#DeleteFacebook: Antecedents of Facebook Fatigue

Abstract

Despite the trend of leaving Facebook (#DeleteFacebook campaign), investigation on both the social and psychological factors affecting Facebook fatigue is limited. This study aims to explore the social and psychological antecedents of Facebook fatigue and identify the effects of the antecedents on overall Facebook fatigue. A total of 327 Facebook users participated in an online survey. Respondents were recruited from a major online panel in Korea. The results from the principal component analysis suggest that there are six social and psychological antecedents: impression management, unwanted posts, reputation concern, personal relative deprivation, privacy concern, and relationship concern. In addition, the results show that personal relative deprivation, privacy concern, impression management, and relationship concern positively predicted overall Facebook fatigue. This research not only sheds light on the antecedents of social networking services (SNS) fatigue that influence overall Facebook fatigue but also suggests practical implications for the everexpanding SNS market.

Introduction

Although social networking services (SNS) help improve the overall quality of life, they also have some negative effects. Indeed, statistics can demonstrate the disadvantages of SNS. According to a report from The Information, personal updates on Facebook decreased by 21 percent because of privacy and relationship concerns in 2014 and 2015.¹ A report from Mavrck notes that the number of “original posts” decreased by 29.49 percent and “engagement per post” decreased by 15.14 percent in 2016 compared to 2015, mainly because of “being tired of using SNS.”² Influenced by this social phenomenon of SNS discontinuance, researchers examining the relationship between SNS usage and negative psychological outcomes started to receive more attention in the field.^3–6

A central negative outcome from SNS is fatigue, or stress caused by using SNS. Because fatigue is a complex concept, the definition of SNS fatigue differs in various sources. For example, although it is defined as a state of awareness describing a range of afflictions, usually associated with physical (i.e., headache and shoulder/neck discomfort) and/or mental weakness (i.e., mental dissonance/stress and depressed mood) in SNS contexts,⁶ it is also expressed as a subjective and self-evaluated feeling of tiredness induced by SNS usage.⁷ SNS require the user to constantly interact with others, which can cause fatigue and consequently have detrimental physical and mental effects on an individual.⁸ A recent study from Korea reports that users suffering from SNS fatigue experience negative emotions, such as loneliness, dejection, and a sense of deprivation, while skimming through updates from others.⁹ In other words, the incessant use of SNS despite experiencing fatigue may lead to stress, depression, dependence, and impaired concentration in daily life.¹⁰ There have been cases in which fatigue has caused users to leave SNS. Moreover, SNS fatigue affects daily lives; SNS users recognize difficulties in their routine and are easily distracted when trying to maintain their job performance.¹¹

With the rise of this phenomenon, prior research examined some antecedents of SNS fatigue. For example, significant user concerns regarding both reputation and impressions have been found to induce social media fatigue in general.^12–14 Concerned with their reputations, a growing number of social media users monitor each other's SNS accounts, with 61 percent of users visiting SNS every few days with this concern in mind.¹⁵ In addition, female users spend up to 5 hours a week taking selfies, with 22 percent reporting that the primary intent of their posting is to obtain “likes” for impression management.¹⁶ Also, the effects of social comparison on Facebook fatigue have been discovered.¹⁷ Recently, it has been found that the SNS environment promotes narcissistic behaviors as SNS users only share the positive aspects of their lives.¹⁸ In other words, social media users naturally make upward comparisons, comparing themselves to their perceived superiors, resulting in negative outcomes. In fact, heavy Facebook users believe that others are happier than they are,¹⁹ with others' posts leaving them dejected.⁹ In addition, they are concerned about their personal information being used illegally,^20,21 as well as about revealing personal details to certain Facebook friends, such as business partners (i.e., privacy concerns).^22,23 Finally, information overload, irrelevant advertising, and messages from unknown others (so-called unwanted feeds) have been identified factors that cause SNS fatigue.^24,25

Even though prior research has attempted to understand SNS fatigue and its antecedents, the findings are still limited to SNS overload, which is an individual's perception that the available information is more than one can handle while using SNS.²⁴ In particular, prior research suggested three dimensions of the overloads as core determinants of the SNS fatigue: system, information, and social overload.^7,23,24,26 Thus, little is known regarding the social and psychological factors that lead to SNS users' fatigue. Facebook users may have Facebook-specific reasons for leaving the network. Therefore, this study focuses on the antecedents of fatigue related to Facebook, which, with the largest number of users in the world,²⁷ represents the popularity of SNS and has as many SNS-leaver simultaneously. In addition, this study examines how such antecedents are related to the overall Facebook fatigue of a Korean user. Thus, the following research questions were proposed:

RQ1: What are the social and psychological antecedents of Facebook fatigue for Korean users?

