Hasil untuk "Commerce"

Sang Hyun Kim, Hyunsun Park

Yulin Fang, I. Qureshi, Heshan Sun et al.

Elizabeth E. Grandón, J. Pearson

R. Rahayu, J. Day

The aim of this study is to investigate those factors that influence SMEs in developing countries in adopting e-commerce. This study is motivated by the fact that the adoption of e-commerce by SMEs, especially in developing countries, is still very far behind the adoption by large companies. Yet to be able to survive in the new economic era, which is the information era; businesses, including SMEs, are forced to adopt e-commerce. Non-adopters will be left behind by the adopters. In addition, studies regarding e-commerce adoption by SMEs are rarely found. Therefore, the results of this study provide a timely understanding of e-commerce adoption by SMEs in developing countries. The model developed in this study is based on the TOE framework. Eleven variables are proposed as the factors that influence SMEs in adopting of e-commerce. These are organized into four groups, namely: technological factors, organizational factors, environmental factors and individual factors. Based on a survey of 292 Indonesian SMEs, it was found that perceived benefits, technology readiness, owners’ innovativeness, owners’ IT ability and owners’ IT experience are the determinant factors that influence Indonesian SMEs in their adopting e-commerce.

Li Tuobang

Against the backdrop of e-commerce restructuring consumption patterns, last-mile delivery stations have substantially fulfilled the function of community retail distribution. However, the current tax system only levies a low labor service tax on delivery fees, resulting in a tax contribution from the massive circulating goods value that is significantly lower than that of retail supermarkets of equivalent scale. This disparity not only triggers local tax base erosion but also fosters unfair competition. Based on the "substance over form" principle, this paper proposes a tax rate calculation method using "delivery fee plus insurance premium" as the base, corrected through "goods value conversion." This method aims to align the substantive tax burden of e-commerce with that of community retail at the terminal stage, effectively internalizing the high negative externalities of delivery stations through fiscal instruments, addressing E-commerce Deterritorialization.

Tian Tang, Zhixing Tian, Zhenyu Zhu et al.

Query and product relevance prediction is a critical component for ensuring a smooth user experience in e-commerce search. Traditional studies mainly focus on BERT-based models to assess the semantic relevance between queries and products. However, the discriminative paradigm and limited knowledge capacity of these approaches restrict their ability to comprehend the relevance between queries and products fully. With the rapid advancement of Large Language Models (LLMs), recent research has begun to explore their application to industrial search systems, as LLMs provide extensive world knowledge and flexible optimization for reasoning processes. Nonetheless, directly leveraging LLMs for relevance prediction tasks introduces new challenges, including a high demand for data quality, the necessity for meticulous optimization of reasoning processes, and an optimistic bias that can result in over-recall. To overcome the above problems, this paper proposes a novel framework called the LLM-based RElevance Framework (LREF) aimed at enhancing e-commerce search relevance. The framework comprises three main stages: supervised fine-tuning (SFT) with Data Selection, Multiple Chain of Thought (Multi-CoT) tuning, and Direct Preference Optimization (DPO) for de-biasing. We evaluate the performance of the framework through a series of offline experiments on large-scale real-world datasets, as well as online A/B testing. The results indicate significant improvements in both offline and online metrics. Ultimately, the model was deployed in a well-known e-commerce application, yielding substantial commercial benefits.

Yipeng Zhang, Hongju Yu, Aritra Mandal et al.

