Advances in Decision Sciences
type: channel
description: 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.
publisher: Scientific and Business World, Singapore / Asia University, Taiwan
language: en
publication name: Advances in Decision Sciences
issn: 2090-3359
items: Multi-Objective Constrained Reinforcement Learning for Joint Routing–MAC–Duty Cycling in Low-Power Wireless Sensor Networks
items: Assessing the Moderating Effect of ESG Investment on Climate Variability Risk and Firm Value Nexus
items: Quantifying the Spillover Effect of U.S. Trade Policy Uncertainty on Economic Growth of BRICS: A Panel Data Investigation
items: Determinants of Reward Crowdfunding Success for Technology Projects: The Moderating Role of Platform Age
items: Role of ESG Disclosure in Determining Earnings Management of GCC Firms: Moderating Role of Corporate Board Diversity
items: The Impact of Firm–Specific Characteristics on the Readability of Bank Annual Reports: Evidence from MENA Countries
items: Exploring Tail Risk Transmission between Volatility Indices and Cryptocurrencies: Evidence from Quantile Connectedness
items: The Role of Perceived Value in Renewable Energy Adoption: A Study of Rural Entrepreneurs in Bangladesh
items: Economic Policy Uncertainty and Stock Market Co-Movements in BRIC Countries: Evidence from Wavelet Coherence and Rolling Bootstrap Granger Causality
items: Worldwide Nickel Ore Trade, Its Stability and the Characteristics: A Fresh Policy Analysis
items: Optimal stopping time strategy for paying tax
items: The Mediating Role of Globalization in the Nexus Between CO₂ Emissions, GHGs, and Economic Growth
items: Optimizing Repair Allocation in Healthcare: Comparing Volume- and Value-Based Models under Capacity Constraints
items: Aligning Curriculum Design with Vocational Skills Development: A Competency-Based Approach
items: Dynamic Spillovers and Portfolio Construction: A TVP-VAR Analysis of the S&P 500, SSE, ESG ETFs, and Commodities
items: Historical Analysis of Land-use Changes in Vietnam's Red River Delta: Bayesian Network Approach to Land Policies and Sustainable Development
items: Forecasting ROA and ROE for Retail Companies in Vietnam by Using Machine Learning Techniques
items: Data-Driven Decision Optimization of Humic and Fulvic Acid Treatments: Experimental Evidence on Enzyme Regulation and Nutrient Bioavailability in Calcareous Soils
items: Sustainability in Sport Management: A Review and Bibliometric Analysis
items: The Role of Social Media Engagement in Shaping Blockchain Adoption: Insights from Thai Users
items: Review Helpfulness to Support Business: Identifying Fake Reviews from User-Generated Content Using Random Forest
items: Energy demand response to the dynamics of the currency valuation: Evidence from G7 countries
items: Hedging Global Stock Markets with Bitcoin, Precious Metals, Copper, Crude Oil, and Agricultural Commodities: Evidence from Bivariate Threshold GARCH Approach
items: Technological Capabilities and Institutional Pressures in Green Logistics Adoption: Evidence from an Emerging Economy's Freight Forwarding Sector
items: Impact of Earnings Management on Corporate Tax Avoidance: The Moderating Role of Corporate Governance – Insights from Asian Markets
items: Ownership Structure and Leverage as Precursors to Financial Distress in an Emerging Economy: A Mediating Model
items: Strategic Decision-Making in SME Growth: Harnessing Digital Transformation and Innovation in Jordan
items: Forecasting the Appearance Frequency of Rafflesia arnoldii in Bengkulu, Indonesia, Using Discrete-valued Time Series Modeling
items: Study of Investor Behavior on Stock Investment Decision Making with Self-Monitoring as a Moderating Variable in Generation Y and Generation Z
items: Strengthening Financial Reporting Quality: The Role of Rule of Law, Public Transparency, and Cultural Change as a Moderator

Multi-Objective Constrained Reinforcement Learning for Joint Routing–MAC–Duty Cycling in Low-Power Wireless Sensor Networks
type: item
description: 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.
subject: Articles
  topic: section
subject: WSNs
  topic: keywords
subject: reinforcement learning
  topic: keywords
subject: multi-objective optimization
  topic: keywords
subject: constrained Markov decision processes
  topic: keywords
subject: cross-layer optimization, energy efficiency
  topic: keywords
subject: energy efficiency
  topic: keywords
subject: C44
  topic: subjects
subject: C61
  topic: subjects
subject: L96
  topic: subjects
subject: C63
  topic: subjects
creator: Ghaida Muttashar Abdulsahib Abdulsahib
creator: Mohammed Awad Mohammed Ataelfadiel
rights: Copyright (c) 2026 Ghaida Muttashar Abdulsahib (Author); Mohammed Awad Mohammed Ataelfadiel (Corresponding Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-04-05
publication date: 2026-04-05
starting page: 197
ending page: 229
doi: 10.47654/v30y2026i2p197-229

