Sage Journals: Discover world-class research

Abstract

Prior social media research has identified various motives within the classic framework of uses and gratifications theory to determine why people use social media. To date, most studies have used a variable-centered approach to investigate TikTok use motives with a composite score and analyzed its linear relationships with other variables in a population, where subpopulations with a different configuration of motives remain unaddressed. This research conducted two studies (N = 680) and identified four TikTok use motive profiles: deep motivators, lone motivators, mood-elevating motivators, and slight motivators. Furthermore, the authors examine how antecedent (labile self-esteem) and distal outcomes (TikTok addiction and subjective well-being) differ across the profiles. The findings contribute to the TikTok use literature by identifying different profiles of TikTok use motives and exploring their different relationships with antecedents and outcomes. Some practical ways to manage users’ needs and improve their experience are also discussed.

Keywords

social media use TikTok motive latent profile analysis uses and gratifications theory

Introduction

TikTok, as a short-form-video mobile-based application (app) that allows users to create videos lasting 15 seconds to 10 minutes and share them with the broader TikTok community (Barta et al., 2023), has gained immense popularity worldwide. According to a TechTipsWithTea analysis report, the platform is now available in over 154 countries globally. It has a user base comprising over 19.58% of the worldwide population, with 1.58 billion monthly active users as of August 2024 (Singh, 2024). While TikTok shares some features with other social media platforms like Facebook and Instagram, such as presenting content in feeds, enabling users to like and comment, and aggregating content via hashtags, it also has some unique aspects in content delivery and user interaction. Unlike other social medias apps (e.g., Facebook, Instagram, Twitter) that rely on scrollable feeds, TikTok’s content delivery is heavily algorithm-driven, serving highly personalized videos one at a time. Additionally, each video is paired with a soundtrack, marked by a spinning musical notes icon that users can tap to access audio details and discover related content. This distinctive interface, which requires users to swipe vertically to navigate between videos, creates a more immersive and recommendation-dependent experience. These innovative design elements contribute to an average of 26 hours of app usage per month worldwide (Duarte, 2025), which raises important research questions: What drives people to engage with TikTok so extensively and how do different users’ motivations shape their experience on the platform?

One of the most widely used theoretical frameworks to explore media usage is uses and gratifications theory (e.g., Katz et al., 1973; Krause et al., 2014; Lonsdale & North, 2011; McQuail et al., 1972; Whiting & Williams, 2013). This perspective posits that individuals engage with specific media platforms to satisfy distinct psychological needs, such as entertainment, identity expression, social interaction, and information-seeking (Lonsdale & North, 2011; McQuail et al., 1972). While prior studies have applied uses and gratifications theory to TikTok usage, most of them have predominantly employed variable-centered approaches (e.g., Bossen & Kottasz, 2020; Meng & Leung, 2021). These studies assume motivational homogeneity within populations and assess motives independently or use composite scores to examine linear relationships with outcomes, such as engagement behaviors (e.g., Bossen & Kottasz, 2020; Meng & Leung, 2021). Although such research yields valuable insights, it obscures the configural diversity of motivations—that is, how different motives may co-occur uniquely in specific subgroups of users.

To address this limitation, person-centered approaches, such as latent profile analysis (LPA), allow researchers to uncover distinct subpopulations (or profiles) of users who share similar combinations of motivational drivers (Howard & Hoffman, 2018). Applying this method to TikTok use motives could reveal, for example, whether some users are primarily motivated by emotional regulation, identity formation, or information-seeking. Although LPA is gaining traction in social media research, empirical studies applying it to TikTok remain scarce. One notable exception is Gu et al.’s (2022) study, which identified four motivational profiles among Chinese Douyin users based on trendiness, novelty, escapist addiction, and socially rewarding self-presentation. While insightful, their study used a limited motive framework, focused on a single cultural group, and did not incorporate theoretically meaningful psychological predictors or outcomes.

Building on and extending this emerging body of work, the present study adopts a more comprehensive and theoretically grounded approach. We examine six TikTok-specific use motives—identity, negative mood management, positive mood management, diversion, surveillance, and social interaction—derived from uses and gratifications theory and tailored to the platform context (e.g., Lonsdale & North, 2011; McQuail et al., 1972). We apply LPA across two demographically and culturally distinct samples (American working adults and Chinese college students) to enhance the external validity and generalizability. To further understand these differences, we also explore individual antecedents that may influence the likelihood of belonging to a specific motivational profile. In particular, we focus on labile self-esteem—the degree to which an individual's self-worth fluctuates in response to daily experiences (Dykman, 1998). Unlike stable trait self-esteem, labile self-esteem reflects psychological reactivity and emotional instability, making it especially relevant in digital environments characterized by rapid feedback and social comparison. Prior research on self-esteem and social media use has yielded mixed findings (e.g., Han & Yang, 2023; Mann & Blumberg, 2022; Valkenburg et al., 2021), which suggests that the effect may depend on user subtypes. Thus, examining labile self-esteem as a predictor of TikTok use motive profiles can help explain why different users engage with the platform in distinct ways.

In addition to antecedents, we examine two distal outcomes that have generated both academic and societal concern: TikTok addiction and subjective well-being. TikTok has been identified as one of the most potentially addictive social media platforms (Marengo et al., 2022), with excessive use linked to reduced productivity, sleep disturbance, and emotional dysregulation (Hou et al., 2019). At the same time, social media use has shown both positive and negative associations with subjective well-being, broadly defined as individuals’ evaluations of their happiness and life satisfaction (Diener et al., 2018). Previous studies have reported inconsistent findings regarding the relationship between social media use and well-being (e.g., Kim, 2017; Masciantonio et al., 2021), possibly due to user heterogeneity in motives. By exploring well-being and addiction across distinct motive profiles, our study aims to clarify how different motivational patterns may buffer or exacerbate these outcomes.

Literature Review

A Review of Variable-Centered Research on TikTok Use Motives

The sustained global popularity of TikTok has drawn increasing scholarly attention to a key question: Why do people use TikTok and what psychological needs does it fulfill? In response, researchers have frequently employed uses and gratifications theory—which posits that media use is goal-directed and driven by users’ attempts to satisfy psychological, social, and emotional needs—as a foundational framework to investigate users’ underlying motivations (e.g., Bossen & Kottasz, 2020; Hossain, 2019; Meng & Leung, 2021; Orchard, 2019; Sundar & Limperos, 2013; Whiting & Williams, 2013). Researchers have systematically identified an extended range of motives underlying TikTok use. For instance, Scherr and Wang (2021) uncovered four motives among Chinese TikTok users: trendiness, novelty, escapist addiction, and socially rewarding self-presentation. Falgoust et al. (2022) delineated six motives—seeking social support, increasing social interaction, entertainment, seeking and sharing information, escaping daily life, and communication convenience—while Meng and Leung (2021) identified a more extensive nine-motive structure involving both personal and technical gratifications. These findings enable researchers to examine the relationships between specific motives and outcomes (e.g., TikTok consuming behaviors, TikTok engagement behaviors, and TikTok use; Meng & Leung, 2021; Omar & Dequan, 2020; Scherr & Wang, 2021).

However, recent studies have raised significant concerns about the dominant use of variable-centered-analysis approaches in studying social media motivations (Howard & Hoffman, 2018). These approaches, which examine motives in isolation or aggregate them into composite scores, rest on the assumption of motivational homogeneity within the population. While useful for modeling linear relationships between specific motives and outcomes, they often overlook the configural nature of motivations—that is, how multiple motives may coexist and interact within individuals. In practice, social media users often exhibit simultaneously varying combinations of motives (e.g., high entertainment but moderate social needs), which variable-centered methods may obscure. Moreover, statistical interaction terms or regression-based clusters tend to yield artificial groupings driven by model specifications, rather than reflecting meaningful user subtypes (Dahling et al., 2017; Morin et al., 2010). This analytical limitation further underscores the need to adopt person-centered approaches to uncover naturally occurring subpopulations of users based on motivational profiles.

