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
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.
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
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.
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.
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.
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.
Special Issue on : Contemporary Management Research
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
Energy demand response to the dynamics of the currency valuation: Evidence from G7 countries
Design/methodology/approach The study employs several traditional methodologies and the newly developed Mixed-TAR Nonlinear ARDL (MTNARDL) model to investigate long-run co-integration. Additionally, Granger causality in the quantile test is applied to enhance the robustness of the findings. Findings The MTNARDL model confirms the presence of long-run co-integration among all sample countries, whereas traditional methodologies fail to detect any such relationship. Furthermore, the Granger causality results reveal that the impact of exchange rate fluctuations on energy demand varies across different quantiles. Research limitations/implications While the study provides robust empirical insights, it is limited by the scope of available data and methodological constraints, which may require further validation in other economic settings. Practical implications The findings highlight the need for policymakers to design exchange rate and energy policies that account for nonlinearities and quantile-specific effects, ensuring economic stability in G7 nations. Originality/value This study extends the literature by integrating extreme exchange rate changes with energy demand in G7 countries, employing advanced methodologies to uncover dynamic relationships that were previously overlooked.
Hedging Global Stock Markets with Bitcoin, Precious Metals, Copper, Crude Oil, and Agricultural Commodities: Evidence from Bivariate Threshold GARCH Approach
Purpose: This study examines the effectiveness of hedging global stock markets with hedge assets, including bitcoin, precious metals, copper, crude oil, and agricultural commodities. To achieve this, we selected eleven global stock market indices, including ASX 200, MSCI US, MSCI Europe, MSCI Japan, HSI, IBOVESPA, BSESN, SSECI, STI, TAIEX, and TSXCI to evaluate the effectiveness of hedging with the aforementioned hedge assets. Consequently, our current study offers a more up-to-date and comprehensive comparison of hedging effectiveness among multiple classes of hedge assets than earlier academic work. Study design/methodology/approach: We collected weekly price data, denominated in USD, from Eikon (https://eikon.refinitiv.com/), covering the period from May 1, 2018, to March 2, 2023. For the analysis, this study utilized the bivariate diagonal BEKK-TGARCH and OLS models to estimate time-varying and static hedge ratios, respectively, with the goal of measuring the effectiveness of hedging. Findings: In the empirical analysis of the BEKK-TGARCH model, we find that the return spillover effects are weak, and past information shocks influence the current variance-covariances of returns on most hedge assets but not on stock market returns. Moreover, the stock markets exhibit a stronger asymmetric leverage effect than the hedge assets. Furthermore, the BEKK-TGARCH model demonstrates greater hedging effectiveness than the OLS. Silver, copper, and crude oil emerge as highly effective hedge assets, whereas agricultural commodities are the least effective. Finally, ASX 200 and TSXCI are the most effectively hedged stock markets. Practical Implications: This study evaluates the effectiveness of various hedge assets for hedging global stock markets and identifies the most effective hedge assets. Thus, our research is connected to the field of decision sciences, providing insights into hedging processes and optimal strategies for portfolio managers and hedgers.
Technological Capabilities and Institutional Pressures in Green Logistics Adoption: Evidence from an Emerging Economy's Freight Forwarding Sector
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, thereby extending sustainability and logistics theory through a combined moderation–mediation framework. Practical implications: Logistics firms should complement green technology investments with organizational capability development and supply chain collaboration. Policymakers should strengthen institutional frameworks and support collaborative sustainability initiatives, particularly for small and medium-sized firms. Originality/value: This study is among the first to integrate extended survey data, dual SEM estimation, comprehensive diagnostics, and moderation–mediation analysis to examine green logistics adoption in an emerging economy, offering robust empirical and methodological contributions to sustainability and decision sciences research.
