Search Results (315)

Industrial tree plantations (ITPs) are increasingly recognized as a sustainable response to deforestation and the decline in native wood resources in the Philippines. This study presents LUNTIAN (Labor, UNiversity, Timber Investment, and Agent-based Nexus), an agent-based model that simulates an experimental ITP operation within a mountain forest managed by University of the Philippines Los Baños. The model integrates biophysical processes—such as tree growth, hydrology, and stand dynamics—with socio-economic components such as investment decision making based on risk preferences, employment allocation influenced by local labor availability, and informal harvesting behavior driven by job scarcity. These are complemented by institutional enforcement mechanisms such as forest patrolling, reflecting the complex interplay between financial incentives and rule compliance. To assess the model’s validity, its outputs were compared to those of the 3PG forest growth model, with results demonstrating alignment in growth trends and spatial distributions, thereby supporting LUNTIAN’s potential to represent key ecological dynamics. Sensitivity analysis identified investor earnings share and community member count as significant factors influencing net earnings and management costs. Parameter calibration using the Non-dominated Sorting Genetic Algorithm yielded an optimal configuration that ensured profitability for resource managers, investors, and community-hired laborers while minimizing unauthorized independent harvesting. Notably, even with continuous harvesting during a 17-year rotation, the final tree population increased by 55%. These findings illustrate the potential of LUNTIAN to support the exploration of sustainable ITP management strategies in the Philippines by offering a robust framework for analyzing complex social–ecological interactions. Full article

With consumers increasing in environmental awareness, manufacturers have integrated green R&D into their strategies, aiming to grasp the green market. However, manufacturers may be too bullish on the market potential of green products and maintain an optimistic attitude toward green R&D. Despite having an optimistic attitude, manufacturers often have no demand information advantage over downstream retailers due to their position in the supply chain, away from the market. It is worth exploring what impact optimistic green R&D in a sustainable supply chain with demand information asymmetry will have. Previous studies have not managed to reveal this. In this study, a stylized model is introduced to explore this question. The main findings are as follows: (1) optimistic green R&D increases the feasibility of the retailer sharing demand information, which facilitates information communication in the sustainable supply chain; (2) in most cases, optimistic green R&D does not bring higher profits for the manufacturer, yet is likely to allow the retailer to earn more, thereby resulting in a loss–win outcome; and (3) depending on the green R&D efficiency of the manufacturer and the consumer’s environmental awareness, optimistic green R&D may not generate higher environmental benefits. Full article

In this study, we utilize a unique quarterly dataset of non-GAAP earnings to re-examine the “informational” role of non-GAAP earnings from the perspective of value relevance and earnings predictability in the post-Reg G period. We find that non-GAAP earnings are more value relevant and can better predict future operating earnings of a firm compared to equivalent GAAP earnings. Additionally, we also find empirical evidence suggesting that the difference in the value relevance and earnings predictability between non-GAAP and equivalent GAAP earnings can vary across but cannot be completely mitigated by firm-level characteristics, such as the market value of equity, accruals quality, analyst coverage, and managerial ability of a firm. Moreover, our supplementary analysis reveals that the superior value relevance and predictive power of non-GAAP earnings persist even after the SEC’s release of the Compliance and Disclosure Interpretations (C&DI) in 2010. Overall, our empirical evidence suggests a superior “informational” role of non-GAAP earnings to equivalent GAAP earnings in terms of valuation and predictability on future operating performance in the post-Reg G period. Full article

China’s internal control framework follows the Committee of Sponsoring Organizations (COSO) framework, emphasizing enterprise risk management and encompassing financial reporting, operations, compliance, and strategies. The authors review research that uses the COSO-based Internal Control Index to assess internal control quality among all publicly listed firms in China. Unlike the binary classification of internal control weaknesses under the Sarbanes-Oxley Act Section 404, this continuous index captures more nuanced variations in internal control effectiveness and provides two key advantages over traditional assessment of internal control over financial reporting (ICFR). First, while financial reporting can enhance a firm’s monitoring and decision-support systems, the underlying information is determined by operations. Thus, internal control over operations has a greater impact on a firm’s performance than ICFR. While U.S.-based research argues that the effects of ICFR extend to operations, the COSO-based index includes operational controls, allowing for a more direct study of internal control effects. Second, many U.S. corporations fail to report internal control weaknesses, particularly during misstatement years. In contrast, the COSO-based index, compiled by independent scholars, avoids managerial incentives to withhold negative internal control information. Covering institutional background and research evidence from China, the authors survey a wide range of internal control studies related to various aspects of enterprise risk management, such as earnings quality, crash risk, stock liquidity, resource extraction, cash holdings, mergers and acquisitions, corporate innovation, receivable management, operational efficiency, tax avoidance, and diversification strategy. Full article

