Abstract:

This study empirically investigates the impact of both the level and risk of cash dividend distributions on the stock value of companies listed on the Saudi Stock Exchange (Tadawul). Utilizing a proportional stratified random sample of 120 companies across 21 sectors over the period 2020-2024, the research employs third-degree polynomial regression models to analyze complex, non-linear relationships. The findings reveal a significant cubic relationship, identifying an optimal dividend per share of 5.91 SAR that maximizes stock price. Furthermore, dividend volatility (risk) exhibits an inverted S-shaped relationship with price, with an optimal standard deviation of 5.04 SAR, indicating that the market rewards a dynamically stable payout policy. The study also uncovers strong sectoral effects, with Telecommunication, Health Care, and Energy sectors commanding significant valuation premiums, while Real Estate and Financial Services trade at discounts. The results robustly confirm that both dividend level and stability are critical, sector-dependent determinants of firm value in the Saudi market. These insights provide valuable guidance for corporate dividend strategy, investment decision-making, and policy formulation within the context of Saudi Vision 2030.

Abstract: Entrepreneurship in South Africa extends beyond conventional sustainable development, incorporating several viewpoints that mirror the nation's distinct socio-economic and cultural context. This study examines several perspectives, emphasising the interaction of technical, artistic, socio-economic, historical, and institutional elements in influencing entrepreneurial activity. Technological advancements and management innovations create new opportunities for small and medium-sized enterprises (SMEs), while cultural diversity fosters creativity and innovation. Socio-economic issues, such as elevated unemployment rates and historical disparities, necessitate targeted entrepreneurial activities to promote job creation and community empowerment. This study elucidates how entrepreneurship in South Africa can extend beyond sustainable development, thereby fostering economic growth and social transformation by examining various perspectives. The qualitative study utilised original data collected from selected participants through interviews. The data was analysed through a thematic data analysis, and results show that entrepreneurship in South Africa is a dynamic and complex phenomenon that transcends the conventional emphasis on sustainable development. This study elucidates the critical influence of technology breakthroughs, cultural diversity, socio-economic issues, and historical disparities on entrepreneurial activity. These elements collectively enhance the resilience, inventiveness, and innovation of South African entrepreneurs.

Abstract: Using daily data ranging from January 2020 to June 2023 we aim to investigate the interconnectedness between the crude oil price and the exchange rate price. As first step we use the impulse response function to measure the interaction between both variables within a shock occurred on one of the studied variables. The findings justify that a shock occurred within the price of the crude oil has a direct effect to exchange rate variability. At the second step we use the VAR-DCC-GARCH to employ the time frequency correlation between both variables. Our funding proof dependency of the Japanese yen , the mexico pesos , the Canadian dollar as well as the indian rupee to the volatility of the price of crude oil. The Russian rubble show great resistance to twards the volatility of the price of crude oil. Our findings suggest that the dollarization of world economy tend to influence significantly the volatility of foreign exchange market on the crude oil price.

Abstract: Global progress towards the 2030 Sustainable Development Goals (SDGs) remains critically off-track, with current trends suggesting that only 17% of targets will be met by the 2030 deadline. This study investigates whether observed divergence reflects temporary setbacks or persistent structural regimes characterised by distinct configurations. Using panel data from over 160 countries (2019–2024), we employ annual latent class analysis to identify hidden structures in SDG performance across 15 goals, introducing intertemporal volatility as a dimension of development dynamics. We complement this with ordered logistic regression to examine structural determinants of regime membership, including governance quality, digital infrastructure, health investment, and macroeconomic indicators. Our analysis identifies three temporally stable development regimes —lagging, transitional, and leading — with fewer than 15% of countries transitioning between classes over the observation period. ANOVA results reveal that internet access and government effectiveness exhibit the most considerable between-regime differences. Ordered logit models indicate that governance quality and digital connectivity are the primary predictors of regime membership, with marginal effects of 18–19 percentage points in regime probability. In contrast, short-term GDP growth exerts a negligible influence. These findings challenge linear convergence assumptions and suggest that achieving the SDGs requires addressing deep structural constraints, particularly digital divides and institutional quality, rather than relying solely on incremental policy adjustments or economic growth.

