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Abstract

The adoption of mobile health (m-Health) applications (apps) has been widely studied, but their continued use has not. This study endeavours to address this gap by examining the factors that motivate consumers to continue to use sugar-related mobile apps in Australia. To do so, this study extrapolates the technology acceptance model for technology adoption to explain the continued use of m-Health technology by adapting and extending its associated tenets informed by prior literature. This study also performs structural equation modelling on 306 usable responses from a randomly crowdsourced sample of sugar-related mobile app users in an online survey in Australia. In doing so, this study finds that consumers’ continuance intention to use m-Health apps is driven primarily by their ease of use and social influence toward such apps. Upon further scrutiny, this study observes that the influence of usefulness and enjoyment of such apps on continuance intention is fully mediated and thus manifests indirectly through the usage habits shaped by the ease of use of m-Health apps. The paper concludes with the implications of the findings for theory and practice, the limitations of the study, and suggestions for future research directions.
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Journal of Strategic Marketing
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/rjsm20
Where there’s sugar, there are sugar-related
mobile apps. What factors motivate consumers’
continued use of m-Health?
Tareq Rasul, Aaron Wijeratne, Samaneh Soleimani & Weng Marc Lim
To cite this article: Tareq Rasul, Aaron Wijeratne, Samaneh Soleimani & Weng Marc Lim (2021):
Where there’s sugar, there are sugar-related mobile apps. What factors motivate consumers’
continued use of m-Health?, Journal of Strategic Marketing, DOI: 10.1080/0965254X.2021.1999307
To link to this article: https://doi.org/10.1080/0965254X.2021.1999307
Published online: 16 Nov 2021.
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Where there’s sugar, there are sugar-related mobile apps. What
factors motivate consumers’ continued use of m-Health?
Tareq Rasul
a
, Aaron Wijeratne
b
, Samaneh Soleimani
a
and Weng Marc Lim
c,d
a
Department of Marketing, Australian Institute of Business, Adelaide, Australia;
b
Pearson Education Victoria,
Melbourne, Australia;
c
Swinburne Business School, Swinburne University of Technology, Hawthorn,
Australia;
d
School of Business, Swinburne University of Technology, Kuching, Malaysia
ABSTRACT
The adoption of mobile health (m-Health) applications (apps) has
been widely studied, but its continuance has not. This study endea-
vors to address this gap by examining the factors that motivate the
continued use of m-Health using consumers of sugar-related
mobile apps in Australia as a case. To do so, this study extrapolates
the technology acceptance model for technology adoption to
explain the technology continuance of m-Health by adapting and
extending its associated tenets informed by prior literature. This
study also performs structural equation modeling on a sample of
306 usable responses from a randomly crowdsource online survey
of sugar-related mobile app users in Australia. In doing so, this
study nds that consumers’ continuance intention of m-Health
apps is driven primarily by the ease of using and social inuence
toward such apps. Upon further scrutiny, this study observes that
the inuence of usefulness and enjoyment of such apps on con-
tinuance intention is fully mediated and thus manifests indirectly
through the usage habits shaped by the ease of m-Health app
usage. The implications of the ndings for theory and practice
and the limitations of and future research directions from the
study conclude the paper.
ARTICLE HISTORY
Received 31 August 2021
Accepted 22 October 2021
KEYWORDS
Australia; continuance
intention; mobile apps;
mobile health; m-Health;
sugar; technology
continuance
1. Introduction
Sugary food is considered harmful for one’s health, and research has demonstrated that
consumers acknowledge sugar’s negative impact that causes a variety of health issues
(Gibson, 2008). Yet, there is still much confusion amongst consumers about how much
sugar various sugary food items and drinks contain. In this digital era propelled by the
Fourth Industrial Revolution (IR4.0) (Lim, 2019), mobile health (m-Health) applications (or
apps) are increasingly being used by consumers to track their health and tackle health-
related issues (Naimark et al., 2015).
Health consciousness refers to the extent to which consumers have an awareness of the
impact of their diet and lifestyle on their health (Becker et al., 1977). The ability of consumers
to have health information at their ngertips has become a dening element of health
CONTACT Weng Marc Lim lim@wengmarc.com; marclim@swin.edu.au; wlim@swinburne.edu.my
This article has been republished with minor changes. These changes do not impact the academic content of the article.
JOURNAL OF STRATEGIC MARKETING
https://doi.org/10.1080/0965254X.2021.1999307
© 2021 Informa UK Limited, trading as Taylor & Francis Group
consciousness among consumers in the digital era. Indeed, m-Health apps can provide
consumers with additional ways to achieve health-related goals. In particular, research has
shown that m-Health apps, especially sugar-related mobile apps, can be very useful in
maintaining a healthy lifestyle (Naimark et al., 2015). Such apps can assist consumers in
monitoring caloric intake and keeping logs of overall food intake in addition to providing
them with nutritional information (e.g. Fitbit, MyFitnessPal), thereby assisting in adherence,
retention, and weight loss during health interventions (Ringeval et al., 2020).
The public health landscape in Australia is interesting and noteworthy. Since 1980,
Australia has encountered a signicant increase in adult obesity, from 16% in 1980 to 29%
in 2013 (Brand-Miller & Barclay, 2017). According to the Australian Bureau of Statistics
(2019), 67% of adults and 24% of children were classied as overweight or obese in 2018.
Overweight and obesity contribute to cancer, cardiovascular disease, and type 2 diabetes,
all of which have signicant health and economic impacts (Sainsbury et al., 2018). It is
interesting to note the increase in obesity as a concern in Australia, where sugar con-
sumption has declined since 2012 (Brand-Miller & Barclay, 2017). This suggests that
obesity is caused by numerous factors (e.g. eating too much, moving too little), and that
the lessons from successful mitigation of contributing factors such as sugar consumption
will need to be unpacked so that they can be extended, if possible, to mitigate other
contributing factors (e.g. frequency of exercise).
Of particular interest in this study is the use of m-Health apps for maintaining good
health. Australian consumers are increasingly using various sugar-related mobile apps
to check sugar levels, sugar types, and the health status of sugar products. Yet, little is
known about the factors that motivate consumers’ continuance intention to
use m-Health apps, particularly sugar-related mobile apps in the context of Australia,
and thus, the current study endeavors to address these gaps. The technology accep-
tance model (TAM) by Davis (1989) has been adapted (not adopted) and extended in
the current study. The adaptation and extension are in line with the recommendations
by Lim (2018) to (1) use TAM as a foundational lens and (2) contextualize and extend the
model based on the goal and unique peculiarities of the investigation. In the case of the
present study, the goal involves understanding consumers’ continuance intentions and
the unique peculiarities revolve around m-Health applications in the form of sugar-
related mobile apps among Australians. Hence, the adaptation (i.e. perceptual to actual
evaluations) and extension of the theory (i.e. adding of new, relevant explanatory
factors) allows us to move beyond technology adoption and into the sphere of technol-
ogy continuance.
To this end, this study makes a couple of key contributions to the literature and
practice. From the theoretical perspective, the contribution of the current research is
that it provides further support to extending the TAM. In particular, examining the TAM in
light of continuance intentions propels the extrapolation and extension of the theory. This
theoretical contribution (1) directly accounts for the impact of technological use on
consumers’ intentions to continue using that technology, and (2) indirectly accounts for
the benets that consumers continue to enjoy when technological use is maintained. In
this study, the technology is m-Health apps (i.e. sugar-related mobile apps) and the
benets are health outcomes. From the practical perspective, health practitioners will
benet from the ndings of this research in motivating consumers to continue to use
2T. RASUL ET AL.
sugar-related mobile apps. In addition, m-Health app developers will also benet from the
ndings of this study, wherein investments on pertinent and non-pertinent factors can be
aorded and redirected accordingly.
This paper proceeds as follows. In the second section, we provide a brief overview of
obesity in Australia and mobile app usage in general and for health purposes, followed by
a thorough articulation of the TAM as the guiding theory and the ensuing hypotheses to be
tested. The third and fourth sections discuss the research methodology, analysis, and results.
Finally, in the fth and last section, we discuss the theoretical and managerial implications of
our ndings, the limitations of the current study, and the directions for future research.
2. Literature review
2.1. Obesity in Australia
It is well established that the consumption of sugary food causes obesity in both adults
and children (Brand-Miller & Barclay, 2017). Since 1980, Australia has encountered
a signicant increase in obesity (Brand-Miller & Barclay, 2017), with 67% of adults and
24% of children were classied as overweight or obese by 2018 (Australian Bureau of
Statistics, 2019). Obesity contributes to cancer, cardiovascular disease, and type two
diabetes, all of which have signicant health and economic impacts (Sainsbury et al.,
2018). Though obesity has increased in Australia, sugar consumption has declined since
2012 (Brand-Miller & Barclay, 2017). Food labelling has played a pivotal role in this case, by
making consumers aware of how much sugar they are consuming (Review Panel, 2011).
