Search Results (120)

The amount of protein and fat in raw milk determines its quality, value in the marketplace, and related payment to suppliers. Technicians use expensive specialized laboratory equipment to measure milk quality in specialized laboratories. The continuous quality monitoring of the milk supply in the supplier’s tanks enables the production of higher quality products, better milk supply chain optimization, and reduced milk waste. This paper presents an inexpensive AI-powered IoT sensor that continuously measures the protein and fat in the raw milk in the tanks of dairy farms, pickup trucks, and intermediate storage depots across any milk supply chain. The proposed sensor consists of an in-tank IoT device and related software components that run on any IoT platform. The in-tank IoT device quality incorporates a low-cost spectrometer and a microcontroller that can send milk supply measurements to any IoT platform via NB-IoT. The in-tank IoT device of the milk quality sensor is housed in a food-safe polypropylene container that allows its deployment in any milk tank. The IoT software component of the milk quality sensors uses a specialized machine learning (ML) algorithm to translate the spectrometry measurements into milk fat and protein measurements. The paper presents the design of an in-tank IoT sensor and the corresponding IoT software translation of the spectrometry measurements to protein and fat measurements. Moreover, it includes an experimental milk quality sensor evaluation that shows that sensor accuracy is ±0.14% for fat and ±0.07% for protein. Full article

In the context of global e-commerce platform supply chains dominated by Alibaba and Amazon, power reconfiguration among tripartite stakeholders (platforms, manufacturers, and retailers) remains a critical yet underexplored issue in supply chain contract design. To analyze the strategic interactions between platforms, manufacturers, and retailers, as well as how platforms select the contract format within a tripartite supply chain, this study proposes a Stackelberg game-theoretic framework incorporating participation constraints to compare fixed-fee and revenue-sharing contracts. The results demonstrate that revenue-sharing contracts significantly enhance supply chain efficiency by aligning incentives across members, leading to improved pricing and sales outcomes. However, this coordination benefit comes with reduced platform dominance, as revenue-sharing inherently redistributes power toward upstream and downstream partners. The analysis reveals a nuanced contract selection framework: given the revenue sharing rate, as the additional value increases, the optimal contract shifts from the mode RR to the mode RF, and ultimately to the mode FF. Notably, manufacturers and retailers exhibit a consistent preference for revenue-sharing contracts due to their favorable profit alignment properties, regardless of the platform’s value proposition. These findings may contribute to platform operations theory by (1) proposing a dynamic participation framework for contract analysis, (2) exploring value-based thresholds for contract transitions, and (3) examining the power-balancing effects of alternative contract formats. This study offers actionable insights for platform operators seeking to balance control and cooperation in their supply chain relationships, while providing manufacturers and retailers with strategic guidance for contract negotiations in platform-mediated markets. These findings are especially relevant for large e-commerce platforms and their partners managing the complexities of contemporary digital supply chains. Full article

Escherichia coli (E. coli) remains a major concern in poultry production due to its ability to incite foodborne illness and public health crisis, zoonotic potential, and the increasing prevalence of antibiotic-resistant strains. The contamination of poultry products with pathogenic E. coli, including avian pathogenic E. coli (APEC) and Shiga toxin-producing E. coli (STEC), presents risks at multiple stages of the poultry production cycle. The stages affected by E. coli range from, but are not limited to, the hatcheries to grow-out operations, slaughterhouses, and retail markets. While traditional detection methods such as culture-based assays and polymerase chain reaction (PCR) are well-established for E. coli detection in the food supply chain, their time, cost, and high infrastructure demands limit their suitability for rapid and field-based surveillance—hindering the ability for effective cessation and handling of outbreaks. Biosensors have emerged as powerful diagnostic tools that offer rapid, sensitive, and cost-effective alternatives for E. coli detection across various stages of poultry development and processing where detection is needed. This review examines current biosensor technologies designed to detect bacterial biomarkers, toxins, antibiotic resistance genes, and host immune response indicators for E. coli. Emphasis is placed on field-deployable and point-of-care (POC) platforms capable of integrating into poultry production environments. In addition to enhancing early pathogen detection, biosensors support antimicrobial resistance monitoring, facilitate integration into Hazard Analysis Critical Control Points (HACCP) systems, and align with the One Health framework by improving both animal and public health outcomes. Their strategic implementation in slaughterhouse quality control and marketplace testing can significantly reduce contamination risk and strengthen traceability in the poultry value chain. As biosensor technology continues to evolve, its application in E. coli surveillance is poised to play a transformative role in sustainable poultry production and global food safety. Full article