This study investigates whether internationally diversified firms substitute between accrual-based and real earnings management and examines how institutional supervision moderates this relationship. Drawing on a sample of Taiwanese firms listed on the Taiwan Stock Exchange from 2003 to 2016, we conduct regression analyses to test our hypothesis. We find that internationally diversified firms actively shift between accrual and real earnings management strategies depending on the constraints they face. Specifically, firms tend to rely more on accrual-based manipulation when information asymmetry is high and switch to real earnings management when accruals are more easily detected. We also show that stronger institutional supervision—measured by information transparency and investor protection—significantly curbs accrual-based earnings management. These findings reflect the higher volatility and agency problems associated with international operations, such as exposure to foreign risks and the distance between parent and subsidiary firms. By highlighting the conditions under which firms manage earnings and the supervisory mechanisms that constrain such behavior, this study offers practical insights for managers seeking to smooth earnings, investors aiming to evaluate firm transparency, and policymakers designing regulations to deter opportunistic financial reporting. Full article

This paper explores the effectiveness of an innovative FinTech risk-scoring model to predict the risk-appropriate return for short-term credit sales. The risk score serves to mitigate the information asymmetry between the seller of receivables (“Seller”) and the purchaser (“Funder”), at the same time providing an opportunity for the Funder to earn returns as well as to diversify its portfolio on a risk-appropriate basis. Selling receivables/credit to potential Funders at a risk-appropriate discount also helps Sellers to maintain their short-term financial liquidity and provide the necessary cash flow for operations and other immediate financial needs. We use 18,304 short-term credit-sale transactions between 23 April 2020 and 30 September 2022 from the private FinTech startup Crowdz and its Sustainability, Underwriting, Risk & Financial (SURF) risk-scoring system to analyze the risk/return relationship. The data includes risk scores for both Sellers of receivables (e.g., invoices) along with the Obligors (firms purchasing goods and services from the Seller) on those receivables and provides, as outputs, the mutual gains by the Sellers and the financial institutions or other investors funding the receivables (i.e., the Funders). Our analysis shows that the SURF Score is instrumental in mitigating the information asymmetry between the Sellers and the Funders and provides risk-appropriate periodic returns to the Funders across industries. A comparative analysis shows that the use of SURF technology generates higher risk-appropriate annualized internal rates of return (IRR) as compared to nonuse of the SURF Score risk-scoring system in these transactions. While Sellers and Funders enter into a win-win relationship (in the absence of a default), Sellers of credit instruments are not often scored based on the potential diversification by industry classification. Crowdz’s SURF technology does so and provides Funders with diversification opportunities through numerous invoices of differing amounts and SURF Scores in a wide range of industries. The analysis also shows that Sellers generally have lower financing stability as compared to the Obligors (payers on receivables), a fact captured in the SURF Scores. Full article

Timely and informed decision-making is essential for the successful execution of construction projects, where delays and cost overruns frequently pose significant risks. Earned value analysis (EVA) provides a robust, integrated framework that combines scope, schedule, and cost performance to support proactive project control. This study investigates the effectiveness of EVA as a decision-support tool by applying it to two real-life construction case studies. Key performance indicators, including Cost Performance Index (CPI), Schedule Performance Index (SPI), Estimate at Completion (EAC), and Estimate to Complete (ETC), are calculated and analyzed over a specific monitoring period. The analysis revealed a 15.36% cost savings and a 10.42% schedule improvement during the monitored period. By comparing planned and actual performance data, the study demonstrates how EVA enables early detection of deviations, thereby empowering project managers to implement timely corrective actions. The findings highlight EVA’s practical utility in improving project transparency, enhancing cost and schedule control, and supporting strategic decision-making in real-world construction environments. Full article

