College students academic motivation, media engagement [614623]

College students’ academic motivation, media engagement
and fear of missing out
Dorit Alt
Kinneret College on the Sea of Galilee, Israel
article info
Article history:
Keywords:
Fear of missing out
Social media engagementSelf-determination theoryAcademic motivationHigher educationabstract
The concerns about the consequences of mental problems related to use of social media among university
students have recently raised consciousness about a relatively new phenomenon termed Fear of MissingOut (FoMO). Drawing on the self-determination theory and on the assumption that low levels of basic
need satisfaction may relate to FoMO and social media engagement, the aim of the present research
was to examine for the first time possible links between FoMO, social media engagement, and threemotivational constructs: Intrinsic, extrinsic and amotivation for learning. Data were gathered from 296
undergraduate students by using the following scales: Social Media Engagement (SME), Fear of
Missing Out (FoMOs) and Academic Motivation. The SME is a new scale, specifically designed for thisstudy to measure the extent to which students used social media in the classroom. This scale includesthree categories: Social engagement, news information engagement and commercial information
engagement. Path analysis results indicated that the positive links between social media engagement
and two motivational factors: Extrinsic and amotivation for learning are more likely to be mediated byFoMO. Interpretation of these results, their congruence within the context of the theoretical frameworks
and practical implications are discussed.
/C2112015 Elsevier Ltd. All rights reserved.
1. Introduction
Students attending colleges today, known as the ‘Millennials’
(Jonas-Dwyer & Pospisil, 2004 ), are heavy users of social media
tools relative to the general population, and use them extensively
for communication with peers, including other students in their
courses ( Ophus & Abbitt, 2009; Subrahmanyam, Reich, Waechter,
& Espinoza, 2008 ). These technologies might play a key role in
keeping college students connected to family and friends to obtain
social support ( Gemmill & Peterson, 2006 ). However, extensive
social media use could also negatively affect psychological
outcomes, such as well-being ( Alabi, 2013; Alavi, Maracy,
Jannatifard, & Eslami, 2011 ). These concerns about the conse-
quences of mental problems related to use of social media among
university students have recently raised consciousness about a
relatively new phenomenon termed Fear of Missing Out, popularly
referred to as FoMO. This phenomenon has been defined as a ‘‘per-
vasive apprehension that others might be having rewarding expe-riences from which one is absent, FoMO is characterized by the
desire to stay continually connected with what others are doing’’
(Przybylski, Murayama, DeHaan, & Gladwell, 2013, p. 1841 ).Drawing on the self-determination theory (SDT; Deci & Ryan,
1985, 2008 ),Przybylski et al. (2013) suggest that FoMO could serve
as a mediator linking deficits in psychological needs to social media
engagement. Their study showed that FoMO plays an essential role
in the explanation of social media engagement over and above sev-
eral individual factors, such as levels of need satisfaction. Based on
this motivation-based perspective, the current study aims to fur-
ther explore FoMO and its set of connections to Millennials’ social
media engagements in higher education settings.
Motivation is considered to be a significant psychological con-
struct in the learning process, and highly connected to academic
achievement and persistence in college ( Donche, Coertjens, Van
Daal, De Maeyer, & Van Petegem, 2014; Linnenbrink & Pintrich,
2002; Ratelle, Guay, Vallerand, Larose, & Senécal, 2007 ), therefore
seems as a useful perspective for framing an empirically based
understanding of FoMO. The current study aims to assess this psy-
chological construct’s connections to college students’ social media
engagement during lessons, mediated by FoMO, hence enables todelve further into the newly defined phenomenon of FoMO by
investigating its correlates with learning motivations.
The current work represents a twofold effort. First, from a
methodological point of view and with the dearth of empirically-
based assessment instruments, a new scale, designed to measure
http://dx.doi.org/10.1016/j.chb.2015.02.057
0747-5632/ /C2112015 Elsevier Ltd. All rights reserved.E-mail address: doritalt@014.net.ilComputers in Human Behavior 49 (2015) 111–119
Contents lists available at ScienceDirect
Computers in Human Behavior
journal homepage: www.elsevi er.com/locate/comphumbeh

features of social media activities in higher education settings, will
be constructed and validated. Moreover, Przybylski et al.’s (2013)
single-factor scale will be adapted to include different facets of
FoMO, corresponding to the different social media utilities suggest-
ed by theory. Thus, in contrast to previous work, the current study
could point to specific elements of FoMO and social media engage-
ments, which may be connected to learning motivations. These
efforts might allow for the examination of more components or
dimensions based on theoretical considerations than have been
assessed thus far.
Second, with relation to college students’ learning processes,
this study could illustrate the role of motivational constructs in
explaining FoMO and social media engagement, when the latter is
not harnessed for pedagogical purposes. This potentially new
avenue of research might encourage a future discussion related toMillennials’ engagement in current higher education learning
environments, and to the investigation of new instructional
approaches incorporating social media usages into current pedago-
gical applications.
2. Theoretical framework
2.1. Psychological correlates of social media engagement
Social media provides a platform for active communication
between friends and an access to new information through a
diverse set of acquaintances ( Burke, Marlow, & Lento, 2010 ).
Lankshear and Knobel (2011) describe social media as new ways
of participating and communicating that rely on collaboration,
remixed texts, and self-publishing. Social media utilities afford
easy access to real-time information about the activities, events,
and conversations happening across diverse social networks
(Przybylski et al., 2013 ). These utilities enable users to articulate
a network of connections of people with whom they wish to share
access to diverse forms of content, such as profile information,
news, or status updates ( Steinfield, Ellison, Lampe, & Vitak,
2013 ). In the field of marketing, the emergence of Internet-based
social media has made it possible for individuals to communicate
with other people about products and the companies that provide
them ( Kietzmann, Hermkens, McCarthy, & Silvestre, 2011;
Mangold & Faulds, 2009 ). Social media has also dramatically
reduced the cost to produce, distribute, and access diverse news
information. With the rise of social media sites, such as Facebook
and Twitter, individuals can share their favorite stories with hun-
dreds of their contacts. According to Mitchell and Guskin’s
(2013) report, nearly one-in-ten U.S. adults (8%) get news through
Twitter and 30% of Americans get news on Facebook.
