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Abstract

This study investigates the psychological factors influencing users’ intention to switch social media platforms in the context of an increasingly overloaded digital environment. By integrating the Self-Determination Theory (SDT) with the Stressor–Strain–Outcome (SSO) model, the research examines how intrinsic motivations and extrinsic pressures jointly shape social media fatigue and the intention to switch digital platforms. Using data from young, urban social media users in Vietnam, a collectivist and rapidly digitalizing context, this study employed the structural equation modeling (SEM) approach to examine the influence of factors such as self-efficacy, self-presentation, and various types of extrinsic pressures (information overload, communication overload, and system features overload) on the level of fatigue, as well as the relationship between fatigue and switching intention. The results show that overload factors and self-presentation increase fatigue, whereas self-efficacy mitigates it. Notably, social media fatigue serves as a key mediator in intention to switch behavior. The findings extend the SSO and SDT frameworks by demonstrating how motivational and environmental pressures interact to explain user disengagement, while offering practical implications for the design of user-centric, autonomy-enhancing, and mentally sustainable social media platforms.

Plain Language Summary

Fatigue in The Virtual World: The Impact of Intrinsic Motivation and External Pressures on Young Vietnamese Users’ Intentions to Switch Social Media Platforms

This study looks at why young people in Vietnam feel tired or stressed from using social media and why they sometimes decide to switch to another platform. The research found that when users receive too much information, too many messages, or face too many app features, they often feel mentally exhausted. At the same time, people who try too hard to look perfect online also feel more pressure. On the other hand, those who feel confident in managing their social media use experience less fatigue. The study highlights that this ‘social media fatigue’ is a main reason people leave one platform for another. These findings can help technology companies design simpler, more user-friendly, and mentally healthy social media environments.

Keywords

social media fatigue intention to switch self-determination theory stressor–strain–outcome model intrinsic motivation extrinsic motivation platform switching

Introduction

Social media fatigue is emerging as a common psychological consequence of excessive and prolonged use of digital platforms (Bright et al., 2015). In recent years, the rapid development of internet technology and social media platforms has dramatically changed the way people communicate, work, and entertain themselves. However, alongside the conveniences brought by social networks, users are increasingly faced with invisible yet persistent pressures stemming from high interaction frequency, constant notifications, and the expectation to maintain continuous presence on digital platforms (A. K. M. N. Islam & Patil, 2015; Maier et al., 2015). These pressures, including information overload, communication overload, and system feature overload, have been shown to cause prolonged stress, deplete users’ psychological resources, and encourage intention to switch on the platform (Guo et al., 2020; Karr-Wisniewski & Lu, 2010). This situation is even more serious in collectivist cultural contexts such as Vietnam, where group harmony and social expectations regarding timely responses tend to be higher than in individualistic cultures (Ho et al., 2022; Nguyen et al., 2006; Triandis, 1995). However, existing studies have mostly approached social media fatigue from either psychological or technological perspectives in isolation, overlooking how motivational and cultural factors interact to shape user fatigue and disengagement. Recent reviews (Yuna et al., 2022; Zheng and Ling, 2021) also emphasize the lack of integrative, cross-cultural investigations, particularly in developing and fast-digitizing societies.

Although social media fatigue has been widely examined in prior (Bright et al., 2015; Dhir et al., 2018), most studies have emphasized individual or technical aspects rather than underlying psychological mechanisms. To provide a more comprehensive explanation, this study integrates the Stressor–Strain–Outcome (SSO) model and Self-Determination Theory (SDT). The SSO model (Koeske and Koeske, 1993) is used to explain how external stressors such as information and communication overload, lead to psychological strain (fatigue) and behavioral outcomes (switching intention). Meanwhile, SDT (Deci & Ryan, 2000) provides the motivational lens to understand how intrinsic factors such as self-efficacy and self-presentation influence emotional responses and self-regulation in digital contexts. Integrating these two frameworks allows the study to capture both environmental and psychological dynamics that contribute to users’ fatigue and intention to switch platforms. Vietnam represents a particularly relevant context for examining these dynamics. As a collectivist society undergoing rapid digital transformation, Vietnamese users experience dual pressures: the social obligation to stay responsive within online communities and the cognitive load from increasingly complex digital features. Collectivism amplifies social conformity and constant connectivity, while technological acceleration intensifies overload—together heightening both fatigue and switching intentions.

The study focuses on young, middle-class social media users living in urban areas of Vietnam, a rapidly digitalizing country deeply influenced by collectivist culture. This is a unique context where users are highly engaged with digital communities while also being easily influenced by social pressure to maintain a constant presence. By integrating SDT and SSO into a single analytical framework, this study extends the application of both theories to the digital cultural context of developing economies, an area that still lacks sufficient empirical evidence. This study therefore aims to (1) examine how intrinsic (self-efficacy, self-presentation) and extrinsic (information, communication, and system-feature overload) factors contribute to social media fatigue, and (2) assess how fatigue subsequently shapes users’ intention to switch platforms. Building on these foundations, the next section develops hypotheses grounded in the integrated SSO–SDT framework.

