Cyber Slacking among University Students: The Role of Internet

Habit Strength, Media Multitasking Efficacy and Self Regulated

Learning

Ermida Simanjuntak

1,2

, Nur Ainy Fardana Nawangsari

, and Rahkman Ardi

Faculty of Psychology, Airlangga University, Surabaya, Indonesia

Faculty of Psychology, Widya Mandala Catholic University, Surabaya, Indonesia

Keywords: cyber slacking, non-academic purpose Internet access, university students

Abstract: University provides Internet in the campus to enhance students’ learning during lectures. However, students

use the Internet in the classrooms not only for academic tasks but also for non-academic purposes. The use

of the Internet for non-academic purposes during class is defined as cyber slacking. The term cyber slacking

was firstly used in the working environment and it is considered as counterproductive behaviour in

organizations. Referring to an educational setting, cyber slacking will make students unproductive in

learning. This current study aims to explore cyber slacking behaviour among university students in

academic settings. There were 385 university students who participated in this survey. Results show that

cyber slacking behaviour correlates with Internet habit strength and media multitasking efficacy. The more

habitual students are to the Internet the more likely they are to engage in cyber slacking activities during

lectures. Students who are confident in media multitasking also tend to cyber slack in the classrooms. The

findings also describe that self-regulated learning does not correlate with cyber slacking. Further research on

cyber slacking behaviour in university settings should be conducted to identify potential factors that prevent

cyber slacking behaviour in the university classrooms.

1 INTRODUCTION

The recent teaching and learning process frequently

uses the Internet to improve the quality of students’

learning results (Weaver and Nilson, 2005; Lee and

Tsai, 2011; Moskal, Dziuban and Hartman, 2013;

Karaoglan Yilmaz et al., 2015). This is in line with

the characteristic of adult learning, which

encourages students to learn independently outside

classroom sessions. This policy implies that the

Internet is one of the supporting learning tools in

academic work, such as finding relevant academic

references for learning task completion (Gaudreau,

Miranda and Gareau, 2014). The high frequency of

Internet use on campus is also related to the

availability of free Wi-Fi (wireless technology)

access provided by the university or students’

personal access on their smartphones. This is in line

with the survey results of Ofcom (2017) which

found that the development of smartphone

technology has made the Internet part of daily

lifestyle among university students. A survey

conducted by the Indonesian Ministry of

Communication and Information showed that

university students were the most active users of

Internet connection, compared to students from other

institutions and professional workers (Kominfo,

2016). Such a wide use of the Internet among

university students may influence their behaviors in

learning (Kolikant, 2010; Barry, Murphy and Drew,

2015).

Researches on students’ learning behavior in

lectures show that the availability of Internet access

tends to lead students to indulge in non-academic

activities such as accessing social media, opening

irrelevant sites and playing online games (Junco and

Cotten, 2012; Wood et al., 2012; Wentworth and

Middleton, 2014; Taneja, Fiore and Fischer, 2015;

Gupta and Irwin, 2016; Akbulut et al., 2016b; Varol

and Yildirim, 2018). Students’ tendency to partake

in non-academic activities during lectures falls into

the category of cyber slacking (Gerow, Galluch and

Thatcher, 2010; Yasar and Yurdugul, 2013; Rana et

al., 2016; Taneja, Fiore and Fischer, 2015). Often,

students who bring laptops to the classes are cyber

Simanjuntak, E., Nawangsari, N. and Ardi, R.

Cyber Slacking among University Students: The Role of Internet Habit Strength, Media Multitasking Efﬁcacy and Self Regulated Learning.

DOI: 10.5220/0008587702390247

In Proceedings of the 3rd International Conference on Psychology in Health, Educational, Social, and Organizational Settings (ICP-HESOS 2018) - Improving Mental Health and Harmony in

Global Community, pages 239-247

ISBN: 978-989-758-435-0

239

slacking by using the them to access sites irrelevant

to the topics being discussed in the lectures (Fried,

2008; Ragan et al., 2014). Thus, cyber slacking is a

challenge for both students and lecturers during

classes where Internet access is available.

Research on cyber slacking in educational

settings uses the approaches in the theory of planned

behavior (TPB) to explain cyber slacking to students

(Askew et al., 2014; Taneja, Fiore and Fischer,

2015). This theory highlights intention as the

important factor in determining the cyber slacking

behavior. Intention is formed by attitude, subjective

norms, descriptive norms and perceived behavioral

control (Taneja, Fiore and Fischer, 2015). A student

with a positive attitude towards cyber slacking tends

to do it during lectures, though considerations are

still required on the subjective norm, descriptive

norm and perceived behavioral control. If that

particular student feels that it is difficult to cyber

slack, then they will lose the intention to do it

(Taneja, Fiore and Fischer, 2015). Furthermore,

Askew et al. (2014) adds two more factors: web

self-efficacy and ability to hide. Thus, if a student

can access the Internet easily and is capable of

hiding his cyber slacking behavior, then he will tend

to do it.

