Search Results (363)

Marine data assets are increasingly recognized as important drivers of value creation in the blue economy, yet their valuation remains challenging due to difficulties in isolating data-related earnings in capital-intensive maritime enterprises. This study proposes a methodological valuation framework that integrates the multi-period excess earnings method with the Analytic Hierarchy Process (AHP) and the Fuzzy Comprehensive Evaluation (FCE) approach, incorporating both financial and non-financial dimensions. The framework follows a “total synergistic return–data contribution separation” logic to isolate data-related excess earnings and applies an AHP–FCE-based adjustment coefficient to account for data quality, application value, and risk. A representative container shipping enterprise is used as an illustrative application to demonstrate the implementation logic of the framework. The results indicate that marine data assets can constitute a non-negligible component of enterprise value under reasonable parameter settings, while sensitivity analysis highlights the influence of key parameters such as the data contribution coefficient and discount rate. The proposed framework provides a transparent methodological reference for marine data asset valuation and supports sustainability-oriented research and practice in the blue economy. Full article

Blockchain is widely promoted as a tool for enhancing transparency, trust, and sustainability in business, yet little is known about how creative micro, small, and medium enterprises (MSMEs) in emerging economies can meaningfully adopt it for finance and accounting purposes in times of global uncertainty. This study explores how blockchain can be harnessed for transparent and sustainable accounting in Indonesian creative MSMEs amid rapid digital disruption. Using an exploratory qualitative design, we conducted semi-structured, in-depth interviews with 18 owners and key decision-makers across diverse creative subsectors and analysed the data thematically through an integrated Technology Acceptance Model (TAM) and Diffusion of Innovation (DOI) lens. The findings show that participants recognise blockchain’s potential benefits for transaction transparency, verifiable records, intellectual property protection, and secure payments, but adoption is constrained by technical complexity, financial constraints, limited digital and accounting capabilities, and perceived regulatory and reputational risks. Government initiatives are seen as important for legitimacy yet insufficient without concrete guidance, capacity-building, and financial support. The study extends TAM–DOI applications to blockchain-enabled accounting in creative MSMEs and highlights the need for sequenced, ecosystem-based interventions to translate blockchain’s technical promise into accessible, ESG- and SDG-oriented accounting solutions in the creative economy. Full article

Financialisation has an increasing influence on the functioning of non-financial enterprises. It is therefore important to examine whether and to what extent food sector enterprises are subject to the process of financialisation. The research objective was to determine the level of financialisation of food industry enterprises in Poland in relation to the whole industry sector. To achieve this objective, the following research hypothesis was formulated: the process of financialisation of food industry enterprises proceeds similarly to the analogous process undergoing in industrial enterprises but varies across different sectors of the food industry. The research was conducted on the basis of statistical data from Statistics Poland (SP) published in various statistical studies. Financial data from 2010 to 2023 were analysed. For this purpose, research tools used in the paper are referred to in the literature as measures of the level of financialisation, so-called balance sheet indicators. The main limitation of the research is that the results can only be applied to countries with similar economic conditions, especially post-communist countries, and that balance sheet indicators are used to measure financialisation, which, although widely used, are limited in their effectiveness because they focus only on balance sheet data. The results support the research hypothesis. The companies in the analysed industries are characterised by a low level of financialisation. The process of financialisation of food industry companies is similar to the one in industrial companies and is more intense in beverage production than in other food industry sectors. There is room for a sustainable financing policy. The results indicate that there is room for higher financing of food industry enterprises in Poland, but excessive financing may lead to excessive concentration and monopolisation of enterprises and even to speculation on agricultural markets. To maintain financial stability, it will be important to pursue a stable monetary policy, limit the risk of food price volatility, improve communication and coordination in international monetary policy, and increase national food self-sufficiency. This study fills a research gap in understanding the process of financialisation, assessing its degree of advancement and diversity in the main sectors of food processing enterprises. Full article