Theoretical Background and Hypotheses

Theoretical Background

Stressor–Strain–Outcome Model

To explain how users psychologically respond to the pressures of the digital environment, this study applies the Stressor–Strain–Outcome (SSO) model by Koeske and Koeske (1993) as a central theoretical framework. Based on the stimulus–organism–response principle, the SSO model provides a comprehensive approach to analyzing micro-level psychological processes under the influence of environmental factors. In this context, stressors are external pressure-inducing factors (e.g., information overload, communication overload, or system features overload), strain refers to negative internal responses, whether emotional or cognitive (e.g., social media fatigue), and outcome refers to behavioral consequences such as avoidance, reduced engagement, or the intention to switch platforms (Dhir et al., 2019; Hu et al., 2024).

The model emphasizes the mediating role of strain between stressors and behavioral outcomes, reflecting users’ emotional or cognitive imbalance under persistent digital exposure. Recent research has reaffirmed the applicability of the SSO framework to technostress and social overload phenomena, which consistently predict withdrawal behaviors and digital fatigue (Fu et al., 2020; Pang et al., 2023).

In this study, self-efficacy, self-presentation, information overload, communication overload, and system feature overload are considered the main stressor factors; social media fatigue is regarded as the strain; and intention to switch is seen as the outcome, a behavioral result reflecting a deliberate tendency to avoid. The application of the SSO model clarifies the psychological process that users experience when facing pressures from the social media environment, thereby providing a foundation for building the research model and developing hypotheses in this study.

Self-Determination Theory

While the SSO model helps explain how stressors from the digital environment lead to user fatigue and withdrawal behavior, Self-Determination theory (SDT; Deci & Ryan, 2000) serves as an important complement by clarifying the internal psychological motivational mechanisms that govern users’ responses to stressors. Integrating SDT into research allows for a deeper understanding of the nature of behavioral responses, which are not only outcomes of external pressures but also manifestations of an imbalance among basic psychological needs.

According to SDT, human behavior is guided by a combination of intrinsic and extrinsic motivation, based on three core psychological needs: autonomy, competence, and social connection (Deci & Ryan, 2000). In the context of social networks, SDT indicates that when users perceive autonomy in their actions, feel competent in controlling information, and maintain positive social connections, they will sustain intrinsic motivation, leading to long-term engagement and positive experiences. Conversely, when platforms create constant pressures such as incessant notifications, the expectation of timely responses, or overwhelming complex features, extrinsic motivation takes over, causing users to feel a loss of control, fatigue, and a tendency to withdraw from the platform (Guo et al., 2020; A. K. M. N. Islam & Patil, 2015).

In this study, intrinsic motivation is manifested through the perception of self-efficacy and self-presentation, while extrinsic motivation is information overload, communication overload, and system feature overload. These two groups of motivations are assumed to have a direct impact on the level of social media fatigue and the intention to switch platforms, reflecting avoidance behavior trends caused by a lack of autonomy and prolonged stress. Therefore, SDT is used in this study as an underlying theoretical framework to supplement the SSO model, aiming to provide a deeper explanation of the motivational factors influencing users’ emotional and behavioral responses in a saturated digital environment. The integration of the SSO and SDT frameworks provides a more comprehensive lens for understanding user behavior in overloaded digital environments. While the SSO model explains how external stressors generate psychological strain and behavioral withdrawal, SDT clarifies why these reactions occur by emphasizing users’ internal motivational resources and psychological needs. When stressors such as information or communication overload undermine autonomy or competence, they translate into emotional exhaustion and avoidance behaviors. Thus, combining SSO and SDT allows this study to capture both the environmental pressures and motivational processes that jointly shape social media fatigue and switching intentions.

Hypotheses

Self-Efficacy and Social Media Fatigue

Self-efficacy is understood as an individual’s belief in their ability to control behavior and achieve desired outcomes in a specific situation (Bandura, 1977). In the context of social media use, self-efficacy reflects the user’s level of confidence in handling content, managing digital functions, and maintaining psychological stability in a highly interactive environment (Hocevar et al., 2014; Logan et al., 2018).

From the perspective of the SSO model, individuals with high self-efficacy are able to control their usage behaviors, thereby minimizing feelings of overload and exhaustion, key manifestations of social media fatigue. They tend to manage their time better and are less likely to be influenced by the pressure to respond or the need to maintain an online personal image (Bright et al., 2015). At the same time, according to SDT, self-efficacy contributes to sustaining a sense of mastery and the ability to self-regulate social media usage in ways that align with personal values. Conversely, users with low self-efficacy are more likely to feel helpless, experience internal conflict, and feel compelled to continue using the platform even when they do not feel satisfied, which leads to fatigue (Ravindran et al., 2014; Islam et al., 2021). Computer self-efficacy has been shown to reduce perceived technostress (Shu et al., 2011). This is consistent with the concept of self-efficacy, which is the extent to which users perceive that they can achieve expected outcomes through social media use (Hocevar et al., 2014; Logan et al., 2018).

