Collaboration in cooperatives helps farmers strengthen their economic position in dynamic agri-food markets. Unlike other types of businesses, agricultural cooperatives are user-owned, user-controlled, and user-benefitting enterprises. Their dual nature as business enterprises and social groups of members complicates performance evaluation. This study attempts to bridge the gap by developing a micro-level conceptual framework for benchmarking agricultural cooperatives. Based on a systematic literature review of 77 empirical studies published in 1987–2025 and thematic analysis, the authors propose an eight-dimensional conceptual framework encompassing competitive, financial, educational, efficiency, environmental, governance, operational, and social performance indicators. The review reveals that existing research prioritises financial indicators while overlooking cooperative-specific characteristics arising from their dualistic nature. The conceptual framework offers a structured conceptual basis for assessing the performance of agricultural cooperatives across sectors and countries. Although applying the framework is beyond the scope of this paper, the authors highlight prospective indicators for future empirical work and practical implementation.

This study investigates corporate failure among financial service institutions listed on the Lusaka Securities Exchange (LUSE), examining the role of financial performance and regulatory oversight. The study data were collected from six (6) LUSE-listed financial institutions, namely, Zambia National Commercial Bank (Zanaco Bank Plc), Absa Bank Plc, Standard Chartered Bank Plc, Investrust Bank Plc, Madison Financial Services Plc, and Zambia Reinsurance Plc. The study finds that weak financial performance, inadequate governance, and regulatory non-compliance are significant predictors of corporate failure, exemplified by the collapse of Investrust Bank. Conversely, strong financial indicators, effective risk management, and robust regulatory oversight correlate with sustainable operations and firm value, as observed in Zanaco Plc and Standard Chartered Plc. The findings highlight the importance of proactive monitoring, risk-based regulation, and governance interventions to prevent financial distress. The study proposes a diagnostics model that will prevent financial institutions from failure. When the model is operationalized and adopted for use, it is envisaged that financial institutions to a larger extent will benefit from its early warning signals, like those used in the traffic lights—red, amber, and green. The red signals a financial institution in the intensive care unit (ICU), meaning that its collapse is invertible; amber signals a financial institution with moderate weaknesses in the variables that requires attention; and green signals a healthy financial institution requiring nurturing and continuous improvement to sustain that healthy condition.

Purpose This paper aims to investigate the impact of diaspora remittances on Nigeria’s monetary dynamics amid domestic capital constraints. It specifically examines how remittance inflows affect key financial indicators, producing potentially contrasting short- and long-run effects, particularly through changes in liquidity and credit availability, as well as unintended inflationary pressures. Design/methodology/approach This paper uses linear and nonlinear Autoregressive Distributed Lag (ARDL) models on annual data spanning 1980–2023. The use of these models enables a detailed analysis of the effects of diaspora remittances on two financial development indicators: credit to the private sector and broad money supply. This approach allows for a clear differentiation between long-run equilibrium relationships and short-run dynamic adjustments resulting from remittance inflows. Findings The empirical results confirm an evident temporal and asymmetric duality in the financial development-remittance nexus. Firstly, in the long run, remittance inflows exhibit a significant positive relationship with both private-sector credit and the broad money supply, confirming their role in financial deepening. However, the short-run dynamics reveal a more complex picture: positive shocks to remittances contemporaneously constrain private credit and money supply, suggesting initial frictions in financial intermediation. Furthermore, the analysis shows significant asymmetry: the effects of rising and falling remittance flows are not mirror images, and adverse shocks also exhibit distinct lagged detrimental impacts. This indicates that the monetary system reacts differently to the acceleration vs the deceleration of remittance inflows. Originality/value This paper complements the existing literature by providing empirical evidence on the asymmetric and temporal effects of remittances on Nigeria’s monetary dynamics. It highlights the dual nature of remittance impacts on Nigeria’s monetary policy, indicating how short-run shocks can diverge significantly from long-run benefits. It emphasises the need for synchronised monetary policy measures that mitigate short-run disruptions while leveraging the long-run advantages of remittance inflows.

