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

Introduction

Over the past decade, online video-sharing platforms, notably TikTok and YouTube, have become dominant and the most popular platforms worldwide in recent years, especially among young adults (Pew Research Center, 2021). The data reported that most young adults (95%; ages 18 to 29) in the United States use YouTube in their daily lives. Similarly, TikTok shows a skewed adoption, with nearly half of its users (48%) falling within the 18–29 age range, contrasted with a minor representation among older cohorts aged 65 and above (4%).

YouTube is an online video-sharing platform where anyone can create, upload, and share videos. It allows users to consume diverse and global content from entertaining videos, live news streaming, music videos, educational videos, and other videos created by amateur creators (Khillar, 2021), and these videos are disseminated to a broad audience of platform visitors worldwide (Khan, 2017). TikTok, another user-generated media (UGM) platform, allows users to create short-form videos (from 3 to 30 min in length) (Stern, 2020). While YouTube typically requires dedicated production and infrastructure (e.g., high-definition audio, visual equipment, and complex software) to support diverse and long-form content, TikTok relies on smartphones (Khillar, 2021). This low barrier to generating videos has changed the ecosystem of online video-sharing platforms and content types, such as entertainment-oriented genres such as short dance clips, memes, and challenges (Sarkar, 2020).

Although there are some differences between the two video-sharing platforms, both platforms share similarities in that they have video-centric features (Perrin and Anderson, 2019; Wang, 2020). Also, TikTok and YouTube are considered social media platforms (Roberts and David, 2025). For instance, both platforms fulfill desires to watch or share information, satisfy needs to communicate and interact with other users, post videos to remember special events in their lifetime, express themselves, and escape or relieve themselves from daily stress and pressures (Hanson and Haridakis, 2008; Khan, 2017; Omar and Dequan, 2020).

Given the dominance of these video-sharing platforms, a fundamental empirical question may emerge: what drives people to adopt and use these technologies in their daily lives? This question can be addressed by Hong et al. (2002) that technological adoption depends on the interplay between individual attributes and platform-specific characteristics. Theoretically, Davis (1989) introduced the Technology Acceptance Model (TAM) to offer a theoretical framework for explaining user adoption by relating external attributes (i.e., technological attributes) and internal factors (i.e., psychological motivations). This framework assumes that individuals are likely to adopt and use technology when it is related to perceived ease of use (PEOU) and perceived usefulness (PU), which in turn shape attitudes toward technology (Davis, 1989).

TAM has been widely used across various contexts (e.g., health, interpersonal, and information systems) to investigate actual technology usage behaviors. Empirical findings have validated the model’s robustness and predictivity across various technologies, ranging from digital libraries to smartphone applications (e.g., Hong et al., 2002; Joo and Sang, 2013) and social media (e.g., Choi and Chung, 2013; Kwon et al., 2014; Pinho and Soares, 2011; Rauniar et al., 2014). However, most extant literature applying TAM to social media has focused on text- and image-based platforms such as Facebook and Twitter, leaving an empirical gap regarding contemporary video-based platforms.

Comparative evaluations of TikTok and YouTube through the TAM exist, such that a single study by Al-Maroof et al. (2021) compared the two platforms. However, there are limitations in that this study mainly targeted medical students and focused on medical purposes. In addition, they excluded one of the key constructs of TAM—attitude—in their research model. That is, the model has rarely been extended to general user engagement with the four key TAM variables. Thus, this study addresses this gap by examining TAM across broader entertainment and everyday information-seeking behaviors, where perceived ease of use and perceived usefulness operate under different user motivations than in specific contexts.

Specifically, this study evaluates the structural validation of TAM for TikTok and YouTube, respectively, and investigates the structural relationships among four key TAM dimensions—PEOU, PU, US, and AU—across both platforms. In doing so, this study directly addresses the overarching research question: “How do adoption patterns and structural factors differ between TikTok and YouTube among young adult users?” To answer this question, we examine the structural framework, including four hypothesized associations with the four key TAM variables across both platforms.

