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Pertanika J. Soc. Sci. & Hum. 25 (S): 391 - 398 (2017)
ISSN: 0128-7702 © Universiti Putra Malaysia Press
SOCIAL SCIENCES & HUMANITIES
Journal homepage: http://www.pertanika.upm.edu.my/
E-mail addresses:
saidonjb@gmail.com (Janiffa Saidon),
dr.rosidahmusa@gmail.com (Rosidah Musa),
miorharris@salam.uitm.edu.my (Mior Harris Mior Harun)
* Corresponding author
Article history:
Received: 15 September 2016
Accepted: 30 December 2016
ARTICLE INFO
Pathological Smartphone Use and Its Consequences
Janiffa Saidon1*, Rosidah Musa2 and Mior Harris Mior Harun1
1Faculty of Business Management, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
2Institute of Business Excellence (IBE), Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
ABSTRACT
Pathological Smartphone Use (PSU) is an emerging phenomenon that needs to be
understood. Although there are extensive studies in Pathological Internet Use (PIU)
empirical research appears to be still insufcient. It is important to note that Media System
Dependency (MSD) Theory assumed that social system and media system as the factors
to explain Pathological Internet Use (PIU).The main objective of this research is to extend
the MSD Theory with a new construct called personality system and examine its effect on
PSU. In this study, the target respondents are the Millennial cohort who are born between
1981 and 1996, often early adopters of new technologies as well as extensive users of
the Smartphone. The preliminary phase of the research uses a qualitative approach the
gain observable facts which is followed by quantitative data analysis aimed at testing the
plausibility of the proposed model among urban Millennial (age 20 - 35 years old). The
research introduced an integrative MSD model and it is suggested personality is the main
factor behind smartphone pathology phenomenon.
Keywords: Integrative Media Dependency Theory, smartphone pathology
INTRODUCTION
Millennials are the last generation born
in the 20th century. They are described as
the fastest growing internet populations.
According to the Economic Planning Unit
report in 2016, the number of internet users
in Malaysia in 2015 was 718 for every 1000
people. And for every 1000 people, there
are 1418 cellular phones owned (Economic
Planning Unit, 2016). Malaysians especially
youth have shown drastic increase in
accepting online technology and services.
Despite the significant growth figures
there is insufcient research in identifying
Janiffa Saidon, Rosidah Musa and Mior Harris Mior Harun
392 Pertanika J. Soc. Sci. & Hum. 25 (S): 391 - 398 (2017)
factors influencing PSU and its impact
among Millennials in Malaysia. This
study will be a comprehensive study
aimed at determining the predictors and
consequences of smartphone pathology use
among Millenials in Malaysia. This study
will be using a modied MSD theory (Hong,
Chiu, & Huang, 2012).
In understanding PSU outcomes, several
studies stressed on the negative effects of
PSU which made it difficult to see the
other side of the coins in technological
revolution. It raises the doubt if there is
the positive effect of using mobile internet.
Evidently, it appears that mobile internet
does not affect young people in a negative
way. It provides users with the opportunity
to connect with people well beyond time
and space constraints. Young (2007) argues
that compulsive behaviour in patients with
Internet addiction reduces an underlying
emotional tension as well as a reward for
future behaviour and some even found a
benecial effect of its use of community
life. PSU requires Millennial Teens to
constantly communicate with their friends
or parents via Facebook, posting pictures
to Instagram and reading some tweets
from their followers, this, in turn, will
make them perceive themselves as good
online consumer self-efcacy in shaping
their cognitive skill and boosting their self-
esteem which increases their well-being and
affective nature. Lastly, their behavioural
addiction may develop them to be better
market mavens.
LITERATURE REVIEW
Smartphone Hardware
The hardware such as the design of the
smartphone can influence usage of a
smartphone.
Thus, the study hypothesizes:
H1: Mobile device hardware positively
inuences Pathological Smartphone
Use.
