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
A New Approach for Image SecurityEnhancement Using Ternary Logic LinearFeedback Shift Register for CryptographicApplications
Purpose: This paper addresses a core decision problem in healthcare data governance: how should healthcare decision-makers optimally select encryption parameters under resource and threat-model constraints? To answer this, a formal multi-criteria decision framework is developed and instantiated through a novel ternary linear feedback shift register (LFSR)-based encryption system, providing clinicians and security engineers with principled, quantitative parameter-selection guidance for lightweight image-encryption deployment on resource-constrained medical devices. Design/Methodology/Approach: The proposed method extends traditional binary LFSRs to the ternary domain GF(3), operating over three logic states {0, 1, 2}, to generate pseudo-random keystreams that drive a pixel-permutation cypher. The system was evaluated on 10–15 images per modality across three clinically distinct modalities: kidney ultrasound, brain MRI, and multiple sclerosis (MS) MRI; reported metrics correspond to the image whose scores were closest to the modality average, ensuring representative rather than cherry-picked results. Evaluation used standard security metrics including NPCR, UACI, information entropy, MSE, PSNR, SSIM, and pixel-correlation coefficients. Findings: The model achieves a Number of Pixels Change Rate (NPCR) of 98.04% and entropy of 6.80 bits for kidney ultrasound images and a Unified Average Changing Intensity (UACI) of 27.96% for brain MRI images. Encrypted images exhibit nearuniform histograms and near-zero pixel correlation coefficients (≤ 0.022), confirming strong randomness. Correct-key decryption recovers the original image with SSIM values of 0.9903–1.0000 and PSNR values of 44.52–52.21 dB. Incorrect-key decryption produces entirely unintelligible output, validating key sensitivity. The entropy gap below the 8-bit theoretical maximum is attributed to the permutation-only design, which preserves pixel intensity values; this limitation and the path towards a diffusion layer are discussed. Originality/Value: This work makes two original contributions. The primary contribution is a formally grounded, evidence-based decision framework that maps LFSR configuration variables (n, P) to measurable security-versus-cost trade-offs across three healthcare deployment tiers, enabling risk-based governance of medical image encryption. The secondary contribution is the ternary LFSR cipher itself — the first deployment of GF(3) logic within an LFSR-based cypher for medical image protection — which serves as the concrete case study instantiating the decision framework, expanding the key space from 2n − 1 to 3n − 1 states while maintaining O(N logN) computational complexity. Implications: The decision framework gives healthcare administrators and security engineers a structured, evidence-based basis for encryption parameter selection, directly supporting risk-based governance and regulatory compliance (e.g., HIPAA, GDPR). The minimax-regret analysis provides robust configuration guidance even under uncertainty about attacker capability and device heterogeneity. The low computational overhead of the ternary LFSR cipher makes the framework practically deployable on embedded and IoTbased medical devices. This work advances decision science methodology by formalizing parameter-selection under resource constraints and threat-model uncertainty—a canonical multi-criteria decision problem—and demonstrating its application to healthcare data governance, where encryption configuration choices directly impact regulatory compliance, patient privacy protection, and operational efficiency under bounded computational budgets.
Copyright (c) 2026 Trapti Sharma, Ayush Ranjan, Harvinder Singh, Rajit Nair, Hasan Alkahtani, Sami Morsi, Ahmed A.F. Osman (Author); Theyazn H.H. Aldhyani (Corresponding Author) https://creativecommons.org/licenses/by-nc/4.0
Does Settling Trade in National Currency Stabilize the Rupee? Nonlinear, Asymmetric, and Horizon-Dependent Evidence from India
Purpose – This study asks whether settling international trade in the national currency enhances exchange rate stability in India, and whether the relationship is nonlinear, asymmetric, and horizon-dependent. Motivated by the Reserve Bank of India's 2022 framework for international trade settlement in rupees and the broader fragmentation of the dollar-centric monetary order, it addresses a gap left by linear, mean-based studies. Design/methodology/approach – Using monthly data from April 2002 to September 2025 (282 observations), the study combines maximal-overlap wavelet decomposition with quantile-on-quantile regression (WQQR) to estimate how a standardized National Currency Trade Settlement Index (NCTSI), a constructed proxy, relates to INR/USD exchange rate volatility (the inverse of stability) across short-, medium-, and long-run horizons and across the full distribution of both variables. Findings – The aggregate association is modestly negative, but this masks pronounced heterogeneity. Over long horizons, national-currency settlement is significantly and negatively associated with volatility across most of the distribution, with the association strongest in high-volatility (turbulent) regimes; over medium horizons, it is weak and occasionally positive; and over short horizons, it is statistically negligible. The patterns are robust to alternative volatility measures, an alternative wavelet basis, and a post-2022 sub-sample that isolates the rupee-settlement framework era. Implications – The results support an invoicing- and settlement-currency interpretation in which reduced reliance on a vehicle currency dampens imported volatility only gradually and most powerfully when external pressure is acute. For policymakers, national-currency settlement operates over long rather than short horizons (conditional on the proxy used), complementing reserves and intervention during periods of external stress rather than substituting for them. Originality/value – The paper provides, to our knowledge, among the first horizon- and regime-explicit assessments of the settlement-stability nexus for the Indian rupee, extending the invoicing- and vehicle-currency literatures to the volatility margin for a major emerging economy.
Copyright (c) 2026 Antanas Laurinavicius, Hussein Moselhy Sayed Ahmed (Author); Algimantas Laurinavicius (Corresponding Author); Safaa Sayed Mahmoud, Komolov Odiljon Sayfidinovich (Author) https://creativecommons.org/licenses/by-nc/4.0
Reduced Dollar Dependence and External Balance Sustainability across Time Horizons: Evidence from Five Emerging Economies (1999–2025)
Purpose – This study attempts to investigate whether de-dollarization, defined as a lessening of reliance on the U.S. dollar for settlement of international trade, improves the sustainability of trade balance (TBS) in emerging economies and if the impact varies by trade balance regime, and by time horizon. It fills a void that linear and mean-based studies leave when assuming a constant effect over the course of time. Design/methodology/approach – The study implements the maximality of information from the panel of five emerging economies (MEX, IDN, MAS, THA, and TUR) from 1999 to 2025, using wavelet decomposition methodology in maximal overlap as well as quantile-on-quantile regression (WQQR) methodology to relate a standardized de-dollarization index (DDX) to TBS across the entire conditional distribution of both indexes and across short-, medium- and long-run frequencies. Findings – There is very little aggregate correlation between de-dollarization and sustainability (≈0.02) and much of the variation. The relationship is positive and statistically significant in the lower-to-middle quantiles of the trade balance, and becomes more negative and stronger at the upper quantile of the trade balance; it is mostly negative and not statistically significant over medium horizons, and is not statistically significant over short horizons. The slopes of these patterns are fairly stable under an alternative wavelet basis and under a current-account-based outcome, although when the outcome is standardized within-country, the slopes become negative, suggesting that the positive long-run pattern is mostly a cross-country (level) regularity. Implications – The results suggest that de-dollarization is a structural lever to be pulled in the long run and not a tool of stabilization, and that it would be most effective when external balance is fragile, consistent with the dominant-currency paradigm. The evidence highlights an effect that is discernible mostly between countries, and this suggests that there should be a gradual move to a diversified mix of reserves and settlements, with a particular focus on the local context. Originality/value – The study offers one of the first horizon- and regime-explicit assessments of the de-dollarization–sustainability nexus for emerging markets, extending the dominant-currency paradigm from the price of the currency to the currency of denomination and demonstrating that a combined wavelet quantile-on-quantile design recovers structure that mean-based models miss.
