Keeping Family Up Ti Date Without Social Media
Int J Environ Res Public Health. 2018 Oct; 15(10): 2319.
Fear of Missing Out as a Predictor of Problematic Social Media Utilize and Phubbing Beliefs among Flemish Adolescents
Vittoria Franchina
1Department of Psychology and Educational Sciences, University of Palermo, 90133 Palermo, Italy; ti.apinu@ococol.aculnaig
Mariek Vanden Abeele
2Tilburg School of Humanities and Digital Sciences, Tilburg University, 5037AB Tilburg, The netherlands
Antonius J. van Rooij
3imec-mict-UGent, Department of Advice Sciences, Ghent University, 9000 Ghent, Belgium; ln.sobmirt@jioort (A.J.v.R.); eb.tnegu@zeramed.neveil (50.D.G.)
ivSection of Youth & Risky Behavior, Trimbos Institute, 3521 VS Utrecht, The Netherlands
Gianluca Lo Coco
1Department of Psychology and Educational Sciences, University of Palermo, 90133 Palermo, Italy; ti.apinu@ococol.aculnaig
Received 2018 Sep 6; Accepted 2018 Sep 24.
Abstract
Fright-of-missing-out (FOMO) refers to feelings of anxiety that arise from the realization that yous may be missing out on rewarding experiences that others are having. FOMO can exist identified as an intra-personal trait that drives people to stay up to engagement of what other people are doing, amid others on social media platforms. Drawing from the findings of a large-scale survey study among 2663 Flemish teenagers, this study explores the relationships between FOMO, social media use, problematic social media use (PSMU) and phubbing behavior. In line with our expectations, FOMO was a positive predictor of both how frequently teenagers use several social media platforms and of how many platforms they actively apply. FOMO was a stronger predictor of the employ of social media platforms that are more private (due east.yard., Facebook, Snapchat) than platforms that are more public in nature (eastward.g., Twitter, Youtube). FOMO predicted phubbing behavior both directly and indirectly via its human relationship with PSMU. These findings back up extant research that points towards FOMO as a factor explaining teenagers' social media use.
Keywords: fright of missing out (FOMO), social media, problematic social media use (PSMU), phubbing, teenagers, adolescents, addiction
1. Introduction
Behavioral addiction researchers argue that the psychological processes that explain problematic behavior require greater attention [1,2,iii,four,5]. Understanding underlying processes is especially relevant when examining problematic forms of (digital) media employ. Digital devices, such as mobile phones, can be used and misused in a diverseness of different ways. Information technology is likely that the way in which problem utilise manifests itself depends on the item underlying psychological mechanism [half-dozen,7].
One psychological process that may underlie problematic digital media use is the Fear-Of-Missing-Out (FOMO). FOMO refers to the "pervasive apprehension that others might be having rewarding experiences from which one is absent" [8]. Persons who take a greater FOMO are assumed to have a greater desire to stay continually up-to-appointment of what others are doing, for instance via the employ of social media. According to Przybylski et al. [8], FOMO originates from psychological deficits in people's competence and relatedness needs [9]. One fashion of satisfying these needs, the authors merits, is through the employ of social media applications, because the dynamic nature of social media applications provides users with a consistent stream of social and advisory rewards [10].
The purpose of the current study is to contribute to the extant trunk of research on FOMO in relation to social media apply. The study has 4 aims. Get-go, it investigates whether teenagers with higher levels of FOMO have more social media accounts (i.e., the breadth of social media utilize) and whether they access these accounts more frequently (i.e., the depth of social media employ [11]) than teenagers with lower levels of FOMO. 2d, assuming that teenagers with college levels of FOMO are mostly motivated to check up on people in their personal social networks, we examine whether FOMO is a stronger predictor of the use of platforms that connect teenagers to their offline networks (e.thou., Facebook, Snapchat) than of the utilise of platforms that connect to a largely unknown audition (e.one thousand., Youtube, Twitter). Third, the study examines if teenagers with greater FOMO report higher levels of problematic social media utilise (PSMU) and, iv, are more likely to report one particular course of problematic social media use, which is the apply of social media during conversations with co-present others (cf. "phubbing"). These research aims are addressed with data from a big-calibration cross-sectional survey that was administered to 2663 Flemish adolescents.
two. Theoretical Framework
People have always had a tendency to think nearly what others are thinking and doing [12]. In the 1970 and 1980 scholars already identified that some people developed anxieties around missing out, contemplating on the rewarding experiences that others might exist having (e.g., in the context of romantic relationships) [xiii]. FOMO is thus not an entirely new concept.
FOMO can be understood as such an feet around missing out on rewarding experiences that results from people's desire for interpersonal attachments [12]. This want, which is grounded in people's need to belong, is an innate and fundamental motivation which humans have [fourteen]. People gratify this need past seeking belongingness to social groups. Social groups nowadays be in both physical and virtual shapes and people have access to their social groups in both ways, online and offline. Social media, which can be defined as "Internet-based applications that build on the ideological and technological foundations of Spider web ii.0, and that allow the creation and exchange of User Generated Content" [xv,16], offer a place where users can continue in bear upon with their social circles. Social network sites (SNS) such as Facebook or Instagram, for instance, offer users an online connection to persons in their personal networks, facilitating the do of keeping in touch.
Present the digital world is considered an extension of the Self [17,18]. In addition to the personal listen and physical body, the Self can be thought to include people, places, physical possessions, too as affiliation groups to which a person feels attached [19]. Social media platforms are a part of this: they are the digital portals to affiliation groups. In contemporary society nosotros thus manage the amalgamation network both offline and online; virtual groups are every bit real and important as the physical ones. Non being able to connect with those amalgamation groups on social media may cause feelings of beingness out of bear upon with "real" life [17]. After all, losing or missing a person one is attached to, can bring feelings of loss and grieving as much as if a part of the Cocky was damaged [xix].
The fearfulness of being socially excluded plays a role in experiencing a FOMO [12]. Social exclusion produces a loss of belongingness, and therefore causes anxiety. Thus, when people cannot access their social media accounts, they might feel anxiety considering of a fear that they are being socially excluded [12].
Social exclusion as well elicits feelings of worthlessness [12]. These feelings lead people to the act of comparing themselves to others on social media [twenty] with the purpose of deciding upon their own personal value [21]. Social networks offering a place where consumers, particularly young generations, can continuously keep upward with what peers are doing and checking on what they are missing out on (e.one thousand., social events, life experiences, life opportunities, and and then on and then forth). FOMO can thus drive social media use, equally checking up on other people may lead to a temporary relief of i's anxiety.
two.1. FOMO and the Utilise of Different Social Media Platforms
Users may use unlike media to gratify different needs [22,23,24]. This can explain differences in the popularity of sure social media platforms. In January 2015, the Global Web Index summary showed that the most pop social media platform was the social network site Facebook, immediately followed past YouTube, Twitter and Instagram. The same year Instagram'southward popularity outperformed Twitter. Co-ordinate to some, this is considering pictures are more constructive than words in achieving cocky-presentational objectives, which are central motivations for social media use [25]. Given that each social media platform is characterized by its own features and affordances, it is relevant to differentiate between social media platforms in research on social media use: Dissimilar platforms may connect users to different persons and networks, and give access to different forms of data of which users may wish to stay upwards-to-appointment.
Current research findings reveal that FOMO is a predictor of the use of SNS with which users connect to people in their personal networks, such as Instagram [25] and Facebook [26]. Instagram use, for example, is plant to exist motivated by the desire to keep in touch with others [25] and to "to keep up with or gain cognition near what others (i.east., friends, family and strangers) are doing" [27]. FOMO has also been constitute to predict Facebook employ [28,29] and Instagram utilise [thirty,31]. The above written report findings thus betoken that FOMO predicts the employ of at least these social network sites, but potentially also the use of other social media platforms.
With respect to social media utilize, a difference can be made between the "depth" and the "breadth" of 1's use, where the depth of use refers to aspects such every bit the frequency and duration of social media employ, and the breadth of apply refers to the diversity of social media platforms that are actively used. For teens in detail, not only frequent use, simply also the use of a broad variety of social media platforms may serve the purpose of relieving anxieties with regard to not knowing what others are thinking and doing, equally the differences in the relational affordances of dissimilar platforms [32] mean that they may give access to at least partially different networks and contents. Hence, for the current study nosotros wait that non simply the depth, operationalized as the frequency of social media use, but also the breadth, operationalized every bit the number of active social media platforms teenagers employ, are predicted by FOMO:
Hypothesisi.
