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) that 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 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. Our results highlight that visual count features are two of the top three
predictors of success. 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. We supplement our article with R and Python codes that help
authors extract image details (https://osf.io/ujnzp/).Comment: 32 pages, 5 figure