E-commerce Community Research Articles

Easily Determining Post-Study System Usability for Anime Community E-Commerce Analysis

The rapid growth of e-commerce as a form of electronic commerce has transformed the global shopping landscape. To maintain user satisfaction and competitiveness, e-commerce sites must effectively understand and improve the usability of their systems after launch. The aims of the study are to improve e-commerce post-launch usability so that anime fans' enthusiasm can be capitalized upon for financial gain and market expansion. In this study, we adopted a combined approach that included user observations, interviews, surveys, and performance measurement for anime community e-commerce analysis Weeboo web commerce. Through this method, we analyze the behavior and views of users towards e-commerce systems. The results show that most users experience a positive experience in shopping online by appreciating the usability of the layout, search process, and fast checkout process. The results indicated that most users have a positive online shopping experience, appreciating the layout, search process, and fast checkout process. The SUS score of 75.375 (grade B) and the overall PSSUQ satisfaction score of 2.0296 indicate that the system is acceptably well-received. The proposed recommendations can help e-commerce companies quickly identify usability issues and implement relevant fixes.

Optimization and Innovation of the Operation Model of Mobile Social E-commerce under AI Empowerment: Taking the Female Community Platform “Little Red Book” as an Example

This article takes the female community platform “Little Red Book” as an example to explore the optimization and innovation of mobile community e-commerce operation mode under Artificial Intelligence (AI) empowerment. Firstly, the relevant concepts were defined, and then the unique attributes of mobile community e-commerce were analyzed. As a typical representative of mobile community e-commerce, Little Red Book introduces the background and characteristics of its platform, analyzes its mobile community operation mode, and focuses on exploring how to establish a mobile community e-commerce platform and effective operation mode under the empowerment of AI technology, to provide some reference and inspiration for the development and operation of Little Red Book and other e-commerce platform enterprises.

Research on Fresh Produce Delivery Scheduling Problems under the Perspective of Community E-commerce

This paper addresses the challenges associated with the high costs and logistical difficulties in the distribution of fresh agricultural products from the perspective of community e-commerce. The paper takes the perspective of merchant scheduling and integrates a classical vehicle scheduling model. The study meticulously examines the various costs associated with the distribution process, encompassing fixed costs, transportation costs, refrigeration costs, storage costs and time subsidies. A vehicle routing optimisation model was developed with the dual objectives of cost control and product quality assurance. The use of a genetic algorithm for analysis and solution indicates that this approach can achieve superior cost efficiency and more optimal delivery routes. The results not only confirm the validity of the model and the effectiveness of the solution algorithm, but also provide theoretical underpinnings and viable optimisation strategies for analogous route optimisation challenges. This research has significant reference value and practical relevance for improving delivery efficiency and cost management in community e-commerce.

Enhancing Community E-Commerce Repurchase Prediction Through Information Entropy Analysis

In this study, we delve into the prediction and recommendation algorithm for user repurchase behavior within the community e-commerce landscape, leveraging the framework of information entropy. By analyzing consumer behavior data from a product-centered perspective, we observe that the majority of consumers engage in approximately 0-8 information search clicks before making a purchase, primarily focusing on product details, with an average of about 4 browsing interactions. Subsequently, a minimal portion of consumers exhibit 0-5 instances of preference behavior after browsing activities. Similarly, consumers who add items to their shopping carts typically engage in this behavior from 0 to 4 times, showcasing limited participation. Leveraging attribute probability, information entropy serves as a driving force for predicting community e-commerce users' repurchase behavior, contrasting with traditional outlier detection methods and underscoring the advantages of information entropy.

Research on the marketing strategy of agricultural product community e-commerce

With the continuous progress and development of the “global e-commerce era”, China's community e-commerce of marketing environment of agricultural products is growing rapidly. At present, the level of e-commerce development in various regions of China is not balanced, resulting in farmers in many regions still lacking marketing awareness of community e-commerce. Through the analysis of the current status of China's e-commerce development of agricultural products, this study found that there are existing problems such as: the imbalance of agricultural products e-commerce development scale in various regions, the difficulty of agricultural products logistics and distribution, the uneven quality level of agricultural products, the lack of community marketing awareness among merchants, and the gap in e-commerce talents. Based on the content marketing theory, this paper draws research conclusions and puts forward suggestions for optimizing community e-commerce marketing.