Teens who experience greater FOMO, apply social media more ofttimes (Hypothesis 1a; i.east., the depth of social media use), and use more different social media accounts (Hypothesis 1b; i.e., the breadth of social media use).
2.ii. FOMO and the Utilize of More Private versus More Public Social Media Platforms
Every bit mentioned higher up, the use of dissimilar social media platforms may gratify different underlying needs. For example, a recent study shows that users significantly differ in the gratifications they derive from using Facebook, Instagram, Twitter and Snapchat [33]. One affordance in which platforms may differ is in the extent to which content is restricted to a (sub-)set of contacts, or fully public—in other words, whether content is shared with a mostly known versus a mostly unknown audience. On platforms such every bit Facebook, Instagram or Snapchat, people's online social network commonly overlaps with their offline amalgamation grouping (e.g., Facebook/Snapchat contacts need to know each other'south names or phone numbers to encounter each other's posts and profiles). Platforms such equally Twitter or Youtube, on the other manus, commonly make content accessible to a wider audience of mainly unknown individuals, and resemble a broadcasting platform rather than a platform in which content is restricted to people who take been accepted as "contacts", "followers" or "friends".
Given this deviation in the public accessibility of platform content, it seems logical to assume that SNSs such every bit Facebook or Snapchat are more apt at providing relief from FOMO than, for example, video sharing platforms such as Youtube or microblogging services such equally Twitter because the sometime provide greater relief from anxieties surrounding what friends and family unit are doing.
Indeed, platforms such equally Facebook or Instagram are more than personal SNS that enable teens to limit content accessibility to the desired public (e.m., friends or friends-of-friends). Every bit a result, these SNS may exist especially attractive venues for teens with a loftier FOMO because it lets them know what people in their primary affiliative groups are thinking and doing. We explore this assumption by asking the following research question:
ResearchQuestion.
Is FOMO a stronger predictor of the utilize of more private social media platforms (that connect generally to offline networks) than more than publicly accessible social media platforms (that connect mostly to online networks)?
two.3. FOMO and Problematic Social Media Use (PSMU)
When social media use is excessive, it can go problematic. Several studies have addressed problematic social media utilize (PSMU) [34,35,36]. There is an ongoing fence in the literature effectually the differences between problematic social media use and a possible social media behavioral addiction [4,37]. An in-depth discussion of this argue goes beyond the scope of this work. In the current report, however, we use the term problematic social media use, which we define as an unhealthy excessive form of social media use, characterized by a lack of control over the behavior, and connected behavior despite adverse life consequences. Our focus is on revealing the factors that predict to problematic social media use in a general population of teenagers (i.e., the aim is non to diagnose or identify pathological cases).
As mentioned before, one of the aims of this study is to explore if teenagers with a greater FOMO report a higher levels of PSMU. Previous studies propose that this is the case [38,39,40,41,42,43], and suggest that those who experience FOMO may try to relieve their anxiety past checking upwardly on other people on social media. Ironically, nonetheless, the more people check their social media accounts, however, the more than they may notice events they are missing out on. Using social media to reduce the feet may cease up to be some other source of FOMO. Therefore this roughshod circle may reinforce itself, gradually turning social media use into problematic social media utilize. Hence we expect:
Hypothesisii.
Teens who feel greater FOMO, report higher PSMU.
2.4. FOMO and Phubbing
A final study purpose is to focus on FOMO equally a predictor of ane item course of problematic social media use, which is the use of social media during conversations with co-nowadays others. This exercise is termed "phubbing" (derived from phone + snubbing), which refers to "the act of snubbing someone in a social setting by concentrating on 1'southward phone instead of talking to the person directly" [44,45]. Experimental studies show that phubbing negatively impacts relational outcomes such as impression formation [46].
Recent findings evidence that people prefer to use smartphones when going online [47]. Smartphones allows the states to be in contact with our amalgamation groups on social media, everywhere we are. Therefore, we assume that if people experience feet, they may temporary endeavour to reduce information technology by accessing their social media accounts on their smartphones [48]. It is likely that those high in FOMO, who apply social media to accost their feet, may overuse social media on their smartphones in such a way that it intersects with their offline social interactions, leading them to phub their offline interaction partners.
Hence, the terminal aim of this study is to explore whether teens with a greater FOMO report to appoint in phubbing behavior more frequently, and whether the latter relationship is mediated past PSMU:
Hypothesis3a.
Teens with a greater FOMO, are more likely to apply social media during conversations with co-nowadays others (cf. "phubbing").
Hypothesis3b.
PSMU mediates the former human relationship.
3. Method
3.1. Sample and Process
In Flemish region, a consortium of non-profit organizations collaborates bi-annually on a large-scale survey project that examines the country of affairs of digital media buying and use of Flemish youths. Apart from a large set of recurring questions, every edition of the survey includes a number of questions on topics that are considered relevant at the fourth dimension.
The data gathered for the current study were part of the 2016 inquiry project [49]. An double-decker survey was administered to the high school pupils of 11 geographically dispersed high schools in Flanders, Belgium. Using the data made available by the ministry of education, quota sampling was used to select schools, and—within the schools—years and classrooms. This procedure resulted in a final sample that is representative for the population in terms of gender, age and school rails (run into Van Waeg, D'Hanens, Dooms & Naesens [49] for further details).
Within each school, a local collaborator (east.g., the school's information and communications technology coordinator) organized the survey administration procedure, according to a set of instructions provided past the projection leaders. The survey was administered online, but to avert self-selection bias, the information collection took part during schoolhouse hours, in the figurer rooms of the schools. Unfortunately, no response rates were registered. However, the local collaborators stated that few pupils did not receive permission to participate. In total, the responses of 3291 pupils were gathered. The project leaders subjected these responses to a rigorous data cleaning process, leading to removal of 452 responses that were either substantially incomplete, either contained multiple invalid responses to validation screening items. This process resulted in a concluding sample of 2663 pupils (57.1% girls; 1000 age = 14.87, SD = 1.67). This final dataset was distributed by the consortium to the collaborating researchers for further analysis.
Informed consent was nerveless from both the participating teenagers and their parents. Given the large sample-size, an opt-out procedure was used for collecting consent from parents. The academy's institutional review lath approved the report.
3.ii. Measures
3.two.1. Breadth of Social Media Platforms Used
Based on interviews with young persons, a listing of 25 ofttimes used social media applications was constructed. We adhered to Kaplan and Haenlein's [sixteen] definition of social media, which includes all platforms in which users can generate content that is (semi-)publicly bachelor to others. The latter definition excludes platforms that focus exclusively on instant messaging (e.g., Whatsapp, Facebook Messenger). The list of included platforms can be consulted in Table 1. The breadth of social media employ was assessed by asking for each platform whether the teenager had an active account (ane = yes, 0 = no), and and then summing the total number of active accounts per participant.
Table 1
Scale items, ways and standard deviations.
| Fear-of-Missing-Out (FOMO) | |||
| Items | M | SD | |
| i | I fearfulness my friends have more rewarding experiences than me | 2.33 | 1.eleven |
| two | Information technology is important that I understand my friends' "within jokes" | 3.09 | one.05 |
| 3 | It bothers me when I miss an opportunity to meet up with friends | 4.xvi | 0.ninety |
| iv | When I continue summer camp or vacation, I continue to keep tabs on what my friends are doing | 2.66 | 1.xiv |
| Problematic Social Media Use (PSMU) | |||
| Items | M | SD | |
| 1 | How often do you notice it difficult to quit using social media? | two.89 | one.19 |
| 2 | How ofttimes do others (east.1000., your parents or friends) tell you that you should spend less fourth dimension on social media? | two.72 | 1.28 |
| iii | How frequently practise you prefer using social media over spending fourth dimension with others (e.m., with friends or family)? | 1.89 | 0.97 |
| 4 | How oft do you feel restless, frustrated or irritated when y'all can't admission social media? | 2.33 | one.16 |
| v | How frequently practice yous do your homework poorly because you prefer existence on social media? | 2.51 | 1.17 |
| half-dozen | How ofttimes practise yous apply social media because you feel unhappy? | 2.31 | 1.23 |
| vii | How frequently do you lack sleep because you spent the night using social media? | two.44 | 1.35 |
| Phubbing | |||
| Items | Thousand | SD | |
| one | How ofttimes practice you apply your mobile phone during a conversation in a bar or restaurant? | 2.39 | 0.99 |
| 2 | How often are y'all engaged with your phone during a conversation? | 2.13 | 0.96 |
| 3 | How oftentimes do you check social media on your phone during a personal conversation? | 1.89 | 0.92 |
3.2.2. Depth of Social Media Platforms Used
The depth of social media use was measured by assessing for each active platform how oft it was used (1 = less than once per week, 5 = multiple times per solar day). Questions with respect to the usage frequency of a platform were only answered by participants who had an active account for the platform. As visible in Table 2, a substantial number of platforms was used past (very) few participants. We opted to only include those platforms who were used by at least 5% of the sample. The analyses for Hypothesis 1a, concerning FOMO as a predictor of platform usage frequency (see Table three and Table 4) are performed on the basis of the subsample of users of each platform.