Recent digital-behavior studies further confirm the protective function of self-efficacy. Individuals with stronger perceived competence experience lower technostress and emotional exhaustion when facing complex mobile-app environments (Pang & Zhang, 2024). Likewise, self-regulatory ability has been shown to buffer the cognitive strain arising from information and communication overload, thereby reducing fatigue levels (Pang & Ruan, 2023). These findings imply that self-efficacy operates as an internal psychological resource that shields users from overload-induced fatigue in highly interactive online settings.

Therefore, self-efficacy not only helps users avoid exhaustion in the digital environment by enabling self-regulation but also enhances intrinsic motivation to maintain positive engagement with the platform. The combination of the SSO and SDT perspectives, along with empirical evidence from previous studies, highlights the role of self-efficacy in reducing social media fatigue. Thus, the study proposes the following hypothesis.

H1: Self-efficacy negatively influences social media fatigue.

Self-Presentation and Social Media Fatigue

Self-presentation on social media is considered a common strategy for managing one’s personal image in front of others (Goffman, 1959; Rice et al., 2017). Accordingly, users often select content, edit images, and adjust their online behavior to maintain a positive and socially acceptable image (Y. Liu & He, 2021). Although this is a natural social need, the continuous process of self-presentation in the online environment can significantly deplete emotional and mental resources, especially when individuals feel compelled to “perform” (Bright et al., 2015).

Through the lens of SSO model, self-presentation acts as a stressor, as this behavior creates pressure to maintain a perfect version of oneself in public. These pressures cause internal strain, and if prolonged, can lead to emotional exhaustion or social media fatigue (T. Islam et al., 2019; Lee et al., 2016). From the SDT viewpoint, when users are unable to act in accordance with their true selves, they tend to lose internal connection, leading to reduced motivation to use social media positively and sustainably (Deci & Ryan, 2000). Prior studies by Hjetland et al. (2024) and Hjetland et al. (2022) show that high levels of self-presentation are associated with emotional stress, anxiety, and reduced self-satisfaction, especially when community feedback does not meet expectations.

Recent studies Meier and Johnson (2022) and Pang and Hu (2025) show that users who constantly compare themselves with others and seek social approval experience greater emotional strain and distraction, which mirrors the mental depletion caused by continuous self-presentation. In collectivist societies like Vietnam, where social approval and harmony are highly valued, the obligation to appear responsive and well-presented can turn self-presentation from voluntary expression into a psychological burden, intensifying fatigue more than in individualistic cultures. Therefore, the study proposes a hypothesis.

H2: Self-presentation positively influences social media fatigue.

Information Overload and Social Media Fatigue

Information overload occurs when the volume of information exceeds an individual’s cognitive processing capacity, causing distraction and reduced behavioral effectiveness, particularly in social media contexts where users face continuous exposure to updates, news, and advertisements (Hunter, 2004; A. K. M. N. Islam et al., 2021; Jones et al., 2004; Maier et al., 2015). In reality, with the rapid development of digital technology, the number of information channels is becoming increasingly diverse, making it easy for people to fall into a state of passive and unfiltered information intake, gradually eroding their ability to concentrate (Luqman et al., 2017).

Within the SSO framework, information overload acts as a stressor that produces emotional exhaustion, cognitive strain, and a loss of behavioral control on social media. Such unfiltered exposure intensifies stress and disrupts users’ daily functioning and social relationships (Zhang et al., 2021). In the fast-paced environment of social networks, unfiltered information intake further increases the risk of social media fatigue, especially among young users and those who use social media frequently (H. Liu et al., 2021; Soroya et al., 2021). The perceived level of information overload is positively correlated with social media fatigue (Lee et al., 2016; Ravindran et al., 2014). When users are surrounded by information without effective filtering mechanisms, they are prone to exhaustion, avoidance, or withdrawal from online interactions. Recent studies further confirm that information overload predicts emotional exhaustion and platform-switching tendencies (Fan et al., 2024; Pang & Wang, 2025). Therefore, information overload serves not only as a technological burden but also as a key psychological driver of social media fatigue. Thus, the study proposes the following hypothesis.

H3: Information overload positively influences social media fatigue.

Communication Overload and Social Media Fatigue

Communication overload is understood as a situation in which users have to handle too many communication requests within a short period of time, exceeding their capacity to receive and respond (Karr-Wisniewski & Lu, 2010). In the context of social networks, users frequently receive messages, comments, group invitations, or notifications related to social activities. When these requests appear continuously, users are more likely to feel pressured, exhausted, and lose control.

Within the SSO framework, communication overload functions as a stressor that triggers emotional exhaustion and cognitive strain once the intensity of interactions exceeds users’ coping capacity (Lee et al., 2016; Maier et al., 2015). The constant need to remain responsive fosters pressure to sustain visibility, turning social interaction from a source of connection into a psychological burden. Research by Cao and Sun (2018) shows that communication overload is a key component of social media overload, which refers to the phenomenon where users are overwhelmed by the pressure of social interaction on digital platforms. Similarly, Karr-Wisniewski and Lu (2010) confirm that when the degree of communication exceeds the optimal threshold, negative consequences begin to emerge. Chen and Lee (2013) further suggest that overload in digital communication can contribute to mental health issues among young people, especially in social network environments with high interaction speeds. Recent empirical work also supports this relationship. Pang et al. (2025) found that persistent communication intensity on mobile applications amplifies perceived social pressure and emotional strain, accelerating fatigue among users. This evidence reinforces the stressor–strain–outcome mechanism proposed in the current model.