We extended real estate property listings on the online prop-tech platform. On the platform, the images were classified into the specified classes according to quality criteria. The necessary interventions were made by measuring the platform’s appropriateness level and increasing the advertisements’ visual appeal. A dataset of 3000 labeled images was utilized to compare different image classification models, including convolutional neural networks (CNNs), VGG16, residual networks (ResNets), and the LLaVA large language model (LLM). Each model’s performance and benchmark results were measured to identify the most effective method. In addition, the classification pipeline was expanded using image enhancement with contrastive unsupervised representation learning (CURL). This method assessed the impact of improved image quality on classification accuracy and the overall attractiveness of property listings. For each classification model, the performance was evaluated in binary conditions, with and without the application of CURL. The results showed that applying image enhancement with CURL enhances image quality and improves classification performance, particularly in models such as CNN and ResNet. The study results enable a better visual representation of real estate properties, resulting in higher-quality and engaging user listings. They also underscore the importance of combining advanced image processing techniques with classification models to optimize image presentation and categorization in the real estate industry. The extended platform offers information on the role of machine learning models and image enhancement methods in technology for the real estate industry. Also, an alternative solution that can be integrated into intelligent listing systems is proposed in this study to improve user experience and information accuracy. The platform proves that artificial intelligence and machine learning can be integrated for cloud-distributed services, paving the way for future innovations in the real estate sector and intelligent marketplace platforms. 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

(1) Live streaming commerce has refined online consumer engagement by fostering a real-time, socially enriched shopping environment. Despite its growing prominence, the role of social scene characteristics in consumer purchase decisions in live streaming remains insufficiently examined. (2) This study uses the Cognition-Affection-Conation (C-A-C) framework to examine how these social characteristics influence purchase intention through the mediating roles of emotional and cognitive trust, with privacy concerns as a moderator factor. The research employs Structural Equation Modeling (SEM) to test the hypothesis using data from 504 valid responses. (3) The results demonstrate that the characteristics of social scenes proposed in this study enhance consumer trust and positively impact purchase intention. Moreover, privacy concerns weaken the effect of interactivity atmosphere and scene immersion on emotion trust, though they do not weaken the effect of social identity on emotion trust. (4) These findings contribute to the theoretical understanding of live commerce by identifying the psychological mechanisms linking the social service scene to purchasing behavior. They also offer practical implications for platforms and merchants seeking to improve consumer engagement and trust in competitive digital marketplaces. This research highlights the importance of integrating social scenes and privacy management into the strategic design of live streaming commerce services. Full article

Lack of digitization in data sharing between enterprises and inspection solutions suppliers negatively affects cash flows between parties, which results in late payments that negatively affect the adoption of automatic inspection equipment. This paper contributes to improving the implementation of a new Inspection as a Service Business Model for deploying automatic inspection solutions using non-destructive inspection solutions, and to enhance workflows by integrating Blockchain and Smart Contracts. The Inspection as a Service offers flexible, cloud-based, or on-premise inspection solutions through the Marketplace, reducing upfront costs with a recurring service fee and automated payments. The marketplace platform supports automatic payment processes and facilitates industry adoption of IaaS solutions. The digital ecosystem offers improved capital expenditure and payback periods. It enhances communication, collaboration, data sharing, and payment processes through a subscription model. The case study demonstrates that the IaaS Business Model (on-premise or cloud) improves the economic feasibility of automatic non-destructive inspection solutions by lowering initial investments and enhancing return on investment and payback periods, even with higher operating costs. The analysis confirms the profitability and sustainability of IaaS Business Model over traditional one-fee selling by emphasizing its potential to improve operational performance and sustainability in manufacturing. The current proposal of automatic non-destructive solutions implements a new revenue model based on pay-per-use or volume, which makes it more financially viable to adopt this technology in industry. Full article

Despite free access to complete information regarding hotel quality and reference prices, consumers perceive significant price differences across different online platforms. We explore how perceived price dispersion on online travel agency platforms influences consumer purchase intention through mental account theory and propose a psychological mechanism explaining why consumers may tolerate and even embrace price discrepancies. Study 1 employs a scenario-based experiment that manipulates differing levels of price dispersion for the same hotel booking, demonstrating that higher PPD significantly amplifies perceived transaction utility and, in turn, acquisition utility. Study 2 corroborates these findings through an online survey with judgment sampling, highlighting that consumers—despite access to comprehensive OTA information—are often motivated, rather than deterred, by price discrepancies; multiple variable combinations were tested to ensure robust findings. This study challenges traditional marketing theories suggesting that price dispersion signals market unfairness and reduces consumers’ purchasing intention; instead, it mentally stimulates consumers. This perception enhances transaction and acquisition utility, positively impacting purchase intention. We also offer a robust model for mechanism study and provide insights for leveraging price dispersion as a cost-less promotional strategy, potentially increasing consumer engagement without additional marketing expenditure. We contribute to the literature by integrating the mental account theory into the context of online marketplaces and developing a price dispersion model with psychological utility in the consumer decision-making process. Full article

