Advances in Decision Sciences is a double-blind, peer-reviewed, Open Access journal that publishes original research articles as well as review articles in all areas of decision sciences. If the paper is accepted, we will charge a USD 300 publication fee (excluding the cost/fee for transferring funds) for each submission. However, we will consider giving authors a discount for very good papers or waiving the entire publication fee. Advances in Decision Sciences is archived in Portico, which provides permanent archiving for electronic scholarly journals, as well as via the LOCKSS initiative. It operates a fully open access publishing model which allows open global access to its published content. Advances in Decision Sciences is included in many leading abstracting and indexing databases. Advances in Decision Sciences was founded in 1997 by Professor Mahyar A. Amouzegar who served as the Editor-in-Chief of the journal between 1997 and 2009. Advances in Decision Sciences is ranked the 4th (16%) out of 25 Decision Sciences (miscellaneous), the 31th (18.5%) out of 168 Statistics, Probability and Uncertainty according to https://www.scopus.com/sourceid/19900193531 the 36th (top 24%) out of 150 Computational Mathematics journals, the 166th top 19.8%) out of 836 Applied Mathematics journals, the 313rd (top 13.9%) out of 2,248 Mathematics journals, the 530th out of 3,140 (top 16.9%) Formal Sciences journals, the 1759th (top 8.7%) out of 20,139 Social Sciences journals, and the 2863rd (top 23.6%) out of 12,155 Physical Sciences journals. according to https://exaly.com/journal/33289/advances-in-decision-sciences Thus, Advances in Decision Sciences is ranked a Q1 journal in Computational Mathematics, Applied Mathematics, Mathematics, Formal Sciences, Social Sciences, and Physical Sciences.
Scientific and Business World, Singapore / Asia University, Taiwan
Optimal stopping time strategy for paying tax
Purpose: In this study, we examine the strategic timing for paying corporate taxes under varying financial market conditions. Design/methodology/approach: We develop a model by using optimal stopping theory and considering three tax regimes: (i) no tax, the firm is never required to pay taxes; (ii) paid tax on a monthly basis, taxes paid based on revenues periodically; and (iii) deferred tax with penalties, delaying taxes but incurring financial penalties. Findings: Our results indicate that the optimal timing of tax payments is highly sensitive to market parameters such as drift, volatility, and the structure of tax penalties. The evidence further suggests that, under certain risk conditions, tax deferral can serve as a legitimate and value-enhancing strategy, allowing firms to retain more earnings and allocate resources more efficiently. Originality: Our main contribution is to identify how timing strategies are shaped by drift, volatility, and tax penalties. The model is developed theoretically and supported by a Monte Carlo simulation. We also address the legal and policy implications of tax deferral, namely that under certain risk conditions, tax deferral increases firm value. Overall, our conclusions are based on mathematically rigorous modeling and simulations.
Copyright (c) 2025 Advances in Decision Sciences https://creativecommons.org/licenses/by-nc/4.0
Energy demand response to the dynamics of the currency valuation: Evidence from G7 countries
Design/methodology/approach The study employs several traditional methodologies and the newly developed Mixed-TAR Nonlinear ARDL (MTNARDL) model to investigate long-run co-integration. Additionally, Granger causality in the quantile test is applied to enhance the robustness of the findings. Findings The MTNARDL model confirms the presence of long-run co-integration among all sample countries, whereas traditional methodologies fail to detect any such relationship. Furthermore, the Granger causality results reveal that the impact of exchange rate fluctuations on energy demand varies across different quantiles. Research limitations/implications While the study provides robust empirical insights, it is limited by the scope of available data and methodological constraints, which may require further validation in other economic settings. Practical implications The findings highlight the need for policymakers to design exchange rate and energy policies that account for nonlinearities and quantile-specific effects, ensuring economic stability in G7 nations. Originality/value This study extends the literature by integrating extreme exchange rate changes with energy demand in G7 countries, employing advanced methodologies to uncover dynamic relationships that were previously overlooked.
Hedging Global Stock Markets with Bitcoin, Precious Metals, Copper, Crude Oil, and Agricultural Commodities: Evidence from Bivariate Threshold GARCH Approach
Purpose: This study examines the effectiveness of hedging global stock markets with hedge assets, including bitcoin, precious metals, copper, crude oil, and agricultural commodities. To achieve this, we selected eleven global stock market indices, including ASX 200, MSCI US, MSCI Europe, MSCI Japan, HSI, IBOVESPA, BSESN, SSECI, STI, TAIEX, and TSXCI to evaluate the effectiveness of hedging with the aforementioned hedge assets. Consequently, our current study offers a more up-to-date and comprehensive comparison of hedging effectiveness among multiple classes of hedge assets than earlier academic work. Study design/methodology/approach: We collected weekly price data, denominated in USD, from Eikon (https://eikon.refinitiv.com/), covering the period from May 1, 2018, to March 2, 2023. For the analysis, this study utilized the bivariate diagonal BEKK-TGARCH and OLS models to estimate time-varying and static hedge ratios, respectively, with the goal of measuring the effectiveness of hedging. Findings: In the empirical analysis of the BEKK-TGARCH model, we find that the return spillover effects are weak, and past information shocks influence the current variance-covariances of returns on most hedge assets but not on stock market returns. Moreover, the stock markets exhibit a stronger asymmetric leverage effect than the hedge assets. Furthermore, the BEKK-TGARCH model demonstrates greater hedging effectiveness than the OLS. Silver, copper, and crude oil emerge as highly effective hedge assets, whereas agricultural commodities are the least effective. Finally, ASX 200 and TSXCI are the most effectively hedged stock markets. Practical Implications: This study evaluates the effectiveness of various hedge assets for hedging global stock markets and identifies the most effective hedge assets. Thus, our research is connected to the field of decision sciences, providing insights into hedging processes and optimal strategies for portfolio managers and hedgers.
