Abstract: Although the number of Islamic mutual funds (IMFs) in Indonesia has grown in recent years, the market remains relatively small compared with countries such as Malaysia and Saudi Arabia. This study explores the key drivers and barriers influencing IMF development and Indonesian investors’ attitudes toward these products. Drawing on 21 semi-structured interviews with investment managers, regulators, and policymakers, the study identifies major constraints, including regulatory complexity, a limited and illiquid pool of Shariah-compliant securities, suboptimal fund performance, tax limitations, low product awareness, limited Islamic financial literacy, and moderate investor risk orientation. Simultaneously, several factors support potential growth, such as robust investor protection regulations, a large and youthful population, opportunities for product innovation, and strong government backing. The findings offer practical guidance for enhancing product offerings, improving the regulatory framework, and promoting financial literacy, while underscoring that fund performance, investor awareness, and financial literacy remain central determinants of investment behavior.

Abstract: This study examines how artificial intelligence (AI) contributes to contemporary processes of authenticity evaluation by functioning as a multimodal diagnostic cue in consumer decision-making. Drawing on survey data collected from 468 visitors at Terra Madre Salone del Gusto in Turin, Italy, the study tests a structural model comprising five latent constructs: Authenticity Trust, Perceived AI Usefulness and Diagnosticity, Multimodal Value, User Engagement, and Behavioural Intentions. The findings indicate that heritage-based and institutional authenticity cues remain foundational in consumers’ evaluations, but are increasingly interpreted and conditionally reinforced through interaction with AI-mediated information perceived as credible and diagnostically informative. Multimodal inputs—particularly the integration of textual, visual, and auditory narratives—are associated with richer authenticity perceptions and higher levels of user engagement. Experiential enjoyment during interaction with the AI system is positively related to intentions to adopt AI-supported evaluation tools, while behavioural intentions also encompass a willingness to pay a premium for products confirmed as authentic. Although the use of a convenience sample limits generalisability, the results highlight the broader potential of multimodal AI systems to reduce evaluative uncertainty and support trust formation in complex cultural and consumer environments. Conceptually, the study advances the notion of augmented authenticity, defined as a hybrid evaluative process in which tradition-based trust mechanisms are dynamically interpreted and reinforced through perceived AI diagnosticity and multimodal coherence. By situating AI within culturally embedded processes of meaning-making rather than purely instrumental evaluation, the findings contribute to interdisciplinary debates on technology-mediated trust, consumer judgement, and the societal implications of AI-assisted decision-making.

Abstract:

The air quality index (AQI) depends on the concentrations of six pollutants (PM_2.5, PM₁₀, SO₂, NO₂, O₃, and CO). In this paper, a Prophet-LSTM model with improved particle swarm optimization (PSO) is proposed to analyze the time series of six pollutant concentrations in Wuhan city. First, the time series are decomposed by Prophet, and Prophet is used to predict the trend term and periodic term. Then, LSTM is used to predict the error term. Finally, the improved PSO algorithm is used for optimization. These experimental results indicated that (1) Prophet’s decomposition method has good applicability to time series with the multiplication form. The Prophet-LSTM model can overcome the influence of PM series irregularity, large fluctuations and multiple noise on the prediction effect, which improves the prediction ability of the model. (2) The improved PSO algorithm can greatly improve the accuracy of the weight solution space and has the attribute of parallel computing, which makes the solution forms more diversified. (3) The hybrid model has better prediction ability than the comparison model (LSTM, Prophet, Prophet-LSTM). The hybrid model combines the advantages of Prophet and LSTM, which has strong adaptability to the randomness of sample selection and has strong accuracy in predicting pollutant concentrations.

