Online Shopping Platform Research Articles

Adaptive incentive mechanism with predictors for on-time attended home delivery problem

The widespread use of the Internet and smart devices has led to a fast growth in online shopping, offering new chances for online retailers to boost profits. However, this expansion has also brought various challenges, such as the heavy workload faced by delivery riders. To meet customers’ delivery time preferences and increase earnings, riders often work long hours, especially during busy periods. This study explores how historical delivery data can be used to balance workload in on-time attended home delivery. Drawing on the actual delivery operations and data of an online shopping platform, we propose a framework that combines delivery demand and customer behavior predictors with an adaptive incentive system to balance rider workload. Specifically focusing on same-day attended home delivery, we introduce a method to forecast future delivery demand, an algorithm to estimate customer choice behavior using a simple model, and an adaptive incentive system to influence customer decisions and achieve workload balance. We show that as order volume increases, the proposed incentive system achieves the pre-determined workload target. Using real data, we conduct numerical experiments which not only underscore the superior predictive performance of our models but also affirm the efficacy of the proposed incentive structure.

The ranking of factors in selecting the online shopping platform in Malaysia based on fuzzy analytic hierarchy process

The ranking of factors in selecting the online shopping platform in Malaysia based on fuzzy analytic hierarchy process

A Novel Group Decision Making Model to Compare Online Shopping Platforms

Over the years, E-commerce industry has been witnessing a phenomenal growth, thanks to rapid technological advancement in Industry 4.0. There has been a standout surge in the use of various online shopping platforms (OSP) for daily use. The recent pandemic has accelerated the growth trajectory and made a transformational change in the digital commerce landscape. As a result, there has been a proliferation of OSPs in the competitive domain. It is therefore pertinent to address the questions: How do the customers select their favorite OSP? To what extent the OSPs differ based on consumers’ preferences? The present work addresses these questions by proposing a novel group decision making framework. The ongoing study provides several innovative extensions of multi criteria decision making models like Borda count, criteria importance assessment (CIMAS), modified preference selection index (MPSI), and root assessment method (RAM). In this paper, the researchers provide a novel use of the Borda count method, integrated with CIMAS for determining criteria weights utilizing ranking of the criteria. Further, a novel extension of MPSI and RAM has been made with multiple normalizations. In this paper, the authors demonstrate a rare combination of vector and non-linear normalization using the Heron mean. The present paper derives the final criteria weights by combining Borda count, CIMAS and multi-normalization based MPSI (MNMPSI) using Bayesian logic. The criteria are selected based on Uses and Gratification theory (UGT). The findings reveal that interactive app interface and features (C16), user-friendly interface and search (C13), convenience in shopping (C14), product availability and variety (C12) and discounts and offers (C8) exert significant influence in selecting the OSP. Further, it is observed that Flipkart (A2) and Amazon (A1) are the top performers in the eyes of the users. The stability and reliability of the proposed methodology are examined by conducting a sensitivity analysis and comparing with several other models. The robustness of the proposed methodology and practical relevance of the findings of the present work shall provide notable impetus to the analysts and strategic decision-makers.

Efficient Algorithms for Range Mode Queries in the Big Data Era

The mode is a fundamental descriptive statistic in data analysis, signifying the most frequent element within a dataset. The range mode query (RMQ) problem expands upon this concept by preprocessing an array A containing n natural numbers. This allows for the swift determination of the mode within any subarray A[a..b], thus optimizing the computation of the mode for a multitude of range queries. The efficacy of this process bears considerable importance in data analytics and retrieval across diverse platforms, including but not limited to online shopping experiences and financial auditing systems. This study is dedicated to exploring and benchmarking different algorithms and data structures designed to tackle the RMQ problem. The goal is to not only address the theoretical aspects of RMQ but also to provide practical solutions that can be applied in real-world scenarios, such as the optimization of an online shopping platform’s understanding of customer preferences, enhancing the efficiency and effectiveness of data retrieval in large datasets.