Therefore, communication overload not only increases social media fatigue but also diminishes the quality of digital experiences by draining emotional energy and natural connections. Synthesis from the SSO model and empirical evidence indicates that communication overload plays a crucial role in explaining the growing prevalence of social media fatigue among users. Consequently, the study proposes a hypothesis.

H4: Communication overload positively influences social media fatigue.

System Feature Overload and Social Media Fatigue

System feature overload is defined as a state in which users feel overwhelmed by a large number of technological features that exceed their actual needs or ability to use them (Karr-Wisniewski & Lu, 2010). In the context of social networks, this occurs when platforms continuously update and expand functions such as livestreams, marketplaces, AI-based recommendations, making users feel that their experience becomes more complicated rather than supported (Fu et al., 2020). As a result, having to adapt to and manage numerous unnecessary features can easily lead to system feature overload, inefficiency, and reduced satisfaction.

Grounded in the SSO model, system feature overload acts as a stressor when the complexity of the platform exceeds the user’s ability to coordinate and master it. This leads to strain, including technical confusion, reduced technological self-confidence, and emotional fatigue. If prolonged, these stresses can accumulate into social media fatigue, causing users to feel drained, out of control, and inclined to avoid the platform (Fu et al., 2020; Maier et al., 2015). Experimental research confirms that when system functions become cumbersome and no longer serve core user needs, individuals lose their sense of control—an essential factor for sustaining engagement. Moreover, attempts to fully utilize excessive features consume cognitive resources, reducing communication efficiency and decision-making in digital environments.

From the SDT perspective, such technological complexity also frustrates users’ need for competence and autonomy, transforming innovation into psychological pressure. This suggests that technological overload, rather than facilitating engagement, can intensify fatigue through continuous adaptation demands.

Therefore, system feature overload is not only a disruptive technological factor but also a psychological stressor that accelerates digital exhaustion. The combination of the SSO model and experimental findings highlights the role of this factor in explaining the growing phenomenon of social media fatigue. Thus, the study proposes the hypothesis.

H5: System feature overload positively influences social media fatigue.

Social Media Fatigue and Intention to Switch

Social media fatigue is defined as a state of emotional, cognitive, or behavioral exhaustion caused by excessive exposure to digital social interactions (Bright et al., 2015). This condition often arises when users feel overwhelmed by information, the pressure to respond, or the need to maintain their personal image, leading to a decline in experience and motivation to use social networks.

Stress-inducing factors such as information overload, communication overload, or system feature overload can lead to social media fatigue (strain), which in turn results in negative behaviors (outcomes) like decreased usage or the intention to switch platforms—an effect explained by the SSO model. When feeling fatigued, users often tend to withdraw from interactions or even consider leaving the current platform in search of a more pleasant experience (Ravindran et al., 2014). Experimental studies show that stress from excessive use of social networks can lead to disruptive behaviors or abandoning the platform altogether (Maier et al., 2015). From the SDT perspective, this behavioral shift can also be interpreted as a self-regulatory response, an attempt to restore autonomy and psychological balance disrupted by continuous digital pressure. Recent empirical findings reinforce this link. Pang et al. (2025) reported that emotional strain and perceived pressure from constant social connectivity significantly increased users’ tendency to shift between mobile applications, confirming that fatigue can translate into platform-switching behavior. This finding affirms that fatigue not only reflects emotional depletion but also acts as a psychological signal driving avoidance and platform-switching behavior. Therefore, this study proposes a hypothesis.

H6: Social media fatigue positively influences intention to switch.

Methods

Sampling

The study employed the convenience sampling method combined with the snowball sampling technique to collect data from social media users in Vietnam. The sample primarily comprised young, middle-class users living in urban areas. This group was intentionally targeted because they represent Vietnam’s most active segment of social media users and are typically among the earliest adopters of new platforms. Their frequent online engagement and exposure to high interaction intensity make them especially vulnerable to information overload and social media fatigue. Therefore, this demographic provides a meaningful context for examining psychological mechanisms related to platform-switching behavior.

The sampling criteria included: being 18 years old or above, currently using at least one popular social media platform within the past 6 months (such as Facebook, TikTok, Instagram, Zalo), and voluntarily agreeing to participate in the survey. Data was collected between February and April 2025 using a Google Form. The survey link was initially shared in community groups on Facebook and Zalo, after which participants were encouraged to forward the survey to eligible friends and acquaintances. To increase the response rate, the survey was introduced with a message encouraging contributions to academic research and a commitment to complete anonymity, solely for scientific purposes. A total of 420 responses were collected. After a screening step to remove invalid responses, 370 valid responses remained for analysis. This sample size not only meets the minimum requirements for the SEM analysis method (Hair et al., 2019) but is also robust enough to test the hypotheses in the proposed theoretical model.

