The integration of the financial industry and financial technology (Fintech) plays a pivotal role in increasing financial services reach and inclusion for the large unbanked population in Indonesia. Fintech adoption optimization expands the financial access to formal financial institutions, especially to vulnerable groups such as the unbanked population who predominantly reside in rural areas far from formal financial institutions. Fintech is viewed as a game changer to bring finance to the unreached communities via information technology and digital financial landscape. In this causal research, data collection was done via online questionnaires to 485 Fintech users between December 2020 and April 2021. Data analysis and path modelling was performed using smartPLS 3.0 software. Result shows user innovativeness as a significant predictor, directly and indirectly affecting the adoption of Fintech in Indonesia, while user attitude found the most important factor towards Fintech adoption. Financial literacy is the least important variable to predict Fintech adoption, contrary to popular belief. This indicates that Fintech usage requires less financial literacy and is potential to reach unbanked population and those with low financial literacy. To make Fintech more inclusive, the government needs to accelerate improving Information and Communications Technology (ICT) infrastructure such as widening mobile broadband penetration and soft infrastructure by encouraging Fintech startup, allowing regulatory sandbox for startups, and driving financial institutions to innovate through Fintech to bring financial services to unbanked population.

Personal safety has had a renewed focus throughout the COVID-19 pandemic, which has led to behavioral change. The adoption of E-wallets facilitates social distancing and thereby helps prevent the spread of the COVID-19 virus. This paper aims to investigate the potential for consumers’ continued usage of an E-wallet service through an integrated framework based on two established models: the Health Belief Model (HBM) and Technology Continuous Theory (TCT). An electronic survey was distributed to a sample of 1080 individuals from academic society in three different Hungarian universities who had used an electronic wallet during the pandemic COVID-19. Structural equation modelling (SEM) was applied in the study and explained the 55.9% variance in consumers’ continuous intention towards E-wallet usage. This study found that while the COVID-19 pandemic strongly influenced the current use of e-wallets; the pivotal factor affecting their continued use is based on consumer self-efficacy. The study has both short and long-term implications; in the short-term, decisionmakers should utilize health threat constructs (as an element of the protective behaviors taken during the COVID-19 pandemic) to motivate consumers to use E-wallets; in the longer-term, banks should develop further strategies that encourage consumer loyalty regarding E-wallets by reassuring customers that these financial services achieve the value and benefits that they expect, resulting in self-efficacy.

The main purpose of this study is to scrutinize the effect of financial inclusion on financial sustainability, financial efficiency, gross domestic product, and human development in the context of G20 nations. This study has employed annual data of 15 developed and emerging economies during the period from 2004 to 2017. The current study has utilized a single index for financial inclusion, financial sustainability, and financial efficiency by employing principal composite analysis (PCA). The outcomes of the panel stationarity test confirmed the ARDL model for both the long and short runs. Equally, the findings of the ARDL Model 1 showed no association between financial inclusion and financial sustainability in the short run, however, in the long run, inclusive finance showed a significant impact on sustainability. Likewise, the ARDL Model 2 showed that financial inclusion has no effect on efficiency in the short run, while it positively influenced financial efficiency in the long run. The results of the ARDL Model 3 are also similar to Models 1 and 2 where inclusive finance showed no effect on poverty in the short run, but a significant effect in long run. Similarly, the ARDL Model 4 also presented no association between GDP and inclusive finance in the short run, while it showed significant relationships in the long run. Moreover, the outcomes of the GMM Model 1 showed a significant impact of inclusive finance on financial stability, and these results were similar to the GMM Model 2 between financial inclusion and financial efficiency. Additionally, GMM Models 3 and 4 have shown that inclusive finance has a statistically significant impact on poverty and economic expansion, respectively. The outcomes of this article are essential for policymakers, academics, regulators, and practitioners with valuable and convincing debate over financial inclusion, economic growth, poverty, sustainability, and financial efficiency.

The paper explores the business sector and firm age effects on firm performance mediated by foreign ownership levels in domestic firms and financial leverage by examining 146 Medium Enterprises (MEs). The results show that except for ownership, the business sector, firm age, foreign ownership level, and financial leverage significantly influence performance. Foreign ownership substantially mediates the correlation between firm age and performance but not leverage. Both foreign ownership and leverage have no substantial mediating effect on the relationship between the business sector and financial performance. Moreover, the findings reveal business sectors whose performance is statistically different from zero based on the referent group.

Predicting bankruptcy of companies has been a hot subject of focus for many economists. The rationale for developing and predicting the financial distress of a company is to develop a predictive model used to forecast the financial condition of a company by combining several econometric variables of interest to the researcher. The study sought to introduce deep learning models for corporate bankruptcy forecasting using textual disclosures. The study constructed a comprehensive study model for predicting bankruptcy based on listed companies in Kenya. The study population included all 64 listed companies in the Nairobi Securities Exchange for ten years. Logistic analysis was used in building a model for predicting the financial distress of a company. The findings revealed that asset turnover, total asset, and working capital ratio had positive coefficients. On the other hand, inventory turnover, debt-equity ratio, debtors turnover, debt ratio, and current ratio had negative coefficients. The study concluded that inventory turnover, asset turnover, debt-equity ratio, debtors turnover, total asset, debt ratio, current ratio, and working capital ratio were the most significant ratios for predicting bankruptcy.

The study examines the determinant factors that influence financial inclusion among small and medium enterprises (SMEs) in Ethiopia. The study uses an explanatory research design and a mixed research approach with both primary and secondary sources of data. More specifically, the study adopts a multiple linear regression model. The finding of the study reveals that; supply-side factors, demand-side factors, market opportunity, and collateral requirements have a positive effect on the firm’s access to finance. On the other hand, institutional framework factors, and the costs of borrowing negatively affect the firm’s access to finance. This study suggests concerned bodies sustain rapid and inclusive economic growth and hence eradicate extreme poverty and hunger, the policymakers must build an efficient, strong, and well-functioning financial market system that provides affordable and sustainable financial service to SMEs.