Distraction or motivation? Unraveling the role of fear of missing out on college students’ learning engagement

Abstract

Background

Despite the recognition of the impact of Fear of Missing Out (FoMO) on learning engagement, research conclusions remain inconsistent. This inconsistency may be due to a lack of exploration of the underlying mechanisms and the singularity of research perspectives.

Methods

Drawing from motivational dynamics model for learning engagement, this study investigated the relationship between FoMO and learning engagement from both variable-centered and person-centered perspectives, and further explored the mediating role of self-control. A total of 1,510 college students from eastern China were selected via an online platform, including 642 males and 868 females.

Results

The results from the variable-centered analysis indicated that FoMO had a significant positive direct effect on learning engagement (effect = 0.293, 95% CI = [0.185, 0.401]). FoMO negatively predicted self-control, which in turn had a negative impact on learning engagement (effect = -0.375, 95% CI = [-0.456, -0.295]). This suggests that self-control acts as a masking effect between FoMO and learning engagement. The person-centered results suggest the presence of four latent profiles of FoMO: the low trait-FoMO low state-FoMO group, the high trait-FoMO low state-FoMO group, the low trait-FoMO high state-FoMO group, and the high trait-FoMO high state-FoMO group. Specifically, compared to the low trait-FoMO low state-FoMO group, self-control acts as a masking role between the low trait-FoMO high state-FoMO group, the high trait-FoMO high state-FoMO group and learning engagement.

Conclusions

FoMO exhibits a “dual-edged sword effect”. Educators should adopt diverse instructional methods to leverage the positive aspects of FoMO while guiding students in self-control training to mitigate its negative effects to enhance college students’ learning engagement.

Introduction

In the era of information technology, the widespread adoption of the internet has greatly enhanced convenience, making information acquisition, communication, learning, and entertainment more accessible than ever. However, this ubiquitous connectivity has also exacerbated the prevalence and intensity of Fear of Missing Out (FoMO), which is defined as a pervasive concern that others may be experiencing rewarding events in one’s absence [1]. A survey revealed that nearly 3/4 of young adults reported having felt FoMO [2]. Milyavskaya et al. employed an experience sampling method to assess the daily experiences of FoMO among college freshmen [3]. The findings revealed that college students frequently encounter situations characterized by FoMO, particularly when engaged in essential tasks such as studying or working. The aforementioned study demonstrates a remarkable prevalence of FoMO experiences within college students. This pervasive state of anxiety not only causes problematic social media use [4], but also increases the incidence of psychiatric disorders, anxiety, and depression [5, 6]. Additionally, it affects college students’ learning engagement [7]. Learning engagement is not only an important observational indicator of students’ learning processes [8], but also an important metric of learning quality [9]. Previous studies have primarily focused on exploring how personal factors, such as personality, emotions, or motivations, influence academic engagement [10–12]. However, in the digital age, certain psychological characteristics, such as FoMO, have become particularly prominent due to their close association with digital technology. Yet, the relationship between these characteristics and learning engagement has not been fully studied.

A few studies have explored the relationship between FoMO and learning engagement, but their conclusions have been inconsistent. Some studies have found a positive or negative correlation [7, 13, 14], while others have found no significant correlation between them [8, 15]. The existing studies have primarily conceptualized FoMO as a mediating variable that operates between individual or environmental factors and academic engagement without extensively examining the underlying mechanisms driving the relationship. Moreover, the prevailing research has predominantly employed a variable-centered perspective, potentially overlooking individual heterogeneity. A people-centered perspective is more helpful for identifying subgroups in heterogeneous populations [16]. Therefore, this study adopts a dual perspective, drawing upon both variable-centered and person-centered approaches, firmly grounded in motivational dynamics model for learning engagement, to extensively explore the influence of FoMO on learning engagement and the mediating mechanism of self-control. This study seeks to elucidate the relationship between FoMO and learning engagement, with the objective of providing more nuanced and evidence-based recommendations to effectively support the developmental needs of college students.

FoMO and learning engagement

The research on FoMO originated from Przybylski’s study, and in recent years, there has been a surge in research on FoMO. However, ambiguity and controversy still surround its conceptualization and structure. Initially, FoMO was introduced and assessed within online contexts [1]. Przybylski suggested that individuals with high levels of FoMO fear being excluded from beneficial experiences or worry about missing out on the latest social media updates, thereby developing a constant need and desire to stay connected with others [1]. However, some scholars argue that FoMO is a stable trait characterized by individuals’ general anxiety about missing out on others’ positive experiences [17–19]. Wegmann et al. proposed a complex multidimensional structure of FoMO, including trait-FoMO and state-FoMO. Trait-FoMO refers to a relatively stable personality trait characterized by general FoMO on certain things, while state-FoMO specifically refers to FoMO on online information [20]. Groenestein et al. employing a longitudinal study design, also provided evidence that FoMO consists of two components: a trait-based component representing interindividual difference and a state-based component reflecting within-individual fluctuations that change over time, experiences, and contexts [21]. Xiao & Liu also confirmed the two dimensions of FoMO among Chinese participants: trait-FoMO and state-FoMO [22].