Low-income individuals are unlikely to save relatively large sums on a regular basis; however, many still fall short of even the modest threshold required for long-term financial security. This study examines the determinants of benchmark-defined undersaving among retail e-payment agents (REAs) operating in the urban slums of Lagos, Nigeria. We use a contingent valuation survey, descriptive analysis, and logistic regression to examine how selected behavioural and demographic factors, alongside a 60-day experimental intervention—the Programmed Microsaving Scheme (PMSS), a hard daily commitment savings device—affect the likelihood of undersaving, defined as saving less than 12% of each REA’s average daily income. While the PMSS appears to have contributed to improvements in post-treatment saving participation and performance among REAs, it did not significantly increase the likelihood of reaching or exceeding the benchmark savings threshold. Consistent with this, average daily income, age, gender, marital status, education, and religion are statistically insignificant predictors of benchmark-defined undersaving. In contrast, self-control, measured using a literature-validated instrument, exhibits a statistically significant negative association with benchmark-defined undersaving, indicating that higher self-control reduces the likelihood of failing to meet the benchmark. Measured risk aversion similarly shows no significant association. Notably, this study introduces a novel 60-day PMSS, co-designed with REAs and neobanks to accommodate daily income savings—a characteristic of the informal sector largely overlooked in the literature on commitment savings devices. From a policy perspective, the findings suggest that while short-horizon commitment devices (such as the 60-day PMSS) and financial literacy are associated with improvements in microsavings among low-income daily earners, achieving benchmark-level saving might require longer-term and more adaptive mechanisms that address income volatility and mitigate other inherent risks. Full article

This study examines how geographic distance to Vietnam’s centralized securities regulator—the State Securities Commission (SSC)—influences firm-level stock price crash risk. In emerging markets characterized by weak governance, corruption, and political connections, distance can erode monitoring effectiveness and heighten managerial incentives to conceal bad news. Using data on Vietnamese listed firms from 2010 to 2024, we find a robust positive association between a firm’s distance to the SSC headquarters in Hanoi and its future crash risk. The effect is stronger for non-state-owned enterprises (non-SOEs) and in provinces with high corruption, but disappears in SOEs and in more transparent regions, where state-related networks provide insulation from weak formal institutions. Exploiting the 2019 Securities Law as a quasi-natural experiment, we show that the distance effect was more pronounced before the reform, suggesting that improved formal regulation can partially offset geographically induced monitoring frictions. Additional tests reveal that the effect is amplified among firms listed on the Ho Chi Minh Stock Exchange (HOSE) and those with higher financial leverage. Our findings provide novel evidence on the spatial dimension of regulatory enforcement in emerging markets. We highlight geographic distance as a significant but previously overlooked source of crash risk, with implications for regulators in designing risk-based supervision and for investors in pricing location-driven risks. Full article

Against the backdrop of heightened global economic uncertainty and increasingly frequent risks in agricultural supply chains, enhancing agricultural supply chain resilience has become a critical issue for safeguarding national food security and promoting high-quality agricultural development. As key actors within agricultural supply chains, the impact of financialization—defined as the shift of resources to non-core financial assets—among agricultural listed firms on supply chain resilience warrants systematic examination. Using panel data from 165 Chinese agricultural listed firms (2010–2022), this study empirically investigates the impact of corporate financialization on agricultural supply chain resilience and its underlying mechanisms. An entropy-weighted composite index based on 16 parameters is used to assess agricultural supply chain resilience. It is composed of three dimensions: resistance capability, recovery capacity, and renewal capacity. The results show that: Financialization significantly undermines supply chain resilience, with the most substantial negative effect on recovery capacity, followed by renewal capacity, and the weakest on resistance capacity. Heterogeneity analyses show more pronounced negative effects among non-state-owned enterprises, non-primary sector firms, and capital-intensive enterprises. Financing constraints and capital expenditures partially mediate the negative relationship between financialization and resilience, while profitability persistence exacerbates the crowding-out effect. These findings suggest that policymakers should strike a compromise between reducing excessive financialization and strengthening agricultural supply chains. While prudently guiding agricultural firms’ financial asset allocation, greater emphasis should be placed on developing a diverse and coordinated industrial support system, thereby diverting financial capital away from crowding out core operations and toward effectively serving the real economy, ultimately contributing to national food security and agricultural modernization. Full article

This study examines the financial resilience of small enterprises in Ukraine during the wartime crisis, addressing the lack of quantitative evidence on how regional military risks and adaptive strategies jointly shape SME stability. The analysis is based on a sample of 30 small agricultural enterprises from the eastern, central, and western regions of Ukraine using annual data for 2022–2024. To capture multidimensional resilience patterns, the study applies factor analysis, cluster analysis, and taxonomic assessment methods to evaluate financial performance, operational adaptability, and access to external resources. The findings show that resilience variation across the sample is strongly associated with enterprises’ ability to sustain revenue flows, control operating costs, and maintain a balanced capital structure. Three distinct resilience profiles were identified: high resilience in western regions (KT = 0.89), moderate resilience in central regions (KT = 0.81), and low resilience in eastern frontline regions (KT = 0.49). These results indicate substantial regional asymmetry linked to differentiated exposure to military threats. Building on these empirical insights, the study proposes a hybrid risk-management approach that integrates digitalization of financial operations, diversification of funding sources, and enhanced social engagement as mechanisms supporting adaptation under prolonged instability. The novelty of the research lies in combining regional risk exposure with multidimensional financial indicators to develop an evidence-based framework for assessing SME resilience in wartime conditions. Full article

