E-commerce Research Articles

ROI constrained optimal online allocation in sponsored search

Sponsored search plays a major role in the revenue contribution of e-commerce platforms. Advertising systems are designed to maximize platform revenue, but other goals also need to be considered, such as user experience, advertiser utility, and how to achieve the long-term revenue goal. A key component of a sponsored search system is online allocation, which makes real-time decisions to match users’ search requests with relevant ad campaigns to maximize platform revenue within constraints such as campaign budgets. Although much progress has been made, most of the research work on allocation problem has focused on satisfying guaranteed deals for display ads, and those challenges for allocation problems in sponsored search are not properly addressed. In this paper, we develop a framework to solve the large-scale sponsored search ad allocation problem, consisting of two main parts. One is an optimization problem solved offline by a parameter-server based architecture, and the other is an online strategy to alleviate the conflict with the auction mechanism during online service. Comprehensive offline evaluation on real production data and online A/B testing on real production system have been made. The experimental results demonstrate that through better allocating user queries to appropriate ads, the proposed model can significantly increase the platform’s revenue without sacrificing advertisers’ ROI.

Analyzing the Alibaba-Suning Partnership: Strategies and Outcomes

This paper deeply discusses the strategic cooperation between Alibaba and Suning Yunshang, and analyzes its strategic significance and cooperation results in the context of new retail. By sharing resources and complementing each other’s strengths, the two sides have built a new retail ecosystem integrating online and offline, and realized the all-round interoperability of goods, services, technologies and data. This cooperation has not only promoted the rapid growth of the two sides’ business, but also promoted the deep integration of the retail industry and the Internet industry, providing a strong reference for the future development of e-commerce and retail industry in China and even the world. The strategic partnership between Alibaba and Suning Yunshang represents a significant milestone in the evolution of new retail, where online and offline channels converge to create a seamless consumer experience. By leveraging each other’s strengths—Alibaba’s robust e-commerce platform and Suning’s extensive offline retail network—the two companies have fostered a new retail ecosystem that integrates goods, services, technologies, and data across both platforms.

Credit Anomaly Detection Method based on Bayesian Networks

Due to the virtual nature of online transactions and the underdeveloped personal credit risk assessment system of Internet finance lending platforms in China, these platforms often struggle to verify the creditworthiness of applicants prone to fraud and default, leading to loan losses. As a result, risk control has become the core of Internet lending platforms. Modern risk control relies on statistical and data mining techniques to analyze and model data, uncover patterns that indicate loan defaults, and identify anomalies. This paper proposes a Bayesian network-based credit anomaly detection method, which detects anomalies by calculating and ranking the joint anomaly probabilities of sample instances. The technique operates under unsupervised learning and can effectively handle missing and imbalanced data. This method analyzes a loan credit dataset with 32,581 samples and 12 features of the customers. The result of the analysis demonstrates the feasibility and effectiveness of this method in detecting anomalies in online loans.

Influence Analysis of The Factors Determining Consumer Decisions on The Marketing Performance of J&T Express in Pontianak

Digital technology, such as e-commerce, has changed the way people live by providing convenience, speed, comfort and efficiency. Freight forwarding services are vital in online transactions to deliver goods from sellers to buyers. The purpose of this study was conducted to determine the effect of customer orientation, promotional attractiveness, and competitive pricing on marketing performance. This research method uses the causality method which aims to test how much the cause-and-effect relationship between the independent variable and the dependent variable is. The population of this study were consumers totaling 13,577, from 2018-2023, and a sample of 100 respondents. The data analysis technique uses quantitative data analysis and uses a range scale measurement. The results of simultaneous testing (F test) indicate that customer orientation, promotional attractiveness, and competitive prices have a significant effect on marketing performance. The results of persial testing (t test) show that customer orientation, promotional attractiveness, and competitive prices have a significant effect on marketing performance.

DEVELOPMENT OF AN E-COMMERCE PLATFORM USING EXTREME PROGRAMMING METHODOLOGY

<span lang="EN-US">The pervasiveness of information technology has reshaped human lifestyles, including shopping practices. Andalas Store, a prominent bookstore, has experienced a decrease in customer visits. Consequently, this research endeavours to establish an e-commerce platform for Toko Andalas utilizing Extreme Programming (XP) and the MERN Stack to broaden customer reach and expand market presence. The e-commerce platform is constructed leveraging the XP software development methodology and undergoes rigorous testing encompassing unit testing, black box testing, Lighthouse, and the System Usability Scale. The research outcomes validate the successful development and testing of the Toko Andalas e-commerce platform, highlighting its website performance in responsiveness, accessibility, adherence to best practices, and SEO optimization. The platform also encompasses comprehensive functionalities, including product search, shopping cart, and online payment. </span>

