Abstract: Digital inclusion is increasingly recognized as a key driver of socioeconomic opportunity in rapidly urbanizing African cities, yet empirical evidence on its structural determinants remains limited. This study advances the literature by developing a multidimensional, data-driven framework to assess digital inclusion in Ziguinchor, Senegal. Using a unique household survey, it integrates technological access, service quality, affordability, electricity reliability, mobility constraints, and social capital. Principal Component Analysis (PCA) is used to construct standardized domain indices and a composite Digital Inclusion Index, while regression models quantify the relative influence of each domain, accounting for gender and age differences. The findings provide new empirical evidence that digital inclusion is driven primarily by material and infrastructural conditions, particularly device access, proximity and mobility constraints, and electricity reliability. In contrast, affordability and service quality play smaller roles, challenging dominant policy narratives focused on data costs. The study also reveals persistent gender and generational inequalities in digital access and use. By quantifying the relative weight of multidimensional constraints and linking them to spatial and infrastructural conditions, the research offers a replicable and policy-relevant analytical framework for secondary cities. It demonstrates that digital inclusion is not solely a connectivity issue but a structurally embedded outcome, requiring integrated interventions across infrastructure, mobility, and social equity domains.

Abstract: This paper studies platform environments in which participation incentives change discontinuously when valuation crosses salient thresholds. Empirical research on digital and tokenized platforms documents valuation plateaus, abrupt participation shifts, and weak short-run links between price stability and usage dynamics, patterns that are difficult to reconcile with smooth adjustment models. We capture these features structurally by modeling adoption and valuation as a coupled equilibrium system with regime-dependent incentives, formulated as a Filippov differential inclusion.Token prices are determined endogenously through market clearing between usage-driven demand and regime-dependent speculative demand. We establish global well-posedness and identify conditions under which valuation thresholds become attracting manifolds. In these regimes, prices remain anchored at the threshold while adoption continues to evolve, generating persistent valuation plateaus and path dependence. When threshold crossings are regular and the induced dynamics are uniformly dissipative, endogenous boom--bust cycles are ruled out.The framework yields design-relevant insights for digital platforms. Valuation anchoring and volatility depend on governance primitives such as effective circulating supply and regime-dependent speculative depth. A diagnostic numerical analysis links time-at-anchor and sliding intensity to adoption volatility and platform risk, providing empirically interpretable indicators of stability in tokenized platforms.

Abstract: The accuracy of financing demand prediction has a direct impact on the return on investment and risk exposure in fintech investment and asset allocation. Nevertheless, the real world financial transaction data often displays significant nonstationary features — for example, cyclical fluctuations, event shocks, and short-term anomalies — which make the traditional forecasting approach unstable in the real investment scenarios. This study builds a data set that includes 34 reproducible variables — including daily financing requirements, transaction peaks, capital occupation duration, and risk exposure levels — on the basis of 180 consecutive days of investment and operating data from a leading financial services firm. It systematically compares ARIMA, Prophet, Random Forest, and XGBoost models for financing demand forecasting. Empirical results show that XGBoost maintains a low forecast error (MAPE of 8.2%) in the case of market fluctuations and unusual events, which reduces the average error by about 22% in comparison with the baseline model. Based on these results, a model is built to analyze the effect of forecast errors on the stability of investment returns and the efficiency of capital turnover. Results show that keeping the forecast error under 10% significantly reduces the risk of capital misallocation in times of high volatility, while at the same time improving the stability of overall investment returns. This study provides a reusable model workflow and engineering reference for the establishment of the investment allocation and risk management system of the financial institutions.