Measurement

The observed variables in the study were measured using standardized scales that have been developed and tested for reliability in previous research. Survey participants evaluated the statements using a 5-point Likert scale, ranging from 1 “Strongly disagree” to 5 “Strongly agree.” Social media self-efficacy is measured by eight items, inherited from the study of Bright et al. (2015), assessing users’ confidence in navigating, controlling, and effectively utilizing social media features. Self-presentation uses three measurement items developed by Rice et al. (2017), reflecting the extent to which users attempt to manage their personal image and adjust their behavior to create a positive impression on social media. Information overload is measured by three items, and the variable communication overload by four items, both based on the scale of Karr-Wisniewski and Lu (2010), aiming to assess the level of content and interaction overload experienced by users when engaging with digital platforms. Social media fatigue is measured by five items from the study of Lee et al. (2016), assessing the state of psychological and emotional exhaustion caused by prolonged social media use under overloaded conditions. Finally, the variable Intention to switch is measured by four items from the scales of McKinney et al. (2002), reflecting the level of readiness or intention to leave the current social media platform for another one.

All measurement items were originally developed in English and then translated into Vietnamese using Brislin’s (1970) back-translation procedure to ensure linguistic and cultural equivalence. A pilot test with 10 university students was then conducted to evaluate the clarity, relevance, and cultural appropriateness of each item before the formal data collection.

Ethical Considerations

This study was conducted in accordance with recognized ethical standards for human research. Participation was entirely voluntary, and all respondents provided informed consent prior to data collection. The online survey was fully anonymous, and no personally identifiable information (e.g., names, email addresses, IP data) was collected. Participants were informed of their right to withdraw at any time without consequence. The study posed minimal risk and complied with the principles outlined in the Declaration of Helsinki (World Medical Association, 2013). Figure 1 illustrates the theoretical framework of the study.

Figure 1.

Theoretical framework.

Data Analysis

To test the relationships in the research model, the collected data were processed using IBM SPSS version 26.0 and analyzed using the structural equation modeling (SEM) method in AMOS version 24.0. First, valid responses were screened and compiled in SPSS to provide descriptive statistics of demographic characteristics and measurement variables. Next, Cronbach’s alpha was computed to assess internal reliability, ensuring consistency among items belonging to the same construct. Although all scales were adapted from previously validated studies, an Exploratory Factor Analysis (EFA) was conducted to verify construct validity and item reliability within the Vietnamese context. This step ensured that adapted items loaded appropriately on their intended factors before performing the Confirmatory Factor Analysis (CFA). Based on the CFA results, convergent and discriminant validity were further evaluated. Finally, the SEM model was employed to test six hypotheses and examine the relationships among constructs in the proposed theoretical model. In addition, tests for common method bias (CMB) and multicollinearity were conducted to ensure data integrity prior to SEM estimation. Harman’s single-factor test (Harman, 1967) and Variance Inflation Factor (VIF) diagnostics (Hair et al., 2019) were applied as standard procedures to confirm that the dataset met reliability and independence assumptions.

Results

A total of 370 valid survey responses were used. In terms of gender, 57.6% of participants were female. The most common age group was 26 to 33 years old (38.1%), followed by 18 to 25 years old (26.2%). Regarding educational level, the majority had at least a college degree, with 30.3% holding a bachelor’s degree and 19.2% holding a postgraduate degree. In terms of income, 41.1% of participants had a monthly income below 10 million VND. The sample achieved reasonable coverage in terms of age, gender, and education, aligning with the research objective of studying social media user behaviors (Table 1).

Table 1.

Sample Profile.

Characteristics	Category	Frequency	Percentage
Gender	Male	157	42.4
	Female	213	57.6
Age	18–25	97	26.2
	26–33	141	38.1
	34–41	53	14.3
	42–49	43	11.6
	50 and above	36	9.8
Education level	Highschool and below	76	20.5
	College	111	30.0
	Bachelor	112	30.3
	Master and higher	71	19.2
Income level	Less than 10 million VND	152	41.1
	10 ≤ 20 million VND	77	20.8
	20 ≤ 30 million VND	37	10.0
	30 ≤ 40 million VND	43	11.6
	From 40 million VND and above	61	16.5

Table 2 presents the results of Cronbach’s alpha and exploratory factor analysis (EFA) for the measurement scales used in the study. The EFA results show that the KMO index reached 0.92 and Bartlett’s test was statistically significant (Sig. = .00), confirming that the data is fully suitable for factor analysis. All measurement scales achieved high reliability, with Cronbach’s alpha coefficients ranging from .90 to .94. Additionally, most observed variables had factor loadings greater than 0.50, reflecting good convergent validity. Specifically, the self-efficacy scale had the highest reliability (α = .94) with factor loadings from 0.70 to 0.92. The remaining scales, including communication overload (α = .92), system overload (α = .93), information overload (α = .92), self-presentation (α = .90), social media fatigue (α = .93), and intention to switch (α = .90), also demonstrated strong reliability. Overall, the results indicate that the measurement scales used in the study exhibit good reliability and convergent validity, meeting the requirements for further analyses. To minimize common method bias (CMB), procedural remedies were applied, including respondent anonymity, randomization of item order, and voluntary participation. Furthermore, Harman’s single-factor test (Harman, 1967) was conducted, and the first factor accounted for 42.75% of the total variance, which is below the 50% threshold, indicating that common method bias was not a major concern.

