Search Results (6,802)

This study investigates how positive and negative framing affect sustainable investment behaviour, emphasising the mediating role of investor confidence and the moderating role of intention. An experimental design with 301 participants was employed, comparing control, positive, and negative framing conditions. Participants allocated both simulated and real monetary endowments to a green investment (recycling) project, and the PROCESS macro for SPSS 29 was used to test mediation and moderation models. The results show that positive framing directly increases allocation to sustainable investment, while negative framing operates indirectly by enhancing investor confidence, which in turn drives greater investment. Moderation analysis further demonstrates that negative framing strengthens the link between intention and real monetary commitment, even though the direct effect of framing on actual financial behaviour remains weak. This paper contributes to behavioural finance by clarifying the differential mechanisms of positive and negative framing in investment decisions and highlighting confidence as a key psychological pathway in sustainable finance behaviour. It also differentiates short-term and long-term behaviour to capture the complexity of sustainable investment. Full article

Climate change represents a serious challenge to agricultural sustainability in arid and semi-arid regions, where farmers increasingly face drought, temperature fluctuations, and resource scarcity. This study aims to assess and compare farmers’ attitudes in Egypt and Iraq toward climate change adaptation mechanisms and to identify the main barriers that limit the effective adoption of adaptive practices. A descriptive–analytical and comparative field approach was applied, and primary data were collected using a structured questionnaire administered to 342 farmers in Egypt and 157 farmers in Iraq. Descriptive statistics and inferential analyses were used to examine attitudes and determine significant differences between the two groups. Farmers’ attitudes toward climate change adaptation mechanisms and practices were measured using a 30-item scale with a three-point Likert response format (1–3), where higher scores indicate more favorable attitudes. The results indicated that farmers in both countries exhibited moderately positive attitudes toward adaptation practices, with mean scores of 2.34 in Egypt and 2.38 in Iraq with no statistically significant difference at the aggregate level, while differences are more clearly expressed at the dimensional and contextual levels rather than in overall attitudes. Major constraints to adaptation included weak institutional support, limited access to financing, absence of early warning systems, and insufficient training opportunities. The study concludes that improving agricultural extension services, expanding credit facilities, and upgrading rural infrastructure are essential to enhance farmers’ adaptive capacity and strengthen the resilience of agricultural systems. Full article

The rapid rise in cryptocurrencies has created an investment environment marked by unprecedented levels of information volume, fragmentation, and volatility. While prior research has examined drivers of trust and adoption in crypto markets, far less is known about the psychological consequences of information overload on investor decision-making. This study addresses this gap through nineteen semi-structured interviews with individual cryptocurrency investors, analyzed using an inductive, manually conducted thematic approach. Findings reveal four interconnected dynamics: decision fatigue and paralysis, heuristic reliance on influencers and peers, emotional strain characterized by anxiety and fear of missing out (FOMO), and diverse coping strategies ranging from selective filtering to withdrawal. These results demonstrate that crypto investing is not only a financial process but also a cognitively and emotionally taxing experience. By linking investor narratives to broader theories of decision fatigue, bounded rationality, and consumer vulnerability, the study contributes to interdisciplinary debates in marketing, behavioral finance, and consumer psychology. Practically, the findings highlight the need for clearer communication strategies, supportive platform design, and financial education initiatives that help investors manage cognitive strain and decision fatigue. In a market where credibility is fluid and decisions are often made under conditions of overload, understanding the psychological dimensions of investment behavior is essential. Full article

Access to finance is believed to be a key enabler that enhances women’s entrepreneurship and improves business performance, sustainability, and their empowerment. Despite its importance, scholarly literature has mentioned financial challenges as one of the factors that hinder women’s entrepreneurship success, not only in South Africa but also in other developing countries. There is, however, scant literature regarding the enablers that help women entrepreneurs navigate financial challenges in South Africa. To address this gap, this study conducted a systematic review of literature, where 21 documents drawn from Google Scholar, Scopus, and Google databases were used to identify the enablers that help women entrepreneurs navigate their financial challenges in businesses. Identified enablers that help women entrepreneurs navigate their financial challenges include stokvels, government finance programs, banks, Microfinance Institutions (MFIs), and financial education programs. The study shows how women entrepreneurs navigate financial gaps through informal networks, MFIs, and banks, while government support, institutional innovations, and tailored financial products promote sustainable business growth. The study also highlights the need for governments, banks, MFIs, universities, and NGOs to improve awareness, accessibility, gender-sensitive financial services, digital solutions, training, and mentorship for women entrepreneurs and, therefore, promote sustainable entrepreneurial growth. Full article

