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Media multitasking and well-being of university students


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This study examines the impact of media multitasking behaviors on university students' social and psychological well-being (indicated by social success, normalcy, and self-control measures). To address inconsistent findings in recent literature, we characterized media multitasking behaviors by motivations, characteristics, and contexts. In particular, we examined the motivation of the primary task and the synchronicity of the task when social interactions were involved. Synchronous social interactions were found to be significantly and positively associated with social success, normalcy, and self-control. However, as predicted, media multitasking during synchronous social interactions was associated with lower social success. Further, although increased media multitasking during cognitive activities was linked with decreased self-control, media multitasking during entertainment activities was correlated with increased social success, normalcy, and self-control.
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Media multitasking and well-being of university students
Shan Xu
, Zheng (Joyce) Wang
, Prabu David
School of Communication, The Ohio State University, Columbus, OH, USA
College of Communication Arts and Sciences, The Michigan State University, East Lansing, MI, USA
article info
Article history:
Received 12 June 2015
Received in revised form
12 August 2015
Accepted 24 August 2015
Available online xxx
Social success
This study examines the impact of media multitasking behaviors on university studentssocial and
psychological well-being (indicated by social success, normalcy, and self-control measures). To address
inconsistent ndings in recent literature, we characterized media multitasking behaviors by motivations,
characteristics, and contexts. In particular, we examined the motivation of the primary task and the
synchronicity of the task when social interactions were involved. Synchronous social interactions were
found to be signicantly and positively associated with social success, normalcy, and self-control.
However, as predicted, media multitasking during synchronous social interactions was associated with
lower social success. Further, although increased media multitasking during cognitive activities was
linked with decreased self-control, media multitasking during entertainment activities was correlated
with increased social success, normalcy, and self-control.
©2015 Elsevier Ltd All rights reserved.
1. Introduction
Media saturation and convergent technologies have made me-
dia multitasking a way of life for many. In the U.S., a majority of
teenagers multitask mostor someof the time when listening to
music (73% of respondents), watching TV (68%), using a computer
(66%), and reading (53%; Rideout, Foehr, &Roberts, 2010). In the UK,
on average, 16- to 24-year-olds use media for 9.5 h a day, of which
52% involves media multitasking (Ofcom &GfK, 2010). Given its
prevalence, media multitasking has drawn considerable interest
from researchers.
Existing research on media multitasking has focused primarily
on its increasing popularity and detrimental effects on cognitive
performance and functions, but recently, its relationship with social
and psychological well-being has gained attention (e.g., Pea et al.,
2012; Shih, 2013). Potential negative consequences of media
multitasking on well-being have been documented. For example,
research has found that among children, it negatively correlates
with the feeling of normalcy and capabilities to develop intimate
relationship with friends (Pea et al., 2012), and it has been associ-
ated with the symptoms of depression and social anxiety in adults
(Becker, Alzahabi, &Hopwood, 2012). Findings, however, have been
inconsistent. For example, Shih (2013) found no signicant corre-
lation between media multitasking and a range of psychosocial
well-being factors, including emotional positivity, sociability, and
impulsivity. In other studies, even positive effects of media multi-
tasking on well-being have been suggested. For example, interact-
ing with family members while viewing television enhanced
children's prosocial behavior (St. Peters, Huston, &Wright, 1989),
and media multitasking was positively correlated with university
students' emotional satisfaction, albeit at the cost of cognitive
performance (Wang &Tchernev, 2012).
Then, is media multitasking harmful, harmless, or benecial to
social and psychological well-being? Before addressing this ques-
tion, we propose to further specify the concept of media multi-
tasking; we suspect that one reason for inconsistent ndings in the
literature is the denition of media multitasking. In recent liter-
ature, media multitasking refers to the simultaneous pursuit of two
or more relatively independent tasks, with at least one of the tasks
involving media (e.g., Jeong &Fishbein, 2007; Sanbonmatsu,
Strayer, Medeiros-Ward, &Watson, 2013). This broad and prac-
tical denition is invoked in everyday conversations, news
coverage, and research. Its breadth, however, makes comparing
ndings across studies a challenge because it encompasses a
plethora of diverse behaviors. This may obscure critical differences
in contexts and characteristics of media multitasking behaviors in
well-being research.
For example, both listening to music while studying and
listening to music while talking face-to-face with people are
*Corresponding author.
E-mail addresses: (S. Xu), (Z. Wang), (P. David).
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Computers in Human Behavior 55 (2016) 242e250
considered media multitasking, although these two behaviors
manifest distinct intentions. On the one hand, individuals who
listen to music while studying do so to make studying fun without
too much distraction, and it is one of the most popular multitasking
behaviors among university students (David, Kim, Brickman, Ran, &
Curtis, 2014). On the other hand, listening to music during a face-
to-face conversation is not common and is likely to be viewed as
discourteous; it may suggest avoidance of social interaction. Hence,
it is possible that frequent multitasking during face-to-fact
communication could be negatively associated with social re-
lationships and well-being in the long run, but we may not easily
draw the same conclusion for multitasking during study. However,
existing research on the relationship between media multitasking
and well-being relies on the popular media multitasking index
(Ophir, Nass, &Wagner, 2009) to gauge media multitasking
behavior. This index, although valuable for assessing general media
multitasking tendencies, aggregates a variety of media multitasking
activities, making it impossible to distinguish the impacts of these
different activities on well-being.
Two important criteria to differentiate media multitasking be-
haviors are motivations and resources demands. There is growing
evidence that different goals motivate different media multitasking
behaviors, which have different impacts on gratifying these goals
(Hwang, Kim, &Jeong, 2014;Wang &Tchernev, 2012; Zhang &
Zhang, 2012). Furthermore, based on the psychological literature,
eleven cognitive dimensions of media multitasking behaviors (e.g.,
relevance of the tasks, modalities of the tasks, behavioral responses
required by the tasks) have been identied as making some media
multitasking behaviors more resource intensive than others and,
thus, impacting behavioral outcomes and choices differently
(Wang, Irwin, Cooper, &Srivastava, 2015). Based on Wang et al.s
cognitive dimensional framework, it is easy to see why, despite the
overwhelming number of studies showing negative consequences
of media multitasking on task performance, some studies have
found an increase in task performance, such as when the tasks are
highly relevant and executed through non-competing modality
channels (e.g., Moreno &Mayer,1999; Wang et al., 2015). Following
these ideas, it seems reasonable to predict that distinct motivations
and cognitive characteristics of media multitasking behaviors can
impact social and psychological well-being in different ways,
leading to divergent ndings on their relationships. This is the
general issue explored in the current study.
In this study, we compared media multitasking behaviors
motivated by different goals and with different cognitive charac-
teristics. Specically, based on recent portrayals of the communi-
cation activities of university students (David et al., 2014; Wang &
Tchernev, 2012), we categorize media multitasking behaviors by
their primary task motivation (social, cognitive, and entertain-
ment); we also consider synchronicity, an important characteristic
of media multitasking behaviors that determines resource de-
mands (Walther, 1996; Wang et al., 2015).
2. Media multitasking among university students and its
Media multitasking has become increasingly popular thanks to
the versatility and accessibility of computers, smartphones, and
tablets, which allow for the seamless integration of work, play, and
social interaction (e.g., Carrier, Cheever, Rosen, Benitez, &Chang,
2009; David et al., 2014; Rosen, Mark Carrier, &Cheever, 2013;
Srivastava, 2013). A recent investigation in the U.S. (David et al.,
2014) revealed the major communication and media activities of
undergraduate students on a typical day based upon self-report of
992 respondents. In this study, estimates of time spent on
communication and media reached 39 h a day. Such an over-
estimation can bedat least partiallydattributed to multitasking.
Media multitasking has been examined mainly for its negative
impact on cognitive performance and functions, such as academic
performance (e.g., Junco, 2012; Junco &Cotten, 2012; Wood et al.,
2012). However, entertainment and social functions of media use
and media multitasking are also important (Hwang et al., 2014;
Wang &Tchernev, 2012). In a longitudinal experience-sampling
study on university students' daily activities over a month, Wang
and Tchernev (2012) found that students sacriced performance
on cognitive tasks for emotional and entertainment gains by
engaging in media multitasking activities. More specically, despite
studentsstated cognitive motivation, emotional and entertain-
ment needs were gratied by media multitasking although they
were not consciously sought after.
