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The COVID-19 pandemic has a massive impact on people’s lives, the wealth of nations, tourism cities, economic and social media. The outbreak of the COVID-19 pandemic has led to challenges and innovations to deal with multiple direct and indirect impacts. This research aims to explore the development of social media research in the tourism city context to highlight emerging research themes, methodological approaches.

This study searched and filtered on the Scopus Web and Web of Science to collect databases and applied hybrid methods. The VOSviewer algorithm was used in the paper to generate maps from bibliographic and text data. Furthermore, a polynomial trend line was used to depict the data fluctuations of papers in the relationship between documents and years. Besides, other mathematical and statistical tools to measure the inter-relationships and impacts of social media publications within the tourism city research area, identify valuable insights and determine a framework for social media research trends.

The study outcomes expressed the development in social media research trends from 2005 to 2021 in the tourism city context of topics (social media, social networking (online)), methodology (sentiment analysis), publication outputs with most extensive collaborations and some other contents to form a framework of social media research trends in tourism cities.

Research data is mainly from reviews of articles that may not account for all trends in reality.

This research is original that will apply to future social media researchers and institutions.

Policy makers could base on the research trends to examine their future tourism development orientation.

This research assists social media scholars in understanding trends and themes in tourism city contexts that they could apply in their business area. Moreover, there is a desire to comprehend an overview of the related literature of social media science in tourism city contexts. The outcomes showed the limitations of lacking journals and volume of social media research. The results contribute to meaningful insights by having a more holistic understanding of social media through aggregated data sources.

This research addresses the pressing need for comprehensive studies in the rapidly evolving field of city tourism. This study aims to understand the overall performance of the International Journal of Tourism Cities (IJTC), the structure of knowledge in city tourism research and the prevalent themes and trends arising from IJTC.

A bibliometric analysis was conducted to scrutinize the publication patterns in IJTC. This involved examining parameters such as the annual count of published articles, the keywords used in them and their respective authors.

The findings reveal that IJTC has a growing and diverse publication output, establishing itself as a reputable and influential publication within urban tourism research. The results reflect various aspects and themes in city tourism research.

The study has certain limitations. The data used for analysis was obtained exclusively from the Scopus database. The analysis was conducted using only one software package, Bibliometrix. Other software packages may offer different features for bibliometric analysis. The study relied exclusively on quantitative methods for data analysis. Qualitative methods could have provided more nuanced interpretations of the data.

Comparative analyses could be conducted between IJTC and other journals within urban tourism or related disciplines. Such research would yield valuable insights into the current state of the field and aid in identifying areas warranting further investigation.

The findings from this study can inform the decisions and actions of various stakeholders involved in urban tourism. Practitioners and policymakers can draw from this research to make informed decisions. Existing and emerging authors can identify relevant topics for their research. Readers can access pertinent information for their needs.

This study offers a unique contribution by thoroughly comprehending the performance of IJTC between 2015 and 2023. It progresses the existing body of knowledge on city tourism research by examining its current status and future trends.

This study aims to explore when and for what purpose is social media used by tourists in their decision-making; and to further interpret the nature of such usage.

An online questionnaire was administered to 530 Indians who qualified as recent tourists and social media users. Partial least square structural equation modeling (PLS-SEM) is used to analyse hypothesised relationships; the results helped us to unveil the tourist social media cycle which is a conceptual model of the results obtained.

PLS-SEM results indicate that social media is predominantly used in the pre-travel stage for evaluation purposes, during travel stage for purchase purposes and post-travel stage for post-purchase behaviour. Also, it is noted that social media use by a tourist is an ongoing process, and thus, a conceptual model in the form of tourist social media cycle is unveiled.

This study reveals the unique behaviour of Indian tourists with respect to using social media for travel decision-making, which establishes a different perspective of understanding and further strategizing social media’s use in tourism for a developing nation. Also, the tourist social media cycle stresses on the importance of social media as a digital repository where continuously the user-generated content is used by self (existing) or “others” (potential tourists).

To the best of the author’s knowledge, this is the first study that addresses the population and knowledge gap of understanding social media–based tourist behaviour providing a perspective of a developing nation, specifically India. Further, it unveils the nature of social media use by tourist in the form of a first ever tourist social media cycle.

