Credit Scoring with AHP and Fuzzy Comprehensive Evaluation Based on Behavioural Data from Weibo Platform

It is increasingly necessary to evaluate the customers\u27 credit. In the era of big data, Information on the Internet is commonly used to judge the credit worthiness of customers. Some users\u27 credit information is incomplete or unavailable, so credit managers cannot judge the true credit situation of these users. However, with the support of social data especially behavioural data and credit evaluation system, this problem can be effectively solved. This study used Weibo to obtain the behavioural data of Chinese users for credit evaluation. Two methods are used to calculate the credit scores of Weibo users, which are the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation methods. By analysing social processes and inviting experts to make decisions, we constructed a credit evaluation system to expose users\u27 behavioural characteristics. We found that the three key indexes determining the user’s social credit are personal identification, behavioural characteristics and interaction among friends. Then, AHP was used to determine the weight of each index. Finally, a static algorithm was proposed to compute the credit evaluation system of Weibo users using fuzzy comprehensive evaluation methods

Effects of background complexity on consumer emotion and purchase intention in live streaming commerce

Visual complexity plays a crucial role in consumer purchase behavior. However, existing research on background complexity in live streaming commerce is limited. Drawing on the stimuli-organism-response (S-O-R) theory, this study aims to explore the relationships between live background complexity, consumers’ emotion and purchase intention. A 3×2 between-subjects online experiment was developed to collect the participants’ related emotion and intention data. The results primarily indicated that consumers’ emotions (i.e., pleasure and arousal) can be evoked by the visual complexity and further positively influence their purchase intention. Consumer emotion shows a nonlinear mediation effect between live complexity and purchase intention. The moderator role of gender in the relationship between complexity and consumer emotion was also examined. The result revealed that the difference between men and women only exists in the pleasure dimension

Donate Time or Money? The Determinants of Donation Intention in Online Crowdfunding

Compared with traditional charities, donation-based crowdfunding has many novel characteristics. Among the variety of factors that influence potential donors before they develop a donation intention, which are the main ones influencing the intention to donate online? The purpose of this paper is to investigate the key potential determinants of both time and money donations. This study attempts to combine the theory of planned behavior and norm activation theory with social presence theory to conceptualize and develop an integration framework to measure the donation intention. The results of the structural equation modeling, based on 350 valid questionnaire responses received from November 19 to December 19, 2018, suggest that the dependent variable of time donations is significantly affected by social presence, trust, and perceived behavioral control. As for the dependent variable of money donations, only subjective norm has an insignificant effect. The study results offer practical guidelines about the unique aspects of donation to managers of crowdfunding platform and fundraisers

Detecting Fraudulent Financial Statements for the Sustainable Development of the Socio-Economy in China: A Multi-Analytic Approach

Identifying financial statement fraud activities is very important for the sustainable development of a socio-economy, especially in China’s emerging capital market. Although many scholars have paid attention to fraud detection in recent years, they have rarely focused on both financial and non-financial predictors by using a multi-analytic approach. The present study detected financial statement fraud activities based on 17 financial and 7 non-financial variables by using six data mining techniques including support vector machine (SVM), classification and regression tree (CART), back propagation neural network (BP-NN), logistic regression (LR), Bayes classifier (Bayes) and K-nearest neighbor (KNN). Specifically, the research period was from 2008 to 2017 and the sample is companies listed on the Shanghai stock exchange and Shenzhen stock exchange, with a total of 536 companies of which 134 companies were allegedly involved in fraud. The stepwise regression and principal component analysis (PCA) were also adopted for reducing variable dimensionality. The experimental results show that the SVM data mining technique has the highest accuracy across all conditions, and after using stepwise regression, 13 significant variables were screened and the classification accuracy of almost all data mining techniques was improved. However, the first 16 principal components transformed by PCA did not yield better classification results. Therefore, the combination of SVM and the stepwise regression dimensionality reduction method was found to be a good model for detecting fraudulent financial statements