Abstract:

Entrepreneurship plays a critical role in addressing youth unemployment in emerging economies, yet the psychosocial mechanisms through which entrepreneurial attitudes are formed among first-year university students remain underexplored. This study examines whether creativity mediates the relationship between motivational factors and entrepreneurial attitude, and whether perceived family support moderates this mediation process. A cross-sectional survey was conducted with 600 first-year students from public and private universities in northern Peru (Trujillo, Piura, and Chiclayo). Results revealed that entrepreneurial creativity fully mediates the relationship between intrinsic motivation and entrepreneurial attitude, while the direct effect was nonsignificant. Family support significantly moderated the creativity-attitude relationship, with stronger effects at higher support levels. The integrated model explained 51% of variance in entrepreneurial attitude. These findings demonstrate that intrinsic motivation operates through creativity development to shape entrepreneurial attitudes, and that family support amplifies this transformation. Universities in collectivist cultures should prioritize creativity-enhancing pedagogies combined with structured family engagement programs to effectively cultivate sustainable entrepreneurial ecosystems among incoming students.

Abstract:

In recent years, industry development has become closely connected with ICT and trade openness. This research explores how industry, ICT, and trade openness affect the environment, highlighting the importance of investing in low-carbon technologies and energy-efficient machinery. The goal of this research is to investigate the long-run and short-run impacts of industrialization, ICT, trade openness, and economic growth on per capita carbon emissions in Lithuania from 2000 to 2024. This study uses the ARDL econometric model along with several diagnostic tests. The Breusch-Godfrey Serial Correlation test confirmed no serial correlation, while the Breusch-Pagan-Godfrey test indicated that no heteroscedasticity exists. The Ramsey RESET test confirmed that the model is correctly specified and significant. Additionally, the VIF multicollinearity test shows that no multicollinearity exists between the research variables. The research outcomes show that industrialization, ICT, and economic growth have a positive relationship with per capita carbon emissions and are harmful to the environment, whereas trade openness has a negative effect on per capita carbon emissions in Lithuania and contribute environmental sustainability. The novelty of this research lies in its long-run and short-run analysis of the interaction among the selected variables. This research provides policy suggestions aimed at enhancing environmental quality.

Abstract: Fixed effects models often rely on the within transformation, which constructs demeaned arrays prior to forming cross-products. This paper develops an estimator that avoids the for- mation of demeaned arrays by exploiting grouped summaries built from per-unit sufficient statistics. A complete derivation shows that the grouped Gram representation reproduces the classical estimator exactly. The difference lies in memory access patterns and byte movement. The grouped estimator concentrates operations into unit-level accumulations, avoiding the writes associated with array centering. Gains arise once the panel reaches a scale where mem- ory traffic governs run time. Simulations examine coefficient accuracy, bootstrap dispersion, run time, and memory use.

Abstract: Understanding how subway stations affect nearby crime is important for urban planners, transit agencies, and public safety officials who must allocate limited resources. Prior research suggests that transit nodes can increase crime by concentrating potential targets, but findings vary depending on station design, ridership levels, and time of day. This study examines whether within-place changes in subway ridership are associated with changes in recorded crime across New York City from 2020 to 2024. The unit of analysis is the quarter-mile "egohood," a buffer around each Census block centroid. Data come from NYPD complaint records, MTA ridership counts, and American Community Survey demographics. Using two-way fixed-effects models that control for stable neighborhood traits and citywide year shocks, the analysis finds that increases in ridership within the same egohood are associated with modest increases in recorded crime. Station presence alone does not predict crime once time-invariant characteristics are held constant. These findings suggest that managing passenger flows, rather than station footprint, should guide safety planning. Practical steps include improved lighting, visible staffing during peak hours, and coordination between transit agencies and local police.

