E-commerce Research Articles

AR technology-based marketing service and the determinants of consumer self-disclosure intention

ABSTRACT Augmented reality (AR) technology has been robustly adopted, especially in the case of online shopping in recent years. The essential technology in AR marketing involves the integration of virtual objects with the real world to create an immersive experience. To accomplish this, users must engage in self-disclosure. Our aim in the present study is to broaden the understanding of consumer self-disclosure intention toward AR marketing applications in electronic commerce (e-commerce) platforms. Using partial least squares structural equation modeling, we quantitatively analyzed data collected from 445 AR marketing users on two trusted e-commerce platforms: Amazon and Shopee and these data come from Taiwan and Vietnam. Results indicate that electronic trust (eTrust) has a direct positive effect on self-disclosure intention and significantly mediates the relationship between electronic word-of-mouth (eWOM), perceived value (PV), and behavioral intention in high-tech contexts where trust and privacy are powerful factors. Our research results identify key factors influencing consumers' self-disclosure intentions in the AR marketing environment. These insights will assist retailers in understanding consumer intentions and in formulating future marketing strategies accordingly. Furthermore, our findings help lay down a sound theoretical foundation for future research examining the eTrust subconstructs of brand trust and platform trust.

Applications and Challenges of Warehouse Optimization Technology in E-commerce Platforms under the Background Big Data

With the rapid growth of e-commerce, warehouse management has become a crucial component in ensuring operational efficiency and customer satisfaction. Integrating big data into e-commerce warehousing has significantly influenced logistics by providing valuable insights into consumer behavior, inventory turnover, and supply chain performance. Warehouses can make better use of space, get orders filled faster, and get more accurate information with the help of technologies like automated storage and retrieval systems (ASRS), inventory forecasting models, real-time tracking with the Internet of Things (IoT), and AI-driven order picking. However, the adoption of these optimization technologies is not without challenges. Successful implementation requires addressing issues related to data quality and integration, high initial costs, workforce adaptation, and data security. By addressing these obstacles, e-commerce platforms can leverage warehouse optimization to reduce costs, improve operational efficiency, and meet increasing consumer demands in a competitive market. This passage will focus on the applications of optimization technology in warehouses based on a big data background and the challenges it will face in the optimization process.

Post-Pandemic Growth of E-Commerce Platforms: A Case Study on Taobaos Strategic Success in China

This study analyzes the reasons for the continued growth of the Taobao platform after the pandemic to reveal the key factors that enable e-commerce platforms to maintain competitiveness in a dynamic market environment. Through a case study of Taobao, it is found that its success not only relies on innovative marketing methods such as the Double Eleven shopping festival and live streaming but also on efficient supply chain management and personalized user experience. This study points out that Taobao has built a strong market advantage through the interactivity of social e-commerce and the optimization of the supply chain, successfully meeting the demand of consumers for online shopping after the pandemic. This study provides valuable practical experience for other e-commerce platforms, emphasizing that focusing on user experience, adopting innovative promotional strategies, and optimizing supply chain management are effective ways to improve market performance. These findings are of great practical significance for the continued development of the e-commerce sector.

An Accented Character-based Captcha System with Usability Test Using Solving Time and Response Time

CAPTCHA is an acronym for Completely Automated Public Turing test to tell Human and Computer Apart. The main purpose of CAPTCHA is to differentiate between human and automated machine during online transaction. Text, image, audio and video are types of CAPTCHAs. However, text-based CAPTCHAs are available in the market in different languages i.e., English, Arabic, Urdu and Chinese but accented character-based text CAPTCHA system namely NAIJACAPTCHA is a newly introduce text-based CAPTCHA developed using Latin characters and accented characters from two Nigerian language: Yorùbá and Igbo. The usability of sixteen accented character-based CAPTCHAs was tested to see if they were suitable for human usage. The usability performance was measured using response time, solving time, accuracy, and success rate. A total of two hundred and twenty-two participants were selected for the study, and 1108 CAPTCHA codes were generated. The response time for Text Distortion with Coloured Background was the fastest, with 1.18×103 ms, while Coloured Texts with No Background (CTNB) had the least response time of 1.09 ms. With a solving time of 2.52×104 ms, Character Fragmentation with No Background was the fastest. The result showed that CTBN's response and problem-solving time is highly promising; as a result, its website application for authentication during online transactions to distinguish between humans and machines will be simple for human beings to solve and user requests will also be swiftly attended to. Lastly, the security aspect of the developed NAIJACAPTCHA will be looked into determine its vulnerability.