Smartphone Software
Consumers who are familiar with their
smartphones are quite comfortable using
the device. These users also have good
knowledge of what m-commerce can
offer, and hence will not be attracted to use
m-commerce based on the perceived ease of
use (Chong, Chan, & Ooi, 2012).
The mobile device is turning into
multifunctional devices that are not
exclusively utilized for communication
purposes; the researcher suggests that due to
the varying applications that mobile device
provides to their users, this software agent
can increase their pathology towards their
smartphone (Agrebi & Jallais, 2015).
Thus, the study hypothesizes:
H2: Mobile device software positively
inuences Pathological Smartphone
Use
User’s Personality
Among many elements associated with
smart phones, personality has been shown to
Pathological Smartphone Use and Its Consequences
393Pertanika J. Soc. Sci. & Hum. 25 (S): 391 - 398 (2017)
profoundly inuence Internet use (Weibel,
Wissmath, & Groner, 2010). Among early
researchers who linked personality to
the internet is Hamburger and Ben-Artzi
(2000), where they measured the level of
extraversion and neuroticism among female
and male users, and reported extrovert
and neurotics are positively related to
social-leisure activities such as random
surng. Andreassen et al. 2013 found that
agreeableness to be negatively associated
with smartphone addiction among college
students.
Thus, the study hypothesizes:
H3: Personality of a mobile device user
positively inuences Pathological
Smartphone Use.
Smartphone Pathology
Understanding the major role smartphones
are having among young adults it is noted
Millennials are more dependent on their
smartphones. In 2016, it was found that
within 15 minutes of walking, 79 percent of
the respondents will start reaching for their
phones. It was also reported in the same
study, 68 percent of the respondents sleep
with their phones switched on. Surprisingly
67 percent of the respondents will check
their smartphones even though it is not
ringing or vibrating and 46 percent admitted
they cannot live without having their
smartphones (Roberts, 2016).
Self-Efcacy
LaRose, Eastin and Gregg (2001) proposed
that self-efcacy may help to reverse the
adverse effects as people have become
expert users. According to Hill and Beatty
(2011), online consumer Self-efcacy is the
degree to which a person perceived that he
or she is capable of engaging effectively as a
shopper and buyer in the online marketplace.
Online shopping self-efcacy is a person’s
perception of his or her skills in searching
for information online, for searching prices
online and for making purchases online.
eMaven
It was published by a six-year study of
Internet usage among children from 1996
to 2002, (79% from the United States),
showed indicates that 25% are heavy
users, spending more than ten hours online
each week. When smartphone users spend
long hours online they are conceived as
heavy media consumers, which will entail
them to become a market maven. The term
‘‘maven’’ describes an individual who
knows a great deal about with numerous
product choices or product class. While,
market mavens on the Internet are widely
known as eMavens. It was observed that
eMavens share information about a product
imperfection and benets through Internet
regularly. Meanwhile, emavens who are
heavy media users share their experiences
and knowledge on certain products through
both online and ofine. The eMaven ability
to share and gather knowledge from
consumers could affect the companies’
reputation and brand positively.
Thus, the study hypothesizes:
H4: Smartphone Pathology positively
inuences self-efcacy.
Janiffa Saidon, Rosidah Musa and Mior Harris Mior Harun
394 Pertanika J. Soc. Sci. & Hum. 25 (S): 391 - 398 (2017)
H5: Smartphone Pathology positively
inuences market maven.
All of the ve hypotheses are shown in
Figure I.
Figure 1. Conceptual Framework
conceived as heavy media consumers, which will entail them to become a market maven. The
term ‘‘maven’’ describes an individual who knows a great deal about with numerous product
choices or product class. While, market mavens on the Internet are widely known as
eMavens. It was observed that eMavens share information about a product imperfection and
benefits through Internet regularly. Meanwhile, emavens who are heavy media users share
their experiences and knowledge on certain products through both online and offline. The
eMaven ability to share and gather knowledge from consumers could affect the companies’
reputation and brand positively.
