The current issue and full text archive of this journal is available on Emerald Insight at:

https://www.emerald.com/insight/1468-4527.htm

Modeling the acceptance and Mobile contact

tracing apps
resistance to use mobile contact
tracing apps: a developing
nation perspective 43
Michael Adu Kwarteng Received 18 October 2021
Revised 16 August 2022
Department of Management and Marketing, 3 January 2023
Faculty of Management and Economics, Tomas Bata University in Zlin, Accepted 14 February 2023
Zl
ın, Czech Republic
Alex Ntsiful
Department of Business Administration,
Faculty of Management and Economics, Tomas Bata University in Zlin,
Zl
ın, Czech Republic
Christian Nedu Osakwe
Rabat Business School, International University of Rabat, Rabat, Morocco;
Department of Customs Affairs, South Ural State University, Chelyabinsk, Russia and
University of Pretoria’s Gordon Institute of Business Science,
Johannesburg, South Africa, and
Kwame Simpe Ofori
Department of Business Administration, Ho Technical University, Ho, Ghana

Abstract
Purpose – This study proposes and validates an integrated theoretical model involving the theory of planned
behavior (TPB), health belief model (HBM), personal norms and information privacy to understand determinants
of acceptance and resistance to the use of mobile contact tracing app (MCTA) in a pandemic situation.
Design/methodology/approach – This study draws on online surveys of 194 research respondents and
uses partial least squares structural equation modeling (PL-SEM) to test the proposed theoretical model.
Findings – The study establishes that a positive attitude towards MCTA is the most important predictor of
individuals’ willingness to use MCTA and resistance to use MCTA. Furthermore, barriers to taking action
positively influence resistance to the use of MCTA. Personal norms negatively influence resistance to the use of
MCTA. Information privacy showed a negative and positive influence on willingness to use MCTA and use the
resistance of MCTA, respectively, but neither was statistically significant. The authors found no significant
influence of perceived vulnerability, severity, subjective norms and perceived behavioral control on either
acceptance or use resistance of MCTA.
Originality/value – The study has been one of the first in the literature to propose an integrated theoretical
model in the investigation of the determinants of acceptance and resistance to the use of MCTA in a single
study, thereby increasing the scientific understanding of the factors that can facilitate or inhibit individuals
from engaging in the use of a protection technology during a pandemic situation.

© Michael Adu Kwarteng, Alex Ntsiful, Christian Nedu Osakwe and Kwame Simpe Ofori. Published by
Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY
4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for Online Information Review
Vol. 48 No. 1, 2024
both commercial and non-commercial purposes), subject to full attribution to the original publication pp. 43-66
and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/ Emerald Publishing Limited
1468-4527
legalcode DOI 10.1108/OIR-10-2021-0533
OIR Peer review – The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-
10-2021-0533
48,1 Keywords Digital contact tracing, Health belief model, Theory of planned behavior, Personal norm,
Information privacy, Use resistance
Paper type Research paper