Higher Precision Is Not Always Better: Search Algorithm and Consumer Engagement

On decentralized e-commerce platforms, search algorithms play a critical role in matching buyers and sellers. A typical search algorithm routinely refines and improves its catalog of data to increase search precision, but the effects of a more precise search are little known. We evaluate such effects via a 2019 quasiexperiment on a world-leading e-commerce platform in which the search algorithm refined some product categories into finer subgroups to allocate consumer queries to more relevant product listings. Our data cover millions of consumers’ search and purchase behaviors over six months across multiple search sessions and product categories, enabling us to investigate trade-offs over time and across categories. We find that a more precise search algorithm improves consumers’ click-through and purchase rates drastically and instantaneously, but it comes at the cost of a significant decrease in consumer engagement and unplanned purchases over a longer time horizon. On average, consumers who used to spend more time searching now conduct 5.5% fewer searches, spend 4.1% less time on the platform, and decrease their spending on related categories by 2.2% in the week after the search precision increases. Our examination of the mechanisms behind these consequences calls for more careful search algorithm designs that account for not only instant conversion based on search precision but also consumer engagement and sellers’ strategic responses in the longer horizon. This paper was accepted by Anindya Ghose, information systems. Funding: This work was supported by the Key Program of the National Natural Science Foundation of China [Grants 72141305 and 72192803] and the Ministry of Education, People’s Republic of China (Fundamental Research Funds for the Central Universities), both to L. Fang. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00478 .

Application of data-driven Decision Support System in Combating Illegal Wildlife Trade on Internet

With the rapid development of the Internet, the illegal wildlife trade is becoming increasingly serious, posing a threat to biodiversity. Using big data and artificial intelligence technology, this paper identifies abnormal transactions and network relationships by analyzing social media and e-commerce platforms, combined with random forests and graph neural networks, to enhance the efficiency of the crackdown. Random forest algorithms reveal nonlinear patterns and abnormal behavior, and graph neural networks help reveal the complex structure of the transaction network. In implementing the strategy, this paper uses a multi-attribute decision-making model (MADM) and analytic hierarchy Process (AHP) to assess and select the best partners, such as the United Nations Environment Programme, to optimize resource allocation and synergies. Through these technologies, this paper hopes to significantly increase the precision and speed of combating illegal wildlife trade, thereby effectively protecting biodiversity and contributing to the sustainable development of the planet and the harmonious coexistence of the natural environment.

How to establish customers’ green brand relationships and behaviors: from green consumption consciousness perspectives

PurposeThis study aimed to understand how the consumer–green brand relationship affects the behavior of consumers of e-commerce platforms for smallholder farmers; it did so by formulating and testing a framework based on social identity perspective.Design/methodology/approachStructural equation modeling was applied to data from 532 questionnaire responses.FindingsAll hypotheses were supported. Customers’ environmental consciousness, green brand consciousness and green brand self-expression strongly affect green consumption consciousness. The effect of environmental consciousness on green brand identification is stronger than that of environmental consciousness on green brand consciousness and that of green brand self-expression on green brand identification.Practical implicationsSuggestions for managing green brands on e-commerce platforms for smallholder farmers and references for strategizing are also provided.Originality/valueThis study accounts for a wide range of antecedents of green consumption consciousness based on a social identity perspective. This study is the first to elucidate how green consumption affects the e-commerce branding of green smallholder farmers.

Research on the Influence of Anchor Influence on Consumers' Purchase Intention in Live Broadcast Room under the Background of Interest E-commerce

In the emerging shopping mode of interest e-commerce live streaming, anchors are the key factors influencing consumers' purchase intentions, and their personal characteristics and behaviors have a significant impact on consumer decision-making. Based on the stimulus-organism-response (S-O-R) model, this study constructed a structural equation model under the framework of influencer marketing theory to explore the internal mechanism between the various dimensions of anchor influence, consumer trust and identity, and its impact on consumers' purchase decision-making behavior. The results show that in different confidence intervals, the five dimensions of anchors' professionalism, interactivity, authenticity, popularity and innovation can significantly enhance consumers' trust and identity, and then significantly improve consumers' purchase intention. In addition, consumers' trust and recognition play a mediating role between the influence of anchors and consumers' purchase intentions. This study can enrich the theoretical discussion of consumer behavior decision-making in the live streaming environment of interest e-commerce, and provide a strategic reference for e-commerce anchors and interest e-commerce platforms.

