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South Asian Journal of Global Business Research
Students’ dependence on smartphones and its effect on purchasing behavior
Imtiaz Arif Wajeeha Aslam Muhammad Ali
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Students’ dependence on
smartphones and its effect on
purchasing behavior
Imtiaz Arif
Department of Management Science, IQRA University, Karachi, Pakistan
Wajeeha Aslam
Department of Marketing, IQRA University, Karachi, Pakistan, and
Muhammad Ali
Department of Economics, University Malaysia Sarawak (UNIMAS),
Sarawak, Malaysia
Abstract
Purpose – The purpose of this paper is to explore how social need, social influence and convenience
affect dependence on smartphones and purchasing behavior among university students in Pakistan’s
emerging economy.
Design/methodology/approach – Survey methods and non-probability purposive sampling
were used to collect data from 337 respondents, and structural equation modeling was used to test
the hypothesis.
Findings – Overall the results provided evidence that social need, social influence and convenience
significantly affect students’ dependence on their smartphones. A significant relationship also existed
between students’ dependence on smartphones and their purchasing behavior.
Originality/value – The availability of 3G/4G mobile networks and the growth in smartphones’
computing power have meant that this form of mobile technology is in great demand. This study
provides an exclusive viewpoint concerning students’ dependence on smartphones and the effect of
this on their purchasing behavior, which is a subject that has not been covered previously in the
Pakistani context.
Keywords Structural equation modelling, Social influence, Smartphones, Dependence,
Purchase behaviour, Social need
Paper type Research paper
1. Introduction
Technological advancements in the past decade have changed the ways we consume,
distribute and create information significantly. Smith and Caruso (2010) reported that
63 percent of North American undergraduate students now have a smartphone or a
mobile device. Moreover, half of them said that they accessed the internet via their
devices on a daily basis to read and send e-mails, check news and weather, receive
maps and directions, and access social networking sites (SNS). A smaller, but growing,
group of students in the US also uses phones to conduct online banking, shopping and
the downloading and streaming of music and videos.
Scholars have argued that university students are often early adopters of new
technologies, particularly with respect to smartphones (Kim and Parka, 2014; Persaud
and Azhar, 2012; Sultan et al., 2009; Auter, 2007). According to a report by
Pew Internet (Smith, 2011), 52 percent of 18-29-year-old owned a smartphone in 2011,
while only 24 percent of 50-64-year-old owned a smartphone in the same year.
Furthermore, a report by eMarketer has shown that 61 percent of university students
South Asian Journal of Global
Business Research
Vol. 5 No. 2, 2016
pp. 285-302
© Emerald Group Publishing Limited
2045-4457
DOI 10.1108/SAJGBR-05-2014-0031
Received 25 May 2014
Revised 7 August 2014
8 December 2014
12 May 2015
30 October 2015
3 December 2015
Accepted 11 December 2015
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/2045-4457.htm
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owned a smartphone in 2011. Other studies have also noted a remarkable increase in
smartphone possession among university students (Paterson and Low, 2011; Jacob and
Issac, 2008). It has also been argued that university students are a “sweet spot” market
segment (Haverila, 2013).
A higher smartphone adoption rate among university students is also prevalent in
emerging nations (Sultan et al., 2009). According to a report by Pew Internet (Rainie and
Poushter, 2014), a large portion of the early adopters of smartphones in emerging
economies are aged between 18 and 29 years, i.e. of university-going age (Heinonen and
Strandvik, 2007). Although a significant difference in age groups and ownership of
smartphones was noted in every country that was polled, consumers aged under 30
were found more likely to own a smartphone than others. In China, 69 percent of
18-29-year-old had a smartphone, as compared to (62 percent) in Lebanon, Chile
(55 percent), Jordan (53 percent) and Argentina (50 percent). Furthermore, the report
noted that the educational level of smartphone owners is significantly positively related
to their ownership of a smartphone. Those with a university degree are more likely to
own a smartphone than those who do not have a university degree. This is especially
true in the Middle East and China.
Until April 2014, or before the auction of its 3G and 4G spectra, Pakistan’s
smartphone market accounted for only 15 percent of its mobile phone industry.