Abstract: There are indices for measuring the different aspects of how companies disseminate information, but none specifically assesses the level of blockchain technology adoption by financial institutions. Given the rapid development and applications of blockchain technology in finance, evaluating its adoption by financial institutions is crucial. In this context, this study develops and tests a formative blockchain technology adoption index to quantify the extent of blockchain adoption in the financial sector, based on data from the annual reports of leading banks in Africa. The research employs a triangulation methodology, which involves identifying indicators that can be combined to form a blockchain technology adoption index. These indicators were evaluated against the SMART criteria for selecting effective metrics. The index was then created based on these indicators and applied using data from 20 leading banks in Africa. These banks were chosen for their regional representation, size, and rankings derived from Africa Business data, a reputable source that uses Tier 1 capital as a key indicator of bank strength. Findings revealed that banks in West Africa lead with the highest adoption score of 72%, followed by South Africa at 66%. Furthermore, an ANOVA test at a 0.05 confidence level was used to examine whether bank size significantly influences blockchain adoption levels. The results showed no statistical difference in the average total assets between larger and smaller banks in the sample, thereby indicating the index’s broad applicability regardless of bank size. This study concludes that the developed blockchain technology adoption index in this study can be applied to banks and other financial sector companies of varying revenue sizes across different regions in Africa and beyond.

Abstract: The transition towards circular economy represents now a key strategy to address the environmental issues we are facing. Within this framework, biochar, a carbon reach material [1] derived from the residual agricultural pyrolysis can represent a sustainable and circular solution. This paper aims at evaluating the possibility of implementing a local biochar production system as part of an economic and social strategy of redevel-opment of an abandoned rural site, Borgo Perolla, in Tuscany, Italy. A cost-benefits analysis (CBA) was conducted to evaluate the economic feasibility of three different scenarios of production and strategies. For each scenario, three indicators were calculated: Net-Present Value (NPV), Internal Rate of Return (IRR) and Breakeven point (BEP). The most evident result that emerged is that the sale of biochar and its by-products alone is not sufficient to ensure the project’s economic sustainability, mainly due to high production costs. Only through carbon credit trading markets bio-char becomes not only an environmentally strategic tool but also an economically re-warding one. In this sense, market infrastructures such as the ETS are essential for the dissemination of circular models, like the biochar, that generate both environmental and economic benefits.

Abstract: Financial resilience has increasingly captured the attention of scholars, as it directly impacts general well-being, inclusive education, and sustainable economic development. This study provides evidence on how university students view financial resilience, based on their perceptions, experiences, and actions taken to address difficult financial situations in recent times. Thus, it contributes to SDG 3 by promoting economic and psychological well-being; to SDG 4 by strengthening financial education as an essential element of quality education; and to SDG 8 by facilitating development. The scale is based on financial health indicators aligned with the Center for Financial Services Innovation. Data were analyzed using SEM methodology. Findings reveal a three-factor model explaining perceptions of financial health indicators, including lived experiences and resilient actions taken in the face of adverse financial situations. The three-dimensional model is not fully supported, as indicators that do not favor the final model due to low factorial loads are excluded.

Abstract: The paper examines the problems of forecasting and modeling consumer demand in the organic agricultural products market in the context of sustainable development and digital marketing, which is due to the growth of environmental awareness of the population and the active spread of digital communication channels, but is constrained by the level of income and a high share of food costs. The methodology is based on consumer surveys, factor and regression analysis, matrix method and forecasting. The results showed that the formation of demand is determined by a combination of economic, social and behavioral factors, among which the key ones are the level of income, perception of affordability, as well as trust in certification and awareness of the benefits of organic products. It is proven that digital marketing increases the influence on purchasing decisions, forms sustainable consumption patterns and supports the development of responsible behavior. It is concluded that the integration of predictive models and digital marketing tools provides a more accurate definition of future demand trends and creates a basis for the formation of effective strategies aimed at increasing the availability and popularization of organic products in accordance with the principles of sustainable development.

Abstract: The oil and gas sector operates in a high-risk environment defined by capital intensity, regulatory uncertainty, and volatile commodity prices. While Artificial Intelligence (AI) technologies such as machine learning and predictive analytics promise to reduce risk and enhance profitability, the precise mechanisms converting AI adoption into tangible financial success remain under-researched. Grounded in the Resource-Based View and Technology Adoption Theory, this study employs a dual-methodological approach: a bibliometric analysis of 201 Scopus-indexed publications (2010–2025) and a focused financial analysis of industry supermajors (BP and Shell). The results demonstrate that AI adoption alone does not guarantee superior financial results. In-stead, the relationship is mediated by operational efficiency, which accounts for up to 45% of the variation in financial performance. Specifically, the application of AI in predictive maintenance and digital twins drives improvements in asset uptime and cost control, which directly correlate with stabilized Return on Average Capital Em-ployed (ROACE), even during periods of oil price volatility. By synthesizing five key research clusters, this study provides a strategic framework verifying that AI’s value is realized through a causal chain: AI enables operational efficiency, which in turn se-cures financial resilience and capital returns.

