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bernd skiera/oliver hinz/martin spann*
SOCIAL MEDIA AND ACADEMIC PERFORMANCE:
DOES THE INTENSITY OF FACEBOOK ACTIVITY RELATE TO
GOOD GRADES?**
A BSTRACT
we analyze how Facebook use and students’ social network positions within it relate to
their academic performance. we use a unique data set obtained from a survey of students’ perceptions, actual Facebook connections to measure social network positions,
and objective grades provided by the university registrar to measure academic performance. we ind that Facebook activities during class relate negatively to academic performance, that students located in densely connected subnetworks earn better grades,
and that in contrast to female students, male students beneit from a general use of
Facebook, particularly if they are highly connected.
Jel classiication:
Keywords:
d83, d85, m3, m30, m31.
academic Performance; Facebook; social media; social network analysis.
1 I NTRODUCTION
The popularity of social media, such as Facebook, LinkedIn, Twitter, and Xing, continues
to grow, providing people with amazing opportunities to interact through social networks (Hinz, Skiera, Barrot, and Becker (2011); Junco (2013); Messerschmidt, Berger,
and Skiera (2010); Nadkarni and Hofmann (2012)). Many people happily make use of
these opportunities by spending significant time on social media (Schulze, Schöler, and
*
Bernd Skiera, Chair of Electronic Commerce, Department of Marketing, School of Business and Economics,
Goethe-University Frankfurt am Main, Grueneburgplatz 1, 60629 Frankfurt, Germany, Phone: +49-69-79834649, Fax: +49-69-798-35001, E-Mail: skiera@skiera.de; Oliver Hinz, Chair of Electronic Markets, Faculty
of Law and Business, Hochschulstraße 1, TU Darmstadt, 64289 Darmstadt, Germany, Phone: +49-6151-1675221, Fax: +49-6151-16-72220, E-Mail: hinz@wi.tu-darmstadt.de; Martin Spann, Institute of Electronic
Commerce and Digital Markets, Munich School of Management, Ludwig-Maximilians-Universität (LMU Munich), Geschwister-Scholl-Platz 1, 80539 Munich, Germany, Phone: +49-89-2180-72050, Fax: +49-89-218072052, E-Mail: spann@spann.de.
** The authors thank Anton Kistner and Janis Stenner for their support in collecting the data.
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Skiera (2014)). For example, roughly one in three Germans is active on Facebook, and
Americans spend on average more than 20 minutes per day on Facebook. We can capture
the different roles that people can have in these networks by sociometric measures such as
degree centrality, betweenness and closeness centrality.
Through the social connections that people typically maintain on these platforms, individuals can gain access to resources that may be key to their professional success (Baldwin,
Bedell, and Johnson (1997)), including information and links to important others (Burt
(1992a)) and functional communication networks that lead to effective organizational
forms (Rogers (1979)). Yet with limited time available, too much time spent socializing
also could have negative effects on professional success (Astin (1984); Chickering and
Gamson (1987); Junco (2012)). For example, for students, who are perhaps the most intensive users of social media, interacting on the platform, playing video games and using
other electronic media (Jacobsen and Forste (2011)), expressing themselves, and maintaining friendships are time-intensive acts that might limit the time available for studying,
thus possibly harming their academic performance (Mihaly (2009)).
To clarify how social media activities relate to academic performance, we analyze the relation of academic performance to social media activity and a person’s social network position. To accomplish this aim, we also provide an overview of measures that characterize
social network positions in full and egocentric social networks.
To our knowledge, our empirical study is the first to investigate this relation among German students. Differences between German and American students might occur because
they use different kinds of content, e.g., because of the stronger privacy concerns in
Germany or cultural differences. As far as we know, ours is also the first study to analyze
actual social network positions, which we measure with students’ Facebook data and
egocentric networks. Furthermore, we are also the first to analyze the impact of interactions between gender and measures of social network positions on academic performance.
Finally, our research is among the first to use actual academic grades, which were provided
to us by a university registrar. Actual grades supply a higher validity than do self-reported
grades (Junco (2012)). This use of diverse data sources contrasts sharply with those studies that use only survey data.
The paper is organized as follows: In Section 2, we consider important social network
analysis metrics. In Section 3 we summarize prior research that tries to specify the links
between these metrics and student performance. From these analyses, we derive our hypotheses, which we test in Section 4 by using an empirical study with students at a large
German university. In Section 5 we summarize our main findings and conclude.
