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
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.
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.
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.
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.
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.
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.
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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.
Special Issue on : Decision Science in Energy Economics and Environment for Reducing Carbon Footprint and Achieving Sustainable Development Goals
Effects of Green Human Resource Management Practices on Environmental Performance of the Organization: The Mediating Role of Green Innovation and Corporate Social Responsibility and the Moderating Role of Green Transformational Leadership
Purpose: This research examines the environmental sustainability of manufacturing companies with a focus on preserving the planet for future generations. It aimed to empirically test the factors (i.e., green human resource management practices (GHRMP), green innovation (GIN), and corporate social responsibility (CSR)) influencing the environmental performance of the organization (EPO) using the natural resource-based view theory. This research also examines the moderation effect of green transformational leadership (GTL) as well as the indirect effects of GIN and CSR in Ghana's manufacturing industry. Design/methodology: 369 manufacturing employees were selected using convenience sampling. Data was collected using an online survey and analyzed through SmartPLS version 4.1. Findings: This research outcome includes the positive effect of GHRMP on GIN, CSR, and EPO. In addition, the GHRMP–EPO link is indirectly affected by GIN and CSR. Moreover, the GTL significantly moderated the GHRMP–CSR link. Overall, the study model explained 56.9% variance for the EPO in the manufacturing sector. Research limitations: This research employed self-reported data, which limits causal inference. Considering this, this study suggests that upcoming studies use a longitudinal or multi-source research design to validate their study results and explore sectoral differences. Originality/value: This study adds to the growing body of literature on green practices by providing evidence from a developing country context, where empirical studies remain limited. Considering the decision science perspective, these research findings offer a practical decision-making model for manufacturing managers, enabling them to strategically allocate resources to GHRMP, GIN, and CSR initiatives to optimize EPO.
green human resource management practices
environmental performance of the organization
Examining the Interplay between Tax Systems and Corporate Finance across Diverse Asian Economies
Objective: This research examines how a country's culture affects a company's decisions regarding financing and corporate tax rates. Methodology: We used a two-step system called the Generalized Method of Moment (GMM) to analyze data from five major Asian economies (China, India, Pakistan, South Korea, and Singapore) from 2010 to 2019. Findings: Our findings suggest a positive correlation between corporate tax rates and debt financing. Increased corporate taxes lead to high tax deduction value, incentivizing firms to enhance their debt financing after-tax income. Furthermore, corporations in a more tax environment prefer leverage financing to exploit the tax deductibility of interest payments. However, this relationship can become negative due to the influence of national culture. In economies with a low power distance culture and less information asymmetry, managers prefer equity financing to debt financing. Reliability, moral sympathy, and trust can attract investors, shareholders, and other stakeholders' attention in these cultures. Similarly, in cultures with low uncertainty avoidance, people are less risk-averse and prefer equity financing to debt financing. Implications: Corporate managers should consider national culture when deciding on financing patterns, especially when dealing with high corporate tax rates. Novelty and Originality: This research stands out due to its novel approach of integrating national cultural factors as moderating variables in analyzing corporate tax rates and financing patterns. Unlike previous studies, this research provides a holistic perspective that underscores the importance of cultural context in financial decision-making processes, thereby offering new insights and practical implications for corporate managers and policymakers aiming to optimize financial strategies in culturally diverse settings. This study is pertinent to decision sciences as it delves into corporate managers' intricate decision-making processes influenced by taxation regimes and cultural dynamics. By clarifying the moderating role of national culture on corporate financial strategies, this research provides valuable insights into the strategic considerations that underpin corporate finance decisions in diverse economic and cultural contexts.