Examining Drivers of B2B Subscription Commerce in Digital Ecosystem Using Fuzzy Cognitive Mapping

This study investigates subscription commerce (SC) in the business-to-business (B2B) marketplace, focusing on transforming legacy incumbents’ relationships from one-time transactions to long-term associations. It aims to identify SC drivers and their role in the digital era’s production and consumption ecosystem. The research follows a three-part approach. First, it distinguishes SC from electronic commerce and subscription billing. Second, it explores the digital ecosystem (DE), emphasizing the production and consumption ecosystem. Third, it proposes a fuzzy cognitive mapping (FCM) framework to analyse SC drivers, specifically emphasizing value derivation from data in the DE transition. This study pioneers the analysis of SC drivers within B2B contexts, showcasing SC’s substantial role in unlocking value for legacy incumbents, particularly through the innovative FCM framework. It stands out as the first comprehensive analysis of SC drivers in B2B settings, showcasing its contribution to the digital transformation of legacy incumbents using FCM. This research offers crucial insights for legacy firm managers, aiding in digital transformation strategies, whether through in-house development or SC procurement. Additionally, consultants can leverage these drivers to support firms in their digitization efforts.

Impact of reviews on e-commerce platforms on customer behavior: A case study of the Arab market

The study investigates the impact of electronic reviews (e-reviews) on consumer purchasing behavior in Arab markets, considering the moderating role of customer characteristics. It is based on a sample of 490 participants from seven countries, including Bahrain, Egypt, Jordan, Kuwait, Qatar, Saudi Arabia, and the UAE, with equal weighting for each market in the second quarter of 2024. The study uses a comprehensive questionnaire based on a 5-point Likert scale and analyzes data using descriptive and inferential statistics, including ANOVA and hierarchical regression. The study found a significant positive correlation between e-reviews and online purchasing behavior. The results highlight the importance of demographic characteristics in moderating the relationship between e-reviews and consumer behavior. The findings suggest that younger consumers, females, and those with higher educational levels tend to place greater importance on e-reviews when making purchasing decisions. The study provides implications for marketers and researchers in future studies.

Fusion of SP-VAE and IMP-VAE for Proxy Attack Detection in ECommerce Systems

Fusion of SP-VAE and IMP-VAE for Proxy Attack Detection in ECommerce Systems

A Decision Framework for Supplier Selection in Digital Supply Chains of E-Commerce Platforms Using Interval-Valued Intuitionistic Fuzzy VIKOR Methodology

A Decision Framework for Supplier Selection in Digital Supply Chains of E-Commerce Platforms Using Interval-Valued Intuitionistic Fuzzy VIKOR Methodology

Value-in-Use and Sustaining Use Intention on E-Commerce Platforms: The Service-Dominant Logic Perspective

With the progressive increase in online shopping, e-commerce platforms’ sustainability has attracted considerable attention from both academia and the business sector. This study employs a service-dominant logic perspective and incorporates value-in-use into the conceptual model to investigate the factors and mechanisms that influence sustained intention to use e-commerce platforms. Drawing on a survey of 358 college students in China, we reveal that users’ perceived usefulness and perceived enjoyment on e-commerce platforms have a positive effect on their sustaining use intention on platforms, whereas perceived uncertainty exerts a negative influence. Furthermore, the effect of perceived uncertainty on confirmation is less pronounced than that of perceived usefulness and enjoyment. Perceived uncertainty primarily stems from information asymmetry and seller opportunism, with the latter having a more significant impact on perceived uncertainty than the former. These findings enrich the empirical literature on the sustainable development of e-commerce platforms by offering new insights into the development of service-dominant logic and value-in-use theory.