The diverse motivations and functions of media multitasking
behaviors point to the importance of the context in which media
multitasking occurs. When listening to music for relaxation or
entertainment, responding to a text message may have no conse-
quences. However, if the motivation for listening to music were to
learn the lyrics of the songs (i.e., cognitive motivation), texting
during listening would interfere with learning. In this example, the
impact of the same media multitasking behavior changes when the
motivation of the primary task changes.
In line with previous studies on university studentstime spent
on communication and media activities (Calderwood, Ackerman, &
Conklin, 2014; David et al., 2014; Wang &Tchernev, 2012), this
study identies three communication contexts of media multi-
tasking: (1) social-interaction activities driven by social needs,
which are comprised of face-to-face communication, phone and
video chat, texting, and social networking; (2) media-based
entertainment activities driven by relaxation, emotional, and
entertainment needs, including listening to music, watching TV or
videos online, and playing video games; and (3) cognitive activities
motivated by cognitive needs, mainly reading and studying (in our
sample of university students).
3. Resource characteristics of media multitasking behaviors
Another important way to specify media multitasking behaviors
is to take into consideration the resource demands of the tasks.
Based on psychological theories and ndings on limited resources
and resource allocation (Lang, 2000; Salvucci &Taatgen, 2008;
Wickens, 2002), media multitasking has been conceptualized as a
multidimensional behavior, with the dimensions of tasks requiring
and attracting different types and amounts of resources (Wang
et al., 2015). For example, multitasking activities with lower
levels of modality sharing and higher levels of control over infor-
mation ows (e.g., listening to music from a playlist while doing
homework) are less demanding than those that compete for the
The media multitasking index was developed by Ophir et al. (2009) and
adapted by Pea et al. (2012) to dene the level of media multitasking. This measure
includes 8 different media forms: (1) watching video content (T V, YouTube, movies,
etc.); (2) playing video games; (3) listening to music; (4) reading or doing home-
work; (5) e-mailing or sending messages/posting on SNS (not including Facebook
chat); (6) texting or instant messaging (including Facebook chat); (7) talking on the
phone or video chatting; and (8) participating in face-to-face conversations. For
each media-use category, respondents reported the total number of hours per week
they spend engaging in it. The question was followed by a multiple-choice scale
with options that were assigned numerical values for analysis: never (0), less than
1h(.5), about 1 to 2 h (1.5), about 2 to 3 h (2.5), about 3 to 4 h (3.5), or more than 4 h
(4.5). The media multitasking index is the weighted sum of the number of addi-
tional media an individual is using when involved in these eight communication
activities. Therefore, the index encompasses a wide range of media multitasking
S. Xu et al. / Computers in Human Behavior 55 (2016) 242e250 243
same modality resources and allow for less control (e.g., watching a
live television program while doing homework).
In the current study, we focused on two aspects of media
multitasking behaviors identied by Wang et al. (2015): task
switching and time pressure. Task switching here refers to the
extent of control people have over switching between tasksdthat
is, whether the multitasking context allows the user to change
when and where mental resources are allocated. For example,
multitasking behaviors involving instant messaging grant users
more control over when to attend to concurrent tasks than do those
involving phone conversations, as individuals' interactions over the
phone tend to be dictated by the social expectation to respond in a
timely fashion to one's partner (Wang, David, et al., 2012). Further,
whether one or more tasks require quick responses adds more
complexity to the dynamics of media multitasking. For example,
multitasking involving face-to-face conversation imposes greater
time pressure than that involving interaction on social networking
sites (SNS).
The characteristics of task switching and time pressure closely
relate to an important feature of mediadsynchronicity, which has
been highlighted in the literature on interactive media technology.
Synchronicity refers to media capabilities through which in-
dividuals can transmit and process information and communicate
in real time (Dennis, Fuller, &Valacich, 2008; Walther, 1996). Some
communication activities are synchronous, such as face-to-face
communication and phone calls/video chat, whereas texting and
SNS activities are typically asynchronous. As underlined by the
hyper-personal model in computer-mediated communication
(Walther, 1996), synchronous social interaction requires both
temporal commitment and simultaneous attention. Although
asynchronous media activities (involving, e.g., texting and SNS)
solve temporal conicts by allowing individuals to attend to social
interactions at their own convenience, synchronous media activ-
ities (involving face-to-face interaction, video conferencing, and
telephone calls) can lead to higher satisfaction and perceived
effectiveness of communication (Nowak, Watt, &Walther, 2009).
As discussed, in the context of media multitasking, synchronous
communication means less control over task switching and greater
time pressure. This indicates higher demands on cognitive re-
sources and, thus, a greater likelihood of suffering from limited yet
divided attention (Wang, David, et al., 2012).
Based on the above discussion, social interaction can be divided
into synchronous (including face-to-face, and phone calls/video
chat) and asynchronous (including texting and using SNS) cate-
gories. Taken together, we examine communication activities in
four contexts (driven by cognitive motivation, driven by enter-
tainment motivation, driven by social motivation and synchronous,
and driven by social motivation and asynchronous) in the current
study, as summarized in Table 1. We hypothesize that in general,
media multitasking in the four different contexts should have
distinctive effects on social and psychological well-beingdspeci-
cally, as indicated by measures of social success, normalcy, and self-
control (Hypothesis 1). These three well-being variables have been
examined in the literature on media multitasking, as reviewed next.
Based on the following review, we also propose more specichy-
potheses below relating each of the well-being variables to the
media multitasking contexts.
4. Social success
Social success is a crucial developmental task during adoles-
cence and emerging adulthood (Erikson, 1968; Pea et al., 2012). It is
conceptualized as having friends and being socially skilled,
including being able to develop and maintain close and meaningful
friendships (Pea et al., 2012). Research has found that those who
report an increase in social support over the time of emerging
adulthood show improved psychological well-being (Galambos,
Barker, &Krahn, 2006), whereas perceived lack of social support
is related to depression and loneliness (Jackson, Soderlind, &Weiss,
The social success of university students is largely connected to
their social-interactions, which are driven by social needs and
carried out through both face-to-face and technology-mediated
communication (Ellison, Steineld, &Lampe, 2007; Wang,
Tchernev, &Solloway, 2012). However, the context of social
interaction has been fundamentally altered by multitasking facil-
itated by mobile technologies such as smartphones and tablets.
Multitasking diverts attention away from one's present social in-
teractions and orients one's thoughts to people, places, and ac-
tivities outside the immediate spatial context (Misra, Cheng,
Genevie, &Yuan, 2014). This split attention invited by media
multitasking has the potential to strain social interactions and
relationships. In fact, a recent study showed that even the mere
presence of mobile phones could diminish the experience of face-
to-face conversations (Przybylski &Weinstein, 2013). Through two
laboratory experiments, the researchers showed that the presence
of a mobile phone placed on a desk next to paired participants but
outside of their direct visual eld had negative effects on their
perceived closeness, connection, and conversation quality, and
these effects were most apparent when the participants were
discussing personally meaningful topics. In a more naturalistic
eld study, the mere presence of a mobile phone placed innocu-
ously near participants while they were having a conversation
was found to interfere with their perceived closeness and
connection and related to the participants' diminished empathetic
concern toward each other (Misra et al., 2014). It is worth noting,
though, that in both studies, the primary tasks were real-time
As discussed, it is important to consider the synchronicity of
social interaction when evaluating the effects of media multitasking.
Synchronous social interaction, such as real-time conversations in
person or over phone or video technologies, demand more cognitive
resources for producing timely responses (Wang, David, et al., 2012;
Wang et al., 2015). Thus it is more likely to be negatively affected by
multitasking than asynchronous social interaction, such as texting
and SNS communication, which does not require real-time re-
sponses. As Wang, David, et al. (2012) have argued based upon re-
sources theories and, in particular, the threaded cognition theory of
multitasking, Except for some critical activities, such as driving, the
immediate loss in performance from multitasking or task-switching
may be gained somewhere down the line(p. 974). Asynchronous
social interaction is unlikely to be among those critical activitiesin
which one has no control over task switches or is under high time
pressure for responding. Instead, media users can actively control
task switches and, hopefully, allocate limited resources along the
timeline in an optimal or at least sensible way to achieve multiple
goals through multitasking.