This study examines the False Reality Bias in treasury management, a cognitive distortion through which small and medium-sized enterprises (SMEs) infer financial stability from salient bank balances while overlooking pending obligations and cash-flow timing. Using a firm-level dataset of 50 Spanish meat-processing SMEs, the analysis develops two behavioral-finance indicators: the Liquidity Misperception Index (PEL), capturing the divergence between salient liquidity cues and effective short-term obligations, and the Liquidity Misconfidence Index (ICEL), measuring managerial overconfidence in liquidity assessments. Results show that 41% of firms overestimate liquidity (average PEL = 1.21), while 40% exhibit excessive confidence (ICEL > 1.3), both significantly associated with liquidity distress. Econometric estimates indicate that firms with PEL values above 1.2 are 4.48 times more likely to experience liquidity crises, even after controlling for bank balance levels. Predictive models are used in an exploratory capacity, achieving classification accuracies above 80% and supporting the robustness of the behavioral signals identified. In addition, AI-assisted cash-flow simulations reduce liquidity misperception by 34.7% (p < 0.01). Overall, the findings provide micro-level evidence that cognitive biases systematically distort SME treasury decisions but can be partially corrected through targeted decision-support tools, offering practical insights for managers, advisors, and policymakers. Full article

Economic crises significantly restrict corporate access to external financing, and regional differences in recovery capacity deserve close attention. This study examines the financial structure and debt of large enterprises in the Visegrád Four (V4) countries (Hungary, Czechia, Poland, Slovakia), focusing on firms with annual revenues above €10 million. Using data from 2021 to 2023, the analysis explores the relationship between corporate debt—including total debt and loan volumes—and regional economic characteristics at the NUTS 3 level. Financial indicators are assessed in comparison with regional productivity data and a sector-specific specialization index sourced from Eurostat. The analysis targets the post-COVID-19 recovery period, which significantly influenced corporate financial behavior. The results indicate that corporate debt increased sharply at the onset of the COVID-19 pandemic and subsequently declined, while remaining strongly concentrated in capital regions. Higher firm concentration and employment scale are associated with greater regional indebtedness, whereas stronger productive capacity is linked to lower reliance on external debt outside metropolitan cores. Overall, the findings highlight pronounced structural and regional heterogeneity, illustrating how spatial concentration and underlying regional characteristics shape corporate debt dynamics during periods of economic stress. Full article

This study examines the determinants of gold returns over the period 2000–2025, a period marked by recurrent financial crises, geopolitical tensions, and major shifts in global monetary conditions. As gold represents both a strategic commodity and a key reserve asset, understanding the channels driving its price dynamics is central to debates in commodity finance and macro-finance. Using Lasso variable selection combined with post-Lasso estimation, block bootstrap inference, and rolling and subsample analyses, the paper investigates the role of major macro-financial factors in shaping gold returns. The results indicate that U.S. Dollar Index (DXY) movements have strong incremental explanatory power for gold returns, consistent with a reduced-form dollar-channel interpretation. At the same time, the marginal contribution of inflation, volatility, and the tariff episode becomes limited once the DXY is included. Overall, the findings contribute to the commodity-finance literature by offering a parsimonious reduced-form interpretation of gold return dynamics and by highlighting implications for commodity price risk, hedging strategies, portfolio allocation, and reserve management in an increasingly interconnected global economy. Full article

Tree-based production systems embedded within Amazonian biocultural landscapes remain systematically undervalued in global climate, biodiversity, and development policy frameworks. This study assessed tree diversity, structural attributes, and carbon stocks across traditional cacao-based Amazonian agroforestry systems (Chakra), tree-rich silvopastoral systems, and old-growth forests in the Andean–Amazon transition zone of Ecuador. Based on 28 sampling plots (DBH ≥ 10 cm), old-growth forests stored the highest aboveground carbon stocks, while agroforestry and silvopastoral systems retained approximately 20–30% of forest carbon, equivalent to ~100–180 Mg CO₂-equivalent ha⁻¹—far exceeding values reported for monocultures or treeless pastures. A total of 151 tree species were recorded across all land-use systems, with forests harboring the highest richness (122 species), followed by agroforestry (35 species) and silvopastoral systems (28 species). Carbon storage was highly concentrated in a limited subset of multifunctional species: in agroforestry systems, eight species accounted for ~80% of total aboveground CO₂-equivalent stocks, whereas in silvopastoral systems only five species explained a similar proportion. Dominant taxa such as Cordia alliodora, Inga edulis, Jacaranda copaia, Piptocoma discolor, and Piptadenia pteroclada illustrate a process of biocultural species filtering, whereby trees providing food, timber, shade, and cultural value are selectively retained while sustaining significant carbon stocks. These findings demonstrate that tree-based productive systems function as biocultural productive landscapes that conserve carbon, biodiversity, and livelihoods beyond forest boundaries. We argue for their formal inclusion, particularly traditional silvopastoral systems, within climate finance mechanisms, nationally determined contributions (NDCs), and biocultural heritage frameworks, alongside forest conservation strategies. Full article