Special Issue on : Decision Science in Energy Economics and Environment for Reducing Carbon Footprint and Achieving Sustainable Development Goals
Copyright (c) 2026 Erkan Duzgun (Corresponding Author); Tuyen Thi Hoang Nguyen (Author) https://creativecommons.org/licenses/by-nc/4.0
Ownership Structure and Leverage as Precursors to Financial Distress in an Emerging Economy: A Mediating Model
Purpose—This study investigates the mediating role of leverage structure in the relationship between corporate ownership composition and financial distress within an emerging economy. Given that stakeholders' influence on leverage decisions can significantly alter a firm's risk profile, we examine the specific pathways through which different ownership categories, via their leverage choices, affect the likelihood of financial distress. Design/methodology/approach – The study examines the impact of leverage structure on the relationship between ownership structure and financial distress. A panel dataset from 2017 to 2021 in Pakistan is analyzed using the Preacher-Hayes (Preacher & Hayes, 2004) method to identify both direct and indirect pathways. Additional sensitivity analysis involves testing different methodological approaches and alternative proxies for mediating variables. Pooled OLS regression serves as the main estimation method, with PCSE (panel-corrected standard error) regression used for robustness checks. Findings – Results show that total leverage mediates the impact of institutional ownership, insider ownership, and foreign ownership on financial distress in Pakistan. Regarding debt maturity, findings confirm that the effects of institutional and foreign ownership on financial distress are mediated by long-term leverage. Additionally, the influence of insider ownership, foreign ownership, and state ownership on financial distress is mediated by short-term leverage. Research limitations/implications – This study focuses on an emerging economy and highlights implications of the tradeoff theory, which limits the extensive use of leverage, whether short-term or long-term. Originality/value – This research explores the less-examined mediating role of leverage structure, which integrates both capital and debt maturity structures. Moreover, there is limited literature linking ownership structure with financial distress in both developed and developing countries. The findings provide a quantitative framework that enhances managerial decision-making by clarifying how different ownership structures, through the mechanism of leverage, impact financial risk, thereby making a significant contribution to the corporate finance literature on risk management and governance. The findings offer a granular framework for decision-makers to understand and mitigate financial risk originating from the interplay of ownership and leverage.
Strategic Decision-Making in SME Growth: Harnessing Digital Transformation and Innovation in Jordan
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 organizational goals. Policymakers are encouraged to design initiatives that support SMEs in overcoming technological and resource barriers. Social implications: Promoting digital transformation in SMEs fosters job creation, innovation, and economic stability, contributing to the broader socio-economic development of emerging economies. Originality/value: This study is original in its context-specific focus on SMEs in a developing economy and differs from prior literature by explicitly demonstrating how digital strategy mediates both financial and non-financial outcomes. By highlighting originality and explicitly situating digital strategy within the Decision Sciences paradigm, the paper shows how structured decision models can guide SMEs in resource-constrained environments.
Forecasting the Appearance Frequency of Rafflesia arnoldii in Bengkulu, Indonesia, Using Discrete-valued Time Series Modeling
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 performance. Practical implications: The forecasting framework can inform the scheduling of conservation monitoring and guide ecotourism planning by anticipating peak flowering periods. This study’s primary relevance to Decision Sciences lies in providing a quantitative, data-driven framework for conservation resource allocation and ecotourism planning under uncertainty, transforming ecological data into actionable management strategies. Originality/value: This study pioneers the application of discrete-valued time series models in forecasting the occurrences of a rare and sporadically flowering plant species. It contributes to the Decision Sciences literature by demonstrating how probabilistic modeling can be applied to biodiversity management under uncertainty.
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
Study of Investor Behavior on Stock Investment Decision Making with Self-Monitoring as a Moderating Variable in Generation Y and Generation Z
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.
Strengthening Financial Reporting Quality: The Role of Rule of Law, Public Transparency, and Cultural Change as a Moderator
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 on how governance principles and cultural factors influence financial reporting quality, which is critical for informed decision-making in public-sector resource allocation and policy formulation. Originality - By incorporating cultural change as a moderating factor in the relationship among the rule of law, transparency, and financial reporting quality, this study makes a significant contribution to the existing literature. Addressing the specific challenges faced by the Indonesian General Election Commission offers a fresh perspective on how governance practices influence financial reporting in developing countries.
