The rapid transformation of e-platforms and the growth of e-commerce in China provide consumers with the most advanced channels and creative options for buying and selling through online podium. Online shopping is categorically going to be the world's future shopping mode. Besides, The Belt and Road Initiative (BRI) of China aims to place the world on a new trajectory of higher growth and human development through the connectivity of infrastructure, increased trade, and investment. Crossborder online shopping has become one of the forthcoming prime business models through the implication of the Belt & Road Initiative. By analyzing the representative group of consumers from Belt & Road countries, this study is to explore possible guiding constraints about online shopping decisions that may assist to understand behavioral traits to cross-border online shopping. In the expansion of online shopping, consumers, particularly young people, and students play the most important role. It is, therefore, imperative for companies to investigate and consider the factors that affect the decision of students and young people to get online shopping. This paper has attempted to explore various factors that will affect the online shopping decision of consumers particularly from Belt & Road countries. During this analysis, a qualitative study approach was adopted and a sample of 105 higher education students from the Belt & Road Initiative (BRI) countries studying at Capital Normal University was drawn up. The study will ascertain the ‘Online Shopping Behaviors’ of typical consumers from BRI (Belt & Road) countries to explore a wider direction for cross-border online shopping decisions. It is discovered that online shopping makes life easier, and it is not so far that online shopping can infinitely overtake conventional physical shopping in the whole world.

Online Shopping Behaviour among International Students from Belt & Road Countries in China

Analysis of customer relationships based on their satisfaction is a practical and motivating success factor for the growth of every company. Web intelligence describes the scientific development that uses information technology and artificial intelligence for new frameworks, services, and products provided by the web. This chapter aims to present the model of analyzing the users' sentiments from their online reviews on an e-commerce platform using machine-learning classifiers, namely naive bayes, logistic regression, support vector machine, and neural network. For data analysis, latent semantic analysis has been applied to examine the most frequent words used in online reviews. Finally, customers' interest in online shopping analysis has been performed to classify the customers' sentiments from their posted reviews on the e-commerce platform. In addition, the authors compared the performance results of these classifiers on the e-commerce dataset. The results reveal that the naive bayes classifier has performed better than all the other three classifiers. 

Development of Effective Electronic Customer Relationship Management (ECRM) Model by the Applications of Web Intelligence Analytics

This study aims to know the adoption of online shopping apps on the digital natives generation. This paper is based on the result of a survey on the process of online shopping apps adoption on 160 digital natives in Pekanbaru City, one of the smart cities in Indonesia. This study found that 89% of respondents have used online shopping apps. That innovation adoption has been through the process of thorough innovation-decision at a high score range. This finding describes that digital natives generation is a potential adopter of online shopping apps innovation because most of them are early adopters and early majority namely the group that adopts technology quickly. If projected for facing the era of demography bonus of Indonesia in the period of 2030 to 2040, this digital natives generation can be counted on for powering the smart economy as one of the important pillars of the smart city.

Adoption of Online Shopping Apps Innovation on Digital Natives Generation

— E-commerce websites are becoming extremely prevalent, with a significant volume of individuals embracing this medium. This seems to be due to the enhanced accessibility and simplicity of use that these networks provide when it start shopping for necessities as well as other things from the convenience of your own home. This has enabled many handicapped people and others with accessibility concerns to successfully remain at home and purchase different products that they require. This has resulted in a plethora of e-commerce companies fighting for consumers' focus and attempting to grow their customer base in order to deliver accurate alternatives with simplicity. The integration of speech and gesture-based searching for items on e-commerce platforms is one example of something like this. The paradigm of utilizing Convolution Neural Network for the recognition of the gestures is one of the most difficult approaches that require extensive computations along with the region of interest estimation. The obtained results are indicate better performance of the system through the use of Root mean square Error evaluation.

Interactive System For Product Purchase Through Gesture and Voice

Web data mining can provide insight into the e-commerce data which will be useful to promote sales, understand customers, and support new business opportunities. But understanding the customers and increasing their participation in online shopping is a great challenge as it requires the analysis of the relationship between customer satisfaction and the most significant factors that influence their online buying decisions. Machine learning comes into play in addressing the challenge faced by the e-commerce companies. In a nutshell, this paper focuses on the analytics of customers’ attitude towards using online shopping and intention to buy, using machine learning classifiers namely Naive Bayes, Logistic regression, Support Vector Machine, and Neural Network. For data analysis, Latent semantic analysis is applied to examine the most frequent words used in the online reviews. Finally, customer’s interest in online shopping analysis has been performed using machine learning classifiers to classify the customers’ sentiment from their posted reviews on the e-commerce platform. Also, we compared the performance results of these classifiers on the ecommerce dataset. The results reveal that the Naive Bayes classifier has performed better than all the other three classifiers in terms of execution time and the measures like accuracy, recall, F1-score, confusion matrix. 

Analyzing Online Reviews of Customers Using Machine Learning Techniques

The rapid development of the Internet has increased the number of users shopping online. Many sellers thus focus on the shopping behavior on online environment. In this competitive environment, managers must understand factors that affect the online purchase behavior of customers to increase and maintain their competitive advantage. Therefore, this study aims to explore antecedents of online shopping behavior. Based on the social cognitive theory, this study proposes a research model of online shopping behavior. This study argues that trust positively affects online shopping behavior; perceived website complexity negatively and directly affects online shopping behavior, and indirectly affects trust online shopping behavior via trust. These research results can be seen as references by future scholars and practical suggestions for platform managers to improve their businesses.

Tzu-Jui Fu

Papers

The Determinants of Online Shopping Behavior