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In this study, 396 university students in Turkey are surveyed online regarding their attitudes towards financial issues, their level of financial knowledge, and their financial behavior. The latest version of OECD/INFE Financial Literacy Survey (2018) is used in the study. Using SPSS and LISREL. Factor analysis, Cronbach's Alpha, and Structural Equation Modeling (SEM) are used to analyze the relationship between financial knowledge of the participants with their financial attitude and behavior as well as the mediation effect of financial attitude in this relationship. In line with the common wisdom that the relationship between knowledge and behavior is higher if knowledge is better reflected in the attitude of the individual, the mediation effect of financial attitude strengthens the financial knowledge and financial behavior relationship.

This paper attempts to explore the issue of enhancing pedagogical skills among the college teachers of Bangladesh through CPD. This is a literature review paper that divided the topic into different themes and sub-themes in order to analyze the issue properly. After analyzing 60 studies including journal articles, conference proceedings, and research reports of different authors and organizations this study found several positive impacts of CPD on the teachers regarding uplifting their professional skills. It has also found some challenges of implementing CPD for college teachers identifying the scarcity of resources at the heart of all the challenges. Finally, the paper ends up with some recommendations to enhance the teachers’ professional skills through CPD in Bangladeshi colleges

The COVID-19 pandemic is anticipated to continue to the year 2022 and could lead to several business failures if entrepreneurs do not respond adequately. Thus, the purpose of this study is to introduce a framework that captures the impacts and potential responses to the ongoing pandemic. To do that, we conducted telephone interviews with ten entrepreneurs who operate enterprises within Nigeria. Data collected for analysis were transcribed, chronologically arranged, coded, and thematically analyzed using NVivo. Our findings revealed that, during the pandemic, businesses experienced limited opening hours, a decline in their clients' patronage, lower turnover, and employee demotivation. In response, business owners should consider re-strategizing their business activities, maintaining connections with clients, and exploiting available support measures. Our findings have profound theoretical and practical implications. For entrepreneurs and policymakers, they offer insights into the form of challenges confronting businesses during a pandemic. It also provides response activities that entrepreneurs could adopt to mitigate the impact of an epidemic on their enterprises’ performance. For the field of entrepreneurship, it advances the need to integrate public health (health-related issues) into entrepreneurship study and consider its multiple levels of influence on the performance of businesses.

This paper employs the R software in identifying the most suitable ARMA model for forecasting the growth rate of the exchange rate between the US dollar and a unit of the British pound. The data is systematically split into two distinct periods identified as the in-sample period and the out of sample period. The best model selected for the in-sample period is used to make forecasts for the out of sample period. Both traditional and rolling window forecasting methods are employed. This research uses the MSE, MAE, MAPE and correct sign prediction criterion to compare the forecasting performance of the rolling window forecasting method and the traditional forecasting method. The results obtained suggest that the traditional forecasting method performs better judging by MSE, MAE and MAPE. In other words, the traditional forecasting method is more suitable for predicting the magnitude (i.e., size) by which the US /UK exchange rate changes over time. However, the results also suggest that the rolling window forecasting method performs better based on the correct sign prediction criterion. In other words, the rolling window forecasting method is more appropriate for predicting the changes in the sign of the US /UK exchange rate. 

 This paper utilizes the Least Squares Dummy Variables (LSDV) technique in investigating the effect of financial depth on economic growth within a sample of middle-income countries, over the period 2005–2017. The research finds that financial depth has a negative impact on real GDP growth within middle-income countries. This result is robust to the use of alternative measures of financial depth, the use of per capita GDP growth as a proxy for economic growth, the inclusion of dummy variables to control for the 2007–2010 global financial crisis, the exclusion of countries with high average growth as well as across income levels. Based on its findings, this study recommends the need for robust regulations to ensure that the credit facilities of domestic financial institutions are channeled towards productive investments rather than debt servicing.