In the global economy, traditional accounting-based ratios alone are often insufficient to fully explain firm performance, increasing the importance of complementary information sources such as sustainability and governance disclosures. In this context, environmental, social, and governance (ESG) indicators, together with corporate governance signals, have increasingly been recognized as important drivers of firm performance. However, the literature does not provide a clear and generalizable view on the impact of ESG indicators on profitability. This study aims to examine whether sustainability and corporate governance signals provide additional information value beyond traditional financial ratios in predicting ROE. To this end, two models were compared using a sample of 428 non-financial publicly traded companies operating in Turkey. The firm-level dataset was constructed using financial statements and independent audit disclosures obtained from the Turkish Public Disclosure Platform (KAP). Tree-based machine learning models were employed to capture potential nonlinear relationships and complex interactions between financial and non-financial indicators. Model performance was evaluated within a Bootstrapped Grouped Cross-Validation framework that considered firm-level dependency; the statistical reliability of performance differences was tested using bootstrap-based confidence intervals and matched tests. Among the evaluated models, Random Forest achieved the strongest overall predictive performance. In conclusion, this study demonstrates that sustainability and corporate governance disclosures provide statistically significant additional information value to ROE prediction. Due to the use of multiple algorithms, it contributes to the literature in a generalizable manner.

ABSTRACT The growing urgency of climate change, alongside global sustainable development initiatives, has brought environmental priorities to the forefront of corporate strategy. This study explores how narrative disclosures related to research and development (R&D) predict carbon performance in European industries with high R&D intensity. Guided by the natural resource‐based view (NRBV), the research examines how qualitative R&D narratives act as strategic tools for communicating innovation‐driven environmental strategies. We introduce a novel methodological approach for analyzing unstructured textual data using advanced machine learning (ML) models, including neural networks (NNs), support vector machines (SVMs), and random forests (RFs). Our results show that firms with extensive and positively framed R&D disclosures are more effective in managing carbon emissions and in progressing toward major sustainability targets such as the Paris Agreement and the EU Green Deal. The findings also reveal that regulation and innovation shape distinct patterns in narrative disclosures across sectors, particularly in technology and pharmaceuticals. Moreover, the tone and thematic focus of these narratives offer strategic insights that go beyond traditional financial indicators, effectively linking innovation with sustainability objectives. This research advances the corporate disclosure literature by deepening our understanding of how sustainability and innovation intersect, while also offering practical guidance for firms and policymakers.

This study investigates creative accounting practices and their effects on reported financial position, performance, and risk indicators in Romanian listed companies. Using a mixed research design, the analysis combines a perception-based survey of financial–accounting professionals with a scenario-based financial case study, allowing for a comparison between perceived and actual effects of discretionary accounting techniques. The survey results indicate that professionals perceive creative accounting practices as having a significant influence on financial reporting outcomes, particularly in areas characterized by high managerial discretion, such as provisions, depreciation policies, inventory valuation methods, asset revaluation, and capitalization of research and development expenditures. The empirical case study confirms that these techniques generate observable changes in key financial indicators; however, the magnitude and direction of their effects vary across accounting methods and reporting periods. A key contribution of this study lies in highlighting a discrepancy between perceived and measured effects of creative accounting. While practitioners accurately identify the accounting areas most exposed to discretion, the empirical results suggest that the financial impact of creative accounting practices is often more moderate and context-dependent than commonly assumed. In addition, a descriptive assessment of fraud risk indicators suggests that extensive use of discretionary accounting practices may be associated with elevated risk exposure, without constituting direct evidence of fraudulent behavior.

New developments and perspectives on financial crisis indicators: How can signal processing enrich existing financial crises indicators?

The financial performance of a region can serve as an indicator of the local government’s overall performance. One methode of analyzing such financial performance is by examining financial ratios based on the Regional Revenue an Expenditure Budget (APBD) that has been determined and implemented. This study aims to measure the financial performance of the local governments of the regencies and cities surrounding the New Capital City (IKN) in East Kalimantan Province during the periode 2020 – 2024. Several ratios used in the analysis of financial performace include Share and Growth, rhe Regional Financial Capability Maping, and the Financial Capability Index (IKK). Based on the financial performance analysis conducted over five years for the four regencies and cities supporting the New Capital City (IKN), the following results were obtained. The share ratio of Samarinda City during the five-year period 2020 – 2024 falls under the low category. The share ratios of Penajem Paser Utara Regency and Kutai Kartanegara Regency fall under the very low category. Meanwhile, Balikpapan City and Penajem Paser Utara Regency falls under the low category. Kutai Kartanegara Regency shows a moderate growth ratio, while Balikpapan City is categorized as very low. Based on the Share and Growth analysis within the Regional Financial Capability Maping, two regions are positioned in Quadrant IV (Samarinda City and Penajem Paser Utara Regency), one region in Quadrant II (Kutai Kartanegara Regency), and one region in Quadrant III (Balikpapan City). Based on the Financial Capability Index (IKK), three regions fall under the high financial capability category, while one region is categorized as moderate.