Special Issue on : Contemporary Management Research
Ownership Structure and Leverage as Precursors to Financial Distress in an Emerging Economy: A Mediating Model
Purpose—This study investigates the mediating role of leverage structure in the relationship between corporate ownership composition and financial distress within an emerging economy. Given that stakeholders' influence on leverage decisions can significantly alter a firm's risk profile, we examine the specific pathways through which different ownership categories, via their leverage choices, affect the likelihood of financial distress. Design/methodology/approach – The study examines the impact of leverage structure on the relationship between ownership structure and financial distress. A panel dataset from 2017 to 2021 in Pakistan is analyzed using the Preacher-Hayes (Preacher & Hayes, 2004) method to identify both direct and indirect pathways. Additional sensitivity analysis involves testing different methodological approaches and alternative proxies for mediating variables. Pooled OLS regression serves as the main estimation method, with PCSE (panel-corrected standard error) regression used for robustness checks. Findings – Results show that total leverage mediates the impact of institutional ownership, insider ownership, and foreign ownership on financial distress in Pakistan. Regarding debt maturity, findings confirm that the effects of institutional and foreign ownership on financial distress are mediated by long-term leverage. Additionally, the influence of insider ownership, foreign ownership, and state ownership on financial distress is mediated by short-term leverage. Research limitations/implications – This study focuses on an emerging economy and highlights implications of the tradeoff theory, which limits the extensive use of leverage, whether short-term or long-term. Originality/value – This research explores the less-examined mediating role of leverage structure, which integrates both capital and debt maturity structures. Moreover, there is limited literature linking ownership structure with financial distress in both developed and developing countries. The findings provide a quantitative framework that enhances managerial decision-making by clarifying how different ownership structures, through the mechanism of leverage, impact financial risk, thereby making a significant contribution to the corporate finance literature on risk management and governance. The findings offer a granular framework for decision-makers to understand and mitigate financial risk originating from the interplay of ownership and leverage.
Are Livestock Effluent Standards of Vietnam Feasible? Insights from Land Use of Wastewater Treatment Plants in Pig Farms
Purpose: This study evaluates the feasibility of implementing livestock effluent standards in Vietnam, a developing country experiencing rapid growth in pig production alongside severe environmental pollution problems. Design/methodology/approach: This study surveyed 118 pig farms, collecting wastewater samples from their waste treatment systems. Through multiple regression analysis, this study modeled the relationship between pollutant levels and land area used for different treatment facilities. Findings: The study revealed that meeting Level B of Vietnam's National Livestock Effluent Standards requires a waste treatment area of 6.09 m²/pig for standalone biogas digesters and 3.61 m²/pig for systems combining biogas digesters with bioponds. However, the surveyed farms allocated only 0.64 m²/pig and 1.16 m²/pig for these respective systems, demonstrating that compliance with the current standards is practically unachievable. Additionally, the results indicate that the combined system (biogas + biopond) achieves higher treatment efficiency than biogas digesters alone. Practical implications: This finding highlights critical shortcomings in Vietnam’s agricultural land policies, including restrictive land quotas, land fragmentation, and short leasing terms for public agricultural land. These constraints discourage enterprises and large-scale farms from investing in infrastructure and waste treatment systems. To enable livestock farms to comply with effluent standards, policymakers should prioritize land consolidation, longer leasing terms for public agricultural land, and stricter enforcement of environmental regulations. Originality/value: This is the first study in Vietnam to examine the feasibility of livestock wastewater standards in the aspect of land-use, one of the increasingly constrained resources in Vietnam.
Corporate Social Responsibility and its Influence on Trade Credit: An Analysis of China's A-Share Corporate Sector
Objective: This study investigates the relationship between Corporate Social Responsibility (CSR) and trade credit financing, focusing on Chinese A-Share listed firms from 2010 to 2022. Methodology: This study employs robust econometric techniques, including Panel Estimated Generalized Least Squares (EGLS) and System Generalized Method of Moments (GMM), to provide empirical evidence of an inverse relationship between CSR activities and firms' reliance on trade credit. Findings: The findings reveal that firms actively engaging in high levels of CSR tend to rely less on trade credit. This reduced dependency is mainly due to the positive impact that socially responsible practices have on fostering trust and building stronger relationships with suppliers. By consistently demonstrating their commitment to ethical, environmental, and social standards, these firms create a reputation for reliability and integrity, strengthening their bonds with suppliers and opening up access to more diverse and favorable financing options beyond trade credit. This shift occurs because the trust built through CSR initiatives makes suppliers more willing to offer flexible payment terms or alternative financial arrangements. At the same time, banks and investors may view these firms as lower-risk and more deserving of credit support. Implications: The study also offers practical insights for corporate managers and policymakers, emphasizing the importance of integrating CSR into strategic financial planning to mitigate reliance on short-term financing mechanisms. Novelty: This study offers novel insights by exploring the complex relationship between CSR and trade credit, specifically in the context of Chinese A-share listed firms.
Copyright (c) 2024 Advances in Decision Sciences https://creativecommons.org/licenses/by-nc/4.0
Carbon emissions and the rising effect of foreign direct investment and trade openness: Evidence from panel data countries
Purpose: The study aims to investigate the effect of FDI and FTO on carbon emissions using sectoral data from 30 industries in China from 2001 to 2024. It will document both the positive and negative influences of FDI and FTO on carbon emissions, presenting a fresh perspective on these economic factors and their relation to environmental sustainability. Design/methodology/approach: The research employed a rigorous threshold regression model to explore the relationship between FDI+FTO and carbon emission. Panel data of industries in China was meticulously analyzed, indicating the changes in sectors and the impacts of controlled variables. This comprehensive approach ensures the validity and reliability of our findings. Findings: The empirical results confirm that FDI and FTO affect sectoral carbon emissions nonlinearly. FDI suppresses carbon dioxide emissions when lower emission threshold values are considered, while FDI at higher thresholds increases them. Similarly, as FTO reduces carbon emissions in high-intensity sectors, carbon emissions increase in low-intensity ones. The control variables concerning carbon emissions are also explained. Practical implications: Policy initiatives based on the peculiar features of each variable would promote FDI in industries with low emissions and regulate FTO in sectors with high carbon intensity. This would balance economic growth with environmental sustainability. Originality/value: The paper contributes to the extant literature by analyzing the relationship between FDI, FTO, and carbon emissions within a sector-specific threshold regression analysis framework. It illustrates the complex dynamics of international trade and investment in environmental outcomes.
