E-commerce Websites Research Articles

BHARAT Decision Making Model: Harness an Innovative MCDM Methodology for Recommending Beneficial E-Commerce Website

The electronic commerce (e-commerce) business has become increasingly important due to the recent hoopla around online shopping. This is due to the volatility of the situation and disasters such as the COVID-19 pandemic. In order to keep customers loyal and avoid customer attrition, service and product suppliers were compelled to adjust to the latest developments in digital marketing. Business-to-consumer (B2C) is a key market niche in this market, as companies utilize their websites to offer goods and services directly to customers. That's why B2C wants to be shaped in a way that helps companies and customers establish a reliable, long-lasting connection. As a result, this study showcases the essential criteria that contribute to constructing a profitable B2C e-commerce website. These criteria are leveraged to prioritize and evaluate e-commerce websites (E-WSs). This is a crucial phase in this study to recommend effective E-WSs amongst a set of competitors for customers. This study achieved this phase by constructing a decision-making model. This model can deal with conflicting criteria for preferring E-WSs over each other. The best Holistic Adaptable Ranking of Attributes Technique (BHARAT) is a novel methodology of Multi-Criteria Decision Making (MCDM). This methodology is proposed in the constructed decision-making model to evaluate and rank E-WSs. Ultimately, we applied this model to real E-WSs, and the findings indicated that E-WS2 is optimal; otherwise, E-WS4 is the worst one.

Business to Consumer Interaction in the Post COVID Era. A Case Study on Customer Reviews for Electronics and Cosmetics

The current paper discusses the topic of business to consumer interaction in the post COVID era. It briefly reviews key literature on aspects related to business communication practices and trends, and analyses a corpus of customer reviews selected from leading online e-commerce sites to see how customers interact with service/product providers to express satisfaction and dissatisfaction. The analysis will try to identify how the language used reflects the relation between participants, their credibility or lack of credibility in front of each other, and whether such aspects may be said to influence online sales trends.

GOVERNMENT PROGRAMS TO TAP ONLINE USER’S POTENTIAL AND ALLOW THEM TO USE IT FOR DIGITAL MARKETING AND BECOME DIGITAL AMBASSADORS

The purpose of the study in this paper is to provide details regarding the initiatives and programs for utilizing the untapped potentials of the users present on different social media sites by training them or guiding them to use their energy in something which will bring good results for them and make them digital influencers. Researchers who are working effectively in this research field are utilizing the qualitative research method. The research data related to it was obtained from Amazon programs, blogs, social media, and e-commerce sites, and it is shared descriptively. The finding from this paper indicates that the creation of programs like this will undoubtedly benefit social media users and improve the way social media is currently used by most people. The way forward to make it possible, multiple hurdles need to be passed for successfully implementing this program. The paper will be limited to the extent to which the motive behind it is clearly described, then describe the possible ways for creating this program and how it will work, and lastly, what effect the initiative will have on the social media users and the e-commerce market.

QNN-MAGDM strategy for E-commerce site selection using quadripartition neutrosophic neutrality aggregative operators

In this paper, we define the Quadripartition Neutrosophic Weighted Neutrality Aggregative (QNWNA) operator and Quadripartition Neutrosophic Ordered Weighted Neutrality Aggregative (QNOWNA) operator for solving Multi-Attribute Group Decision Making (MAGDM) problems. The basic properties of both operators are discussed thoroughly. A new MAGDM strategy is developed using these developed operators. A case study of e-commerce site selection in India is discussed to show the applicability of the proposed MADM strategy. Moreover, the performance of the QNWNA and QNOWNA operators are compared with Quadripartition Neutrosophic Weighted Arithmetic Aggregation (QNWAA) operator and Quadripartition Neutrosophic Weighted Geometric Aggregation (QNWGA), Quadripartition Single valued Neutrosophic Dombi Weighted Arithmetic Aggregation (QSVNDWAA) and QSVN Dombi Weighted Geometric Aggregation (QSVNDWAA) operator.

