Purpose This study aims to examine how the evolving European Union (EU) sustainability regulatory landscape, shaped by the Corporate Sustainability Reporting Directive (CSRD) and recent developments such as the Stop the Clock Directive and the Omnibus I Package, and embodying differing political, economic and institutional interpretations of sustainability, may influence the future configuration of the sustainability assurance market for both mandatory and voluntary adopters. Design/methodology/approach The paper adopts a scenario analysis approach, supported by an inductive content analysis of legislative texts related to CSRD transposition and the Omnibus I Package. Key variables, including the scope of authorised providers, the role of incumbent auditors, professional qualifications and training requirements, guide the construction of alternative scenarios. Findings For mandatory adopters, the analysis outlines three possible trajectories for the sustainability assurance market: (1) a monopoly of statutory auditors, (2) competitive coexistence between statutory auditors and independent assurance service providers and (3) thematic specialisation between financial and sustainability auditors. For voluntary adopters, the analysis adds (4), a fragmented and largely optional assurance market shaped by the Omnibus I framework. Practical implications The scenarios identify actionable regulatory levers that policymakers and oversight bodies can use to mitigate fragmentation risks and safeguard assurance quality under both mandatory and voluntary regimes. Social implications By clarifying how regulatory recalibration may affect sustainability assurance practices, the study informs stakeholders who rely on sustainability reports for accountability and decision-making. Originality/value This study presents a novel approach by employing scenario analysis to link the diversity of CSRD implementation with potential assurance market outcomes. By connecting expected divergence in CSRD transposition to broad political and sustainability dynamics within the EU, the study demonstrates how competing policy directions and differing emphases on the economic, social and environmental pillars shape the emerging assurance market.

Active lock-in options are a class of complex derivatives characterized by pronounced path dependence and optimal decision making features, and they possess significant application value in the design of structured financial products and risk management. This paper investigates the pricing of active lock-in call options under a stochastic volatility framework. The lock-in decision is formulated as an optimal stopping problem and is further reformulated as a partial differential equation with obstacle constraints. By introducing a linear complementarity problem formulation, the structural properties of the option value function and the optimal lock-in boundary are systematically characterized. From a numerical perspective, an IMEX time discretization scheme is employed to transform the continuous problem into a sequence of time-layered discrete complementarity systems. These systems are efficiently solved using the projected successive over relaxation (PSOR) algorithm. Numerical experiments are conducted to analyze the structural features and economic interpretations of the value function and the associated free boundary surface.

This study compares machine learning and econometric approaches for forecasting agricultural export values in volatile global markets, examining predictive accuracy and economic interpretability trade-offs. Monthly data from January 2014 to December 2023 were analyzed using five models: Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Hybrid ANN-LSTM, Ordinary Least Squares (OLS), and Autoregressive Distributed Lag (ARDL). Key predictors included durian, mangosteen, and longan export values/volumes, plus China's GDP. Performance evaluation used MAE, RMSE, MAPE, and R² metrics with systematic hyperparameter optimization through grid search and 5-fold cross-validation. ANN achieved the highest absolute accuracy (MAE: 1,684,667,401.55; RMSE: 2,602,671,952.28), while Hybrid ANN-LSTM delivered superior relative accuracy (MAPE: 1.58%). ARDL demonstrated exceptional explanatory power (R²=0.83) for structural economic relationships. China's GDP emerged as the strongest determinant across all models. Longan export value showed contrasting effects between approaches, positive in machine learning models versus negative in econometric models, reflecting different paradigmatic interpretations of market substitution dynamics. This research introduces the first comprehensive comparative framework integrating advanced hybrid neural networks with traditional econometric methods for multi-commodity agricultural forecasting, addressing cross-commodity substitution effects previously unexplored while offering complementary perspectives for both predictive accuracy and economic policy interpretation.

