The Technology Acceptance Model, introduced by F. D. Davis in 1989, holds that people adopt a technology when they believe it is easy to use and useful, that these two beliefs shape their attitude toward the technology, and that attitude in turn drives actual use. Perceived ease of use describes the degree to which a person believes using a system would be free of effort, while perceived usefulness reflects the belief that the system enhances performance or helps accomplish goals. Although the model has been applied extensively to text- and image-based social media such as Facebook and Twitter, comparatively little work has extended it to video-centric platforms, and only one prior study had directly compared TikTok and YouTube, focusing narrowly on medical students and omitting the attitude construct entirely.

To fill that gap, the team recruited participants from a departmental research pool at a large Southwestern university in the United States. All respondents were over 18, ranged in age from 18 to 36 with a mean of about 20 years, and reported using both TikTok and YouTube. Participants completed a self-administered online questionnaire taking roughly 15 to 20 minutes, rating 24 items for each platform covering perceived ease of use, perceived usefulness, attitude toward the platform, and actual use, all on five-point Likert scales. The items were adapted from established TAM instruments, with wording modified for each platform, for example asking whether it is easy to explore videos on TikTok or to search YouTube for topics of interest.

Because the scales were modified, the researchers first ran confirmatory factor analyses to verify that the measurements behaved as intended. Problematic items with low factor loadings, below the 0.40 threshold, were removed iteratively until the remaining items showed convergent validity and reliability. Cronbach’s alpha values exceeded the standard 0.70 benchmark, composite reliability ranged from 0.82 to 0.92, and average variance extracted ranged from 0.58 to 0.80 across the latent constructs in both models, all comfortably above accepted cutoffs. Model fit statistics, including the comparative fit index, the root mean square error of approximation, and the standardized root mean square residual, indicated that the measurement models for both platforms fit the data well.

The structural equation modeling results then confirmed all four hypothesized pathways on both platforms. Perceived ease of use was positively associated with perceived usefulness for TikTok, with a standardized coefficient of 0.42, and for YouTube, with a coefficient of 0.63. Ease of use also shaped attitudes toward each platform, with coefficients of 0.37 for TikTok and 0.23 for YouTube, while perceived usefulness exerted a stronger influence on attitude, at 0.55 for TikTok and 0.60 for YouTube. Finally, attitude predicted actual usage on both platforms, with a coefficient of 0.58 for TikTok and 0.28 for YouTube, supporting the cognitive-behavioral consistency logic that underpins the model.

The most striking finding emerged when the researchers compared the explanatory power of the two models. The TAM accounted for 47 percent of the variance in actual TikTok usage but only 10 percent for YouTube. In other words, the psychological chain running from perceived usability and utility through attitude to behavior is far more complete a description of why young adults use TikTok than of why they use YouTube. The authors suggest that long-form platform adoption may depend on factors outside the original model, such as habits and other motivations, whereas TikTok’s short-form, algorithmically curated experience maps closely onto the cognitive evaluations TAM captures.

Paired-sample t-tests added a second layer of insight by revealing significant mean-level differences between the platforms on all four constructs. Participants rated TikTok as easier to use than YouTube, a difference that was statistically significant. However, they rated YouTube as significantly more useful and held significantly more favorable overall attitudes toward it. Despite those lower utility and attitude scores, participants reported spending significantly more time actually using TikTok, a pattern the authors interpret as reflecting the platform’s low barrier to entry and its design for immediate gratification and passive browsing, in contrast to YouTube’s task-oriented, long-form utility.

The findings also diverge from the earlier comparison by Al-Maroof and colleagues in 2021, which found that medical students accepted YouTube more readily than TikTok for medical purposes. The authors attribute this inconsistency to TikTok’s rapid proliferation and near-universal adoption among young adults since that study, along with evolving consumption preferences that increasingly favor micro-length content delivered by recommendation algorithms. The result underscores that acceptance of a platform is not fixed but shifts with the broader media ecosystem and the motivations users bring to it.

The study carries practical weight for platform designers, user experience teams, and digital marketers competing in hyper-competitive video markets. Because perceived ease of use and perceived usefulness drive favorable attitudes and behavioral engagement, the authors argue that optimizing system affordances, simplifying interfaces, and delivering content users perceive as genuinely valuable remain the most reliable strategies for converting casual viewers into engaged ones. Theoretically, the work demonstrates that even as information technologies evolve rapidly, cognitive appraisals of usability and utility leading to attitude formation continue to anchor technology adoption in emerging media landscapes.

The authors also note important limitations. The data were cross-sectional and self-reported, which introduces the possibility of common method variance and social desirability bias, and self-reported usage durations often differ from objectively measured screen time. The sample, drawn from a single American university, also limits generalizability across generations and cultures. Future research, they suggest, should employ longitudinal or experimental designs, replicate the models across more diverse age cohorts and cultural contexts, and incorporate additional variables to better explain engagement with long-form platforms like YouTube, where the original model leaves most of the variance unaccounted for.

Subject of Research: Technology Acceptance Model analysis of TikTok and YouTube adoption among young adults

Article Title: Technology Acceptance Model in video-sharing platforms: comparisons between TikTok and YouTube

Article References: Technology Acceptance Model in video-sharing platforms: comparisons between TikTok and YouTube. (n.d.). https://doi.org/10.3389/fcomp.2026.1883472

Image Credits: AI Generated

DOI: 10.3389/fcomp.2026.1883472

Keywords: Technology Acceptance Model, TikTok, YouTube, perceived ease of use, perceived usefulness, structural equation modeling, social media, video-sharing platforms, young adults, technology adoption, confirmatory factor analysis, user attitude