Rancang Bangun E-Commerce Terintegrasi untuk Toko Atlanta Sport Berbasis Yii Framework

E-commerce has become a dominant trend in buying and selling transactions; however, Atlanta Sport Store still relies on a manual approach to managing data, marketing, product sales, and generating sales reports. To address this challenge, we propose an innovative e-commerce platform design for Atlanta Sport Store. The development methodology involves the waterfall method and Unified Modeling Language (UML) modeling. The programming stack includes PHP as the programming language, MySQL as the Database Management System (DBMS), and XAMPP as the web server, with coding performed through browsers such as Google Chrome. The primary objective of this design is to create an effective e-commerce site, facilitating customers in finding information and making product purchase transactions while simplifying store management activities. This e-commerce design not only addresses the needs of online sales but also optimizes inventory management, transaction reporting, and financial reporting for store owners. This research makes a significant contribution to optimizing business operations through the implementation of an integrated e-commerce solution, opening up opportunities for increased efficiency and competitiveness in the retail sector. With this approach, it is expected that Atlanta Sport Store can strengthen its position in the market and provide a better shopping experience for its customers.

Personalized Advertising in E-Commerce: Using Clickstream Data to Target High-Value Customers

The growing popularity of e-commerce has prompted researchers to take a greater interest in deeper understanding online shopping behavior, consumer interest patterns, and the effectiveness of advertising campaigns. This paper presents a fresh approach for targeting high-value e-shop clients by utilizing clickstream data. We propose the new algorithm to measure customer engagement and recognizing high-value customers. Clickstream data is employed in the algorithm to compute a Customer Merit (CM) index that measures the customer’s level of engagement and anticipates their purchase intent. The CM index is evaluated dynamically by the algorithm, examining the customer’s activity level, efficiency in selecting items, and time spent in browsing. It combines tracking customers browsing and purchasing behaviors with other relevant factors: time spent on the website and frequency of visits to e-shops. This strategy proves highly beneficial for e-commerce enterprises, enabling them to pinpoint potential buyers and design targeted advertising campaigns exclusively for high-value customers of e-shops. It allows not only boosts e-shop sales but also minimizes advertising expenses effectively. The proposed method was tested on actual clickstream data from two e-commerce websites and showed that the personalized advertising campaign outperformed the non-personalized campaign in terms of click-through and conversion rate. In general, the findings suggest, that personalized advertising scenarios can be a useful tool for boosting e-commerce sales and reduce advertising cost. By utilizing clickstream data and adopting a targeted approach, e-commerce businesses can attract and retain high-value customers, leading to higher revenue and profitability.

Design Scheme of High Concurrent and High Usable Cache based on TB Level Data Capacity

With the continuous development of the Internet in recent years, the Internet business has also changed greatly. In the past, when accessing the database, it usually takes the way of direct access, in order to resist the reading and writing traffic, often use a master and data fragmentation methods. However, the current traffic is more and more, and the amount of data is also accumulating. The data capacity of many e-commerce websites is getting bigger and bigger, even exceeding the TB level. If we still use the previous way, all the traffic using the database to bear, its stability will be greatly reduced, and it will also bring higher cost and lower efficiency, is extremely undesirable. Therefore, there is an urgent need to find technologies and architectures related to high concurrency and high availability. For the above reasons, this topic first understands and analyzes the previous application platform cache and Redis database cluster cache, and then builds a multi-level cache strategy based on Njinx local cache, which will be described from requirements analysis and architecture design.