Abstract: This study employed machine learning methods to predict household credit access concerns using comprehensive financial and demographic data from the 2022 Survey of Consumer Finances (SCF), analyzing 4,595 households to examine which characteristics predict whether families have been turned down for credit or feared credit denial in the past five years, a critical measure of financial vulnerability affecting approximately 84% of surveyed households. Two classification models were developed and compared: an XGBoost gradient boosting model and a logistic regression model, both using 263 principal components derived from the original feature space. The XGBoost model (Model A) achieved exceptional predictive performance (AUC = 0.9885, accuracy = 96.65%, precision = 97.52%, recall = 82.01%), substantially outperforming the logistic regression model (Model B: AUC = 0.7955, accuracy = 80.34%, precision = 44.93%, recall = 78.37%), demonstrating that credit access concerns follow highly systematic patterns. Feature importance analysis revealed that asset-based financial indicators dominated predictions, with Equity to Income, Homeownership, Credit application history, Emergency Savings, and Leverage Ratios emerging as the top five predictors, while behavioral and historical factors particularly payment delinquencies and prior credit experiences exhibited substantial importance, supporting path-dependent theories of financial exclusion. Race ranked 14th among predictors, suggesting that observed disparities operate substantially through differential economic circumstances, though structural barriers persist. The findings have immediate implications for financial institutions, which can deploy similar predictive models to identify at-risk customers for targeted financial counseling, develop alternative credit scoring approaches accounting for asset ownership and emergency savings, and design early warning systems flagging households with declining liquid assets or increasing leverage ratios for preventive assistance. Financial advisors can use these insights to prioritize asset-building strategies over simple income increases, emphasize emergency savings establishment as critical for credit access maintenance, counsel clients on credit history management following past delinquencies, and recognize that payment delinquencies create enduring barriers requiring proactive rehabilitation.

Abstract: Cognitive biases are evolutionarily adaptive mental shortcuts rooted in automatic processing, yet their expression varies widely across individuals due to differences in personality structure, cultural communication patterns, and generational socialization. Drawing on research in behavioral psychology, cognitive science, organizational behavior, and cross-cultural communication, this paper presents an integrated framework for predicting dominant cognitive biases by combining three complementary models: Kahler’s process communication model, Lewis’s cultural communication model, and Strauss and Howe’ generational cohort theory. The study outlines the design of an 11-item instrument grounded in these frameworks and evaluates its preliminary validity, reliability and perceived accuracy. By identifying how psychological, cultural, and temporal factors shape bias tendencies, the model offers insight into how individuals interpret organizational purpose, challenge assumptions, and adapt their decision-making in uncertain environments. This predictive approach also supports talent mapping, and the formation of cognitively diverse teams, which strengthen strategic adaptability, and contribute to more effective and inclusive organizational practices.

Abstract: The rapid integration of artificial intelligence (AI) and smart technologies is transforming hospitality operations, yet guest acceptance remains uneven, shaped by utilitarian, experiential, ethical, and cultural evaluations. This study develops and empirically tests a multicomponent framework to explain how these factors jointly influence two behavioral outcomes: whether AI-enabled features affect hotel choice and whether guests are willing to pay a premium. A cross-sectional survey of 689 hotel guests in Tirana, Albania, an emerging hospitality market and rapidly growing tourist destination in the Western Balkans, was analyzed using cumulative link models, partial proportional-odds models, nonlinear and interaction extensions, and binary robustness checks. Results show that prior experience with smart or AI-enabled hotels, higher awareness, and trust in AI, especially trust in responsible data handling, consistently increase both acceptance and willingness to pay. Perceived value, operationalized through the breadth of identified benefits and desired features, also exhibits robust positive effects. In contrast, privacy concerns selectively suppress strong acceptance, particularly financial willingness, while cultural–linguistic fit and support for human–AI collaboration contribute positively but modestly. Interaction analyses indicate that trust can mitigate concerns about reduced personal touch. Open-ended responses reinforce these patterns, highlighting the importance of privacy, human interaction, and staff–AI coexistence. Overall, findings underscore that successful AI adoption in hospitality requires aligning technological innovation with ethical transparency, experiential familiarity, and cultural adaptation.