Previous research on social media has been mainly focused on
the influence of technological tools for seeking social connections
on attitudes and behaviors that enhance individuals’ social capital
(Acquisti & Gross, 2006; Ellison, Steinfield, & Lampe, 2007; Putnam,
2000; Steinfield et al., 2013 ). Several studies have examined the
underlying mechanisms through which social capital benefits are
generated by the use of social media. These studies have indicated
several advantages of social tools like Facebook and Twitter for the
general population. For instance, use of these sites has been asso-
ciated with greater levels of emotional support from close friends
(Putnam, 2000 ).Steinfield et al. (2013) maintain that these social
media utilities have become important for managing relationships
with a large network of people who provide social support and
serve as conduits for useful information. Other studies show that
young people are motivated to join social media sites to keep
strong ties with friends and to strengthen ties with new acquain-
tances ( Acquisti & Gross, 2006; Ellison et al., 2007 ). In a similar
route, Steinfield, Ellison, and Lampe (2008) found that the intensityof Facebook use strongly predicted higher levels of social capital
outcomes. Social networks could also enable individuals to develop
norms of trust and reciprocity, which are necessary for successful
engagement in collective activities ( Valenzuela, Park, & Kee, 2009 ).
While the aforementioned studies reveal positive associations
between online social network use and well-being, others suggest
the opposite. For instance, Chou and Edge (2012) have examined
the impact of using Facebook on undergraduate students’ percep-
tions of others’ lives. Their study indicated that those who have
used Facebook longer, agreed more that others were happier and
had better lives, and agreed less that life is fair; furthermore, stu-
dents who included more people whom they did not personally
know as their Facebook ‘friends’ agreed more that others had bet-
ter lives. Kross et al.’s (2013) study indicated that Facebook use
could predict declines in two components of subjective wellbeing:How people feel moment to moment and how satisfied they are
with their lives.
2.2. FoMO and social media use in college
The generation of students attending colleges today is known as
the ‘Millennials’. The characteristics typically attributed to millen-
nial students are an information technology mindset and a highly
developed skill in multitasking. Millennials are described as having
a focus on social interaction and connectedness with friends,
family and colleagues by using SMS, mobile phones, chat-rooms
and email while they simultaneously play computer games, listen
to music and watch TV ( McMahon & Pospisil, 2005 ).Gemmill and
Peterson (2006) posit that on the up side, these technologies might
play a significant role in keeping college students connected to
family and friends to obtain social support, as a buffer to ‘‘exces-
sive’’ levels of stress they experience as they grapple with a host
of academic, personal, and social pressures. On the down side, their
study on college students indicated that technology may also dis-
rupt and occupy the time of a college student, and that could
enhance higher levels of perceived stress. Others suggest that
social media could also afford an outlet for addictive behaviors
(Alabi, 2013 ), or psychiatric symptoms, such as sensitivity,
depression and anxiety ( Alavi et al., 2011 ).
2.3. Psychological correlates of FoMO
Whereas the above-surveyed studies have focused on the
impact of social media use on psychological outcomes, such as
well-being, a recent study ( Przybylski et al., 2013 ) examined possi-
ble potential personal predictors of social media engagement, in
particular aspects of deficits in psychological need satisfactions.
Drawing on the self-determination theory (SDT; Deci & Ryan,
1985, 2008 ), Przybylski et al. contend that FoMO could serve as a
mediator linking deficits in psychological needs to social media
engagement. Their study’s results indicated that individuals who
evidenced less satisfaction of the basic psychological needs for
competence (efficacy), autonomy (meaningful choice), and related-
ness (connectedness to others) also reported higher levels of FoMO.
In the field of educational psychology, the psychological con-
struct of motivation for learning, is one of the most highly studied
variable, and has been extensively investigated in previous
research ( Donche et al., 2014; Linnenbrink & Pintrich, 2002;
Ratelle et al., 2007 ). Therefore, in the context of higher education,
the SDT regarding learning motivation appears to be a particularly
useful perspective to empirically explore FoMO.
Previous studies on learning motivations have placed an
emphasis on the motivation behind the choices that students
make, and on how social factors affect their sense of volition and
initiative, as well as their well-being and the quality of their
academic performance ( Linnenbrink & Pintrich, 2002; Ratelle112 D. Alt / Computers in Human Behavior 49 (2015) 111–119

et al., 2007 ). The SDT defines intrinsic and extrinsic sources of
motivation. Intrinsic motivation refers to internal factors, such as
enthusiasm and pleasure experienced while engaging in a task.
In contrast, extrinsic motivation refers to external factors, such as
obtaining good grades or passing exams. Studies on the quality of
motivation (e.g., Donche et al., 2014 ) suggest that although basical-
ly distinguished as intrinsic and extrinsic, it can be further refined
by making a distinction between autonomous and controlled moti-
vation. Studies assessing the connections between these motiva-
tions, learning strategies and achievements have associated
controlled motivation with surface processing and weak coping
strategies in the case of failing ( Ryan & Connell, 1989 ). The surface
approach to learning is based on an intention that is extrinsic to
the real purpose of the task ( Biggs, 2001 ). One of the most common
strategies for the surface approach is rote learning content withoutunderstanding, in order to subsequently reproduce the material.