Table 2.

Results of Cronbach’s alpha and Exploratory Factor Analysis (EFA).

Items	Factor loadings	Cronbach’s alpha
SE2	0.92	.94
SE3	0.86
SE4	0.83
SE8	0.82
SE6	0.81
SE1	0.80
SE7	0.79
SE5	0.70
CO4	0.95	.92
CO1	0.94
CO3	0.87
CO2	0.75
SO3	0.93	.93
SO1	0.91
SO2	0.87
IO3	0.91	.92
IO2	0.87
IO1	0.86
SP1	0.93	.90
SP2	0.88
SP3	0.76
SM3	0.86	.93
SM5	0.86
SM2	0.85
SM1	0.85
SM4	0.85
IW1	0.90	.90
IW2	0.87
IW3	0.85
IW4	0.62

Note. SE = self-efficacy; CO = communication overload; SO = system feature overload; IO = information overload; SP = self-presentation; SM = social media fatigue; IW = intention to switch.

To address potential multicollinearity, the Variance Inflation Factor (VIF) values were examined. All constructs showed VIF ranging from 1.58 to 3.98, well below the threshold of 5.0 (Hair et al., 2019), indicating no multicollinearity issue.

Following the EFA, Confirmatory Factor Analysis (CFA) was conducted to validate the measurement model. The model demonstrated excellent fit to the data (χ²/df = 1.207, GFI = 0.925, AGFI = 0.910, CFI = 0.991, TLI = 0.990, IFI = 0.992, RMR = 0.070), all within the recommended thresholds (Hair et al., 2019).

Table 3 presents the results of the assessment of convergent validity and discriminant validity of the measurement scales through the indices Composite Reliability, Average Variance Extracted, and Maximum Shared Variance. All scales have a Composite Reliability greater than 0.70 and an Average Variance Extracted greater than 0.50, indicating that the constructs achieve good convergent validity (Fornell & Larcker, 1981). Specifically, the Average Variance Extracted ranges from 0.67 to 0.82, with the highest being the system feature overload scale (0.82) and the lowest being self-efficacy and intention to switch (both 0.67).

Table 3.

Assessment of the Convergent and Discriminant Validity of the Constructs.

Construct	CR	AVE	MSV	SE	CO	SP	IO	SO	IW	SM
SE	0.94	0.67	0.43	0.82
CO	0.93	0.78	0.44	−0.35**	0.88
SP	0.90	0.75	0.40	−0.51**	0.39**	0.87
IO	0.92	0.78	0.46	−0.43**	0.43**	0.35**	0.88
SO	0.93	0.82	0.42	−0.38**	0.35**	0.39**	0.44**	0.90
IW	0.89	0.67	0.45	−0.26**	0.47**	0.31**	0.55**	0.38**	0.82
SM	0.93	0.73	0.46	−0.66**	0.66**	0.63**	0.68**	0.65**	0.67**	0.85

Note. CR = composite reliability; AVE = average variance extracted; MSV = maximum shared variance; SE = self-efficacy; CO = communication overload; SO = system feature overload; IO = information overload; SP = self-presentation; SM = social media fatigue; IW = intention to switch.

Table 4 presents the results of the SEM analysis, showing that all hypotheses from H1 to H6 are supported with a high level of statistical significance (p = .00), confirming the validity of the relationships in the research model. Specifically, self-efficacy has a significant negative impact on social media fatigue (β = −.23), implying that when users are more confident in controlling and using social networks, they are less likely to experience fatigue. Conversely, self-presentation (β = .20), information overload (β = .28), communication overload (β = .30), and system feature overload (β = .25) all have a significant positive impact on SM, indicating that when users feel pressured by social presence, information overload, or excessive interaction, their level of social media fatigue increases accordingly. Notably, a strong and positive effect on intention to switch (β = .66), reinforcing the mediating role of social media fatigue in the relationship between overload factors and the behavior of withdrawing from social networks. These results not only support all hypotheses proposed in the model but also highlight the importance of managing users’ emotions and perceptions in the context of modern social network usage.

Table 4.

Result of SEM.

Path	β	p-Value	Hypothesis	Decision
SE → SM	−.23	.00	H1	Supported
SP → SM	.20	.00	H2	Supported
IO → SM	.28	.00	H3	Supported
CO → SM	.30	.00	H4	Supported
SO → SM	.25	.00	H5	Supported
SM → IW	.66	.00	H6	Supported

Note: SE = self-efficacy; CO = communication overload; SO = system feature overload; IO = information overload; SP = self-presentation; SM = social media fatigue; IW = intention to switch.