RQ2. To what extent do the social and psychological antecedents of Facebook fatigue predict the overall Facebook fatigue of Korean users?

Methods

Participants

A total of 327 Facebook users participated in this study. The final sample included 168 females and 159 males, 15 to 59 years of age, with an average age of 35.25 (SD = 13.58). On average, participants indicated having used Facebook for 38.01 months (SD = 24.99), ranging from 1 to 120 months. Participants reported having logged on to their Facebook account approximately five times per day and had an average of 172 friends (3–3,500 friends, SD = 303.21). Table 1 shows the sample characteristics.

Table 1.

Sample Characteristics (n = 327)

Sample profile	Mean (min–max)
Age	35.25 years old (15–59, SD = 13.58)
Gender
Male	48.6 percent
Female	51.4 percent

Facebook usage	Mean (min–max)
Frequency of log on to Facebook per day	4.80 times (1–7)
Daily time spent on SNS usage	45.16 minutes (1–360, SD = 50.25)
Usage period	38.01 months (3–120, SD = 24.99)
Number of friends	172.38 friends (3–3,500, SD = 303.21)

Main activity on Facebook	Frequency (n)	Percentage
Check friend's feed	154	47.1
Upload photos, videos, writings	75	23.0
Check out a specific person's (ex. celebrity's) page	86	26.3
Interaction with friends	12	3.7

Motivation for using Facebook	Frequency (n)	Percentage
To acquire information	120	36.7
To communicate	90	27.5
To kill time	73	22.3
To browse daily lives of people	23	7.0
To express my self	21	6.4

SD, standard deviation; SNS, social networking services.

Instrumentation

Scale items for antecedents of Facebook fatigue were derived from prior literature on the Internet and social networking sites.^14,17,21,23 Because there were many items, several of which overlapped, it was deemed appropriate to eliminate some of them to obtain more manageable and relevant items applicable to Facebook. Therefore, several similar items were eliminated and revised for this study. Subsequently, an independent group of active Facebook users (n = 30; 15 females) was asked to eliminate items that were not relevant to the context of Facebook. Finally, they were asked to list any additional reason for fatigue resulting from Facebook use. A total of 33 unique statements on the antecedents of Facebook fatigue were identified. Respondents were asked to indicate how much they agreed or disagreed with each item on a 7-point Likert-type scale (1 = ”strongly disagree” and 7 = ”strongly agree”).

Facebook usage variables were operationalized by asking respondents to examine how often they log on to their accounts, for how long they have used Facebook, how long they stay on their accounts per day, and the number of their Facebook friends. Furthermore, respondents were asked about their primary activities on Facebook. Finally, overall Facebook fatigue was measured with three items using a seven-point Likert-type scale, which were identified as valid and reliable¹²: “Managing Facebook is a burden,” “I feel uncomfortable using Facebook,” and “I feel tired after using Facebook” (α = 0.85).

Data collection and analysis

An online survey was conducted during a 2-week period in August 2017; respondents were recruited from an online panel in Korea. There were 336 respondents, of which 327 were included in the final sample after the elimination of 9 respondents, who did not complete the survey and had extreme or inconsistent response patterns. For the first research question, an exploratory factor analysis was performed to identify the social and psychological antecedents of Facebook fatigue. Furthermore, a standard multiple regression analysis was conducted to answer the second research question.

Results

Exploratory factor analysis was performed to identify what underlying structure exists for the antecedents of Facebook fatigue. A principal component analysis (PCA) with varimax rotation was evaluated using the following criteria: eigenvalue (>1.0), variance explained by each component, loading score for each factor (≥|0.50|), and meaningfulness of each dimension. We deleted six items that had high loadings on more than two components and two additional items that did not tap into any of the components. The PCA was rerun with the remaining 25 items. Consequently, a meaningful and interpretable six-component solution was obtained.