However, after 3G and 4G mobile networking became available in Pakistan via its three
major network operators, the demand for smartphones shot up. It was expected that
within a year this market’s consumers would increase to 50 percent of the population
(Zafar, 2014). Perhaps, because it was relying on this positive outlook, United Mobile,
which had been one of the country’s major distributors for Nokia, launched its own
smartphone recently. The optimism of mobile phone makers ahead of the spectra
auctions was also reflected by the figures concerning the country’s telecoms imports.
Mobile phone imports for the month of February 2014 increased by 20 percent to
US$60million compared to US$50million in February, 2013 (Baloch, 2014).
The Pakistan Advertising Society has also forecast a rapid expansion of the
smartphone market and stated that Pakistani youth will account for a larger share of
that growth. According to them, 77 percent of smartphone users in Pakistan are
between just 21 and 30-year old (Smart Phone Usage in Pakistan, 2014).
The recent hike in demand for smartphones in Pakistan, and their early adoption
among university students, both highlight the importance of understanding the
determinants that are creating a dependency on smartphones in the student market
segment and shaping the willingness of young people to purchase smartphones. In this
context, Ting et al. (2011) and Mohd Suki (2013) have highlighted the fact that students’
purchasing behaviors are mainly associated with their dependence on smartphones,
while the dependence on smartphones has been created thanks to social pressure,
consumer desire and the handiness of smartphones for students. Additionally, any
negative experiences of smartphone use have ultimately been outweighed by positive
experiences which, in turn, has led to even greater smartphone usage. Therefore, to
appreciate better the importance of the works by Ting et al. (2011) and Mohd Suki
(2013), this study’s focus has been to understand whether social needs, social influences
or convenience have stimulated dependence on smartphones among university
students in Pakistan. Furthermore, our findings have also confirmed the results of
similar researches that have established evidence of a relationship between smartphone
dependence and users’ purchasing behaviors. Hence, under the umbrellas of holistic
marketing management and strategic planning, this study should be useful for
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determining customers’ needs in order to provide up-to-date technologically advanced
smartphones. It is also our belief that this present research can provide guidance to
smartphone manufacturers and distributors concerning the development of strategic
marketing management and planning in respect of potential smartphone sales.
The rest of the paper is organized as follows. In the next section, a literature review
relating to students’ dependence on smartphones and hypotheses about that are
discussed. Next, we have presented the methodology that describes the sampling and
data collection techniques, and the methods employed for measurement of the stimuli
underpinning purchasing behavior. The test of the proposed model using structural
equation modeling (SEM) and the study’s findings are presented in the results section,
which is followed by a discussion of the results’ implications, whereby our findings
are expounded.
2. Empirical studies
There has been created a greater dependency on smartphones for consumers,
especially when communicating while traveling, or while at offices, at home and so on
(Genova, 2010). In the same vein, consumers have come to perceive smartphones as
necessities and so they have a higher propensity to use them now (Tian et al., 2009).
In this sense, future purchasing behavior by consumers will be influenced by their
positive perceptions of smartphones. Similarly, their dependency on smartphone usage
has underlying implications for the purchasing behavior of students (Kuhlmeier and
Knight, 2005). One study by Mohd Suki and Mohd Suki (2007) has also drawn our
attention to consumers’ acceptance of smartphones. Their findings suggest that intensive
use of smartphones leads to greater levels of knowledge and better social networking.
Later on, research by Paterson and Low (2011) also supported these results. More recently,
Mohd Suki (2013) and Duffett (2015) recommended that investigations into the
dependency on smartphone usage among university students should be conducted in
developing countries in order to obtain more holistic and accurate findings about
smartphone buying behaviors and how they reflect the perceptions of those students.
In the past decade or so, we have noticed incredible advances in the services
provided by mobile phone service providers and smartphone manufacturers in
Pakistan. This has provided new opportunities for users and created new avenues for
research. Although some studies have focussed on the university students’ reasons for
using smartphones and related services (Auter, 2007; Shin et al., 2011; Chun et al., 2012;
Kim and Park, 2011; Kim and Parka, 2014), there are several unexplored dimensions
that are related to understanding the factors that influence smartphone usage among
young university students in Pakistan. For this research, we have built a model to
examine social needs, social influences and convenience as motivations for dependency
on smartphones and to determine the control that those factors have on dependency
which, in its turn, affects purchasing behaviors. Hence, our next section discusses the
development of our relevant hypotheses.
