Abstract: This paper introduces a formal behavioral model to explain how perfectionist tendencies, especially those associated with obsessive-compulsive traits, can lead to systematic violations of transitive preferences and inefficient decision-making. Drawing from behavioral economics and clinical psychology, we propose a deficiency-penalized utility framework, in which individuals evaluate each option not only by its intrinsic merits but by the psychological costs of its perceived shortcomings relative to other alternatives.Unlike traditional models of bounded rationality, which emphasize cognitive limitations such as limited attention or computational power, our model focuses on affective distortions in the evaluation process. Specifically, it formalizes how emotionally salient comparisons across options can create asymmetric deficiency penalties that result in non-transitive preference cycles, such as A ≻ B ≻ C ≻ A. These cycles, we argue, emerge from a structured but emotionally charged decision mechanism aimed at minimizing flaws rather than maximizing utility.We develop the model using a multi-attribute utility framework, incorporating direction-dependent salience functions and a sensitivity parameter γ to capture perfectionist intensity. We demonstrate through a numerical example how such a framework systematically generates preference intransitivity. The model also accounts for observed phenomena such as decision paralysis, excessive deliberation, and post-choice regret among individuals with high evaluative sensitivity.Beyond its theoretical contribution, this framework has practical implications for consumer behavior, mental health economics, and behavioral policy design. It provides a psychologically grounded rationale for why increasing choice sets may reduce welfare for certain individuals and why emotionally informed decision environments can enhance both coherence and well-being. The model generates testable predictions for experimental economics and offers pathways for integrating affective distortions into broader welfare analysis.By formalizing how imperfection aversion reshapes preference logic, this paper expands the theoretical foundations of behavioral economics and offers a structured lens through which to understand emotionally bounded rationality.

Abstract: The leap from laboratory to marketplace represents the critical chasm in longevity technology innovation. While scientific breakthroughs in aging biology continue to accelerate (López-Otín et al., 2023; DePinho et al., 2024), the commercialization pathway remains fragmented, creating a unique "valley of death" where promising discoveries often fail to reach consumers despite substantial investment interest (Campisi et al., 2019). At stake is a projected $46.62 billion market by 2033 (Business Research Insights, 2024) that could revolutionize healthcare by shifting from disease treatment to prevention of aging itself—potentially the most transformative healthcare paradigm since antibiotics (Deloitte, 2024). The implications of successful longevity technology commercialization extend far beyond the healthcare sector, as increased lifespans would necessitate dramatic changes in workforce participation to compensate for declining new entrants to the labor force (Papapetrou & Tsalaporta, 2020), restructuring of pension and insurance systems that collectively manage trillions of dollars (Amaglobeli et al., 2019), and fundamental adjustments to social structures across developed economies (Grand View Research, 2024).

Abstract: This study explores nanostores’ identity—micro, independent grocery retailers through a systemic, stakeholder-informed lens to promote their survivability and competitiveness. Moving beyond traditional operational descriptions, it introduces a multidimensional framework that examines what nanostores do (X), how they do it (Y), and why they matter (Z), complemented by the TASCOI tool, as identity statements. Based on survey data collection and thematic analysis from nanostore stakeholder responses in Mexico City, the research categorises identity statements into six 2×2 matrices across four dimensions: operational, functional, relational, and adaptive. This produces twenty-four archetypes that capture nanostores’ diversity, complexity, and adaptability. Findings reveal that nanostores are not a homogeneous category. They simultaneously exhibit characteristics of multiple archetypes, blending retail function, social embeddedness, and entrepreneurial adaptation. The study contributes to nanostore and micro-enterprise literature by operationalising identity description and offers practical insights for supporting diverse shop types through context-sensitive policy and business strategies. While the study ensures internal validity and reliability through systematic coding and stakeholder feedback, it acknowledges limitations in generalisability. Future research may build on this work through comparative studies, longitudinal tracking, and direct engagement with nanostore owners and their communities to further understand identity dynamics and resilience in evolving retail landscapes.