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2 S OCIAL N ETWORK A NALYSIS
In modern sociology, social network analysis defines social relations by using a network
theoretic approach (Freeman (2006)). A basic network consists of actors (or nodes) and
the connections (or relations) among them, which are represented as ties. Figure 1 offers
examples of social networks in which each tie represents a connection between two actors and indicates the directionality and intensity of their link. Social network analysis
uses information about these connections to characterize the whole network, the position
of each social entity in that network, and the local network of each social entity (Freeman (1979)). A social entity can be an individual, such as a consumer or employee, an
organization, or an organizational unit. Two basic types of networks appear frequently in
business research: full social networks and egocentric social networks. Different metrics
are useful to characterize individuals in these two types of networks.
2.1 F ULL
AND
E GOCENTRIC S OCIAL N ET WORKS
A full social network consists of all the network’s nodes and ties within a given boundary,
such as the entire network of students attending a university or enrolled in a particular
department or class. In principle, the whole population of the planet could be considered
a single social network, although it would be difficult to capture all these connections.
The analysis of a full social network usually requires some boundary specification, called
network specification strategy (Laumann, Marsden, and Prensky (1992)). Unlike a full
social network, an egocentric social network results from the perspective of one actor, that
is, the ego (Van den Bulte and Wuyts (2007)). This type of network encompasses only the
ego and the direct connections that might be alter egos or peers, and the relationships that
exist among these connections (Burt (1984)).
The left-hand panel of Figure 1 depicts a full social network of 14 actors; the right-hand
panel shows the egocentric social networks of Actors A and B. By definition, egocentric
social networks are embedded in a full social network, as illustrated by Figure 1. Focusing
on isolated networks of Actors A and B in the form of egocentric social networks makes
it easier to obtain information, e.g., from surveys of egos, Burt (1984); downloaded data
from Facebook using NodeXL; maps provided by LinkedInMaps, the Facebook app
“MyFnetwork,” the “VK online graph” on the vkontakte social network. However, this
focus ignores relationships across egocentric social networks. For example, it cannot identify how Actor C in Figure 1 connects the two subnetworks.
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Figure 1:
Full Social Network and Two Egocentric Social Networks
Full Social Network
Egocentric Social
Networks
B
B
C
C
A
C
A
Actor (Node)
2.2 M ETRICS
TO
D ESCRIBE E ACH A CTOR
IN A
Tie
S OCIAL N ET WORK
Centrality measures describe the position of an actor in a social network, which may relate to his or her importance in the network (Van den Bulte and Wuyts (2007); Schlereth,
Barrot, Skiera, and Takac (2013)). The most prominent centrality measures are degree,
betweenness, and closeness (Freeman (1979)). The degree of centrality indicates the
number of direct connections an actor (ego) has in a full network (egocentric network). A
high degree of centrality indicates popularity. In Figure 1, the degree of centrality values
for Actors A and B are 5 and 4, respectively, in both the full social network and their
respective egocentric social networks.
Betweenness centrality captures the extent to which an actor falls between the dispersed
parts of the social network. We measure betweenness by measuring the degree of the actor’s placement on the shortest path between any two actors (Freeman (1979)). An actor
with high betweenness centrality is a broker, who can access and pass information across
different parts of the social network (Scott (2000)). In Figure 1, Actor A is on 5.67 of the
shortest paths in the full social network, Actor B is on 39, and Actor C is on 48.17. In
the egocentric social networks, A is on the 3 shortest paths, and B is on 7 shortest paths
between any pair of alter egos.
Closeness centrality measures the inverse of the sum of the shortest distances from an actor to all the other actors he or she can reach within a social network. Thus, closeness centrality indicates communication efficiency (Freeman (1979)). Closeness centrality is not
applicable to egocentric social networks, because only connections between the ego and
his or her neighbors and their neighbors come into play (Everett and Borgatti (2005)).
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In Figure 1, the closeness centrality of Actors A and B in the full social network are 0.038
and 0.04, respectively (Actor C’s closeness centrality is 0.048).
The concept of local density provides the clustering coefficient, which measures the density of the egocentric social network of one actor. (We note that the focal actor is not
included in this calculation.) In Figure 1, the clustering coefficients for the egocentric
social networks of Actors A and B are 5/(5 ´ 4 / 2) = 50% and 1/(4 ´ 3 / 2) = 16.67%,
respectively.