Of particular interest in this study is Australian consumers’ increasing use of sugar-related
mobile apps to check sugar levels, sugar types, and the health status of sugar products.
The idea is that if we can understand and replicate the use of m-Health apps in the sugar
context for other health-related context (e.g. exercise), then we might have a better
chance to make greater progress in the achievement of other health outcomes, such as
obesity. This study endeavors to contribute to this understanding, which will be unpacked
in the next sections.
2.2. Usage of mobile apps in Australia
In changing food consumption habits, mobile apps have played an important role
(DiFilippo et al., 2015). Tonkin et al. (2017) indicated that m-Health apps can provide
specic nutrition information to individuals and give them the initiative to change their
food consumption behavior. Badawy et al. (2017) also found that m-Health apps improve
health-related behaviors. The internet, increased network coverage, and aordable smart-
phones have enabled many Australians to use sugar-related mobile apps. According to
Deloitte (2018), 89% of Australians own a smartphone, and, on an average, they spend
three hours a day on it for work, nding information, playing, and connecting with family
and friends. In 2019, Statista reported that there were an estimated 17.9 million smart-
phones in Australia, about a quarter of which had health-related mobile apps down-
loaded on them. Badawy et al. (2017) found that mobile apps improved adolescents’
health-related well-being. While there are thousands of m-Health apps used by Australian
consumers, only those used to track sugar consumption were considered in this study.
JOURNAL OF STRATEGIC MARKETING 3
2.3. Mobile apps and health consciousness
For many consumers, mobile apps have become part of daily life in maintaining a healthy
lifestyle. The number of m-Health apps is increasing exponentially. Skardziute ((2016))
noted that there were 259,000 m-Health apps, which was 57% more than in 2015. A study
by Pohl (2017) identied 325,000 apps, a 25% increase over 2016. m-Health apps oer
a variety of functions, such as providing information about calories per food item or
quantity, tracking exercise, and weight management (Kao & Liebovitz, 2017). Naimark
et al. (2015) highlighted that m-Health apps promote a healthy lifestyle by allowing
consumers to monitor their diet and physical activities. It is worth noting that
a signicant percentage of m-Health apps are connected with external devices, such as
digital scales, blood pressure monitors, and heart rate monitors, which can further
increase consumers’ interest in using them (Kao & Liebovitz, 2017). Finally, in the age of
social media, research has shown that many m-Health apps allow their users to employ
them as social media platforms, thereby promoting motivation by peers (Kao & Liebovitz,
2017).
2.4. Technology acceptance model
The TAM has been extensively used within information technology research to under-
stand user intentions toward technological adoption, providing insight into beliefs about
myriad technologies. Previously, TAM was applied to aid in the understanding of the
adoption of technologies within organizations (Natarajan et al., 2018). However, TAM has
now become one of the most accepted theories for understanding consumer acceptance
and usage of technologies (Ahmad et al., 2020). TAM was adapted (not adopted) as the
theoretical framework for the current study as it can allow us to understand consumers’
continuance intentions regarding m-Health applications. The TAM has undergone many
theoretical developments, but its eectiveness for examining technological adoption in
a range of contexts has been earmarked and thus its continued use for new technological
explorations has been highly encouraged (Lim, 2018).
For various technologies, TAM has been applied, in its original or extended form, to
determine customers’ intention to use and adopt them (Venkatesh & Davis, 2000),
including health-related technologies and services (Egea & González, 2011; Yarbrough &
Smith, 2007). In particular, TAM has been used to examine female adoption and accep-
tance of m-Health applications (S. Lim et al., 2011; Lishan et al., 2009); the role of
ubiquitous information systems in healthcare (Maass & Varshney, 2012); the adoption
of m-Health by hospital professionals (Wu et al., 2011); end-user acceptance of biometrics
in the privacy context (Miltgen et al., 2013); and the acceptance of preventive m-Health
services by the elderly (Guo et al., 2013).
While technology adoption has been widely examined, limited research exists on
technology continuance (Lim, 2018). In addition, past research has indicated that proper
conceptualization and denition for key technology continuance factors in healthcare
studies remain lacking (Moores, 2012). More importantly, previous scholars have noted
that using only the key factors prescribed by grand theories may be inadequate, and thus,
requires further extension (Lim, 2018). To address these gaps, the TAM is used as the main
4T. RASUL ET AL.
theoretical lens in this study, wherein the core tenets of the theory are adapted (not
adopted) and extended to account for the unique peculiarities of the m-Health phenom-
enon under study.
In essence, TAM consists of two key factors that explain technology adoption, namely
perceived usefulness and perceived ease of use, wherein the measuring of these factors
assists in understanding an individual’s intention to adopt and use a specic technology
(Davis, 1989). Perceived usefulness refers to the extent to which an individual believes
that a particular technology will improve his or her performance or will benet him or her
in some way (Davis, 1989; Gefen et al., 2003; Horst et al., 2007), whereas perceived ease of
use refers to the extent to which an ‘individual believes that the use of specic technology
will be free of much mental eort’ (Davis, 1989, p. 320).
Although TAM has been applied in many studies in the past, this study argues that the
‘adaptation’ of TAM is warranted as it goes beyond ‘adoption,’ and thus, avoids the
problem of ‘replication.’ More specically, the adaptation is performed by (1) contextua-
lizing the theory to the novel phenomenon under study (see Figure 1), and (2) recongur-
ing the core tenets for study of technology continuance (e.g. actual evaluations of
usefulness and ease of use) rather than technology adoption (e.g. perceptual evaluations
of usefulness and ease of use). Further extension is also considered in this study by
including new, relevant factors informed by prior research to enrich our understanding
of technology continuance among consumers of m-Health apps.
2.4.1. Usefulness
The usefulness of a technology is one of the critical components of TAM (Davis, 1989). It
refers to the extent to which an individual nds that a specic technology improves their
performance or benet them in some way (Davis, 1989; Gefen et al, 2003; Horst et al.,
2007). Due to the increasing expectations of consumers in today’s world, technological
advancements are very rapid. Therefore, consumers consider the usefulness of the tech-
nology (McLean & Wilson, 2016) when deciding whether to adopt and continue to use
them. For health-conscious consumers, usefulness means being able to use technologies
that allow them to track health indicators regularly (Okumus et al., 2018). Having health
Figure 1. Continuance of sugar-related mobile apps usage.
JOURNAL OF STRATEGIC MARKETING 5
information that is both readily accessible and meaningful is therefore a contributing
factor to remaining health conscious (Gould, 1988). Thus, the usefulness of m-Health apps
is expected to inuence both continuance intention and ease of use evaluations of such
apps among consumers.
H1. Usefulness of sugar-related mobile apps positively inuences consumers’ intention to
continue using sugar-related mobile apps.
H2. Usefulness of sugar-related mobile apps positively inuences consumers’ ease of use
of sugar-related mobile apps.
2.4.2. Ease of use
The ease of use of a technology is another indispensable element of TAM (Davis, 1989). It
refers to the extent to which an ‘individual believes that the use of specic technology will
be free of much mental eort’ (Davis, 1989, p. 320). Many studies have conrmed ease of
use as a signicant inuence on individual intentions to adopt a technology (Agarwal &
Karahanna, 2000; Khalifa & Shen, 2008; J. -H. Wu & Wang, 2005), albeit from a perceptual
rather than an actual evaluation perspective. According to McLean and Wilson (2016) and
Muñoz-Leiva et al. (2017), the ease of use of a mobile app will positively inuence an
individual’s experience. Mobile apps, in this case, m-Health apps, need to be simple and
easy to manage, as this is important for the user to understand the relevant health-related
information (i.e. sugar consumption) (Norman & Skinner, 2006). The lack of ease of use of
a mobile app can therefore hinder an individual’s ability to interpret the health-related
information (i.e. sugar consumption). Thus, the ease of use of m-Health apps is expected
to inuence continuance intention of such apps among consumers.
H3. Ease of use of sugar-related mobile apps positively inuences consumers’ intention to
continue to use sugar-related mobile apps.
Past research has argued that TAM is too parsimonious for helping us to understand
technology adoption across industries (I. -L. Wu et al., 2011). Additionally, Chen et al.
(2007), demonstrates that when based only on the usefulness and ease of use factors,
TAM is not able to explain behavioral intentions toward the new technology suciently.
In overcoming these drawbacks, the current study extends the TAM to understand the use
of sugar-related mobile applications. As this study seeks to capture continuance inten-
tions in the use of sugar-related mobile apps, associated factors such as enjoyment
(Venkatesh, 2000) and social inuence (Venkatesh et al., 2003) are considered based on
the recommendations of prior studies (Cheng et al., 2006; Chiou & Shen, 2012; Venkatesh
et al., 2003).
