Paper—An Investigation into the Factors Influencing the Purchase Intentions of Smart Wearable…
An Investigation into the Factors Influencing the
Purchase Intentions of Smart Wearable Technology by
Students
https://doi.org/10.3991/ijim.v13i05.10255
Evelyne Kasongo Nkonko (*), Norman Chiliya
University of the Witwatersrand, Johannesburg, South Africa
Evelyne.nkonko@students.wits.ac.za
Tinashe Chuchu, Tinashe Ndoro
University of Pretoria, Pretoria, South Africa
Abstract—The purpose of this study was to examine the factors which in-
fluence the purchase intentions of Smart wearable technology by students in
South Africa. The researchers, guided by literature developed a conceptual
framework with five constructs, namely, product quality, design, price, con-
sumer attitudes and purchase intentions. The study followed a quantitative re-
search design. Data was collected from 416 registered students at a selected
higher education institution in South Africa who were older than 18 years. Data
analysis techniques comprised of structural equation modelling which focused
on confirmatory factory analysis to confirm conceptual relations and causal re-
lations between the factors. SPSS 23 and AMOS 23 software were used to per-
form the data analysis. The results revealed that product quality and product de-
sign had a significant positive effect on purchase intentions. Price and attitudes
were found to be mediating the effect of product quality and product design on
purchase intentions. The paper contributes to existing literature on the purchase
intentions of Smart wearable technology. Furthermore, it provides technology
organisations with adequate insight into the factors which influence the pur-
chase of Smart wearable technology.
Keywords—Technology, mobile application, students, higher education
1 Introduction
The advent of mobile education technologies into teaching and learning has given
rise to both new opportunities and challenges to educators (Handal, El-Khoury,
Campbell & Cavanagh, 2013) [21].The rapid advance in broadband and wireless in-
ternet technologies has promoted the utilisation of wireless applications in our daily
lives (Hwang, Yang, Tsai &Yang, 2009). Application software, also referred to as an
application or an app, relates to a software designed to assist users in performing spe-
cific or various related tasks (Handal, El-Khoury, Campbell and Cavanagh, 2013)
[21]. Advancement in mobile technology and learning applications have broadened
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Paper—An Investigation into the Factors Influencing the Purchase Intentions of Smart Wearable…
the scope of learning areas outside of formal education by allowing flexible and in-
stant access to rich digital learning sources (Cheon, Lee, Crooks & Song, 2012) [13].
Access to mobile online lectures can provide an opportunity for learning by students
while commuting (Massey, Ramesh & Khatri, 2006) [37]. For example eSchoolBag,
is a platform that allows students to download/upload homework, access class an-
nouncements and complete exercises, anywhere, anytime (Massey et al., 2006) [37].
Education in particular has benefitted from technologies such as computers and the
internet (Abdullah, Ward & Ahmed, 2016) [1]. Being economical, flexible and acces-
sible without constraints of time and distance, technologies such as electronic learning
(e-learning) systems are becoming increasingly relevant in the Higher Education con-
text (Abdullah et al., 2016 [1]; Lin, Lu & Liu, 2013) [35]. An e-learning system is
defined by Lee, Hsieh and Ma (2011) [32] as an information system that can integrate
a wide variety of instructional elements through audio, video, and text delivered
through live chat sessions, online discussions, forums, tests and assignments. The
present study will primarily focus on the adoption of a mobile applications as educa-
tional tools. The study used the Technology Acceptance Model (Davis, Bagozzi &
Warshaw, 1989) [18] to examine the determinants of the adoption of educational
mobile applications in higher education.
In numerous empirical studies (Ong & Lai, 2006 [42]; Pituch & Lee, 2006 [45];
Sánchez & Hueros, 2010) the utility and applicability of the Technology Acceptance
Model (TAM) has been supported in a wide range of educational settings. Wang,
Wiesemes and Gibbons (2012) define mobile applications for educational purposes as
learning tools used to gain knowledge through mobile devices. Mobile devices in-
clude mobile devices such as tablets and smartphones. Nonetheless, mobile devices
facilitate mobile learning (m-learning) which involves a form of learning that makes
use of mobile communication technologies that give students the capacity to continu-
ously learn anywhere and anytime (Moreira, Santos & Durao, 2017) [41]. According
to Rainie (2012) [46] over 60% of young adults aged between 18-29 years, own
smartphones and use them for a variety of purposes such as, surfing the internet for
information, texting , social networking and reading emails. This therefore reveals
how significant a role smartphones play in young adult’s lives. Smartphones and mo-
bile apps have developed into an every day staple in the lives of young people includ-
ing Higher education students (Green, Cantu & Wardle, 2014 [20]; Moreira et al.,
2017) [46].