2.3 M ETRICS
TO
C HARACTERIZE
THE
S TRUCTURE
OF A
S OCIAL N ET WORK
The number of nodes and connections are the elements that characterize the structure
of the full social network. For example, the full social network in Figure 1 has 14 actors
(nodes) and 22 connections. Density measures the proportion of actual to possible connections (Van den Bulte and Wuyts (2007)). For 14 actors, the number of possible connections is 14 ´ (14 – 1) / 2 = 91, so the density is 22/91, 24.18%.
We use the average values of the metrics that describe the position of each actor in a social
network to characterize the structure of the social network. In the full social network in
Figure 1, the average degree centrality is 3.14, the average betweenness centrality is 9.29,
the average closeness centrality is 0.03, and the average clustering coefficient is 0.44. Another measure is the maximum geodesic distance, or diameter. It equals 5, because it takes
a maximum of five connections to get from one actor to another in the social network.
The average geodesic distance is 2.25.
3 C ONCEPTUAL B ACKGROUND
3.1 L ITERATURE R EVIEW
Previous research on the relation between social media activities and students’ academic
achievement is growing, but still relatively scarce and concentrated on U.S. students. Table 1 summarizes the research findings to date. Despite some variation, most studies find
a negative relation between time spent on Facebook and academic performance. Some
differences might be driven by measurement variance, because most of the earlier studies
rely on a self-reported, non-continuous, grade-based measurement of performance grades
(see Junco (2012)). Other differences might occur because not all of the studies control
for prior grades in high school as Pasek, More, and Hargittai (2009) do. Previous studies
do not measure the effect of social network positions on Facebook, and none of them
identify causality (see Junco (2012)). This latter lacuna is a limitation that remains valid
for our study.
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Table 1:
Previous Research into the Relation of Social Media Activities and
Academic Performance
Study
Main Finding
Measure of
Academic
Achievement
self-reported
GPa, four-point
scale
Measure of
Social Media
Activities
self-reported
time-diary on
use of electronic
media
Measure
of Social
Network Position
None
Jacobsen and
Forste (2011)
Negative relation
between exposure to
social network sites
and academic
performance
Negative relation
between time spent
on Facebook and academic performance
GPa provided
by university
registrar
self-reported
time spent on
Facebook
None
Negative relation between social network
use and academic
performance
Negative relation
between Facebook use
and academic performance
No relation between
Facebook use and academic performance
self-reported
GPa
self-reported
measure of social
network use
None
self-reported
GPa
self-reported
measure of Facebook use
None
self-reported
GPa, on eightpoint scale
None
Negative relation
between time spent
on social networks and
academic performance
notes: GPa = grade point average.
self-reported
GPa
self-reported
dichotomous
measure of Facebook use
self-reported
measure of time
spent on social
networks
Junco (2012)
Karpinski,
Kirschner, ozer,
Mellott, and
ochwo (2013)
Kirschner and
Karpinski (2010)
Pasek, More, and
hargittai (2009)
Paul, Baker, and
cochran (2012)
None
3.2 H YPOTHESES
As mentioned in the previous section, prior research indicates that Facebook use might
relate negatively to academic performance. An explanation of this negative relation notes
that time is a limited resources(e.g., Falkinger (2007)), so time on Facebook, and particularly time spent on gaming and self-expression, decreases the time available for learning.
Smith and Wilson (2005) find that workers’ functional IQs fall ten points when they are
distracted by external elements, such as ringing telephones and incoming e-mails. Karpinski, Kirschner, Ozer, Mellott, and Ochwo (2013) summarize research that shows that
frequently switching between tasks leads to poorer learning and performance on tasks
than serial completion of the same tasks. Therefore, we hypothesize:
H1. The intensity of Facebook use relates negatively to academic performance.
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In addition, the use of Facebook in class distracts from learning (see Paul, Baker, and
Cochran (2012)), so we predict:
H2. Facebook use in classes relates negatively to academic performance.
However, more favorable arguments suggest Facebook use can enable information exchanges and help students prepare for exams. Many streams of literature in social network
analysis, marketing, and sociology assert that social positions determine information access. For example, Borgatti and Foster (2003) argue that cohesive subgroup membership
in a network positively affects performance, and that the magnitude of connectivity in
a subgroup can be captured by the clustering coefficient. Members of such densely connected cliques tend to achieve better local cooperation (Chwe (2000)); accordingly, students in cliques might share exam-relevant information. Furthermore, access to cliques
has a strong impact on members’ performance and their position in society (Oppenheim
(1955)). Thus, we hypothesize:
H3. Students in closely connected subnetworks (measured by high clustering coefficients)
achieve better academic performance than do those in loosely connected networks.
