Hasil untuk "Commerce"

Dan J Kim, Donald L. Ferrin, H. Rao

Trust and satisfaction are essential ingredients for successful business relationships in business-to-consumer electronic commerce. Yet there is little research on trust and satisfaction in e-commerce that takes a longitudinal approach. Drawing on three primary bodies of literature, the theory of reasoned action, the extended valence framework, and expectation-confirmation theory, this study synthesizes a model of consumer trust and satisfaction in the context of e-commerce. The model considers not only how consumers formulate their prepurchase decisions, but also how they form their long-term relationships with the same website vendor by comparing their prepurchase expectations to their actual purchase outcome. The results indicate that trust directly and indirectly affects a consumer's purchase decision in combination with perceived risk and perceived benefit, and also that trust has a longer term impact on consumer e-loyalty through satisfaction. Thus, this study extends our understanding of consumer Internet transaction behavior as a three-fold (prepurchase, purchase, and postpurchase) process, and it recognizes the crucial, multiple roles that trust plays in this process. Implications for theory and practice as well as limitations and future directions are discussed.

Ting-Peng Liang, E. Turban

David Gefen

Hsin‐Hui Lin, Yi-Shun Wang

Sichen Zhao, Zhiming Xue, Yalun Qi et al.

Malicious bots pose a growing threat to e-commerce platforms by scraping data, hoarding inventory, and perpetrating fraud. Traditional bot mitigation techniques, including IP blacklists and CAPTCHA-based challenges, are increasingly ineffective or intrusive, as modern bots leverage proxies, botnets, and AI-assisted evasion strategies. This work proposes a non-intrusive graph-based bot detection framework for e-commerce that models user session behavior through a graph representation and applies an inductive graph neural network for classification. The approach captures both relational structure and behavioral semantics, enabling accurate identification of subtle automated activity that evades feature-based methods. Experiments on real-world e-commerce traffic demonstrate that the proposed inductive graph model outperforms a strong session-level multilayer perceptron baseline in terms of AUC and F1 score. Additional adversarial perturbation and cold-start simulations show that the model remains robust under moderate graph modifications and generalizes effectively to previously unseen sessions and URLs. The proposed framework is deployment-friendly, integrates with existing systems without client-side instrumentation, and supports real-time inference and incremental updates, making it suitable for practical e-commerce security deployments.

Hart O. Awa, Ojiabo Ukoha Ojiabo, Bartholomew C. Emecheta

E. Turban

Arnav Attri, Anuj Attri, Pushpak Bhattacharyya et al.

Customer reviews on e-commerce platforms capture critical affective signals that drive purchasing decisions. However, no existing research has explored the joint task of emotion detection and explanatory span identification in e-commerce reviews - a crucial gap in understanding what triggers customer emotional responses. To bridge this gap, we propose a novel joint task unifying Emotion detection and Opinion Trigger extraction (EOT), which explicitly models the relationship between causal text spans (opinion triggers) and affective dimensions (emotion categories) grounded in Plutchik's theory of 8 primary emotions. In the absence of labeled data, we introduce EOT-X, a human-annotated collection of 2,400 reviews with fine-grained emotions and opinion triggers. We evaluate 23 Large Language Models (LLMs) and present EOT-DETECT, a structured prompting framework with systematic reasoning and self-reflection. Our framework surpasses zero-shot and chain-of-thought techniques, across e-commerce domains.

Urvashi Kishnani, Sanchari Das

E-commerce mobile applications are central to global financial transactions, making their security and privacy crucial. In this study, we analyze 92 top-grossing Android e-commerce apps (58 U.S.-based and 34 international) using MobSF, AndroBugs, and RiskInDroid. Our analysis shows widespread SSL and certificate weaknesses, with approximately 92% using unsecured HTTP connections and an average MobSF security score of 40.92/100. Over-privileged permissions were identified in 77 apps. While U.S. apps exhibited fewer manifest, code, and certificate vulnerabilities, both groups showed similar network-related issues. We advocate for the adoption of stronger, standardized, and user-focused security practices across regions.