Assessing the Moderating Effect of ESG Investment on Climate Variability Risk and Firm Value Nexus
type: item
description: 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 literat…
subject: Articles
  topic: section
subject: Climate Risk
  topic: keywords
subject: ESG Investment
  topic: keywords
subject: Firm Value
  topic: keywords
subject: GMM
  topic: keywords
subject: FGLS
  topic: keywords
subject: G32
  topic: subjects
subject: Q54
  topic: subjects
subject: M14
  topic: subjects
subject: Q56
  topic: subjects
creator: Mohammed Ahmed Yousef Al-qadhi
creator: Shen Zunhuan
creator: Umar Farooq
creator: Mosab I. Tabash
creator: Abdelhafid Belarbi
creator: Zokir Mamadiyarov
rights: Copyright (c) 2026 Advances in Decision Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-27
publication date: 2026-01-27
starting page: 40
ending page: 71
doi: 10.47654/v30y2026i1p40-71

Quantifying the Spillover Effect of U.S. Trade Policy Uncertainty on Economic Growth of BRICS: A Panel Data Investigation
type: item
description: 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 mini…
subject: Articles
  topic: section
subject: Trade Policy Uncertainty
  topic: keywords
subject: Economic Growth
  topic: keywords
subject: Real Option Theory
  topic: keywords
subject: BRICS Economies
  topic: keywords
subject: F13
  topic: subjects
subject: F21
  topic: subjects
subject: F43
  topic: subjects
creator: Mustafa Ahmed Hamed Mansour
creator: Salem Hamad Aldawsari
rights: Copyright (c) 2026 Advances in Decision Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-29
publication date: 2026-01-29
starting page: 72
ending page: 102
doi: 10.47654/v30y2026i1p72-102

Determinants of Reward Crowdfunding Success for Technology Projects: The Moderating Role of Platform Age
type: item
description: 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 an…
subject: Articles
  topic: section
subject: Reward-crowdfunding
  topic: keywords
subject: Technology projects
  topic: keywords
subject: Crowdfunding success
  topic: keywords
subject: GLS regression
  topic: keywords
subject: Quantile regression
  topic: keywords
subject: C51
  topic: subjects
subject: G24
  topic: subjects
subject: M13
  topic: subjects
subject: O33
  topic: subjects
creator: Ichrak Dridi
creator: Oussama Gafrej
creator: Jabeur Salhi
rights: Copyright (c) 2026 Ichrak DRIDI (Corresponding Author); Oussama Gafrej, Jabeur Salhi (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-28
publication date: 2026-03-28
starting page: 68
ending page: 113
doi: 10.47654/v30y2026i2p68-113

Role of ESG Disclosure in Determining Earnings Management of GCC Firms: Moderating Role of Corporate Board Diversity
type: item
description: 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, …
subject: Articles
  topic: section
subject: Environmental social governance (ESG)
  topic: keywords
subject: Board Diversity
  topic: keywords
subject: Earnings management
  topic: keywords
subject: GLS regression
  topic: keywords
subject: G34
  topic: subjects
subject: M40
  topic: subjects
subject: Q56
  topic: subjects
subject: C33
  topic: subjects
creator: Laila Maswadi
creator: Muzammal Ilyas Sindhu
creator: Ruby Khan
creator: Ameera Hakami
rights: Copyright (c) 2025 Advances in Decision Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 141
ending page: 160
doi: 10.47654/v29y2025i4p141-160

The Impact of Firm–Specific Characteristics on the Readability of Bank Annual Reports: Evidence from MENA Countries
type: item
description: 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.
subject: Articles
  topic: section
subject: Annual Report
  topic: keywords
subject: Chairman Letter
  topic: keywords
subject: MENA Region
  topic: keywords
subject: Narrative
  topic: keywords
subject: Readability
  topic: keywords
subject: M41
  topic: subjects
creator: Rasha Mohamad Mahboub
rights: Copyright (c) 2026 Rasha Mohamad Mahboub (Corresponding Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 157
ending page: 196
doi: 10.47654/v30y2026i2p157-196

Exploring Tail Risk Transmission between Volatility Indices and Cryptocurrencies: Evidence from Quantile Connectedness
type: item
description: 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 method…
subject: Articles
  topic: section
subject: Quantile connectedness
  topic: keywords
subject: Implied volatility indices
  topic: keywords
subject: Cryptocurrencies
  topic: keywords
subject: Risk transmission
  topic: keywords
subject: Tail spillover
  topic: keywords
subject: Hedging
  topic: keywords
subject: C32
  topic: subjects
subject: G15
  topic: subjects
subject: G11
  topic: subjects
subject: G17
  topic: subjects
creator: ENNADIFI Imane
creator: KADIL Ghizlane
rights: Copyright (c) 2025 Advances in Decision Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2025-11-19
publication date: 2025-11-19
starting page: 119
ending page: 157
doi: 10.47654/v29y2025i3p119-157

