China is currently at a critical juncture in implementing the rural revitalization strategy, with urbanization and tourism development as crucial components. This study investigates 41 counties (cities) in the Wuling Mountain area of central China, constructing an evaluation system for the coordinated development of these two sectors. The coupling coordination degree is calculated using a combination weighting method and the coupling coordination degree model. Spatio-temporal evolution characteristics are analyzed through spatial autocorrelation, while the geographic detector explores the driving factors of spatial variation. The findings reveal a significant increase in coupling coordination between urbanization and tourism, transitioning towards a coordinated phase. Spatially, urbanization and tourism exhibit positive correlations, with high-value clusters in the southeast and northwest and low-value clusters in the south. The geographical detector identifies industrial factors as the most critical drivers of spatial variation. This study offers novel insights into the dynamics of urbanization and tourism, contributing to the broader literature by providing practical implications for regional planning and sustainable development. The results are relevant to the Wuling Mountain area and serve as a reference for similar regions globally. However, the study has certain limitations, such as regional specificity and data availability, which should be considered in the context of this research.

Despite the proliferation of corporate social responsibility (CSR) studies, it is accruing academic interest since there still remains a lot to be further explored. The purpose of the study is to examine whether/how CSR perception affect employee/intern thriving at work and its mediator through perceived external prestige in the hospitality industry. Data from 501 hospitality industry employees and interns in China were collected using a quantitative survey consisting of 35 questions. Statistical findings showed that CSR perception and thriving at work were positively related. Additionally, perceived external prestige partially mediated the connection between CSR perception and thriving at work. Furthermore, the study found that hotel interns generally exhibited lower levels of CSR perception and thriving at work compared with frontline or managerial staff. The study underscores the importance of collaborative efforts between hotel practitioners and university educators to enhance CSR perception and promote thriving among hotel interns. By prioritizing the improvement of CSR perception and thriving at work, the hotel sector can potentially mitigate workforce shortages and reduce high turnover rates.

Indonesia’s tourism industry has emerged as a strategic sector, contributing to the country’s foreign exchange earnings. Given the prominence of this sector, there is significant potential for further development. Indeed, a mapping study to assess the dissemination of the trend and the potential for further issues to emerge would be highly beneficial. It is encouraging to note that academics have produced substantial literature on the subject, offering insights into its many facets. However, there is still a need for more in-depth analysis to understand the trends and issues currently facing the sector entirely. Consequently, this article examines the core themes in Indonesia’s tourism studies and maps the potential for future research on tourism issues and regulations. To this end, it employs a qualitative, four-year data set (2020–2023) and a SWOT analysis to identify critical aspects of Indonesian tourism issues. The data was collected in three forms: government reports, statistical data, and research articles (n = 252 samples) from the Scopus database. The results demonstrate that the predominant trend in Indonesia’s tourism industry is the widespread embrace of ecotourism at both the local and regional levels. Instead of identifying a limited number of leading destinations, the focus has shifted towards developing tourism villages and multi-stakeholder tourism. The primary concerns are the Indonesian tourism industry’s growth potential and sustainability. The development potential of Indonesian destinations based on SWOT objectives is a crucial aspect, and its score shows that Indonesia’s tourism sector is strategically positioned to take advantage of strengths and opportunities.

Catfish (Pangasianodon hypothalamus) are known in Asia, specifically in Southeast Asia. Currently, this fish has been exported to almost all countries in the world. This research aimed to examine the existing conditions of the solid waste produced, analyze the chemical composition of the waste, and look for alternatives for the policy and economical use of waste in the catfish processing business. Using the survey method, data were gathered through measurement at the research location and laboratory, interviews with business owners, and field observations. Proximate analysis was conducted on pink slime meat, belly fat, bones, and fish innards. Analysis of acid number, saponification number, iodine number, and fat fatty acid was carried out on stomach fat. Meanwhile, amino acid analysis was carried out for pink slime meat. Handling catfish industrial waste has yet to be carried out properly, which causes a foul smell and disturbs the environment. The catfish industry waste’s chemical content (protein, fat, water content, carbohydrates, and fatty acids) (pink slime meat, belly fat, fish bones, and innards) is still relatively applicable. The study processed fish waste into products like instant porridge, analogous fish sago rice, and fish sago noodles. The proximate analysis results of these products show figures that exceed the minimum standards for similar products.

The cars industry has undergone significant technological advancements, with data analytics and artificial intelligence (AI) reshaping its operations. This study aims to examine the revolutionary influence of artificial intelligence and data analytics on the cars sector, particularly in terms of supporting sustainable business practices and enhancing profitability. Technology-organization-environment model and the triple bottom line technique were both used in this study to estimate the influence of technological factors, organizational factors, and environmental factors on social, environmental (planet), and economic. The data for this research was collected through a structured questionnaire containing closed questions. A total of 327 participants responded to the questionnaire from different professionals in the cars sector. The study was conducted in the cars industry, where the problem of the study revolved around addressing artificial intelligence in its various aspects and how it can affect sustainable business practices and firms’ profitability. The study highlights that the cars industry sector can be transformed significantly by using AI and data analytics within the TOE framework and with a focus on triple bottom line (TBL) outputs. However, in order to fully benefit from these advantages, new technologies need to be implemented while maintaining moral and legal standards and continuously developing them. This approach has the potential to guide the cars industry towards a future that is environmentally friendly, economically feasible, and socially responsible. The paper’s primary contribution is to assist professionals in the industry in strategically utilizing Artificial Intelligence and data analytics to advance and transform the industry.

The proportion of national logistics costs to Gross Domestic Product (NLC/GDP) serve as a valuable indicator for estimating a country’s overall macro-level logistics costs. In some developing nations, policies aimed at reducing the NLC/GDP ratio have been elevated to the national agenda. Nevertheless, there is a paucity of research examining the variables that can determine this ratio. The purpose of this paper is to offer a scientific approach for investigating the primary determinants of the NLC/GDP and to advice policy for the reduction of macro-level logistics costs. This paper presents a systematic framework for identifying the essential criteria for lowering the NLC/GDP score and employs co-integration analysis and error correction models to evaluate the impact of industrial structure, logistics commodity value, and logistics supply scale on NLC/GDP using time series data from 1991 to 2022 in China. The findings suggest that the industrial structure is the primary factor influencing logistics demand and a significant determinant of the value of NLC/GDP. Whether assessing long-term or short-term effects, the industrial structure has a substantial impact on NLC/GDP compared to logistics supply scale and logistics commodity value. The research offers two policy implications: firstly, the goals of reducing NLC/GDP and boosting the logistics industry’s GDP are inherently incompatible; it is not feasible to simultaneously enhance the logistics industry’s GDP and decrease the macro logistics cost. Secondly, if China aims to lower its macro-level logistics costs, it must make corresponding adjustments to its industrial structure.