Asian Journal of Economics, Business and Accounting
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The Impact of Financial Structure and Director Diversity on Financial Performance: The Mediating Role of Intellectual Capital in Indonesia’s Financial Sector
No full textAims: Corporate performance management is very important in supporting the achievement of organizational goals. This research investigates how financial structure and board diversity influence financial performance, with intellectual capital serving as a mediating factor.
Study Design: This study is a quantitative analysis that employs a purposive sampling approach to investigate companies in the financial services sector listed on the Indonesia Stock Exchange (IDX) from 2020 to 2023.
Methodology: This study used 104 companies as the population, and 30 companies with 82 data were used as the sample. The analysis technique used was the classic assumption test, regression analysis, and SobelSobel test on SPSS 26.
Conclusion and Recommendations: The study revealed that financial structure positively influences intellectual capital, whereas gender diversity has no effect, and nationality diversity negatively impacts intellectual capital. Additionally, intellectual capital positively affects financial performance and serves as a mediator between financial structure and financial performance; however, it does not mediate the relationship with gender diversity and negatively mediates the relationship with nationality diversity. These results align with agency theory and the resource-based view, highlighting the significance of effectively managing intellectual capital to enhance financial performance. In conclusion, companies should focus on managing diversity and resources to attain a competitive advantage
The Impact of Corporate Governance on Firm Performance: A Review
No full textAims: The purpose of this paper is to analyze the results of recent empirical research on the impact of corporate governance on firm performance and reflect on potential research design issues that lead to inconsistent results.
Study design: Literature Review.
Place and Duration of Study: Archival Literature from Emerald Journal’s Data.
Methodology: This study aims to explore the impact of financing decisions on firm value in Indonesia, focusing on the implementation of Good Corporate Governance (GCG). This study synthesizes existing studies by collecting data from scientific literature sourced from online databases such as Google Scholar, Mendeley, and others. The literature review includes articles published in the last decade (2020-2024).
Results: Empirical evidence shows that financing decisions positively and significantly affect firm value. Optimal financing choices can increase firm value sustainably. In addition, the implementation of GCG principles is essential in overseeing management practices, ensuring transparent, responsible, and shareholder-friendly financing decisions.
Conclusion: This study provides in-depth insights into the relationship between financing decisions, firm value, and the importance of GCG practices in the Indonesian business environment
Navigating Ethics and Regulation: The Role of AI in Modern Financial Services
No full textIn this paper, I delve into the ethical and regulatory aspects of using artificial intelligence (AI) in the finance sector. As AI technologies increasingly influence financial decision-making, addressing issues of fairness, transparency, and regulation becomes crucial. The research will involve reviewing relevant academic literature, industry reports, and regulatory documents to gather information and insights on the ethical and regulatory dimensions of AI in finance. My research focuses on three main areas: the presence of bias in AI-driven financial decisions, the regulatory challenges hindering ethical AI deployment, and the need for transparency and explainability in AI processes. By examining these aspects, I argue that mitigating bias, enhancing regulatory frameworks, and promoting clarity in AI applications are essential for building trust and ensuring ethical practices in financial services. Key findings include the presence of bias in AI-driven financial decisions, the need for updated regulatory frameworks to address AI complexities, and the importance of transparency and explainability in AI processes. These elements are crucial for building trust and ensuring ethical practices in financial services. Ultimately, this work advocates for a collaborative approach among regulators, financial institutions, and AI developers to create a more equitable financial ecosystem
The Effect of ESG Disclosure and Financial Performance on Company Value with Company Size as a Control Variable on Companies Listed on the ESG Leaders Index
No full textThis study aims to evaluate the influence of ESG Disclosure and Financial Performance aspects on Company Value with Company Size as a control variable. This study uses a population of companies included in the ESG Leaders index during the 2021-2023 period and a sample collection method using Purposive Sampling which resulted in 90 research samples. The results of the study reveal that Environmental Social Governance (ESG) Disclosure, Profitability, Liquidity and Company Size as control variables have a positive and significant effect on company value. While Leverage has a negative and insignificant effect on company value. This finding emphasizes the importance of integrating ESG principles into corporate strategy not only to mitigate risk but also to increase the company\u27s attractiveness in the eyes of investors. This study provides a relevant empirical contribution, especially in the context of developing countries and can be used as a reference for other companies in optimizing company value through the implementation of effective sustainability practices
