E-commerce Industry Research Articles

Developing A Conceptual Model of Factors Influencing Express Delivery Packaging Recycling Behaviors of E-Commerce Enterprises in China

The rapid growth of the e-commerce industry has led to a significant increase in express delivery service volumes, resulting in substantial environmental pollution and resource depletion from express delivery packaging (EDP) waste. Recycling express delivery packaging (EDPR) has become a critical global concern, particularly in China, the largest contributor to EDP waste for decades. Despite numerous government regulations aimed at promoting recycling behavior to support carbon peaking and neutrality targets, China’s recycling rate remains low, at just 25%-30%, compared to rates as high as 65% in EU countries, with some exceeding 80%. Drawing on Structuration Theory (ST), this study posits that the authority effect positively influences the recycling behavior of e-commerce organizations. It also examines the mediating role of recycling intention in the relationship between authority effect and recycling behavior and the moderating role of social sanction on recycling intention and behavior. This research aims to develop a comprehensive model for understanding the factors influencing EDPR and offers valuable insights from an organizational perspective. The findings will guide future empirical studies and help policymakers devise more effective strategies by understanding the impacts of authority and social sanction on recycling behavior.

Analysis of How Industrial Policy and Competition Policy Promote the Development of Cross-border E-commerce EnterprisesTake the Unicorn Company Shein as an Example

Abstract: Since the 20th National Congress of the Communist Party of China, the central government has proposed to realize the economic development model of domestic and international dual circulation. The development of China's cross-border e-commerce industry is in the ascendant, and the unicorn company Shein has achieved successive revenue achievements in recent years. However, there are still problems with the external environment, which is dominated by industrial policy and competition policy. This paper analyzes the current external environment of China's cross-border e-commerce, analyzes the relationship between China's industrial policy and competition policy, and enlightens the competition policy adjustment of China's cross-border e-commerce industry through foreign experience.

AI Enabled E-commerce Platform

An overview of AI-based e-commerce plat forms and their effects on the online retail sector is given in this survey. Businesses are starting to use artificial intelligence (AI) to improve customer experience, streamline processes, and boost revenue development in the e-commerce industry as a result of the technology’s rapid advancement. This research offers a thorough examination of the state of AI-based e-commerce platforms today, stressing their salient characteristics, advantages, difficulties, and potential. The first part of the poll introduced artificial intelligence (AI) and how it relates to e-commerce. It looks at how artificial intelligence (AI) technologies like computer vision, machine learning, natural language processing, and recommendation systems are being incorporated into e-commerce platforms to provide intelligent customer support, enhance product search and discovery, and offer personalized shopping experiences. Additionally, it explores the role of AI in enhancing customer engagement through chatbots, virtual assistants, and personalized marketing campaigns

Investigating factors influencing AI customer service adoption: an integrated model of stimulus–organism–response (SOR) and task-technology fit (TTF) theory

PurposeArtificial intelligence (AI) customer service has grown rapidly in recent years due to the emergence of COVID-19 and the growth of the e-commerce industry. Therefore, this study employs the integration of the stimuli–organism–response (SOR) and the task-technology fit (TTF) frameworks to understand the factors that affect individuals’ intentions towards AI customer service adoption in Malaysia.Design/methodology/approachThe study utilised a survey-based research approach to investigate the factors that affect individuals’ intentions towards AI customer service adoption in Malaysia. The data were collected by conducting an online survey targeting individuals aged 18 or above who had prior customer service interaction experience with human service agents but had not yet adopted AI customer service. A sample of 339 respondents was used to evaluate the hypotheses, adopting partial least squares structural equation modelling as a symmetric analytic technique.FindingsThe PLS-SEM analysis revealed that social influence and anthropomorphism have a positive direct relationship with emotional trust. Furthermore, communicative competence, technology characteristics and perceived intelligence were positively correlated with TTF. Moreover, emotional trust significantly impacts AI customer service adoption. In addition, AI readiness positively moderates the association between task technology fit and AI customer service adoption.Practical implicationsThe study provides insights to individuals, organisations, the government and educational institutions to improve the features of AI customer service and its development in Malaysia.Originality/valueThe originality of this study is found in its adoption of the SOR theory and TTF to understand the factors affecting AI customer service adoption. Additionally, it incorporates moderating variables during the analysis, adding depth to the findings. This approach introduces a new perspective on the factors that impact the adoption of AI customer service and offers valuable insights for practitioners seeking to formulate effective strategies to promote its adoption.