The article presents a comprehensive study and comparative analysis of the capabilities of modern generative artificial intelligence models in the context of their application for solving practical tasks in econometric modeling. The study focuses on models of various architectural types: the «advanced» versions with enhanced reasoning capabilities – Google Gemini 2.5 Pro and ChatGPT-5 Thinking + Study, as well as their optimized «light» versions – Google Gemini 2.5 Flash and the basic ChatGPT-5 model. The empirical basis of the study was built using real data from the Ukrainian residential real estate market, specifically a representative sample of 100 properties, including both quantitative and qualitative variables. The experimental methodology involved the sequential execution of the full cycle of econometric research: preliminary data processing, exploratory analysis and visualization, construction of a multifactor linear regression model, diagnostics for multicollinearity and heteroscedasticity, calculation of elasticity indicators for economic interpretation, as well as testing the predictive capabilities of the model on a test sample. The verification of results obtained using generative artificial intelligence models was carried out by comparing them with benchmark calculations manually performed in the MS Excel environment. The results of the experiment revealed a significant difference in the performance of the examined models. It was found that Pro/Thinking class models (Gemini 2.5 Pro, ChatGPT-5 Thinking) demonstrate absolute mathematical accuracy, correctly calculating regression coefficients, the coefficient of determination, the F-statistic, and indicators of average and marginal efficiency. In contrast, the basic and «light» versions of the models (Gemini 2.5 Flash, ChatGPT-5) showed a tendency toward critical errors, including hallucinations in the form of generating fictitious data, loss of context when processing large datasets, and an inability to independently validate input information. A common weakness was also identified across all tested models in tasks requiring qualitative classification of heteroskedasticity types, as well as a tendency to ignore macro indicators in favor of microanalysis of individual variables. Based on the obtained data, it was concluded that at the current stage of development, generative artificial intelligence cannot fully replace humans; however, «advanced» models can be effectively used as an auxiliary tool for automating routine operations, writing code, and preliminary data processing, provided that the results are verified by a specialist.

This study develops and evaluates a machine learning framework for hourly EUR/USD directional forecasting that emphasizes temporal alignment, economic interpretability, and out-of-sample validation. Despite extensive research on algorithmic trading strategies, a critical disconnect persists between reported classification accuracy and actual economic profitability, often arising from methodological issues related to temporal misalignment between predictions, positions, and realized returns. Employing hourly EUR/USD data spanning 2005 to 2020, this research implements a logistic regression classifier with simple price-based features evaluated through walk-forward validation. The model achieves approximately 58.5 percent mean out-of-sample directional accuracy across multiple validation folds, demonstrating statistically stable predictive performance. Translation of probabilistic forecasts into trading positions occurs through a confidence-based long-short strategy that exploits bidirectional price movements while remaining inactive during periods of low prediction certainty. Under strict temporal alignment ensuring causal consistency between information availability and return realization, the machine learning strategy generates positive cumulative returns and superior risk-adjusted performance compared to passive buy-and-hold benchmarks. The buy-and-hold strategy experiences severe drawdowns and terminal cumulative returns of approximately negative 19 percent, while the machine learning approach maintains positive terminal returns of approximately 12 percent with substantially improved downside protection across heterogeneous market regimes including the 2008 financial crisis and European sovereign debt crisis.

This study builds a reproducible detector of earnings manipulation in Iraqi banks using a bank-year panel from 2010 to 2024 sourced from audited annual reports, IFRS 9 credit risk notes, Iraq Stock Exchange disclosures, and Central Bank of Iraq publications. The feature set aligns with banking mechanics discretionary loan loss provisioning residuals scaled by lagged loans, a three-year smoothing index between changes in NPL and provisions, asset growth, fee share dynamics, and leverage changes. The label flags the top quintile of discretionary provisioning within each year to focus on relative deviations. Data are winsorized within year, standardized on the training sample, and split chronologically into training 2010–2021 and testing 2022–2024. Two classifiers are compared a class-weighted logistic regression and a class-weighted SVM. Evaluation uses ROC-AUC, PR-AUC, F1, accuracy, and Brier score, with thresholds tuned on validation folds and probabilities calibrated. Results show that the SVM delivers stronger ranking and better operating tradeoffs than the logistic baseline when inputs are standardized and the decision threshold targets screening objectives. Out-of-sample gains appear in ROC-AUC and PR-AUC with a lower Brier score. Confusion matrices confirm higher specificity and controlled false alarms at useful recall. SHAP analysis validates economic interpretability delta leverage, asset growth, and DLLP drive the score, followed by fee share changes and smoothing. The framework supports an audit workflow that screens bank-years, routes alerts to document-level review of allowance movements and write-offs, and updates models annually with rolling windows while preserving time integrity and comparability.