People with a high degree of centrality, and thus more links to others, also tend to have
greater access to information (Burt (1992b); Hinz and Spann (2008); Hinz et al. (2011)).
Research in management, public administration, and organizational sociology (Hinz,
Spann, and Hann (2014); Meier and O’Toole (2003); Schalk, Torenvlied, and Allen
(2010)) asserts that agents with a high degree of centrality have ample access to resources
and potential for learning and cooperation, which ultimately may lead to better academic
performance. Because the number of friends might relate positively to academic performance, we predict:
H4. The number of friends on Facebook (measured by degree centrality) relates positively to
academic performance.
In our study we control for the high school grade point average (HSGPA, or Abiturnote
in German), which captures students’ latent capabilities and serves as a good predictor of
academic performance at university (Junco (2012)). We also control for age (“age”) and
semester (“semester”), because a student’s average grades may vary in earlier compared
to later semesters; and for students’ part-time jobs (“PartTimeJob”), which may have a
negative impact on academic performance (“AcademicPerformance”). Additional control
variables refer to academic effort (“effort”) and whether the students use Facebook to
stay in contact with fellow students (“UseFBforUniversity”). Furthermore, the time at
which the student started using Facebook (“AdoptionTime”) may have an impact on the
academic performance.
These considerations lead to the following model that we estimate in the following empirical study and in which index i describes the respective student:
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AcademicPerformancei = β0 + β1 ⋅ FBUseInGeneral i + β 2 ⋅ FBUseDuringClass i
(1)
+ β3 ⋅ Clustering i + β 4 ⋅ Degree i + β5 ⋅ HSGPA i + β 6 ⋅ Gender
+ β 7 ⋅ Semester i + β8 ⋅ Age i + β9 ⋅ PartTimeJob i + β10 ⋅ Effort i
+ β11 ⋅ UseFBforUniversity i + β12 ⋅ AdoptionTime i + ε i ,
where FBUseInGeneral denotes Facebook use in general, and FBUseDuringClass the use of
Facebook during classes (see also Table 3). Clustering serves as the clustering coefficient,
Degree is degree centrality, and Gender is defined as one for females and zero for males.
4 E MPIRICAL S TUDY
To analyze the relation between the use of Facebook and students’ academic performance,
we examine if more successful students are more connected on Facebook than are less
successful students. In contrast with the studies listed in Table 1, for our data sources we
rely on surveys, academic grades provided by the university registrar, and social position
on Facebook and distinguish between Facebook use in general and in class. In addition,
we focus on German instead of American students and incorporate their social positions,
which may be decisive for their academic performance.
4.1 D ATA
The subjects of our empirical study are bachelor-degree students who attended a major
German university in early 2012. To facilitate the comparison of their academic achievements, we selected students studying business and economics. For each student we collected data from three sources. First, we required each student to respond to a survey
conducted with the online survey platform DISE (Schlereth and Skiera (2012)). Second,
each student submitted a copy of his or her academic transcript from the university registrar. Third, each student provided access to his or her egocentric network on Facebook,
through an interface with NodeXL software. Thus, in contrast to most of the earlier
studies, we were able to collect observable, reliable information about students’ grades
and social networks on Facebook. Each student received €10 in compensation for his or
her effort, and a third party deleted personal information (e.g., students’ names) from
our data set.
Of the 117 students who agreed to participate, five stopped their participation before
the end of the study, and technical problems prevented us from collecting Facebook data
from 9 participants. Hence, we built our analysis on the remaining 103 participants,
confirming that they took sufficient time to answer the online questionnaire. Table 2
describes the sample in more detail.
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Table 2:
Sample Description
Variable
academic performance (average grades at university)a
high school grade point average (Abiturnote)a
Gender (0: male; 1: Female)
semester
age
Part-time job (hours per week)
degree (number of friends)
Clustering coeicient
notes: a scale from 1.0 (best) to 5.0 (fail). N = 103.
Mean
2.46
1.99
0.48
5.12
22.55
9.23
350.14
0.09
Standard Dev.
.54
.42
1.37
1.74
9.53
159.79
.04
The sample compares well with the larger population of students in business and economics: 43.2% of all students in the winter semester of the 2011–2012 school year were
women (48% in our sample). Moreover, their average age was 22.5 years, and on average
they worked for outside jobs for 9.23 hours per week. They maintained an average of 350
friends on Facebook, although female students averaged 338 and male students averaged
361 friends on Facebook. The number of friends varied from 22 to 869.