Development of the hypotheses
Social needs. The need for social interaction with others is referred to as a social need,
which is fulfilled via communication with friends, family members and affiliates, such
as coworkers and fellow group or club members (Tikkanen, 2009). Social need is one
crucial factor underpinning consumers’ dependence on smartphones. The versatility of
smartphones and the availability of social networking applications (or “apps”) that are
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used via them has allowed consumers to increase their smartphone usage for
communication and maintaining relationships with other individuals (Yuan, 2012;
Pearson et al., 2010; Lippincott, 2010). Smartphones (which make the internet available
on the go) have made it easy to use SNS such as Twitter, WhatsApp and Facebook.
People have become dependent on smartphones because they enable them to shop,
research and connect with the world and feel more active among their social circles
(Raskin, 2006; Goldman, 2010; Jung, 2014; Kang and Jung, 2014). The most popular
mobile activities on smartphones are sending and receiving short text messages;
sending and receiving e-mails, transferring files and using SNS (Jung, 2014).
As in any other place in today’s world, people in Pakistan – particularly young
university-going students – are continually finding and adapting to new ways of
communicating electronically to fit their needs. Yet, it remains unclear to what extent
university students’ perceived needs for social media have created a dependence on
smartphones. As a result, the underlying hypothesis is proposed:
H1. Students’ needs to connect to social media have positively affected their
dependence on smartphones.
Social influence. Social influence arises when one person’s feelings, emotions and
activities are affected or influenced by others in social groups (Mason et al., 2007). It has
been identified by Lee et al. (2009) and Chun et al. (2012) hat social relationships are
strongly connected to consumers’ decisions to adopt a technology. Social influences
come from a variety of people such as neighbors, relatives, family members and friends,
as well as from inspirational figures in the media, such as sports celebrities or movie
stars. Generally, it has been noted that friends and family members are the major
influences who affect consumer evaluations concerning product selections (Schiffman
et al., 2010; Auter, 2007; Algesheimer et al., 2005; Dholakia et al., 2004). Several
researchers have identified social influence as a key variable that inspires both usage
intention and usage behavior, hence it plays an important role in consumers’ adoptions
of new technology (Rice and Aydin, 1991; Nysveen et al., 2005; Lee, 2014; Ting et al.,
2011; Vannoy and Palvia, 2010). A satisfied smartphone user’s dependency on
smartphones will increase and consequently will lead to positive word-of-mouth
communication about the technology with others (Brown et al., 2005). Consumers who
rely on positive word-of-mouth opinions given by members of a common social group
have their usage initiated by either a transformation of their beliefs or through a
process of imitation (Ting et al., 2011).
This leads to the following hypothesis:
H2. Social influence positively affects university students’ dependence on
smartphones.
Convenience. Convenience refers to a situation where work is simplified, made easy or
can be done with less effort, without discomfort or difficulty. Consumers have a high
need for convenience where they are able to use their smartphones at any time and in
any place without having to park the smartphone in a fixed workstation (Ting et al.,
2011; Genova, 2010; Holub et al., 2010). Smartphones provide quick access to multiple
products on multiple channels with greater levels of quality, efficiency and
personalization, and they can do almost everything that a laptop can (Persaud and
Azhar, 2012; Basaglia et al., 2009). The fusion of normal mobile phone and laptop
functionalities in smartphones was accomplished merely for consumers’ convenience
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(Stephens and Davis, 2009). The dual-use nature of smartphones has increased their
usage (Hahn, 2010). Moreover, with the availability of high-speed 3G/4G and Wi-Fi
networks, especially on university campuses, in malls, restaurants and at home, surfing
the internet has become more convenient for users who are bound by severe time
constraints (Lu and Su, 2009).
Hence, consumers have become more dependent on smartphones than they have
been before for the retrieval of useful information, as they have become ubiquitous
devices and users are with them always when they commute, relax at home, travel
overseas and so on (Genova, 2010). The convenience offered by smartphones makes
consumers more dependent on them.
As a result, the third hypothesis is:
H3. Convenience positively affects students’ dependence on smartphones.