A Person-Centered Approach to TikTok Use Motives

The person-centered approach provides a powerful lens for examining how constellations of motivational variables jointly shape behavioral outcomes. Rather than treating individual variables in isolation, this approach assumes the presence of latent heterogeneity in the population—namely, that users cluster into subgroups characterized by distinct configurations of use motives (Howard & Hoffman, 2018; Wang & Hanges, 2011; Woo et al., 2018). In the context of TikTok, existing research based on uses and gratifications theory has identified a diverse set of motives that serve varying psychological functions (Falgoust et al., 2022; Meng & Leung, 2021; Scherr & Wang, 2021), suggesting that users may form unique subpopulation profiles that distinguish TikTok engagement from other social media platforms.

To address the limitations of variable-centered analysis, we adopted a person-centered approach and developed a six-motive framework for profiling TikTok users. This framework—comprising personal identity, positive mood management, negative mood management, diversion, surveillance, and social interaction—is adapted from Lonsdale and North's (2011) validated model of media use motives in the music domain, which has strong theoretical grounding in uses and gratifications theory. Although originally developed for music consumption, this framework is well suited to TikTok's media environment, which similarly revolves around short-form, emotionally resonant content and highly individualized engagement patterns. The framework includes surveillance, personal identity, social interaction, diversion, negative mood management, and positive mood management (Katz et al., 1973; Lonsdale & North, 2011; McQuail et al., 1972). Each motive reflects a key psychological need: surveillance, or information-seeking, captures users’ desire to stay informed about current events, trends, or developments in their environment; personal identity fulfills self-understanding and self-expression needs; social interaction meets relational and communal needs; diversion, which encompasses both entertainment and escapism, reflects users’ motivation to seek pleasure and avoid real-life concerns; and the two mood-management motives correspond to emotion-based gratifications, with negative mood management focused on alleviating unpleasant states and positive mood management on enhancing enjoyment.

Compared to prior TikTok studies that use overlapping or unbalanced motive sets (e.g., Meng & Leung, 2021; Scherr & Wang, 2021), our six-factor model offers several advantages. First, it captures both hedonic (e.g., mood management, diversion) and utilitarian/social (e.g., surveillance, interaction, identity) motives. Second, it distinguishes between positive and negative mood management, allowing for a more nuanced understanding of emotional motivations, consistent with modern affective science (e.g., Gross, 2015; Tamir, 2011). Third, the six-motive framework is parsimonious yet comprehensive. For instance, the escapism motive found in Meng and Leung (2021) and Falgoust et al. (2022) aligns with our diversion dimension, while Omar and Dequan's (2020) emphasis on social interaction and self-expression maps onto our social and identity motives. It avoids redundancy while enabling effective use in person-centered methods such as LPA.

This study employs the technique of LPA to uncover both quantitative differences (e.g., high, moderate, or low levels of motive endorsement) and qualitative differences (i.e., unique motivational patterns) across user profiles (Spurk et al., 2020; Zyphur, 2009). LPA allows us to identify subgroups based on shared patterns of motivations and to examine how profile membership relates to antecedents and outcomes (Dahling et al., 2017; Lanza et al., 2013; Spurk et al., 2020). This approach enables a more holistic understanding of how multiple motives co-occur within individuals and jointly influence TikTok engagement and its psychological consequences. By using a coherent model and LPA, we establish a strong foundation for identifying distinct motivational profiles and, ultimately, answering the following research question:

Research Question 1 (RQ1): Are there quantitatively and qualitatively distinct profiles of TikTok use motives and, if so, how are they combined?

Labile Self-Esteem as the Antecedent of Profile Membership

Labile self-esteem occurs when an individual's self-esteem fluctuates as a result of daily positive or negative experiences (Dykman, 1998). Unlike trait self-esteem, which is relatively stable over time, labile self-esteem is characterized by volatile self-cognitions and heightened emotional reactivity, making individuals more susceptible to external evaluations and mood swings (Hayes et al., 2004). Individuals with a high level of labile self-esteem tend to exhibit exaggerated responses to minor setbacks or successes (Roberts & Gotlib, 1997), often manifesting in maladaptive behavioral patterns—such as compulsive validation-seeking, excessive self-monitoring, and the frequent checking of social media feedback (Nesi & Prinstein, 2015).

These tendencies are especially relevant in the context of TikTok, a platform that delivers personalized, short-form, emotionally engaging content through algorithmic reinforcement mechanisms. Users with labile self-esteem may be particularly vulnerable to the dynamic reward structures of TikTok—where algorithmic feedback (e.g., likes, views, tailored content) is immediate, unpredictable, and emotionally salient. As such, these users may develop stronger psychological dependencies on specific usage motives (e.g., emotional regulation, identity expression), which in turn may shape distinct motivational configurations.

From a theoretical perspective, labile self-esteem is more likely than trait self-esteem to explain intraindividual motivational diversity because it captures moment-to-moment variations in need salience and self-perception (Crocker & Wolfe, 2001). Given that person-centered profiles are derived from co-occurring motives within individuals, labile self-esteem is especially suitable as a predictor—it reflects dynamic psychological states that can differentially activate motivational clusters (e.g., high escapism with low information-seeking). Prior studies have also noted inconsistent relationships between global self-esteem and social media use (e.g., Brougham, 2021; Chamsi et al., 2022; Savira et al., 2022), suggesting that static trait measures may obscure important motivational distinctions across user subtypes.

To address this gap, Cingel et al. (2022) proposed that the relationship between self-esteem and social media engagement should be approached from a person-oriented perspective, taking into account the daily dynamics and psychological fluidity of users. Following this recommendation, we examine labile self-esteem as an antecedent of TikTok motivational profile membership, proposing that individuals with different levels of self-esteem fluctuation are likely to cluster into distinct motive-based subgroups. Thus, the following research question is proposed:

Research Question 2 (RQ2): Does labile self-esteem differentially predict profile membership in TikTok use motives?

TikTok Addiction and Subjective Well-Being as Outcomes of Profile Membership

The consequences of social media use are increasingly receiving scholarly attention, particularly in relation to maladaptive behaviors and psychological outcomes. In this study, we focus on two such outcomes—TikTok addiction and subjective well-being—as theoretically relevant and practically important consequences of motivational profile membership.

First, social media addiction has become a major concern globally, with TikTok identified as the platform with the highest potential for addictive use due to its immersive short-form, algorithm-driven content (e.g., Gu et al., 2022; Marengo et al., 2022; Scherr & Wang, 2021). Defined as a psychological dependence that disrupts daily functioning (Cao et al., 2020), TikTok addiction has been linked to symptoms such as compulsive checking, withdrawal, and impaired social or occupational functioning (e.g., Hou et al., 2019; Procházka et al., 2021; Yang et al., 2016). Despite these established correlates, current research has not yet identified which specific user motivations contribute most to such addictive outcomes. From a uses and gratifications theory perspective, media use driven by emotional escapism, negative mood regulation, or identity concerns may signal deeper psychological vulnerabilities and lead to excessive engagement. By identifying motivational profiles that correspond with elevated addiction risk, we aim to reveal which configurations of motives (rather than isolated motives) are most predictive of problematic TikTok use.

Second, we examine subjective well-being, defined as individuals’ evaluation of their life satisfaction and emotional experiences (Diener et al., 2018). While some studies report that social media use enhances well-being (e.g., Gerson et al., 2016), others suggest that it undermines mood and satisfaction (e.g., Sagioglou & Greitemeyer, 2014), particularly when use is habitual or comparison-driven. Previous research has highlighted these inconsistencies, often attributing them to methodological differences or individual variability in why users engage with social media (Kim, 2017; Masciantonio et al., 2021). Accordingly, it has been proposed that user motivations—especially distinct combinations of motivations—may help explain divergent well-being outcomes (Masciantonio et al., 2021; Webster et al., 2021).