This paper examines whether information-theoretic complexity measures enhance industry-group return forecasting and portfolio construction within a machine-learning framework. Using daily data for 25 U.S. GICS industry groups spanning more than three decades, we augment gradient-boosted decision tree models with Shannon entropy and fuzzy entropy computed from recent return dynamics. Models are estimated at weekly, monthly, and quarterly horizons using a strictly causal rolling-window design and translated into two economically interpretable allocation rules, a maximum-profit strategy and a minimum-risk strategy. Results show that the top performing strategy, the weekly maximum-profit model augmented with Shannon entropy, achieves an accumulated return exceeding 30,000%, substantially outperforming both the baseline model and the fuzzy-entropy variant. On monthly and quarterly horizons, entropy and fuzzy entropy generate smaller but robust improvements by maintaining lower volatility and better downside protection. Industry allocations display stable and economically interpretable patterns, profit-oriented strategies concentrate primarily in cyclical and growth-sensitive industries such as semiconductors, automobiles, technology hardware, banks, and energy, while minimum-risk strategies consistently favor defensive industries including utilities, food, beverage and tobacco, real estate, and consumer staples. Overall, the results demonstrate that entropy-based complexity measures improve both economic performance and interpretability, yielding industry-rotation strategies that are simultaneously more profitable, more stable, and more transparent.

This study aimed to analyze the impact of the digital economy and artificial intelligence (AI) on GDP growth in 10 developed and developing countries during the period 2010–2024. It was based on the hypothesis that increased digitalization and AI investments promote sustainable economic growth by improving national productivity and efficiency, in accordance with modern technological growth theory, which links digital innovation to economic development. The study used tablet data comprising 150 observations, which were analyzed using fixed- and random-effects models, controlling for traditional variables such as employment, human capital, and investment. The results showed that the Digitalization Indicators (DIGI) had a significant positive impact on growth (fixed: 0.003479, p < 0.01; random: 0.003325, p < 0.01), and that investment in AI also had a significant positive impact (fixed: 0.063695, p < 0.05; random: 0.066548, p < 0.05). In contrast, workforce size had a limited impact, while education and human capital emerged as key drivers of sustainable growth (Constant: 0.003257, p < 0.01; Random: 0.003264, p < 0.01). The inclusion of dummy variables further differentiated between developed and developing countries in the random-effects model, reinforcing the economic interpretation of the findings. The study suggests that integrating digitalization, education, and investment in artificial intelligence is an effective strategy for promoting sustainable economic growth, while emphasizing the importance of workforce skills development to maximize its impact.

This study examines optimal investment and reinsurance strategies for two competing insurers who are concerned with their relative performance. Each insurer can purchase reinsurance and invest in a financial market consisting of one risk‐free asset and one risky asset, with the risky asset’s price modeled using the Heston local‐stochastic volatility (HLSV) model, which combines the characteristics of both the CEV and Heston models. When optimizing strategies under an exponential utility function, an analytical solution is not attainable due to the complex nonlinearity of the resulting partial differential equation. To address this, we employ a dual method, Legendre transformation, and an asymptotic expansion technique to obtain an approximate solution considering only the slow‐varying volatility factor. The analysis is conducted under two premium calculation frameworks: the variance premium principle in the first part and the expected value principle in the second part. Finally, we complement the theoretical findings with numerical studies and provide economic interpretations for the optimal reinsurance strategies derived under both principles.

The article examines the current role of the economic interpretation of tax law in the context of the GAAR and SAAR anti-abuse clauses. The first part presents how this interpretative approach is presently applied in the practice of administrative courts. The second part considers the possibility of its further use. According to the Author, although the scope of the economic interpretation has been limited by anti-abuse regulations, it remains a useful tool for assessing the economic rationality of a taxpayer’s actions in situations where anti-abuse clauses do not apply.