Furthermore, the more resource demanding media multitasking
behaviors are, the less frequently they should be selected in
everyday life because of the law of less workto conserve re-
sources (Hull, 1943; Kool, McGuire, Rosen, &Botvinick, 2010; Wang
et al., 2015). Likely because of this, multitasking during synchro-
nous social interactions is perceived as less appropriate than
multitasking during asynchronous social interactions. Therefore,
we expect media multitasking during synchronous social interac-
tion to cause unpleasant social experiences and thus, in the long
run, decrease perceived social success. We predict that social suc-
cess is negatively correlated with media multitasking during syn-
chronous social interaction, but not during asynchronous social
interaction (Hypothesis 2).
S. Xu et al. / Computers in Human Behavior 55 (2016) 242e250244
5. The feeling of normalcy
Another indicator of social well-being is normalcy, the feeling of
being understood and accepted by peers (Reis &Shaver,1988). Peer
acceptance is considered to promote the development of mean-
ingful friendships, whereas peer rejection results in challenges in
establishing them (Nangle, Erdley, Newman, Mason, &Carpenter,
2003) and leads to troubling issues in later personality develop-
ment (Ladd, 2006). Much research has found that by late adoles-
cence, peers are typically the strongest inuence on personal
behavior, and university students appear to be no exception in
trying to become in-group members among their peers (e.g.,
Perkins, 2002). Given that university students are away from par-
ents and lacking frequent contact with siblings and members of
other reference groups, such as religious communities and full-
time jobs, peer acceptance and the sense of normalcy are particu-
larly crucial for their social well-being.
Similar to social success, the feeling of normalcy is gradually
established through interactions with peers. Whereas social suc-
cess is cultivated through interpersonal conversations, closeness,
and connection, the feeling of normalcy is closely related to social
and peer norms and is shaped by observing group behaviors,
adopting group attitudes, and behaving in accordance with peer
expectations (Festinger, 1954; Reno, Cialdini, &Kallgren, 1993;
Rhodes &Ewoldsen, 2009). There are two types of norms:
descriptive norms, which specify what most people do in a
particular situation, and injunctive norms, which specify what is
typically approved in society(Reno et al., 1993, p.104). Given the
popularity and prevalence of media multitasking behavior among
university students (David et al., 2014), media multitaskingdeven
during face-to-face communication and in classroom lecturesdhas
become a new normamong many university students under
certain situations (Gabriel, Campbell, Weibe, MacDonald, &
McAuley, 2012). For example, surveys and interviews of students
and faculty at a Canadian university showed that students in gen-
eral held a strong belief in their media multitasking capabilities
(Gabriel et al., 2012). One faculty member interviewed in the study
reported that in lectures, students come with their laptops, they're
doing their Facebook while they're taking the odd note while
they're checking e-mail, and so their attention span is all over the
place(pp. 9e10). Similarly, during face-to-face conversions, young
people are getting used to checking their text messages, Facebook
pages, or Twitter feeds (Turkle, 2012).
However, as discussed earlier, depending on their primary
motivation and characteristics, the four contexts of media multi-
tasking behaviors may impact normalcy differentlydat least in the
current social environment. For communication activities that are
primarily motivated by social interaction or cognition, the trend of
media multitasking becoming expected and normative probably
occurs chiey at the level of descriptive norms (i.e., Many people
do it) but not at the level of injunctive norms (i.e., This is an
approved behavior), as suggested by the disapproving tone of the
researchers (e.g., Gabriel et al., 2012). However, for media multi-
tasking that is primarily motivated by entertainment, this norma-
tive perception of media multitasking may have been constructed
at both the descriptive and injunctive norm levels. Engaging in
media multitasking is expected and approved, as media often can
facilitate entertainment and enjoyment (e.g., chatting on mobile
devices about a TV show while watching the show; Giglietto &
Selva, 2014;Nielsen, 2013). Therefore, although the feeling of
normalcy is closely related to social success, we expect that media
multitasking behavior should have different effects on normalcy
than on social success because of the emerging social norms of
media multitasking perceived by university students, but the ef-
fects may differ depending on contexts. Specically, we posit that
media multitasking in the four communication contexts should not
signicantly decrease the feeling of normalcy, and that normalcy
may be positively correlated with multitasking in the context of
entertainment-driven media use (Hypothesis 3).
6. Self-control
Academic growthda main goal and function of university
experiencedrequires students to sustain attention to learning
tasks, which demands self-control. Self-control, often used inter-
changeably with self-regulation (Baumeister &Alquist, 2009), has
been used to predict cognitive learning outcomes in school (e.g.,
grade point average; see Duckworth &Seligman, 2005; Tangney,
Baumeister, &Boone, 2004). Self-control has been dened by
Zimmerman (2002) as the self-directive process through which
learners transform their mental abilities into task-related academic
skills(p. 65). Based on the theory of self-regulated learning, self-
regulated learners tend to block out distracters in a learning envi-
ronment (Pintrich &de Groot, 1990) and actively engage in cogni-
tive processing of learning materials (Zimmerman, 2002). If that is
the case, then over time, the decreased top-down attentional
control associated with frequent media multitasking during situa-
tions in which undivided attention is required (e.g., learning tasks)
may lead to lower self-regulation (self-control). Research has found
that compared to light media multitaskers, heavy media multi-
taskers are more likely to be distracted by irrelevant stimuli and
less likely to sustain their attention on cognitive tasks (Ophir et al.,
2009). Furthermore, accumulated evidence shows that media
multitasking while studying is associated with shallower process-
ing (Carr, 2010), poor performance in the classroom (Rosen et al.,
2013; Wood et al., 2012), and lower grade point average (Junco,
2012), which can collectively hinder success and well-being in
life. More important, self-control is associated with psychological
well-being directly, as low self-control is associated with a wide
range of deviant behaviors, whereas a higher degree of self-control
is positively related to better planning and decision-making
(Ridder, Lensvelt-Mulders, Finkenauer, Stok, &Baumeister, 2012).
For example, in one study, students with low self-control were at
greater risk for reporting binge drinking, marijuana use, and
prescription-drug misuse (Ford &Blumenstein, 2013).
In light of self-control's important effects on the well-being of
university students, we examine whether university students'
media multitasking behavior relates to their self-control above and
beyond their media and communication activities in general. More
specically, for our sample of university students, we predict that
media multitasking during media cognitive activities would be
negatively correlated with self-control (Hypothesis 4).
7. Method
7.1. Participants
An online survey was conducted between March and May of
2014. Participants were undergraduate and graduate students
recruited from 59 universities in Beijing, China. Participants were
recruited online to complete an online survey created using Qual-
trics software (Qualtrics, 2013). The survey link was distributed
online through major SNS used widely by university students in
Beijing. As an incentive, students were offered the opportunity to
enter a lottery with a chance to win one of 10 cell phone gift cards (a
$17 value).
In total, 375 students completed the survey. They were 18e38
years old (M¼20.90, SD ¼2.66), and 61.9% were female. Most
(69.5%) reported that they were single, about one-third (29.1%)
reported being in a relationship, and 1.3% were married. Many were
S. Xu et al. / Computers in Human Behavior 55 (2016) 242e250 245
freshmen (40.2%), and the rest were sophomores (19.7%), juniors
(17.0%), and seniors (8.4%), or graduate students (14.8%). Almost all
reported owning a smartphone (97.3%). On average, participants
reported keeping in touch with 7.53 (SD ¼5.62) family members
and 10.89 (SD ¼17.24) close friends through SNS and having 186.36
(SD ¼258.68) friends on SNS.
7.2. Measures
7.2.1. Communication activities
Participants were asked how many hours on a typical day they
spent on the following activities: (1) watching video content (e.g.,
TV, online video, and movies); (2) playing video games; (3) listening
to music; (4) reading, studying, or doing homework; (5) e-mailing
or sending messages/posting on SNS (excluding chatting on SNS);
(6) texting or instant messaging; (7) talking on the phone or video
chatting; (8) having face-to-face conversations in person. These
items were adopted from Wang and Tchernev (2012) and David et al.
(2014), and included typical college studentsdaily communication
activities. For each activity, participants reported their daily level of
engagement by selecting one of the following options: (numerical
values assigned to each option are labeled in parentheses): never
(0); less than 1 h (.5), about 1 to 2 h (1.5),about 2 to 3 h (2.5), about 3 to
4h(3.5), about 4 to 5 h (4.5), or more than 5 h (5.5).