Dependency and purchasing behavior
The recent proliferation of smartphones and the functions they offer suggest that soon
their use will overtake that of primitive mobile phones. Smartphones offer diverse
internet content with multimedia options. For instance, users can download various
kinds of mobile apps to their smartphones, which has enabled users to customize their
mobile devices and services by installing the functionalities that they want ( Jung, 2014;
Tossell et al., 2012; Verkasalo et al., 2010; Tam and Ho, 2006). This user-empowering
smartphone attribute of customization is perceived by consumers as being liberating
and it is viewed by them now as a necessity, which has increased the propensity for
continuous high smartphone usage. Using smartphones engages consumers by
allowing them to gain personal knowledge about their characteristics and how they can
be made to work to satisfy needs and improve personal experiences (Keaveney and
Parthasarathy, 2001). This affects consumers’ expectations of future purchases further,
as they are highly dependent on smartphones because of the benefits they are
extracting from them (Kuhlmeier and Knight, 2005). Ting et al. (2011) and Mafé and
Blas (2006) have also observed that users’ high dependence on smartphones is
positively correlated with their future purchasing behavior. Thus:
H4. Students’ dependence on smartphones positively affects their purchasing
behavior.
In the existing literature, a persuasive positive relationship has been established with
respect to the implications of social need, social influence and convenience for
dependence on smartphones. This dependence on smartphones, plays a further
important role by translating to positive purchasing behaviors (Ting et al., 2011; Suki
and Suki, 2013; Lee, 2014; Genova, 2010; Stephens and Davis, 2009; Jung, 2014; Kang
and Jung, 2014).
Conceptual framework. In the past, many empirical studies have been conducted
based on theoretical frameworks, research focus, study variables and research design.
Moreover, it is to be noted that these empirical works relied on theories already
developed, such as that of Roach (2009) who applied Rogers’s diffusion of innovations
theory (DOI). For their work, Heinonen and Strandvik (2007) used the Advertising
Research Foundation model. The Q methodology was tested by Andrews et al.
Grant and O’Donohoe (2007) employed uses and gratifications theory and
phenomenology. Ali et al. (2015) and Ali and Raza (2015) considered the theory of
reasoned action (TRA), while the theory of planned behavior (TPB) and the technology
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smartphones

acceptance model (TAM) were adopted by Muk (2007), Karjaluoto and Alatalo (2007)
and Bauer et al. (2005). Additionally, previous researchers have also reported that each
of the TRA, TAM and TPB conjectures are successfully modified in the absence of the
original constructs of the theoretical models (see Sultan et al., 2009; Yang and Zhou,
2011; Karjaluoto et al., 2008; Gao et al., 2010, and many more). In the same vein, the
studies by Hsu et al. (2007) and Roach (2009) were conducted using Rogers’s DOI
theory, but the original variables were removed from the analysis and replaced with
new ones. However, the evidence presented in their studies varied widely and required
further exploration of some new but relevant factors. This argument has also been
supported by Persaud and Azhar (2012) who set out to measure the intentions of
smartphone users. Similarly, a study by Kim et al. (2011) argued that the purchasing
intentions of smartphone users may have a different set of related behaviors and so
that has required further exploration of other new variables. Their study focussed on
four antecedents of purchasing intentions, namely, entertainment, networking,
productivity and information. Based on this discussion, the authors of this study
have understood the need for further exploration of the key constructs that determine
the purchasing intentions of smartphone users without using any theoretical
framework. Therefore, we have constructed our research hypotheses based on the
findings of past empirical studies. This methodology has also been used by Kim et al.
(2011), Persaud and Azhar (2012), Awan and Shahzad Bukhari (2011), Ting et al. (2011),
Naser et al. (1999), Mohd Suki (2013) and many more. Furthermore, Baumgartner and
Steenkamp (1996) have argued that it is better to develop a list of important variables
which affect consumers’ intentions than it is to test or choose a particular theoretical
model (Figure 1).
3. Methodology
Data were collected in spring 2014 from students at different universities in Karachi,
who were using smartphones, since this market segment is viewed as important for the
continued advancement of the telecommunications industry (Haverila, 2013). A self-
administered questionnaire was designed for use as a survey instrument to record the
respondents’ experiences with, and perceptions about, smartphones on a five-point
Likert-type scale that varied from “strongly disagree” (1) to “strongly agree” (5). Each
item used for the development of the survey instrument was adopted from earlier
studies and the measurements taken are given in the next section.