Copyright (c) 2026 Algimantas Laurinavicius (Corresponding Author); Raheel Gohar, Antanas Laurinavicius, Yücel Ergün, Komolov Odiljon Sayfidinovich (Author) https://creativecommons.org/licenses/by-nc/4.0
A Review of Behavioral Finance and Econometrics: Theories and Applications
Purpose: Traditional financial theories based on the assumption of rationality cannot explain many paradoxes and anomalies in the financial markets. Behavioral Finance models are then developed to incorporate ideas from psychology and financial economics to explain market phenomena. Additionally, advanced Behavioral Econometric methods are proposed for testing theories in Behavioral Finance. This review paper introduces readers to theoretical frameworks and econometric modeling techniques for studying Behavioral Finance, with numerous applications in the literature. Design/methodology/approach: This review provides comprehensive profiles of theoretical evolution, econometric methodology, and applied studies in Behavioral Finance, along with salient readings. Findings: The results emphasize the ongoing development of theories in Behavioral Finance and Econometrics, from risk-seeking utility functions and stochastic dominance to advanced risk measures, econometric testing methods, and ultimately financial applications with testable implications. These advancements help readers understand the main research areas and emerging trends in this field. Practical Implications: This review paper is valuable for academics and practitioners, as it advances Behavioral Finance and Behavioral Econometric theories, and also supports their application to portfolio construction, investment analysis, and risk management. Our study contributes to decision sciences by helping financial analysts understand their clients’ behavioral traits and make optimal decisions that improve their risk-return trade-off. Originality/value: Many studies treated the evolution of Behavioral Finance theory, Behavioral Econometrics, and empirical investigations in isolation. Our review paper not only summarizes existing work but also synthesizes these strands, ranging from theoretical model building to econometric testing and financial applications, into a unified framework.
Copyright (c) 2026 Kai-Yin Woo (Corresponding Author); Hassan Zada, Shin-Hung Pan (Author) https://creativecommons.org/licenses/by-nc/4.0
Multi-Objective Constrained Reinforcement Learning for Joint Routing–MAC–Duty Cycling in Low-Power Wireless Sensor Networks
Introduction: Wireless Sensor Networks (WSNs) face significant challenges in balancing energy efficiency, latency, and reliability while operating under severe resource constraints. Current methods either optimize network layers separately or use static cross-layer coordination, which doesn't work well when conditions change. Purpose: The aim of this study is to introduce a Constrained Multi-Objective Reinforcement Learning Model (CMORLM) for optimizing Joint Routing, Medium Access Control (MAC), and Duty Cycling Optimization (DCO) in low-power WSNs. Methods: In this paper, we have suggested the CMORLM approach as a constrained Markov Decision Process (MDP) with three competing goals: lowering Energy Consumption (EC), lowering End-to-End-Latency (EEL), and raising Packet Delivery Ratio (PDR). There are strict limits on the amount of residual energy, the buffer size, and the Quality of Service (QoS) requirements. Lagrangian Constraint Handling (LCH) and multi-objective policy gradients are combined within the primal-dual optimization method. For routing, MAC, and DCO, the policy network uses a shared encoder with factorized heads. Federated Gradient Aggregation (FGA) is used for distributed learning across Sensor Nodes (SN). Results: Testing in NS-3 shows that EC is 34.2% lower, EEL is 41.3% higher, and PDR is 16.5% higher than Traditional Layered Protocols (TLP). Network Lifetime (NL) goes up by 38.4%. The constraint violation rate (CVR) is still below 1%. This is 23 times less than the CMORLM that was suggested. Ablation studies show that joint optimization increases the EC by 44.7% over single-layer control. Conclusion: The suggested CMORLM works well on networks with 50 to 200 nodes and can handle changes in traffic, node failures, and mobile sinks. To enable operator control over performance trade-offs through weight configuration, Pareto frontier analysis is performed.
Copyright (c) 2026 Ghaida Muttashar Abdulsahib (Author); Mohammed Awad Mohammed Ataelfadiel (Corresponding Author) https://creativecommons.org/licenses/by-nc/4.0
Mitigating Corporate Biodiversity Risk through Green Finance: The Role of Green Development and Climate Risk in the USA
Purpose - This study examines whether green finance mitigates corporate biodiversity risk in the United States, focusing on the decision-making mechanisms underlying this relationship. It specifically evaluates the mediating role of corporate green development and the moderating effect of climate risk in shaping firms’ responses to sustainability-oriented financial incentives. Design/methodology/approach - Using a balanced panel of U.S. publicly listed firms from 2003 to 2025, corporate biodiversity risk is measured through BERT-based textual analysis of 10-K disclosures aligned with the Taskforce on Nature-related Financial Disclosures (TNFD) framework. Green finance is captured using a state-level Green Finance Development Index constructed via the Entropy Weight Method. The empirical strategy employs firm and year fixed effects, Propensity Score Matching (PSM), and a Difference-in-Differences (DID) approach to address endogeneity. Mediation analysis is applied to examine corporate green development as a transmission mechanism, while moderation analysis is used to assess whether climate risk strengthens the effect of green finance on corporate green development. Findings - The results show that green finance significantly reduces corporate biodiversity risk (p < 0.01). Corporate green development, proxied by Green Total Factor Productivity (GTFP), serves as a significant mediating channel, indicating that financial incentives promote environmentally efficient practices. Climate risk strengthens this relationship, suggesting that firms in high-risk environments respond more strongly to green finance signals. The findings are robust across multiple specifications. Originality/Value - This study is among the first to integrate green finance, climate risk, and biodiversity exposure within a unified decision-making framework at the firm level. Employing TNFD-aligned textual analysis and a mediation–moderation structure provides rare empirical evidence on how financial systems influence corporate responses to nature-related risks in a developed economy. The study contributes to Advances in Decision Sciences by offering evidence-based insights into how sustainability-oriented financial incentives shape corporate risk management, resource allocation, and strategic adaptation under environmental uncertainty, thereby enhancing decision-making in complex and evolving risk.
Copyright (c) 2026 Salim Bagadeem (Author); Salem Hamad Aldawsari (Corresponding Author) https://creativecommons.org/licenses/by-nc/4.0
The Role of AI Tools in Enhancing Student Satisfaction and Learning Outcomes in Developing Countries
Purpose - This study examines the determinants of students’ acceptance and use of artificial intelligence (AI)-based academic support systems in higher education. It focuses on how performance expectancy, information accuracy, and pedagogical fit influence behavioral intention, actual use, and academic performance of the students. Additionally, this study evaluated the mediating roles of behavioral intention and satisfaction in shaping students’ learning experiences in AI-enabled environments across different institutional contexts. Design/methodology/approach - An extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework is employed using cross-sectional survey data collected from students in higher education institutions in the United Arab Emirates (UAE) and India. Structural Equation Modeling (SEM) was applied to examine the relationships among the constructs. A mediation analysis was conducted to capture indirect effects, and cross-country validation was performed to assess the robustness and generalizability of the model across diverse educational settings. Findings - The findings revealed that performance expectancy, information accuracy, and pedagogical fit significantly enhanced students’ behavioral intention to adopt AI-based tools (p < 0.01). Behavioral intention positively influences actual usage, which, in turn, improves student satisfaction and academic performance. Satisfaction and behavioral intention were significant mediating mechanisms in the model. Cross-country analysis confirms the structural consistency of the framework, with minor variations reflecting the contextual differences between the UAE and India. Originality/Value - This study extends the UTAUT framework by integrating AI-specific constructs, including information accuracy and pedagogical fit, into a unified behavioral model. By providing cross-country empirical evidence and incorporating both cognitive and affective mechanisms, this study offers a comprehensive understanding of AI adoption in higher education. Importantly, this study contributes to decision science by explaining how students’ behavioral intentions and satisfaction shape technology adoption decisions and learning outcomes in AI-enabled environments. It provides evidence-based insights for policymakers and educators to support informed decision-making regarding AI integration, resource allocation, and strategic planning in conditions of technological uncertainty.