This study analyzes the implementation of fintech technology in online financing in Islamic banks, focusing on operational efficiency and compliance with sharia principles. Using secondary data from the February 2025 LPBBTI Statistics published by the Financial Services Authority (OJK), this study applies descriptive statistical analysis and trend analysis to assess the impact of fintech on the performance of Islamic banks. The results show that the implementation of fintech improves operational efficiency and expands access to financing services, despite the decline in some financial indicators. Challenges related to sharia compliance were also found, especially in fintech products that potentially involve riba and gharar. This study suggests the importance of a deeper study of sharia fintech regulations in Indonesia, strengthening the role and competence of the Sharia Supervisory Board to ensure sharia compliance in the implementation of fintech in Islamic banks.

Stock prices reflect firm value and serve as a key reference for investors’ decision-making in the capital market. This study examines the effect of the Loan to Deposit Ratio (LDR) and Return on Equity (ROE) on the stock prices of banking companies listed on the Indonesia Stock Exchange during the 2022–2024 period. Using a quantitative approach, the study applies purposive sampling to obtain 41 banking firms, resulting in 123 firm-year observations. Multiple linear regression analysis is employed using IBM SPSS 27. The results indicate that LDR has a positive and statistically significant effect on stock prices, suggesting that banks’ effectiveness in channeling third-party funds into credit acts as a positive signal to investors regarding liquidity management and income generation. Likewise, ROE shows a positive and significant influence on stock prices, indicating that higher profitability enhances investor confidence in management’s ability to generate returns from shareholders’ equity. These findings support signaling theory, which posits that financial performance indicators convey important information to the market and influence investor behavior. The study implies that liquidity and profitability ratios are fundamental considerations for investors in valuing banking stocks and for bank management in maintaining market credibility.

As economies worldwide confront the urgent need to transition toward low-carbon and socially inclusive development models, the interaction between green finance, sustainable entrepreneurship, and innovation ecosystems has become central to both academic debate and policy agendas. This article investigates how these dimensions jointly shape national trajectories across 30 developed and developing countries by combining key sustainability indicators, including the Green Growth Index, Global Green Finance Index, Global Innovation Index, ESG scores, and the number of unicorns. Using Principal Component Analysis (PCA), the study identifies two main latent dimensions integrated sustainability and technological dynamism and reveals a typology of sustainability-driven, tech-driven, and lagging economies. Countries such as Switzerland and Sweden demonstrate a strong convergence between ESG performance, green growth, and innovation capacity, whereas others, including the United States, India, and Kenya, exhibit more fragmented patterns. The findings highlight the critical mediating role of institutional quality in translating green financial flows and innovative potential into sustainable entrepreneurial outcomes. For policymakers, the results offer a comparative diagnostic tool to prioritise interventions in green finance infrastructure, ESG regulation, and ecosystem support, and to design more inclusive regional cooperation frameworks. The study also points to avenues for future research, including the integration of blue economy indicators and subnational data, as well as the use of causal modelling approaches, to further inform evidence-based strategies for sustainable and resilient development. Keywords: finance; sustainable entrepreneurship; ESG; innovation; green and blue economy; Principal Component Analysis (PCA)

The construction industry confronts persistent challenges related to inefficiencies, rework, and cost overruns, driving the need for advanced digital solutions like Building Information Modelling (BIM). This research appraises BIM clash detection's financial and operational benefits, emphasizing its pivotal role in improving project performance. Centered on case study projects, the research employs a comprehensive cost-benefit analysis framework, integrating quantitative data from BIM clash reports, project rework logs, and qualitative insights from stakeholder interviews. The analysis evaluates financial indicators such as Net Present Value (NPV), Benefit-Cost Ratio (BCR), and Return on Investment (ROI) to determine the viability of BIM clash detection. Findings reveal that implementing this technology lessens rework frequency, enhances project timelines, and fosters stakeholder communication and coordination. A significant reduction in errors and rework also ensures higher cost savings and more efficient resource utilization. The study utilizes advanced techniques like federated BIM modelling, sensitivity analyses, and scenario-based evaluations to simulate real-world conditions and quantify outcomes. Results confirm a positive NPV, a BCR greater than 1, and a high ROI, underscoring BIM clash detection's economic feasibility and long-term value. The research illustrates how this technology mitigates construction risks, improves stakeholder satisfaction, and ensures superior project delivery quality. Through its rigorous methodological approach and robust analysis, this research demonstrates the transformative potential of BIM in modern construction. It offers actionable insights for stakeholders seeking to enhance efficiency, reduce costs, and adopt innovative technologies to revolutionize project delivery and management processes.

