E-commerce Domain Research Articles

From Fun to Fear: Unpacking the Emotional Triggers of Cross-Border E-Commerce Repurchases

This study explores how emotional triggers—specifically the interplay between perceived fun and fear—affect repurchase intentions on cross-border e-commerce platforms through the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Additional variables, including online shopping fun and online shopping risk, are incorporated to provide a comprehensive analysis. Data were collected through an online questionnaire survey, resulting in 270 valid responses. The study employed SmartPLS 4.0 to analyze the data, utilizing descriptive statistics, reliability analysis, validity tests, and structural equation modeling (SEM) to evaluate the research hypotheses. Effort expectancy, facilitating conditions, and online shopping fun positively influence repurchase intention and behavior. However, performance expectancy and social influence do not significantly impact repurchase intention. Online shopping risk has a negative effect on repurchase intention, indicating that consumers’ concerns about security and privacy reduce their likelihood of repurchasing. Cross-border e-commerce platforms should prioritize improving usability and providing support features to enhance customer experiences. Incorporating engaging features and mitigating perceived risks can significantly encourage repeat purchases. These insights are critical for platform developers and marketers aiming to foster customer loyalty. This study extends the UTAUT framework by incorporating shopping fun and risk as contextual variables, providing novel insights into consumer behavior on cross-border e-commerce platforms. The findings offer practical recommendations for enhancing user experience and addressing specific challenges in the e-commerce domain.

A Comprehensive Review of User Interaction for Recommendation Systems

In recent years, Recommendation Systems have become integral to the online experiences of consumers, particularly those that effectively integrate user interactions into their algorithms, enhancing both efficiency and adaptability. This article presents a comprehensive systematic review of the literature addressing classical problems in recommendation systems, specifically focusing on the consideration of user interaction. We employed a rigorous systematic literature review methodology, critically analyzing various proposals to identify their limitations, characteristics, and potential avenues for further research. Our investigation involved mapping relevant studies that examine how user interaction with recommendation systems is addressed and determining the extent to which this aspect has been explored. We established strict inclusion and exclusion criteria to select academic publications, resulting in a curated set of 29 scientific papers. The findings offer a snapshot of the primary characteristics of the identified works, revealing significant gaps that can inform future research directions. Our analysis indicates that most studies addressing user interaction emphasize preference elicitation and feedback mechanisms, predominantly focusing on improving the accuracy of recommendation rankings, with a notable concentration on the e-commerce domain.

Advancing explainable MOOC recommendation systems: a morphological operations-based framework on partially ordered neutrosophic fuzzy hypergraphs

Recommendation systems constitute an integral part of nearly all digital service platforms. However, the common assumption in most recommendation systems in the literature is that similar users will be interested in similar items. This assumption holds only sometimes due to the inherent inhomogeneity of user-item interactions. To address this challenge, we introduce a novel recommendation system that leverages partially ordered neutrosophic hypergraphs to model higher-order relationships among users and items. The partial ordering of nodes enables the system to develop efficient top-N recommendations with very high Normalized Discounted Cumulative Gain (NDCG). Our approach incorporates the morphological operation of dilation, applied to user clusters obtained through fuzzy spectral clustering of the hypergraph, to generate the requisite number of recommendations. Explanations for recommendations are obtained through morphological erosion applied on the dual of the embedded hypergraph. Through rigorous testing in educational and e-commerce domains, it has been proved that our method outperforms state-of-the-art techniques and demonstrates excellent performance for various evaluation parameters. The NDCG value, a measure of ranking quality, surpasses 0.10, and the Hit Ratio (HR) consistently falls within the range of 0.25 to 0.30. The Root Mean Square Error (RMSE) values are minimal, reaching as low as 0.4. These results collectively position our algorithm as a good choice for generating recommendations with proper explanations, making it a promising solution for real-world applications.

E-commerce ecosystems as catalysts for sustainability: A multi-case analysis

Single organizations encounter intricate challenges in meeting the UN’s Sustainable Development Goals (SDGs) on their own and thus are increasingly concerned with forming ecosystems. This is also the case in the e-commerce domain. It is assumed that particularly focal actors of such ecosystems have the power to foster initiatives towards more sustainability. We conducted an embedded multi-case study with 135 initiatives collected from three types of e-commerce ecosystems to uncover what sustainability goals are addressed by different ecosystem participants. This paper’s analysis explores (1) what sustainability initiatives are reported from focal actors and differences depending on a specific type of ecosystem, (2) dependencies between SDGs within the initiatives, and (3) the degree of involvement of other ecosystem actors and how they contribute to certain SDGs. Our work synthesizes existing initiatives towards sustainable development to give orientation and impulses for practice and academia as well as outlines avenues for future research at the intersection of ecosystems and sustainability.