The analysis of social E-commerce with artificial intelligence

Nowadays, with the widespread popularization and development of the internet, the e-commerce industry has begun to rise, among which social e-commerce, as a new community, has become popular on the Internet. At the same time, the field of artificial intelligence is slowly infiltrating into every field of today's society. The diversified data contained in the social e-commerce platform has great potential value, but artificial intelligence, as an important technology of information analysis, is rarely applied in this direction. This paper fundamentally discusses the role of artificial intelligence in e-commerce. Taking Xiaohongshu as an example, the SWOT framework is used to analyze the advantages and drawbacks, potential benefits and risks caused by the application of artificial intelligence in an e-commerce platform with rich user data. The limitation and extensibility of artificial intelligence in e-commerce platforms, finally put forward the application prospect of artificial intelligence in the e-commerce direction. This study recommends that the social e-commerce community establish a robust data privacy protection system, increase investment in technology research and development, and fully leverage the potential of AI technology.

A Comprehensive Exploration of Machine Learning Models for Predicting Online Auction Prices

The transformative impact of traditional commerce by online marketplaces is exemplified through eBay, a global platform that facilitates diverse transactions via auctions. In this research, the dynamics of eBay auctions, crucial for buyers, sellers, and researchers, are delved into. The central inquiry revolves around the key factors shaping auction outcomes, examining bid behaviors and types. The study leverages a robust dataset from eBay, meticulously curated to encompass auction identifiers, bid details, pricing information, auction types, and temporal aspects. A comprehensive approach involves data preprocessing, ensuring reliability by addressing missing values and outliers. Rigorous exploration and validation validate the dataset's integrity. Machine Learning Techniques, including MLP, SVR, Linear Regression, Extra Trees, and Gradient Boosting, form the analytical backbone. Model evaluation reveals top-performing candidates, such as MLP Regressor (0.8084), SVR (0.8210), and Linear Regression (0.8173), exhibiting superior accuracy and reliability. These models are identified for adoption in future work, emphasizing nuanced predictions in eBay auctions. This research contributes to understanding online auction dynamics, offering practical insights for eBay users and the broader e-commerce community. The models identified pave the way for enhanced predictive capabilities and continuous refinement in deciphering factors influencing auction outcomes.

Examining Network Effects and Platform Differentiation in Trend Community E-commerce: Case Study of Poizon

With the rapid rise of platform-based enterprises in the digital economy, particularly in the post-epidemic era, online sales have become the primary sales channel for trendy goods. Many platform-based enterprises that combine community and e-commerce attributes have flourished. This paper examines the network effect of community e-commerce platforms in the trend industry, using Poizon as a case study. The success of Poizon provides valuable insights for similar enterprises, and network effects are one of the keys to its success. How-ever, Poizon still faces some challenges, which are analyzed from the perspective of net-work effects, and corresponding solutions are proposed. Chapter two provides an overview of network effects in the network platform economy, while chapter three analyzes the net-work effect of Poizon as a power-flow community e-commerce platform. Finally, chapter four concludes the paper.

Analysis and Recommendations for Community E-commerce Word-of-Mouth Marketing Strategies Based on Trust Theory

Based on the perspective of trust theory, this paper conducts in-depth research and analysis on the word-of-mouth marketing strategy of community e-commerce. In the course of the research, the basic concepts of trust theory and the characteristics of community e-commerce are sorted out, and the importance of word-of-mouth marketing in community e-commerce is clarified. The research results show that the success of community e-commerce word-of-mouth marketing is closely related to users' trust in the platform. Community e-commerce word-of-mouth marketing has challenges to the authenticity of word-of-mouth, the proliferation of false word-of-mouth, and the response to public opinion crises. In order to solve these problems , through the implementation of strategies such as strengthening the monitoring and management of user evaluations, establishing a true and credible word-of-mouth communication mechanism, strengthening the identification and prevention of false word-of-mouth, and establishing a crisis management mechanism to respond to public opinion crises in a timely manner, these strategies will help improve community e-commerce The effect of word-of-mouth marketing and users' trust in the platform will promote the innovation and sustainable development of the community e-commerce industry.