Copyright (c) 2026 Mohamed Elsayed Abdelsalam Ghanem, Asma Salman, Muthanna G. Abdul Razzaq, Safaa Sayed Mahmoud (Author); Mohammed Abdul Imran Khan (Corresponding Author) https://creativecommons.org/licenses/by-nc/4.0
Behavioral Biases and Market Fluctuations: An Empirical Study of Herding and Volatility in Vietnam
Purpose: This research assesses the intensity and presence of the herding behavior in the Vietnamese stock market and its influence on the trading activity and price volatility. Given the dominance of retail investors and limited market transparency, the study examines how collective investor sentiment impacts volatility before, during, and after the COVID-19 pandemic. Design/methodology/approach: Using intraday transaction data from the Ho Chi Minh Stock Exchange (HOSE) between March 2019 and January 2025, the herding intensity is assessed through Patterson and Sharma's (2006) run-based statistic. The GARCH(1,1) approach is utilized to explore how herding and trading volume influence market volatility, with the dataset divided into three sub-periods: pre-COVID, during COVID, and post-COVID. Stationarity and approach robustness are confirmed by utilizing the Augmented Dickey-Fuller (ADF) test. Findings: The results confirm statistically strong herding behavior, particularly during the COVID‑19 period, as both buyer (Hu) and seller (Hd) herding intensities frequently fall below the −1.96 threshold (p < 0.01), indicating significant directional clustering. Trading volume and herding intensity both significantly increase conditional market volatility (p < 0.01), with volatility remaining highest during the pandemic and moderating but still elevated in the post‑COVID period. These patterns show that intraday herding measures can serve as early‑warning indicators of volatility spikes in a retail‑dominated emerging market. Originality/Value: This research is the first to integrate Patterson–Sharma intraday herding metrics with a GARCH(1,1) volatility model to study the Vietnamese stock market across pre‑crisis, crisis, and post‑crisis regimes. It provides rare intraday evidence from a developing market dominated by retail investors and quantifies how directional and zero‑return herding affect volatility dynamics. By treating Hu, Hd, and Hz as behavioral risk indicators, the study contributes to Decision Sciences by offering empirically grounded analytics that support decision‑making under ambiguity for market makers, regulators, and investors.
Copyright (c) 2026 Thanh Pham (Corresponding Author); Huyen Thu Nguyen, Thanh Trung Le (Author) https://creativecommons.org/licenses/by-nc/4.0
Assessing the Moderating Effect of ESG Investment on Climate Variability Risk and Firm Value Nexus
Purpose: This study investigates how climate variability risk (CVR) influences the firm value (EW) and examines the moderating role of environmental, social, and governance (ESG) investment in mitigating the impact. The objective is to examine whether firms with stronger ESG engagement demonstrate greater resilience to climate-induced uncertainties. Design: A panel dataset of 1,720 U.S.-listed firms covering the period from 2005 to 2020 is utilized. Both Feasible Generalized Least Squares (FGLS) and Generalized Method of Moments (GMM) techniques are employed to examine heteroscedasticity, endogeneity, and dynamic relationships between the variables. Findings: Our empirical analysis reveals a significant and negative association between CVR and EW, suggesting that heightened climate uncertainty reduces corporate valuation. In contrast, ESG investment exhibits a positive and significant relationship with EW, underscoring its contribution to sustainable value creation. Furthermore, ESG investment moderates the CVR–EW linkage by diminishing the adverse impact of climate variability risk on firm value. Research Limitations: The analysis is confined to the U.S.-listed firms, which may limit the applicability of the findings to contexts characterized by different environmental and regulatory structures. Future studies could expand this scope through cross-country comparisons or sector-specific analyses. Practical Implications: The results underscore the importance for corporate managers, investors, and policymakers to reinforce ESG-driven strategies, as such investments can buffer firms against climate-related risks and support long-term value enhancement. Originality/Value: This study uniquely incorporates ESG investment as a moderating mechanism within the CVR–EW nexus, offering novel insights into how sustainability-oriented strategies transform climate risk into value-enhancing opportunities in corporate finance. The findings contribute to the Decision Sciences literature by demonstrating how ESG-based decision frameworks can improve corporate resilience and strategic responses to climate uncertainty, thereby supporting more informed, risk-aware corporate policies.
Copyright (c) 2026 Advances in Decision Sciences https://creativecommons.org/licenses/by-nc/4.0
Quantifying the Spillover Effect of U.S. Trade Policy Uncertainty on Economic Growth of BRICS: A Panel Data Investigation
Purpose: Amid escalating geopolitical risks and rising global uncertainty, fluctuations in trade policy have emerged as powerful forces shaping macroeconomic outcomes. Against this backdrop, the present study evaluates the impact of U.S. trade policy uncertainty (TPU) on the economic growth (ECG) of BRICS economies over the period 1985–2023. Design/methodology/approach: To ensure methodological robustness and reliable inference, the study employs advanced panel econometric techniques, including the Cross-Sectional Autoregressive Distributed Lag (CS-ARDL) model, Fully Modified Ordinary Least Squares (FMOLS), and the two-step System Generalized Method of Moments (GMM). These approaches address issues of cross-sectional dependence, endogeneity, and heterogeneity across BRICS economies. Findings: Empirical results reveal a statistically significant negative relationship between U.S. TPU and both GDP per capita growth and GNI per capita growth in BRICS economies. Heightened trade policy uncertainty amplifies investor risk perceptions, delays investment decisions, and contracts capital inflows. Furthermore, TPU disrupts trade planning, increases transaction costs, and weakens export performance, particularly for economies reliant on external demand. These outcomes underscore the need for adaptive and resilient domestic economic policies to mitigate the spillover effects of global uncertainty. Research limitations/implications: The analysis is constrained by the availability of long-term comparable data for all BRICS members and focuses solely on the effects of U.S. trade policy uncertainty. Future research could incorporate regional trade uncertainties or domestic policy volatility to broaden the understanding of cross-country resilience mechanisms. Practical implications: The findings provide actionable insights for policymakers in emerging markets. Strengthening domestic institutions, diversifying trade partnerships, and building flexible policy frameworks can help minimize the adverse effects of global trade policy shocks. Additionally, coordination among BRICS members can enhance collective stability amid evolving global trade tensions. Originality/value: This study is original in empirically validating Real Options Theory within a cross-country trade uncertainty framework, highlighting how global policy ambiguity drives cautious investment behavior and delayed economic activity. Unlike prior studies that focus on single-country or regional contexts, this research uniquely examines the long-term macroeconomic consequences of U.S. trade policy uncertainty on the BRICS bloc, using comprehensive and robust econometric techniques. The paper contributes to the field of Decision Sciences by demonstrating how uncertainty in global trade policy shapes economic decision-making and risk management under external shocks. Importantly, the findings offer valuable guidance for policymakers and decision-makers in designing strategies that enhance economic resilience and stability under uncertainty.
