International Journal of Educational Research 103 (2020) 101618

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International Journal of Educational Research

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Smartphone use and academic performance: A literature review

T
Simon Ameza,c,*, Stijn Baertb,c
a
Ghent University, Belgium
b
Ghent University, Research Foundation – Flanders, University of Antwerp, Université catholique de Louvain, Institute for Labour Economics (IZA),
Global Labour Office (GLO), IMISCOE, Belgium
c
Sint-Pietersplein 6, B-9000 Ghent, Belgium

A R T IC LE I N F O ABS TRA CT

Keywords: We present the first systematic review of the scientific literature on smartphone use and academic
Smartphone use success. We synthesise the theoretical mechanisms, empirical approaches, and empirical findings
Smartphone addiction described in the multidisciplinary literature to date. Our analysis of the literature reveals a
Academic performance predominance of empirical results supporting a negative association between students’ frequency
Literature review
of smartphone use and their academic success. However, the strength of this association is het-
Causality
erogeneous by (a) the method of data gathering, (b) the measures of academic performance used
in the analysis, and (c) the measures of smartphone use adopted. The main limitation identified
in the literature is that the reported associations cannot be given a causal interpretation. Based on
the reviewed findings and limitations, directions for further research are discussed.

1. Introduction

In 2018, approximately 77 percent of America’s inhabitants owned a smartphone (Pew Research Center, 2018), defined here as a
mobile phone that performs many of the functions of a computer (Alosaimi, Alyahya, Alshahwan, Al Mahyijari, & Shaik, 2016). In
addition, a survey conducted in 2015 showed that 46 percent of Americans reported that they could not live without their smart-
phone (Smith, 2015). Similar numbers can be observed in other parts of the (Western) world (OECD, 2017). Therefore, it should come
as no surprise that in recent years discussions about the (potential) consequences of (heavy) smartphone use have earned an im-
portant place in societal debates (see, e.g. Eliahu, 2014; OECD, 2017). Simultaneously, the possible effects of smartphone use re-
ceived increasing interest from scientists in different disciplines. As such, scholars have investigated associations between smart-
phone use and smartphone addiction and, for example, (a) driving performance (Choudhary & Velaga, 2019); (b) sleep quality and
quantity (Demirci, Akgönül, & Akpinar, 2015); (c) anxiety, loneliness, and depression (Boumosleh & Jaalouk, 2017); (d) satisfaction
with life (Samaha & Hawi, 2016); (e) social relationships (Chen & Peng, 2008); (f) substance addictions (Ho et al., 2014); and (g)
attention deficit and hyperactivity disorder (Ho et al., 2014).

⁎
Corresponding author at: Sint-Pietersplein 6, B-9000 Ghent, Belgium.
E-mail addresses: Simon.Amez@UGent.be (S. Amez), Stijn.Baert@UGent.be (S. Baert).

https://doi.org/10.1016/j.ijer.2020.101618
Received 14 February 2020; Received in revised form 2 April 2020; Accepted 25 May 2020
Available online 10 June 2020
0883-0355/ © 2020 The Authors. Published by Elsevier Ltd.
S. Amez and S. Baert International Journal of Educational Research 103 (2020) 101618

In addition to the (potential) impact on people’s private life, smartphone use is also expected to interfere with individuals’
educational and professional life. In particular, it has been related to tertiary students’ academic performance. Smartphone ownership
is highest among people aged 18–29 (Pew Research Center, 2018), an age group in which students are highly represented. Moreover,
there are many theoretical reasons based on which a direct effect of (heavy) smartphone use on academic performance is expected. To
the best of our knowledge, 23 studies confront the theoretical expectations with the empirical reality. The present review is the first to
compile the existing literature on the impact of general smartphone use (and addiction) on performance in tertiary education.1
We believe that a synthesis of this literature is valuable to both academics and policy makers. Firstly, as we focus on divergences
in the empirical findings—ergo, aspects in which there is no consensus in the literature—and (methodological) limitations of existing
studies, we explicitly provide scholars with directions for fruitful future research. Secondly, while in several countries interventions
have been developed to discourage heavy smartphone use in class because it is believed to obstruct knowledge acquisition (e.g. in
France, a smartphone ban was introduced into schools in 2017; Samuel, 2017), it is unclear whether these popular perceptions
correspond with a consensus in the related scientific literature.
This literature review is structured as follows. In the next section, we discuss the different theoretical mechanisms reported in the
literature that could lead to improved or deteriorated educational outcomes due to (heavy) smartphone use. In Section 3, we discuss a
systematic overview of the empirical findings, with a focus on how the empirical research results converge concerning the overall
negative association between smartphone use and academic performance but diverge according to (a) the method of data gathering,
(b) the measures of academic performance used in these studies, and (c) the measures of smartphone use adopted in the research. A
final section concludes with directions for future research based on the discussed findings and the limitations of the current literature.

