Information In E-commerce Research Articles

Unveiling the Secrets of Effective Information Management: How Does Introductory Information Influence Purchase Intention in Live-Streaming

There is growing evidence that product introductory information plays a crucial role in online consumer purchase behavior. However, existing researches on the effect of introductory information on individuals’ purchase intention in live-streaming were limited. In this setting, based on the Stimulus-Organism-Response (S-O-R) model, we explored the relationship among introductory information, trust, purchase intention and display uniqueness. It was done by collecting questionnaire survey data from 500 consumers who have participated in live-streaming e-commerce. The results showed that the introductory information in live-streaming e-commerce has a significant positive effect on consumers’ purchase intention through trust. Additionally, the results revealed a significant moderating effect of display uniqueness on the relationship between introductory information and trust. Display uniqueness could significantly and positively moderate the predictive effect of introductory information on trust. Thus, this study will extend the applicability of S-O-R model to further understand the influence mechanism of introductory information on purchase intention and enrich the research in the live-streaming e-commerce. Also, it can enhance the understanding of consumers’ purchase behaviors to optimize the live-streaming system and online marketing tools, thereby helping companies achieve their business goals.

Application of big data search based on collaborative filtering algorithm in cross-border e-commerce product recommendation.

With the deepening of cross-border e-commerce, the trend of buying and selling goods through the Internet is rising. It is necessary to establish a cross-border e-commerce platform that meets the above functions, and improve the ability to process big data in search. For example, the emergence of large amounts of data can not only help users make choices, but also increase the difficulty of users in choosing. At present, there are many problems in the big data search system in the market, such as inaccurate user personality analysis and low importance of product recommendation. E-commerce is developing rapidly in the new era, and new users are increasing every day. Many researchers invest in finding excellent cross-border e-commerce recommendation system as a business platform. The number of information in cross-border e-commerce shows a rapid growth pattern, and the rapid growth of data and information has seriously affected people's judgment. The big data search system based on collaborative filtering algorithm can meet the product recommendation system of cross-border e-commerce. The user matrix label is an attribute of construction. For the label quantification, the new user preference is the model of building the label, and the concept of weight is added to the label. The collaborative filtering algorithm works based on the created weight label.

The role of information quality processing in determining purchase intentions through social psychological distance

The expansion of e-commerce users in Indonesia parallels the growing internet penetration. However, this does not necessarily positively affect e-commerce service providers, as there are other obstacles, including a healthy environment. Because the quality of the information in e-commerce is maintained, a healthy ecosystem develops. Consequently, this study will delve deeper into the significance of information quality in e-commerce services. Data gathered from 143 respondents and analyzed using Structural Equation Modeling (SEM) reveal that the quality of information has a positive and statistically significant effect on purchase intention via social psychological distance and trust.

The Use of Electronic Media as an Innovation in Law by the Notary in Handling Agreement and Contracts: The Role of Electronic Information and Transactions (ITE) Law in Indonesia's E-Commerce

The Use of Electronic Media as an Innovation in Law by the Notary in Handling Agreement and Contracts: The Role of Electronic Information and Transactions (ITE) Law in Indonesia's E-Commerce

Introduction to the Special Issue on “Innovation and Application of Intelligent Processing of Data, Information and Knowledge in E-Commerce”

Introduction to the Special Issue on “Innovation and Application of Intelligent Processing of Data, Information and Knowledge in E-Commerce”

Internet business Information The board

Product management is crucial for e-commerce businesses, and it can help you crush the competition if you have the correct e-commerce data management software in place. You are only as successful in the realm of online commerce as the administration of your product data. Data is the new money in the online and digital world. You can outperform your rivals and boost your profitability if you know how to use it to your advantage. Ecommerce data management, to put it simply, is a highly collaborative and organised modern system that assists you in managing, sharing, and accessing vast quantities of essential product data that are highly valuable to your company. By allowing you to respond to market demands more swiftly, effective data management also aids in keeping you competitive. When meeting the needs of your consumers, EDM enables you to work more efficiently and with less time and human mistake [13]. Product Data Management is a structured approach to assist in managing, gaining access to, and disseminating vast amounts of essential product data that is highly beneficial to your company. The product catalog, attributes, product content, and the consumer data gathered are typically included in this information in eCommerce. In this paper , we have studied How to choose a database management system. There are several database management systems available. Each of them has benefits and downsides in its own particular ways. We tried to compare the databases that are frequently used for both small web applications and massive data warehousing systems, even though we haven't even touched on a third of all databases. So, how do for your own software program? Did you choose the best choice? Databases like MySQL might be a logical starting point for local eCommerce businesses that are just getting off the ground and are compatible with OLTP and web-based BI TOOLS. If you want to create a massive eCommerce company that offers a full customer journey, Cassandra is a good option.. You may also attach the Elasticsearch database solution to it to give it a strong search engine as an addition. In relation to Cassandra, it's also a respectable option for data centres and real-time analytics with massive amounts of data. It may be fair to choose NoSQL databases like MongoDB when discussing analytical tools without numerous data layers. It works great for product catalogs as well. MSSQL is also important to note in the context of data warehousing applications, especially for businesses that have several different Microsoft subscriptions. Oracle is a strong option as well for developing OLTP solutions and data warehousing applications. Our list of the finest use cases for Redis will be summarized by IoT applications and microservice architecture that prefer to scale its data hosting. Yes, there are more database systems to take into account. Your business concept and needs will determine everything. Currently, e-commerce websites not only supply massive amounts of data to e-commerce businesses, but also provide insights data using so many analytics techniques [5].