Abstract: Over the past few years, the banking sector has undergone a rapid digital transformation. The combination of AI and ML is disrupting the old age systems and rule-based systems like anything. Various sectors are benefiting from implementing Artificial Intelligence (AI) and Machine Learning (ML) technologies to automate, personalise, and predict further changes in the customary banking model. In this paper, the study analyses the role of Machine Learning in improving customer experience and managing risk in the banking industry. Additionally, this paper discusses recent banking use-cases for chatbots, credit scoring systems, fraud detection systems, and anti-money laundering systems built using AI and ML. It also covers the ethical and regulatory aspects of AI and ML in the banking industry, including the concerns of privacy, algorithmic accountability, and algorithmic transparency. The report ends with a brief description of new technologies such as Explainable AI, Quantum Computing, and the use of Blockchain technology in financial systems. With the help of Artificial Intelligence and ML, customer experience has continued to improve, and risk management in the financial system is getting better. This study adopts a conceptual and literature-based analytical approach, and also makes an attempt to draw insights from recent empirical studies and industry reports are used to synthesise the applications of AI and ML in modern banking. The paper contributes by integrating dual perspectives, viz., customer experience and risk management, into a unified analytical framework. This study provides a structured understanding of how AI and ML combinedly enhance banking operations, offering theoretical and managerial insights for future empirical research.

Abstract: Although researchers document the political utility of cabinet reshuffles in African presidential systems extensively, they devote insufficient empirical attention to the trade-offs these reshuffles impose on economic performance. This paper delivers robust evidence that frequent ministerial changes inflict substantial, often underestimated costs on economic growth. The analysis draws on data from 19 African nations spanning 2006 to 2023 and applies a polynomial dynamic panel model, which uncovers a nonlinear relationship between cabinet stability and economic performance. Empirical estimates indicate that each cabinet reshuffle lowers annual GDP per capita growth by roughly 1.7 to 2.9 percentage points. The study further detects an inverted U-shaped relationship between ministerial tenure and economic outcomes. The Least Squares Dummy Variable Corrected (LSDVC) estimator reveals that ministers maximize economic growth at an optimal tenure of approximately 51.9 months (4.3 years). Beyond this threshold, moral hazard effects dominate the gains from accumulated experience. These results underscore a pivotal governance dilemma for African presidents: they must weigh the political benefits of cabinet reshuffles against their economic costs. The study thus advances insights into governance dynamics and economic performance in African settings.

Abstract: This paper explores the implementation and strategic development of Electronic Records Management Systems (ERMS) across diverse governmental contexts, with attention to both developed and developing nations. Grounded in ISO 15489:2001, the paper examines the core functions of ERMS, including the creation, maintenance, storage, and disposal of digital records. It outlines key implementation strategies, such as policy development, stakeholder engagement, and data migration and conversion. The findings highlight common challenges, such as inadequate infrastructure, limited Internet access, and shortages of skilled personnel. Conversely, the paper emphasizes the benefits of ERMS, including secure information handling, improved organizational efficiency, and enhanced service delivery. This work contributes to the field of information management by providing a practical and comparative overview of ERMS adoption. It identifies critical success factors and offers guidance for policymakers and practitioners aiming to enhance record management in the digital age.

Abstract: This paper explores the link between economic performance and multidimensional well-being in the Italian context using a combination of the ISTAT BES approach (Benessere Equo e Sostenibile) and machine learning and clustering analysis. On the basis of a dataset of 19 Italian regions and the Autonomous Provinces of Trento and Bolzano from 2012 to 2023, it will be examined how the three BES components—Benessere (B), Equità (E), and Sostenibilità (S)—are intertwined with the Gross Domestic Product of the regions. Regarding the Benessere (B) component of well-being, the Gross Domestic Product will be analyzed using a regression approach of the K-Nearest Neighbors type to reveal the complex linkages between health outcomes, education outcomes, working conditions, social participation, and economic performance. The clustering of the B indicators and the Gross Domestic Product will be done using Hierarchical Clustering analysis to identify homogeneous territories characterized by different levels of quality of life and economic prosperity. Regarding the Equità (E) component of well-being, the regression analysis will be done using the Boosting algorithm to model the linkages between the Gross Domestic Product and the indicators of income distribution, poverty, material deprivation, and inclusion in the labor market. Boosting regression analysis will be particularly useful for this purpose since it models the complex interactions and thresholds of social and economic inequalities. Hierarchical Clustering analysis will be applied to identify the territories characterized by different levels of equity and economic growth. Regarding the Sostenibilità (S) component of well-being, the Gross Domestic Product will be modeled using Boosting regression analysis to reveal the very complex linkages between the economic performance of the territories and the indicators of environmental quality, risk of climate change, innovation outcomes, and the quality of public services. For this purpose, the analysis will use the Random Forest algorithm to identify the territories characterized by different levels of sustainability and economic performance. The analysis will show that the BES approach provides a very useful framework to identify the very different levels of linkages between the economic performance of the territories and the outcomes of the BES approach. The analysis will provide evidence that the BES approach is a very useful framework for the analysis of the linkages between the economic performance of the territories and the outcomes of the BES approach.