Research on the Development of Logistics Performance of E-Commerce Enterprises and its Influencing Mechanism on Customer Satisfaction

With the booming development of e-commerce industry, logistics performance has become one of the key factors of competitiveness for e-commerce enterprises, directly related to the customer satisfaction and market position of the enterprises. Under the background of digitalization and intelligentization, online shopping has become one of the main ways of daily shopping for consumers. As an important link in the transaction chain of e-commerce, logistics efficiency and quality directly affect the shopping experience and loyalty of consumers. This paper conducts an in-depth analysis of the current situation of logistics performance of e-commerce enterprises, the mechanism analysis of how it affects customer satisfaction, and explores solutions to the problem. Through the mechanism analysis, it is found that e-commerce enterprises can improve logistics performance by enhancing logistics speed, improving logistics service quality, reducing costs and increasing efficiency, strengthening green logistics, and enhancing cross-border e-commerce logistics capabilities. This can improve customer satisfaction, and the paper proposes suggestions such as optimizing logistics networks, enhancing logistics service training, fine-tuning management, promoting environmentally friendly packaging, optimizing transportation methods, and improving cross-border e-commerce logistics networks.

Research on the Impact of Big Data and Enterprise Digitalization on the E-commerce Industry and Coping Strategies

Abstract: Big data, enterprise digitalization, and e-commerce all have one thing in common: they were born from the Internet. Since the onset of the 21st century, the global Internet industry has experienced rapid growth, with e-commerce emerging as a key player. Participants in e-commerce engage with both the global online marketplace and the physical market, utilizing modern communication networks and information technology to conduct business activities through e-commerce platforms or service platforms. E-commerce embodies characteristics such as market globalization, rapid transactions, virtualization, and cost-effectiveness, making it highly favored by enterprises. Presently, e-commerce relies heavily on robust databases, as well as the data collection, processing, and analysis capabilities of big data. This reliance compels traditional enterprises to undergo digital transformation. This study delves into the impact of big data and enterprise digitalization on the e-commerce industry, exploring the mechanisms underlying their influence on cross-border e-commerce. By examining these dynamics, the study aims to provide insights into the evolving landscape of e-commerce and the transformative potential of big data and digitalization in shaping its future trajectory.

Intelligent E-commerce under the Trend of Integration of Artificial Intelligence and E-commerce: A Case Study of Alibaba International Station

Abstract: China has fully entered the era of digital economy, and undoubtedly, the e-commerce industry needs to keep pace with the times and evolve into a new stage of development: integrating artificial intelligence with e-commerce. Nowadays, an abundance of new productivity tools have unveiled new opportunities for the e-commerce industry. This study focuses on Alibaba International, a premier global digital trading platform, to illustrate the applications of artificial intelligence tools in e-commerce, examine both the positive and negative impacts of these tools on e-commerce, and suggest strategies for enhancing e-commerce Operation Management. The aim is to offer enlightenments for small and medium-sized cross-border e-commerce businesses. Through this case analysis, it can be understood that intelligent e-commerce has provided many conveniences for the e-commerce industry, but also faces a series of challenges, such as AI generated false or infringing content, and increased operational management difficulties. In this situation, e-commerce companies should take strengthening the cultivation of intelligent talents as a guarantee, focusing on customer experience and valuing personalized product needs as advantages, and improving operational management efficiency through the reasonable use of favorable policies to bring profits to the enterprise.

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.

A Study on Revenue Distribution of Chinese Agricultural E-Commerce Supply Chain Based on the Modified Shapley Value Method

With the rapid development of e-commerce in China, “live broadcast + e-commerce” has become an emerging mode of the e-commerce industry. The speedy growth of the digital economy has also provided a favorable external environment for fresh agricultural products to ride on the e-commerce express train. As a result, more and more suppliers of fresh agricultural products choose to sell their products on e-commerce platforms and conduct live streaming. This paper utilizes the e-commerce supply chain field survey data, takes tomato as the specific analyzed variety, and studies the revenue distribution pattern of each subject in the supply chain. The purpose of this paper is to explore the optimization strategy of the revenue distribution of an agricultural e-commerce supply chain, and the results show that the agricultural e-commerce supply chain effectively promotes the matching of supply and demand of medium- and high-end agricultural products, realizes the quality premium of medium- and high-end agricultural products, and significantly improves the overall revenue level of the supply chain. The field survey found that under the supply chain model of “farmers + cooperatives + e-commerce platform”, farmers, cooperatives, and e-commerce platforms gained $2865, $3098, and $1111, respectively, for operating one mu of tomatoes in a year. According to the traditional Shapley value method, from the point of view of the contribution to the cooperative income, part of the income of the farmers and cooperatives should be compensated to the e-commerce platform, and the specific results are as follows: the income of the farmers should be reduced from $2865 to $2729, the income of the cooperatives should be reduced from $3099 to $2955, and the income of the e-commerce platform should be increased from $1111 to $1390. According to the Shapley value method based on the risk coefficient correction, from the perspective of risk compensation, part of the earnings of the e-commerce platform should be compensated to farmers and cooperatives, so as to establish a more equitable and reasonable pattern of earnings distribution, with the specific results: the earnings of farmers should be increased to $3205, the earnings of cooperatives should be increased to $3148, and the earnings of the e-commerce platform should be reduced to $722.