2. Theoretical mechanisms

Multiple arguments in the existing literature argue for an association—positive or negative—between smartphone use and aca-
demic performance. In this section, we review the main theoretical mechanisms.
On the one hand, (particular functions of) smartphones could—when used properly—lead to better educational performance.
Smartphones’ mobility allows students to access the same (internet-based) services as a computer almost anywhere, almost every
time (Lepp, Barkley, & Karpinski, 2014). Easy accessibility to these functionalities offers students the chance to search continuously
for study-related information. Thus, smartphones provide a multi-media platform to facilitate learning which cannot be replaced by
reading a textbook (Zhang, Ho, & Ho, 2014). Furthermore, social networking sites and communication applications may contribute to
the quick sharing of relevant information. Faster communication between students and between students and faculty staff may
contribute to more efficient studying and collaboration (Chen & Ji, 2015; Lepp, Barkley, & Karpinski, 2015).
On the other hand, research has suggested that university students think of their smartphones as a source of entertainment, rather
than as a working instrument (Lepp, Barkley, Sanders, Rebold, & Gates, 2013). These findings support the idea of a time trade-off à la
Becker (1965) between smartphone use and study-related activities. That is, the time spent on smartphone use is time lost for study
activities. As such, university students’ decision to use their smartphones could have a deleterious effect on their academic perfor-
mance.
Furthermore, smartphone use may interfere with study-related activities. The proximity of the mobile device can be a tempting
distraction, leading to multitasking or task-switching. A growing body of literature (see, e.g. Junco, 2012; Junco & Cotten, 2012;
Levine, Waite, & Bowman, 2012) has shown this behaviour’s negative implications with respect to educational performance. We
discuss four potential causes for this multitasking or task-switching behaviour. Firstly, visual and auditory notifications on the
smartphone may draw students’ attention during class and/or during study time (Junco & Cotten, 2012). Secondly, the desire not to
miss out on what is happening online and to continuously interact with the rest of the world (nowadays labelled as ‘FOMO’, i.e. fear of
missing out) may lead to a lack of focus necessary to achieve good study performance (Chen & Yan, 2016; Firat, 2013). Thirdly, but
related, smartphone use during study-related activities may be the result of addiction behaviour and cyberslacking, which can be
defined as the interference of personal (online) life during working or study activities (Garrett & Danziger, 2008; Vitak, Crouse, &
LaRose, 2011). Finally, due to the lack of academic motivation, students can experience a sense of boredom for which smartphone
applications provide a fast and tempting escape (Hawi & Samaha, 2016).
Besides the direct effect smartphone use might have on both time spent and productivity for academic-related activities,
smartphone use can have an additional indirect impact on educational performance by influencing students’ health. In recent years,
an increasing amount of literature provides evidence for a negative relation between technology use, including smartphone use, and
health indicators. As such, negative associations were uncovered between smartphone use and (a) sleep quality (Christensen et al.,
2016; Demirci et al., 2015; Rosen, Carrier, Miller, Rokkum, & Ruiz, 2016; Tavernier & Willoughby, 2014), (b) mental health (Lepp
et al., 2014), and (c) physical fitness (Jackson, von Eye, Fitzgerald, Witt, & Zhao, 2011). These health indicators have in turn been

1
We concentrate on the interference of smartphone use and performance in tertiary education and, thereby, ignore the corresponding literature
based on data for secondary education for two different reasons. Firstly, this strategy implies that the reviewed studies are conducted in comparable
settings. Typically, differences between the organisation and regulation of secondary education institutions across and within countries exist (see
e.g. Beland & Murphy, 2016 and Gao, Yan, Zhao, Pan and Mo (2014)). Therefore, many institutional modalities should have been considered in our
discussion when also focussing on secondary education. Secondly, the body of research on the association between smartphone use and educational
outcomes in tertiary education is more substantial than the literature on the same association in secondary education so that a literature review
focussing on tertiary education is more appropriate than one focussing on secondary education.

