Financial inclusion and social protection have been recognised as the primary essential stimuli from the potential they carry as avenues for economic development, especially with respect to reduction in poverty and inequalities, the creation of employment and the enhancement overall welfare and livelihood. However, inclusive access to financial resources and equitable access to social protection interventions have remained a significant concern in Nigeria. In addition, the emergence of the COVID-19 pandemic exposed the weakness of Nigeria in all sectors of the economy such as energy, health, education and food systems and low-level inclusive access to financial resources and social protection coverage. On the other hand, this study argues that financial inclusion and social protection has the potential to mitigation shocks orchestrated by the COVID-19 pandemic. This study empirically examines how social protection interventions and access to financial resources responded to COVID-19 pandemic. The study made use of data sourced from the World Bank’s COVID-19 national longitudinal phone survey 2020 and applied the logit regression. The findings show that social protection and access to financial resources significantly associated with the likelihood of shock mitigation during the COVID-19 pandemic. The results show that social protection intervention reduces the probability of being severely affected by shocks by 0.431. Given this result, the study recommends that the government should put more effort into proper social protection intervention to mitigate the effect of the COVID-19 pandemic.

Rapid urban expansion gives rise to smart cities which pose immense logistical and supply chain challenges. The COVID-19 pandemic transformed the holistic system identified by Zhao et al. in 2021. The system encompasses logistics and supply chain integral to the concept of smart cities, with a focus on sustainability. This transformation requires an in-depth study on challenges of a common framework of policies for smart cities in countries comprising the Organisation for Economic Cooperation and Development (OECD). The study employs an extensive literature analysis for the period 2020–2022. an approach which contextualizes the model. The model identifies the causes, impact, and spillovers of new trends in logistics and supply, including the sustainability of adopted technologies. The study includes the variables involved, and barriers to creating a shared model. The results reveal that the two elements affecting the supply chain and transport in smart cities are Industry 4.0 and 5.0 technologies supporting specific sectors. The resilience of small and medium-sized enterprises positively impacts the sustainability of large urban centres. The study presents both factors that help and hinder the adoption of environmental, social, and economic sustainability technologies.

The research utilizes a comprehensive dataset from MENA-listed companies, capturing data from 2013 to 2022 to scrutinize the influence of capital structure (CapSt) level on corporate performance across 11 distinct countries. This study analyzed 6870 firm-year observations using a quantitative research method through static and dynamic panel data analysis. The primary analysis reveals a positive correlation between the CapSt ratio and company performance using fixed effects (FE) techniques. Hence, the preliminary results were re-examined and affirmed using a two-step system generalized method of moment (GMM) estimator to address potential endogeneity concerns. This finding aligns with most studies conducted in advanced countries, indicating a positive correlation between CapSt and corporate performance. Furthermore, it is also consistent with some research conducted in less-developed markets. This research argues that, in the MENA region, the advantages of debt, such as tax saving, may outweigh the potential financial distress cost. Furthermore, it offers insights into the monitoring role of CapSt in MENA-listed companies. We strengthen our research results by employing various methodologies and using alternative measures of accounting performance and controlling size, notably panel quantile regression analysis.

The government’s land registration program aims to protect communities from future land disputes. However, lack of community support presents challenges to its process and implementation. Utilizing a qualitative case study approach, this article examines these challenges from the community’s perspective, focusing on land registration, community participation, and implementation dynamics. It suggests that learning from these dynamics can enhance the program’s effectiveness, highlighting the need for a systematic approach to community involvement.

This study investigates the impact of the metaverse on English language teaching, focusing on the perspectives of students from the University of Boyacá. The use of the metaverse was compared with the Moodle platform in a virtual educational environment. A mixed-method approach combining quantitative and qualitative methods was employed. The sample consisted of 30 university students enrolled in English courses, randomly assigned to two groups: one using the metaverse and the other using Moodle. Students’ grades on different activities and assessments throughout the course were collected, and semi-structured interviews were conducted to explore students’ perceptions of the educational platforms. Results revealed that while students recognize the potential of the metaverse to enhance interactivity and learning experience, they also identified technical and accessibility challenges. Although no significant differences in grades were found between the groups, less variability in grades was observed in the metaverse group. The mixed design allowed for a more comprehensive understanding of the impact of the metaverse on English language teaching, while providing a variety of student perspectives on their experience with educational technology. This research contributes to understanding the role of the metaverse in English language teaching and highlights key areas for future research and developments in the field of virtual education.

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.