The Impact of Social Media Marketing on Consumer Purchase Intention and Customer Loyalty on E-commerce Websites

Thailand’s e-commerce industry has significantly improved in recent years, driven by widespread Internet access and smartphone adoption. The diverse online marketplace caters to the changing needs and preferences of Thai consumers. However, the impact of social media marketing on consumer purchase intention and loyalty to e-commerce sites has not received sufficient attention. This study focuses on online shopping platforms in Thailand to investigate the role of social media marketing in influencing consumer purchase intention and loyalty to e-commerce platforms. For this purpose, 400 online questionnaires were required for data collection and used for testing the hypothesis, based on the suitable statistics through the advanced statistical program. The findings indicated that demographic factors, consumer behavior, and social media engagement have significantly influenced consumer purchase intention. This research also highlighted implications for how e-commerce sites can enhance their marketing strategies and foster customer loyalty to their products. Keywords: social media marketing, purchase intention, customer loyalty

Analyzing the Impact of Interior Public Space on User Satisfaction in Shopping Malls Using Virtual Reality Simulation Experiments

The growth in per capita consumption levels and the e-commerce industry have shifted the shopping mall model from a product consumption to a time-based consumption focus. This paper evaluates customer satisfaction with shopping malls using an importance–performance questionnaire, which identified eight key areas for improvement. Through field surveys and virtual reality (VR) simulation experiments, the impact of the components of public spaces within shopping malls on user satisfaction was assessed using both quantifiable and unquantifiable elements. Our findings inform the formulation of optimization design strategies in the areas of multipurpose functionality of facilities, intensification of boundaries, complexity of functions, and integration of resources, and have implications for the future design of internal public spaces in shopping malls.

Creating Competitive Advantage through Digital Innovation: Insights from Startupreneurs in E-Commerce

Digital innovation has become a crucial factor in creating competitive advantage for startups in the e-commerce industry. In the fast-paced digital economy, startupreneurs must leverage technological advancements to differentiate themselves and ensure sustainable growth. This research aims to explore how digital innovation is utilized by startupreneurs in e-commerce to gain a competitive edge, focusing on the strategies that enhance market presence and customer loyalty. A mixed-method approach was adopted, combining surveys and in-depth interviews with e-commerce startupreneurs, along with a comprehensive literature review to provide a theoretical foundation. The findings indicate that startups implementing digital innovations such as automation, data analytics, and personalized customer experiences see significant improvements in customer satisfaction and operational efficiency. These innovations play a pivotal role in helping e-commerce startups maintain competitiveness in a saturated market. In conclusion, this study highlights the practical importance of adopting digital innovation for e-commerce startups and contributes to the theoretical understanding of how digital tools can drive entrepreneurial success in modern markets

The Impact of Blockchain Technology on E-commerce Product Development: Case Studies of Walmart and LVMH

In the current digital era, the emergence of blockchain technology has profoundly impacted business models, especially in e-commerce. Blockchain technology brings new opportunities and challenges to product sales, transactions, and management. This study examines the impact of blockchain technology on the development of e-commerce products, particularly through the cases of Walmart and Louis Vuitton (LVMH). It demonstrates the application of blockchain and its effects on product traceability, anti-counterfeiting verification, data transparency and privacy protection. It was found that blockchain technology improves data security and user trust, reduces transaction costs, and enhances supply chain management efficiency. However, blockchain also needs help regarding energy consumption, performance bottlenecks, privacy protection, inconsistent technical standards, and unclear laws and regulations. It is necessary to take appropriate measures to reduce risks and ensure the effective application of technology. This study concludes with a series of recommendations to promote the effective application of blockchain technology in e-commerce to facilitate the e-commerce industry's sustainable development.

Explore the AI Anxiety Attitude of E-Commerce Employees

With the rapid development and widespread application of artificial intelligence (AI) technology in the e-commerce industry, understanding practitioners’ attitudes and psychological reactions to AI has become critical. This study explores the impact of AI on the anxiety of e-commerce professionals, establishes a structural equation model by constructing an integrated Artificial Intelligence Anxiety (AIA) framework, and analyzes the path relationships between various anxiety factors. The results show that Learning Anxiety and Job Replacement Anxiety are associated with Existential Risk Anxiety, AI Configuration Anxiety, and Sociotechnical Blindness Anxiety. The study further extends its analysis using a machine learning algorithm to assess the impact of each AIA variable and individual variables on job optimism. The results indicate that among the AIA variables, Job Replacement Anxiety has a significant negative impact on job optimism, while among the individual variables, gender has a significant negative impact. This study provides important insights into understanding the AI-related anxiety of e-commerce practitioners and offers implications for the management and development of AI in practice.