Income level is a key indicator of the standard of living and the economic efficiency of undertaken activities. This paper aims to evaluate the earnings of Polish dairy farmers compared to those in other economic sectors between 2005 and 2022. The analysis covered 1688 family-run farms that participated continuously in the FADN system throughout the study period, with particular emphasis on farms that expanded their dairy cow herds. The remuneration for the labour of farmers and their families was estimated ex post by subtracting the opportunity costs of owned land and capital from farm income. The alternative cost of engaging one’s own land was determined on the basis of actual rental prices for farmland occurring in the surveyed farm groups in the years analysed. This information is collected in the FADN system from which the studied group of farms was drawn. The basis for determining the alternative cost of involvement of own capital was the average interest rates on deposits for households, concluded for a period of 6 months to 1 year inclusive, reported by the National Bank of Poland. The analysed population was divided into seven groups based on the number of dairy cows maintained. The analysis focused on two three-year reference periods: 2005–2007 and 2020–2022. The results were compared with average salaries and wages in non-agricultural sectors of the economy. Structural changes in agriculture, increased productivity, and the expansion of production scale in dairy farms indicate a growing professionalisation of the sector. The rise in farm incomes during the analysed period contributed to a significant increase in the remuneration for farmers’ and their families’ labour. The highest growth in remuneration was observed among farms with the greatest production potential and scale. While in 2005–2007 the remuneration for labour in dairy farms was lower than in non-agricultural sectors, this situation changed in 2020–2022. During this latter period, the average remuneration for labour on dairy farms slightly exceeded the average salary and wages in other sectors of the economy. Full article

This study analyses the economic impact of tourism in Iceland, focusing on its contributions to GDP, employment, and foreign currency earnings. This study employs descriptive and comparative secondary data analysis based on available statistics and an extensive literature review to assess the sector’s development, resilience, and sustainability within global and national contexts. The findings confirm that tourism is a key pillar of Iceland’s economy, surpassing traditional export industries in value and generating significant employment opportunities. However, the sector’s volatility exposed during the COVID-19 pandemic and its dependence on international markets reveal structural vulnerabilities that threaten a sustainable future. Beyond economic considerations, this study critically engages with the growing pressures of over-tourism, seasonality, and environmental degradation, particularly in ecologically sensitive areas. Recent scholarship and policy shifts emphasise the need for sustainability indicators, equitable taxation mechanisms, and participatory governance to guide Iceland’s tourism development. This research highlights that balancing economic growth with environmental limits and community well-being is essential for building a more resilient and future-proof tourism model. These insights help inform policymakers, stakeholders, and researchers in aligning tourism strategies with sustainability and diversification goals. Full article

Objective: To characterize the nutritional status of the child population and to identify the most relevant determinants that could influence the early development of obesity and overweight. Methods: A descriptive cross-sectional study was conducted, recruiting a representative sample (698 schoolchildren, aged between 5 and 14, and obtaining information from a total of 414 households), using of anthropometric methods for nutritional assessment, the FIES scale, the KidMed index, and questions from the COSI survey. Results: Overall, 27% of the sample had healthy weight, 22.6% were overweight, and 18.1% were obese, with a higher prevalence among boys, and 86.7% of the sample did not adhere to dietary habits consistent with the Mediterranean diet. Food insecurity was present in 45.6% of the sample. A mean weight increase of 2.6 kg (95% CI: 1.0–4.3) was observed for each additional kilogram the child weighed at birth, 2.0 kg (95% CI: −0.2–4.3) in children living in households with some difficulty making ends meet, and 4.8 kg (95% CI: 1.3–8.3) in those from households reporting that they do not or barely manage to make ends meet. A statistically significant association was found with gross annual household income, with children from households earning less than EUR 12,000 having a 3.6 times higher risk of overweight/obesity compared to those from households earning more than EUR 36,000. Conclusions: The prevalence of obesity and overweight is considerably higher in low-income families and is significantly associated with family economic capacity. Continued epidemiological surveillance of these conditions and their relationship with social determinants is necessary. Full article

Background/Objectives: Students with specific learning disorders (SLDs) are at increased risk for emotional and academic difficulties. While teacher characteristics can influence student outcomes, few studies have examined the role of teacher personality in supporting students with SLDs. This study investigated whether teacher personality traits predicted student emotional well-being and academic achievement in a school-based intervention context. Methods: Participants were 64 students with SLDs (Mage = 13.28) nested within 21 teachers. Students completed measures of emotional well-being at baseline and post-intervention, and grade point average (GPA) was obtained from school records at the end of the school year. Teachers completed the Big Five Inventory mid-intervention. Two-level multilevel models were conducted in Mplus using maximum likelihood estimation with robust standard errors (MLR). The models controlled for student and teacher demographics, baseline emotional well-being, and the intervention group. Missing data were addressed using full information maximum likelihood (FIML). Results: Teacher female sex, higher neuroticism, and lower teaching experience were associated with higher student emotional well-being post-intervention. Follow-up analyses confirmed that teacher sex, neuroticism, and conscientiousness each explained substantial between-teacher variance. In the GPA model, student sex and teacher openness were significant predictors, with female students and students taught by more open teachers earning higher GPAs. Conclusions: Teacher personality traits, specifically neuroticism, conscientiousness, and openness, were associated with emotional and academic outcomes among students with SLDs. The findings highlight the importance of considering teacher characteristics in designing school-based interventions to support the development of learners with SLDs or other neurodevelopmental disorders. Full article