1.1 Problem investigated
The South African higher education landscape is faced by a plethora of challenges
which include transformation, student unrests and poor student graduation rates
(Barkhuizen, Rothmann & Van de Vijver, 2013 [6]; Barkhuizen & Rothmann, 2006
[5]; Letseka & Maile, 2008 [34]). Mobile applications have the potential to positively
support teaching and learning in higher education institutions by providing universal
communication, study aids and flexible location-based services for learners (Cheon et
al., 2012) [12]. Moreover, the higher education landscape is particularly suitable for
the integration of student centred mobile educational applications to be adopted be-
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Paper—An Investigation into the Factors Influencing the Purchase Intentions of Smart Wearable…
cause mobile devices have become ubiquitous on university campuses among both
students and staff members in both developed and developing countries (Cheon et al.,
2012 [12]; Rogers, Palmer & Miller, 2017). According to the International Telecom-
munication Union (ITU) (2015), seven billion people in the world have access to
mobile devices coverage. Africa has the second largest and fastest growing mobile
phone market in the world (ITU, 2015). According to Phuangthong and Malisawan
(2005) [44], most researchers have focused on mobile applications, users’ acceptance
and the application of mobile learning in developed countries (Brown, Ryu & Par-
sons, 2006 [7]; Liu, 2008 [36] and Chao and Chen (2009) [10]. However, limited
research has explored the adoption of mobile devices to facilitate learning in higher
education institutions within the African context (Kaliisa & Picard, 2017) [26].
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2 Literature Review
Baker (2000) [4] considers reviewing current literature relevant to a research inter-
est to be an essential initial step and basis for undertaking the research study. In an
educational environment students can utilise mobile devices to support their learning.
Mobile devices , such as personal digital assistants (PDAs), mobile phones, or porta-
ble computers are increasingly being incorporated in learning activities by educators
(Wu, Hwang, Tsai, Chen & Huang, 2011) [57]. In this respect, mobile technology,
allows learning activities to be carried out inside and outside of the classroom (Wu et
al., 2011) [57]. Researchers have established that what really matters is students being
able to access the right educational resources at the right time in the right place (Shih,
Chu & Hwang, 2011[50] and Wu et al., 2011) [57].
As a form of learning, mobile learning involves learning which is facilitated by
mobile devices. Mobile learning provides continuous opportunities to extend spaces
and times for learning by learners (McCaffrey, 2011) [38]. There are four types of
learning approaches that can be supported by mobile devices, namely, individualised
learning, situated learning, collaborative learning and informal learning. Through
individualised learning, mobile learning allows students to pace themselves as they
learn and acquire knowledge. On the other hand, situated learning occurs when stu-
dents utilise mobile devices to learn within a real life context. Collaborative learning
occurs when students utilise mobile devices to interact and share knowledge with
other students. Finally, informal learning occurs when students are able to utilise their
mobile devices out of the class room setting at their convenience (Wu et al., 2011)
[57]. Several studies focusing on the use of mobile devices to facilitate learning have
provided empirical evidence supporting the effectiveness of mobile devices in the
learning process. For example, in a study conducted by Hwang, Yang, Tsai and Yang
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Paper—An Investigation into the Factors Influencing the Purchase Intentions of Smart Wearable…
(2009) [24] mobile and wireless communication technologies were used in a Chemis-
try course to train students on the operating procedure of the single-crystal X-ray
diffraction experiment. In this vein, mobile applications have been reported to facili-
tate learning activities which include, sharing of information, robust debates and the
discussions of important topics.