Tabular array 2
Pct of teenagers with an account on various social media platforms and average frequency of utilise among account holders (1 = less than once/week, 5 = multiple times/solar day).
| Social Media Platform | N | % of Total Sample | Average Usage Frequency | Less than Once per Week | Once per Calendar week | Multiple Times per Calendar week | Daily | Multiple Times per Day | |
|---|---|---|---|---|---|---|---|---|---|
| M | SD | % | % | % | % | % | |||
| 2360 | 89% | 4.49 | 0.87 | one.4 | iii.half-dozen | half dozen.4 | 22.2 | 66.5 | |
| Snapchat | 1937 | 73% | four.36 | 1.00 | 2.4 | iv.5 | ten.1 | 20.five | 62.5 |
| 1680 | 63% | 4.36 | 0.97 | 1.v | 4.4 | 15.7 | 31.8 | 46.6 | |
| YouTube | 1596 | 60% | four.17 | 0.95 | ii.i | 4.4 | nine.7 | 22.3 | 61.4 |
| Google+ | 925 | 35% | 2.67 | one.twoscore | 28.2 | 21.v | 19.8 | 16.4 | 14.1 |
| 582 | 22% | 3.63 | 1.35 | 8.eight | fourteen.nine | 19.4 | 18.6 | 38.3 | |
| Swarm | 510 | 19% | 4.26 | 1.09 | iii.seven | 5.5 | x.8 | 20.6 | 59.four |
| We Heart It | 329 | 12% | 3.42 | 1.34 | 10.6 | 16.4 | 21.9 | 22.2 | 28.9 |
| 324 | 12% | 2.6 | ane.34 | 28.ane | 21.half dozen | 24.four | 14.5 | 11.4 | |
| Tumblr | 298 | eleven% | 3.52 | 1.35 | ix.4 | 16.4 | 21.1 | xviii.8 | 34.2 |
| Vine | 232 | nine% | three.12 | ane.38 | 15.9 | 19.8 | 23.three | xviii.5 | 22.4 |
| Foursquare | 172 | six% | 3.03 | 1.72 | 34.three | 8.7 | 9.9 | xiii.4 | 33.7 |
| Tinder | 120 | 5% | two.52 | 1.51 | 36.7 | 20 | fifteen.8 | 9.two | eighteen.three |
| Kiwi | 117 | iv% | 3 | 1.58 | 25.vi | 18.8 | xiii.seven | xiii.seven | 28.2 |
| Ask.fm | 92 | 3% | 3.6 | one.60 | 20.7 | six.5 | 12 | 14.ane | 46.7 |
| Runkeeper | 72 | 3% | ii.17 | 1.x | 31.9 | 34.7 | 23.half dozen | four.2 | 5.6 |
| 60 | 2% | 3.07 | 1.48 | 20 | twenty | 18.three | 16.7 | 25 | |
| Happening | 48 | ii% | two.65 | 1.42 | 29.two | xx.8 | 20.8 | 14.6 | xiv.six |
| Vimeo | 32 | one% | 2.91 | i.61 | 34.4 | 6.3 | fifteen.half dozen | 21.9 | 21.9 |
| Strava | 25 | one% | ii.six | 1.44 | 28 | 28 | 16 | 12 | sixteen |
| 24 | ane% | ii.08 | 1.38 | 50 | 16.seven | 20.8 | 12.5 | / | |
| Periscope | 15 | 1% | iii.07 | one.28 | 13.three | 20 | 26.vii | 26.seven | thirteen.3 |
| Endomondo | 11 | 0% | 2.73 | ane.74 | 36.4 | 18.two | 9.1 | 9.1 | 27.3 |
| Ello | 8 | 0% | 1.v | i.41 | 87.5 | / | / | / | 12.v |
| Meerkat | half dozen | 0% | 2.33 | 2.07 | 66.vii | / | / | / | 33.3 |
Table 3
Gender, age, school track and Fear-of-Missing-Out (FOMO) equally predictors of the frequency of Facebook, Snapchat, Instagram, Youtube, Google Plus and Twitter.
| Snapchat | Youtube | Google Plus | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PE | SE | Wald | PE | SE | Wald | PE | SE | Wald | PE | SE | Wald | PE | SE | Wald | PE | SE | Wald | |
| Gender (boy) | −0.50 | 0.09 | 32.55 *** | −0.68 | 0.09 | 52.79 *** | −0.61 | 0.10 | 35.85 *** | 0.72 | 0.x | 56.21 *** | 0.xviii | 0.12 | 2.23 | −0.25 | 0.xv | 2.58 |
| Gender (girl) | a | |||||||||||||||||
| Age | 0.15 | 0.03 | 28.1 *** | 0.02 | 0.03 | 0.30 | 0.12 | 0.03 | fourteen.36 *** | −0.01 | 0.03 | 0.14 | −0.09 | 0.04 | half dozen.42 * | 0.02 | 0.05 | 0.fifteen |
| School track (voc) | 0.41 | 0.fourteen | eight.72 ** | 0.40 | 0.14 | eight.00 ** | −0.34 | 0.14 | 5.75 * | 0.54 | 0.15 | xiii.58 *** | 0.51 | 0.17 | 8.79 ** | 0.36 | 0.25 | 2.18 |
| School rail (s-voc) | 0.28 | 0.12 | v.67 * | 0.35 | 0.12 | 8.88 ** | −0.01 | 0.12 | 0.00 | −0.01 | 0.12 | 0.00 | 0.40 | 0.fifteen | 6.84 ** | 0.76 | 0.18 | 19.06 *** |
| School track (air conditioning) | a | |||||||||||||||||
| FOMO | 0.48 | 0.07 | 55.09 *** | 0.28 | 0.07 | 16.89 *** | 0.34 | 0.07 | 22.48 *** | 0.18 | 0.07 | vii.08 ** | 0.04 | 0.09 | 0.22 | 0.15 | 0.eleven | 2.02 |
| Pearson GOF | X 2(1821) = 1635.34, p = 1.00 | X 2(1731) = 1842.31, p = 0.031 | X 2(1643) = 1716.37, p = 0.102 | X ii(1623) = 1621.94, p = 0.503 | Ten 2(1335) = 1370.00, p = 0.247 | X ii(1083) = 1131.58, p = 0.148 | ||||||||||||
| −2LL GOF | Xtwo (five) = 150.10, p < 0.001 | Ten2 (5) = 88.07, p < 0.001 | Xtwo (five) = 79.xix, p < 0.001 | 102 (5) = 75.47, p < 0.001 | Ten2 (five) = 17.20, p = 0.004 | 102 (v) = 29.41, p < 0.001 | ||||||||||||
| Nagelkerke R2 | 0.07 | 0.05 | 0.05 | 0.05 | 0.02 | 0.05 | ||||||||||||
Table 4
Gender, age, schoolhouse track and Fear-of-Missing-Out (FOMO) equally predictors of the frequency of Swarm, Nosotros Centre It, Pinterest, Tumlbr, Vine, Foursquare and Tinder.