Abstract: Modeling dependence between cryptocurrency returns has received great attention in the last years. Most papers use Pearson’s correlation measure to quantify the degree of dependence, while other use parametric copula methods together with sophisticated linear or nonlinear regression methods to unearth complicated dependence patterns between them. In this article, we show that simple dependence measures might lead to misleading results and provide alternative and more robust measures of dependence, some quite recent ones. In addition, utilizing the flexibility of copula functions in characterizing the joint dependence, we employ sophisticated methods in estimating and plotting the copula density function non-parametrically, therefore avoiding issues of model misspecification. Using almost 2 years of daily return data of the 10 most traded cryptocurrencies we have first properly quantified the degree of dependence between different pairs and by visualizing the non-parametrically estimated bivariate copula density for most pairs, we have uncovered asymmetric tail dependence between them, hence shedding further light on this issue.

Abstract: Regulatory risk analysis allows you to anticipate the impact of regulatory changes on products and business strategies. The process is structured in nine steps: identification of the regulatory event, classification of the type of action, evaluation of the status of implementation, impact analysis, evaluation of international precedents, projection of future measures, identification of collateral risks, assignment of the level of risk and definition of recommendations for action. To quantify these risks, a mathematical algorithm is proposed where the Regulatory Risk (RR) is calculated as (I×G×T×P)×(1+L), combining intensity of the measure, degree of implementation, urgency, proportion of the affected portfolio and expected litigation. Each variable is scored on a standardized scale, and the outcome classifies the risk as low, moderate, high, or critical. The model is based on international standards such as the Codex Alimentarius, ISO 31000 and FDA frameworks. Its value lies in operational simplicity, adaptability to different types of regulatory changes, strategic prioritization capacity and technical robustness for audits or regulatory defenses. Applied to the elimination of synthetic dyes in the United States, the algorithm yielded a critical risk (RR = 450), recommending urgent action through reformulations, adaptation of labels and legal mitigation strategies.

Abstract: The Potential Payback Period (PPP) extends the traditional Price-to-Earnings (P/E) ratio by incorporating earnings growth, discount rate, and risk. This article provides a mathematical demonstration that when both the earnings growth rate and the discount rate are zero (i.e., g = r = 0), the PPP simplifies to the P/E ratio. A similar result holds when g equals r and both are nonzero. These findings confirm the PPP’s consistency with the P/E ratio under simplified conditions, and reveal far-reaching implications for how stocks can and should be valued across varying economic environments.

Abstract: Accounting Information Systems (AIS) have become the majority used across diverse sectors, as they provide a systematic method for managing financial performance with timeliness, transparency, and accuracy AIS is one of the computer-based systems for business entities to collect, record, and track the financial and accounting information that is used by decision-makers. The high quality of AIS allows business firms to effectively manage their financial activities. It shows that there is a higher demand for investment opportunities to improve their financial management and maintain long-term business operations. Therefore, in term of enhancing their financial strategy and overcoming unexpected business venture. Financial performance involves a comprehensive assessment that enables businesses to gauge their overall financial health and assess their capability to meet long-term financial obligations indicated that most business managers need the financial and accounting data provided by AIS to evaluate their past performance and map their future plans. This research aims to assess Management Accounting System as a mediating variable in the effect of Accounting Information Systems and Financial Performance, Information Technology as a Moderating variable in the effect Accounting Information Systems, Management Accounting System and Financial Performance. In this research the variables were divided into four types, financial performance as the dependent variable, Information Technology as the moderator, management accounting system as a mediator variable and Accounting Information Systems as the independent variable. This research applied on industrial companies listed on the Amman Stock Exchange, by using randomly method sampling, the data was obtained from 263 respondents. The data were tested using Smart PLS 3. The data analysis was carried out using the partial least squares (PLS) technique of structural equation modelling was used for data analysis. PLS is suitable for small sample sizes such as sample of this research Findings: The results of the analysis showed that Accounting Information Systems had a positive effect on Financial Performance. The mediating effect of Management Accounting System increases the positive effect between Accounting Information Systems and Financial Performance, the results shows that mediation is partial. Also, the interaction with information technology increases more than the financial performance of the companies. Research limitations/implications: The limitation of this research was using Industrial companies Listed on the Amman Stock Exchange for the 2024 period, so I recommended more studies by applying other companies and using panel data analysis. Originality/value: This research assessed the moderated mediating role that plays in the nexus between Accounting Information Systems and Financial Performance. This indicated that there was pressure from stakeholders to focus and have attention to Accounting Information Systems and Verify that the financial performance is as good as possible, The Jordanian mangers companies has to assure Accounting Information Systems to be perfect.