Analysis and Optimization of the Usability of E-Commerce Platforms through Evaluation Heuristics

This paper extends previous work on usability evaluation heuristics for e-commerce platforms by conducting a detailed experimentation to validate and refine the proposed heuristic set. A group of international expert evaluators applied the new heuristics to an e-commerce platform, providing both individual assessments and qualitative feedback. The results revealed significant insights into the platform's usability, highlighting strengths and areas needing improvement. This process not only identified deficiencies but also allowed for the adjustment and optimization of the heuristic questions to better reflect user needs and expectations. The validated heuristics, which include 15 guidelines and 60 questions, provide a comprehensive evaluation framework, quantifying usability through a percentage score. The findings contribute to the ongoing enhancement of e-commerce platform usability and offer valuable knowledge for the usability and user experience research community.

Purchase intention of Millennial Muslims toward B2C e-commerce

Indonesia’s e-commerce market, particularly B2C, continues to expand, attracting both domestic and foreign investments. Despite this growth, a notable decline in B2C sales occurred. This drop is largely attributed to rising competition in the market, which has made it increasingly difficult for platforms to retain consumer interest. This study investigates the factors that influence Millennial Muslim consumers’ purchase intentions on B2C e-commerce in Indonesia using structural equation modeling. The respondents in this study were taken from the Millennial generation of e-commerce users. This generation was chosen because they are economically more established than Generation Z and in terms of technology use, are more expert than Baby Boomers. Initially, 700 responses were collected, but after excluding incomplete or non-qualifying data, 434 valid responses were analyzed. The findings reveal that perceived risk (β: 0.206), perceived quality (β: 0.440), and enduring involvement (β: 0.240) positively affect perceived trust, whereas situational involvement (β: -0.331) has a negative impact. Additionally, enduring involvement (β: 0.659), situational involvement (β: 0.133), and perceived trust (β: 0.103) were found to positively influence purchase intention, while perceived risk (β: -0.093) negatively affects it. The study emphasizes that enduring involvement (β: 0.659) is the strongest variable that increase purchase intention in B2C e-commerce. Enduring involvement refers to a consumer’s long-term interest and engagement with a product category, driven by personal relevance, values, or consistent motivations. AcknowledgmentThe authors would like to thank the Research and Innovation Institute (LRI), Universitas Muhammadiyah Surakarta, for the enormous financial support in writing this study through the funding scheme of the 2025 Outstanding Research of Study Program (RU-PRODI).

Consumer negative opinions on stock returns: evidence from E-commerce reviews in China

This study investigates the significance of e-commerce consumer opinions regarding value in China’s A-share market. By analyzing a large dataset comprising over 18 million online consumer reviews on JD.com, we demonstrate that sentiments expressed in e-commerce reviews can influence stock returns. This indicates that consumer opinions on the e-commerce platform contain valuable information that can impact the stock market. Our findings show that Consumer Negative Sentiment Tendency (CNST) and One-Star Tendency (OST) have a negative effect on expected stock returns, even after controlling for firm characteristics such as market risk, illiquidity, idiosyncratic volatility, and asset growth. Further analysis indicates that CNST demonstrates stronger predictive power within the home appliance industry, under high sentiment conditions, in growth companies, and among firms with lower accounting transparency. We also find that CNST negatively predicts revenue surprises, earnings surprises, and cash flow shocks. These results suggest that consumer opinions and sentiments derived from e-commerce reviews highlight firms’ intrinsic worth and prospects. Future research could explore how firms, including suppliers and logistics companies, can leverage the information conveyed by consumer opinions on e-commerce platforms.

Enhancing loyalty in luxury fashion with AI: the mediation role of customer relationships

PurposeThis study aims to investigate the impact of artificial intelligence (AI) on customer loyalty in the luxury fashion market. It explores how AI-driven tools influence customer trust, satisfaction, commitment and engagement, which in turn affect loyalty. By examining these relationships, the study provides insights into the acceptance and effectiveness of AI technologies in enhancing customer loyalty within the luxury fashion sector.Design/methodology/approachThis study employs structural equation modelling (SEM) to analyse data collected from 406 luxury consumers in Iran. The data was gathered using a targeted sampling procedure, leveraging DigiKala’s e-commerce platform. A comprehensive literature review informed the measurement items, and a seven-point Likert scale was used. The methodology includes confirmatory factor analysis (CFA) to assess the reliability and validity of the constructs, followed by hypothesis testing through SEM.FindingsThe study reveals that AI significantly enhances customer loyalty in the luxury fashion market by positively influencing trust, satisfaction, commitment and engagement. Satisfaction and engagement were found to be key mediators between AI and loyalty, while trust had no direct impact on loyalty. The results underscore the importance of AI-driven personalized experiences in fostering stronger customer relationships and loyalty.Originality/valueThis study is one of the first to explore the impact of AI on customer loyalty in the luxury fashion market, using a comprehensive model that includes trust, satisfaction, commitment and engagement as mediators. It extends the stimulus-organism-response (SOR) and technology acceptance model (TAM) frameworks, offering valuable insights for luxury brands on how AI can be leveraged to enhance customer relationships and loyalty.