Based on the theoretical reasons described earlier, the following
four multitasking contexts were considered: (1) media-based
entertainment activities, including watching video content, listening
to music, and playing video games; (2) media-based cognitive ac-
tivities, including reading, studying, and doing homework using
media; (3) asynchronous social interaction, including e-mailing,
most SNS use (excluding real-time chatting on SNS), and texting
and instant messaging; and, nally, (4) synchronous social interac-
tion, including talking on the phone or video chatting (including
real-time chatting on SNS) and having in-person, face-to-face
7.2.2. Media multitasking tendencies
For each of the eight communication activities listed above,
participants were also asked to indicate the percentage of time
(1%e100%) they would typically be engaging in another form of
communication activity. For example, While you are having a face-
to-face conversation in person, what percentage of the time are you
also doing any of the following activities?The listed activities
included all the communication activities described earlier,
excluding face-to-face communication. Then, the sum of all the
percentages was used as the indicator of multitasking tendency
during face-to-face conversations. Finally, we computed multi-
tasking tendency for the four communication activity contexts by
taking the average of all activities within a context.
7.2.3. Social success
Social success was measured using an index adopted from Pea
et al. (2012). Participants were asked to rate seven statements us-
ing 5-point Likert scale (1 ¼strongly disagree,5¼strongly agree)d
for example, I feel like I have a lot of friends,People my age
understand me, and I feel like I have a lot of close friends. The
average of the ratings was used to create the nal score for social
success. The index was reliable (Cronbach's
7.2.4. Normalcy
Following Pea et al. (2012), we had participants rate three
statements using a 5-point Likert scale (1 ¼strongly disagree,
5¼strongly agree). The statements were Compared to people my
age, I feel normal,I often feel like I'm not normal compared to
people my age(reverse-coded), and I often feel rejected by other
people my age. The average of the ratings was used to create the
nal score for normalcy. The index was reliable (Cronbach's
7.2.5. Self-control
Following Tangney et al. (2004), we used a 5-point Likert scale to
measure self-control. Participants rated 11 statements, such as I
have trouble concentrating,I have worked or studied all night at
the last minute, and Getting up in the morning is hard for me.
The average of the ratings (after reverse-coding some items) was
used to indicate self-control. The scale was reliable (Cronbach's
7.2.6. Demographics
To collect some basic information about the sample, we also had
participants report their age, gender, year in university, marriage
and relationship status, ownership of a smartphone, and number of
family members, close friends, and friends on SNS.
8. Results
The analysis began with a descriptive analysis of the variables.
Then, hierarchical regression models were used to examine the
effects of multitasking tendencies on well-being.
8.1. Descriptive summary of key variables
As summarized in Table 1, on average, participants spent 4.29 h
(SD ¼2.26) per day on entertainment-driven media activities
(gaming, music, and video), 4.05 h (SD ¼2.46) on asynchronous
social interaction (SNS and texting), 3.36 h (SD ¼1.90) on syn-
chronous social interaction (face-to-face and phone call/video
chat), and 2.32 h (SD ¼1.39) on cognitive media activities (reading,
studying, and doing homework).
Table 1
Time spent (in hours) on four categories of communication activities and percentage of time spent multitasking during
these activities.
Communication activities Mean hours spent (SD)
Synchronous social interaction 3.36 (1.90)
Asynchronous social interaction 4.05 (2.46)
Entertainment-driven media activities 4.29 (2.26)
Cognitive media activities 2.32 (1.39)
Multitasking tendencies during communication activities Mean percentage of hours (SD)
MT during syn social interaction 62 (83)
MT during asyn social interaction 116 (89)
MT during entertainment media activities 110 (77)
MT during cognitive media activities 109 (89)
Note: The percentage can be greater than 100% because of simultaneous engagement of more than two tasks at a time.
S. Xu et al. / Computers in Human Behavior 55 (2016) 242e250246
Multitasking tendencies during these activities also are sum-
marized in Table 1. It is interesting to note that rates of multitasking
in three of the four contexts were greater than 100%. This may be
explained by the fact that sometimes more than two communica-
tion tasks were added to a primary communication task. On
average, participants were most likely to multitask during asyn-
chronous social interaction (M¼1.16 or 116%, SD ¼.89), followed
entertainment-driven media activities (M¼110%, SD ¼.77) and
cognitive media use (M¼109%, SD ¼.89). Consistent with previous
ndings (Wang et al., 2015), multitasking was least common during
synchronous social interaction (M¼62%, SD ¼.83).
8.2. Hierarchical regression analysis
Hierarchical regression models were used to examine the effects
of media multitasking tendencies on the three well-being in-
dicators (social success, normalcy, and self-control). The assump-
tions of multicollinearity based on the values for tolerance and the
variation ination factor were examined, which were above .10 and
below 10, respectively, for all models. Normal probability plots of
the regression standardized residual and the scatterplot were also
were checked to ensure that the assumptions of linearity,
normality, independence of residuals, and homoscedasticity were
For each of the three well-being indicators, in Step 1, we entered
the four contexts of communication as predictors. Gender and age
were controlled as well. In Step 2, multitasking tendencies in the
four communication contexts were entered. In Step 3, two-way
interactions between each communication context and its corre-
sponding multitasking tendency were entered.
Two-way interactions from Step 3 did not signicantly
contribute to the changed R-squared value for any of the well-being
indicators and were subsequently dropped. However, multitasking
variables added in Step 2 signicantly increased the R-squaredvalue
compared to Step 1 models (see Table 2). Hence, the nal selected
models were the Step 2 models for all three well-being variables,
which are summarized in Table 2. As predicted in Hypothesis 1,
media multitasking during the four communication contexts (i.e.,
variables added in Step 2) inuenced social and psychological well-
being variables in different waysdthere were positive, negative,
and null effects. Hence, Hypothesis 1 was supported.
8.2.1. Social success
The more complex model with media multitasking variables
added in Step 2 signicantly increased the explained variance
when compared to the model that included only communication
activities, age, and gender (
¼.035, p<.01). This suggests that
media multitasking tendencies during communication are posi-
tively associated with social success. The model predicting social
success was statistically signicant, F(10, 364) ¼3.69, p<.001;
the R-squared value was .092, meaning the model explained 9.2%
of the variance in social success, whereas 3.5% was contributed by
media multitasking tendencies. As shown in Ta bl e 2, synchronous
social-interaction activities signicantly increased perceived so-
cial success (
¼.25). However, media multitasking tendency
during synchronous social interaction signicantly decreased
social success. When the tendency increased by one unit (e.g., 1%
of time), perceived social success decreased by .22 points (on the
1e5 point scale). However, and also as predicted, the media
multitasking tendency during asynchronous social interaction
was not signicantly related to social success. Together, these
ndings support our Hypothesis 2dthat multitasking during
synchronous, but not asynchronous, social interaction would
decrease perceived social success. In addition, it is interesting to
note that the media multitasking tendency during entertainment-
driven media activity signicantly increased social success
8.2.2. Normalcy
The more complex model with media multitasking variables
signicantly increased the explained variance in normalcy
¼.043, p<.005). The overall model predicting normalcy was
statistically signicant, F(10, 364) ¼2.53, p<.01; the R-squared
value was .065, meaning the model explained 6.5% of the variance
of normalcy, with 4.3% contributed by media multitasking variables
(see Table 2). On the one hand, as with social success, synchronous
social interaction activities (
¼.17) signicantly increased
normalcy. On the other hand, as predicted by Hypothesis 3 and in
contrast to our nding regarding social success, none of the four
media multitasking variables was negatively correlated with
normalcy. Instead, the media multitasking tendency during
entertainment-driven media activities positively predicted
normalcy (
¼.19). The 95% condence intervals for estimated
regression coefcients were [e.302, .041] for media multitasking
during synchronous social interaction, [e.275, .146] for media
multitasking during asynchronous social interaction, and [e.349,
.004] for media multitasking during cognitive media activities. All
CIs were tightly around 0, supporting the null-effect predictions of
media multitasking in these contexts.
Table 2
Summary of regression results for predicting well-being indicators.