The surface approach is generally related to lower quality out-
comes of learning ( Kyndt, Dochy, & Cascallar, 2014 ).
Autonomous motivation has been found directly and positively
connected to a deep approach to learning, that is the use of more
information processing, high concentration while studying and
better time management, and indirectly to higher academic
achievement ( Vansteenkiste, Zhou, Lens, & Soenens, 2005 ). A deep
approach is based on a perceived need, such as an intrinsic interest
to engage the task appropriately and meaningfully. Examples of
deep-approach strategies are reflecting, using various information
sources, relating ideas and looking for patterns ( Kyndt et al., 2014 ).
According to Burnett, Pillay, and Dart’s (2003) study, students who
adopted a deep approach liked learning new things and indirectly
viewed learning as experiential, involving social interaction, and
directly viewed learning as personal development.
Apart from intrinsic and extrinsic motivations, a third construct
assessed in the present study is amotivation (an absence of motiva-
tion) for learning ( Ratelle et al., 2007 ). Amotivated students are nei-
ther intrinsically nor extrinsically motivated. They cannot predict
the consequences of their behavior, and may feel disintegrated or
detached from their action and will thus invest little effort in its
effectuation ( Deci & Ryan, 1985 ). In the academic domain, amotiva-
tion has been associated with boredom and poor concentration in
class ( Vallerand et al., 1993 ), poor psychosocial adjustment to col-
lege ( Baker, 2004 ), and dropout ( Vallerand, Fortier, & Guay, 1997 ).
Despite increased interest in FoMO, there has been to date scant
scholarly literature on its potential links with psychological health
and well-being. In fact, Przybylski et al.’s (2013) research was the
first to provide a number of insights into how fear of missing out
constellates with motivational, behavioral, well-being, and demo-
graphic factors. Their study mainly showed that FoMO plays a
key role in explaining social media engagement over and aboveseveral individual factors, such as levels of need satisfaction, gen-
eral mood, and overall life satisfaction. The concerns about the con-
sequences of the alarming rate of mental problems related to use of
social media among university students give additional reasons to
expect FoMO linked to motivational deficits.
2.4. The present research
Based on the SDT and on the assumption that low levels of basic
need satisfaction may relate to FoMO and social media engage-
ment, the aim of the present research was to examine for the first
time possible links between these constructs in the academic are-
na. In order to properly assess these connections, a new scale was
constructed and validated to map and measure social media activ-
ities students engaged in during classes.
The following hypotheses were formulated to guide the study:H1. Students, who are high in basic need satisfaction, in terms of
being intrinsically motivated for learning, would be less inclined
toward social media use in the classroom. Whereas amotivated or
extrinsically motivated students would tend toward using social
media tools available.
H2. Based on the assumption that psychological need deficits can
lead some toward a general sensitivity to FoMO, it is hypothesized
that FoMO would serve as a mediator linking motivational deficits
to social media engagement. Furthermore, background variables,
such as gender, age, and socio-economic status, will also be
addressed in this research in order to assess how these variables
intersect and may contribute to the measured variables. Fig. 1
demonstrates the theoretical structure of the proposed framework.
3. Method
3.1. Participants
Data were gathered from 296 undergraduate Social-Science stu-
dents (14.7% males and 85.3% females) from one major college
located in the Northern Galilee. The distribution regarding eth-
nicity was: 65.9% Jewish students, 27.3% Muslim students, 5.1%
Christian students, and 1.7% Druze students, with a mean age of
25.4 ( SD= 7.1) years. Based on the report of The Central Bureau
of Statistics. (2011) and The Council for Higher Education. (2009)
in Israel, the gender and ethnicity breakdown of Northern Galilee
college students, majoring mainly in Social-Science studies is 20%
males and 80% females of whom 50% Jews, 45% Muslims, and 5%
belonging to other religions, thus the current study’s sample repre-
sents, to some extent, the gender breakdown of regional colleges
located in the Northern Galilee. However, although including all
four ethnic groups studying in Northern Galilee colleges, the sam-
ple is less representative concerning the distribution of ethnicity.
The distribution regarding the year of study was: 42.4%
first-year students, 31% second-year students and 26.6% third-year
students. The participants’ faculty enrollment breakdown was as
follows: Education – 35.1%, Criminology – 11.9%, Sociology – 8.4%,
Management – 36.5%, Economics – 6%, Political Science – 2.1%.
3.2. Instrumentation
3.2.1. Student characteristics
Data were gathered using a questionnaire aimed at measuring
the student’s cultural group, gender, age, socioeconomic-status
(SES), year of study, and current education achievements. SES
was assessed by the student’s father’s educational attainment
(FEA) and mother’s educational attainment (MEA), both definedon a six-level scale from 0 = lack of education ,t o5= doctoral degree .
Another SES factor was the participants’ report on their current
economic condition (EC), defined on a six-level scale from
1=extremely difficult to 6 = comfortable ,no financial worries .
Social media
engagementFoMO Academic
motivations
Student
characteristics
Fig. 1. Model 1. The theoretical structure of the proposed framework.D. Alt / Computers in Human Behavior 49 (2015) 111–119 113

Finally, students’ current education achievements were measured
by their self-reported grade point average (GPA).
3.2.2. Social Media Engagement (SME) questionnaire
This scale was specifically designed for this study to measure
the extent to which students used social media in the classroom.