Copyright (c) 2025 Novi Natalia Padang (Corresponding Author); Idhar Yahya, Badaruddin, Nurlinda (Author) https://creativecommons.org/licenses/by-nc/4.0
Rice (Oryza sativa L.) Bioeconomy: A Comprehensive DEA Analysis
Purpose: The research focuses on analyzing the technical efficiency of a sample of 612 rice (Oryza sativa L.) producers in Ecuador during the year 2019. The objective is to explore productivity and efficiency gaps to guide decision-making and sustainable policies in the rice sector. Design/methodology/approach: By employing Data Envelopment Analysis (DEA)- a non-parametric method used to measure productive performance- this study includes BCC model (which reflect pure technical efficiency by comparing farm only against others of similar sizes for variable returns to scale) and CCR model (which reflect overall technical efficiency, including the effect o the farm scale assumes constant returns to scale). It also incorporates Super Efficiency and Bootstrapping methods to ensure robust statistical validation. Findings: The results reveal significant technical inefficiencies; the mean technical efficiency (the ability to produce maximum output with inputs) under the BCC model was 0.3641, while the CCR model was 0.2817. These findings indicate that rice producers could potentially reduce their input consumption (seeds, fertilizers, labor, water) by 63.59% to 71.83% without compromising current output levels, highlighting a critical need for improved resource management. Implications: The findings highlight the need for cooperative initiatives and sustainable farming practices to enhance productivity in Ecuador's rice bioeconomy. Originality: This research pioneers the application of DEA combined with bootstrapping in the Ecuadorian rice sector, offering a novel framework for the agricultural policy and resource allocation.
Copyright (c) 2025 M. D. Castro-Alvarez (Author); C.A. Zuniga-Gonzalez (Corresponding Author); W. Mercado, R.S. Andrade (Author) https://creativecommons.org/licenses/by-nc/4.0
Big Five Personality Traits in the Selection of Retention Strategies in the IT-ITES Industry: A Fuzzy MCDM Approach
Purpose: The potential of any organization to flourish while sustaining competition and delivering excellent service depends upon its human capital. There is no one-size-fits-all approach to retaining employees, as every individual is unique, necessitating the use of multiple strategies. Multi-criteria decision-making (MCDM) methods are employed to rank employee retention techniques based on each personality . This study contributes to Decision Sciences by developing and validating a hybrid Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) framework that systematically links the Big Five Personality traits with specific retention strategies under conditions of uncertainty, thereby providing a quantifiable tool for strategic human resource management. Design/methodology/approach: Expert opinions have been obtained with the objective of determining the most effective strategy based on each type. Based on the recommendations of experts, Organizational Culture, Inclusion and Belongingness, Employee Engagement, Employee Empowerment, and Employee Well-Being were taken into account as alternatives. The Big 5 traits, Openness to new experiences, Conscientiousness, Extroversion, Agreeableness, and Neuroticism, are used as criterion factors. The criterion weights established using FAHP were ranked using FTOPSIS. Sensitivity analysis was conducted to test the robustness and stability of the proposed method. Findings:. The Consistency Ratio of the fuzzy influence matrix was within the specified value, hence the scores given by the experts were valid and reliable. The study identified that Conscientiousness was the most preferred trait according to HR experts. Based on the criteria, i.e., Big Five personalities, organizational culture is ranked number one among the retention techniques to be employed for retention. Sensitivity analysis was performed and proved the reliability of the data. Practical implications:. Based upon the results of the model, organizations can customize their model with other personality types as criteria and build their own set of alternatives. By experimenting with various MCDM approaches, researchers may widen the scope of their investigation. Different sectors, combinations of factors, and geographical locations can be used for the studies. Social implications: The study highlights that Fuzzy MCDM techniques can be used to predict retention techniques using personality. The algorithms and variables create a base for further restructuring that could be used irrespective of industry and organization. Originality/value: The study broadens the scope of the literature by using mathematical models. A decision-making model has been customized for deciding upon employee retention strategies based on employee Personalities that are pertinent to the IT&ITES organizations in India.
