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BRIEF RESEARCH REPORT
published: 13 January 2021
doi: 10.3389/fpsyg.2020.568492
Edited by:
Andrej Košir,
University of Ljubljana, Slovenia
Reviewed by:
Domen Novak,
University of Wyoming, United States
Mehdi Elahi,
University of Bergen, Norway
*Correspondence:
Yang Yang
hi.yangyang@hotmail.com
Specialty section:
This article was submitted to
Human-Media Interaction,
a section of the journal
Frontiers in Psychology
Received: 19 June 2020
Accepted: 09 December 2020
Published: 13 January 2021
Citation:
Bai J, Mo K, Peng Y, Hao W,
Qu Y, Lei X and Yang Y (2021) The
Relationship Between the Use
of Mobile Social Media and Subjective
Well-Being: The Mediating Effect
of Boredom Proneness.
Front. Psychol. 11:568492.
doi: 10.3389/fpsyg.2020.568492
The Relationship Between the Use of
Mobile Social Media and Subjective
Well-Being: The Mediating Effect of
Boredom Proneness
Jie Bai, Kunyu Mo, Yue Peng, Wenxuan Hao, Yuanshan Qu, Xiuya Lei and Yang Yang*
Department of Psychology, School of Humanities and Social Science, Beijing Forestry University, Beijing, China
Objective: This study took users of short-form mobile videos as research participants
to explore the role of their boredom proneness in mediating the relationship between the
use of mobile social media (UMSM) and subjective well-being (SWB).
Methods: A sample of 656 users was evaluated by the Problematic Mobile Social
Media Usage Assessment Questionnaire, General Well-Being Schedule, and Boredom
Proneness Scale.
Results: Firstly, significant interactions were found between monthly living expenses
and the UMSM of the participants, which were recognized as factors affecting SWB.
Secondly, the level of living expenses had little effect on the high-level and low-level
groups of the UMSM but imposed a significant impact on the medium-level group.
Thirdly, the UMSM showed an influence that could positively predict boredom; both
the UMSM and boredom demonstrated a negative predictive effect on SWB.
Conclusion: The findings indicate that the inappropriate use of mobile social
media negatively affects users’ subjective well-being; boredom partially mediated the
relationship between the use of mobile social media and SWB.
Keywords: short video, problematic use of mobile social-media, subjective well-being/SWB, the boredom
proneness, network environment
INTRODUCTION
In recent years, mobile social media have been used by more and more people due to its
convenience. Although online social activities have become a supplement to the offline social life to
a certain extent, excessive dependence on the Internet would inevitably induce more or less negative
effects on the users (Jiang, 2018a). For instance, a decrease in subjective well-being was reported in
individuals with Internet addiction (AFROZ, 2016;Mei et al., 2016;Nie et al., 2016;Koç, 2017;
Suresh et al., 2018).
Subjective Well-Being and the Use of Mobile Social Media
Subjective well-being (SWB) is a subjective, holistic, and relative indicator, which is widely used in
psychological research as an overall assessment of the quality of life (Diener, 1984). SWB is also
regarded as one of the standards for measuring mental health. People with high-level SWB could
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Bai et al. Mobile Social-Media and Subjective Well-Being
experience a higher level of self-esteem (Joshanloo and Daemi,
2015) and be more tolerant of others (Datu, 2013). Several
factors have been reported to be effective to influence SWB
(Yamaguchi et al., 2013), such as individual personality traits,
style of attribution, physical health, socioeconomic status, social
support, etc. (Diener et al., 2013;López Ulloa et al., 2013;Zhang
et al., 2014). Among the affecting factors, the critical role of social
support has been repeatedly verified (Khan and Husain, 2010;
Wang, 2014;Tian et al., 2015). When people obtained more social
support, they experienced less loneliness and more happiness (Q.