Theoretical foundation and hypotheses development

Technology Acceptance Model (TAM)

Derived from the theory of reasoned action (TRA) by Fishbein and Ajzen (1975), Davis (1989) proposed the Technology Acceptance Model (TAM) to explain an individual’s decision-making process of a certain information system in the context of workplaces. This model assumes that one’s perceptions, beliefs, and attitudes influence the adoption and use of a specific technology or device. Individuals’ motivations are driven by three psychological constructs: perceived ease of use (PEOU), perceived usefulness (PU), and attitude toward a technology (UA), and these ultimately lead to actual system adoption and use (Davis, 1989).

PEOU refers to “the degree to which a person believes that using a particular system would be free of effort,” and PU indicates “the degree to which a person believes that using a particular system would enhance his or her job performance” (Davis, 1989, p. 320). These two constructs are considered extrinsic benefits that users of a technology can perceive while using the technology. An individual’s attitude indicates their feelings about enacting a specific behavior (Fishbein and Ajzen, 1975). Regarding technology use, UA refers to a user’s personal feelings or evaluations toward using a specific technology or service (Davis, 1989).

Based on these conceptualizations, Davis (1989) suggests the associations between these constructs of TAM by arguing that PEOU and PU directly influence an individual’s attitudes toward using technology (UA), and the attitude of the user induced by PEOU and PU positively impacts whether individuals will use technology or not. Taken together, these three types of motivations to use the system (i.e., PEOU, PU, and UA) are significant factors determining its actual use (AU).

Further studies have refined and extended the model by adding other variables and modifying the causal relationships that Davis (1989) initially proposed. For instance, Venkatesh and Davis (2000) proposed an extended model (TAM2) by identifying a significant relationship between PEOU and PU and empirically demonstrating that PEOU has a direct and positive effect on PU. In addition, the authors emphasized the important role of behavioral intention in using technology. Several studies have also added external variables that play roles as antecedents of PEOU and PU (e.g., subjective norms, job relevance, self-efficacy, and prior experience; Oh et al., 2003; Venkatesh, 2000; Venkatesh and Davis, 2000). Furthermore, other variables have been identified and added to influence attitudes toward using technology, such as perceived behavioral control and perceived playfulness (PP) (Chau and Hu, 2001; Moon and Kim, 2001).

Social media and TAM

With the rapid growth of social media, many scholars have applied TAM to understand social media usage behaviors. For instance, Pinho and Soares (2011) examined the determinants of social media adoption among university students from the TAM perspective. The authors specifically focused on the effects of PEOU, PU, and UA on behavioral intention (BI) to use social media. The results of their study showed that students who perceive Facebook as useful and easy to use were likely to have positive attitudes toward Facebook, and these led to higher intention to use this platform. Rauniar et al. (2014) extended TAM by adding three variables: critical mass (CM), perceived playfulness (PP), and capability (CP), and found that those who have higher levels of PEOU, CM, CP, and PP were more likely to perceive Facebook as useful, which in turn led to a more positive attitude toward Facebook. Their study also demonstrated the greater degree of favorable attitude also increased behavioral intention and actual Facebook use.

As such, prior research has used TAM to predict actual social media usage. However, very little is known about online video-sharing platforms such as TikTok and YouTube. As mentioned, a single study by Al-Maroof et al. (2021) investigated PEOU and PU across both platforms and compared the differences in the acceptance of TikTok and YouTube. They found that both PEOU and PU were positively associated with YouTube and TikTok adoption. It was also found that there were significant differences between the acceptance of TikTok and YouTube, such that medical students were more likely to accept YouTube than TikTok.

Based on the theoretical and empirical previous studies above, we propose the following four hypotheses related to the TAM and thus address the ultimate research question of this study: “How do university-aged users of TikTok and YouTube adopt them differently?” (see Figure 1):

RQ1. How do adoption patterns of TikTok and YouTube differ?

H1. PEOU is positively associated with PU of (a) TikTok and (b) YouTube.

H2. PEOU is positively associated with UA toward (a) TikTok and (b) YouTube.

H3. PU is positively associated with UA toward (a) TikTok and (b) YouTube.

H4. UA toward (a) TikTok and (b) YouTube is positively associated with actual (a’) TikTok and (b’) YouTube usage, respectively.

