PurposeExtant research shows mixed results on the impact of expressed negative emotions on donations in online charitable crowdfunding. This study solves the puzzle by examining how different types of negative emotions (i.e. sadness, anxiety and fear) expressed in crowdfunding project descriptions affect donations.Design/methodology/approachData on 15,653 projects across four categories (medical assistance, education assistance, disaster assistance and poverty assistance) from September 2013 to May 2019 come from a leading online crowdfunding platform in China. Text analysis and regression models serve to test the hypotheses.FindingsIn the medical assistance category, the expression of sadness has an inverted U-shaped effect on donations, while the expression of anxiety has a negative effect. An appropriate number of sadness words is helpful but should not exceed five times. In the education assistance and disaster assistance categories, the expression of sadness has a positive effect on donations, but disclosure of anxiety and fear has no influence on donations. Expressions of sadness, anxiety and fear have no impact on donations in the poverty assistance category.Research limitations/implicationsThis work has important implications for fundraisers on how to regulate the fundraisers' expressions of negative emotions in a project's description to attract donations. These insights are also relevant for online crowdfunding platforms.Originality/valueOnline crowdfunding research often studies negative emotions as a whole and does not differentiate project types. The current work contributes by empirically testing the impact of three types of negative emotions on donations across four major online crowdfunding categories.
There is a trend that customers increasingly join the online brand community. However, evidence shows that there are nuances between different user segments, and only a small group of users are active. Thus, one key concern marketers face is identifying and targeting specific segments and decreasing user churn rates in an online environment. To this end, this study aims to propose a UGC-based segmentation of online brand community users, identify the characteristics of each segment, and consequently reduce online brand community users' churn rate. We used python to obtain users' post data from a well-known online brand community in China between July 2012 and December 2019, resulting in 912,452 posts and 20,493 users. We then use text mining and clustering methods to segment the users and compare the differences between the segments. Three groups—information-oriented users, entertainment-oriented users, and multi-motivation users—were emerged. Our results imply that entertainment-oriented users were the most active, yet, multi-directional users have the lowest probability of churn, with a churn rate of only 0.607 times than that of users who focus either on information or entertainment. Implications for marketing and future research opportunities are discussed.
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