Tian, 2014). In contrast, people with less social support are likely
to turn to mobile social media to get noticed (e.g., by posting a
tweet and attracting comments). Some studies have shown that
using mobile social media can strengthen the connection with
others and provide social support, to help people enhance their
SWB (Boyd and Ellison, 2007;Koroleva et al., 2011;Wenninger
et al., 2018). However, with the advancement of research, it has
been demonstrated that individuals who use mobile social media
frequently are more inclined to develop addictive behaviors,
which may cause a series of negative effects such as anxiety,
depression, etc. (Labrague, 2014). The improper use of mobile
social media imposes negative impacts on people both physically
and psychologically, thereby affecting their SWB (Hanprathet
et al., 2015;Hawi and Samaha, 2016). A negative correlation has
been found between the levels of SWB and problematic use of the
Internet (AFROZ, 2016;Mei et al., 2016;Nie et al., 2016;Koç,
2017;Suresh et al., 2018).
Boredom Proneness and SWB
Boredom is a state of being weary and restless through lack
of interest. Also, boredom proneness refers to a persistent
personality trait reflecting how easy an individual is apt to feel
bored (Farmer and Sundberg, 1986;Eastwood et al., 2012). The
individual feels bored when the environment cannot provide
enough emotional stimuli. One with higher boredom proneness
is more possible to generate negative emotions, which may
lead to depression, anxiety, loneliness, and lower levels of SWB
(German and Latkin, 2012). Thus, individuals tend to look for
something new and exciting from the environment to alleviate
boredom (Skues et al., 2015). Once an individual becomes
overdependent on a stimulus that was novel, the adaptation to
the novelty can reduce the interest and lead to new boredom.
On the contrary, boredom proneness also acts as a predictor
of the overdependence on a certain object. It was suggested
that individuals whose personality trait is easy to feel bored are
more possible to indicate Internet addiction (Chaney and Blalock,
2006). Titilope (2014) examined the use of mobile phones from
a psychosocial dimension and found that boredom proneness
could significantly predict the degree of dependence on mobile
phones in adolescents. Leung (2008) found that people who were
more likely to be bored used mobile phones more frequently.
So, the interplay between boredom proneness and excessive
reliance on certain specific activities or tools appears mutual
and complex. In the latest decade, the use of mobile social
media keeps rising, owing to the increasing interest of people
in the new product that integrates features of the Internet and
mobile phone. Nevertheless, to answer the question whether
the gradually unfolding dependence on mobile social media is
associated with boredom proneness, further investigations are
still necessary.
In addition, a negative correlation between boredom
proneness and SWB has been revealed. Individuals manifesting
a high degree of boredom tended to show negative emotions
and a lower level of SWB (German and Latkin, 2012). The study
by Wang et al. (2014) confirmed that boredom proneness could
negatively predict SWB. Combined with the aforementioned
evidence indicating the links between the use of mobile social
media and SWB, the two variables and boredom proneness
constitute a new psychological framework. Rosen et al. (2013)
suggested that bored people utilized social networks to relieve
their boredom. In the Internet environment, it seems feasible
to reduce the boredom by using the Internet appropriately and
consequently improve SWB (Rosen et al., 2013;Greyling, 2018).
A number of investigations have also been carried out focusing
on the relationship between Internet-based utility and negative
emotions (Bozoglan et al., 2013;Banjanin et al., 2015;Chen et al.,
2019), whereas few studies have explored the role of boredom in
the interrelationship between Internet use and SWB.
Aims and Hypotheses
Some evidence has proven that social media imposed a significant
effect on SWB (Uysal et al., 2013;Brooks, 2015). However, the
mechanism underlying the interactions remains ambiguous (Nie
et al., 2016). In our study, we surveyed users of short video who
had habitual use of Internet-based social media, to figure out
the pathways on how the individuals’ well-being is influenced
by the improper use of social media and boredom proneness in
the cyber world.
Given that boredom proneness is related to the use of social
media and enables the prediction of the level of SWB, the
current study raised the following hypotheses: (1) correlations
exist among the use of mobile social media, boredom proneness,
and SWB; (2) boredom proneness is a mediator variable between
the problematic use of mobile social media and SWB, which
means that the problematic use of mobile social media affects
SWB via boredom proneness.
MATERIALS AND METHODS
Ethics
The recruitment of participants for this study was approved
by the Ethics Committee of the Department of Psychology,
Beijing Forestry University. A survey was carried out with all the
participants online or offline. All data were collected with the
consent of the participants. Before the survey, the participants
were informed about the research content and their rights.
