E-commerce Research Articles

Optimal Strategies for E-Commerce Platform Supply Chain: Carbon Emission Reduction and Financing

In the context of global carbon emission reduction (CER) targets and slowing economic growth, it is imperative for suppliers to make informed choices regarding CER and financing strategies. However, limited research has explored the impact of financing strategies on CER. This paper develops a supply chain model that includes a supplier, a manufacturer, an E-commerce platform (E-C platform), and consumers with a preference for low-carbon products. The supplier sets the wholesale price, while the manufacturer controls both the production quantity and the unit amount of CER. We examine whether the manufacturer will invest in CER with sufficient capital or under various financing scenarios, namely (1) traditional production with sufficient capital (Scenario ST); (2) CER implementation with sufficient capital (Scenario SG); (3) CER implementation with E-C platform financing (Scenario EG); (4) CER implementation with bank financing (Scenario BG). Through comparative analysis, the analysis reveals that, regardless of the financing method chosen, the supplier’s profit and the manufacturer’s production quantity increase when the manufacturer invests in CER technology innovation compared to the traditional scenario. Furthermore, in terms of the manufacturer’s profit, if the service cost of bank financing exceeds a certain threshold, the manufacturer should either seek financing from the E-C platform or abandon the CER investment. Additionally, with respect to CER outcomes, Scenario BG outperforms Scenario EG. These findings provide a theoretical foundation and decision-making support for supply chain participants when addressing carbon reduction and financing strategy decisions.

Monitoring the trade in bat taxidermy and specimens on e-commerce platforms

Wildlife trade is a major economic activity but can pose a threat to many species. Previous research has focused on the trade of bats (Chiroptera) for food and medicine but little is known about the online, international trade in bat taxidermy and specimens, which is thought to be an emerging threat. We aimed to characterise this trade by sampling two major e-commerce platforms over an 18-month period. We used an automated pipeline, whereby data were accessed programmatically; filtered using a neural-network classifier; and the locations and species mentioned in relevant listings were identified using Named Entity Recognition. We retrieved 40,412 relevant results, representing 2363 and 2116 unique listings from Etsy and eBay, respectively. Although the accuracy of listings could not be verified, most (57 %) provided species-level information. Hence, 47 bat species were listed for sale, of which 32 are not currently identified by the IUCN Red List as traded for any purpose. The highest number of listings were sold from USA, Hong Kong and the UK, whilst traded species' ranges were predominantly located in South East Asia. We provide one of the first longitudinal datasets on the online bat trade, indicating that several species are being traded at potentially high volumes, with demand from North America and Europe driving exploitation in South East Asia. This is concerning for highly-traded species which occur at low densities, such as the painted wooly bat Kerivoula picta. Our automated pipeline can be used for subsequent monitoring of this trade, or could be adapted for monitoring of trade in other taxa.

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.

Empowering sports facilities and sports physiotherapy performance with Integrated Management Information System (IMIS): Enhanced user experience and service quality

This study investigates the development of Integrated Management Information Systems (IMIS) to improve user experience and service quality in sports facilities. This research conducts a systematic review (2018-2023) of 7 articles from Scopus, Google Scholar, and Semantic Scholar to explore how IMIS can address evolving industry needs. The system facilitates user access to information, online transactions, and feedback mechanisms while enabling personalization and recommendations. However, the review also highlights the potential of IMIS to revolutionize sports physiotherapy through enhanced athlete care, data-driven treatment plans, and streamlined operations. Successful IMIS implementation requires careful planning, stakeholder involvement, and continuous adaptation to emerging technologies like AI and IoT. By overcoming challenges and embracing opportunities, IMIS can usher in a new era of personalized, effective, and efficient athlete care. IMIS implementation involves evaluating facility needs, staff training, and introducing the system to users. Data security and privacy are paramount. Future integration of AI, IoT, and mobile applications holds promise for further enhancing IMIS effectiveness. Overall, IMIS implementation has demonstrably improved user experience and service quality. By prioritizing user experience and adapting to technological advancements, sports facilities can remain competitive in a digital age.

Enhancing the efficiency of e-commerce through digital technologies: Economic and legal aspects

The research is aimed at analyzing the economic and legal aspects of the functioning of digital technologies in the field of electronic commerce, as well as studying the impact of these technologies on the efficiency of processes and their interaction with various aspects of legislation regulating the field of electronic commerce. In the course of the research, literary sources and legal acts were analyzed with the aim of generalizing approaches to understanding electronic commerce and establishing the main principles of its regulation. The analysis of statistical data made it possible to identify trends in the development of the e-commerce market and the influence of negative external factors. In turn, the application of methods of generalization and systematization made it possible to determine the key aspects of the implementation of digital technologies to increase the efficiency of electronic commerce and identify regulatory gaps in this area. The results of the research confirm that e-commerce in Ukraine plays an important role in the digital economy, ensuring efficiency and profitability for businesses. An analysis of the e-commerce market showed the growth of the use of the Internet for shopping, even in difficult socio-political conditions. Therefore, despite the challenges facing the country, e-commerce remains a sustainable and powerful tool for business development and consumer activity. However, it is necessary to improve the legal framework in order to meet the requirements of modernity and to ensure the protection of consumers’ and market participants’ rights from technical and other risks related to electronic commerce.