Table 2 indicates that the first component, impression management, accounted for 13.41 percent of the variance with four items. The second component, unwanted posts, with four items, explained 13.33 percent of the variance, and the third component, personal relative deprivation, containing four items, accounted for 12.91 percent of the variance. The fourth component, reputation concerns, explained 11.96 percent of the variance and consisted of four items, and the fifth component, relationship concerns, with three items, accounted for 10.29 percent of the variance. Finally, the sixth component, privacy concerns, explained 10.27 percent of the variance with three items. The alpha coefficient for the entire scale was 0.76. Cronbach's alphas indicated high levels of internal reliability for all six subscales: impression management = 0.85, unwanted posts = 0.85, personal relative deprivation = 0.87, reputation concerns = 0.81, relationship concerns = 0.81, and privacy concerns = 0.82. Thus, the six-component solution, which explains ∼72.1 percent of the total variance, is appropriate. Specific items and loadings are reported in Table 2.

Table 2.

Antecedents of Facebook Fatigue (n = 327)

	1	2	3	4	5	6
Impression management (α = 0.85)
When I feel the burden of selecting a profile picture	0.87	0.05	0.14	0.14	0.03	0.03
When I feel like I have to upload a post after visiting a great place	0.79	−0.02	0.22	0.22	0.05	0.08
When I take too much time to select nice pictures to upload	0.77	0.12	0.20	0.23	0.05	0.08
When I am obsessed with updates	0.73	−0.03	0.08	0.11	0.23	0.04
Unwanted posts (α = 0.85)
When I am exposed to advertising posts	−0.01	0.90	0.00	0.08	0.09	0.15
When I am exposed to false advertisements	−0.02	0.90	−0.00	0.02	0.10	0.15
When I am exposed to provocative content such as swearing	0.01	0.81	0.11	0.08	0.08	0.13
When I have to see a post of people I don't like or I am not close to	0.18	0.59	0.16	0.10	0.23	0.16
Personal relative deprivation (α = 0.87)
When I found out my friend looks better and happier than me	0.11	−0.04	0.81	0.08	0.33	0.03
When I see a happy post that is different from my situation	0.23	0.14	0.79	0.30	0.05	0.09
When I see pictures of others who look better than before	0.15	0.18	0.79	0.30	0.08	0.13
When I see other pictures taken at a decent restaurant	0.32	0.02	0.71	−0.01	0.31	−0.04
Reputation concern (α = 0.81)
When there are no/few feedbacks (comment or like) of posts I upload	0.20	0.13	0.13	0.83	0.02	−0.04
When I am obsessed with checking others' feedbacks (comment or like) to my post	0.19	0.17	0.21	0.78	0.02	−0.04
When I upload a post being conscious of others	0.12	−0.07	0.14	0.68	0.31	0.26
When I cannot upload my honest feelings or thoughts	0.20	0.04	0.09	0.66	0.28	0.15
Relationship concern (α = 0.81)
When the distinction between the public and private is unclear online	0.13	0.21	0.12	0.10	0.81	0.19
When I have to care about offline relationships even online	−0.00	0.16	0.35	0.16	0.71	0.13
When I have to face people who I don't feel comfortable with from work/school at online	0.22	0.16	0.22	0.21	0.71	0.04
Privacy concern (α = 0.82)
When my privacy I do not want to disclose is revealed	0.09	0.16	0.06	0.04	0.06	0.86
When I am tagged in unwanted contents	0.05	0.14	−0.01	0.03	0.06	0.86
When I am worried about personal information leakage	0.04	0.26	0.12	0.14	0.24	0.72
Eigenvalue	2.9	2.9	2.8	2.6	2.2	2.2
Percent of variance	13.4	13.3	12.9	11.9	10.2	10.2
Cumulative, percent	13.4	26.7	39.6	51.6	61.8	72.1

Note: Loadings that were 0.50 or larger are in boldface.

As shown in Table 3, the six antecedents were positively correlated both with overall Facebook fatigue and with each other. First, impression management was positively correlated with an overall Facebook fatigue of r = 0.41 and p < 0.001. The remaining antecedents, unwanted posts (r = 0.28, p < 0.001), reputation concerns (r = 0.47, p < 0.001), personal relative deprivation (r = 0.35, p < 0.001), relationship concerns (r = 0.43, p < 0.001), and privacy concerns (r = 0.37, p < 0.001) also yielded a positive correlation. To test the relative influence of the six identified components, a regression analysis was performed. Because our objective was to determine how much each component uniquely contributes to overall Facebook fatigue, we employed a standard multiple regression. Before undertaking the regression analysis, the assumptions of linearity, outliers, normality, and homoscedasticity were examined to ensure that the results are valid. These assumptions were examined using the analysis of residuals, plots of the standardized residuals against predicted values, and a P-P Plot and Histogram. The results found no issues of linearity, outliers, normality, or homoscedasticity. In addition, we used variance inflation factors (VIF) to eliminate multicollinearity, which are the diagonal elements of the inverse correlation matrix. We found that the VIF values of all the variables were less than 5.0, suggesting that there was no multicollinearity problem.