Special Economic Zones (SEZs) have increasingly been adopted worldwide as policy instruments for industrialization, export promotion, and employment creation. However, despite their rapid expansion, the long-term sustainability of SEZ projects remains uneven, particularly in emerging economies such as Kenya, where several zones continue to operate below expected performance levels. Existing studies largely emphasize financial viability while paying limited attention to how governance and institutional factors jointly influence multidimensional sustainability outcomes. This study therefore examines the combined influence of project finance structuring, public sector participation, and institutional capacity on the sustainability of SEZ projects in Kenya. In this study, sustainability is conceptualized through the triple bottom line dimensions of economic, social, and environmental sustainability. The study adopted a cross-sectional research design and collected primary data from stakeholders across SEZ projects using structured questionnaires administered to project managers, government officials, and community representatives. Reliability and validity of measurement instruments were confirmed through Cronbach’s alpha and factor analysis, while diagnostic tests verified compliance with regression assumptions. Data were analyzed using descriptive statistics, Pearson correlation, and multiple linear regression techniques. Descriptive findings indicate moderate overall project sustainability, with economic sustainability recording relatively stronger outcomes compared to social and environmental sustainability, suggesting uneven progress across sustainability dimensions. Regression results show that public sector participation emerged as the strongest predictor of SEZ projects’ sustainability, followed by institutional capacity, while project finance structuring demonstrated only a moderate relationship and became statistically insignificant when public sector participation and institutional factors were jointly considered. Collectively, the integrated model explained approximately 76.5% of the variation in SEZ projects’ sustainability. The study concludes that sustainable SEZ projects in Kenya depends less on project finance structuring alone and more on strong institutional systems and proactive public sector participation capable of balancing economic growth with social and environmental objectives. The findings contribute to policy and practice by emphasizing a shift from finance-centric SEZ projects development toward integrated governance frameworks that promote inclusive and environmentally responsible industrialization. Full article

Fifth generation (5G) networks have significantly enhanced connectivity, speed, and reliability, transforming industries with faster and more efficient communication. The Internet of Things (IoT) has introduced unprecedented convenience and automation, revolutionizing sectors such as healthcare, finance, and smart infrastructure. However, both 5G networks and IoT environments are experiencing a high frequency of attacks. Intrusion detection systems (IDSs) built on federated learning (FL) are being proposed to boost data privacy and security. However, these IDSs are related with the inherent drawbacks of FL, namely the existence of non-independently and identically (non-IID) distributed features and the machine learning model complexity. To address these limitations, we present a study that integrates a Mixture of Experts (MoE) into an FL setting in the task of intrusion detection. Specifically, to mitigate the issues of model complexity within the FL setting, we use a sparsely gated MoE layer consisting of a router/gating network and a set of experts. Only a subset of experts is selected via applying noisy top-k gating. To alleviate the issue of non-IID data, we adopt the Label-based Dirichlet Partition method, utilizing Dirichlet sampling with a hyperparameter $\alpha$ to simulate a label-based non-IID data distribution. Four FL strategies are employed. We perform our experiments on the 5G-NIDD and BoT-IoT datasets. Findings show that the proposed approach achieves competitive performance across both datasets under heterogeneous federated settings. Full article

A considerable proportion of perishable goods, including fruits and vegetables, deteriorate prior to reaching customers. Inadequate refrigeration infrastructure, particularly in developing nations with arid climates and markets distant from agricultural sources, accounts for most of these losses. A food cold chain has three primary phases: pre-cooling, cold storage, and refrigerated transportation. All phases of the cold chain rely fundamentally on refrigeration to preserve perishable products at designated temperatures, relative humidity, and CO₂ concentrations, thus prolonging their shelf life. Solar-driven or aided refrigeration systems use solar energy to power cooling systems and preserve the food in the cold chain. These systems are especially beneficial in off-grid or developing areas for preserving perishable goods such as fruits, vegetables, and other food items, mitigating postharvest losses that can exceed 30–50% in areas with inconsistent energy supplies. Despite progress in efficiency and scalability, numerous research gaps remain across technological, economic, social, policy, and regional dimensions, including technical aspects, optimization, and integration. There is a need to enhance energy-efficient designs, particularly by managing solar intermittency to address non-uniform cooling, which leads to inconsistent ripening and spoilage, and by integrating sustainable refrigerants to mitigate environmental impact. Further development is necessary for micro-scale, transportable, or decentralized systems designed for small farms, while economic and financing obstacles include high upfront costs and limited financial accessibility. Substantial deficiencies exist in creating affordable models and funding channels for small-scale agriculturalists. Addressing these deficiencies could expedite adoption, thereby reducing global food loss and waste (accounting for 8–10% of GHG emissions) while improving food security. Future research must emphasize multidisciplinary methodologies that amalgamate engineering, economics, and social sciences to provide comprehensive solutions. Full article