Participants
In this study, an online questionnaire survey platform called
“Questionnaire Star”1, as well as offline questionnaires, was
used to collect data on the use of mobile social media from
1https://www.wjx.cn/
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Bai et al. Mobile Social-Media and Subjective Well-Being
short-video users. Due to the COVID-19 pandemic and the
restriction of close social contact, the offline data collection
could not continue. As a result, a mix of online and offline data
collection was adopted.
After eliminating unqualified questionnaires (e.g.,
questionnaires that were filled out randomly), a total of 656
valid samples were collected, including 237 male samples (36.1%)
and 419 female samples (63.9%).
Measures
Problematic Use of Mobile Social Media
The Problematic Mobile Social Media Usage Assessment
Questionnaire developed by Jiang (2018b) was used in this
study. This questionnaire includes 20 questions, divided into
five subdimensions, which are used to measure five different
aspects of the use of mobile social media (UMSM). (1) Viscosity
increase is used to measure the time length, frequency, and
intensity of the use of mobile social media. For example, “Always
extend the time of using mobile social-media without awareness”
and “I have a certain dependence on mobile social-media, and
sometimes cannot control the using time.” A higher score on
this factor means that individuals use mobile social media for
a longer time and more frequently and individuals are more
dependent on mobile social media. (2) Physiological damage
refers to the negative physical responses of individuals after
excessive use of mobile social media, such as impaired vision,
lack of sleep, shoulder pain, etc. A higher score on this factor
means that the use of mobile social media has caused more
serious physiological damage to individuals. To some certain
extent, the improper use of mobile social media can be reflected
by a physical condition. (3) Omission anxiety refers to the anxiety
caused by individuals’ concerns about missing messages due
to their inability to check their mobile social media in time.
A higher score on this factor indicates a higher level of anxiety
caused by an individual’s uncontrollable worry about missing
information. This emotion could affect people’s concentration on
their ongoing tasks. (4) Cognitive failure refers to the negative
consequences of using mobile social media for cognition, such
as memory loss and thinking stagnant. “Due to the convenience
of mobile phones and mobile networks, I rarely remember
things by myself, which made my memory gradually decline.”
“Because of excessive dependence on the mobile social media,
a great amount of information is no longer needed to be
thought and processed by individuals, which dulls our mind and
causes memory loss.” This is also a manifestation of excessive
reliance on mobile social media. (5) Guilt is the feeling of being
unable to complete an individual’s work or study schedule on
time due to using mobile social media for a long time, for
example, “I often regret wasting too much time on mobile
social media.” This feeling may make the individual fall into
constant self-blame and compunction. A higher score on this
factor indicates that an individual feels guiltier for not completing
a task on schedule due to unreasonable arrangement of using
mobile social media. All items are scored from “1 = not at
all” to “5 = completely true.” The higher total score of the
UMSM represents the higher tendency in the problematic use
of mobile social media. In this study, the internal consistency
coefficient of the scale was 0.93, showing that this scale was highly
reliable in the survey.
Subjective Well-Being
The Overall Happiness Scale revised by Duan (1996) was used
in this study. The scale has 18 items, covering satisfaction and
interest of life, energy, concerns about health, depressed or
positive emotions, and control of emotions and behavior, as well
as tension and relaxation. A higher score on this scale means
a higher level of SWB. In this study, the internal consistency
coefficient of the scale was 0.84, revealing its high reliability.
Boredom Proneness
The Boredom Proneness Scale was developed by Huang et al.
(2010). The questionnaire has a total of 30 items, including two
dimensions—external stimuli and internal stimuli. The external
stimuli include four factors, and they are monotony, loneliness,
tension, and restraint. On the other hand, the internal stimuli
include two factors, and they are self-control and creativity. All
items are scored from “1 = not at all” to “5 = completely true.” In
this study, we only used the total score to measure the individual’s
boredom proneness. Individuals showing a higher total score
of boredom proneness are characterized by higher boredom
proneness and the tendency to be bored easily. The internal
consistency coefficient of the scale was 0.92 in the current study.
Data Processing
All data were processed and analyzed by using statistical software
SPSS 24.0. A series of analyses were implemented to check the
systematic errors and explore the relationship among various
psychological variables.