| Swarm | We Heart It | Tumblr | Vine | Foursquare | Tinder | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PE | SE | Wald | PE | SE | Wald | PE | SE | Wald | PE | SE | Wald | PE | SE | Wald | PE | SE | Wald | PE | SE | Wald | |
| Gender (male child) | −0.48 | 0.xix | half-dozen.58 ** | −ane.41 | 0.58 | 5.87 * | −0.59 | 0.29 | 4.18 * | −0.41 | 0.29 | 2.01 | 0.19 | 0.24 | 0.64 | −0.04 | 0.29 | 0.02 | −0.11 | 0.34 | 0.eleven |
| Gender (girl) | a | ||||||||||||||||||||
| Age | 0.21 | 0.06 | 11.25 * | −0.12 | 0.07 | ii.69 | −0.03 | 0.07 | 0.sixteen | −0.03 | 0.07 | 0.16 | −0.xiii | 0.08 | 2.48 | 0.03 | 0.eleven | 0.x | −0.17 | 0.eleven | 2.36 |
| School track (voc) | −0.58 | 0.25 | 5.34 * | 0.65 | 0.31 | 4.36 * | −0.10 | 0.27 | 0.15 | −0.l | 0.33 | 2.30 | −0.21 | 0.36 | 0.33 | 0.25 | 0.37 | 0.46 | 0.75 | 0.48 | 2.45 |
| Schoolhouse track (s-voc) | 0.36 | 0.22 | ii.78 | 0.01 | 0.24 | 0.00 | −0.17 | 0.25 | 0.43 | 0.07 | 0.25 | 0.08 | 0.05 | 0.29 | 0.03 | 0.33 | 0.34 | 0.93 | 0.48 | 0.42 | 1.33 |
| Schoolhouse rails (air-conditioning) | a | ||||||||||||||||||||
| FOMO | 0.sixteen | 0.thirteen | one.49 | 0.24 | 0.14 | ii.86 | 0.19 | 0.17 | 1.28 | 0.29 | 0.15 | iii.77 * | 0.37 | 0.eighteen | 4.22* | 0.47 | 0.21 | 5.26 * | 0.04 | 0.23 | 0.03 |
| Pearson GOF | 102 (915) = 856.28, p = 0.917 | X2 (571) = 607.49, p = 0.141 | Ten2 (619) = 627.27, p = 0.400 | 102 (619) = 620.67, p = 0.474 | Xii (599) = 616.26, p = 0.304 | 102 (475) = 488.52, p = 0.324 | Tentwo (371) = 373.34, p = 0.456 | ||||||||||||||
| −2LL GOF | X2 (5) = 28.53, p < 0.001 | 102 (5) = 12.53, p = 0.028 | 10two (5) = 6.24, p = 0.283 | Ten2 (five) = 689.76, p = 0.123 | X2 (v) = 8.29, p = 0.141 | Xii (5) = 7.47, p = 0.188 | Ten2 (5) = 4.26, p = 0.513 | ||||||||||||||
| Nagelkerke R2 | 0.06 | 0.04 | 0.02 | 0.03 | 0.04 | 0.05 | 0.04 | ||||||||||||||
3.2.three. Private Versus Public Accessibility of Social Media Platforms Used
With respect to the private versus public accessibility of platforms, we consider Facebook and Snapchat as social media platforms on which content is generally less publicly accessible (i.due east., content is oftentimes shielded off to a public of "approved" contacts), and Youtube and Twitter as social media platforms on which content is generally publicly attainable (i.e., content is commonly accessible to everyone who visits the platform).
iii.two.4. Fear of Missing Out (FOMO)
The jitney format of the survey implied a constraint on the number of items nosotros could use. We chose to select four items from Przybylski et al.'s [viii] 10-detail FOMO-scale, as this scale had been pre-validated past the authors. In Przybylski et al.'due south study, the ten scale items loaded on 1 factor, and were internally consistent. In the absence of information on the factor loadings of the individual items in the original study, we chose to select a subset of four items that reverberate the diversity of the original scale items well. Those items were: "It bothers me when I miss an opportunity to meet up with friends", "I fear my friends have more than rewarding experiences than me", "When I go on holiday or summer military camp, I continue to continue tabs on what my friends are doing", and "It is important that I empathise my friends "within jokes"". The items were measured on a 5-point Likert-scale (i = completely disagree; 5 = completely agree).
A well-known risk of using a diverse ready items to construct a brusk calibration, is that internal consistency may be jeopardized [l]. Indeed, although an exploratory gene analysis revealed that the four items loaded onto one factor, with all factor loadings to a higher place 0.55, the total variance explained past the factor (42%) was below the advised sixty% threshold, and the overall Kaiser-Meier-Olkin measure (0.65) indicated mediocre sampling capability. A farther examination of the scale's reliability, confirmed that the internal consistency of the scale was weak (α = 0.56), and revealed that it could non exist further improved via item choice, every bit the highest inter-item correlation was 0.36 (p < 0.001). Appendix A shows the inter-particular correlation matrix. Means and standard deviations tin can exist consulted in Tabular array 1.
A solution to the issue of low internal consistency, is to perform analyses with individual calibration items, rather than with a scale variable. For the current written report, however, such procedure would imply an inflation of results—particularly when answering Hypothesis 1a, which reduces the comprehensibility of the study findings. The culling is to continue with a sub-optimal measure out, knowing that the main risk of using a scale-measure that suffers from a weak Cronbach alpha, is underestimation of the real relationship [51]. In the context of the current study, we opted for the latter solution for reasons of comprehensibility, and thus work with the scale variable to answer Hypothesis 1. The adventure of an underestimation of the real relationship should exist kept in mind, however, when interpreting the results. For Hypotheses two, iii and 4, we study both the results using the FOMO scale variable and the individual scale items.
3.2.5. Problematic Social Media Use (PSMU)
Problematic social media use was assessed using an adapted version of the C-VAT musical instrument [52], which is a scale based on the CIUS-scale that was developed and validated by Meerkerk, Van den Eijnden et al. [53] and Meerkerk [54]. The adapted version addresses social media use rather than videogaming. The items were measured on a five signal Likert-scale (α = 0.82). The scale items, their ways and standard deviations, and their factor loadings can be consulted in Table 1.
3.ii.6. Phubbing Behavior
At the time of amalgam the questionnaire, we were unaware of scales measuring phubbing behavior. Hence, we measured phubbing behavior with 3 self-synthetic items: "How oft do you use your mobile phone during a conversation in a bar or restaurant?", "How often are you engaged with your phone during a conversation?", and "How frequently do you check social media on your telephone during a personal chat?". The items were measured on a 5-signal Likert scale, ranging from 1 (never) to 5 (almost all the time; α = 0.77). The scale items, their ways and standard deviations can exist consulted in Tabular array 1.
3.2.7. Control variables
Gender (one = boy, 2 = girl), age and schoolhouse track (1 = vocational, 2 = semi-vocational, three = bookish) were included as control variables in the linear regression analyses.
3.3. Analyses
Hypothesis 1a states that teens who have a greater FOMO apply social media platforms more frequently. We used ordinal regression assay in to test this hypothesis because the frequency of utilise-measures have ordinal response scales. In the analyses with the frequency of utilize of Snapchat, We Heart information technology, Pinterest and Tinder every bit the dependent variables, the assumption of proportional odds was violated; this occurs more frequently in large samples, considering minor violations of the parallel lines test may already exist statistically significant [55]. Yet, we advise to interpret these results with caution.
Hypothesis 1b states that FOMO positively predicts the latitude of social media utilise. The "breadth of social media utilize" variable was operationalized past counting the number of active social media accounts that teens have (min = 0, max = 25). Nosotros used multiple linear regression analysis to exam the hypothesis, after assessing that the standardized residuals of the variable were normally distributed (and thus that the supposition of normality was not violated: because measurements gathered in large samples typically accept very minor standard errors, it is advised to appraise normality on the footing of the absolute values of skewness and kurtosis rather than on Z-scores of skewness and kurtoses. Advised disquisitional values for skewness, respectively kurtosis in large samples are 2, respectively 7 [56]. Using these guidelines, the skewness (1.22) and kurtosis (v.95) values of the residuals indicated that the assumption of normality for regression analysis was sufficiently met).
To explore our research question on the comparative strength of the correlations between FOMO and individual platforms on the one hand, and between FOMO and public platforms on the other, we get-go calculated the correlations, and next compared the strength of these correlations using Steiger's Z-test [57] in Lee and Preacher's [58] web-based software. The Steiger Z-exam operates on the basis of Pearson correlations between two dependent correlations with i variable in common. Because the correlations take to be fatigued from the same sample, we first recoded the 'frequency of platform apply' variables then that persons without an agile account received the everyman usage score (rather than a missing value). This recoding process ensured that there were 2663 responses for each variable. Next, we calculated the Pearson correlation coefficients, which class the input for the Steiger's Z-test. The reader may discover that the Pearson correlation examination is a parametric examination, whereas the "frequency of platform utilize" variables are ordinal. Yet, the variables met the standard guidelines for skewness and kurtosis in big samples [56], and the Pearson correlation coefficients and the Spearman rho correlation coefficients were highly similar (i.eastward., for only two out of ten correlations the divergence between the Pearson and the Spearman correlation coefficient exceeded a value of 0.03).
In social science enquiry, scale variables are oft treated as interval variables, based on the idea that the sets of items that comprise each scale grade an index that represents an underlying latent factor [59]. The required assumptions for parametric testing were met. Hence, to examine Hypotheses 2 and iii, we fitted a arbitration model using model iv of Hayes' [60]. Process macro for SPSS with FOMO as the independent variable, PSMU as the mediator and phubbing beliefs as the dependent variable. The indirect effect was estimated for 5000 bootstrap samples with a 95% bias-corrected confidence interval.