Discussion and Conclusion

Discussion

This study aims to explore the factors influencing social media fatigue and intention to switch by integrating SSO and SDT. However, because of the study’s cross-sectional design, these relationships should be interpreted as correlational rather than causal. The SEM model results show that all hypotheses are supported with a high level of statistical significance. Given that the sample primarily consisted of young, urban, and well-educated users, the findings should be interpreted within this demographic boundary and may not generalize to the broader population. Specifically, extrinsic factors, including information overload, communication overload, and system feature overload, along with intrinsic factors such as self-presentation, have a positive impact on social media fatigue, while self-efficacy has a negative effect. Notably, social media fatigue has a strong influence on intention to switch, serving as an important mediating variable in the model.

This study expands prior research by incorporating intrinsic motivational factors (self-efficacy, self-presentation) to clarify how personal regulation shapes social media fatigue. The finding that self-efficacy has a mitigating effect on social media fatigue, while self-presentation contributes to increased fatigue, is entirely in line with SDT (Deci & Ryan, 2000). This mitigating effect extends beyond a mere sense of capability; users with higher self-efficacy maintain stronger autonomy and emotional resilience, which helps them regulate stress and prevent fatigue even under high digital pressure. Conversely, when they are under external pressure to maintain their self-image on social media, feelings of fatigue tend to increase significantly. These findings also complement recent research on user gratifications and social network effects. Pang & Zhang (2024) observed that perceived gratifications and network externalities significantly shape continuance intention on mobile social platforms. Similarly, Pang and Zhang (2024) found that multidimensional benefits, including hedonic, social, and utilitarian gratifications, significantly enhance user satisfaction and engagement, reinforcing the motivational pathways emphasized by SDT. The current results extend this understanding by showing that when such gratifications are disrupted by overload or fatigue, users’ motivation to remain engaged may decline.

In practical terms, the findings from this study suggest that to limit user disengagement from the platform, technology companies should not solely focus on expanding features or increasing content volume, but need to pay closer attention to users’ emotional experiences and their self-regulation abilities in the digital environment. Simplifying the interface, minimizing distracting notifications, and providing tools to control information flow and interaction levels will help reduce cognitive and emotional pressure, thereby alleviating social media fatigue. Specifically, reducing information overload can be achieved through content curation tools and AI-based filtering systems; minimizing communication overload may involve message-priority settings and customizable notification frequency; and lowering system feature overload requires adaptive interface modes that allow users to hide or simplify underused functions. At the same time, encouraging users to develop proactive technology usage skills such as through time management features, rest reminders, or digital literacy education, also plays an important role in enhancing perceived competence. Designers could further implement adaptive notification systems, personalized interface modes, and time-use dashboards that promote users’ autonomy and self-regulation while maintaining engagement. These design adjustments not only help reduce fatigue but also contribute to maintaining long-term user engagement and loyalty to the platform. This suggests that maintaining a sense of identification and community could help buffer the negative psychological outcomes of overload and fatigue, fostering a healthier form of engagement.

The findings should also be interpreted in light of Vietnam’s collectivist cultural context, where social relationships and group harmony are highly valued. In such environments, individuals often feel a stronger sense of social obligation to remain responsive and connected, which amplifies the perceived pressure to engage continuously on social media platforms. This constant social demand can heighten information and communication overload, eventually leading to greater fatigue. Moreover, collectivist tendencies may discourage users from openly disengaging from platforms, even when they feel exhausted, because doing so might be perceived as neglecting social norms or relationships. These cultural dynamics help explain why users in collectivist societies may experience more intense fatigue and slower withdrawal behavior compared to those in more individualistic cultures. While these findings are rooted in Vietnam, similar patterns may emerge across other collectivist or emerging markets such as Indonesia, Thailand, or India, where social connectedness and conformity similarly shape digital fatigue dynamics.

Finally, the research results suggest a new direction for future studies in the fields of technology and user psychology. Incorporating dynamic variables into digital behavior models opens up great potential for developing hybrid theoretical frameworks that connect technological platforms, culture, and individual structures in an increasingly digital society.

Conclusion

This study aims to explore the psychological mechanisms that govern users’ intentions to switch social media platforms, particularly in the context of social media fatigue caused by both intrinsic motivations and external pressures. By integrating SDT and the SSO model, the research has developed a comprehensive analytical framework to explain how psychological needs and technological factors influence user behavior in a saturated digital environment.

The results of the SEM analysis confirmed all six proposed hypotheses. Specifically, self-efficacy has a negative impact on the level of social media fatigue, while factors such as self-presentation, information overload, communication overload, and system feature overload all have a positive impact on this social media fatigue. Notably, social media fatigue has a strong and positive effect on the intention to switch, thereby confirming the mediating role of strain in the relationship between stressors and avoidance behavior.