To measure academic performance, we used the weighted average of all the grades noted
on the official university transcript, according to the credit points associated with the
respective course, which in turn reflected the time required to invest in each particular
course. In this case, lower scores mean better academic performance: The best grade possible is 1.0; a 5.0 indicates a failed exam. The mean grade in our sample was 2.46; the best
student earned a grade of 1.22, and the student with the worst grade showed a score of
3.66. Visual assessment see Figure 2 as well as the Shapiro-Wilk normality test (p > 0.33)
and the Shapiro-Francia normality test (p > 0.47) do not reject the null hypothesis of
normality. The HSGPAs ranged from 1.0 to 3.3 with a mean of 1.99, so grades in high
school were slightly better than those at university.
We measured Facebook use in general, use of Facebook during classes, effort toward studies, and use of Facebook for university contacts. As Table 3 shows, the internal validity
of the multi-item constructs is good; we used the average value of the respective items to
reflect the value of the construct.
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Figure 2:
Table 3:
Distribution of Academic Performance
(Dependent Variable, Measured by Weighted Average of Grades)
Construct Description
Construct
(Source)
Item
Facebook
use in general
i spend too much time on Facebook.
on an average day i use Facebook … hours.
compared to my friends, i do not use Facebook
very frequently (reverse scale).
Facebook is an important part of my daily life.
4.874
4.311
3.951
Corrected
Item-to-Total
Correlation
0.749
0.643
0.657
4.670
0.729
use of
Facebook
during classes
i use Facebook during lectures ("vorlesungen").
i use Facebook during classes ("Übungen, mentorien").
3.757
2.835
0.804
0.804
0.891
Efort toward
studies
i attend lectures ("vorlesungen").
i attend classes ("Übungen, mentorien").
i study much more than my classmates.
i invest much time in preparing my lecture and
classes.
i always prepare the exercises of my class.
5.563
6.029
3.544
3.816
0.615
0.637
0.416
0.593
0.744
3.019
0.322
i am in daily contact with my fellow students via
Facebook.
4.427
use of
Facebook for
university
contacts
Mean
Cronbach’s
Alpha
0.847
Notes: N = 103. items measured on seven-point likert scales from 1 (completely disagree) to 7 (completely
agree).
4.2 R ESULTS
Before estimating our model, explorative analyses suggest that Facebook use and networking behavior are gender specific. Chow tests indicate that the coefficients for degree
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centrality, Facebook use, and effort expended on studies are different between men and
women, so pooling is not adequate without further considerations. We address these differences by including interaction effects between gender and degree centrality, between
gender and Facebook use, and between gender and effort.
We estimate a linear regression model with robust standard errors and obtain the results
for the model outlined in Equation (1) plus interactions. Table 4 shows the results. We
also estimate several alternative models, the results of which are displayed in Table 6. In
all models we used the Huber (1967) and White (1980) sandwich estimators to estimate
the standard errors. Doing so enables us to deal with minor concerns about the potential
failure to meet assumptions, such as normality or heteroscedasticity, or observations that
exhibit large residuals, leverage, or influence. The point estimates of the coefficients are
the same as in ordinary least squares (OLS), but the standard errors account better for the
limitations of OLS due to heteroscedasticity or potential lack of normality.
Table 4:
Determinants of Academic Performance
Independent Variable
Facebook use in general (h1)
Facebook use during classes (h2)
clustering coeicient (h3)
degree (h4)
degree × Gender
Fb use in general × Gender
Gender (0: male; 1: Female)
High school grade point average
semester
age
Part-time job
Efort toward studies
efort × Gender
use Facebook for university contacts
adoption time in months
Constant
F-Value
Prob > F
R²
root mean squared error (rmse)
Coeicient
-0.141***
0.049*
-2.397*
-0.001*
0.0007**
0.202***
-2.227***
0.650***
-0.113***
0.053*
-0.00003
-0.127**
0.211***
-0.042
0.001
2.227***
Standard.
Coef.
