论文标题
提取视觉信息以预测众筹成功
Extraction of Visual Information to Predict Crowdfunding Success
论文作者
论文摘要
研究人员越来越多地转向众筹平台，以洞悉企业家活动和动态。虽然先前的研究探索了影响众筹成功的各种因素，例如技术，沟通和营销策略，但可以自动从图像中自动提取的视觉元素的作用受到了较少的关注。考虑到众筹平台强调了吸引注意力和高分辨率图像的重要性，这令人惊讶，并且先前的研究表明，图像特征可以显着影响产品评估。实际上，对利用Kickstarter数据的经验文章的全面综述（n = 202），重点是将视觉信息纳入其分析。我们的发现表明，只有29.70％的图像数量控制，少于12％的图像详细信息。在本手稿中，我们回顾了有关图像处理及其与业务领域相关的文献，突出了两种类型的视觉变量：视觉计数（图片数量和视频数量）和图像详细信息。在讨论颜色，构图和图形关系的作用的先前工作的基础上，我们介绍了尚未在众筹中探讨的视觉场景元素，包括面孔的数量，所描绘的概念数量以及识别这些概念的易于性。为了证明视觉计数和图像细节的预测价值，我们分析了Kickstarter数据。我们的结果表明，视觉计数特征是成功的前三个预测指标中的两个。我们的结果还表明，简单的图像细节特征（例如颜色）很重要，我们提出的视觉场景元素测量也可能很有用。我们用R和Python代码补充文章，这些代码可帮助作者提取图像详细信息（https://osf.io/ujnzp/）。
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/).