Other researches also mention the effect of self-

regulation on cyber slacking (Gokcearslan et al.,

2016). Discussion on self-regulation in educational

settings uses the concept of self-regulated learning

(SRL) by Zimmerman and Schunk (2011). SRL is

one of the influential factors in students’ learning

achievements. A student with good SRL tends to be

able to achieve the pre-determined learning targets

(Pintrich, 2004; Kitsantas, Winsler and Huie, 2008;

Nandagopal and Ericsson, 2012; Zhu, Au and Yates,

2016). Related to cyber slacking, a student with

good SRL will tend to focus on things related to

academic topics during lectures; he will tend to

minimize access to non-academic topics.

Internet habit strength is related on how strong

someone’s habit is to access the Internet (LaRose

and Eastin, 2004; Ang, 2017). Someone with

stronger Internet habit will be more likely to access

the Internet. Teenagers usually have a strong

Internet habit (Wohn, 2012; Ang, 2017). Teenagers

and the current university student generation are

often perceived as “Digital Natives”, which refers to

people who have technology exposure and

technology experience (Akcayir, Dundar and

Akcayir, 2016). These students seem to have

habitual access to the Internet during their early

childhood, resulting in them being referred to as

Digital Natives. Related to cyber slacking, it is

assumed that students with a strong Internet habit

would tend to continuously access the Internet.

Rosen et al., (2013) found that for most students, it

would be difficult to learn without technology and to

stop checking their gadgets such as laptops,

computer tablets or smartphones for more than 10

minutes. This phenomenon is related to the

classroom situation in which students meet

difficulties in understanding the lectures and thus

tend to access the Internet (Calderwood et al., 2016).

This habit to access the Internet seems to urge

students to stay connected to the Internet and try to

find a method to do so although the Internet

connection might be unavailable in the classroom

(LaRose and Eastin, 2004; Ang, 2017). If this

behavior is repeated continuously, it will result in a

habit. Related to media habit, such as the Internet

habit, the frequency of accessing the Internet would

then lead to the Internet habit (Wohn, 2012). Related

to cyber slacking, students’ habit of accessing the

Internet for non-academic materials during lectures

would then lead to non-academic Internet habit

during lectures, such as accessing social media and

doing status updates although the lectures are still

running.

Discussion on cyber slacking is also related to

media multitasking. A number of previous

researches also found students’ tendency to do

unproductive multitasking activities during lectures,

which is accessing non-academic materials during

lectures or classroom tutorials (Judd, 2014; Bellur,

Nowak and Hull, 2015; Junco and Cotten, 2012;

Wentworth and Middleton, 2014; Gupta and Irwin,

2016). This media multitasking behavior seems to

happen if the particular student has the confidence to

do things by multitasking. Wu (2017) points to this

confidence as the media multitasking efficacy

(MME). If a student feels confident that he can

multitask in a situation, such as accessing social

media while listening to the lectures, he will tend to

cyber slack in lectures. Students with higher MME

seem to get involve more in media multitasking,

including the possible unproductive media

multitasking.

This paper aims to test the relationship between a

few antecedents of cyber slacking behaviors:

Internet habit strength, media multitasking efficacy

(MME) and self regulated learning. The hypotheses

proposed in this study are:

H1. Internet habit strength correlates with

students’ cyber slacking behavior. Students with

stronger Internet habit tend to cyber slack during

lectures.

ICP-HESOS 2018 - International Conference on Psychology in Health, Educational, Social, and Organizational Settings

240

H2. Media multitasking efficacy (MME)

correlates with students’ cyber slacking behavior.

Students with more confidence in their ability to do

multitasking will tend to cyber slack during lectures.

H3. Self-regulated learning corresponds to cyber

slacking among students. Students with good self-

regulated learning tend to avoid cyber slacking in

lectures.

1.1 Academic Cyber Slacking Among

University Students

Cyber slacking, also referred as cyber loafing, is

originally studied in the field of the work behavior

related to technology, which is when workers

frequently access the Internet for personal matters

unrelated to work during working hours (Block,

2001; Lim, 2002; Vitak, Crouse and LaRose, 2011).

Perceived as unproductive behavior during working

hours, cyber slacking is regarded as a problem to

solve in order to improve workers’ productivity at

work (Ugrin and Michael Pearson, 2013; Lim, 2002;

Vitak, Crouse and LaRose, 2011).

Later, the cyber slacking concept is applied in

educational settings, specifically in higher education.