The Role of Perceived Value in Renewable Energy Adoption: A Study of Rural Entrepreneurs in Bangladesh
type: item
description: 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.
subject: Articles
  topic: section
subject: rural entrepreneurs
  topic: keywords
subject: renewable energy technologies
  topic: keywords
subject: environmental sustainability
  topic: keywords
subject: environmental awareness
  topic: keywords
subject: Smart PLS 4.0
  topic: keywords
subject: Q42
  topic: subjects
subject: Q55
  topic: subjects
subject: Q56
  topic: subjects
subject: L26
  topic: subjects
subject: O33
  topic: subjects
subject: C83
  topic: subjects
creator: Mohammad Rashed Hasan Polas
creator: Ahmed Imran Kabir
creator: Abu Saleh Md. Sohel-Uz-Zaman
creator: Ridoan Karim
rights: 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
date: 2026-06-14
publication date: 2026-06-14
starting page: 297
ending page: 335
doi: 10.47654/v30y2026i1p297-335

Economic Policy Uncertainty and Stock Market Co-Movements in BRIC Countries: Evidence from Wavelet Coherence and Rolling Bootstrap Granger Causality
type: item
description: 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.
subject: Articles
  topic: section
subject: EPU
  topic: keywords
subject: Stock Returns
  topic: keywords
subject: Wavelet coherence
  topic: keywords
subject: Bootstrap rolling window
  topic: keywords
subject: F21
  topic: subjects
subject: C22
  topic: subjects
creator: Houssem Ben-Ammar
creator: Riadh El Abed
rights: Copyright (c) 2026 Advances in Decision Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-03
publication date: 2026-02-03
starting page: 103
ending page: 135
doi: 10.47654/v30y2026i1p103-135

Worldwide Nickel Ore Trade, Its Stability and the Characteristics: A Fresh Policy Analysis
type: item
description: 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.
subject: Articles
  topic: section
subject: Nickel ore trade
  topic: keywords
subject: Network analysis
  topic: keywords
subject: Supply disruption
  topic: keywords
subject: Scale-free network
  topic: keywords
subject: Trade stability
  topic: keywords
subject: F14
  topic: subjects
subject: Q37
  topic: subjects
subject: L72
  topic: subjects
subject: C63
  topic: subjects
subject: O13
  topic: subjects
creator: Algimantas Laurinavicius
creator: Asma Salman
creator: Mohamed Elsayed Abdelsalam Ghanem
creator: Antanas Laurinavicius
creator: Mohammed Ahmar Uddin
rights: Copyright (c) 2025 Advances in Decision Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2025-11-16
publication date: 2025-11-16
starting page: 85
ending page: 118
doi: 10.47654/v29y2025i3p85-118

Optimal stopping time strategy for paying tax
type: item
description: 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.
subject: Articles
  topic: section
subject: Optimal stopping problem
  topic: keywords
subject: Markov processes
  topic: keywords
subject: tax payment
  topic: keywords
subject: reinvestment
  topic: keywords
subject: penalty
  topic: keywords
subject: H26
  topic: subjects
subject: C61
  topic: subjects
subject: C63
  topic: subjects
creator: Soufiene ASSIDI
creator: Noureddine JILANI BEN NAOUARA
creator: Wing-Keung Wong
rights: Copyright (c) 2025 Advances in Decision Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-28
publication date: 2025-09-28
starting page: 55
ending page: 82
doi: 10.47654/v29y2025i1p55-82

The Mediating Role of Globalization in the Nexus Between CO₂ Emissions, GHGs, and Economic Growth
type: item
description: 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 econom…
subject: Articles
  topic: section
subject: Special Issue on : Decision Science in Energy Economics and Environment for Reducing Carbon Footprint and Achieving Sustainable Development Goals
  topic: category
subject: CO₂ emissions
  topic: keywords
subject: Greenhouse gas emissions
  topic: keywords
subject: Globalization
  topic: keywords
subject: Economic growth
  topic: keywords
subject: G20
  topic: keywords
subject: F43
  topic: subjects
subject: O44
  topic: subjects
subject: Q43
  topic: subjects
subject: Q56
  topic: subjects
creator: Abdelbasset Dhouioui
rights: Copyright (c) 2026 Abdelbasset DHOUIWI (Corresponding Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-04-05
publication date: 2026-04-05
starting page: 230
ending page: 257
doi: 10.47654/v30y2026i2p230-257

Optimizing Repair Allocation in Healthcare: Comparing Volume- and Value-Based Models under Capacity Constraints
type: item
description: 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 …
subject: Articles
  topic: section
subject: Healthcare equipment maintenance
  topic: keywords
subject: Optimization model
  topic: keywords
subject: Repair task allocation
  topic: keywords
subject: Eligibility-weighted assignment
  topic: keywords
subject: Capacity-constrained scheduling
  topic: keywords
subject: C44
  topic: subjects
subject: C61
  topic: subjects
subject: P36
  topic: subjects
creator: Chartchai Leenawong
rights: Copyright (c) 2025 Advances in Decision Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-11
publication date: 2025-12-11
starting page: 38
ending page: 62
doi: 10.47654/v29y2025i4p38-62