ANALYZING USER BEHAVIOR PATTERNS FOR PERSONALIZED RECOMMENDER SYSTEMS IN E-COMMERCE: A LITERATURE REVIEW

Abstract: E-commerce thrives on a user-centric strategy, and recommender systems are at the cutting edge of personalizing the purchasing experience. These systems may forecast preferences and recommend appropriate items by analyzing user behavior patterns, resulting in many benefits such as increased customer satisfaction, increased sales and conversions, and increased efficiency. To accomplish these benefits, recommender systems utilize complex algorithms that examine numerous elements of user behavior such as purchase history, browsing behavior, search queries, demographic data, and implicit feedback. Sophisticated algorithms can recognise complicated patterns in user data, resulting in more accurate and personalized suggestions. Analyzing user reviews, product descriptions, and social media interactions may help you better understand consumer preferences and product features. Systems can make real-time suggestions depending on a user’s current browsing session, resulting in a more dynamic purchasing experience. Personalized recommender systems will play an increasingly important role in molding the future of e-commerce as user behavior analysis techniques are constantly refined. The study intends to make important advances to the field of personalized recommender systems by undertaking a thorough research of user behavior patterns in the e-commerce domain. We strive to improve the performance of recommender systems by extracting insightful features from various data sources and exploring sophisticated machine learning techniques, resulting in a more engaging and tailored user experience that fosters customer satisfaction and drives business growth. A comprehensive review of user behavior patterns and their influence on personalized recommender systems in the e-commerce industry reveals the critical role of data analysis and machine learning algorithms in tailoring product suggestions to individual preferences, thereby enhancing customer satisfaction and driving sales growth. By implementing the tactics and approaches expressed in this study, e-commerce platforms may stay ahead of the curve, providing a smooth and tailored purchasing experience that surpasses customer expectations and contributes to their competitive advantage in the changing e-commerce environment.

Enhancing Predictive Accuracy in Machine Learning: Techniques for Model Optimization and Feature Selection"s

Artificial intelligence (AI) is very relevant in areas like healthcare, finance and e-commerce where a close estimate is crucial in decision making. There are still problems with performance even with the recent developments of Machine Learning models, which stem from insufficient preprocessing, unsuitable feature selection, and poor hyperparameter optimization, which restrict their applicability to multiple domains. This research aims at developing a systematic, step-by-step, and stage-wise improvement of the accuracy of the ML models through data preprocessing and feature selection, and model selection. This study evaluates techniques such as normalization, Recursive Feature Elimination (RFE), and Bayesian hyperparameter tuning by applying this approach to datasets in the healthcare, finance, and e-commerce domains. The findings show that preprocessing increases the accuracy by 5-8%, while RFE maintains 95% of the accuracy with a feature reduction of 30-50%, Bayesian optimization also increases the accuracy by 10-15%, making the overall accuracy of the models to be 96%. This work underscores the importance of the proposed integrated approach for constructing reliable, explainable, and scalable ML models for various fields. The results of the study provide a clear and easily replicable approach useful for future studies in the field and for industries that require high levels of accuracy and model parsimony.

The Effects of E-Commerce Recommendation System Transparency on Consumer Trust: Exploring Parallel Multiple Mediators and a Moderator

Recommendation systems are used in various fields of e-commerce and can bring many benefits to consumers but consumers’ trust in recommendation systems (CTRS) is lacking. Recommendation system transparency (RST) is an important factor that affects CTRS. Applying a three-layered trust model, this paper discusses the influence of RST on CTRS in the e-commerce domain, demonstrating the mediating role of perceived effectiveness and discomfort and the moderating role of consumers’ domain knowledge. We recruited 500 participants for an online hypothetical scenario experiment. The results show that consumers’ perceived effectiveness and discomfort can mediate the relationship between RST and CTRS. Specifically, RST (vs. non-transparency) leads to higher perceived effectiveness ( promoting CTRS) and lower levels of discomfort (which inhibits CTRS), in turn increasing CTRS. Domain knowledge positively moderates the positive impact of RST on perceived effectiveness, while negatively moderating the negative impact of RST on discomfort. Further, gender has a negative impact on CTRS when consumers are purchasing experience products but there is no effect when purchasing search products.