Abstract: The global regulatory landscape is shifting from voluntary corporate social responsibility (CSR) to mandatory Environmental, Social, and Governance (ESG) disclosure. This study investigates the causal impact of mandatory ESG disclosure on firm value and operational decarbonization using a comprehensive balanced panel of 1,612 listed firms from the EU and the US between 2018 and 2025.Employing a Difference-in-Differences (DiD) design and an event study analysis, our empirical results yield three primary findings. First, consistent with Agency Theory, mandatory disclosure significantly increases firm value (Tobin’s Q) immediately following the 2021 regulatory shock (Post×Treat=0.5212, p< 0.01), indicating that standardized transparency reduces information asymmetry (H1). Second, we document a progressive and cumulative reduction in carbon intensity, providing robust evidence of substantive execution rather than mere ceremonial compliance (H2). The "downward-sloping" trajectory in the event study confirms that the mandate drives real-world operational transitions over time, refuting Decoupling Theory. Third, we find that internal governance mechanisms play a crucial moderating role in this transition (H3); the reduction in carbon intensity is significantly more pronounced in firms with higher board independence and established ESG committees. These findings suggest that "hard-law" transparency mandates effectively align corporate incentives with global climate goals. The synergy between external regulatory pressure and internal governance oversight is essential for bridging the "talk-walk" gap, offering critical implications for global policymakers designing the next generation of climate-related reporting standards.

Abstract: The European Union aims to achieve the target of 25% of land under organic farming by 2030. Italy reached the share of 18.7% in 2022, although significant regional differences persist. This study analyzes farms’ conversion response in the Lazio region (Italy) to evaluate the effectiveness of higher economic incentives in promoting organic conversion. The agro-economic supply model AGRITALIM is applied to a sample of 587 FADN farms. The model simulates individual farm conversion choice, distinguishing between conversion and maintenance phases, and accounting for conversion costs, yield, and price variations associated with each period. Results show limited effects of increased economic support: the 2023–2027 Common Agricultural Policy reform, characterized by higher support, leads to a 5.1% increase in the area under organic farming, while a 40% increase in financial support generates an expansion of 12%. Farm responses are highly heterogeneous: rural provinces, larger and arable farms are more responsive, while smaller farms and livestock are less likely to convert. These findings highlight the need for integrated policy strategies combining financial support, reduced costs, technical assistance, and improved market access. The methodological approach adopted in this study provides a useful tool for supporting the design of targeted and effective policy interventions.

Abstract: Under resource constraints, technology-based SMEs are highly sensitive to the return on training investment. This study analyzes the impact of different training strategies on employee performance, focusing on the relationship between skills gap and training effectiveness. Based on skills assessment, training records, and performance appraisal data of 2,784 technical personnel in a technology-based SME, a skills gap index was constructed, and two types of investment methods were distinguished: general training and job-oriented training. A multiple regression model was used to analyze the relationship between training duration and performance changes. The results show that implementing job-oriented training for employees with skills gaps in the upper quartile resulted in an average performance score improvement of 0.21 standard deviations, while the improvement from general training was less than 0.06. The research results provide a quantitative basis for SMEs to optimize the allocation of training resources.

Abstract: This study aims to design a context-sensitive data governance framework for nonprofit sport organizations in Iran within the era of digital transformation. A qualitative research design grounded in Corbin and Strauss’s grounded theory was employed. Seventeen semi-structured interviews were conducted with managers, coaches, IT specialists, and decision-makers in Iranian nonprofit sport organizations. Participants were selected based on their experience with data-driven projects, decision-making authority, and familiarity with organizational information systems. Data analysis involved open, axial, and selective coding to identify causal, contextual, and intervening conditions influencing data governance. The constant comparative method ensured conceptual consistency and saturation. Key dimensions such as data ownership, quality, infrastructure, organizational culture, and literacy were explored in depth to develop an empirically grounded conceptual model. Results reveal that digital transformation acts as a major causal condition, increasing pressure on organizations to manage large, heterogeneous data sets. Contextual constraints such as limited financial resources, informal structures, and fragmented data processes interact with intervening factors including leadership commitment, staff data literacy, and acceptance of technology to shape governance strategies. These strategies, when implemented, enhance decision quality, transparency, accountability, and organizational trust. The study demonstrates that without formal governance mechanisms, data remain underutilized despite technological adoption.