Aligning Curriculum Design with Vocational Skills Development: A Competency-Based Approach
type: item
description: 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…
subject: Articles
  topic: section
subject: Special Issue on : Contemporary Management Research
  topic: category
subject: Competency-Based Curriculum
  topic: keywords
subject: Vocational Skills Development
  topic: keywords
subject: Student Learning Engagement
  topic: keywords
subject: Perceived Skill Relevance
  topic: keywords
subject: Institutional Support
  topic: keywords
subject: I23
  topic: subjects
subject: J24
  topic: subjects
subject: M53
  topic: subjects
creator: Mingjing Yang
creator: Muhantha Paramalingam
creator: Yashu Chen
rights: Copyright (c) 2025 Advances in Decision Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-16
publication date: 2025-12-16
starting page: 188
ending page: 221
doi: 10.47654/v29y2025i3p127-160

Dynamic Spillovers and Portfolio Construction: A TVP-VAR Analysis of the S&P 500, SSE, ESG ETFs, and Commodities
type: item
description: 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-f…
subject: Articles
  topic: section
subject: Dynamic connectedness
  topic: keywords
subject: TVP-VAR Framework
  topic: keywords
subject: portfolio optimization
  topic: keywords
subject: Hedging effectiveness
  topic: keywords
subject: ESG
  topic: keywords
subject: Equity Markets
  topic: keywords
subject: Crude Oil
  topic: keywords
subject: C01
  topic: subjects
subject: C02
  topic: subjects
subject: C32
  topic: subjects
subject: C58
  topic: subjects
subject: G11
  topic: subjects
subject: G15
  topic: subjects
creator: Rihab Belguith
rights: Copyright (c) 2026 RIHAB BELGUITH (Corresponding Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-19
publication date: 2026-02-19
starting page: 186
ending page: 221
doi: 10.47654/v30y2026i1p186-221

Historical Analysis of Land-use Changes in Vietnam's Red River Delta: Bayesian Network Approach to Land Policies and Sustainable Development
type: item
description: 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 uncer…
subject: Articles
  topic: section
subject: LUCC
  topic: keywords
subject: Bayesian Network
  topic: keywords
subject: Red River Delta
  topic: keywords
subject: agricultural policy
  topic: keywords
subject: sustainable land use
  topic: keywords
subject: Q15
  topic: subjects
subject: Q24
  topic: subjects
subject: Q58
  topic: subjects
subject: R14
  topic: subjects
subject: O13
  topic: subjects
creator: Trang Dao Thi Thu
creator: Huyen Ngo Khanh
creator: Phuong Tran Thi
rights: Copyright (c) 2026 Trang Dao Thi Thu, Huyen Ngo Khanh (Author); Phuong Tran Thi (Corresponding Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-01
publication date: 2026-03-01
starting page: 1
ending page: 38
doi: 10.47654/v30y2026i2p1-38

Forecasting ROA and ROE for Retail Companies in Vietnam by Using Machine Learning Techniques
type: item
description: 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.
subject: Articles
  topic: section
subject: Financial performance
  topic: keywords
subject: forecasting
  topic: keywords
subject: ROA
  topic: keywords
subject: ROE
  topic: keywords
subject: retail enterprises
  topic: keywords
subject: Vietnam
  topic: keywords
subject: C01
  topic: subjects
subject: C02
  topic: subjects
subject: G17
  topic: subjects
creator: Quang Hung Do
rights: Copyright (c) 2025 Advances in Decision Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-11
publication date: 2025-12-11
starting page: 63
ending page: 93
doi: 10.47654/v29y2025i4p63-93

Data-Driven Decision Optimization of Humic and Fulvic Acid Treatments: Experimental Evidence on Enzyme Regulation and Nutrient Bioavailability in Calcareous Soils
type: item
description: 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, allow…
subject: Articles
  topic: section
subject: Humic acid
  topic: keywords
subject: Fulvic acid
  topic: keywords
subject: Soil enzyme
  topic: keywords
subject: Nutrient bioavailability
  topic: keywords
subject: Calcareous soil
  topic: keywords
subject: Q15
  topic: subjects
subject: Q24
  topic: subjects
subject: Q53
  topic: subjects
subject: O13
  topic: subjects
subject: C93
  topic: subjects
creator: Qian Sun
creator: Mohamad Anuar Kamaruddin
creator: Kai Huang
creator: Yun Cao
creator: Jianlong Liu
rights: Copyright (c) 2025 Advances in Decision Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-11
publication date: 2025-12-11
starting page: 158
ending page: 187
doi: 10.47654/v29y2025i3p158-187