2.4.3. Enjoyment
Enjoyment refers to the extent to which the use of technology generates feelings of
pleasure, aside from any anticipatable performance consequences (Venkatesh, 2000).
Studies have demonstrated that enjoyment plays a signicant role in motivating con-
sumers to use technology (Davis et al., 1992; Venkatesh et al., 2012). Additionally, it
contributes signicantly to ease of use of that technology (Koenig-Lewis et al., 2015).
6T. RASUL ET AL.
For users of smartphones, enjoyment is likely to encourage them to continue their
engagement with its associated technologies (e.g. mobile apps) (Kim, 2008). Thus, the
enjoyment from m-Health apps is expected to inuence continuance intention, ease of
use, and usefulness of such apps among consumers.
H4. Enjoyment from sugar-related mobile apps positively inuences consumers’ intention
to continue to use sugar-related mobile apps.
H5. Enjoyment from sugar-related mobile apps is positively related to usefulness of sugar-
related mobile apps.
H6. Enjoyment from sugar-related mobile apps is positively related to ease of use of
sugar-related mobile apps.
2.4.4. Social influence
Social inuence has been dened by Venkatesh et al. (2003, p. 451) as ‘the degree to which
an individual perceives that important others believe he or she should use the new system.’
Many previous studies have already established the fact that social inuence has a positive
inuence on the process of adopting new technologies (Kesharwani & Tripathy, 2012;
Venkatesh et al., 2003). According to Yuan et al. (2015), mobile apps allow users to connect
with other people who are important to them and can inuence their adoption of new
technology (Hong & Tam, 2006). Previous studies have also found that there is a positive
connection between social inuence and usefulness, as well as between social inuence
and enjoyment (Y. Lee et al., 2006; Venkatesh & Morris, 2000). These ndings, which were
limited to the perceptual lens in prior technology adoption studies, are now extrapolated
for the present study to account for actual evaluations that manifest in technology
continuance. Thus, social inuence toward m-Health apps is expected to inuence con-
tinuance intention, ease of use, and usefulness of such apps among consumers.
H7. Social inuence positively inuences consumers’ intention to continue to use sugar-
related mobile apps.
H8. Social inuence is positively related to the usefulness of sugar-related mobile apps.
H9. Social inuence is positively related to the enjoyment of sugar-related mobile apps.
2.5. Continuance intention
The intention to continue to use a technology after the initial adoption of that technology
has been dened by Bhattacherjee (2001) as continuance intention. Cho (2016) indicated
that continuance intention toward a technology may be inuenced by the usefulness and
ease of use of that technology. Lisha et al. (2017) added that enjoyment may also impact
the continuance of technology use, whereas Venkatesh (2000) and B. Wu and Chen (2017)
represent the many studies that advocated the scrutiny of social inuence in technology
continuance.
JOURNAL OF STRATEGIC MARKETING 7
The preceding discussion is summarized visually in Figure 1 and the nine hypotheses
are tested empirically and reported in the next sections.
3. Method
The quantitative research approach was chosen for this study to test the research
hypotheses. In particular, this study employed an online survey to collect data from
randomly selected respondents who use sugar-related mobile apps on a regular basis.
Qualtrics was used for data collection in two stages. First, a pilot study was conducted,
followed by the main study.
3.1. Pilot study
A pilot study was conducted with a random sample of 45 participants who regularly used
sugar-related mobile apps in order to rene the survey instrument. The aim was to ensure
that the survey did not create participant fatigue and to evaluate the attention check
questions. No signicant issues were identied, but some renements to question ow
and word use were made based on the pilot study.
3.2. Main study
The main study consists of 306 usable survey responses out of a total of 345 survey
responses received (see Table 1). All participants were required to respond to all questions
in the initial page in order to proceed to the next page and the same process continues
thereafter. As a result, 39 incomplete survey responses were excluded from this study,
which included participants who did not respond to attention check questions appro-
priately. Survey participants for this study were randomly recruited through a market
research company called Pureprole that is based in Melbourne, Australia.
Following the recommendations by Goodman et al. (2013), four techniques were used
in this study to solicit rened crowdsource data. First, the minimum time threshold set for
each of the 49 questions is 90 seconds, and thus, data from respondents who took less than
90 seconds to complete the survey were not included in this study. Second, we included
three pre-screening questions for participants, namely whether they were a minimum of
18 years old, whether they lived in Australia, and whether they currently used sugar-related
mobile apps. A ‘no’ answer to any of these three questions terminated that survey. Third, to
improve the response rate, $1 was given to every respondent approved by the ethics
committee. Finally, there were two attention check questions in the survey to identify and
remove non-attentive respondents. The validity and generalizability of the data were
therefore improved as a result of this structured approach. Qualtrics was used for the
survey and it took approximately two months to collect the data.
3.3. Measurement and analysis techniques
The survey instrument was designed from previously validated scales and adapted to suit
the context of sugar-related applications. Usefulness was measured using four items
adapted from Thong et al. (2006) as well as Venkatesh et al. (2011). The four-item measure
8T. RASUL ET AL.
of ease of use was adapted from Wang (2014). The three items measuring enjoyment were
taken from Lin and Bhattacherjee (2008). The ve items testing for social inuence were
derived from the research by Sim et al. (2014). Finally, four items were adapted from
Bhattacherjee and Barfar (2011) to measure continuance intention to use sugar-related
mobile apps. Gender and age were included as control factors for this study. A ve-point
Likert scale anchored at one for ‘strongly disagree’ and ve for ‘strongly agree’ was used
for the measurement of all factors. The items measuring each factor are depicted in
The data collected for this study was analyzed using two techniques informed by
past studies (Dash & Paul, 2021; Hair et al., 2017; Hosen et al., 2021; Lim, 2015; W. M. Lim
et al., 2021). First, conrmatory factor analysis (CFA) was performed to assess dimen-
sionality, reliability, and validity of items measuring each factor. In essence, CFA is
a multivariate statistical analysis technique used to verify the measurement structure of
factors in a study. In other words, CFA is used to empirically test whether the items
used to measure the factors are consistent with the literature-informed understanding
of the nature of factors in a study. Second, structural equation modelling (SEM) was
Table 1. Profile of participants (n = 306).
Category n%
Gender
Male 152 50%
Female 154 50%
Age
18–20 years 32 10%
21–29 years 56 18%
30–39 years 58 19%
40–49 years 54 18%
50–59 years 50 16%
≥60 years 56 18%
Marital status
Married/De facto 183 60%
Separated/Divorced 28 9%
Widowed 8 3%
Never married 87 28%
Employment status
Work full-time 144 47%
Work part-time 82 27%
Away from work 6 2%
Unemployed 23 8%
Not in the labor force 51 17%
Education level
No formal education 1 0%
High school 71 23%
Certificate level 82 27%
Undergraduate degree 103 34%
Postgraduate degree 48 16%
Other 1 0%
Area of residence
Australian Capital Territory 4 1%
New South Wales 100 33%
Northern Territory 2 1%
Queensland 64 21%
South Australia 23 8%
Tasmania 6 2%
Victoria 73 24%
Western Australia 34 11%
JOURNAL OF STRATEGIC MARKETING 9
conducted to test the hypotheses. In essence, SEM is a multivariate statistical analysis
technique used to analyze structural relationships in a study. SEM is more powerful
than its alternative, multiple regression analysis (i.e. conducting regression analysis
multiple times), because it is capable of analyzing multiple relationships in a single
analysis (i.e. one-time analysis). Noteworthily, covariance-based (CB) SEM was per-
formed over partial least squares (PLS) SEM because this study is (1) extending an
existing theory (i.e. technology acceptance model), and (2) interested in theory testing
(i.e. technology continuance for m-Health), which are in line with the focus of CB SEM,
as opposed to theory prediction (i.e. predicting key factors), which represents the focus
of PLS SEM.
4. Results
4.1. Measurement model evaluation
CFA was conducted to assess the validity of the measures used to study focal factors.
A maximum likelihood method was used to demonstrate the factorial validity of each of
the factors. The results of CFA are presented in Table 2.Table 2.
Table 2. Measurement items.
Factor Item FL α CR AVE
Usefulness of sugar-related
mobile apps
I find sugar-related mobile apps useful in my daily life. .61 .88 .82 .54
Using sugar-related mobile apps helps me accomplish things
more quickly.
.80
Using sugar-related mobile apps increases my productivity. .86
Using sugar-related mobile apps helps me to perform many
things more conveniently.
.85
Ease of use of sugar-related
mobile apps
Learning to operate sugar-related mobile apps is easy for me. .69 .86 .82 .54
I found sugar-related mobile apps help me to do what I want to
do.