A Semiotic Analysis of Cultural Differences Between Australian and Emirati E-commerce Websites

This study aimed to investigate cultural differences in the design of e-commerce websites by undertaking a semiotic analysis. The study utilized semiotic analysis methods to examine the relationships among signs, symbols and their meanings on e-commerce websites. Specifically, six popular e-commerce websites from two culturally distinct countries, Australia and the United Arab Emirates (UAE), were selected for analysis. The semiotic analysis of the e-commerce websites revealed significant differences in design attributes between the two countries. Specifically, the findings showed that UAE websites emphasize visual elements such as graphics and layout to engage users aesthetically, whereas Australian websites prioritize text-based content to ensure clear and direct communication. This divergence can be attributed to cultural disparities, with Arab culture valuing additional context and Australian culture favouring a more straightforward approach. This implies that different interface designs may be necessary to effectively communicate with and cater to the diverse cultural groups within each country.

The Role of Digitalization in Cross-Border E-Commerce Performance of Italian SMEs

The utilization of digital technologies is rapidly increasing businesses’ capacities for innovation and growth, especially in the case of small and medium-sized enterprises (SMEs), with strong benefits in efficiency, competitiveness, and market reach. We aimed to study the impact of digital technologies on cross-border e-commerce (CBEC) in Italian SMEs, with regard to three main ICT areas: e-business, e-marketing, and e-commerce. Using a regression analysis, the study found that e-business tools have an unclear impact on cross-border e-commerce, while e-marketing tools, such as data tracking for medium-sized enterprises and social media for all SMEs, have a positive and significant impact on the online export performance. Finally, we examined the impact of being present on a marketplace rather than having a proprietary e-commerce website, and we found that having a presence on marketplaces such as Amazon or Alibaba is more effective than having a proprietary e-commerce website in terms of cross-border online sales. These results are useful both for policy makers and managers, since making informed decisions to develop SMEs is crucial for industrial strategy effectiveness.

Intellectual Property Protection in Cross-Border E-Commerce

With the rapid development of Internet technology, electronic trade has become a significant player in the national and global economy. This thesis examines the complex issues surrounding intellectual property rights (IPR) in cross-border electronic trade. The rapid growth of internet technology has led to an increase in transnational economic disputes, particularly in online shopping platforms and e-commerce websites. The presence of pirated products, trademark counterfeiting, brand infringement, and copyright protection are major concerns in this context. Several factors contribute to these disputes, including different legal systems, cultural backgrounds, economic development levels, conflicting interests of countries, and varying stakeholder attitudes towards IPR protection. Notable cases, such as the Apple-Samsung patent dispute and the challenges faced by Alibaba regarding counterfeit goods, highlight the need for comprehensive solutions to safeguard IPR in a globalized marketplace. The thesis also explores how technology can contribute to addressing these challenges. Blockchain technology can provide enhanced security, transparency, and efficiency in managing intellectual property rights. Big data and artificial intelligence can automate the analysis and monitoring of digital content, enabling faster identification and action against infringement. Establishing global collaborative IP protection platforms and innovative dispute resolution mechanisms are also crucial steps forward.

UI/UX Research & Design Aplikasi E-commerce: Fixup Waste With Design Thinking Methode

The problem of waste and unused waste has reached an alarming level, where it has become the main focus for the government. This problem raises special concern because the number of waste production continues every year. The reason is the consumptive lifestyle of new products that are trending among the public. It is this consumptive lifestyle that causes many items that were previously owned by individuals to be wasted, most of them even end up in landfills or the sale of used goods, without any guarantee that these items can be recycled into new products. To overcome this problem, innovation is needed in the form of digital products such as e-commerce applications and websites. This innovation serves as a platform to promote trending items and invites the public to avoid excessive consumption patterns of these products through the offered Upcycling Products. The process of developing this digital product uses the Design Thinking method, which places a focus on the needs and problems faced by current users. The stages in this method start from Emphatize (understand the user), Define (define the problem), Ideate (ideate), Prototype (prototype), and Testing (conducting tests on users). Each stage is carried out repeatedly to ensure optimal results for users. The end result of this design process is the E-commerce application prototype design and the Fixup Waste website.