This leads cyber slacking to be defined as the use of

the Internet by students for non-academic purposes

during lectures (Lavoie and Pychyl, 2001; Galluch

and Thatcher, 2006; Gerow, Galluch and Thatcher,

2010). Later researches on cyber slacking started to

apply a cyber slacking concept to describe students’

behavior on Internet access during lectures (Taneja,

Fiore and Fischer, 2015; Akbulut et al., 2016b; Rana

et al., 2016; Varol and Yildirim, 2018). Research

conducted by Akbulut, O. O. Dursun, et al. (2016)

reveals that cyber slacking behaviors during lectures

include sharing, shopping, real time updating,

accessing online content, gaming and gambling.

These indicators are used in the measurement tool

for cyber slacking.

1.2 Internet Habit Strength

Ang (2017) defines Internet habit strength as the

strength of someone’s habit to connect to the

Internet. This habit is acquired from a series of

practices or repeated behaviors, which in this case is

the behavior to connect to the Internet. LaRose and

Eastin (2004) also mention the influence of this

habit on someone. High Internet habit strength

results in integrating the Internet as part of

someone’s routine, with a tendency to access the

Internet without further consideration (LaRose and

Eastin, 2004).

Other research conducted by Wohn, (2012)

mentioned that Internet habit is less connected to the

motivation to connect to the Internet, but more

connected to the habit of accessing the Internet due

to the stimulus from the environment, such as the

availability of Internet access, which leads to

accessing the Internet as a somehow natural deed to

do.

Wohn (2012) argues that this Internet habit will

grow stronger since that particular person will get

involved more in social media, such as playing

online games on social media. Related to the

routines, then it seems that playing online games on

a daily basis will result in a higher Internet habit

strength. In other words, if someone accesses the

social media habitually, then the Internet habit

strength is formed resulting in the action to connect

to the Internet whenever there is available access

(Ang, 2017). Regarding the Internet habit, repetitive

behaviors or connecting to the Internet repeatedly is

the key to the formation of the Internet habit, in

which an Internet access pattern is mentally formed

in someone (Ang, 2017).

1.3 Media Multitasking Efficacy

(MME)

The concept of media multitasking efficacy is rooted

in Bandura’s self-efficacy theory (Wu, 2017) about

the confidence of an individual in completing a

specific task. In this concept, media multitasking

efficacy (MME) is defined as the confidence of an

individual to simultaneously use several media in his

activities (Wu, 2017). Related to the Internet access,

MME is someone’s confidence to use several

software applications simultaneously.

Related to the cyber slacking academic context,

students accessing non-academic materials during

lectures is connected to the concept of MME since it

is plausible for students to access several gadget

applications while accessing academic materials

(Gaudreau, Miranda and Gareau, 2014). On the

premise that someone with confidence to do

something will tend to really do it, then someone

who can multitask well will tend to multitask (Wu,

2017). However, it is interesting to note that students

often overestimate their capability in multitasking.

Brooks (2015) mentioned that confidence in

multitasking with computers will drive the person to

multitask when working with computers. Related to

cyber slacking, students who are confident in their

capability in media multitasking tend to access

several online software applications during lectures

because they are confident that they can join the

Cyber Slacking among University Students: The Role of Internet Habit Strength, Media Multitasking Efﬁcacy and Self Regulated Learning

241

lectures while also engaging in non-academic

activities. On the other hand, academic achievement

which is often used as an indicator to measure

students’ understanding towards the lectures is often

negatively correlated to the students’ media

multitasking (Junco and Cotten, 2011; Calderwood,

Ackerman and Conklin, 2014; Wentworth and

Middleton, 2014). This notion is consistent with Wu

(2017) who argues that confidence in multitasking

sometimes has no correlation to a poor performance.

1.4 Self Regulated Learning (SRL) and

Academic Cyber Slacking

Self-regulated learning (SRL) is defined as

someone’s capability to direct his cognition,

affection and behavior to achieve the pre-determined

learning objectives (Zimmerman and Schunk, 2011).

The assumption of this theory is that a student is an

active agent in constructing his learning process.

The learning process is directed to attain particular

pre-determined objectives and the learner can make

necessary adjustments to do that (Zimmerman and

Schunk, 2011).

Schunk (2012) argues that self-regulated learning

involves the choice of the learner to engage in a

certain behavior in learning situations. In this notion,

the learner will choose and formulate his desired

learning objective. This objective may vary from

one learner to another, based on the learner’s own

considerations towards his capability and his

external environment. After formulating the learning

objective, the learner will then monitor his own

learning behavior and assess whether that particular

behavior is capable of helping him achieve his

learning objective.

A study conducted by Kadioglu, Uzuntiryaki and

Aydin (2011) resulted in SRL indicators, which are

motivation regulation, planning, effort regulation,

attention focusing, task strategies, using additional

resources and self-instruction. These findings are

rooted in the research on self-regulated learning by

Zimmerman, Bonner and Kovach (1996). On those

indicators, there is a difference in learning behaviors

between individuals with high SRL and those with

low SRL. For instance, a learner with a high SRL on

motivation regulation could maintain his learning

motivation to achieve his learning objective despite

the difficult academic tasks. Besides that, an

individual with high SRL seems capable of focusing

his attention on things supportive for his learning

processes.