The transition of Sketchfab, a widely used platform for hosting and sharing 3D cultural heritage content, to Epic Games’ Fab marketplace has raised concerns within the cultural heritage community about the potential loss of years of work and thousands of 3D models, highlighting the risks of relying on commercial solutions for preservation and dissemination. This shift, together with the unprecedented investments by the European Commission on infrastructures for digitised heritage, present a critical opportunity to restart conversations about the future of 3D scholarship and infrastructures for cultural heritage. Using a mixed-methods approach, this paper analyses data from a literature review, two surveys, a focus group, and community responses to Sketchfab’s announced changes. Our findings reveal critical user requirements, including robust metadata and paradata for transparency, advanced analytical tools for scholarly use, flexible annotation systems, mechanisms for ownership, licensing, and citation, as well as community features for fostering engagement and recognition. This paper proposes models and key features for a new infrastructure and concludes by calling for collaborative efforts among stakeholders to develop a system that will ensure that 3D cultural heritage remains accessible, reusable, and meaningful in an ever-changing technological landscape. Full article

The global energy sector is rapidly changing due to decentralization, renewable energy integration, and digitalization, challenging traditional energy business models. This paper explores a startup concept for an AI-assisted regional marketplace for renewable energy, specifically suited for small- and medium-sized enterprises (SMEs). Driven by advancements in artificial intelligence (AI), big data, and Internet of Things (IoT) technology, this marketplace enables efficient energy trading through real-time supply–demand matching with dynamic pricing. Decentralized energy systems, such as solar and wind power, offer benefits like enhanced energy security but also present challenges in balancing supply and demand due to volatility. This research develops and validates an AI-based pricing model to optimize regional energy consumption and incentivize efficient usage to support grid stability. Through a SWOT analysis, this study highlights the strengths, weaknesses, opportunities, and threats of such a platform. Findings indicate that, with scalability, the AI-driven marketplace could significantly support the energy transition by increasing renewable energy use and therefore reducing carbon emissions. This paper presents a viable, scalable solution for SMEs aiming to participate in a resilient, sustainable, and localized energy market. Full article

The circular economy implementation in the built environment is hindered by the complexity of CE strategies and unique nature of the construction industry. Digital technologies have been explored as promising solutions to aid decision making and enable circular solutions in the architecture, engineering, and construction sector. The literature on both circular economy and digital technology fields has grown exponentially in the past few years, and there is a need for a comprehensive review of the state-of-the-art applications, integrations, potential, and limitations of digital technologies in the circular economy context. Through a systematic literature review, this study identified ten key digital technologies to enable circularity in the building sector: building information modeling, spatial data acquisition, artificial intelligence and machine learning, Internet of Things, blockchain, digital twin, augmented and virtual realities, digital platform/marketplace, material passports, and additive manufacturing and digital fabrication. In this study, we review current applications, discuss their integrations, match digital technology opportunities with circular economy barriers, and map the digital technologies applications along a building’s lifecycle. Blockchain and material passport technologies demonstrated potential to enable circular economy strategies throughout the whole building’s lifecycle, but their application remains limited in the construction industry. Building information modeling was found to be at the core of most technological integrations, but more research is needed to understand the impact of such integrations in supporting circular economy policies, standards, and assessment methods. Finally, collaborative research efforts are needed to unveil the risks of digitalization in the built environment, including risks concerning privacy and cybersecurity. Full article

The ceramic and glass industries, integral to the EU Emissions Trading System (EU ETS), face significant challenges in achieving decarbonization despite advancements in energy efficiency. The circular economy offers a promising pathway, emphasizing the reuse and recycling of waste materials into secondary raw materials (SRMs) to reduce resource consumption and emissions. This study investigates a standardized waste supply chain framework, developed collaboratively with stakeholders, tailored for the ceramic sector. The Waste Resource Platform (WRP) integrates Industry 4.0 paradigms, utilizing a modular, layered architecture and a process-centric design. The framework includes experimental tests and co-creation methodologies to refine a digital marketplace that connects stakeholders, facilitates SRM exchange, and fosters industrial symbiosis. The WRP demonstrates the potential for SRMs to replace virgin materials, reducing environmental impacts and production costs. It enhances supply chain transparency through digital traceability, promotes predictive material sourcing, and streamlines logistics via algorithmic optimization. Challenges such as regulatory gaps and quality standards are addressed through standardized processes, open data governance, and innovative algorithms. The WRP project advances circular economy goals in the ceramic sector, promoting waste reuse, industrial symbiosis, and supply chain resilience. Its standardized, open-access platform offers a scalable model for other industries, fostering sustainable practices and resource efficiency while addressing global climate targets. Full article