Sustainability in Sport Management: A Review and Bibliometric Analysis
type: item
description: 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 …
subject: Articles
  topic: section
subject: Sustainability
  topic: keywords
subject: Sports Management
  topic: keywords
subject: Information Systems
  topic: keywords
subject: Digital Innovations
  topic: keywords
subject: Sustainable Practices
  topic: keywords
subject: Bibliometric Analysis
  topic: keywords
subject: L83
  topic: subjects
subject: Q01
  topic: subjects
subject: M14
  topic: subjects
creator: Alfonso Pellegrino
creator: ALESSANDRO STASI
rights: Copyright (c) 2025 Advances in Decision Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-12
publication date: 2025-12-12
starting page: 94
ending page: 140
doi: 10.47654/v29y2025i4p94-140

The Role of Social Media Engagement in Shaping Blockchain Adoption: Insights from Thai Users
type: item
description: 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 framew…
subject: Articles
  topic: section
subject: Social Media Engagement
  topic: keywords
subject: Blockchain Technology
  topic: keywords
subject: Information Systems Management
  topic: keywords
subject: Structural Equation Modeling (SEM)
  topic: keywords
subject: Digital Communication
  topic: keywords
subject: O33
  topic: subjects
subject: D83
  topic: subjects
subject: G20
  topic: subjects
subject: O10
  topic: subjects
subject: L86
  topic: subjects
creator: Alfonso Pellegrino
creator: Alessandro Stasi
rights: Copyright (c) 2026 Alfonso Pellegrino (Author); Alessandro Stasi (Corresponding Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-18
publication date: 2026-03-18
starting page: 261
ending page: 296
doi: 10.47654/v30y2026i1p261-296

Review Helpfulness to Support Business: Identifying Fake Reviews from User-Generated Content Using Random Forest
type: item
description: 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…
subject: Articles
  topic: section
subject: Special Issue on : Contemporary Management Research
  topic: category
subject: Text Similarity
  topic: keywords
subject: BERT Embeddings
  topic: keywords
subject: Fake Reviews
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Review Helpfulness
  topic: keywords
subject: Random Forest Classifier
  topic: keywords
subject: L81
  topic: subjects
subject: O33
  topic: subjects
subject: L86
  topic: subjects
subject: C38
  topic: subjects
subject: C45
  topic: subjects
creator: Syed Imran Abbas Qazmi
creator: Midhun Chakkaravarthy
creator: Syed Hassan Raza
creator: Farrah Aslam
creator: Shahbaz Aslam
creator: Moneeba Iftikhar
rights: 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
date: 2026-03-28
publication date: 2026-03-28
starting page: 114
ending page: 156
doi: 10.47654/v30y2026i2p114-156

Energy demand response to the dynamics of the currency valuation: Evidence from G7 countries
type: item
description: 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.
subject: Articles
  topic: section
subject: NARDL model
  topic: keywords
subject: MTNARDL model
  topic: keywords
subject: Granger causality in quantile test
  topic: keywords
subject: traditional ARDL model
  topic: keywords
subject: E32
  topic: subjects
subject: F31
  topic: subjects
subject: Q43
  topic: subjects
subject: C32
  topic: subjects
subject: O11
  topic: subjects
creator: Antanas Laurinavicius
creator: Chaleun Vongmileuth
creator: Sonesavanh Vongmileuth
creator: Algimantas Laurinavicius
creator: Shin Hung Pan
creator: Bisharat Hussain Chang
rights: Copyright (c) 2025 Advances in Decision Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2025-06-06
publication date: 2025-06-06
starting page: 1
ending page: 34
doi: 10.47654/v29y2025i1p1-34

Hedging Global Stock Markets with Bitcoin, Precious Metals, Copper, Crude Oil, and Agricultural Commodities: Evidence from Bivariate Threshold GARCH Approach
type: item
description: 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.
subject: Articles
  topic: section
subject: Special Issue on : Contemporary Management Research
  topic: category
subject: hedge ratio
  topic: keywords
subject: hedging effectiveness
  topic: keywords
subject: BEKK-GARCH
  topic: keywords
subject: C58
  topic: subjects
subject: G11
  topic: subjects
creator: Zhuangxing ZHENG
creator: Kai Yin WOO
rights: Copyright (c) 2025 Advances in Decision Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2025-06-08
publication date: 2025-06-08
starting page: 35
ending page: 54
doi: 10.47654/v29y2025i1p35-54