The potential benefits of mobile applications as learning tools has received exten-
sive support in terms of being cost saving, ubiquitous, and convenient (Cheon et al.,
2012) [12]. According to Young (2011) mobile applications on mobile devices can be
used as study aids that can be easily accessed by learners when they are at home dur-
ing any time of the day. The characteristics of mobile devices are three fold, (1) port-
ability - mobile device can be taken to any location because of their size, (2) instant
connectivity - because of the wide spread accessibility of the internet mobile devices
can be used to access any information instantly (3) context sensitivity - with regard to
the availability of the internet, any use of mobile devices can be tracked and measured
to gather necessary data and information (Churchill & Churchill, 2008 [14]; Klopfer,
Squire & Jenkins, 2002 [28]; Sharples, 2000 [49]). Recent research showed that 67%
of students’ smartphones and tablets are reportedly being used for academic purposes
(Chen & Denoyelles, 2013) [11]. Research also indicates that most students use mo-
bile devices for academic applications including university applications (such as, UCF
mobile, Tegrity, Mobile learn), educational application (such as, Flash cards, Khan
Academy and iTunes U), e-books (such as, Course Smart and Inkling), Google and
Safari for accessing information (Chen et al, 2012) [11].
The following sections in the paper will comprise of the theoretical grounding un-
derpinning the study, research objectives, hypotheses, research methodology, find-
ings/results, managerial and academic implications, conclusions and lastly sugges-
tions for future research.
2.1 Theoretical grounding
For the purpose of this study the theoretical grounding will be guided by, the Tech-
nology Acceptance Model (Davis et al., 1989) [18], Theory of Planned Behaviour
(Ajzen, 1991) and the Theory of Reasoned Action (Ajzen & Fishbein, 1975). These
theories were be used to explain students’ behaviour towards the adoption of mobile
applications as learning tools. Moreover, this study will add to our understanding of
theory through the application of the aforementioned theories within the African high-
er education context to comprehend the adoption of mobile applications as learning
tools.
Technology Acceptance Model: The technology acceptance model (TAM) pro-
posed by Davis (1989) [17] is the most widely used and recognised theory for ex-
plaining an individual’s acceptance and adoption of information technology (Lee,
Hsieh & Hsu, 2011) [32]. TAM determines users attitudes and recognises the role of
perceived ease of use (PEOU) and perceived usefulness (PU) in the comprehension of
users acceptance of information systems (Min, So & Jeong, 2018 [39]; Taylor &
Todd, 1995 [52]; Venkatesh & Davis, 2000) [54]. Increasingly, TAM has been used
as an explanatory tool in investigating m-learning amongst students. In a study con-
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Paper—An Investigation into the Factors Influencing the Purchase Intentions of Smart Wearable…
ducted by Park, Nam and Cha (2012) [43] it was found that the TAM is an acceptable
model to explain student’s acceptance of m-learning. TAM highlights the importance
of two key dimensions, namely, Perceived Usefulness (PU) and Perceived Ease of
Use (PE). In this vein, PU represents the extent to which individuals believe that tech-
nology will aid them in achieving their intended outcomes. On the other hand, PE
denotes the extent to which an individual believes that adopting technology will ease
and support their cognitive efforts (Park et al., 2012) [43].
Theory of Reasoned Action: According to Tsai, Chen and Chien (2012) [53] the
theory of reasoned action is widely used to explain human behaviour. According to
theory of reasoned action (Ajzen & Fishbein 1975) [3], intentions are the sole deter-
minant of the behaviour (Sommer, 2011) [51]. According to the theory of reasoned
action (Ajzen & Fishbein, 1975) [3] in order for an individual to fully engage in a
certain behaviour, their behaviour is driven by their intentions.
Theory of Planned Behaviour: The theory of planned behaviour (TPB) is a theory
intended to predict and explain human behaviour in specific settings (Ajzen, 1991)
[2]. The theory of planned behaviour is an extension of the theory of reasoned action
which addresses the limitation of the theory of reasoned action in not accounting for
behaviours in which individuals do not have complete voluntary control. Hence, the
theory of planned behaviour has the additional component of perceived behaviour
control has a determinant for behaviour intention (Ajzen, 1991) [2].
3 Research Objectives
The main objective of the literature was to investigate the determinants of the
adoption of mobile applications as learning tools by students in higher education.
4 Research Conceptual Model
In the conceptual model adapted from the TAM (Davis, Bagozzi & Warshaw,
1989) [18], Theory of Planned Behaviour (Ajzen, 1991) [2] and the Theory of Rea-
soned Action (Ajzen & Fishbein, 1975) [3], perceived usefulness, perceived ease of
use, attitudes on mobile applications, intentions to use mobile applications and the
actual use of mobile applications will be presented. Based on the conceptual model,
hypotheses are developed for the present study.
Fig. 1. The proposed conceptual model
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