Learning engagement is defined as a positive state characterized by learners’ active participation in learning activities [23], which manifests as having ample energy and mental fortitude to learn, being enthusiastic about learning, and being fully immersed in the learning process [24]. Skinner and Pitzer, building on self-determination theory (SDT), proposed a motivational dynamics model for learning engagement [25]. The model posits that basic psychological needs serves as the underlying foundation influencing students’ learning engagement. When students’ basic psychological needs are met, they can develop self-regulation processes that provide a motivational basis for learning engagement. Conversely, when these needs are thwarted, such as when individuals experience FoMO, it may lead to avoidance or withdrawal behaviors in activities.

However, research exploring the relationship between FoMO and learning engagement has shown complex and inconsistent findings. Some studies have shown that FoMO negatively predicts learning engagement. For instance, Al-Furaih & Al-Awidi reported that individuals with high FoMO are more prone to distraction and exhibit lower levels of memory retention and task performance [7]. They also have weaker motivation to engage in learning and show signs of disengagement from learning. Similarly, Rozgonjuk et al. found that individuals with high FoMO tend to employ surface learning strategies and invest minimal effort in their learning [14]. Lin et al. also found that FoMO increases individuals’ attentiveness to information on social media, subsequently undermining their learning engagement [26]. However, Lemay et al. found that FoMO positively predict academic performance, hypothesizing that this may be due to individuals with higher levels of FoMO having a greater need for social approval, which manifests as a stronger inclination towards conformity [13]. Additionally, there are studies that have found no significant correlation between FoMO and learning engagement [8] or academic performance [15].

These inconsistent research findings can be attributed to several factors. Firstly, divergent conceptualizations of FoMO have resulted in variations in prior research. Early studies, largely grounded in Przybylski’s framework, have defined FoMO as the anxiety about missing out on others’ online activities or information. However, a growing body of research supports the notion that FoMO comprises both trait and state components, which are not exclusively related to anxiety about missing out on online social activities. Moreover, FoMO has been found to exhibit cultural specificity, with its structure and intensity varying across distinct cultural contexts [27]. Chinese college students, who are deeply influenced by Confucian relationalism, tend to place greater emphasis on interpersonal relationships [28] and may therefore be more prone to exhibiting FoMO. Secondly, previous studies have focused on different outcome variables. Some research has examined the process of learning engagement, while others have focused on its outcomes, such as academic performance. Although there is a positive correlation between learning engagement and academic performance [29], learning engagement fosters intrinsic motivation and autonomy in learners, promoting deep learning and continuous improvement, thereby offering greater research value. Finally, prior research has not thoroughly explored the mediating mechanisms between FoMO and learning engagement, potentially overlooking the influence of certain mediating variables on outcomes. Therefore, this study specifically targets Chinese college students to investigate the impact of FoMO on learning engagement and the mediating role of self-control, aiming to provide a more comprehensive understanding of the intricate relationships among these factors.

In summary, drawing on Wegmann’s conceptual framework of FoMO, this study utilizes learning engagement as an outcome variable to investigate the impact of FoMO on Chinese college students’ learning engagement. Despite the inconsistent findings of previous research, based on the motivational dynamics model and the previously discussed negative effects of FoMO, such as distraction, surface learning, and media multitasking, this study hypothesizes that FoMO negatively predicts learning engagement (H1).

The mediating role of self-control

According to the motivational dynamics model of learning engagement, the influence of FoMO on learning engagement is mediated by self-control, which is a crucial psychological function through which individuals actively regulate irrational thoughts, emotions, and behaviors to align with social norms and support the achievement of long-term goals [30]. The essence of self-control lies in the capacity to inhibit undesirable thoughts or behavioral tendencies (such as impulsivity) and restrain oneself from engaging in inappropriate actions [31]. Individuals with high levels of FoMO may be preoccupied with concerns about missing out on the activities and updates on social media, leading to distractions from learning-related tasks. One key function of self-control is to counteract distraction, which involves ignoring intrusive thoughts or resisting temptations [31]. According to the limited-resource theory, individuals have finite control resources [30]. Thus, after engaging in activities requiring self-control (such as FoMO), these resources become depleted, leaving individuals in a weakened state of control. In addition, FoMO is essentially a pervasive form of anxiety and is regarded a subcategory of anxiety [32]. Attentional control theory suggests that anxiety impairs the central executive functions of goal-directed individuals, thereby reducing their attentional control and self-regulation abilities [33]. Research has found that individuals with high levels of FoMO exhibit impaired executive functioning and are more prone to impulsivity [34], which is a manifestation of difficulties in inhibitory control [35]. Xu & Tian conducted an ERP study and found that FoMO consumes more cognitive resources during the early stage of conflict detection, leading to insufficient cognitive resources for later inhibitory processes, ultimately undermining individuals’ inhibitory control [36]. Empirical studies have demonstrated a significant negative correlation between FoMO and self-control [37], as well as a positive correlation between FoMO and failures in social media self-control among college students [38].