Measurement
The dimensions assessed for this study (i.e. social need, social influence, convenience,
dependence and purchasing intention) and the survey items that comprised them were
derived from different studies listed in Table I. The instruments used in this paper are
Social Needs
Students’ Dependence on smart phone Purchase BehaviorSocial Influence
Convenience
Sources: Adopted from Ting et al. (2011) and Mohd Suki (2013)
Figure 1.
Conceptual
frame work
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those which have established reliabilities that have been used in similar contexts by
previous researchers.
Social need scale. For the social need scale, one item related to maintaining social
relations was adapted from Campbell (2007) – “Smartphones allow me to stay
Items Adapted source
Factor
loadings
Social needs (Cronbach’s α ¼ 0.695)
A smartphone allows me to stay connected with those I care about Ting et al. (2011) 0.758
I use a smartphone to stay connected with friends and family
through social networking websites (Twitter, Facebook,
WhatsApp, etc.) 0.761
It is easy for me to observe other’s activities by using the
smartphone 0.701
I use my smartphone to catch up with friends and relatives 0.742
A smartphone allows me to transfer photo/audio or other data with
whomever I want to share
Campbell (2007) 0.577
Social influence (Cronbach’s α ¼ 0.712)
The pressure from friends and family is likely to influence the
usage rate of my smartphone
Ting et al. (2011)
0.619
I would buy a smartphone if it helped me fit in with my social group
better 0.667
It is important that my friends like the brand of smartphone
I am using 0.746
I would be open to being persuaded into using a smartphone if I had
low self-esteem 0.733
I have seen that smartphones attract people’s attention Basaglia et al. (2009) 0.561
Convenience (Cronbach’s α ¼ 0.792)
Having a smartphone is like having both a mobile phone and a
computer together
Ting et al. (2011)
0.541
In my work, a smartphone saves me time and effort 0.693
I would prefer carrying my smartphone rather than my laptop 0.694
A smartphone enables me to receive learning materials anywhere
I go 0.742
Using a smartphone would allow me to accomplish tasks more
quickly 0.746
Dependency (Cronbach’s α ¼ 0.764)
I always use my smartphone to deal with my job Ting et al. (2011) 0.704
I cannot do anything with my job without a smartphone 0.8
I will feel insecure if my smartphone is not with me 0.804
I am totally dependent on my smartphone 0.642
Purchasing behavior (Cronbach’s α ¼ 0.700)
I intend to keep using a smartphone in the future Ting et al. (2011) 0.578
On the whole, I am satisfied with the smartphone experience 0.699
I intend to have purchase a better smartphone in the future from my
experience 0.692
Overall, my positive experiences outweigh my negative experiences
with smartphones 0.655
I think about a smartphone as a choice when buying a mobile phone Park and Chen (2007) 0.721
Note: All the items were measured on a five-point Likert scaling
Table I.
Factor loadings
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connected with those I care about” – and four items were adapted from measures of
social needs devised by Ting et al. (2011), of which an exemplar item was: “I use a
smartphone to stay connected with friends and family through social networking
websites (Twitter, Facebook, WhatsApp etc.).”
Social influence scale. Social influence was assessed using four items from the
work of Ting et al. (2011). An example was: “The pressure from friends and family is
likely to influence the usage rate of a smartphone” and one item was adapted from
Basaglia et al. (2009).
Convenience scale. Convenience was assessed by adopting a five-item scale
developed and validated by Ting et al. (2011). An exemplar item was: “Having a
smartphone is like having both a mobile phone and a computer together.”
Dependence scale. Dependence was measured by employing four items adopted from
Ting et al. (2011). An exemplar item was: “I am totally dependent on my smartphone”
and one item was adapted from Hjorthol (2008) which has not been included in Table I,
because it did not have a factor loading threshold value of 0.50.
Purchasing behavior scale. Purchasing behavior was measured via four items
adopted from the work of Ting et al. (2011). An exemplar item was: “I intend to keep
continuing to use a smartphone in the future” and one item was adopted from Park
and Chen (2007), which was: “I think of a smartphone as a choice when buying a
mobile phone.”
Before launching the survey finally, a pilot testing for the instrument was carried
out on a sample of 50 university-going smartphone users. This helped the researcher to
improve minor editorial errors and to fine-tune the survey instrument.