Research on Crowdsourcing Distribution Model of Community E-commerce Logistics Development

This paper provides an overview of the concept and characteristics of community e-commerce logistics, crowdsourcing logistics, existing models and the current situation and problem analysis of community logistics development, analyzes the necessity and feasibility of developing crowdsourcing distribution model for community e-commerce logistics, designs a crowdsourcing distribution platform for community e-commerce logistics from the perspective of considering security, reliability and flexibility, and proposes measures such as information risk guarantee, quality risk guarantee, security risk guarantee, capital risk guarantee and legal risk guarantee, in order to provide reference and reference for the study of community e-commerce logistics crowdsourcing distribution model. It also proposes measures such as information risk protection, quality risk protection, security risk protection, capital risk protection and legal risk protection to provide reference and reference for the research on the crowdsourcing delivery model of community e-commerce logistics, with a view to promoting the development of community e-commerce logistics delivery and improving the competitiveness of community e-commerce logistics.

Study of Consumers’ Purchase Intentions on Community E-commerce Platform with the SOR Model: A Case Study of China’s “Xiaohongshu” App

As e-commerce flourishes, a new model known as community e-commerce has sprung up, which gathers users of the same interests and values and provides precise content marketing for specific groups, to satisfy the various demands of customers. Studying the influencing factors of community e-commerce platforms on the purchasing willingness of customers can offer valuable references for a healthily developed community e-commerce. On the basis of the SOR model, this work establishes a structural model of the consumers' purchase intentions on community e-commerce platforms. Citing the Xiaohongshu application, an analysis is conducted, using structural-equation modeling and questionnaire. The results show that the perceived value is positively influenced by the product features, content marketing, and community factors which prod customers to be ready to purchase. Based on the research conclusions, management suggestions are proposed, including building products with utmost cost-performance, emphasizing precise content-marketing, creating an active atmosphere in the community, and establishing the social responsibility of platforms, etc.

A Comprehensive Exploration of Machine Learning Models for Predicting Online Auction Prices

The transformative impact of traditional commerce by online marketplaces is exemplified through eBay, a global platform that facilitates diverse transactions via auctions. In this research, the dynamics of eBay auctions, crucial for buyers, sellers, and researchers, are delved into. The central inquiry revolves around the key factors shaping auction outcomes, examining bid behaviors and types. The study leverages a robust dataset from eBay, meticulously curated to encompass auction identifiers, bid details, pricing information, auction types, and temporal aspects. A comprehensive approach involves data preprocessing, ensuring reliability by addressing missing values and outliers. Rigorous exploration and validation validate the dataset's integrity. Machine Learning Techniques, including MLP, SVR, Linear Regression, Extra Trees, and Gradient Boosting, form the analytical backbone. Model evaluation reveals top-performing candidates, such as MLP Regressor (0.8084), SVR (0.8210), and Linear Regression (0.8173), exhibiting superior accuracy and reliability. These models are identified for adoption in future work, emphasizing nuanced predictions in eBay auctions. This research contributes to understanding online auction dynamics, offering practical insights for eBay users and the broader e-commerce community. The models identified pave the way for enhanced predictive capabilities and continuous refinement in deciphering factors influencing auction outcomes.

Retracted: Supply Chain Decision Analysis of Community E-Commerce Platform under Different Power Structures: Considering the Influence of Value Cocreation.

[This retracts the article DOI: 10.1155/2021/2522245.].

Supervision Timing Simulation Analysis of Community E-commerce Platform Supply Chain Based on Tripartite Game Model

Supervision Timing Simulation Analysis of Community E-commerce Platform Supply Chain Based on Tripartite Game Model

Retracted: Research on Dynamic Evaluation Modeling of Community e-Commerce Service Satisfaction under the Background of New Retail

Retracted: Research on Dynamic Evaluation Modeling of Community e-Commerce Service Satisfaction under the Background of New Retail

The Analysis of the Mode of Community E-commerce Platform about The Small Red Book

In recent years, with the rapid development of Internet data communication and network technology, big data technology has penetrated into all walks of life, and also has a crucial impact on the future development of new media, an emerging industry. The integration of new media into all aspects of people's life deeply affects people's behaviors and habits, and its development is closely related to the life of individuals and even the development of the country. Taking Small red book as an example, this paper uses 4Ps and SWOT analysis to analyze its brand positioning, APP marketing status and existing problems, and focuses on the development strategy of new media Small red book from the perspective of big data. The research results show that Small red book has its unique development advantages due to its unique and effective business model, making its strength remarkable in the same industry. This research has certain practical significance for developers of community e-commerce platforms.