Technological Capabilities and Institutional Pressures in Green Logistics Adoption: Evidence from an Emerging Economy's Freight Forwarding Sector
type: item
description: Purpose: This study examines how access to green technologies, institutional pressures, and supply chain collaboration influence green logistics adoption and organizational performance in Vietnam’s logistics industry. Motivated by inconsistent findings in prior sustainability research and concerns about model validity in emerging-market studies, the paper integrates the Resource-Based View and Institutional Theory to explain how internal capabilities and external constraints jointly shape sustainability outcomes. Design/methodology/approach: Data were collected through a structured questionnaire survey, yielding 334 valid responses from logistics firms operating in Vietnam. Partial least squares structural equation modeling (PLS-SEM) using SmartPLS was employed to test direct, moderating, and mediating relationships, supported by bootstrapping. To enhance robustness and address spurious inference concerns, extensive diagnostic tests were conducted, and key results were cross-validated using covariance-based SEM (CB-SEM). Findings: The results indicate that access to green technologies has a limited direct effect on green logistics adoption, whereas supply chain collaboration significantly enhances adoption and environmental awareness. Institutional pressures primarily influence organizational performance rather than operational adoption decisions. Moderation and mediation analyses show that organizational size and environmental awareness condition and partially transmit the effects of technological access and collaboration. By clarifying how organizational and institutional factors shape sustainability adoption under uncertainty, these findings provide evidence-based insights that support managerial and policy decision-making in Decision Sciences. Theoretical implications: The study challenges technology-centric explanations of green logistics adoption by demonstrating that organizational and institutional mechanisms play a more decisive role in emerging markets, th…
subject: Articles
  topic: section
subject: Special Issue on : Decision Science in Energy Economics and Environment for Reducing Carbon Footprint and Achieving Sustainable Development Goals
  topic: category
subject: Green logistics adoption
  topic: keywords
subject: Institutional pressures
  topic: keywords
subject: Supply chain collaboration
  topic: keywords
subject: Organizational size
  topic: keywords
subject: Emerging markets
  topic: keywords
subject: Structural equation modeling
  topic: keywords
subject: L14
  topic: subjects
subject: L91
  topic: subjects
subject: M11
  topic: subjects
subject: O33
  topic: subjects
subject: Q56
  topic: subjects
creator: Erkan Duzgun
creator: Tuyen Thi Hoang Nguyen
rights: Copyright (c) 2026 Erkan Duzgun (Corresponding Author); Tuyen Thi Hoang Nguyen (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-14
publication date: 2026-03-14
starting page: 222
ending page: 260
doi: 10.47654/v30y2026i1p222-260

Impact of Earnings Management on Corporate Tax Avoidance: The Moderating Role of Corporate Governance – Insights from Asian Markets
type: item
description: Purpose - This study aims to investigate how corporate governance moderates the relationship between earnings management and corporate tax avoidance. Methodology - Data were collected from 620 non-financial companies in six Asian markets. A two-step generalized method of moments (GMM) approach was used to analyze the relationship between the variables. Findings - The study reveals that higher levels of earnings management are associated with greater degrees of corporate tax avoidance. However, strong corporate governance practices, such as board independence and gender diversity, can mitigate this relationship, reducing corporate tax avoidance even in the presence of earnings management. Implications - The findings have significant theoretical and practical implications for regulators, policymakers, and managers. They highlight the necessity of adapting tax regulations to account for the influence of corporate governance and earnings management. Additionally, the study underscores the importance of organizations enhancing their internal audit systems and regularly examining internal controls and procedures to ensure the accuracy and integrity of financial statements. Originality/Value - This study provides novel insights into the relationship between earnings management, corporate governance, and corporate tax avoidance, particularly in Asian markets. It offers valuable recommendations for strengthening corporate governance practices and aligning them with effective tax regulatory models. This study contributes to the field of decision sciences by presenting a rigorous quantitative framework that allows regulators, investors, and corporate leaders to make informed decisions, particularly when dealing with corporate governance issues and tax regulations. The analysis of Asian markets shows how institutional factors such as legal traditions, cultural norms, and governance capability influence the efficacy of corporate control. The study addresses a significant gap in …
subject: Articles
  topic: section
subject: Tax Avoidance
  topic: keywords
subject: Earnings Management
  topic: keywords
subject: Corporate Governance
  topic: keywords
subject: Asian Markets
  topic: keywords
subject: Generalized Method of Moments
  topic: keywords
subject: G34
  topic: subjects
subject: H26
  topic: subjects
subject: L25
  topic: subjects
creator: Muhammad Furqan
creator: Tahira Awan
creator: Hassan Zada
creator: Wing-Keung Wong
creator: Naveed Khan
rights: Copyright (c) 2025 Muhammad Furqan, Tahira Awan (Author); Hassan Zada (Corresponding Author); Wing-Keung Wong, Naveed Khan (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 161
ending page: 194
doi: 10.47654/v29y2025i4p161-194

Ownership Structure and Leverage as Precursors to Financial Distress in an Emerging Economy: A Mediating Model
type: item
description: 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 countrie…
subject: Articles
  topic: section
subject: Ownership structure
  topic: keywords
subject: leverage structure
  topic: keywords
subject: financial distress
  topic: keywords
subject: mediation
  topic: keywords
subject: Pooled OLS
  topic: keywords
subject: PCSE
  topic: keywords
subject: C51
  topic: subjects
subject: C54
  topic: subjects
subject: C58
  topic: subjects
subject: G32
  topic: subjects
subject: G34
  topic: subjects
creator: Muhammad Usman Akmal
creator: Syed Ahmed Salman
creator: Rana Yassir Hussain
creator: Kai-Yin WOO
rights: Copyright (c) 2025 Advances in Decision Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2025-10-05
publication date: 2025-10-05
starting page: 1
ending page: 43
doi: 10.47654/v29y2025i3p1-43