By using LPA to identify user subgroups based on motivational patterns, we seek to clarify these mixed findings. For instance, users characterized by a balanced motivational profile (e.g., moderate levels of identity, social, and informational motives) may experience greater well-being, while those dominated by escapism or negative mood-management motives may report lower life satisfaction and higher psychological strain. Therefore, examining addiction and well-being as downstream consequences of motivational profiles offers theoretical insight into the differential risks and benefits of TikTok use. We propose the following research question:

Research Question 3 (RQ3): Do the profiles of TikTok use motives differ in levels of (a) TikTok addiction and (b) subjective well-being?

Overview of Current Research

This research employed LPA to determine whether distinct motivational configurations emerge among TikTok users and whether these configurations differ meaningfully in their psychological correlates. Across two studies, we examined labile self-esteem as a psychological antecedent and explored TikTok addiction and subjective well-being as key outcomes.

The two studies employed intentionally distinct samples. Study 1 recruited American working adults via an online panel, while Study 2 involved Chinese college students. This cross-sample design was guided by both theoretical and contextual considerations. TikTok (known as Douyin in China) is especially popular among Chinese users, and much of the early empirical work on the platform has used Chinese samples (e.g., Lu & Shen, 2023). As noted by Hsu et al. (2021), cultural values play a pivotal role in shaping how people engage with social media, including their motivations for use. By comparing American and Chinese users, we aimed to explore cross-cultural variation in motivational structures and enhance the external validity and generalizability of our findings.

Second, the two samples differ in key life-stage and usage-context characteristics. Chinese college students typically use TikTok for entertainment through social interaction and identity exploration (Zhu et al., 2024). In contrast, American working adults tend to engage with TikTok more passively, primarily for personal entertainment rather than social interaction, and often as a form of emotional regulation or brief diversion amid daily work routines. According to a research report by Bestvater (2024), this group primarily uses the platform to relax or relieve stress, rather than to engage socially. At the time of data collection, the US version of TikTok did not yet offer e-commerce features, while its Chinese counterpart had already become a hub for news, commerce, and lifestyle content—primarily consumed by older adults. Thus, the college-student sample in China and the working-adult sample in the USA represent distinct but theoretically relevant populations, enabling us to examine how motivational profiles may emerge differently across age, cultural context, and platform use habits.

Study 1

Method

Participants and Procedure

We recruited our participants through Prolific, an online data collection platform, and paid them £0.90 to complete the required questionnaires. On the Prolific platform, 394 persons responded to the request. In the recruitment advertisement, we set up two criteria: people working full-time and active TikTok users (for at least one year). We used two attention-check questions to screen for these two criteria. Those who met the criteria participated in the study; those who did not were automatically withdrawn. Prior to completing the formal questionnaire, participants were provided with information about the study’s purpose, requirements, compensation, and voluntary nature and then asked to provide informed consent. The formal questionnaire included a set of scales on motives for TikTok use, TikTok addiction, subjective well-being, labile self-esteem, and demographic information. Finally, owing to serious missing values and the failure of attention checks of 31 participants (7.9%), we retained 363 (92.1%) valid participants for the subsequent data analysis.

Overall, all of the participants were from the USA. The sample was 71% female, with a mean age of 29.8 years (SD=6.55). Regarding education, 22.9% were high school graduates, 57.2% held a bachelor’s degree (or equivalent), and 19.6% had a master’ degree (or equvalent). The average organizational tenure was 4.72 years (SD=5.75).

Measures

TikTok Use Motives

We used a scale of 26 items to establish the participants’ motives for TikTok use (Lonsdale & North, 2011), which were scored on a 7-point Likert scale (0 = not at all important to 7 = extremely important). The scale had six subscales: (1) personal identity (e.g., “to create an image for myself”); (2) negative mood management (e.g., “to help get through difficult times”); (3) positive mood management (e.g., “to be entertained”); (4) diversion (e.g., “to pass the time”); (5) surveillance (e.g., “to obtain useful information for daily life”); and (6) social interaction (e.g., “to spend time with friends”). The Cronbach's alpha for the scale was .88.

Labile Self-Esteem

We used a 5-point Likert scale to assess the tendency to experience fluctuations in self-esteem on a 7-point scale (1 = strongly disagree to 7 = strongly agree; Dykman, 1998). An example item is “My self-esteem shifts rapidly from feeling good about myself one day to feeling bad about myself on the next.” The Cronbach's alpha was .88.

TikTok Addiction

To assess the participants’ levels of TikTok addiction, we adapted the Short Smartphone Addiction Scale developed by Kwon et al. (2013). The original 10-item measure was originally designed to capture general smartphone dependency; however, for the purposes of this study, we systematically revised the item wordings to specifically reflect TikTok-related behaviors. For example, the original item “Missing planned work due to smartphone use” was modified to “Missing planned work due to TikTok use.” All of the items were rated on a 7-point Likert scale (1 = strongly disagree to 7 = strongly agree), with higher scores indicating stronger addictive tendencies. This adaptation ensured greater contextual relevance and comprehensibility for the respondents, who primarily engaged with TikTok. The revised scale demonstrated strong internal consistency in our sample. The Cronbach's alpha was .88.

Subjective Well-Being

To measure the participants’ subjective well-being, we used the World Health Organization-Five Well-Being Index (Topp et al., 2015). The items were assessed on a 7-point Likert scale (1 = strongly disagree to 7 = strongly agree). The Cronbach's alpha was .90.

Analytical Approach

Following Nylund et al.'s (2007) guidelines, we initially specified a two-profile solution and then gradually increased the number of profiles until the model fit statistics did not warrant the loss of parsimony. This approach is inductive and has been widely used in LPA (Nylund et al., 2007). Seven fit statistics were used to evaluate the models: the log-likelihood (LL), Akaike information criterion (AIC), Bayesian information criterion (BIC), sample-size-adjusted BIC (ABIC), Lo–Mendell–Rubin likelihood ratio test (LMR), bootstrap likelihood ratio test (BLRT), and entropy.

There were no cutoff values for the LPA fit statistics. Instead, the ideal model has the following fit statistics: the values for the LL, AIC, BIC, and ABIC should be lower than those for other profile solutions; the entropy should be larger than that for other profile solutions; and the LMR and BLRT should be significant (p < .05). Researchers should also consider the theoretical implications of solutions when choosing an appropriate profile structure (Foti et al., 2012).

We applied an automated three-step approach to explore potential profile solutions. As described above, we implemented LPA in the first phase to establish the number of profiles that fit the data. Given the inductive nature of LPA (Gabriel et al., 2015; Wang & Hanges, 2011), we did not hypothesize a priori about the number or shape of the profiles of TikTok use motives. Second, we confirmed the most probable membership of the profile based on the posterior distribution from the preceding stage. This step can be interpreted as “the estimated probability that each individual belongs to each of the profiles” (Morin et al., 2010, p. 66). Finally, auxiliary variables concerning the profiling solution were evaluated according to the chance of being members of a particular class and the classification error rate.

For the antecedents, we ran the R3STEP command in Mplus, which uses multinomial logistic regression to determine whether an increase in an antecedent makes an individual more or less likely to belong to one profile over another (Asparouhov & Muthén, 2014; Bakk & Vermunt, 2016). For outcomes, we examined the relationship between profile membership and outcome variables at this stage using the BCH command in Mplus (Asparouhov & Muthén, 2014; Bakk & Vermunt, 2016). This step explores whether each profile differs in the outcome variables.

Results

Table 1 shows the means, standard deviations, and Pearson correlations of the variables in Study 1.

Table 1.

Means, Standard Deviations, and Pearson Correlations of Variables in Study 1 and Study 2.