Cosmetic Shop Sentiment Analysis on TikTok Shop Using the Support Vector Machine Method

User reviews are crucial in today's digital world for determining a product's quality. Nevertheless, these remarks are frequently disorganized and erratic, which confuses people and makes it challenging for them to make wise purchases. The erratic character of these reviews breeds uncertainty and makes determining a product's actual value more difficult. To help consumers more effectively evaluate and select products on platforms such as TikTok Shop, this study uses sentiment analysis tools. It hopes to accomplish this by improving the overall shopping experience and empowering customers to make more confident and informed selections. This research aims to assist consumers in evaluating and selecting products on TikTok Shop, an online shopping platform, by employing sentiment analysis techniques that help consumers make more informed decisions. In this study, a total of 500 comments from TikTok Shop users were collected as data. 350 comments have been set aside for training and 150 comments were set aside for testing. Data was gathered employing scraping, an automated process that makes use of the Python library's Selenium module to retrieve data from the internet. We employed the Support Vector Machine approach, an efficient machine learning tool for text classification, to assess the comments. 121 comments were categorized as having positive sentiment and 29 as having negative sentiment based on the test results. The system successfully recommended the "Ourluxbeauty" cosmetics store as a shop with many positive sentiments, indicating a recommendation level of 0.7 on the positive sentiment scale. The system's accuracy was measured using a Confusion Matrix, resulting in an accuracy rate of 78% and an inaccuracy rate of 22%. This demonstrates that the system can accurately classify comment sentiments and has significant potential for application in e-commerce practices to enhance the online shopping experience.

Online Shopping Behavior: Factors Affecting Consumer Trust and Purchase Intensions in E-commerce

This is new era of shopping and buying almost anything. The customer does not have to shake a leg to secure or get hold of anything. Just a little time and few clicks on the online shopping platform will do the job. But as the offline shopping patterns have changed so have changed the shopping style and preferences of the customers. Now they have plentiful of choices, delivery preferences, types of discounts and perks. Despite of all these the people who do not shop online are a common finding. What are the reasons that they do not have trust or is trust a stable feature among whose who regularly shop online are the skeptical aspects that have been explored in this paper.

Memperkuat Kesadaran Merek Ramah Lingkungan dan Jangkauan Pemasaran UMKM Bumbu Rempah Ratoeh Indah dengan Pemasaran Digital

Digital marketing plays an important role in strengthening brand awareness and expanding the marketing reach of Micro, Small, and Medium Enterprises (MSMEs). Realizing this important role, the STIE Indonesia Banking School community service team carried out community service activities to support Ratoeh Indah, an MSME, in utilizing digital marketing tools and strategies. The results of initial observations and interviews show that Ratoeh Indah has not utilized the potential of social media for product promotion, nor has she used online shopping platforms or digital advertising to achieve competitive advantage and innovation. Historically, Ratoeh Indah relied on conventional door-to-door sales methods, limiting its reach and growth potential. To address this gap, our intervention includes a comprehensive approach that utilizes SWOT analysis, interviews, observation, and direct mentoring. The main activities include creating and optimizing an Instagram account for product promotion, creating targeted advertisements on Instagram, establishing an online shop on the e-commerce platform Shopee, and implementing more environmentally friendly packaging solutions. The results of the service activities carried out are very significant. There has been a marked increase in brand awareness, evidenced by the growth of Instagram followers and engagement metrics. Additionally, newly established Shopee stores experienced increased visitor traffic, indicating successful penetration into the online shopping realm. The community service undertaken underscores the transformative impact of digital marketing on MSMEs, providing a model for similar companies seeking to strengthen brand awareness and expand marketing reach.

PENGARUH PROMOSI, HARGA, DAN KEPUASAN TERHADAP LOYALITAS PELANGGAN E-COMMERCE SHOPEE

Abstrak (Abstract) Customer loyalty has a very important role. Data shows that Shopee occupies the first position in e-commerce which is most often used by Indonesian people to shop online. Tujuan (Purpose): This research aims to determine the level of loyalty in the use of shopee as an online shopping platform among students of the 2020-2023 cohorts in the Business Management at Pradita University. This aspects in this study include promotion, pricing, and satisfaction. Metodologi penelitian (Research methodology): The approach taken in this research is a quantitative approach to studying data collection using research tools. This research using a survey method and questionnaire techniques for collecting data, the data is processed using SPSS version 26. Hasil (Results): The results show promotion does not influence customer loyalty, while price and satisfaction influence customer loyalty. Promotion, price, and satisfaction simultaneously influence customer loyalty. Limitasi (Limitations): The limitation in this research is that because distributing questionnaires via gform is not completely helpful, sometimes there are still some respondents who do not fill out the questionnaire as honestly as possible. Distributing questionnaires via form and offline will predominately not be filled in by potential respondents. The satisfaction variable in this researcher is affected by symptoms of heteroscedasticity. Kontribusi (Contribution): The author advises that Shopee can increase customer loyalty by maintaining prices and satisfaction so that it can increase Shopee customer loyalty. Kata kunci (Keywords): Customer Loyalty, Promotion, Pricing, Satisfaction.