A common method bias test was conducted to find out
whether the properties of the data in this study affected the
results. The common method bias is a systematic error which
can be attributed to several environmental factors, such as the
experimental settings, the way how the participants answer the
questionnaires, and so forth. These factors can enlarge the errors
and bias the final results of the study. Therefore, the aim of the
implementation of the common method bias effect testing was
to confirm whether such systematic error exists in our collected
dataset. According to the test method introduced by Zhou and
Long (2004), the method of “separating the first common factor”
was used to compare the model fitting degree before and after
controlling the deviation of the common method.
After we eliminated the possibility of common method biases,
we performed the variance tests to explore the significance in
the use of mobile social media, SWB, and boredom proneness,
influenced by differences in gender, age, daily short-video viewing
duration, and daily mobile social media usage time.
The Pearson correlation analysis was conducted to examine
the correlations between pairs of variables, followed by a stepwise
regression analysis to explore the linear relationship between the
five factors of the use of mobile social media and SWB.
We also used a two-factor ANOVA to analyze whether the
main effects of demographic variables and the use of mobile social
media exist to influence SWB, as well as the potential interaction
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TABLE 1 | The results of difference analysis on UMSM.
Variable Number UMSM t/F p
Gender
Male 237 3.28 ±0.82 2.825** 0.005
Female 419 3.10 ±0.78
Age
Under the age of 18 95 2.84 ±0.78 11.382*** 0.000
Aged 18–24 426 3.25 ±0.76
Aged 24 and above 135 3.10 ±0.80
UMSM, use of mobile social media; **correlation is significant at the 0.01 level (two
tailed); ***correlation is significant at the 0.001 level (two tailed).
TABLE 2 | The results of difference analysis on SWB.
Variable Number SWB t/F p
Gender
Male 237 4.25 ±0.70 −1.986* 0.047
Female 419 4.37 ±0.77
Age
Under the age of 18 95 4.38 ±0.78 2.418 0.09
Aged 18–24 426 4.28 ±0.75
Aged 24 and above 135 4.44 ±0.74
SWB, subjective well-being. *Correlation is significant at the 0.05 level (two tailed).
between the two factors. Notably, among the demographic
variables of the participants, we focused on the “monthly living
expenses,” as we considered it an indicator of an individual’s
economic status, which might be related to SWB. Finally, the
PROCESS 2.1 program and bootstrap method were employed to
verify the mediating effect of boredom proneness.
RESULTS
Common Method Bias Test
The results of the single-factor test showed that for the 12 factors
with eigenvalues greater than 1, the first factor explained 26.04%
of the variation, which was lower than the critical value of 40%.
Therefore, the common method deviation had little effect on the
following analyses (see Supplementary Table 1).
Descriptive Statistics and Difference
Tests
Gender, age, daily viewing duration of short videos, and daily
usage time of mobile social media were utilized as grouping
variables for the short-video users in the t-tests or one-way
ANOVA analyses, which were carried out to examine the
differences caused by the demographic factors in UMSM, SWB,
and boredom proneness. The results are shown in Tables 1–3and
Supplementary Tables 2–4.
Males’ total scores of the UMSM and boredom proneness were
higher than those of females (t= 2.825, p<0.005; t= 4.377,
p<0.000); males’ SWB score was significantly lower than that
of females (t=−1.986, p<0.047).
TABLE 3 | The results of difference analysis on BP.
Variable Number BP t/F p
Gender
Male 237 3.62 ±0.92 4.377*** 0.000
Female 419 3.29 ±0.90
Age
Under the age of 18 95 3.66 ±0.92 8.366*** 0.000
Aged 18–24 426 3.43 ±0.88
Aged 24 and above 135 3.17 ±0.99
BP, boredom proneness. ***Correlation is significant at the 0.001 level (two tailed).
Significant differences among different age groups were found
in scores of both UMSM and boredom proneness (F= 11.382,
p<0.000; F= 8.366, p<0.000). The following multiple
comparisons showed that, regarding the UMSM scores, the group
“Under the age of 18” had lower scores than the group “Aged
18–24” and the group “Aged 24 and above.”
For the scores of boredom proneness, the group “Under the
age of 18” had higher scores than the group “Aged 18–24,” and
the group “Aged 18–24” had higher scores than the group “Aged
24 and above.” That is, boredom proneness decreased with age.
Correlation Among Boredom Proneness,
UMSM, and SWB
This study used the Pearson correlation to analyze the
relationship among boredom proneness, UMSM, and SWB.