Variables	M	SD	1	2	3	4	5	6	7	8	9	M	SD
1. TikTok addiction	2.56	1.14		−.08	.30**	.40**	.47**	.34**	.32**	.19**	.24**	2.84	1.21
2. Subjective well-being	4.94	1.23	−.01		−.02	.11*	.12*	.04	.09	.02	.01	4.85	1.28
3. Labile self-esteem	4.03	1.45	.28**	−.32**		.11*	.12*	.04	.09	.02	.01	3.79	1.21
4. Personal identity	3.78	1.35	.44**	.16**	.19**		.74**	.61**	.51**	.63**	.68**	3.86	1.09
5. Negative mood management	4.36	1.39	.41**	.03	.31**	.77**		.67**	.62**	.51**	.57**	3.97	1.18
6. Positive mood management	5.62	.96	.34**	.16**	.16**	.57**	.69**		.73**	.65**	.48**	4.77	1.11
7. Diversion	5.40	1.05	.24**	.05	.17**	.41**	.53**	.59**		.53**	.41**	4.55	1.09
8. Surveillance	5.18	1.35	.33**	.13**	.17**	.68**	.62**	.59**	.43**		.43**	4.92	1.23
9. Social interaction	2.82	1.59	.24**	.29**	.03	.70**	.56**	.42**	.32**	.44**		3.73	1.41

Note. Below the diagonal are the means, standard deviations, and Pearson correlations for the variables in Study 1 (American working adults). Above the diagonal are the same statistics for Study 2 (Chinese college students).

*p < .05. **p < .01.

The fit statistics for the possible latent profile structures are listed in Table 2. We chose the four profiles because they exhibited lower LL, AIC, BIC, and ABIC values than the two- and three-profile solutions. In addition, the entropy explains how precisely participants are classified into profiles. Although the five- and six-profile solutions had slightly lower LL, AIC, BIC, and ABIC statistics than the four-profile solution, the entropy was lower. Moreover, in addition to using the fit indices, the proportion of the four-profile solution was better balanced in terms of class proportions than the five- and six-profile solutions. Thus, we retained the four-profile structure.

Table 2.

Fit Statistics for Profile Structures in Study 1 and Study 2.

No. of profiles	LL	FP	AIC	BIC	ABIC	LMR(p)	BLRT(p)	Entropy	CP
Study 1
2	−3205.68	19	6449.36	6523.30	6463.02	.0002	0	.864	.45/.54
3	−3058	26	6168.00	6269.18	6186.69	.0299	0	.856	.17/.45/.37
4	−2993.67	33	6053.33	6181.76	6077.06	.0114	0	.848	.12/.34/.34/.17
5	−2966.82	40	6013.64	6169.30	6042.40	.1406	0	.82	.11/.1/.28/.31/.17
6	−2935.43	47	5964.85	6147.75	5998.64	.044	0	.83	.08/.22/.18/.09/.24/.16
Study 2
2	−2710.19	19	5458.38	5529.80	5469.50	0	0	.955	.14/.85
3	−2563.95	26	5179.89	5277.62	5195.20	.0188	0	.852	.13/.28/.58
4	−2503.37	33	5072.73	5196.78	5092.10	.0374	0	.878	.1/.03/.56/.29
5	−2440.54	40	4961.07	5111.42	4984.60	.0349	0	.879	.03/.09/.38/.06/.41
6	−2420.72	47	4935.44	5112.11	4963	.3089	0	.889	.03/.03/.41/.07/.38/.05

Note. LL = log-likelihood; FP = free parameters; AIC = Akaike information criteria; BIC = Bayesian information criteria; ABIC = sample-size-adjusted BIC; LMR = Lo–Mendell–Rubin likelihood ratio test; BLRT = bootstrapped log-likelihood ratio test; CP = class proportions.

The scores for the six TikTok use motive dimensions yielded four profiles (see Figure 1). Class 1 comprises 12.98% of the sample (n = 47) and represents individuals with the lowest levels of personal identity (M = 1.97, SE = .09), negative mood management (M = 2.20, SE = .14), positive mood management (M = 4.17, SE = .13), diversion (M = 4.19, SE = .18), surveillance (M = 2.97, SE = .28), and social interaction (M = 1.31, SE = .08). Accordingly, we refer to this profile as slight motivators. Class 2 represents 34.81% of the sample (n = 126), and we refer to this as mood-elevating motivators because these respondents reported relatively higher levels of positive mood management (M = 5.32, SE = .11), diversion (M = 5.21, SE = .12), and surveillance (M = 4.87, SE = .10), but low levels of personal identity (M = 2.89, SE = .09), negative mood management (M = 3.67, SE = .14), and social interaction (M = 2.01, SE = .10). Class 3 constitutes 34.25% of the sample (n = 124) and is termed lone motivators. This is because the motive of social interaction (M = 4.16, SE = .20) was the lowest, and the other motives were at a moderate but above-average level among the participants. Finally, Class 4 comprises 17.96% of the sample (n = 65), and these individuals reported the highest values of TikTok use motives. We refer to this profile as deep motivators. In response to RQ1, these results suggest that there are quantitatively different motivators among TikTok users.

Figure 1.

Latent Profiles for Different Employee TikTok Users in Study 1.

Based on the four-profile structure, we considered the predicting role of labile self-esteem using the R3STEP approach (see Table 3; RQ2). Our results indicated that experiencing higher labile self-esteem made individuals more likely to belong to deep motivators and lone motivators, followed by mood-elevating motivators. Although, compared with lone motivators, individuals with higher labile self-esteem were more likely to belong to deep motivators, there were no significant differences between deep motivators and lone motivators (p = .8).

Table 3.

Three-Step Results for Antecedents and Distal Outcomes for Study 1 and Study 2.

	Mood versus slight	Mood versus deep	Mood versus lone	Slight versus deep	Slight versus lone	Deep versus lone
Study 1
Labile self-esteem	.30*	−.19*	−.28*	−.51***	−.59***	.08
TikTok addiction	5.47*	23.38***	39.65***	34.25***	53.73***	.002
Subjective well-being	.24	16.29***	.05	7.52***	.11	12.45***
Study 2
Labile self-esteem	.21*	−.15*	−.11*	−.37*	−.33*	−.04
TikTok addiction	10.13**	40.37***	28.25***	118.61***	124.21***	4.31*
Subjective well-being	3.79*	14.06***	1.99	.01	1.99	21.19***

Note. Values for labile self-esteem are estimates from the R3STEP logistic regression analyses. Values for TikTok addiction and subjective well-being are chi-square from the BCH command in Mplus.

*p < .05. **p < .01. ***p < .001.

Next, we examined the different distal outcomes. Figure 2 shows that slight motivators were more likely to have the lowest TikTok addiction (M = 1.75, SE = .13) and second-lowest subjective well-being (M = 4.89, SE = .19). Mood-elevating motivators had the lowest subjective well-being (M = 4.77, SE = .12) and second-lowest TikTok addiction (M = 2.12, SE = .07). Lone motivators had the highest TikTok addiction (M = 3.06, SE = .11) and second-highest subjective well-being (M = 4.81, SE = .12). Deep motivators had the highest subjective well-being (M = 5.56, SE = .15) and second-highest TikTok addiction (M = 3.05, SE = .17).

Figure 2.

Standardized Means of Distal Outcomes by Latent Profiles for Study 1.

Table 3 shows the results of a two-by-two comparison of the profiles for each outcome. For TikTok addiction, only the comparison between deep motivators and lone motivators was not statistically significant (p = .97). For labile self-esteem, the comparison between mood-elevating motivators and deep motivators (p = .08) and the comparison between deep motivators and lone motivators were not significant (p = .49), while other profile comparisons were significant. Moreover, for subjective well-being, the comparisons between mood-elevating motivators and slight motivators (p = .62), mood-elevating motivators and lone motivators (p = .82), and slight motivators and lone motivators (p = .74) were not significant.

In summary, these results speak to the importance of RQ2 and RQ3, illustrating that labile self-esteem differently predicts the profiles of TikTok users. Also, different profiles of TikTok users relate to different levels of distal outcomes (TikTok addiction and subjective well-being).

Study 2

Method

Participants and Procedure

Using the convenience sampling approach, we sent online invitations to about 400 college students from three universities in mainland China to participate in this study, and 349 (87.2%) responded. We used Qualtrics, an online survey tool, to collect data from the college students, who signed an informed consent form before starting the study. Likewise, we used an attention-check question to set the criterion for participation—that is, at least one year of active use of TikTok (Douyin). The survey questionnaire included a set of scales on motives for TikTok use, TikTok addiction, labile self-esteem, subjective well-being, and demographic information. We removed 32 students (9.2%) due to serious missing values and failure to meet the attention-check criterion. Therefore, a final sample of 317 participants (90.8%) was retained. Overall, most of the participants were female (67.2%), and the average age was 19.18 years (SD = 1.14).