Abstract: This study investigates the complex relationship between the performance of logistics and Environmental, Social, and Governance (ESG) performance drawing upon the multi-methodological framework of combining econometric with state-of-the-art machine learning approaches. Employing IV panel data regressions, viz. 2SLS and G2SLS, with data from a balanced panel of 163 countries covering the period from 2007 to 2023, the research thoroughly investigates how the performance of the Logistics Performance Index (LPI) is correlated with a variety of ESG indicators. To enrich the analysis, machine learning models—models based upon regression, viz. Random Forest, k-Nearest Neighbors, Support Vector Machines, Boosting Regression, Decision Tree Regression, and Linear Regressions, and clustering, viz. Density-Based, Neighborhood-Based, and Hierarchical clustering, Fuzzy c-Means, Model Based, and Random Forest—were applied to uncover unknown structures and predict the behaviour of LPI. Empirical evidence suggests that higher improvements in the performance of logistics are systematically correlated with nascent developments in all three dimensions of the environment (E), the social (S), and the governance (G). The evidence from econometrics suggests that higher LPI goes with environmental trade-offs such as higher emissions of greenhouse gases but cleaner air and usage of resources. On the S dimension, better performance in terms of logistics is correlated with better education performance and reducing child labour, but also demonstrates potential problems such as social imbalances. For G, better governance of logistics goes with better governance, voice and public participation, science productivity, and rule of law. Through both regression and cluster methods, each of the respective parts of ESG were analyzed in isolation, allowing to study in-depth how the infrastructure of logistics is interacting with sustainability research goals. Overall, the study emphasizes that while modernization is facilitated by the performance of the infrastructure of logistics, this must go hand in hand with policy intervention to make it socially inclusive, environmentally friendly, and institutionally robust.

Abstract: The forestry industry, both primary (sawn wood production) and secondary (output of reels, pallets, boxes, cooperage, and tool handles), is growing in importance in Honduras. In 2023, exports from this industry were mainly destined for Central America (58.19%), followed by North America (22.92%) and the Caribbean region (17.20%), with hardly any Honduran wood reaching Asia or Europe. The objective of this article is twofold: 1) to analyze the current situation of the Honduran timber industry and 2) to propose solutions to avoid the current trade deficit (USD 2.3 million) in the sector and the environmental deterioration caused by the overexploitation of timber resources. To this end, we will apply a descriptive methodology based on official statistical sources. The results of our analysis show the urgent need to implement a series of public policies in the medium and short term to strengthen this industry. Failure to do so is expected to have adverse effects, such as increased pressure on natural forests, rising unemployment, and the loss of an important source of income for both forest owners and the Public Administration.

Abstract: The logistics sector, being a part of global supply chains, is under increasing pressure to incorporate sustainability in its operations with the growing emphasis on Environmental, Social, and Governance (ESG) metrics. This paper addresses the uptake and use of ESG metrics in logistics with a particular emphasis on how integrated reporting frameworks can be used to bring transparency and sustainability to the sector. The study is mainly concerned with the effectiveness of the ESG reporting procedures in logistics firms, transport, storage, and distribution, and examines the application of integrated reporting frameworks in addressing global sustainability needs. The study used a quantitative approach in researching, utilising a survey method in collecting data from 350 South African logistics firms. The study analyses opportunities and challenges to the adoption of ESG, creates best practices in making reporting more reliable, and establishes a conceptual framework for including ESG indicators in logistics reporting. The findings reference the challenges of logistics companies in embracing ESG practices, particularly concerning financial constraints, technological challenges, and regulatory challenges, and introduce innovations such as digital traceability systems and artificial intelligence-based analytics that could drive improved decisionmaking and reporting procedures. Lastly, the paper aims to contribute to the body of work from scholars and practitioners with an in-depth comprehension of how ESG factors can be best applied in logistics operations for aiding corporate social responsibility and sustainable development.

