Search Results (551)

With the rapid development of e-commerce, ensuring product quality on online platforms has become increasingly important, especially in developing countries where market regulations are still underdeveloped. By treating different sellers offering the same brand’s products as an industry, this study examines the impact of flagship store entry on online product quality by constructing a multiple period difference-in-difference model and conducts detailed empirical tests using full-category and large-span data from Taobao. The empirical results demonstrate that flagship store entry not only prompts the exit of incumbent sellers and deters potential new entrants due to the competition effect, but also facilitates the exit of low-quality sellers while attracting high-quality sellers as a result of a consumer-learning effect. Consequently, the overall quality of the industry is improved, and this effect is more pronounced in high-priced and durable goods industries. The findings of this study have important implications for market structure design and online quality governance in online marketplaces. Full article

Background: This study was performed to validate the addition of capacitive-based pressure sensors to an existing smart sock developed by the research team. This study focused on evaluating the accuracy of soft robotic sensor (SRS) pressure data and its relationship with laboratory-grade Kistler force plates in collecting ground force reaction data. Methods: Nineteen participants performed walking trials while wearing the smart sock with and without shoes. Data was collected simultaneously with the sock and the force plates for each gait phase including foot-flat, heel-off, and midstance. The correlation between the smart sock and force plates was analyzed using Pearson’s correlation coefficient and R-squared values. Results: Overall, the strength of the relationship between the smart sock’s SRS data and the vertical ground reaction force (GRF) data from the force plates showed a strong correlation, with a Pearson’s correlation coefficient of 0.85 ± 0.1; 86% of the trials had a value higher than 0.75. The linear regression models also showed a strong correlation, with an R-squared value of 0.88 ± 0.12, which improved to 0.90 ± 0.07 when including a stretch-SRS for measuring ankle flexion. Conclusions: With these strong correlation results, there is potential for capacitive pressure sensors to be integrated into the proposed device and utilized in telehealth and sports performance applications. Full article

As diversity, equity, and inclusion (DEI) efforts in higher education face increasing political resistance, it is critical to understand how equity-centered institutional change is fostered, and who is transformed in the process. This study examines the intended and emergent outcomes of faculty professional development initiatives implemented through the Howard Hughes Medical Institute’s Inclusive Excellence (HHMI IE) program. We analyzed annual institutional reports and anonymous reflections from four public universities in a regional Peer Implementation Cluster (PIC), focusing on how change occurred at individual, community, and institutional levels. Guided by Kezar’s Shared Equity Leadership (SEL) framework, our thematic analysis revealed that while initiatives were designed to improve student outcomes through inclusive pedagogy, the most profound outcome was the development of equity consciousness among faculty. Defined as a growing awareness of systemic inequities and a sustained commitment to address them, equity consciousness emerged as the most frequently coded theme across all levels of change. These findings suggest that equity-centered faculty development can serve as a catalyst for institutional transformation, not only by shifting teaching practices but also by building distributed leadership and deeper organizational engagement with equity. This effort also emphasizes that documenting emergent outcomes is essential for recognizing the holistic impact of sustained institutional change. 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

The international trade in agricultural products is complex and diverse. Global buyers must diversify their import sources, while sellers must explore new market opportunities. In the past, there has been no analysis on how second-tier exporters, with a smaller market share compared to dominant exporters, interact in the same target market and within an existing trade market and what factors affect trade prices and market forces. Based on Vollrath’s revealed competitive advantage index framework, this study analyzes the global tangerine trade (HS08052100) and means of production from 2008 to 2021, performs clustering, and estimates the residual demand elasticities of two main second-tier exporting countries—South Africa and Morocco—in four major importing countries for empirical analysis. The results show that South African tangerines have a lower market share than Moroccan tangerines in the Netherlands, the United States, and the United Kingdom. However, all data indicate that the residual demand elasticity for the country’s products in the target markets is negative, indicating that South African exporters have market influence in all three markets and significantly affect the prices of Moroccan products in these markets. Unlike other studies that have focused on the ranking analysis of export indices, the novelty of this study is that it provides an oligopolistic framework based on agricultural value chain analysis, which can be used for many countries with limited export scales. The method proposed in this study is expected to help citrus traders to effectively find export markets by evaluating the remaining market niches using key market data and the prices of similar competitors in the same category. Full article

Specialty coffee commercialization has experienced a consistent upward trend over the past several years. The prevalence of specialty coffee consumption has increased considerably, particularly among younger demographics and people who engage in physical activities. Sellers are actively involved in the development of innovative formulas and modifications to maintain the competitiveness of their product in the market. Here, we propose a naturally infused caffeine drink with cascara extract as an alternative social beverage. This beverage was formulated using extracts derived from Arabica Ethiopia coffee beans and coffee cherry shells. The final cascara-infused caffeine drink comprises a 50% Ethiopian Arabica coffee infusion and a 50% coffee cherry shell infusion. This beverage is characterized by an average caffeine content of 0.28 mg/mL, a caffeic acid content of 0.24 mg/mL, and a chlorogenic acid content of 0.13 mg/mL. Furthermore, 100 mL of the cascara-infused coffee drink is enriched with polyphenol compounds at an amount of 80.6 mg of Gallic Acid Equivalents per liter (mg GAE/L), including 67.6 mg of catechin equivalent per liter (mg CAE/L) flavonoids. The formulation of the infused caffeine drink contains natural sugars such as glucose, sucrose, and fructose, in amounts of 0.17 mg/mL, 0.97 mg/mL, and 1.66 mg/mL, respectively. The developed procedure has the potential to enhance the coffee-sale market. Full article