-0.379
0.181
-0.161
-0.216
0.432
0.870
-2.075
0.509
-0.305
0.171
-0.001
-0.268
0.937
-0.154
0.034
Robust
Standard
Error
0.053
0.027
1.244
0.000
0.001
0.061
0.414
0.136
0.034
0.029
0.005
0.054
0.070
0.027
0.004
0.650
9.090
0.000
0.493
0.415
Change
in R2
F-test
0.0002
0.0218
0.0087
0.0013
0.0009
0.0003
0.0512
0.2832
0.0426
0.0162
0.0000
0.0118
0.0388
0.0153
0.0008
0.02
2.90*
0.74
0.15
0.10
0.04
5.09**
43.55***
7.60***
2.84*
0.00
1.70
8.64***
2.38
0.13
* p < 0.1, ** p < 0.05, *** p < 0.01, two-tailed signiicance.
notes: the dependent variable was academic performance, measured by the weighted average of grades at
the university. the change in R2 denotes the increases in R2 from adding each additional variable in the order
of their appearance in this table. the F-test depicts the signiicance of the change in R2.
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4.2.1 R ELATION
BET WEEN
F ACEBOOK U SE
AND
A CADEMIC P ERFORMANCE
Table 4 shows that the R-square value for our full model is 49.3%, which is quite good.
The F-value allows us to reject the null hypothesis that sets of coefficients are jointly zero
(p < 0.01). The results do not support H1, which predicts that the intensity of Facebook
use in general relates to lower academic performance. We find that males benefit academically from Facebook use (p < 0.01), but women who use Facebook intensively have
a lower academic performance, captured by the interaction effect FB use in general ×
Gender (p < 0.01). We also check for nonlinear effects of Facebook use and estimate the
model with a quadratic term for Facebook use, because a moderate level of Facebook use
might relate positively to academic performance while excessive use might relate negatively. However, we find no significant linear or quadratic effects of Facebook use. One
possible explanation for this finding could be the clear distinction between Facebook use
in general and use in class, which seems relevant: The results shown in Table 4 support
H2 (p < 0.1) as Facebook use in class is associated with poorer grades.
4.2.2 R ELATION
BET WEEN
N ET WORK C HARACTERISTICS
AND
A CADEMIC P ERFORMANCE
We find support for H3, our hypothesis that students who are located in a strong clique,
as measured by a high clustering coefficient, earn better grades (p < 0.1). In such a clique,
nearly everyone is connected to everyone else, and members tend to share information
and trust one another (Hinz and Spann (2008); Krackhardt (1999)). In contrast, a collection of isolated individuals with few or no ties tend to have difficulties exchanging resources (Balkundi and Harrison (2006)). In our study context, this finding indicates that
students in a strong clique use their relations to achieve better academic performance.
We find support for H4, which concerns the number of connections. Highly interconnected students exhibit better grades (-0.001, p < 0.1). Again, this result strongly supports
a gender-specific effect of Facebook use. Women and men seem to use the social network
differently, which may also explain differences in academic performance.
Female students achieve better grades on average (p < 0.01). The interaction effect Degree × Gender (0.001, p < 0.05) shows that the effect of degree on grades among male students is nullified for female students. Highly connected male students earn better grades,
but female students who are similarly interconnected have relatively worse grades. This
gender-specific finding is in line with previous research that indicates that men use their
social contacts for networking purposes and thereby achieve greater career performance
than women (e.g., Forret and Dougherty (2004)).
Studies on gender-specific communication and networking behavior also cite some
strong differences between men and women (e.g., Brett and Stroh (1997); Dreher and
Cox (2000); Schneer and Reitman (1997); Stroh, Brett, and Reilly (1992)). Forret and
Dougherty (2004) find that involvement in networking behavior is more beneficial to
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men’s career progress. The reason could be that women tend to exhibit expressive, socialemotional behavior, but men perform stereotypical male roles, behaving in non-emotional, instrumental ways (Athenstaedt, Haas, and Schwab (2004)). In our context, men
might utilize their social capital through the use of Facebook while women use Facebook
for socializing purposes. This statement is supported by the significant interaction between Facebook usage and gender (p < 0.01). While men connect to generate and exploit
social capital, women groom their relationships, which requires more time (that could
also be used for studying) than does the goal-oriented approach that men pursue.
4.2.3 R ELATION
BET WEEN
C ONTROL V ARIABLES
AND
A CADEMIC P ERFORMANCE
The coefficient for HSGPA (Abiturnote) is 0.65 and highly significant (p < 0.01); school
grades provide good predictors of university performance. Moreover, students in later
semesters achieved better grades (p < 0.01), likely due to a selection bias, indicating that
students with bad grades might have already left the university. However, we identified
no impact of part-time jobs on grades (p > 0.1). Although study effort related to better
grades, the effect was again gender-specific. The use of Facebook for university contacts
and the time of adoption returned insignificant effects (p > 0.1).