Self-control is a significant determinant of academic development. Throughout the learning journey, nearly all students experience conflicts between academic goals (e.g., higher income levels, or higher social status) and non-academic goals (e.g., stress relief or entertainment) associated with short-term benefits [39]. Individuals with a high level of self-control possess the capacity to minimize conflicts between academic and non-academic goals by employing proactive strategies and exerting deliberate control over impulsive behaviors, thereby ensuring their engagement in learning activities [40]. In contrast, individuals with low self-control are unable to effectively regulate and modulate their emotions and engagement behaviors during learning processes [41]. Empirical studies have also shown that self-control significantly predicts the level of learning engagement, with lower levels of self-control corresponding to decreased learning engagement [42]. In summary, this study posits that self-control plays a mediating role between FoMO and learning engagement (H2).

Latent profile analysis of FoMO

The inconsistent findings regarding the relationship between FoMO and learning engagement may also be partly explained by the limited perspective of previous studies. Most prior research has been conducted from a variable-centered perspective, which often overlooks individual differences. In contrast, a person-centered perspective aims to uncover heterogeneity within the population by identifying subgroups with similar relationships, levels, or patterns in the variable system [43]. Latent profile analysis (LPA) is a commonly employed method for person-centered analysis, partitioning participants into mutually exclusive groups based on their response patterns to measurement items, with individuals within the same group sharing similar characteristics and response patterns [44]. Wegmann et al. propose that the FoMO encompasses two dimensions: trait-FoMO and state-FoMO [20]. Individuals with high trait-FoMO are concerned about missing out on beneficial events, information, or opportunities, and they seek to fulfill their unmet needs through specific channels or platforms. Some of them resort to browsing social media to alleviate their unease. This triggers a sense of online social comparison, leading individuals to worry about missing out on others’ online activities, consequently exhibiting high state-FoMO [45]. However, others may focus more on offline social activities [32] and may not necessarily exhibit high levels of state-FoMO. Therefore, FoMO may manifest as different combinations of trait and state aspects in reality. Translating the varied combinations of FoMO and their effects on self-control and learning engagement is of paramount importance in formulating targeted educational intervention measures. Elhai et al. conducted a latent profile analysis of FoMO among Chinese college students and identified four latent categories, and found significant variations in anxiety and stress levels across different categories of FoMO [46]. Li identified five latent classes of FoMO among Chinese college students and found that the combination of high trait and high state FoMO exhibited higher levels of social media engagement [16]. To summarize, this study intends to employ LPA to explore different profiles of trait-FoMO and state-FoMO, and further examine the mediating role of self-control in the relationship between different FoMO latent profiles and learning engagement. Given the scarcity of relevant research, this study will undertake an exploratory investigation of the latent profiles of FoMO and their impact on self-control and learning engagement. No specific hypotheses will be formulated.

The present study

Based on prior theoretical and empirical evidence, FoMO is expected to predict learning engagement, with self-control mediating this relationship. We also assume that effect of FoMO on self-control and learning engagement exhibits heterogeneity. This study validates the aforementioned hypotheses from both the variable-centered and person-centered perspectives.

Methods and participants

Participants

This study was approved by the ethics committee of the authors’ affiliated institution (2024JNXYLL-028). The sample for this study consisted of college students from four universities located in the eastern province of China (Shandong). We contacted teachers from these schools to distribute the online survey links to their students. Participants voluntarily completed the questionnaire on their computers or mobile devices. Prior to participating in the online survey, all respondents were required to provide informed consent. The informed consent information was presented at the beginning of the online questionnaire, which outlined the purpose of the study, the procedures involved, any potential risks or benefits, and the voluntary nature of participation. Only those who checked a box indicating their consent were allowed to proceed with completing the online survey questionnaires. It took approximately 10 min to answer all the questions. A total of 1,667 questionnaires were collected, of which 1,510 met the criteria for validity, resulting in a validity rate of 90.58%. The exclusion criteria for invalid data were as follows: 1) a sincere response to the integrity question; 2) evidence of response regularity; and 3) an appropriate answer completion time. The participants included 642 males (42.5%) and 868 females (57.5%), with ages ranging from 16 to 24 years (M = 19.49, SD = 1.45). Participants were distributed across four academic years: 661 (43.8%) were in Grade 1, 406 (26.9%) in Grade 2, 244 (16.2%) in Grade 3, and 199 (13.2%) in Grade 4. Regarding academic disciplines, 314 participants (20.8%) were from the humanities, 513 (34.0%) from the sciences, 284 (18.8%) from engineering, and 399 (26.4%) from arts and physical education. In terms of social media usage, 587 participants (38.9%) reported spending on social media for more than 4 h per day, 261 (17.3%) for 3–4 h, 358 (23.7%) for 2–3 h, 227 (15.0%) for 1–2 h, and 77 (5.1%) for less than half an hour.