In Table 4, it suggested that SWB showed a significantly
negative correlation with the UMSM; boredom proneness
showed a significantly positive correlation with the UMSM and
a significantly negative correlation with SWB.
According to the total score of the UMSM, participants
were divided into the high-level group (the top 27%), medium-
level group (the middle 46%), and low-level group (the bottom
27%). The independent sample test was used to compare
SWB and boredom proneness between groups of high and
low levels. The results showed that the SWB of the low-
level group was significantly higher than that of the high-level
group (t= 9.996, p<0.001), and the low-level group had
significantly lower boredom proneness than the high-level group
(t=−10.807, p<0.001).
In the group with low-level UMSM, the Pearson correlation
coefficient (PCC) between the UMSM and SWB was −0.200
(p<0.01), and the PCC between the SWB and boredom
proneness was −0.446 (p<0.001). In the high-level group, the
PCC between the UMSM and SWB was −0.162 (p<0.05), the
PCC between the UMSM and boredom proneness was 0.244
(p<0.01), and the PCC between SWB and boredom proneness
was −0.575 (p<0.001). In the medium-score group, there was no
correlation between the UMSM and SWB, and the PCC between
the SWB and boredom proneness was −0.576 (p<0.001)
(Supplementary Tables 5–7).
Main Effects and Interaction Analysis
A two-factor ANOVA analysis was implemented to explore
the main effects of the UMSM and monthly living expenses
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TABLE 4 | Correlation between variables.
M SD 1 2 3 4 5 6 7 8
(1) SWB 4.33 0.75 1
(2) UMSM 3.16 0.80 −0.392*** 1
(3) Viscosity increase 3.49 0.93 −0.260*** 0.801*** 1
(4) Physiological damage 2.99 0.95 −0.346*** 0.847*** 0.547*** 1
(5) Omission anxiety 3.06 0.99 −0.354*** 0.851*** 0.636*** 0.635*** 1
(6) Cognitive failure 3.07 0.95 −0.343*** 0.837*** 0.527*** 0.647*** 0.652*** 1
(7) Guilt 3.17 1.19 −0.295*** 0.698*** 0.426*** 0.528*** 0.492*** 0.610*** 1
(8) BP 3.41 0.92 −0.604*** 0.420*** 0.307*** 0.341*** 0.368*** 0.383*** 0.313*** 1
UMSM, use of mobile social media; SWB, subjective well-being; BP, boredom proneness; ***correlation is significant at the 0.001 level (two tailed).
on SWB, as well as the possible interaction between the
two factors. Multiple levels of each factor were taken into
the analysis. As a result, the main effect of the UMSM
on SWB was significant (F= 43.77, p<0.001); the main
effect of monthly living expenses on SWB was not significant
(F= 0.02, p>0.05), whereas the interaction between
the UMSM and monthly living expenses was significant
(F= 3.943, p<0.05).
Further simple effect analysis revealed that for the groups
with high- and low-level UMSM, living expenses had no
significant effect on SWB; for the group with medium-
level UMSM, the SWB of participants reporting high living
expenses (more than CNY 2,000) was higher than that of
participants reporting low living expenses (less than CNY 2,000)
(p<0.05). The results demonstrated that different levels of
living expenses are related to discrepancies in SWB, which
is valid only for the group showing medium-level UMSM
(see Figure 1).
Regression Analysis of the UMSM and
SWB
Multiple stepwise regression analysis was performed based on
the correlation analysis. The SWB was used as the dependent
variable, and the five factors constituting the UMSM were
used as the predictor variables. As shown in Table 5, three of
the five factors—omission anxiety, physiological damage, and
guilt—exerted negative predictive effects on the SWB of short-
video users (β=−0.148, p<0.001; β=−0.128, p<0.01;
β=−0.072, p<0.01); the contribution rates reached 12.4, 14.8,
and 15.5%, respectively.
The Mediating Effect of Boredom
Proneness
On basis of the test method proposed by Ye and Wen (2013),
we examined the mediating effect of boredom proneness on the
relationship between the UMSM and SWB. The result showed
FIGURE 1 | Interaction between living expense and mobile social media use on SWB. A 3 (use of mobile social media) * 2 (living expense) analysis of variance to
show the interaction between the two factors.