4.3
R OBUSTNESS
OF
R ESULTS
We conduct several robustness tests. First, we check the model for multicollinearity. Models 1–3 in Table 6 show that the models without interactions have low variance inflation
factors (VIFs) with a mean VIF < 1.44 and a maximum VIF of 1.72. To further mitigate
the concern of potential multicollinearity, we provide the correlation matrix in Table 5
and estimate the models on split samples for male and female students. The results do
not change substantially.
Another concern is that the high school grade, another proxy for academic performance,
is also a function of Facebook use, since the subjects may already have already begun
to use Facebook in high school. To check for such an impact, we exclude HSGPA as
control variable in Model 5 of Table 6. This exclusion leads to minor changes in our
results. The same holds for excluding the efforts towards studies, which we present as
Model 6 of Table 6.
Beside the interesting gender-specific use effect, we also check for further moderating
effects of age, semester, and high-school grade point average, but these analyses did not
reveal further insights.
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Table 5:
Correlation Matrix
January 2015
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
1. Facebook use in general
54–72
-0.013
3. clustering coeicient
0.130
0.411
4. degree
-0.078
-0.327
-0.100
5. high school grade point average
0.015
0.493
0.352
-0.424
6. semester
0.534
0.063
0.114
-0.057
0.033
7. age
0.000
-0.054
0.196
-0.071
0.011
0.214
8. Part-time job
0.343
-0.117
-0.031
-0.111
-0.223
0.478
0.340
9. efort toward studies
0.113
-0.108
0.126
-0.140
0.147
0.109
0.112
0.029
10. use Facebook for university contacts
-0.224
-0.320
-0.387
0.086
-0.278
-0.316
-0.015
0.062
-0.154
11. adoption time in months
-0.084
0.499
0.269
-0.296
0.224
0.033
-0.151
-0.142
0.005
-0.122
SoCial MEdia aNd aCadEMiC PErForMaNCE
2. Facebook use during classes
67
68
Robustness Checks and Extended Models
Model 1
Controls Only
coef.
Model 2
FB Use in General
& Controls
rob. s.e. coef.
Fb use in general (h1)
Model 3
Model 4:
Model 5:
Facebook Use &
FB Use &
Full Model w/o
Controls
Network & Controls High school GPA
rob. s.e. coef.
-0.003
0.041
Fb use during classes (h2)
rob. s.e. coef.
-0.021
0.043
0.044
0.028
rob. s.e. coef.
-0.037
0.053*
Model 6:
Full Model w/o
Efort
rob. s.e. coef.
0.043
-0.046
0.044
0.028
0.045
0.030
Model 7: Full
Model & All sign.
Interactions
rob. s.e. coef.
-0.029
0.059**
rob. s.e.
0.044
-0.141***
0.053
0.027
0.049*
0.027
clustering coeicient (h3)
-2.424*
1.246
-1.441
1.711
-2.406*
1.263
-2.397*
1.244
degree (h4)
-0.001**
0.0005 -0.001
0.001
-0.001*
0.000
-0.001*
0.000
0.001
06
0.002***
0.001
07**
0.001
0.202***
0.061
degree × Gender
0.002***
0.0014**
Fb use in general × Gender
Gender (1: Female)
0.002
0.099
0.002
0.101
0.016
0.101
high school GPa
0.571***
0.141
0.571***
0.143
0.577***
0.137
0.616***
semester
age
Part-time job
Efort toward studies
-0.577**
0.224
-0.526**
0.238
0.141
-0.075**
0.036
-0.075**
0.036
-0.091**
0.037
-0.102***
0.035
0.061*
0.032
0.061*
0.032
0.064**
0.032
0.057*
0.032
-0.557**
0.660***
-0.094**
0.040
0.140***
0.029
0.003
0.005
0.003
0.005
0.002
0.005
-0.001
0.005
-0.001
0.005
-0.048
0.050
-0.049
0.049
-0.030
0.051
-0.046
0.047
-0.120**
0.047
-0.104***
0.05
0.228
-2.227***
0.414
0.127
0.650***
0.136
0.035
-0.113***
0.034
0.031
0.053*
0.029
0.004
-04
-0.127**
efort × Gender
use Fb for university contacts
0.054
0.211***
-0.033
0.025
-0.032
adoption time in months
0.002
0.004
0.002
Constant
0.585
0.616
0.598
F-Value
0.005
-003
7.881
0.031
-0.038
04
0.003
0.606
0.434
7.112
0.031
0.070
-0.045
0.029
-0.031
0.032
-0.048
0.029
-0.043
0.027
04
0.002
0.004
0.005
0.004
0.002
0.004
0.001
0.004
0.630
1.368**
0.673
0.705
0.829
1.174*
0.668
2.227***
6.364
6.249
3.549
6.406
0.650
9.090
sbr 67
Prob > F
rMSE
0.454
0.457
0.453
0.439
0.488
0.439
0.415
January 2015
0
0
0
0
0
0
0
R²
0.344
0.344
0.362
0.419
0.276
0.413
0.493
viF mean/max
1.29 / 1.67
1.39 / 1.68
1.44 / 1.72
2.57 / 7.49
1.27 / 7.45
2.60 / 7.36
7.57 / 43.53
* p < 0.1, ** p < 0.05, *** p < 0.01, two-tailed signiicance.