Unperplexing The Nexus Between E-Commerce Website Assistance and Online Brand Advocacy: Roles of Self-Presentation and Hedonic Value

ABSTRACT The complexities of online brand advocacy (OBA) were investigated using social exchange theory. Recognizing the need to understand better the psychological antecedents influencing OBA, this research examines the interaction of e-commerce website assistance, self-presentation, hedonic value, and OBA. Data was gathered from consumers of e-commerce websites (i.e. AliExpress, Rakuten, and Amazon). The multigroup analysis findings show that e-commerce website assistance, self-presentation, and hedonic value positively influence OBA. Moreover, the nexus between self-presentation and OBA was positively affected by the hedonic value. This paper aims to comprehensively understand the psychological and rational motivations driving OBA behaviors, contributing to the theoretical foundations of consumer behaviors in e-commerce domains. Findings are expected to provide valuable insights for marketers and academics, guiding strategies to foster positive consumer–brand relationships in the ever-changing landscapes of online commerce.

Evaluation and optimization of intelligent recommendation system performance with cloud resource automation compatibility

This paper comprehensively explores the integration of cloud computing and advanced recommendation systems, emphasizing their pivotal roles in enhancing user experiences and operational efficiencies across digital platforms. It reviews the evolution of recommendation algorithms, highlighting their application in diverse domains such as e-commerce and media. The study evaluates the performance of advanced models like UniLLMRec against traditional counterparts using datasets from news and e-commerce domains. Additionally, the paper discusses the infrastructure architecture of cloud computing, demonstrating its capability to support scalable and efficient data processing. Through experimental insights and methodology, the research underscores the transformative impact of cloud technologies on optimizing recommendation system performance, thereby advancing digital engagement and competitiveness.

LLMs-based machine translation for E-commerce

Large language models(LLMs) have shown promising performance for various downstream tasks, especially machine translation. However, LLMs and Specialized Translation Models (STMs) are designed to handle general translation needs, they are not well-suited for domains with specialized terms and writing styles, such as e-commerce, legal, and medicine. In the e-commerce domain, the text often contains many domain-specific terms and keyword-stacked structures, leading to poor translation quality with existing NMT methods. To tackle these problems, we have collected two resources specifically for the e-commerce domain, including aligned Chinese-English bilingual terms and parallel corpus from real e-commerce scenarios for model fine-tuning. We propose an LLMs-based E-commerce machine translation approach(LEMT) which includes LLMs utilization, e-commerce resources collection, and tokenizer optimization. We conduct two-stage fine-tuning and self-contrastive enhancement based on general LLMs to enable the model to learn translation features in the e-commerce domain. Through comprehensive evaluations on real e-commerce titles, our LEMT methodology demonstrates superior translation quality and robustness, outperforming leading NMT models such as NLLB, LLaMA, and even GPT-4.

Emerging synergies between large language models and machine learning in e-commerce recommendations

This paper explores the integration of large language models (LLMs) into collaborative filtering algorithms to enhance recommendation systems in the e-commerce domain. The proposed approach combines user-based and item-based collaborative filtering with LLMs to improve recommendation accuracy and personalization. Specifically, the study introduces a novel framework called PALR, which leverages LLMs to refine user-item interactions and enrich item representations. PALR utilizes historical user behavior data, such as clicks, purchases, and ratings, to guide candidate retrieval and generate recommended items. This study highlights the importance of integrating LLMs into recommendation systems to deliver more accurate and personalized suggestions, ultimately improving user satisfaction and driving sales in e-commerce platforms.

The Effect of Product Personalization, User Experience, and Consumer Trust on the Level of E-Commerce Consumer Satisfaction in Indonesia

This study looks into how user experience, consumer trust, and product personalization affect Indonesian e-commerce customer happiness. To investigate the associations between these characteristics, data was gathered via surveys and analyzed quantitatively using 225 Indonesian e-commerce customers. The results show that consumer trust is positively influenced by product customisation, and consumer trust is positively influenced by user experience. Furthermore, customer satisfaction and confidence in e-commerce are strongly impacted by product customisation and user experience. These findings highlight the significance of personalized experiences, frictionless communication, and reliability in fostering customer satisfaction among Indonesian online shoppers. Through comprehension of these characteristics, enterprises can formulate focused approaches to augment customer contentment and cultivate enduring connections inside the Indonesian e-commerce domain.