ANALYSIS OF THE COLLABORATIVE MARKETING STRATEGY OF THE TOKOPEDIA AND TIKTOK SHOP PLATFORMS IN INCREASING SALES.

The Tiktok application itself has developed over time along with the development of technology, so that the use of Tiktok is not just about sharing information, but has become an online shopping platform. The reason is, Tiktok also presents new innovations in its own application, namely on April 17 2021 it introduced a new feature in the form of Tiktokshop. Then in September 2023, Tiktokshop was closed due to several conflicts and allegations of poor financial performance. in markets that monitor economic activity manually. Additionally, on December 11, 2023, we partnered with Tiktok to enable product sales between Tokopedia via the Tiktok application. Tokopedia and Tiktok Shop collaborate with large shipping agents in Indonesia to make it easy to check the status of goods delivery. The research method used is a qualitative research method, where this research method does not use data in the form of numbers, but data that comes from words and also from existing images. The goal of studying the results of a merger between two or more companies is to increase profitability between companies. Combining capital and assets will increase asset growth so that company profits increase. By carrying out a merger, the financial scale of a company or economic entity also becomes larger, because the business concerned becomes more productive, stronger, efficient and effective. Companies can also increase their market share by introducing new innovations and minimizing existing risks. Based on the results and discussion of the marketing strategy for the Tokopedia and Tiktok applications, it can be concluded that Tokopedia and Tiktok are effective social media in implementing marketing strategies. Advertisements implemented in the Tokopedia and Tiktok applications include advertisements, sales content, live streaming features, and others. with the aim of offering products and services to potential buyers, arousing purchasing interest and creating shopping transactions to increase sales

SCHOOL SHOP ECOMMERCE PROJECT

Abstract This research paper explores the development of an e-commerce platform specifically designed for a school shop using the MERN stack, which comprises MongoDB, Express.js, React, and Node.js. The primary objective is to create a user-friendly, efficient, and scalable online store that caters to the needs of students, parents, and school administrators for purchasing school-related products. The study highlights the increasing importance of e-commerce in educational settings and discusses the advantages of employing the MERN stack, such as seamless integration, high performance, and ease of development. The methodology section provides a comprehensive overview of the development process, including system design, frontend and backend development, database management, and integration of key functionalities like user authentication, product catalogue, shopping cart, and payment gateway. The implementation phase details the practical application of each MERN stack component, showcasing how MongoDB’s flexibility supports varied data types, how Express.js simplifies server-side development, how React enhances user experience through dynamic interfaces, and how Node.js ensures efficient server-side execution. The project undergoes rigorous testing, including performance, usability, and security assessments, to validate its functionality and reliability. Index Terms – E-commerce, Online Shopping Platform, Web Development, School Shop, User Authentication, Product Management, Shopping Cart, Payment Processing, Scalability, Web Application Development

Elevating Online Shopping Using MERN Stack

Abstract: This project presents the design, development, and implementation of an online shopping platform aimed at enhancing the retail experience for both consumers and businesses. The rapid growth of e-commerce has revolutionized the way consumers shop, creating a need for robust and efficient online shopping platforms. This research paper explores the potential of the MERN stack (MongoDB, Express.js, React.js, and Node.js) in developing a comprehensive and user-friendly online shopping solution. By leveraging the strengths of these modern technologies, we aim to elevate the online shopping experience, addressing key challenges such as performance, scalability, and seamless user interactions. Through a combination of theoretical analysis and practical implementation, we demonstrate the effectiveness of the MERN stack in creating a responsive, secure, and featurerich online shopping platform.

