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Internet + and Electronic Business in China is a comprehensive resource that provides insight and analysis into E-commerce in China and how it has revolutionized and continues to revolutionize business and society. Split into four distinct sections, the book first lays out the theoretical foundations and fundamental concepts of E-Business before moving on to look at internet+ innovation models and their applications in different industries such as agriculture, finance and commerce. The book then provides a comprehensive analysis of E-business platforms and their applications in China before finishing with four comprehensive case studies of major E-business projects, providing readers with successful examples of implementing E-Business entrepreneurship projects.

Internet + and Electronic Business in China is a comprehensive resource that provides insights and analysis into how E-commerce has revolutionized and continues to revolutionize business and society in China.

The purpose of this paper is to present the state-of-the-art E-commerce logistics in supply chain management by investigating worldwide implementations and corresponding models together with supporting techniques via furniture industry.

Typical E-commerce logistics companies from North America, Europe, and Asia Pacific are comprehensively investigated so as to get the lessons and insights from these practices.

Future technologies like Internet of Things, Big Data Analytics, and Cloud Computing would be possibly adopted to enhance the E-commerce logistics in terms of system level, operational level, and decision-making level that may be real time and intelligent in the next decade.

This paper takes the furniture industry for example to illustrate the E-commerce logistics and supply chain management (LSCM). Other industries like electronic appliance industry are not considered.

Opportunities and future perspectives are summarized from practical implementations so that interested parties like E-commerce and logistics companies are able to get some guidance when they are contemplating the business.

E-commerce is booming with the development of new business models and will be continuously boosted in the near future. With large number of enterprises carrying out E-commerce, logistics has been largely influenced.

Insights and lessons from this paper are significant for academia and practitioners for considering E-commerce LSCM.

This paper explores how the blockchain food traceability system (BFTS) affects consumers' affective brand commitment and subsequent willingness to pay premium prices.

From February 11 to May 23, 2023, this study collected data from 236 Chinese customers, who had purchased blockchain-traced food in Jingdong Mall within the past three months. Structural equation modelling was used to analyse the data.

The main findings were as follows: (1) BFTS information transparency, information immutability and product diagnosticity are significant predictors of consumer-perceived trustworthiness; BFTS information transparency, product diagnosticity and product safety are significant predictors of consumer-perceived informativeness, (2) Perceived trustworthiness and perceived informativeness build consumers' affective brand commitment, (3) Affective brand commitment affects willingness to pay premiums and (4) Health consciousness positively moderates the relationship between consumers' affective brand commitment and willingness to pay premiums.

This paper complements the research on consumer behaviour in the BFTS, and the research results provide important enlightenment for guiding food enterprises to formulate reasonable and perfect marketing strategies of blockchain-traced food.

The money raised for projects on crowdfunding platforms is determined not only by the merit of the projects themselves but also, to a large extent, by the design of the project introduction on the platforms. The purpose of this study is to identify the clear relationships between the financing result and the design factors to give preferable design suggestions to the fund initiators.

The data for this study were abstracted with Python from the Jingdong crowdfunding platform on a certain day. In all, 11 hypotheses are put forward in the study with the two main purposes of exploring the function of the quality signaling and level setting.

All design factors can impact the money raised for a project. However, the impact differs in certain situations. The level setting can significantly impact the amount of money raised. The quality of signals has a complex impact on the two dimensions.

Three major contributions are made by this paper. First, the level setting is explored after overcoming the difficulty of scrawling data. Second, the authors create the Success Index and Weighted Gap Index to measure the level of success from comprehensive dimensions and quantify the dispersion of the levels. Third, this study shows a comprehensive theoretical framework which can function as a reference for research in the future.

Because online shopping is risky, there is a strong need to develop better presentation of online reviews, which may reduce the perceived risk and create more pleasurable shopping experiences. To test the impact of online reviews’ sentiment polarity presentation, the purpose of this paper is to adopt a scenario experiment to study consumers’ decision-making process under the two scenarios of mixed presentation and classified presentation of online reviews collected from Jingdong.com in China: focusing on the comparative analysis on the differences of the consumers’ perceived risk, purchase intention and purchase delay, and further studying the interaction effect of involvement and online reviews’ sentiment polarity presentation.

This paper employed a 2×2 factorial experiment to test the hypothesis. The experimental design is divided into four groups: 2 (online reviews’ sentiment polarity presentation: mixed presentation vs classified presentation) × 2 (involvement: low vs high), each of which contains 90 samples. Through the data analysis, the main effect, mediation effect and moderating effect were examined.

The results show that compared with mixed presentation, classified presentation can reduce purchase intention and increase purchase delay due to the existence of loss aversion and availability heuristic. Furthermore, the paper also confirms that there is a significant interaction effect between involvement and online reviews’ sentiment polarity presentation.