Measures

Utrecht Work Engagement Scale-Student (UWES-S)

The measurement of learning engagement employed in this study was the University of Utrecht Work Engagement Scale (UWES-S), which was originally developed by Schaufeli [24] and translated into Chinese by Li & Huang, demonstrating good reliability and validity [47]. Specifically, six items measured vigor (e.g., “When I get up in the morning, I feel like going to class”; α = 0.882); five items measured dedication (e.g., “I’m enthusiastic about my study”; α = 0.892); six items measured absorption (e.g., “When I’m studying, I forget everything around me”; α = 0.888). A 5-point Likert scale was used, with response options ranging from “never” (1) to “always” (5). Higher scores on the scale indicate elevated levels of learning engagement.

Trait-State Fear of Missing Out Scale (T-S FoMOS)

The measurement of FoMO in this study employed the Chinese version of the scale, initially developed by Wegmann et al. [20] and translated into Chinese by Xiao & Liu, which exhibited satisfactory reliability and validity among Chinese participants [22]. On this questionnaire 4 items measured trait-FoMO (e.g., “I am worried that my friend has more valuable experiences than me”; α = 0.862); 7 items measured state-FoMO (e.g., “I am continuously online in order not to miss out on anything”; α = 0.894). All items were rated on a 5-point Likert scale ranging from 1 (totally disagree) to 5 (totally agree). A higher total score on the scale indicates a greater level of FoMO.

Self-Control Scale (SCS)

Self-control was measured using a scale developed by Tangney et al. [31] and translated into Chinese by Tan & Guo, which exhibited satisfactory reliability and validity [48]. The scale encompasses a total of 19 items (e.g., “I can resist temptation very well” and “It is difficult for me to change bad habits.”), which can be categorized into five dimensions: impulse control, healthy habits, resisting temptation, focusing on learning, and abstaining from entertainment. The respondents rated each item on a 5-point scale ranging from 1 (totally disagree) to 5 (totally agree), with higher scores indicating higher levels of self-control. In this study, the Cronbach’s α coefficient for the scale was 0.874.

Data analysis

First, we conducted reliability tests, descriptive statistics, correlation analyses, and tests for common method bias using SPSS 25. Second, we performed variable-centered mediation effect analyses using Mplus 8. A structural equation model was constructed with FoMO as the predictor variable, learning engagement as the outcome variable, and self-control as the mediating variable, while controlling for grade and gender. The criteria for assessing the fit of a structural equation model are as follows: CFI and TLI values greater than 0.90 [49], and RMSEA and SRMR values less than 0.08 [50] indicate an acceptable model fit. CFI and TLI values greater than 0.95, and RMSEA and SRMR values less than 0.05 [51] indicate an excellent model fit. Third, FoMO was subjected to a latent profile analysis, with the original item scores used as indicators, following the methodology outlined in the study conducted by Li et al. [16]. We utilized Mplus to evaluate the 1- to 5-class solutions of the LPA models and compared them based on fit indices. The most optimal model was determined based on lower Akaike’s information criteria (AIC), lower Bayesian information criteria (BIC), lower adjusted BIC (aBIC), significant p values of the Lo-Mendell-Rubin likelihood ratio test (LMR) and bootstrap likelihood ratio test (BLRT), higher entropy value, and conceptual meaning. Finally, we utilized one-way analysis of variance (ANOVA) in SPSS to examine potential significant differences in self-control and learning engagement among the latent classes. Subsequently, person-centered mediation analyses were conducted using Mplus. A structural equation model was constructed with latent profiles of FoMO as the predictor variable, learning engagement as the outcome variable, and self-control as the mediating variable, while controlling for grade and gender.

Results

Common method bias test

To address potential common method bias, we implemented various strategies during both the data collection and statistical analysis stages. First, the subjects’ self-reports were collected anonymously to minimize any potential bias in the responses. Additionally, reverse scoring techniques were employed to further mitigate common method bias. To examine the presence of common method bias, we conducted Harman’s one-way test [52]. Specifically, exploratory factor analysis was performed on the measured items of the three variables. This analysis yielded a total of seven factors with eigenvalues greater than 1. Notably, the largest common factor accounted for 24.62% of the total variance. This value is below the critical threshold of 40%, which suggests no significant common method bias in this study, supporting the validity of the collected data.