Profitability Analysis of Women Entrepreneurs on Palm Oil Refinery in Kigoma Municipal, Tanzania
No full textThis paper was analyses the profitability of the women entrepreneurs on oil palm refinery at Kigoma Municipal in Tanzania for year 2024 using variables of age of respondent, education level of respondent, experience in business, purchasing cost, labor cost, transportation costs during buying, refinery charge and selling price. Data were collected using a structured questionnaire administered to a random sample of 65 oil palm women entrepreneurs. The data were analyzed using (SPSS) to determine descriptive statistics, profitability analysis and Multiple Linear Regression Model (MLRM)results. The obtained results showed that the highest cost for oil palm refinery process stages is Tsh 1,200,000.00 = (70.5%) for labor charges of average total variable costs per year. The profitable analysis showed that oil palm refinery process in the study area is an average gross margin (TR)-(TVC) per women enterprises of oil palm refinery was Tshs. 2,589,000.00 per year while the net profit was estimated at Tshs. 2,533,000.00. The benefit cost ratio of the entire enterprise was 2.44, thus indicating an additional return for every one shilling selling on oil palm refinery or processed. The multiple regression models revealed that the experience in oil palm refinery process enterprises, cost of seed purchasing, labor cost, costs of refinery process and cost of selling oil has a positive and significant relationship with profitability of women entrepreneurs’ success (β = 0.108, 0.084, 0.214, 0.347and 0.088, p value > 0.05). The adjusted R2 of the model was 0.86 which means the independent variables explain 86% of the variation in the profit per 20 litres of the oil palm enterprises. This paper recommends Small and Medium Enterprises (SMEs), policymakers, and practitioners to encourage women entrepreneurs to run their businesses for the long term by providing a variety of incentives and supports related to those internal and external factors. Also to expanding and up-scaling oil palm refinery production for providing adequate investment capital to operate so that can be guaranteed loans to the oil palm women entrepreneurs untouched and touched of refinery process specifically at Kigoma Municipal in Tanzania
Mediation Analysis in Structural Equation Modeling (Sem): Theoretical Foundations, Statistical Methods and Practical Implications
No full textThis study offers a comprehensive investigation of mediation analysis in Structural Equation Modelling, highlighting its theoretical basics, statistical practices, and real-world applications. It differentiates mediation from moderation, explaining how mediation helps in understanding indirect relationships between latent variables. Various proposed mediation models, including simple mediation, multiple mediation, and moderated mediation, are discussed in detail. The study also analyses statistical methods such as the Causal Steps Approach (Baron & Kenny, 1986), the Product-of-Coefficients Method (Sobel Test), Bootstrapping, the Bayesian Estimation Method, and Monte Carlo Simulation, each with its respective advantages and limitations. Additionally, advanced Structural Equation Modelling techniques, such as multigroup mediation, longitudinal mediation, and latent variable mediation, are examined to address complex research scenarios. Employing a literature review-based methodology, the study synthesizes existing knowledge on best practices for estimating mediation effects using Structural Equation Modelling. Software tools like AMOS, Mplus, LISREL, and SmartPLS are discussed in the context of model specification, estimation, and evaluation. Real-world applications in business, psychology, human resource management, and marketing are illustrated, including customer trust mediating the relationship between service quality and purchase intention, employee engagement mediating the effect of transformational leadership on job performance, and social media engagement mediating brand trust and purchase intention. Key findings highlight bootstrapping as a better method for estimating indirect effects due to its non-reliance on normality of the data assumptions and Bayesian SEM as a robust substitute for handling small sample sizes and incorporating preceding knowledge. The study also discusses crucial challenges such as measurement error, model misspecification, the need for longitudinal data to establish causal inference, and comparisons between Structural Equation Modelling-based mediation and regression-based mediation using the PROCESS macro. By presenting a structured framework for mediation analysis in Structural Equation Modelling, this current study contributes to advancing causal modelling methods across various disciplines and provides directions for future research
A Review of Omni-channel Strategies in E-commerce: Integrating Online and Offline Customer Journeys