Abstract: Over the past two decades, the world has experienced significant and relentless increase in environmental degradation, measured through carbon emissions (CO2). These emissions have been one of the persistent global concerns. South Africa boosts abundance of natural resources and some of the world’s most substantial mineral deposits endowment in the form of precious metals, diamonds and gold. The paper aims to examine impact of socio-economic and energy-related factors on environmental degradation from South African perspective. Using multivariate annual data spanning from 1991 to 2022, Autoregressive Distributed Lag Model (ADRL) was employed to determine both short-run and long-run impact of financial development (FD), renewable energy(RE), non-renewable energy (NRE), unemployment rate (UNE), economic growth (GDPPC), and population growth (PoPG) on CO2 emission. The results show that FD, RE, GDPPC, and PoPG promote environmental quality in the long run while NRE has opposite impact. The study thus calls for actions by relevant policymakers to stimulate economic growth and promote access to climate change finance, thereby encouraging investment in green energy technologies and consumption, to enhance and promote environmental quality in South Africa.

Abstract: This study investigates factors influencing public-sector employees’ acceptance of an EDRMS in Saint Vincent and the Grenadines by extending the Technology Acceptance Model (TAM) with organisational and information-systems quality factors. A quantitative, cross-sectional survey was conducted among government employees with prior EDRMS experience (N = 122). Structural equation modelling using AMOS tested relationships among system quality, service quality, subjective norm, top management support, trust, perceived ease of use, perceived usefulness, and behavioural intention. Behavioural intention was mainly driven by perceived usefulness (β = 0.58, p < .001) and perceived ease of use (β = 0.17, p = .043), explaining 47.8% of the variance. Service quality positively influenced perceived usefulness (β = 0.45, p < .001) and ease of use (β = 0.32, p < .001), while system quality positively affected ease of use (β = 0.49, p < .001). Subjective norm was positively related to perceived usefulness (β = 0.27, p = .002). Top management support and trust were not significant predictors. The study contributes evidence from a small-island developing-state public sector, highlighting the importance of service support and usability beyond classic TAM. Policymakers should emphasise system usability, reliability, and responsive training and support services to enhance EDRMS adoption.

Abstract: This study examined the relationship between perceptions of financial health indica-tors, lived experiences, and actions taken to address economic crises, while also ex-ploring potential gender differences. A non-experimental, quantitative, cross-sectional design was applied to a sample of 499 working professionals who had graduated from universities in Veracruz and were employed in either the public or private sector. A 24-item Likert-scale instrument was used to assess financial health perceptions, per-sonal experiences, and crisis-related actions. Reliability was confirmed with Cronbach’s alpha and McDonald’s omega values above 0.7. Data were analyzed using Exploratory Factor Analysis with Varimax rotation, Structural Equation Modeling, and Bayesian analysis to assess gender differences. A four-factor structure explained 64.86% of the variance. Moderate correlation was observed between financial wellbe-ing and resilience (r = 0.32), a weaker correlation between wellbeing and experiences (r = 0.18), and a strong correlation between experiences and actions in crises (r = 0.47). No significant gender differences were found. Findings highlight strategies for man-aging financial crises, maintaining credit health, and improving resilience, proposing a refined three-factor model linking experiences and actions to financial outcomes.