Table 3.

Intercorrelations Among the Variables

	1	2	3	4	5	6	7	M (SD)
1. Impression management	1.00							4.13 (1.34)
2. Unwanted posts	0.14^*	1.00						5.26 (1.15)
3. Reputation concern	0.48^**	0.23^**	1.00					4.31 (1.31)
4. Personal relative deprivation	0.47^**	0.24^**	0.46^**	1.00				4.68 (1.19)
5. Relationship concern	0.34^**	0.38^**	0.53^*	0.43^**	1.00			4.75 (1.26)
6. Privacy concern	0.19^**	0.41^**	0.21^**	0.25^**	0.35^**	1.00		5.47 (1.18)
7. Overall Facebook fatigue	0.41^**	0.28^**	0.47^**	0.35^**	0.43^**	0.37^**	1.00	4.39 (1.10)

p < 0.01, ^**p < 0.001.

M, mean.

As can be seen in Table 4, the overall regression model was significant (R² = 0.35, F = 28.42, p < 0.001). Personal relative deprivation was found to be the strongest predictor of overall Facebook fatigue (β = 0.24, t = 4.06, p < 0.001), followed by privacy concerns (β = 0.21, t = 4.08, p < 0.001), impression management (β = 0.19, t = 3.50, p < 0.01), and relationship concerns (β = 0.13, t = 2.18, p < 0.05). However, unwanted posts (t = 0.99, p = 0.32) and reputation concerns (t = 0.66, p = 0.51) were not found to be significant predictors.

Table 4.

Results of the Multiple Regression Analyses

	Overall Facebook fatigue
	B	SE	β	t
Antecedents
Impression management	0.16	0.04	0.19	3.50^**
Unwanted posts	0.05	0.05	0.05	0.99
Personal relative deprivation	0.20	0.05	0.24	4.06^***
Reputation concern	0.03	0.05	0.04	0.66
Relationship concern	0.19	0.04	0.13	2.18^*
Privacy concern	0.11	0.05	0.21	4.08^***
Adjusted R²	0.35
F	28.42

Note: β is the standardized regression coefficient.

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

Discussion

By surveying a sample of 327 active Facebook users in Korea, this study sheds light on the antecedents of Facebook fatigue. Unlike previous studies, which found system or information overload to have the greatest impact on SNS fatigue,^7,23,24,26 our findings reveal that there are six types of social and psychological antecedents of Facebook fatigue.

The findings suggest that impression management, reputation concerns, personal relative deprivation, and relationship concerns are the key social factors that lead to overall Facebook fatigue. Because one main function of Facebook is self-expression, users feel overwhelmed primarily by impression management and reputation concerns. For instance, Facebook users feel pressured to display themselves positively through updates and feel worn out by worrying about others' reactions before and after uploading posts.¹⁷ In addition, users may experience negative feelings, such as personal relative deprivation, by engaging in continuous upward social comparison.^28,29 A disinclination to extend online relationships to unwanted offline acquaintances is also an aspect of Facebook fatigue that is widely experienced.^22,23 It is noteworthy that Facebook, which was originally meant to facilitate social interaction,³⁰ now leads users to feel exhausted by relationships.

Unwanted posts and privacy concerns are psychological factors that cause Facebook fatigue. In fact, Facebook users feel tired of being exposed to various unwanted messages, including advertising, spam, and other commercial messages on the site. Furthermore, users feel anxious about personal information leakage or unwanted privacy exposure.²⁶ These are understood to be the types of “fatigue” that can result from using Facebook, which is a major platform for advertising,²⁵ and an “open SNS”³¹ where unknown others can have easy access.

