This study explores the intricate relationship between emotional cues present in food delivery app reviews, normative ratings, and reader engagement. Utilizing lexicon-based unsupervised machine learning, our aim is to identify eight distinct emotional states within user reviews sourced from the Google Play Store. Our primary goal is to understand how reviewer star ratings impact reader engagement, particularly through thumbs-up reactions. By analyzing the influence of emotional expressions in user-generated content on review scores and subsequent reader engagement, we seek to provide insights into their complex interplay. Our methodology employs advanced machine learning techniques to uncover subtle emotional nuances within user-generated content, offering novel insights into their relationship. The findings reveal an inverse correlation between review length and positive sentiment, emphasizing the importance of concise feedback. Additionally, the study highlights the differential impact of emotional tones on review scores and reader engagement metrics. Surprisingly, user-assigned ratings negatively affect reader engagement, suggesting potential disparities between perceived quality and reader preferences. In summary, this study pioneers the use of advanced machine learning techniques to unravel the complex relationship between emotional cues in customer evaluations, normative ratings, and subsequent reader engagement within the food delivery app context.

This study investigates the influence of service quality, destination facilities, destination image, and tourist satisfaction on tourist loyalty in the Pasar Lama Chinatown area of Tangerang City. Utilizing data from 400 respondents, the study employed structured questionnaires analyzed through descriptive statistics, reliability analysis, exploratory and confirmatory factor analysis, and structural equation modeling (SEM). The results reveal that service quality (β = 0.47, p < 0.001), destination facilities (β = 0.33, p < 0.001), and destination image (β = 0.4, p < 0.001) all significantly enhance tourist satisfaction, which in turn has a strong positive effect on loyalty (β = 0.58, p < 0.001). Direct paths also show that service quality, destination facilities, and destination image independently contribute to tourist loyalty. Bootstrapping confirms satisfaction’s mediating role between these factors and loyalty. Practical recommendations suggest prioritizing service quality improvements, facility enhancements, and a positive destination image to foster loyalty and promote tourism sustainability in Pasar Lama, China. These insights assist tourism managers in developing strategies to enhance long-term visitor retention and engagement in the area.

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%.

This research focuses on patients’ perceptions regarding the accessibility of dental services in Slovenia across four dimensions: financial accessibility, time accessibility, geographical accessibility, and service quality. We observed how specific factors impact accessibility dimensions of dental services in Slovenia, that patients perceive important. A cross-sectional quantitative survey was conducted using proportionate stratified sampling. Data was collected through an online questionnaire, and 599 completed responses were received from patients regarding their experiences and perceptions of accessibility to dental care. A SEM (structural equation model) approach was used to examine the data. The analysis revealed that patients perceive all four dimensions of accessibility: financial, time, geographical, and service quality important and they all constitute the perception of dental accessibility. The findings of this study can assist policymakers in developing a more accessible dental health system by considering the results proposed in our model.

The paper analyzes the corporate carbon emissions and GDP contributions of the top ten companies by turnover for 2020–2023 in Germany, South Korea, China and the United Kingdom. Focusing on Scope 1, 2, and 3, the study explores the contribution of these companies to carbon intensity across different sectors and economies. The analysis shows that there are significant gaps in carbon efficiency, with the UK’s and Germany’s firms emitting the lowest emissions per unit of GDP contribution, followed by China and South Korea. Additionally, the study further examines the impact of Economic Policy Uncertainty on both firm carbon intensity and economic productivity. While EPU is positively associated with GDP contributions, its impact on emissions is nuanced. Firms apparently respond to policy uncertainty by increasing energy efficiency in direct (Scope 1) and energy-related (Scope 2) emissions but find it more difficult to manage supply chain emissions (Scope 3) in that case. The results point out the critical role of comprehensive ESG reporting frameworks in enhancing transparency and addressing Scope 3 emissions, which remain the largest and most volatile component of corporate carbon footprints. The paper then emphasizes the importance of standardized ESG reporting and bespoke policy intervention for promoting sustainability, especially in carbon-intensive industries. This research contributes to the understanding of how industrial and policy frameworks affect carbon efficiency and economic growth in different national contexts.

In developing countries, urban mobility is a significant challenge due to convergence of population growth and the economic attraction of urban centers. This convergence of factors has resulted in an increase in the demand for transport services, affecting existing infrastructure and requiring the development of sustainable mobility solutions. In order to tackle this challenge, it is necessary to create optimal services that promote sustainable urban mobility. The main objective of this research is to develop and validate a comprehensive methodology framework for assessing and selecting the most sustainable and environmentally responsible urban mobility services for decision makers in developing countries. By integrating fuzzy multi-criteria decision-making techniques, the study aims to address the inherent complexity and uncertainty of urban mobility planning and provide a robust tool for optimizing transportation solutions for rapid urbanization. The proposed methodology combines three-dimensional fuzzy methods of type-1, including AHP, TOPSIS and PROMETHEE, using the Borda method to adapt subjectivity, uncertainty, and incomplete judgments. The results show the advantages of using integrated methods in the sustainable selection of urban mobility systems. A sensitivity analysis is also performed to validate the robustness of the model and to provide insights into the reliability and stability of the evaluation model. This study contributes to inform decision-making, improves policies and urban mobility infrastructure, promotes sustainable decisions, and meets the specific needs of developing countries.