Abstract: The Glycaemic Index (GI) serves as a critical indicator of carbohydrate quality linked to postprandial glycaemic response. As “Low-GI” claims proliferate on front-of-pack la-bels, it remains unclear how consumers value this complex signal. This study quantifies willingness to pay (WTP) for Low-GI labeling and tests a “motivation–capability” mechanism, positing that health orientation motivates label use, while objective Low-GI knowledge facilitates targeted evaluation across nutritional contexts. A dis-crete choice experiment was conducted in China using plain yogurt (N = 910). Mixed logit models analyzed how the valuation of the Low-GI claim is moderated by carbo-hydrate context, health orientation, and objective knowledge. Results indicate a sig-nificant average premium for Low-GI labeling, with health orientation acting as a consistent motivational amplifier. Objective knowledge functions as a critical moder-ator interacting with carbohydrate context, driving label valuation only in specific low- or high-carbohydrate profiles while triggering skepticism in regular-carbohydrate ones. These findings suggest that the public-health effectiveness of emerging physiological claims depends jointly on consumer motivation and label-specific literacy. Conse-quently, policy interventions should combine label standardization with targeted ed-ucation, equipping consumers with the capability to decode the claim’s physiological meaning rather than relying on a generalized health halo.

Abstract: This study aimed to jointly characterize destination diversification and revealed competitiveness in the international shrimp and prawn trade of Ecuador, India, Viet Nam, and Indonesia during 2020–2024. A quantitative, descriptive–comparative approach was applied using annual FOB values at the exporter–destination level obtained from Trade Map (ITC). Destination diversification was proxied by the Herfindahl–Hirschman Index (HHI), while market-level competitiveness was measured through the Normalized Revealed Comparative Advantage (NRCA). Results show that Ecuador expanded exports while maintaining persistently high destination concentration. India exhibited broad revealed comparative advantage across multiple markets, yet remained highly concentrated, with episodes of deconcentration that were not sustained. Viet Nam recorded relative stagnation, moderate concentration, and heterogeneous competitiveness across destinations. Indonesia experienced contraction with extremely high concentration, characterized by a pronounced advantage in the United States alongside disadvantages in alternative markets. Overall, a positive NRCA did not necessarily imply a low HHI, and vulnerability increased when competitive advantage aligned with a small set of anchor markets, constraining adjustment capacity under shocks. The study recommends institutionalizing integrated HHI–NRCA dashboards, strengthening destination-specific market intelligence and early-warning systems, setting operational deconcentration targets, and implementing differentiated market strategies—consolidating advantages where robust, converting near-neutral positions into sustained advantages, and redesigning entry into unfavorable markets through improvements in quality, traceability, and logistics efficiency.

Abstract: Electric-vehicle (EV) diffusion exhibits nonlinear, path-dependent dynamics shaped by interacting economic, technological, and social constraints. This paper develops a unified hybrid-systems framework that captures these complexities by integrating microfounded household choice, capacity constrained firm behavior, local network spillovers, and multi-level policy intervention within a Filippov differential-inclusion structure. Households face heterogeneous preferences, liquidity limits, and network-mediated moral and informational influences; firms invest irreversibly under learning-by-doing and profitability thresholds; and national and local governments implement distinct financial and infrastructure policies subject to budget constraints. The resulting aggregate adoption dynamics feature endogenous switching, sliding modes at economic bottlenecks, network-amplified tipping, and hysteresis arising from irreversible investment. We establish conditions for the existence of Filippov solutions, derive network-dependent tipping thresholds, characterize sliding regimes at capacity and liquidity constraints, and show how network structure magnifies hysteresis and shapes the effectiveness of local versus national policy. Optimal-control analysis further demonstrates that national subsidies follow bang--bang patterns and that network-targeted local interventions minimize the fiscal cost of achieving regional tipping. The framework provides a complex-systems perspective on sustainable mobility transitions and clarifies why identical national policies can generate asynchronous regional outcomes. These results offer theoretical foundations for designing coordinated, cost-effective, and network-aware EV transition strategies.

