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/).

翻译：研究人员越来越依赖众筹平台来洞察创业活动及其动态。尽管已有研究探讨了影响众筹成功的多种因素（如技术、沟通和营销策略），但可自动从图像中提取的视觉元素的作用却较少受到关注。这一现象令人意外，因为众筹平台强调吸引眼球的高分辨率图像的重要性，且先前研究已表明图像特征能显著影响产品评价。事实上，一项对利用Kickstarter数据的实证文章（n=202）的综合评述显示，仅有29.70%的研究控制了图像数量变量，而考虑图像细节的比例不足12%。在本文中，我们综述了图像处理领域的文献及其与商业领域的相关性，重点强调两类视觉变量：视觉计数（图片数量与视频数量）和图像细节。基于前人关于色彩、构图及图形-背景关系作用的讨论，我们引入了尚未在众筹领域探索的视觉场景元素，包括人脸数量、所描绘概念的数量及其识别难易度。为验证视觉计数与图像细节的预测价值，我们分析了Kickstarter数据。研究结果表明，视觉计数特征在成功预测因素中占据前三中的两位；同时，简单的图像细节特征（如色彩）也具有重要影响，而本文提出的视觉场景元素度量同样具备实用价值。我们随文附带了用于提取图像细节的R和Python代码（https://osf.io/ujnzp/）。