Item information, such as titles and attributes, is essential for effective user engagement in e-commerce. However, manual or semi-manual entry of structured item specifics often produces inconsistent quality, errors, and slow turnaround, especially for Customer-to-Customer sellers. Generating accurate descriptions directly from item images offers a promising alternative. Existing retrieval-based solutions address some of these issues but often miss fine-grained visual details and struggle with niche or specialized categories. We propose Optimized Preference-Based AI for Listings (OPAL), a framework for generating schema-compliant, high-quality item descriptions from images using a fine-tuned multimodal large language model (MLLM). OPAL addresses key challenges in multimodal e-commerce applications, including bridging modality gaps and capturing detailed contextual information. It introduces two data refinement methods: MLLM-Assisted Conformity Enhancement, which ensures alignment with structured schema requirements, and LLM-Assisted Contextual Understanding, which improves the capture of nuanced and fine-grained information from visual inputs. OPAL uses visual instruction tuning combined with direct preference optimization to fine-tune the MLLM, reducing hallucinations and improving robustness across different backbone architectures. We evaluate OPAL on real-world e-commerce datasets, showing that it consistently outperforms baseline methods in both description quality and schema completion rates. These results demonstrate that OPAL effectively bridges the gap between visual and textual modalities, delivering richer, more accurate, and more consistent item descriptions. This work advances automated listing optimization and supports scalable, high-quality content generation in e-commerce platforms.

Qianye Wu, Chengxuan Xia, Sixuan Tian

The rapid growth of e-commerce has led to an overwhelming volume of customer feedback, from product reviews to service interactions. Extracting meaningful insights from this data is crucial for businesses aiming to improve customer satisfaction and optimize decision-making. This paper presents an AI-driven sentiment analysis system designed specifically for e-commerce applications, balancing accuracy with interpretability. Our approach integrates traditional machine learning techniques with modern deep learning models, allowing for a more nuanced understanding of customer sentiment while ensuring transparency in decision-making. Experimental results show that our system outperforms standard sentiment analysis methods, achieving an accuracy of 89.7% on diverse, large-scale datasets. Beyond technical performance, real-world implementation across multiple e-commerce platforms demonstrates tangible improvements in customer engagement and operational efficiency. This study highlights both the potential and the challenges of applying AI to sentiment analysis in a commercial setting, offering insights into practical deployment strategies and areas for future refinement.

Zhanheng Nie, Chenghan Fu, Daoze Zhang et al.

Recent Multimodal Large Language Models (MLLMs) have significantly advanced e-commerce product understanding. However, they still face three challenges: (i) the modality imbalance induced by modality mixed training; (ii) underutilization of the intrinsic alignment relationships among visual and textual information within a product; and (iii) limited handling of noise in e-commerce multimodal data. To address these, we propose MOON2.0, a dynamic modality-balanced MultimOdal representation learning framework for e-commerce prOduct uNderstanding. It comprises: (1) a Modality-driven Mixture-of-Experts (MoE) that adaptively processes input samples by their modality composition, enabling Multimodal Joint Learning to mitigate the modality imbalance; (2) a Dual-level Alignment method to better leverage semantic alignment properties inside individual products; and (3) an MLLM-based Image-text Co-augmentation strategy that integrates textual enrichment with visual expansion, coupled with Dynamic Sample Filtering to improve training data quality. We further release MBE2.0, a co-augmented Multimodal representation Benchmark for E-commerce representation learning and evaluation at https://huggingface.co/datasets/ZHNie/MBE2.0. Experiments show that MOON2.0 delivers state-of-the-art zero-shot performance on MBE2.0 and multiple public datasets. Furthermore, attention-based heatmap visualization provides qualitative evidence of improved multimodal alignment of MOON2.0.

Anand A. Rajasekar, Praveen Tangarajan, Anjali Nainani et al.

The emergence of conversational assistants has fundamentally reshaped user interactions with digital platforms. This paper introduces Flippi-a cutting-edge, end-to-end conversational assistant powered by large language models (LLMs) and tailored for the e-commerce sector. Flippi addresses the challenges posed by the vast and often overwhelming product landscape, enabling customers to discover products more efficiently through natural language dialogue. By accommodating both objective and subjective user requirements, Flippi delivers a personalized shopping experience that surpasses traditional search methods. This paper details how Flippi interprets customer queries to provide precise product information, leveraging advanced NLP techniques such as Query Reformulation, Intent Detection, Retrieval-Augmented Generation (RAG), Named Entity Recognition (NER), and Context Reduction. Flippi's unique capability to identify and present the most attractive offers on an e-commerce site is also explored, demonstrating how it empowers users to make cost-effective decisions. Additionally, the paper discusses Flippi's comparative analysis features, which help users make informed choices by contrasting product features, prices, and other relevant attributes. The system's robust architecture is outlined, emphasizing its adaptability for integration across various e-commerce platforms and the technological choices underpinning its performance and accuracy. Finally, a comprehensive evaluation framework is presented, covering performance metrics, user satisfaction, and the impact on customer engagement and conversion rates. By bridging the convenience of online shopping with the personalized assistance traditionally found in physical stores, Flippi sets a new standard for customer satisfaction and engagement in the digital marketplace.