Cross-regional enterprise financial distress can undermine long-term corporate viability, weaken regional industrial resilience, and amplify systemic risk, making robust early-warning tools essential for sustainable financial governance. This study investigates the problem of cross-regional enterprise delisting-related distress identification under heterogeneous economic structures and highly imbalanced risk samples. We propose a cross-domain learning framework that aims to deliver stable, interpretable, and transferable risk signals across regions without requiring access to labeled data from the target domain. Using a multi-source empirical dataset covering Beijing, Shanghai, Jiangsu, and Zhejiang, we conduct leave-one-domain-out evaluations that simulate real-world regulatory deployment. The results demonstrate consistent improvements over representative sequential and graph-based baselines, indicating stronger cross-regional generalization and more reliable identification of borderline and noisy cases. By linking cross-domain stability with uncertainty-aware risk screening, this work contributes a practical and economically meaningful solution for sustainable corporate oversight, offering actionable value for policy-oriented financial supervision and regional economic sustainability. Full article

Growing global awareness of climate change and environmental protection has fueled the rapid expansion of the green bond market. Building upon a theoretical framework that links green bond issuance with corporate governance and green innovation effects, this study employs a sample of Chinese A-share listed firms from 2014 to 2022 and applies a staggered difference-in-differences (DID) approach to empirically examine the impact of green bond issuance on corporate risk-taking and the underlying mechanisms. The results indicate that green bond issuance significantly reduces firms’ risk-taking levels. This effect operates primarily through three channels: increasing agency costs, enhancing information transparency, and exacerbating structural imbalances in green innovation. Furthermore, the risk-mitigating effect of green bonds is more pronounced in state-owned enterprises, firms with low audit quality, and firms operating in heavily polluting industries. These findings offer important implications for accelerating the diversification of China’s green financial system, improving firms’ risk management capabilities, and fostering the development of green productivity. Full article

Predictive analytics for financial distress plays an important role in enterprise risk management and everyday business decisions. Most past studies mainly use accounting indicators that come from standard financial reports. This study adds analyst-forecast financial indicators and places them in a data-driven business intelligence setup to improve how companies predict financial distress. We work with seven real datasets to test several predictive models and run statistical checks to see how analyst forecasts work with historical financial data. The results show that analyst-forecast indicators can clearly improve prediction accuracy and make the results easier to understand. From an enterprise systems view, this study pushes traditional financial distress prediction toward a smarter analytics setup that supports real-time, explainable, and data-based risk assessment. The findings provide useful ideas for both the theory and practice of designing business intelligence systems and financial decision-support tools for companies. Full article

As enterprises increasingly depend on software systems, security defects such as vulnerability disclosures, exploitations, and misconfigurations have become economically relevant risk events. However, their short-term impacts on capital markets remain insufficiently understood. This study examines how different types of software security defects affect short-horizon stock market behavior. Using a multi-model event-study framework that integrates the Constant Mean Return Model (CMRM), Autoregressive Integrated Moving Average (ARIMA), and the Capital Asset Pricing Model (CAPM), we estimate abnormal returns and trading-activity responses around security-related events. The results show that vulnerability disclosures are associated with negative abnormal returns and reduced trading activity, while exploitation events lead to larger price declines accompanied by significant increases in trading activity. Misconfiguration incidents exhibit weaker price effects but persistent turnover increases, suggesting that markets interpret them primarily as governance-related issues. Further analyses reveal that market reactions vary with technical severity, exposure scope, industry context, and firm role, and that cyber shocks propagate through both price adjustment and liquidity migration channels. Overall, the findings indicate that software security defects act as short-term information shocks in financial markets, with heterogeneous effects depending on event type. This study contributes to the literature on cybersecurity economics and provides insights for firms, investors, and policymakers in managing software-related risks. Full article

High cybersecurity risks and attacks cause information theft, unauthorized access to data and information, reputational damage, and financial loss in small and medium enterprises (SMEs). This creates a need to adopt information security systems of SMEs through innovation and compliance with information security policies. This study seeks to develop an integrated research model assessing the adoption of InfoSec systems in SMEs based on three existing theories, namely the technology acceptance model (TAM), theory of reasoned action (TRA), and unified theory of acceptance and use of technology (UTAUT). A thorough review of literature identified prior experience, enjoyment of new InfoSec technology, top management support, IT infrastructure, security training, legal-governmental regulations, and attitude as potential determinants of adoption of InfoSec systems. A self-developed and self-administered questionnaire was distributed to 418 employees, mid-level managers, and top-level managers working in SMEs operating in Riyadh, Saudi Arabia. The study found that prior experience, top management support, IT infrastructure, security training, and legal-governmental regulations have a positive impact on attitude toward InfoSec systems, which in turn positively influences the adoption of InfoSec systems. Gender, education, and occupation significantly moderated the impact of some determinants on attitude and, consequently, adoption of InfoSec systems. Such an integrated framework offers actionable insights and recommendations, including enhancing information security awareness and compliance with information security policies, as well as increasing profitability within SMEs. The study findings make considerable theoretical contributions to the development of knowledge and deliver practical contributions towards the status of SMEs in Saudi Arabia. Full article