.68
It is easy for me to become skillful at using sugar-related mobile
apps.
.69
I found sugar-related mobile apps easy to use. .67
Enjoyment from sugar-related
mobile apps
I have fun interacting with sugar-related mobile apps. .73 0.89 .72 .61
Using sugar-related mobile apps provides me with a lot of
enjoyment
.83
I enjoy using sugar-related mobile apps. .81
Social influence toward sugar-
related mobile apps
Friends and family members have influenced my decision to use
sugar-related mobile apps.
.84 .86 .87 .57
Mass media (e.g. TV, radio, and newspaper) have influenced my
decision to use sugar-related mobile apps.
.81
It is the current trend to use sugar-related mobile apps. .68
People whose opinions that I value prefer that I use sugar-
related mobile apps in reducing my sugar consumption.
.90
I will use sugar-related mobile apps if my colleagues use them. .90
Intention to continue using
sugar-related mobile apps
I intend to continue using sugar-related mobile apps, rather
than to discontinue their use.
.69 .93 .82 .67
I plan to continue using sugar- related mobile apps. .74
I will continue using sugar-related mobile apps. .81
I predict I will continue using sugar-related mobile apps in the
future.
.80
Fit indices: χ
2
(160) = 420.46, p < .001. CFI = .94. GFI = .87. IFI = .94. PNFI = .76. TLI = .93. RMSEA = .07. SRMR = .06.
KMO = .93. Bartlett’s Test of Sphericity Approximate χ
2
= 4366.944, df = 190, p < .001. FL = Factor Loading.
α = Cronbach’s Alpha. CR = Construct Reliability. AVE = Average Variance Extracted. CFI = Comparative Fit Index.
GFI = Goodness of Fit Index. NFI = Normed Fit Index. TLI = Tucker Lewis Index. RMSEA = Root Mean Square Error of
Approximation. SRMR = Standardized Root Mean Residual.
10 T. RASUL ET AL.
The t indices of CFA indicated an acceptable level of model t to the sample data
(Fornell & Larker, 1981). In particular, the Kaiser-Meyer-Olkin (KMO) value was .93, which
exceeds the recommended value of .60, and thus demonstrating sample adequacy. The
CFA results revealed that the factor loading of all factors was signicant (p < .01) and
above the minimum threshold value of .50. The average variance explained (AVE) values
ranged from .78 to .89, which establish the convergent validity of the factors under study.
The square root of the AVE values presented in the upper diagonal of Table 3 for each
factor was also greater than each factor’s correlation coecient with other factors,
thereby establishing discriminant validity. In addition, the Cronbach’s alpha coecient
of each factor presented in Table 2 was above or equal to .70, thereby demonstrating the
reliability of factor measures. Finally, the results demonstrate that the factors are signi-
cantly correlated with each other, with correlation coecients ranging from .30 to .64,
which are were less than .90, and thus indicating that no issue of multi-collinearity exist
between the factors under study (Tabachnick & Fidell, (2012)).
4.2. Common method bias
To assess the potential for common method bias in the dataset, a Harman one-factor test
was conducted (Podsako & Organ, Podsako and Organ, (1986)). The results of an
unrotated factors analysis with the Eigenvalue >1 criterion revealed a solution that
accounted for 75% of the variance, with the rst factor accounted for 46% of the variance.
Therefore, common method bias does not appear to be a problem within the current
dataset.
4.3. Structural model evaluation
The relationships between the proposed factors were tested using SEM. The t indices
suggest an acceptable level of model t to the data (see Table 4). The Variance Ination
Factor (VIF) value of the model was below the cut of value of 10, revealing no issues with
multi-collinearity in the model. The results of the analysis depict that most of the
hypotheses were supported.
Overall, the model explained 47% of the variance in the intention to continue using
sugar-related mobile apps, demonstrating a moderate eect on the latent factor. As the
results suggest, the usefulness of sugar-related mobile apps had a non-signicant rela-
tionship with the intention to continue using such apps (β = .19, p > .05), and thus, H1 is
Table 3. Correlation matrix.
Factor M SD 1 2 3 4 5 6 7
1 Gender
b
1.50 .50 -
2 Age
c
3.67 1.63 −.10 -
3 Usefulness 13.99 3.32 −.03 −.09 .78
a
4 Ease of use 15.17 2.93 −.03 −.07 .64** .88
a
5 Enjoyment 10.23 2.51 −.06 −.10 .64** .59** .89
a
6 Social influence 15.33 4.47 −.10 −.15** .47** .30** .48** .86
a
7 Intention to continue 15.11 3.15 −.06 −.04 .60** .57** .55** .47** .93
a
*Correlation is significant at p < .05. **Correlation is significant at p < .01. M = Mean. SD = Standard Deviation.
a
Diagonal
value indicates the square root of AVE of individual latent factor.
b
1 = Male, 2 = Female.
c
1 = 18–20 years. 2 = 21–
29 years. 3 = 30–39 years. 4 = 40–49 years. 5 = 50–59 years. 6 = ≥60 years.
JOURNAL OF STRATEGIC MARKETING 11
not supported. However, the usefulness of sugar-related mobile apps was found to
signicantly inuence the ease of use of such apps (β = .51) at the p < .001 level, and
thus, H2 is supported. In addition, the ease of use of sugar-related mobile apps signi-
cantly inuences the intention to continue using such apps positively (β = .36, p < .001),
and thus, H3 is supported.
Nevertheless, the enjoyment from sugar-related mobile apps did not have a signicant
inuence on the intention to continue using such apps (β = .09, p > .05), and thus H4 is not
supported. However, the enjoyment from sugar-related mobile apps did produce
a signicantly positive inuence on the usefulness = .28, p < .001) and ease of use
(β = .33, p < .05) of such apps, and thus, H5 and H6 are supported.
Finally, the social inuence toward sugar-related mobile apps had a signicantly
positive inuence on the intention to continue using such apps = .21, p < .001), and
thus, H7 is supported. The same eect from social inuence also manifested toward the
usefulness (β = .34, p < .001) and enjoyment (β = .31, p < .05) of sugar-related mobile apps,
and thus H8 and H9 are supported.
4.4. Post-hoc mediation analysis
The non-signicance of the usefulness of and the enjoyment from sugar-related mobile
apps prompted the performance of a post-hoc analysis to detect potential mediation
eects. In particular, the mediating role of ease of use of sugar-related mobile apps was
examined in relation to the relationships between (1) usefulness and intention to
Table 4. Structural model.
Hypothesis Coefficient (β) t-value Hypothesis testing
Effects on intention to continue using sugar-related mobile apps
H1: Usefulness Intention to continue .19 1.82
NS
Not supported
H3: Ease of use Intention to continue .36 4.54*** Supported
H4: Enjoyment Intention to continue .09 1.24
NS
Not supported
H7: Social influence Intention to continue .21 3.98** Supported
Effects on ease of use of sugar-related mobile apps
H2: Usefulness Ease of use .51 4.82*** Supported
H6: Enjoyment Ease of use .33 4.06** Supported
Effects on usefulness of sugar-related mobile apps
H5: Enjoyment Usefulness .28 6.39*** Supported
H8: Social influence Usefulness .34 6.88** Supported
Effects on enjoyment from sugar-related mobile apps
H9: Social influence Enjoyment .31 7.66* Supported
Fit indices: χ
2
(161) = 419.25, p < .001. CFI = .94. GFI = .87. IFI = .94. PNFI = .76. TLI = .93. RMSEA = .07. SRMR = .06.
***p < .001. **p < .01. *p < .05. NS = Not Significant. CFI = Comparative Fit Index. GFI = Goodness of Fit Index.
NFI = Normed Fit Index. TLI = Tucker Lewis Index. RMSEA = Root Mean Square Error of Approximation.
SRMR = Standardized Root Mean Residual.
Table 5. Post-hoc mediation.
Mediation relationship Estimate
Lower
CI
Upper
CI p
Ease of use as a mediator between usefulness and intention to continue using
sugar-related mobile apps
.254 .168 .370 .000
Ease of use as a mediator between enjoyment and intention to continue using
sugar-related mobile apps
.152 .096 .233 .001
CI = Confidence Interval. p = p-value.