To ensure that the sample was representative, both private and public universities
were targeted. Questionnaires were distributed in person and a non-probability
convenience sampling method was adopted. For content validity, the measurement
items used in the questionnaire were adapted from a wide range of earlier relevant
researches (see Table I) and these were used to operationalize the research constructs of
this study. A total of 400 questionnaires were distributed, 337 usable completed
questionnaires were received (84 percent) and statistical procedures were applied to
analyze the data. Cronbach’s α was used to evaluate the internal consistency of the
items and to construct validity exploratory factor analysis. Principal component
analysis was performed by using IBM SPSS Statistics 22.0. To test the hypothesized
relationships among the latent variables further, the SEM was employed using
IBM SPSS Amos 22.
4. Results
To evaluate whether the dataset used in this research was valid for the suggested
model or not, model fitness analysis was carried out for the confirmation and
modification of the model. The model’s fitness was verified by using three types of fit
measures which were an absolute fit measure that included χ2
, a goodness-of-fitness
index (GFI) and root mean square error of approximation (RMSEA); incremental
fit measures that included an adjusted goodness-of-fit index (AGFI), a normed fit index
(NFI), a comparative fit index (CFI), an incremental fit index (IFI) and a relative fit
index (RFI); and parsimony fit measures that included a parsimony comparative
fit index (PCFI), a parsimony normed fit index (PNFI) (Bollen, 1989; Hair et al., 2010).
The skewness of all the items’ ranges from −1.53 to 0.03, and the values for kurtosis
ranges from −0.75 to 2.65 were within the threshold values of ±2.0 and ±10 for
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skewness and kurtosis, respectively, which supported the requirements for “normally
distributed” data approximately.
Finally, by performing a simultaneous test, the structural model was estimated to
provide an empirical measure of the hypothesized relationships among the research
variables and constructs.
Based on the model-fit indices obtained, the model had adequate and acceptable
GFIs: χ2
/df ¼ 1.347(o3), GFI ¼ 0.954(W0.90), RMSEA ¼ 0.032(o0.08), AGFI ¼ 0.931
(W0.80), NFI ¼ 0.931(W0.90), CFI ¼ 0.981(W0.95), IFI ¼ 0.981(W0.95), RFI ¼ 0.907
(W0.90), PCFI ¼ 0.728(W0.50) and PNFI ¼ 0.691(W0.50). These indices have been
among the most frequently used, as they are less affected by sample size (Hair et al.,
2010; Adil, 2014, Arif et al., 2016).
The results indicated that convenience, social needs, social influences and
university students’ dependency on smartphones were positively related at p⩽0.01
levels. Convenience was found to be significantly related to university students’
dependency on smartphones ( β ¼ 0.32, p ⩽ 0.01). Thus, H1 was supported. Moreover,
the results indicated that social needs had a significant impact on the dependency on
smartphones ( β ¼ 0.21, p ⩽ 0.05). Therefore, the H2 was supported. Finally, social
influences were significantly related to university students’ dependency on
smartphones ( β ¼ 0.59, p ⩽ 0.01) hence, H3 was supported. Figure 2 shows that the
R2
between the independent variables on dependency was found to be 0.80. This
indicated that 80.0 percent of the variation in university students’ dependency on
smartphones could be explained by convenience, social needs and social influences.
This evidence supported the interaction effect of convenience, social needs and social
influences on university students’ dependency upon smartphones. Hence, were all
supported (Tables II-IV).
As for the path between dependency and purchasing behavior, it too was found to be
significant ( β ¼ 0.70, p ⩽ 0.01). Therefore, H4 was supported. The adjusted R2
for this
path was 0.48. This explained that 48.0 percent of the variation in future purchasing
behavior was accounted for by the university students’ dependency on smartphones.
This supported the effect that university students’ dependency on smartphones has upon
their future purchasing behavior. Thus, H4 was supported (Table V).
0.32***
0.57***
Convenience
Social Need Dependency Purchase Behavior
Social Influence
0.70***0.21***
0.59***
0.49***
0.20***
Notes: **p-value<0.05; ***p-value<0.01
Figure 2.