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TABLE 5 | Regression analysis of the use of mobile social media and SWB.
Dependent variable Independent variable R R21R2FβBeta t
SWB Omission anxiety 0.354 0.124 0.126 93.877 −0.148 −0.195 −4.085***
Physiological damage 0.388 0.148 0.025 57.717 −0.128 −0.162 −3.309**
Guilt 0.399 0.155 0.009 41.081 −0.072 −0.113 −2.605**
SWB, subjective well-being; **correlation is significant at the 0.01 level (two tailed); ***correlation is significant at the 0.001 level (two tailed).
that the UMSM enabled a significantly negative prediction of
SWB (β=−0.1683, p<0.001). Boredom proneness allowed a
significantly negative prediction of SWB as well (β=−0.5334,
p<0.001). The UMSM gave rise to a significantly positive
prediction of boredom proneness (β= 0.4202, p<0.001),
indicating that boredom proneness played a partial mediating
role in the interplay between the UMSM and SWB. In summary,
the mediation model hypothesized in this study was supported
(see Figure 2).
In order to verify the model, this study used the PROCESS
program compiled by Hayes to conduct the bootstrap test (5,000
times). The result showed that the confidence interval of 95% for
the UMSM to influence SWB through boredom proneness was
[−0.2848, −0.1728] (see Table 6).
DISCUSSION
Demographic Analysis of the UMSM,
SWB, and Boredom Tendency
Results of the descriptive statistics showed that there were gender
differences in the UMSM. Males’ scores on the UMSM were
significantly higher than females’ scores. That is to say, males
tend to have more problematic use of mobile social media than
females. This was consistent with the result of previous research
(Tomaszek and Muchacka-Cymerman, 2019). On the overall
score of SWB, females’ score was significantly higher than that of
males, which may be caused by the different motivations of males
and females on using mobile social media and their different
preferences on specific functions when using it. Males are more
inclined to use mobile social media for work-related instrumental
purposes, while females use it more as a way to communicate
FIGURE 2 | The mediating effect of boredom proneness. Test of the
mediating role of boredom proneness between the use of mobile social media
and subjective well-being. ∗p<0.05, ∗∗p<0.01, ∗∗∗ p<0.001. UMSM, use
of mobile social media; SWB, subjective well-being; ∗correlation is significant
at the 0.05 level (two tailed); ∗∗correlation is significant at the 0.01 level (two
tailed); ∗∗∗correlation is significant at the 0.001 level (two tailed).
with important people, maintain contact, or perform some
entertainment activities to achieve satisfaction (Walsh et al.,
2011). This is the reason why females’ SWB is higher. At the same
time, this study found that the longer time individuals use mobile
social media, the lower SWB they could perceive.
Moreover, there were differences in the scores of the UMSM
and boredom proneness of different age groups. There was no
difference in SWB among different age groups, which showed
that SWB of short-video users was not affected by age. That is, in
the long run, SWB is a relatively stable measure (Diener, 1984).
For the scores of the UMSM, the users in the “Under the age of
18” group showed lower scores than those in the “Aged 18–24”
group, and the users in the “Aged 18–24” group presented lower
scores than those in the “Aged 24 and above” group. Overall, the
tendency of problematic use of mobile social media increases with
age. As to the scores of boredom proneness, the “Under the age
of 18” group had higher scores than the “Aged 18–24” group, and
the “Aged 18–24” group had higher scores than the “Aged 24 and
above” group. That is, boredom proneness decreases with age. In
China, most of the teenagers under the age of 18 are trapped in
academic stress and do not have enough time for extracurricular
activities out of school, particularly using mobile phones, which
is usually limited under the supervision of parents. When staying
in a highly constrained environment, people often feel more
bored (Chin et al., 2017). Besides, numerous students feel caught
by rigid routines from which they cannot escape (Daschmann
et al., 2011). As the students grow up, the time at their disposal
increases proportionally, by which they are able to engage in more
activities according to their own ideas, to enrich their lives.