From Friends to Feedback: Effect of Social Influence on Mobile Shopping in the Post-COVID Era

Although mobile shopping is a new norm after the pandemic, its proliferation is still not very mature in developing nations. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, this research employs the SEM technique to examine the effect of mobile shopping frequency and repurchase intent among 198 young mobile shopping consumers in Pakistan. Our findings suggest that purchase frequency is a key variable in consumer behavior and plays a significant role in building mobile shopping repurchase intentions. Findings further suggest that effort expectancy, unlike the performance expectancy, also strongly affects the relationship between purchase frequency and repurchase intention as an intervening mechanism, while a strong interaction effect from the social influence is also noted. This research offers insightful implications for researchers and marketers in the realm of e-commerce and mobile shopping domains.

Providing healthcare shopping advice through knowledge-based virtual agents

Knowledge-based virtual shopping agents, that advise their users about which products to buy, are well used in technical markets such as healthcare e-commerce. To ensure the proper adoption of this technology, it is important to consider aspects of users’ psychology early in the software design process. When traditional adoption models such as UTAUT-2 work well for many technologies, they overlook important specificities of the healthcare e-commerce domain and of knowledge-based virtual agents technology. Drawing upon health information technology and virtual agent literature, we propose a complementary adoption model incorporating new predictors and moderators reflecting these domains’ specificities. The model is tested using 903 observations gathered through an online survey conducted in collaboration with a major actor in the herbal medicine market. Our model can serve as a basis for many phases of the knowledge-based agents software development. We propose actionable recommendations for practitioners and ideas for further research.

Enhancing fairness of trading environment: discovering overlapping spammer groups with dynamic co-review graph optimization

Within the thriving e-commerce landscape, some unscrupulous merchants hire spammer groups to post misleading reviews or ratings, aiming to manipulate public perception and disrupt fair market competition. This phenomenon has prompted a heightened research focus on spammer groups detection. In the e-commerce domain, current spammer group detection algorithms can be classified into three categories, i.e., Frequent Item Mining-based, graph-based, and burst-based algorithms. However, existing graph-based algorithms have limitations in that they did not adequately consider the redundant relationships within co-review graphs and neglected to detect overlapping members within spammer groups. To address these issues, we introduce an overlapping spammer group detection algorithm based on deep reinforcement learning named DRL-OSG. First, the algorithm filters out highly suspicious products and gets the set of reviewers who have reviewed these products. Secondly, taking these reviewers as nodes and their co-reviewing relationships as edges, we construct a homogeneous co-reviewing graph. Thirdly, to efficiently identify and handle the redundant relationships that are accidentally formed between ordinary users and spammer group members, we propose the Auto-Sim algorithm, which is a specifically tailored algorithm for dynamic optimization of the co-reviewing graph, allowing for adjustments to the reviewers’ relationship network within the graph. Finally, candidate spammer groups are discovered by using the Ego-Splitting overlapping clustering algorithm, allowing overlapping members to exist in these groups. Then, these groups are refined and ranked to derive the final list of spammer groups. Experimental results based on real-life datasets show that our proposed DRL-OSG algorithm performs better than the baseline algorithms in Precision.

Intent Recognition and slot filling for ecommerce chatbot

With the continuous evolution of natural language processing (NLP), conversational agents have become pivotal in enhancing user engagement and satisfaction in the e-commerce domain. This paper presents a comprehensive study and implementation of intent recognition and slot filling techniques tailored for an e-commerce chatbot. Leveraging a dataset of 8,000 samples, we employed a Convolutional Neural Network (CNN) combined with Long Short-Term Memory (LSTM) networks for intent recognition, achieving an accuracy of 87%. For slot filling, we utilized the Bidirectional Encoder Representations from Transformers (BERT) model, also attaining an accuracy of 87%. The chatbot seamlessly integrates with an e-commerce database, using OpenAI's GPT-3 to generate natural language responses from query results. Our system demonstrates significant advancements in processing user queries, generating precise database queries, and providing coherent and relevant responses, thereby enhancing the overall user experience. This work aims to equip researchers and practitioners with insights into the methodologies and challenges in developing sophisticated e-commerce chatbots, fostering further innovation in this field. Key Words: Intent Recognition, Slot Filling, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT), GPT-3, Natural Language Understanding (NLU), Chatbot