ANALISIS KECURANGAN DALAM MENGHADAPI PENIPUAN DI SITUS E-COMMERCE MENGGUNAKAN RANDOM FOREST ; PENDEKATAN MACHINE LEARNING BERBASIS AI

In this rapidly growing digital era, the phenomenon of e-commerce has become a major highlight, the rapid growth of e-commerce has attracted more and more users. However, cases of sophisticated and dynamic fraud are increasing as the volume of transactions increases. This phenomenon not only poses a risk of financial loss for buyers and sellers but also threatens the trust that is so important in the e-commerce industry. To solve this problem, the author uses a random Forest AI-based Machine Learning approach in analyzing and finding fraud patterns to deal with fraud on e-commerce sites. The Random Forest model was chosen because of its excellent ability to handle complex e-commerce transaction data, including the ability to find non-linear patterns, its resistance to overfitting, and its scalability on large datasets. This model is expected to identify suspicious fraud patterns in e-commerce transactions. The method will involve data processing, feature selection, and model training using a dataset that includes ecommerce transactions. The results of this research are expected to contribute to a better understanding of fraud on e-commerce sites in the face of future fraud. Effective fraud detection is also expected to reduce the losses caused by fraud on e-commerce sites and protect users from the risk of fraud.

Detecting Positive and Negative Deviations in Cross-Domain Product Reviews using Adaptive Stochastic Deep Networks

The analysis of sentiment in product reviews across diverse platforms such as e-commerce website and social media presents a challenging task due to the inherent differences in user behaviour and review formats. This research introduces an innovative methodology for detecting positive and negative deviations in cross-domain product reviews using Adaptive Stochastic Deep Networks (ASDN) tailored for multi-platform sentiment analysis. ASDNs possess mechanisms that enable dynamic adaptation to changes in data distributions, domain shifts, or varying complexities within the input data. The proposed framework aims to capture refined variations in sentiment expression across disparate platforms by incorporating adaptive stochasticity within deep neural networks. By adapting dynamically to changes in review styles, language use, and sentiment patterns unique to each platform, the ASDN architecture facilitates the identification of nuanced sentiment shifts. Through extensive experimentation on comprehensive datasets spanning Amazon, Facebook, and Instagram, the efficacy of the ASDN model in detecting positive and negative sentiment deviations across diverse platforms is demonstrated. This research contributes to advancing the understanding of sentiment dynamics across distinct social platforms and e-commerce sites, paving the way for more robust and adaptable models in cross-domain sentiment analysis.

Research on personalized recommendation system for social media marketing content in big data environment

Abstract A personalized recommendation system is an effective and consistent marketing method that caters to consumer demand for goods. It solves the problem of consumer shopping choices on e-commerce websites and is currently a hot spot in the field of network information services. In this paper, from the perspective of the user’s shopping process, we summarize the four factors that can maximally reflect the consumer’s interest and preference, i.e., product browsing, collection, adding to cart, and purchasing behavior. To achieve static user interest weights, we quantify each index factor and set the corresponding rules. Considering the change in user interest, the decaying interest value over time is designed to compensate for the deficiencies of static system recommendations. We use implicit user feedback data to identify content that truly interests users and then construct a personalized recommendation algorithm for social media marketing content using multi-source data. The test results show that UIPR has an MRR value of more than 0.8 and an NDCG value of more than 0.55 in both the MovieLens and Taobao datasets, compared to the baseline model’s best value. This proves that UIPR consistently performs better and makes better recommendations.