Descriptive Statistics

Table 1 displays the results of the descriptive statistics and correlation analyses conducted to examine the associations among FoMO, self-control, and learning engagement.

Table 1.

Descriptive statistics and correlations between variables (N = 1510)

	M ± SD	1	2	3	4	5	6	7
1. Trait-FoMO	2.37 ± 0.92	1
2. State-FoMO	2.62 ± 0.87	0.564***	1
3. FoMO	2.53 ± 0.79	0.817***	0.937***	1
4. Self-control	3.30 ± 0.57	0.438***	0.375***	0.447***	1
5. Learning engagement	3.19 ± 0.73	0.103***	0.001	0.044	0.377***	1
6. Gender		0.108***	0.096***	0.112***	0.058*	0.004	1
7. Grade		0.111***	0.003	0.045	0.129***	0.058*		1

Gender is coded as a dummy variable: male = 1, female = 2

^*p < 0.05, ^***p < 0.001

Variable-centered analysis

Following the mediation effect test procedure proposed by Wen & Ye [53], we first examined the direct effect of FoMO on learning engagement. Subsequently, we assessed the changes in the direct effect coefficients and model fit after introducing the mediating variables. The results of the direct effect analysis indicated a good fit of the model, with χ²/df = 10.737, CFI = 0.979, TLI = 0.965, RMSEA = 0.08, and SRMR = 0.064. Notably, FoMO did not significantly predict learning engagement (β = -0.033, SE = 0.034, p > 0.05). However, an increasing number of researchers have suggested the possibility of mediating effects even in the absence of a significant direct effect path between variables [54]. Consequently, this study further explored the mediating role of self-control between FoMO and learning engagement.

The structural equation model showed an acceptable fit, with χ²/df = 9.563, p < 0.001, CFI = 0.939, TLI = 0.914, RMSEA = 0.080, and SRMR = 0.064. The results are presented in Fig. 1. FoMO had a significant negative effect on self-control (β = -0.643, SE = 0.030, p < 0.001). Self-control positively predicted learning engagement (β = 0.584, SE = 0.052, p < 0.001). Additionally, FoMO had a significant positive direct effect on learning engagement (β = 0.293, SE = 0.055, p < 0.001). Further mediation effect tests were conducted, revealing that the direct effect c’ was significant (effect = 0.293, 95% CI = [0.185, 0.401]), the indirect effect ab (mediated by self-control) was significant (effect = -0.375, 95% CI = [-0.456, -0.295]), and the absolute value of the ratio of the indirect effect to the direct effect (|ab/c’ |) was 1.28. Importantly, the indirect effect had the opposite sign to the direct effect, and the absolute value of the direct effect c’ was larger than the absolute value of the total effect c. Therefore, the relationship between variables should be interpreted as a masking effect. In the broadest sense, the absence of a significant relationship between the independent and dependent variables due to masking or influence by a third variable constitutes a mediation effect [55]. This implies that while the direct effect of FoMO on learning engagement is positive, the masking effect of self-control ultimately renders the total effect of FoMO on learning engagement non-significant.

Fig. 1.

Model path diagram based on the variable-centered perspective

Note. Path values are the path coefficients (standardization coefficient); ^***p < 0.001. FoMO, fear of missing out; IC, impulse control; FOL, focusing on learning; HH, healthy habits; RT, resisting temptation; AFE, abstaining from entertainment

Person-centered analysis

LPA of FoMO

The profile models (1–5) were examined to identify the optimal model fit (see Table 2). As the number of groups increased, the information criteria AIC, BIC, and aBIC consistently decreased, indicating improved model fit. Additionally, both the LMRT and BLRT values remained significant. The entropy values exceeded 0.80 for the remaining 2 to 5 categories, indicating the acceptable accuracy of these profile models. However, the p value of the LMRT for the 5-profile model exceeded 0.05, suggesting that the addition of a 5-profile solution did not improve the model significantly compared to the 4-profile solution. Therefore, the 4-profile solution was deemed the most parsimonious fit for the data (LMR < 0.001, BLRT < 0.001, entropy = 0.88). Subsequent analyses were conducted using the 4 identified subgroups.

Table 2.