Research methodology

Participants and procedures

This study received approval from the Institutional Review Board of the first author’s previous institution (see Ethics Statement). Quantitative methods were used to understand how university-aged users accept and adopt TikTok and YouTube. University students were targeted mainly as they were the most active users of both platforms (Pew Research Center, 2021). The final sample of this research comprised 351 TikTok and YouTube users aged between 18 and 36 years (M = 20.03, SD = 2.02). Additional demographic information of participants is indicated in Table 1.

Table 1. Participants’ demographic information (n = 351).

A self-reported online survey was employed to examine the adoption of TikTok and YouTube. We recruited participants from the departmental research pool at a large Southwestern university in the United States. Participants were required to be above the age of 18 and have used both TikTok and YouTube platforms.

Informed consent was obtained from all participants who satisfied the requirements of this study’s eligibility, and they were informed about the purpose of this study, that their participation was voluntary, and that they could participate in the survey participation anytime they wanted. After agreeing to participate in the study, they were asked to answer the questionnaire regarding their experiences as users of both TikTok and YouTube. Participants first provided basic demographic information (e.g., gender, age, ethnicity) and were asked to indicate the following research measures: acceptance of TikTok and YouTube with PU, PEOU, attitude toward each platform, and AU, respectively. It took approximately 15–20 min to complete the questionnaire. Participants received course credit for their participation.

Measures

To measure the key constructs of the TAM, we adapted existing scales from prior literature. PEOU and PU were assessed using items adapted from Chintalapati and Daruri (2017) and Davis (1989). We used items from Davis (1989) and Wixom and Todd (2005) to measure UA. Finally, this study used measurements for AU by Rauniar et al. (2014). Based on our review of existing measurements relevant to this study, we modified the language of the items to measure PEOU, PU, UA, and AU of TikTok. For example, we replaced the words from specific technology names (e.g., Chart-master or social media) (e.g., PEOU—“I find Facebook easy to use”) with TikTok (e.g., “I find TikTok easy to use”) and added some items (e.g., PEOU—“I think TikTok has a friendly and intuitive interface and an easy operation process). Similar to the measurement of TikTok acceptance, we adapted the existing items aforementioned and modified them by changing the word choice (i.e., editing the words to YouTube) and adding some statements to reflect the features of YouTube (e.g., PEOU—“It is easy to search for relevant YouTube videos of topics that I am interested in). All these study measures were rated on 5-point Likert scales ranging from 1 (strongly disagree) to 5 (strongly agree).

Because our study measures were rephrased and modified in accordance with the purpose of this study, confirmatory factor analysis (CFA) was conducted to re-evaluate the factor structure, factor loadings, and construct validity of the modified measurement instruments. The specific standards are indicated in the section of Data analyses below.

TikTok acceptance

To measure TikTok acceptance, we used the acceptance of TikTok measure consisted of 24 items with four constructs: PEOU (9 items, e.g., “It is easy to explore videos on TikTok), PU (6 items; e.g., “TikTok has sufficient content for different topics), UA (6 items; e.g., “I am willing to spend some time using TikTok”), and AU (3 items; e.g., “How often do you use TikTok per day?”). The responses were computed with higher scores of each construct representing higher levels of PEOU and PU, positive attitudes toward TikTok, and heavier TikTok use.

YouTube acceptance

The measurement of YouTube acceptance included 24 items: PEOU (9 items; e.g., “I can use YouTube in all kinds of free places”), PU (6 items; e.g., “The variety of content on YouTube covers all topics of interest for me), UA (6 items; e.g., “I am positive about YouTube”), and AU (3 items; e.g., “Approximately, how much time do you spend on YouTube per day?). Higher scores on each construct of YouTube indicated that participants were likely to have higher PEOU and PU, positive attitudes toward using YouTube, and be heavy users of YouTube.

Data analysis

We conducted descriptive statistics and correlation analysis using SPSS 28.0. Then, a series of confirmatory factor analyses (CFAs) were conducted using AMOS 24.0 for the four independent constructs (i.e., PEOU, PU, and UA), because this study was the first to apply the existing items to TikTok and YouTube platforms. The following criteria were used to investigate the convergent validity of the measurement items used in this study and further to evaluate the fit of the models (Hu and Bentler, 1999): comparative fit index (CFI) ≥ 0.90, the root mean square error of approximation (RMSEA) ≤ 0.06, and the standardized root mean square residual (SRMR) ≤ 0.08.