Related to academic cyber slacking, a student

with high SRL seems to be able to exercise control

over his desire to access non-academic materials

when he has to complete his academic tasks

(Simanjuntak, 2018). Students with high SRL seem

to exercise control over their behaviors when

accessing the Internet by focusing only on matters

relevant to their learning process in class. During

lectures, these students will limit themselves by

avoiding access to irrelevant materials. By

conducting research on students bringing laptops to

lectures Zhang (2015) proved that self-regulation is

an important factor for students to stay focused on

learning materials despite the possible access to non-

academic materials.

2 METHOD

2.1 Procedure

This study was conducted in a private university in

Surabaya by asking students to fill in questionnaires

disseminated by research assistants at the end of

lectures. Before disseminating the questionnaires,

the research assistants asked permission from the

lecturers in charge to briefly explain the

questionnaires and that participating in this study

was to be done voluntarily. The questionnaires were

anonymous, recording only demographic data such

as gender and age.

2.2 Participants

Participants were students of a private university in

Surabaya with a total number of 385 students, within

the ages of 17 – 28 years old. There were 289 female

participants and 96 male participants from the

Faculty of Psychology, Pharmacy, Nursing,

Medicine and Philosophy. Most participants did

cyber slacking during lectures, proven by the answer

towards the item “Do you use the Internet to access

non-academic materials during classroom lectures,

(e.g. chatting with friends, accessing social media,

browsing sites unrelated to lectures)?”. There were

344 participants answering yes while only 41

participants answered no. This indicates that most

participants do cyber slacking during classroom

lectures.

2.3 Measures

Cyber slacking scale in this study is the translated

version of a cyber slacking scale developed by

Akbulut et al. (2016). This scale consists of 30 items

measuring 5 cyber slacking indicators: sharing,

ICP-HESOS 2018 - International Conference on Psychology in Health, Educational, Social, and Organizational Settings

242

shopping, real time updating, accessing online

content and gaming or gambling. The answers range

from Never to Great Extent. Cronbach’s alpha

coefficient of cyber slacking scale is 0.925.

Internet habit strength was measured by using an

Internet habit strength scale developed by Ang

(2017). The scale is adopted in Bahasa Indonesia

and it consists of 3 items with answers ranging

Strongly Disagree, Disagree, Neutral, Agree and

Strongly Agree. The Internet habit strength scale has

Cronbach’s alpha coefficient 0.703.

Media multitasking efficacy (MME) was

measured by using a media multitasking efficacy

scale developed by Wu (2017) and it is adopted in

Bahasa Indonesia. The author of the MME scale has

approved the scale to be translated and used for

subjects in Indonesia. This scale consists of 5 items

with 6 alternative answers, which are 1 (not at all

like me), 2 (not much like me), 3 (neutral), 4

(somewhat like me) and 5 (very much like me).

Cronbach’s alpha coefficient for MME scale is

0.751.

Self-regulated learning (SRL) scale was the

development of the self-regulatory strategies scale

(SRSS), which is developed by Kadioglu,

Uzuntiryaki and Aydin (2011) and it is adopted in

Bahasa Indonesia. The scale consists of 28 items and

has Cronbach’s alpha coefficient 0.759, which

measures indicators of motivation regulation,

planning, effort regulation, attention focusing, task

strategies, using additional resources and self-

instruction.

Table 1: Descriptive statistic of the participants (N = 385).

Questions

Frequency

Doing non-academic access during lectures

Yes

344

89.3

10.6

Average time for non-academic access during

lectures

< 1 hour

23.6

30 minutes – 1 hour

15.3

15 – 30 minutes

24.6

> 15 minutes

118

30.6

Reasons to do cyber slacking during lectures

Boring lectures

235

(participants can answer more than one reason)

Uninteresting teaching methods

150

38.9

Communicating with friends

106

27.5

Searching information

156

40.5

Table 2: Correlation between cyber slacking, internet habit strength, media multitasking efficacy and self regulated

learning (N = 385).

Variables

1. Internet habit strength

2. Media multitasking efficacy

.263*

3. Self regulated learning

-.030

-.083

4. Cyber slacking behaviour

.300*

.324*

-.018

*p < .01

Table 3: Regression results for cyber slacking, internet habit strength, media multitasking efficacy and self regulated

learning (N = 385).

