PurposeThe purpose of this study is to unpack the antecedents and consequences of clickbait prevalence in online media at two different levels, namely, (1) Headline-level: what characteristics of clickbait headlines attract user clicks and (2) Publisher-level: what happens to publishers who create clickbait on a prolonged basis.Design/methodology/approachTo test the proposed conjectures, the authors collected longitudinal data in collaboration with a leading company that operates more than 500 WeChat official accounts in China. This study proposed a text mining framework to extract and quantify clickbait rhetorical features (i.e. hyperbole, insinuation, puzzle, and visual rhetoric). Econometric analysis was employed for empirical validation.FindingsThe findings revealed that (1) hyperbole, insinuation, and visual rhetoric entice users to click the baited headlines, (2) there is an inverted U-shaped relationship between the number of clickbait headlines posted by a publisher and its visit traffic, and (3) this non-linear relationship is moderated by the publisher's age.Research limitations/implicationsThis research contributes to current literature on clickbait detection and clickbait consequences. Future studies can design more sophisticated methods for extracting rhetorical characteristics and implement in different languages.Practical implicationsThe findings could aid online media publishers to design attractive headlines and develop clickbait strategies to avoid user churn, and help managers enact appropriate regulations and policies to control clickbait prevalence.Originality/valueThe authors propose a novel text mining framework to quantify rhetoric embedded in clickbait. This study empirically investigates antecedents and consequences of clickbait prevalence through an exploratory study of WeChat in China.

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Although Fogg's (1999, 2003) ideas of persuasive technologies are widely accepted, few attempts have been made to test his ideas, particularly in a team context. In this article, we 1) theoretically extend Fogg's ideas by identifying contexts in which virtual teams are more likely to use persuasive technologies; 2) empirically measure technology visualness, a factor that likely makes technologies more or less persuasive; and 3) assess the association between the use of persuasive technologies, judgment shifts, and forecast performance in a real-world virtual team context. We identify visual representation technologies (VRTs) as a class of technologies used by virtual teams to select, transform, and present data in a rich visual format. We propose that such technologies play a persuasive, as well as diagnostic, role in virtual team decisions. Over a three-year period, we examine the daily chat room discussions and decisions of a virtual team that makes smog forecasts with large economic and health consequences. We supplement regression models of field data with an experiment, interviews with team members, and analyses of imagery processing and group cohesion in team language use. Experiment results show that, relative to non-VRTs, the use of a VRT in a forecasting task increases imagery processing. Field data results show that team members increase their use of VRTs during chat room discussions when initial team consensus is low and the environment is more exacting. Greater use of VRTs in team discussions relates to greater shifts in the initial to final consensus forecasts of the team and greater odds of the team shifting its forecast policy to issue a smog alert. Increased use of VRTs is associated with lower forecast bias but is not significantly associated with forecast accuracy. VRT use is also associated with greater imagery processing and increased group cohesion, as shown through language use.

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