The Economics and Finance Letters

Office activities performance as a mediator between organizational drivers and firm performance: Evidence from Vietnamese SMEs

2025-07-02T07:51:14-05:00

Office activities performance is a critical but underexplored component of business operations, particularly among small and medium-sized enterprises (SMEs) in Vietnam, an emerging market. This study aims to evaluate both the direct effects of internal and external factors on office activities performance and, more importantly, to assess its mediating role in the relationship between these factors and overall firm performance. Grounded in the Resource-Based View (RBV) and Stakeholder Theory, the proposed model positions leadership as a key driver of internal capabilities, including organizational culture, technological capability, and financial capability, while also accounting for external stakeholder pressures such as government support and customer pressure. Data were collected from 209 SMEs across Vietnam and analyzed using partial least squares structural equation modeling (PLS-SEM) via SmartPLS software. The results reveal that both internal drivers and external pressures significantly and positively influence office activities performance, which in turn mediates their impact on firm performance. These findings emphasize the strategic role of office operations as an intermediary mechanism that connects organizational resources and stakeholder engagement to business outcomes. The study provides theoretical contributions to office management literature and offers practical guidance for SME leaders aiming to strengthen operational effectiveness and competitiveness through enhanced office performance.

Market competition and firm investment decisions: An empirical study of Asian economies

2025-07-02T07:25:53-05:00

This study examines how market competition can influence corporate investment decisions in Asian countries. Using firm-level data collected from Refinitiv from 2004 to 2023, this study analyzes the Herfindahl-Hirschman Index (HHI) as a proxy to measure market concentration and how it affects corporate investment. The results show that market concentration positively impacts corporate investments, suggesting that higher market concentration encourages investment. Monopolies or less competitive markets have greater pricing power and financial viability to sustain investments. Furthermore, the results establish that developed countries are more responsive to market concentration. Developed countries appear more responsive to market concentration due to stronger institutions, regulatory stability, and political certainty, enabling investments in infrastructure and innovation. In contrast, developing countries invest in immediate development through capital expenditure, often constrained by weaker regulatory environments and limited access to capital. This discourages innovation and long-term growth. Instead, these economies rely more on state-owned enterprises and family conglomerates that can hinder competition. This study discusses the importance of competition in stimulating corporate investments. Such insights may be useful to policymakers in determining what is required for economic growth, while also serving as valuable guidance for corporate leaders in making investment decisions across various economic and institutional environments.

Adoption of Islamic financing: The case of MSMEs in Indonesia

2025-07-03T12:58:49-05:00

This study aims to investigate the role of non-economic factors that can influence MSMEs' financial decisions in Indonesia. The data was analyzed using partial least squares. 320 MSMEs participated in a questionnaire survey that was used to gather data. The findings demonstrated that behavioral intentions to adopt Islamic funding are positively impacted by attitudes. If MSMEs believe that the Sharia financial products offered are beneficial to improve MSMEs' performance, then their attitudes towards Sharia financing will be positive. Subjective norms influence behavioral intentions to adopt Islamic financing positively. The behavioral intentions to adopt Islamic funding can be impacted by coworkers, friends, and family. The behavioral intentions to adopt Islamic financing are positively impacted by perceived behavioral control. The realization of a person's intention to choose Islamic financing depends on the ability and accessibility possessed by the individual according to his/her capacity. It was discovered that the desire to adopt Islamic financing was influenced both directly and indirectly by the mediating attitudes for the relationship between pricing fairness and religiosity and behavioral intentions to embrace Islamic financing. The findings of this study extended the TPB model by adding religiosity and price fairness to Islamic financing.

How is the adoption of advances of ICT valued by the market through the Tobin’s Q indicator? The case of Vietnamese commercial banks

2025-07-08T21:55:24-05:00

This study aims to examine the impact of information and communication technology (ICT) investment on the market-based performance of Vietnamese commercial banks. Using panel data from 26 banks over the period 2006–2022, the research adopts the system generalized method of moments (System-GMM) to address potential endogeneity and dynamic panel bias. ICT investment is measured through a composite ICT index, while performance is captured by Tobin’s Q, a market-based indicator reflecting investor expectations. The findings reveal that ICT investment has a positive and significant effect on Tobin’s Q, underscoring the strategic role of technology in enhancing a bank’s market valuation. In addition to ICT, factors such as bank size, the loan-to-deposit ratio, the loan loss provision ratio, and macroeconomic conditions are also found to significantly influence Tobin’s Q in ways consistent with prevailing banking practices and the economic context during the study period. The study concludes that ICT investment plays a strategic role in improving market-based performance and signaling future value to investors. These findings suggest that Vietnamese banks should adopt long-term, strategic ICT investment plans not only to enhance competitiveness but also to support broader goals of innovation and digital transformation in the banking sector, particularly in the context of Industry 4.0.