Special Issue on : Decision Science in Energy Economics and Environment for Reducing Carbon Footprint and Achieving Sustainable Development Goals
Examining the Interplay between Tax Systems and Corporate Finance across Diverse Asian Economies
Objective: This research examines how a country's culture affects a company's decisions regarding financing and corporate tax rates. Methodology: We used a two-step system called the Generalized Method of Moment (GMM) to analyze data from five major Asian economies (China, India, Pakistan, South Korea, and Singapore) from 2010 to 2019. Findings: Our findings suggest a positive correlation between corporate tax rates and debt financing. Increased corporate taxes lead to high tax deduction value, incentivizing firms to enhance their debt financing after-tax income. Furthermore, corporations in a more tax environment prefer leverage financing to exploit the tax deductibility of interest payments. However, this relationship can become negative due to the influence of national culture. In economies with a low power distance culture and less information asymmetry, managers prefer equity financing to debt financing. Reliability, moral sympathy, and trust can attract investors, shareholders, and other stakeholders' attention in these cultures. Similarly, in cultures with low uncertainty avoidance, people are less risk-averse and prefer equity financing to debt financing. Implications: Corporate managers should consider national culture when deciding on financing patterns, especially when dealing with high corporate tax rates. Novelty and Originality: This research stands out due to its novel approach of integrating national cultural factors as moderating variables in analyzing corporate tax rates and financing patterns. Unlike previous studies, this research provides a holistic perspective that underscores the importance of cultural context in financial decision-making processes, thereby offering new insights and practical implications for corporate managers and policymakers aiming to optimize financial strategies in culturally diverse settings. This study is pertinent to decision sciences as it delves into corporate managers' intricate decision-making processes influenced by taxation regimes and cultural dynamics. By clarifying the moderating role of national culture on corporate financial strategies, this research provides valuable insights into the strategic considerations that underpin corporate finance decisions in diverse economic and cultural contexts.
Impact of Oil Prices on Islamic Stock Prices: Evidence from Pakistan using Bootstrap ARDL Approach
Purpose: This research inspects the dynamic association between the oil prices and Islamic stock prices in Pakistan, an area relatively uninvestigated, regardless of the rising significance of Islamic finance. Design/methodology/approach: The research utilizes daily data from January 2011 to April 2022. The SOR unit root assessment is employed to recognize smooth and sharp structural breaks, and the Bootstrap ARDL technique is employed to explore short- and long-run cointegration between oil prices and Islamic stock prices. Findings: The findings divulge that certain Islamic stocks show substantial long-run cointegration with the oil prices, whereas others illustrate short-run causal associations. The outcomes highlight the non-linear and time-varying effect of the oil price fluxes on the dynamics of the Islamic stock market. Research limitations/implications: The assessment is restricted to thirty Islamic stocks in Pakistan and does not include sectoral or regional heterogeneity, signifying a need for comprehensive future investigation. Practical implications: The outcomes deliver valuable information for investors and policymakers by featuring the prominence of monitoring the dynamics of the oil market when articulating risk management and investment approaches in Islamic financial markets. Originality/value: This research contributes to the inadequate empirical literature by employing innovative econometric procedures to assess the oil-Islamic stock price association in a developing market, delivering vigorous evidence that augments and complements the current study.
Assessing the Efficiency of Hong Kong Public Hospital Sector During the COVID-19 Pandemic: An Input-oriented Structural Efficiency Approach
Purpose: The available literature on the impact of COVID-19 on the healthcare system is relatively limited. This paper aims to evaluate the efficiency of the public hospital sector in Hong Kong during the COVID-19 pandemic. Design/methodology/approach: One characteristic of the Hong Kong public hospital sector is the cluster-based management model. This paper developed an input-oriented structural efficiency measure with subgroups not yet documented in the literature. This measure is then applied to the data from 38 public hospitals in 7 hospital clusters managed by the Hong Kong Hospital Authority (HA) from fiscal year 2017-18 to 2022-2023. The study contributes to the field of Decision Sciences by applying and extending Data Envelopment Analysis (DEA), a core operations research technique, to provide actionable insights for strategic planning and resource allocation in public health systems. Findings: The results show that the excess resource usage of the whole hospital sector increased from around 24% before the pandemic to around 40% during the late pandemic period. The results further reveal that it mainly originated from the inefficiency within individual hospitals. Resource allocation among hospital clusters is very efficient, whether before or during the COVID-19 pandemic. However, resource allocation within hospital clusters worsened in three clusters but improved in another three clusters. Originality/value: This paper makes an original contribution by developing and applying a novel input-oriented structural efficiency measure that accommodates subgroups, a methodology not previously documented in the literature for this context. By applying the new measure developed, this paper evaluates the efficiency of the Hong Kong public hospital sector during the COVID-19 pandemic and decomposes such inefficiency into individual hospital clusters and levels of the public hospital sector.
Long-run relationship between insurance premiums and driving factors in Mongolia
Purpose: The insurance industry in Mongolia is indeed expanding, though it has received comparatively less attention than other sectors within the financial industry. However, it is crucial to understand the factors that reflect and influence long-run development trends of the insurance industry, in order to actively work towards its growth and development in the future. This study examines the variables in terms of their long-run effects on the insurance industry in Mongolia. Study design/methodology/approach: Data are sourced from the World Bank’s databases. Spanning the period from 1995 to 2022, with a total of 28 observations. Johansen Cointegration rank tests, estimation of cointegrating vector, and testing of hypotheses on long-run coefficients are used to investigate the long-run relationship between insurance premiums and variables that drive them. Findings: Our results show that price level, internet usage, and household income are the long-run variables influencing the total amount of insurance premiums in Mongolia. The long-run coefficients of these variables determine the long-run elasticities of insurance premiums. The long-run coefficient of household income is the largest and then household income is the primary factor of insurance premiums. Also, life expectancy is excluded from the cointegrating relation because zero restriction on its coefficient cannot be rejected. The role of life expectancy is ambiguous and is needed for further investigation. Practical Implications: Based on the main results, it is imperative to focus on long-run factors influencing the aggregate insurance premium income in Mongolia. Consequently, our findings offer valuable perceptions for the insurance sector, shedding light on their influence on the development of the industry. The development of the insurance industry in Mongolia is full of challenges and uncertainties. Hence, our study is related to the practice of decision sciences, providing insights into decision-making processes in optimal ways under conditions of uncertainty faced by government officials and practitioners.