This study investigates the impact of financial and pension factors, including funding ratios, pension accounting modifications, and anticipated future cash flows, on disclosure practices in accordance with International Financial Reporting Standards (IFRS). Although previous studies have provided significant insights into the correlation among pension variables, corporate attributes, and disclosure levels, empirical information in IFRS jurisdictions is still few. To rectify this deficiency, we examine the factors influencing key actuarial estimate (CAE) disclosures and the actual variations in discount rates and anticipated asset returns, utilising panel data from 173 German companies (DAX, MDAX, SDAX), encompassing 1,057 and 1,352 firm-year observations, respectively. Employing the deferred remuneration theory as a framework, the findings reveal that greater firm size and regulatory status enhance the sensitivity of discount rates and anticipated returns, whereas extensive litigation disclosures diminish both. Profitability is positively correlated with sensitivity, whereas a weaker financed status exerts a mitigating influence. The correlations are most robust and consistent among DAX and MDAX companies, but less so among SDAX companies. The model exhibits a high overall fit, with R² values between 0.38 and 0.72. The results indicate that management of larger, regulated enterprises must be particularly vigilant about the formulation and dissemination of pension-related assumptions, as disclosure decisions can significantly affect market reactions. This study enhances comprehension of the factors and implications of pension assumption disclosures by presenting comparative evidence from significant German indices, thereby proposing practical recommendations for company policy and regulatory supervision. This work theoretically expands the deferred remuneration theory (DRT) by illustrating that the intensity and orientation of compensation–disclosure interactions differ systematically based on firm size, governance structure, and financial attributes. This indicates that the amount and direction of the relationship between remuneration and disclosure are not consistent, contrary to DRT's assumption. Instead, they exhibit consistent variation based on firm size, governance frameworks, and critical financial performance metrics.

This study investigates the use of artificial intelligence (AI) to improve operational performance in Zimbabwean road haulage enterprises, with a focus on driver training as a moderator. As the logistics industry faces new difficulties, AI technologies have great promise for increasing efficiency and decision making. However, the usefulness of these technologies is determined by the skill levels of the drivers using them. This study demonstrated how extensive driver training improves the capacity to comprehend AI-generated insights, resulting in better route management, lower operating costs, and increased safety. This study examines how AI affects key performance variables such as cost savings, productivity, customer happiness, and environmental sustainability, using real data from road haulage companies. Key findings demonstrate how AI is transforming decision-making, improving operational effectiveness, and optimizing routes. The research highlights several noteworthy obstacles in addition to their obvious advantages, such as budgetary limitations, difficulty in obtaining pertinent data, and the need for more regionalized AI solutions. The findings, which are based on case studies and performance data from diverse enterprises, indicate that (i) organizations that invest in both AI and driver training benefit from a synergistic impact, resulting in higher operational outcomes, (ii) there is need to combine technical improvements with human experience to achieve maximum performance in Zimbabwe's competitive road-haulage market and finally (iii) this study offers helpful recommendations for successfully integrating artificial intelligence (AI) into haulage processes, along with insights into best practices and alternative approaches to overcome current obstacles. This study emphasizes the importance of context-specific solutions in emerging regions, enhancing the expanding corpus of knowledge on AI applications, particularly in logistics.

High unemployment levels means that graduates are now encouraged to consider entrepreneurship as a career, and it is necessary to understand how entrepreneurship education influences their career choices. We investigated the relationship between entrepreneurial education and entrepreneurial intentions of business management students at selected Universities of Technology (UoT) in KwaZulu-Natal, South Africa.  We used quantitative research questionnaire to survey the entrepreneurial intentions and skills of students of 224 students enrolled in business courses and had completed a module in entrepreneurship at undergraduate level. Regression analysis of entrepreneurial education variables and entrepreneurial intention was conducted. The findings of this study indicated that Entrepreneurial intention is positively affected by entrepreneurial skills, creativity, leadership, and business experience but negatively by financial knowledge. These results show that while higher levels of financial knowledge have a negative impact on entrepreneurial intention of business students. Therefore, the university curriculum must cultivate traits like resourcefulness, leadership, and ambition which encourages beneficial outcomes of entrepreneurial intention amongst students.

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.

This study investigates how organisational and geographical factors affect the efficacy of business architecture in enterprises. Employing a quantitative research methodology, the research analyses data from a diverse range of organisations using ordinal logistic regression. The findings validate the hypothesis that factors like organisational alignment, industry sector, geographic region, organisational size, and adoption duration significantly influence business architecture's maturity and strategic impact. The study contributes to the understanding of business architecture's role in organisational strategy, highlighting the importance of contextual factors. It suggests that business architecture strategies should be tailored to specific organisational contexts, offering valuable insights for policy formulation and organisational strategy development. This research provides a foundation for future studies and practical applications in business architecture and enterprise transformation, emphasizing the need for a nuanced approach considering different organisational and regional characteristics.