Zida Liang, Changfa Wu, Dunxian Huang et al.

Recommendation systems are essential tools in modern e-commerce, facilitating personalized user experiences by suggesting relevant products. Recent advancements in generative models have demonstrated potential in enhancing recommendation systems; however, these models often exhibit limitations in optimizing retrieval tasks, primarily due to their reliance on autoregressive generation mechanisms. Conventional approaches introduce sequential dependencies that impede efficient retrieval, as they are inherently unsuitable for generating multiple items without positional constraints within a single request session. To address these limitations, we propose TBGRecall, a framework integrating Next Session Prediction (NSP), designed to enhance generative retrieval models for e-commerce applications. Our framework reformulation involves partitioning input samples into multi-session sequences, where each sequence comprises a session token followed by a set of item tokens, and then further incorporate multiple optimizations tailored to the generative task in retrieval scenarios. In terms of training methodology, our pipeline integrates limited historical data pre-training with stochastic partial incremental training, significantly improving training efficiency and emphasizing the superiority of data recency over sheer data volume. Our extensive experiments, conducted on public benchmarks alongside a large-scale industrial dataset from TaoBao, show TBGRecall outperforms the state-of-the-art recommendation methods, and exhibits a clear scaling law trend. Ultimately, NSP represents a significant advancement in the effectiveness of generative recommendation systems for e-commerce applications.

Haiyang Yang, Qinye Xie, Qingheng Zhang et al.

Structured representation of product information is a major bottleneck for the efficiency of e-commerce platforms, especially in second-hand ecommerce platforms. Currently, most product information are organized based on manually curated product categories and attributes, which often fail to adequately cover long-tail products and do not align well with buyer preference. To address these problems, we propose \textbf{G}enerative \textbf{S}emantic \textbf{I}n\textbf{D}exings (GSID), a data-driven approach to generate product structured representations. GSID consists of two key components: (1) Pre-training on unstructured product metadata to learn in-domain semantic embeddings, and (2) Generating more effective semantic codes tailored for downstream product-centric applications. Extensive experiments are conducted to validate the effectiveness of GSID, and it has been successfully deployed on the real-world e-commerce platform, achieving promising results on product understanding and other downstream tasks.

Devi Yulia Rahmi, Fikri Alwi, Ratni Prima Lita et al.

Objective: The study aims to examine the effect of halal awareness, halal logo, religiosity, and price on consumers' intention to buy food products with a halal logo. This study also examines the effect of purchase intention on consumer purchase behaviour of food products with the halal logo Research Design & Methods: This study uses a quantitative method with 200 respondents who consume food products with the halal logo in West Sumatra. Data were analyzed using the PLS-SEM (Partial Least Square-Structural Equational Modeling) method. Findings: The results of the study show that the halal logo, religiosity, and price are significant for purchase intention. No significant effect was found on halal awareness and attitude towards purchase intention. Moreover, the study's results show that purchase intention significantly affects purchase behaviour. Implications and Recommendations: This research implies that consumers of food products labelled halal consider the halal logo on a product before consuming it, so food producers must try to sell products that have a halal logo Contribution & Value Added: This study addresses the existing literature by modifying the research model regarding the purchase behaviour of halal products. In practical, companies in the halal food industry should focus on maintaining and enhancing product quality, obtaining halal certification, and establishing a trusted halal label to foster consumer purchase intention and behavior.