Measures

TikTok Use Motives

As in Study 1, we used a 26-item scale to measure the participants’ motives for TikTok use (Lonsdale & North, 2011). The Cronbach's alpha was .89.

Labile Self-Esteem

As in Study 1, we applied a 5-item scale to measure labile self-esteem (Dykman, 1998). The Cronbach's alpha was .82.

TikTok Addiction

As in Study 1, we used the Short Smartphone Addiction Scale (Kwon et al., 2013) to measure TikTok addiction. The Cronbach's alpha was .91.

Subjective Well-Being

As in Study 1, we applied the World Health Organization-Five Well-Being Index (Topp et al., 2015). The Cronbach's alpha was .93.

Analytical Approach

We used the same analytical approach as in Study 1.

Results

Table 1 displays the means, standard deviations, and correlations of the variables in Study 2.

The fit statistics for the possible latent profile solutions are listed in Table 2. We chose the four profiles because they exhibited lower LL, AIC, BIC, and ABIC values than the two- and three-profile solutions. Although the six-profile solutions had lower LL, AIC, and ABIC statistics and higher entropy than the four-profile solution, the LMR statistics were not significant. Although there was only a slight difference between the four and five profiles, combined with the results of Study 1, we retained the four-profile structure.

Figure 3 provides a visual representation of the four profiles. Class 1 constitutes 3.79% of the sample (n = 12) and represents individuals with the lowest levels of personal identity (M = 1.53, SE = .15), negative mood management (M = 1.40, SE = .16), positive mood management (M = 1.79, SE = .32), diversion (M = 1.93, SE = .39), surveillance (M = 2.04, SE = .53), and social interaction (M = 1.52, SE = .25). Accordingly, this profile is referred to as slight motivators. Class 2 represents 10.41% of the sample (n = 33), and we term it mood-elevating motivators because the levels of positive mood management (M = 3.88, SE = .18) and diversion (M = 3.82, SE = .17) are relatively higher compared to other motives. Class 3 constitutes 56.46% of the sample (n = 179) and we call it lone motivators. The motive for social interaction (M = 3.70, SE = 0.09) was relatively low, while other motives representing individual needs were high. Finally, Class 4 comprises 29.34% of the sample (n = 93), and these individuals reported the highest levels of personal identity (M = 4.72, SE = .14), negative mood management (M = 4.98, SE = .18), positive mood management (M = 5.79, SE = .14), diversion (M = 5.57, SE = .16), surveillance (M = 5.86, SE = .11), and social interaction (M = 4.75, SE = .24). We refer to this profile as deep motivators. Thus, the results are consistent with those of Study 1. In response to RQ1, these results suggest that there are quantitatively different motivators for student TikTok users.

Figure 3.

Latent Profiles for Different Student TikTok Users in Study 2.

As shown in Table 3, higher levels of labile self-esteem made individuals more likely to belong to deep motivators and lone motivators, followed by mood-elevating motivators, replicating Study 1. In Study 2, compared with deep motivators, individuals with higher labile self-esteem were more likely to belong to lone motivators. There were also no significant differences between these two profiles.

In terms of outcomes, the analyses yielded some pieces of evidence (Figure 4). Regarding TikTok addiction and subjective well-being, slight motivators reported the lowest level of TikTok addiction (M = 1.23, SE = .12), followed by mood-elevating motivators (M = 1.89, SE = .16), lone motivators (M = 2.91, SE = .08), and finally deep motivators, who reproted the highest levels (M = 3.28, SE = .14). Moreover, mood-elevating motivators reported the lowest level of subjective well-being (M = 4.17, SE = .30), followed by lone motivators, (M = 4.63, SE = .09), slight motivators (M = 5.38, SE = .52), and deep motivators, who reported the highest level of subjective well-being (M = 5.42, SE = .13).

Figure 4.

Standardized Means of Distal Outcomes by Latent Profiles for Study 2.

Based on the four-profile structure, Table 3 reveals that all of the profile comparisons for TikTok addiction were significant. For labile self-esteem, the comparison between mood-elevating motivators and deep motivators (p = .08) and the comparison between lone motivators and deep motivators (p = .49) were not significant. For subjective well-being, the comparisons between mood-elevating motivators and deep motivators (p < .001, χ ² = 14.06) and between lone motivators and deep motivators (p < .001, χ² = 21.19) were significant.

These results also provide insight into RQ2 and RQ3, illustrating that labile self-esteem differently predicts the profiles of student TikTok use motives. Different profile solutions of student TikTok users are related to different levels of TikTok addiction and subjective well-being. In summary, Study 2 replicated the findings of Study 1 on the profile solution and its distinct associations with antecedent and distal outcomes.

Discussion

This study aimed to explore latent profiles based on a set of TikTok use motives and to assess their distinctiveness regarding predictors and outcomes. Using a person-centered approach, we identified unique profiles among 362 American working adults and 317 Chinese college students. The results revealed a four-profile solution as optimal in both studies.

Since there was no interaction between the motives, qualitatively distinct profiles did not exist. We only identified the quantitatively distinct profiles of TikTok use motives (RQ1). These profiles were labeled slight motivators, mood-elevating motivators, lone motivators, and deep motivators, with each representing a subpopulation of TikTok users. The deep motivator profile was characterized by high endorsement across all six motives—personal identity, negative mood management, positive mood management, diversion, surveillance, and social interaction. In contrast, lone motivators showed high levels of identity, mood management, diversion, and surveillance motives, but low social interaction, indicating a preference for self-oriented rather than social gratifications. The mood-elevating motivators profile endorsed high positive affect regulation, diversion, and surveillance motives, but low identity, social interaction, and negative mood management, suggesting a focus on hedonic enhancement.

The slight motivator profile differed most sharply from the others, with relatively low scores across all six motives. While these motive scores were low, they were not zero, indicating a pattern of low-intensity or situational use rather than an aversion to the platform. In Study 1, the profile shape resembled a subtle inverted U, while in Study 2 it was more uniformly low—although positive mood management and diversion were slightly elevated in both samples. Interestingly, social interaction motives were consistently low across all profiles, which may reflect TikTok's algorithmically driven feed structure, which deprioritizes direct interpersonal exchange (Bhandari & Bimo, 2020; Montag et al., 2021).

Importantly, the contrast between slight motivators and deep motivators illustrates a motivational intensity continuum rather than mutually exclusive user types. While deep motivators represent users who engage with TikTok in immersive, multifaceted ways across emotional, informational, and social domains, slight motivators likely use the app in a more passive, sporadic, or context-driven manner—without strong psychological investment (Chen et al., 2024). Rather than viewing slight motivators as “non-users,” their profile reflects a compensatory or minimal-engagement pattern, where usage may be habitual, curiosity-driven, or peripheral to their digital routines. This interpretation aligns with prior uses and gratifications research emphasizing not just types of motives, but also their relative salience and integration into daily life (Song et al., 2004).

Across the two studies, the four-profile structure showed high cross-sample robustness, despite demographic and cultural differences. In terms of distribution, mood-elevating motivators were most prevalent among American working adults, whereas lone motivators dominated in the Chinese student sample. These variations may reflect contextual needs—working adults may prioritize emotional regulation and personal entertainment, whereas students may emphasize identity expression and social interactions. Still, the dominant motives across all of the profiles—particularly positive affect regulation, entertainment, and real-time information-seeking—align closely with TikTok's stated mission to “inspire creativity and bring joy.”¹

Furthermore, the results revealed that labile self-esteem significantly predicted profile membership, supporting RQ2. Users with more fluctuating self-worth were more likely to fall into high-motivation profiles, such as deep motivators or lone motivators. With regard to RQ3, the outcome patterns were consistent across both samples: TikTok addiction was highest among deep motivators, followed by lone motivators, mood-elevating motivators, and slight motivators. Interestingly, subjective well-being was highest among deep motivators and slight motivators, suggesting a nonlinear relationship between motivation intensity and psychological benefit—possibly moderated by the type and coherence of the motives pursued.