Transit-Oriented Development is a crucial strategy for addressing urban sprawl and improving mobility, particularly in rapidly urbanizing regions like Indonesia. Transit-Oriented Development integrates high-density, mixed-use developments with public transportation infrastructure, offering sustainable urban growth solutions. However, despite its benefits, the financial feasibility of Transit-Oriented Development in emerging economies remains under-explored. This study assesses the financial viability of a Transit-Oriented Development project in Tangerang, Greater Jakarta, focusing on key financial indicators such as Capital Expenditure, Operational Expenditure, Internal Rate of Return, and Net Present Value. The study uses financial modeling, analyzing data from the project's feasibility reports and investment summaries. The results show positive Internal Rate of Return, and Net Present Value, indicating financial viability under base case assumptions. However, the analysis reveals sensitivity to changes in Capital Expenditure and revenue assumptions, with increased Capital Expenditure reducing profitability and extending the payback period. This research fills the gap in Transit-Oriented Development financial feasibility studies in developing economies, providing valuable insights for similar projects in Southeast Asia and beyond. The findings underscore the importance of effective financial planning and risk management in ensuring the long-term success and sustainability of Transit-Oriented Development projects, highlighting the need for strong public-private partnerships to mitigate financial risks.

Purpose: This study aims to analyze the influence of the implementation of Islamic accounting based on AAOIFI standards on the financial performance of Islamic banks in Qatar. Methodology: Financial performance is measured using three key indicators: Return on Assets (ROA), Return on Equity (ROE), and Net Profit Margin (NPM). The research data were collected from six leading Islamic banks in Qatar during the period of 2019 to 2023, employing a quantitative method and linear regression analysis to test the relationship between the level of compliance with Islamic accounting standards and financial performance. The AAOIFI compliance measurement instrument was developed through a Likert-scale questionnaire that was tested for validity and reliability. Findings: The results indicate that the implementation of Sharia accounting has a positive and significant effect on all three financial performance indicators. Specifically, every one-point increase in the AAOIFI compliance score raises ROA by 0.02%, ROE by 0.105%, and NPM by 0.215%. The regression model explains 38% to 47% of the variance in financial performance, suggesting that applying Sharia accounting standards plays a critical role in improving asset management, equity efficiency, and net profit generation in Islamic banks. Orginility: These findings reinforce the importance of integrating AAOIFI standards into Islamic banks’ financial reporting to enhance transparency, accountability, and competitiveness. This study provides practical implications for Islamic bank management and Islamic financial regulators, particularly in promoting the global harmonization of Sharia accounting standards. Furthermore, this research serves as a reference for future studies to examine Sharia governance aspects and other moderating variables.

This article presents the concept of a multidimensional accounting model as an evolutionary vector in the development of the Russian Accounting System (RAS) in the era of digital economic transformation. The authors argue that the limitations of the traditional two-dimensional accounting architecture based on the «debit-credit» plane are not due to imperfections in the double-entry principle itself, but rather in the implementation of this principle within an information space predominantly focused on financial indicators. Based on a critical examination of domestic and international scientific research, regulatory frameworks, and conceptual foundations of financial reporting, the article reveals the essence of the multidimensional accounting model as an n-dimensional coordinate system where each economic event is reflected through the prism of financial, tax, managerial, social, environmental, and risk-oriented dimensions. The paper substantiates that the consolidation of these dimensions in a unified information environment, implemented using OLAP technologies, data warehouses, and metadata systems, forms the foundation for enhancing accounting information reliability in the context of the Accounting Concept in Russia’s Market Economy and the Conceptual Framework of IFRS. The examples provided demonstrate how multidimensional representation of transactions eliminates the information gap between traditional financial accounting, requirements for integrated and ESG reporting, and risk management needs. The article identifies methodological, organizational, and regulatory barriers to implementing the multidimensional model in Russian accounting practice and formulates priority directions for further scientific research in standardizing multidimensional measurements and adapting the Russian Accounting System to the digital environment.