Impact of religious proximity on energy trade: Policy implications based on international evidence

2025-08-05T18:43:14-05:00

This study aims to examine the role of religious proximity in shaping bilateral energy trade. It explores how shared religious beliefs, values, and practices between countries influence their engagement in the trade of fossil energy resources—specifically coal, oil, and natural gas. The analysis uses a panel dataset comprising 3,407 country pairs from 88 nations over the period 1996–2019. Religious proximity is operationalized through a Grubel–Lloyd index constructed from three religiosity dimensions: religious affiliation, the perceived importance of religion, and attendance at religious services. An extended gravity model is employed to estimate the relationship between religious proximity and energy trade, using Poisson pseudo-maximum likelihood (PPML) to account for heteroskedasticity and zero trade flows. The results reveal a statistically significant negative association between religious proximity and bilateral energy trade. Countries that are more religiously aligned are less likely to engage in energy trade with one another. This relationship holds across various model specifications and sub-periods. Among the three religiosity dimensions, the perceived importance of religion exerts the strongest and most consistent negative effect, particularly during specific historical intervals. Religious proximity, while often assumed to foster cooperation, may instead reflect underlying ideological or cultural barriers that limit economic exchange in energy markets. The findings challenge conventional assumptions about cultural similarity facilitating trade and suggest a more nuanced, context-dependent role of religion in international economic relations. Policy-makers and international energy negotiators should consider cultural-religious dynamics as potential sources of friction in energy cooperation. Understanding the inhibiting role of religious alignment can inform strategies to mitigate soft barriers in trade policy and multilateral energy agreements, particularly in geopolitically sensitive regions.

Forecasting the South African tax-to-GDP ratio series utilizing seasonal autoregressive integrated moving average and artificial neural networks models

2025-09-27T02:15:04-05:00

South Africa has experienced successful tax collections but has not achieved the governance outcomes desired to establish an efficient fiscal contract compared to most developed countries. The objective of this study is to compare the performance of the conventional Seasonal Autoregressive Integrated Moving Average (SARIMA) model with that of the recently developed machine learning approach, the Artificial Neural Network (ANN) model in forecasting the South African Tax-To-GDP ratio. The focus is on accurately forecasting the South African tax-to-GDP ratio using the historical series from January 2008 to November 2024. The sampled period is characterized by random, irregular, and seasonal fluctuations, which are critical for accurate forecasting in the context of macroeconomic policy. The lower mean absolute percentage error indicates that the machine learning model outperformed the conventional time series model in terms of accuracy and reliability when forecasting South Africa’s tax-to-GDP ratio. The findings further show that there will be slight growth in the tax-to-GDP ratio during the financial year of 2025, with a sharp decline forecasted between the end of 2025 and the beginning of 2026. These results add to the growing literature on the application of machine learning methods to economic forecasting. For policy considerations, this study suggests that South Africa's policy to expand its tax base, enhance tax administration efficiency, diversify revenue sources, and promote sustainable economic growth to minimize tax distortions and maintain macroeconomic stability during economic downturns.

Combining fundamental and technical analyses for better stock selection: An empirical study on Bursa Malaysia

2025-09-30T01:36:09-05:00

This research aims to examine the impact of fundamental and technical analyses in stock selection, focusing on the FBM 100 index stocks in Bursa Malaysia from 2021 to 2023. The design of this research is based on the filtration of variables within fundamental and technical analyses and the application of Monte Carlo simulations using Python. Using a filtration approach, the Monte Carlo simulations are applied to varying combinations of fundamental metrics, namely price-earnings ratio, dividend yield, price-to-book ratio, and earnings per share, as well as technical indicators such as moving average convergence divergence (MACD), moving average crossovers, and relative share price momentum. The strategy that employs all the fundamental and technical analysis filters achieved a monthly return of 3.34% and a weekly return of 1.05%, compared to just 0.60% and 0.23% without filters. Although the higher returns are accompanied by higher volatility, the hybrid combination of both fundamental and technical analyses yields better risk-adjusted performance. Within the scope of fundamental analysis, this study further reveals that sector affiliation is a significant determinant of share price performance. This study highlights the complementary nature of fundamental and technical analyses, which offer a practical, flexible, and hybrid stock selection framework for enhancing equity investment outcomes.