4. Results
iv.one. Descriptives
Earlier addressing the hypotheses and research questions, we briefly written report some descriptive statistics for the media apply variables. The means and standard deviations for the items of the FOMO-scale, the PSMU-scale and the phubbing calibration can be consulted in Table 1.
For 25 social media platforms, we asked the teenagers in our sample whether they had an active account, and if so, how ofttimes they use it. In terms of active account ownership, Facebook (89% of teens with an active account), Snapchat (73%), Instagram (63%) and Youtube (lx%) are the most popular social media platforms. The teenagers in our sample had on boilerplate four.35 (Median = four, Mode = 4, Min = 0, Max = 25) agile social media accounts. The standard departure (SD = ii.29) reveals that there is substantial variability between teenagers. Most teens who have an active account on Facebook, Snapchat, Instagram and Youtube, reported using the platform more than once per day (see Table two). There are other social media platforms that are frequently used, such as the location-sharing platform Swarm (M = four.26, SD = 1.09), simply these ofttimes accept pocket-size user bases (eastward.g., just 19% of teens has an active Swarm account).
The teenagers in our sample on average had a fairly neutral FOMO score (Chiliad = 3.06, SD = 0.69). Using a multiple regression assay, nosotros explored whether FOMO was predicted by gender, age and school rail. Our analysis showed that girls (ß = 0.08, p < 0.001) reported a higher FOMO, whereas historic period (ß = 0.03, p = 0.109) and school track (ß = −0.01, p = 0.800) were no pregnant predictors, R² = 0.01, F(iii.2659) = v.96, p < 0.001).
4.two. FOMO as a Predictor of the Depth and Breadth of Social Media Use
Hypotheses 1a and 1b concern FOMO as a predictor of the depth and breadth of social media use. We tested Hypothesis 1a using ordinal regression assay, with gender, age, schoolhouse rails and FOMO every bit the predictor variables, and the frequency of use of each corresponding social media platform that was used by 5% of the sample or more as the dependent variable.
Table iii and Table 4 show the results. Afterward decision-making for gender, age and school runway, FOMO positively predicted the use of the four most popular social media platforms: Facebook, Snapchat, Instagram and Youtube, as well as the frequency of using foursquare, Tumblr and Vine. These findings lend fractional back up to our hypothesis. While FOMO appears to be a pocket-sized predictor of the virtually common social media platforms, as well as of platforms that are used more rarely, it was not a consistent predictor of social media platform employ.
With respect to the latitude of social media use (Hypothesis 1b), a multiple linear regression analysis with gender, age, school runway and FOMO as predictors, revealed that gender (ß = 0.08, p < 0.001), age (ß = 0.twenty, p < 0.001), school track (ß = −0.05, p = 0.006) and FOMO (ß = 0.14, p < 0.001; R² = 0.08, F(4.2658) = 57.27, p < 0.001) were meaning, positive predictors of the number of active social media accounts that teenagers have (H1b supported).
four.3. FOMO in Relation to the Public Accessibility of Platforms
We posited that FOMO would be a stronger predictor of social media utilise when the social media platform examined is a more private platform than when information technology as a more public platform (RQ1), because more private platforms such as Facebook or Snapchat connect teenagers more strongly to their offline ties, which can exist considered the dominant affiliative grouping on which they desire to continue tabs. We calculated Steiner's Z to statistically compare the correlations between FOMO and Facebook, respectively Snapchat utilize on the one manus, and betwixt FOMO and YouTube, respectively, and Twitter on the other hand (see Table five and Table half-dozen).
Tabular array 5
Correlations between FOMO and frequency of utilise of less publicly accessible platforms versus more than publicly accessible platforms.
| Pearson'southward r | Snapchat | YouTube | ||
|---|---|---|---|---|
| Snapchat | 0.48 *** | 1 | ||
| Youtube | 0.13 *** | 0.04 * | 1 | |
| 0.15 *** | 0.19 *** | 0.18 *** | 1 | |
| FOMO | 0.16 *** | 0.17 *** | 0.00 | 0.06 *** |
Table half-dozen
Comparison of Pearson correlation strength between FOMO and more private platforms versus FOMO and more public platforms.
| Steiner's Z (rFOMO, column var vs. rFOMO, row var ) | Snapchat | YouTube | ||
|---|---|---|---|---|
| Snapchat | −0.41 | |||
| Youtube | six.37 *** | 6.38 *** | ||
| iv.18 *** | 4.62 *** | −2.30 * |
The findings testify that the correlations between FOMO and Facebook (r = 0.16, p < 0.001), respectively Snapchat use (r = 0.17, p < 0.001), are significantly stronger than the correlations between FOMO and YouTube (r = 0.00, p = 0.921), respectively Twitter employ (r = 0.06, p = 0.002), thus lending support to our research question.
4.four. FOMO as a Predictor of PSMU and Phubbing Behavior
Hypotheses 2 stated that FOMO positively predicts PSMU and Hypothesis 3a stated that FOMO positively predicts phubbing beliefs. Hypothesis 3b stated that PSMU mediates the relationship between FOMO and phubbing behavior. Nosotros tested these hypotheses by estimating a arbitration model. The results are depicted in Figure 1. All hypotheses were supported. FOMO has a direct, positive predictor of both PSMU (ß = 0.40, p < 0.001) and phubbing beliefs (ß = 0.20, p < 0.001). When PSMU is accounted for, the relationship between FOMO and phubbing behavior weakens considerably. The indirect upshot is pregnant (ß = 0.16, p < 0.001).
Mediation model of the relationship between FOMO, PSMU and phubbing behavior (*** p < 0.001). FOMO: Fearfulness-of-missing-out; PSMU: problematic social media employ.
Given that the internal consistency of the FOMO scale was unsatisfactory, we performed the arbitration analysis on the individual items of the FOMO scale (see Table seven). For three items, the mediation analysis resulted in a similar results pattern. For the item "It bothers me when I miss an opportunity to meet with friends", however, the direct relationship with PSMU was much weaker (albeit still significant; ß = 0.05, p = 0.008), and the straight relationship with phubbing behavior was negative (ß = −0.04, p = 0.011).
Table 7
Mediation analysis results for the individual items of the FOMO scale and the FOMO scale variable.
| a | b | c | c' | Indirect Effect Gauge | |
|---|---|---|---|---|---|
| FOMO (4-item scale) | 0.40 *** | 0.41 *** | 0.04 | 0.twenty *** | 0.xvi [0.14; 0.19] |
| Individual items | |||||
| I fear my friends take more than rewarding experiences than me | 0.21 *** | 0.42 *** | 0.00 | 0.09 *** | 0.09 [0.07; 0.ten] |
| It is important that I empathise my friends' "inside jokes" | 0.15 *** | 0.43 *** | −0.01 | 0.05 *** | 0.06 [0.05; 0.08] |
| It bothers me when I miss an opportunity to meet up with friends | 0.05 ** | 0.43*** | −0.04 * | −0.02 | 0.02 [0.00; 0.04] |
| When I go on summer camp or vacation, I continue to keep tabs on what my friends are doing | 0.22 *** | 0.38 *** | 0.09 *** | 0.eighteen*** | 0.09 [0.07; 0.10] |
v. Discussion
Cartoon from the results of a survey study amongst 2663 Flemish teenagers, the aims of this report were fourfold: (one) to examine FOMO every bit a predictor of the depth and breadth of social media use; (2) to examine whether FOMO relates more than strongly to the apply of more individual social media platforms than more public platforms; (3) to test whether FOMO predicts PSMU and (4) whether FOMO predicts, both directly and indirectly via PSMU, phubbing beliefs.
With respect to the latitude of social media utilise (Hypothesis 1b), nosotros found support for the hypothesis that teens who have a greater FOMO utilize a wider variety of social media platforms. With respect to the depth of social media employ, the study findings partially back up the hypothesis that teenagers with a higher FOMO utilize social media more frequently (Hypothesis 1a), every bit FOMO was identified as a predictor for the frequency of use of some, simply non all social media platforms examined. We did notice a consistent relationship with the usage frequency of the 4 nigh used platforms: Facebook, Snapchat, Instagram and YouTube. This finding suggests that the relationship between FOMO and the frequency of apply of these popular platforms is generalizable to the population of at least Flemish teenagers. These findings also support the findings of earlier piece of work that characterize FOMO as an intrapersonal characteristic that predicts the use of Facebook [28,29] and Instagram [30,31].
