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This study explores how problem-based learning (PBL) programs can address Sustainable Development Goals (SDGs) via the higher education (HE) curriculum, teaching materials and relevant assessments, supporting learning at scale for HE institutions.

Employing SDGs and their indicators as the coding framework, our two-phase study evaluates the curriculum and teaching materials of seven PBL programs at a leading higher education institution (HEI). The first phase involved a content analysis to assess the degree of sustainability integration in 156 relevant courses. The second phase applied a semi-automated mapping protocol to analyze learning and teaching materials in 120 relevant courses.

The school aligns with 17 SDGs (100%), covering 94 indicators (55.62%). On average, each program within the school addresses over ten of these goals and incorporates more than 24 associated indicators. However, the study reveals an imbalance in the incorporation of SDGs, with some goals not yet deeply and comprehensively embedded in the curriculum. While there is a substantial focus on sustainability theories, the practical implications of SDGs in emerging countries, particularly through case studies and assessments, require significant enhancement.

Mapping SDGs allows HEIs to identify strengths and gaps in SDG integration, thereby improving the PBL approach to enhance student work readiness in sustainability-focused careers.

Through the lens of transformative learning theory, this study provides evidence of SDG integration into PBL curricula. It highlights a mapping methodology that enables HEIs to evaluate their sustainability readiness in curriculum, teaching materials and relevant assessments.

Artificial intelligence (AI) allows the brand to co-create value with young customers through mobile apps. However, as many brands claim that their mobile apps are using the most updated AI technology, young customers face app fatigue and start questioning the authenticity of this touchpoint. This paper aims to study the mediating effect of authenticity for the value co-creation of AI-powered branded applications.

Drawing from regulatory engagement theory, this study conceptualize authenticity as the key construct in customers’ value experience process, which triggers customer value co-creation. Two scenario-based online experiments are conducted to collect data from 444 young customers. Data analysis is performed using ANOVA and Process Hayes.

The results reveal that perceived authenticity is an important mediator between media richness (chatbot vs AI text vs augmented reality) and value co-creation. There is no interaction effect of co-brand fit (high vs low) and source endorsement (doctor vs government) on the relationship between media richness and perceived authenticity, whereas injunctive norms (high vs low) strengthen this relationship.

The finding provides insights for marketing managers on engaging young customers suffering from app fatigue. Authenticity holds the key to young customers’ technological perceptions.

This research highlights the importance of perceived authenticity in encouraging young customers to co-create value. Young customers consider authenticity as a motivational force experience that involves customers through the app’s attributes (e.g. media richness) and social standards (e.g. norms), rather than brand factors (e.g. co-brand fit, source endorsement).

This study aims to examine the relationship between corporate social responsibility (CSR) and worker turnover rate (WTR) and worker turnover cost (WTC) in Vietnamese garment companies, using the mediating role of symbolic image dimensions (SID) of employer image and the moderating role of unemployment rate.

This study uses Signaling Theory and Job Embeddedness Theory as theoretical frameworks to examine the mediating role of SID and the moderating role of the unemployment rate on the link between CSR and WTR, as well as the associated cost. Data from 119 Vietnamese garment companies are analyzed using structural equation modeling – AMOS software.

The findings suggest that CSR has a direct adverse effect on the WTR and an indirect effect on WTC through WTR. The results also indicate a direct relationship between the level of CSR adopted by companies and workers’ perception of the symbolic attributes of their company’s employer image as sincere, innovative, competent and prestigious. This, in turn, leads to a reduced rate of worker turnover. In addition, this study discovers the moderating influence of the region’s unemployment rate on the correlation between companies’ CSR and WTR.

The results indicate that companies should view CSR as a strategic tool to obtain better performance by achieving a win-win state of affairs with their workers. It also implies the influence of external economic factors on the relationship between CSR and worker turnover.

This study contributes to the literature by highlighting the economic benefits of CSR and the SID.

This paper aims to design a PD controller for an active suspension system to improve the car’s moving smoothness.

The controller parameters are optimized by an in-loop genetic algorithm (iL-GA). Unlike previous studies that only used conventional GAs to tune coefficients for the controller, the iL-GA designed in this paper provides outstanding efficiency when determining the optimal value range for the system. The optimal value range of parameters is determined by the in-loop algorithm based on criteria related to systematic errors. The optimal values are then calculated by the GA based on this range instead of an uncertain one.

