This research digs into the revolutionary environment of e-commerce and online marketplaces, illuminating disruptive forces and transformational potential in the digital domain. The study's goal is to thoroughly examine the influence of e-commerce on traditional company models, providing insights into strategic adaptation and innovation. The study recognizes the primary issue of little qualitative research in this sector, particularly in terms of e-commerce's localized consequences. It undertakes an exhaustive review of existing literature, theories, and concepts using a qualitative methodology. This approach fills a vacuum in the literature by giving qualitative insights on e-commerce's disruptive patterns and transformational potential. The key conclusions emphasize the disruptive role of technology, the transformation of supply systems, and changes in consumer behavior. It is advised that traditional firms embrace digital transformation, collaborate with e-commerce platforms, diversify revenue streams, and adapt to changing market dynamics. Policy proposals emphasize the significance of balanced laws that promote innovation while protecting consumers and maintaining fair competition. Policymakers should also prioritize digital literacy and cybersecurity measures. In conclusion, this study contributes to our understanding of e-commerce's transformative power, offering practical insights for businesses and policymakers. Navigating the evolving digital landscape requires adaptability, innovation, and a proactive approach to thrive in the era of e-commerce and online marketplaces.

Women entrepreneurs have been more common in recent years, but little is known about how they operate. This research explores the present dynamics of women's entrepreneurship based on a quantitative survey of 300 female business owners in Kano, Nigeria. The study examines the elements influencing women's entrepreneurial activities and the difficulties they encounter using a cross-sectional approach and stratified random sampling. The main findings show that, when analysed with SPSS, entrepreneurial networking, entrepreneurial education, and general self-efficacy have a substantial impact on women's entrepreneurial intention in Kano, Nigeria. Women entrepreneurs exhibit high levels of inventive talents in this moderately favourable entrepreneurial environment. However, limited access to financial inclusion and entrepreneurial support remains a challenge. To address these issues, the study recommends several strategies. First, formal financial institutions should raise awareness about loans, credit, and financial inclusion to attract more customers. Second, encouraging women entrepreneurs to obtain insurance can protect them from risk. Third, providing access to training and mentoring programs can enhance their leadership and management skills. Fourth, developing additional support mechanisms within formal financial institutions can assist female business owners in expanding their operations. Finally, training friends and family members of women entrepreneurs in entrepreneurship best practices can provide effective advice. These findings and recommendations shed light on the multifaceted landscape of women's entrepreneurship in Kano, Nigeria, emphasizing the need for targeted support to foster growth and empowerment within this dynamic community.

This research explores the untapped potential of farmstay tourism in Uttarakhand, India, leveraging its scenic landscapes, cultural richness, and diverse agriculture. Despite promising prospects, challenges such as inadequate infrastructure, seasonal variations, and regulatory complexities hinder full realization. To optimize this potential, governmental efforts should prioritize infrastructure enhancement and regulatory simplification, coupled with effective marketing strategies. Emphasizing sustainability, community involvement, and the fusion of modern experiences with traditional settings is crucial. Striking a balance between development and preservation is essential for Uttarakhand to evolve into a sustainable and distinctive farmstay destination. Success lies in crafting authentic, unique experiences that resonate with both tourists and locals, ensuring a flourishing and harmonious future for rural Uttarakhand.

This study examines factors determining a bank's credit risk in Ghana, using bank-specific factors such as size, liquidity, profitability, and operational efficiency. Moreover, industry-specific factors such as finance sector expansion, inflation, and capitalization, and economic variables like inflation and GDP growth rate were also used. The study uses panel data from 23 commercial banks from 2010-2020. The study found that bank-specific determinants like bank size and operational efficiency significantly influence credit risk. However, profitability has a negative correlation, while industry-specific variables like financial sector development and capitalization positively affect credit risk. Macroeconomic variables like inflation also have a negative relationship with credit risk. Bank management should focus on these determinants to prevent NPLs and ensure proper monitoring and supervision.

This study explores the transformative potential of integrating Big Data analytics and machine learning techniques in redefining market intelligence within the context of the Indian stock market. Leveraging the vast volume and variety of financial data available, the research aims to provide a comprehensive analysis of market dynamics, with a particular focus on accurate predictions and enhanced decision-making capabilities for investors. The application of machine learning algorithms seeks to uncover intricate patterns, trends, and anomalies within the financial data landscape, thereby offering unprecedented insights into market behavior. The study envisions a future where advanced analytics not only revolutionize stock market analysis but also contribute to regulatory compliance and monitoring processes. However, this transformative journey is not without its challenges. Concerns related to data privacy, potential biases in machine learning models, and the dynamic nature of financial markets need careful consideration. Furthermore, the reliance on historical data for predictions may present limitations, particularly in volatile market conditions. By addressing these challenges and striking a delicate balance between embracing technological advancements and mitigating associated risks, the study anticipates the potential for a paradigm shift in Indian stock market analysis, where market intelligence is redefined through the synergy of Big Data analytics and machine learning methodologies.