IMPACT OF BLOCKCHAIN TECHNOLOGY IN THE E-COMMERCE SECTOR

Blockchain technology is considered as a significant development after the invention of the internet. Blockchain technology records the transactions on database which are encrypted and distributed over many computer networks like a digital ledger of online transactions. This technology can be utilized in the e-commerce industry to develop a decentralized online buying and selling platform. E-commerce industry presently facing many unique challenges like security measures of the e-commerce system, transparency, and trust, efficiency, etc. These challenges can be answered by the implementation of blockchain technology in the e-commerce industry. This paper discussed opportunities for using blockchain technology in the e-commerce industry. Blockchain applications and opportunities are discussed for various aspects of e-commerce like Payment, Security, Supply chain, Work automation with Smart contract, Ethical practices for transparency in e-commerce transactions. The paper seeks to explore the possible uses of blockchain technology within the e-commerce sector.

E-commerce market entry strategy, adoption and market performance: Basis for e-commerce industry synergy framework

E-commerce market entry strategy, adoption and market performance: Basis for e-commerce industry synergy framework

Evaluating the Impact of Digital Technology Adoption on Business Strategy Transformation: An SmartPLS Analysis in the E-Commerce Industry

Evaluating the Impact of Digital Technology Adoption on Business Strategy Transformation: An SmartPLS Analysis in the E-Commerce Industry

패션산업 전자상거래에서 물류 서비스가 고객만족도에 미치는 영향

Purpose: A s t he c ommerce m arket r apidly c hanges d ue t o t echnological a dvancements and consumer preferences, the competitiveness of logistics services is highlighted. This study analyzes the factors of logistics in fashion industry e-commerce that affect customer satisfaction. The goal is to provide insights to fashion companies to develop logistics strategies. Research design, data, and methodology: In the research model for empirical analysis, the logistics service factors are reliability, economy, convenience, and speed. Data obtained via questionnaire were analyzed for multiple regression and moderating effects. Results: As a result of the analysis, the logistics service factors that provide economy, convenience, and speed had a significant impact on customer satisfaction. There were significant differences in customer satisfaction based on gender and age. Conclusions: Fashion companies must provide a variety of logistics services, and need to improve customer satisfaction through continuous service improvement.

Segmentasi Pelanggan E-Commerce Menggunakan Fitur Recency, Frequency, Monetary (RFM) dan Algoritma Klasterisasi K-Means

The rapid growth in the e-commerce industry demands the development of smarter and more focused marketing strategies. One approach that can be applied is customer segmentation using various features such as Recency, Frequency, and Monetary (RFM), along with machine learning-based clustering methods. The objective of this study is to design and develop a web-based e-commerce customer segmentation application using a combination of RFM features and clustering methods. The study proposes the K-Means algorithm and compares it with K-Medoids and Fuzzy C Means using publicly available e-commerce datasets. Experimental results showed that the K-Means algorithm outperformed K-Medoids and Fuzzy C Means (FCM) based on the Silhouette Score of 0.67305, Davies Bouldin Index of 0.51435, and Calinski Harabasz Index of 5647.89. Through analysis and testing, the designed application has proven effective in grouping customers into relevant segments. These segments are divided into three categories: Loyal, Need Attention, and Promising, visualized in a web-based application dashboard using Streamlit. The developed application allows e-commerce business owners and users from the business, management, and marketing divisions to categorize customers based on transaction data. The results of this study are expected to provide valuable insights to e-commerce management and marketing professionals who are facing increasingly fierce competition.

Exploring the synergy of guided numeric and text analysis in e-commerce: a comprehensive investigation into univariate and multivariate distributions

This research adopts a holistic approach to analyze customer reviews in the e-commerce industry by utilizing a combined approach of numerical and text analysis. Specifically, this study integrates univariate, multivariate, and sentiment analysis to gain comprehensive insights into product preferences and customer satisfaction. The methodology includes a detailed examination of univariate distributions to uncover numerical trends in product ratings and preferences. Multivariate distributions are explored to understand the complex relationships between related variables. Sentiment analysis is performed using the Sentiment Intensity Analyzer to categorize reviews into positive, neutral, and negative sentiments. Additionally, N-gram analysis is applied to both recommended and non-recommended reviews to identify key themes, such as dissatisfaction with product size and satisfaction with fit. Logistic regression and naive Bayes models are employed to classify sentiment, with logistic regression achieving high accuracy on both training (91.3%) and validation data (89.2%). This research highlights the significant role of product recommendations as indicators of positive sentiment, while product ratings reveal the complexity in consumer judgment. The study contributes significantly to understanding the dynamics of customer reviews in the e-commerce industry, providing a solid foundation for smarter decision-making to improve customer experience and product quality.