The relationships found between FOMO and the frequency of using less popular social media platforms such as Foursquare, Tumblr and Vine are more hard to interpret, equally these social media platforms differ considerably from each other in what they afford the user to exercise: Foursquare is a location-based social network site, Tumblr is a blogging service, and Vine let users share brusk videoclips. Future enquiry may explore the relationship between FOMO and the employ of these detail platforms more in-depth via the employ of interviews with teenagers, as greater insight into their personal experiences with these platforms and their anxieties concerning missing out may shed new light on what makes these particular platforms attractive.
Every bit mentioned in a higher place, a pertinent question is whether the social gratification provided by social media utilize sooths or aggravates the anxieties of teenagers; subsequently all, studies on social comparison on social media platforms [61] advise that exposure to other people's social media accounts may make the experience of missing out on rewarding experiences even greater. A limitation of our study is its correlational nature, which prevents from making claims concerning the potential bi-directional causality of the human relationship betwixt FOMO and social media utilize. Future research may address this question via the use of longitudinal report designs that enable the modelling of cross-lagged path models.
Information technology is essential for our understanding of FOMO to unravel how information technology resemblances, but also differs from other, associated personality factors. I cistron that has been identified in the extant literature and that appears relevant to consider, is sociotropy [62], which refers to an insatiable need for belongingness to others, visible in an over-reliance on approval from others, which tin can go at the expense of personal autonomy. In contempo work, sociotropy is linked to the ritualistic monitoring of, oft multiple social media platforms [63]. Time to come research may explore how the fear of negative feedback or rejection that is typical for sociotropic individuals aligns with a fear of missing out.
We questioned whether FOMO would exist a stronger predictor of more private social media platforms such as Facebook or Snapchat than of more public platforms such as YouTube and Twitter (RQ1). Our exploratory analysis suggests this is indeed the case. This is unsurprising, given that FOMO itself has been conceptualized and operationalized in the current study as a fear to miss out on what friends are thinking and doing, and information virtually these friends tin exist plant more often than not on platforms that connect to people who are part of one'southward offline network. Nonetheless the stronger human relationship betwixt FOMO and these individual platforms, nosotros need to remark that FOMO still remains a weak, yet significant predictor of some more than publicly attainable platforms (e.k., Tumblr). A pertinent question is what still drives those higher on FOMO to utilize the latter platforms, and so. As mentioned above, qualitative research seems relevant to further expand on the affordances in which various social media platforms resemble or differ from one another, and how these affordances are perceived, valued and acted upon by those with a college versus lower FOMO.
FOMO was found to predict PSMU (Hypothesis two), a result that aligns with the findings from other recent studies which showed that greater FOMO is associated with more problematic internet and smartphone employ [40,64,65]. Moreover, FOMO was as well associated with phubbing behavior (Hypothesis 3a), consistently with previous research that showed a similar pattern of associations [44]. Interestingly, our results showed that the relationship between FOMO and phubbing behavior was mediated past PMSU (cf. Hypothesis 3b), appropriately to the proposed conceptual model by Chotpitayasunondh & Douglas [44], which constitute that FOMO was a positive predictor of smartphone habit, and that smartphone habit predicted smartphone behavior. Thus, adolescents who are high in their fear of missing out are more likely to overuse the social media and smartphones, which in turns leads them to phub their offline interaction partners [66]. Scholars in the field of problematic media use research advocate to invest greater effort into the study of the pathways that lead to trouble behavior [6]. Our study findings stand for such an endeavour, as they reveal that FOMO is an intra-personal characteristic that leads to phubbing behavior by inducing excessive, uncontrolled social media utilise.
The findings reported in this study are generalizable as they stalk from a large-calibration survey study that was administered to representative sample of Flemish teenagers. There are a number of limitations to the electric current study, however. Nosotros used a shortened, four item version of the FOMO-scale adult by Przybylski et al. [8] to assess teenagers' FOMO. The internal consistency of the calibration was unsatisfactory, which increases the risk of underestimating the real relationship (see Schmitt [51]). The low internal consistency for our scale illustrates the importance for enquiry in this field to use consummate and validated scales—for the reason of making reliable claims about one's study, and for enabling valid comparisons between studies.
In light of the unsatisfactory internal consistency of our FOMO-scale, we tested Hypotheses 2 and 3 using both the scale variable and the individual items. This analysis revealed that for one detail the human relationship with phubbing behavior was reversed: The more teens agreed to feel bothered when missing an opportunity to meet with friends (i.e., indicative of a greater fear-of-missing-out), the less they study phubbing their interaction partner during a face-to-face interaction. This finding indicates that some teenagers adhere neat importance to face up-to-confront interactions with friends, leading them to prioritize these interactions over smartphone interactions. This finding suggests that it is relevant to further investigate how FOMO relates to relational behavior, non merely online (in the form of social media utilize), but also offline.
2d, our study used a narrow definition of social media [xvi], which excludes mobile messaging applications. It is likely that those with a greater FOMO besides heavily rely on the employ of these messengers to soothe their anxieties nearly what others in their social networks are doing. The latter applications are unlike from social media, notwithstanding, in that they are often used for pocket-sized-group communication, and therefore are more dialogical in nature [67]. It is more hard—if not impossible—to "lurk" in dyadic and small-scale-group conversations, equally they by and large rely on a certain degree of reciprocity. With respect to FOMO, this difference in the interactional affordances of social media platforms and (mobile) instant messengers raises interesting questions. Information technology may be the case that people with a high FOMO are particularly attracted to social media because they tin lurk on these platforms without risking a label of "voyeur", and without having to engage in reciprocal disclosures about themselves. As mentioned above, it seems relevant to explore the difference between FOMO and sociotropy [62] in this context, as active disclosures on social media platforms and messaging platforms may provide sociotropic individuals with a means to gather the social approval they long for, while those with a loftier FOMO may seek information about other people'southward experiences without necessarily wanting to engage in interactions with them. Time to come research may explore this.
In brusque, this study is valuable because information technology provides generalizable findings on the relationships between FOMO, social media use, PSMU and phubbing behavior amid teenagers. As such, it tin can serve as a starting ground for future inquiry. This research needs to await into the pathways via which FOMO leads to particular forms of (problematic) media use, and how these pathways are similar to or unlike from other pathways.
6. Conclusions
To conclude, and on the footing of the findings presented in this article, FOMO is an important factor explaining teenagers' social media use. The present report found back up for the hypothesis that teens who have a greater FOMO use a wider variety of social media platforms. Also, the present study establish partially support for the hypothesis that teenagers with a higher FOMO use social media more oft: FOMO was identified every bit a predictor for the frequency of use of some, simply not all social media platforms examined. In particular, there was a consistent human relationship betwixt FOMO and the usage frequency of Facebook, Snapchat, Instagram and YouTube. Moreover, FOMO was found to predict PSMU. This result aligns with the findings from other recent studies which showed that greater FOMO is associated with more than problematic internet and smartphone use [twoscore,64,65]. Finally, FOMO was associated with phubbing behavior. Our results additionally showed that the human relationship between FOMO and phubbing beliefs was mediated by PSMU. Teens who are high in their fright of missing out are more than likely to overuse the social media and smartphones, which in turns may lead them to phub their offline interaction partners [66].
Appendix A
Table A1
Intercorrelation matrix of items measuring the Fear-of-Missing-Out (FOMO).
| Items FOMO Scale | one | 2 | 3 | 4 |
|---|---|---|---|---|
| I fear my friends have more than rewarding experiences than me | 1 | 0.36 *** | 0.17 *** | 0.26 *** |
| Information technology is important that I understand my friends' "inside jokes" | 1 | 0.25 *** | 0.21 *** | |
| It bothers me when I miss an opportunity to come across up with friends | 1 | 0.15 *** | ||
| When I continue summertime camp or vacation, I keep to proceed tabs on what my friends are doing | 1 |
Author Contributions
V.F. and M.Five.A. performed analyses and wrote initial drafts of the manuscript. A.J.v.R., Chiliad.L.C., and L.D.M. proofread drafts, contributed ideas and edited the diverse versions.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflict of interest.