Abstract: Understanding when and where to shift land from agriculture to forestry is essential for developing sustainable land-use strategies that balance climate, biodiversity, and rural development goals. Traditional profitability comparisons rely on long-term discounting, which is sensitive to assumptions and misaligned with the decision-making horizons of landowners and policymakers. This study introduces a deposit-based framework that treats annual timber biomass growth as accumulating economic value, enabling direct comparison with yearly agricultural profits on a per-hectare basis. By integrating parcel-level spatial data, land quality indicators, national statistics, and expert input, the framework generates high-resolution maps of annual profitability for both land uses. Applied in Latvia, the analysis reveals significant regional variation in agricultural returns, with many low-quality areas showing marginal or negative profits, while forestry offers stable, modest gains across diverse biophysical conditions. The results highlight where afforestation becomes a financially rational alternative and suggest transition pathways that enhance overall land-use profitability while supporting climate and biodiversity objectives. The framework is transferable to other contexts by substituting context-specific data on land quality, prices and growth, and can complement policy instruments such as performance-based CAP payments and afforestation support. The approach supports future-oriented differentiated land-use planning using annually updated spatial economic signals.

Abstract: This article proposes an interpretable, multi-layered recruitment model that balances predictive performance with decision transparency in AI-supported HR processes, ad-dressing risks related to opacity, auditability, and ethically sensitive decision-making. The architecture combines an expert rule layer for minimum-threshold screening, an unsupervised clustering layer to structure candidate profiles and generate pseudo-labels, and a supervised classification layer trained and evaluated via repeated k-fold cross-validation. Model behavior is explained using SHAP to identify feature contribu-tions to cluster assignment, and cluster quality is additionally diagnosed using Necessary Condition Analysis (NCA) to assess minimum competency requirements for attaining a target overall quality level. The approach is illustrated in a Data Scientist recruitment case study, where centroid-based clustering predominates (K-Means is most frequently se-lected), while linear classifiers show the highest effectiveness and stability (logistic re-gression performs best). SHAP highlights competencies that differentiate candidates beyond the initial threshold, and NCA further distinguishes candidates within the recommended cluster by identifying profiles that meet (or fail) the necessary-condition bottleneck. The proposed framework is replicable and supports transparent, auditable recruitment decisions.

Abstract: The incidence of technologies created with the support of federal funding at universities and federal laboratories that are transferred to the private sector is nowhere close to its potential. The literature suggests that technology maturity level can possibly be a useful lever to increase the incidence of technology transfer. Orthodox approaches to technology transfer research have significant limitations that negatively impact their usefulness for investigating this issue. This paper presents a theoretical framework to address this gap and the results of a study that applied this framework in combination with Bayesian analysis to understand whether technology maturity level holds promise as a lever that practitioners and policymakers can use to substantially increase the incidence and societal benefits of technology transfer from universities and federal laboratories. The results of the study indicate that there is about a 55% probability that insufficient maturity is the primary reason that private sector organizations do not pursue 5% or more of available university and federal laboratory technologies. Thus, implementing public policies, programs, and initiatives to further mature technologies created at universities and federal laboratories that private sector firms would otherwise eschew because of insufficient maturity is likely to increase the overall incidence of technology transfer slightly but even a slight increase could produce substantial societal benefits. The potential economic benefits of commercializing such technologies are roughly 1.7 to 2.4 times greater than strategically redistributing the research funding used to create them to induce consumption and spur economic activity.

Abstract: Love of the homeland is a sincere attitude shown by citizens and is manifested in actions for the glory of the homeland and the happiness of the nation. High school students are part of Indonesia’s demographic bonus defined as the productive age population. With a large demographic bonus, the concept of loving the homeland to achieve glory must be well internalized. This study aims to identify students perceptions and consumption behavior towardtowards national products in the personal care and perfume sectors and examine how the practice of consuming domestic products internalizes the value of love for the homeland. The study was conducted in the SMA Komplek Surabaya environment (Jalan Kusuma Bangsa and Wijaya Kusuma) with informants from SMAN 1, SMAN 2, SMAN 5, SMAN 6, and SMAN 9 using a descriptive qualitative approach through short interviews with 12 students. The results of the study indicate that although students have a positive attitude toward Indonesian-made products, the consistency of their use is still low due to the influence of brand image, perceived quality, and social media. These findings emphasize the need for participatory education and contextual digital communication to foster a sense of patriotism while simultaneously strengthening the values ​​of the third principle of Pancasila through economic behavior that supports national industrial independence.

