E-commerce Research Articles

Revolutionising Skincare Through Airtificial Intelligence

Abstract: Background: Over the last 10years there has been a drastic shift in the development of computer-based systems, artificial intelligence, algorithms and technology in the world of dermatology be it cosmetic or even retail production of products sold to customers with high-tech technology and innovative means to build their client base through customer delight. Artificial Intelligence is particularly helpful in recognizing the type of skin customers have and suggesting solutions which help customers pick from the range of options given to them. Most e-commerce platforms have begun using such AI driven soft wear to help customers while they are in the comfort of their homes to choose from a range of cosmetic options right from lipsticks, concealers, foundation, eyeshadows, shades of tins, serums etc. Given that every person has a different skin type, these applications ensure 100 percent personalized suggestions. This new trend is sending shock waves across the retail sector around the world attracting a massive customer base to adapting to AI Derma logy and retail. The intelligent software used by sever companies not only benefits the customer, but also helps companies with their operational efficiency as they can use AI algorithms to predict and forecast the inventory to help reduce over production and excessive stock production minimizing product wastage and improving sustainably.AI is booming in both clinical diagnosis as well as giant companies such as Loreal and Olay who are dedicated to providing customers with advanced tools like skin analyzers or apps based on predefined rules which help AI-powered tools analyze skin conditions, age, and personal skincare history to provide highly personalized product recommendations. In clinical diagnosis AI offers valuable insight to the patient’s skin and condition be it cancer or other skin diseases making judgments to assist the doctors and dermatologist in making informed decisions regarding the line of treatment for the patient in question. This includes screening, diagnosis, treatments and predictions of the treatment outcomes. These new innovative and robust AI systems leaving customers feeling confident and have not begun relying on AI driven treatments and procedures

Dynamic Price Monitoring in E-Commerce: Leveraging Web Scraping, Data Analysis, and Email Automation for Dynamic Pricing Insights

This paper focuses on the development of a web application that leverages web scraping, data storage, and email automation technologies to track real-time price changes for specific products on e-commerce platforms. Built using Next.js for the frontend, MongoDB as the database, and Nodemailer for email notifications, the system aims to provide users with a comprehensive and user-friendly interface to monitor price fluctuations. By scraping data from product pages, the application gathers key pricing metrics, including the average, lowest, highest, and current prices, and presents this information visually to help users make informed purchasing decisions. By utilizing the combination of web scraping and real-time data analysis, this project provides a dynamic solution for both consumers and businesses, offering valuable insights into price changes and trends in the competitive e-commerce landscape.

A comprehensive outline towards conciliation in the contemporary world of E-commerce

Since the start of the millennial era, technical advancements have led to exponential growth in electronic commerce, or e-commerce. According to the United Nations Conference on Trade and Development (UNCTAD), e-commerce sales reached about US$25 trillion globally in 2021 [1] and are expected to grow at a compound annual growth rate of 9% through 2027, which is twice as fast as the 4% annual growth rate predicted for traditional in-person retail [2]. Online dispute resolution (ODR) techniques that aim to settle disagreements and lower the likelihood of expensive lawsuits are becoming more and more necessary as both individuals and corporations use online shopping platforms. Due of its legally binding nature, arbitration is the most formal method, as was briefly discussed [3]. Online arbitration is carried out using electronic methods, like email and digital tools designed to make the duties of arbitrators and parties to a dispute easier. It adheres to the same rules and processes as traditional arbitration. On the plus side of e-arbitration, advancements in technology allow for time and cost savings without sacrificing the ease of remote filing. The conclusion of e-arbitration agreements raises several negative issues, including privacy and data protection issues for the consumer signing the agreements and power imbalances between corporate entities and the consumer. The degree to which artificial intelligence (AI) is effectively and sufficiently analysing a dispute between parties is up for debate because it's unclear how new this adjudication process is.

Transformation of e-commerce business models in the digital economy

Purpose. Research on the transformation of e-commerce business models in the conditions of the digital economy, analysis of the main directions of changes in business processes, as well as the determination of key differences between Internet commerce, e-commerce and online commerce, substantiation of the difference in the sources of generation of added value and identification of the factors that facilitate the transition from traditional e-commerce business models to digital ones. Methodology. When conducting the research, the trends of the development of electronic commerce and the factors contributing to their transformation in the conditions of the digital economy were revealed by the method of formal analysis. Findings. This study presents an analysis of trends and factors affecting the development of digital commerce and the transformation of digital commerce business models. The directions of integration of digital technologies into business processes and strategic aspects of the implementation of digital solutions into business practice are substantiated. The key factors that contribute to the transformation of e-commerce business models into digital commerce business models are identified, in particular, in the creation and implementation of new business models that allow for effective adaptation to the conditions of the digital economy. It is found that the economic basis of transformations of business models is the sources of creation of added value. It has been proven that digital technologies, which form the basis of e-business, are carriers and main sources of added value creation, which is reflected in business monetization models. The impact of artificial intelligence on the prospects and further development of digital commerce business models is analyzed. Originality. The scientific novelty obtained as a result of this research consists in the definition and substantiation of a new factor – added value, which is the main factor influencing the transformation of e-commerce business models into a digital commerce business model, which takes place under the influence of the digital economy. The novelty of the obtained research results in terms of substantiation of added value as a factor of transformation is proven by confirming the hypothesis that the formation of e-commerce business models and their transformation into digital commerce models take place under the influence of digital sources of added value creation.

