A mediation–moderation framework of consumers’ intention to participate in crowdfunding

The purpose of this paper is to examine the role of perceived risk and shopping frequency as a mediator and a moderator in supporting a reward-based crowdfunding (CF) project by potential backers. A research framework is developed based on consumer decision-making styles and literature studies. A total of 218 valid responses are collected from offline shoppers through an online questionnaire to examine their perceptions and motivation to participate in a CF project on Indiegogo, one of the largest reward-based CF platforms. Descriptive statistics and Hayes’ PROCESS macro are used to analyze data. The results reveal five decision-making styles of Thai offline shoppers. When combining these styles, they significantly directly increase the tentative of offline shoppers to support a CF project, but indirectly decrease their backing intention through perceived risk. Past behavior in terms of respondents’ offline shopping behavior insignificantly moderate the relationships between consumer style inventory (CSI) and perceived risk, perceived risk and intention, and CSI and intention, but significantly help to lower their perceived risk. The results guide project owners in reward-based CF platforms in drawing attention from future backers, expanding their market, and creating marketing strategies for potential consumers with different decision-making styles. This work is one of the first papers that explores offline shoppers as potential backers, examines the impact of consumer decision-making styles, and analyze mediation and moderation models in the context of a reward-based CF platform

Finding customer behavior insights for content creation in material and product sourcing using specialized topic analysis

In content creation, customer behavior insights are very important as they help creators find and create the content that drives sales. To comprehend customer needs, content creators need not just generalized information but also specific information, which can be different across markets and cultures. This information also needs some standards so it can be analyzed systematically. This paper aims to obtain customer insight into web content. Inside the web content, one possible source of this information is the tags based on customer feedback and the related entities. In this case, the product review data were collected and analyzed. However, manually analyzing feedback is a time-consuming activity. In this work, we formulated the topic analysis problem specialized for material and product sourcing, which could benefit product analysis and development. Technically, we also compared different text processing and classification methods, which set the benchmarks for reviewing the model performance in the future

VISHIEN-MAAT: Scrollytelling visualization design for explaining Siamese Neural Network concept to non-technical users

The past decade has witnessed rapid progress in AI research since the breakthrough in deep learning. AI technology has been applied in almost every field; therefore, technical and non-technical end-users must understand these technologies to exploit them. However existing materials are designed for experts, but non-technical users need appealing materials that deliver complex ideas in easy-to-follow steps. One notable tool that fits such a profile is scrollytelling, an approach to storytelling that provides readers with a natural and rich experience at the reader’s pace, along with in-depth interactive explanations of complex concepts. Hence, this work proposes a novel visualization design for creating a scrollytelling that can effectively explain an AI concept to non-technical users. As a demonstration of our design, we created a scrollytelling to explain the Siamese Neural Network for the visual similarity matching problem. Our approach helps create a visualization valuable for a short-timeline situation like a sales pitch. The results show that the visualization based on our novel design helps improve non-technical users’ perception and machine learning concept knowledge acquisition compared to traditional materials like online articles