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This paper aims to examine the financial performance of Bangladeshi listed commercial banks. The study analyzed the financial performances of banks categorized into three generations—first, second, and third—based on their year of commencement of operations in the country. The data of a total of 24 banks, 8 from each generation, listed in the Dhaka Stock Exchange (DSE), has been collected from audited financial statements for the time period of 13 years, from 2010 to 2022. Data has been analyzed using different ratios of profitability, liquidity, capital adequacy, operational efficiency, and credit risk. The ANOVA test also has been used to make a comparison of profitability—the return on assets (ROA), and return on equity (ROE) of the banks of different generations. According to the results, the average profitability ratios in terms of ROA, and ROE of the second-generation banks showed their credibility than banks of other generations; while first generation banks kept higher liquidity, and capital adequacy with higher credit risk than the banks of other generations. The first-generation banks also showed a lower cost-expense ratio, and a higher efficiency in terms of operation, than the banks in the second and third generations. The research also discovered that the profitability of banks of different generations significantly varies from one another.  

The fraudulent claims for Unemployment Insurance (UI) have also risen massively in the United States especially during the onset of COVID-19 pandemic with billions of dollars that were lost. These approaches applied formerly in fraud detection and prevention have been challenged by new and advanced fraud systems. For this reason, AI, Big Data and Predictive Analytics are now crucial for improving fraud mitigation in UI programs. The aim of this research is to understand how far AI, Big Data and Predictive Analytics have been utilized, for how effective they are and the barriers they pose in tackling unemployment insurance fraud in the United States. An online quantitative self-administered questionnaire was administered where 200 participants from the unemployment insurance agencies of the U.S, covering fraud investigators, IT personnel, data analysts and policymakers, were included in the study. Data were analyzed to establish the correlation between technology adoption to application of anti-fraud measures using descriptive statistics, chi-square test and regression analysis. The survey shows that about 42.5% of agencies actively use AI and 34% of them use Big Data very often. However, difficulties like high costs, integration troubles and limited availability of specialized staff are still issues that make adoption a problem. The results of using AI and Big Data were moderate and optimistic but rather elevated, as seen through regression analysis; on the other hand, predictive analytics demonstrated large potential when applied successfully. The study’s results indicate that AI, Big Data and Predictive Analytics present enormous potential for mitigating UI fraud, although broad integration will not be possible without overcoming essential infrastructural and resource limitations.

Using A-share listed companies in Shanghai and Shenzhen from 2010 to 2019 and the density of Confucian temples and schools near sample company headquarters, this paper uses regression analysis to estimate the impact of Confucian culture on corporate environmental governance.  Enterprise environmental capital investment as a percent of total assets is the dependent variable used as a proxy for environmental governance. The log of the number of the number of Confucian temples and Confucian academies within a 200-kilometer radius of the company's registered location plus one is the independent variable. This study finds that the environmental capital expenditures increase by 0.007% for every unit increase in the independent variable. Similar results are found when the radius is expanded to 300 kilometers. The greater the influence of Confucian culture is, the more inclined enterprises are to participate in corporate environmental governance. Additionally, environmental governance information disclosure can be a key influence path for this correlation.  Further analysis finds that the relationship between Confucian culture and environmental governance is stronger with more experienced managers, a more developed institutional environment, and after 2015 following the People's Republic of China’s enactment of the Environmental Protection Law.  This study expands the literature on the factors that influence Confucian culture on corporate environmental governance, enriches the empirical evidence of the influence of the informal system of corporate environmental governance on enterprise decision-making, and confirms the modern commercial value of Confucian culture. 

Although the onset of the pandemic pressured monetary and fiscal policy across the globe, deficit finance was aided by the large, established debt markets in countries like the United States.  The expansionary efforts in emerging markets like Latin America put greater strains on their more limited capacity.  Our study analyzes data from seven Latin American countries, three European countries, three Asian countries, and the United States. We utilize panel vector autoregressive (VAR) models to explore the dynamic interactions and feedback mechanisms among the policy variables, while accounting for unobserved country-specific effects.  Our results demonstrate strong and significant interactions between monetary and fiscal policy around the time of the pandemic, with the primary causal effects running from fiscal to monetary policy.  Additionally, our results yield evidence of countercyclical policy reactions to inflation for both monetary and fiscal policy over the pandemic period.  