No full textThis study explores the evolution and implementation of omnichannel strategies in e-commerce, emphasizing the seamless integration of online and offline channels to enhance customer experience and operational efficiency. Omnichannel strategies in e-commerce integrate online and offline channels to create seamless, personalized customer experiences. This approach addresses evolving consumer behavior, characterized by a preference for convenience, flexibility, and cross-platform shopping. By merging platforms like physical stores, mobile apps, and social media, omnichannel retail enhances operational efficiency, customer loyalty, and revenue generation. The integration, however, poses challenges, including data synchronization, legacy system compatibility, and channel conflicts. Emerging technologies such as artificial intelligence, IoT, and big data play a pivotal role in overcoming these challenges by enabling real-time synchronization, predictive analytics, and personalized marketing. Practical implementations include click-and-collect systems, dynamic pricing, and tailored in-store experiences, while theoretical foundations like the cognitive-affective-conative paradigm support strategies that improve consumer satisfaction. Despite significant advancements, high implementation costs and technological complexities remain barriers, emphasizing the need for cost-effective innovations. Future research should focus on augmented reality, blockchain applications, and cross-cultural studies to broaden the scope and applicability of omnichannel strategies. By addressing these challenges and leveraging advanced technologies, businesses can adapt to dynamic market demands, fostering sustainable growth and enhanced customer experiences
Impact of Government Expenditure on Economic Growth of Ghana
No full textThe paper focuses on the effect of government expenditure on economic growth in Ghana. The study sought to discover and analyze the effect of Government recurrent and capital expenditure on economic growth. The independent variables considered in this research constitute Government recurrent and capital expenditure while gross domestic product (GDP) is considered as dependent variable. The work is analyzed using time series data from 1972 to 2021. A unit root test is conducted to determine whether the time series variable is non-stationary and possesses a unit root. The results imply that capital government expenditure (GCE) has an important effect on GDP growth with a coefficient of 0.26160 (p < 0.01) meaning that a 1% increase in GCE results in a 0.26% increase in GDP. On the other hand, the coefficient for recurrent government expenditure (RGE) is given as 0.05581 with a p-value of 0.2664 which shows a positive relationship between GDP and RGE, but the p-value is also not significant. The Granger causality test indicates the presence of a bidirectional relationship between capital expenditure and economic growth while unidirectional causality from economic growth to recurrent expenditure. These results support the Keynesian perspective that more government involvement in the economy is associated with growth. However, the study also goes against the Peacock and Wiseman hypothesis, suggesting government spending is more responsive during crises than as an inner cause of economic growth. The study recommends that to achieve sustained economic growth, the government and policymakers should concentrate on investing in the basic infrastructure as they are catalysts for economic growth
Financial Risk Management in Zimbabwe
No full textAims: The study aimed to develop a comprehensive framework tailored to the specific challenges faced by businesses
Study Design: Qualitative research design.
Place of Study: Financial stakeholders in Zimbabwe
Methodology: The researcher chose 15 important players in Zimbabwe\u27s financial industry in order to conduct interviews and collect primary data. This includes academics with expertise in finance as well as professionals employed by banks, regulatory agencies and investment businesses.
Results: Key challenges Zimbabwean businesses face include economic instability, political uncertainty, lack of access to finance, foreign exchange risks and regulatory challenges. Economic instability, political instability, policy changes, limited access to financing options, capital constraints and complex regulatory requirements add to the challenges. The study proposed a comprehensive risk management framework for Zimbabwean businesses, including risk assessment tools, mitigation strategies, and monitoring mechanisms.
Conclusion: It can be alluded that incorporating the components proposed in the framework into risk management procedures can help companies better recognize, evaluate, reduce and track financial risks in the nation\u27s distinct economic landscape. Further research can however be required on a larger scale and incorporating other research methods
Moderation Analysis in Business Research: Concepts, Methodologies, Applications and Emerging Trends
No full textModeration analysis is an essential statistical technique employed to examine the effect of the strength or direction of the relationship between two variables by a third variable, which is referred to as a moderator. This essay seeks to provide an all-encompassing model of moderation by explaining the definition, outlining different methodologies, and illustrating the practicability in various fields, such as human resource management (HRM), marketing, psychology, finance, and organizational behaviour (OB). In HRM, moderation analysis is used to determine how variables like perceived organizational support affect the relationship between work-life balance policies and employee commitment. In marketing, moderators such as brand trust affect the influence of corporate social responsibility (CSR) efforts on customer loyalty. Likewise, financial research applies moderation to determine how risk tolerance influences the relationship between financial literacy and investment choices. In OB and psychology, moderators like organizational culture and psychological safety are critical in influencing team dynamics and leadership effectiveness. In addition, new trends in moderation analysis, including the use of AI and ML, have improved the capability to identify intricate interactions in diverse fields. The paper addresses these developments and presents future research directions, such as longitudinal modelling and multi-level moderation analysis. By combining these findings, companies and researchers can improve contextual factors affecting decision-making and align strategies to maximize them