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.

Online Impulse Buying: A Systematic Review of 25 Years of Research Using Meta Regression

ABSTRACTIn the dynamic realm of online commerce, the phenomenon of Online Impulse Buying (OIB) has become a focal point of extensive research. Despite the multitude of studies exploring antecedents, mediators, and moderating factors, inconsistencies in findings have hindered generalization. To bridge this gap, we conducted a comprehensive meta‐analysis, synthesizing data from 84 empirical results across 75 research articles up to December 2023. Encompassing a cumulative sample size of 139,545 participants and 341 effects, our analysis aimed to evaluate the strength and significance of associations with OIB. Results revealed substantial influences on OIB, with Situational stimuli (ESr = 0.477), Marketing stimuli (ESr = 0.433), Customer related factors (ESr = 0.388), and Platform related factors (ESr = 0.362) emerging as key contributors. The heightened impact of situational and marketing stimuli reflects the evolving landscape of OIB. Additionally, we explored six potential moderators—culture, sample type, sampling method, online commerce type, and data collection technique—uncovering statistically significant effects on some aggregated correlations. By shedding light on the changing dynamics of OIB, our findings provide valuable insights for both researchers and practitioners navigating the intricate landscape of online impulse buying. Finally, we outline future research directions to guide ongoing exploration in this rapidly evolving field.

PENERAPAN INOVASI DIGITAL PEMASARAN UNTUK PENINGKATAN OMZET PEMBERDAYAAN EKONOMI RUMAH TANGGA PKK KELURAHAN RAGUNAN

The background of this Community Service activity is that PKM partners of PKK cadres in Ragunan Village have several limitations in terms of identity, packaging and product marketing. For product identity, there is no logo (label) for packaging, so the product that has been made is less known. In addition, there is no safe packaging design for the delivery of business products sold. So far, sales have only been carried out through event bazaars around Ragunan village so that there is a lack of marketing networks. Limited sales range, and the target market is not wide and diverse, revenue or sales turnover does not increase. The purpose of this PKM activity is to expand market reach, build strong product appeal, increase sales turnover and increase economic independence. The method for this PKM activity is to provide socialization and training regarding new product logos for the packaging production process on packaging labels, packaging design for shipping packaging, socializing the e-commerce application that has been made to PKK cadres of Ragunan Village. Providing training in using e-commerce platforms to market their products. The targeted output is to create an online sales application (e-commerce) to be able to market the business products of PKK cadres in Ragunan Village widely. This community service reached the main conclusion that digital marketing strategies effectively help the economic empowerment of PKK cadres' households and this strategy shows positive results in increasing their economic independence and income.

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

Gluten-free and conventional breads and pastas sold in Southern Brazil: a comparative study on cost, nutritional composition and ingredients

A gluten-free diet is the only treatment for patients with coeliac disease, who report dissatisfaction with the availability and variety of products, especially bread and pasta. The aim was to compare availability, cost, nutritional composition and ingredients of gluten-free and conventional bread and pasta products. Census of breads and pastas available in online commerce belonging to the third largest supermarket chain in southern Brazil. Information was collected at supermarket′s and brand’s websites. One hundred and fourteen products were analysed (n = 37 breads and n = 77 pastas): 32.4% (n = 12) gluten-free breads and 18.2% (n = 14) gluten-free pastas. Both gluten-free breads and pastas were significantly more expensive when compared to gluten-containing breads and pastas. Gluten-free breads have higher fat content, gluten-free pastas have higher content of sodium and ingredients. Gluten-free breads and pastas are less available, more expensive and have lower nutritional value compared to conventional versions.

Research on Rural e-commerce Development under the Background of Rural Revitalization — Take Daji Town of Cao County as an Example

Rural revitalization has always been an important proposition of The Times. In the context of rural revitalization, the traditional sales methods have also taken a new turn, and the shopping form of "Internet + sales" is gradually accepted by the public. Rural society has also been exposed to the Internet, and has quietly changed the new sales mode. Some rural areas have combined local resources with the Internet, expanded product sales channels, and increased economic income through e-commerce. Thanks to the development of e-commerce, many poor villages have gradually been lifted out of poverty and embarked on the road of prosperity. Now it has become a wealthy Taobao town. Daji Town, Caoxian County, Heze City, Shandong Province, is such a typical village for Internet sales. Through the analysis of rural e-commerce in Daji town, this paper puts forward relevant suggestions for the existing problems of rural e-commerce in Daji town.

Optimal strategies for contract farming supply chain with government subsidies under e-commerce assistance to farmers.