Furthermore, four of the six antecedents (i.e., impression management, personal relative deprivation, privacy concerns, and relationship concerns) were significant predictors of overall Facebook fatigue. The prior research conducted in South Korea also noted that both Facebook fatigue and intention of discontinuance are positively predicted by the three key antecedents: impression management, personal relative deprivation, and privacy concerns.^32,33 However, unwanted posts, such as commercial messages, did not predict overall Facebook fatigue. It is possible that, having already been exposed to excessive marketing messages, Facebook users no longer feel overwhelmed by them in South Korea. In addition, uploading a Facebook post with concerns about one's reputation did not predict overall Facebook fatigue. For reputational issues, previous studies have shown that posting activities not only increases users' subjective well-being³⁴ but also decreases the intention to discontinue using Facebook.³² In a sense, it is understandable that the positive effects of posting would exceed any exhaustion generated.

This study has both theoretical and practical implications in terms of expanding the ongoing discussion on SNS fatigue among scholars. As found in this study, it is noteworthy that “with whom they interact” or “how active they are” are more important than “for how long they have used Facebook” in predicting fatigue. Such social media fatigue on a particular SNS platform causes users to switch to newer platforms. In fact, new online services (e.g., Suicide Machine, Seppukoo) that help users delete inactive social media accounts and disconnect from unwanted social relationships are available and popular among social media users. In addition, anonymous peer-to-peer-based social network services (e.g., AROUND) emerged and quickly gained popularity among social media users in Korea, as many people seek a level of privacy in our digital age.

The anonymity of social media does not typically require the disclosure of personal information; private profiles collect very little information on users. Those who participate in posting behaviors on online communities do not feel any “privacy concerns” and are allowed to express themselves freely, while experiencing neither vulnerability nor the pressures of “impression management” or “reputation concerns.” Moreover, those anonymous users are allowed to browse with no hindrance from negative experiences such as suffering from “personal relative deprivation” or encountering “unwanted postings.” Rather, they are comforted by the browsing lifestyles of other users who are in similar circumstances.

This study has two key limitations. First, it mainly focused on users of Facebook, owing to its popularity and vast number of users. The results could be generalized across different SNS platforms if SNS were examined from a broader perspective. Second, the relationship between the antecedents and user discontinuance of SNS was not examined. If the fatigue caused by various factors had eventually reduced the degree of SNS usage, there would be more empirical implications for SNS research.

Footnotes

Acknowledgments

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2016S1A3A2924760).

Author Disclosure Statement

No competing financial interests exist.

References

Griffith

Facebook users are sharing fewer personal updates and it's a big problem. http://fortune.com/2016/04/07/facebook-sharing-decline (accessed Dec. 5, 2017).

Armstrong

Facebook users posted a third less content in 2016 than in 2015. www.forbes.com/sites/paularmstrongtech/2017/02/14/facebook-users-posted-a-third-less-content-in-2016-than-in-2015/#5384d096776d) (accessed Dec. 6, 2017).

Jelenchick

, Eickhoff

, Moreno

. Facebook depression? Social networking site use and depression in older adolescents. Journal of Adolescent Health, 2013; 52:128–130.

Kross

, Verduyn

, Demiralp

, et al. Facebook use predicts declines in subjective well-being in young adults. PLoS One, 2013; 8:e69841.

Tandoc

, Ferrucci

, Duffy

. Facebook use, envy, and depression among college students: is facebooking depressing?. Computers in Human Behavior, 2015; 43:139–146.

Yamakami

(2012) Towards understanding SNS fatigue: exploration of social experience in the Virtual World. In 2012 7th International Conference on Computing and Convergence Technology (ICCCT). Seoul, Republic of Korea: IEEE, pp. 203–207.

Lee

, Son

, Kim

. Information and communication technology overload and social networking service fatigue: a stress perspective. Computers in Human Behavior, 2016; 55:51–61.

Hind

Captured by technology. CIO Magazine, 1998; 9:22–23.

Park

, Ryu

, Lee

. A study on the negative emotion of using Social Networking Services and its discontinuance intention. Knowledge Management Research, 2014; 15:89–106.

10.

Moreno

, Jelenchick

, Egan

, et al. Feeling bad on Facebook: depression disclosures by college students on a social networking site. Depression and Anxiety, 2011; 28:447–455.

11.

Park

, Kim

. A study on exploring the antecedents and consequences of SNS fatigue: the moderating effect of habit. Yonsei Business Review, 2016; 53:43–73.

12.

Lee

Why do people leave SNS?: the influence of the users' characteristics and SNS fatigue. Journal of the HCI Society of Korea, 2018; 13:1–9.

13.

Lee

, Jung

. Study on the use of Twitter and Facebook suspended. Korean Society for Journalism & Communication Studies, 2013; 57:269–293.

