An Exploration of the Relation between the Visual Attributes of Thumbnails and the View-Through of Videos: The Case of Branded Video Content
Decision Support Systems, 160, September 2022
Posted: 22 Jun 2020 Last revised: 15 Jul 2022
Date Written: May 27, 2020
Abstract
While browsing through an online video platform, potential viewers decide which videos to click and watch based on the information and impression they obtain from thumbnails. Therefore, a thumbnail needs to be able to tell potential viewers what the video is about (i.e., be informative), and at the same time, a thumbnail needs to grab potential viewers' attention (i.e., be visually appealing). Drawing from the elaboration likelihood model (ELM), we take the visual attributes that are relevant to the informativeness of thumbnails (e.g., element complexity and object complexity) as central route cues, take the visual attributes that are relevant to the aesthetics of thumbnails (e.g., celebrity endorsement, colorfulness, brightness, and image quality) as peripheral route cues, and explore the relation between these visual attributes of thumbnails and the view-through of videos using 3,745 marketing videos posted by 38 top brands across 4 industries – the automobile, beverage, restaurant, and toy industries – on YouTube. We construct our variables by extracting features from thumbnails using image mining techniques. Our study contributes to the literature on information systems and marketing by enhancing our understanding of the role of the visual attributes of images in persuading viewers in the online environment. Furthermore, our study provides a possible theoretical basis for studies on information processing and decision support that focus on automatic thumbnail selection. Finally, our study provides useful and practical guidelines for designing templates for optimal thumbnails that grab potential viewers' attention and yield more video views.
Keywords: Thumbnails, visual persuasion, video view-through, branded video content, elaboration likelihood model, image mining.
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