mediated by income loss.

Due to data limitations, the authors could not use better indicators to define and measure the variables of interest (e.g. COVID-19 disruptions to agricultural activities, income loss and food insecurity). Another similar concern was that our models did not account for unobservables, causing some estimation biases.

This research is among the first attempts that examined the direct and indirect (mediated by income loss) effects of the COVID-19 disruptions to agricultural activities on food insecurity.

This paper proposes an approach to identify categorical influencers (i.e. influencers is the person who is active in the targeted categories) in social media channels. Categorical influencers are important for media marketing but to automatically detect them remains a challenge.

We deployed the emerging deep learning approaches. Precisely, we used word embedding to encode semantic information of words occurring in the common microtext of social media and used variational autoencoder (VAE) to approximate the topic modeling process, through which the active categories of influencers are automatically detected. We developed a system known as Categorical Influencer Detection (CID) to realize those ideas.

The approach of using VAE to simulate the Latent Dirichlet Allocation (LDA) process can effectively handle the task of topic modeling on the vast dataset of microtext on social media channels.

This work has two major contributions. The first one is the detection of topics on microtexts using deep learning approach. The second is the identification of categorical influencers in social media.

This work can help brands to do digital marketing on social media effectively by approaching appropriate influencers. A real case study is given to illustrate it.

In this paper, we discuss an approach to automatically identify the active categories of influencers by performing topic detection from the microtext related to the influencers in social media channels. To do so, we use deep learning to approximate the topic modeling process of the conventional approaches (such as LDA).