Structural equation
model
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5. Discussion and implication
The study examined students’ dependence on smartphones and the effect of that
dependency on their purchasing behavior with the help of a SEM. Social need, social
influence and convenience were examined to assess students’ dependence on
smartphones while their purchasing behavior was measured by taking students’
dependence as an influencing variable. It was found that social influence, social needs
and convenience significantly affected students’ dependence on smartphones.
This result demonstrates that social influences and social need do influence the
amount which university students use smartphones and this creates a dependency to
the extent that they would buy a smartphone if they believed it would help them to fit
in better with their social group. This finding is consistent with the conclusions
drawn by Park and Chen (2007). This study’s results also found that convenience is
another important factor that affects students’ dependency on smartphones. Having a
smartphone is like having both a mobile phone and a computer together.
Smartphones enable students to receive learning materials wherever they go and
Constructs Items Standardized loadings Composite reliability Average variance extracted
Social needs SN1 0.66 0.762 0.677
SN2 0.72
SN5 0.71
Social influence SI1 0.7 0.785 0.609
SI2 0.68
SI3 0.74
SI4 0.65
Convenience C2 0.7 0.737 0.659
C3 0.66
C5 0.73
Dependency D2 0.68 0.77 0.685
D3 0.74
D4 0.72
Purchasing PI1 0.59 0.752 0.5811
behavior PI2 0.71
PI3 0.74
PI4 0.64
Table II.
Reliability and
confirmatory
factor loading
1 2 3 4 5
(1) Social needs 0.823
(2) Convenience 0.417** 0.812
(3) Purchasing behavior 0.532** 0.438** 0.762
(4) Social influence 0.367** 0.207** 0.367** 0.78
(5) Dependency 0.243** 0.246** 0.271** 0.480** 0.827
Mean 4.19 3.83 4.02 3.21 2.79
SD 0.81 0.85 0.7 0.92 1.02
Skewness −1.53 −0.9 −1.38 −0.25 0.03
Kurtosis 2.65 0.96 3.42 −0.31 −0.75
Note: **Correlation is significant at the 0.01 level (two-tailed)
Table III.
Correlations analysis
between variables
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they prefer to carry smartphones rather than laptops. Preceding research by
Goldman (2010) concluded comparably.
Further investigation during the study showed that there is a positive significant
relationship between the influence of students’ dependency on smartphones and
their purchasing behaviors. The results concerning these variables imply that
students are deeply dependent on smartphones, which causes them to feel insecure
when their smartphones are not with them. Students’ positive experiences with
smartphones have outweighed their negative experiences, hence their usage of
smartphones is high.
A significant positive relationship between social influence and dependency on
smartphones shows how persuasive societal factors have been in making smartphones
become perceived as essential within social communities (Bearden and Etzel, 1982; Raento
et al., 2009). This should be recognized by marketers as an important factor that influences
university students’ smartphone dependency. Marketers should promote smartphones
as social necessities. This could be achieved by spreading positive word-of-mouth
messaging, which is a major component of marketing diffusion models (Uncles et al.,
2013). Positive word-of-mouth communication may be disseminated via promotions and
endorsements made by effective reference groups that are at the center of students’
attention. Positive word-of-mouth marketing would allow social influencers to increase
awareness about smartphone functions and make positive recommendations by giving
potential purchasers greater encouragement to use smartphones (Brown, 1989).
Fit indices Recommended level of fit Model value
Absolute fit measures
χ² 136.053
df 101
χ²/df o3a
1.347
Goodness-of-fit index (GFI) W0.9a
0.954
Root mean square error of approximation (RMSEA) o0.08b
0.032
Incremental fit measures
Adjusted goodness-of-fit index (AGFI) W0.90a
0.931
Normed fit index (NFI) W0.90a
0.931
Comparative fit index (CFI) W0.90a
0.981
Incremental fit index (IFI) W0.90a
0.981
Relative fit index (RFI) W0.90a
0.907
Parsimony fit measures
Parsimony comparative fit index (PCFI) W0.50a
0.728
Parsimony normed fit index (PNFI) W0.50a
0.691
Sources: a
Bagozzi and Yi (1988) and b
Browne and Cudeck (1993)
Table IV.
Goodness-of-fit
indices for
structural model
Path β SE CR p Results
Convenience → dependency 0.32 0.059 3.395 0 Supported
Social need → dependency 0.21 0.096 2.014 0.044 Supported
Social influence → dependency 0.59 0.083 5.695 0 Supported
Dependency → purchasing behavior 0.7 0.113 5.678 0 Supported
Table V.
