Explore the evidence.
Understand the reach.

Data quality in banking and financial sector is one of the most researched topics nowadays. With the increasing regulatory burden and increased importance of targeted sales, data quality directly influences funds and performance of banking system. In this paper, the author is aiming to define universal reasons and causes of data quality problem and apply the case to local Azerbaijani banks taking into account local managers’ personal view based on their banking experience. Key finding of the research is that unintegrated software, wrong data insertion, aging of data with the growing speed of market, corporate governance and inability to calculate true costs of low data quality to the local banks are the reasons of data quality issue in the local banks. Moreover, main costs of the data quality issue are time and money, appearance of hidden data factories, obstacles to apply and measure KPIs, uncorrelations in sensitivity analysis and ineffective marketing strategies.

<p>The paper employs various measures of law enforcement to provide new evidence on the importance of legal institutions for different dimensions of financial development in transition economies. It offers a critical assessment of law enforcement measures employed in recent studies by showing that some proxies for law enforcement in the credit market may not be appropriate. Hence, care should be taken in how the quality of institutions is measured and the context which it represents. An original approach to measuring law enforcement in the credit market is developed by embodying the legal theory of dispute resolution and assessing this approach by collecting primary data for Kosovo. The findings suggest that Kosovo compares well with countries in the region and other transition economies in terms of the enforcement of creditor rights.</p>

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.

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.

The aim of this study is to examine the dynamic relationships among alternative investment instruments in Turkey using advanced time-series methods. The findings indicate evidence of granger causal relationships among some of the variables. However, the direction and statistical significance of these relationships vary across the variables examined. The analysis employs monthly data covering the period from February 2010 to December 2018. The stationarity properties of the variables were examined using the Augmented Dickey–Fuller (ADF) test. Causal relationships among the variables were subsequently investigated using the Toda–Yamamoto causality test. The findings indicate that there are statistically significant short-term and long-term relationships among alternative investment instruments. It was determined that the exchange rate and gold prices have on the stock market. Furthermore, its interest rates play in both financial markets and housing prices. This study contributes to the literature by examining the relationships between alternative investment instruments using linear methods. Moreover, the results offer researchers and policymakers important implications for financial stability for investors' portfolio diversification strategies at both the national and international levels.

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.

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.