Abstract: Recent advances in cultural psychology elucidated a number of cultural differences in diverse psychological characteristics and behaviors from perceptions, and economic decisions to religiosity. Also, quantum models of cognition and decision making have been developed to mathematically characterize perceptions, and human judgement and decision making. This study proposes cultural quantum modelling approaches to cultural psychology and neuroscience, by utilizing the mathematical model of quantum cognition and decisions in psychology, economics, and decision science. This approach may help better quantitatively rigorous understandings of cultural differences between Westerners and Easterners, Catholics and Protestants, and other cross-cultural variations in psychological and behavioral characteristics and normative principles of rationality.

Abstract: This review systematically explores the potential of the active inference framework in illuminating the cognitive mechanisms of decision-making within repeated games. Characterized by multi-round interactions and social uncertainty, repeated games more closely resemble real-world social scenarios, where the decision-making process involves interconnected cognitive components such as inference, policy selection, and learning. Unlike traditional reinforcement learning models, active inference, grounded in the free energy minimization principle, unifies perception, learning, planning, and action within a single generative model. Belief updating is achieved by minimizing variational free energy, while the exploration-exploitation dilemma is balanced by minimizing expected free energy. Formulated based on partially observable Markov decision processes, the framework naturally incorporates social uncertainty, and its hierarchical structure allows for simulating mentalizing processes, thereby offering a unified account of social decision-making. Future research can further validate its effectiveness through model simulation and behavioral fitting.

Abstract: Innovation adoption in primary sectors—agriculture, horticulture, forestry, and aquaculture—is essential for addressing pressing global challenges including climate change, resource degradation, and food security. However, a persistent gap exists between innovation potential and actual implementation, with many promising technologies failing to achieve widespread adoption despite substantial research investments. This paper presents the Extended Integrated Adoption Model Framework (EIAMF), a systemic approach that addresses critical gaps in adoption theory by integrating four quadrants: technologies, users, finance, and institutions. The EIAMF explicitly recognizes adoption as a systemic process requiring alignment across multiple dimensions. The framework’s distinctive contribution lies in its emphasis on inter-quadrant relationships, revealing how variables across different domains interact, compound, and cascade to create either enabling conditions or barriers. We demonstrate how the framework can enable practitioners to proactively identify potential adoption barriers early in the innovation development process by providing structured diagnostic protocols that reveal when barriers in multiple quadrants compound to create obstacles, when cascade effects amplify constraints across the system, and where strategic interventions can address multiple barriers simultaneously. We discuss theoretical contributions and practical implications for practitioners and policy designers, highlighting how the EIAMF provides stakeholders with a tool for designing more effective adoption strategies.

Abstract: In this paper, we evaluate the profitability for firms in Greece and Cyprus from 2005 to 2020. More specifically, we investigate the effect of non-accounting variables, which affect the daily life of companies, on the firms' level profitability. We seek to investigate the impact of corruption, unemployment, part-time employment, and R&D on the performance of companies that can help managers by giving them more information and serving them in future decision making. We show that these variables do not have a large effect on the firm-level profitability of these two countries, which is largely influenced by profit margin and other interaction variables, such as profit margin on asset turnover ratio and equity multiplier. Based on the results, we see that companies should put more emphasis on R&D and strengthen the labor market by reducing any negative speculation in the country.

Abstract: Consumer preferences for beef are increasingly driven by a desire for hygienic and nutritious meat with excellent organoleptic qualities. This paper investigates the impact of cattle breed on key quality attributes—colour, marbling, and tenderness—central to consumer choice. Six different bovine breeds were taken into consideration: German Red Pied, Piemontese, Chianina, Angus, Holstein and a Polish crossbreed. The muscle taken into consideration was the Longissimus thoracis et lumborum. Colorimetric assessments, marbling evaluations, fatty acid profiling, and tenderness measurements, were conducted on meat cuts from each breed. Results revealed that Chianina, Holstein, and the Polish crossbreed exhibited distinct colour characteristics, with Chianina displaying notably brighter meat. Angus emerged as the most marbled breed, while Chianina and Piemontese showed lower marbling. Total lipids content was correlated with visible marbling. Tenderness assessments identified Angus and Holstein as the most tender breeds. The study's findings contribute to a proposed grading scale for colour, marbling, and tenderness, offering potential labelling infographics to assist consumers in making informed choices based on individual preferences and needs. These insights underscore the importance of breed-specific information on labels to enhance consumer understanding and facilitate more informed purchasing decisions.