Abstract: Emerging hospitality markets confront a two-speed ecosystem where operational digitalization outpaces strategic AI readiness, creating a benefit-feasibility gap. Providers recognize substantial technology value yet face implementation constraints from costs, integration complexity, and skills shortages, while guests demonstrate acceptance conditional on trust with privacy concerns suppressing willingness to pay. Drawing on dual-perspective empirical evidence derived from Albania's accommodation sector, integrating a national provider readiness assessment and a guest acceptance study, this Design Science Research study develops a segment-differentiated technological blueprint through systematic integration of Design Thinking, service blueprinting, and systems thinking methodologies. Integrated TAM-TOE-DOI framework analysis reveals three distinct provider segments requiring differentiated implementation pathways: Tech Leaders positioned for AI capabilities, Selective Adopters benefiting from smart modules, and Skeptics requiring foundational capabilities. Empirical evidence establishes that regional ecosystem characteristics outweigh organizational scale in determining adoption feasibility, trust operates as gating condition moderating acceptance and financial commitment, and supply-demand misalignment creates bottlenecks invisible to single-perspective assessments. Theoretical contributions extend TAM-TOE-DOI frameworks from explanatory constructs to design requirements, conceptualize supply-demand alignment as adoption mechanism, and generate two generalizable design principles: dual-constraint satisfaction requiring simultaneous provider feasibility and guest acceptance, and trust-as-architecture embedding trust mechanisms as structural properties. Practically, the blueprint provides differentiated guidance for policymakers, technology vendors, education providers, and accommodation providers, with transferability to Western Balkans, Mediterranean, and post-transition economies facing comparable heterogeneous readiness and resource constraints.

Abstract: This paper investigates the behavioral bases supporting real estate investment choices within the newly developing European market through the analysis of EU-accession optimism-driven cognitive and social heuristic activation. Based on the cross-sectional survey design using data collected from 462 respondents within the Vlora region in Albania, this study uses the PLS-SEM model, including hypotheses that examine the mediating and moderating effects of anchoring, overconfidence, and herding. Results indicated that the positive direct effect of EU-related optimism on investment intention is significant (β = 0.42, p < 0.001), as well as that on price expectations (β = 0.37, p < 0.01). The partial mediation effect of anchoring between optimism and investment intention is significant (β = 0.31, p < 0.05), which is additionally strengthened by overconfidence and herding. The multi-group test further confirmed the presence of significant behavioral divergence among locals, diaspora Albanians, Kosovo Albanians, and European visitors, underlining the crucial influence of informational distance and external reference pricing on bias creation. By treating the process of EU accession as a sentiment-based process rather than a strictly macroeconomic process, this study seeks to make a contribution to the field of behavioral finance related to real assets.

Abstract: Covid-19 supply chain disruptions clearly illustrated deficiencies in central coordination. Meaningful improvement in central coordination of supply chains will require transparency into resource stocks and flows. Latest technology, like 5G, blockchain and IoT, are primed to provide this transparency for collaboration during pandemics. This will improve agility and service, reduce inventory and enable reverse logistics benefits. Furthermore, transparent global networks can allow more inclusive and equitable distribution of critical supply, yielding quicker resolution to pandemics. However, many challenges exist that portend to delay the adoption of a holistic and transparent digitalized supply chain. This paper explores the most recent pandemic with attention to the limiting factors at all levels of emergent global crisis response.