Determinants of Reward Crowdfunding Success for Technology Projects: The Moderating Role of Platform Age
Purpose: Moving beyond the average effects identified in prior literature, this study investigates the differential drivers of technology crowdfunding success and how platform maturity reshapes the competitive landscape. Specifically, it examines whether strategies ensuring basic viability can secure top-tier success and how key drivers evolve as a platform ages. Design/methodology/approach: The study analyzes 600 technology projects from Kickstarter (2009-2023) using a complementary analytical approach. Generalized Least Squares regression and subsample analysis establish baseline relationships and test the moderating role of platform age. Quantile regression then uncovers how the influence of key determinants shifts across the entire conditional distribution of campaign success. Findings: The results confirm the significance of fundamental determinants, with subsample analysis showing their influence is contingent on a project’s final outcome. A key finding is that platform age positively moderates the relationship between media richness and success. Quantile regression further reveals a performance-level hierarchy, showing that influential criteria evolve from establishing baseline viability for lower-performing campaigns to signaling superior credibility and comprehensive development for top performers. Originality/value: This study advances decision sciences by demonstrating context-dependent crowdfunding strategies and employing quantile regression to reveal a hierarchical decision model, with platform age as a key moderator. In contrast to earlier studies that overlook decision heterogeneity, this research proposes an original quantile regression approach that incorporates platform age as a moderator, enabling a differentiated analysis of reward-based crowdfunding success across performance levels. It identifies the key determinants of success in reward-based crowdfunding, offering a decision-oriented framework to understand how creators’ strategic choices and backers’ responses jointly drive funding outcomes. Practical implications: For decision sciences in practice, the findings prescribe distinct contingent strategies for creators based on performance targets and platform maturity. They further advocate for platform administrators to develop level-specific decision support systems, moving beyond one-size-fits-all analytics to improve strategic outcomes.
Copyright (c) 2026 Ichrak DRIDI (Corresponding Author); Oussama Gafrej, Jabeur Salhi (Author) https://creativecommons.org/licenses/by-nc/4.0
Role of ESG Disclosure in Determining Earnings Management of GCC Firms: Moderating Role of Corporate Board Diversity
Purpose - Drawing on the stakeholder theory and legitimacy theory, this study aimed to examine the impact of ESG disclosure on earnings management with the moderating influence of board diversity. Design/methodology/approach – This study employed the random GLS regression and moderation analysis to investigate the relationships. To investigate the aforementioned relationship, panel data for 91 firms from six countries of the GCC region were collected from DataStream. Findings – Key findings posit a significant and positive association with ESG, its components, including environmental, social, and governance factors. Furthermore, there is a negative association with board diversity. Moreover, the remaining relationships were also found in light of theoretical assumptions and shown with optimal levels. In addition, the random GLS regression demonstrated a significant and positive relationship with ESG and earnings management. Our findings enable stakeholders to assess corporate ESS performance to determine the information asymmetry and make optimal asset allocation decisions. Practical/Social Implications – Investors and regulators need to critically observe the firms in the GCC region, as ESG disclosures in emerging markets may not inherently signal reduced opportunism. However, firms with diverse boards provide stronger assurance of authentic ESG engagement, reducing information risk and enhancing the credibility of financial reporting. Our study results imply that regulators must ensure that corporations protect the public interest by providing reliable information to make informed decisions. Originality/value – This study contributed to existing literature by uncovering the paradoxical role of ESG in facilitating earnings management in emerging markets and validating stakeholder theory through the moderating effect of board diversity. Our findings remain novel in determining that ESG measures can be used to determine the information asymmetry in emerging markets, thereby enabling GCC investors and regulators to focus on more stringent ESG disclosure and regulations.
Copyright (c) 2025 Advances in Decision Sciences https://creativecommons.org/licenses/by-nc/4.0
The Impact of Firm–Specific Characteristics on the Readability of Bank Annual Reports: Evidence from MENA Countries
Purpose: This study examines how firm–specific characteristics—profitability, size, liquidity, age, and leverage—affect the readability of the narrative sections of annual reports of major MENA banks. While prior research focuses on developed countries, evidence from the MENA region remains limited. This study addresses this gap by integrating textual readability measures with firm–level financial data across several MENA countries, offering a novel regional perspective. Design/methodology/approach: Panel data from 29 banks across five MENA countries (Lebanon, Qatar, Saudi Arabia, UAE, and Oman) for 2020–2023 were analyzed using multiple regression models. Findings: Profitability and liquidity positively influence annual report readability, whereas bank age has a negative effect. Size and leverage do not significantly affect readability. These findings suggest that financially stronger and more liquid banks produce narratives that are easier to process, thereby reducing readers’ cognitive load and facilitating faster and more accurate decision-making by investors, regulators, and analysts. Research limitations/implications: The study is limited to five countries and five characteristics. Future studies might build on these results by looking at other regions, industries, or corporate characteristics. Practical implications: improved readability enhances information processing efficiency, supporting better investment evaluation, regulatory oversight, and risk assessment in banking decisions. Originality/value: This research contributes to Decision Sciences by demonstrating how firm-level financial characteristics influence the cognitive accessibility of financial narratives, thereby shaping the quality and efficiency of stakeholder decision-making in emerging markets.
Copyright (c) 2026 Rasha Mohamad Mahboub (Corresponding Author) https://creativecommons.org/licenses/by-nc/4.0
Exploring Tail Risk Transmission between Volatility Indices and Cryptocurrencies: Evidence from Quantile Connectedness
Purpose: This paper examines tail spillover and quantile connectedness between implied volatility measures (VIX, OVX, GVZ, and major cryptocurrencies such as Bitcoin Cash, Ripple, Litecoin, Ethereum, and Bitcoin). The investigation examines risk transmission for the bullish, bearish, as well as normal market conditions. Methodology: Cross-quantilogram and quantile connectedness frameworks are utilized in a quantile vector autoregressive (QVAR) framework. Using the QVAR method with the day-ahead data from June 5, 2020, to June 8, 2024, generalized forecast error variance decomposition (GFEVDs) are evaluated to account for static as well as dynamic connectedness across different regimes of market conditions. Results: The results indicate that the TCI (Total Connectedness Index) is broadly flat under typical conditions but increases under bullish conditions as well as under bearish conditions. Cryptocurrencies, besides Ripple under certain conditions, are a net transmitter of shocks, while volatility indices are the key net receivers. Such results feature non-linear risk transmission processes and carry useful implications for hedging as well as for diversification of portfolios. Research limitations/implications: The paper focuses on cryptocurrencies and implied indices during a particular time interval (2020-2024) that might restrict the generalizability of results extended to other economic assets or timeframes. Future research might extend the coverage by incorporating the other asset classes or machine learning-based connectivity methods. Practical Implications: It provides actionable advice for policymakers as well as for portfolio managers to control risk transmission between cryptocurrencies as well as volatility indices. Enhancing monitoring mechanisms as well as adaptive hedging policies remains helpful in mitigating systematic risk exposure under severe market conditions. Originality/Value: This study is original in applying the quantile connectedness methodology to simultaneously study implied volatility indexes and cryptocurrencies under different market settings. Unlike earlier studies that focus on just the mean connectedness, our evaluation reveals the tail risk behavior that causes systemic exposure and contagion. The results add value to the body of work under Decision Science by offering policymakers and investors useful insights about risk transmission between volatility indexes and cryptocurrencies, thus facilitating decision-making about portfolio diversification and hedging under a volatile market.
