This study examined socio-economic factors affecting Micro, Small, and Medium Enterprises (MSME) e-commerce adoption, focusing on gender, income, and education. Using the 2022 National Socio-Economic Survey (Susenas) data, a logistic regression model was employed to analyze key determinants of e-commerce utilization. Additionally, an online survey of 550 MSMEs across 29 provinces was conducted to assess the impact of digitalization on business performance. In comparison, an offline study of 42 MSMEs with low digital adoption provided insights into the barriers hindering digital transformation. A natural experiment was conducted to evaluate the effectiveness of behavioral interventions in promoting the adoption of e-payments and e-commerce. The main contribution of this study lies in integrating large-scale national survey data with experimental approaches to provide a deeper understanding of digital adoption among MSMEs. Unlike previous studies focusing solely on socio-economic determinants, this research incorporated a digital nudging experiment to examine how targeted incentives influenced e-commerce participation. The findings revealed that digital transformation significantly enhanced MSME performance, particularly in turnover, product volume, customer base, and worker productivity. Socio-economic factors such as gender, household head status, and social media access significantly influenced digital adoption decisions. Behavioral nudging proved effective in increasing MSME participation in e-commerce. Although this study was limited to Susenas 2022 data and survey responses, it bridges a critical research gap by linking socio-economic factors with behavioral interventions in MSME digitalization. The findings offer key insights for policymakers in formulating evidence-based strategies to drive MSME digital transformation and e-commerce growth in Indonesia.

The consensus is that price stability promotes sustainable economic growth while excessive inflation harms growth. This study assesses the linkage between inflation and economic growth in South Africa to determine the optimal inflation rate threshold for the sustainable growth of the economy. Quarterly data from 1995 to 2022 was analysed through the ARDL and threshold regressions. The ARDL and threshold regressions estimate established a relationship between inflation and economic growth and computed the optimal inflation rate threshold for economic growth at 6 percent. The results also established that both the repo rate (repurchase rate) and real effective exchange rate have a negative relationship with economic growth. The Toda-Yamamoto causality test result indicated a unidirectional causality runs from inflation to economic growth. These results are crucial for the South African Reserve Bank to discharge its monetary policy functions to attain and maintain price stability. Therefore, this study offers the Bank a roadmap for targeting an inflation rate that aligns with the nation’s long-term objectives for sustainable economic growth.

India has experienced notable advancements in trade liberalization, innovation tactics, urbanization, financial expansion, and sophisticated economic development. Researchers are focusing more on how much energy consumption of both renewable and non-renewable accounts for overall system energy consumption in light of these dynamics. In order to gain an understanding of this important and contentious issue, we aim to examine the impact of trade openness, inventions, urbanization, financial expansion, economic development, and carbon emissions affected the usage of renewable and non-renewable energy (REU and N-REU) in India between 1980 and 2020. We apply the econometric approach involving unit root tests, FE-OLS, D-OLS, and FM-OLS, and a new Quantile Regression approach (QR). The empirical results demonstrate that trade openness, urbanization and CO₂ emissions are statistically significant and negatively linked with renewable energy utilization. In contrast, technological innovations, financial development, and economic development in India have become a source of increase in renewable energy utilization. Technological innovations were considered negatively and statistically significant in connection with non-renewable energy utilization, whereas the trade, urbanization, financial growth, economic growth, and carbon emissions have been established that positively and statistically significant influence non-renewable energy utilization. The empirical results of this study offer some policy recommendations. For instance, as financial markets are the primary drivers of economic growth and the renewable energy sector in India, they should be supported in order to reduce CO₂ emissions.

In rural areas, land use activities around primary arterial roads influence the road section’s traffic characteristics. Regulations dictate the design of primary arterial roads to accommodate high speeds. Hence, there is a mix of traffic between high-speed vehicles and vulnerable road users (pedestrians, bicycles, and motorcycles) around the land. As a result, researchers have identified several arterial roads in Indonesia as accident-prone areas. Therefore, to improve the road user’s safety on primary arterial roads, it is necessary to develop models of the influence of various factors on road traffic accidents. This research uses binary logistic regression analysis. The independent variables are carelessness, disorderliness, high speed, horizontal alignment, road width, clear zone, road shoulder width, signs, markings, and land use. Meanwhile, the dependent variable is the frequency of accidents, where the frequency of accidents consists of multi-accident vehicles (MAV) and single-accident vehicles (SAV). This study collects data for a traffic accident prediction model based on collision frequency in accident-prone areas. The results, road shoulder width, and road sign factor all have an impact on the frequency of traffic accidents. According to a realistic risk analysis, MAV and SAV have no risk difference. After validation, this model shows a confidence level of 92%. This demonstrates that the model generates estimations that accurately reflect reality and are applicable to a wider population. This research has the potential to assist engineers in improving road safety on primary arterial roads. In addition, the model can help the government measure the impact of implemented policies and engage the public in traffic accident prevention efforts.

The study investigates the impact of corporate gender diversity on dividend payouts in Asia-Pacific countries. The study used the data of 610 listed firms in the Asian Pacific region over eleven years, from 2006 to 2016, with 6710 observations. The regression results revealed that the representation of women on board and at least 30% on board positively relates to dividend payout. Board size and board independence have a significant negative relationship with dividend payouts. Overall, results suggest that gender diversity on corporate boards has a greater propensity to pay dividends in the mix of ownership structure, strong and weak corporate governance compliance, and horizontal agency conflict.

The present study attempted to assess the impact of fundamental ratios on the share prices of selected telecommunication companies in India. India has dramatically expanded over the past ten years to become the second-biggest telecoms market worldwide, with 1.17 billion users. The Indian telecom industry has proliferated thanks in part to the government of India’s liberal and reformist policies and strong customer demand. It has become a lucrative investment sector for investors due to its recent and prospective growth. Data on 13 telecom firms indexed in the S&P BSE telecommunication index from 2013 to 2022 were taken from companies’ annual reports, the BSE website (Bombay Stock Exchange), and other secondary sources. Six firm-specific fundamental factors viz. Debt to Equity ratio (D/E), Current ratio (CR), Total Assets Turnover ratio (ATR), Earnings per share (EPS), Price to earnings ratio (P/E), Return on equity (ROE), and three country-specific fundamental factors viz. Gross Domestic Product, Inflation rate, and S&P BSE Sensex return were considered. Fixed effect panel regression through Generalized Least Square (GLS) model was performed to find inferences. Debt Equity ratio and Inflation rate were found to impact share price negatively. Conversely, the Total Assets Turnover ratio (ATR), Earnings per share (EPS), Price to Earnings ratio (P/E), and Return on Equity (ROE) positively impacted selected companies’ share prices. The study results will benefit individual & institutional investors in formulating their investment and portfolio diversification strategies for gaining a high effective rate of return on their investments.