Newton-Raphson Method: Overview and Applications
Purpose: This paper provides a comprehensive overview of the Newton-Raphson method (NRM) and illustrates the use of the theory by applying it to diverse scientific fields. Design/methodology/approach: This study employs a systematic approach to analyze the key characteristics of the NRM that facilitate its broad applicability across numerous scientific disciplines. We thoroughly explore its mathematical foundations, computational advantages, and practical implementations, emphasizing its versatility as a problem-solving tool. Findings: The findings of this paper include a detailed examination of the NRM, demonstrating its efficacy in solving non-linear equations, systems of equations, and optimization problems. The study further highlights the relevance of NRM in addressing complex challenges within probability, statistics, applied mathematics, and other related fields. Originality/value: This study contributes to the existing literature by providing a comprehensive and in-depth analysis of the NRM’s diverse applications. It effectively bridges the gap between theoretical understanding and practical utilization, thereby serving as a valuable resource for researchers and practitioners seeking to leverage the NRM in their respective domains. Practical implications: This research showcases the practical utility of the NRM through two illustrative case studies: optimizing loudspeaker placement for COVID-19 public health communication and determining the submersion depth of a floating spherical object in water. Additionally, the paper demonstrates the NRM's extensive use in estimating parameters of probability distributions and regression models. It highlights its significance across various areas within Decision Sciences, including applied mathematics, finance, and education. This paper contributes both a theoretical overview and a display of diverse practical applications of the NRM.
Bibliometric Insights into Crisis Management: A Review of Key Literature
Purpose: The study explores the current situation of crisis management research and future directions through a comprehensive visual analysis by using VOSviewer. Methodology: To achieve our study's objective, we retrieved published papers from the Scopus Database covering 1995 to 2022, resulting in 1853 publications. The study used bibliometric indicators to analyze the publications and conducted co-authorship and co-occurrence analyses to identify the main research strengths and topics in crisis management. Findings: The findings provide insight into the seven crucial facets of the crisis management environment, including methodology, developed, emerging, and developing countries, governance and SMEs, public policy and relations, crisis response, COVID-19 response, and risk communication and knowledge management. The findings also identify current research trends such as urban resilience, crisis communication, sustainability, climate change, mental health, and future hotspot areas such as family business, organizational resilience, lockdown, COVID-19 crisis, and digitalization in crisis management. Novelty: This research introduces insights into the crisis management literature by identifying research gaps and submitting new arrangements for emerging hotspots' future keywords. It employs a rigorous methodology that offers a robust analysis of the crisis management literature. Crisis management research explores key areas, research trends, and future perspectives, revealing the application of decision-making principles. Rigorously using decision science, the study provides valuable insights and improves understanding of crisis management literature.
Does Intellectual Capital Foster Deposit Growth in Banking System? Empirical Evidence from a Developing Economy
Objective: This study investigates the relationship between intellectual capital (IC) efficiency and deposit growth in Vietnamese banks. IC refers to intangible knowledge-based assets like employee competencies, processes, and customer relationships. Methodology: The paper analyzes a sample of 26 commercial banks in Vietnam from 2006 to 2020 and uses the Value-added Intellectual Coefficient model to measure IC efficiency. Findings: The results indicate a positive influence of overall IC on deposit growth. However, the effect of individual IC components varies, with human and structural capital enhancing deposits while capital employed efficiency reducing deposits. The findings suggest smaller banks leverage IC more effectively for deposit mobilization than larger banks. Implications: The research offers insights for bank executives on utilizing IC to attract deposits and for policymakers on regulating IC investment. Practical strategies for developing IC and theoretical directions for future research are discussed. Novelty: The study provides the first empirical evidence of the linkage between IC and bank deposit growth.
A CRITIC-I-TOPSIS Approach to Assess Public Satisfaction with Cultural Services in Urban Communities
Purpose: This paper aims to propose a multi-criteria evaluation method for assessing public cultural service quality satisfaction in urban communities. The method is validated by evaluating satisfaction with public cultural service quality in the Baihe Yuan community of Nanan District, Chongqing, China. The study also explores the factors influencing public cultural service quality satisfaction in this community. Evaluating satisfaction with public cultural service quality requires assessment across multiple dimensions, making it a classic multi-criteria decision-making problem. Design/methodology/approach: In the evaluation process, we utilized the CRITIC method to determine the weights of influencing factors and the I-TOPSIS-Sort method to assess the factors affecting public cultural service quality satisfaction in this area. Among them, I-TOPSIS-Sort is a multi-criteria sorting method improved in this study. Findings: The research findings indicate that the factor of greatest concern to residents of Baiheyuan Community in Nan'an District is "Equipment for Community Cultural Services," while the least concerning indicator is "Personnel of Community Public Cultural Services." Furthermore, in terms of evaluation ratings, all indicators for this community were classified as C, except for "Personnel of Community Public Cultural Services," which was categorized as D. This suggests that residents of the community are generally satisfied with the quality of public cultural services. However, there is still room for improvement in the quality of public cultural services in the community. Practical Implication: This study focuses on Baiheyuan Community in Nan'an District as the research subject and improves the CRITIC and I-TOPSIS-Sort methods to evaluate the satisfaction level of public cultural service quality in the community. This research is beneficial for assisting policymakers and decision-makers in assessing the quality of public cultural services, thereby aiding them in policy formulation and decision-making. Ultimately, it aims to further refine management systems and better meet the public cultural service needs of residents. Originality/value: This paper evaluates public cultural service quality satisfaction in urban communities using the CRITIC-I-TOPSIS-Sort method to address the contradictions between the increasing public cultural demands and the original supply. Among them, CRITIC-I-TOPSIS-Sort is the multi-criteria sorting framework proposed in this study.
Urban Community Public Cultural Services
Mitigating Financial Crimes: How Anti-Money Laundering Mechanisms Shape Bank Outcomes
Purpose: This study examines the influence of anti-money laundering (AML) mechanisms on the financial performance of Jordanian banks, alongside assessing how macroeconomic and financial factors—such as inflation, GDP, bank size, quick ratio, and financial leverage—affect performance dynamics. Design/methodology/approach: Using a descriptive-analytical approach, the research analyzes data from 12 Jordanian banks listed on the Amman Stock Exchange (ASE) from 2007 to 2021. Given the unbalanced nature of the panel data, fixed-effect models were employed to evaluate the relationship between AML indices, key bank performance metrics, and broader economic variables. Findings: The results demonstrate a significant negative relationship between AML intensity and bank performance, indicating that stringent AML enforcement can impose short-term financial burdens. In contrast, liquidity (as measured by the quick ratio) and bank size positively and significantly impact profitability. Inflation was also found to significantly increase bank performance. Meanwhile, financial leverage displayed a negative but statistically insignificant influence on performance. These findings collectively illustrate the complex interplay between compliance obligations, operational characteristics, and macroeconomic conditions in shaping bank outcomes. Research limitations/implications: The study focuses exclusively on Jordanian banks, and caution is advised when generalizing the findings to banks operating under different regulatory frameworks or economic systems. Practical implications: The findings suggest that banks must carefully balance regulatory compliance costs with operational resilience strategies. Policymakers should consider frameworks that support banking sector stability without imposing excessive burdens on institutions, particularly smaller banks. Social implications: Effective AML measures not only protect financial institutions but also foster public trust and contribute to the overall health of the financial system. Originality/value: This paper provides comprehensive empirical insights into the intertwined effects of AML compliance and macro-financial factors on banking sector performance within an emerging market context.