Menghan Wang, Yuchen Guo, Duanfeng Zhang et al.

How to leverage large language model's superior capability in e-commerce recommendation has been a hot topic. In this paper, we propose LLM-PKG, an efficient approach that distills the knowledge of LLMs into product knowledge graph (PKG) and then applies PKG to provide explainable recommendations. Specifically, we first build PKG by feeding curated prompts to LLM, and then map LLM response to real enterprise products. To mitigate the risks associated with LLM hallucination, we employ rigorous evaluation and pruning methods to ensure the reliability and availability of the KG. Through an A/B test conducted on an e-commerce website, we demonstrate the effectiveness of LLM-PKG in driving user engagements and transactions significantly.

Lynn Huang

Amidst the era of networking, the e-commerce sector has undergone notable expansion, notably with the advent of Cross-border E-commerce (CBEC) in recent times. This growth trend persists, necessitating robust logistical frameworks to sustainably support operations. However, the current e-commerce logistics paradigm faces challenges in meeting evolving user demands, prompting a quest for innovative solutions. This research endeavors to address these complexities by undertaking a comprehensive analysis of CBEC logistics models and integrating embedded technology into logistical frameworks, resulting in the development of an advanced logistics tracking system. Moreover, employing the ant colony algorithm, the study conducts experimental investigations into optimizing logistics package distribution route planning. Noteworthy enhancements are observed in key metrics such as average delivery time, signaling the efficacy of this approach. In essence, this research offers a promising pathway towards optimizing logistics package distribution routes and bolstering package transportation efficiency within the CBEC domain.

Siddiqui Muhammad Yasir, Hyunsik Ahn

There are mostly two approaches to tackle trust management worldwide Strong and crisp and Soft and Social. We analyze the impact of integrated trust mechanism in three different e-commerce services. The trust aspect is a dormant element between potential users and being developed expert or internet systems. We support our integration by preside over an experiment in controlled laboratory environment. The model selected for the experiment is a composite of policy and reputation based trust mechanisms and widely acknowledged in e-commerce industry. The integration between policy and trust mechanism was accomplished through mapping process, weakness of one brought to a close with the strength of other. Furthermore, experiment has been supervised to validate the effectiveness of implementation by segregating both integrated and traditional trust mechanisms in learning system

Bruno Amaral Teixeira de Freitas, Roberto de Alencar Lotufo

This work presents Retail-GPT, an open-source RAG-based chatbot designed to enhance user engagement in retail e-commerce by guiding users through product recommendations and assisting with cart operations. The system is cross-platform and adaptable to various e-commerce domains, avoiding reliance on specific chat applications or commercial activities. Retail-GPT engages in human-like conversations, interprets user demands, checks product availability, and manages cart operations, aiming to serve as a virtual sales agent and test the viability of such assistants across different retail businesses.

Guandong Li

Generating style-consistent images is a common task in the e-commerce field, and current methods are largely based on diffusion models, which have achieved excellent results. This paper introduces the concept of the QKV (query/key/value) level, referring to modifications in the attention maps (self-attention and cross-attention) when integrating UNet with image conditions. Without disrupting the product's main composition in e-commerce images, we aim to use a train-free method guided by pre-set conditions. This involves using shared KV to enhance similarity in cross-attention and generating mask guidance from the attention map to cleverly direct the generation of style-consistent images. Our method has shown promising results in practical applications.