Social success Normalcy Self-control
Step 1
Intercept 2.948 (.349) 3.462 (.400) 1.797 (.298)
Synchronous social interaction .100 (.023) .251
.079 (.027) .174
.064 (.020) .180
Asynchronous social interaction .037 (.019) .119 .009 (.022) .026 .039 (.017) .142
Entertainment media activities .002 (.019) .006 .028 (.022) .074 .011 (.016) .037
Cognitive media activities .018 (.028) .032 .024 (.032) .038 .110 (.024) .227
Age .003 (.015) .012 .019 (.017) .060 .046 (.013) .180
Gender .136 (.084) .087 .084 (.097) .047 .065 (.072) .047
Step 2
MT during syn social interaction .224 (.076) .245
.131 (.087) .127 .030 (.065) .038
MT during asyn social interaction .086 (.093) .101 .064 (.107) .067 .016 (.080) .021
MT during entertainment media activities .210 (.084) .213
.212 (.096) .190
.210 (.071) .241
MT during cognitive media activities .023 (.078) .026 .172 (.090) .178 .182 (.067) .240
for step 1 .058
.022 .120
for step 2 .035
S. Xu et al. / Computers in Human Behavior 55 (2016) 242e250 247
8.2.3. Self-control
As for the other two well-being indicators, the more complex
model with media multitasking variables signicantly increased
the explained variance in self-control (
¼.030, p<.01). The
overall model predicting self-control was statistically signicant,
F(10, 364) ¼6.41, p<.001; the R-squared value was .150, meaning
the model explained 15%of the total variance of self-control, and 3%
was contributed by media multitasking (see Table 2). As with the
other two well-being variables, synchronous social interaction ac-
tivities were signicantly and positively related to self-control
¼.18). However, asynchronous social interaction (
was signicantly and negatively related to self-control. It is prob-
ably not surprising that cognitive activities, such as study and
reading, were positively related to self-control (
The coefcients estimated for media multitasking variables
helped conrm Hypothesis 4: The media multitasking tendency
during cognitive media activities was signicantly and negatively
related to self-control (
¼.24). However, as with social success
and normalcy, the media multitasking tendency during
entertainment-driven media activities was signicantly and posi-
tively related to self-control (
9. Discussion
The primary goal of this study was to explore different impacts
of media multitasking on social and psychological well-being in
four media multitasking contexts. Based on motivational research
on media use and media multitasking behaviors (David et al., 2014;
Hwang et al., 2014;Wang &Tchernev, 2012; Zhang &Zhang, 2012),
we examined three types of motivations: social, cognitive, and
entertainment. We also considered synchronous and asynchronous
communication, an important distinction that has been identied
in the literature (Walther, 1996; Wang et al., 2015). As predicted, for
different contexts, media multitasking inuenced the three in-
dicators of social and psychological well-being in different way-
sdyielding positive, negative, and null effects.
The variance explained ranged from relatively small to moder-
ate in the current sample, with the models explaining 9.2%, 6.5%,
and 15% of social success, normalcy, and self-control, respectively.
Additional variance accounted for by media multitasking variables
entered in Step 2 was 3.5% in social success, 4.3% in normalcy, and
3% in self-control. Considering that only a simple set of key vari-
ables relevant to communication activities and media multitasking
tendencies were included, a small effect is to be expected. Further,
other variables that contribute to well-being, such as physical
health, social economic status, and family and social support
(Penedo &Dahn, 2005; Pinquart &S
orensen, 2000), were not
considered in this study. In addition, as dynamic systems theories
indicate, smallmedia effects may accumulate over time and
through the life span, lead to a greater impact on individuals (e.g.,
Wang, Lang, &Busemeyer, 2011; Wang, Tchernev, &Solloway,
2012; Ward, 2002; ). This warrants even more attention, consid-
ering the accelerating media multitasking trend among younger
people (Carrier et al., 2009).
9.1. Media multitasking in different communication contexts
A large body of research has established that media multitasking
during cognitive activities, such as reading and studying, produces
negative consequences. Prior research has revealed that media
multitasking during cognitive activities is associated with
decreased comprehension of and memory for lectures (Rosen et al.,
2013; Wood et al., 2012), shallow processing (Carr, 2010), failure to
satisfy cognitive needs (Wang &Tchernev, 2012), lower grade point
average (Junco, 2012), and susceptibility to distractions from
irrelevant information (Ophir et al., 2009). All of these may be
related to decient self-control during required cognitive activities
for university students and may further lead to interference with
daily life, hindering career success and well-being (David et al.,
Interestingly, however, in a different communication context,
media multitasking showed positive effects on well-being. More
specically, our study revealed that media multitasking during
entertainment-driven media activities was positively related to
indicators of social and psychological well-beingdnamely, social
success, normalcy, and self-control. The benecial outcomes of
multitasking during entertainment-driven media activities have to
do with the motivation of the primary task. Generally, people
involved in entertainment-driven activities want to have fun and be
relaxed, so the goals of such activities are generally less pressing
and do not require intense and potentially exhausting concentra-
tion. Therefore, competition for cognitive resources might not be
acute during entertainment-driven activities, which should free up
resources for other activities in a seemingly effortless way. Conse-
quently, the characteristics of multitasking during these activities
allow the concurrency of multitasking behaviors without inter-
fering with primary-task performance or leading to feelings of
stress. In many entertainment-based situations, multitasking can
even increase enjoyment and positive social outcomes, such as
providing a shared interactive experience with friends or other fans
on social media while watching a common-interest TV show or live
sports telecast (e.g., Nielsen, 2013; Shim, Oh, Song, &Lee, 2015).
Media multitasking during social interactions had more com-
plex effects on well-being, depending on the synchronicity of such
interactions. On the one hand, during synchronous social in-
teractions (e.g., face-to-face conversations, phone conversations,
video chats), the higher the media multitasking tendency, the
lower the degree of social success. On the other hand, during
asynchronous social interactions (e.g., emailing, texting, online
chatting), the tendency of media multitasking made no difference
on indicators of well-being. Thus, the synchronicity of social
interaction is critical and should be taken into consideration in
discussions of the effects of media multitasking.
9.2. The critical role of synchronous social interactions in well-
The results of this study resonate with previous ndings on the
importance of synchronous social interactions in building mean-
ingful relationships and maintaining mental health, even as our
social relationships now have been shaped by SNS, e-mail, and
short text messages (e.g., Misra et al., 2014; Pea et al., 2012). Syn-
chronous social interactions are communication activities that
provide greater immediacy of feedback, leading to simultaneous
sender-and-receiver exchanges (Walther, 1996), including face-to-
face communication and phone calls/video chats. Synchronous
social interactions lead to higher satisfaction of team relations
(Nowak et al., 2009) and are associated with a wide range of pos-
itive social and psychological feelings (Pea et al., 2012).
Given the crucial role of synchronous social interactions, it is
even more important to note that media multitasking during such
activities has a deleterious impact on well-being. Today, the context
of social interactions has been fundamentally altered by media
multitasking facilitated by mobile devices. We often face situations
in which multiple goals and needs are combined and a natural
solution is to juggle multiple tasks. Mobile technologies have
facilitated and encouraged divided attention by providing cues for
other tasks (e.g., alerts for incoming e-mails and text messages) and
providing multitasking capabilities (e.g., apps with different func-
tions on smartphones) that enable the distribution of attention to
S. Xu et al. / Computers in Human Behavior 55 (2016) 242e250248
people and matters outside the immediate spatial and temporal
contexts (Misra et al., 2014). As discussed, media multitasking
during synchronous social interaction may gradually become more
and more expected and normative among university students at
the descriptive-norm level, though it is still viewed as inappro-
priate by many (e.g., Bauerlein, 2008). Indeed, experimental
research has suggested that this split attention due to multitasking
has ramications in the form of restraining the synchronous social
interactions in which people expect immediate responses and
focused conversation, and, further, on their interpersonal re-
lationships generally (Wang, David, et al., 2012). In comparison,
multitasking during asynchronous social interaction was not
signicantly related to any indicators of well-being. Asynchronous
social interaction, compared with synchronous interaction, allows
communicators to create a message at one time and recipients to
obtain it later, without interfering with conversational ow
(Walther, 1996). This interaction system does not impose a pressing
time constraint, which makes extra cognitive resources available
for multitasking activities (Wang, David, et al., 2012). These ndings
can help guide multitasking training and design for workplaces. For
example, when a job requires signicant task switches between
communication and information navigation (e.g., customer ser-
vices), it is advisable to use an asynchronous medium (e.g., texting
or online chatting) instead of a synchronous one (e.g., video chat).