The Lapakemane Digital Market is an innovation we developed to answer the challenges faced by MSMEs in the District Brebes. The Lapakemane digital market has the same goal as traditional markets in general: to be a forum for buying and selling transactions. However, the digital market has its characteristics, including that transactions can be carried out anytime and anywhere, using electronic media as the primary tool, the buying and selling process is carried out without face-to-face. By building a digital market system, Lapakemane can help simplify and increase the promotion of MSME products via the Internet to increase buyer interest. The impacts obtained from the stages and the entire program are as follows. First, it can increase market exposure (market share). Using a digital market, you can promote or offer various goods for sale to various regions. Second, increase customer loyalty. Implementing the Lapakemane digital market makes interaction easy, such as without waiting in line or coming directly to the outlets, making it easier to shop from anywhere. Third, increase brand awareness. With the broad reach of marketing, many consumers know about the products sold at the district MSME centers. Brebes.

The study investigates whether institutional environment play any role on the impact of public debt on poverty level in sub-Sahara region from 2000 to 2022. The study uses cross-sectional data of 43 Sub-Saharan Africa countries. System GMM estimator was employed, the result shows a positive impact of health and education expenditure respectively on poverty level in the Sub-Sahara African region. Institutional environment appears to be a friend and a signifIcant mediating variable explaining how increasing public debt affect povert in Sub-Saharan african countries. The coefficients are statistically significant for education and health expenditure respectively, implying that human capital development through spending on education and health have not aided the attainment of the required threshold for drastically reducing poverty rate in the sub-Saharan African region contrary to theoretical expectation. The positive coefficient and 5% statistically significant of the GDP is not by any means amazing, an increase in the gross domestic product is a curse or a blessing. The evidence from the empirical result further shows that a trivial and indirect drag of GDP on poverty rate in the region. The study therefore recommends that if institutional environment can be enhanced, then public debt should align with a corresponding development level that can reduce the level of poverty in the African sub- Sahara region specifically since education expenditure appears to be one of the main dominant contents of public debt, government can continue in this area.

Every firm, regardless of its location, sector, or size, is susceptible to the issue of employee turnover, which is a problem that affects all enterprises. Accurately estimating employee turnover is one of the most important objectives of Human Resources (HR) in many firms. This is because it is a significant concern for an organization. Many firms are confronted with the challenge of employee turnover, which occurs daily and results in the departure of valued and experienced workers from the organization. A great number of companies all around the world are working toward the elimination of this significant problem. The primary purpose of this study endeavour is to develop a model that can assist in determining whether or not an employee will quit the organization. Many businesses incur considerable expenditures due to employee turnover, which is a key problem that leads these businesses to spend considerable costs. Providing human resources departments with a beneficial decision support system and, consequently, preventing a significant amount of time and resources from being wasted may be accomplished by utilizing machine learning and artificial intelligence techniques to predict the possibility of an employee resigning and the reasons for their departure. This study aims to present a preliminary exploratory examination of the application of machine learning approaches for predicting employee turnover. Organizations are confronted with enormous expenditures as a consequence of staff turnover. Implementing the Random Forest Classifier method will be utilized to make a prediction regarding employee turnover, which is now feasible thanks to developments in machine learning and data science. Human resources are one of the most valuable assets that an organization possesses. The success of any company or organization is contingent on the people working for that company or organization accomplishing their goals, meeting their deadlines, preserving quality, and ensuring that their customers are satisfied. Employee turnover, often known as employee attrition, is one of the most significant challenges businesses must face in a highly competitive environment. Attrition of employees is a predictable phenomenon under stable conditions, in which a predetermined pattern can be inferred from specific characteristics that influence both the individual and the business at all times. Some of these characteristics may be foreseen, such as the age at which one may retire. In contrast, others may be unforeseeable, such as the success of the firm, other sources of money, management shakeups, and so on. To lower the employee turnover rate, it is vital to evaluate the efficiency of employee evaluations and the degree to which workers are satisfied with their jobs within the organization. Within the scope of this study, a novel strategy that centres on machine learning was utilized to improve various retention strategies for specifically targeted employees. This report also tries to shed some insight into the many elements that influence the attrition rate of workers and the potential remedies to these problems. Many firms are confronted with the challenge of employee turnover, which occurs daily and results in the departure of valued and experienced workers from the organization. A great number of companies all around the world are working toward the elimination of this significant problem. The primary purpose of this study endeavour is to develop a model that can assist in determining whether or not an employee will quit the organization. Having competent personnel is a rare commodity when it comes to having a great company. An issue that poses a danger to business owners is the difficulty of retaining skilled workers with years of expertise. Since it requires much money to reward employees for their experience and efficiency, the problem of employee turnover may be quite expensive for companies. Because of this, the purpose of this research is to propose an automated model capable of predicting employee turnover based on various predictive analytical methodologies. Various pipeline topologies have utilized these methodologies to determine which champion model is the most effective. To recognize valuable employees leaving the firm, implementing this concept will assist management in employee evaluation and decision-making. We aim to create a general attrition prediction platform independent of the application domain and founded on the features of bipartite graphs and machine learning methods. By utilizing this program, it is possible to discover the hidden causes behind the departure of employees, and management can take preventative measures concerning the departure of each employee on an individual basis.