Are Livestock Effluent Standards of Vietnam Feasible? Insights from Land Use of Wastewater Treatment Plants in Pig Farms
Purpose: This study evaluates the feasibility of implementing livestock effluent standards in Vietnam, a developing country experiencing rapid growth in pig production alongside severe environmental pollution problems. Design/methodology/approach: This study surveyed 118 pig farms, collecting wastewater samples from their waste treatment systems. Through multiple regression analysis, this study modeled the relationship between pollutant levels and land area used for different treatment facilities. Findings: The study revealed that meeting Level B of Vietnam's National Livestock Effluent Standards requires a waste treatment area of 6.09 m²/pig for standalone biogas digesters and 3.61 m²/pig for systems combining biogas digesters with bioponds. However, the surveyed farms allocated only 0.64 m²/pig and 1.16 m²/pig for these respective systems, demonstrating that compliance with the current standards is practically unachievable. Additionally, the results indicate that the combined system (biogas + biopond) achieves higher treatment efficiency than biogas digesters alone. Practical implications: This finding highlights critical shortcomings in Vietnam’s agricultural land policies, including restrictive land quotas, land fragmentation, and short leasing terms for public agricultural land. These constraints discourage enterprises and large-scale farms from investing in infrastructure and waste treatment systems. To enable livestock farms to comply with effluent standards, policymakers should prioritize land consolidation, longer leasing terms for public agricultural land, and stricter enforcement of environmental regulations. Originality/value: This is the first study in Vietnam to examine the feasibility of livestock wastewater standards in the aspect of land-use, one of the increasingly constrained resources in Vietnam.
Forecasting Vietnam inflation using machine learning approaches: A comprehensive analysis
Purpose: This study investigates the predictive ability of selected machine learning methods for inflation prediction in Vietnam. Design/methodology/approach: This study computes forecasts using autoregressive integrated moving average, extreme gradient boosting, linear regression, random forest, K-nearest neighbour, four variants of the recurrent neural network, and causal convolutional neural network. This research assesses their properties according to criteria from the optimal forecast literature. Then, their performance is compared with the predictions of the International Monetary Fund and Asian Development Bank used by the State Bank of Vietnam as a policy benchmark tool. Findings: Although there is no single best model to predict inflation for various horizons, the findings suggest that the K-nearest neighbour (KNN) model provides better forecasts than others for the 12-month horizon. These forecasts are relatively in line with the projections of well-known international organisations under several conditions. The KNN forecast even outperformed those when considering the COVID-19 crisis. Research implications: The results suggest that the machine learning models selected in this study could be used as an additional benchmark tool for policy decision-making under uncertainty, offering a data-driven approach to supplement traditional economic judgment. Originality/value: This study is the first attempt to employ different advanced machine learning methods to predict inflation in Vietnam. More importantly, these results are then compared with other conventional ones and benchmark forecasts for robustness checks.