The Role of Perceived Value in Renewable Energy Adoption: A Study of Rural Entrepreneurs in Bangladesh
Purpose- This study investigates the determinants of rural entrepreneurs’ intention to adopt renewable energy technologies (RET) in Bangladesh and examines whether environmental awareness strengthens these relationships. Design/methodology/approach- Using a positivist, hypothetical-deductive design, survey data were collected from 388 rural SME entrepreneurs in the Dhaka division (Manikganj, Dhamrai, and peripheral rural zones of Gazipur). The research model integrates Technology Acceptance Model (TAM) constructs (perceived usefulness; perceived ease of use) and Diffusion of Innovation (DOI) logic (relative advantage). The proposed relationships were estimated using PLS-SEM with bootstrapping in SmartPLS 4.0, alongside diagnostic checks for measurement quality and potential method bias. Findings- Perceived usefulness and perceived ease of use positively predict intention to adopt RET, while the direct effect of relative advantage is not statistically significant. Environmental awareness significantly mediates the effects of relative advantage, perceived usefulness, and perceived ease of use on adoption intention. Originality/value- This study advances RET adoption research by focusing specifically on rural SME entrepreneurs in Bangladesh and by explaining how environmental awareness translates perceived benefits (usefulness, ease, and advantage) into adoption intention in a resource-constrained rural context. Implications- Policy interventions should combine usability-focused support (training, technical guidance) with targeted environmental awareness programs and financing mechanisms (subsidies/low-interest credit) to accelerate RET adoption among rural SMEs.
Copyright (c) 2026 Mohammad Rashed Hasan Polas (Corresponding Author); Ahmed Imran Kabir, Abu Saleh Md. Sohel-Uz-Zaman, Ridoan Karim (Author) https://creativecommons.org/licenses/by-nc/4.0
Economic Policy Uncertainty and Stock Market Co-Movements in BRIC Countries: Evidence from Wavelet Coherence and Rolling Bootstrap Granger Causality
Purpose: The relationship between economic policy uncertainty (EPU) and stock returns in the BRIC countries (Brazil, Russia, India, and China) is examined by analyzing both static and dynamic interactions across different time horizons, with particular attention to major global crises. Design/methodology/approach: Monthly data from 2004 to 2022 are used, and wavelet coherence analysis is applied together with bootstrap rolling-window and full-sample Granger causality tests to assess the dynamic and causal links between EPU and stock returns. Findings: The results show unidirectional causality from EPU to stock returns in Brazil, Russia, and India. In these countries, higher policy uncertainty reduces stock returns, while no significant causal relationship is found for China. Wavelet coherence results reveal strong short-term co-movements during crisis periods, medium-term synchronization in India and Russia, and persistent long-term correlations in China. The findings highlight the time-varying nature of the EPU–return relationship and its sensitivity to global shocks and institutional conditions. Originality/value: By integrating wavelet coherence with bootstrap rolling-window Granger causality, the study provides a multi-scale and dynamic framework for analyzing the EPU–stock return nexus in BRIC economies, offering useful insights for portfolio management, risk assessment, and decision-making in the field of Decision Sciences. Practical/Social implications: The results suggest that investors adopt horizon-sensitive investment strategies, while policymakers improve policy transparency and communication to limit market volatility. Opportunities for future sectoral and cross-market research are also highlighted.
Worldwide Nickel Ore Trade, Its Stability and the Characteristics: A Fresh Policy Analysis
Purpose: The study is conceptualized to examine structural characteristics and the strength of nickel ore network trading from 2011 to 2025, and focuses on studying major nations' involvement and their risk potential for disruptions in supply. Design/methodology/approach: Network analysis is utilized to examine the topology of the nickel ore trade network and recognize influential players. Robustness simulations are directed to evaluate the influence of targeted and random disruptions on network stability, with emphasis on vulnerabilities associated with critical nodes. Findings: The findings reveal that the nickel ore trade network shows a scale-free structure, where a few dominant states exercise disproportionate influence on the trade flows. Simulation findings confirm that disruptions among these key players abruptly decrease network stability, escalate systemic risks, and endanger international supply chains. Research limitations/implications: The analysis is constrained by data availability and doesn't encompass informal networks of trade or future policy changes that could transform network structure. Practical implications: Findings provide actionable policy recommendations for governments and industry stakeholders to design strategies that enhance resilience, diversify trade routes, and ensure a sustainable nickel supply. Originality/value: The originality of this study lies in the application of network robustness simulations to the global nickel ore trade, offering a novel, evidence-based assessment of systemic risks from targeted disruptions—a dimension underexplored in prior literature. Relevance to Decision Sciences: This research contributes to the field of Decision Sciences by providing a quantitative framework that supports policymakers and corporate strategists in evaluating supply chain vulnerabilities, anticipating risks, and making informed decisions regarding resource security and international trade policy.
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.
Non-Performing Loans, Bank Performance, and Financial Stability: The Moderator Effect of Digitalization
Purpose- This paper examines how digitalization moderates the relationship between non-performing loans (NPLs), bank performance, and financial stability in the banking sector. Design/methodology/approach - The study applies panel data regression techniques to banks operating in 18 MENA countries over the period 2000–2023. Bank performance is measured using return on assets (ROA) and return on equity (ROE), while financial stability is proxied by the Z-score. Digitalization indicators are included both directly and through interaction terms with NPLs to capture their moderating effect. Several robustness tests are conducted to validate the results. Findings - Results show that NPLs negatively affect bank performance and financial stability, highlighting the destabilizing impact of deteriorating credit portfolios. Digitalization exerts a direct positive effect by improving credit monitoring, information processing, and operational efficiency. Interaction results indicate that higher digitalization levels significantly reduce the negative effect of NPLs on profitability and resilience. Managerially, banks investing in advanced credit-risk analytics, automated loan monitoring, and digital customer screening tools are better able to mitigate NPL-related losses, detect early loan deterioration, and improve recovery strategies. These findings contribute to Decision Sciences by demonstrating how technological adoption enhances risk prediction, managerial decision-making, and strategic resource allocation. Originality/value - This study offers new empirical evidence on the moderating role of digitalization in the NPL–performance–financial stability nexus through one of the first long-term panel analyses of the MENA banking sector spanning more than two decades. It extends existing literature by quantifying the impact of technological adoption on credit risk–performance dynamics in emerging markets. Practical implications - The findings provide actionable insights for managers and policymakers. Banks should prioritize investments in (1) AI and machine learning for predictive credit scoring and early-warning systems, (2) integrated digital loan management platforms for improved monitoring and recovery, and (3) digital customer data infrastructures to reduce information asymmetry. Regulators can support digital transformation through regulatory sandboxes and fintech partnerships to strengthen financial stability. Overall, targeted digitalization strategies can reduce NPL accumulation, enhance efficiency, and improve long-term resilience.