Aim. To substantiate the role of Living Labs as a source of relevant, multidimensional databases for the functioning of decision support systems (DSS) in agrotechnological decision-making within modern farming systems. Methods. The abstract–logical method for theoretical generalization and the formulation of conclusions; structural–functional analysis to identify the role and place of Living Labs in ensu­ring the functioning of an interactive DSS; and comparative and synthesis methods. Results. It is shown that the effectiveness of such systems is largely determined by the quality, structure, and contextual relevance of the data on which economic calculations and managerial recommendations are based. The key characteristics of data generated within Living Labs are identified, including their practical orientation, multidimensionality, spatial and temporal linkage, and reflection of real production, resource, and organizational constraints of farms. The functional role of Living Labs in supporting the economic model of an interactive DSS is revealed, ensuring the transition from primary production data to a substantiated selection of agrotechnological alternatives. The feasibility of integrating agrobio­logical, economic, environmental, and socio-organizational data into a unified information framework for assessing efficiency and risks is justified. An approach to structuring Living Lab databases by key categories is proposed, and their economic interpretation within decision support systems is defined. It is demonstrated that the use of Living Lab data enhances the adaptability of the DSS economic model through a feedback mechanism that allows the refinement of cost parameters, performance indicators, and risk estimates based on actual results of production seasons. It is substantiated that a network-based organization of Living Labs creates conditions for developing typical profiles of agrotechnological alternatives and increasing the reproducibility of managerial decisions in farms with different organizational structures and resource endowments. Conclusions. The obtained results can be used to further develop interactive decision support systems aimed at improving the economic efficiency and sustainability of modern farming systems.

The article analyzes the economic effects of the impact of artificial intelligence technologies on the global air transport industry in the context of the digital transformation of the international aviation market and the growing need for increased safety. The relevance of the research is determined by the dynamic expansion of global air traffic, the increasing complexity of international aviation logistics, rising operational costs, and the strategic shift of aviation enterprises toward datadriven decisionmaking and technological innovation. In this context, AI is considered as a strategic economic resource that reshapes business models, management practices, and competitive behavior in the global aviation sector. The methodological framework of the study is based on a combination of system analysis, comparative analysis, statistical generalization, economic interpretation of empirical data, and a structuralfunctional approach. The application of comparative analysis allows for the identification of differences between traditional management approaches and AIbased solutions in the aviation industry, while statistical generalization provides an assessment of global trends and investment dynamics related to the implementation of AI technologies. The research results demonstrate that AI optimizes air traffic management systems, reduces fuel consumption and maintenance costs, improves the accuracy of forecasting and operational planning, enhances passenger service personalization, and increases the adaptability of aviation enterprises to external challenges such as weather disruptions, market volatility, and fluctuations in global demand. At the same time, the study identifies a number of critical risks associated with AI integration, including data protection issues, cybersecurity threats, algorithmic bias, technological dependence, and regulatory constraints that may limit the speed and effectiveness of digital adoption in international aviation markets. The practical value of the article lies in providing scientifically substantiated conclusions regarding the economic feasibility and strategic advantages of AI implementation in international aviation business practices. The results of the study may be used by airline managers, airport authorities, and policymakers to develop effective digital transformation strategies, improve regulatory frameworks, minimize technological risks, and enhance the resilience, sustainability, and global competitiveness of the air transportation industry.

The article investigates the relationship between population welfare and key macroeconomic indicators in the context of modern global markets. Population welfare is measured by gross domestic product per capita, while explanatory variables include total GDP, GDP growth rates, the share of exports of goods and services in GDP, the industrial sector’s contribution to GDP, and unemployment rates. The empirical framework of the study is based on statistical data for 2024 covering a sample of twelve countries representing different models of economic development. The research highlights and practically implements statistical analysis methods widely used in applied market research, including correlation analysis and cluster analysis. These methods make it possible to identify similarities and structural differences between national economies and to assess the explanatory power of traditional macroeconomic indicators. The results reveal a high degree of heterogeneity in the relationships between GDP per capita and the selected macroeconomic factors. In particular, the analysis demonstrates that export orientation, industrial structure, and shortterm economic growth rates do not necessarily determine a high level of population welfare in a crosscountry comparison. Special attention is devoted to Norway as an economy with a unique structural configuration and exceptionally high income levels. The findings show that Norway exhibits weak linear correlations between GDP per capita and most macroeconomic variables, which explains its atypical position in the cluster analysis results. This confirms that standard statistical models applied to modern market analysis may have limited explanatory capacity when structural, institutional, and resourcebased characteristics of national economies are not taken into account. These methods make it possible to identify similarities and structural differences between national economies and to assess the explanatory power of traditional macroeconomic indicators. The results reveal a high degree of heterogeneity in the relationships between GDP per capita and the selected macroeconomic factors. In particular, the analysis demonstrates that export orientation, industrial structure, and shortterm economic growth rates do not necessarily determine a high level of population welfare in a crosscountry comparison. Special attention is devoted to Norway as an economy with a unique structural configuration and exceptionally high income levels. The findings show that Norway exhibits weak linear correlations between GDP per capita and most macroeconomic variables, which explains its atypical position in the cluster analysis results. This confirms that standard statistical models applied to modern market analysis may have limited explanatory capacity when structural, institutional, and resourcebased characteristics of national economies are not taken into account. The study emphasizes the importance of combining classical statistical tools with a contextual economic interpretation in comparative economic research and applied market analysis.

