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.

The purpose of the study is to create proposals and recommendations to improve the system evaluating the quality of governance and efficient use of budget funds in order to improve public welfare and sustainable development. The research methodology included application of statistical methods to review scientific articles, legislative acts and other documents, study models for evaluating the quality of governance and efficient use of budget funds. Mathematical modeling and forecasting methods were also used to assess aspects of governance and predict the results when changes are made, including building a trend model and determining the forecast values of accrued taxes and mandatory payments for 2024–2026. The conclusions highlight there is a positive correlation between the accrued taxes and mandatory payments to the budget of the Republic of Kazakhstan, and an economic growth and changes in tax legislation. The key factors influencing the quality of governance and efficient use of budget funds were identified. Recommendations were developed to improve the quality assessment system and governance of budget funds in order to increase efficiency and responsibility in financial management. The results of the study can be used by public administration bodies and financial institutions to optimize the governance of budget funds.

Academic integrity has been at the centre of the discussion of the adoption of Chat GPT by academics in their research. This study explored how academic integrity mitigates the desire to use ChatGPT in academic tasks by EFL Pre-service teachers, in consideration of the time factor, perceived peer influence, academic self-effectiveness, and self-esteem. The study utilized web-based questionnaires to elicit data from 300 EFL Pre-service teachers across educational fields drawn from different schools across the world. Analysis was conducted using relevant statistical measures to test the projected four hypotheses. The findings provide evidence in support of Hypothesis 1, with a statistically significant path coefficient (β) of 0.442, a t-value of 3.728, and a p-value of 0.000. The hypothesis acceptance implies that when academic integrity improves, the impact of the time-saving aspect of the use of ChatGPT Across educational fields study decreases. This suggests that EFL Pre-service teachers who have a firm dedication to academic honesty are less influenced by the tempting appeal of ChatGPT’s time-saving features, highlighting the ethical factors that influence their decision-making. The data also provide support for Hypothesis 2, indicating a substantial inverse relationship with a path coefficient (β) of 0.369, a t-value of 5.629, and a p-value of 0.001. These findings indicate that stronger adherence to academic integrity is linked to a diminished effect of colleagues on the choice to use ChatGPT in Academic tasks. The results suggest that a firm dedication to academic honesty serves as a protective barrier against exogenous pressures or influences from colleagues when it comes to embracing cutting-edge technology. However, in general, these findings revealed there was a negative association between academically related factors (e.g., time factor, sense of peer pressure, language study self-confidence, and academic language competence), as well as an attitude toward adoption of ChatGPT and commitment towards academic integrity.

Through Qualitative Comparative Analysis (QCA) on destination attractiveness characteristics at the country level, this study identifies attribute configurations in the pre- and post-pandemic period to analyze the changes and differences generated by an exogenous event (COVID-19). The results suggest that the destination attractiveness attributes work together, in multidimensional configurations, to increase leisure travel volume. We found an important change in pat-terns/configurations of attractiveness between the pre- and post-pandemic scenarios. Our findings suggest that the destination attributes may change in importance and valuation or disappear for some configurations. The conclusion has implications for the stakeholders related to the destination attractiveness development, showing possible patterns of tourism attributes to guide the action to improve the resilience in the tourism sector and recover these activities in a disaster scenario.

Luxembourg institutions have the opportunity to reconcile environmental goals with financial stability by implementing Green Fintech solutions, as the banking sector increasingly recognizes the importance of sustainability. This study employs a quantitative approach and analyzes data collected from 150 participants working in the banking industry of Luxembourg. The research aims to assess the consequences of adopting Green Fintech on sustainable development. Banking institutions can boost their financial resilience and mitigate climate-related risks by adopting Green Fintech, which improves their sustainability. The paper emphasizes the importance of Green Fintech in the Luxembourg banking sector for advancing sustainable development goals. To effectively address the increasingly complex environmental concerns, it is crucial to embrace innovative Fintechs.

How can social enterprises implement Total Quality Management (TQM) to tackle urgent social issues within their organizational framework while also ensuring their continued viability? To address this question, this study aims to explore the organizational approach to the adoption and implementation of TQM practices and their efficacy in mitigating pressing social challenges and maintaining financial sustainability. It adopts a qualitative multiple-case research design involving 3 social enterprises to explore the research phenomenon. Following qualitative research analysis process using NVivo, our findings highlight a prevalent, short-term outlook in managing TQM, hindering the full potential of TQM to achieve both social impact and organizational sustainability. More specifically, they expose a significant dissonance within the case organizations’ TQM implementations: the contrast between the current state, indicative of what it is, and the ideal state, indicative of what it should be. Altogether, the study advocates leveraging TQM for long-term excellence and alignment in social enterprises (as opposed to short-term mediocrity and disarray), thereby facilitating the achievement of both social impact and financial sustainability.

The study’s purpose is to evaluate the influence of some factors of the model of planned behavior (TPB) and the perceived academic support of the university on the attitude toward entrepreneurship and entrepreneurial intention of students. The results of Structural Equation Modeling (SEM) linear structural model analysis with primary data collected from 1162 students indicated that entrepreneurial intention is influenced by attitude toward entrepreneurship, subjective norm, perceived educational support, and perceived concept development support. In addition, this study also found the positive influence of perceived educational support, concept development support, and business development support on attitude towards entrepreneurship. Interestingly, the influence of perceived business development support on entrepreneurial intention was rejected, and personal innovativeness is demonstrated to promote an attitude toward entrepreneurship. Notably, this study also highlights the moderating role of personal innovativeness on the relationship between attitude toward entrepreneurship and entrepreneurial intention. Based on these findings, several implications were suggested to researchers, universities, and policymakers.