The scale was constructed in three steps. The first step included
collecting statements from 54 college students who were asked
to describe their social media activities in the classroom. In the sec-
ond step, duplicates and irrelevant statements were omitted. The
rest were analyzed by three raters; all are experts in the research
area of media and digital literacy. Inter-rater Cohen’s Kappa ( k)
reliability ( Cohen, 1960 ) was used. The raters were asked to
categorize the students’ reports. The kvalues were interpreted as
follows: k< 0.20 poor agreement; 0.21 < k< 0.40 fair agreement;
0.41 < k< 0.60 moderate agreement; 0.61 < k< 0.80 good agree-
ment; 0.81 < k< 1.00 very good agreement. Results of 0.61 < k<1
were considered acceptable. Three meaningful categories have
emerged from the analysis:
1. Social engagement – refers to sharing individual or social infor-
mation with the close social environment, such as family and
friends, using social media sites (e.g. Facebook, Twitter,
Whatsapp, Instagram).
2. News information engagement – includes news-related activ-
ities, for example, responding to alerts or getting updates via
social media sites.
3. Commercial information engagement – pertains to activities,
such as getting or sharing updates (e.g. current discounts/sales,
available coupons) via social media sites.
The statements were formulated as short items. Each item was
given a Likert-type score ranging from 1 = never to 5 = always .
Consequently, a 14-item scale was submitted to 65 undergraduate
students in order to assess the clarity of the items. Accordingly,
four items were excluded due to unclear phrasing.
Third step: The 10-item scale (hereinafter: Social Media
Engagement scale [SME]) was submitted to 296 undergraduate
students. Participants were asked: ‘To what extent do you do the
following activities by using your laptop computer or mobile
phone during class?’ Students were also asked whether these
activities were used during class for learning purposes. It should
be noted that as reported by the students, none of the activities
were requested for academic purposes.
All items were subjected to a principal component analysis fol-
lowed by a Varimax rotation with an eigenvalue >1.00 as acriterion for determining the number of factors. The analysis
resulted in three factors, which accounted for 69.60% of the vari-ance. Table 1 provides the item loadings (>.40) on each of the three
factors and the computed internal consistencies (Cronbach’s alpha)
for each factor and for all items, indicating high overall and within
factor reliability results. Item 5 was omitted due to a low item
loading result (<.40). Convergent validity has been shown by posi-
tive statistically significant correlations between all factor pairings
(.29 < r< .37; p< .01). The generally small to moderate correlations
among the dimensions suggest that the factors are, to some extent,
independent each from the other. Table 2 provides descriptive
statistics for the SME factors.
3.2.3. Fear of Missing Out Scale (FoMOs)
Based on a review of popular and industry writing on FoMO
(e.g., JWT., 2011; Morford, 2010; Wortham, 2011 ),Przybylski
et al. (2013) created this 10-item scale meant to reflect the fears,
worries, and anxieties people may have in relation to being in (or
out of) touch with the events, experiences, and conversations hap-
pening across their extended social environment. The scale mea-
sures the extent to which people feared missing out on
rewarding experiences, activities, and methods of discourse, for
example: ‘I get worried when I find out my friends are having
fun without me’. In accordance with the above new constructed
SME scale, eight items were added to the FoMO scales: Four of
which were aimed at measuring the extent to which people feared
missing out news information, for example: ‘It bothers me when
my friends know what’s happening on the news ahead of me’; four
items were aimed at assessing the extent to which people feared
missing out commercial information, for example: ‘When I go on
vacation, it is important to me to continue following commercial
information (e.g. current discounts/sales, available coupons)’. The
overall scale included 18 items, scored on a five-point Likert scale
from 1 = not at all true of me to 5 = extremely true of me .
The 18-item scale was submitted to 65 undergraduate students
in order to assess the clarity of the items. Accordingly, one social
FoMO item was excluded due to unclear phrasing. All 17 items
were subjected to a principal component analysis followed by aTable 1
The SME questionnaire: Factors, item descriptions, item loadings and internal consistencies (Cronbach’s alpha).
Factor Item Item
loadingCronbach’s
alpha
Social media
engagement1 Reading updates about what is happening with others (e.g., your friends or family members) by using social
media sites (e.g. Facebook, Twitter, Whatsapp, Instagram).857 .85 (four
items)
3 Responding to social or personal updates of others (e.g., your friends or family members) in social media
sites.834
2 Updating personal information in social media sites .767
4 Holding conversations (chats) with others (e.g., your friends or family members) in social media sites .759
News information
engagement5 Reading news updates via social media sites <.04
6 Responding to news information (e.g. by talkbacks) via social media sites .856 .77 (two
items)
7 Sharing news alerts via social media sites .846
Commercial
informationengagement8 Buying ‘‘on sale’’ products via social media sites .832 .83 (three
items) 9 Sharing commercial updates via social media sites .826
10 Reading commercial updates (e.g. current discounts/sales, available coupons) via social media sites .812
.84 Total
(nine items)
Table 2
Descriptive statistics for the SME measured factors.
Factor Mean SD Skewness Kurtosis
Social engagement 3.20 1.06 /C00.30 /C00.54
News information engagement 1.72 0.90 1.10 0.75Commercial information engagement 1.87 0.92 0.94 0.50114 D. Alt / Computers in Human Behavior 49 (2015) 111–119

Varimax rotation with an eigenvalue >1.00 as a criterion for deter-
mining the number of factors. Four items (F5, F7, F9, and F11) were
removed from the scale due to low item loading results (<.40). The
analysis resulted in three factors, which accounted for 54.56% of
the variance: Social FoMO (based on the original FoMOs’ items),
including six items (Cronbach’s alpha equals to .79); three items
of fear of missing news information (Cronbach’s alpha equals to
.70); and fear of missing commercial information, including four
items (Cronbach’s alpha equals to .83). The intercorrelation results
among the factors indicated positive connections (.33 < r< .39;
p< .01).