The research aims to measure and analyze the impact of the economic failure trilogy (poverty, unemployment, and corruption) on the migration of Iraqi talent during the period (2006-2023). To achieve the research objectives and prove its hypothesis, the researchers adopted an approach thatcombines descriptive and analytical methods to investigate the nature of the relationship between the economic failure trilogy and the migration of Iraqi talent. The inductive approach based on economic measurement was used by applying the autoregressive variable-lag (ARDL) model to measure the impact of the economic failure trilogy on the migration of Iraqi talent during the period (2006-2023), based on (Eviews.9). The long-term results also showed a direct and significant relationship between talent migration and each of the rates of corruption, unemployment, and poverty. It was found that a one-unit increase in the corruption rate leads to an increase in talent migration by (0.680), which reflects the impact of corruption on the deterioration of the institutional environment and the decline in fair job opportunities, thus pushing talent to emigrate. The results also showed that a rise in the unemployment rate leads to an increase in brain drain by 0.053, which is consistent with the economic vision that views unemployment as a primary driver of skilled migration in search of better job opportunities. The results also revealed that a one-unit increase in the poverty rate leads to an increase in brain drain by (0.203), in light of deteriorating living conditions and the desire to improve the standard of living. These long-term equilibrium relationships confirm the validity of the research hypothesis and support the economic interpretation of the brain drain phenomenon. Among the most important recommendationsof the research is the provision of a set of strategies that can help break the vicious cycle of economic failure. This can be achieved by moving towards structural reforms in economic sectors, in addition to developing oversight and accountability systems to reduce the rampant corruption in the country, and developing programs to provide job opportunities.

Introduction . Due to temporary disability of employees, Russian Federation annually loses about 4% of GDP. A significant issue for the national economics and occupational health is the problem of presenteeism – attending work while being ill – which poses dangers to the employer’s financial well-being and the health of the country’s working population. However, the phenomenon of presenteeism remains largely unexplored worldwide. The significance of the issue of presenteeism is added by the varying results obtained through different assessment methods. Lack of understanding of their frequency, correlation and influence on the choice of approaches hinders both inter-research comparisons and the creation of unified strategies and recommendations to prevent and reduce economic loss associated with presenteeism. The purpose of the study is to systematize data on the prevalence, assessment methods, and economic burden of presenteeism and analyze their regional characteristics. Materials and methods . The systematic review was prepared in accordance with the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The literature search was performed using PubMed, Scopus, and eLibrary databases. The review includes 84 original articles published in English and Russian between January 2020 and June 2025. Results . The undertaken systematic review has identified a number of key trends and methodological challenges in the research on presenteeism. There is a pronounced regional peculiarity, particularly in European studies, which focus on economic aspects and financial loss. The prevalence of the WPAI questionnaire in global research has been confirmed, while in Asian countries (especially Japan) the WFun scale, reflecting cultural peculiarities, is widely used. There is also a discrepancy in the approach to sampling: the focus on specific nosologies prevails in Western countries, whereas in China and Brazil, certain areas of employee activity are more frequently explored. The thematic analysis shows a focus on pain syndromes, while among the occupation groups, medical workers prevail. An extreme heterogeneity of quantitative result presentation turned out to be the major methodological challenge, significantly complicating comparisons and meta-analyses. This indicates an urgent need to develop uniform reporting standards to improve data validity and comparability. Conclusion . The literature review has identified significant methodological challenges in the research on presenteeism, requiring unification of approaches to its assessment and economic interpretation. The study has proposed the most significant areas for further research, including the development of consensus documents for standardizing assessment methods and data presentation, cross-validation of existing scales, unification of approaches to economic evaluation, expansion of the range of studied nosologies, and the creation of clinical recommendations and guidelines for practical applications to effectively manage presenteeism.