12 T. RASUL ET AL.
continue and (2) enjoyment and intention to continue using adecision tree for assessing
mediation eects. The rst step in the decision tree was to determine whether signicant
relationships exist between (1) usefulness and intention to continue and (2) enjoyment
and intention to continue, and as depicted in Table 4, their relationships were insignif-
icant. The second step in the decision tree was to determine whether a signicant
relationship exist between ease of use and intention to continue, and as depicted in
Table 4, the relationship was signicant. The third step in the decision tree is to examine
the signicance of the mediating eect of ease of use between (1) usefulness and
intention to continue and (2) enjoyment and intention to continue. The indirect eect
dening the mediating relationships was tested using bootstrapping procedures, which
indicate the presence of signicant mediating eects on both relationships. For the
relationship between usefulness and intention to continue, the bootstrapped unstandar-
dized indirect eect through ease of use was .254 and within the 95% condence interval
that ranged from .168 to .370, thereby indicating that the indirect eect in this mediating
relationship was statistically signicant (Table 5). For the relationship between enjoyment
and intention to continue, the bootstrapped unstandardized indirect eect through ease
of use was .152 and within the 95% condence interval that ranged from .096 to .233,
thereby indicating that the indirect eect in this mediating relationship was also statisti-
cally signicant. Thus, ease of use is a signicant (full) mediator, wherein usefulness and
enjoyment have a signicantly indirect inuence on the intention to continue using
sugar-related mobile apps through the ease of use of such apps. The implications of
these ndings along with those found in the preceding SEM analysis are discussed in the
next sections.
5. Conclusion
This study endeavored to provide fresh insights into the factors that motivate the continued
use of m-Health technology using the case of sugar-related mobile apps usage in Australia.
In particular, the study sought to demonstrate the utility of TAM and the potential extra-
polation of that theory from technology adoption to technology continuance. In total, seven
out of the nine hypotheses were supported and the reasons behind the two hypotheses
that were not supported were revealed through a post-hoc mediation analysis.
The core tenets of usefulness and ease of use from TAM produced dierential ndings.
Though ease of use was a signicant predictor of continuance intention, usefulness was not,
which is in contrast to the assumptions of TAM for technology adoption (Davis, 1989; Lim,
2018). Similarly, the inclusion of additional factors to explain technology continuance proved
to be interesting and refreshing. In particular, social inuence was a signicant predictor of
continuance intention, but enjoyment was not, which could be attributed to increased app
usage (Linnho & Smith, 2017). Nonetheless, the post-hoc mediation analysis revealed that
usefulness and enjoyment had an indirect eect on continuance intention via ease of use,
which rearms the importance of ease of use in predicting continuance intention.
From a theoretical standpoint, the ndings of this study suggest that consumers’
continuance of m-Health technology use such as sugar-related mobile apps are primar-
ily driven by the ease of use of and the social inuence toward such apps. The former
nding may be attributed to the habitual formation of using sugar-related mobile apps
as a result of its ease of use (Gefen et al., 2003), wherein usefulness and enjoyment are
JOURNAL OF STRATEGIC MARKETING 13
observed to be indirect motivators that consumers unknowingly experience when they
nd using such apps easy to use. That is to say, usefulness and enjoyment are camou-
age through ease of use to drive technology continuance. Whereas, the latter nding
may be attributed to the expectations and normalization of using mobile apps in society
(Kesharwani & Tripathy, 2012; Venkatesh et al., 2003), thereby making the utilitarian and
hedonic aspects of usefulness and enjoyment from the individual point of view negli-
gible in determining technology continuance. That is to say, technology continuance, as
revealed in this study, is driven externally by the app itself and by the society.
From a managerial standpoint, the ndings of this study suggest that m-Health app
developers will need to ensure that any app updates do not disrupt the habits of
consumers using the app, as ease of use was found to be the main driver of continued
usage of such apps. Though consumers who use such apps do nd these apps useful and
enjoyable, any improvements in these aspects should not disrupt existing consumer
habits as they are not the direct but indirect drivers of technology continuance. If any
updates are implemented, then m-Health app developers could keep consumers
informed about ‘what’s new’ using in-app interactive informative gestures (e.g. ‘tap to
continue’, ‘tap for a quick tip’, ‘tap to learn more’) to ensure that the app continues to
deliver an easy-to-use experience to them (e.g. easy to learn about the [updated] app,
easy to become skillful at using the [updated] app to do what consumers want it to do).
The ndings of this study also suggest that m-Health advocates and app developers will
need to work on maintaining and strengthening the culture of m-Health monitoring and
reliance given that social inuence was found to be a strong direct predictor of continued
usage of such apps. Community-based app functions (e.g. social networking and sharing
of information) can be considered as they leverage o the power of social inuence and
strengthen the usefulness and enjoyment of app experience whilst keeping habits emer-
ging from the routine ease of using the apps in check.
Notwithstanding the theoretical and managerial contributions from this study, several
limitations avail, which could pave the way for further research. First, this study is cross-
sectional and measures continuance intentions only, and thus, future research could pursue
a longitudinal study that enables actual continuance of m-Health apps to be ascertained.
Second, this study, which is exploratory in nature, includes only two additional factors to
extend TAM for technology continuance. The rich and refreshing insights revealed signal
the potential fruitfulness of adding new factors, which future research can advocate or
identify through critical (W. M. Lim et al., 2020) or systematic reviews (Lim & Weissmann,
2021; Paul et al., 2021) of technology continuance and pursue in empirical investigations
accordingly. Alternatively, future research can also pursue causal investigations using con-
ditional research designs that focus on prescriptive solutions (Lim, 2021b; W. M. Lim et al.,
2019) to elaborate and substantiate the managerial implications in this study (e.g. avenues
of social inuence that are most eective – e.g. digital versus traditional marketing, celebrity
versus non-celebrity social media inuencers). Technology continuance in underexplored
and susceptible populations, such as digital immigrants, older adults, and rural communities
(Anderson, 2019; Bacsu et al., 2020; Carrasco et al., 2020; De Morais et al., 2020; Okonji et al.,
2019; Özsungur, 2021; Serwe & Walmsley, 2020; Talmage et al., 2020), including its potential
impact (e.g. integrated care) (Lim, 2021a) and utility during and beyond the COVID-19
pandemic (Lim, 2021c), should also be explored more enthusiastically.
14 T. RASUL ET AL.
Disclosure statement
No potential conict of interest was reported by the author(s).
Funding
This project was supported by the Australian Institute of Business (AIB) through an internal research
grant [AIB2017/L1/05] scheme.
ORCID
Tareq Rasul http://orcid.org/0000-0002-1274-7000
Aaron Wijeratne http://orcid.org/0000-0002-5256-6327
Samaneh Soleimani http://orcid.org/0000-0002-2316-2825
Weng Marc Lim http://orcid.org/0000-0001-7196-1923
References
Agarwal, R., & Karahanna, E. (2000). Time ies when you’re having fun: Cognitive absorption and
beliefs about information technology usage. Management Information Systems Quarterly, 24(4),
665–694. https://doi.org/10.2307/3250951
Ahmad, A., Rasul, T., Yousaf, A., & Zaman, U. (2020). Understanding factors inuencing elderly
diabetic patients’ continuance intention to use digital health wearables: Extending the technol-
ogy acceptance model (TAM). Journal of Open Innovation: Technology, Market, and Complexity, 6
(3), 81. https://doi.org/10.3390/joitmc6030081
Anderson, K. A. (2019). The virtual care farm: A preliminary evaluation of an innovative approach to
addressing loneliness and building community through nature and technology. Activities,
Adaptation & Aging, 43(4), 334–344. https://doi.org/10.1080/01924788.2019.1581024
Arning, K., & Ziee, M. (2007). Understanding age dierences in PDA acceptance and performance.
Computers in Human Behavior, 23(6), 2904–2927. https://doi.org/10.1016/j.chb.2006.06.005
Australian Bureau of Statistics. (2019). Population clock. Retrieved September 26, 2020, from https://
www.abs.gov.au/statistics/health/health-conditions-and-risks/national-health-survey-rst-results
/latest-release
Bacsu, J. D., Viger, M., Johnson, S., Novik, N., & Jeery, B. (2020). Rural older adults’ perspectives of
activities to support cognitive health. Activities, Adaptation & Aging, 44(3), 177–191. https://doi.
org/10.1080/01924788.2019.1626203
Badawy, S. M., Barrera, L., Sinno, M. G., Kaviany, S., O’Dwyer, L. C., & Kuhns, L. M. (2017). Text
messaging and mobile phone apps as interventions to improve adherence in adolescents with
chronic health conditions: A systematic review. JMIR mHealth and uHealth, 5(5), 66. https://doi.
org/10.2196/mhealth.7798
Becker, M. H., Maiman, L. A., Kirscht, J. P., Haefner, D. P., & Drachman, R. H. (1977). The health belief
model and prediction of dietary compliance: A eld experiment. Journal of Health and Social
Behavior, 18(4), 348–366. https://doi.org/10.2307/2955344
Beglaryan, M., Petrosyan, V., & Bunker, E. (2017). Development of a tripolar model of technology
acceptance: Hospital-based physicians’ perspective on EHR. International Journal of Medical
Informatics, 102, 50–61. https://doi.org/10.1016/j.ijmedinf.2017.02.013
Bhattacherjee, A., & Barfar, A. (2011). Information technology continuance research: Current state
and future directions. Asia Pacic Journal of Information Systems, 21(2), 1–18.