95% Confidence

Interval

Predictor

Estimate

Odds ratio

Lower

Upper

Intercept

-2.59766

1.10631

-2.348

0.019

0.0744

0.00851

0.651

Internet habit strength

0.20433

0.04479

4.562

0.001*

1.2267

1.12361

1.339

Media multitasking

efficacy

0.06052

0.02199

2.752

0.006*

1.0624

1.01757

1.109

Self regulated learning

-0.00428

0.00977

-0.438

0.661

0.9957

0.97685

1.015

*p < .01

Cyber Slacking among University Students: The Role of Internet Habit Strength, Media Multitasking Efﬁcacy and Self Regulated Learning

243

3 RESULTS

Descriptive results showed mean and standard

deviation for cyber slacking behaviour (M = 69.38;

SD = 20.29), media multitasking efficacy (M =

16.43, SD = 5.22), Internet habit strength (M = 9.78;

SD = 2.63) and self-regulated learning (M = 97.37;

SD = 11.12). Regarding the reasons for participants

cyber slacking, Table 1 shows that there were

several reasons and some participants might have

more than one answer. The most chosen reason was

the boredom felt during lectures (235 participants),

uninteresting teaching method of the lecturers (143

participants), wishing to communicate with friends

(106 participants), finding information for personal

interests (156 participants). The data showed that

boring lectures is the main reason for students to do

cyber slacking in class.

Hypotheses are analyzed using ordinal regression

analysis and the results of regression are presented

in Table 3. Based on hypothesis test on H1, results

show that H1 is accepted with r = 0.30 (p < .01). It

means that the Internet habit strength is related to

cyber slacking behavior among students. Thus, it is

concluded that there is a significant relationship

between Internet habit strength and cyber slacking

behavior.

Hypothesis test H2 on the media multitasking

efficacy (MME) proved that MME correlates with

the cyber slacking behavior (r = 0.32, p < .01). Thus,

students confident in their ability to multitasking

tend to cyber slack during lectures.

Hypothesis test H3 on the relationship between

self-regulated learning (SRL) with cyber slacking

proved that there is no significant correlation

between SRL and cyber slacking (r = -0.01). Thus, it

was possible for students with high SRL to cyber

slack in lectures. It also applies to students with low

SRL that they also do cyber slacking during lectures.

Regression results in Table 3 show that media

multitasking efficacy and Internet habit strenght are

strong predictors for cyber slacking behavior (p <

0.01). However, self-regulated learning is not a

significant predictor of cyber slacking behavior.

Students with high and low SRL might also engage

in cyber slacking behavior.

4 DISCUSSION

Results of hypothesis 1 and 2 are consistent with the

theories and results of previous researches about

Internet habit strength and media multitasking

efficacy. Someone with high Internet habit strength

tends to get used to connecting to the Internet

wherever he was (LaRose and Eastin, 2004; Wohn,

2012; Ang, 2017). Thus, a student with high Internet

habit strength will try to access the Internet during

classroom lectures. The environment is quite

supportive by the availability of the Internet access

at campus or the Internet access through students’

own smartphones. This makes it difficult for

students to avoid accessing the Internet for their

favorite sites. This is in accordance with Rosen et

al., (2013) stating that it is difficult for young people

of this generation to leave their technology gadgets

for more than 10 minutes. Besides that, this Internet

habit strength drives the person to automatically

search for things that make him/her comfortable and

relaxed.

Descriptive data shows that the reason for

students cyber slacking is their boredom towards the

lectures. Related to the Internet habit strength, this

boredom is related to finding things which are

entertaining through the technology in his

possession, including playing online games in social

media such as Facebook (Wohn, 2012). Research

conducted by Ang (2017) on Internet habit strength

also mentioned the connection between Internet

habit strength and online communication between an

individual and his peers. It is supported by

descriptive data in this study that most cyber

slacking behavior conducted was communicating

with friends. Thus, students’ Internet habit strength

is capable of predicting the possibility of those

students cyber slacking.

The hypothesis about the correlation between

media multitasking efficacy and cyber slacking

showed that students’ confidence in their ability in

media multitasking would influence their cyber

slacking behavior. This is in line with the findings of

Wu (2017) that students with confidence in

multitasking on the completion of certain tasks as

well as operating several software applications

simultaneously would have a high probability of

cyber slacking during lectures. These students are

confident that they would understand the lectures

while accessing the Internet at the same time. Wu

(2017) argues that the problems were that some

students tend to be overconfident about their ability,

leading to the lack of awareness that not all lectures

could be understood properly while cyber slacking.

This student’s perception and confidence are not

totally correct, because the theory of thread

cognition by Salvucci and Taatgen (2011) stated that

an individual is able to use two or more information

channels if that particular individual has already

mastered one of the tasks to complete. Salvucci and

ICP-HESOS 2018 - International Conference on Psychology in Health, Educational, Social, and Organizational Settings

244

Taatgen (2011) explained that multitasking is

possible if an individual is able to do one of the tasks

well. Thus, it is unnecessary for that particular

individual to recount the steps to do one task since

that particular task is no longer on the level of

declarative memory, but is already in the procedural

memory, which involves skills. This condition is

doable when one of the tasks at hand is no longer

something new. In classroom cyber slacking, it is

possible that both things conducted by students

(explanations of new materials from their lecturers

and reading a friend’s chat on a new information)

are relatively new, making students unable to

understand the lectures well despite their strong

confidence in their media multitasking ability

(Zhang, 2015; Wu, 2017).