Strategic Decision-Making in SME Growth: Harnessing Digital Transformation and Innovation in Jordan
type: item
description: Purpose: This study investigates the strategic role of digital transformation (DT), digital innovation (DI), and digital strategy (DS) in enhancing the performance of small and medium enterprises (SMEs) in Jordan, a developing economy. The research focuses on the mediating influence of digital strategy in translating technological advancements into financial and non-financial organizational outcomes. Design/methodology/approach: A quantitative approach was employed, utilizing cross-sectional survey data from 117 SMEs. The study used partial least squares structural equation modeling (PLS-SEM) to examine the relationships among digital transformation, digital innovation, digital strategy, and firm performance. The model was validated with diagnostic tests for reliability, validity, and model fit indices. Findings: The findings reveal significant direct effects of digital transformation and digital innovation on digital strategy. Mediation analysis confirms that digital strategy partially mediates the relationships between digital advancements and performance. For financial and non-financial outcomes, digital strategy enables SMEs to leverage technological resources effectively. The study identifies the potential of strategic digital alignment for sustainable growth in resource-constrained environments. This research explicitly contributes to the field of Decision Sciences by modeling how SMEs optimize digital resources under uncertainty, linking decision-making processes with performance outcomes. Research limitations/implications: The cross-sectional design limits the ability to observe long-term impacts of digital strategies. Moreover, the sample is restricted to Jordanian SMEs, which may limit generalizability. Future longitudinal and cross-country studies are recommended to capture dynamic effects and comparative insights. Practical implications: The research highlights actionable strategies for SME leaders to adopt and align digital technologies with organizatio…
subject: Articles
  topic: section
subject: Digital Transformation
  topic: keywords
subject: Digital Innovation
  topic: keywords
subject: Digital Strategy
  topic: keywords
subject: SME Performance
  topic: keywords
subject: PLS-SEM
  topic: keywords
subject: Organizational Growth
  topic: keywords
subject: Developing Economies
  topic: keywords
subject: O33
  topic: subjects
subject: M15
  topic: subjects
subject: L25
  topic: subjects
subject: L26
  topic: subjects
subject: C83
  topic: subjects
creator: Yousef Alsafadi
creator: Manaf Al-Okaily
creator: Rejaul Karim
creator: Bablu Kumar Dhar
creator: Md. Mustaqim Roshid
creator: Sharmila Devi Ramachandaran
rights: Copyright (c) 2026 Advances in Decision Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-25
publication date: 2026-01-25
starting page: 1
ending page: 39
doi: 10.47654/v30y2026i1p1-39

Forecasting the Appearance Frequency of Rafflesia arnoldii in Bengkulu, Indonesia, Using Discrete-valued Time Series Modeling
type: item
description: Purpose: This study aims to develop a probabilistic forecasting model to estimate the frequency of Rafflesia arnoldii appearances, a rare flower species endemic to Bengkulu Province, Indonesia. Motivated by the need for effective conservation and ecotourism planning, the study addresses a gap in the literature by applying discrete-valued time series models to ecological count data. Design/methodology/approach: This study analyzes monthly data on the frequency of Rafflesia arnoldii appearances from January 2016 to December 2023. We applied and compared multiple discrete-valued time series models, including Poisson INAR, Negative Binomial INAR, Zero-Inflated INAR, INARMA, NB-INARMA, and INGARCH. The models were fitted using Maximum Likelihood Estimation and the EM algorithm. Model performance was evaluated using AIC, BIC, RMSE, and MAAPE, with post-estimation diagnostics conducted via Ljung-Box, Jarque-Bera, and Lagrange Multiplier tests. Findings: The analysis found that the optimal models were INAR(1)-INGARCH(1,1) for South Bengkulu, and INAR(1)-INGARCH(1,1) for Kepahiang. These models effectively captured the discrete, overdispersed, and zero-inflated characteristics of the data, producing reliable short-term forecasts. The variation in model performance across regencies highlights underlying ecological differences and reinforces the need for region-specific forecasting strategies. Research limitations/implications: Limitations include a focus on four regencies and the exclusion of exogenous environmental variables such as rainfall and temperature. Implications suggest that future studies should expand the data scope, incorporate environmental covariates, and explore spatio-temporal extensions to improve predictive accuracy. In addition, future extensions may explore integer-valued time series frameworks designed to accommodate non-stationary count data, including signed-thinning or generalized INAR formulations, to further enhance model flexibility and predictive …
subject: Articles
  topic: section
subject: Rafflesia arnoldii
  topic: keywords
subject: Discrete-valued
  topic: keywords
subject: INARMA-INGARCH models
  topic: keywords
subject: Bootstrap Analysis
  topic: keywords
subject: C22
  topic: subjects
subject: C35
  topic: subjects
subject: C53
  topic: subjects
subject: Q23
  topic: subjects
subject: Q56
  topic: subjects
creator: Jose Rizal
creator: Nur Afandi
creator: Gusman Juliadi
creator: Indah Wahyuliani
creator: Cinta Rizki Oktarina
rights: Copyright (c) 2026 Jose Rizal (Corresponding Author); Department of Mathematics, The University of Bengkulu, Bengkulu, Indonesia, Gusman Juliadi, Indah Wahyuliani, Cinta Rizki Oktarina (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-04
publication date: 2026-03-04
starting page: 39
ending page: 67
doi: 10.47654/v30y2026i2p39-67

