Cities are no longer viewed as creatures with a linear-climax-established cycle but as ecosystems with dynamic and complicated processes, with people as the primary component. Thus, we must understand urban ecology’s structure and function to create urban planning and appreciate the mechanisms, dynamics, and evolution that connect human and ecological processes. The ecological city (ecocity) is one of the city conceptions that has evolved with the perspective of urban ecology history. The concept of ecocity development within urban ecology systems pertains to recognizing cities as complex ecosystems primarily influenced by human activities. In this context, individuals actively engage in dynamic problem-solving approaches to address environmental challenges to ensure a sustainable and satisfactory quality of life for future generations. Therefore, it is necessary to study how ecocity has developed since it was initiated today and how it relates to the urban ecology perspective. This study aims to investigate the progression of scholarly publications on ecocity research from 1980 to 2023. Additionally, it intends to ascertain the trajectory of ecological city research trends, establish connections between scientific concepts, and construct an ecological city science network using keyword co-occurrence analysis from the urban ecology perspective. The present study used a descriptive bibliometric analysis and literature review methodology. The data was obtained by utilizing the Lens.org database, was conducted using the VOS (Visualization of Similarities) viewer software for data analysis. The urban ecology research area ecology of cities can be studied further from density visualization of ecosystem services and life cycle assessment. Finally, the challenges and future agenda of ecocity research include addressing humans by modeling functions or processes that connect humans with ecosystems (ecology of cities), urban design, ecological imperatives, integration research, and improving the contribution to environmental goals, spatial distribution, agriculture, natural resources, policy, economic development, and public health.

In a time of a growingly age-diverse workforce, modern organizations are facing the challenge of simultaneously maintaining job satisfaction for both younger and older workers. In that regard, this study aims to analyse and further explore the difference in job expectations of employees from the IT industry who belong to different age groups. Based on the extant literature, an appropriate research model was designed, which was subsequently tested using the data gathered through the surveys conducted over the past fourteen years. The research results show that the main difference between younger and older employees within the IT industry is related to professional and personal growth. Specifically, younger employees primarily look for personal development and rapid professional advancement, which are of minor importance to their older counterparts. Intriguingly, the obtained results showed no difference between the younger and older employees regarding the work environment, including its competitiveness.

Artificial intelligence (AI) has rapidly evolved, transforming industries and addressing societal challenges across sectors such as healthcare and education. This study provides a state-of-the-art overview of AI research up to 2023 through a bibliometric analysis of the 50 most influential papers, identified using Scopus citation metrics. The selected works, averaging 74 citations each, encompass original research, reviews, and editorials, demonstrating a diversity of impactful contributions. Over 300 contributing authors and significant international collaboration highlight AI’s global and multidisciplinary nature. Our analysis reveals that research is concentrated in core journals, as described by Bradford’s Law, with leading contributions from institutions in the United States, China, Canada, the United Kingdom, and Australia. Trends in authorship underscore the growing role of generative AI systems in advancing knowledge dissemination. The findings illustrate AI’s transformative potential in practical applications, such as enabling early disease detection and precision medicine in healthcare and fostering adaptive learning systems and accessibility in education. By examining the dynamics of collaboration, geographic productivity, and institutional influence, this study sheds light on the innovation drivers shaping the AI field. The results emphasize the need for responsible AI development to maximize societal benefits and mitigate risks. This research provides an evidence-based understanding of AI’s progress and sets the stage for future advancements. It aims to inform stakeholders and contribute to the ongoing scientific discourse, offering insights into AI’s impact at a time of unprecedented global interest and investment.

The augmentation of firm performance via customer concentration is particularly indispensable for organizational evolution. Both trade credit financing and financing constraints play pivotal roles in the nexus between customer concentration and performance. This research constructs a moderated mediation model to rigorously investigate the impact of customer concentration on firm performance, positing trade credit financing as the mediating variable and financing constraints as the moderating variable. The relevant hypotheses are evaluated empirically using panel data compiled from listed manufacturing firms in China over the period 2013–2020, yielding 8 firm-year observations. The empirical outcomes denote that customer concentration exerts a positive influence on firm performance, albeit having a negative impact on trade credit financing. Trade credit financing serves as a partial mediator in the relationship between customer concentration and manufacturing firm performance. Financing constraints are found to positively moderate the mediating role of trade credit financing in the relationship between customer concentration and firm performance. This research broadens the understanding of the implications of customer relationships on trade credit financing and performance, thereby enriching the knowledge base for managing a firm’s financing channels more effectively.

Environmental, social and governance (ESG) goes beyond its function as a business to maximize profits for the shareholders to work for societal purposes. Meanwhile, the green credit policy in China is still in its infancy, and the impact of green loans on the efficiency of commercial banks is significantly different. In this context, this paper details the company’s performance in crucial aspects such as low-carbon operations, eco-friendly financial innovation, a sustainable economic system, data security and the development of organizational capabilities to provide a sustainable development paradigm for supply chain finance technology peers. Based on ESG portfolio, we found that adding ESG holdings to a company affects its compliance with delivery or environmental rules, and anode and cathode of ESG combined Dual Carbon (DC) are presented in terms of emission levels. Our further research indicates the implementation of Green Credit Guideline has a positive impact on ESG performance of both green and polluting firms in comparison with others. The result was fully supported by different methods and models including PSM-DID (Propensity Score Matching-Differences-in-Differences), QDID (Quantiles Differences-in-Differences), and Kernel approaches, which can provide more implications and references for policy makers. Investors, politicians, and other essential stakeholders perceive ESG as a strategy to protect enterprises from future risks.

Improving the competitiveness of tourism destinations is crucial for driving local economies and achieving income growth. In light of this evidence, numerous government departments strive to assess specific factors that impact the competitiveness of tourism destinations, enabling them to issue appropriate new tourism policies that promote more effective forms of tourism business. Therefore, the primary objective of this paper is to investigate how various elements such as tourism resources, tourism support, tourism management, location conditions, and tourism demand influence regional competitiveness in the Northern Bay region of Guangxi Province in China. To accomplish this goal, an online survey was conducted to collect data from 420 visitors who had experienced North Gulf Tourism; yielding an impressive response rate of 95 percent. The findings reveal that all aforementioned factors—namely: Tourism resources, tourism support, tourism management, location conditions and tourist demand—significantly impact destination competitiveness. Notably though, it was found that among these factors influencing destination competitiveness; it is primarily determined by effective local-level management (β = 0.345). Following closely behind are tourist demand (β = 0.133) as the second most influential factor affecting destination competitiveness; followed by location conditions (β = 0.116) ranking third; then comes tourist support (β = 0.03) as fourth in line impacting destination competitiveness; finally with least impact being exerted by available tourist resources (β = 0.016). Consequently, highlighting that regional competitiveness within Guangxi’s Northern Bay area predominantly hinges on efficient local-level management practices thus strongly recommending relevant authorities formulate novel work policies aimed at enhancing levels of local-level competitive advantage within the realm of regional touristic offerings.