Analysis of machine learning approaches to determine online shopping ratings using naïve bayes and svm

This research aims to identify and compare the effectiveness of Naïve Bayes and Support Vector Machine (SVM) algorithms in classifying ratings based on customer comments on the Lazada online shopping platform. The main issues identified include data uncertainty, model selection and optimization, as well as improving efficiency and scalability. Using a dataset of comments and reviews from Lazada, this study conducts an analysis using both algorithms to determine which is most effective in classifying comments into appropriate ratings. The research methodology includes data collection, text preprocessing, algorithm implementation, and evaluation using a confusion matrix to measure accuracy, precision, recall, and F-measure. This analysis is supported by data visualization using Python, allowing for in-depth interpretation and understanding of the results. The research findings show significant differences in the performance of both algorithms, with each having strengths in certain aspects of classification. The discussion in this study interprets these results to address the research questions formulated and demonstrates the practical application of machine learning theory in real-world data processing. This study concludes that both algorithms have significant potential in sentiment classification but require further adjustment and optimization to improve accuracy and efficiency. Recommendations for further research include the development of hybrid models or new approaches that can address the identified limitations, as well as exploration of more diverse datasets to test the scalability of the proposed solutions.

Research on Influencing Factors of AI Chat Robot on Customer Satisfaction of Online Shopping Platform

Based on the technology acceptance model, this study deeply investigates the factors influencing customer satisfaction of AI Chatbot, and portrays the relationships between different latent variables from both TAM and external factors to reveal the information quality, usability, self-efficacy, perceived enjoyment, subjective norms, perceived ease of use, perceived usefulness and customer satisfaction. A total of 481 valid questionnaires were obtained through online questionnaire distribution and offline interview research, and the samples were analyzed by SPSS software for reliability and heterogeneity, and then the SEM model path analysis was conducted by Amos software to derive the direct or indirect relationships among the variables. Main research conclusions include:(1) information quality, usability, self-efficacy, perceived enjoyment have a direct impact on customers' perceived ease of use (2) subjective norms can positively affect customers' perceived usefulness; (3) perceived ease of use and perceived usefulness have a direct positive impact on customers' satisfaction. Last, this study further discusses the research results, elaborates the theoretical significance and practical significance of this study, and finally puts forward the limitations and looks forward to future studies.

The Effect Of Customer Satisfaction And Trust On Customer Loyalty In The Shopee Marketplace

This research aims to test the influence of trust and customer satisfaction on customer loyalty, test the influence of trust on customer loyalty and test the influence of customer satisfaction on customer loyalty. This research uses a quantitative descriptive method by distributing questionnaires via Google Form with purposive sampling, namely Shopee users as an online shopping platform for consumers in Depok. A purposive sampling method of 150 respondents was used for the sampling. SPSS 26 was used to calculate the multiple linear regression variable test to analyze the research data results. The research results show that customer satisfaction and trust are important in forming customer loyalty to Shopee. Factors such as ease of use, wide product selection, responsive customer service, and security of online transactions influence customer satisfaction levels. In addition, customer trust in the security of online transactions and product quality also plays a key role in building long-term relationships between customers and e-commerce platforms. The R square value of 0.725 indicates that most of the variation in customer loyalty levels can be explained by customer satisfaction and trust in Shopee in Depok. At the same time, the rest is influenced by other factors, such as quality. Product and brand preferences.

Utilizing Multi-Armed Bandit Algorithms for Advertising: An In-Depth Case Study on an Online Retail Platform's Advertising Campaign

In recent years, the advertising sector has increasingly embraced Multi-armed Bandit Algorithms (MAB) for their versatile applications. This article delves into four stochastic MAB algorithms: the Explore-Then-Commit (ETC), the Upper Confidence Bound (UCB), the Thompson-Sampling (TS), and their respective variants, applied in an online shopping platform's advertising campaign to optimize click-through rates. Our findings indicate that each of these algorithms successfully identifies the most effective advertisement. Both the UCB and TS algorithms demonstrate logarithmic regret relative to the time horizon. The ETC algorithm exhibits a delayed onset of logarithmic regret but surprisingly holds up well against the UCB algorithms. Notably, the TS algorithm class outshines the UCB class due to its inherent randomness, offering a more robust performance. Furthermore, two variants, the UCB algorithm with the Mini-max Optimal Strategy in the Stochastic case (MOSS) and the paralleled TS algorithm, show promising results. They not only maintain logarithmic regret but also enhance efficiency, indicating further potential in the UCB and TS algorithm classes for advanced applications.

