This study explores the factors that affect consumer adoption of reusable packaging in South Korea’s food delivery market. Adopting a mixed-method that includes interviews and an online survey of 137 consumers aged 18 to 30, the analysis, using an ordered probit model, reveals key drivers of the likelihood of switching to food delivery services using reusable packaging. Positive influences include environmental concerns, intention to take action on disposable packaging, willingness to pay extra, and awareness that reusable packaging does not require washing. However, challenges such as hygiene concerns and higher delivery fees deter consumers from switching to reusable package option. Demographic factors like living arrangements and gender show minimal impact. In response to the findings, the study suggests strategic solutions, including a pilot program, to overcome barriers and effectively demonstrate the benefits of reusable containers.

Infrastructure decision-making has traditionally been focused on the use of cost-benefit analysis (CBA) and multicriteria decision analysis (MCDA). Nevertheless, there remains no consensus in the infrastructure sector regarding a favored approach that comprehensively integrates resilience principles with those tools. This review focuses on how resilience has been evaluated in infrastructure projects. Initially, 400 papers were sourced from Web of Science and Scopus. After a preliminary review, 103 papers were selected, and ultimately, the focus was narrowed down to 56 papers. The primary aim was to uncover limitations in both CBA and MCDA, exploring various strategies for amalgamating them and enhancing their potential to foster resilience, sustainability, and other infrastructure performance aspects. Results were classified based on different rationalities: i) objectivist, ii) conformist, iii) adjustive, and iv) reflexive. The analysis revealed that while both CBA and MCDA contribute to decision-making, their perceived strengths and weaknesses differ depending on the chosen rationality. Nonetheless, embracing a broader perspective, fostering participatory methods, and potentially integrating both approaches seem to offer more promising avenues for assessing the resilience of infrastructures. The goal of this research proposal is to devise an integrated approach for evaluating the long-term sustainability and resilience of infrastructure projects and constructed assets.

This study examines how circular economy (CE) practices contribute to energy resilience by mitigating the impacts of energy shocks and supporting sustainable development. Through a systematic literature review (SLR) of recent studies, we analyze the ways in which CE strategies—such as resource recovery, renewable energy integration, and closed-loop supply chains—enhance energy security and reduce vulnerability to energy disruptions. Our research draws on academic databases, focusing on publications from 2018 to 2024, to identify key themes and practices that illustrate the transformative potential of the circular economy. Findings reveal that CE practices at macro, mezzo, and micro levels support resilience by fostering efficient resource use, reducing dependency on non-renewable energy sources, and promoting sustainable economic growth. Additionally, we highlight the roles of foreign direct investment (FDI), research and development (R&D), and supportive policies in accelerating the adoption of circular systems. The study concludes with recommendations for future research to address identified gaps, suggesting a roadmap for advancing circular economy practices as a means to enhance energy resilience and sustainability aims to reveal how wide array of factors affect transition towards more sustainable or circular economy.

The study investigates the impact of artificial intelligence (AI)-powered chatbots on brand dynamics within the banking sector, focusing on the interrelationships between AI implementation and key brand dimensions, including awareness, equity, image, and loyalty. Using structural equation modeling (SEM) analysis on data collected from 520 banking customers, the study tests eight hypotheses to explore the direct and indirect effects of AI-driven interactions on brand development. The findings reveal that AI chatbots significantly enhance brand awareness in banking services, demonstrating moderate positive effects on both brand equity and brand image. Notably, while brand awareness exerts a strong influence on brand image, it does not have a significant direct effect on brand loyalty. Instead, the study shows that brand loyalty is primarily developed through the mediating effects of brand equity and image, with brand image exerting a particularly strong influence on brand equity. For banking practitioners, these insights suggest a need to integrate AI chatbots within a comprehensive brand strategy that merges technological innovation with traditional relationship-building approaches. Limitations of the study and potential directions for future research are also discussed, providing avenues for further exploration of AI’s role in brand management.

