Like, comment, and subscribe: investigating the effectiveness of digital engagement prompts

Purpose This study investigates the effectiveness of different types of calls-to-action (CTAs) in YouTube videos, specifically examining how “like,” “comment,” and “subscribe” prompts affect user engagement behaviors and how their placement within videos (beginning, middle, or end) influences viewer response rates. Design/methodology/approach The research analyzes 8,500 English-language YouTube videos from major English-speaking markets (USA, UK, Canada, Australia) using PLS-SEM. Video transcripts were analyzed to identify CTA presence and placement, while engagement metrics were collected via YouTube’s API. Findings Results show that “like” CTAs significantly increase video likes, particularly when placed mid-video. However, neither “comment” nor “subscribe” CTAs show significant effect on their respective engagement metrics. Cross-country analysis reveals variations in CTA effectiveness across markets, with the strongest effects observed in the USA. Research limitations/implications The sample of English-language content from Western markets limits generalizability to other cultural contexts. The analysis also relied solely on verbal CTAs, excluding non-verbal elements and content quality factors. Practical implications Content creators should strategically place “like” CTAs mid-video to maximize low-effort engagement, while recognizing that direct “comment” and “subscribe” requests have limited effectiveness without additional incentives or value propositions. Market-specific engagement strategies are recommended even within seemingly homogeneous English-speaking markets. Originality/value This study provides one of the first large-scale empirical tests of CTA effectiveness on YouTube, challenging the assumption that all CTAs boost engagement. By integrating Parasocial Relationship Theory and Expectancy-Value Theory, it demonstrates how both emotional connections and rational cost-benefit analyses determine viewer responses to prompts, expanding our theoretical understanding of digital consumer behavior.