Role of Live E-Commerce on Consumer Purchase Intentions

Purpose: One of the many ways that e-commerce allows shops to use live video to promote products in real time is live streaming. Theoretical structure: Perceived value and trust received are important factors of cognitive response showing interaction with external stimuli in the context of online purchasing study. Approach, methodology, and design: In the end, it influences the customers' information-seeking behaviour and helps the business create long-term profits by providing information and enabling synchronous information exchange between the online viewer (consumer) and the Internet celebrity. China has set the standard for innovation in internet commerce worldwide, from live broadcasting to cutting-edge purchasing. Results: For live commerce, social engagement, entertainment, and buy intent were found to be positively correlated. However, no relationship between purchase intent and professionalisation or visualisation was found. During live streaming, entertainment has the most influence on viewers' intention to purchase. Research, Practical, and Social Implications: With the quick advancement of contactless communication, it is projected that the live-streaming e-commerce sector will keep expanding. E-commerce that is live-streamed transcends the traditional notion of solely selling products or services. Originality/value: This study can be used as a standard by future academics and business experts to boost viewers' propensity to make a purchase while live streaming.

Unified Cart Systems: A Paradigm Shift in E-commerce User Experience

This article examines the impact of unified cart systems on e-commerce platforms, focusing on their role in enhancing user experience and driving business performance. Unified cart systems, which enable customers to purchase products across multiple categories in a single transaction, represent a significant innovation in e-commerce. Through a comprehensive analysis of existing literature and industry case studies, this article explores the benefits of unified cart systems, including seamless customer experiences, increased conversion rates, and improved cross-selling opportunities. The article also addresses the challenges in implementing these systems, such as integration complexity and security concerns, and proposes best practices for successful deployment. Our findings suggest that unified cart systems can significantly improve e-commerce performance metrics and provide a competitive advantage in the digital marketplace. This article contributes to the growing body of literature on e-commerce innovations and provides practical insights for businesses looking to optimize their online retail strategies.

A Study On Present Status of E Banking Services in Tamil Nadu

E-banking has rapidly transformed the banking landscape in Tamil Nadu, driven by technological advancements and increasing internet penetration. This paper examines the current status of e-banking services in the state, highlighting key features such as mobile banking, online transactions, and digital wallets. The rise of fintech companies has introduced innovative solutions, enhancing customer experience and accessibility. Despite the growth, challenges remain, including cybersecurity threats and digital literacy gaps among certain demographics. The government's initiatives, such as the Digital India campaign, aim to promote e-banking and financial inclusion. Overall, Tamil Nadu is witnessing a robust adoption of e-banking, making it a crucial player in India's digital financial ecosystem.

Conceptualizing the Implications of Artificial Intelligence (AI) Tools and Personalization Marketing on Consumer Purchase Intention: Insights from the Malaysian E-Commerce Market

Artificial intelligence (AI) has emerged as a powerful tool, enabling online retailers to offer highly personalized shopping experiences tailored to individual preferences and behaviors. As e-commerce continues to grow in Malaysia, understanding the influence of AI and personalization marketing on consumer purchase intentions has become increasingly important for businesses seeking to remain competitive. However, the rapid adoption of AI also raises concerns about data privacy, ethical AI usage, and compliance with emerging data protection regulations, such as Malaysia’s Personal Data Protection Act (PDPA). This study aims to explore the potential impact of artificial intelligence (AI) and personalization marketing on consumer purchase intention within the Malaysian e-commerce market. By reviewing previous literature and theoretical frameworks, the study explores how the use of AI tools including predictive analytics automation, and personalization experiences might influence consumer behavior in online shopping environments. The study adopts a quantitative approach, in which quota sampling will be used for the participant selection. A self-administered questionnaire with a five-point Likert scale will be employed to gather data from e-commerce users in Malaysia. The findings from this study have important implications for both e-commerce businesses and policymakers in Malaysia. For businesses, understanding which aspects of AI and personalization most influence consumer purchase intentions can help them strategically implement these technologies to enhance customer engagement and drive sales. For policymakers, the study highlights the need to consider ethical and legal issues, such as data privacy and policy issues, in the growing use of AI in the e-commerce market.

ENHANCING REPEAT PURCHASE INTENTION (ZALORA INDONESIA ON GEN Y) THROUGH E-SERVICE QUALITY, ETRUST, AND MODERATING ROLE OF PRIOR ONLINE EXPERIENCE

E-commerce has emerged as one of the most developed sectors in the nation's digital economy, consisting of many transactions conducted over the internet, referred to as online sales and purchases. The fundamental success of an e-commerce comes from the results of the sales transactions carried out by consumers, therefore it is essential to convert prospective consumers into buyers who are motivated to conduct repeat purchases, which is the key to achievement in purchasing online, ensuring all businesses carries out various strategies by providing excellent service quality to create satisfaction and keep consumers loyal consequently to encourage repeat purchases. This research contributes to the understanding of how the factors or factors influence consumer behavior, specifically in the context of repeat purchase intention on ecommerce platforms like Zalora Indonesia. The study uses SEM-PLS (SmartPLS 3.0 software) and carried out through three stages, Outer model, Inner model, and hypothesis which inclusive direct and indirect effects. The measurement results show that the variables or factors generate positive and significant hypotheses about repeat purchase intentions, with the exception of the prior online experience variable, which produces negative results concerning the correlation between e service quality and repeat purchase intent, as evidenced by the classification of moderators, which serves as a predictor variable (Predictor Moderation). The study provides managerial implications for the city of Cilegon (particularly among Gen Millennials) to increase Repeat Purchase Intention in order to compete more effectively, especially against Shopee and Tokopedia.