Today, sustainability extends beyond its traditional environmental and economic interpretations and has become a key issue within human resource management as well. The long-term success and competitiveness of organizations increasingly rely on their ability to retain, develop, and motivate employees, with special emphasis on effectively managing generational differences. This study aims to investigate how the intentional development of managerial tools can support sustainable human resource practices, focusing on the distinct motivational drivers and value systems of various generations—Baby Boomers and Generations X, Y, Z, and Alpha. Worldwide trends show that employees' expectations regarding recruitment, selection, career development, and retention are becoming more diverse. Our research seeks to identify both the divergences and commonalities among these generational groups, as well as the strategies managers can apply to address their heterogeneous needs. To achieve this, the study employs a mixed-methods approach: it combines an extensive literature review with empirical data collection examining managerial motivation practices and their perceived effectiveness. The findings indicate that sustainable HR policy is grounded in the integration of generation-specific motivational techniques into organizational culture, alongside an adaptive, value-oriented reinterpretation of managerial roles. The study’s practical relevance lies in its recommendations for developing a managerial toolkit that strengthens intergenerational collaboration and supports the long-term enhancement of employee engagement, satisfaction, and performance.

IntroductionHorizon scanning (HS) systematically identifies emerging health technologies to support future decision-making. However, HS methods face challenges due to inconsistent terminology and lack of reporting standards. This hinders dissemination and increases research waste. Unlike PRISMA for evidence synthesis, no reporting guidelines exist to support HS. This work proposed a preliminary reporting checklist.MethodsIn July 2024, a working group comprising HS analysts, evidence synthesis researchers, and information specialists was established at the Innovation Observatory (IO), the UK’s national HS organization. The group convened three times to define objectives, refine methods, and develop the reporting checklist. In the first meeting, we outlined a prototype by adapting the PRISMA Extension for Scoping Reviews (PRISMA-ScR). We internally validated it on 25 IO HS reports (2017 to 2024). The second meeting assessed the relevance of each checklist item following our internal validation. In the third meeting, additional items were proposed based on the validation’s results, and the group reached consensus on the final checklist.ResultsThe final checklist prototype comprised a total of 35 items of which 29 were essential and six optional. It introduced new key elements absent in PRISMA-ScR: “interest holder description” in the introduction, “horizon-scanning scope” in the methods, “technology characteristics” in the results and “political, economic, societal, technological, legal and environmental (PESTLE) interpretation” in the discussion. Additionally, the checklist was tailored to promote transparency, equity, fairness, and sustainability in signal detection processes over those characteristics in evidence synthesis, aligning with the goals of HS. The checklist is available to download in the Open Science Framework.ConclusionsOur checklist addressed a critical methodological gap, advancing HS research by promoting standardization and transparency of HS methods. By making the draft checklist available, we encourage HS practitioners to pilot it. Feedback is welcomed to contribute to the final checklist. Its adoption and endorsement will improve output visibility, ensuring this meets necessary methodological standards underpinning unbiased healthcare policy decision-making.