Thus, the study hypothesizes:
H4: Smartphone Pathology positively influences self-efficacy.
H5: Smartphone Pathology positively influences market maven.
All of the five hypotheses are shown in Figure I.
Figure 1. Conceptual Framework
METHODS
Using the snowball method, the data was
collected through on-line questionnaire.
272 respondents qualied to proceed until
the end. Since this is an exploratory survey,
therefore Smart Partial Least Squares
(SmartPLS) will be utilised to analyse the
data.
The respondents are confined to
the millennial ranges from age 20 to 35
years old. Nevertheless, they must have
experienced on-line purchases using their
mobile device. The mobile device could be
a mobile phone, tablet or PDA.
On the basis of exploratory research, six
subscales have been adapted and extended
to suit the research setting. The scale
consists of 52 items covering the subjects of
hardware, software, personality, smartphone
pathology, self efcacy and market maven.
The instrument was employed by using a
seven-point Likert scale (1 denotes strongly
disagree, while 7 denotes strongly agree).
The questionnaire was divided into three
broad sections; Section A covers generic
questions pertaining to the respondents’
smartphone. Section B measures the
smartphone systems influencing the
smartphone pathology. While section C
measures the smartphone pathology. Section
D measures the personality that inuences
smartphone pathology and Section E
measures the outcomes of smartphone
pathology. At the end of the instruments,
Section F covers information about the
respondents’ demographic backgrounds.
RESULTS AND DISCUSSION
Validity was measured using two criteria:
convergent validity and discriminant
validity. Convergent validity consists of
factor loadings, average variance extracted
Pathological Smartphone Use and Its Consequences
395Pertanika J. Soc. Sci. & Hum. 25 (S): 391 - 398 (2017)
(AVE) and composite reliability (CR) as in
Table 1 while discriminant validity using
Fornell and Larcker as summarized in
Table 2.
Table 1
Convergent validity
Construct Item Loadings Composite Reliability Average Variance Extracted
Hardware HW1 0.795 0.906 0.660
HW2 0.790
HW4 0.792
HW6 0.820
HW7 0.862
Software SW1 0.799 0.936 0.708
SW2 0.834
SW3 0.917
SW4 0.888
SW5 0.858
SW7 0.742
Personality 0.735 0.957
P. Extrovert PE1 0.945
PE2 0.752
P. Neurotism PN1 0.789
PN3 0.845
PN4 0.910
PN5 0.846
P. Openness PO4 0.887
PO5 0.866
S. Pathology PSU4 0.709 0.866 0.565
PSU5 0.712
PSU6 0.730
PSU8 0.808
PSU9 0.793
Self Efcacy SE1 0.888 0.903 0.651
SE2 0.888
SE3 0.701
SE4 0.788
SE5 0.754
Market Maven MM1 0.773 0.930 0.690
MM2 0.870
MM3 0.802
MM4 0.838
MM5 0.859
MM6 0.839
Janiffa Saidon, Rosidah Musa and Mior Harris Mior Harun
396 Pertanika J. Soc. Sci. & Hum. 25 (S): 391 - 398 (2017)
In this study, the factor loadings
exceeded 0.7 at the acceptance rate of 0.7
(Hair et al., 2010). The factor loadings
ranged from 0.701 to 0.917. The AVE of the
result indicates that all the variables have a
value greater than 0.5 which means that less
error remains (Hair et al., 2011). The highest
AVE is personality which is 0.957 followed
by smartphone software 0.708. The lowest
AVE is smartphone pathology which is
0.565. Based on Table I, it is initiated that all
of the AVE and CR values are more than 0.5.
Fornell and Larcker analysis summarized
in Table 2 also shows that all the diagonal
values are above their horizontal and
verticals values respectively. Hence, all
variables achieved reliable and valid results
as they are near to 1.0 (Henseler, Ringle, &
Sarstedt, 2015).