Abstract: This study explores potential challenges Indonesian MSMEs may face over the next 10-20 years and proposes strategies to address them. Using strategic foresight methods, the research highlights the importance of digital transformation and legal reforms in enhancing MSME resilience and sustainability. Continuous legal and educational support is emphasized to empower MSMEs and ensure their significant contribution to Indonesia's economic prosperity and food security. Strategic foresight, scenario planning, and trend analysis are employed to identify emerging challenges and opportunities, ensuring MSMEs can navigate future trends effectively. Digital transformation and anticipation of future trends are key to creating resilient and sustainable MSMEs. The report notes that Indonesian MSMEs, which make up 99% of businesses, are vital for the economy. Digitalization is seen as a critical tool to expand market access, reduce costs, and optimize transactions. Legal reforms, such as the Omnibus Law, and enhanced legal and educational support, are essential for MSMEs' growth and sustainability. By leveraging digital transformation and strategic foresight, Indonesia can ensure resilient and sustainable MSMEs that contribute significantly to food security and economic prosperity. Continuous legal and educational support will empower MSMEs to navigate future challenges and thrive in a dynamic economic environment. Embracing digitalization for broader market access and cost reduction is crucial. Policymakers should use strategic foresight to prepare for future trends and challenges, and continuous legal reforms and educational programs are essential to empower MSMEs and foster innovation.

Abstract: Artificial intelligence is heralded as a vehicle for “social good” in agriculture, ostensibly advancing the UN Sustainable Development Goals while uplifting smallholders. This study examines that claim through a mixed-methods review of eleven Thai Agricultur-al-AI providers, analysing both governance practices and the framing of impact. Findings show that ‘social good’ essentially equated with economic gains, reflecting farmers’ immediate financial predicament. However, this productivist approach often confines to incremental market optimisation and diverts attention away from long-term environ-mental goals and resolving structural issues. Moreover, governance practices are confined to cultivating functional trust (high predictive accuracy and mobile-friendly interfaces) over procedural safeguards (algorithmic transparency, stakeholder participation, redress mechanism). This can be problematic when many services are free-of-charge in exchange for data commodification, but current legal framework provides scant guardrails to farmers. Thailand’s Agricultural-AI landscape thus embodies a “hybrid intersection”: it delivers tangible, albeit incremental, benefits to marginalised farmers but lacks the procedural and structural foundations required for transformative change.

Abstract: The Ecuadorian agroindustrial sector continues to face persistent structural challenges, largely due to the predominance of linear production models characterized by intensive resource use, low waste recovery, and institutional fragmentation. This systematic review explores the potential of circular bioeconomy principles to support sustainable transformations in rural and agroindustrial ecosystems. A comprehensive literature search was conducted across Scopus, Web of Science, SciELO, and Google Scholar for the period 2000–2024, using Boolean operators and multilingual keywords. Eligibility criteria included peer-reviewed articles and technical documents addressing circular practices in rural or agro-industrial contexts within developing countries. From 145 identified records, 25 duplicates were removed and 84 studies were selected for full-text analysis and thematic synthesis. The findings reveal the emergence of circular initiatives in Ecuador’s coastal region—particularly in the banana, cocoa, and oil palm value chains—focusing on waste valorization, resource reuse, and collaborative innovation. These practices remain fragmented and face significant barriers, such as financial limitations, infrastructural gaps, and weak territorial governance. Nonetheless, industrial symbiosis, regenerative design, and adaptive circular ecosystems show strong potential to promote environmental sustainability, economic diversification, and social inclusion. The review was conducted in accordance with PRISMA 2020 guidelines. The review protocol was not registered.

