Focusing on Shanghai Port, this in-depth study explores how government support can make port organizations more competitive. This study shall implement qualitative analysis based on in-depth interviews with key industry and government leaders to break down the complicated actions taken by the government and how they have changed the operational and strategic skills of the port industry. Seven factors were found in our study to be the most crucial support factors: Financial, regulatory, infrastructure growth, talent, market, policy, and organizational support. In their ways, each of these groups undermines the ability of port businesses to compete. For instance, finance can make ports more competitive in aspects such as tax cuts, lower interest rates, innovation and R&D funds, financing programs, venture capital funds, and putting up R&D sites. Supporting regulations makes sure that there is fair competition and smooth operations. This is done by protecting intellectual property, keeping the market going smoothly, improving the business environment, and monitoring market regulations. Building new infrastructure, such as innovation and updated buildings, enables the smooth running of the port businesses and minimizes wastage of time; thus, more time is spent on production. Supporting talent, the market, and policy all work together to make the human capital, international cooperation, and strategic regulatory framework that a company needs to stay ahead in the long run. It is clear from organizational support how important collaborative networks are for making ports more competitive. These networks, for instance, can be of assistance in helping schools and businesses work together, create new technologies, and find ways for companies and colleges to study together. This study examines these support systems to determine where the government should step in and how the systems can be made better to make ports more competitive. In terms of practical contribution, this in-depth study helps policymakers and port workers plan for the future. This study shows a fair way for the government to support the port business, which changes with its needs and stays competitive in the world of trade.

The success of a city’s entrepreneurial ecosystem (EE) depends on a combination of interconnected factors that foster innovation, collaboration and growth. Urban planning, infrastructure management and an entrepreneurial culture are essential factors for the success of cities’ Entrepreneurial Ecosystems (EEs). Land use and infrastructure management create opportunities for growth and industry expansion. EEs are local, social, business, institutional and cultural stakeholders that encourage and enhance the formation and growth of new businesses, which are supported by enabling infrastructure. The objective of this study was to investigate how urban planning affects EEs in the metropolitan region, Nelson Mandela Bay (NMB), South Africa. NMB is known for poor land use management, which hinders the management of diverse spatial needs, as well as bureaucratic processes for land rezoning for commercial activity. In order to better understand the fundamental issues, a qualitative case study was conducted. The data were collected from fifteen economic development role players from NMB using semi-structured interviews combined with secondary data from the NMB Integrated Development Plan (IDP). The data analysis included thematic analysis using Atlas.ti and Claude 2.0. In order to validate the findings, qualitative data were cross-referenced with secondary sources from the NMB IDP. The key themes that emerged effect the NMB metropole’s management of infrastructure to support the EE. These include, Land use issues, Poor oversight by metropolitan leadership, Lack of infrastructure maintenance and pushing out potential investment and economic growth. The results highlight that the NMB metropole fails to prioritise land use and infrastructure challenges, impacting the NMB metropolitan area’s economic development and worsening inequality among different groups. The findings from this study add to the current research on cities’ EEs and The Right to the City Theory, which supports the UN Sustainable Development Goals 8, 9 and 11.

Creating products and services that satisfy individual and community needs is impossible without raw materials. This study takes a novel approach by integrating the economic dynamics and raw material consumption indicators of the European Union (EU). The study uses different econometric methods to analyze the relationship between GDP (gross domestic product) and the EU’s raw material consumption (RMC) from 2014–2023. Among the results, the panel data analysis model shows that the resource productivity of the EU improved during the period under review, whereas the material intensity decreased significantly. These trends significantly contributed to the relative decoupling of material consumption from GDP in the last decade. The results of the K-means cluster analysis highlight the regional economic differences within the EU. According to the results of the correlation analysis, EU member countries differ significantly in the efficiency of raw material use. Nevertheless, five member countries are robustly vulnerable to large-scale raw material use. The divergence calculation results show that while some countries use raw materials extremely efficiently to produce GDP, others achieve low efficiency. This unique approach and the resulting findings provide a new perspective on the complex relationship between economic growth and raw material use in the EU.

The transfer of knowledge and the preservation of traditions is passed down from generation to generation. The main objective of this study was to explore people’s knowledge of the gastronomic heritage of the Kisalföld regions through an analysis of the county’s (attendance to, decision-making and willingness to spend on food and beverages) taking place in the county, such as the Flavours of Szigetköz, the County Wines Festival, the Flavours of Rábaköz or Eszterházy Baroque Food Festival at Fertőd. A quantitative research was used to analyse the topic (N = 666), the sample is not representative and the selection of respondents was random. Data were collected between 1 September 2023 and 31 October 2023 using electronic questionnaires shared on Google Drive. Data were processed using SPSS 25.0 and MS Office Excel in addition to the descriptive statistical data (modus, median, standard deviation), correlation, and crosstabulation analyses. Important research questions of the study were whether the respondents’ place of respondents influences gastronomic awareness whether age determines willingness to travel to attend a gastronomy event, The most popular gastronomic event in the county was the Vegetables of Hanság Region (mean 3.35), and the least popular was the Szigetköz Flavours of Szigetköz festival (mean 3.01). The key finding of the study is that an essential aspect of sustainability for decision-makers is to know the characteristics of tourists (middle-aged female target group), to select and maximize the different program packages in the marketing of the offer, to distribute the traffic and to avoid mass tourism.

As the aging trend intensifies, the Chinese government prioritizes technological innovation in smart elderly care services to enhance quality and efficiency, catering to the diverse needs of the elderly. This study examines the acceptance and usage behavior of smart elderly care services among elderly individuals in Xi’an, using a modified Unified Theory of Acceptance and Use of Technology (UTAUT) model that includes digital literacy as a moderating variable. Data were collected via a survey of 299 elderly individuals aged 60 and above in Xi’an. The study aims to identify factors influencing the acceptance and usage behavior of smart elderly care services and to understand how digital literacy moderates the relationship between these factors and usage behavior. Regression analysis assessed the direct effects of Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) on usage behavior. These dimensions were then integrated into a comprehensive index Service Acceptance to evaluate their overall impact on usage behavior, with behavioral intention examined as a potential mediating variable. Results indicate that EE and SI significantly impact the adoption of smart elderly care services, whereas PE and FC do not. Behavioral intention mediates the relationship between these variables and usage behavior. Additionally, gender, age, and digital literacy significantly moderate the impact of service acceptance on usage behavior. This study provides valuable theoretical and practical insights for designing and promoting smart elderly care services, emphasizing the importance of usability and social promotion to enhance the quality of life for the elderly.

A precise risk assessment in a production line constitutes a significant item to identify susceptible areas where there is a possibility of product quality degradation. This also applies to the precast concrete production line in Indonesia that has a spun pile product. Based on a risk assessment activity conducted in this study, it is proposed to build a traceability model in order to maintain and even improve the spun pile product quality in Indonesia. The approach used was the Neural Network of the perceptron model for weighing and will result in a defined traceability path in the context of reducing defects and even failed spun pile products. The simulation result showed that the model has been able to detect risky path possibilities to reduce product quality. The accumulation result of high-risk and medium-risk paths in this study showed that closer to product finalization, the risk will be higher. It is evident that when assessing Indicators, the order from the highest accumulation value first is Curing & Demolding and Stressing & Spinning at 29% each, Casting at 14%, Forming & Setting at 14%, and lastly Cutting & Heading at 14%. Regarding the risk assessment for activities, the first position is Curing & Demolding and Stressing & Spinning with 30% each, the second is Casting and Forming & Setting with 15% each, and the third is Cutting & Heading with 10%.