2
S. Amez and S. Baert International Journal of Educational Research 103 (2020) 101618

associated with educational performance (see, e.g. Baert, Verhaest, Vermeir, & Omey, 2015; Galambos, Vargas Lascano, Howard, &
Maggs, 2013).
Taken together, the aforementioned arguments suggest good reasons to expect an association between university students’
smartphone use and their academic performance. Smartphones can be supportive in the academic setting by providing easy and fast
communication and to search for relevant information. However, the potential negative influence of smartphones through distrac-
tion, multitasking, and/or health implications are dominantly advanced in the literature to date.

3. Empirical findings

In this section, we summarise the literature’s empirical findings on the association between general smartphone use and tertiary
educational outcomes. This review is the result of a systematic search. In the first step, all 490 article abstracts indexed in Web of
Science including a combination of ‘smartphone use’ with ‘educational outcome’ or ‘academic performance’ were screened for re-
levance, which provided an initial list of studies for our review. In the second step, we explored (a) the articles included in the studies’
references and (b) the articles citing these studies in Web of Science. This second step was re-iterated whenever an additional relevant
article was found.
Studies that examine the association between the use of specific smartphone applications and study performance, such as
Jacobsen and Forste (2011) and Bun Lee (2014), were excluded. This choice was made to realise a homogeneous literature to review.
Moreover, studies investigating the association between the use of particular applications and academic performance ignore the fact
that the use of an electronic device for this particular application is strongly correlated with its use for other activities (Chen & Yan,
2016), potentially resulting in an omitted variable bias.
A schematic overview of the studies can be found in Table 1. The reviewed articles are ordered alphabetically based on the
authors’ names in column (1). Column (2) contains information on the authors’ data: we provide details on how and where the data
were gathered and the sample size is reported. Columns (3) and (4) list the variables used in the reviewed studies to capture academic
performance and smartphone use, respectively. Finally, column (6) reports the main findings concerning the association between the
variables in columns (3) and (4), based on the empirical approach listed in column (5).

3.1. Main findings

A quick look at Table 1 reveals the predominance of studies reporting a significantly negative association between smartphone use
and academic performance in tertiary education. More specifically, 18 of the 23 included studies (i.e. 78.3 percent), relying on
diverging empirical approaches elaborated upon below, conclude such a negative association. The remaining five articles find no
statistically significant association between overall smartphone use and performance at university. However, no single study to date
reports a positive overall association.2 This first look at the literature provides an indication3 that the negative mechanisms, as
discussed in the previous section, contribute more to the association between smartphone use and academic outcomes than the
positive mechanisms.
In column (6) of Table 1, we report significant Pearson correlation coefficients with respect to the studies’ measures of these
outcomes—they could be gathered for 18 of the 23 studies4 . These correlation coefficients do not take into account any confounding
variables and therefore are not the analysis end point in most reviewed articles. However, they allow us to compare a raw indicator of
the magnitude of the association between smartphone use and academic performance across studies. The reported coefficients range
from a correlation coefficient of −0.380 between total phone use in class and self-reported grade point average (GPA) in Kim et al.
(2019) to a correlation coefficient of 0.047 between the problematic mobile phone use scale of Sert, Yilmaz, Kumsar and Aygin
(2019) and self-reported GPA. When considering only correlation coefficients of studies that concluded a statistically significantly
negative association, not surprisingly the interval is less wide. Then it moves from a very small correlation of −0.079 (Lin & Chiang,
2017) to a (rather) small correlation of −0.380 (Kim et al., 2019) In sum, the reviewed literature seems to suggest a negative
association between overall smartphone use and academic performance that is small in magnitude (at most). However, this general
picture may conceal interesting (further) convergences and divergences in the literature that can be observed only after investigating
the studies more carefully, which is addressed in the next two subsections.

3.2. Convergences in the empirical literature

The statistical and economic magnitude of the association between smartphone use and academic performance seems to be rather
homogeneous by (a) the public versus private nature of tertiary education institutions, (b) the method of data analysis as well as by
(c) the region and sample size of the realised data.
Firstly, one could expect that the students in samples from private universities are positively selective with respect to non-

2
Chen and Peng (2008) report a positive association in the particular situation when personal electronic devices are (mainly) used for educational
purposes.
3
The reported associations cannot be given a causal interpretation, though. We return to this point below.
4
The Pearson correlation coefficient was reported in 15 of the articles. For the other articles, we attempted to obtain this coefficient from the
authors via email, or, when our email was not answered, via telephone (using both personal and institution numbers).

