Like, comment, and subscribe: investigating the effectiveness of digital engagement prompts
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".