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.

Purpose: This study adopts self-determination theory and stimulus-organism-response framework to develop a model that explores the motivations of such donors by considering their self-determination needs and extrinsic and intrinsic motivations.Design/methodology/approach: Based on online survey data collected from 436 crowdfunding donors in China, this study follows a structural equation modeling analysis to test hypotheses.Findings: The results indicate that perceived ease of use, perceived self-efficacy and social connection have positive effects on the donation intentions of backers through a combination of extrinsic and intrinsic motivations.Originality/value: The findings shed light on various extrinsic and intrinsic motivations advancing knowledge of individual fund motivation in donation-based crowdfunding and provide guidelines for the development of donation-based crowdfunding theory and practice.

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.

Aiming to solve the cluster lag consensus (CLC) problem of networked nonlinear agents with a switching topologies, this paper proposes a control algorithm based on the measurements of position and velocity. When the network has several clusters of agents, under the conditions that each cluster is a strongly connected digraph including isolated agents, and at least one agent in each cluster can communicate with the common leader each time, a sufficient condition is derived for reaching CLC. Finally, a numerical simulation is demonstrated to validate the proposed control algorithm and the developed conditions.

Purpose: The aim of this study is to investigate how social media users' experience of seeking emergency information affects their engagement intention toward emergency information with a reciprocity framework integrated with information adoption model.Design/methodology/approach: Drawing on reciprocity theory, indebtedness theory, and information adoption model, an integrative research model is developed. This study employs a questionnaire survey to collect data of 325 social media users in China. Structural equation modeling analyses are conducted to test the proposed theoretical model.Findings: Social media users' experience of seeking emergency information has a strong effect on their perceived information usefulness and indebtedness, while perceived information usefulness further influences community norm, indebtedness, and engagement intention. The authors also found that perceived information usefulness mediates the relationships between experience of seeking emergency information and community norm/indebtedness.Originality/value: This study offers a new perspective to explain social media users' engagement intention in the diffusion of emergency information. This study contributes to the literature by extending the theoretical framework of reciprocity and applying it to the context of emergency information diffusion. The findings of this study could benefit the practitioners who wish to leverage social media tools for emergency response purposes.