This article examines the growing relevance of photovoltaic (PV) energy amid rising electricity demand, sustainability goals, and the need for flexible energy management in households and enterprises. It analyzes six PV business models, ownership, leasing, Power Purchase Agreement (PPA), energy communities/peer-to-peer (P2P), crowdfunding, and subscription-based Solar-as-a-Service, using the Business Model Canvas (BMC) framework. A systematic literature review was combined with a unified BMC for each model, enabling structured comparison of value propositions, customer segments, cost structures, revenue streams, and risk allocation. The results show that no single universal model exists; each addresses different financial capacities, risk preferences, and strategic needs of households, SMEs, large enterprises, and energy communities. Significant differences were found in investment requirements, operational involvement, scalability, and potential for energy independence. The study’s novelty lies in providing a coherent, cross-model comparison using a standardized BMC approach, offering insights not systematically explored in previous research. These findings support informed decision-making for organizations considering PV adoption and provide a basis for further research on innovative energy management strategies. The topic is highly relevant in the context of the accelerating global energy transition, technological advances, regulatory changes, and increasingly diverse customer profiles, highlighting the need for comprehensive comparative analyses to guide flexible photovoltaic deployment. Full article

Artificial Intelligence (AI) is a transformational force reshaping business processes, financial decision-making, and enabling firms to create, deliver and capture value more effectively. While large corporations in South Asian countries, particularly Bangladesh, India, Pakistan and Sri Lanka have started leveraging AI to drive Business Model Innovation (BMI), Small and Medium Enterprises (SMEs) continue to face significant challenges. These include limited infrastructure, poor bandwidth penetration, unreliable electricity, weak institutional capacity and governance immaturity, along with ethics and compliance concerns. These challenges hinder SMEs from fully exploiting AI-driven BMI and reduce their financial resilience and competitiveness in increasingly digital and globalised markets. This paper examines how South Asian countries are adopting AI technologies in SMEs by comparing patterns and variations in adoption, capability, ethics, risks, compliance, and financial outcomes. The paper proposes a tailored, actionable framework, called TRIAD (Target, Restructure, Integrate, Accelerate, and Democratise)-AI, designed to address technical, organisational and institutional challenges that shape AI-driven BMI across South Asian SMEs and to meet regional and global SME needs. The framework integrates the best practices from global AI leaders such as China, Estonia and Singapore, emphasising responsible AI adoption through robust ethics and compliance standards, and risk management, and offering practical guidance for South Asian SMEs. By adopting this framework, South Asian countries can gain a competitive advantage, enhance operational efficiency, support GDP growth across the region and ensure adherence to all relevant international AI standards for responsible, sustainable, and financially sound innovation. Full article

This study presents a methodology for optimizing repair intervals of mining equipment by integrating economic efficiency and reliability criteria. A review of existing maintenance strategies revealed their limitations, and a mathematical model was developed that incorporates both projected financial expenditures and the probability of equipment failures, enabling more accurate prediction of the optimal repair timing. This study introduces a novel integration of the Weibull reliability distribution with a cost-convolution optimization model, explicitly capturing the trade-off between economic efficiency and failure risk. Unlike traditional fixed-schedule approaches, the proposed model provides analytically optimized repair intervals derived from observed degradation trends. Statistical analysis demonstrates that unplanned repairs are, on average, 56% more costly than scheduled ones, highlighting the need to revise current preventive maintenance practices. The cost comparison is based on 34 restoration records collected from publicly available supplier price lists and field maintenance logs, converted into a unified currency. Based on operational data and reliability parameter estimation, the optimal repair interval was determined to be 5129 machine hours, which minimizes both the probability of failure and total maintenance-related financial losses, while reducing unplanned downtime. Unlike traditional fixed-schedule approaches, the proposed model allows adaptive adjustment of maintenance intervals according to the actual degradation characteristics of the equipment. The practical significance of the research lies in its ability to help mining enterprises reduce expenditures on corrective repairs, extend the service life of machinery, and improve overall operational efficiency. The findings contribute to advancing maintenance optimization in the mining industry, supporting more sustainable and cost-effective equipment management. Full article