In Urban development, diversity respect is needed to prioritize and balance the urban development design for sustainable eco-city development. As a result, this research aimed to investigate the causal factor pathways of social network factors influencing sustainable eco-city development in the northeastern region of Thailand through a quantitative research approach. With the aim to survey insightful information, the analysis unit was conducted at the individual level with three hundred and eighty-three (383) samplings in Khon Kaen and Udon Thani provinces, including univariate analysis and multivariate analysis, using path analysis and multiple linear regression. The study results indicated that two pathways of social network factors influencing sustainable eco-city development were indirect influence factors. The indirect influence factor consists of information exchange, benefits exchange in the network, and members’ role in the social network. Additionally, the study revealed that the pathway has influences through social network types and the economic and social dimensions of sustainable cities (R² = 0.330). Therefore, this study concluded that sustainable eco-city development should be implemented through community networks and economic and social network development for environmental development through social network types.

Increasing number of smart cities, the rise of technology and urban population engagement in urban management, and the scarcity of open data for evaluating sustainable urban development determines the necessity of developing new sustainability assessment approaches. This study uses passive crowdsourcing together with the adapted SULPiTER (Sustainable Urban Logistics Planning to Enhance Regional freight transport) methodology to assess the sustainable development of smart cities. The proposed methodology considers economic, environmental, social, transport, communication factors and residents’ satisfaction with the urban environment. The SULPiTER relies on experts in selection of relevant factors and determining their contribution to the value of a sustainability indicator. We propose an alternative approach based on automated data gathering and processing. To implement it, we build an information service around a formal knowledge base that accumulates alternative workflows for estimation of indicators and allows for automatic comparison of alternatives and aggregation of their results. A system architecture was proposed and implemented with the Astana Opinion Mining service as its part that can be adjusted to collect opinions in various impact areas. The findings hold value for early identification of problems, and increasing planning and policies efficiency in sustainable urban development.

Food security presents a complex challenge that spans multiple sectors and levels, involving diverse stakeholders. Such a challenge necessitates collaborative efforts and the creation of shared value among participants. Through the lens of service-dominant logic (S-D logic), food security can be redefined to achieve a more comprehensive understanding and sheds light on the dynamic interplay among stakeholders, enabling the realization of potential value co-creation. As a theoretical contribution, this research addresses the gap in explaining stakeholder interactions. This aspect is crucial for fostering collaboration, and the study accomplishes this by leveraging Social Network Analysis to identify clusters and assign them roles as sub-orchestrators to support the National Food Agency as the main orchestrator who responsible to implement co-creation management strategy (involvement, curation, and empowerment). The study also proposes stakeholder roles in the context of food security: regulator, operator, dominator, niche player, and supporter. Moreover, the practical significance of this research is highly relevant to the early stages of the National Food Agency (NFA) since its establishment in 2021. As the NFA seeks optimal structure, networks, and resources to enhance Indonesia’s existing food system, the study offers valuable insights. This comprehensive study highlights key issues in developing food security in Indonesia and provides recommendations for overcoming future challenges.

This paper focuses on studying the impact of institutional distance between home and host countries on the entry mode choice of multinational enterprises (MNEs). Based on theories of transaction costs and institutional theory, we predict the trend of choosing investment forms of wholly-owned enterprises (WOEs) and joint venture enterprises (JVEs) in the agricultural sector of Vietnam in the context of free trade agreement implementation. The data of 364 MNEs from 22 different nations that directly invested in the agricultural sector of Vietnam in the period 1996–2019 were extracted from Worldwide Governance Indicators (WGI), which is provided by World Bank. An empirical investigation has employed logistic regression. The results show a positive relationship between institutional distance with regard to rule of law and regulatory quality and WOE choice. Furthermore, the entry mode choices of MNEs in Vietnam’s agricultural sector are also noticeably influenced by the implementation of freedom trade agreements (FTAs).

Artificial Intelligence (AI) has become a pivotal force in transforming the retail industry, particularly in the online shopping environment. This study investigates the impact of various AI applications—such as personalized recommendations, chatbots, predictive analytics, and social media engagement—on consumer buying behaviors. Employing a quantitative research design, data was collected from 760 respondents through a structured online survey. The snowball sampling technique facilitated the recruitment of participants, focusing on diverse demographics and their interactions with AI technologies in online retail. The findings reveal that AI-driven personalization significantly enhances consumer purchase intentions and satisfaction. Multiple regression analysis shows that AI personalization (β = 0.35, p < 0.001) has the most substantial impact on purchase intention, followed by chatbot effectiveness (β = 0.25, p < 0.001), predictive analytics (β = 0.20, p < 0.001), and social media engagement (β = 0.15, p < 0.01). Similarly, AI personalization (β = 0.30, p < 0.001), predictive analytics (β = 0.25, p < 0.001), and chatbot effectiveness (β = 0.20, p < 0.001) significantly influence consumer satisfaction. The hierarchical regression analysis underscores the importance of ethical considerations, showing that ethical and transparent use of AI increases consumer trust and engagement. Model 1 explains 45% of the variance in consumer behavior (R² = 0.45, F = 154.75, p < 0.001), while Model 2, incorporating ethical concerns, explains an additional 10% (R² = 0.55, F = 98.25, p < 0.001). This study highlights the necessity for retailers to leverage AI technologies ethically and effectively to gain a competitive edge, improve customer satisfaction, and drive long-term success. Future research should explore the long-term impacts of AI on consumer behavior and the integration of emerging technologies such as augmented reality and the Internet of Things (IoT) in retail.