In the context of structural transformation of the agro-industrial complex and increasing external economic constraints, the formation of an effective institutional environment for innovation-driven and investment-oriented development of agriculture in Russia becomes critically important. The purpose of this study is to analyze the role of institutions of innovation and investment development in enhancing the investment attractiveness of the agricultural sector and to identify key mechanisms through which these institutions influence investment processes. The paper examines public and quasi-public development institutions, financial and non-financial support mechanisms for agricultural innovation, as well as instruments aimed at stimulating private investment in agriculture. The methodological framework of the research is based on institutional and systems approaches, complemented by methods of comparative analysis, generalization, and economic interpretation of statistical and regulatory data. The study demonstrates that institutions of innovation and investment development contribute to reducing investment risks, creating long-term incentives for technological modernization, developing agricultural innovation infrastructure, and improving access to financial resources for agricultural producers. Special attention is paid to the role of state programs, interest rate subsidy schemes, guarantee mechanisms, and regional development institutions. It is substantiated that the effectiveness of institutional impact largely depends on the coherence of agricultural, investment, and innovation policies, as well as on the quality of institutional design and implementation. The findings indicate that further development and improvement of innovation and investment institutions is a key factor in increasing the investment attractiveness of Russian agriculture, accelerating technological renewal, and ensuring sustainable socio-economic development of rural areas in the Russian Federation.

<p>This study examined how Nigerians perceive inflation and how these perceptions shape their spending habits, with a focus on residents of Umuahia City. Using a descriptive-correlational research design, data were gathered from 384 respondents selected through the Raosoft sample size calculator at a 95% confidence level. A pilot test conducted with 30 participants produced a reliability coefficient of 0.814, confirming the instrument’s internal consistency. The findings showed that respondents’ perceptions of inflation were strongly influenced by prior experiences with rising prices, which made them more cautious and financially restrained. Social interaction also played a major role, as individuals relied on peer discussions, media reports, and online platforms to interpret ongoing price changes. Cognitive interpretations were shaped by personal struggles, with many respondents linking inflation to governance issues and economic policies. In terms of purchase behavior, Nigerians increasingly compared prices, substituted cheaper alternatives for preferred brands, and significantly reduced or postponed non-essential purchases. A statistically significant relationship emerged between inflation perception and purchase behavior, demonstrating that stronger perceptions of inflation led consumers to adopt more cautious and needs-based spending patterns. Respondents also expressed concerns about government interventions, noting issues related to subsidy removal, transparency, and inadequate agricultural support. The study concluded that Nigerians’ spending habits are deeply shaped by their experiences, social information sources, and economic interpretations, resulting in more deliberate and survival-oriented consumption patterns. It recommends that government agencies improve transparency, strengthen communication on economic measures, and expand targeted interventions that stabilize essential goods. Consumer education programs and community-based financial awareness campaigns are also advised to help households make informed spending decisions during inflationary periods.</p><p><strong>JEL Classification: </strong>E31 – Price Level; Inflation; Deflation; D12 – Consumer Economics: Empirical Analysis; D84 – Expectations and Perceptions; E21 – Consumption, Saving, and Household Behavior<strong></strong></p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0857/a.php" alt="Hit counter" /></p>

In the article analyzing, organizing and summarizing scientific works, a comparison of legislative and extended economic interpretation of the concept of "accounting policy", which showed that the first has a narrower, normative nature and focuses on ensuring compliance with the requirements of state regulation, while the second considers accounting policies as a comprehensive development. The combination of these approaches makes it possible to form an effective accounting policy that will simultaneously meet the requirements of the legislation and meet the management needs of the enterprise. Particular attention is paid to the specifics of agricultural enterprises, with the specific features of the enterprise, such as sectoral specificity, seasonality of production and complexity of the organizational structure, directly determine the approaches to the formation of its accounting policy. It is established that the peculiarities of agricultural enterprises necessitate the adaptation of traditional methods of accounting to specific production processes. It is noted that the formation of a modern model of accounting policy of agricultural enterprise is an important element of ensuring the transparency and efficiency of its financial and economic activity. Unlike enterprises of other sectors of the economy, agricultural producers function in special conditions, which necessitates the development of such accounting policy, which not only would meet the requirements of legislation and accounting standards, but also reflect the industry specificity, providing information and analytical. It is emphasized that the current stage of development of the agricultural sector is characterized by the active introduction of digital technologies. Accordingly, this requires the adaptation of accounting policy to the calls of today, which provides: the use of automated accounting systems, which take into account the specifics of agricultural enterprises (BAS Agro Accounting, BAS Agro CME, IN-AGRO, Agribusiness ERP); integration of accounting with precision agriculture technologies (GPS-navigation, drones, sensors); construction of internal management reporting by cultures, industries, units); use of electronic document flow; Using Big Data and analytics to forecast financial results.