We also explored potentially problematic items with their reliability and validity and removed them from the data, such as low factor loadings (<0.40; Hair et al., 2010). As a result, we tested convergent validity and reliability by examining Cronbach’s alpha (α > 0.70), composite reliability (CR > 0.70), and the average variance extracted (AVE > 0.50) as Fornell and Larcker (1981) suggested. Structural equation modeling (SEM) was used to test the hypotheses of this study, and then a series of paired sample t-tests were performed to examine the differences of PEOU, PU, UA, and AU between the cases of TikTok and YouTube.

Covariates

Previous studies showed significant differences in age and gender in social media use (e.g., Correa et al., 2010; Muscanell and Guadagno, 2012). Therefore, we included these demographic characteristics as control variables in this analysis to test relationships between key constructs of the TAM more rigorously and to avoid possible confounding effects of age and gender on TikTok and YouTube usage.

Results

Preliminary analyses

Before testing the study hypotheses, distributional normality was examined with the values of skewness and kurtosis. It was found that the data were normally distributed as absolute skewness (ranged from −1.12 to −0.14) and kurtosis (ranged from −0.71 to 1.43) values were less than 2 and 4, respectively (Kim, 2013). In terms of measurement reliability and construct validity, internal consistency for all measures in Model 1 (TikTok) and Model 2 (YouTube) was tested, and it was demonstrated that the study scales were reliable with Cronbach’s alpha coefficients exceeding the standard 0.70 threshold. In addition, the values of CR ranged from 0.82 to 0.92, and AVE values ranged from 0.58 to 0.80 across all latent constructs in both models, exceeding the thresholds of 0.70 and 0.50, respectively (see Table 2).

Table 2. Validity and reliability of the study variables.

CR and AVE are omitted for AU in both models because it was modeled as a composite/observed variable.

Furthermore, we conducted CFA to confirm validity across all constructs and ensure that all items demonstrated standardized factor loadings before evaluating the structural path. It was found that there were problematic items due to poor factor loadings (i.e., TikTok: PEOU: items #2, #5, #7, #8, and #9, PU: items #1 and #6; UA: item #2; YouTube: PEOU: items #2, #7, #8, and #9, PU: items #1 and #6; UA: items #2, #4, and #5; AU: item #3). We followed this removal process until the model met the thresholds and satisfied the model fit. After removing these items, it was found that each measurement of TikTok model including PEOU and PU [χ²(19) = 46.87, χ²/df = 2.47, p = <0.001, CFI = 0.98, RMSEA = 0.05 and SRMR = 0.06.] and UA [χ²(4) = 4.42, χ²/df = 1.11, p = <0.001, CFI = 1.00, RMSEA = 0.02 and SRMR = 0.01] had an acceptable model fit. The constructs of the YouTube model including PEOU and PU [χ²(26) = 51.48, χ²/df = 1.98, p = <0.001, CFI = 0.99, RMSEA = 0.04 and SRMR = 0.05], and UA [χ²(2) = 1.94, χ²/df = 1.01, p = <0.001, CFI = 1.00, RMSEA = 0.01 and SRMR = 0.01], showing good model fit. Taken together, based on these findings, we concluded that the study measures were reliable and valid to examine the structural associations among the TAM constructs for both TikTok and YouTube.

Pearson’s correlation analysis showed that all the key variables were significantly correlated across both models, supporting the conceptual framework (see Table 3). Specifically, in the TikTok model, actual use was positively correlated with PEOU (r = 0.39, p < 0.001), PU (r = 0.45, p < 0.001), and UA (r = 0.52, p < 0.001). This indicated that user engagement on TikTok was strongly aligned with subjective evaluations of usability, utility, and attitude. Similarly, in the YouTube model, actual usage of YouTube was positively correlated with PEOU (r = 0.22, p < 0.001), PU (r = 0.21, p < 0.01), and UA (r = 0.23, p < 0.01). The magnitudes of YouTube correlations were weaker than those in the TikTok model.

Table 3. Descriptive statistics and correlations between key constructs of this study.

^*p < 0.05, ^**p < 0.01, ^***p < 0.001.