Machine learning algorithms to prevent fraudulent transactions in real-time

In the rapidly evolving landscape of digital finance, the proliferation of online transactions has been accompanied by a significant increase in fraudulent activities. This paper explores the application of machine learning algorithms to detect and prevent fraudulent transactions in real-time, thereby enhancing the security and reliability of financial systems. We investigate various machine learning techniques, including supervised learning models such as Random Forest, Logistic Regression, and Neural Networks, as well as unsupervised methods like anomaly detection. By leveraging these algorithms, we aim to identify patterns and anomalies indicative of fraudulent behavior. Our approach integrates robust preprocessing techniques and real-time data analysis to ensure high accuracy and efficiency. The results demonstrate that machine learning models can significantly reduce the incidence of fraud, providing financial institutions with a powerful tool to safeguard their customers’ assets. This study underscores the potential of machine learning to transform fraud detection, offering a scalable and adaptive solution to one of the most pressing challenges in the financial sector

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 .

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.

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.

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.

Transformasi Digital dan Pertumbuhan UMKM: Analisis Dampak Teknologi pada Kinerja Usaha Kecil dan Menengah di Indonesia

This study examines the impact of digital transformation on the growth of Small and Medium Enterprises (SMEs) in Indonesia, focusing on the impact of technology on SME performance. A quantitative approach with a survey method was employed, involving 100 SMEs across various sectors in Indonesia. Data was collected through questionnaires measuring variables such as the use of e-commerce, social media, and management information systems as indicators of digital transformation, and increased revenue, efficiency, and market reach as indicators of SME performance. Multiple linear regression analysis revealed that digital transformation has a positive and significant impact on SME performance. The findings demonstrate that every one-unit increase in the digital transformation variable results in a 1.5-unit increase in SME performance. Furthermore, the study identified factors influencing the adoption of technology by SMEs, such as internet access, digital literacy levels, and government support. The analysis indicates that high internet access and digital literacy, along with strong government support, are crucial factors in increasing technology adoption and maximizing the benefits of digital transformation for SMEs. The study recommends strategies to encourage digital technology adoption by SMEs in Indonesia, including improving digital infrastructure, providing training and mentoring programs, and offering incentive schemes for digitally transforming SMEs.

Digital Transformation Strategy of Macys: Addressing Challenges and Achieving Revival

Abstract: This study explores the challenges faced by Macys in the United States and its strategy for digital transformation. Firstly, it introduces the rise and fall of the U.S. department store industry and the challenges Macys faces as a representative of the traditional department store industry, such as the rise of e-commerce and changes in consumer shopping habits. Then, taking Macys as a case study, it describes its digital transformation strategy, Polaris, including cost reduction, accelerated digitization, reshaping the supply chain and other initiatives and the results achieved. It then proposes specific plans for staffing adjustments, enterprise transaction process optimization and online business model modifications. Finally, the challenges that may be faced in implementing digital transformation programs, such as data silos, change management and financial overheads, are analyzed, and solutions are proposed. By summarizing and learning from the successful experiences of modern enterprises, this study proposes a series of feasible digital transformation plans for Macy's to emerge from the predicament of the traditional department store industry, meet the challenges of the digital era, and achieve a renaissance.

The effect of the product categories diversity recommended on cross‐buying in electronic commerce platforms: The moderating role of user navigation heterogeneity

AbstractE‐commerce platforms encourage consumers' cross‐buying behavior to boost user traffic and trading volume. However, balancing the precision and richness of recommended information with the mechanisms influencing consumer cross‐buying remains unclear, which also poses challenges to the specific operation of these platforms. Therefore, this study leverages extensive online behavioral data from an e‐commerce platform, encompassing detailed records of 26,034 consumers. By employing unsupervised machine learning algorithms to distinguish the heterogeneity of consumer browsing, we apply clickstream data to explore the mechanisms of the impact of information diversity, specifically the breadth and depth of recommended product categories, on cross‐buying. This study reveals that the effect of diverse recommendations on online decision‐making undergoes marginal variations due to alterations in both the breadth and depth of these recommendations. Specifically, the depth and breadth of recommended product categories exhibit an inverted U‐shaped relationship with cross‐buying, initially promoting, and subsequently suppressing it. Additionally, consumers with different browsing behavior characteristics respond differently to recommendation diversity. Confused visitors and hedonic visitors positively moderate the depth of information and negatively moderate the breadth of recommendations with the inverted U‐shaped effect on cross‐buying. Search‐oriented visitors positively adjust the inverted U‐shaped relationship between the depth of information and the breadth of recommendations on consumer cross‐buying. This study enriches research on cross‐buying in the domain of recommendations on e‐commerce platforms from the perspective of information diversity, providing valuable insights for optimizing recommendation strategies, promoting cross‐buying behavior, and enhancing consumer loyalty.