3
 Table 1
Summary of the literature.
(1) Study (2) Data (3) Outcome variable (4) Explanatory variable (5) Main empirical (6) Association reported between (3) and (4)
approach
S. Amez and S. Baert

Alosaimi, Alyahya, Alshahwan, Al Online survey, public university, Saudi- Academic achievement Total phone use (hours/day). Linear regression Negative association.m
Mahyijari and Shaik (2016) Arabia (N = 2367). subscale.a analysis.
Asante and Hiadzi (2018) Paper and pen survey, public university, Self-reported GPA. Total phone use in class Linear regression Negative association.n
Ghana (N = 150). (minutes/class). analysis.
Bun Lee (2015) Paper and pen survey, public university, Self-reported GPA. Smartphone addiction scale.g Linear regression No significant association.n
United States (N = 304). analysis.
o,p
Chen and Ji (2015) Paper and pen survey, private university, Actual GPA. Total personal electronic device Linear regression Negative association (r = −0.09).
Taiwan (N = 506). use (minutes/day). analysis.
Felisoni and Godoi (2018) Paper and pen survey combined with G-MNPS score.b Total phone use (minutes/day). Linear regression Negative association (r = −0.301).
logging data, business school, Brazil (N = analysis.
43).
Ibrahim et al. (2018) Paper and pen survey, public university, Self-reported GPA. Problematic mobile phone use Correlational analysis. Negative association.n
Saudi-Arabia (N = 610). questionnaire.h
Hawi and Samaha (2016) Online survey, private university, Lebanon Actual GPA. Smartphone addiction scale.g Logistic regression Negative association (r = −0.2).o
(N = 293). analysis.
Jankovic et al. (2016) Paper and pen survey, two public College adjustment Total phone use (hours/day). Linear regression Negative association (r = −0.111).
universities, Serbia (N = 485). scale.c analysis.
Kim et al. (2019) Paper and pen survey combined with Self-reported GPA. Total phone use in class Linear regression Negative association (r = −0.380).p
logging data, public university, South Korea (minutes/day). analysis.
(N = 84).
Lepp et al. (2014) Paper and pen survey, public university, Actual GPA. Total phone use (minutes/day). Path analysis. Negative association (r = −0.203).

4
United States (N = 536).
Lepp et al. (2015) Paper and pen survey, public university, Actual GPA. Total phone use (minutes/day). Linear regression Negative association (r = −0.234).
United States (N = 536). analysis.
Li, Lepp and Barkley (2015) Online survey, public university, United Self-reported GPA. Total phone use in class Path analysis. Negative association (r = −0.173).
States (N = 516). (minutes/day).
Lin and Chiang (2017) Online survey, private university, Singapore Self-reported GPA. Smartphone dependency Path analysis. Negative association (r = −0.079).p
(N = 438). symptom scale.i
Nayak (2018) Paper and pen survey, public university, Academic performance Total phone use (minutes/day). Correlational analysis. Negative association (r = −0.276).
India (N = 429). scale.d
Olufadi (2015) Paper and pen survey, two public Self-reported GPA. Mobile phone use behaviours Linear regression No significant association (r = −0.060).
universities, Nigeria (N = 286). scale.j analysis.
Rashid and Asghar (2016) Online survey, private university, Saudi- Self-reported GPA. Media and technology use and Path analysis. No significant association (r = −0.01).o
Arabia (N = 761). attitude scale.k
Rosen et al. (2018) Paper and pen survey combined with Social science course Total phone use (minutes/day). Correlational analysis. Negative association (r = −0.13).o
logging data, public university, United grade.e
States (N = 216).
Samaha and Hawi (2016) Online survey, private university, Lebanon Actual GPA. Smartphone addiction scale.g Linear regression Negative association (r = −0.143).
(N = 293). analysis.
Sert et al. (2019) Paper and pen survey, public university, Self-reported GPA. Problematic mobile phone use Correlational analysis. No significant association (r = 0.047).
Turkey (N = 743). scale.h
Uzun and Kilis (2019) Paper and pen survey, public university, Self-reported GPA. Media and technology use and Correlational analysis. Negative association (r = −0.107).
Turkey (N = 631). attitude scale.k
Wentworth and Middleton (2014) Paper and pen survey, private university, Self-reported GPA. Total phone use (minutes/ Linear regression No significant association (r = 0.01).o
United States (N = 483). week). analysis.
(continued on next page)
International Journal of Educational Research 103 (2020) 101618
 Table 1 (continued)