3.2.4. Academic motivation
Academic motivation was measured by three constructs from
the Academic Motivation Scale – College (CEGEP) version
(Vallerand, Blais, Brière, & Pelletier, 1989 ): Intrinsic motivation
(IN), for example: ‘I go to college because I experience pleasure
and satisfaction while learning new things’; extrinsic motivation(EX), for instance: ‘I go to college because with only a high-school
degree I would not find a high-paying job later on’; and amotivation
(AM), for example: ‘I can’t see why I go to college and frankly, I
couldn’t care less’. The overall scale included 12 items, scored on
a five-point Likert scale from 1 = strongly disagree to 5 = strongly
agree . The structural validity of the scale is shown in Fig. 2 (which
presents the combined measurement and path models). The inter-
correlation results among the motivational factors indicated a posi-
tive connection between intrinsic and extrinsic motivation ( r= .23,
p< .01), and negative connections between amotivation and intrin-
sic/extrinsic motivations ( /C0.31 < r</C0.35, p< .01). (Cronbach’s
alpha results for the sub-scales ranged from 0.70 to 0.82).
3.3. Procedure
The questionnaires were administered by research assistants to
the participants in the classrooms in which they studied without
the instructor being present. The purpose of the study was
explained as examining social media engagement in higher educa-
tion. Prior to obtaining participants’ consent, it was specified that
the questionnaire was anonymous and that no pressure would be
applied should they choose to return the questionnaire unfilled
or incomplete. Debriefing information was sent to the participants
on the completion of the study via the academic institutions’ Web-
site and face-to-face presentations, in which they could raise ques-
tions. Finally, participants were assured that no specific identifying
information about the courses would be processed.
4. Findings
Structural equation modeling (SEM) was employed to
empirically test the current research hypotheses and to further
assess the construct validity of the SME and FoMO scales, using a
confirmatory factor analysis. Data used for the SEM were analyzed
with the maximum likelihood method. Three fit indices were com-
puted in order to evaluate model fit (the parenthetical values by
the fit indices indicate the suggested cut-offs for good quality of
fit):v2(df)(p> .05), CFI(>0.9), and RMSEA (<0.08) ( Bentler, 2006 ).
4.1. The first hypothesis ( H1)
A structural model ( Fig. 2 ) was constructed to measure the con-
nections between the motivational and SME constructs. The model
included the SME latent factor with its three latent sub-factors:
Social engagement (M1), news information engagement (M2),
and commercial information engagement (M3); and the motiva-
tional latent factors of extrinsic motivation (EX), intrinsicmotivation (IN), and amotivation (AM). Observed items were
entered in accordance with the aforementioned measurement
descriptions. The goodness-of-fit of the data to the model yielded
sufficient fit results ( v2= 478.10, df= 183, p= .000; CFI= .910;
RMSEA = .071). Results indicated positive, low to moderate, sig-
nificant coefficients between SME and the following constructs:
Amotivation ( b= .46, p< .001), and extrinsic motivation ( b= .30,
p< .001). An insignificant path coefficient was found between the
intrinsic motivation and SME constructs.
The model’s capacity to explain the variation in each dependent
variable was measured by the squared multiple correlation (SMC)
values, for each structural equation (path) in the model. This coef-
ficient is a measure of how well a given variable can be predicted
using a linear function of a set of other variables. According to the
results, the motivational factors explained 30% of the SME factorvariance.
4.2. The second hypothesis ( H2)
In order to test the second hypothesis, several background vari-
ables and the FoMO latent variable accompanied by three latent
variables: Social FoMO (FO1), news information FoMO (FO2), and
commercial information FoMO (FO3) were entered into Model 3.
The path model ( Fig. 3 ) was constructed as follows: Paths were
specified between the following student characteristic variables
and several latent factors: Age, gender ( Male =1 , Female = 2), and
Cultural group (CG: Jewish students =1 , Non-Jewish students = 2).
The latter dummy variable was created due to insignificant differ-
ences found among the non-Jewish groups (Muslim, Christian, and
Druze) on the dependent variables. The student characteristic vari-
ables were entered into the analysis based on the results of several
linear regression analyses, in which the FoMO, SME, and the moti-
vational factors were separately measured as dependent variables,
and the following student characteristic variables were entered
into the analyses as independent variables: The student’s cultural
group, gender, age, year of study, FEA, MEA, EC, and GPA.
Paths were specified between the three motivational variables
and the FoMO variable; and, based on Model 2, between the fol-
lowing factors: Amotivation, extrinsic motivation and SME. An
additional path was created between the FoMO and SME factors
(v2= 1219.68, df= 609, p= .000; CFI= .912; RMSEA = .058). The
results showed a positive high significant coefficient between the
FoMO and SME factors ( b= .68, p< .001), and positive (moderate)
significant coefficients between the FoMO factor and the following
variables: Amotivation ( b= .36, p< .001), and extrinsic motivation
(b= .21, p< .01). Insignificant coefficient results were indicated
between the FoMO and the intrinsic motivation variables, and
between the motivational and SME factors.
Regarding the student characteristic factors, positive connec-
tions were found between the following factors: Age and intrinsic
motivation ( b= .23, p< .01); gender (females) and intrinsic motiva-
tion ( b= .14, p< .05); cultural group (non-Jewish students) and the
variables of FoMO ( b= .49, p< .001) and amotivation ( b= .21,
p< .001). An insignificant connection result was found between
the non-Jewish group and SME. A negative connection was indicat-
ed between gender (females) and amotivation ( b=/C0.16, p< .01).
An inverse correlation was indicated between the non-Jewish
group and the age variable ( b=/C0.38, p< .001).
The amotivation factor explained 38% of the FoMO factor vari-
ance (with additional 10% of the variance explained by the cultural
group variable and 1% explained by the extrinsic motivation vari-
able) which in turn explained 67% of the SME construct variance.