Abstract: This study examines the dynamics of nonfarm diversification among rural households in Ethiopia, focusing on the patterns of upward, downward, and lateral mobility across economic activities. Rural households face persistent challenges, including low agricultural productivity, climatic shocks, and constrained access to resources, which have driven many to diversify into nonfarm activities. Nonfarm diversification is critical for mitigating risks and enhancing rural livelihoods, yet its outcomes vary significantly depending on access to resources, education, and infrastructure. Using panel data from the Ethiopian Socioeconomic Survey (ESS) for 2018/19 and 2021/22, this study employs multinomial logit models to analyze transitions between pure agriculture, low return, and high return nonfarm activities. The findings reveal that upward mobility into high return activities is driven by education, infrastructure access, and access to financial, while downward mobility is often triggered by shocks such as climate and livestock loss. Lateral mobility, characterized by shifts between pure agriculture and low-return activities, reflects survival strategies in response to agricultural constraints. The study underscores the importance of addressing systemic barriers, such as infrastructural deficits and gender disparities, to promote sustainable and inclusive rural development. Targeted policies that enhance resilience, expand educational opportunities, and improve market access are essential for maximizing the potential of nonfarm diversification and improve the pathway for upward mobility in Ethiopia.

Abstract: This article extensively examines the correlation between environmental, social, and governance (ESG) scores and credit ratings across diverse geographic regions and industrial sectors. The study reveals that despite obtaining higher ESG scores, European corporations frequently receive lower credit ratings for combined ESG scores and components such as Human Rights and Governance Pillar scores. Conversely, Resource Use and Shareholder Engagement positively correlate with credit ratings in non-European regions. The analysis of the sectors demonstrates that superior environmental performance positively impacts credit ratings in the Healthcare sector, while effective controversy management is essential in the consumer defense sector. The study delves into the components of ESG, emphasizing that different factors influence credit ratings differently across regions and sectors. Utilizing a dataset from 2013 to 2023, the research examines 188 companies from Europe and non-Europe with consistent ESG scores. An ordered probit model is employed to assess the influence of ESG ratings on credit ratings while accounting for control variables such as leverage, size, profitability, sales growth, and dividend yield. The findings indicate that incorporating ESG factors into credit rating assessments necessitates a region and sector-specific approach. Policymakers, regulators, and investors must consider these differences when making decisions. The study suggests further research on the role of stakeholder pressure in the ESG-credit rating relationship. This investigation aims to provide constructive insights into corporate strategy, policy decisions, and investment practices, empowering the audience with practical knowledge.

Abstract: This study examines the impact of Village Community Banks (VICOBA) on household welfare in Kilosa District, Tanzania. While VICOBA is recognized for improving livelihoods, concerns remain about its effectiveness on the welfare outcome of the participants. Using data from a randomly selected sample of 99 VICOBA members and 203 non-members, the research employed Propensity Score Matching (PSM) and Endogenous Switching Regression (ESR) methods to examine the impacts of VICOBA membership on welfare status of the households in Tanzania. Results showed a significant positive effect of VICOBA membership on household consumption, a key welfare indicator, with a 1% significance level. The ESR analysis confirmed these findings. The study concludes that VICOBA membership positively impacts household welfare and recommends that policymakers enhance operational systems and regulatory frameworks to better integrate these community banks into local development initiatives.

Abstract: Background: Balochistan, Pakistan's largest province, grapples with profound health economics challenges, including inadequate healthcare infrastructure, high treatment costs, and limited access to quality care, particularly in rural areas. Despite recent policy initiatives, such as the Balochistan Health Card Program launched in November 2023, the region continues to face significant disparities in healthcare access and outcomes. Objective: This review aims to critically examine the prevailing health economics issues in Balochistan, assess the effectiveness of current interventions, and propose evidence-based strategies to enhance healthcare accessibility and equity in the province. Methods: A comprehensive literature review was conducted, encompassing peer-reviewed articles, government reports, and reputable news sources published between 2023 and 2025. Key databases such as PubMed, Scopus, and regional publications were utilized to gather relevant data. Studies focusing on healthcare infrastructure, financing mechanisms, and policy interventions in Balochistan were prioritized. Findings: The review identifies several critical issues: ∙ High Healthcare Costs: Despite the introduction of the health card program, many residents remain unaware or face administrative barriers, limiting its reach. ∙ Inadequate Infrastructure: Approximately 62% of healthcare facilities are partially damaged, with 47% only partially functional, and 16% partially accessible. ∙ Workforce Shortages: The doctor-to-patient ratio stands at 1:1,000, and the nurse-to-patient ratio is 1:50, indicating a severe shortage of healthcare professionals. ∙ Limited Access: Geographical isolation and poor transportation further hinder access to healthcare services, especially in rural areas. Conclusion: Addressing Balochistan's health economics challenges necessitates a multifaceted approach, including: Enhancing Awareness and Accessibility: Implementing robust information campaigns to educate the public about available health services and insurance programs. Infrastructure Development: Investing in the repair and modernization of healthcare facilities to improve service delivery. Workforce Expansion: Recruiting and training healthcare professionals to meet the growing demand for services. Policy Reforms: Strengthening public-private partnerships and ensuring equitable distribution of healthcare resources. By adopting these strategies, Balochistan can move towards achieving universal health coverage and improving the overall health outcomes of its population.