Financial Consequences of Fraud in Amman Stock Exchange Firms
Purpose: This study investigates the impact of financial fraud risk, proxied by the Beneish M-Score, and key macroeconomic variables on the financial performance (Return on Assets - ROA) of firms listed on the Amman Stock Exchange (ASE). Methodology: Employing panel data from 140 ASE-listed firms between 2015 and 2020, the research utilizes Ordinary Least Squares (OLS) regression and several machine learning regression models (Support Vector Machines, Random Forest, Gradient Boosting). The analysis examines the influence of the Beneish M-Score, GDP growth, inflation, and company size on return on assets (ROA). Findings: The results reveal a significant positive impact of GDP growth and firm size on ROA. While inflation's linear effect was insignificant, we uncovered a compelling non-linear, inverted U-shaped relationship between the Beneish M-Score and ROA. This suggests that while moderate levels of earnings management risk may coincide with performance-enhancing activities, higher levels are unequivocally detrimental. Notably, machine learning models, particularly Random Forest, demonstrated superior predictive accuracy over traditional OLS regression, underscoring the importance of capturing these non-linear dynamics. Recommendations: Jordanian firms are advised to strengthen internal controls and foster transparent financial reporting. Regulators should enhance oversight and consider advanced analytical tools, including machine learning, for risk assessment. Investors should critically evaluate fraud risk indicators, recognizing their complex impact on performance. Originality: This study offers novel insights into the nonlinear performance implications of financial fraud risk in an emerging market context (Jordan). It distinctively integrates macroeconomic factors and compares traditional econometric techniques with machine learning approaches, contributing to the financial fraud literature in developing economies by highlighting the complex dynamics between earnings manipulation risk and firm performance. This study contributes to the field of Decision Sciences by demonstrating how hybrid econometric-ML models can enhance fraud risk assessment and corporate decision-making in developing economies.
Soil Particle Prediction using Spatial Ordinary Logistic Regression
Purpose: This study embarks on a novel journey to create a predictive model for soil texture. We utilize the Spatial Ordinary Logistic Regression model to estimate soil particles in the topsoil with unprecedented accuracy. This involves employing Geographically Weighted Ordinary Logistic Regression to analyze and map the spatial distribution of these particles based on primary data collected from the field. Design/methodology/approach: This study distinguishes itself by adopting a meticulous approach to gathering soil particulate and geospatial data from various random locations. This method is crucial in addressing the complexity of modelling soil texture, an essential aspect of soil management. The study analyzes soil texture, a combination of sand, silt, and clay, using Digital Elevation Model (DEM) data. By leveraging topographical variations, the study predicts soil texture, employing Geographically Weighted Ordinary Logistic Regression for areas without direct observations. This approach significantly enhances both understanding and prediction in soil science. Findings: The proposed model will be cross-validated to ensure precision. Aimed at aiding land and resource management, this study focuses on examining spatial variations in topsoil particle sizes and their influencing factors. The Geographic Weighted Ordinary Logistic Regression (GWOLR) model, designed for estimating soil particle sizes using a fixed bi-square weight, demonstrated superior effectiveness with a 90% accuracy rate compared to the standard model's 88%. Further findings show that all topographical predictors exhibit significant spatial autocorrelation (Moran’s I, p < 0.05), justifying the spatial approach. The GWOLR model also provides localized parameter estimates, revealing spatial heterogeneity in the influence of terrain features. Specifically, vertical curvature and slope positively associate with sandy textures, while lower northness aspects correlate with higher clay and silt presence. Spatial prediction maps generated from the model align closely with actual field data, affirming its practical value in precision agriculture, land-use planning, and targeted soil conservation strategies. Practical Implications: In 2023, soil particle size data were gathered from the Kalikonto Watershed Area in Batu City, East Java, Indonesia. This data, divided into three categories, was analyzed using the Geographically Weighted Ordinal Logistic Regression method, incorporating spatial factors. Originality/value: This study presents innovative methods for enhanced spatial analysis, notably the Geographically Weighted Ordinary Logistic Regression technique. This approach improves spatial and statistical data integration for analyzing geographic information, offering insights into how spatial variables influence soil properties. Focusing on estimating particle-size fractions in the soil's top layer, the research underscores the significance of soil attributes on plant growth and agricultural productivity. Furthermore, it provides new perspectives in the crucial field of soil property investigation.
The Interaction Between Asset Tangibility, Cash Holdings, and Financial Development: An Evidence from Emerging Economy
Objective: This research delves into the intricate relationships among asset tangibility, corporate cash holdings, and corporate financial development within corporate firms. The primary aim is to estimate these interactions and discern their direct and combined impacts through empirical analysis. Methodology: Utilizing Financial Statement Analysis (FSA) data spanning from 2010 to 2019, comprising 220 corporate firms and 1,947 observations, this study employs panel data analysis techniques. The research evaluates the direct influence of asset tangibility on corporate cash holdings and examines the joint effect of corporate financial development on both asset tangibility and corporate cash holdings. The methodology involves various analytical tools, including descriptive statistics, correlation analysis, fixed effect models, random effect models, Hausman tests, and various diagnostic tests. These methodologies ensure the rigor and validity of the estimations and econometric approaches. Findings: The findings reveal significant associations. Firstly, asset tangibility negatively correlates with corporate cash holdings, indicating that firms with higher tangible asset structures tend to maintain lower cash reserves. Moreover, a negative relationship exists between corporate financial development and asset tangibility, suggesting that financially developed firms tend to possess lower tangible assets. Implications: Understanding the inverse relationship between asset tangibility and cash reserves can guide firms in optimizing asset composition, potentially leading to more efficient resource allocation and strategic decision-making, particularly in emerging economies. Novelty: This study underscores the interdependent nature of asset tangibility, corporate cash holdings, and financial development within corporate entities. The outcomes contribute valuable insights into the strategic management of tangible assets and cash reserves, particularly in financial development among corporate firms.