Abstract: This study examines the determinants of goodwill impairment recognition under IFRS 3 in the context of Moroccan listed firms. Using an unbalanced panel of 62 companies observed from 2006 to 2024, we employ a three-stage empirical strategy that integrates a Probit model to estimate the likelihood of impairment, a Tobit model to assess the magnitude of the loss, and a Heckman two-step procedure to correct for potential self-selection. The results show that goodwill impairment reflects key economic and financial fundamentals, including revenue growth, book-to-market ratios, and operating performance. However, both real and accrual-based earnings management significantly influence the probability and intensity of impairment, particularly through abnormal cash flows and income-smoothing behavior. Discretionary accruals become significant only after correcting for selection bias, indicating that they do not drive the recognition decision but contribute to determining the size of the impairment once it has been recorded. The findings are robust across multiple specifications and contribute to the broader literature on financial reporting quality under IAS/IFRS, while enriching empirical evidence on managerial discretion and earnings management in emerging-market environments.

Abstract: We investigate the impact of government economic policy uncertainty (GEPU) on bank risk, distinguishing short- and long-term effects. We argue that heightened GEPU increases bank risk in the short run by raising borrowers’ default probabilities under adverse economic conditions, while reducing risk in the long run by discouraging banks from extending risky loans due to the higher option value of waiting under uncertainty. Using bank-level data from 22 countries over 1998–2017, we find that elevated GEPU raises bank risk contemporaneously but lowers it with a lag of two to four years. These results are robust to endogeneity concerns, alternative measures of bank risk and GEPU, variations in sample composition, and different estimation techniques. Our findings highlight the dual role of policy uncertainty in shaping bank risk-taking behavior and have implications for regulatory design and macroprudential policy.

Abstract: This article examines the growing inadequacy of Gross Domestic Product (GDP) as a measure of human progress in a world shaped by ecological fragility, socio-technical transformations, and civilizational transitions. While GDP served as a convenient post-war metric for national accounting, it now obscures critical dimensions of wellbeing, including ecological sustainability, relational capabilities, and systemic resilience. Drawing from complexity economics, relational sociology, and post-growth political economy, the article proposes a renewed understanding of value as emergent, interconnected, and ecologically embedded.Complexity economics demonstrates that economies are not linear production machines but adaptive systems shaped by feedback loops, cooperation, and innovation. Relational perspectives from Sen, Nussbaum, and Appadurai highlight capabilities, agency, and aspiration as fundamental components of wellbeing beyond monetary aggregates. Post-growth scholarship—including recent contributions from Hickel, Raworth, and Stiglitz—calls for civilizational metrics aligned with planetary boundaries and distributive justice.The article synthesizes these paradigms to propose a multidimensional framework integrating ecological boundaries, relational wellbeing, and systemic capabilities. Special attention is given to Africa and the Global South, where informal economies, urban complexity, and community resilience constitute fertile ground for post-GDP experimentation.Overall, the analysis argues that moving beyond GDP is not merely a technical adjustment but a civilizational shift toward a regenerative, capability-enhancing, and complexity-aware understanding of prosperity fit for the twenty-first century.

Abstract:

The research focused on Sustainable Development Goal 8, which promotes decent work and economic growth, by studying theories related to determining the profile of online shoppers. The overall objective was to determine the characteristics of the digital consumer profile and the segments to which digital customers of optical stores in Chimbote belong in 2025. The research was applied, with a quantitative approach, a non-experimental design, and a descriptive-correlational level. The population consisted of 1,800 customers from 2024, with a sample of 317 customers. Simple random sampling was used to obtain data through a survey. The results showed the existence of five segments based on the profiles found: exclusive aesthetics, natural aesthetics, whimsical aesthetics, practical naturals, and traditional naturals. This was corroborated by the hypothesis test, where the resulting p-value of 0.018 was less than 0.05, confirming the existence of digital consumer profile characteristics according to the segments to which the digital customer belongs. In conclusion, the data obtained made it possible to determine the main characteristics that define the profiles of digital consumers in the optical sector of Chimbote.