In order to gain further insights into how the FoMO and SME
sub-factors are interrelated, an additional analysis was conducted
to assess these connections. Table 3 displays the bivariate correla-
tion analysis results between these sub-factors. Results indicatedD. Alt / Computers in Human Behavior 49 (2015) 111–119 115

positive statistically significant correlations between all factor
pairings. As can be learned from Table 3 , the correlation coefficient
between the social FoMO (FO1) and social engagement (M1) sub-
constructs was relatively higher ( r= .325, p< .01) than the results
found between this FoMO sub-construct and other SME sub-fac-
tors. Similar results were indicated for the fear of missing news
information (FO2) and news information engagement (M2) sub-
factors ( r= .364, p< .01); and between the fear of missing commer-
cial information (FO3) and commercial information engagement
(M3) sub-constructs ( r= .524, p< .01).
5. Discussion
The aim of the present research was to examine possible con-
nections between academic motivation of college students, FoMO
and social media engagement constructs in the academic arena.
Path analysis results have confirmed the assumption that
extrinsically and a-motivated students would be more likely to
use social media tools available in the classroom. However, when
those links were mediated by the FoMO variable, insignificant
direct relations between the above academic motivations and
social media engagement were shown. Thus, both motivational
variables were positively associated with FoMO, which in turn
led to increased levels of social media engagement in the class-
room. These findings illustrate the robust mediating role of FoMO
in explaining social media engagement. It can be inferred that the
link between motivational deficits and social media engagement is
more likely to be indirect, and that these psychological deficits
could be linked to social media use only insofar as they are linked
to FoMO, in accordance with Przybylski et al.’s (2013) study.
Another result that warrants mentioning is the statistically
insignificant inverse relation indicated between intrinsic
motivation and FoMO, contrary to expectations. Yet, the slight
statistically insignificant tendency toward a negative correlation
between the factors may imply that a larger sample, which couldmore reliably reflect the population mean, may increase the chance
of finding a significant negative connection between the factors in
future work.
The connections between FoMO and social media engagement
were further assessed by examining the relations between these
constructs’ sub-factors. Bivariate correlation analysis results indi-
cated higher connections between factors sharing the same realm
of content: Social, news or commercial. This may suggest that
FoMO should not be perceived as a general factor, but rather as a
multiple dimension phenomenon. In the same route, different
instantiations of media engagement in the classroom should be
acknowledged.
Concerning student characteristics, a positive connection was
indicated between the non-Jewish group of students and FoMO,
directly, and indirectly through academic amotivation. A possible
explanation for this result might be related to the ongoing parental
pressure on minority students to attain high grades, which oftenresults in placing emphasis on outcomes rather than on learning
goals, and to feelings of disintegration or detachment from academic
actions ( Alt & Geiger, 2012 ). Thus, it may be implied that the parental
involvement associated with the minority group of non-Jewish stu-
dents could affect their learning motivations, and as also suggested
byPrzybylski et al. (2013) , FoMO could serve as a mediator linking
this deficit in psychological needs to social media engagement.
An alternative explanation for the non-Jewish cultural group
and FoMO connection might be related to age. The non-Jewish stu-
dents were significantly younger than the Jewish students.
Although a direct connection between age and FoMO was not con-
firmed by the path model, it is plausible to infer that the positive
connection between the non-Jewish group and the FoMO variable
could be explained by the age factor, in line with Przybylski et al.’s
(2013) study which indicated a negative connection between age
and tendency toward higher levels of FoMO.
Some limitations of the present investigation and further
directions for future research must be noted. First, this study was
Fig. 2. Model 2. The structural model, with standardized parameter estimates for the assessment of H1.116 D. Alt / Computers in Human Behavior 49 (2015) 111–119

conducted in a single country and was limited to a single regional
college; therefore, the results cannot necessarily be generalized to
students of other colleges. Furthermore, although all four ethnic
groups studying in Northern Galilee colleges were represented,
the current research sample failed to accurately reflect the distri-
bution of ethnicity. A cross-cultural validation of the results is
needed to substantiate these findings.
Second, future research should consider expanding the model
tested here with additional variables that could be related to learn-
ing motivations, such as self-efficacy in performing academically
(Linnenbrink & Pintrich, 2002 ).Third, it should be further acknowledged that alternate models
might explain the relationships in these data as well as the one
tested in this study. The cross-sectional nature of the data can pre-
vent definitive statements about causality. In fact, many relation-
ships in the model are likely reciprocal. For example, althoughthe analysis implies that some motivational constructs could indi-
rectly increase social media different engagements, it is equally
plausible that an excessive social engagement in the classroom
might disrupt college students’ learning processes and affect their
motivational outcomes.
Fourth, all of our measures were self-report. Future research
should employ diverse methods in assessing this research con-
structs, including different approaches to survey measurement,
as well as experimental and qualitative techniques. A triangulating
methodological approach can lend more confidence to conclusions
about FoMO.
Despite its limitations, this study lends support to previous
work by showing the robust mediating role of FoMO in explaining
the links between motivational deficits, namely amotivation for
learning, and social media engagement over and above background
factors, such as age, gender, and ethnicity. Because amotivation
could be accompanied by feelings of incompetence and expectan-
cies of uncontrollability, as found in previous studies ( Deci &
Ryan, 1985 ), it should be of interest to further investigate its links
to FoMO.
Fig. 3. Model 3. The structural model, with standardized parameter estimates for the assessment of H2.
Table 3
Bivariate correlation matrix for the FoMO and SME sub-factors.
FoMOs sub-factorsSME sub-factors
Social
engagementNews information
engagementCommercial
informationengagement
Social FoMO .325
**.218**.277**
News FoMO .140*.364**.322**
Commercial
FoMO.231**.520**.524**
*p< .05.
