Abstract Despite evidence that currency denomination can influence spending, researchers have yet to examine whether the physical appearance of money can do the same. This is important because smaller denomination bills tend to suffer greater wear than larger denomination bills. Using real money in the context of real purchases, this article demonstrates that the physical appearance of money can override the influence of denomination. The reason being, people want to rid themselves of worn bills because they are disgusted by the contamination from others, whereas people put a premium on crisp currency because they take pride in owning bills that can be spent around others. This suggests that the physical appearance of money matters more than traditionally thought, and like most things in life, it too is inextricably linked to the social context. The results suggest that money may be less fungible than people think.

We suggest that text readability plays an important role in driving consumer engagement on social media. Consistent with a processing fluency account, we find that easy‐to‐read posts are more liked, commented on, and shared on social media. We analyze over 4,000 Facebook posts from Humans of New York, a popular photography blog on social media, over a 3‐year period to see how readability shapes social media engagement. The results hold when controlling for photo features, story valence, and other content‐related characteristics. Experimental findings further demonstrate the causal impact of readability and the processing fluency mechanism in the context of a fictitious brand community. This research articulates the impact of processing fluency on brief word‐of‐mouth transmissions in the real world while empirically demonstrating that readability as a message feature matters. It also extends the impact of processing fluency to a novel behavioral outcome: commenting and sharing actions.

Researchers have increasingly turned to crowdfunding platforms to gain insights into entrepreneurial activity and dynamics. While previous studies have explored various factors influencing crowdfunding success, such as technology, communication, and marketing strategies, the role of visual elements that can be automatically extracted from images has received less attention. This is surprising, considering that crowdfunding platforms emphasize the importance of attention‐grabbing and high‐resolution images, and previous research has shown that image characteristics can significantly impact product evaluations. Indeed, a comprehensive review of empirical articles ( n = 202) utilized Kickstarter data, focusing on the incorporation of visual information in their analyses. Our findings reveal that only 29.70% controlled for the number of images, and less than 12% considered any image details. In this manuscript, we contribute to the existing literature by emphasizing the significance of visual characteristics as essential variables in empirical investigations of crowdfunding success. We review the literature on image processing and its relevance to the business domain, highlighting two types of visual variables: visual counts (number of pictures and number of videos) and image details. Building upon previous work that discussed the role of color, composition, and figure–ground relationships, we introduce visual scene elements that have not yet been explored in crowdfunding, including the number of faces, the number of concepts depicted, and the ease of identifying those concepts. To demonstrate the predictive value of visual counts and image details, we analyze Kickstarter data using flexible machine learning models (Lasso, Ridge, Bayesian additive regression trees, and eXtreme Gradient Boosting). Our results highlight that visual count features are two of the top three predictors of success and highlight the ease at which researchers can incorporate some information about visual information. Our results also show that simple image detail features such as color matter a lot, and our proposed measures of visual scene elements can also be useful. By supplementing our article with R and Python codes that help authors extract image details ( https://osf.io/ujnzp/ ), we hope to stimulate scholars in various disciplines to consider visual information data in their empirical research and enhance the impact of visual cues on crowdfunding success.