Research on HCI and Interface Design in VR: Understanding Virtual Perceptions

The Virtual reality (VR) affords a promising route for revolutionizing online shopping (OS) experiences, imparting human-computer interaction (HCI) and more desirable product expertise. By integrating the VR into OS platforms, users can delve deeper into product details, along with substance, shade, and different vital characteristics, thereby improving choice-making and fulfillment. This investigation specializes in knowledge of the synergy among VR and OS, utilizing structural equation modeling (SEM) to evaluate user interaction (UI) and experience (UE). Through UI achievement surveys and professional panels, insights into VR-integrated OS are collective, informing the improvement of design standards. The research focused on generating evidence-primarily based layout requirements, validating their feasibility, and assessing their impact. By emphasizing sizeable design additives, VR creators can enhance consumer enjoyment and pleasure, ultimately contributing to greater effective OS interfaces. This research presents sensible pointers for VR improvement but underscores the potential for VR to elevate overall user satisfaction in online retail environments.

Peran Influencer, Fitur Live Streaming dan Electronic Word Of Mouth Terhadap Keputusan Pembelian Produk Skincare The Originote pada Aplikasi Tik Tok

The Tik Tok application has become one of the most popular digital media that is widely used by entrepreneurs as an online shopping platform and has around 99.1 million users in Indonesia in 2021, with an average of 68% female users and 32% male users. This research aims to examine the role of influencers, live streaming features and electronic word of mouth on purchasing decisions for The Originote skincare products on the Tik Tok application. With an unknown population, the Lemeshow formula is used to calculate the sample size, with 100 respondents. This research is quantitative and uses the Smart PLS analysis tool. The research results show that Influencers have a significant positive effect on Purchasing Decisions, the Live Streaming Feature has a significant positive effect on Purchasing Decisions and Electronic Word of Mouth has no effect on Purchasing Decisions for The Originote Skincare Products on the Tik Tok Application.

Live streaming commerce as communication media at Social Bread

Live streaming commerce is the most up-to-date promotional tool connecting sellers and buyers. Social Bread utilizes this phenomenon as an institution that provides services for businesses to optimize product sales through the live-stream commerce method. Social Bread is a medium that bridges the buying and selling process. Much research on similar topics has been conducted, especially from China and the United States. However, little has been done in Indonesia, even though Indonesian businesses and society widely use this feature. This research aims to determine how live-streaming commerce is implemented and how the institution implementing live-streaming commerce becomes a communication medium for conveying messages to the audience. This research uses a qualitative case study methodology through interview techniques with the CEO, VP Operations, and Streamer from Social Bread. As a result, live streaming commerce embodies the Communicator – Medium – Communicator communication process, namely the business represented by the Streamer as the Communicator, an online shopping platform with live streaming features mediated by Social Bread as the medium, and the audience as the Communicant. The process of preparing live-stream commerce on Social Bread starts from implementing the client's brief by the Streamer to get the predicted results: increasing sales from live streaming. The consumers' cognitive responses previously in the audience are recorded, and a report is made to be presented to the client as evaluation material.

How Shopping Platforms Play a Role in the Credit Card Industry

<p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">We develop a four-party model involving banks, merchants, consumers, and platform firms to explain why large shopping malls can offer interest-free installment services to consumers. Using a collaboration between an online shopping platform firm and multiple banks to issue co-branded credit cards as a natural experiment, we find that consumers’ spending on the platform fulfills the requirements for merchants and banks to choose a four-way model. This study highlights the pivotal role of platform firms in the credit card market.</span></p>

The Influence of Financial Literacy, Pocket Money, and Lifestyle on the Financial Behavior of College Students who Use Online Shopping Platforms

This study aimed to determine the effect of financial literacy, pocket money, and lifestyle on the financial behavior of students who use online shopping platforms at the Faculty of Economics and Business, University of Mataram. This study uses primary data from questionnaires to 120 respondents who are active students of the Faculty of Economics and Business, University of Mataram, and students who use online shopping platforms such as Shopee and TikTok Shop. The method used is quantitative research with multiple linear regression analysis on the IBM SPSS software application version 25. This study concluded that financial literacy and pocket money positively and significantly affect the financial behavior of students who use online shopping platforms. This means that the better the understanding of financial literacy and the higher the amount of pocket money owned, the better the students' financial behavior. However, lifestyle does not affect students' financial behavior using online shopping platforms. This means that a high or low lifestyle does not affect students' financial behavior using online shopping platforms