9.3. Limitations and implications
The study is among the rst to differentiate the impacts of
media multitasking on social and psychological well-being by
specifying the motivations and cognitive characteristics of media
multitasking behaviors. As predicted, indeed the impacts vary
depending on the contexts and nature of the media multitasking
behavior. However, given that this study was based on survey
methods, these ndings are correlational, not causal. We cannot
determine whether different types of media multitasking behaviors
affected or resulted from social and psychological well-being. For
example, media multitasking during synchronous communication
could disrupt attention and conversation quality, thereby leading to
decreased social success in the long run. Alternatively, it is possible
that people who are socially anxious use media multitasking as a
distraction tactic to avoid social interactions.
In fact, such a relationship between individual traits and media
multitasking behaviors is likely to have reciprocal causality in the
long run. Individual differences in mental states and traits can
determine media multitasking behaviors, which in turn can further
reinforce or change the individual states and traits (Slater, 2007;
Wang, 2014; Wang &Tchernev, 2012). Future research should try
to replicate these results and also formally test the causal rela-
tionship between media multitasking and well-being through ex-
periments and longitudinal studies. As a rst attempt, the current
study has provided evidence for the plausibility of this causality.
Bauerlein, M. (2008). The dumbest generation: How the digital age stupees young
Americans and jeopardizes our future. New York, NY: Jeremy P. Tarcher/Penguin
Baumeister, R. F., & Alquist, J. L. (2009). Is there a downside to good self-control? Self
and Identity, 8(2e3), 115e130.
Becker, M. W., Alzahabi, R., & Hopwood, C. J. (2012). Media multitasking is associ-
ated with symptoms of depression and social anxiety. Cyberpsychology,
Behavior, and Social Networking, 16(2), 132e135.
Calderwood, C., Ackerman, P. L., & Conklin, E. M. (2014). What else do college stu-
dents dowhile studying? an investigation of multitasking. Computers &Ed-
ucation, 75,19e29.
Carr, N. (2010). The shallows: What the internet is doing to our brains. New York:
W.W. Norton &Company, Inc.
Carrier, L. M., Cheever, N. A., Rosen, L. D., Benitez, S., & Chang, J. (2009). Multitasking
across generations: multitasking choices and difculty ratings in three gener-
ations of Americans. Computers in Human Behavior, 25(2), 483e489. http://
David, P., Kim, J.-H., Brickman, J. S., Ran, W., & Curtis, C. M. (2014). Mobile phone
distraction while studying. New Media &Society.
1461444814531692. Advance online publication.
Dennis, A. R., Fuller, R. M., & Valacich, J. S. (2008). Media, tasks, and communication
processes: a theory of media synchronicity. MIS Quarterly, 32(3), 575e600.
Duckworth, A. L., & Seligman, M. E. P. (2005). Self-discipline outdoes IQ in pre-
dicting academic performance of adolescents. Psychological Science, 16(12),
Ellison, N. B., Steineld, C., & Lampe, C. (2007). The benets of Facebook friends:
social capital and college students' use of online social network sites. Journal of
Computer-Mediated Communication, 12(4), 1143e1168 .
Erikson, E. (1968). Identity: Youth and crisis. New York: Norton.
Festinger, L. (1954). A theory of social comparison processes. Human Relations, 7(2),
117 e140.
Ford, J. A., & Blumenstein, L. (2013). Self-control and substance use among college
students. Journal of Drug Issues, 43,56e68.
Gabriel, M. A., Campbell, B., Weibe, S., MacDonald, R. J., & McAuley, A. (2012). The
role of digital technologies in learning: expectations of rst year university
students. Canadian Journal of Learning and Technology, 38(1), 1e18.
Galambos, N. L., Barker, E. T., & Krahn, H. J. (2006). Depression, self-esteem, and
anger in emerging adulthood: seven-year trajectories. Developmental Psychol-
ogy, 42(2), 350e365.
Giglietto, F., & Selva, D. (2014). Second screen and participation: a content analysis
on a full season dataset of tweets. Journal of Communication, 64(2), 260e277.
Hull, C. L. (1943). Principles of behavior. New York, NY: Appleton-Century.
Hwang, Y., Kim, H., & Jeong, S. H. (2014). Why do media users multitask? Motives
for general, medium-specic, and content-specic types of multitasking.
Computers in Human Behavior, 36, 542e548.
Jackson, T., Soderlind, A., & Weiss, K. E. (2000). Personality traits and quality of
relationships as predictors of future loneliness among American college stu-
dents. Social Behavior and Personality, 28(5), 463e470.
Jeong, S.-H., & Fishbein, M. (2007). Predictors of multitasking with media: media
factors and audience factors. Media Psychology, 10(3), 364e384. http://
Junco, R. (2012). In-class multitasking and academic performance. Computers in
Human Behavior, 28(6), 2236e2243.
Junco, R., & Cotten, S. R. (2012). No A 4 U: the relationship between multitasking
and academic performance. Computers &Education, 59(2), 505e514. http://
Kool, W., McGuire, J. T., Rosen, Z. B., & Botvinick, M. M. (2010). Decision making and
the avoidance of cognitive demand. Journal of Experimental Psychology: General,
139 , 665e682.
Ladd, G. W. (2006). Peer rejection, aggressive or withdrawn behavior, and psy-
chological maladjustment from ages 5 to 12: an examination of four predictive
models. Child Development, 77(4), 822e846.
Lang, A. (2000). The limited capacity model of mediated message processing.
Journal of Communication, 50(1), 46e70.
Misra, S., Cheng, L., Genevie, J., & Yuan, M. (2014). The iPhone effect the quality of
in-person social interactions in the presence of mobile devices. Environment
and Behavior,1e24.
Moreno, R., & Mayer, R. E. (1999). Cognitive principles of multimedia learning: the
role of modality and contiguity. Journal of Educational Psychology, 91(2), 358.
Nangle, D. W., Erdley, C. A., Newman, J. E., Mason, C. A., & Carpenter, E. M. (2003).
Popularity, friendship quantity, and friendship quality: Interactive inuences on
children's loneliness and depression. Journal of Clinical Child And Adolescent
Psychology, 32(4), 546e555.
Nielsen. (2013). A look across screens. The cross-platform report. New York. Retrieved
Nowak, K. L., Watt, J., & Walther, J. B. (2009). Computer mediated teamwork and the
efciency framework: exploring the inuence of synchrony and cues on media
satisfaction and outcome success. Computers in Human Behavior, 25(5),
110 8 e1119 .
Ofcom and GfK. (2010). The consumer's digital day. Retrieved from: http://
Ophir, E., Nass, C., & Wagner, A. D. (2009). Cognitive control in media multitaskers.
Proceedings of the National Academy of Sciences, 106(37), 15583e15587. http://
Pea, R., Nass, C., Meheula, L., Rance, M., Kumar, A., Bamford, H., et al. (2012). Media
use, face-to-face communication, media multitasking, and social well-being
among 8- to 12-year-old girls. Developmental Psychology, 48(2), 327e336.
S. Xu et al. / Computers in Human Behavior 55 (2016) 242e250 249
Penedo, F. J., & Dahn, J. R. (2005). Exercise and well-being: a review of mental and
physical health benets associated with physical activity. Current Opinion in
Psychiatry, 18(2), 189e193.
Perkins, H. W. (2002). Social norms and the prevention of alcohol misuse in col-
legiate contexts. Journal of Studies on Alcohol and Drugs, Supplement, 14,
164 e172 .
Pinquart, M., & S
orensen, S. (2000). Inuences of socioeconomic status, social
network, and competence on subjective well-being in later life: a meta-anal-
ysis. Psychology and Aging, 15(2), 187e224.
Pintrich, P. R., & de Groot, E. V. (1990). Motivational and self-regulated learning
components of classroom academic performance. Journal of Educational Psy-
chology, 82(1), 33e40.
Przybylski, A. K., & Weinstein, N. (2013). Can you connect with me now? how the
presence of mobile communication technology inuences face-to-face con-
versation quality. Journal of Social and Personal Relationships, 30(3), 237e246.
Qualtrics. (2013). Qualtrics research suite (Version 43051) (Computer software).
Provo, UT: Qualtrics.
Reis, H. T., & Shaver, P. (1988). Intimacy as an interpersonal process. In S. Duck,
D. F. Hay, S. E. Hobfoll, W. Ickes, & B. M. Montgomery (Eds.), Handbook of per-
sonal relationships: Theory, research and interventions (pp. 367e389). Oxford,
England: John Wiley &Sons.