The Impact of AI-Personalized Recommendations on Clicking Intentions: Evidence from Chinese E-Commerce

AI-personalized recommendation technology offers more accurate and diverse choices to consumers and increases click-through rates and sales on e-commerce platforms. Yet, data on consumers’ experiences of AI-personalized recommendations and their impact path on clicking intention are scarce. This article addressed these issues through three studies. In study 1, we adopted the Grounded Theory approach to conduct in-depth interviews with 30 Chinese consumers and constructed a scale to measure the impact of consumer experience on click intention. In study 2, we adopted the empirical research method to conduct reliability and validity tests on 347 valid questionnaires to finalize the scale officially. In study 3, based on the SOR theory, we constructed a model and formulated hypotheses and then conducted empirical analysis using 1097 valid questionnaires. We found that the relevance, inspiration, and insightful experiences of AI-personalized recommendations can significantly promote consumers’ clicking intention. Moreover, immersive experience mediates between the former three factors and clicking intention, and technology acceptance mediates between relevance, inspiration, and clicking intention. When consumers perceive a high degree of information privacy infringement, the immersive experience’s positive impact on clicking intention will be weakened. Meanwhile, the promoting effect of technology acceptance on clicking intention will also be inhibited. When information quality improves, the positive impact of technology acceptance on clicking intention will be enhanced. This research fills the gap in the literature on consumers’ experiences of AI-personalized recommendations and clarifies how these experiences affect the clicking intention. It offers valuable insights for e-commerce platforms to continuously optimize personalized recommendation algorithms and boost the click conversion rate of online shopping.

Research on Optimal Model Combination of Cross-Border E-Commerce Platform Operation Relying on Robot Hybrid Algorithm

Research on Optimal Model Combination of Cross-Border E-Commerce Platform Operation Relying on Robot Hybrid Algorithm

Home Environment as a Therapeutic Target for Prevention and Treatment of Chronic Diseases: Delivering Restorative Living Spaces, Patient Education and Self-Care by Bridging Biophilic Design, E-Commerce and Digital Health Technologies

A high prevalence of chronic diseases exposes diverse healthcare pain points due to the limited effectiveness of pharmaceutical drugs and biologics, sedentary lifestyles, insufficient health literacy, chronic stress, unsatisfactory patient experience, environmental pollution and competition with commercial determinants of health. To improve patient care and long-term outcomes, the impact of the home environment is overlooked and underutilized by healthcare. This cross-disciplinary work describes perspectives on (1) the home environment as a therapeutic target for the prevention and treatment of chronic diseases and (2) transforming health-centric household goods e-commerce platforms into digital health interventions. We provide a rationale for creating therapeutic home environments grounded in biophilic design (multisensory, environmental enrichment) and supporting physical activities, quality sleep, nutrition, music, stress reduction, self-efficacy, social support and health education, hence providing clinical benefits through the modulation of the autonomic nervous system, neuroplasticity and behavior change. These pleiotropic “active non-pharmacological ingredients” can be personalized for people living with depression, anxiety, migraine, chronic pain, cancer, cardiovascular and other conditions. We discuss prospects for integrating e-commerce with digital health platforms to create “therapeutic home environment” interventions delivered through digital therapeutics and their combinations with prescription drugs. This multimodal approach can enhance patient engagement while bridging consumer spending with healthcare outcomes.