In the housing sector emission reduction builds on a shift from fossil fuels to renewable energy sources and increasing the efficiency of energy usage, with heating playing a dominant role in comparison to that of electricity. For electricity production in the residential sector, research shows that different settings of (co-)ownership in renewables are linked to a greater tendency to invest in energy-efficient devices or to adopt more energy-conscious behaviours. The empirical analysis demonstrates that fully-fledged prosumers, i.e., consumers who have the option to choose between self-consumption and selling to third parties or the grid, exhibit a higher tendency to invest in energy efficiency and that only this group manifests a greater likelihood of engaging in conscious-energy consumption behaviour. This paper extends the analysis to include heating in the residential sector. The study conducted an ANCOVA based on a sample of 2585 German households. The findings show that, depending on the (co-)ownership setting, the willingness to invest and to adopt energy-efficient practices grows considerably. Consumer-sellers demonstrate the highest willingness to invest and adapt energy conscious behaviour. Furthermore, regarding heating in particular, self-consumers are also inclined to invest and engage in energy-savings behaviour. Full article

The study constructed a supply chain inventory model for sellers and buyers that integrates payment-time-dependent demand, product defects, misclassification risks, and carbon emission tax considerations. The model was designed to optimize payment time, replenishment time, and order quantities to maximize the seller’s profit per unit time. Theoretical analysis showed that profit exhibited joint concavity with respect to both payment time and replenishment time. An algorithm was also formulated to derive optimal solutions. Finally, numerical experiments and sensitivity analyses validated the model and offered practical insights for managing inventories involving imperfect products. Results indicated that higher responsiveness of demand to payment timing, greater demand coefficients, better product prices, and higher scrap values led to increased seller profits, while greater misclassification, credit default risks, and carbon tax rate reduced it. These insights help decision-makers select suitable parameter values for efficient operations. Full article

This paper considers the critical role of local markets in sustaining rural communities and forests through the trade of Boswellia serrata Roxb. gum-resin as a culturally significant non-timber forest product (NTFP). Despite its cultural significance in Hindu rituals, little is known about the market dynamics at the consumer end of the value chain. This is one of the first detailed studies on consumer behavior and seller economics of B. serrata gum-resin in temple contexts. Open-ended surveys with sellers and consumers reflect seller activities, incomes, and consumer perceptions within the markets, providing insights into the dynamics of the gum-resin value chain and the implications for sustainability. Challenges gum-resin sellers face are brought to light, with a notable struggle to secure a significant portion of the final product’s value. Consumer perceptions are identified as a pivotal aspect influencing this NTFP’s market dynamics. The study emphasizes the importance of understanding consumer demand and preferences in shaping market size and sustainability practices. The research advocates for establishing structured markets to enhance returns for harvesters and reduce costs for consumers. In providing insights into the socio-economic aspects of temple markets for B. serrata gum-resin, this study contributes to the understanding of NTFP value chains and their broader impact on the sustainability of forest-dwelling communities and forest ecosystems. The findings underscore the need for informed interventions and policy measures to address challenges, promote equitable practices, and ensure the long-term viability of NTFP-based economies. Full article

Background/Objectives: Traumatic brain injury (TBI) affects millions of people worldwide, with approximately 2.8 million cases occurring in the United States each year. These injuries may be mild, moderate, or severe based on intensity of impact. The damage caused by TBI results not only from the initial injury, but also from secondary damage due to oxidative stress. Oxidative stress is the increase in reactive oxygen and nitrogen species and the decrease in overall antioxidant capacity, which can lead to a loss of protein function. There is currently no treatment for TBI, only alleviation of symptoms. Glutathione, the most potent antioxidant in the brain, is capable of reducing oxidative damage. Methods: This study investigates the efficacy of gamma-glutamylcysteine ethyl ester (GCEE), a glutathione analog, as a post-therapeutic treatment option in moderate TBI using enzymatic analysis. Enzymatic analysis indicates that key metabolic enzymes of TBI samples treated with GCEE significantly increase in activity relative to traumatically brain injured rats treated with a saline treatment. Protein and gene expression of TBI samples treated with GCEE was also analyzed and compared to that of control and saline-treated samples. Results: Glutathione-related enzymes were found to be increased in GCEE-treated animals compared to saline, thereby showing an increase in antioxidant defense from gamma-glutamylcysteine ethyl ester. Conclusions: Results demonstrate GCEE as a promising post-therapeutic treatment for moderate TBI. Full article

