E-commerce Research Articles

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.

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

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

DEVELOPMENT OF AN E-COMMERCE PLATFORM USING EXTREME PROGRAMMING METHODOLOGY

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

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

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

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

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

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

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

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

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

Role of Live E-Commerce on Consumer Purchase Intentions

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

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

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