Сергій Гарапко

Стаття розкриває актуальну проблему оцінювання стану й рівня економічної безпеки страхового ринку України в умовах воєнних викликів. Проаналізовано основні показники функціонування страховиків з позицій впливу на рівень економічної безпеки та ідентифікації існуючих загроз. На основі використання індексів Герфінадаля-Гіршмана, Тідемана-Хола, Розенблюта доведено, що страховий ринок залишається помірно концентрованим з низьким рівнем монополізації, проте загрозливою є ситуація високої концентрації на ринку страхування життя та його монополізації. Представлено аналітичні дослідження та авторські узагальнення щодо екзистенційних ризиків в умовах війни. Акцентовано, що одним із викликів сучасності є необхідність швидкого впровадження міжнародних принципів нагляду як основи забезпечення економічної безпеки страхового ринку в умовах війни.

عمار عبد الزهرة عبد الجبار, حيدر جاسم عبيد

يهدف البحث الحالي الى معرفة مدى تأثير هيكل رأس المال كمتغير مستقل في قيمة المنشأة كمتغير تابع، في عيّنة من شركات التصنيع العراقية والاردنية. اعتمد البحث على إجمالي الديون الى إجمالي الموجودات وإجمالي الديون طويلة الاجل اى إجمالي الموجودات وإجمالي الديون الى إجمالي حقوق الملكية كمؤشرات لقياس هيكل رأس المال في شركات التصنيع العراقية والاردنية، كما اعتمد البحث على معدل نمو القيمة السوقية كمؤشر لقياس قيمة المنشأة. وتم وضع مجموعة من الفرضيات الخاصة بالبحث. ومن أجل تحقيق أهداف البحث الحالي تم اختبار الفرضيات لعيّنة من الشركات الصناعية والمدرجة في سوق العراق للأوراق المالية وسوق عمّان للأوراق المالية للمدة (2011-2022)، وشملت العينة (9) شركات صناعية عراقية و(9) شركات صناعية اردنية. تم تحليل البيانات واختبار الفرضيات وفق اسلوب الانحدار البسيط والمتعدد باستخدام برنامج SPSS-26)) وتوصل البحث إلى مجموعة من الاستنتاجات كان أهمها ان هنالك تأثيراً مباشراً لهيكل رأس المال في قيمة المنشأة، أما اهم توصيات البحث فكانت ضرورة قيام الشركات الصناعية بالعراق، بتنويع هيكل رأس المال، ومحاولة تنمية مصادر التمويل المختلفة، وتحقيق التوازن بين المصادر الداخلية والخارجية للتمويل.

Tuhin Subhra De, Pranjal Singh, Alok Patel

In the context of developing nations like India, traditional business to business (B2B) commerce heavily relies on the establishment of robust relationships, trust, and credit arrangements between buyers and sellers. Consequently, ecommerce enterprises frequently. Established in 2016 with a vision to revolutionize trade in India through technology, Udaan is the countrys largest business to business ecommerce platform. Udaan operates across diverse product categories, including lifestyle, electronics, home and employ telecallers to cultivate buyer relationships, streamline order placement procedures, and promote special promotions. The accurate anticipation of buyer order placement behavior emerges as a pivotal factor for attaining sustainable growth, heightening competitiveness, and optimizing the efficiency of these telecallers. To address this challenge, we have employed an ensemble approach comprising XGBoost and a modified version of Poisson Gamma model to predict customer order patterns with precision. This paper provides an in-depth exploration of the strategic fusion of machine learning and an empirical Bayesian approach, bolstered by the judicious selection of pertinent features. This innovative approach has yielded a remarkable 3 times increase in customer order rates, show casing its potential for transformative impact in the ecommerce industry.

Houye Ji, Ye Tang, Zhaoxin Chen et al.