Testing for TikTok and YouTube models and hypotheses

According to the results of the CFA, the four-construct TAM model of TikTok and YouTube had good fit to the data, respectively: TikTok—χ²(124) = 255.75, χ²/df = 2.06, p = <0.001, CFI = 0.96, RMSEA = 0.05 and SRMR = 0.05; YouTube—χ²(93) = 162.51, χ²/df = 1.75, p = <0.001, CFI = 0.98, RMSEA = 0.04 and SRMR = 0.04. For the TikTok model (Model 1).

We conducted an SEM to test the hypotheses of this study and found that all the hypotheses were supported by the results (see Figure 2). We found that PEOU was positively related to PU on both TikTok (β = 0.42, p < 0.01) and YouTube (β = 0.63, p < 0.01), supporting H1a and H1b. The results for both TikTok and YouTube models revealed that there were positive associations between PEOU and UA (TikTok: β = 0.37, p < 0.01; YouTube: β = 0.23, p < 0.01) and between PU and UA (TikTok: β = 0.55, p < 0.01; YouTube: β = 0.60, p < 0.01). Thus, H2a and H2b and H3a and H3b were confirmed in this study. Finally, UA was positively and significantly associated with AU in both TikTok (β = 0.58, p < 0.01) and YouTube (β = 0.28, p < 0.01) models, thereby supporting H4a and H4b.

Testing the differences between TikTok and YouTube

Beyond testing the hypothesized models, we carried out a series of paired sample t-tests to examine whether there were significant construct differences in their extent between TikTok and YouTube (see Table 4). The results revealed statistically significant differences across all four TAM constructs. Specifically, university participants perceived TikTok to be slightly easier to use than YouTube, t(350) = 2.78, p < 0.01. However, participants reported significantly higher PU for YouTube, t(350) = −13.60, p < 0.001, as well as more favorable overall attitudes toward YouTube, t(350) = −7.64, p < 0.001. Despite lower utility ratings, participants reported significantly greater actual usage time for TikTok compared to YouTube, t(350) = 10.85, p < 0.001.

Table 4. Results from paired t-tests.

Statistical support for the comparative explanatory power of the TAM across TikTok and YouTube was evaluated using variance explained (R²) and standardized path coefficients (β) derived from SEM. As shown in Figure 2, the model accounted for 47% of the variance in actual usage for TikTok (AU R² = 0.47), compared to 10% for YouTube (AU R² = 0.10). Along with these findings, the structural relationship between attitude and actual usage was significantly stronger for TikTok (β = 0.59, p < 0.01) than for YouTube (β = 0.28, p < 0.01). These empirical findings showed that while TAM effectively explains user attitudes toward both platforms, it provided superior predictive validity for actual behavioral usage in short-form video contexts (i.e., TikTok) relatively to long-form video platforms (i.e., YouTube).

Discussion

Based on a well-established theoretical model of technology acceptance (TAM; Davis, 1989), this study examined whether the theory also explained the case of two video-sharing social media platforms: TikTok and YouTube, thereby addressing the research question of whether people adopt these two platforms differently in terms of the perspectives of TAM.

First, in line with existing studies (e.g., Al-Adwan et al., 2013; Sek et al., 2010), it was found that PEOU was positively and significantly associated with PU. Those who evaluated the platforms as easy to navigate were more likely to recognize its operational utility. These findings suggest that functional simplicity plays a significant role in efficacy. That is, as interface complexity diminishes, users tend to perceive the platform as a more competent and reliable tool for fulfilling their media consumption motivations.

Second, across both video-sharing platforms, participants who perceived ease of use and perceived usefulness toward TikTok and YouTube were more likely to have positive attitudes toward the platforms, respectively. This is consistent with the original TAM (Davis, 1989) and previous studies (e.g., Al-Maroof et al., 2021; Pinho and Soares, 2011; Rauniar et al., 2014) that highlight user attitude formation relies on foundational cognitive appraisals of effortlessness and functional value. This can be interpreted as meaning that low cognitive effort and meaningful instrumental values remain crucial factors to foster favorable user attitudes toward various emerging technologies and media.