**p< .01.D. Alt / Computers in Human Behavior 49 (2015) 111–119 117

From a methodological point of view, this mix-method study
represents an effort to develop an instrument designed to measure
features of social media activities in higher education settings. The
newly detected instantiations of social media activities helped
revealing new facets of FoMO. Thus, in addition to the social
FoMO scale, designed by Przybylski et al. (2013) , the new scale
enables measuring the extent to which people feared missing out
news ( Mitchell & Guskin, 2013 ), and commercial information
(Kietzmann et al., 2011; Mangold & Faulds, 2009 ) as suggested
by theory. This effort could help bridging the gap between the
theoretical definitions of FoMO and social media engagement and
their operational levels. The positive correlations found between
the newly identified sub-factors in both scales may suggest that
different forms of media engagement follow different FoMO
trajectories.
This study’s main implication is to understanding the psycho-
logical precursors to students’ social media engagements in the
classroom, which were, by definition, not requested for academic
purposes. The current work may suggest that students use media
tools during classes mainly for social activities, which are not relat-
ed to the subject matter or their learning processes. However, an
interesting avenue for future research that was not addressed in
the current study should be related to the question of how differ-
ent learning environments can affect social media engagements
and motivational outcomes. Because Millennials are described as
having a focus on social media interaction and preferring group-
based approaches to study and social activities ( McMahon &
Pospisil, 2005 ), the question of how social media can be incorpo-
rated into current pedagogical applications and processes is of
importance. A current research ( Tarantino, McDonough, & Hua,
2013 ) has indicated that by encouraging engagement with social
media, students develop connections with peers, establish a virtual
community of learners and ultimately increase their overall learn-
ing skills. Thus, future work should evaluate the context in which
social media is being used for learning purposes and its possible
impact on learning motivations.
References
Acquisti, A., & Gross, R. (2006). Imagined communities: Awareness, information
sharing, and privacy on the Facebook .I n Proceedings from privacy enhancing
technologies workshop (pp. 1–22). < http://people.cs.pitt.edu/~chang/265/proj10/
zim/imaginedcom.pdf >.
Alabi, O. F. (2013). A survey of Facebook addiction level among selected Nigerian
university undergraduates. New Media and Mass Communication, 10 , 70–80 .
Alavi, S. S., Maracy, M. R., Jannatifard, F., & Eslami, M. (2011). The effect of
psychiatric symptoms on the internet addiction disorder in Isfahan’s university
students. Journal of Research in Medical Sciences: The Official Journal of Isfahan
University of Medical Sciences, 16 (6), 793–800 .
Alt, D., & Geiger, B. (2012). Goal orientations and tendency to neutralize academic
cheating: An ecological perspective. Psychological Studies, 57 (4), 404–416 .
Baker, S. R. (2004). Intrinsic, extrinsic, and amotivational orientations: Their role in
university adjustment, stress, well-being, and subsequent academic
performance. Current Psychology: Developmental, Learning, Personality, Social,
23, 189–202 .
Bentler, P. M. (2006). EQS 6 structural equations program manual . Encino, CA:
Multivariate Software Inc. .
Biggs, J. (2001). Enhancing learning: A matter of style or approach? In R. J. Sternberg
& L. F. Zhang (Eds.), Perspectives on thinking, learning, and cognitive styles
(pp. 61–84). Mahwah, NJ: Erlbaum .
Burke, M., Marlow, C., & Lento, T. (2010). Social network activity and social
wellbeing. Postgraduate Medical Journal, 85 , 455–459 .
Burnett, P. C., Pillay, H., & Dart, B. C. (2003). The influences of conceptions of
learning and learner self-concept on high school students’ approaches to
learning. School Psychology International, 24 , 54–66 .
Chou, H.-T. G., & Edge, N. (2012). ‘‘They are happier and having better lives than I
am’’: The impact of using Facebook on perceptions of others’ lives. Cyber
Psychology, Behavior & Social Networking, 15 (2), 117–121 .
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and
Psychological Measurement, 20 , 37–46 .
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human
behavior . New York: Plenum .
Deci, E. L., & Ryan, R. M. (2008). Self-determination theory: A macrotheory of human
motivation, development, and health. Canadian Psychology, 49 (3), 182–185 .Donche, V., Coertjens, L., Van Daal, T., De Maeyer, S., & Van Petegem, P. (2014).
Understanding differences in student learning and academic achievement in
first year higher education: An integral research perspective. In D. Gijbels, V.
Donche, J. T. E. Richardson, & J. D. Vermunt (Eds.), Learning patterns in higher
education: Dimensions and research perspectives (pp. 214–231). London and New
York: Routledge and EARLI .
Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook ‘‘friends:’’
Social capital and college students use of online social network sites. Journal of
Computer-Mediated Communication, 12 , 1143–1168 .
Gemmill, E., & Peterson, M. (2006). Technology use among college students:
Implications for student affairs professionals. NASPA Journal, 43 (2), 280–300 .
Jonas-Dwyer, D., & Pospisil, R. (2004). The millennial effect: Implications for
academic development. In Proceedings of the 2004 annual international
conference of the Higher Education Research and Development Society of
Australasia (HERDSA ) (pp. 194–207). < http://www.herdsa.org.au/wp-content/
uploads/conference/2004/PDF/P050-jt.pdf >.
JWT. (2011). Fear of Missing Out (FOMO). < http://www.jwtintelligence.com/
production/FOMO_JWT_TrendReport_May2011.pdf >.
Kietzmann, J. H., Hermkens, K., McCarthy, I. P., & Silvestre, B. S. (2011). Social
media? Get serious! Understanding the functional building blocks of social
media. Business Horizons, 54 , 241–251 .
Kross, E., Verduyn, P., Demiralp, E., Park, J., Lee, D. S., Lin, N., et al. (2013). Facebook
use predicts declines in subjective well-being in young adults. PLoS One, 8 (8).
http://dx.doi.org/10.1371/journal.pone.0069841 .