References
1. Moreno M.A., Jelenchick L., Koff R., Eikoff J., Diermyer C., Christakis D.A. Internet use and multitasking among older adolescents: An experience sampling approach. Comput. Hum. Behav. 2012;28:1097–1102. doi: x.1016/j.chb.2012.01.016. [CrossRef] [Google Scholar]
ii. Billieux J., Schimmenti A., Khazaal Y., Maurage P. Are nosotros overpathologizing everyday life? A tenable design for behavioral addiction inquiry. J. Behav. Addict. 2015;4:119–123. doi: 10.1556/2006.4.2015.009. [PMC gratis article] [PubMed] [CrossRef] [Google Scholar]
3. Bean A.M., Nielsen R.K., Van Rooij A.J., Ferguson C.J. Video game addiction: The push to pathologize video games. Prof. Psychol. Res. Pract. 2017;48:378–389. doi: x.1037/pro0000150. [CrossRef] [Google Scholar]
iv. Kardefelt-Winther D., Heeren A., Schimmenti A., Van Rooij A., Maurage P., Carras M., Edman J., Blaszczynski A., Khazaal Y., Billieux J. How tin we conceptualize behavioral addiction without pathologizing mutual behaviours? Addiction. 2017;112:1709–1715. doi: 10.1111/add together.13763. [PMC complimentary commodity] [PubMed] [CrossRef] [Google Scholar]
5. Van Rooij A.J., Daneels R., Liu S., Anrijs Southward., Van Looy J. Children'southward motives to start, continue, and cease playing video games: Confronting popular theories with real-world observations. Curr. Aficionado. Rep. 2017;4:323–332. doi: 10.1007/s40429-017-0163-x. [CrossRef] [Google Scholar]
6. Billieux J. Problematic use of the mobile phone: A literature review and a pathways model. Curr. Psychiatry Rev. 2012;viii:299–307. doi: 10.2174/157340012803520522. [CrossRef] [Google Scholar]
7. Billieux J., Maurage P., Lopez-Fernandez O., Kuss D.J., Griffiths K.D. Tin can matted mobile phone use exist considered a behavioral addiction? An update on current bear witness and a comprehensive model for future inquiry. Curr. Addict. Rep. 2015;2:156–162. doi: 10.1007/s40429-015-0054-y. [CrossRef] [Google Scholar]
eight. Przybylski A.Chiliad., Murayama K., DeHaan C.R., Gladwell Five. Motivational, emotional, and behavioral correlates of fear of missing out. Comput. Hum. Behav. 2013;29:1841–1848. doi: 10.1016/j.chb.2013.02.014. [CrossRef] [Google Scholar]
ix. Deci E.L., Ryan R.M. Intrinsic Motivation and Self-Determination in Human Behavior. Plenum Press; New York, NY, Us: 1985. [Google Scholar]
10. Oulasvirta A., Rattenbury T., Ma L., Raita E. Habits make smartphone use more pervasive. Personal Ubiquitous Comput. 2012;16:105–114. doi: x.1007/s00779-011-0412-2. [CrossRef] [Google Scholar]
11. LaPlante D.A., Nelson South.E., Grey H.M. Breadth and depth involvement: Understanding Cyberspace gambling involvement and its relationship to gambling problems. Psychol. Addict. Behav. 2014;28:396. doi: 10.1037/a0033810. [PubMed] [CrossRef] [Google Scholar]
12. Abel P.J., Buff C.Fifty., Burr Southward.A. Social media and fear of missing out: Scale development and assessment. J. Bus. Econ. Res. 2016;fourteen:34–44. doi: 10.19030/jber.v14i1.9554. [CrossRef] [Google Scholar]
13. Simon J. Love: Addiction or route to self-realization, a second await. Am. J. Psychoanal. 1982;42:253–263. doi: 10.1007/BF01253492. [PubMed] [CrossRef] [Google Scholar]
14. Baumeister R.F., Leary Chiliad.R. The demand to vest: Desire for interpersonal attachments as a fundamental human motivations. Psychol. Bull. 1995;117:497–529. doi: 10.1037/0033-2909.117.3.497. [PubMed] [CrossRef] [Google Scholar]
15. Boyd D., Ellison N.B. Social network sites: Definition, history and scholarship. J. Comput. Mediat. Commun. 2008;thirteen:210–230. doi: 10.1111/j.1083-6101.2007.00393.x. [CrossRef] [Google Scholar]
16. Kaplan A.M., Haenlein K. Users of the world, unite! The challenges and opportunities of Social Media. Bus. Horiz. 2010;53:59–68. doi: ten.1016/j.bushor.2009.09.003. [CrossRef] [Google Scholar]
17. Clayton R.B., Leshner G., Almond A. The extended iself: The impact of iphone separation on noesis, emotion and physiology. J. Comput. Mediat. Commun. 2015;20:119–135. doi: 10.1111/jcc4.12109. [CrossRef] [Google Scholar]
18. Kruger D.J., Djerf J.One thousand. Loftier ringxiety: Attachment anxiety predicts experiences of phantom cell telephone ringing. Cyberpsychol. Behav. Soc. Netw. 2016;19:56–59. doi: 10.1089/cyber.2015.0406. [PubMed] [CrossRef] [Google Scholar]
xix. Belk R.W. Possession and the extended self. J. Consum. Res. 1988;15:139–168. doi: 10.1086/209154. [CrossRef] [Google Scholar]
20. Tandor E.C., Ferrucci P., Duffy M. Facebook use, envy, and depression amongst higher students: Is facebooking depressing? Comput. Hum. Behav. 2014;43:139–146. doi: 10.1016/j.chb.2014.ten.053. [CrossRef] [Google Scholar]
21. Casale S., Rugai L., Fioravanti K. Exploring the role of positive metacognitions in explaining the clan betwixt the fear of missing out and social media addiction. Addict. Behav. 2018;85:83–87. doi: 10.1016/j.addbeh.2018.05.020. [PubMed] [CrossRef] [Google Scholar]
22. Katz East. Mass communications research and the written report of popular culture: An editorial note on a possible future for this periodical. Stud. Public Commun. 1959;2:i–6. [Google Scholar]
23. Katz E., Blumler J.G. The Uses of Mass Communications: Electric current Perspectives on Gratifications Research. Sage Publications; Beverly Hills, CA, United states of america: 1974. [Google Scholar]
24. Ajzen I. The theory of planned beliefs. Organ. Behav. Hum. Decis. Procedure. 1991;50:179–211. doi: 10.1016/0749-5978(91)90020-T. [CrossRef] [Google Scholar]
25. Lee E., Lee J.A., Moon J.H., Sung Y. Pictures speak louder than words: Motivations for using Instagram. Cyberpsychol. Behav. Soc. Netw. 2015;eighteen:552–556. doi: 10.1089/cyber.2015.0157. [PubMed] [CrossRef] [Google Scholar]
26. Sheldon K.Thousand., Abad N., Hirsch C. A two-process view of Facebook use and relatedness need-satisfaction: Disconnection drives u.s.a. and connection rewards information technology. J. Personal. Soc. Psychol. 2011;100:766–775. doi: x.1037/a0022407. [PubMed] [CrossRef] [Google Scholar]
27. Sheldon P., Bryant K. Instagram: Motives for its employ and relationship to narcissism and contextual historic period. Comput. Hum. Behav. 2015;58:89–97. doi: 10.1016/j.chb.2015.12.059. [CrossRef] [Google Scholar]
28. Beyens I., Frison E., Eggermont S. "I don't want to miss a thing": Adolescents' fear of missing out and its human relationship to adolescents' social needs, Facebook utilise, and Facebook related stress. Comput. Hum. Behav. 2016;64:one–8. doi: ten.1016/j.chb.2016.05.083. [CrossRef] [Google Scholar]
29. Błachnio A., Przepiórka A. Facebook Intrusion, fearfulness of missing out, narcissism, and life satisfaction: A cross-sectional study. Psychiatry Res. 2018;259:514–519. doi: 10.1016/j.psychres.2017.eleven.012. [PubMed] [CrossRef] [Google Scholar]
thirty. Barry C.T., Reiter S.R., Anderson A.C., Schoessler Chiliad.L., Sidoti C.Fifty. "Allow me take another selfie": Further examination of the relation betwixt narcissism, self-perception, and Instagram posts. Psychol. Popul. Media Cult. 2017 doi: 10.1037/ppm0000155. [CrossRef] [Google Scholar]
31. Salim F., Rahardjo W., Tanaya T., Qurani R. Are self-presentation of instagram users influenced past friendship-contingent self-esteem and fear of missing out? Makara Hubs Asia. 2017;21:70–82. doi: x.7454/mssh.v21i2.3502. [CrossRef] [Google Scholar]
32. Bayer J.B., Ellison N.B., Schoenebeck South.Y., Falk East.B. Sharing the small moments: Ephemeral social interaction on Snapchat. Inf. Commun. Soc. 2016;xix:956–977. doi: 10.1080/1369118X.2015.1084349. [CrossRef] [Google Scholar]