Analysis of Consumer Behavior in Community and Campus Based on Questionnaire-Taking Tianjin University Students and the Elderly as Examples

With the gradual improvement of the supply chain and the rapid development of the e-commerce economy, community e-commerce and campus e-commerce is getting closer to daily life. The outbreak of the new crown epidemic in 2020 has promoted online community group buying, fresh food e-commerce and campus e-commerce. With the rapid development of other formats, the consumer group has been greatly expanded. This article conducts a questionnaire survey on the community e-commerce and campus e-commerce consumer groups in the post-epidemic period, after using the "Questionnaire Star" software to make questionnaires, the research members disseminated the questionnaires among the surrounding college students through various social channels. At the same time, the researchers also visited universities and communities in Tianjin, and randomly invited college students and elderly people to fill out the questionnaires, then analyzes the consumption behavior of consumers of different age groups and living conditions in terms of consumption time, reasons for purchase, types of products purchased, and preferences for promotional activities characteristics, and then further compare the differences and similarities of the consumption behavior of the elderly and college students, deeply explore the needs of consumers, integrate and optimize data, and provide scientific decision-making suggestions for the operation of e-commerce companies between the two groups. The choice of community and campus e-commerce supply models is more realistic.

Study on Deep Learning Technology to Construct E-Commerce Industry Marketing Promotion Model

In recent years, with the development of Internet, Internet technology has swept the world and become a new round of scientific and technological revolution in the world economy. Network marketing has become the “darling” of marketing, with a multidimensional and multilevel marketing layout, which provides a carrier platform for the promotion of enterprise marketing strategy. The traditional network marketing mode is mainly in the form of advertising push, with pop-ups, screen overlays, top search rankings, loading videos, texts, and pictures. Nowadays, Internet technology and social media have promoted the development of community e-commerce models. Live delivery is a new sales model, which has promoted economic development. It has many advantages, such as interactivity, real-time, and transparency. Compared with the traditional e-commerce model, it is more suitable for the development requirements of the new era. Live delivery is a new sales model, which promotes economic development. This paper starts with the AdaBoost algorithm and marketing mode of STP and 4P marketing theory, in order to reduce the execution cost of marketing. In this way, it proves that using online marketing can not only reduce the cost but also improve the marketing effect. Through the new network marketing promotion mode and algorithm described in this paper, the cost can be reduced by 17%, and the promotion effect is remarkable.

Effect of e-service quality on customer engagement behavior in community e-commerce

Customer engagement behavior is a critical success factor for community e-commerce. While many community e-commerce websites are currently improving service quality to enhance customer engagement behavior, little is known about how such e-services affect customer engagement behavior. Building upon the stimulus-organism-response (SOR) model, this study developed a research model to explain how e-service quality of community e-commerce platform affects customer engagement behavior through customer trust and perceived risk. The research model was empirically evaluated by surveying 326 customers who have shopped through the community e-commerce platforms. The results indicate that e-service quality (system design, intelligent fulfillment, security assurance, and interactive service) positively affects customer engagement behavior. Besides, customer trust and perceived risk play a mediating role between e-service quality and customer engagement behavior. This study offers recommendations to managers on how to build an attractive community e-commerce platform to stimulate customer engagement behavior.

Game Analysis on the Influence of Participants' Psychology on Value Co-Creation in Community E-Commerce Platform Supply Chain.

As e-commerce continues to develop, online shopping is becoming more and more popular, and community e-commerce platform emerges as the times require. To make the participants in the supply chain of community e-commerce platform play a greater role in value creation, the supply chain of community e-commerce platform can get more benefits. This paper studies the psychology of all participants in the supply chain of community e-commerce platform, so as to establish the value creation mechanism of community e-commerce platform. Through the research, it is found that by studying the psychology of the two main participants in the supply chain of community-based online store, community e-commerce platform and community leader, and considering whether they will do their best to participate in value co-creation, a two-party evolutionary game model is constructed. By calculating the dynamic replication equation, the influence of the psychology of the participants in the supply chain of the community-based online store on the supply chain revenue of the community-based online store is obtained, and the evolutionary trend of both parties in the game is simulated by Matlab. It is concluded that the key factors that affect the co-creation of the value of a community-based online store supply chain are the proportion of increased income, the cost of participating in co-creation of value, and the purchasing power of consumers brought by participating with both parties. Finally, some suggestions and countermeasures are put forward through the analysis of simulation results.