The existing research pays less attention to the impact of online reviews presentation on consumers’ decision making, especially the lack of discussion on the interaction effect between involvement and online reviews presentation. For this reason, this paper proposes a problem, which concerns whether mixed presentation and classified presentation of online reviews will affect consumers’ decision making differently.

As a standard source of capital for entrepreneurs, crowdfunding has recently gained wide attention in business and academia. With scientific endorsement, some research is conducted to explore the antecedents of online crowdfunding success. The factors that can influence the backers’ investment which is the key to success are information from prior backers’ and creators’ behaviors. Based on the signaling theory, the purpose of this paper is to systematically investigate the dynamic influences and interaction effects of signals with different forms (action-based or opinion-based signals) and sources (creator-sourced or backer-sourced signals) on backers’ investment behaviors over a project-funding cycle.

A panel data set of 3,010 projects with 640,625 transaction records from April 28, 2013 to September 31, 2017 is collected from a famous online crowdfunding platform – Zhongchou.cn in China and the negative binomial panel data model with fixed effect is used to obtain our empirical results.

The findings demonstrate that the work of different signals is significantly effective at the early stage of a project and decreases with time. Furthermore, our results show that there are both synergistic effect and substitution effect among different signals. Specifically, the direction of interaction effect depends on the forms of signals and the backers’ sensitivity toward that signal, and the interaction effects are also dynamic.

This paper has shed light on the roles of different signal types and their interactions in influencing funding behavior over a project-funding cycle, enriched the literature on crowdfunding and provided both theoretical and practical implications.

In China, supply chain finance (SCF) has gradually emerged as a new service for the retail industry. This case systematically discusses how JD conducts product design and risk control of supply chain finance and related financial services, and analyze the impact of supply chain finance on JD's retail operations. The case also analyzes the relationship between JD supply chain finance and traditional financial institutions, and explore the future development of retail supply chain finance.

This paper aims to explore whether crowdfunding creators can learn from previous experiences to have a better financing performance of future crowdfunding projects.

This paper uses Python to capture the data of 6,267 crowdfunding projects from one of the largest crowdfunding platforms in China (JingDong Crowdfunding) and the author use the negative binomial regression model and the OLS model in this empirical study.

The empirical results show that both the early-stage experience of creating a crowdfunding project and the early-stage experience of supporting projects of other crowdfunding creators can improve the financing performance of their newly launched projects. The social network of the previous projects and the “Blockbuster” projects initiated before can also make the newly initiated projects obtain better financing performance.

Current research on entrepreneurial experience shows that serial entrepreneurs have significantly different success rates than novice or inexperienced entrepreneurs but there is limited literature on the learning effect of crowdfunding creators. This study adds to the literature on entrepreneurial learning and provides suggestions to crowdfunding creators.

The rapid development of e-commerce has brought not only great convenience to people but a great challenge to online stores. Phenomenon such as out of stock and slow sales has been common in recent years. These issues can be managed only when the occurrence of the sales volume is predicted in advance, and sufficient warnings can be executed in time. Thus, keeping in mind the importance of the sales prediction system, the purpose of this paper is to propose an effective sales prediction model and make digital marketing strategies with the machine learning model.

Based on the consumer purchasing behavior decision theory, we discuss the factors affecting product sales, including external factors, consumer perception, consumer potential purchase behavior and consumer traffic. Then we propose a sales prediction model, M-GNA-XGBOOST, using the time-series prediction that ensures the effective prediction of sales about each product in a short time on online stores based on the sales data in the previous term or month or year. The proposed M-GNA-XGBOOST model serves as an adaptive prediction model, for which the instant factors and the sales data of the previous period are the input, and the optimal computation is based on the proposed methodology. The adaptive prediction using the proposed model is developed based on the LSTM (Long Short-Term Memory), GAN (Generative Adversarial Networks) and XGBOOST (eXtreme Gradient Boosting). The model inherits the advantages among the algorithms with better accuracy and forecasts the sales of each product in the store with instant data characteristics for the first time.

The analysis using Jingdong dataset proves the effectiveness of the proposed prediction method. The effectiveness of the proposed method is enhanced and the accuracy that instant data as input is found to be better compared with the model that lagged data as input. The root means squared error and mean absolute error of the proposed model are found to be around 11.9 and 8.23. According to the sales prediction of each product, the resource can be arranged in advance, and the marketing strategy of product positioning, product display optimization, inventory management and product promotion is designed for online stores.

The paper proposes and implements a new model, M-GNA-XGBOOST, to predict sales of each product for online stores. Our work provides reference and enlightenment for the establishment of accurate sales-based digital marketing strategies for online stores.