Introduction Financial markets operate as dynamic networks in which institutional cross-holdings shape the diffusion of information and the propagation of risk. Forecasting the evolution of stock information networks is critical for anticipating herding behavior and safeguarding systemic stability, yet remains challenging due to high-dimensional heterogeneity, structural non-stationarity, and the need for economically interpretable predictions. Methods Using a quarterly fund–stock holding panel from 2016 to 2024, we construct time-indexed bipartite fund–stock graphs and project them onto the stock layer. From these graphs, we compute two key network indicators: degree centralization (cen_d), capturing market-wide concentration, and residual density (den), reflecting firm-level anomalies. We then develop a large language model (LLM)–enhanced forecasting framework that transforms numeric time series and textual fund disclosures into promptable sequences, incorporates retrieval-augmented historical context, and performs multi-step forecasting of both cen_d and abnormal den spikes. Results Extensive experiments show that the proposed LLM-based framework significantly reduces mean absolute error and root mean square error, and improves directional accuracy, compared with ARIMA, Prophet, and Temporal Fusion Transformer benchmarks. Attention-weight analysis further indicates that the model assigns higher importance to historical quarters characterized by sharp fund co-movement or policy shocks. Discussion These findings demonstrate that LLM-driven time-series forecasting can provide early warnings of systemic risk and generate economically interpretable insights for investors and regulators. The results highlight the broader potential of language-informed graph forecasting as a new paradigm for financial market surveillance and policy design.

Toward sustainable carbon utilization: Integrated LCA-TEA assessment of carbon dioxide-derived polymers.

Abstract This study examines the impact of financial inclusion on key socio-economic outcomes, namely economic growth, financial stability, and income inequality, during the pre-COVID era. We develop a composite Financial Inclusion Index (FII) using a multidimensional framework encompassing indicators of availability, usage, and penetration dimensions. The analysis covers 60 developing countries from 2005 to 2019 and employs the two-step System Generalised Method of Moments (GMM) to address potential endogeneity and dynamic panel bias. The results reveal that financial inclusion significantly promotes economic growth, particularly by improving access to banking. However, it shows no statistically significant relationship with financial stability. In contrast, higher financial inclusion appears to widen income inequality, likely due to unequal access to financial services and uneven distribution of benefits. By applying a unified FII across three macroeconomic outcomes using a consistent sample and methodology, this study provides comprehensive insights into the multifaceted effects of financial inclusion in developing economies. The findings underscore the need for policies that enhance access to finance, strengthen financial literacy, and promote responsible lending to ensure that financial inclusion contributes to inclusive and stable economic development.

This study provides empirical evidence that parcel-level socioeconomic indices can be reliably inferred from urban form using deep learning. Building on a representation that links cadastral parcels with digital surface models, we train EfficientNet variants to predict return on investment (ROI)—used here as a representative parcel-level economic indicator—directly from volumetric form. Across more than 24,000 parcels in a North American city, the models exhibit stable convergence and consistent generalization, capturing systematic variations across residential, commercial, and industrial morphologies. Although errors increase in dense commercial cores—where financial volatility and vertical complexity are highest—predictions remain bounded and preserve spatial gradients of ROI. The learned embeddings further reveal coherent manifolds structured by building massing, footprint geometry, and block configuration, indicating that the model extracts underlying spatial principles that correlate with economic outcomes. These findings demonstrate that urban form encodes measurable signals of economic performance and establish morphological learning as a viable pathway for integrating financial, environmental, and socio-cultural indices into form-based urban analysis and design.

Telegram, along with WhatsApp and Signal, has become very popular due to its hybrid capabilities, including both instant private and public messaging, making it an effective tool for quickly broadcasting content to a wide audience. This article presents TGEconomicDataset, a new dataset containing more than 2.9 million messages from the most popular Russian-language Telegram channels in the field of economics, as well as synthetically generated labeled mixtures of these channels. These mixtures are specifically designed to model authorship change scenarios for testing various methods for solving the problem of continuous authentication, which is of particular interest due to the need for organizations and companies to rely on data posted on social media. The presented dataset is enriched with quotes of important financial instruments such as gold futures, the USD/RUB currency pair, BRENT oil, the dollar index (DXY), and bitcoin (BTC), synchronized with the message timestamps. A detailed joint analysis of the collected data is provided. In addition to the presented dataset, we publish the scripts used to collect the data, integrate the financial indicators, and generate the synthetic mixtures for the continuous authentication task, ensuring full reproducibility of the research.