(1) Study (2) Data (3) Outcome variable (4) Explanatory variable (5) Main empirical (6) Association reported between (3) and (4)
approach

Winskel et al. (2019) Online survey, one private and one public Self-reported GPA. Total phone use (minutes/day). Correlational analysis. No significant association (South Korea, r =
S. Amez and S. Baert

university, South Korea and Australia (N = 0.04) and negative association (Australia, r =
389). -0.30).o
Witecki and Nonnecke (2015) Online survey, public university, Canada (N Student course Information technology habits Correlational analysis. Negative association.q
= 972). engagement scale.f during lectures scale.l

The following abbreviation is used: GPA (grade point average).

a
The academic achievement subscale is part of the authors’ problematic use of mobile phones scale. This subscale relates to the item that assesses whether the respondent believes her/his smartphone
use adversely affects her/his academic achievement, rated on a 5-point Likert scale. To allow the reader to compare column (6) row by row, we refer to an implicitly reverse coded version of the measured
subscale in the article.
b
The G-MNPS score is the student’s weighted average score calculated by the business school, based on the student’s rank for every course included in the curriculum.
c
The college adjustment scale comprises three items that assess self-related academic motivation, academic performance, and social adjustment to college, rated on a 5-point Likert scale.
d
The academic performance scale comprises three items that assess self-related academic participation, academic performance, and time management, rated on a 5-point Likert scale.
e
The final course grade is the final grade obtained for the specific course in which the survey was held, as provided by the lecturer of this course.
f
The student course engagement scale comprises 23 items that assess four factors found to relate to student course engagement (skills engagement, emotional engagement, participation and interaction
engagement, and performance engagement), rated on a 5-point Likert scale.
g
The smartphone addiction scale comprises 10 items that assess smartphone use primarily to identify the level of smartphone addiction risk, rated on a 6-point Likert scale.
h
The problematic mobile phone use scale comprises 27 items that assess smartphone use primarily to identify the level of problematic mobile phone use, rated on a 5-point Likert scale.
i
The smartphone dependency symptom scale comprises 16 items that assess smartphone use primarily to identify the level of smartphone dependency, rated on a 5-point Likert scale.
j
The mobile phone use behaviour scale comprises 13 items that assess the smartphone behaviour in different circumstances, rated on a 3-point Likert scale.

5
k
The media and technology usage scale comprises 44 items clustered in 11 subscales on smartphone use for different activities, rated on a 9- or 10-point frequency scale.
l
The information technology habits during lectures scale comprises 9 items that assess the frequency and the purpose of technology use during lectures, rated on a 5-point Likert scale and by open
answers.
m
This negative association represents the reported positive association between total phone use and the full problematic use of mobile phones scale (r = 0.311). The academic achievement subscale
(see above) is highly correlated to the other subscales of the full problematic use of mobile phones scale (correlation between academic achievement subscale and full problematic use of mobile phones
scale: r = 0.474).
n
The author(s) could not be reached via both email or telephone communication to obtain the Pearson correlation coefficients.
o
The article and/or electronic communication provided Pearson correlation coefficients with fewer than three decimals.
p
The reported Pearson correlation coefficient was obtained via personal communication with the author(s).
q
The authors indicated that based on the available data they were not able to provide a Pearson correlation coefficient.
International Journal of Educational Research 103 (2020) 101618
S. Amez and S. Baert International Journal of Educational Research 103 (2020) 101618