Special Issue on : Contemporary Management Research
Copyright (c) 2026 Amir SAADAOUI (Corresponding Author); Mohamed Naceur SOUISSI (Author) https://creativecommons.org/licenses/by-nc/4.0
The Mediating Role of Globalization in the Nexus Between CO₂ Emissions, GHGs, and Economic Growth
Purpose - This study investigates the mediating role of globalization in the relationship between carbon dioxide (CO₂) emissions, greenhouse gas (GHG) emissions, and economic growth in G20 countries. It seeks to determine whether globalization can transform environmental pressures into drivers of sustainable economic performance. Design/methodology/approach - The analysis relies on balanced panel data from 20 G20 countries covering the period 2010–2024. Advanced panel econometric techniques are employed to estimate direct and indirect (mediating) effects, while controlling for cross-country heterogeneity and time-specific effects. Findings - The empirical results reveal that CO₂ and GHG emissions exert a negative but statistically insignificant direct effect on economic growth. However, globalization has a positive and significant impact on growth and fully mediates the relationship between emissions and economic performance. Specifically, higher levels of globalization reduce the adverse growth effects of emissions by facilitating technology transfer, cleaner production processes, and access to environmentally efficient markets. Research limitations/implications - The study is limited to G20 economies and does not account for country-specific environmental regulations or sectoral emission structures, which may influence the magnitude of the effects. Practical implications - Policymakers should integrate globalization-oriented strategies with environmental policies to enhance green technology diffusion and sustain long-term economic growth. Originality/value - This study is among the first to empirically demonstrate the mediating role of globalization in the emissions–growth nexus within G20 countries over a recent and extended period, offering a dynamic perspective absent from prior studies. From a Decision Sciences perspective, this study provides analytical evidence that supports strategic policymaking by helping decision-makers evaluate trade-offs between economic growth and environmental sustainability under conditions of global interdependence and uncertainty.
Special Issue on : Decision Science in Energy Economics and Environment for Reducing Carbon Footprint and Achieving Sustainable Development Goals
Copyright (c) 2026 Abdelbasset DHOUIWI (Corresponding Author) https://creativecommons.org/licenses/by-nc/4.0
Optimizing Repair Allocation in Healthcare: Comparing Volume- and Value-Based Models under Capacity Constraints
Purpose: This study compares two optimization models—volume-based and value-based—for allocating repair resources in healthcare equipment maintenance. It introduces a dual-model framework with eligibility-weighted fairness constraints, a combination not previously explored in the literature. The study highlights trade-offs between service volume and strategic value, offering practical and adaptable solutions, including for low-resource settings. Design/methodology/approach: A linear optimization approach evaluates both models under identical capacity and eligibility constraints. The volume-based model maximizes repair volume, while the value-based model maximizes total repair value based on equipment criticality and facility performance. Both incorporate eligibility-weighted proportionality constraints to ensure fair and feasible assignments. Real-world–inspired data simulate multiple demand scenarios to compare assignment patterns and system outcomes. Findings: The value-based model prioritized high-impact repairs and achieved a higher total repair value score of 1,157,270 compared to 1,130,998 in the volume-based model, with only a one-unit difference in repair volume. These dimensionless scores reflect strategic benefit rather than monetary value. The results highlight trade-offs between broad service coverage and targeted system impact. This study contributes to Decision Sciences by applying linear programming to optimize repair allocation under resource constraints. Practical implications: Healthcare administrators can use these insights to align repair strategies with institutional priorities—whether maximizing throughput or clinical value. Social implications: Timely repair of critical equipment enhances patient safety, service reliability, and health system resilience. Originality/value: This research presents a novel comparison of volume- and value-based optimization models for healthcare repair. Both models incorporate eligibility-weighted proportionality constraints to support fair, effective planning across diverse facilities.
Free Cash Flow as a Moderator Between Board Characteristics and Dividend Policy: Evidence from Jordan’s Industrial Sector
Purpose: This study investigates the relationship between board characteristics and dividend policy in the context of emerging markets, where agency problems and governance effectiveness remain critical concerns. Although prior research has investigated the direct effects of board attributes on dividend policy, the moderating influence of firms’ internal financial resources, notably free cash flow, remains underexplored. Design/Methodology/Approach: Using panel data from industrial firms listed on the Amman Stock Exchange over the period 2016–2020, this study employs panel regression techniques to examine the effects of board independence, board size, and board meeting frequency on dividend payouts, while explicitly modeling free cash flow as a moderating variable. Findings: The empirical findings indicate that board independence and board size are positively and significantly associated with dividend payouts, whereas board meeting frequency does not exhibit a significant direct effect. Furthermore, interaction analysis indicates that higher free cash flow significantly amplifies the effect of board independence and size on dividend policy, implying that governance mechanisms are more effective in guiding dividend decisions under greater internal liquidity. By incorporating free cash flow as a moderating factor, this study offers new insights into the mixed evidence reported in the corporate governance and dividend policy literature. These findings enhance understanding of the interplay between internal governance mechanisms and financial conditions in mitigating agency conflicts, especially within emerging market contexts. Practical Implications: The findings offer practical implications for policymakers, investors, and corporate directors. The study recommends enhanced transparency in free cash flow reporting and improved governance guidelines to strengthen dividend decision-making in developing economies. Originality/Value: This research introduces an interaction-based framework that evaluates how board characteristics and free cash flow jointly influence dividend decisions. This originality provides a new explanation for why previous studies have reported inconsistent findings regarding the impact of board structure on dividend policy. This study is directly related to Decision Sciences because it examines how corporate boards and managers make strategic financial decisions under varying levels of free cash flow, highlighting the governance mechanisms that shape dividend decision-making in emerging markets.
Copyright (c) 2026 Mahmoud Nassar (Corresponding Author); Yousef Abu Siam, Mohammad Haroun Sharairi, Khalil Nimer (Author) https://creativecommons.org/licenses/by-nc/4.0
Aligning Curriculum Design with Vocational Skills Development: A Competency-Based Approach
Purpose: This study examines the impact of competency-based curriculum design on the learning of vocational skills, with student learning engagement and perceived skill relevance mediating and institutional support moderating. The study aims to contribute empirical evidence to the impact of well-designed competency-based learning on students' skills acquisition and employability. Design/Methodology/Approach: A quantitative approach was employed, and data were collected from 224 vocational education students. Standardized scales were adapted from previous research, and data analysis was performed using SmartPLS 4.0. Partial least squares structural equation modeling (PLS-SEM) was applied to test construct reliability, validity, and hypothesized relationships. Findings: The results show that competency-based curriculum design has a significant and positive effect on vocational skills development. Student learning engagement and perceived skill relevance were found to be significant mediators of this relationship, highlighting their central role in driving effective skills acquisition. Moreover, institutional support significantly moderated the primary relationship, strengthening the positive impact of competency-based curriculum design when resources, infrastructure, and guidance were adequately provided. Practical implications: The findings provide actionable insights for curriculum designers, vocational educators, and policymakers by emphasizing the importance of aligning curricula with industry demands, enhancing student engagement strategies, and providing sufficient institutional support to maximize skill development outcomes. Research limitations/implications: The study was conducted on a sample of 224 students in a specific vocational context, which may limit generalizability. Future studies could explore longitudinal designs, cross-cultural comparisons, and sector-specific vocational streams to deepen theoretical and empirical insights. Originality/value: This research contributes to vocational education and curriculum design literature by simultaneously examining the mediating effects of student engagement and perceived skill relevance alongside the moderating role of institutional support. This simultaneous investigation of mediating and moderating factors offers a novel and integrated perspective rarely addressed in prior research. This research aligns with the domain of Decision Sciences by offering evidence-based insights that support decision-making processes in curriculum design, institutional policy, and workforce development.