A deep neural network model for fashion collocation recommendation using side information in e-commerce

A fashion collocation recommendation model is a useful tool to enhance customer experiences and increase cross-selling in e-commerce. Prior research efforts focus on using titles and images to build the model. However, accessible side information in e-commerce, such as textual descriptions, purchase data and category information of items, generally bear valuable information regarding this task. This study seeks to develop a fashion collocation recommendation model that leverages accessible side information in e-commerce. First, a knowledge graph is built to model the purchase data and category information of items and an approach is subsequently proposed to learn the knowledge embeddings in the knowledge graph. To cope with the challenge of Chinese titles, a model is developed to encode the Chinese titles into vectors. Finally, a fashion collocation recommendation model combines the knowledge embeddings of the knowledge graph and the encoded titles to compute the probability of fashion collocation between items. The experiments are conducted on two datasets. Compared with baseline models, our model obtains nearly 6% and 1% AUC improvement in TIANCHI and POG dataset, respectively. The experimental results demonstrate that our efforts to leverage side information to improve fashion collocation recommendations are feasible and effective.

Spam review detection using self attention based CNN and bi-directional LSTM

Opinion reviews are a valuable source of information in e-commerce. Indeed, it benefits users in buying decisions and businesses to enhance their quality. However, various greedy organizations employ spammers to post biased spam reviews to gain an advantage or to degrade the reputation of a competitor. This results in the explosive growth of opinion spamming. Due to its nature and their increasing volume, spam reviews are a fast-growing serious issue on the internet. Until now, researchers have developed many Machine Learning (ML) based methods to identify opinion spam reviews. However, the traditional ML methods cannot effectively detect spam messages due to the limited feature representations and the data manipulations done by spammers to escape from the detection mechanism. As an alternative to ML-based detection, in this paper, we proposed a Deep Learning (DL) based novel framework called Self Attention-based CNN Bi-LSTM (ACB) model to learn document level representation for identifying the spam reviews. Our approach computes the weightage of each word present in the sentence and identifies the spamming clues exists in the document with an attention mechanism. Then the model learns sentence representation by using Convolution Neural Network (CNN) and extracts the higher-level n-gram features. Then finally, sentence vectors are combined using Bi-directional LSTM (Bi-LSTM) as document feature vectors and identify the spam reviews with contextual information. The evaluated experiment results are compared with its variants and the result shows that ACB outperforms other variants in terms of classification accuracy.

Information and communication technologies in e-commerce and e-governance

The development of information and communication technologies has reached a level where their use in business and governance is becoming not only expedient but also inevitable. The Internet and ICT have created the technical and technological foundation that can significantly improve the efficiency of public administration, provide businesses and citizens with access to necessary information, simplify bureaucratic procedures and reduce the time for decision making. The paper describes the new conditions in which the business is developing. The article presents the features of the interaction of e-government and business, the impact of e-government on the development of the economy as a whole. The authors analyzed the process of development of technologies for e-governance and e-commerce. The authors identified the tasks that are solved by means of e-government. Also, the authors set up indicators for evaluating the effectiveness of the e-governance. The implementation of the e-government demonstrates advantages for the further development of the economic sector and the whole state in general.

Involve Convolutional-NN to Generate Item Latent Factor Consider Product Genre to Increase Robustness in Product Sparse Data for E-commerce Recommendation

Online shopping also popular named e-commerce business need a computer machine to provide product information for customer or buyer candidate. Relevant information served by ecommerce system engine famous dubbed recommender system. It will impact seriously in increasing of marketing target achievement. The character of information in ecommerce have to be specific, personalized, relevant and fit according to customer profiling. There are four kind of recommender system to provide ecommerce recommender system, however only one model that most successful to applied in real ecommerce industry that namely collaborative filtering. These approaches rely on rating as basic calculation to generate product recommendation. However, just a little number of rating that given by customer reference to several convince datasets. The problem causes of sparse product rating, it will bring the impact to product recommendation accuracy. Sometime, in extreme condition impossible to generate product recommendation. Some work efforts have been developing to tackle lack of rating, one of them is considering to involving text sentences document such as text genre, product review, abstract, product description, synopsis, etc. All of material text sentences useful as raw component to predict the product rating. Reference previous work method aims extract text sentences document such as product review to become rating value based on bag of word and word order, sometimes they have got the mis in deeper understanding of text sentences description about product. Therefore, it influenced the result of predict the rating was inaccurate. In this research, author proposed novel method enhance variant of convolutional neural network dubbed dynamic convolutional neural network to improve scalability and increase deeper understanding to increase accuracy level. Based on our experiment, our model outperforms over existing state of the art in previous work in extracting text sentences product description based on evaluation approach uses RMSE.

