Convince Me on Social Media: The Impact of Visual Information Entropy on Consumer Responses

In an era where retailers and brands increasingly embed purchase links in their social media posts, consumer convenience is crucial. Yet the overwhelming volume of visual content on these platforms often leaves consumers feeling disoriented. While prior research has examined this complexity through the lens of subjective visual entropy—defined as perceived visual disorder—the objective measurement of information volume, known as visual information entropy, remains largely unexplored. To address this gap, we refine an algorithm to objectively and accurately compute the information entropy of images, moving beyond previous studies that rely on broad, intuition-based classifications of entropy as “high” or “low” (Biliciler et al., 2022). Our research includes one field study and four online experiments, contributing to the literature on visual marketing and Construal Level Theory (CLT). First, we introduce information theory (Shannon, 1948) as a novel framework for empirically quantifying visual information entropy in marketing contexts. Second, we challenge prior claims that consumers prefer low-entropy images due to a general preference for visual order (Palmer et al., 2013), showing instead that high- entropy images can increase engagement and enhance ad persuasiveness. Third, we reconcile mixed findings in the literature regarding the relationship between information entropy and construal levels (e.g., Biliciler et al., 2022; Carney, 2020). Finally, we suggest that the superior performance of color images over black-and-white images may stem from their higher levels of visual information entropy. Our findings offer both theoretical insights and practical implications for social media marketers seeking to optimize visual communication and consumer engagement.