Despite the significant correlation between

media multitasking efficacy and Internet habit

strength regarding cyber slacking behavior, the

effect level of both variables is relatively low

compared to other researches (Ang, 2017; Wu,

2017). There are some possible explanations for

these findings. First, both variables are from internal

aspects of the subjects and there are some

possibilities that external factors influence the cyber

slacking behavior of the subjects such as regulations

on Internet use and teaching methods used by

lecturers in the classrooms ((Lim, 2002; Vitak,

Crouse and LaRose, 2011; Ugrin and Michael

Pearson, 2013). Second, most of the students as

digital natives use the Internet every day for

completing the tasks and there could be no

difference in cyber slacking in the classrooms

between students who believe they have capabilities

in doing multitasking and students who are not

confident in doing multitasking. Most of the students

use the Internet in the classrooms due to social

reasons in order to communicate with friends

(Simanjuntak, 2018). Referring to this condition,

cyber slacking could happen in the classrooms due

to students’ need to communicate with peers despite

their belief in their multitasking capabilities. These

possible explanations also apply to Internet habit

strength in which most of the students as digital

natives have a strong internet habit (LaRose and

Eastin 2004; Kolikant, 2010; Akcayir, Dundar and

Akcayir, 2016).

The result of this study is in opposition to the

hypothesis on self-regulated learning (SRL) which

indicates that there is no significant correlation

between SRL and cyber slacking behavior in

classroom lectures. Thus, students with high SRL or

students with low SRL both have the same tendency

to do cyber slacking. Correlated to the descriptive

data, most participants (89.3%) tend to cyber slack

in classrooms, which explains why self-regulation

has nothing to do with cyber slacking. When

correlated to the reason for participants cyber

slacking, then it is caused by the feeling of boredom

towards the lectures. This is in line with the findings

of Gupta and Irwin (2016) stating that high interest

lectures will influence cyber slacking behavior.

Students who feel that the lectures are uninteresting

tend to alternate their sights to Internet access in

accordance to their interests (Junco and Cotten,

2012; Gupta and Irwin, 2012). This actually

indicates how weak the self-regulation factor is,

especially the aspect of attention focus. However,

the rejection of this hypothesis might also be due to

the absence of data to describe the participants’ SRL

condition since the SRL scales are yet to measure

the real behavior of the participants.

Related to the cyber slacking in educational

settings, the results of this study are in line with

several researches which find that Internet access

availability tends to drive students to access non-

academic materials during lectures (Askew et al.,

2014; Taneja, Fiore and Fischer, 2015; Akbulut et

al., 2016). The results of this study confirm that

cyber slacking is also happening in educational

settings aside from work contexts. Future researches

on cyber slacking in educational settings are

necessary to better described cyber slacking

behavior in classroom contexts in order to prepare

proper countermeasures.

5 CONCLUSIONS AND

LIMITATIONS

The results of this research prove that the cyber

slacking that happens in the work field context is

also happening in educational settings, when

students access the Internet for non-academic

purposes during classroom lectures. The results

show that there is a correlation between Internet

habit strength and media multitasking efficacy on

cyber slacking behavior conducted by the students.

Stronger Internet habit tends to lead students to do

more cyber slacking. There is also a correlation

between students’ confidence in their abilities in

media multitasking towards their tendency to cyber

slack. Results also show that there is no correlation

between self-regulated learning and cyber slacking.

The drawback of this research lies in the cyber

slacking measurement tool which can be further

developed into observation in experimental

Cyber Slacking among University Students: The Role of Internet Habit Strength, Media Multitasking Efﬁcacy and Self Regulated Learning

245

situations in order to better observe the students’

cyber slacking behavior in reality. Besides that, the

SRL measurement tool is still limited to self-report,

which is limited in describing the real condition of

students’ SRL. Future researches to develop a cyber

slacking model of university students should include

internal factors such as personality, procrastination

tendency and ability to hide cyber slacking behavior.

Besides that, there are external factors to be

considered, such as extrinsic learning motivation,

lecturers’ teaching skills to foster learning

engagement and classroom Internet access policy.

REFERENCES

Akbulut, Y., Dursun, O.O., Donmez, O. and Sahin, Y.L.,

2016a. In search of a measure to investigate

cyberloafing in educational settings. Computers in

Human Behavior, 55, pp.616–625.

Akbulut, Y., Dursun, Ö.Ö., Dönmez, O. and Şahin, Y.L.,

2016b. In search of a measure to investigate

cyberloafing in educational settings. Computers in

Human Behavior, 55, pp.616–625.

Akcayir, M., Dundar, H. and Akcayir, G., 2016. What

makes you a digital native? Is it enough to be born

after 1980? Computers in Human Behavior, 60,

pp.435–440.