Academic publications from the Web of Science Core Collection on “e-business” and “artificial intelligence” (AI) are investigated to reveal the role of AI, extract latent themes and identify potential research topics. The proposed methodology includes relevant graphical representations (trends, co-occurrence networks, Sankey diagrams), sentiment analyses and latent topics identification. A renewed interest in these publications is evident post-2018, with a sharp increase in publications around 2020 that can be attributed to the COVID-19 pandemic. Chinese institutions dominate the collaboration network in e-business and AI. Keywords such as “business transformation”, “business value” and “e-business strategy” are prominent, contributing significantly to areas like “Operations Research & Management Science”. Additionally, the keyword “e-agribusiness” recently appears connected to “Environmental Sciences & Ecology”, indicating the application of e-business principles in sustainable practices. Although three sentiment analysis methods broadly agree on key trends, such as the rise in positive sentiment over time and the dominance of neutral sentiment, they differ in detail and focus. Custom analysis reveals more pronounced fluctuations, whereas VADER and TextBlob present steadier and more subdued patterns. Four well-balanced topics are identified with a coherence score of 0.66 using Latent Dirichlet Allocation, which is a probabilistic generative model designed to uncover hidden topics in large text corpora: Topic 1 (29.8%) highlights data-driven decision-making in e-business, focusing on AI, information sharing and technology-enabled business processes. Topic 2 (28.1%) explores AI and Machine Learning (ML) in web-based business, emphasizing customer service, innovation and workflow optimization. Topic 3 (23.6%) focuses on analytical methods for decision-making, using data modeling to enhance strategies, processes and sustainability. Topic 4 (18.5%) examines the semantic web, leveraging ontologies and knowledge systems to improve intelligent systems and web platforms. New pathways such as voice assistance, augmented reality and dynamic marketplaces could further enhance e-business strategies. Full article

The rapid advancement and widespread adoption of artificial intelligence (AI) across diverse industries, including healthcare, finance, manufacturing, and retail, underscore the transformative potential of AI technologies. This necessitates the development of viable AI model marketplaces that facilitate the development, trading, and sharing of AI models across the pervasive industrial domains to harness and streamline their daily activities. These marketplaces act as centralized hubs, enabling stakeholders such as developers, data owners, brokers, and buyers to collaborate and exchange resources seamlessly. However, existing AI marketplaces often fail to address the demands of modern and next-generation application domains. Limitations in pricing models, standardization, and transparency hinder their efficiency, leading to a lack of scalability and user adoption. This paper aims to target researchers, industry professionals, and policymakers involved in AI development and deployment, providing actionable insights for designing robust, secure, and transparent AI marketplaces. By examining the evolving landscape of AI marketplaces, this paper identifies critical gaps in current practices, such as inadequate pricing schemes, insufficient standardization, and fragmented policy enforcement mechanisms. It further explores the AI model life-cycle, highlighting pricing, trading, tracking, security, and compliance challenges. This detailed analysis is intended for an audience with a foundational understanding of AI systems, marketplaces, and their operational ecosystems. The findings aim to inform stakeholders about the pressing need for innovation and customization in AI marketplaces while emphasizing the importance of balancing efficiency, security, and trust. This paper serves as a blueprint for the development of next-generation AI marketplaces that meet the demands of both current and future application domains, ensuring sustainable growth and widespread adoption. Full article

This study aims to understand how InsurTech has developed in Korea. To achieve this, we collected InsurTech-related news articles published in the Korean media over the past eight years. Using a relatedness analysis based on the TopicRank algorithm, a text-mining technique, we extracted the top keywords associated with InsurTech by year. The extracted keywords were analyzed and discussed in terms of development trends: which technologies gained prominence over time, who the key players were, and what solutions were introduced. The analysis revealed several key trends in InsurTech’s development in Korea. First, regarding changes in InsurTech technology, blockchain and the Internet of Things initially garnered significant attention, but artificial intelligence and big data later emerged as more critical technologies. Second, in terms of market players, government agencies and research institutes initially created forums for discussion, such as seminars to draw social attention to InsurTech. Over time, innovative startups entered the market, general agencies specializing in insurance brokerage gained prominence in the online marketplace, and the entry of Big Tech platforms further diversified the market. Finally, in terms of InsurTech-related insurance solutions, early attention was focused on developing new products. However, the trend gradually shifted toward improving the accessibility and convenience of existing insurance services. Additionally, asset management and payment settlement services—linked to financial services beyond traditional insurance—emerged, along with new concepts such as healthcare, which reshaped the approach to insurance services. This study contributes to understanding how InsurTech has evolved by identifying key trends in emerging technologies, leading market players, and innovations in the insurance value chain. The Korean case provides insights that may help explore similar patterns in other countries. Full article