Reno, R. R., Cialdini, R. B., & Kallgren, C. A. (1993). The transsituational inuence of
social norms. Journal of Personality and Social Psychology, 64,104e112 . http://
Rhodes, N., & Ewoldsen, D. R. (2009). Attitude and norm accessibility and cigarette
smoking. Journal of Applied Social Psychology, 39, 2355e2372.
Ridder, D. T., Lensvelt-Mulders, G., Finkenauer, G., Stok, F. M., & Baumeister, R. F.
(2012). Taking stock of self-control a meta-analysis of how trait self-control re-
lates to a wide range of behaviors. Personality and Social Psychology Review, 16(1),
Rideout, V. J., Foehr, U. G., & Roberts, D. F. (2010). Generation M
: Media in the lives of
8- to 18-year-olds. Menlo Park, CA: Kaiser Family Foundation. Retrieved from
Rosen, L. D., Mark Carrier, L., & Cheever, N. A. (2013). Facebook and texting made me
do it: Media-induced task-switching while studying. Computers in Human
Behavior, 29(3), 948e958.
Salvucci, D. D., & Taatgen, N. A. (2008). Threaded cognition: an integrated theory of
concurrent multitasking. Psychological Review, 115(1), 101e130. http://
Sanbonmatsu, D. M., Strayer, D. L., Medeiros-Ward, N., & Watson, J. M. (2013). Who
multi-tasks and why? multi-tasking ability, perceived multi-tasking ability,
impulsivity, and sensation seeking. PLoS One, 8(1), e54402.
Shih, S.-I. (2013). A null relationship between media multitasking and well-being.
PLoS One, 8(5), e64508.
Shim, H., Oh, P., Song, H., & Lee, Y. (2015). An exploration of motivations for two
screen viewing, social interaction behaviors, and factors that inuence viewing
intentions. Cyberpsychology, Behavior, and Social Networking, 18(3), 158e164.
Slater, M. D. (2007). Reinforcing spirals: the mutual inuence of media selectivity
and media effects and their impact on individual behavior and social identity.
Communication Theory, 17(3), 281e303.
Srivastava, J. (2013). Media multitasking performance: role of message relevance
and formatting cues in online environments. Computers in Human Behavior,
29(3), 888e895.
St Peters, M., Huston, A. C., & Wright, J. C. (1989). Television and families: Parental
coviewing and young children's language development, social behavior, and tele-
vision processing. Retrieved from
Tangney, J. P., Baumeister, R. F., & Boone, A. L. (2004). High self-control predicts good
adjustment, less pathology, better grades, and interpersonal success. Journal of
Personality, 72(2), 271e324.
Turkle, S. (2012). Alone together: Why we expect more from technology and less from
each other. New York, NY: Basic Books.
Walther, J. B. (1996). Computer-mediated communication impersonal, interper-
sonal, and hyperpersonal interaction. Communication Research, 23(1), 3e43.
Wang, Z. (2014). Bridging media processing and selective exposure: a dynamic
motivational model of media choices and choice response time. Communication
Research, 41(8), 1064e1087.
Wang, Z., David, P., Srivastava, J., Powers, S., Brady, C., D'Angelo, J., et al. (2012).
Behavioral performance and visual attention in communication multitasking: a
comparison between instant messaging and online voice chat. Computers in
Human Behavior, 28(3), 968e975.
Wang, Z., Irwin, M., Cooper, C., & Srivastava, J. (2015). Multidimensions of media
multitasking and adaptive media selection. Human Communication Research, 41,
Wang, Z., Lang, A., & Busemeyer, J. R. (2011). Motivational processing and choice
behavior during television viewing: an integrative dynamic approach. Journal of
Communication, 61,72e94.
Wang, Z., & Tchernev, J. M. (2012). The mythof media multitasking: reciprocal
dynamics of media multitasking, personal needs, and gratications. Journal of
Communication, 62(3), 493e513.
Wang, Z., Tchernev, J. M., & Solloway, T. (2012). A dynamic longitudinal examination
of social media use, needs, and gratications among college students. Com-
puters in Human Behavior, 28(5), 1829e1839.
Ward, L. M. (2002). Dynamical cognitive science. Cambridge, MA: MIT Press.
Wickens, C. D. (2002). Multiple resources and performance prediction. Theoretical
Issues in Ergonomics Science, 3(2), 159e177.
Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012).
Examining the impact of off-task multi-tasking with technology on real-time
classroom learning. Computers &Education, 58(1), 365e374.
Zhang, W., & Zhang, L. (2012). Explicating multitasking with computers: gratica-
tions and situations. Computers in Human Behavior, 28, 1883e189 1. http://
Zimmerman, B. J. (2002). Becoming a self-regulated learner: an overview. Theory
into Practice, 41(2), 64e70.
S. Xu et al. / Computers in Human Behavior 55 (2016) 242e250250
... Media multitasking is a subtype of multitasking that specifically involves the use of digital devices for accessing social media [98] and is defined by Xu et al. [99] as "the simultaneous pursuit of two or more largely independent tasks where at least one of those tasks involves media." The Mobile Fact Sheet published by the Pew research center reported that the American smartphone ownership rate continued to increase and reached 85% in 2021, indicating the increasing likelihood of media multitasking. ...
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As online learning becomes an indispensable component of the current education system, students benefit from the advantages of online education. At the same time, students are also facing more challenges during online learning. Multitasking, mind-wandering, and using digital devices are the extensively discussed types of distractions that detriment students’ learning performance by impairing their focused attention. However, the consensus on the definition of distraction and what represents distraction during online learning are still lacking. This literature review develops a comprehensive definition of distraction, summarizes three main types of distraction (multitasking, mind-wandering, and using digital devices), and proposes two new types of distraction (unexpected interruption and consistent interference). Since the detrimental effects of distraction on online learning are salient, more explorations on helping students to resist distractions and maintain focused attention are imperative for future studies.
... Questi dati rilevano un potenziale rischio per l'apprendimento degli studenti. Infatti, come dimostrato da numerose ricerche (Burak, 2012;Junco, 2012;Junco & Cotten, 2012;Xu, Wang, & David, 2016), in ambito apprenditivo il multitasking rappresenta una pratica che compromette l'attenzione continuativa richiesta da una lezione. Con la disponibilità di più dispositivi digitali accesi contemporaneamente i discenti si ritrovano a ridurre i tempi di concentrazione sul singolo compito scolastico (Frank, Reibel, Broderick, Cantrell, & Metz, 2013;Rosen, Carrier, & Cheever, 2013). ...
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In this study, conducted on over 1300 students from second level secondary schools in the province of Udine, investigate the impact of distance learning (DAD) on relationships and emotional well-being. The analysis of two questionnaires filled out anonymously by students, highlighted a significant experience of suffering that opens up a critical reflection on the challenges that the school system is called to face since the end of the pandemic. Le ricadute della Didattica a Distanza sulle relazioni e sul benessere emotivo degli adolescenti. In questo studio, condotto su oltre 1300 studenti/esse delle scuole secondarie di secondo grado della provincia di Udine, è stato indagato l’impatto della didattica a distanza (DAD) sulle relazioni e sul benessere emotivo. L’analisi di due questionari compilati anonimamente da studentesse e studenti ha messo in luce un rilevante vissuto di sofferenza che apre una riflessione critica sulle sfide che il sistema scolastico è chiamato ad affrontare dalla fine della pandemia.
... As classes often incorporate multiple modes of conveying content that can be utilized simultaneously, students find it increasingly desirable to multitask. Multitasking in the classroom has been shown to distract students and reduce performance (Wood et al. 2012;David et al. 2015;Chiang and Sumell 2019) and to negatively impact their social success (Xu et al. 2016). ...
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Technological improvements have changed the way class content is delivered and absorbed, with new modes of communications and collaboration creating viable alternatives to the traditional classroom. While online learning has many benefits in terms of greater flexibility and convenience, the lack of face-to-face interaction creates potential consequences in the form of greater propensity for procrastination in completing coursework. This paper investigates whether traditional methods of mitigating these habits by way of nudging is enhanced using techniques that motivate students to be more engaged with class content. We introduce microcommitments, small daily tasks delivered with a soft commitment device and social accountability. We randomly assigned 276 students in an online course into a treatment or control group to isolate the effects of microcommitments on delaying the completion of assignments. Our results indicate that engaging students with course content, using a microcommitment, significantly reduces procrastination. Students are nearly twice as likely to complete at least some of the assigned work more than a week prior to the due date as opposed to leaving all of the work for the last week.