Impact of Oil Prices on Islamic Stock Prices: Evidence from Pakistan using Bootstrap ARDL Approach
Purpose: This research inspects the dynamic association between the oil prices and Islamic stock prices in Pakistan, an area relatively uninvestigated, regardless of the rising significance of Islamic finance. Design/methodology/approach: The research utilizes daily data from January 2011 to April 2022. The SOR unit root assessment is employed to recognize smooth and sharp structural breaks, and the Bootstrap ARDL technique is employed to explore short- and long-run cointegration between oil prices and Islamic stock prices. Findings: The findings divulge that certain Islamic stocks show substantial long-run cointegration with the oil prices, whereas others illustrate short-run causal associations. The outcomes highlight the non-linear and time-varying effect of the oil price fluxes on the dynamics of the Islamic stock market. Research limitations/implications: The assessment is restricted to thirty Islamic stocks in Pakistan and does not include sectoral or regional heterogeneity, signifying a need for comprehensive future investigation. Practical implications: The outcomes deliver valuable information for investors and policymakers by featuring the prominence of monitoring the dynamics of the oil market when articulating risk management and investment approaches in Islamic financial markets. Originality/value: This research contributes to the inadequate empirical literature by employing innovative econometric procedures to assess the oil-Islamic stock price association in a developing market, delivering vigorous evidence that augments and complements the current study.
Assessing the Efficiency of Hong Kong Public Hospital Sector During the COVID-19 Pandemic: An Input-oriented Structural Efficiency Approach
Purpose: The available literature on the impact of COVID-19 on the healthcare system is relatively limited. This paper aims to evaluate the efficiency of the public hospital sector in Hong Kong during the COVID-19 pandemic. Design/methodology/approach: One characteristic of the Hong Kong public hospital sector is the cluster-based management model. This paper developed an input-oriented structural efficiency measure with subgroups not yet documented in the literature. This measure is then applied to the data from 38 public hospitals in 7 hospital clusters managed by the Hong Kong Hospital Authority (HA) from fiscal year 2017-18 to 2022-2023. The study contributes to the field of Decision Sciences by applying and extending Data Envelopment Analysis (DEA), a core operations research technique, to provide actionable insights for strategic planning and resource allocation in public health systems. Findings: The results show that the excess resource usage of the whole hospital sector increased from around 24% before the pandemic to around 40% during the late pandemic period. The results further reveal that it mainly originated from the inefficiency within individual hospitals. Resource allocation among hospital clusters is very efficient, whether before or during the COVID-19 pandemic. However, resource allocation within hospital clusters worsened in three clusters but improved in another three clusters. Originality/value: This paper makes an original contribution by developing and applying a novel input-oriented structural efficiency measure that accommodates subgroups, a methodology not previously documented in the literature for this context. By applying the new measure developed, this paper evaluates the efficiency of the Hong Kong public hospital sector during the COVID-19 pandemic and decomposes such inefficiency into individual hospital clusters and levels of the public hospital sector.
Long-run relationship between insurance premiums and driving factors in Mongolia
Purpose: The insurance industry in Mongolia is indeed expanding, though it has received comparatively less attention than other sectors within the financial industry. However, it is crucial to understand the factors that reflect and influence long-run development trends of the insurance industry, in order to actively work towards its growth and development in the future. This study examines the variables in terms of their long-run effects on the insurance industry in Mongolia. Study design/methodology/approach: Data are sourced from the World Bank’s databases. Spanning the period from 1995 to 2022, with a total of 28 observations. Johansen Cointegration rank tests, estimation of cointegrating vector, and testing of hypotheses on long-run coefficients are used to investigate the long-run relationship between insurance premiums and variables that drive them. Findings: Our results show that price level, internet usage, and household income are the long-run variables influencing the total amount of insurance premiums in Mongolia. The long-run coefficients of these variables determine the long-run elasticities of insurance premiums. The long-run coefficient of household income is the largest and then household income is the primary factor of insurance premiums. Also, life expectancy is excluded from the cointegrating relation because zero restriction on its coefficient cannot be rejected. The role of life expectancy is ambiguous and is needed for further investigation. Practical Implications: Based on the main results, it is imperative to focus on long-run factors influencing the aggregate insurance premium income in Mongolia. Consequently, our findings offer valuable perceptions for the insurance sector, shedding light on their influence on the development of the industry. The development of the insurance industry in Mongolia is full of challenges and uncertainties. Hence, our study is related to the practice of decision sciences, providing insights into decision-making processes in optimal ways under conditions of uncertainty faced by government officials and practitioners.