In the rapidly evolving field of e-commerce, precise and efficient attribute extraction from product descriptions is crucial for enhancing search functionality, improving customer experience, and streamlining the listing process for sellers. This study proposes a large language model (LLM)-based approach for automated attribute extraction on Trendyol’s e-commerce platform. For comparison purposes, a deep learning (DL) model is also developed, leveraging a transformer-based architecture to efficiently identify explicit attributes. In contrast, the LLM, built on the Mistral architecture, demonstrates superior contextual understanding, enabling the extraction of both explicit and implicit attributes from unstructured text. The models are evaluated on an extensive dataset derived from Trendyol’s Turkish-language product catalog, using performance metrics such as precision, recall, and F1-score. Results indicate that the proposed LLM outperforms the DL model across most metrics, demonstrating superiority not only in direct single-model comparisons but also in average performance across all evaluated categories. This advantage is particularly evident in handling complex linguistic structures and diverse product descriptions. The system has been integrated into Trendyol’s platform with a scalable backend infrastructure, employing Kubernetes and Nvidia Triton Inference Server for efficient bulk processing and real-time attribute suggestions during the product listing process. This study not only advances attribute extraction for Turkish-language e-commerce but also provides a scalable and efficient NLP-based solution applicable to large-scale marketplaces. The findings offer critical insights into the trade-offs between accuracy and computational efficiency in large-scale multilingual NLP applications, contributing to the broader field of automated product classification and information retrieval in e-commerce ecosystems. Full article

Managing risk, matching resources efficiently, and ensuring fair allocation are fundamental challenges in both finance and decision-making processes. In many scenarios, participants contribute unequally to collective outcomes, raising the question of how to distribute costs, benefits, or opportunities in a justifiable and optimal manner. This paper applies the Shapley value—a solution concept from cooperative game theory—as a principled tool in the following two specific financial settings: first, in tax cooperation games; and second, in assignment markets. In tax cooperation games, we use the Shapley value to determine the equitable tax burden distribution among three firms, A, B, and C, which operate in two countries, Italy and Poland. Our model ensures that countries participating in coalitions face a lower degree of tax evasion compared to non-members, and that cooperating firms benefit from discounted tax liabilities. This structure incentivizes coalition formation and reveals the economic advantage of joint participation. In assignment markets, we use the Shapley value to find the optimal pairing in a four-buyers and four-sellers housing market. Our findings show that the Shapley value provides a rigorous framework for capturing the relative importance of participants in the coalition, leading to more balanced tax allocations and fairer market transactions. Our theoretical insights with computational techniques highlights the Shapley value’s effectiveness in addressing complex allocation challenges across financial management domains. Full article

Landslides are hazardous events that occur mainly in mountainous areas and cause substantial losses of various kinds worldwide; therefore, it is important to investigate them. In this study, a specific Machine Learning (ML) method was further analyzed due to the good results obtained in the previous stage of this research. The algorithm implemented is Extreme Gradient Boosting (XGBoost), which was used to evaluate the susceptibility to landslides recorded in the city of Cuenca (Ecuador) and its surroundings, generating the respective Landslide Susceptibility Maps (LSM). For the model implementation, a landslide inventory updated to 2019 was used and several sets from 15 available conditioning factors were considered, applying two different methods of random point sampling. Additionally, a hyperparameter tuning process of XGBoost has been employed in order to optimize the predictive and computational performance of each model. The results obtained were validated using AUC-ROC, F-Score and the degree of landslide coincidence adjustment at high and very high susceptibility levels, showing a good predictive capacity in most cases. The best results were obtained with the set of the six best conditioning factors previously determined, as it produced good values in validation metrics (AUC = 0.83; F-Score = 0.73) and a degree of coincidence of landslides in the high and very high susceptibility levels above 90%. The Wilcoxon text led to establishing significant differences between methods. These results show the need to perform susceptibility analyses with different data sets to determine the most appropriate ones. Full article

I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macroeconomic factors (MEF), including an inflation metric (CPI), US Treasury rates (10-yr), Gross Domestic Product (GDP), and portfolio size of central banks (ECB, FED). This set of parameters covers all the parties involved in a transaction (buyer, seller, and financing facility) while ignoring the intrinsic properties of each asset and encompassing local (inflation) and liquidity issues that may impede each transaction composing a market. The model here takes the point of view of a real estate trader who is interested in both the financing and the price of the transaction. Machine learning allows for the discrimination of two periods within the dataset. First, and up to 2015, I show that, although the US Treasury rates level is the most critical parameter to explain the change of house-price indices, other macroeconomic factors (e.g., consumer price indices) are essential to include in the modeling because they highlight the degree of openness of an economy and the contribution of the economic context to price changes. Second, and for the period from 2015 to today, I show that, to explain the most recent price evolution, it is necessary to include the datasets of the European Central Bank programs, which were designed to support the economy since the beginning of the 2010s. Indeed, unconventional policies of central banks may have allowed some institutional investors to arbitrage between real estate returns and other bond markets (sovereign and corporate). Finally, to assess the models’ relative performances, I performed various sensitivity tests, which tend to constrain the possibilities of each approach for each need. I also show that some models can predict the evolution of prices over the next 4 quarters with uncertainties that outperform existing index uncertainties. Full article