The literature laments college students’ lack of financial literacy and their lackadaisical interest in learning. However, our research demonstrates that students can be enticed such that they get interested in acquiring financial skills. Generally, college students live hand-to-mouth and are happy to learn about money-making opportunities (not necessarily financial management). Students were informed that qualified faculty will conduct a series of workshops whereby they will learn financial tools and skills and will also get an opportunity to trade stocks and options first-hand. Literature on pedagogy confirms that experiential learning is the best way to learn. Students also prefer practical, hands-on learning more than listening to a lecture or reading a book. Moreover, if workshops (for which professionals charge exorbitant sums) are offered for free, students’ interest soars, and they enrol in hordes. This is confirmed by our workshops which proved successful in enhancing student interest and imparting financial skills. The workshops were innovative and unique as they were online, interactive, tutored, and free. They were open to students from all majors, faculty, and staff. Students are excited about stock trading but are generally apprehensive of and intimidated by financial options. This was true of our sample of students as well, but the faculty-led, friendly, and riskless environment of hands-on learning provided a great opportunity, and over 100 individuals enrolled in our workshops. A comparison of pre- and post-survey quizzes shows that participants’ interest in, and knowledge of options trading increased significantly due to the workshops.  

Controlling corporate governance decision-making RISK is one of the prime safeguarding directives in the PCAOB’s requirement that the financials of Market Trading Firms [MTFs] are reasonably free from material error and thus are reliable indications of the results of operations. In this Corporate Governance Risk [CGR] context, it is important to understand the Interacting- & Integrated-Systemic Components of CGR and how they have evolved over time. We examine these aspects from Circa 1985 to date. We argue that the Driving Control System is: The Firm’s decision-making system of Internal Control over Financial Reporting [ICoFR] that impacts the Nature of CGR—to wit, Increasing CGRisk results as management experiences Increasing difficulties in maintaining adequate ICoFR. We examine the historical development of these systemic components and how they have spawned an Economic Sector of Firms that Rate & Score MTFs on the nature of their CGR profile, one currently predominate aspect of which is: The Nature of the MTF’s Caretaker Persona. With this as context, we present a duel-effect inferential design that examines the reliability of the Bloomberg™ Market Navigation Platform’s Institutional Shareholder Services™ [ISS]-Profiler as an information-link to be used in selecting MTFs to create CGR-sensitive Investment Portfolios. Our inferential results suggest for the MTFs in the ISS-Groups [{1: Lowest CGR} & {5: Mid-Level CGR} & {10: Highest CGR]}, it is likely that: (i) The decisions made by all involved in Management’s System of ICoFR, in its current COSO-version, are reflective of these MTF’s Caretaker Personas, (ii) The effects of these Caretaker Personas are analytically perceptible as reflected in the MTF’s Financials, and (iii) are aligned with the expected Nature of the ISS-Groups.

The fraudulent claims for Unemployment Insurance (UI) have also risen massively in the United States especially during the onset of COVID-19 pandemic with billions of dollars that were lost. These approaches applied formerly in fraud detection and prevention have been challenged by new and advanced fraud systems. For this reason, AI, Big Data and Predictive Analytics are now crucial for improving fraud mitigation in UI programs. The aim of this research is to understand how far AI, Big Data and Predictive Analytics have been utilized, for how effective they are and the barriers they pose in tackling unemployment insurance fraud in the United States. An online quantitative self-administered questionnaire was administered where 200 participants from the unemployment insurance agencies of the U.S, covering fraud investigators, IT personnel, data analysts and policymakers, were included in the study. Data were analyzed to establish the correlation between technology adoption to application of anti-fraud measures using descriptive statistics, chi-square test and regression analysis. The survey shows that about 42.5% of agencies actively use AI and 34% of them use Big Data very often. However, difficulties like high costs, integration troubles and limited availability of specialized staff are still issues that make adoption a problem. The results of using AI and Big Data were moderate and optimistic but rather elevated, as seen through regression analysis; on the other hand, predictive analytics demonstrated large potential when applied successfully. The study’s results indicate that AI, Big Data and Predictive Analytics present enormous potential for mitigating UI fraud, although broad integration will not be possible without overcoming essential infrastructural and resource limitations.