... 102) for the absence of a relationship between VR usage/response and age, we recognize that further research to confirm this preliminary finding is recommendable. Further research should examine how an older sample group reacts to innovative media, as younger people are, for instance, often more likely to seek sensation (Xu et al., 2016) and are more open to new technologies (Venkatesh et al., 2003). A study considering a more diverse respondent sample, considering all age groups could allow for the comparison between age groups, possibly identifying age as a moderator. ...
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Despite the power of VR in immersing viewers in an experience, it generally only targets viewers via visual and auditory cues. Human beings use more senses to gather information, so expectedly, the full potential of this medium is currently not yet tapped. This study contributes in answering two research questions: (1) How can conventional VR ads be enriched by also addressing the forgotten sense of smell?; and (2) Does doing so indeed instill more engaging experiences? A 2 × 3 between-subjects study (n = 235) is conducted, whereby an existing branded VR commercial (Boursin Sensorium Experience) is augmented with “sound” (on/off) and (congruent/incongruent/no) “scents.” The power of these sensory augmentations is evaluated by inspecting emotional, cognitive and conative dimensions of customer engagement. The results identify product-scent congruence (with sound) as a deal-maker, albeit product-scent incongruence is not necessarily a deal-breaker. The article concludes with further research avenues and a translation into managerial implications.
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(up to 120 words) Despite the prevalence of social media multitasking (SMM), its effects on well-being have received little attention. Of the nine studies that directly examined this topic, six found a negative relationship between SMM and well-being variables, and three found mixed or no effects. We compare the findings with those from five meta-analyses of general media multitasking (GMM) and 15 recent studies on GMM and well-being. We propose three pathways for the impact of SMM on well-being, with a potential mediating role of cognitive functions and performance. Based on the review, we suggest four directions for future research by emphasizing: (1) characteristics of SMM, (2) multiple goals of SMM, (3) different types and aspects of well-being (e.g., enjoyment, purpose), and (4) person-specific effects.
Background College students frequently identify social media sites (SMSs) as in-class distractions, although students continue to use these sites during class. In a technology-driven world, students’ fear of missing out (FOMO) may drive SMS behaviors, whereby classes and study time serve as obstacles to fulfilling one’s social desires. Objective The current study investigated whether students’ use of SMSs during class and study time was predicted by demographic characteristics and students’ FOMO. Method Participants ( N = 198) completed an online survey assessing their media use during class and study time, FOMO, and their perceived advantages/disadvantages of media use. Results In-class Twitter and Instagram use were predicted by students’ FOMO, whereas Snapchat and Facebook use were only predicted by age. Age also predicted Snapchat use during study time. Most participants indicated that media was a distraction, while also reporting a range of benefits from media multitasking. Conclusion Given that students recognize both benefits and drawbacks of media multitasking, they may trade-off between their desire to engage with learning materials and their desire to stay socially connected with others. Teaching Implications Educators can begin to address the socio-emotional needs of students through modifications made to course design and student-centered learning materials.
With the ubiquitous presence of media devices, media multitasking has become prevalent in an educational context. Several authors have synthesized the literature on this topic, but no systematic review has been carried out so far. The present study fills this gap by examining the academic papers in the past decade to delineate the research trends, gaps, and directions for future research. Following the Standard Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), we analyzed 88 papers from various aspects including study focus, contexts, participants, and methods. Findings point to the necessity to focus on the reasons behind multitasking, include more K-12 learners, and adopt qualitative methodology. To support future work on the predictors of media multitasking, we propose a conceptual framework that includes nine variables in technology, personal, and environment domains.
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This paper expands, refines, and explicates media synchronicity theory, originally proposed in a conference proceeding in 1999 (Dennis and Valacich 1999). Media synchronicity theory (MST) focuses on the ability of media to support synchronicity, a shared pattern of coordinated behavior among individuals as they work together. We expand on the original propositions of MST to argue that communication is composed of two primary processes: conveyance and convergence. The familiarity of individuals with the tasks they are performing and with their coworkers will also affect the relative amounts of these two processes. Media synchronicity theory proposes that for conveyance processes, use of media supporting lower synchronicity should result in better communication performance. For convergence processes, use of media supporting higher synchronicity should result in better communication performance. We identify five capabilities of media (symbol sets, parallelism, transmission velocity, rehearsability, and reprocessability) that Influence the development of synchronicity and thus the successful performance of conveyance and convergence communication processes. The successful completion of most tasks involving more than one individual requires both conveyance and convergence processes, thus communication performance will be improved when individuals use a variety of media to perform a task, rather than just one medium.
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This study examined the major motives for multitasking, and how those motives are related to general, medium-specific, and content-specific types of multitasking. The major motives for multitasking identified in this study are as follows: information, social, enjoyment, efficiency, and habit. Of these motives, general multitasking behavior was predicted by information, efficiency, and habit. In terms of medium-specific types of multitasking, TV-based multitasking was predicted by habit motive, Internet-based multitasking was predicted by information and enjoyment, and mobile-based multitasking was predicted by information motives. In terms of content-specific multitasking, news-related multitasking was predicted by information motives, entertainment-related multitasking was predicted by information and enjoyment motives, and advertising-related multitasking was predicted by information and social motives.
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The mobile phone is a breakthrough advance for human communication. But with the plethora of choices available via smartphone, individuals who are deficient in self-regulation or with a propensity for addiction may face challenges in managing these choices strategically. To examine this potential dysfunctional aspect, we examined the effect of multitasking when studying or doing homework and found that both frequency and attention to texting and social media were positively related to mobile phone interference in life (MPIL). However, frequency of music use during study was not associated with MPIL, although allocated attention to music while studying was positively associated with MPIL. Ownership of a smartphone and the number of Facebook friends were positively associated with MPIL and women reported more MPIL than men.
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This study explores whether, and how, motivations for two screen viewing predicted social interaction behaviors and subsequent viewing intention of TV programs. A total of 453 respondents who responded that they use social networking sites (SNSs) via smartphones and actively watch entertainment programs completed an online survey questionnaire. In agreement with uses and gratifications assumptions, motivations for TSV predicted distinctive sets of social interaction behaviors, which mediated the influence of motivations on viewing intentions. Respondents' two screen viewing was meaningfully related with social interaction, engagement with programs, information seeking, and passing time. Results suggest that two screen viewing could provide shared experiences nourishing social capital and reintegrate TV audiences by social adhesive resulting from TV with SNSs.
A correlational study examined relationships between motivational orientation, self-regulated learning, and classroom academic performance for 173 seventh graders from eight science and seven English classes. A self-report measure of student self-efficacy, intrinsic value, test anxiety, self-regulation, and use of learning strategies was administered, and performance data were obtained from work on classroom assignments. Self-efficacy and intrinsic value were positively related to cognitive engagement and performance. Regression analyses revealed that, depending on the outcome measure, self-regulation, self-efficacy, and test anxiety emerged as the best predictors of performance. Intrinsic value did not have a direct influence on performance but was strongly related to self-regulation and cognitive strategy use, regardless of prior achievement level. The implications of individual differences in motivational orientation for cognitive engagement and self-regulation in the classroom are discussed.
Bacterial cell wall and membrane are associated with a variety of glycoconjugates and polysaccharides which aids in structural formation as well as performing various functions in the bacterial cell. In gram-negative bacteria, peptidoglycan is majorly present in the periplasmic space and it provides mechanical strength as well as shape to the cell. In some cases, the periplasm contains membrane-derived oligosaccharides (MDOs), which are involved in osmoregulation. The outer membrane mainly contains lipopolysaccharides (LPSs) that bind to divalent cations or chelators for structure stabilization and to increase outer membrane permeability. This LPS contains lipid A, also known as endotoxin, which has shown a powerful biological effect in mammals such as fever, septic shock, multiple organ failure, and mortality. The mucoid (slime-producing) strains contain capsular polysaccharide which aids as virulence factor. The gram-positive bacteria lack an outer membrane and have a much thicker peptidoglycan layer along with a specialized polysaccharide known as teichoic acid. It provides cell wall integrity through complex formation with cations and also assists in cell growth regulation. The present report attempts to provide an overview of bacterial polysaccharide structure, occurrence, and their important functions, along with the biosynthesis and major inhibitors to block biosynthetic pathways.