Dynamic Spillovers and Portfolio Construction: A TVP-VAR Analysis of the S&P 500, SSE, ESG ETFs, and Commodities
Purpose - This study explores the dynamic return spillovers and portfolio implications of key global financial assets, including U.S. and Chinese equities, crude oil, and ESG-focused investments, with the aim of analyzing whether investors in the S&P 500 and Shanghai Stock Exchange can mitigate portfolio risk through strategic allocation to ESG and commodity assets. This research provides a quantitative framework for investors, portfolio managers, and policymakers to make evidence-based decisions on risk diversification, hedging strategies, and performance enhancement in both equity and multi-asset portfolios under conditions of financial and economic uncertainty. Design/methodology/approach - A Time-Varying Parameter Vector Autoregressive (TVP-VAR) model is applied to examine evolving interdependencies among the S&P 500 Index, Shanghai Stock Exchange Composite Index, West Texas Intermediate (WTI) crude oil, and the SPDR S&P 500 ESG ETF (EFIV). Four dynamic portfolio optimization strategies—Minimum Variance, Minimum Correlation, Minimum Connectedness, and Risk Parity—are implemented and evaluated under varying market conditions. Findings - Results indicate that the S&P 500 and EFIV consistently act as net transmitters of volatility, while WTI and SSE function predominantly as receivers. Portfolios optimized using Minimum Connectedness and Correlation strategies demonstrate superior cumulative returns, whereas those using Minimum Variance and Risk Parity approaches achieve better risk-adjusted performance. Bivariate hedging analyses highlight the effectiveness of ESG assets, especially in equity pairings. Practical implications - Findings provide valuable insights for institutional investors and portfolio managers seeking to optimize diversification, manage risk, and incorporate ESG principles in asset allocation strategies, particularly under conditions of global financial uncertainty. Originality/value - This study contributes to the literature by integrating ESG-focused instruments within a dynamic connectedness framework and demonstrating their role in portfolio risk mitigation and performance enhancement. Specifically, it introduces a novel combination of TVP-VAR modeling with multiple dynamic portfolio optimization strategies, demonstrating how ESG assets can systematically mitigate portfolio risk and enhance performance, offering new guidance for evidence-based decision-making in financial markets.
Copyright (c) 2026 RIHAB BELGUITH (Corresponding Author) https://creativecommons.org/licenses/by-nc/4.0
Historical Analysis of Land-use Changes in Vietnam's Red River Delta: Bayesian Network Approach to Land Policies and Sustainable Development
Purpose: Vietnam’s Red River Delta experiences rapid and uneven land-use transformations driven by market liberalization and urban expansion, severely impacting agricultural land and rural livelihoods. Accurate modeling of these changes is critical for sustainable land governance. To address this gap, this study uses Bayesian network modeling to retrospectively investigate LUCC in the Red River Delta, clarifying how land-policy transitions and agricultural expansion have influenced land-use decision-making and highlighting sustainable development implications. Design/methodology/approach: This study proposes a Bayesian network-based panel decision support framework that synthesizes (i) multi-temporal satellite-derived spatial data (1979–2022), (ii) farmer land-use decision behavior, and (iii) historical land-policy and institutional change to evaluate and project LULC dynamics in the Red River Delta. To the best of our knowledge, this is the first region-wide, long-horizon application that explicitly links LUCC trajectories to policy shifts, quantifies transformation trends, and identifies the key drivers shaping land-use change. Findings: The Red River Delta has undergone a clear shift from rice-based agriculture toward urban–industrial land uses between 2008 and 2022. Agricultural land declined sharply (7%), while forest land decreased only modestly (0.8%) and pastureland expanded (6.3%). The Bayesian network results indicate that industrial land prices are among the most influential economic drivers of these transitions, while the strongest governance levers relate to land-use zoning and conversion controls that steer agricultural-to-urban/industrial reallocations. In addition, the slight rebound of previously diminishing undefined agricultural zones suggests a move toward more structured land management. Research limitations/implications: Limitations include medium-resolution satellite imagery potentially overlooking small-scale features and classification uncertainties from traditional algorithms. Practical implications and Originality/value: This study presents a Bayesian network panel decision support framework that enables ex ante policy evaluation of land governance in the Red River Delta by simulating policy scenarios before implementation and estimating their likely effects on LUCC. It tests how changes in land use zoning, conversion controls, and industrial land prices shift the probability of major transitions, especially the conversion of rice-based agricultural land to urban and industrial uses, and highlights the most influential governance levers. The region-wide, long-horizon application in the Red River Delta offers a transferable approach for Asian delta regions facing similar trade-offs between urban industrial expansion, agricultural protection, and sustainability goals.
Copyright (c) 2026 Trang Dao Thi Thu, Huyen Ngo Khanh (Author); Phuong Tran Thi (Corresponding Author) https://creativecommons.org/licenses/by-nc/4.0
Forecasting ROA and ROE for Retail Companies in Vietnam by Using Machine Learning Techniques
Purpose: This study aims to forecast the financial performance of Vietnamese retail companies by predicting both Return on Assets (ROA) and Return on Equity (ROE) by using artificial intelligence (AI) models, thereby enhancing predictive decision-making in the retail sector. Design/methodology/approach: Financial statements from publicly listed retail firms covering the period 2010 to 2024, together with macroeconomic variables (CPI, exchange rate, gold price, VN-Index, oil prices), are combined. Three machine learning algorithms—Random Forest, XGBoost, and Multilayer Perceptron—are trained and tested by using an 80/20 split. Model accuracy is assessed by using RMSE, MAE, MAPE, Pearson’s R, and Theil’s U. Findings: Random Forest achieved the lowest RMSE of 0.0926 and delivered the highest forecasting accuracy for both ROA and ROE, followed by XGBoost, while MLP performed less effectively. Decision tree–based ensemble models better capture non-linear relationships in financial data than neural networks in this context. Research limitations/implications – The study covers only listed retail firms and annual data, omitting private firms and intra-year fluctuations. The models identify associations, not causality. Practical implications: From a Decision-Sciences perspective, the proposed AI-driven forecasting framework operates as a data-rich decision-support system that transforms multi-source information into actionable guidance on capital allocation, risk mitigation, and performance control. Originality/value: This is the first study to jointly forecast both ROA and ROE for Vietnamese retailers using three complementary ML families, integrating firm-level and macro-financial variables, and providing reproducible benchmarks for AI-driven financial forecasting in emerging markets.
Data-Driven Decision Optimization of Humic and Fulvic Acid Treatments: Experimental Evidence on Enzyme Regulation and Nutrient Bioavailability in Calcareous Soils
Purpose: This study explores the influence of humic and fulvic acids on soil enzymatic activities and nutrient bioavailability in calcareous soils, aiming to enhance understanding of their environmental roles in soil management and fertility. Design/methodology/approach: Four humic substances—fulvic acid (FA), OMRI, SP85, and HS—were applied at varying concentrations (0–1000 mg·kg⁻¹) to calcareous soils. Enzymatic activities (urease, phosphodiesterase, acid and alkaline phosphatase) and the bioavailability of key nutrients (P, K, NH₄⁺, Mg, Ca, Cu, Zn) were assessed after 40 and 70 days. Findings: Humic substances significantly affected soil enzymatic processes and nutrient availability. Urease activity was consistently inhibited, with the strongest effect observed under SP85. Phosphodiesterase and phosphatase activities were notably enhanced, especially under FA and HS treatments. Optimal phosphatase activity was observed at 800 mg·kg⁻¹, while excessive concentrations (e.g., 1000 mg·kg⁻¹ of SP85) led to decreased activity. HS treatment induced the greatest increase in phosphomonoesterase activity by day 70. Nutrient responses varied: OMRI improved P and Mg availability; HS increased K and Zn; FA promoted Cu. Calcium levels declined under all humic acid treatments. An application rate of 400 mg·kg⁻¹ was found to be optimal. Practical implications: Appropriate application of humic substances can improve soil biochemical health, supporting efficient nutrient management in high-pH soils. Originality/value: This study provides novel insights into the enzyme–nutrient interactions influenced by humic substances in calcareous soils. This study also contributes to Decision Sciences by providing a data-driven optimization framework for selecting the most effective type and concentration of humic substances to support evidence-based agricultural decision-making. This research is original in integrating soil biochemical experimentation with quantitative decision modeling, allowing readers to see how data-driven optimization links biological evidence with managerial decision-support. Academics may cite this paper for its methodological synthesis of experimental design and optimization analysis, while practitioners can adopt its findings to enhance real-world nutrient-management efficiency in calcareous soils.
