PurposeThis study aims to investigate the interplay between renewable energy development, unemployment and GDP growth within Bangladesh, India, Pakistan and Sri Lanka. The research underscores the significant role of renewable energy plays in stimulating economic growth and mitigating unemployment, offering crucial policy insights for sustainable growth in South Asia.Design/methodology/approachUtilizing the autoregressive distributive lag (ARDL) framework and Toda Yamamoto causality through the vector autoregressive (VAR) approach, the study analyzes the long-term and short-term impacts of these variables from 1990 to 2019.FindingsThis study reveals a significant co-integration among renewable energy consumption, unemployment and GDP growth in selected South Asian countries. The long-term estimation shows renewable energy consumption influences negatively economic progression in Bangladesh, with no notable correlation with unemployment. In contrast, Sri Lanka demonstrates an optimal relationship among all the variables. Short-run assessments reveal a significant positive relationship between renewable energy consumption and economic growth in India, while an inverse relationship is evident in Pakistan. Moreover, the relationship between unemployment and economic progression, the result shows a negative and significant relationship in India and Sri Lanka.Research limitations/implicationsThe study emphasizes the need for policy development concerning renewable energy development, unemployment reduction and sustainable economic growth in South Asia. While limitations exist, future research can expand upon this work by incorporating varied data, additional countries or alternative modeling techniques.Originality/valueThis research offers a unique exploration into the multidimensional impacts of renewable energy consumption, unemployment and economic growth in the South Asian context, an area previously unexplored in such depth.
Non-bank financial institutions (NBFIs) are recognized as the fundamental of a financial market as they complement the banking institutions. Since 1981, NBFIs have been playing a vital role in the economic growth of Bangladesh. Unfortunately, in the recent years most of the NBFIs have been found financially distressed. However, few NBFIs that were included in our sample claimed themselves as potential companies with sound financial performance though it was highly criticized. Therefore, the motivation for conducting this study is to examine the financial soundness of selected NBFIs using Altman’s Z score (1995). This study involved 20 NBFIs out of 23 Dhaka Stock Exchange (DSE) listed institutions, which were selected based on information availability by considering A, B and Z categories from 2014 to 2018 period. The secondary data were collected from the annual reports of the selected companies over the period. The findings are as follows: 95% of the 20 NBFIs were in distress zone during the study period and only 5% NBFIs were in safe zone during 2017-2018 period. Therefore, the analysis predicted that within the upcoming years a few of the NBFIs will be approaching bankruptcy. Finally, it is suggested that the government, respective regulatory authority, and policy makers to pay an immediate attention on mitigating the factors affecting the financial distress.
Nowadays, the forecast of the financial crisis is a significant concern for all companies and stakeholders. As a fast-growing economy, the insurance industry in Bangladesh is also a significant concern. Nevertheless, the performance and contribution of Bangladeshi insurance companies are highly criticized by scholars. Therefore, with the motivation to provide a comprehensive overview of the financial health of the general insurance industry in Bangladesh, we have done secondary research on a total of 18 general insurance companies in Bangladesh from 2014-2018. Throughout the study, we tested the widely accepted Altman Z Score model to predict a major financial concern called bankruptcy. We found that 95% of the selected companies secured the safe but not in the highly satisfactory calculated value of the Altman Z score model. Therefore, as expected, this finding highlights the success of the growing nature insurance business in Bangladesh. Also, as the Z score model has high predictive power in the case of predicting financial distress, our findings could be valuable path for stakeholders in making the right decision for investment.
This study investigates the factors influencing customers' choice of financial institutions in Bangladesh, focusing specifically on the role of Digital Financial Services (DFS). The study explores the relationship between trust, risks, benefits, social influences and intention to choose financial institutions through DFS. The research adopts a quantitative methodology and employs Structural Equation Modeling (SEM) to analyze the data collected through a survey that use digital financial services in Bangladesh. The study examines by the modified UTAUT model to explore the direct and indirect relationships that influence the intention to choose financial institutions through using DFS. The results reveal that 'Benefits' and 'Social Influences' have a significant positive impact on the choice of financial institutions through DFS in Bangladesh, while 'Trust' and 'Perceived Risks' demonstrate an insignificant relationship with 'Users' Intention' to choose financial institutions through DFS. This study contributes to the literature on digital financial services (DFS) adoption by examining the factors influencing customers' choice of financial institutions in Bangladesh. Also, the research adds value by incorporating a modified theory of UTAUT model for finding the appropriate relationship among the variables. The findings highlight the importance of understanding the complex factors affecting customers' preferences for financial institutions and emphasize the need for financial institutions in Bangladesh to prioritize not only building trust and managing risks but also leveraging social influences and the benefits of using DFS to attract and retain customers. Finally, it also suggests important insights for financial institutions in Bangladesh to better attract and retain digitally-savvy customers.
This paper aims to investigate the Mutual Fund industry's performance in Bangladesh, considering the close-end mutual funds of 32 listed funds in the Dhaka Stock Exchange, Bangladesh. The study employed unbalanced panel data analysis throughout 2014 to 2019. By using an error-corrected panel data regression model with an attempt to investigate the performance of mutual funds considering several fundamental factors such as Return on Assets, Earning per unit, Fund Size, Fund Age, Dividend payout ratio, Net Asset Growth and Management Fees, etc. After correcting autoregressive disturbance, the RE GLS regression model is selected to describe this panel data analysis. It demonstrates a significant positive connection between earning per unit and return on assets. The study identifies a significant negative relationship with fund age and asset growth in response to the change of return on assets. It also concludes there is no significant predictive power among the fund size, dividend payout ratio, management fees with return on assets while defining mutual fund performance. The study fills the gap by investigating the significant relationship of these following variables with the fundamental performance indicator. Findings of the empirical analysis suggest that the investors should pay close attention to earnings, fund age and assets growth while selecting mutual funds for investment. It is noticed that the company generally tends to pay lower dividend when the performance and profitability starts decrease. Policy makers should also pay attention to defining the downward characteristics of asset growth and dividend payout compared to the basic profitability ratios.
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