Copyright (c) 2026 Tu DQ Le (Corresponding Author); Son H Tran, Thanh Ngo, Hung D Bui (Author) https://creativecommons.org/licenses/by-nc/4.0
Corporate Social Responsibility and its Influence on Trade Credit: An Analysis of China's A-Share Corporate Sector
Objective: This study investigates the relationship between Corporate Social Responsibility (CSR) and trade credit financing, focusing on Chinese A-Share listed firms from 2010 to 2022. Methodology: This study employs robust econometric techniques, including Panel Estimated Generalized Least Squares (EGLS) and System Generalized Method of Moments (GMM), to provide empirical evidence of an inverse relationship between CSR activities and firms' reliance on trade credit. Findings: The findings reveal that firms actively engaging in high levels of CSR tend to rely less on trade credit. This reduced dependency is mainly due to the positive impact that socially responsible practices have on fostering trust and building stronger relationships with suppliers. By consistently demonstrating their commitment to ethical, environmental, and social standards, these firms create a reputation for reliability and integrity, strengthening their bonds with suppliers and opening up access to more diverse and favorable financing options beyond trade credit. This shift occurs because the trust built through CSR initiatives makes suppliers more willing to offer flexible payment terms or alternative financial arrangements. At the same time, banks and investors may view these firms as lower-risk and more deserving of credit support. Implications: The study also offers practical insights for corporate managers and policymakers, emphasizing the importance of integrating CSR into strategic financial planning to mitigate reliance on short-term financing mechanisms. Novelty: This study offers novel insights by exploring the complex relationship between CSR and trade credit, specifically in the context of Chinese A-share listed firms.
Copyright (c) 2024 Advances in Decision Sciences https://creativecommons.org/licenses/by-nc/4.0
Asset Pricing: A Comparative Analysis of Fama-French Five-Factor with Human Capital-Based Six-Factor Model
Purpose: Decisions on investments in financial securities rely on robust asset pricing models. Investors continually seek more effective ways to make their portfolios more efficient by using pricing models that are more accurate and obtain better yields. This study compares the performance of the Fama-French five-factor model with a new six-factor model that incorporates the human capital (HC, hereafter) factor. Methodology: The study employs the Fama and French time series regression and the Fama-MacBeth two-pass time series regression approach to analyze data of 170 firms (non-financial) listed on the Pakistan Stock Exchange (PSX) over the period between July 2010 and June 2020. Findings: Findings indicate that HC is a significant factor that enhances the efficiency of asset pricing models. The six-factor model outperforms the Fama-French five-factor model, suggesting that the inclusion of HC yields a more effective asset pricing model. Implications: The findings have important implications for investors, policymakers, and academics, indicating that the six-factor model is more effective in estimating expected returns and pricing financial securities. Hence, the findings of this study are strongly related to the practice of decision sciences, thereby providing insights to assess how the factors in factor models affect the variation in risk-adjusted returns, and it is useful for portfolio managers to make optimal asset-management decisions. Originality/Value: This research contributes to the development of asset pricing models by introducing and validating the HC factor, offering a novel approach to improving the efficiency of valuation models used in financial markets.
Carbon emissions and the rising effect of foreign direct investment and trade openness: Evidence from panel data countries
Purpose: The study aims to investigate the effect of FDI and FTO on carbon emissions using sectoral data from 30 industries in China from 2001 to 2024. It will document both the positive and negative influences of FDI and FTO on carbon emissions, presenting a fresh perspective on these economic factors and their relation to environmental sustainability. Design/methodology/approach: The research employed a rigorous threshold regression model to explore the relationship between FDI+FTO and carbon emission. Panel data of industries in China was meticulously analyzed, indicating the changes in sectors and the impacts of controlled variables. This comprehensive approach ensures the validity and reliability of our findings. Findings: The empirical results confirm that FDI and FTO affect sectoral carbon emissions nonlinearly. FDI suppresses carbon dioxide emissions when lower emission threshold values are considered, while FDI at higher thresholds increases them. Similarly, as FTO reduces carbon emissions in high-intensity sectors, carbon emissions increase in low-intensity ones. The control variables concerning carbon emissions are also explained. Practical implications: Policy initiatives based on the peculiar features of each variable would promote FDI in industries with low emissions and regulate FTO in sectors with high carbon intensity. This would balance economic growth with environmental sustainability. Originality/value: The paper contributes to the extant literature by analyzing the relationship between FDI, FTO, and carbon emissions within a sector-specific threshold regression analysis framework. It illustrates the complex dynamics of international trade and investment in environmental outcomes.