Abstract Resources and capabilities in SMEs are only advantageous when the SMEs have the potential to execute the right strategies and processes. SMEs must analyze their business environments and understand the resources available to operate efficiently and effectively and achieve a sustained performance. This study is the subject of the influence of valuable resources on performance of SMEs. This study used a cross-sectional survey design. The target population for this study comprised of 121, 680 SMEs. The research used stratified random sampling technique. The  sample size of this study was 399 SMEs. A structured questionnaire was used to collect data. Findings showed that there was a positive significant correlation between valuable resource and SMEs performance (r=.270; p<.01). According to the linear regression analysis valuable resources account for 7.3% of SMEs performance. According to the regression coefficients, one-unit increase in valuable resources improved SMEs performance by 0.269 units (? = 0.269). The t-statistics results showed that t = 4.778, p<0.05. Therefore, there was enough evidence to reject the null hypothesis and conclude that valuable resources have a statistically significant influence on SMEs Performance. The study concluded that valuable resource has a significant relationship with SMEs Performance. Valuable resources have a statistically significant influence on SMEs Performance. Increasing valuable resources in an SMEs will help the enterprise register higher performance. Key Words: Organizational Resources, Valuable Resources, Performance

Using A-share listed companies in Shanghai and Shenzhen from 2010 to 2019 and the density of Confucian temples and schools near sample company headquarters, this paper uses regression analysis to estimate the impact of Confucian culture on corporate environmental governance.  Enterprise environmental capital investment as a percent of total assets is the dependent variable used as a proxy for environmental governance. The log of the number of the number of Confucian temples and Confucian academies within a 200-kilometer radius of the company's registered location plus one is the independent variable. This study finds that the environmental capital expenditures increase by 0.007% for every unit increase in the independent variable. Similar results are found when the radius is expanded to 300 kilometers. The greater the influence of Confucian culture is, the more inclined enterprises are to participate in corporate environmental governance. Additionally, environmental governance information disclosure can be a key influence path for this correlation.  Further analysis finds that the relationship between Confucian culture and environmental governance is stronger with more experienced managers, a more developed institutional environment, and after 2015 following the People's Republic of China’s enactment of the Environmental Protection Law.  This study expands the literature on the factors that influence Confucian culture on corporate environmental governance, enriches the empirical evidence of the influence of the informal system of corporate environmental governance on enterprise decision-making, and confirms the modern commercial value of Confucian culture. 

This study investigates the intricate relationships between transformational leadership behaviours (TLB), job satisfaction (JS), organizational commitment (OC), and organizational citizenship behaviours (OCB) within the context of county governments in the United States. Drawing on a theoretical model that hypothesizes both direct and indirect effects of TLB on OCB, mediated by JS and OC, this research employs structural equation modelling to analyze survey data collected from 372 matched leader-follower pairs across various county governments. The findings substantiate the hypothesized model, revealing that TLB indirectly influences OCB through the mediating effects of JS and OC. Specifically, the study underscores the pivotal role of job satisfaction and organizational commitment as conduits through which transformational leadership exerts its positive impact on organizational citizenship behaviours. This research contributes to the literature by empirically validating a comprehensive model within the public sector context, thereby extending our understanding of the mechanisms through which transformational leadership fosters a conducive work environment that promotes OCB. The study's implications for leadership development and organizational policy within public sector organizations are discussed, alongside limitations and directions for future research.