observed characteristics like internal motivation and ability (Di Pietro & Cutillo, 2006; James, 1994; Ladd, 2002). For these students,
several of the theoretical mechanisms supporting a negative association between smartphone use and academic performance might
be less relevant. In 15 cases, the research population was matriculated at a publicly funded university at the time of the research.
Twelve of them report a significantly negative association. In seven studies, the research was conducted at a private university or a
business school. Five of these studies report a significantly negative association. Winskel, Kim, Kardash and Belic (2019) studied
students from both private and publicly funded institutions but only find a negative significant association for the subsample of
students matriculated at a publicly funded institution. Thus, the empirical evidence to date suggest a negative association between
smartphone use and academic performance, regardless of the public or private nature of the education institution.
The association between smartphone use and academic performance has been investigated on all continents. However, most
studies were conducted in North America and Asia. No remarkable differences were noticed between the studies relying on data from
these two continents. Among the North American studies, Bun Lee (2015) and Wentworth and Middleton (2014) found no significant
association while a negative association was concluded in the five other articles. Similarly, two of the Asian-based investigations
(Rashid & Asghar, 2016; Sert et al., 2019) did not report a significant association while a negative association was reported by the
nine other studies. Furthermore, Winskel et al. (2019) found no significant association for their Korean subsample. For their Aus-
tralian (Oceania) they reported a significantly negative association. Besides, Felisoni and Godoi (2018) and Jankovic, Nikolic,
Vukonjanski and Terek (2016) reported a significantly negative association in Brazil (South America) and Serbia (Europe), respec-
tively. Finally, among the studies relying on African data, Olufadi (2015) found no significant association in Nigeria while Asante and
Hiadzi (2018) reported a negative association in Ghana.
Except for Jankovic et al. (2016), Nayak (2018), and Winskel et al. (2019), all studies were based on surveys with students from
one single educational institution. However, the sample sizes range from 43 to 2367 participants. Therefore, it could be postulated
that the reported non-significant associations in Table 1 are due to a lack of statistical power in the smaller samples. Of the eleven
articles with a sample size lower than the median (N = 483), only Olufadi (2015) and Bun Lee (2015) did not report a significant
association. Likewise, two studies (Rashid & Asghar, 2016; Sert et al., 2019) with a sample size higher than the median sample size
found no significant association. Finally, Wentworth and Middleton (2014), whose sample size (N = 483) coincides with the median
sample size, reported no significant association between smartphone use and academic performance. In sum, the insignificant as-
sociations reported in the literature do not appear to be driven by (studies with) smaller sample sizes.
Finally, the research results are rather homogeneous according to the level of control for confounding factors in the main em-
pirical approach. Importantly, none of the empirical results summarised in Table 1 can be given a causal interpretation; that is, to
date there is only evidence for heavy smartphone users performing worse at higher education institutions, with no support for heavy
smartphone use causing this worse performance. None of the approaches mentioned in column (5) of Table 1 can fully control for the
endogeneity of smartphone use and academic achievement because they all rely on observational, cross-sectional data. Thus, they can
only control for a limited set of confounders. However, factors such as motivation, intellectual capabilities and perceived academic
pressure are typically not included in their collected survey data but might influence both smartphone use and academic perfor-
mance. Thereby, it is unclear whether the worse performance of heavy smartphone users reported by many of these studies is really
the result of smartphone use or reflects variation in unobserved personal characteristics.
Nevertheless, the studies included in Table 1 differ in the extent to which they attempt to control for confounding variables. In this
respect, the negative association between smartphone use and academic performance is not more or less outspoken when such
controls are taken into account in a regression analysis. Among the nine reviewed articles that only calculate correlation coefficients,
only Olufadi (2015) and Sert et al. (2019) did not find a significant association. The remaining 14 articles perform at least one form of
regression analysis. Nine of them perform a linear regression analysis, four conduct a path analysis and one opts for a logistic
regression as main approach. Eleven times the regression coefficient with respect to the association between overall smartphone use
and academic performance is significantly negative.

3.3. Divergences in the empirical literature

By contrast, the association between smartphone use and academic performance seems to be heterogeneous by (a) the method of
data gathering, (b) the measures of academic performance used in the analysis, and (c) the measures of smartphone use adopted in
the research.
Firstly, all studies in Table 1 are (mainly) based on survey data: Seven rely on an online survey and another 12 rely on a paper and
pencil survey. Winskel et al. (2019) rely partly on an online survey for the Korean subsample and partly on a paper and pencil survey
for their Australian subsample. In addition, Felisoni and Godoi (2018), Rosen et al. (2018), and Kim et al. (2019) combined a paper
and pencil survey with objective logged data, which is a strong point for these studies as Boase and Ling (2013) reported a limited
correlation between self-reported smartphone use and actual logged data—we return to this point below. Four of the five studies
reporting no significant association between smartphone use and academic performance are relying on data gathered by a paper and
pencil questionnaire. A possible explanation might be that those questionnaires are filled in during a course and therefore partici-
pants are surrounded by peers which might increase the tendency to social desirable answers (Krumpal, 2013).
Secondly, across the reviewed studies, three measures of academic performance are used. Six studies use data on students’ actual
grades received from the lecturer or from the faculty or university administration. All of these studies conclude a significantly
negative association between smartphone use and these outcomes. In addition, 11 studies rely on self-reported grades. Remarkably,
all five articles not reporting a negative association fall within these 11 studies. Finally, The six studies using self-reported academic
performance scales all found a negative association. This contrast may indicate that errors of measurement occurred in the self-