Simulation results show that vehicle body acceleration and displacement values are significantly reduced when using the active suspension system compared to the conventional passive suspension system. The phase difference phenomenon does not occur in the iL-GA situation. In addition, the frequency domain investigation also shows the system’s stability when using iL-GA instead of conventional GA.

To the best of the authors’ knowledge, this is a new application that provides positive effects to the suspension controller. This algorithm can be applied to tune coefficients for direct controllers in the future.

The purpose of this paper is to expand and analyze deeply customer emotions, concretize the levels of positive or negative emotions with the aim of using machine learning methods, and build a model to identify customer emotions.

The study proposed a customer emotion detection model and data mining method based on the collected dataset, including 80,593 online reviews on agoda.com and booking.com from 2009 to 2022.

By discerning specific emotions expressed in customers' comments, emotion detection, which refers to the process of identifying users' emotional states, assumes a crucial role in evaluating the brand value of a product. The research capitalizes on the vast and diverse data sources available on hotel booking websites, which, despite their richness, remain largely unexplored and unanalyzed. The outcomes of the model, pertaining to the detection and classification of customer emotions based on ratings and reviews into four distinct emotional states, offer a means to address the challenge of determining customer satisfaction regarding their actual service experiences. These findings hold substantial value for businesses operating in this domain, as the findings facilitate the evaluation and formulation of improvement strategies within their business models. The experimental study reveals that the proposed model attains an exact match ratio, precision, and recall rates of up to 81%, 90% and 90%, respectively.

The study has yet to mine real-time data. Prediction results may be influenced because the amount of data collected from the web is insufficient and preprocessing is not completely suppressed. Furthermore, the model in the study was not tested using all algorithms and multi-label classifiers. Future research should build databases to mine data in real-time and collect more data and enhance the current model.

The study's results suggest that the emotion detection models can be applied to the real world to quickly analyze customer feedback. The proposed models enable the identification of customers' emotions, the discovery of customer demand, the enhancement of service, and the general customer experience. The established models can be used by many service sectors to learn more about customer satisfaction with the offered goods and services from customer reviews.

The research paper helps businesses in the hospitality area analyze customer emotions in each specific aspect to ensure customer satisfaction. In addition, managers can come up with appropriate strategies to bring better products and services to society and people. Subsequently, fostering the growth of the hotel tourism sector within the nation, thereby facilitating sustainable economic development on a national scale.

This study developed a customer emotions detection model for detecting and classifying customer ratings and reviews as 4 specific emotions: happy, angry, depressed and hopeful based on online booking hotel websites agoda.com and booking.com that contains 80,593 reviews in Vietnamese. The research results help businesses check and evaluate the quality of their services, thereby offering appropriate improvement strategies to increase customers' satisfaction and demand more effectively.

This study analyses the effects of firm efficiency on firm performance and how controlling shareholders moderate the link between the two variables.

This study employs data envelopment analysis to estimate firm efficiency and the panel regression method to assess the hypothesised relationships among 1,295 firm-year observations of publicly listed firms in Malaysia from 2015 to 2019.

The results indicate that firm efficiency (technical efficiency, pure technical efficiency and scale efficiency) has mixed relationships with firm performance (return on assets, market-to-book ratio and operating cash flows), all of which are being moderated by controlling shareholdings.

This study highlights the importance of assessing firm efficiency as the key success factor for improving firm performance. Industrial managers should manage efficiently their resources or operating costs in achieving their corporate financial goals. Moreover, this study notes the presence of controlling shareholders, who can be either self-interested or company goal aligned.

This study suggests becoming efficient in transforming inputs into outputs is a prerequisite before investigating accrual-based and cash-based firm performance measures, and the presence of controlling shareholders matters in these regards.

This study aims to comprehensively assess how digital maturity degree (DMD) impacts sustainable supply chain management (SSCM) performance through the mediating role of SSCM practices in businesses in Vietnam.

The hypotheses were performed using partial least square-structural equation modeling (PLS-SEM) with data collected from a survey of over 234 managers having responsibility in the supply chain field in Vietnam. Qualitative data were collected through semistructured interviews with 6 experts to deepen understanding of the relationship between DMD and SSCM.

The results show the mix-results in the relationship between SSCM practices and SSCM performance dimensions while DMD strongly impacts SSCM practices. Also, this study finds the mediating role of SSCM practices on the relationship between DMD and SSCM performance.