Bhattacherjee, A. (2001). Understanding information systems continuance: An
expectation-conrmation model. Management Information Systems Quarterly, 25(3), 351.
https://doi.org/10.2307/3250921
JOURNAL OF STRATEGIC MARKETING 15
Blewett, N. (2011). Labelling logic: Review of food labelling law and policy. Department of Health and
Ageing. https://www.yumpu.com/en/document/view/44605237/labelling-logic-review-of-food-
labelling-law-and-policy
Brand-Miller, J. C., & Barclay, A. W. (2017). Declining consumption of added sugars and
sugar-sweetened beverages in Australia: A challenge for obesity prevention. The American
Journal of Clinical Nutrition, 105(4), 854–863. https://doi.org/10.3945/ajcn.116.145318
Brinkman, W. -P., Haakma, R., & Bouwhuis, D. G. (2009). The theoretical foundation and validity of a
component-based usability questionnaire. Behaviour & Information Technology, 28(2), 121–137.
https://doi.org/10.1080/01449290701306510
Carrasco, M., Ortiz-Maqués, N., & Martínez-Rodríguez, S. (2020). Playing with Nintendo Wii Sports:
Impact on physical activity, perceived health and cognitive functioning of a group of
community-dwelling older adults. Activities, Adaptation & Aging, 44(2), 119–131. https://doi.org/
10.1080/01924788.2019.1595261
Chen, C. -D., Fan, Y. -W., & Farn, C. -K. (2007). Predicting electronic toll collection service adoption: An
integration of the technology acceptance model and the theory of planned behavior.
Transportation Research Part C: Emerging Technologies, 15(5), 300–311. https://doi.org/10.1016/j.
trc.2007.04.004
Cheng, T. C. E., Lam, D. Y. C., & Yeung, A. C. L. (2006). Adoption of internet banking: An empirical
study in Hong Kong. Decision Support Systems, 42(3), 1558–1572. https://doi.org/10.1016/j.dss.
2006.01.002
Chib, A., & Lin, S. H. (2018). Theoretical advancements in mHealth: A systematic review of mobile
apps. Journal of Health Communication, 23(10–11), 909–955. https://doi.org/10.1080/10810730.
2018.1544676
Chiou, J. -S., & Shen, -C. -C. (2012). The antecedents of online nancial service adoption: The impact
of physical banking services on Internet banking acceptance. Behaviour & Information Technology,
31(9), 859–871. https://doi.org/10.1080/0144929X.2010.549509
Cho, J. (2016). The impact of post-adoption beliefs on the continued use of health apps.
International Journal of Medical Informatics, 87, 75–83. https://doi.org/10.1016/j.ijmedinf.2015.
12.016
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. https://doi.org/10.1016/j.dss.2011.12.001
Chung, J. E., Park, N., Wang, H., Fulk, J., & McLaughlin, M. (2010). Age dierences in perceptions of
online community participation among non-users: An extension of the technology acceptance
model. Computers in Human Behavior, 26(6), 1674–1684. https://doi.org/10.1016/j.chb.2010.06.016
Dash, G., & Paul, J. (2021). CB-SEM vs PLS-SEM methods for research in social sciences and
technology forecasting. Technological Forecasting and Social Change, 173, 121092. https://doi.
org/10.1016/j.techfore.2021.121092
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use
computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132. https://
doi.org/10.1111/j.1559-1816.1992.tb00945.x
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information
technology. Management Information Systems Quarterly, 13(3), 319. https://doi.org/10.2307/
249008
De Morais, M. A., De Lima, B. E., & Bandeira Santos, L. C. (2020). Acute eect of Xbox exercise on
mood states in older adults. Activities, Adaptation & Aging, 44(2), 146–156. https://doi.org/10.
1080/01924788.2019.1618689
Deloitte. (2018). Mobile consumer survey 2017.
DiFilippo, K. N., Huang, W. -H., Andrade, J. E., & Chapman-Novakofski, K. M. (2015). The use of mobile
apps to improve nutrition outcomes: A systematic literature review. Journal of Telemedicine and
Telecare, 21(5), 243–253. https://doi.org/10.1177/1357633X15572203
Egea, J. M. O., & González, M. V. (2011). Explaining physicians’ acceptance of EHCR systems: An
extension of TAM with trust and risk factors. Computers in Human Behavior, 27(1), 319–332.
https://doi.org/10.1016/j.chb.2010.08.010
16 T. RASUL ET AL.
Fornell, C., and Larcker, D.F. 1981 Structural equation models with unobservable variables &
measurement error: Algebra and statistics. Journal of Marketing Research, 18 (3), 382–388
Gefen, D., Karahanna, E., and Straub, D.W. 2003 Trust and TAM in online shopping: An integrated
model MIS Quarterly 27 (1) 51–90
Gibson, S. (2008). Sugar-sweetened soft drinks and obesity: A systematic review of the evidence
from observational studies and interventions. Nutrition Research Reviews, 21(2), 134–147. https://
doi.org/10.1017/S0954422408110976
Goodman, J. K., Cryder, C. E., & Cheema, A. (2013). Data collection in a at world: The strengths and
weaknesses of mechanical Turk samples. Journal of Behavioral Decision Making, 26(3), 213–224.
https://doi.org/10.1002/bdm.1753
Gould, S. J. (1988). Consumer attitudes toward health and health care: A dierential perspective.
Journal of Consumer Aairs, 22(1), 96–118. https://doi.org/10.1111/j.1745-6606.1988.tb00215.x
Granwal, L. (2019). Smartphone users in Australia 2015–2022. Statista. https://www.statista.com/
statistics/467753/forecast-of-smartphone-users-in-Australia/
Guo, X., Sun, Y., Wang, N., Peng, Z., & Yan, Z. (2013). The dark side of elderly acceptance of preventive
mobile health services in China. Electronic Markets, 23(1), 49–61. https://doi.org/10.1007/s12525-
012-0112-4
Hair, J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: Updated
guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2),
107–123. https://doi.org/10.1504/IJMDA.2017.10008574
Hair, J. F. (2008). Essentials of marketing research. McGraw-Hill/Higher Education.
Holden, R. J., & Karsh, B. -T. (2010). The technology acceptance model: Its past and its future in health
care. Journal of Biomedical Informatics, 43(1), 159–172. https://doi.org/10.1016/j.jbi.2009.07.002
Hong, S. -J., & Tam, K. Y. (2006). Understanding the adoption of multipurpose information appli-
ances: The case of mobile data services. Information Systems Research, 17(2), 162–179. https://doi.
org/10.1287/isre.1060.0088
Horst, M., Kuttschreuter, M., and Gutteling, J.M. 2007 Perceived usefulness, personal experiences,
risk perception and trust as determinants of adoption of e-government services in The
Netherlands. Computers in Human Behavior 23 (4) 1838–1852
Hosen, M., Ogbeibu, S., Giridharan, B., Cham, T. H., Lim, W. M., & Paul, J. (2021). Individual motivation
and social media inuence on student knowledge sharing and learning performance: Evidence
from an emerging economy. Computers & Education, 172, 104262. https://doi.org/10.1016/j.
compedu.2021.104262
Hung, S. -Y., Chang, C. -M., & Kuo, S. -R. (2013). User acceptance of mobile e-government services: An
empirical study. Government Information Quarterly, 30(1), 33–44. https://doi.org/10.1016/j.giq.
2012.07.008
Jahns, R. -G. (2016, October 11). The mHealth apps market is getting crowded. Research2Guidance.
https://research2guidance.com/mhealth-app-market-getting-crowded-259000-mhealth-apps-
now/
Kalisch, D. W. (2015). National health survey. First results 2014–2015. Australian Bureau of Statistics.
Kao, C. -K., & Liebovitz, D. M. (2017). Consumer mobile health apps: Current state, barriers, and future
directions. PM&R, 9(5S), S106–S115. https://doi.org/10.1016/j.pmrj.2017.02.018
Kesharwani, A., & Tripathy, T. (2012). Dimensionality of perceived risk and its impact on internet
banking adoption: An empirical investigation. Services Marketing Quarterly, 33(2), 177–193.
https://doi.org/10.1080/15332969.2012.662461
Khalifa, M., & Shen, K. N. (2008). Explaining the adoption of transactional B2C mobile commerce.