Purchasing Power Parity Between China and Selected BRI Countries in Asia
Purpose: This study tests the validity of purchasing power parity (PPP) between China and the 18 other Asian countries that have joined the ‘Belt and Road Initiative’ (BRI). The Chinese Government launched the BRI plan to enhance trade and promote market integration among the participating countries. The validity of PPP reflects the degree of market integration and serves as a prerequisite for future economic integration. Study design/methodology/approach: Nonparametric nonlinear rank tests are used to examine whether this is any cointegration in the PPP models. This testing approach is valid even if the functional form of PPP is unknown or nonlinear. Additionally, we address the rank problem in the multivariate PPP models. Findings: Our empirical results reveal strong evidence of the PPP relationship and reasonably strong evidence of nonlinearity in the data. Our findings show that some Asian BRI countries have experienced integration of their markets with that of China during the recent decade but not over the entire sample period. This implies that China’s economic power has been rising with larger trade volumes with Asian trading partners, especially during the most recent decade, which, in turn, infers that the integration of markets through BRI projects has been enhanced. However, hidden transaction costs due to geopolitical risks may have resulted in the failure of the PPP relationship between China and some Asian countries during the past decade. The findings of PPP in our paper are useful for academics, practitioners, and policymakers in making better decisions for trading between China and the Asian BRI countries. Practical Implications: The findings imply that China should get better economic cooperation and greater economic integration with all the Asian BRI members. However, the fear of geopolitical risks and military conflicts in Asia could lead to an increase in transaction costs, resulting in drawbacks from the PPP relationship between China and the Asian countries, especially after the Russia-Ukraine war.
Copyright (c) 2023 Advances in Decision Sciences https://creativecommons.org/licenses/by-nc/4.0
Online Marketing Trends and Purchasing Intent: Advances in Customer Satisfaction through PLS-SEM and ANN Approach
Purpose - The research aims to discern the factors of online marketing that influence consumer intention and enhance satisfaction, particularly in Bangladesh. Methods - The study uses quantitative data, targeting respondents from urban areas and cities from various socio-economic classes. This study uses a two-stage structural equation modeling-artificial neural network approach. Initially, the analysis utilized the PLS-SEM method to assess the structural model. Finally, the analysis used the ANN approach to check the robustness of the findings. Results - The study's findings reveal that convenience, comparison, ease of use, and variety-seeking significantly influence customer satisfaction in online shopping. Conversely, promotional activities and customer service were found to have less impact on customer satisfaction. Customers anticipate prompt and efficient service, and a failure to meet these expectations can strain the customer-seller relationship. Practical implications - This study presents an alternative business model without needing physical store visits. However, despite the growth of internet technology in Bangladesh and its potential to provide products and services at lower costs, convincing customers to shop online remains a challenge for online traders. Originality - This research offers a unique perspective on the dynamics of online marketing and consumer satisfaction in Bangladesh, shedding light on the factors that drive or deter online shopping in a developing nation using the two-staged SEM-ANN approach. This provides actionable knowledge for decision-makers in online service provision, aligning with the quantitative methodology’s characteristic of Decision Sciences.
Advancing Trending Statistical Techniques to Examine Growth and Variability in Scottish Sustainable Business Enterprises
Purpose: This study aims to explore the growth and variability of enterprises in four key Scottish industrial sectors between 2008 and 2021, emphasizing the importance of sustainable business practices in today's evolving economic landscape. Design/methodology/approach: The research employs a quantitative and testing approach, using advanced statistical methods by ORANGE data mining, namely Compound Annual Growth Rate (CAGR), Mean, Standard Deviation (SD), Coefficient of Variation (CoV), Root Mean Square Error (RMSE), and Average Growth Rate (AGR). Data from sectors like Electronics, Information Technology, Information and communication technology (ICT), and Telecommunications form the basis of this analysis. Findings: The study reveals varied growth patterns across sectors. Information Technology displayed a steady growth (CAGR of 1.03%), while the Electronics sector exhibited more variability (Coefficient of Variation of 5.79%). These findings highlight the differing dynamics and stability of enterprises in the context of economic and technological changes. Research limitations/implications: The analysis is limited to Scottish enterprises and may not reflect trends in other geographical contexts. Further research expands to compare with global trends, or utilize machine learning-based analysis for regression and future probabilistic forecasts. Practical implications: The insights are valuable for business strategists and policymakers, aiding in informed decision-making and strategic planning for sustainable business development. Social implications: The study contributes to understanding the sustainability of business practices, which is critical in the current socio-economic climate. Originality/value: This paper enriches the discourse in business administration by integrating modern statistical analyses, offering a novel perspective on the management of sustainable enterprises during significant global economic shifts.
Measuring Circular Economy Index in Agriculture at the Farm Level by using the Fuzzy Analytical Hierarchy Process
Purpose: The Circular economy has emerged as a vital framework for achieving sustainable agriculture in the face of global climate change, environmental degradation, and rising food demand. However, most existing indicators measure circularity from isolated aspects, resulting in biased or incomplete assessments. To get a solution to the issue, this study develops a comprehensive Circular Economy Index (CEI) for agriculture at the farm level by using the Fuzzy Analytical Hierarchy Process (FAHP), and by providing a multidimensional and consistent approach to evaluate circular performance. Design/methodology/approach: The study integrates expert judgments and farm-level data to assess circularity across the technical and impact dimensions—covering environmental, economic, and social aspects. The FAHP method enables systematic weighting of multiple indicators under uncertainty, offering a robust framework for both empirical analysis and policy evaluation. Findings: The results demonstrate that impact-related criteria account for 61% of the total CEI weight, underscoring the dominance of environmental and social factors in assessing circularity. Among six agricultural models examined, the integrated VAC (Vuon-Ao-Chuong) system achieved the highest CEI score (42.2), highlighting its superior performance in nutrient recycling, biodiversity conservation, and overall sustainability. Originality/value: This study is the first to design and implement a comprehensive, multi-dimensional CEI tailored to farm-level agriculture in Vietnam, overcoming the limitations of conventional single-aspect measures. It contributes to the literature of decision sciences by demonstrating how FAHP can operationalize complex and multi-criteria evaluations in uncertain contexts. For academics, the study offers a replicable model for quantitative CE assessment; and for policymakers and practitioners, it provides an evidence-based tool to identify, benchmark, and scale up circular agricultural systems that promote both economic efficiency and environmental stewardship.