33. Alhabash S., Ma M. A tale of four platforms: Motivations and uses of Facebook, Twitter, Instagram, and Snapchat among college students? Soc. Media Soc. 2017;iii:1–13. doi: 10.1177/2056305117691544. [CrossRef] [Google Scholar]
34. Caplan S. Relations among loneliness, social anxiety, and problematic internet use. Cyberpsychol. Behav. 2006;10:234–242. doi: ten.1089/cpb.2006.9963. [PubMed] [CrossRef] [Google Scholar]
35. Milani L., Osualdella D., Di Blasio P. Quality of interpersonal relationships and problematic cyberspace use in adolescents. CyberPsychol. Behav. 2009;12:681–684. doi: 10.1089/cpb.2009.0071. [PubMed] [CrossRef] [Google Scholar]
36. Chakraborty 1000., Basu D., Vijaya K. Internet Addiction: Consensus, Controversies and the Way Ahead. East Asian Arch Psychiatry. 2010;twenty:123–132. [PubMed] [Google Scholar]
37. Griffiths M.D., Kuss D.J. Adolescent Social Media Addiction (revised) Educ. Health. 2017;35:49–52. [Google Scholar]
38. Buglass Southward.L., Binder J.F., Betts L.R., Underwood J.D. Motivators of online vulnerability: The affect of social network sites apply and FOMO. Comput. Hum. Behav. 2017;66:248–255. doi: 10.1016/j.chb.2016.09.055. [CrossRef] [Google Scholar]
39. Fuster H., Chamarro A., Oberst U. Fear of missing out, online social networking and mobile phone addiction: A latent profile approach. Revista de Psicologia Ciències de l'Educació i de l'Esport. 2017;35:23–30. [Google Scholar]
40. Oberst U., Wegmann East., Stodt B., Brand G., Chamarro A. Negative consequences from heavy social networking in adolescents: The mediating role of fear of missing out. J. Adolesc. 2017;55:51–sixty. doi: 10.1016/j.adolescence.2016.12.008. [PubMed] [CrossRef] [Google Scholar]
41. Wegmann Due east., Oberst U., Stodt B., Make Chiliad. Online-specific fear of missing out and Internet-utilize expectancies contribute to symptoms of Net-communication disorder. Addict. Behav. Rep. 2017;v:33–42. doi: 10.1016/j.abrep.2017.04.001. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
42. Elhai J.D., Levine J.C., Alghraibeh A.M., Alafnan A.A., Aldraiweesh A.A., Hall B.J. Fright of missing out: Testing relationships with negative affectivity, online social appointment, and problematic smartphone use. Comput. Hum. Behav. 2018;89:289–298. doi: x.1016/j.chb.2018.08.020. [CrossRef] [Google Scholar]
43. Scott H., Wood H.C. Fear of missing out and sleep: Cognitive behavioural factors in adolescents' nighttime social media use. J. Adolesc. 2018;68:61–65. doi: ten.1016/j.adolescence.2018.07.009. [PubMed] [CrossRef] [Google Scholar]
44. Chotpitayasunondh Five., Douglas K.M. How "phubbing" becomes the norm: The antecedents and consequences of snubbing via smartphone. Comput. Hum. Behav. 2016;63:nine–18. doi: 10.1016/j.chb.2016.05.018. [CrossRef] [Google Scholar]
45. Kenny Thousand. Bachelor's Thesis. The Florida State Academy; Tallahassee, FL, United states: 2016. Cell Phones, College Students, and Conversations: Exploring Mobile Technology Utilise during Contiguous Interactions. [Google Scholar]
46. Vanden Abeele Thousand.K.V., Antheunis M.L., Schouten A.P. The consequence of mobile messaging during a conversation on impression formation and interaction quality. Comput. Hum. Behav. 2016;62:562–569. doi: 10.1016/j.chb.2016.04.005. [CrossRef] [Google Scholar]
47. Ofcom . The Communications Market Reports. Ofcom; Warrington, UK: 2015. [Google Scholar]
48. Kuss D.J., Kanjo Due east., Rumsey-Crook M., Kibowski F., Wang G.Y., Sumich A. Problematic mobile phone use and addiction beyond generations: The roles of psychopathological symptoms and smartphone utilise. J. Technol. Behav. Sci. 2018;3:141–149. doi: 10.1007/s41347-017-0041-3. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
49. Van Waeg S., D'hanens G., Dooms V., Naesens J. Onderzoeksrapport Apestaartjaren 6/Research Report Apestaartjaren half-dozen. [(accessed on 17 Baronial 2018)]; Bachelor online: www.apestaartjaren.be/onderzoek/apestaartjaren-six.
51. Schmitt N. Uses and Abuses of Coefficient Alpha. Psychol. Appraise. 1996;4:350–353. doi: 10.1037/1040-3590.8.4.350. [CrossRef] [Google Scholar]
52. Van Rooij A.J., Ferguson C.J., Van de Mheen D., Schoenmakers T.Thousand. Time to abandon Cyberspace Habit? Predicting problematic Internet, game, and social media apply from psychosocial well-being and application apply. Clin. Neuropsychiatry. 2017;14:113–121. [Google Scholar]
53. Meerkerk G., Van den Eijnden R.J.J.M, Vermulst A.A., Garretsen H.F.L. The Compulsive Internet Use Calibration (CIUS): Some psychometric properties. Cyberpsychol. Behav. 2009;i:1–6. doi: 10.1089/cpb.2008.0181. [PubMed] [CrossRef] [Google Scholar]
54. Meerkerk G., Pwned by the Internet . Ph.D. Thesis. Erasmus University; Rotterdam, The Netherlands: 2007. Explorative research into the causes and consequences of compulsive net use. [Google Scholar]
55. Williams R. Generalized ordered logit/fractional proportional odds models for ordinal dependent variables. Stata J. 2006;6:58. [Google Scholar]
56. Kim H. Statistical notes for clinical researchers: Assessing normal distribution (2) using skewness and kurtosis. Restor. Paring. Endod. 2013;38:52–54. doi: x.5395/rde.2013.38.1.52. [PMC gratis commodity] [PubMed] [CrossRef] [Google Scholar]
57. Steiger J.H. Tests for comparing elements of a correlation matrix. Psychol. Balderdash. 1980;87:245–251. doi: 10.1037/0033-2909.87.two.245. [CrossRef] [Google Scholar]
58. Lee I.A., Preacher K.J. Calculation for the Test of the Difference between Ii Dependent Correlations with I Variable in Common (Estimator Software) [(accessed on ii October 2018)]; Available online: http://quantpsy.org.
59. Allen I.E., Seaman C.A. Likert scales and data analyses. Qual. Prog. 2007;40:64–65. [Google Scholar]
60. Hayes A.F. Introduction to Arbitration, Moderation, and Provisional Process. Analysis. Guilford Press; New York, NY, U.s.: 2013. [Google Scholar]
61. Fox J., Moreland J.J. The dark side of social networking sites: An exploration of the relational and psychological stressors associated with Facebook use and affordances. Comput. Hum. Behav. 2015;45:168–176. doi: 10.1016/j.chb.2014.11.083. [CrossRef] [Google Scholar]
62. Brook A.T. Cerebral therapy of depression: New perspectives. In: Clayton P.J., Barrett J.E., editors. Treatment of Depression: Old Controversies and New Approaches. Raven Press; New York, NY, USA: 1983. pp. 265–290. [Google Scholar]
63. Orchard L.J., Fullwood C., Galbraith North., Morris N. Individual differences as predictors of social networking. J. Comput. Mediat. Commun. 2014;19:388–402. doi: 10.1111/jcc4.12068. [CrossRef] [Google Scholar]
64. Elhai J.D., Levine J.C., Dvorak R.D., Hall B.J. Fear of missing out, need for touch, anxiety and low are related to problematic smartphone utilize. Comput. Hum. Behav. 2016;63:509–516. doi: 10.1016/j.chb.2016.05.079. [CrossRef] [Google Scholar]
65. Stead H., Bibby P.A. Personality, fearfulness of missing out and problematic internet utilize and their relationship to subjective well-being. Comput. Hum. Behav. 2017;76:534–540. doi: ten.1016/j.chb.2017.08.016. [CrossRef] [Google Scholar]
66. Roberts J.A., David Grand.E. My life has become a major distraction from my cell phone: Partner phubbing and relationship satisfaction amidst romantic partners. Comput. Hum. Behav. 2016;54:134–141. doi: 10.1016/j.chb.2015.07.058. [CrossRef] [Google Scholar]
67. Ling R., Lai C. Microcoordination ii.0: Social Coordination in the Historic period of Smartphones and Messaging Apps. J. Commun. 2016;66:834–856. doi: 10.1111/jcom.12251. [CrossRef] [Google Scholar]
Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6211134/
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