Abstract: Central banks around the globe are rapidly progressing towards digital currency. However, its adoption rate has been consistently low among both emerging and advanced economies. This study examines the user adoption of Indian digital currency, e₹ based on primary survey conducted between July 2025 to September 2025 of 751 respondents. The study adopted TAM for the first time in the digital currency domain and the study stands novel in blending the nudge theory with stated preference method in finance literature to understand the willingness to shift to e₹ in India. Using binary logit regression, we test two hypotheses. Result show that apart of socio-economic predictors, adoption of e₹ is significantly influenced by digital financial literacy. With respect to willingness to shift to e₹, the study found TAM constructs like perceived convenience, perceived belief in the study as the key predictors. Unlike the current literature, our study finds that, trust is not a significant predictor in e₹ adoption. The findings highlight the importance of digital financial literacy and behavioral intensions, apart from technical viability, as the key factors in digital currency adoption in India.

Abstract: This paper looks at an emergency lending scheme offered by Germany’s national development bank (NDB) KfW during the Covid crisis. We analyze the design of the KfW scheme and identify obstacles to efficient contracting in these two-tier lending relationships, involving the NDB, the participating commercial banks, and the ultimate firm borrowers. Theoretical arguments and empirical evidence help to understand the main risks of subsidized public lending schemes. We propose a smart set of public lending contracts which induces banks to refrain from applying for public support if firms don’t need the funds because they re financially strong, or if they don’t deserve the funds because they are zombie firms. In the lending scheme, a firm chooses the contract which maximizes the subsidy, reveals its rating, and obtains public funds according to its crisis-induced needs thereby mitigating information asymmetries. In order to ensure incentive alignment, banks retain a share on borrower default risk. This co-insurance component should increase with crisis duration in order to contain moral hazard, i.e. zombification risk.

Abstract: Enset plays a vital role in providing both marketable and non-marketable goods and services to farming communities in southern and Central Ethiopia. Recognizing the key attributes of enset production and leveraging scientific knowledge is essential for maximizing the resource's potential and enhancing community welfare. This study aims to identify and estimate the economic goods and services of enset production. Using a cross-sectional survey with multistage sampling techniques, the study identified several significant economic benefits associated with enset production, including food, feed, fuel, medicine, fertility enhancement, soil moisture conservation, construction materials, fences, and soil and water conservation measures. The study found that the combined annual value of unprocessed enset and its three major products Kotcho, Bulla, and fiber amounted to 41.61 million and 3.79 billion Ethiopian birr, respectively. The feed, wrapping, income and cook benefits of the commodity’s estimated to be 171.9Billion Birr annually. Out of these additional attributes, feed value take the lion share (85%).The preferences of attributes varied across districts and households. The study results revealed that economic attributes of enset production were defined as a function of demographic, socioeconomic and institutional features of the commodity producers. The key attributes identified in the farming system of enset for immediate and long term research and development intervention include food, feed, wrapping, biodiversity, social and natural resource rehabilitation (fertility, soil and water conservation) and fuel benefits. These findings underscore the critical importance of enset and emphasize the urgent need for strong policy and institutional support to ensure optimal utilization and sustainable development.

Abstract: This research examines the dynamic interplay between producers and consumers in Korea’s bottled water market and water purifier market through a socio-cognitive dynamics lens. It explores how environmental concerns, legal frameworks, and media events shape evolving conceptual systems and product meanings regarding water. Using qualitative data from 69 stakeholders (producers, consumers, and experts), the study finds that distrust in tap water and heightened health awareness spurred different product perceptions – from “pure” bottled water to high-tech “functional” purification services. These shifts drove innovation across markets, including new technologies, service models, and branding strategies. Key findings indicate that regulatory limits initially constrained the growth of the bottled water market, but sustainability-driven innovations eventually revitalized it. In contrast, the water purifier sector rapidly expanded through service innovation and consumer education. The co-evolution of product meaning and stakeholder behavior underscores the importance of adaptive socio-cognitive strategies in sustainable water management. Overall, the study demonstrates how socio-cognitive dynamics can inform market development and guide policy and industry innovation to promote sustainable water use.