Study of Investor Behavior on Stock Investment Decision Making with Self-Monitoring as a Moderating Variable in Generation Y and Generation Z
type: item
description: Purpose: This study examines the influence of psychological biases (trait anger, trait anxiety, overconfidence, and herding) on stock investment decisions among Generation Y and Z investors in North Sumatra. It uniquely investigates the role of self-monitoring as a moderating variable to determine if high self-regulation can mitigate irrational investment behaviors. Design/Methodology/Approach: This research employs a quantitative approach, utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze data from 384 retail investors. Additionally, an independent sample t-test is conducted to identify generational differences in decision-making patterns. Findings: The results indicate that trait anger and anxiety negatively affect investment decisions, while herding and self-monitoring have a positive influence. Crucially, self-monitoring significantly moderates the relationship between trait anger and herding behavior on investment decisions. Generation Z is found to be more risk-tolerant and technology-driven, whereas Generation Y is more cautious and analytical. Practical Implications: The findings suggest that financial literacy programs for young investors should go beyond technical analysis to include psychological conditioning. Regulators and investment managers can utilize these insights to design "cooling-off" mechanisms in trading apps or educational modules that enhance self-monitoring skills, thereby reducing impulsive trading. Originality/Value: Unlike previous studies that solely focus on the direct effects of behavioral biases, this study bridges a theoretical gap by introducing self-monitoring as a psychological buffer. It provides a novel behavioral model for emerging markets, demonstrating how self-regulation can dampen the adverse effects of herding and emotional instability.
subject: Articles
  topic: section
subject: Investment Decision
  topic: keywords
subject: Generation Y
  topic: keywords
subject: Generation Z
  topic: keywords
subject: Self-Monitoring
  topic: keywords
subject: Trait Anger
  topic: keywords
subject: K16
  topic: subjects
subject: H11
  topic: subjects
subject: H83
  topic: subjects
subject: D73
  topic: subjects
creator: Eni Duwita Sigalingging
creator: Azhar Maksum
creator: Rina Bukit
creator: Muammar Khaddaf
rights: Copyright (c) 2025 Advances in Decision Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-16
publication date: 2026-01-16
starting page: 222
ending page: 255
doi: 10.47654/v29y2025i3p222-255

Strengthening Financial Reporting Quality: The Role of Rule of Law, Public Transparency, and Cultural Change as a Moderator
type: item
description: Purpose - The purpose of this study is to investigate how the Indonesian General Election Commission's financial reports are affected by the rule of law, transparency, and cultural shift. From a decision sciences perspective, this study provides a quantitative framework for policymakers to allocate resources effectively by identifying critical determinants of financial reporting quality. Unlike previous studies that focus solely on accounting standards, this research uniquely integrates cultural change as a moderating variable within the public sector context, offering a novel behavioral perspective on financial accountability. From a decision sciences perspective, this study provides a quantitative framework for policymakers to allocate resources effectively by identifying critical determinants of financial reporting quality Design/methodology/approach - This quantitative study employs PLS-SEM. Key members of the Election Commission's finance and administration staff were surveyed to gather primary data, while the Supreme Audit Agency's financial audit report provided secondary data. Findings - The findings demonstrate that transparency (β = 0.419, p < 0.05) and the rule of law (β = 0.312, p < 0.05) have a positive and significant effect on financial reporting quality. Conversely, cultural change was found to weaken this relationship, highlighting that in the presence of strong cultural shifts, reliance on legal compliance and transparency becomes even more critical. Practical implications - This study provides valuable insights for government agencies seeking to improve financial reporting quality. It highlights the importance of the rule of law, transparency, and fostering positive cultural change for accurate financial reporting. Policymakers and auditors can utilize these findings to enhance governance practices and address deficiencies in government financial control systems. This study is closely linked to decision sciences, as it provides empirical evidence …
subject: Articles
  topic: section
subject: Special Issue on : Contemporary Management Research
  topic: category
subject: Rule of Law
  topic: keywords
subject: Public Transparency
  topic: keywords
subject: Cultural Change
  topic: keywords
subject: Financial Reporting Quality
  topic: keywords
subject: K16
  topic: subjects
subject: H11
  topic: subjects
subject: H83
  topic: subjects
subject: D73
  topic: subjects
creator: Novi Natalia Padang
creator: Idhar Yahya
creator: Badaruddin
creator: Nurlinda
rights: Copyright (c) 2025 Novi Natalia Padang (Corresponding Author); Idhar Yahya, Badaruddin, Nurlinda (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 138
ending page: 167
doi: 10.47654/v29y2025i2p138-167