The auditor's psyche: Unveiling the hidden links between mood, independence, uncertainty, and audit quality in an emerging market

2025-09-30T02:42:08-05:00

This study investigates the complex relationships between auditor mood, auditor independence, environmental uncertainty, and audit quality in a developing economy. Specifically, it examines the direct effects of auditor mood (AUMO) and auditor independence (AUIN) on audit quality (AUQA). Furthermore, the study explores the moderating influence of environmental uncertainty (ENUN), measured by sales volatility, on the relationships between AUMO and AUQA, and AUIN and AUQA. The research hypotheses were tested using panel data derived from Vietnamese listed companies. The sample comprises firm-year observations from 2020 to 2024, resulting in 835 firm-year observations. The Arellano-Bond two-step system generalized method of moments (S-GMM) regression technique was employed for the analysis. The results reveal novel insights specific to the Vietnamese context: (1) AUMO significantly contributes to enhancing AUQA; (2) AUIN was found to have an unexpected negative association with AUQA; (3) ENUN negatively moderates the positive link between AUMO and AUQA, while its moderation of the AUIN-AUQA relationship was found to be positive in the full model; (4) The impact of these psychological and structural factors on AUQA is a multidimensional phenomenon profoundly shaped by contextual factors and intricate interactions among these variables. Our findings provide valuable guidance for policymakers in considering psychological dimensions and context-specific factors when designing regulatory frameworks to foster AUQA in Vietnam. This research illuminates the complex, context-dependent nature of these interactions, providing a nuanced understanding of how specific psychological elements (AUMO), structural attributes (AUIN), and contextual pressures (ENUN) interplay to influence audit outcomes.

Sovereign credit ratings in South Africa: Does institutional quality matter?

2025-09-30T05:26:30-05:00

The study aimed to empirically assess whether governance quality influences sovereign credit scores in South Africa. The country faces a significant infrastructure financing gap that requires not only public sources but also the issuance of securities on global capital markets. Consequently, sovereign credit scores are a critical component of South Africa's development finance discourse. An Autoregressive Distributed Lag (ARDL) framework was applied to credit scores from Fitch, Standard & Poor's, and Moody's. The ARDL model is advantageous because it is efficient in both small and finite samples. Data for economic indicators were collected over 23 years, ending in 2023. Institutional quality was evaluated using governance indicators reported by the World Bank. The evidence indicates that, individually, corruption, politics, governance, freedom of expression, lawfulness, and regulatory quality do not significantly influence credit scores in South Africa. Variations in gross domestic product (GDP) and foreign investor activities also have an insignificant impact on ratings. In South Africa, sovereign scores are influenced by the reserves-to-external-debt ratio, the current account balance to GDP ratio, and inflation. From a policy perspective, although governance variables are individually insignificant, the government of South Africa should aim to improve institutional quality. Even though governance variables do not directly enhance credit scores, they reduce the likelihood of sovereign downgrades. Further research could explore whether governance variables collectively determine ratings in South Africa. The study could be expanded by including a broader sample that encompasses the least developed economies, thereby improving the generalizability of the results.

Application of artificial intelligence using linear regression and the Naïve Bayes model in forecasting and analyzing the consumer price index in Vietnam

2025-09-30T06:14:05-05:00

The investigation of applying Artificial Intelligence (AI) and Naïve Bayes to forecast the Consumer Price Index (CPI) in Vietnam marks a significant contribution to advancing accurate inflation prediction capabilities. The study leverages rigorous methodological standards and reliable data sources by utilizing a comprehensive 2003 to 2023 dataset comprising seven input variables and the CPI as the output variable. A correlation coefficient of 0.99 indicates a robust correlation between the predicted value and the actual value. The model demonstrates efficacy in forecasting the CPI in both the training dataset and the testing dataset. Furthermore, the histogram visually represents the distribution of errors. The errors are primarily clustered at a minimal magnitude, predominantly falling within the range of -0.05 to 0.03. This suggests that the model tends to make predictions that are quite close to the actual value. The achieved Mean Squared Error (MSE) value of 0.03 demonstrates the model's remarkable accuracy, validating the effectiveness of AI in capturing intricate patterns within CPI data. This research paves the way for further exploration of advanced machine-learning techniques tailored to Vietnam's economic landscape, contributing to improved economic forecasting, proactive policy decisions, and sustainable growth.