Fit indices for five models using LPA (N = 1510)

Profile	LL	AIC	BIC	aBIC	Entropy	LMR(p)	BLRT(p)	Smallest class proportions
1-profile	-25,274.87	50,593.75	50,710.78	50,640.90
2-profile	-22,500.39	45,068.78	45,249.66	45,141.65	0.92	< 0.001	< 0.001	0.38
3-profile	-22,017.80	44,127.59	44,372.31	44,226.18	0.88	< 0.001	< 0.001	0.18
4-profile	-21,601.28	43,318.56	43,627.11	43,442.86	0.88	< 0.001	< 0.001	0.19
5-profile	-21,238.76	42,617.52	42,989.91	2767.54	0.88	0.09	< 0.001	0.09

LL loglikelihood value, AIC Akaike information criterion, BIC Bayesian information criterion, aBIC adjusted Bayesian Information Criterion, LMR(p) p value for the Lo–Mendell–Rubin likelihood ratio test, BLRT(p) p value for the bootstrapped likelihood ratio test value

As depicted in Fig. 2, Class 1 was designated the low trait-FoMO low state-FoMO group (LLG, n = 282, 18.68%). Similarly, Class 4 was labeled the high trait-FoMO high state-FoMO group (HHG, n = 641, 42.45%). Class 2 (n = 306, 20.26%) exhibited a relatively high level of trait-FoMO but a much lower level of state-FoMO and was therefore categorized as the high trait-FoMO low state-FoMO group (HLG). In contrast, Class 3 (n = 281, 18.61%) consisted of participants reporting a relatively low level of trait-FoMO but a higher level of state-FoMO; thus, it was labeled the low trait-FoMO high state-FoMO group (LHG).

Fig. 2.

Latent profiles of participants based on trait-FoMO and state-FoMO (scores)

Note. 1–4 items = trait-FoMO, 5–11 items = state-FoMO; Class 1 = low trait-FoMO low state-FoMO group (LLG), class 2 = high trait-FoMO low state-FoMO group (HLG), class 3 = low trait-FoMO high state-FoMO group (LHG), class 4 = high trait-FoMO high state-FoMO group (HHG)

Differences in self-control and learning engagement across potential profiles

We performed one-way analysis of variance (ANOVA) to examine the differences in self-control and learning engagement across the four FoMO profiles. The results presented in Table 3 indicate significant variations in both self-control and learning engagement among the four profiles.

Table 3.

Differences in self-control and learning engagement across the 4 FoMO profiles

	LLG (M ± SD)	HLG (M ± SD)	LHG (M ± SD)	HHG (M ± SD)	F	Partial η2
Self-control	3.71 ± 0.62a	3.36 ± 0.52b	3.37 ± 0.52b	3.07 ± 0.47	102.302***	0.169
Learning engagement	3.30 ± 0.96a	3.17 ± 0.66b	3.22 ± 0.37	3.14 ± 0.65b	3.098*	0.006

The same superscripts (i.e., a and b) indicate nonsignificant differences between classes in mean score of self-control and learning engagement, while non-same superscripts indicate significant differences (ps > 0.05)

LLG low trait-FoMO low state-FoMO group, HLG high trait-FoMO low state-FoMO group, LHG low trait-FoMO high state-FoMO group, HHG high trait-FoMO high state-FoMO group

^*p < 0.05, ^***p < 0.001

The mediating role of self-control

Using LLG as the reference group, we constructed a structural equation model to examine the relationships among the latent FoMO profiles, learning engagement, and self-control as a mediator. The model demonstrated good fit indices: χ²/df = 8.793, CFI = 0.935, TLI = 0.920, RMSEA = 0.072, SRMR = 0.064. Figure 3 shows that, in comparison to those in the LLG group, college students in the HLG, LHG, and HHG groups exhibited significantly lower levels of self-control, which subsequently resulted in decreased engagement in learning. Both the LHG and HHG had significant direct positive effects on learning engagement.

Fig. 3.

Model path diagram based on the person-centered perspective

Note. The low trait-FoMO low state-FoMO group (LLG) was used as the reference group, and path values are the path coefficients (standardization coefficient), HLG = high trait-FoMO low state-FoMO group, LHG = low trait-FoMO high state-FoMO group, HHG = high trait-FoMO high state-FoMO group, IC = impulse control, FOL = focusing on learning, HH = healthy habits, RT = resisting temptation, AFE = abstaining from entertainment, ^*p < 0.05, ^***p < 0.001

Discussion

Based on motivational dynamics model, this study examined the impact of FoMO on learning engagement by exploring the mediating role of self-control from both variable-centered and person-centered perspectives. The variable-centered analysis revealed that self-control served as a mediator in the relationship between FoMO and learning engagement. Specifically, self-control masked the direct positive prediction of FoMO on learning engagement. However, due to its negative indirect impact on learning engagement through self-control, FoMO ultimately failed to predict learning engagement. Hypothesis H1 was not supported, while H2 was supported. Furthermore, the person-centered findings identified four distinct profiles associated with FoMO. Specially, the present study further showed heterogeneity in the role of self-control in masking between FoMO and engagement in learning. The subsequent section provides a comprehensive discussion on the potential profiles of FoMO and the intricate relationships among the variables under scrutiny.