Abstract: The digital transformation of industry has placed immense pressure on small and medium-sized enterprises (SMEs), which often lack the resources and technical infrastructure to adopt complex AI pipelines and data visualization systems. This creates a pressing challenge: how can SMEs leverage real-time business intelligence without heavy computational or financial burdens. We propose the DecisionFlow framework to generate interactive KPI dashboards on the fly using Vega-Lite declarative syntax through a low-latency visualization engine. To run at the edge, we distilled the long sequence of self-attention into linear Performer blocks, using cross-prediction step weight sharing and 8-bit quantization, so that the model only contained 4.3 M parameters and could be deployed on a common CPU or browser WebAssembly environment. Secondly, the model couples continuous probability prediction with rare event detection with a joint uncertainty loss function, as well as a cross-layer visual feedback closed-loop in which the user interacts with real-time update of the attention mask, so as to support online incremental learning. On Online Retail II (UCI), a dataset for SMEs, DecisionFlow reduced inference latency by 68%, demand forecasting MAE by 17%, and anomaly detection F1 to 0.91.

Abstract: Our study analyzes the combined impact of geopolitical risks and investor sentiment on the major cryptocurrencies, Bitcoin and Ethereum, using monthly data from December 1, 2020, to the end of April 2025. Through a rigorous econometric approach-including unit root tests (Dickey-Fuller (1979-1981) and Perron (1998)), cointegration techniques (Engle and Granger (1987) and Johansen (1990)), and error correction models (ECM and VECM)-we examined the long- and short-term dynamics between cryptocurrencies and three indices: investor sentiment, crypto market sentiment, and the composite geopolitical risk index. Our results confirm the existence of cointegration relationships between these crypto-assets and the indices, indicating structural interdependence during periods of global uncertainty. In the short term, fluctuations in investor sentiment and geopolitical risks significantly affect the returns of Bitcoin and Ethereum, with a rapid adjustment toward long-term equilibrium. Moreover, Ethereum appears to be slightly more sensitive to emotional and geopolitical shocks than Bitcoin. However, our study has certain limitations, notably the use of composite indices that may not capture all the qualitative nuances of the phenomena studied and the assumption of linearity in the modeled relationships. For future research, we suggest integrating nonlinear models and leveraging real-time sentiment data derived from artificial intelligence, as well as expanding the analysis to other segments of the crypto-asset market. Ultimately, our study enhances the understanding of exogenous factors influencing cryptocurrencies in an unstable global environment.

Abstract: Crop conversion from inefficient rice fields to lotus cultivation has been strongly promoted in low-lying areas of Thua Thien Hue Province in recent years. To assess the opportunities and constraints of lotus farming, this research applied a mixed-methods approach, including key informant interviews, focus group discussions, and a household survey with 95 lotus farmers. Two main factors that drove the shift to lotus cultivation were the limitations of the rice farming system in low-lying areas and farmers’ expectations of high economic returns from lotus crops. In addition, crop conversion policies have played an important role in facilitating land access for farming households. Despite being perceived as a high-value crop, lotus production has proven economically unviable for most surveyed households in 2024. Smallholders who rely on family labor tend to achieve higher efficiency, highlighting a paradox where increased scale does not lead to greater profitability. Consecutive crop failures caused by nematode diseases and unfavorable weather, combined with high production costs, have posed serious challenges, particularly for larger farms. These findings suggest that promoting indigenous lotus varieties, expanding mechanization, encouraging farmer cooperation, and improving post-harvest practices are essential to enhance production efficiency and support the sustainability of the lotus sector.