Fb: Facebook, rmse: root mean squared error, viF: variance inlation factor, GPa: Grade point average, rob. s.e.: robust standard errors.
notes: the dependent variable is academic performance, measured by the weighted average of grades at the university level.
b. sKiera/o. hinz/m. sPann
Table 6:
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SoCial MEdia aNd aCadEMiC PErForMaNCE
5 S UMMARY , I MPLICATIONS ,
AND
L IMITATIONS
In this article we empirically analyze the impact of Facebook activities on academic performance. Unlike previous studies, we use multiple data sources (we measure perceptions
by a survey, network positions by actual Facebook connections, and grades by the university registrar) and focus on German instead of U.S. students. Facebook activities during
class relate negatively to academic performance for both genders. This finding is also supported by students’ own average response (5.1 out of 7 (7=totally agree)) to the question:
“My ability to study suffers if I simultaneously use Facebook.”
In line with previous research that examines the impact of gender-specific networking
behavior on career progress, we observe a gender-specific effect of close friendships on
academic performance. The number of contacts relates positively to academic performance for men but negatively for women. According to social role theory, gender differences in behavior occur because men and women usually have different social roles
(Eagly (1987)); behavioral difference such as gender-specific communication styles result
from role-related expectations for women and men (Hall and Briton (1993)). In a traditionally female role, women tend to exhibit expressive, social-emotional behavior, and
men performing stereotypical male roles behave in non-emotional, instrumental ways
(Athenstaedt, Haas, and Schwab (2004)). Thus, instrumental, task- and goal-oriented approaches are often associated with men, but a communal approach oriented more toward
social relationships is associated with women (Iacobucci and Ostrom (1993)).
In the context of our study, this association could imply that men, on average, use platforms such as Facebook to gather valuable information from their peers, but women may
invest time on Facebook to cultivate relationships, which reduces the time available for
their academic studies. However, this conclusion is speculative. Further research should
examine gender-specific communication behavior in social media more closely to explain
these differences.
A limitation of this study, which it shares with previous studies of the impact of Facebook
use on academic performance (Junco (2012)), is that we observe only correlations; we do
not provide evidence of causality. Our cross-sectional data make it difficult to separate the
effect of students’ use of Facebook from other effects, such as social contagion, homophily, and exogenous influences. Social contagion describes how behavior is transmitted in
social networks, which also involves how interpersonal communications affect sales (e.g.,
Hinz et al. (2011)). Homophily describes the tendency of people to associate and bond
with similar others. For example, young people stick together, obese people stick together,
and smokers prefer to hang out with other smokers. Exogenous influences describe other
variables that could influence behavior, including exposure to similar advertising messages or friends.
Although we observe many variables, we cannot rule out the possibility that other (unobserved) variables might be correlated with our treatment. We also lack good control vari-
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69
b. sKiera/o. hinz/m. sPann
ables (Van den Bulte and Lilien (2001); Hinz, Schulze, and Takac (2013)) or instrumental variables (Gaviria and Raphael (2001)). Thus, we can make claims about relations but
not about causal effects, even though the relations are in line with the expectations stated
by our hypotheses. Most notably, we cannot provide evidence that halting Facebook use
during classes would necessarily improve academic performance; it could be that students
with weaker academic performance self-select into the group of students who use Facebook during class. Still, the result is in line with our own and colleagues’ findings.
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