Kyndt, E., Dochy, F., & Cascallar, E. (2014). Students’ approaches to learning in
higher education: The interplay between context and student. In D. Gijbels, V.
Donche, J. T. E. Richardson, & J. D. Vermunt (Eds.), Learning patterns in higher
education: Dimensions and research perspectives (pp. 249–272). London and New
York: Routledge and EARLI .
Lankshear, C., & Knobel, M. (2011). New literacies: Everyday practices and social
learning (3rd ed.). London, England: Open University Press .
Linnenbrink, E. A., & Pintrich, P. R. (2002). Motivation as an enabler for academic
success. School Psychology Review, 31 , 313–327. < http://prof.usb.ve/jjramirez/
PREGRADO/CCP114/CCP114%20Motivaci on%20Linnenbrink%20y%20Pintrich.
pdf>.
Mangold, W. G., & Faulds, D. J. (2009). Social media: The new hybrid element of the
promotion mix. Business Horizons, 52 (4), 357–366 .
McMahon, M., & Pospisil, R. (2005). Laptops for a digital lifestyle: Millennial
students and wireless mobile technologies. In Proceedings of the 22nd ASCILITE
conference (pp. 421–431). < http://www.ascilite.org.au/conferences/brisbane05/
blogs/proceedings/49_McMahon%20&%20Pospisil.pdf >.
Mitchell, A., & Guskin, E. (2013). Twitter news consumers: Young, mobile and
educated. Pew Research Journalism Project .<http://www.journalism.org/2013/
11/04/twitter-news-consumers-young-mobile-and-educated/ >.
Morford, M. (2010). Oh my God you are so missing out. San Francisco. San Francisco
Chronicle (5). < http://www.sfgate.com/cgi bin/article.cgi?f=/g/a/2010/08/04/
notes080410.DTL&ao=all >.
Ophus, J. D., & Abbitt, J. T. (2009). Exploring the potential perceptions of social
networking systems in university courses. MERLOT Journal of Online Learning
and Teaching, 5 (4), 639–648 .
Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational,
emotional, and behavioral correlates of fear of missing out. Computers in Human
Behavior, 29 , 1841–1848 .
Putnam, R. D. (2000). Bowling alone: The collapse and revival of American community .
New York: Simon & Schuster .
Ratelle, C. F., Guay, F., Vallerand, R. J., Larose, S., & Senécal, C. (2007). Autonomous,
controlled, and amotivated types of academic motivation: A person-oriented
analysis. Journal of Educational Psychology, 99 , 734–746 .
Ryan, R. M., & Connell, J. P. (1989). Perceived locus of causality and internalization:
Examining reasons for acting in two domains. Journal of Personality and Social
Psychology, 57 , 749–761 .
Steinfield, C., Ellison, N. B., & Lampe, C. (2008). Social capital, self-esteem, and use of
online social network sites: A longitudinal analysis. Journal of Applied
Developmental Psychology, 29 (6), 434–445 .
Steinfield, C., Ellison, N., Lampe, C., & Vitak, J. (2013). Online social network sites and
the concept of social capital. In F. L. Lee, L. Leung, J. S. Qiu, & D. Chu (Eds.),
Frontiers in new media research (pp. 115–131). New York: Routledge .
Subrahmanyam, K., Reich, S., Waechter, N., & Espinoza, G. (2008). Online and offline
social networks: Use of social networking sites by emerging adults. Journal of
Applied Developmental Psychology, 29 (6), 420–433 .
Tarantino, K., McDonough, J., & Hua, M. (2013). Effects of student engagement with
social media on student learning: A review of literature. The Journal of
Technology in Student Affairs., 1 (8), 1–8. < http://www.studentaffairs.com/
ejournal/Summer_2013/EffectsOfStudentEngagementWithSocialMedia.html >.
The Central Bureau of Statistics. (2011). Women in higher education. < http://
www1.cbs.gov.il/www/publications/desc_exp/women.pdf > Retrieved 21.12.12
(Hebrew).
The Council for Higher Education. (2009). Planning and budgeting committee 34/35
report. < http://www.che.org.il/download/files /contents_1.pdf > Retrieved
20.07.10 (Hebrew).
Valenzuela, S., Park, N., & Kee, K. (2009). Is there social capital in a social network
site? Facebook use and college students’ life satisfaction, trust, and
participation. Journal of Computer-Mediated Communication, 14 (4), 875–901 .
Vallerand, R. J., Blais, M. R., Brière, N. M., & Pelletier, L. G. (1989). Construction et
validation de l’Échelle de Motivation en Éducation (EME). Revue canadienne des
sciences du comportement, 21 , 323–349 .118 D. Alt / Computers in Human Behavior 49 (2015) 111–119

Vallerand, R. J., Fortier, M. S., & Guay, F. (1997). Self-determination and persistence
in a real-life setting: Toward a motivational model of high school dropout.
Journal of Personality and Social Psychology, 72 , 1161–1176 .
Vallerand, R. J., Pelletier, L. G., Blais, M. R., Briere, N. M., Senecal, C., & Vallieres, E. F.
(1993). On the assessment of intrinsic, extrinsic, and amotivation in education:
Evidence on the concurrent and construct validity of the Academic MotivationScale. Educational and Psychological Measurement, 53 , 150–172 .Vansteenkiste, M., Zhou, M., Lens, W., & Soenens, B. (2005). Experiences of
autonomy and control among Chinese learners: Vitalizing or immobilizing.
Journal of Educational Psychology, 97 , 468–483 .
Wortham, J. (2011). Feel like a wallflower? Maybe it’s your Facebook wall. The New
York Times .<http://www.nytimes.com/2011/04/10/business/10ping.html >.D. Alt / Computers in Human Behavior 49 (2015) 111–119 119