The result of the main effect and simple effect analyses showed
that after dividing the tendency of problematic use of mobile
social media into three groups of high, medium, and low level, it
can be found that there were significant differences in the impact
of living expenses on SWB among the different groups. In this
study, most of the short-video users were from 18 to 24 years
old, accounting for 64.93% of the total sample. This group of
participants was primarily comprised of college students, who
had limited income, so the CNY 2,000 was determined as the
standard for dividing samples into high- and low-level economic
status. An interaction effect influencing SWB was found in our
study between two factors—the UMSM and the economic status
of the participants. In general, a co-variation was exhibited
between economic status and SWB in the medium-level UMSM
group, where the SWB level of people with living expenses above
CNY 2,000 was higher than that of people with living expenses
below CNY 2,000. It is consistent with the findings of the positive
correlation between income and SWB (Clark et al., 2005;Carroll
et al., 2007). However, an inconformity was revealed in the low-
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TABLE 6 | Test of the mediating effect of the use of mobile social media, boredom proneness, and SWB.
Mediator Effect Effect size Effect ratio Boot SE BootCI LL BootCI UL
Boredom proneness Total effect −0.3924*** 0.0360 −0.4630 −0.3218
Direct effect −0.1683*** 0.0337 −0.2345 −0.1021
Indirect effect −0.2241*** 57.11% 0.0284 −0.2848 −0.1728
***Correlation is significant at the 0.001 level (two tailed).
and high-level UMSM groups, in which the SWB of participants
was independent of their economic status. Within the low-level
UMSM group that showed the greatest SWB, no significant
difference was identified between the high-expense and low-
expense subgroups in this degree of SWB. This might be caused
by the “marginal utility” which implies that the benefit to one of
an additional unit of happiness is inversely related to the number
of units of happiness he already owns. The satisfaction derived
from higher income barely progresses further in the people
who are already satisfied with their economic status (Kahneman,
2006). Therefore, the change in SWB was not significant when
economic status was taken as the independent variable for the
high-SWB (low-UMSM) group. Within the high-level UMSM
group demonstrating the lowest SWB, no apparent difference was
revealed between the subgroups with discrepant economic status.
This result is inconsistent with previous evidence showing that
lower-level economic status exacerbated the distress of people
with poorer SWB caused by life events, such as divorce, illness,
and being alone (Kahneman and Deaton, 2010). In the present
study, differences in economic status did not affect the degree
of SWB in the individuals showing less happiness that was
associated with their problematic use of mobile social media. The
result implies that low-level SWB linked to the UMSM differs
from that induced by stress events or life pressure, which reflects
the complexity among the UMSM, economic status, and SWB. To
unveil the panorama, more in-depth investigations are needed.
Impact of UMSM on SWB
This study confirmed that mobile social media was an important
variable for predicting SWB. The higher the tendency of the
problematic use of mobile social media, the lower the SWB
which was perceived by individuals. This result showed that
the use of mobile social media imposed a significant effect on
SWB and enabled a significantly negative prediction of SWB.
This was consistent with previous research results (Uysal et al.,
2013;Brooks, 2015). On the one hand, the social comparison
theory points out that the happiness of an individual results
from comparing himself with other individuals. It will reduce
an individual’s subjective happiness when he/she compares
himself/herself with a happier person (Festinger, 1954). The
convenience of mobile social media makes it easier for individuals
to access information about what is happening in their social
community by browsing social media platforms. People are more
inclined to post interesting and delightful life stories via mobile
social media to build up popular personal images. When such
stories are captured by the audience, the unconscious comparison
of themselves with the “leading characters” in stories may make
them feel gloomy, which consequently reduces their level of SWB.
More than 50% of social media users think that their friends
are happier than themselves (Tamir and Mitchell, 2012). On
the other hand, when individuals need to handle negative life
events, they are more likely to adopt a coping style of avoidance
(Wu et al., 2014) and gain more positive emotions through
mobile social media. However, the prolonged use of mobile social
media is more likely to cause individuals to feel guilty and fall into
self-blame. This negative emotional experience can also reduce an
individual’s sense of well-being (Katana et al., 2019).