6
S. Amez and S. Baert International Journal of Educational Research 103 (2020) 101618

reported grade variables. Indeed, these variables may be biased due to recall issues or socially desirable answering (Krumpal, 2013).5
Thirdly, we distinguish a similar difference in research results depending on how smartphone use is measured. In 12 articles, the
researchers investigate the association between academic performance and total smartphone use. Except for Felisoni and Godoi
(2018), Rosen et al. (2018), and Winskel et al. (2019), who used objectively tracked information, these frequencies are self-reported.
Of these twelve studies, only Wentworth and Middleton (2014) did not report a significant association while Winskel et al. (2019)
find a significant negative association for their Australian subsample and no significant association for the Korean subsample. In
contrast, of the other 11 studies, using a scale instrument to measure smartphone attitude and addiction, only seven find a sig-
nificantly negative association. In line with our explanation for the former two divergences in the literature, it might be the case that
students tend more to social desirable answers (Krumpal, 2013) when inquired about attitude or addiction compared to frequency of
phone use.

4. Conclusion

In this article, we reviewed the scientific literature to date on the relationship between smartphone use and academic perfor-
mance in tertiary education. Our analysis of the literature reveals a predominance of empirical results supporting a negative asso-
ciation. However, this predominance is less outspoken in studies analysing data gathered by paper and pen questionnaires (compared
with studies on data gathered by online surveys) and studies relying on self-reported grade point averages (compared with studies
using actual grades). In general, when scholars use methods of data gathering which are more susceptible to social desirable be-
haviour, a non-significant association is found more often.
The main limitation identified in the literature is that the existing studies all conduct correlational analyses and/or linear or
logistic regression analyses on cross-sectional data so that their results cannot be given a causal interpretation. We suggest two
different forms of analysis to counter this endogeneity problem that could be explored in further research. Firstly, longitudinal data
could be collected in view of regression analyses controlling for individual fixed effects. By integrating fixed effects into the analysis,
it is possible to control for time-invariant unobserved characteristics of university students that may affect both smartphone use and
academic performance. Secondly, instrumental variables correlated with smartphone use but not (directly) affecting educational
attainment (such as perceived quality of the WiFi network in the classrooms) may be surveyed. These can be used to capture an
exogenous prediction of smartphone use. Thereafter, the causal impact on academic performance of this exogenous prediction can be
estimated.
A second limitation of the literature is related to the analysed data. As pointed out in the empirical findings section, 20 out of 23
reviewed articles used self-reported measures of smartphone use in their analysis. Felisoni and Godoi (2018), Rosen et al. (2018), and
Kim et al. (2019) made a first attempt to introduce actual tracked use in their analysis but they had a rather small sample size (N =
43, 216, and 84 respectively). However, Boase and Ling (2013) provided evidence for only a limited correlation between actual
smartphone use and that measured by self-reported instruments. So, it is recommended for future research to further investigate
whether the results based on self-reported measures can be confirmed when analysing actual data.
A third shortcoming in the scientific literature so far is the lack of research investigating the empirical validity of the reviewed
theoretical mechanisms for a potential impact of smartphone use on academic performance. However, uncovering the mechanisms at
work is of great importance for policy making. To implement adequate policy measures on smartphone use in academic settings, we
need to know what precisely causes their (potential) relationship. Tracking university students’ smartphone use, as advocated above,
may also help in this respect. Actual tracked data would provide more insight into the timing of students’ smartphone use. This could
reveal indications for the multitasking mechanism and/or the time trade-off argument behind the association between smartphone
use and academic performance.

Appendix A. Supplementary data

Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.ijer.2020.
101618.

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