This comprehensive review examines recent innovations in green technology and their impact on environmental sustainability. The study analyzes advancements in renewable energy, sustainable transportation, waste management, and green building practices. To accomplish the specific objectives of the current study, the exploration was conducted using the PRISMA guidelines in major academic databases, such as Web of Science, Scopus, IEEE Xplore, and ScienceDirect. Through a systematic literature review with a research influence mapping technique, we identified key trends, challenges, and future directions in green technology. Our aggregate findings suggest that while significant progress has been made in reducing environmental impact, barriers such as high initial costs and technological limitations persist. Hence, for the well-being of societal communities, green technology innovations and practices should be adopted more widely. By investing in sustainable practices, communities can reduce environmental degradation, improve public health, and create resilient infrastructures that support both ecological and economic stability. Green technologies, such as renewable energy sources, eco-friendly construction, efficient waste management systems, and sustainable agriculture, not only mitigate pollution but also lower greenhouse gas emissions, thereby combating climate change. Finally, the paper concludes with recommendations for policymakers and industry leaders to foster the widespread adoption of green technologies.

Institutions of higher learning are crucial to sustainability. They play a crucial role in preparing the next generation of leaders who will successfully execute the Sustainable Development Goals of the United Nation. This research therefore intends to present a preliminary conceptual approach in examining how industrial revolution 4.0 (I.R. 4.0) technologies, and lean practices affect sustainability in South Africa’s Higher Education Institutions (HEIs). The study shall employ survey questionnaire to collect data from the employees of the institutions. This preliminary study reveals that hybrid IR 4.0 technologies and lean practices as enablers of sustainability has not gained enough attention in the HEIs. Existing literature show the important role plays by performance variance of lean practices to improve sustainable performance when deployed from industry to education sector. The report validates the HEI’s future course, which has been incorporating new technology into its services processes recently. Using the created items, researchers may utilize empirical analysis to look into the combined effects of lean practices and IR 4.0 technologies on sustainability in HEIs. The following conclusions may be drawn: HEIs are essential for the application of sustainability principles; curriculum focused on sustainability and culture change are critical for attitude development; and the political climate and stakeholder interests impact the implementation of sustainability.

Night tourism, increasingly recognized as integral to the travel experience, has gained attention for its impact on overall tourist satisfaction. This article offers a comprehensive analysis of night tourism development in Vietnam’s coastal cities, focusing on Nha Trang and Quang Ngai, as representative cases of mature and emerging destinations, respectively. Utilizing the Importance-Performance Analysis (IPA) tool, the study aims to provide practical insights for sustainable night tourism. Surveys with 524 domestic tourists were conducted to evaluate perceptions and satisfaction levels. Nha Trang emphasizes accessibility and vibrant nightlife, with a focus on the night market and outdoor shows. Conversely, Quang Ngai highlights its night landscape, dining options, and shopping areas. Recommendations for both destinations include enhancing entertainment offerings and reassessing priorities based on tourist preferences. The study underscores the need for tailored strategies to foster sustainable night tourism development that aligns with evolving tourist demands in coastal cities like Nha Trang and Quang Ngai.

The power of Artificial Intelligence (AI) combined with the surgeons’ expertise leads to breakthroughs in surgical care, bringing new hope to patients. Utilizing deep learning-based computer vision techniques in surgical procedures will enhance the healthcare industry. Laparoscopic surgery holds excellent potential for computer vision due to the abundance of real-time laparoscopic recordings captured by digital cameras containing significant unexplored information. Furthermore, with computing power resources becoming increasingly accessible and Machine Learning methods expanding across various industries, the potential for AI in healthcare is vast. There are several objectives of AI’s contribution to laparoscopic surgery; one is an image guidance system to identify anatomical structures in real-time. However, few studies are concerned with intraoperative anatomy recognition in laparoscopic surgery. This study provides a comprehensive review of the current state-of-the-art semantic segmentation techniques, which can guide surgeons during laparoscopic procedures by identifying specific anatomical structures for dissection or avoiding hazardous areas. This review aims to enhance research in AI for surgery to guide innovations towards more successful experiments that can be applied in real-world clinical settings. This AI contribution could revolutionize the field of laparoscopic surgery and improve patient outcomes.