To achieve the Sustainable Development Goals (SDGs) in the era of Logistics 4.0, machine learning (ML) techniques and simulations have emerged as highly optimized tools. This study examines the operational dynamics of a supply chain (SC) in Vietnam as a case study utilizing an ML simulation approach.

A robust fuel consumption estimation model is constructed by leveraging multiple linear regression (MLR) and artificial neural network (ANN). Subsequently, the proposed model is seamlessly integrated into a cutting-edge SC simulation framework.

This paper provides valuable insights and actionable recommendations, empowering SC practitioners to optimize operational efficiencies and fostering an avenue for further scholarly investigations and advancements in this field.

This study introduces a novel approach assessing sustainable SC performance by utilizing both traditional regression and ML models to estimate transportation costs, which are then inputted into the discrete event simulation (DES) model.

This study empirically investigates how ambidextrous innovations and their balancing affect the supply chain financing performance (SCFP) of small and medium-sized enterprises (SMEs), based on signaling theory. Moreover, this study explores the moderating effect of the breadth and depth of digital technology deployment on the relationship between ambidextrous innovations and the SCFP of SMEs.

A mixed-methods design is used, including a qualitative study and a quantitative study. Qualitative data have been collected from six multi-cases in different industries. Questionnaire data have been collected from 259 SMEs in China, and a multiple regression model is used to verify the research hypotheses.

The findings indicate that, in supply chain financing, both exploitative innovation and exploratory innovation are helpful in improving the SCFP of SMEs. For resource-constrained SMEs, a relative balance between exploitative innovation and exploratory innovation can help improve SCFP. The breadth of digital technology deployment can strengthen the relationship between exploitative innovation and SCFP, while the depth of digital technology deployment can weaken the relationship between exploratory innovation and SCFP. In addition, increasing the depth of digital technology deployment strengthens the positive correlation between the relative balance of ambidextrous innovations and SCFP.

To effectively obtain supply chain financing, SMEs can either concentrate their limited resources on a single type of innovation or use relative balance strategies to simultaneously pursue two innovations. In addition, in the process of obtaining supply chain financing by ambidextrous innovations, SMEs should appropriately deploy digital technologies.

This study first deconstructs the impact mechanism of ambidextrous innovation capabilities on SCFP based on signaling theory, and then discusses the balancing effect of ambidextrous innovations on SCFP in the cases of resource-constrained SMEs. This study also goes further and finds the negative moderating effect of digital technology deployment in the process of supply chain financing.

This chapter examines the significant role of non-fungible tokens (NFTs) and blockchain technology in fostering a sustainable economy in the metaverse. Blockchain allows the saving and transfer of decentralized and secure data. As a primary component of the metaverse economy, NFTs are distinct and secure virtual assets saved on the blockchain. These assets facilitate possessing, trading, and monetizing digital assets. These advancing technologies have also revolutionized the method by which creators and artists test and exchange their digital work, introducing a novel period of ownership and value in the digital realm. However, the negative environmental effects of some blockchain technologies constitute a considerable constraint, pushing a shift to a sustainable economy. Platforms like The Sandbox have implemented initiatives to address environmental concerns. As a case study, The Sandbox play-to-earn model with tokenized assets showcases its ability to create value and encourage user participation. It shows the ability of NFTs and blockchain to support a sustainable economy.

Prior research consistently found maximizers to experience greater regret over their choice than satisficers. Moreover, research also found maximizers to be trapped in a “maximization-regret-maximization” cycle. This paper aims to assess the role of construal level theory in alleviating regret felt by maximizers.

The authors examine the construal level theory (CLT) in conjunction with the choice context (comparable and non-comparable choices). Three experimental studies tested our assertion that a match between CLT mindset and choice set relieves regret for maximizers.

The authors show maximizers experience similar levels of regret compared to satisficers when considering comparable options in a concrete mindset, and non-comparable options in an abstract mindset. However, maximizers experience heightened regret in comparison to satisficers when considering non-comparable (comparable) options in a concrete (abstract) mindset. Choice difficulty mediates our effect.

Future research is needed to replicate our results in real-life settings.