The agricultural issue is a focal point of concern for each country, and e-commerce assistance to farmers, as an emerging model, is gaining increasing attention. Considering this trend, this paper bases on e-commerce assistance to farmers and considers four types of government subsidies: no subsidy, price subsidy model with the farmer as the subsidy target, price subsidy model with the e-commerce platform as the subsidy target, and area subsidy model. Based on this, a game model of the contract farming supply chain involving a farmer and an e-commerce platform was established to explore the optimal decision-making in the contract farming supply chain considering various government subsidies under the background of e-commerce assistance to farmers. The results indicate that: (1) Consumer preference for supporting farmers and consumer premiums can stimulate the farmer to expand farm size, increase agricultural production, increase the purchase price of agricultural products and promote the growth of profits for the farmer and the e-commerce platform, contributing to the growth of social welfare. Output volatility can lead to a decrease in farm size and a reduction in the purchase price of agricultural products. (2) Government subsidies can support the farmer in expanding farm size and contribute to increased profits for the farmer and the e-commerce platform. However, government subsidies do not universally have a positive impact on every variable. The influence of government subsidies on the purchase price of agricultural products is contingent upon the method of subsidy distribution. Similarly, the effects of government subsidies on social welfare are influenced by factors such as price elasticity and agricultural productions per unit area. (3) From the perspectives of farm size, the purchase price of agricultural products, and profits of the farmer and the e-commerce platform, the government will have different and more targeted subsidy models, and the corresponding subsidy models are influenced by the agricultural productions per unit area. The research conclusion can provide references for optimal decision-making in contract farming under the model of e-commerce assistance to farmers.

RESPONSE OF LIQUID MANURES AS ALTERNATIVE NUTRIENT ON GROWTH AND YIELD OF TOMATO

This research explores the transformative role of advanced technologies, particularly AI, machine learning (ML) and block chain, in the development of smart e-commerce solutions that enhance user experiences and operational efficiencies. It also explores how customized algorithms can improve customer engagement, boost conversion rates and encourage sustainable shopping practices. Additionally, the research investigates AI-driven solutions to encourage sustainable shopping behaviors and optimize shopping post-purchase experiences. Specifically, it aims to assess the impact of AI-based personalization algorithms, examine the trade-offs between data privacy and user experience and analyze AI's influence on consumer trust and decision-making processes. Primary data from surveys and interviews are supplemented with secondary data on customer behavior and sales metrics to evaluate the effectiveness of technologies like AI, machine learning and block chain in driving personalized, ethical and efficient e-commerce. A mixed-method research design, combining qualitative and quantitative approaches, employed to capture insights from key stakeholders (e.g., consumers, developers and e-commerce managers) and assess the impact of smart e-commerce platforms on shopping behavior, trust and loyalty. Implementing the Agile Model, the research iteratively addresses system requirements, testing and deployment challenges to create a flexible, scalable solution. Key findings underscore the role of predictive modeling in inventory management, the ethical potential of sustainable product recommendations and the positive consumer responses to transparency initiatives. The study contributes valuable insights into the development of secure, personalized and ethical e-commerce platforms that align with modern consumer expectations for convenience and responsibility.

Analysis of the Digital Transformation Path of Chinese E-commerce Companies

Digital technology has revolutionized China’s e-commerce sector, changing company operations and client relationships. This article analyzes the impact of digital transformation on online purchasing patterns. This article concentrated on the important of cloud computing, AI, and big data have become essential to online retail tactics. Digitalization is fueling economic development and innovation in China as its digital economy grows and impacts GDP. Digital technologies enable e-commerce and other industrial sectors to interact easily, improving operational efficiency and market reach. Moving from isolated firm models to connected platforms is a sign of a more responsive and integrated economy. This integration fosters a value model of co-creation in which stakeholders (including companies) collaborate to develop product and service offerings, improving customer experience and operational productivity. The route to digital maturity is not without challenges. E-commerce companies struggle with inefficient logistics, costly digital transformation, and a dearth of competent digital workers, as this study shows. Companies must make dramatic organizational and technological adjustments to overcome these constraints and maximize digital technologies. The analysis concludes that China’s e-commerce market will depend on enterprises’ digital adoption. E-commerce enterprises must embrace ongoing learning and development and remove digital integration barriers to compete in today’s dynamic online industry. The study illuminates digital transformation and how it will impact e-commerce.

Text Comment Classification and Sentiment Analysis Model Based on LSTM

This study addresses the challenge of sentiment analysis and text classification in user-generated comments, an essential task for understanding public opinion in the digital age. With the widespread adoption of social media, the sheer volume of text data has increased exponentially, thereby necessitating advanced analytical tools. The study integrates Long Short-Term Memory (LSTM) networks with attention mechanisms to develop a model adept at both classifying and analyzing the sentiment of textual comments. The research methodology commences with data cleaning, tokenization, and feature extraction, subsequently followed by the implementation of a bidirectional LSTM architecture augmented with an attention mechanism. The model underwent rigorous training and testing on a comprehensive dataset, yielding high accuracy rates in sentiment analysis and text classification. The results underscore the model’s proficiency in capturing sentiment nuances and underscore its potential for deployment in e-commerce and content recommendation systems, emphasizing the practical implications of this research for enhancing decision-making processes in businesses and among policymakers.

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.