Theoretical Implications

First, our research advances the analytical approach in social media motivation studies by shifting from a traditional variable-centered perspective to a person-centered framework (Zyphur, 2009). Specifically, we introduce LPA to identify unobserved subpopulations of users characterized by distinct configurations of use motives. Unlike variable-centered methods that assume population-level homogeneity, the person-centered approach acknowledges that individuals often pursue multiple, co-occurring motives in diverse combinations. This enables a more nuanced understanding of how interacting motivational forces jointly shape TikTok engagement, rather than treating each motive as an isolated predictor. Our application of LPA thus expands the methodological repertoire of uses and gratifications research and opens new avenues for studying motivation in psychologically meaningful user subtypes.

Second, our findings contribute to the literature on TikTok use by uncovering four empirically distinct motivational profiles: deep motivators, lone motivators, mood-elevating motivators, and slight motivators (RQ1). These profiles differ quantitatively in their endorsement of six core motives—identity, social interaction, surveillance, diversion, negative mood management, and positive mood management—revealing meaningful intra-population heterogeneity in TikTok engagement. This configural perspective challenges the assumption that all users engage with TikTok for similar reasons, and instead highlights subgroup-specific psychological functions of the platform’s use. For example, the lone motivators profile suggests a pattern of low social needs (e.g., identity, social interaction) but moderate emotional regulation (e.g., diversion, positive and negative mood management) and information-seeking (e.g., surveillance) motives, indicating a compensatory use pattern. These findings enrich the understanding of TikTok's motivational ecology by moving beyond aggregate motive scores and emphasizing the structure, strength, and balance of motives within individuals.

Third, we extend prior research by examining both the antecedents (RQ2) and outcomes (RQ3) of these motivational profiles, offering theory-driven insights into their psychological correlates. Our results demonstrate that labile self-esteem—the tendency for self-worth to fluctuate in response to external cues—predicts profile membership, such that individuals with more unstable self-esteem are more likely to belong to high-intensity motivational profiles. This finding suggests that self-concept fragility may heighten individuals’ motivational complexity and reliance on social media to fulfill emotional or identity-related needs. Furthermore, TikTok addiction progressively increased across profiles from slight motivators to deep motivators, supporting a motive–addiction linkage consistent with gratification-seeking and reinforcement theory. Importantly, subjective well-being was highest among both deep motivators and slight motivators but lowest among lone motivators and mood-elevating motivators. This curvilinear pattern implies that neither under-engagement nor over-engagement with motives uniformly promotes well-being—rather, it is the type and configuration of motives, not their sheer number, that matters. By connecting motivation to both internal traits and external consequences, our findings integrate the fragmented literature on self-esteem, addiction, and well-being in the context of short-form video platforms.

Practical Implications

Our study has several practical implications. First, TikTok developers and marketing teams can leverage the identified motivational profiles to design segment-specific engagement strategies. For instance, content recommendations and promotional features could be tailored to the dominant needs of each subgroup—such as emphasizing creativity tools for identity-driven users or stress-relief content for emotionally motivated users—thereby enhancing user satisfaction and platform stickiness.

Second, the finding that labile self-esteem predicts profile membership and that addiction and well-being levels vary across profiles suggests that users with more unstable self-worth may be more vulnerable to problematic usage patterns. For educators, managers, and mental health practitioners, this highlights the need for targeted interventions. Organizations and schools might implement preventive digital literacy programs, promote healthy self-regulation habits, or establish boundaries for non-task-related use during work or study hours. Additionally, individualized support strategies—such as self-monitoring tools, emotional regulation workshops, or artificial-intelligence-driven usage feedback—could be effective in addressing high-risk user groups (Hou et al., 2019; Syrek et al., 2018).

Taken together, our findings suggest that understanding users’ motivational configurations can inform both user-centered platform design and evidence-based strategies to reduce digital overuse and improve well-being, especially among psychologically vulnerable populations.

Limitations and Future Directions

This study has some limitations. First, based on uses and gratifications theory, we used a six-factor motivational framework (Lonsdale & North, 2011; McQuail et al., 1972) to identify latent TikTok use profiles. However, previous studies have proposed alternative motive structures (e.g., Falgoust et al., 2022; Meng & Leung, 2021; Scherr & Wang, 2021), and it is possible that different or more nuanced profiles could emerge under alternative frameworks. Future research should replicate and extend our findings using varied motive models to assess the generalizability of the identified profiles.

Second, our study focused on one psychological antecedent (labile self-esteem) and two outcomes (TikTok addiction and subjective well-being), leaving out other potential predictors and consequences of profile membership. Individual differences (e.g., personality traits, emotion regulation styles, social comparison tendencies) and environmental factors (e.g., TikTok features, peer norms, media literacy) may also shape motivational configurations. In addition, although we used two demographically and culturally distinct samples—American working adults and Chinese college students—we did not formally test profile invariance across the groups. These populations differ in age, life stage, and media habits, which likely influence not only the salience but also the structure of use motives. Future studies should employ multigroup latent profile modeling to examine context sensitivity and assess whether similar motivational profiles hold across cultures and user types. This may also help explain inconsistent findings in the literature regarding social media use and its psychological effects.

Finally, the study relied on cross-sectional online survey data. While our analyses linked motivational profiles to meaningful predictors and outcomes, the direction of these relationships remains unclear. A promising avenue for future research would be to examine how TikTok use motives evolve over time. Longitudinal designs, such as latent transition analysis (Ryoo et al., 2018), could identify how users shift between profiles in response to changes in self-esteem, platform experience, or life context. This would provide deeper insight into the dynamics of social media engagement and the psychological processes underlying motivational change.

Conclusion

This study used latent profile analysis to identify four TikTok use motive profiles—deep, lone, mood-elevating, and slight motivators—among American working adults and Chinese college students, revealing quantitative differences in motive combinations. Labile self-esteem predicted membership in high-intensity profiles, which were linked to higher TikTok addiction and varying subjective well-being, with deep motivators showing the highest levels of both. These findings advance uses and gratifications theory by highlighting user heterogeneity and suggest tailored platform design and interventions to address problematic use. Future research should explore longitudinal profile dynamics and additional predictors.

Footnotes

ORCID iD

Bin Ling

Bowen Dong

Ethical Approval and Informed Consent

Informed consent was obtained from all of the participants in the study. All of the procedures involving the participants were performed in accordance with the standards of the ethics committee of the Business School at Hohai University.

Funding

This research was supported by the Fundamental Research Funds for the Central Universities (grant number B250207063) and the Soft Science Research Plan Project for Nanjing (grant number 202303021).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability

The data for this study is available at

Notes

References

Asparouhov

Muthén

(2014). Auxiliary variables in mixture modeling: Using the BCH method in Mplus to estimate a distal outcome model and an arbitrary secondary model. Mplus Web Notes, 21, 1–22. https://www.statmodel.com/download/asparouhov_muthen_2014.pdf

Bakk

Vermunt

J. K.

(2016). Robustness of stepwise latent class modeling with continuous distal outcomes. Structural Equation Modeling, 23(1), 20–31. https://doi.org/10.1080/10705511.2014.955104

Barta

Belanche

Fernández

Flavián

(2023). Influencer marketing on TikTok: The effectiveness of humor and followers’ hedonic experience. Journal of Retailing and Consumer Services, 70, Article 103149. https://doi.org/10.1016/j.jretconser.2022.103149

Bestvater

(2024). How U S adults use TikTok. Pew Resarch Center. https://www.pewresearch.org/internet/2024/02/22/how-u-s-adults-use-tiktok/

Bhandari

Bimo

(2020). TikTok and the “algorithmized self”: A new model of online interaction. In Paper presented at AoIR 2020: The 21th annual conference of the association of internet researchers (pp. 1–3). Virtual Event: AoIR. Retrieved from http://spir.aoir.org

Bossen

C. B.

Kottasz

(2020). Uses and gratifications sought by pre-adolescent and adolescent TikTok consumers. Young Consumers, 21(4), 463–478. https://doi.org/10.1108/YC-07-2020-1186

Brougham

J. K.

(2021). The impact of social media on the mental health of student-athletes across NCAA divisions. Journal of Issues in Intercollegiate Athletics, 14(1), Article 33. https://scholarcommons.sc.edu/jiia/vol14/iss1/33

Cao

Gong

Dai

(2020). Exploring the mechanism of social media addiction: An empirical study from WeChat users. Internet Research, 30(4), 1305–1328. https://doi.org/10.1108/INTR-08-2019-0347

Chamsi

F. Z.

Katir

Korchi

Belbachir

Ouanass

(2022). The impact of social media on self-esteem. European Psychiatry, 65(S1), S551–S551. https://doi.org/10.1192/j.eurpsy.2022.1410

10.

