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Recent scholarship has explored higher education institutions’ (HEIs) role in transitioning to a sustainable society, but empirically, questions remain regarding their impact on the sustainability of surrounding areas. This study aims to examine the correlation between HEIs’ sustainability actions and local sustainability performance.

This study uses a linear regression model and principal component analysis to examine the sustainability performance of 105 US metropolitan statistical areas (MSAs) using the US cities sustainable development goal (SDG) index, which hosts 427 HEIs known for sustainability efforts. The weighted HEI sustainability performance score is calculated based on the QS sustainability universities ranking.

The correlation between MSA and HEI sustainability performance exhibits a mix of positive and negative associations, with individual and interlinked SDGs serving as proxies. These correlations encompass a wide range of goals, from economic aspects of SDG 1, 2, 3, 7, 9, social aspects of SDG10 and 16, to socio-environmental aspects of SDG12.

Further exploration is needed to identify the causal mechanisms behind associations between SDG measures and HEI sustainability performance, whether influenced by the institution, the individual or both.

This study suggests that HEIs are already associated with some aspects of community sustainability, but greater contributions to a broader array of sustainability measures are possible.

The correlation found between HEI sustainability actions and SDG10, 12 and 16 index performance in an MSA highlights a connection between HEIs and the attainment of societal goals.

To the best of the authors’ knowledge, this study is the first to examine the correlation between HEI and MSA sustainability performance in the US through individual and interlinked SDG proxies. It provides novel empirical evidence that demonstrates an association between HEI and some aspects of community sustainability performance.

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 aims to use the voice of the customer (VoC) strategy to collect user-generated content (UGC) compare customer expectations with reality, make the necessary improvements for the business and create personalized strategies for each customer to maximize revenue, focus on hospitality industry in Vietnam market.

This study proposes a synthesis of techniques for a deep understanding of the VoC based on online reviews in the hospitality industry. First, 409,054 comments were collected from websites in the hospitality sector. Second, the data will be organized, stored, cleaned, analyzed and evaluated. Next, research using business intelligence (BI) solutions integrating three models, including net promoter score (NPS), graph model and latent Dirichlet allocation (LDA), based on natural language processing (NLP) technique, experiment on Vietnamese and English data to explore the multidimensional voice of customer’s row. Finally, a dashboard system will be implemented to visualize analysis results and recommendations on marketing strategies to improve product and service quality.

Experimental results allow analysts and managers to “listen to the customer’s voice” accurately and effectively, identify relationships between entities, topics of discussion in favor of positive and negative trends.

The novelty in this study is the integration of three models, including NPS, graph model and LDA. These models are combined based on the BI solution and NLP technique. The study also conducted experiments on both Vietnamese and English languages, which ensures more effective practical application.

The integration of ESD is a complex problem. It calls for an innovative, student-centred curriculum, as well as professional learning and agency, by which university teachers feel empowered to change their practice and direct their peers and institutions towards ESD. This study aims to explore what university teachers consider to be the most important attitudes in supporting their agency to deliver Education for Sustainable Development (ESD) via a Problem Based Learning (PBL) programme.

This study presents a theoretical framework for professional agency comprising three domains: intrapersonal, action and environmental. A Q methodology is adopted to explore university teachers’ perceptions of the most important environmental factors in supporting their ability to deliver ESD via a problem-based learning (PBL) programme. Twenty-eight participants from six Southeast Asian universities took part in a PBL-based professional development programme designed to improve teachers’ ESD- and PBL-based skills and competencies.

The results indicate that the participants were confident in their ability to implement PBL and saw PBL as an approach suitable for addressing current educational, professional and societal challenges. This study offers a series of recommendations to help university teachers develop their ESD and PBL practices.

Although the literature on human agency is extensive, research surrounding teachers’ professional agency in the context of ESD and PBL in higher education is lacking. The present study addresses this gap by capturing individual teachers’ beliefs, perceptions and views and by using Q methodology to examine the subjectivity of study participants.