Newton-Raphson Method: Overview and Applications
Purpose: This paper provides a comprehensive overview of the Newton-Raphson method (NRM) and illustrates the use of the theory by applying it to diverse scientific fields. Design/methodology/approach: This study employs a systematic approach to analyze the key characteristics of the NRM that facilitate its broad applicability across numerous scientific disciplines. We thoroughly explore its mathematical foundations, computational advantages, and practical implementations, emphasizing its versatility as a problem-solving tool. Findings: The findings of this paper include a detailed examination of the NRM, demonstrating its efficacy in solving non-linear equations, systems of equations, and optimization problems. The study further highlights the relevance of NRM in addressing complex challenges within probability, statistics, applied mathematics, and other related fields. Originality/value: This study contributes to the existing literature by providing a comprehensive and in-depth analysis of the NRM’s diverse applications. It effectively bridges the gap between theoretical understanding and practical utilization, thereby serving as a valuable resource for researchers and practitioners seeking to leverage the NRM in their respective domains. Practical implications: This research showcases the practical utility of the NRM through two illustrative case studies: optimizing loudspeaker placement for COVID-19 public health communication and determining the submersion depth of a floating spherical object in water. Additionally, the paper demonstrates the NRM's extensive use in estimating parameters of probability distributions and regression models. It highlights its significance across various areas within Decision Sciences, including applied mathematics, finance, and education. This paper contributes both a theoretical overview and a display of diverse practical applications of the NRM.
Bibliometric Insights into Crisis Management: A Review of Key Literature
Purpose: The study explores the current situation of crisis management research and future directions through a comprehensive visual analysis by using VOSviewer. Methodology: To achieve our study's objective, we retrieved published papers from the Scopus Database covering 1995 to 2022, resulting in 1853 publications. The study used bibliometric indicators to analyze the publications and conducted co-authorship and co-occurrence analyses to identify the main research strengths and topics in crisis management. Findings: The findings provide insight into the seven crucial facets of the crisis management environment, including methodology, developed, emerging, and developing countries, governance and SMEs, public policy and relations, crisis response, COVID-19 response, and risk communication and knowledge management. The findings also identify current research trends such as urban resilience, crisis communication, sustainability, climate change, mental health, and future hotspot areas such as family business, organizational resilience, lockdown, COVID-19 crisis, and digitalization in crisis management. Novelty: This research introduces insights into the crisis management literature by identifying research gaps and submitting new arrangements for emerging hotspots' future keywords. It employs a rigorous methodology that offers a robust analysis of the crisis management literature. Crisis management research explores key areas, research trends, and future perspectives, revealing the application of decision-making principles. Rigorously using decision science, the study provides valuable insights and improves understanding of crisis management literature.
Effects of Digital Transformation and Green Technology Innovation on Manufacturing's Financial Performance: Evidence from China
Purpose: This study aims to examine the impact of digital transformation on the financial performance of manufacturing firms, with a particular focus on the mediating role of green technology innovation. Design/methodology/approach: This study employs panel data from Chinese A-share manufacturing firms spanning 2013 to 2022. A fixed-effects model and a mediation model are constructed to analyze the relationships, and the Driscoll-Kraay standard errors are applied to address potential cross-sectional dependence and autocorrelation, ensuring robust estimation of the effects of digital transformation and green technology innovation on financial performance. Findings: The results reveal that digital transformation significantly enhances the financial performance of manufacturing firms. These findings remain robust across alternative dependent variables, exclusion of specific samples, and the inclusion of additional control variables. Furthermore, green technology innovation serves as a mediating mechanism in this relationship, accounting for approximately 5.1% of the total effect. Heterogeneity analysis further suggests that the positive impact of digital transformation is more pronounced in state-owned enterprises (SOEs), large-scale firms, and those located in China’s central and western regions. Research limitations/implications: Corporate financial performance is shaped by a wide range of factors, yet this research focuses primarily on digital transformation and green innovation. Future studies could extend this scope by incorporating additional determinants such as artificial intelligence adoption and environmental regulation. Moreover, although the measurement of digital transformation in this study is relevant and widely used, it may not fully capture the depth of firms' digitalization. Originality/value: This research clarifies the mediating mechanism and transmission pathway through which digital transformation affects corporate financial performance, thereby addressing a critical gap in the literature regarding the performance–transformation–innovation nexus. By introducing an integrated framework that connects digital capabilities with innovation efficiency, the study offers new theoretical and empirical insights distinct from prior works. The findings provide actionable guidance for managers and policymakers to leverage digital capabilities and enhance innovation efficiency to improve corporate financial outcomes.