If marketers think that their product is likely to be compared with other comparable products, they should adopt product-specific information that focusses on how the product would be used. However, if marketers think that consumers will compare across non-comparable products, then they should focus on why their product is the most suitable to fulfil consumers’ needs.

This research represents the first attempt at reducing regret for maximizers and answers the call for an examination of the relationship between maximization and CLT. The research adds to the maximization literature by evidencing a CLT-based strategy that attenuates the negative experience of regret for maximizers.

Sustainable waste management (SWM) leads to human safety by eliminating dangerous substances, increasing cost efficiency and reducing environmental impacts. Integrating social, economic and environmental factors is the standard for successfully implementing SWM. However, prior studies have not incorporated the triple bottom line with technological performance and occupational safety in establishing SWM. To drive sustainability in waste management, this study aims to provide a set of SWM attributes and identify a causality model based on the interrelationships among the attributes.

This study used the Delphi method to list the relevant attributes and the decision-making trial and evaluation laboratory (DEMATEL) involving 18 experts from the medical and health-care industry to present the interrelationships indicating the group of cause–effect attributes of SWM.

The study selected 5 aspects and 20 criteria as the relevant attributes of SWM. The cause group consists of environmental impacts and occupational safety, with positive values of 27.031 and 24.499, respectively. The effect group includes technological performance, economic performance and social performance. In particular, the challenges and practices of technological performance are linked to environmental impacts and occupational safety.

The top four criteria for industrial improvement are green practices, government policy and rules, the awareness of workers and waste separation and collection. These results present deeper insights into theoretical and managerial implications.

This study contributes to addressing the challenges and practices of SWM in technological performance leading to environmental impacts and occupational safety. Studies on the technological performance aspect in the causality relationships between environmental impacts and occupational safety are lacking. This study describes SWM using qualitative information and quantitative data.

The theoretical background bases on the big data analytics-artificial intelligence (BDA-AI) technologies and supply chain ambidexterity (SCAX) in the firms to assess their sustainability endeavors such as green supply chain management (GSCM) to improve their green communication and corporate image.

Around 220 participants in the manufacturing firms are participants' industry expertise, diverse roles, and representation as key stakeholders.

The results show BDA-AI and SCAX affected on GSCM and found the significant relationships with green communication and corporate image. Green communication was discovered to impact corporate image significantly.

Prior studies are neglected to address the relationship among the AI, powered by rapid computational and BDA breakthroughs, redefines cognitive tasks, achieving feats previously deemed impossible-making implicit judgments, simulating emotions, and driving operations. This study selects manufacturing firms as respondents due to their forefront of BDA-AI and supply chain ambidexterity adoption to benefit the operational efficiency and competitiveness. The firms intricate supply chains, diverse stakeholders, and strategic emphasis on corporate image make it an ideal context to examine the nuanced impact of these technologies.

The data economy, emerging from the current hyper-technological landscape, is a global digital ecosystem where data is gathered, organized and exchanged to create economic value. This paper aims to shed light on the interplay of the different topics involved in the data economy, as found in the literature. The study research provides a comprehensive understanding of the opportunities, challenges and implications of the data economy for businesses, governments, individuals and society at large, while investigating its impact on business value creation, knowledge and digital business transformation.

The authors conducted a literature review that generated a conceptual map of the data economy by analyzing a corpus of research papers through a combination of machine learning algorithms, text mining techniques and a qualitative research approach.

The study findings revealed eight topics that collectively represent the essential features of data economy in the current literature, namely (1) Data Security, (2) Technology Enablers, (3) Business Implications, (4) Social Implications, (5) Political Framework, (6) Legal Enablers, (7) Privacy Concerns and (8) Data Marketplace. The study resulting model may help researchers and practitioners to develop the concept of data economy in a structured way and provide a subset of specific areas that require further research exploration.

Practically, this paper offers managers and marketers valuable insights to comprehend how to manage the opportunities deriving from a constantly changing competitive arena whose value is today also generated by the data economy.

Socially, the authors also reveal insights explaining how the data economy features may be exploited to build a better society.

This is the first paper exploring the data economy opportunity for business value creation from a critical perspective.