This study investigates the financial and strategic implications of ESG implementation in the hotel sector, focusing on cost structures, stakeholder engagement, and risk-related outcomes. Empirical evidence remains limited in tourism-intensive economies, particularly regarding operational practices. Using a mixed-methods approach, we combine survey data from hotel managers in Crete, Greece, with a case study of a leading hotel enterprise. The findings reveal that environmental initiatives require substantial investment but can lead to operational efficiencies and regulatory alignment. Social and governance practices, while less capital intensive, play a key role in internal stakeholder trust. The study concludes that the integration of ESG into measurable key performance indicators, when strategically aligned with corporate objectives, contributes to long-term financial viability. These insights reinforce ESG’s growing role in enhancing resilience and governance effectiveness within the hospitality sector. Full article

The continued digitalisation of banking systems has raised a demand for more reliable data-based decision-making, in particular when referring to financial forecasts as covered by e-banking applications. This research also investigates the usage of AI-based decision-making systems to facilitate forecasting effectiveness in Jordanian commercial banks. Field research was carried out and 390 employees, working at 14 commercial banks in Jordan, responded to an organised questionnaire. Although the minimum required sample size was 384 respondents, a total of 390 valid responses were collected and used in the final analysis, thereby exceeding the minimum sample requirement. This research concentrates on three dominant categories of AI applications, including expert systems (ES), machine learning (ML), and Robotic Process Automation (RPA), which together are analysed for their effect on forecasting results in the context of customer churn, debt repayment, as well as investment analysis. The results of the multiple regression analysis indicate that AI applications contribute to improvements in forecasting accuracy, with machine learning and RPA showing relatively stronger effects. Expert systems were found to support investment analysis and debt repayment forecasting; however, their influence on customer churn prediction was more limited. In general, the findings indicate that AI applications are not confined to routine automation but are increasingly used as decision-support tools that assist financial analysis and forecasting activities in banking systems. Full article

Under the background of the “dual-carbon” goals and the ecological ecological-civilization-construction strategy, improving the synergistic efficiency of pollution reduction and carbon abatement is a key to promoting green high-quality development. Based on a panel of 30 provincial-level regions in China for 2012–2022, this paper evaluates the impact of digital financial inclusion on the synergistic efficiency of pollution reduction and carbon abatement. First, using a global-frontier directional-distance function (DDF), we characterize the improvement space of “desirable-output expansion—simultaneous contraction of pollution and carbon emissions” under given input constraints, and construct a synergistic efficiency indicator (eff_main). Second, we present a correlation benchmark within a two-way fixed-effects (TWFE) framework and use lead/lag (placebo) tests to probe potential endogeneity; we further construct a Bartik (shift–share) instrumental variable and employ Two-Stage Least Squares (2SLS) to strengthen causal identification. The results show that in TWFE regressions, digital financial inclusion (dif100) is positively and significantly correlated with synergistic efficiency, with a coefficient of 0.113 (i.e., an increase of 100 index points in the digital financial inclusion index is associated with an average increase of 0.113 in eff_main), but a significant lead effect is present, so this result should be interpreted as correlational only; 2SLS estimates indicate a robust positive causal effect of digital financial inclusion on synergistic efficiency, with a baseline coefficient of 0.405, rising to 0.501 under lagged specifications—exhibiting a dynamic feature of “gradual release in subsequent years.” The study suggests that developing digital financial inclusion helps raise regions’ comprehensive green-transition performance and sustainable development capacity; policy implications include accelerating the closing of digital infrastructure gaps, improving green-finance institutions and performance constraints, and guiding funds more effectively toward energy-saving, emission reduction and low-carbon technology areas. Full article

Federated learning (FL) has emerged as a transformative distributed learning paradigm that enables collaborative model training without sharing raw data, thereby preserving privacy across large, diverse, and geographically dispersed clients. Despite its rapid adoption in mobile networks, Internet of Things (IoT) systems, healthcare, finance, and edge intelligence, FL continues to face several persistent and interdependent challenges that hinder its scalability, efficiency, and real-world deployment. In this survey, we present a systematic examination of six core challenges in federated learning: heterogeneity, computation overhead, communication bottlenecks, client selection, aggregation and optimization, and privacy preservation. We analyze how these challenges manifest across the full FL pipeline, from local training and client participation to global model aggregation and distribution, and examine their impact on model performance, convergence behavior, fairness, and system reliability. Furthermore, we synthesize representative state-of-the-art approaches proposed to address each challenge and discuss their underlying assumptions, trade-offs, and limitations in practical deployments. Finally, we identify open research problems and outline promising directions for developing more robust, scalable, and efficient federated learning systems. This survey aims to serve as a comprehensive reference for researchers and practitioners seeking a unified understanding of the fundamental challenges shaping modern federated learning. Full article