Since 2019, Togo has resolutely engaged in the decentralization process marked by communalization and elections of municipal councilors. Financial autonomy constitutes an essential lever for the free administration of municipalities, allowing them to ensure decision-making and the implementation of development projects. However, despite a legal and regulatory framework defining taxation specific to local authorities, Togolese municipalities are often perceived as needing more financial resources. This study aims to map the financing mechanisms for decentralization in Togo and analyze their contribution to municipal budgets. By adopting a quantitative approach combining documentary analysis and interviews with 188 experts and practitioners of local finance from various Togolese structures, four main financing mechanisms were identified: local, national, Community, and international. Among these mechanisms, own resources (in particular from the sale of products and services, fiscal and non-fiscal taxes) and state transfers via the Support Fund for Local Authorities emerge as the primary sources of financing for municipalities. However, the study reveals that several instruments of local mechanisms, although institutionally defined, still need to be updated in many municipalities, thus limiting their effectiveness in resource mobilization. These results highlight the importance of optimizing the management of local mechanisms to strengthen municipalities’ financial autonomy and support territories’ sustainable development.

This study investigates the willingness of Indonesian consumers, particularly in West Java, to pay for green products by applying and expanding the Theory of Planned Behavior (TPB). It examines how perceived green product value and willingness to pay premiums influence consumer intentions and behavior toward green purchases. The research highlights the gap between consumers’ willingness to pay for environmentally friendly products and the actual sales of such products. By incorporating perceived value and willingness to pay into the TPB framework, the study aims to find what factors that can address the gap particularly in a developing country context to contribute to shaping a pro-environmental socio-cultural community in Indonesia and mitigates country’s significant environmental challenges. In the context of 251 young consumers in Indonesia, this study finds that subjective norms do not significantly influence purchase intentions. However, attitudes and behavioral controls do effectively encourage green behavior, suggesting that societal norms for green behavior may not be fully established. In addition, while willingness to pay a premium and perceived value of green purchases can influence green behavior, consumers are generally reluctant to pay higher prices for environmentally friendly products.

A fresh interest has been accorded to metal iodides due to their fascinating physicochemical properties such as high ionic conductivity, variable optical properties, and high thermal stabilities in making micro and macro devices. Breakthroughs in cathodic preparation and metallization of metal iodides revealed new opportunities for using these compounds in various fields, especially in energy conversion and materials with luminescent and sensory properties. In energy storage metal iodides are being looked at due to their potential to enhance battery performance, in optoelectronics the property of the metal iodides is available to create efficient LEDs and solar cells. Further, their application in sensing devices, especially in environmental and medical monitoring has been quite mentioned due to their response towards environmental changes such as heat or light. Nevertheless, some challenges are still in question, including material stability, scale-up opportunities, and compatibility with other technologies. This work highlights the groundbreaking potential of metal iodide-based nanomaterials, emphasizing their transformative role in innovation and their promise for future advancements.

Electronic Word of Mouth (eWOM) has become a pivotal factor influencing consumers’ decisions, particularly in the context of hotel services. With the advent of social media, it provides individuals with powerful tools to share its experiences and opinions about hotels. In this digital age, customers increasingly rely on online reviews and recommendations from their peers when selecting accommodations. eWOM on social media platforms has a substantial impact on customers’ perceptions and decision-making processes. This study aims to better understand the influence of eWOM by social media platforms on purchase intention of hotel services. To understand the influence of eWOM, this study uses the information adoption model as the model has been widely used in previous eWOM studies. The information quantity construct has been added to strengthen the model. The online questionnaire was distributed to social media users by using Google forms via social media platforms and only 210 of them were responded. The SmartPLS 4.0 software is used to analyze the data as the Partial Least Square-Structural Equation Modelling (PLS-SEM) is a method to confirm the structural equation models and to test the link between inert developments. Based on results, the information quantity and information quality of hotel services on eWOM positively influences the information usefulness and the information usefulness of hotel services on eWOM positively influences the purchase intention. The results lead to increase sales of hotel services and contribute to economic growth.

This study conducts a systematic literature review to analyze the integration of artificial intelligence (AI) within business excellence frameworks. An analysis of the findings in the reviewed articles yielded five major themes: AI technologies and intelligent systems; impact of AI on business operations, strategies, and models; AI-driven decision-making in infrastructure and policy contexts; new forms of innovation and competitiveness; and the impact of AI on organizational performance and value creation in infrastructure projects. The findings provide a comprehensive understanding of how AI can be integrated into organizational excellence emerged frameworks to address challenges in infrastructure governance, and sustainable development. Key questions addressed include: how AI affects consumer behavior and marketing strategies. What AI’s capabilities for businesses, especially marketing and digital strategies? How can organizations address the drivers and barriers to help make better use of AI in these business operations? Should organizations even do anything with these insights? These questions and more will be tackled throughout this discussion. This paper attempts to derive a comprehensive conceptual framework from several fields of human resources, operational excellence, and digital transformation, that can help guide organizations and policymakers in embedding AI into infrastructure and development initiatives. This framework will help practitioners navigate the complexities of AI integration, ensuring profitability and sustainable growth in a highly competitive landscape. By bridging the gap between AI technologies and development-related policy initiatives, this research contributes to the advancement of infrastructure governance, public management, and sustainable development.

The Mass Rapid Transit (MRT) Purple Line project is part of the Thai government’s energy- and transportation-related greenhouse gas reduction plan. The number of passengers estimated during the feasibility study period was used to calculate the greenhouse gas reduction effect of project implementation. Most of the estimated numbers exceed the actual number of passengers, resulting in errors in estimating greenhouse gas emissions. This study employed a direct demand ridership model (DDRM) to accurately predict MRT Purple Line ridership. The variables affecting the number of passengers were the population in the vicinity of stations, offices, and shopping malls, the number of bus lines that serve the area, and the length of the road. The DDRM accurately predicted the number of passengers within 10% of the observed change and, therefore, the project can help reduce greenhouse gas emissions by 1289 tCO₂ in 2023 and 2059 tCO₂ in 2030.