14.

Yao

, Phang

, Ling

. (2015). Understanding the influences of trend and fatigue in individuals' SNS switching intention. In System Sciences (HICSS), 2015 48th Hawaii International Conference on Service Science and Innovation. Kauai, HI: IEEE, pp. 324–334.

15.

Madden

, Smith

Reputation management and social media. www.pewinternet.org/2010/05/26/reputation-management-and-social-media (accessed Jun. 9, 2018).

16.

Homer

Selfies takeup five hours of a woman's week. www.mirror.co.uk/news/uk-news/selfies-tak e-up-five-hours-5700345 (accessed Jun. 21, 2018).

17.

Cramer

, Song

, Drent

. Social comparison on Facebook: motivation, affective consequences, self-esteem, and Facebook fatigue. Computers in Human Behavior, 2016; 64:739–746.

18.

Mehdizadeh

Self-presentation 2.0: narcissism and self-esteem on Facebook. Cyberpsychology, Behavior, and Social Networking, 2010; 13:357–364.

19.

Chou

HTG

, Edge

. “They are happier and having better lives than I am”: the impact of using Facebook on perceptions of others' lives. Cyberpsychology, Behavior, and Social Networking, 2012; 15:117–121.

20.

Jones

. Facebook

Users' privacy concerns up since

. 2011. https://news.gallup.com/poll/232319/facebook-users-privacy-concerns-2011.aspx (accessed Jun. 22, 2018)

21.

Lin

KM.

(2013) The negative critical incidents of social network service: an exploratory study. In Service Science and Innovation (ICSSI), 2013 Fifth International Conference on Service Science and Innovation. Kaohsiung, Taiwan: IEEE, pp. 96–99.

22.

Bohnert

, Ross

. The influence of social networking web sites on the evaluation of job candidates. Cyberpsychology, Behavior, and Social Networking, 2010; 13:341–347.

23.

Stankovic

, Passant

, Laublet

. (2010) Directing status messages to their audience in online communities. In Padget

, et al., eds. Coordination, Organizations, Institutions and Norms in Agent Systems V. Berlin, Heidelberg: Springer, pp. 195–210.

24.

Zhang

, Zhao

, Lu

, et al. Do you get tired of socializing? An empirical explanation of discontinuous usage behavior in social network services. Information & Management, 2016; 53:904–914.

25.

Ingram

How Google and Facebook have taken over the digital ad industry. http://fortune.com/2017/01/04/google-facebook-ad-industry (accessed Dec. 8, 2017).

26.

Bright

, Kleiser

, Grau

. Too much Facebook? An exploratory examination of social media fatigue. Computers in Human Behavior, 2015; 44:148–155.

27.

Lee

, Lee

, Moon

, et al. Pictures speak louder than words: motivations for using Instagram. Cyberpsychology, Behavior, and Social Networking, 2015; 18:552–556.

28.

Ellison

, Steinfield

, Lampe

. The benefits of Facebook ‘‘friends'’: social capital and college students use of online social network sites. Journal of Computer-Mediated Communication, 2007; 12:1143–1168.

29.

Feinstein

, Hershenberg

, Bhatia

, et al. Negative social comparison on Facebook and depressive symptoms: rumination as a mechanism. Psychology of Popular Media Culture, 2013; 2:161–170.

30.

Haythornthwaite

, Wellman

. Work, friendship, and media use for information exchange in a networked organization. Journal of the American Society for Information Science, 1998; 49:1101–1114.

31.

Jang

, Kim

. Evaluation study about the utilization of applications which provide closed-type social network service—focusing on ‘Naver Band’ and ‘Kakao Group.’ Journal of Digital Design 2014; 14:377–386.

32.

Lee

The Antecedents of SNS Fatigue: influences on Intention to Continuous Usage and Discontinuing Intention. Journal of the HCI Society of Korea, 2018; 13:21–29.

33.

Lee

, Jung

. A study on the discontinuance intention of Twitter and Facebook. Korean Journal of Journalism & Communication Studies, 2013; 57:269–293.

34.

Burke

. Reading, writing, relationships: the impact of social network sites on relationships and well-being. Doctoral dissertation, Carnegie Mellon

University

, 2011. https://kilthub.cmu.edu/articles/Reading_Writing_Relationships_The_Impact_of_Social_Network_Sites_on_Relationships_and_Well-Being/6721256 (accessed Dec. 1, 2017)