Hypothesis testing
295
Students’
dependence on
smartphones

The positive significant relationship between social needs and university students’
dependency on smartphones signifies that there is a perceived need for university
students to stay connected (Kang and Jung, 2014). Smartphone providers should design
smartphones that are provided with high-speed data connections for online multimedia
applications, which will allow multimedia connectivity between university students
and their social circles. Multimedia communication, and feedback derived from it,
would allow students to engage more using their smartphones, and it would also
contribute to embedding their sense of community via networks where
communications are encouraged. Additionally, marketers might exploit the need for
belonging and the importance of staying connected as part of their promotional
strategies to engage university students to use smartphones.
Similarly, a positive significant relationship between convenience and university
students’ dependence on smartphones indicates that the convenience of smartphones
(Verkasalo, 2009; Basaglia et al., 2009) has enhanced the dependence on smartphones
(Ting et al., 2011). For some time, it has been noted that the demands for convenience
made by consumers have risen (Brown, 1989). Since university students consider that
the convenience of smartphones is a factor that has motivated them to increase their
smartphone usage, smartphone manufacturers should emphasize convenience when
promoting smartphones to students. They may increase the convenience of using
smartphones further by providing greater memory space, more user-friendly
interfaces, high-speed internet connections, options for connecting input and output
devices to smartphone models and an ability to write, edit and view documents, images,
and presentations more easily.
Finally, this research shows a strong positive and significant relationship between
university students’ dependency on smartphones and their future purchasing
behavior which indicates that their dependency on smartphones has a direct effect on
the formation of predictive expectations concerning future purchasing behavior.
This finding is similar to the findings of earlier studies in which the impact of
dependency on the willingness to purchase was studied (Peters, 2009; Ruiz Mafé and
Sanz Blas, 2006).
This research offers practical insights for mobile industry players in emerging
markets. It will help marketers understand consumers’ purchasing behavior and their
usage behavior, and it will give them the ability to develop appropriate marketing
strategies. Smartphone manufacturers can use the findings of this research for
advertising. They can promote the concept that smartphone users are able to acquire
information anywhere, at any time, which will further enhance the user’s confidence in
life. Advertising could also suggest that smartphones make users feel at ease, by
offering a better communication environment supported by mobile messenger services
and SNS apps, which offer constant connection with friends.
Limitations and future directions
There are some limitations in relation to the sampling techniques adopted for this
study. The analysis and interpretation of the results was not based on a nationwide
systematic random sample. Rather, a non-probability sampling technique was adopted
and data were only collected from universities in Karachi. The choice of this sampling
strategy may limit the generalizability of our findings. Even though the applied
sampling technique supported collection data from the youth market, which is a vital
consumer segment within the smartphone market, the findings from this research were
limited to the slender sampling frames of university students.
296
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Although, this study has provided several useful pieces of information for policy-
makers and academics, there were several limitations which should suggest new
directions for future studies. First, the generalization of the results would require extra
attention where smartphone markets are stable, since Pakistan’s smartphone market
has shown relatively rapid growth and the expectations of smartphone users there are
overheated and extraordinary. So, further studies should be conducted in places where
the smartphone user numbers are stable. That would provide support for the existing
body of literature. Second, this study has explored some new but relevant factors
concerning smartphone users’ purchasing intentions, which therefore require
theoretical frameworks for study such as TAM, TRA, TPB, DOI, etc. Additionally,
future research should also use broader sample frames and should examine variances
related to gender, age, socioeconomic and cultural factors further. They should also
endeavor to construct a comprehensive model of Pakistani consumers’ dependencies on
smartphones and consequent purchasing behavior by examining more determinants
such as customer satisfaction, the strengths of social ties, perceived social benefits,
rewards, personality strengths, opinion leadership and altruism. Furthermore, it would
be interesting to examine these factors in a cross-cultural context as cultural differences
between countries may also shape smartphone usage and adoption. For example, in
countries where self-regulation trumps government regulation, research studies
considering those different cultures and regulatory contexts may yield different but
important findings.
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Corresponding author
Imtiaz Arif can be contacted at: imtiaz.arif@iqra.edu.pk
For instructions on how to order reprints of this article, please visit our website:
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