China as data coloniser? rethinking cultural production, cultural mediation, and consumer agency on Kenyan and Chinese e-commerce platforms

Has China become a neo-colonizer, exporting its cultural and economic power to the world based on its agenda of building soft power? Existing scholarship on neocolonialism and data colonialism largely focuses on how China's infrastructural expansion and increasingly platformised cultural sectors can achieve its ambitious platformised cultural sectors overseas. Yet, how China's cultural power is manifested, negotiated, or resisted in people's daily lives in a South–South setting remains under-researched and under-theorised. This article uses everyday fashion in Kenya as a case study to investigate China's cultural and economic power expansion in the Global South. We examined how cultural differences are negotiated and mediated on two Chinese(-invested) e-commerce platforms. Through focus groups and platform walkthrough method, our findings serve to enrich existing theories of cultural production–platform relationships applicable in the study of various cultural and creative sectors, to offer new understandings of how symbolic, sociopolitical and cultural meanings of fashion are constructed through divergent platform affordances, and to reveal the various forms of cultural negotiations and resistance in different contexts, multiplying our frames of reference.

Research on the Matching Mechanism Between E-Commerce Channels and Advertising Appeals

An increasing number of enterprises are adopting a dual-channel business strategy that combines short video e-commerce with traditional e-commerce in their online activities. Previous research on advertising appeals has primarily focused on ad content, products, and consumers. However, the lack of attention given to shopping channels raises a question for the retail industry: How can enterprises create differentiated ads that cater to the needs of consumers in different channels? Across three studies, this research builds upon the regulatory focus theory to demonstrate that consumers on e-commerce platforms, as opposed to short video platforms, are more likely to adopt a prevention-focused mindset rather than a promotion-focused one and vice versa. This positive consumer response arises from the flow experience generated by the regulatory fit between e-commerce channels and advertising appeals. The findings of this research offer both theoretical and practical contributions to the realms of marketing and e-commerce.

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.

Multi-Channel Supply Chains Considering Live Streaming Sales and Spillover Effects

Abstract: With the rapid development of e-commerce, an increasing number of companies, such as Huawei, Apple, and Adidas, are no longer confined to offline physical stores for product sales. They leverage third-party e-commerce platforms or establish online stores on their official websites. Modern consumers have access to various purchasing channels, including e-commerce platforms, offline stores, and manufacturer direct sales. In recent years, a new e-commerce model, live streaming sales, has emerged. In live streaming sales, professional salespersons or key opinion leaders (KOLs) showcase product performance, aiming to sell products online. Unlike traditional online channels, live streaming sales alleviate new consumers' uncertainty about product information through real-time interaction or comments from KOLs. Live streaming has been adopted by e-commerce giants worldwide, such as Taobao, JD.com, and Amazon, due to its significant sales revenue potential. Additionally, channels with KOLs may generate negative or positive spillover effects on other channels. These effects are crucial parameters for exploring the impact of seller profits in the multi-channel supply chain under live streaming conditions. Both domestic and international research have focused on live streaming sales, multi-channel sales, and spillover effects, with many studies yielding significant economic and social benefits. However, challenges faced by multi-channel supply chains, including complex channel management, difficulty in information synchronization, brand consistency issues, and increased costs, need to be addressed. To overcome these challenges, recommendations include integrating channel resources, optimizing channel experience, establishing cross-channel data analysis systems, strengthening supply chain coordination, continuous innovation and improvement, and strengthening brand building.

Digital Transformation Governance Model and Implications for Cosmetic Companies: A Case Study of LOreal

Abstract: As digital technology advances, more companies are undergoing digital transformation, leading to benefits and profits. The cosmetics industry is also following this trend. This study aims to explore the governance model of cosmetic companies during digital transformation and its impact on the development of the industry, using LOreal as a case study for in-depth analysis. By examining LOreals digital transformation case study, this paper reveals the key initiatives and impacts of digital transformation in the cosmetics industry. Specifically, the paper examines LOreals digital transformation initiatives in virtual reality applications, supply chain and operations optimisation, e-commerce platforms and AI product innovation, and analyses the positive impact of these initiatives on the company's business, customer experience and market performance. The study also offers some recommendations for cosmetic companies to address the challenges and risks that may arise during the digital transformation process. By analysing LOreals digital transformation experience in depth, this paper provides a valuable reference for the cosmetics industry to guide other companies to succeed in digital transformation and enhance the overall competitiveness of the industry.

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.