Chen

Zhang

Zhong

Zhao

(2024). Motivations behind problematic short video use: A three-level meta-analysis. Telematics and Informatics, 95, Article 102196. https://doi.org/10.1016/j.tele.2024.102196

11.

Cingel

D. P.

Carter

M. C.

Krause

H.-V.

(2022). Social media and self-esteem. Current Opinion in Psychology, 45, Article 101304. https://doi.org/10.1016/j.copsyc.2022.101304

12.

Crocker

Wolfe

C. T.

(2001). Contingencies of self-worth. Psychological Review, 108(3), 593–623. https://doi.org/10.1037/0033-295X.108.3.593

13.

Dahling

J. J.

Gabriel

A. S.

MacGowan

(2017). Understanding typologies of feedback environment perceptions: A latent profile investigation. Journal of Vocational Behavior, 101, 133–148. https://doi.org/10.1016/j.jvb.2017.05.007

14.

Diener

Oishi

Tay

(2018). Advances in subjective well-being research. Nature Human Behaviour, 2(4), 253–260. https://doi.org/10.1038/s41562-018-0307-6

15.

Duarte

(2025, June 23). Average time spent on TikTok statistics (2025). Exploding Topics. Retrieved from https://explodingtopics.com/blog/time-spenton-tiktok

16.

Dykman

B. M.

(1998). Integrating cognitive and motivational factors in depression: Initial tests of a goal-orientation approach. Journal of Personality and Social Psychology, 74(1), 139–158. https://doi.org/10.1037/0022-3514.74.1.139

17.

Falgoust

Winterlind

Moon

Parker

Zinzow

Chalil Madathil

(2022). Applying the uses and gratifications theory to identify motivational factors behind young adult's participation in viral social media challenges on TikTok. Human Factors in Healthcare, 2, Article 100014. https://doi.org/10.1016/j.hfh.2022.100014

18.

Foti

R. J.

Bray

B. C.

Thompson

N. J.

Allgood

S. F.

(2012). Know thy self, know thy leader: Contributions of a pattern-oriented approach to examining leader perceptions. Leadership Quarterly, 23(4), 702–717. https://doi.org/10.1016/j.leaqua.2012.03.007

19.

Gabriel

A. S.

Daniels

M. A.

Diefendorff

J. M.

Greguras

G. J.

(2015). Emotional labor actors: A latent profile analysis of emotional labor strategies. Journal of Applied Psychology, 100(3), 863–879. https://doi.org/10.1037/a0037408

20.

Gerson

Plagnol

A. C.

Corr

P. J.

(2016). Subjective well-being and social media use: Do personality traits moderate the impact of social comparison on Facebook? Computers in Human Behavior, 63, 813–822. https://doi.org/10.1016/j.chb.2016.06.023

21.

Gross

J. J.

(2015). Emotion regulation: Current status and future prospects. Psychological Inquiry, 26(1), 1–26. https://doi.org/10.1080/1047840X.2014.940781

22.

Gao

(2022). What drives me to use TikTok: A latent profile analysis of users’ motives. Frontiers in Psychology, 13, 992824. https://doi.org/10.3389/fpsyg.2022.992824

23.

Han

Yang

(2023). Will using social media benefit or harm users’ self-esteem? It depends on perceived relational-closeness. Social Media + Society, 9(4). https://doi.org/10.1177/20563051231203680

24.

Hayes

A. M.

Harris

M. S.

Carver

C. S.

(2004). Predictors of self-esteem variability. Cognitive Therapy and Research, 28, 369–385. https://doi.org/10.1023/B:COTR.0000031807.64718.b9

25.

Hossain

M. A.

(2019). Effects of uses and gratifications on social media use. PSU Research Review, 3(1), 16–28. https://doi.org/10.1108/PRR-07-2018-0023

26.

Hou

Xiong

Jiang

Song

Wang

(2019). Social media addiction: Its impact, mediation, and intervention. Cyberpsychology, 13(1), Article 4. https://doi.org/10.5817/CP2019-1-4

27.

Howard

M. C.

Hoffman

M. E.

(2018). Variable-centered, person-centered, and person-specific approaches: Where theory meets the method. Organizational Research Methods, 21(4), 846–876. https://doi.org/10.1177/1094428117744021

28.

Hsu

T. W.

Niiya

Thelwall

Knutson

Tsai

J. L.

(2021). Social media users produce more affect that supports cultural values, but are more influenced by affect that violates cultural values. Journal of Personality and Social Psychology, 121(5), 969–983. https://doi.org/10.1037/pspa0000282

29.

Katz

Blumler

J. G.

Gurevitch

(1973). Uses and gratifications research. Public Opinion Quarterly, 37(4), 509–523. https://doi.org/10.1086/268109

30.

Kim

J.-H.

(2017). Social media use and well-being. In Maddux

(Ed.), Subjective well-being and life satisfaction (pp. 253–271). Routledge.

31.

Krause

A. E.

North

A. C.

Heritage

(2014). The uses and gratifications of using Facebook music listening applications. Computers in Human Behavior, 39, 71–77. https://doi.org/10.1016/j.chb.2014.07.001

32.

Kwon

Kim

D.-J.

Cho

Yang

(2013). The smartphone addiction scale: Development and validation of a short version for adolescents. PloS One, 8(12), Article e83558. https://doi.org/10.1371/journal.pone.0083558

33.

Lanza

S. T.

Tan

Bray

B. C.

(2013). Latent class analysis with distal outcomes: A flexible model-based approach. Structural Equation Modeling, 20(1), 1–26. https://doi.org/10.1080/10705511.2013.742377

34.

Lonsdale

A. J.

North

A. C.

(2011). Why do we listen to music? A uses and gratifications analysis. British Journal of Psychology, 102(1), 108–134. https://doi.org/10.1348/000712610X506831

35.

Shen

(2023). Unpacking multimodal fact-checking: Features and engagement of fact-checking videos on Chinese TikTok (Douyin). Social Media + Society, 9(1). https://doi.org/10.1177/20563051221150406

36.

Mann

R. B.

Blumberg

(2022). Adolescents and social media: The effects of frequency of use, self-presentation, social comparison, and self esteem on possible self imagery. Acta Psychologica, 228, Article 103629. https://doi.org/10.1016/j.actpsy.2022.103629

37.

Marengo

Angelo Fabris

Longobardi

Settanni

(2022). Smartphone and social media use contributed to individual tendencies towards social media addiction in Italian adolescents during the COVID-19 pandemic. Addictive Behaviors, 126, Article 107204. https://doi.org/10.1016/j.addbeh.2021.107204

38.

Masciantonio

Bourguignon

Bouchat

Balty

Rimé

(2021). Don’t put all social network sites in one basket: Facebook, Instagram, Twitter, TikTok, and their relations with well-being during the COVID-19 pandemic. PloS One, 16(3), Article e0248384. https://doi.org/10.1371/journal.pone.0248384

39.