Furthermore, three of the five subdimensions constituting the
UMSM—physiological damage, omission anxiety, and guilt—
had negative predictive effects on SWB, which was revealed
by the stepwise regression analysis. This might be due to the
fact that physiological damage caused individuals to develop
excessive worries about their health. Both emotional distress
and physical discomfort can affect quality of life (Estévez-López
et al., 2015). Therefore, the worries resulted in a decline of
SWB. Omission anxiety refers to unrest when one is unable
to view the information in time and worry about missing
messages. This emotion could reduce the individual’s level of
SWB (Malone and Wachholtz, 2017). When individuals are
immersed in mobile social media, they might unconsciously
increase the time and frequency of the use of mobile social
media, and this results in failure to complete relevant work
or achieve study goals. This is a situation that can lead to
self-blame and self-criticism, which can also decrease well-
being (Przybylski et al., 2013). These results suggest that when
individuals have more awareness of their discomfort related to
the use of mobile social media, whatever it is from the physical
aspect (e.g., physical damage) or the psychological aspect (e.g.,
guilt), the level of SWB tends to decrease. On the contrary,
if the individuals are not aware of these, their SWB level will
not be affected.
The Mediating Effect of Boredom
Proneness
This study showed that boredom proneness played a partial
mediating role in the interaction between the UMSM and
SWB. Our findings provide evidence to explain the mechanism
underlying the interplay between the inappropriate use of the
Internet and SWB reported in previous researches (Boyd and
Ellison, 2007;Koroleva et al., 2011;Wenninger et al., 2018).
The fundamental reason why boredom proneness is involved
in mediation is that boredom per se influences SWB. Another
explanation is the weakened attention induced by the improper
use of social media.
The excessive use of the Internet has been linked to attention
deficits (Kawabe et al., 2019). When a person is overly reliant on
the use of mobile social media, his/her attentiveness is vulnerable.
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Bai et al. Mobile Social-Media and Subjective Well-Being
Meanwhile, boredom is sometimes led by the absence of attention
to the current goals (Hunter and Eastwood, 2016). Individuals
with high boredom proneness are more inclined to experience
boredom. In a state of boredom, a lack of attention drives negative
emotions, and when attention is not fully engaged, the activities
would be negatively treated, which results in a negative emotional
state and affects the level of SWB (Fahlman et al., 2011).
LIMITATIONS
This study also has some limitations. Firstly, according to the
collected questionnaire results, most of the short-video users
were from 18 to 24 years old. In future research, the age
range of the participants could be expanded, and more in-
depth research could be conducted based on a population with
different age groups. Secondly, according to the results, this
study divided the monthly living expenses into above CNY 2,000
and below CNY 2,000. In future studies, the impact of living
expenses on mobile social media could be further explored by
considering people from different occupations and collecting
more detailed data related to living expenses. Thirdly, the survey
adopted the self-reported approach, which may be affected by the
social desirability effect, resulting in biases in the measurement
results. Fourthly, this study is a preliminary exploration, and
the factor usage style of short-video users could be taken into
consideration in the future study. Finally, this study showed
that omission anxiety, physical damage, and guilt involved in
the use of mobile social media imposed a negative predictive
effect on SWB. In future research, more effort could be made
to explore the mechanism underpinning the relations among the
relevant factors.
CONCLUSION
Previous investigations have revealed the negative impact of
the inappropriate use of mobile social media on one’s SWB.
However, the mechanism underlying the relationship between
the two variables is unclear. The current study focused on another
variable—boredom proneness, and figured out its mediating role
between the use of mobile social media and SWB. This study not
only provides new evidence to verify the influence path proposed
in previous studies but also demonstrated the principle of how
the dynamic model works.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
ETHICS STATEMENT
This study involved human participants, and has been reviewed
and approved by Ethics Committee of Department of Psychology,
Beijing Forestry University. The ethics committee waived
the requirement for written informed consent. However,
written informed consent was implied via completion of
the questionnaire.
AUTHOR CONTRIBUTIONS
JB, XL, and YY designed the study. JB, KM, and YP collected
the data. JB, WH, and YQ analyzed the data. JB and YY wrote
the manuscript. JB, YY, XL, KM, YP, WH, and YQ revised the
manuscript. All authors contributed to the article and approved
the submitted version.
FUNDING
This work was supported by the Fundamental Research
Funds for the Central Universities (Nos. BLX201948 and
2019RW10).
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fpsyg.
2020.568492/full#supplementary-material
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Conflict of Interest: The authors declare that the research was conducted in the
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Copyright © 2021 Bai, Mo, Peng, Hao, Qu, Lei and Yang. This is an open-access
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