LPA of FoMO

The present study identified four latent profiles of FoMO, namely, the low trait-FoMO low state-FoMO group (LLG), high trait-FoMO low state-FoMO group (HLG), low trait-FoMO high state-FoMO (LHG), and high trait-FoMO high state-FoMO group (HHG). These findings align with the results of previous study [16]. According to the whole trait theory, traits are considered density distributions of behavior across different states. States reflect individuals’ behavior at specific moments, serving as “momentary expressions” of their traits [56]. Consequently, various combinations of traits and states can manifest in real-life contexts.

The LLG group accounted for a relatively small proportion (18.68%), whereas the HHG group had a significantly larger representation (42.45%). This suggests that FoMO is a prevalent phenomenon among college students. Contemporary college students, who are growing up in the era of information technology, are considered digital natives and have become accustomed to constant connectivity and online interactions. The abundance of information available to them, coupled with the constantly evolving dynamics of the digital world, makes them highly susceptible to experiencing FoMO. Given the potential negative impact of FoMO on college students’ mental well-being, social media addiction, and other relevant aspects, educational administrators should prioritize addressing this issue.

Relationships among FoMO, self-control and learning engagement

The results of the variable-centered analysis indicated a negative association between FoMO and self-control. The person-centered analysis further demonstrated significant differences in self-control among individuals with different FoMO profiles. Notably, the self-control of the HLG, LHG, and HHG groups was significantly lower than that of the LLG group. The findings are align with those reported by Barber & Santuzzi [37] and Chen & Zheng [38]. This finding suggests that regardless of whether individuals exhibit high trait-FoMO, high state-FoMO, or a combination of both, their self-control abilities are compromised. According to SDT, the satisfaction of basic psychological needs, such as competence, autonomy, and relatedness, plays a crucial role in supporting self-control [24]. Since FoMO results from unmet basic psychological needs, high levels of FoMO tend to predict lower internal resources, leading to a decline in self-control. Moreover, FoMO can be considered a form of pervasive anxiety that falls within the spectrum of negative emotions [1]. Previous research has demonstrated a negative correlation between anxiety and self-control, possibly due to the detrimental effects of heightened anxiety on individuals’ central executive functions, thereby impairing cognitive control and self-regulation abilities [57]. Furthermore, some researchers have posited that FoMO is an indication of blocked self-regulation [58], and that self-regulatory activities, as a form of self-control behavior, deplete individuals’ regulatory resources, resulting in self-depletion [30].

An interesting finding was that self-control acted as a masking effect between FoMO and learning engagement from a variable-centered perspective. Specifically, FoMO directly and positively affected learning engagement. However, because FoMO reduced individuals’ level of self-control, and low self-control reduced their engagement in learning. Ultimately, this direct positive effect was masked, and it overall showed that FoMO was not correlated with learning engagement. Person-centered results also showed that self-control acted as a masking effect between the LHG / HHG and learning engagement, as the LLG was the control group. This may be an important reason for the inconsistent results of previous studies on the relationship between FoMO and learning engagement. Previous studies had not explored the intrinsic mechanism of these two processes in depth, while this study explored the mediating effect of self-control between FoMO and learning engagement based on motivational dynamics model and revealed the real relationship between them. Learning engagement, as a positive psychological state in educational settings, is primarily characterized by students demonstrating high levels of energy and strong mental resilience, particularly when encountering challenges [59]. However, this requires students to have adequate resources for self-control [29]. FoMO decreases students’ level of self-control, which means that they can regulate their behavior less. Due to students’ decreased ability to manage the learning process and their limited capacity to regulate their cognitive resources flexibly, this further results in disengagement from learning [60]. Thus, the negative impact of self-control on learning engagement masks the direct positive effect of FoMO on learning engagement.

The direct positive effect of FoMO on learning engagement can be explained from several perspectives. Firstly, FoMO not only increases college students’ social media engagement but also motivates them to use these platforms for educational purposes [61]. Research has indicated that students with higher levels of FoMO tend to use social media in class to seek help and information from peers [62], or to collaborate and communicate through online forums, social media, online searches, and instant messaging [44], which in turn enhances their learning engagement. Secondly, while most studies have examined FoMO in the context of social media use, FoMO can also occur offline [5]. In a learning context, FoMO may present itself as a worry about missing important learning resources, opportunities or activities. Therefore, individuals with high FoMO also pay more attention to interactions with the teacher and classmates, thereby enhancing their engagement in learning. Moreover, FoMO elicits an elevated need for social affiliation and a desire for belonging among individuals. In the school context, this manifests as a strong desire to please significant others, such as teachers, and a tendency to exhibit greater compliance with the demands of others [13]. In order to fulfill their unmet psychological needs, individuals engage more actively and demonstrate a heightened level of commitment in their academic pursuits. Finally, FoMO implies constant “upward social comparisons” and unreasonable expectations, which can negatively impact one’s self-esteem [63]. Research has shown that individuals with high FoMO exhibit greater decreases in self-esteem [64]. Consequently, to elevate their self-esteem, these individuals engage in active learning strategies to attract the attention of others [65].