Journal of Enterprise Information Management, 21(2), 110–124. https://doi.org/10.1108/
17410390810851372
Kim, S. H. (2008). Moderating eects of job relevance and experience on mobile wireless technology
acceptance: Adoption of a smartphone by individuals. Information & Management, 45(6),
387–393. https://doi.org/10.1016/j.im.2008.05.002
King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information &
Management, 43(6), 740–755. https://doi.org/10.1016/j.im.2006.05.003
JOURNAL OF STRATEGIC MARKETING 17
Koenig-Lewis, N., Marquet, M., Palmer, A., & Zhao, A. L. (2015). Enjoyment and social inuence:
Predicting mobile payment adoption. The Service Industries Journal, 35(10), 537–554. https://doi.
org/10.1080/02642069.2015.1043278
Lee, M. K. O., Cheung, C. M. K., & Chen, Z. (2005). Acceptance of Internet-based learning medium: The
role of extrinsic and intrinsic motivation. Information & Management, 42(8), 1095–1104. https://
doi.org/10.1016/j.im.2003.10.007
Lee, Y., Lee, J., & Lee, Z. (2006). Social inuence on technology acceptance behavior: Self-identity
theory perspective. SIGMIS Database, 37(2–3), 60–75. https://doi.org/10.1145/1161345.1161355
Lim, S., Xue, L., Yen, C. C., Chang, L., Chan, H. C., Tai, B. C., Duh, H. B. L., & Choolani, M. (2011). A study on
Singaporean women’s acceptance of using mobile phones to seek health information. International
Journal of Medical Informatics, 80(12), 189–202. https://doi.org/10.1016/j.ijmedinf.2011.08.007
Lim, W. M., Ahmed, P. K., & Ali, M. Y. (2019). Data and resource maximization in business-to-business
marketing experiments: Methodological insights from data partitioning. Industrial Marketing
Management, 76, 136–143. https://doi.org/10.1016/j.indmarman.2018.08.007
Lim, W. M., Gupta, S., Aggarwal, A., Paul, J., & Sadhna, P. (2021). How do digital natives perceive and
react toward online advertising? Implications for SMEs. Journal of Strategic Marketing, 1–35.
https://doi.org/10.1080/0965254X.2021.1941204
Lim, W. M., Rasul, T., & Clarke, R. (2020). A post-publication review of “The challenges involved in
establishing a research technique”. Australasian Journal of Information Systems, 24. https://doi.
org/10.3127/ajis.v24i0.3103
Lim, W. M., & Weissmann, M. A. (2021). Toward a theory of behavioral control. Journal of Strategic
Marketing, 1–27. https://doi.org/10.1080/0965254X.2021.1890190
Lim, W. M. (2015). Antecedents and consequences of e-shopping: An integrated model. Internet
Research, 25(2), 184–217. https://doi.org/10.1108/IntR-11-2013-0247
Lim, W. M. (2018). Dialectic antidotes to critics of the technology acceptance model: Conceptual,
methodological, and replication treatments for behavioural modelling in technology-mediated
environments. Australasian Journal of Information Systems, 22. https://doi.org/10.3127/ajis.v22i0.1651
Lim, W. M. (2019). To what degree is the Fourth Industrial Revolution an opportunity or a threat for
the ASEAN community and region? Modern Applied Science, 13(9), 105–106. https://doi.org/10.
5539/mas.v13n9p105
Lim, W. M. (2021a). A marketing mix typology for integrated care: The 10 Ps. Journal of Strategic
Marketing, 29(5), 453–469. https://doi.org/10.1080/0965254X.2020.1775683
Lim, W. M. (2021b). Conditional recipes for predicting impacts and prescribing solutions for extern-
alities: The case of COVID-19 and tourism. Tourism Recreation Research, 46(2), 314–318. https://
doi.org/10.1080/02508281.2021.1881708
Lim, W. M. (2021c). History, lessons, and ways forward from the COVID-19 pandemic. International
Journal of Quality and Innovation, 5(2), 101–108.
Lin, C.P., and Bhattacherjee, A. (2008) Elucidating individual intention to use interactive information
technologies: The role of network externalities. International Journal of Electronic Commerce 13 (1)
85–108
Linnho, S., & Smith, K. T. (2017). An examination of mobile app usage and the user’s life satisfaction.
Journal of Strategic Marketing, 25(7), 581–617. https://doi.org/10.1080/0965254X.2016.1195857
Lisha, C., Goh, C. F., Yifan, S., & Rasli, A. (2017). Integrating guanxi into technology acceptance: An
empirical investigation of WeChat. Telematics and Informatics, 34(7), 1125–1142. https://doi.org/
10.1016/j.tele.2017.05.003
Lishan, X., Chiuan, Y. C., Choolani, M., & Chuan, C. H. (2009). The perception and intention to adopt
female-focused healthcare applications (FHA): A comparison between healthcare workers and
non-healthcare workers. International Journal of Medical Informatics, 78(4), 248–258. https://doi.
org/10.1016/j.ijmedinf.2008.07.014
Maass, W., & Varshney, U. (2012). Design and evaluation of ubiquitous information systems and use
in healthcare. Decision Support Systems, 54(1), 597–609. https://doi.org/10.1016/j.dss.2012.08.007
McLean, G., & Wilson, A. (2016). Evolving the online customer experience . . . is there a role for online
customer support? Computers in Human Behavior, 60, 602–610. https://doi.org/10.1016/j.chb.
2016.02.084
18 T. RASUL ET AL.
Miltgen, C. L., Popovič, A., & Oliveira, T. (2013). Determinants of end-user acceptance of biometrics:
Integrating the “Big 3” of technology acceptance with privacy context. Decision Support Systems,
56, 103–114. https://doi.org/10.1016/j.dss.2013.05.010
Moores, T. T. (2012). Towards an integrated model of IT acceptance in healthcare. Decision Support
Systems, 53(3), 507–516. https://doi.org/10.1016/j.dss.2012.04.014
Muñoz-Leiva, F., Climent-Climent, S., & Liébana-Cabanillas, F. (2017). Determinants of intention to
use the mobile banking apps: An extension of the classic TAM model. Spanish Journal of
Marketing, 21(1), 25–38. https://doi.org/10.1016/j.sjme.2016.12.001
Naimark, J. S., Madar, Z., & Shahar, D. R. (2015). The impact of a web-based app (eBalance) in promoting
healthy lifestyles: Randomized controlled trial. Journal of Medical Internet Research, 17(3), 56.
Natarajan, T., Balasubramanian, S. A., & Kasilingam, D. L. (2018). The moderating role of device type
and age of users on the intention to use mobile shopping applications. Technology in Society, 53,
79–90. https://doi.org/10.1016/j.techsoc.2018.01.003
Norman, C. D., & Skinner, H. A. (2006). eHEALS: The eHealth literacy scale. Journal of Medical Internet
Research, 8(4), 27. https://doi.org/10.2196/jmir.8.4.e27
Okonji, P. E., Bailey, C., Lhussier, M., & Cattan, M. (2019). Adaptation to loss of visual function:
Implications for rehabilitation on subtle nuances of communication. Activities, Adaptation &
Aging, 43(3), 169–185. https://doi.org/10.1080/01924788.2018.1500056
Okumus, B., Ali, F., Bilgihan, A., & Ozturk, A. B. (2018). Psychological factors inuencing customers’
acceptance of smartphone diet apps when ordering food at restaurants. International Journal of
Hospitality Management, 72, 67–77. https://doi.org/10.1016/j.ijhm.2018.01.001
Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction
decisions. Journal of Marketing Research, 17(4), 460–469. https://doi.org/10.1177/
002224378001700405
Özsungur, F. (2021). The eects of successful aging in the workplace on technology acceptance and
use behaviour. Activities, Adaptation & Aging, 45(2), 159–178. https://doi.org/10.1080/01924788.
2020.1744826
Paul, J., Lim, W. M., O’Cass, A., Hao, A. W., & Bresciani, S. (2021). Scientic procedures and rationales
for systematic literature reviews (SPAR-4-SLR). International Journal of Consumer Studies, 45(4),
O1–O16. https://doi.org/10.1111/ijcs.12695
Podsako, P.M., and Organ, D.W. (1986) Self-reports in organizational research: Problems and
prospects. Journal of Management, 12 (4), 531–544.
Pohl, M. (2017, November 7). 325000 mobile health apps available in 2017-Android now the leading
mHealth platform. Research2guidance. https://research2guidance.com/325000-mobile-health-
apps-available-in-2017/
Ringeval, M., Wagner, G., Denford, J., Paré, G., & Kitsiou, S. (2020). Fitbit-based interventions for
healthy lifestyle outcomes: Systematic review and meta-analysis. Journal of Medical Internet
Research, 22(10), e23954. https://doi.org/10.2196/23954
Sainsbury, E., Hendy, C., Magnusson, R., & Colagiuri, S. (2018). Public support for government
regulatory interventions for overweight and obesity in Australia. BMC Public Health, 18(1), 513.
https://doi.org/10.1186/s12889-018-5455-0
Serwe, K. M., & Walmsley, A. L. (2020). Pilot study of a telehealth delivery method for powerful tools
for caregivers. Activities, Adaptation & Aging, 44(4), 292–308. https://doi.org/10.1080/01924788.