The Influence of Promotion, E-Wom and Trust on Purchasing Decisions on E-Commerce Lazada in Batam City

The primary objective of this investigation is to examine how promotion, electronic word of mouth (e-WOM), and trust impact buying choices on Lazada e-commerce in Batam City. This quantitative study used a replication approach and statistical methods to analyse primary data obtained from 100 participants through online questionnaires, sampled using non-probability techniques based on specific criteria. Utilising the SPSS application, the data was analysed to determine the impact of the independent variables on the dependent variable. The results indicated that promotion, e-WOM, and trust individually have a favourable and significant effect on buying decisions. Furthermore, these three factors collectively have a notable impact on purchasing decisions on Lazada e-commerce. The outcomes suggest that successful promotional strategies, positive feedback from consumers, and consumer trust are critical factors influencing purchasing decisions on e-commerce platforms. Based on these results, researchers suggest that Lazada increase the frequency of promotional campaigns such as Harbolnas or flash sales, provide incentives for consumers to leave product reviews, and strengthen consumer trust through guarantees of product authenticity and transparency of payment security systems. This strategy is expected to strengthen consumer trust and increase purchasing decisions on the Lazada platform.

Operational Research Models for E-Commerce Platforms: New Technology Innovation

New technology innovations and operational research (OR) models can enhance the efficiency and competitiveness of e-commerce platforms. The rapid expansion of e-commerce has led to increased complexity in operations, including dynamic pricing, inventory management, last-mile delivery, and fraud detection. This special issue features nine articles exploring how advanced OR models, such as game-theory-based approaches, machine learning, and big data analytics, offer solutions to these challenges by enabling platforms to make data-driven decisions. As e-commerce evolves, these technological advancements, combined with OR models, will continue to drive platform innovation, improve efficiency, and strengthen resilience in the face of disruptions and fluctuating market demands.

A Novel Ensemble Belief Rule-Based Model for Online Payment Fraud Detection

In recent years, with the rapid development of technology and the economy, online transaction fraud has become more and more frequent. In the face of massive records of online transaction data, manual detection methods are long outdated, and machine learning methods have become mainstream. However, although traditional machine learning methods perform well in fraud detection tasks, the lack of interpretability and class imbalance issues have always been pain points that are difficult to resolve for such methods. Unlike traditional methods, the belief rule base, as a rule-based expert system model, can integrate expert knowledge and has excellent interpretability. In this paper, we propose an innovative ensemble BRB (belief rule base) model to solve the credit card fraud detection problem by combining an ensemble learning framework with the BRB model. Compared with traditional machine learning methods, the proposed model has the advantage of high interpretability. And compared with traditional BRB models, the ensemble framework enables better performance in dealing with highly imbalanced classification tasks. In an experimental study, two datasets of credit card fraud detection from Kaggle are used to validate the effectiveness of this work. The results show that this new method can achieve excellent performance in the application of fraud detection and is capable of effectively mitigating the impact of an imbalanced dataset.

User avoidance behavior in pharmaceutical e-commerce intelligent customer service: a stressor-strain-outcome perspective

IntroductionThis study explores the implementation of Intelligent Customer Service (ICS) in pharmaceutical e-commerce, aiming to enhance user acceptance and service efficiency while addressing the psychological factors influencing user behavior. It expands the boundaries of technology acceptance research by focusing on ICS use and avoidance in high-risk environments.MethodA total of 418 valid questionnaires were collected from participants, ensuring data quality through rigorous screening. The study employed SPSS for data normality tests and SmartPLS for structural equation modeling to analyze the relationships between emotional stress, system overload, and user avoidance behavior.ResultsThe findings indicate that system overload, information overload, and service overload significantly contribute to user emotional stress, which in turn drives avoidance behavior. The analysis revealed strong explanatory power (R2 values ranging from 0.450 to 0.586) and confirmed the mediating role of emotional stress in the relationship between overload factors and user avoidance.DiscussionThis research highlights the critical role of emotional stress in user interactions with ICS, suggesting that pharmaceutical e-commerce companies must refine their ICS design to meet diverse user needs and reduce cognitive burdens. By leveraging big data and establishing robust feedback mechanisms, companies can enhance user experience and loyalty. The study also identifies limitations in demographic representation and suggests future research should incorporate qualitative methods for a deeper understanding of user behavior.