In today’s fast-paced technological environment, spotting emerging trends and anticipating future developments are important tasks in strategic planning and business decision-making. However, the volume and complexity of unstructured data containing relevant information make it very difficult for humans to effectively monitor, analyze, and identify inflection points by themselves. In this paper, we aim to prove the potential of integrating large language models (LLMs) with a novel finite state chain machine (FSCM) with output and graph databases to extract insights from unstructured data, specifically from earnings call transcripts of 40 top Technology Sector companies. The FSCM provides a modular, state-based approach for processing texts, enabling entity and relationship recognition. The extracted information is stored in a knowledge graph, further enabling semantic search and entity clustering. By leveraging this approach, we identified over 20,000 hidden (overlapping) trends and topics across various types. Our experiment on real-world datasets confirms the scalability and effectiveness of the method in extracting valuable knowledge from large datasets. The present work contributes to the field of Natural Language Processing (NLP) by showcasing the proposed method in addressing real-world business problems. The findings shed new light on current trends and challenges faced by tech companies, highlighting the potential for further integration with other NLP methods, leading to more robust and effective outcomes. Full article

Waste electrical and electronic equipment (WEEE) is a continuously growing concern, with issues arising from intensive resource consumption and the environmental and human impacts being caused by inadequate practices. The purpose of this study is to evaluate the impacts of end-of-life management options generated by Information Technology (IT) and telecommunication equipment in Romania during the period of 2018–2021 from a sustainability point of view, including environmental aspects, such as greenhouse gas emissions (GHG) and energy consumption, economic aspects, considering workforce earnings and revenues collected for the public budget, and social impacts through job creation. To achieve the main objective, a two-step methodology is used, with one step to determine the relevant quantities of WEEE generated by the subcategories of IT and telecommunication equipment, using the European Union’s WEEE Calculation Tool based on two approaches, WEEE reported in Eurostat (Scenario 1) and apparent consumption (Scenario 2), and a second step to evaluate the environmental, economic, and social impacts of the WEEE management system by applying Waste Reduction Model (US EPA WARM). Regarding the six WEEE categories evaluated, in both scenarios, Flat-Panel Displays is the category with the lowest environmental impact and highest economic and social benefits, while, on the opposite side, the Cathode-Ray Tube (CRT) category displays the highest environmental impact and lowest economic and social benefits. Full article

The present study focuses on how various firm characteristics influence their dividend payout policies. The study finds empirical evidence with regard to primarily two aspects of corporate dividend decisions—dividend increase and decrease, whose exploration is inadequate in the past literature. The random effect logistic regression has been considered in order to analyze the panel dataset from 2001–2002 to 2021–2022 including 3739 listed Indian firms. The empirical models are formatted based on the relevant dividend-related theories in the Indian context such as the residual theory, transaction cost theory, signalling theory, etc. Further, additional tests are conducted regarding the robustness of the reported results. The empirical results document that firm size, profitability, promoter holdings, cash holdings, and life cycle have a favourable influence on the propensity of both increasing and decreasing dividend payouts. In contrast, earnings volatility, leverage, and free cash flow reduce firms’ tendency to increase and decrease dividend payments. These results indicate that higher liquidity and ownership concentration provide firms with greater financial flexibility to adjust their dividend policies as per their prevailing opportunities. The findings of the study offer insightful information about how to arrange dividend policies with firm-specific traits which will be helpful for managers and investors to make better decisions. Full article

Corporate debt financing capacity is a critical factor for a firm’s survival and development. As climate change intensifies, examining the impact of climate risk on corporate debt financing is crucial for addressing climate change challenges. This study integrates data from the China Climate Risk Index (2007–2021) and A-share-listed companies on the Shanghai and Shenzhen stock exchanges, providing an in-depth analysis of the effects of climate risk on corporate debt financing and its underlying mechanisms. The research finds that climate risk significantly inhibits corporate debt financing, with a notable suppressive effect on both long-term and short-term debt financing. Mechanism tests indicate that climate risk suppresses corporate debt financing by weakening firm profitability, reducing asset turnover rates, increasing earnings uncertainty, and raising external financing costs. The moderating effect indicates that national climate risk responses mitigate the impact of climate risk on short-term debt financing while significantly suppressing long-term debt financing. Furthermore, corporate environmental information disclosure demonstrates a stronger inhibitory effect on short-term debt financing when climate risk is elevated. The study provides practical insights for firms and policymakers to address financing constraints under climate risks. Full article