Examining the Benefits and Drawbacks of Open Network Architectures in E-Commerce

The advent of digital commerce has revolutionized the way businesses operate and interact with customers. One of the key factors contributing to this transformation is the emergence of open network architectures, which facilitate seamless communication and data exchange between various entities within the e-commerce ecosystem. This research aims to provide a comprehensive analysis of the benefits and drawbacks associated with open network architectures in the context of digital commerce. And to investigate how open network architectures promote accessibility and interoperability among diverse systems, enabling seamless integration and collaboration within the e-commerce landscape. Explore the potential of open networks to foster innovation and scalability, empowering businesses to rapidly adapt to changing market demands and technological advancements. Evaluate the economic advantages of open network architectures, including reduced infrastructure costs, improved resource utilization, and streamlined operational processes. Examine the potential vulnerabilities and risks associated with open network architectures, such as data breaches, cyber threats, and privacy issues, and propose strategies to mitigate these challenges. Investigate the role of governance frameworks and industry standards in facilitating the effective implementation and management of open network architectures within the e-commerce domain. Identify and analyze the potential barriers to the widespread adoption for overcoming these challenges. This paper aims to provide a comprehensive analysis of the benefits and drawbacks associated with open network architectures in the context of e-commerce. Through real-world case studies, theoretical frameworks, and a multi-faceted approach encompassing technical, business, and strategic perspectives, we strive to equip e-commerce stakeholders with the insights necessary to navigate this complex landscape and make informed decisions that align with their organizational goals and market dynamics.

Slickdeal Aggregator: A Web Application using Web Scraping

Abstract: An innovative method to facilitate well-informed purchasing decisions by consolidating and analyzing product data sourced from various online platforms. Given the rapid expansion of the e-commerce sector, numerous online retail outlets offer a wide assortment of products, complicating the process of comparing prices and discounts for consumers. By harnessing web scraping technology, we streamline the collection and organization of product information, thereby improving efficiency and accuracy. Our approach involves employing ReactJS for frontend interface design and Node.js for backend server development. At the heart of our methodology lies the integration of web scraping capabilities through Puppeteer, enabling automated extraction of product data. The resultant platform provides users with a seamless browsing experience and comprehensive product insights. Beyond its immediate applications, our research contributes to the advancement of web scraping technology within e-commerce domains.

Does Scarce Inventory Information Disclosure Strategy Promote Online Sales?

In pursuit of promoting sales, certain e-commerce vendors post scarce inventory messages on product pages to signal impending stockouts. To explore how disclosure information strategy regarding scarce inventory influences online sales, this study utilizes Chinese e-commerce data collected from February 1st to April 30th, 2023 and constructs an empirical model that delves into the relationship between the disclosure of scarce inventory information and online sales based on the signal theory. The empirical findings reveal a positive impact of scarce inventory information disclosure on online sales, with this impact being more pronounced under the moderating role of the commodity discount rate. These results hold substantial theoretical implications and offer valuable insights for practical applications in the e-commerce domain.

VSEM-SAMMI: An Explainable Multimodal Learning Approach to Predict User-Generated Image Helpfulness and Product Sales

Using user-generated content (UGC) is of utmost importance for e-commerce platforms to extract valuable commercial information. In this paper, we propose an explainable multimodal learning approach named the visual–semantic embedding model with a self-attention mechanism for multimodal interaction (VSEM-SAMMI) to predict user-generated image (UGI) helpfulness and product sales. Focusing on SHEIN (i.e. a fast-fashion retailer), we collect the images posted by consumers, along with product and portrait characteristics. Moreover, we use VSEM-SAMMI, which adopts a self-attention mechanism to enforce attention weights between image and text, to extract features from UGI then use machine learning algorithms to predict UGI helpfulness and product sales. We explain features using a caption generation model and test the predictive power of embeddings and portrait characteristics. The results indicate that when predicting commercial information, embeddings are more informative than product and portrait characteristics. Combining VSEM-SAMMI with light gradient boosting (LightGBM) yields a mean squared error (MSE) of 0.208 for UGI helpfulness prediction and 0.184 for product sales prediction. Our study offers valuable insights for e-commerce platforms, enhances feature extraction from UGI through image–text joint embeddings for UGI helpfulness and product sales prediction, and pioneers a caption generation model for interpreting image embeddings in the e-commerce domain.