Ang, C.-S., 2017. Internet habit strength and online

communication: Exploring gender differences.

Computers in Human Behavior, 66, pp.1–6.

Askew, K., Buckner, J.E., Taing, M.U., Ilie, A., Bauer,

J.A. and Coovert, M.D., 2014. Explaining

cyberloafing: The role of the theory of planned

behavior. Computers in Human Behavior, 36, pp.510–

519.

Barry, S., Murphy, K. and Drew, S., 2015. From

deconstructive misalignment to constructive

alignment: Exploring student uses of mobile

technologies in university classrooms. Computers &

Education, 81, pp.202–210.

Bellur, S., Nowak, K.L. and Hull, K.S., 2015. Make it our

time: In class multitaskers have lower academic

performance. Computers in Human Behavior, 53,

pp.63–70.

Block, W., 2001. Cyberslacking, Business Ethics and

Managerial Economics. Journal of Business Ethics,

33(3), pp.225–231.

Brooks, S., 2015. Does personal social media usage affect

efficiency and well-being? Computers in Human

Behavior, 46, pp.26–37.

Calderwood, C., Ackerman, P.L. and Conklin, E.M., 2014.

What else do college students “do” while studying?

An investigation of multitasking. Computers &

Education, 75, pp.19–29.

Calderwood, C., Green, J.D., Joy-Gaba, J.A. and Moloney,

J.M., 2016. Forecasting errors in student media

multitasking during homework completion. Computers

& Education, 94, pp.37–48.

Fried, C.B., 2008. In-class laptop use and its effects on

student learning. Computers & Education, 50(3),

pp.906–914.

Galluch, P.S. and Thatcher, J.B., 2006. Slacking and the

Internet in the Classroom : A Preliminary

Investigation. In: Proceedings of the Fifth Annual

Workshop on Human Computer Interaction Research

in MIS. Wisconsin: Association for Information

Systems AIS Electronic Library (AISeL), pp.25–29.

Gaudreau, P., Miranda, D. and Gareau, A., 2014.

Canadian university students in wireless classrooms:

What do they do on their laptops and does it really

matter? Computers and Education, .

Gerow, J.E., Galluch, P.S. and Thatcher, J.B., 2010. To

Slack or Not to Slack: Internet Usage in the

Classroom. JITTA: Journal of Information Technology

Theory and Application, 11(3), pp.5–23.

Gokcearslan, S., Mumcu, F.K., Haslaman, T. and Cevik,

Y.D., 2016. Modelling smartphone addiction: The role

of smartphone usage, self-regulation, general self-

efficacy and cyberloafing in university students.

Computers in Human Behavior, 63, pp.639–649.

Gupta, N. and Irwin, J.D., 2016. In-class distractions: The

role of Facebook and the primary learning task.

Computers in Human Behavior, 55, pp.1165–1178.

Judd, T., 2014. Making sense of multitasking: The role of

Facebook. Computers and Education, .

Junco, R. and Cotten, S.R., 2011. Perceived academic

effects of instant messaging use. Computers and

Education, .

Junco, R. and Cotten, S.R., 2012. No A 4 U: The

relationship between multitasking and academic

performance. Computers & Education, 59(2), pp.505–

514.

Kadioglu, C., Uzuntiryaki, E. and Aydin, Y.C., 2011.

Development of Self-Regulatory Strategies Scale

(SRSS). Education and Science, 36(160), pp.11–23.

Karaoglan Yilmaz, F.G., Yilmaz, R., Ozturk, H.T., Sezer,

B. and Karademir, T., 2015. Cyberloafing as a barrier

to the successful integration of information and

communication technologies into teaching and

learning environments. Computers in Human

Behavior, .

Kitsantas, A., Winsler, A. and Huie, F., 2008. Self-

Regulation and Ability Predictors of Academic

Success During College: A Predictive Validity Study.

Journal of Advanced Academics, 20(1), pp.42–68.

Kolikant, Y.B.D., 2010. Digital natives, better learners?

Students’ beliefs about how the Internet influenced

their ability to learn. Computers in Human Behavior,

26(6), pp.1384–1391.

Kominfo, 2016. Pengguna internet di Indonesia tahun

2016.

LaRose, R. and Eastin, M.S., 2004. A Social Cognitive

Theory of Internet Uses and Gratifications: Toward a

New Model of Media Attendance. Journal of

Broadcasting & Electronic Media, 48(3), pp.358–377.

Lavoie, J.A.A. and Pychyl, T.A., 2001. Cyberslacking and

the Procrastination Superhighway. Social Science

ICP-HESOS 2018 - International Conference on Psychology in Health, Educational, Social, and Organizational Settings

246

Computer Review, 19(4), pp.431–444.

Lee, S.W.Y. and Tsai, C.C., 2011. Students’ perceptions

of collaboration, self-regulated learning, and

information seeking in the context of Internet-based

learning and traditional learning. Computers in Human

Behavior, 27(2), pp.905–914.