Evidence from School Principals: Academic Supervision Decision-making on Improving Teacher Performance in Indonesia
Purpose: Decision Science is important in many fields, including education. In the current educational landscape, as educational processes evolve to meet the challenges of the fourth industrial revolution, this study utilizes a well-established impact factor, academic supervision, to enhance teacher performance. The expectations of school principals are considered critical factors in the implementation of decision supervisors. This study underscores the significance of elements influencing the improvement of teacher performance, with a focus on planning, decision-making, and implementation pressures within the context of academic supervision. Design/methodology/approach: This study employs a case study approach and collects data through interviews, observations, and document analysis. Data were analyzed by transcribing, taking notes, recording videos, and examining documents to generate ideas, codify data, develop themes, interpret the information, and draw conclusions. Findings: The study's findings reveal that the planning expectations for an academic supervision program decision are high and sufficient to consider the needs of teachers, involve all school apparatus components, and integrate the program with other activities. The expectations for making decisions and implementing the academic supervision program are met by fostering solid teamwork, increasing the frequency of classroom supervision visits, advancing the profession, and instilling a professional attitude among teachers. However, in terms of reviewing decisions, discussing them, seeking improvement solutions, and enhancing teacher performance, the evaluation expectations and follow-up results of academic supervision still need improvement. The findings of this study suggest that the decision-making expectations of principals as supervisors are crucial for decision performance and the success of academic supervision programs designed to make decisions and improve teacher performance. Originality/value: Considering the time constraints of this study, future researchers should explore school principals' expectations as supervisors in greater depth, especially concerning decision-making and the improvement of teacher performance. Practical implications: This study benefits school principals in their roles as decision-makers and supervisors, encompassing planning, implementation, and the monitoring of academic supervision decisions. It also covers outcomes such as reviews, discussions, and proposed solutions for enhancing teacher performance, with indirect implications for student learning.
Copyright (c) 2022 Advances in Decision Sciences https://creativecommons.org/licenses/by-nc/4.0
How Does External Environment Affect Individual Service Innovation Performance? Empirical Evidence from The Tourism and Hospitality Industry
Purpose: This study investigates the internal and external factors determining individual service innovation performance in the tourism and hospitality industry (T&H). The research gives decision-makers insights into the external environmental factors that can promote individual innovation. Internal environmental factors, knowledge sharing, and information literacy are vital in this process. Design/methodology/approach: This study adopts a quantitative methodology by collecting the data from questionnaires of mid- and high-level management in high-star hotels in China. CB-SEM methodology is used to test the research hypotheses and model. Findings: The study results support the proposed model that the external environmental factors of technological turbulence and market turbulence can positively affect individual service innovation performance through the chain-mediating effect of the internal environmental factors of information literacy and knowledge sharing. Originality/value: Based on the resource-based view (RBV), the knowledge-based view (KBV) and contingency theory, this study proposes a research paradigm of "external environment-individual-innovation performance" and a novel chain mediation model, which confirms the complementarity of the three theories. The results provide operative solutions for innovation decision makers and management of T&H firms on ways to promote individual innovative service innovation performance by utilizing the external environment turbulence factors.
Stock market Reaction to General Election in Pakistan: An Event Study Methodology
Objective: This study investigates the impact of the 2018 General Election on stock prices of firms listed on the Pakistan Stock Exchange (PSX). Methodology: A sample of 34 listed companies selected from PSX and the KSE-100 index were used to investigate the impacts of the 2018 General Elections in Pakistan, on firms listed on the Pakistan Stock Exchange. We adopt the event study for analysis. The research timeline spans 120 days for estimation window, and 21 days for event window with the day after the General Election as the event date. The impact of the national elections is market-related. Hence, unlike the market model used in most of the previous studies, we use the constant mean model to calculate the abnormal returns. In addition to OLS, we apply the t-distributed threshold GARCH model to obtain more efficient parameter estimates in the context of fat tails and asymmetry in conditional variance of errors. Results: The results of this study indicate a positive impact of the event (2018 General Election in Pakistan) on the sample of listed companies and KSE-100 index. This positive impact can be attributed to the stock market response to the favorable informational content of the general election outcome. The information is fully reflected in the stock market over about 3 days around the event date. Such fast response indicates the existence of semi-strong form of market efficiency in Pakistan stock market. Implications: The empirical analysis presented in this research not only sheds light on the correlation between political events and stock market performance but also offers valuable insights for formulation of equity investment and portfolio management strategies. This study, therefore, serves as a vital contribution to the existing literature by providing innovative empirical evidence on the intricate relationship between political events, investor sentiment and stock prices in the specific context of Pakistan's 2018 General Election.
The Impact of Climate Change on Financial Efficiency and The Financing Choices of Electricity Industrial Companies: Evidence from Vietnam
Purpose: This study examines the 'climate-finance paradox' in Vietnam's electricity sector, focusing on how the paradoxical forces of physical climate risks and economic incentives for carbon-intensive growth influence the firms' financial policies. Our research contributes to Decision Sciences by examining the trade-offs and decision-making strategies that firms use to navigate the conflicting environmental and economic objectives under uncertainty. Design/Methodology/Approach: We employ a fixed-effects two-way panel regression model using data from 40 Vietnamese power companies spanning 2003–2022, capturing both physical and transition risk dimensions. Findings: Physical climate risks (rising temperatures and natural disasters) significantly impair financial health, reducing profitability and long-term debt while increasing short-term debt and precautionary cash holdings. In contrast, higher greenhouse gas emissions (indicating intensive economic activity) correlate with improved financial performance, enhanced long-term debt capacity, and reduced cash hoarding. This reveals a fundamental conflict between climate risk mitigation and the incentives for short-term economic growth. Research Limitations/Implications: The paradox highlights a market failure in which short-term financial signals reward activities that undermine long-term sustainability. For academics, this necessitates theoretical models that incorporate the conflicting dynamics between climate and economy in emerging markets. Practical Implications: Managers and investors must recognize emission-linked financial performance as a misleading indicator of long-term value, while policymakers require interventions (e.g., carbon pricing, green financing) to realign corporate incentives with climate objectives. Originality/Value: This is the first empirical study on the climate-finance paradox in Vietnam's electricity sector, revealing how physical climate risks and emission-intensive growth simultaneously influence corporate financial policies. Our analysis establishes boundary conditions for mainstream climate finance theories in emerging economies with high climate vulnerability and rapid industrialization, utilizing a unique 20-year panel dataset that captures previously unexamined dual risk dimensions in Vietnam.