Abstract: This study critically examines the economic, reputational, and structural dimensions of chargeable journals within the open access (OA) publishing model, with a particular focus on Article Processing Charges (APCs). While open access increases visibility and accessibility of research, it shifts substantial financial and intellectual burdens onto authors, raising concerns about fairness and exploitation. Using a conceptual and thematic analysis of peer-reviewed literature from 2015 to 2025, the study highlights how commercial publishers capture disproportionate economic benefits, leverage prestige, and maintain structural control over scholarly communication. Findings reveal systemic inequities, including financial barriers for researchers from underfunded institutions and low- and middle-income countries, the rise of predatory publishing, and market-driven APC pricing structures. Despite these challenges, alternatives such as Diamond Open Access, institutional support, and policy reforms offer more equitable pathways. The study contributes to debates on scholarly equity and provides recommendations for more transparent, ethical, and inclusive publishing models.

Abstract: Anticipating disruptive technologies often reveals deep social divides in how individuals interpret change. This paper examines the psychological consequences of imagining an Artificial Intelligence (AI) revolution in the Democratic Republic of Congo (DRC), with particular attention to perceived behavioral control (PBC). Drawing on the Theory of Planned Behavior, we conceptualize PBC through two key dimensions: self-efficacy and locus of control. Using a randomized experimental design with treatment, placebo, and control conditions, participants were primed with narratives of AI disruption, after which shifts in their sense of agency were measured. Findings indicate that AI primes significantly reduced perceptions of controllability, especially among disadvantaged groups, while self-efficacy remained largely stable. Conversely, individuals with relative advantages, proxied by car ownership and male gender, demonstrated resilience, and in certain cases even a rebound effect, suggesting that access to material and symbolic resources protects against the disempowering effects of disruptive change. These results underscore a critical psychological cleavage: whereas advantaged participants are inclined to view AI as opportunity, marginalized participants experience it as an uncontrollable threat. The study contributes to debates on inequality, perceived behavioral control, and technology adoption by revealing how social class moderates psychological responses to anticipated technological transformations. Policy implications emphasize that reducing inequality requires not only digital infrastructure and skill-building, but also the cultivation of psychological resources such as Locus of control, inclusion, and self-efficacy to mitigate the risk of AI amplifying existing divides.

Abstract: This paper is devoted to the sustainable management problem of a multipolar world. The description of a multipolar world as a global organizational system is given. For the first time in the literature, all elements of the global organizational system mathematical model are specified in terms of a multipolar world order where civilizations act as agents. The peculiarities of viability conditions for such a global socio-ecological-economic system are indicated. The solvability conditions of the sustainable management problem under multipolarity are identified.

Abstract: This review critically examines training and testing strategies in youth athletes, focusing on growth, maturation, and specialization. Early specialization, occurring in athletes younger than 12 years, is associated with training volumes above 15 h/week and with dropout rates of nearly 30–35% before age 15. The aim was to evaluate age- and maturity-specific protocols, identify limitations of adult-derived models, and provide evidence-informed recommendations. A structured search in PubMed, Scopus, and Web of Science between 2000 and 2025 identified 325 articles; after screening, 87 were included, with 19 addressing maturation, 16 testing, 18 training load, 15 motor skill development, and 19 specialization. Results show that growth variability, particularly during peak height velocity (10–12 y girls; 12–14 y boys), increases injury risk two- to threefold. Standardized adult testing (e.g., VO₂max treadmill) is often unreliable in adolescents, while field-based protocols such as the Yo-Yo IR1, CMJ, and FMS provide safer and more valid alternatives. Flexible periodization with deload cycles of 1–2 weeks and monitoring through RPE, GPS, and HR improves both safety and adaptation. Diversified sport participation further reduces burnout and supports long-term outcomes. Integrating maturity status into training and testing optimizes development, minimizes injury, and sustains lifelong athlete engagement.