Table 2
Discriminant validity
HW MM PE PERSONALITY SPathology SW
HW 0.812
MM 0.400 0.831
PE 0.198 0.019 0.854
PERSONALITY 0.110 0.044 0.630 0.680
SPathology 0.238 0.266 0.345 0.480 0.752
SW 0.781 0.418 0.108 -0.024 0.093 0.842
Table 3 summarizes the results of the
hypotheses. It shows all hypotheses,
apart from Smartphone software towards
Smartphone Pathology, were supported.
Moreover, it also has no effect towards this
investigated phenomenon with the effect
size value of 0.006. The most signicant
path is indicated through personality
towards smartphones pathology with the
t-value of 7.826. The value showed that
personality of a person is very much
important in determining a person whether
Table 3
Structural Analysis
Hypothesis Std Beta Std Error t-value Decision f2 (effect size)
H1 HW ->SPathology 0.271 0.097 2.800** Supported 0.039 Small
H2 SW ->SPathology -0.122 0.114 1.073 Not
Supported
0.006 No
H3 PERSONALITY
->SPathology
0.458 0.059 7.826** Supported 0.262 medium
H4 SPathology -> SE 0.262 0.057 4.577** Supported 0.073 Small
H5 SPathology ->
MM
0.266 0.053 5.047** Supported 0.076 Small
Pathological Smartphone Use and Its Consequences
397Pertanika J. Soc. Sci. & Hum. 25 (S): 391 - 398 (2017)
to be pathology to his or her smartphone or
vice versa. This result is also supported by
the effect size analysis, which indicates a
medium value of 0.262 which is the highest
or the most important factor that inuenced
this research phenomenon. On the other
hand, being smartphone pathology seems
to inuence ones to be more market maven
than self-efcacy with the t-value of 5.047
and 4.577 respectively.
CONCLUSION
Our results indicate that the hardware of
a smartphone and ones’ personality are
positively related to smartphone pathology,
thereby supporting the view that smartphone
pathology influences self-efficacy and
market maven. As mentioned by Lorette
in 2015, these ndings will be benecial to
retailers in strategizing their m-commerce
platform and preparing themselves in the
world market.
ACKNOWLEDGEMENTS
The authors gratefully acknowledge the
help of the Ministry of Malaysia (MOHE)
for providing nancial assistance through
the Fundamental Research Grant Scheme
(FRGS), and also to Universiti Teknologi
MARA for its cooperation.
REFERENCES
Agrebi, S., & Jallais, J. (2015). Explain the intention
to use smartphones for mobile shopping. Journal
of Retailing and Consumer Services. 22, 16-23.
Andreassen, C. S., Grifths, M. D., Gjertsen, S. R.,
Krossbakken, E., Kvam, S., & Pallesen, S. (2013).
The relationships between bahavioral addictions
and the ve-factor model of personality. Journal
of Behavioural Addiction. 2, 90-99.
Augner, C., & Hacker, G. W. (2012). Associations
between problematic mobile phone use and
psychological parameters in young adults.
International Journal of Public Health. 57,
437-441.
Balasubramanian, S., Peterson, R. A., & Jarvenpaa,
S. L. (2002). Exploring the implications of
m-commerce for markets and marketing. Journal
of Marketing. 30(4), 348–361.
Batthyany, D., Muller, K. W., Benker, F., & Woling,
K. (2009) Computer game playing: Clinical
characteristics of dependence and abuse among
adolescents. Wien Klin Wochenschr. 121,
501–509.
Bianchi, A., & Phillips, J.G. (2005). Psychological
predictors of problem mobile phone use.
Cyberpsychology Behavior. 8,39-51.
Chigona, W., Kankwenda, G., & Majoo, S. (2008).
The uses and gratications of mobile internet
among the South African Students. PICMET
2008 Proceedings, 27-31 July, Cape Town,
South Africa.