Impact of Board Ownership Structure on Firm Value and Excessive Cash Holdings: Evidence from Pakistan
Objective: This study investigates the influence of board ownership structure on firm value and the management of excessive cash holdings. Methods: Data from the financial statements of 72 Pakistani firms were collected and analyzed using a Fixed Effect Model (FEM). Findings: The statistical analysis reveals a significant correlation between the board ownership structure and firm value, emphasizing the pivotal role of efficient management in enhancing firm value and optimizing the utilization of excess cash holdings. The study also demonstrates the significance of the coefficient of determination about board size and firm value. Implications: Based on the findings, we recommend that companies actively promote diversity among board members, encompassing not only in terms of gender, age, and nationality but also in knowledge, skills, and information. Such diversity plays a crucial role in ensuring compliance with good corporate governance mechanisms and governance codes, offering valuable insights for firm managers seeking to optimize their firm's value. Additionally, we suggest that firms consider the sensitivity of their board ownership structure when making decisions regarding excessive cash holdings and firm value. This study's focus on board ownership structure, firm value, and cash holdings in Pakistan provides valuable insights that can inform decision-making theory, corporate governance decisions, and policy discussions, making it relevant to the broader field of decision sciences. Novelty: While the study acknowledges the well-explored relationship between corporate governance and firm performance, it focuses explicitly on the impact of board ownership structure, highlighting the significance of understanding how the composition of boards, including ownership patterns, influences firm value and financial practices.
The Impact of Market Liquidity on The Stock Returns During the COVID-19 Outbreak: New Evidence from Vietnam
Research aims: This article examines the impact of market liquidity, as measured by market tightness and depth, on the stock returns of non-financial companies listed on the Vietnam Stock Market during the COVID-19 outbreak. Design/Methodology/Approach: We collected daily data spanning from January 30, 2020, to December 31, 2021, for 647 non-financial companies listed on the Vietnamese stock market and employed a fixed-effects panel data regression model to analyze the data. Research findings: Our findings show a statistically significant and negative relationship between market tightness and stock returns. Additionally, market depth demonstrates a noteworthy positive relationship with stock returns. These findings suggest that stocks with lower liquidity tended to yield higher returns during the COVID-19 pandemic. Notably, this phenomenon was accentuated during periods of lockdown. The study also disclosed a noteworthy difference in the influence of market liquidity on stock returns among companies listed on the HOSE and HNX stock exchanges. Further analysis unveils a marked variance in the impact of market liquidity on stock returns when examined across different sectors and market capitalizations. Notably, the liquidity of the service and manufacturing sectors has the strongest influence on stock returns during COVID-19. Theoretical contribution/Originality: No published studies have investigated the impact of market liquidity on the stock returns of companies listed in emerging markets, such as the Vietnamese stock market, during the COVID-19 outbreak. This study is the first to examine how market liquidity, as measured by market tightness and depth, affects the stock returns of non-financial firms listed on the Vietnam Stock Market during the COVID-19 outbreak. An examination of the impact of market liquidity on stock returns is conducted, taking into account industry-specific variations and differences in market capitalization. Practitioner/Policy implications: Our study demonstrates the critical role of liquidity and the attractiveness of low-liquidity stocks during the COVID-19 outbreak in Vietnam. The findings provide novel and substantial evidence for the field of Decision Science. Through an in-depth analysis of market fluctuations amid the pandemic, the article offers essential insights into the intricate interaction between market liquidity and stock returns, uncovering valuable implications for policy decisions and investment strategies for individual investors and organizations. Research limitations: This study focuses on listed non-financial firms, so the results may not be generalizable to the whole market. Future studies may consider adding financial firms to the sample.
Factors Influencing Online Professional Skills Training Programs for Lecturers: A Case Study of Vietnam National University, Hanoi, and its Implications on Decision Sciences
Purpose: The application of decision science combines qualitative and quantitative frameworks to offer valuable insights into decision-making processes, spanning beyond business, computer science, public health, environmental science, economics, and finance, to include the field of education. Under the impact of the COVID-19 pandemic and digital transformation, online training has become a trend in many universities worldwide. This study aims to develop a new integrated multi-criteria decision-making model to determine the priority levels of factors impacting the effectiveness of online professional skills training programs for lecturers at the Vietnam National University, Hanoi (VNU-Hanoi). Design/methodology/approach: A novel integrated approach has been developed, which combines the generalized fuzzy Analytic Hierarchy Process (AHP) with the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). This approach effectively determines the priority levels of influencing factors by using the fuzzy TOPSIS method with generalized trapezoidal fuzzy numbers (TrFNs). The data for this study was collected through in-depth interviews with experts and managers at the VNU-Hanoi. Findings: The research findings indicate that learners and training program content are the most influential factors in the effectiveness of online training courses in enhancing lecturers' professional competence at the VNU-Hanoi. Drawing on principles from Decision Science, several recommendations have been proposed for the VNU-Hanoi and its member universities and schools: (i) allocate sufficient time and provide resource support to enable lecturers' participation in training courses; (ii) create comprehensive training content for lecturers, encompassing political ideology, ethics, professional knowledge, modern pedagogical skills, and life skills; (iii) strengthen the utilization of scientific, technological, and information technology skills, along with a digital transformation in management, teaching, and learning processes, to meet the broader development needs of society. The study results also demonstrate the feasibility of the proposed model in addressing practical issues. Originality/value: This study proposes a novel integrated generalized fuzzy AHP-TOPSIS approach to determine the priority levels of factors influencing the effectiveness of online training courses in enhancing lecturers' professional competence at the VNU-Hanoi. The study considers 21 sub-factors across five factors, including the policies of higher education institutions, training program content, learners, instructors, and technology for online training. This is new in the literature.
the generalized fuzzy Analytic Hierarchy Process