Theoretically, this study advances the current understanding of social media fatigue by extending the SSO and SDT frameworks into a unified model that captures both motivational and technological determinants of user strain. It contributes new insights into how internal psychological resources (e.g., self-efficacy) interact with external overload factors to shape users’ emotional and behavioral outcomes. Furthermore, by contextualizing the research in Vietnam’s collectivist culture, where social harmony and digital participation are strongly emphasized, this study enriches the cross-cultural perspective on digital fatigue and switching behavior.

Practically, the findings provide actionable implications for social media developers and digital marketers. Designing user-centered platforms that support self-regulated use, simplify system features, and moderate notification frequency could effectively mitigate fatigue and sustain long-term engagement. Promoting digital well-being initiatives, such as time management prompts or rest reminders, may also help users maintain psychological balance.

In summary, social media fatigue should not be viewed merely as a passive emotional state but as a signal that users are struggling to reconcile their psychological needs for autonomy, competence, and connection in an overloaded environment. By bridging motivational psychology and information systems research, this study not only strengthens the theoretical foundation of digital fatigue but also offers a practical roadmap for creating healthier and more sustainable social media ecosystems.

Limitations and Future Directions

Although the study has provided valuable insights into the psychological mechanisms underlying social media fatigue and users’ platform-switching intentions in Vietnam, it still has several limitations that should be noted. First, the use of a cross-sectional quantitative design means that conclusions are limited to correlational relationships and cannot determine causality. Therefore, future studies should employ longitudinal or experimental designs to clarify the process of social media fatigue formation over time. Experimental manipulations, such as varying levels of information or communication overload, could help identify causal pathways between digital stressors and fatigue. Longitudinal tracking of users’ emotional responses over extended periods would also reveal how fatigue evolves and influences platform-switching behavior. Second, the data collected through self-reported measures may be subject to social desirability bias or recall bias; thus, it is recommended to combine other methods such as tracking actual behavior or conducting in-depth interviews to enhance reliability. Third, the survey sample was obtained using convenience and snowball sampling, which may not fully represent the characteristics of all social media users in Vietnam; future research should broaden the sample scope to improve generalizability.

Additionally, this study focused primarily on negative emotional outcomes such as social media fatigue, without considering positive psychological experiences (e.g., enjoyment, satisfaction, connectedness) or alternative behavioral outcomes like reduced usage time or selective participation. Incorporating these aspects in future models could provide a more balanced understanding of users’ responses to social media environments. The current research model has yet to account for the influence of moderating or mediating variables like psychological resilience, the level of platform engagement, or cultural factors, which might offer more profound insights into intention to switch in a crowded digital landscape. Although the proposed model primarily captures the mediating role of social media fatigue, this design was intentionally chosen to ensure analytical clarity and parsimony. Introducing additional moderators (e.g., resilience, platform engagement, or cultural values) would have substantially increased model complexity and reduced statistical power, given the current sample size. Future cross-cultural comparative studies are also recommended to test whether the observed fatigue mechanisms hold across other collectivist or emerging digital markets, such as Indonesia, Thailand, or India, where social obligations and digital engagement patterns may differ from the Vietnamese context. Future studies are encouraged to extend this framework by examining such moderating mechanisms to provide a more comprehensive understanding of users’ behavioral dynamics across different cultural and technological contexts.

Overall, future research should focus on expanding an integrated theoretical framework while also combining actual behavioral data with longitudinal research designs to track users’ psychological and behavioral changes over time. This will help strengthen the academic foundation and enhance the practical application of research in designing more humane and sustainable social media platforms.

Footnotes

Acknowledgements

The author would like to thank the participants for their time and valuable insights.

ORCID iD

Tuan Hai Nguyen

Ethical Considerations

All participants were informed about the purpose of the research and voluntarily consented to participate before completing the survey. The study involved no physical, social, or psychological risk to respondents, and all data were collected anonymously to ensure confidentiality. As the research focused on general user perceptions and did not involve any vulnerable populations, formal institutional ethics approval was not required. This study was conducted in accordance with the ethical principles of the Declaration of Helsinki (World Medical Association, 2013) and SAGE’s Publication Ethics and Research Integrity Policy.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

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

Data Availability Statement

Data will be made available upon reasonable request.

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Fatigue in the Virtual World: The Impact of Intrinsic Motivation and External Pressures on Young Vietnamese Users’ Intentions to Switch Social Media Platforms

Abstract

Plain Language Summary

Keywords

Introduction

Theoretical Background and Hypotheses

Theoretical Background

Stressor–Strain–Outcome Model

Self-Determination Theory

Hypotheses

Self-Efficacy and Social Media Fatigue

Self-Presentation and Social Media Fatigue

Information Overload and Social Media Fatigue

Communication Overload and Social Media Fatigue

System Feature Overload and Social Media Fatigue

Social Media Fatigue and Intention to Switch

Methods

Sampling

Measurement

Ethical Considerations

Data Analysis

Results

Discussion and Conclusion

Discussion

Conclusion

Limitations and Future Directions

Footnotes

Acknowledgements

ORCID iD

Ethical Considerations

Funding

Declaration of Conflicting Interests

Data Availability Statement

References