Kostiantyn Fuks

The purpose of this article is to provide a comprehensive examination of the informational and organisational capabilities of marketing activities in the market for digital products and services. It highlights the importance of data analysis, web analytics and technology partnerships for success in the digital marketplace. It also examines modern organisational strategies to help IT companies effectively implement marketing initiatives and adapt quickly to changing business landscapes. Methodology. This article is based on a theoretical and methodological review of the existing scientific literature on digital technologies, the marketing of digital products and services, and an overview of current technological and organisational solutions in the digital field. In addition, it includes a survey of marketing managers from renowned IT companies with the aim of delineating the typology of organisational structures within marketing departments. Results. Information delivery, data analytics, monitoring tools and web analytics are critical to digital marketing in IT organisations, facilitating the collection and analysis of data from multiple sources such as websites, social media and CRM systems. By leveraging big data and machine learning algorithms, it is possible to identify complex dependencies and predict consumer behaviour. Technological partnerships and collaborations with startups are becoming increasingly important for IT companies' marketing efforts, providing access to fresh ideas, technologies and a competitive edge. Organisational structures in the marketing departments of IT companies emphasise agility and cross-functional teamwork, often using agile methodologies. This promotes adaptability to market changes. Marketing structures typically include inbound approaches, flexible growth-oriented setups, and streamlined hierarchies. Practical implications. These marketing tools and organisational methods are recommended for implementation in the marketing departments of IT companies. The correlation between informational and organisational capabilities contributes to the achievement of marketing goals and the competitive advantage of IT companies in the marketplace. Scrum and Kanban, widely used agile frameworks, are not limited to technology companies but are also common in financial services and retail. Value / Оriginality. In the context of the ongoing military conflict, successful operation of Ukrainian IT companies in the modern world requires not only technological superiority, but also effective marketing and a well-organised internal structure. To accelerate the recovery of the Ukrainian IT sector and improve existing practices, the following recommendations have been made.

Lukić-Nikolić Jelena, Lazarević Snežana, Antić Sunčica

This paper investigates how demographic factors (gender and age), and contextual factors (length of team membership and work type) affect communication, shared values, collaborative growth and improvements in agile software development teams in Serbia. Empirical research was conducted using a specially designed online questionnaire which consisted of profile questions and three highly reliable scales focusing on agile software development team communication, shared values, and collaborative growth and improvements. In the period from April to October 2024, a total of 107 agile software development team members from Serbia participated in the research. Data analysis was conducted using descriptive statistics, the Mann-Whitney U-test, and the Kruskal-Wallis H-test. The findings reveal no statistically significant differences in communication, shared values, or collaborative growth based on age, length of team membership, and work type. However, a notable gender difference was observed, with female team members reporting a higher level of agreement on shared values within their teams. These results underscore the critical role of gender dynamics in fostering a cohesive team environment in agile settings. Understanding these dynamics is essential for enhancing team collaboration and performance, suggesting that organizations should consider gender inclusivity when developing agile software development teams.

Nick Hajli, Julian M. Sims

Hemn Barzan Abdalla, Awder Ahmed, Bahtiyar Mehmed et al.

Online reviews play a crucial role in shaping consumer decisions, especially in the context of e-commerce. However, the quality and reliability of these reviews can vary significantly. Some reviews contain misleading or unhelpful information, such as advertisements, fake content, or irrelevant details. These issues pose significant challenges for recommendation systems, which rely on user-generated reviews to provide personalized suggestions. This article introduces a recommendation system based on Passer Learning Optimization-enhanced Bi-LSTM classifier applicable to e-commerce recommendation systems with improved accuracy and efficiency compared to state-of-the-art models. It achieves as low as 1.24% MSE on the baby dataset. This lifts it as high as 88.58%. Besides, there is also robust performance of the system on digital music and patio lawn garden datasets at F1 of 88.46% and 92.51%, correspondingly. These results, made possible by advanced graph embedding for effective knowledge extraction and fine-tuning of classifier parameters, establish the suitability of the proposed model in various e-commerce environments.

Haixun Wang, Taesik Na

E-commerce search and recommendation usually operate on structured data such as product catalogs and taxonomies. However, creating better search and recommendation systems often requires a large variety of unstructured data including customer reviews and articles on the web. Traditionally, the solution has always been converting unstructured data into structured data through information extraction, and conducting search over the structured data. However, this is a costly approach that often has low quality. In this paper, we envision a solution that does entirely the opposite. Instead of converting unstructured data (web pages, customer reviews, etc) to structured data, we instead convert structured data (product inventory, catalogs, taxonomies, etc) into textual data, which can be easily integrated into the text corpus that trains LLMs. Then, search and recommendation can be performed through a Q/A mechanism through an LLM instead of using traditional information retrieval methods over structured data.