2019.1691313
Sim, J.J., Tan, G.W.H., Wong, J.C., Ooi, K.B., and Hew, T.S. 2014 Understanding and predicting the
motivators of mobile music acceptance–a multi-stage MRA-articial neural network approach.
Telematics and Informatics, 31 (4) 569–584.
Skardziute, A. (2016) The mHealth apps market is getting crowded https://research2guidance.com/
Tabachnick, B.G., and Fidell, L.S. (2012) A practical approach to using multivariate analyses (Boston,
MA: Pearson)
Talmage, C. A., Knopf, R. C., Wu, T., Winkel, D., Mirchandani, P., & Candan, K. S. (2020). Decreasing
loneliness and social disconnectedness among community-dwelling older adults: The potential
of information and communication technologies and ride-hailing services. Activities, Adaptation &
Aging. https://doi.org/10.1080/01924788.2020.1724584
JOURNAL OF STRATEGIC MARKETING 19
Thong, J. Y. L., Hong, S. -J., & Tam, K. Y. (2006). The eects of post-adoption beliefs on the
expectation-conrmation model for information technology continuance. International Journal
of Human-Computer Studies, 64(9), 799–810. https://doi.org/10.1016/j.ijhcs.2006.05.001
Tonkin, E., Jes, L., Wycherley, T. P., Maher, C., Smith, R., Hart, J., Cubillo, B., & Brimblecombe, J. (2017).
A smartphone app to reduce sugar-sweetened beverage consumption among young adults in
Australian remote indigenous communities: Design, formative evaluation and user-testing. JMIR
mHealth and uHealth, 5(12), 192. https://doi.org/10.2196/mhealth.8651
Vartanian, L. R., Schwartz, M. B., & Brownell, K. D. (2007). Eects of soft drink consumption on
nutrition and health: A systematic review and meta-analysis. American Journal of Public Health, 97
(4), 667–675. https://doi.org/10.2105/AJPH.2005.083782
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model:
Four longitudinal eld studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/
mnsc.46.2.186.11926
Venkatesh, V., & Goyal, S. (2010). Expectation disconrmation and technology adoption: Polynomial
modeling and response surface analysis. Management Information Systems Quarterly, 34(2), 281.
https://doi.org/10.2307/20721428
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information
technology: Toward a unied view. Management Information Systems Quarterly, 27(3), 425–478.
https://doi.org/10.2307/30036540
Venkatesh, V., & Morris, M. G. (2000). Why don’t men ever stop to ask for directions? Gender, social
inuence, and their role in technology acceptance and usage behavior. Management Information
Systems Quarterly, 24(1), 115–139. https://doi.org/10.2307/3250981
Venkatesh, V., Thong, J. Y. L., Chan, F. K. Y., Hu, P. J. -H., & Brown, S. A. (2011). Extending the two-stage
information systems continuance model: Incorporating UTAUT predictors and the role of context.
Information Systems Journal, 21(6), 527–555. https://doi.org/10.1111/j.1365-2575.2011.00373.x
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information
technology: Extending the unied theory of acceptance and use of technology. Management
Information Systems Quarterly, 36(1), 157. https://doi.org/10.2307/41410412
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motiva-
tion, and emotion into the technology acceptance model. Information Systems Research, 11(4),
342–365. https://doi.org/10.1287/isre.11.4.342.11872
Walker, G., & Johnson, N. (2008). Faculty intentions to use components for web-enhanced
instruction. International Journal on E-Learning, 7(1), 133–152.
Wang, C. (2014). Antecedents and consequences of perceived value in mobile government con-
tinuance use: An empirical research in China. Computers in Human Behavior, 34, 140–147. https://
doi.org/10.1016/j.chb.2014.01.034
Wei, T. T., Marthandan, G., Yee-Loong Chong, A., Ooi, K., & Arumugam, S. (2009). What drives
Malaysian m-commerce adoption? An empirical analysis. Industrial Management & Data
Systems, 109(3), 370–388. https://doi.org/10.1108/02635570910939399
Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology
acceptance model (TAM) and task technology t (TTF) model. Computers in Human Behavior,
67, 221–232. https://doi.org/10.1016/j.chb.2016.10.028
Wu, I. -L., Li, J. -Y., & Fu, C. -Y. (2011). The adoption of mobile healthcare by hospital’s professionals:
An integrative perspective. Decision Support Systems, 51(3), 587–596. https://doi.org/10.1016/j.
dss.2011.03.003
Wu, J. -H., & Wang, S. -C. (2005). What drives mobile commerce? An empirical evaluation of the
revised technology acceptance model. Information & Management, 42(5), 719–729. https://doi.
org/10.1016/j.im.2004.07.001
Yarbrough, A. K., & Smith, T. B. (2007). Technology acceptance among physicians: A new take on
TAM. Medical Care Research and Review, 64(6), 650–672. https://doi.org/10.1177/
1077558707305942
Yuan, S., Ma, W., Kanthawala, S., & Peng, W. (2015). Keep using my health apps: Discover users’
perception of health and tness apps with the UTAUT2 model. Telemedicine and E-Health, 21(9),
735–741. https://doi.org/10.1089/tmj.2014.0148
20 T. RASUL ET AL.
... In this sense, satisfaction and perceived usefulness drawn from Expectation-Confirmation Model (Bhattacherjee, 2001) have been recognized as two salient determinants of technology continuance intention in the existing literature (Nabavi et al., 2016;Yan et al., 2021). Besides, by extrapolating the Technology Acceptance Model (TAM) (Davis, 1989), Rasul et al. (2021) pointed out that the factors leading to initial technology adoption (e.g., perceived usefulness, perceived ease of use) has evolved under the lens of technology continuance. On the other hand, some researchers argued that technology continuance intention may also be attributed to unconscious factors; for example, continued use of technology may be due to habit, in addition to deliberation and conscious thought (Ashraf et al., 2021;De Guinea & Markus, 2009;Jeyaraj, 2022;Mirkovski et al., 2018). ...
... In the second step, structural equation modeling (SEM) was performed to test the hypotheses in the structural model. In essence, SEM is a statistical technique used to test and estimate causal relationships between variables based on qualitative causal assumptions and statistical data (Rasul et al., 2021;Urbach & Ahlemann, 2010). In addition, covariance-based SEM (i.e., CB-SEM) instead of partial least squares SEM (i.e., PLS-SEM) approach was employed because this study is extending the existing theory (i.e., expectation-confirmation model) and focusing on theory testing (i.e., technology continuance intention for food delivery service), which are consistent with the focus of CB-SEM (Rasul et al., 2021). ...
... In essence, SEM is a statistical technique used to test and estimate causal relationships between variables based on qualitative causal assumptions and statistical data (Rasul et al., 2021;Urbach & Ahlemann, 2010). In addition, covariance-based SEM (i.e., CB-SEM) instead of partial least squares SEM (i.e., PLS-SEM) approach was employed because this study is extending the existing theory (i.e., expectation-confirmation model) and focusing on theory testing (i.e., technology continuance intention for food delivery service), which are consistent with the focus of CB-SEM (Rasul et al., 2021). Meanwhile, PLS-SEM is often used for studies with the objective of theory prediction (Hair Jr et al., 2017). ...
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Advertising was once touted as a 'big boys' marketing tool, but the advent of the Internet has led to the emergence of online advertising , which has benefitted marketing organizations of all sizes, especially small and medium enterprises (SMEs), for 20 years since the new millennium. However, the emergence and proliferation of digital natives as a new generation of consumers necessitate further research to ensure the continued attractiveness, effectiveness, and relevance of online advertising. Furthermore, there is a need to consolidate the multifaceted nature of technology and media in online advertising and the corresponding behavior enacted by consumers, particularly digital natives, today. In this regard, this paper endeavors to examine the perceptions and reactions of digital natives toward online advertising using the theory of planned behavior, technology acceptance model, and uses and gratifications theory as an integrated theoretical lens to guide this examination. Using covariance structural equation modeling on a sample of 380 digital natives, this paper finds that the ease of use, usefulness, entertainment gratification, credibility, design, and personalization of online advertisement positively influence the attitude of digital natives toward online advertisement. However, assurance from online advertisement did not produce any significant effect, a unique finding that exemplifies the confidence that digital natives exhibit in digital environments as a self-assured generation. Finally, this paper finds that the attitude and subjective norms among digital natives toward online advertisement and the security and responsiveness of the website of the online advertised product positively influence the intention of digital natives to purchase the online advertised product. The implications of these findings for theory and practice as well as its limitations and future research directions conclude the paper. ARTICLE HISTORY
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