Blockchain-Based NFT Warranty System: A Software Implementation

As the e-commerce industry continues to grow, the challenges around warranty issuing and its management have become more significant. This research paper presents a software implementation of a Blockchain-based E-commerce warranty system that utilises Non-Fungible Tokens to simplify the warranty management process for both consumers and manufacturers. Its features include automatic warranty issuing, verification, transfer of ownership and warranty expiration. The system was made available as a service and was integrated into an existing E-commerce website clone to evaluate the ease of use. Additionally, a page listing all warranty NFTs owned by a user, irrespective of the e-commerce site of purchase, was created and linked to the original e-commerce website. As a result, the implemented system simplified the manufacturer/seller’s overhead of issuing and managing warranties and improved the user experience for consumers by showing all warranties owned by them in one place. This study illustrates the potential of public blockchains in the e-commerce sector, which has important implications for the use of blockchain-based warranty management systems.

A Collaborative Model in Persuasive Web Design: Multiple case study of Lazada and Shopee

This study examined online persuasion through website design by using the Elaboration Likelihood Model (ELM) and Persuasive System Design (PSD). This study investigates the phenomenon of the success of Lazada and Shopee to provide guidelines for website designers and local sellers to enhance the e-commerce website's persuasiveness. The persuasive design features in Lazada's and Shopee's e-commerce websites were evaluated based on the design criteria of the ELM and the PSD model. This study has examined (1) the influence of central route elements (Dialogue Support and Primary Task Support) towards Lazada and Shopee user's attitude change, (2) the influence of Lazada and Shopee user's attitude change towards behavioral intention and (3) the influence of Lazada and Shopee user's behavioral intention towards actual online buying behavior. A total of 414 samples were analyzed using Partial Least Square Structural Equation Modelling (PLS-SEM) technique. The results from the analysis indicated that Primary task support through navigation design (PTN) was found to be the most influential persuasive design criteria, followed by Social support through connectedness (SSC), and Dialogue support through argument quality (DSA).

Decision Support Model in Compiling Owner Estimate for FMCGs Products from Various Marketplaces with TF-IDF and LSA-based Clustering

In Indonesia, e-commerce platforms are rapidly growing as the number of visitors continues to increase. There is a product that is most often used, called Fast-Moving Consumer Goods. The customer's side wants affordable prices, so it is challenging for the product procurement team to prepare their own estimated prices and compare them quickly. In this study, a decision support system was designed by automating the product grouping process. The experiment used FMCGs textual data from various e-commerce sites and cluster data preparation with the help of Latent Semantic Analysis to reduce the TF-IDF weighting data dimension. The results showed that the effect of data preparation yielded a result of 0.092 outperforming data without stoplist and word-based stoplists available by the stopwords library, even though the execution time was 0.01 seconds longer than others.

Examination of the effect of consumer comments on e-commerce sites on continuance intention to online shopping within the scope of social exchange theory

Examination of the effect of consumer comments on e-commerce sites on continuance intention to online shopping within the scope of social exchange theory

The Analysis of Neural Network Models to Distinguish AI generated faces from Real faces

The rise of Artificial Intelligence (AI)-generated faces that are identical to actual ones is both a technological innovation and a major concern. While considering the implications, some of the existing security systems are unable to differentiate between a high-quality deep-fake and an actual intruder's face. For instance, the risks are quite high at an airport security checkpoint, where facial recognition is the first line of security against unauthorized entry. The primary concern here is how trustworthy the computer programs and algorithms will be in recognizing counterfeits among a plethora of actual and artificially generated faces. This necessitates the need to introduce machine learning approaches to differentiate between actual and fraudulent faces, particularly when AI-generated faces are involved. Effective artificial intelligence systems must be adaptive and change quickly in the face of more complex threats rather than simply recognizing them. AI-generated faces are becoming more convincing by the day, increasing the risk of their exploitation. This poses the need to ensure that the technology on which people rely should be robust and trustworthy in critical situations, whether it is a security checkpoint or an e-commerce site. In this perspective, this study has attempted to develop and implement Artificial Intelligence-powered solutions to detect the artificial faces while ensuring reliability for critical functions in an age where reality constantly blends with fiction.

Analysis of Users’ Behaviour in Structured E-Commerce Websites

Analysis of Users’ Behaviour in Structured E-Commerce Websites