McQuail

Blumler

Brown

J. R.

(1972). The television audience: A revised perspective. In McQuail

(Ed.), Sociology of mass communications (pp. 135–165). Penguin.

40.

Meng

K. S.

Leung

(2021). Factors influencing TikTok engagement behaviors in China: An examination of gratifications sought, narcissism, and the Big Five personality traits. Telecommunications Policy, 45(7), Article 102172. https://doi.org/10.1016/j.telpol.2021.102172

41.

Montag

Yang

Elhai

J. D.

(2021). On the psychology of TikTok use: A first glimpse from empirical findings. Frontiers in Public Health, 9, 641673. https://doi.org/10.3389/fpubh.2021.641673

42.

Morin

A. J. S.

Morizot

Boudrias

J.-S.

Madore

(2010). A multifoci person-centered perspective on workplace affective commitment: A latent profile/factor mixture analysis. Organizational Research Methods, 14(1), 58–90. https://doi.org/10.1177/1094428109356476

43.

Nesi

Prinstein

M. J.

(2015). Using social media for social comparison and feedback-seeking: Gender and popularity moderate associations with depressive symptoms. Journal of Abnormal Child Psychology, 43(8), 1427–1438. https://doi.org/10.1007/s10802-015-0020-0

44.

Nylund

K. L.

Asparouhov

Muthén

B. O.

(2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14(4), 535–569. https://doi.org/10.1080/10705510701575396

45.

Omar

Dequan

(2020). Watch, share or create: The influence of personality traits and user motivation on TikTok mobile video usage. International Journal of Interactive Mobile Technologies, 14(4), 121–137. https://doi.org/10.3991/ijim.v14i04.12429

46.

Orchard

L. J.

(2019). Uses and gratifications of social media: Who uses it and why? In Attrill-Smith

Fullwood

Keep

Kuss

D. J.

(Eds.), The Oxford handbook of cyberpsychology (pp. 330–348). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780198812746.013.19

47.

Procházka

Suchá

Dostál

Dominik

Dolejš

Šmahaj

Kolařík

Glaser

Viktorová

Friedlová

(2021). Internet addiction among Czech adolescents. PsyCh Journal, 10(5), 679–687. https://doi.org/10.1002/pchj.454

48.

Roberts

J. E.

Gotlib

I. H.

(1997). Temporal variability in global self-esteem and specific self-evaluation as prospective predictors of emotional distress: Specificity in predictors and outcome. Journal of Abnormal Psychology, 106(4), 521–529. https://doi.org/10.1037/0021-843X.106.4.521

49.

Ryoo

J. H.

Wang

Swearer

S. M.

Hull

Shi

(2018). Longitudinal model building using latent transition analysis: An example using school bullying data. Frontiers in Psychology, 9, 675. https://doi.org/10.3389/fpsyg.2018.00675

50.

Sagioglou

Greitemeyer

(2014). Facebook’s emotional consequences: Why Facebook causes a decrease in mood and why people still use it. Computers in Human Behavior, 35, 359–363. https://doi.org/10.1016/j.chb.2014.03.003

51.

Savira

Rifai

Wahyunengsih

(2022). Correlation between TikTok use and teenagers’ self esteem. Indonesian Journal of Learning Studies, 2(1), 19–24. https://dmi-journals.org/ijls/article/view/215

52.

Scherr

Wang

(2021). Explaining the success of social media with gratification niches: Motivations behind daytime, nighttime, and active use of TikTok in China. Computers in Human Behavior, 124, Article 106893. https://doi.org/10.1016/j.chb.2021.106893

53.

Singh

(2024). TikTok user statistics 2024—age, gender demographics. TechTipsWith Tea. https://techtipswithtea.com/data/tiktok-users-statistics/

54.

Song

Larose

Eastin

M. S.

Lin

C. A.

(2004). Internet gratifications and Internet addiction: On the uses and abuses of new media. CyberPsychology & Behavior, 7(4), 384–394. https://doi.org/10.1089/cpb.2004.7.384

55.

Spurk

Hirschi

Wang

Valero

Kauffeld

(2020). Latent profile analysis: A review and “how to” guide of its application within vocational behavior research. Journal of Vocational Behavior, 120, Article 103445. https://doi.org/10.1016/j.jvb.2020.103445

56.

Sundar

S. S.

Limperos

A. M.

(2013). Uses and grats 2.0: New gratifications for new media. Journal of Broadcasting & Electronic Media, 57(4), 504–525. https://doi.org/10.1080/08838151.2013.845827

57.

Syrek

C. J.

Kühnel

Vahle-Hinz

De Bloom

(2018). Share, like, twitter, and connect: Ecological momentary assessment to examine the relationship between non-work social media use at work and work engagement. Work & Stress, 32(3), 209–227. https://doi.org/10.1080/02678373.2017.1367736

58.

Tamir

(2011). The maturing field of emotion regulation. Emotion Review, 3(1), 3–7. https://doi.org/10.1177/1754073910388685

59.

Topp

C. W.

Østergaard

S. D.

Søndergaard

Bech

(2015). The WHO-5 Well-Being Index: A systematic review of the literature. Psychotherapy and Psychosomatics, 84(3), 167–176. https://doi.org/10.1159/000376585

60.

Valkenburg

Beyens

Pouwels

J. L.

van Driel

I. I.

Keijsers

(2021). Social media use and adolescents’ self-esteem: Heading for a person-specific media effects paradigm. Journal of Communication, 71(1), 56–78. https://doi.org/10.1093/joc/jqaa039

61.

Wang

Hanges

P. J.

(2011). Latent class procedures: Applications to organizational research. Organizational Research Methods, 14(1), 24–31. https://doi.org/10.1177/1094428110383988

62.

Webster

Dunne

Hunter

(2021). Association between social networks and subjective well-being in adolescents: A systematic review. Youth & Society, 53(2), 175–210. https://doi.org/10.1177/0044118(20919589

63.

Whiting

Williams

(2013). Why people use social media: A uses and gratifications approach. Qualitative Market Research, 16(4), 362–369. https://doi.org/10.1108/QMR-06-2013-0041

64.

Woo

S. E.

Jebb

A. T.

Tay

Parrigon

(2018). Putting the “person” in the center: Review and synthesis of person-centered approaches and methods in organizational science. Organizational Research Methods, 21(4), 814–845. https://doi.org/10.1177/1094428117752467

65.

Yang

Liu

Wei

(2016). Social capital on mobile SNS addiction: A perspective from online and offline channel integrations. Internet Research, 26(4), 982–1000. https://doi.org/10.1108/IntR-01-2015-0010

66.

Zhu

Xia

Salle

S. M.

Huang

Sannusi

S. N.

(2024). Self-perception evolution among university student TikTok users: Evidence from China. Frontiers in Psychology, 14, 1217014. https://doi.org/10.3389/fpsyg.2023.1217014

67.

Zyphur

M. J.

(2009). When mindsets collide: Switching analytical mindsets to advance organization science. Academy of Management Review, 34(4), 677–688. https://doi.org/10.5465/amr.34.4.zok677

A Latent Profile Analysis of TikTok Use Motives: Structure,Antecedents,and Consequences

Abstract

Keywords

Introduction

Literature Review

A Review of Variable-Centered Research on TikTok Use Motives

A Person-Centered Approach to TikTok Use Motives

Labile Self-Esteem as the Antecedent of Profile Membership

TikTok Addiction and Subjective Well-Being as Outcomes of Profile Membership

Overview of Current Research

Study 1

Method

Participants and Procedure

Measures

TikTok Use Motives

Labile Self-Esteem

TikTok Addiction

Subjective Well-Being

Analytical Approach

Results

Study 2

Method

Participants and Procedure

Measures

TikTok Use Motives

Labile Self-Esteem

TikTok Addiction

Subjective Well-Being

Analytical Approach

Results

Discussion

Theoretical Implications

Practical Implications

Limitations and Future Directions

Conclusion

Footnotes

ORCID iD

Ethical Approval and Informed Consent

Funding

Declaration of Conflicting Interests

Data Availability

Notes

References