With the rapid development of the short video industry, traditional e-commerce has encountered a new paradigm, video-driven e-commerce, which leverages attractive videos for product showcases and provides both video and item services for users. Benefitting from the dynamic and visualized introduction of items,video-driven e-commerce has shown huge potential in stimulating consumer confidence and promoting sales. In this paper, we focus on the video retrieval task, facing the following challenges: (1) Howto handle the heterogeneities among users, items, and videos? (2)How to mine the complementarity between items and videos for better user understanding? In this paper, we first leverage the dual graph to model the co-existing of user-video and user-item interactions in video-driven e-commerce and innovatively reduce user preference understanding to a graph matching problem. To solve it, we further propose a novel bi-level Graph Matching Network(GMN), which mainly consists of node- and preference-level graph matching. Given a user, node-level graph matching aims to match videos and items, while preference-level graph matching aims to match multiple user preferences extracted from both videos and items. Then the proposed GMN can generate and improve user embedding by aggregating matched nodes or preferences from the dual graph in a bi-level manner. Comprehensive experiments show the superiority of the proposed GMN with significant improvements over state-of-the-art approaches (e.g., AUC+1.9% and CTR+7.15%). We have developed it on a well-known video-driven e-commerce platform, serving hundreds of millions of users every day

Pedro Herrero-Vidal, You-Lin Chen, Cris Liu et al.

We introduce VARM, variant relationship matcher strategy, to identify pairs of variant products in e-commerce catalogs. Traditional definitions of entity resolution are concerned with whether product mentions refer to the same underlying product. However, this fails to capture product relationships that are critical for e-commerce applications, such as having similar, but not identical, products listed on the same webpage or share reviews. Here, we formulate a new type of entity resolution in variant product relationships to capture these similar e-commerce product links. In contrast with the traditional definition, the new definition requires both identifying if two products are variant matches of each other and what are the attributes that vary between them. To satisfy these two requirements, we developed a strategy that leverages the strengths of both encoding and generative AI models. First, we construct a dataset that captures webpage product links, and therefore variant product relationships, to train an encoding LLM to predict variant matches for any given pair of products. Second, we use RAG prompted generative LLMs to extract variation and common attributes amongst groups of variant products. To validate our strategy, we evaluated model performance using real data from one of the world's leading e-commerce retailers. The results showed that our strategy outperforms alternative solutions and paves the way to exploiting these new type of product relationships.

Xinyi Ling, Hanwen Du, Bo Peng et al.

Leveraging multimodal data to drive breakthroughs in e-commerce applications through Multimodal Foundation Models (MFMs) is gaining increasing attention from the research community. However, there are significant challenges that hinder the optimal use of multimodal e-commerce data by foundation models: (1) the scarcity of large-scale, high-quality multimodal benchmark datasets; and (2) the lack of effective multimodal information integration methods. To address these challenges, in this paper, we introduce MMECInstruct, the first-ever, large-scale, and high-quality multimodal instruction dataset for e-commerce. We also develop CASLIE, a simple, lightweight, yet effective framework for integrating multimodal information for e-commerce. Leveraging MMECInstruct, we fine-tune a series of e-commerce MFMs within CASLIE, denoted as CASLIE models. Our comprehensive evaluation demonstrates that CASLIE models substantially outperform 5 categories of advanced baseline models in the in-domain evaluation. Moreover, CASLIE models show strong generalizability to out-of-domain settings. MMECInstruct and CASLIE models are publicly accessible through https://ninglab.github.io/CASLIE/.

Олег Микитин

Стаття присвячена дослідженню сучасних особливостей діагностування як ключового елементу системи управління (менеджменту) експортно-імпортною діяльністю підприємств в умовах турбулентності. Проведено огляд літератури для системного трактування понять «турбулентність», «діагностування експортно-імпортної діяльності». В статті охарактеризовано ключові аспекти діагностування як елементу системи управління експортно-імпортною діяльністю підприємств в умовах турбулентності. На основі огляду, аналізування, літературних джерел, систематизування наукових здобутків за проблемою діагностування експортно-імпортної діяльності підприємств (як елементу системи менеджменту) в умовах турбулентності запропоновано схему поетапного процесу діагностування експортно-імпортною діяльністю підприємств в умовах турбулентності.