Platform Externality, Asymmetric Information, and Counterfeit Deterrence in E-Commerce

ABSTRACTThe fight against online sales of counterfeit goods has received much attention. To the best of our knowledge, existing literature on online counterfeiting lacks a theoretical framework. To fill this gap, this article proposes a two-period model with sellers, buyers, and a platform. We focus on the relationship among platform structure (characterized by the ratio of buyers to sellers), asymmetric information, deterrence strength, and the ratio of counterfeits. Cross-network externalities of platform make platform managers worry about potential exodus of buyers and sellers due to counterfeits. This externality provides platform with strong incentives to fight counterfeits by itself, even without external regulatory requirement. We further show that the higher the ratio of buyers to sellers, the lower the ratio of counterfeits. Moreover, a lower degree of information asymmetry or a higher degree of punishment can reduce the ratio of counterfeits. We suggest that governments and e-commerce platforms work together in the fight against counterfeiting.

Disclosure of personal data in ecommerce: A cross-national comparison of Estonia and the United States

This study examines how demographic variables affect willingness to disclose and perceived risks of disclosing personally identifying information (PII, also referred to as personal data in Europe) in ecommerce in the United States and Estonia. The study utilized a 17-item list of potential disclosure items (name, email address, etc.), categorized reliably into six sub-indices: contact information, payment information, life history information, financial/medical information, work-related information, and online account information. Online disclosure consciousness (ODC) is introduced as a framework to conceptualize, explain the study’s findings, and empirically measure the gap between one’s willingness to disclose and perceived risk pertaining to the overall 17-item index used in the study, the sub-indices, and particular items. The results show significant gaps among participants both within and across nations. Despite Estonia’s advanced adoption and progressive policies and practices toward the Internet, Americans are more willing to disclose, and less concerned about perceived risks. The findings suggest willingness to disclose and risk aversion can and should be analyzed empirically together. The theoretical model provides an alternative conceptualization to the ideas of the privacy paradox, privacy calculus, and privacy cost-benefit ratios. Implications for theory, consumers, marketing practice, and public policy are discussed. Importantly, the study can inform increased adoption of ecommerce and the digital economy, while also protecting consumer’s personal data.

Imperfect information about product description as a component of asymmetric information in e-commerce

Asymetria informacji jest pojeciem szeroko wykorzystywanym w roznych naukach ekonomicznych. W wiekszości przypadkow jej definiowanie opiera sie wylącznie na podejściu ilościowym, nie uwzgledniając jakości informacji jako elementu niedoskonalości. W artykule zaprezentowano wskaźnik poziomu poinformowania klientow w handlu internetowym jako skladową obliczania asymetrii informacji w oparciu o niedoskonalośc informacji. Wyniki opierają sie na badaniach ankietowych.

E-Commerce Adoption among Small and Medium Enterprises (SMEs) in Northern State of Malaysia

Electronic commerce (E-commerce) which is a practice of buying, selling, transferring, or exchanging goods, information or service by means of internet has established in 1994 and until now hordes of organizations have exploited the internet to handle the information and fit in e-commerce into their business processes. This study analyses the relationship between organizational context, technological context, and environmental context towards E-commerce adoption among SMEs in Northern state of Malaysia which comprise of Perlis, Kedah, Penang and Northern part of Perak. Above and beyond, this study is conducted based on survey design and descriptive research investigation. Entire numbers of respondents collected through questionnaires are 364 CEOs or managers diversified from different sectors. The research data is analyzed using frequency analysis, reliability analysis, descriptive analysis as well as multiple regression analysis. DOI: 10.5901/mjss.2015.v6n5p37

The Effects of Consumer Risk Perception on Pre-purchase Information in Online Auctions: Brand, Word-of-Mouth, and Customized Information

This study examines how consumer information processing affects consumers' perception of risk prior to purchase. In particular, this research focuses on pre-purchase information such as brand, word-of-mouth, and customized information. The results show that customized information and word-of-mouth communication influence consumers more than do other types of information from online auctions. Consumers rely on these two factors because they are based on consumer experience and relevant to product purchase. Nevertheless, brand also has a significant effect upon consumer perceived risk. Pre-purchase information processing is directly related to reducing consumers' risk perception. In particular, information processing associated with product performance plays a crucial role in reducing consumers' perceived risk in online transactions. The results offer insights to e-marketers and e-marketing researchers about the role of pre-purchase information in management and e-commerce.