This is the first study to investigate the role of DMD on SSCM practices and SSCM performance, using empirical evidence. Moreover, the authors integrate both qualitative and quantitative for understanding complex SSCM phenomena. The present study also helps businesses improve their SSCM performance by leveraging SSCM practices and developing their digital technologies in the long-term view.

The purpose of this paper is to evaluate the current situation of buying external technology in Vietnamese manufacturing small and medium enterprises (SMEs) from 2013 to 2018. This paper also aims to examine the effect of buying technology on employment and environmental quality in Vietnam.

External technology purchase and its impact on employment and environmental quality are taken from the Vietnam annual enterprise survey and the using technology in production survey from 2013 to 2018. The estimation is based on a feasible generalized least squares method.

This study finds that external technology purchase in manufacturing SMEs increases the employment rate in the industries. External technology purchase in Vietnamese manufacturing SMEs also increases the level of environmental pollution. This is a negative side of purchasing technology for production because if the technology used is old, outdated and of poor quality, it will increase environmental pollution. In addition, the growth in employment increases air pollution. This shows that purchasing advanced technology selectively helps to increase the employment rate in Vietnamese manufacturing sector, which is followed by a positive impact on environmental quality.

The limitation of this study is that the research period was only investigated from 2013 to 2018 because the General Statistics Office of Vietnam no longer surveys the dataset about using technology in production surveys after 2018. This study has been conducted at the firm level, so results cannot be easily extended to the macroeconomic level, also taking into account the data limitations in terms of sector coverage.

This paper provides necessary policy recommendations for the government and manufacturing SMEs to solve negative technology issues related to the labour market and air quality in Vietnam. The results are expected to help policymakers in Vietnam propose appropriate national science and technology programmes towards sustainable development in the coming decades.

This paper focuses on assessing the effects of buying external technology by Vietnamese small and medium-sized manufacturing enterprises on employment and air quality.

The research aims to assess the relationship between ecological innovation, green productivity, sustainable development and agricultural productivity in Vietnam. The agricultural sector of Vietnam has been observed with new opportunities which have fostered its productivity and growth.

The study uses a range of methods where initially, the researcher used descriptive analysis, cointegration and unit root tests. Secondly, Quantile Autoregressive Distributed Lag (QARDL) is used to assess the short and long run effects. The QARDL methodology is employed to capture the relationship between variables. Through this approach, the researcher is able to examine the scale of the interaction between dependent and independent variables.

The unique findings drawn through statistical techniques are also a great addition to the context of literature related to Vietnam’s agricultural productivity. Practical insights can also not be denied as the study provides beneficial guidelines for Vietnam’s agricultural sector to refine agricultural productivity.

Scholars are advised to use strong literary techniques to overcome these limitations and give a more thorough investigation into the same ideas. The availability and dependability of data was one of the primary challenges in carrying out this study. Vietnam has made significant advances in the collection and documentation of agricultural data, but there might still be gaps in the availability of thorough and current data on ecological innovation, green production and sustainable development.

Vietnam’s unique socioeconomic, cultural and environmental features influence how ecological innovation, green productivity, sustainable development and agricultural production are interconnected. Consequently, consideration should be taken when applying the results to various scenarios.

Informal migrant workers and street vendors have long been recognized as vulnerable groups in urban areas of Global South countries. However, limited studies exist on the economic challenges faced by migrant street vendors during crises. We aim to address this gap by shedding light on their livelihood and welfare losses during a public health crisis.

This research uses descriptive and qualitative analyzes to triangulate the results. Data are derived from surveys and in-depth interviews with migrant street vendors in the two biggest cities in Vietnam during the COVID-19 pandemic.

The street vendors experienced significant business loss and consumption reduction during social distancing as well as encountered difficulties in recovering their businesses in the “new normal.” These adverse consequences were also found to disproportionately affect women vendors. Additionally, despite adopting various strategies and mitigation mechanisms to sustain their businesses and consumption, these efforts proved insufficient.

This research underscores the importance of short-term and long-term urban policies aimed at supporting and promoting the social inclusion of street vendors, particularly migrant and women vendors.

This research represents one of the early attempts to explore the adverse effects of a public health crisis on migrant street vendors and to examine whether the crisis disproportionately affected vendors from different genders and educational backgrounds. It also examines their business recovery in the “new normal.”