In summary, prior research has predominantly identified associations between FoMO and various negative outcomes, such as fatigue, stress, physical symptoms, adverse mental health effects, insomnia, loneliness, social disconnection, and diminished sleep quality [3, 15, 61, 66]. Within the conceptual framework of trait and state FoMO, the present study uncovers the “dual-edged sword” nature of FoMO. It has the potential to directly and positively predict learning engagement (indicating a positive influence), while simultaneously exerting a negative impact on self-control (indicating a detrimental influence). Consequently, in confronting FoMO, it is imperative to acknowledge its potential negative implications while also recognizing its possible positive contributions. Therefore, we recommend adopting a positive perspective to explore strategies for effectively managing and leveraging the beneficial aspects of FoMO, with the aim of enhancing learning efficiency and fostering other favorable behaviors.

Research implications and limitations

The present study not only elucidates the mechanisms underlying the impact of FoMO on college students’ academic engagement, but also reveals the heterogeneity of this effect across groups. The findings of this study contribute to the advancement of research in related fields, revealing that a dual approach targeting FoMO and self-control can effectively enhance college students’ engagement in academic pursuits. On one hand, leveraging the constructive aspects of FoMO to enhance learning engagement is crucial. As digital natives, contemporary college students prefer activities with a strong social component [67]. Therefore, teachers can utilize social media platforms to create virtual learning communities for learners [59]. Within these virtual communities, interactions can occur among teachers and students as well as among students themselves, with a focus on learning content. High-quality interactions not only help fulfill students’ FoMO but also enhance psychological and emotional connections between teachers and students [68], ultimately boosting students’ learning engagement. This type of instructional model requires educators to not only be proficient in using information technologies but also to effectively bridge the virtual and real worlds, achieving an organic unity of virtualized teaching and emotional connection, ultimately integrating knowledge teaching with emotional engagement [69]. On the other hand, to counteract the decrease in self-control associated with FoMO, educators should guide students to actively report their learning progress and achievements while providing timely and comprehensive feedback throughout the instructional process. Moreover, they should guide students in autonomously adjusting their cognition, emotions, and behaviors to enhance their self-control abilities, thereby increasing their engagement in learning [70].

However, the present study has several limitations. Primarily, this study relies solely on self-reported cross-sectional data, thereby precluding any definitive conclusions regarding the causal nexus between FoMO, self-control and learning engagement. Future research could further clarify the relationships among the three through experimental or longitudinal studies. Furthermore, FoMO is a complex concept, and its theoretical structure needs to be further validated. In addition, FoMO varies according to a range of factors, such as psychological needs, social media use patterns, and age demographics. Hence, the developmental trajectory of FoMO can be analyzed through longitudinal tracking studies in the future to further explore its dynamic impact on learning engagement. Finally, the effect of FoMO on learning engagement may be moderated by factors such as learning attitudes, recognition of teachers, and the quality of peer interactions. Therefore, future research could further explore the interplay among these variables.

Conclusion

This study presented that FoMO can directly and positively predict learning engagement. However, its negative impact on self-control, which subsequently exerted a detrimental effect on learning engagement, offset the positive direct effect, ultimately resulting in a non-significant overall correlation between FoMO and learning engagement. Moreover, the results also showed that FoMO can be categorized into four distinct profiles that have different effects on self-control and learning engagement. Furthermore, the study found that, when using LLG as a reference group, self-control masked the relationship between LHG, HHG, and learning engagement. The findings indicate that educational management should consider leveraging the dual-edged nature of FoMO by focusing on mitigating its adverse effects while capitalizing on its potential positive outcomes. Specifically, enhancing learning engagement among college students may involve effectively utilizing FoMO while simultaneously strengthening self-control. Therefore, based on these findings, further research is warranted to develop more effective intervention strategies.

Abbreviations

FoMO

Fear of missing out

SDT

Self-determination theory

LPA

Latent profile analyses

LLG

Low trait-FoMO low state-FoMO group

HLG

High trait-FoMO low state-FoMO group

LHG

Low trait-FoMO high state-FoMO group

HHG

High trait-FoMO high state-FoMO group

IC

Impulse control

HH

Healthy habits

RT

Resisting temptation

FOL

Focusing on learning

AFE

Abstaining from entertainment

Authors’ contributions

Lingfang Kong and Wenguang He is responsible for the design of ideas and the interpretation of results. Hui Sun is responsible for data collection and analysis. Lingfang Kong and Wenyue Hu is responsible for the preparation of the first draft. All authors reviewed the results and approved the final version of the manuscript.

Funding

This study was not funded by any grants.

Data availability

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

All methods were carried out in accordance with relevant guidelines and regulations.The studies involving human participants were reviewed and approved by Jining university. Each participant provided informed consent and was fully aware of the study details before participating in the survey.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.