Lim, V.K.G., 2002. The IT way of loafing on the job:

cyberloafing, neutralizing and organizational justice.

Journal of Organizational Behavior, 23(5), pp.675–

694.

Moskal, P., Dziuban, C. and Hartman, J., 2013. Blended

learning: A dangerous idea? The Internet and Higher

Education, 18, pp.15–23.

Nandagopal, K. and Ericsson, K.A., 2012. An expert

performance approach to the study of individual

differences in self-regulated learning activities in

upper-level college students. Learning and Individual

Differences, 22(5), pp.597–609.

Ofcom, 2017. Adults’ media use and attitudes : report

2017.

Pintrich, P.R., 2004. A Conceptual Framework for

Assessing Motivation and Self-Regulated Learning in

College Students. Educational Psychology Review,

16(4), pp.385–407.

Ragan, E.D., Jennings, S.R., Massey, J.D. and Doolittle,

P.E., 2014. Unregulated use of laptops over time in

large lecture classes. Computers & Education, 78,

pp.78–86.

Rana, N., Yogesh, D., Slade, E. and Banita, L., 2016.

Cyber-Slacking: Exploring Students’ Usage of

Internet-Enabled Devices for Non-Class Related

Activities. In: 22nd Americas Conference on

Information Systems. Atlanta: Association for

Information Systems (AIS), pp.2159–2168.

Rosen, L.D., Whaling, K., Carrier, L.M., Cheever, N.A.

and Rokkum, J., 2013. The Media and Technology

Usage and Attitudes Scale: An empirical investigation.

Computers in Human Behavior, .

Salvucci, D.D. and Taatgen, N.A., 2011. The Multitasking

Mind. Oxford: Oxford University Press.

Schunk, D.H., 2012. Learning Theories : An Educational

Perspective. 6th ed. Boston: Pearson.

Simanjuntak, E., 2018. First Year Challenge: The Role of

Self-Regulated Learning to Prevent Internet Addiction

among First-Year University Students. In:

Proceedings of the 3rd ASEAN Conference on

Psychology, Counselling, and Humanities (ACPCH

2017). Paris, France: Atlantis Press, pp.180–184.

Taneja, A., Fiore, V. and Fischer, B., 2015. Cyber-

slacking in the classroom: Potential for digital

distraction in the new age. Computers & Education,

82, pp.141–151.

Ugrin, J.C. and Michael Pearson, J., 2013. The effects of

sanctions and stigmas on cyberloafing. Computers in

Human Behavior, .

Varol, F. and Yildirim, E., 2018. An Examination of

Cyberloafing Behaviors in Classrooms from Students’

Perspectives. Turkish Online Journal of Qualitative

Inquiry, .

Vitak, J., Crouse, J. and LaRose, R., 2011. Personal

Internet use at work: Understanding cyberslacking.

Computers in Human Behavior, 27(5), pp.1751–1759.

Weaver, B.E. and Nilson, L.B., 2005. Laptops in class:

What are they good for? What can you do with them?

New Directions for Teaching and Learning,

2005(101), pp.3–13.

Wentworth, D.K. and Middleton, J.H., 2014. Technology

use and academic performance. Computers &

Education, 78, pp.306–311.

Wohn, D.Y., 2012. The Role of Habit Strength in Social

Network Game Play. Communication Research

Reports, 29(1), pp.74–79.

Wood, E., Zivcakova, L., Gentile, P., Archer, K., De

Pasquale, D. and Nosko, A., 2012. Examining the

impact of off-task multi-tasking with technology on

real-time classroom learning. Computers and

Education, .

Wu, J.Y., 2017. The indirect relationship of media

multitasking self-efficacy on learning performance

within the personal learning environment:

Implications from the mechanism of perceived

attention problems and self-regulation strategies.

Computers and Education, .

Yasar, S. and Yurdugul, H., 2013. The Investigation of

Relation Between Cyberloafing Activities and

Cyberloafing Behaviors in Higher Education.

Procedia - Social and Behavioral Sciences, 83,

pp.600–604.

Zhang, W., 2015. Learning variables, in-class laptop

multitasking and academic performance: A path

analysis. Computers & Education, 81, pp.82–88.

Zhu, Y., Au, W. and Yates, G., 2016. University students’

self-control and self-regulated learning in a blended

course. The Internet and Higher Education, 30, pp.54–

62.

Zimmerman, B.J., Bonner, S. and Kovach, R., 1996.

Developing Self Regulated Learners – Beyond

Achievement to Self-Efficacy. Washington: American

Psychological Association.

Zimmerman, B.J. and Schunk, D.H., 2011. Handbook of

Self-Regulation of Learning and Performance. New

York: Routledge-Taylor & Francis.

Cyber Slacking among University Students: The Role of Internet Habit Strength, Media Multitasking Efﬁcacy and Self Regulated Learning

247