Chong, A. Y. L., Chan, F. T. S., & Ooi, K. B. (2012).
Predicting consumer decisions to adopt mobile
commerce: Cross country empirical examination
between China and Malaysia. Decision Support
Systems. 53(1), 34-43.
Economic Planning Unit Malaysia. (2016). Syndicated
Report. Retrieved on 25th July 2016 from http://
www.epu.gov.my/documents/
Ehrenberg, A., Juckes, S., White, K. M., & Walsh,
S. P. (2008). Personality and self-esteem as
predictors of young people’s technology use.
CyberPsychology and Behavior, 11(6), 739–741.
Janiffa Saidon, Rosidah Musa and Mior Harris Mior Harun
398 Pertanika J. Soc. Sci. & Hum. 25 (S): 391 - 398 (2017)
Fornell, C. G., & Larcker, D. F. (1981). Evaluating
structural equation models with unobservable
variables andmeasurement error. Journal of
Marketing Research, 18(1), 39-50.
Hair, J. F, Black, W., Babin, B., & Anderson, R.
(2010). Multivariate data analysis, 7/e, Pearson
Prentice Hall.
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena,
J.A. (2011). An assessment of the use of partial
least squares structural equation modeling in
marketing research. Journal of the Academy of
Marketing Science, 40(3), 414-433.
Hamburger, Y. A., & Ben-Artzi, E. (2000). The
relationship between extraversion and
neuroticism and the different uses of the Internet.
Computers in Human Behavior. 16, 441-449.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015).
A new criterion for assessing discriminant
validity in variance-based structural equation
modeling. Journal of the Academy of Marketing
Science.43(1), 115-135.
Hill, W. W., & Beatty, S. E. (2011). A model of
adolescents’ online consumer self-efficacy
(OCSE). Journal of Business Research. 64(10),
1025-1033.
Hong, F. Y., Chiu, S. I., & Huang, D. H. (2012). A
model of the relationship between psychological
characteristics, mobile phone addiction and use
of mobile phones by Taiwanese university female
students. Computers in Human Behavior, 28(6),
2152-2159..
LaRose, R., Eastin, M. S. & Gregg, J. (2001).
Reformulating the Internet paradox: Social
cognitive explanations of internet use and
depression. Journal of Online Behavior. 1(2),
10-15.
Lorette, K. (2015). The importance of marketing
for the success of a business. Small Business -
Chron.com. Web. 19 Feb. 2015.
Nielsens Mobile Insights Malaysia. (2010). Syndicated
Report. Retrieved at 25th July 2013 from http://
www.nielsen.com/my.html.
Roberts, J. A. (2016). The talking dead: How
personality drives smartphone addiction. The
Conversation. Retrieved at 6th August 2016
from https://theconversation.com/the-talking-
dead-how-personality-drives-smartphone-
addiction-62411.
Roberts, J. A., Pullig, C., & Manolis, C. (2015). I
need my smartphone: A hierarchical model of
personality and cell-phone addiction. Journal
of Personality and Individual Differences. 79(1),
13-19.
Roberts, J. A., Yaya, L. H. P., & Manolis, C. (2014).
The invisible addiction: Among male and
female college students. Journal of Behavioral
Addictions. 79, 13-19.
Hooi, P. S. C. (2011). Influence of parents and
peers on internet usage and addiction amongst
school-going youths in Malaysia (Doctoral
dissertation), Multimedia University, Malaysia.
Weibel, D., Wissmath, B., & Groner, R. (2010).
Motives for creating a private website and
personality of personal homepage owners in
terms of extraversion and heuristic orientation.
Cyber psychology: Journal of Psychosocial
Research on Cyberspace, 4(1), 5.
Young, K. (2007). Cognitive behavior therapy with
Internet addicts: Treatment outcomes and
implications. Cyberpsychology and Behavior,
10(5), 671–679.