Sustainability in Sport Management: A Review and Bibliometric Analysis
Purpose: This paper provides a comprehensive bibliometric review of the literature on sustainability in sport management to identify the most prominent thematic areas, underlying conceptual frameworks, and future research directions. Design/methodology/approach: A bibliometric analysis of 393 journal articles from the Scopus database covering the years 2000–2024 was conducted. Using VOSviewer, the study employed co-occurrence and co-citation analyses to map the intellectual structure and thematic evolution of the research field. Findings: The analysis reveals three primary thematic clusters: environmental sustainability, economic impacts, and social impacts, with a notable recent surge in research on technological innovations. A significant increase in research output is evident since 2020. However, the literature is dominated by North American contexts, indicating a significant geographical gap. Research limitations/implications: The review is limited to the Scopus database and English-language articles. The findings imply a critical need for research from more diverse global perspectives to develop a more comprehensive understanding of sustainability in the worldwide sports industry. Relevance to Decision Sciences: This study provides a structured, quantitative map of the sustainability in sport management literature, offering a decision-making tool for academics, journal editors, and policymakers to identify critical research gaps, influential frameworks, and emerging trends, thereby guiding strategic research investments and evidence-based policy formulation. Within this broader Decision Sciences context, the study holds particular relevance for the field of Information Systems and Management, where bibliometric mapping and data-driven visualization function as decision-support tools for managing knowledge flows, evaluating digital sustainability practices, and optimizing research and policy strategies in sport management. Originality/value: This study offers a broad, quantitative overview of the field over a 24-year period. It systematically identifies key research clusters, intellectual foundations, and critical gaps, providing a robust roadmap for future inquiry into integrating sustainability in sport management.
The Role of Social Media Engagement in Shaping Blockchain Adoption: Insights from Thai Users
Purpose – This study examines the effect of social media engagement (SME) on blockchain technology adoption, focusing on awareness, trust, and perceived usefulness among Thai social media users. Design/methodology/approach – Structural equation modeling (SEM) was employed to test six hypotheses using data from 400 Thai social media users collected via an online survey. Findings – SME significantly increases blockchain awareness, which strongly influences adoption. However, SME did not directly affect trust or perceived usefulness. Trust moderately influenced adoption, while perceived usefulness was a key driver. Awareness is a critical mediator, indicating SME's limited direct impact on trust and usefulness. Research limitations/implications – The cross-sectional design limits causal claims. Findings may not generalize beyond Thailand due to cultural and regulatory variations. Practical implications – Marketers and policymakers should complement social media campaigns with deeper educational initiatives and trusted institutional backing to foster blockchain adoption. Social implications – Insights inform strategies for responsible blockchain promotion, ensuring public understanding and trust in emerging digital technologies. Relevance to Decision Sciences – The findings directly inform Decision Sciences by modeling the cognitive and social factors that drive user adoption decisions for complex, high-risk technologies, offering a new framework for predicting technology uptake in uncertain environments. Within the broader Decision Sciences field, this research specifically advances Information Systems and Management by demonstrating how digital engagement and behavioral data can be integrated into technology adoption models, providing actionable insights for system designers and managers seeking to enhance trust, awareness, and perceived usefulness in digital innovation ecosystems. Originality/value – This study integrates social media engagement into TAM/UTAUT frameworks in an emerging market, refining adoption theory and offering evidence-based guidance for digital engagement in technology uptake.
Copyright (c) 2026 Alfonso Pellegrino (Author); Alessandro Stasi (Corresponding Author) https://creativecommons.org/licenses/by-nc/4.0
Review Helpfulness to Support Business: Identifying Fake Reviews from User-Generated Content Using Random Forest
Purpose: Valid and Helpful reviews on an e-commerce platform provide important information regarding customers' perception of a product, which is crucial to the existence and growth of any business. False reviews, which are created to tarnish a product's image through spam fraudulently, continue to be a significant challenge for all e-commerce platforms. Another challenge remains in identifying helpful review content on the platform that can significantly alter a customer's opinion of a product. Hence, the increasing prevalence of fake and unhelpful reviews compromises the credibility of online reviews, resulting in information overload and a misleading consumer decision-making process. Motivated by this challenge, this study aims to develop an automated system capable of retaining only applicable and valid reviews to support the identification of customer needs, which is a valuable area of research. Design/methodology/approach: This study involves three main aspects: helpfulness classification, fake review detection, and topic identification on various categories of the Amazon Dataset. The model leveraged a feature set that included the sentiment polarity of the review in detail, word count indicating the length of feedback, word diversity in the review, comprehension analysis of parts of speech in the review reflecting its grammatical structure and complexity, and authenticity metrics. Moreover, for helpful review classification, the utilized features included review and product metadata, review content informativeness score encoded with the help of Sentence Bidirectional Encoder Representations from Transformers (SBERT), and reviewer attributes. A topic extraction model has been implemented that leverages Gemini to extract sentiment-based topic analysis over reviews. Findings: The study provides useful reviews classification over 6 different Amazon categories using a Random Forest classifier (RFC) by achieving 94% accuracy, precision, and F1-Score, a recall of 93%, and an AUC Score of 98%. While the Gradient Boosting classifier yielded comparable performance with an AUC Score of 98% and 94% accuracy, precision, recall, and F1-Score. For fake reviews detection in the Toys and Games category, the RFC achieved 85% accuracy, 86% precision, a 97% recall, 91% F1-Score, and 79% AUC Score. The findings indicate that combining textual, semantic, reviewer, and product-level features can improve the reliability of review quality assessment. Finally, to enhance the decision-making process for businesses, a topic extraction model utilizing the Gemini tool has been employed to extract significant topics from valid and helpful reviews, categorizing them separately into negative and positive reviews, thereby gaining nuanced insights into customer feedback. Originality/value: Unlike prior studies that either examine review helpfulness or fake review detection in isolation, this study moves beyond single-task and small-sample-based approaches. Our proposed framework offers a comprehensive analysis of patterns in reviews across e-commerce platforms, thereby enhancing brands' ability to integrate customer needs and expectations into future marketing communications and advertising campaigns. This study contributes to Decision Sciences by proposing a data-driven two-stage framework that retains only helpful and valid reviews to enhance content quality, thereby practically supporting better decision-making by content moderation, reducing information overload, and improving consumer trust in reviews.
Copyright (c) 2026 Syed Imran Abbas Qazmi (Corresponding Author); Midhun Chakkaravarthy, Syed Hassan Raza, Farrah Aslam, Shahbaz Aslam, Moneeba Iftikhar (Author) https://creativecommons.org/licenses/by-nc/4.0