Third, in line with prior research (e.g., Davis, 1989; Rauniar et al., 2014), attitudes toward both platforms were positively and significantly associated with actual platform usage. That is, attitudes toward technology serve as significant predictors of actual behavioral engagement. This can be explained by the cognitive behavioral consistency approach by Fishbein and Ajzen (1975) that one’s beliefs, attitudes, and behaviors align harmoniously. In this regard, it is reasonable to predict consumptions and frequencies of online video-sharing platforms through users’ affective evaluations.

Finally, these collective empirical findings directly address the core research question by demonstrating that young adult users evaluate and adopt TikTok and YouTube through distinct internal (i.e., cognitive evaluations toward the platforms) and external (i.e., actual usage) pathways. The notable difference in explanatory power (R²) showed that TikTok adoption was strongly captured by key TAM constructs, where usability and functional utility directly dictate attitude, leading to actual usage. In contrast, the lower proportion of variance explained for YouTube revealed that long-form platform adoption may be dependent upon extraneous theoretical factors (e.g., habits and other motivations) that are not included in the original TAM. These findings contrast with those of Al-Maroof et al. (2021), who reported that medical students had greater acceptance of YouTube than TikTok for medical purposes. This empirical inconsistency can be explained by the rapid proliferation and universal adoption of TikTok among general young adult populations. In addition, it can reflect evolving shifts in audience consumption behavior that contemporary users increasingly prefer micro-length, algorithmically curated short-form video content for immediate gratification and passive browsing, whereas long-form platforms are for task-oriented utility (Khillar, 2021; Sarkar, 2020).

Theoretical and practical implications

Theoretically, this research validates and clarifies the significant relationships among fundamental TAM constructs within contemporary video ecosystems. This contributes to the extant literature of TAM by demonstrating the framework for both TikTok and YouTube. Thus, when evaluating user adoption of new media and/or technologies, the robust TAM can be considered in evolving media environments. In other words, the findings of this study reinforce that despite rapid development of information and communication technologies, cognitive evaluations about usability and utility, which lead to attitudes toward them, remain important theoretical factors for understanding and predicting technology adoption in emerging media landscapes.

Practically, these empirical findings offer insights for platform developers, user experience designers, and digital marketers and content strategists. Within these hyper-competitive video-sharing markets nowadays, the successful strategies are to sustain the highest number of users and to convert casual viewers into active and engaged ones. In this regard, our study shows that perceived ease of use and perceived usefulness are key drivers of favorable user attitudes and behavioral engagement. Therefore, those interested parties should focus on optimizing system affordances.

Limitations and future directions

Despite these contributions, there are limitations that should be considered by future research. First, this study relied on cross-sectional and self-reported survey data. This may introduce common method variance and social desirability bias. Self-reported usage duration often significantly differs from actual platform usage. Thus, future research should conduct a longitudinal design study or objective research methods (e.g., observational and/or experimental methods) to track how user acceptance and engagement patterns change over time across short-form and long-form media. Second, the data of this study were collected targeting university-aged TikTok and YouTube users from a single university in the United States. This limits the external validity of the findings. It is suggested that future researchers to replicate the study models across more diverse age cohorts (e.g., adolescents and older adults) and across various cultural contexts. These cross-generational and cross-cultural examinations allow the application of the findings to broader populations. Finally, our empirical findings revealed the lower explanatory power of YouTube. This suggests future studies to consider additional unmeasured variables to understand long-form platform engagement.

Conclusion

The present study found that across two platforms, perceived ease of use was positively associated with perceived usefulness. Perceived ease of use and perceived usefulness were significantly and positively related to attitudes toward TikTok and YouTube, respectively. Furthermore, attitudes toward the platforms were positively associated with actual usage behaviors. Taken together, these findings offer robust foundations for both theoretical model extension and practical platform designs and marketing. Collectively, this study provides empirical clarity regarding the key factors driving young adult adoption of TikTok and YouTube. In directly addressing the research question, our findings show that university-aged users evaluate and adopt each platform differently. By validating the TAM across both platforms, this study demonstrates that usability and utility remain primary factors of digital media engagement, even as digital platform environments are constantly evolving.

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Footnotes

Edited by: Zeinab Esmaeili, Thompson Rivers University, Canada

Reviewed by: Mónica Cruz, University Institute of Lisbon (ISCTE), Portugal

Failasofah Failasofah, Universitas Jambi, Indonesia