44 1. Introduction
Considering the pandemic and especially ways the spread of the coronavirus and its variants
can be adequately monitored and controlled, numerous scholars and industry practitioners
(Alsaad and Al-Okaily, 2021; Duan and Deng, 2021; Munzert et al., 2021; Saw et al., 2021;
Trang et al., 2020; Viktor et al., 2020; Walrave et al., 2020) have suggested the importance of
integrating mobile contact tracing apps (MCTA) into the existing COVID-19 disease
surveillance system. Ntsiful et al. (2022) briefly explain that the MCTA is an application
downloadable on smart devices and uses Bluetooth to warn people when they come close to
SARS-CoV-2 contagious areas or infected persons. Indeed, the MCTA uses Bluetooth and
location services that are compatible with android and apple devices and works as follows:
When the MCTA user comes into contact with a person with a smart device anchored with
Bluetooth, the two devices automatically communicate and exchange codes via Bluetooth.
When later a person with whom the MCTA user had contacted is declared COVID-19 positive,
the Bluetooth of the infected person shares this information with all Bluetooth that had come
into contact with the exchanged codes. Further, the location service on the MCTA user’s
device also records and saves all the places the user visits so that when those places are
declared COVID-19 contagious, the MCTA user can also know. Both privacy information
shared by Bluetooth and the location services are essential for the MCTA user to self-isolate
and, by extension, reduce the spread of the COVID-19 virus. The MCTA has also been shown
to be helpful to human contact tracers (Lu et al., 2021). Extant studies have explained that
although human tracers provide additional benefits such as health advice and emotional
reassurance to the infected persons, the procedures involved are cumbersome and saddled
with reporting inaccurate data (Braithwaite et al., 2020; Lu et al., 2021). These deficiencies are
minimized with the use of MCTA.
While the idea behind the introduction of this app is excellent, an important concern
remains whether the intended users and, in particular, citizens intend to use the MCTA,
especially given the existential worries about a potential breach of privacy (Rowe, 2020;
Villius Zetterholm et al., 2021). Although studies on this important topic (MCTA) are in their
infancy, contributions of prior research (Duan and Deng, 2021; Lin et al., 2021; Trang et al.,
2020) deserve acknowledgment as they shape our understanding on individuals’ intention to
use or resist the MCTA. Despite these prior contributions on the issue, our review of the
emerging studies on this topic reveals that studies have been primarily focused on the citizen
contexts in very few parts of the world, such as Australia (Duan and Deng, 2021; Lin et al.,
2021), Western European countries (Fox et al., 2021; Guillon and Kergall, 2020; Oldeweme
et al., 2021; Tomczyk et al., 2021; Trang et al., 2020; Walrave et al., 2020), the US (Hassandoust
et al., 2021; Li et al., 2021) and Asian countries such as South Korea, Jordan, China and
Singapore (Alsaad and Al-Okaily, 2021; Garrett et al., 2021; Joo and Shin, 2020; Kim, 2021;
Saw et al., 2021). In particular, until now, there has been scant research on MCTA acceptance
or resistance in developing countries such as those in the African continent and where only
(about) 1% of its population remains vaccinated to date (The New York Times, 2021) due
mostly to low vaccine supplies (Otu et al., 2021; The Economist Intelligence Unit [EIU], 2021)
and partly also to vaccine hesitancy (Cooper et al., 2021; Reuters, 2021; Sisay, 2021). Second,
the review of the existing studies also shows that prior studies have only concentrated on the
acceptance or adoption of the MCTA without recourse to what might fuel the citizenry to
resist this innovation to tackle a critical situation in which the world finds itself. Following Mobile contact
these findings, we see a considerable gap in the literature that needs to be filled. tracing apps
To contribute our quota to fill this critical gap in the literature, we set the objective in this
study to explore the factors influencing the acceptance of MCTA, especially in developing
economies, where scholars are yet to pay much attention, as earlier highlighted. At the same
time, we also explore what could account for the citizenry’s unwillingness to use this digital
technology in the fight against COVID-19. In achieving these two objectives, we draw on
several theories’ strengths and propose and validate a comprehensive research model 45
involving the theory of planned behavior (TPB), health belief model (HBM), and contextual
variables such as information privacy and personal norms. While the single utility of TPB
and HBM has been useful in studies where they were applied, we believe that the proposal to
integrate and extend them with personal norms and information privacy is important for
several reasons:
(1) One theory could not help explain the two-way outcome (acceptance and resistance)
our study seeks to achieve. The integration of TPB and HBM, which we will elaborate
on in section 2.3, has been proven effective in several health-related studies (Gerend
and Shepherd, 2012). Apart from their integration, we also extend the two theories
with privacy information and personal norms. These two variables are critical in
technology acceptance and decisions. As highlighted earlier, although people may see
the relevance of MCTA, such as an opportunity to detect COVID-19 infection early,
individual personal norms and the privacy issues with this digital technology need to
be explored.
(2) By adopting an integrated theoretical approach, we deviate from prior studies whose
models or concepts were either based on a mere literature review or, at best, applying
one theory. In particular, most of the research so far on MCTA acceptance has been
anchored on a single theoretical perspective (Alsaad and Al-Okaily, 2021[protection
motivation theory]; Joo and Shin, 2020 [coping theory]; Oldeweme et al., 2021
[uncertainty reduction theory]; Walrave et al., 2020 [HBM]) or based on selected
variables in the literature (Guillon and Kergall, 2020; Li et al., 2021; Saw et al., 2021;
Trang et al., 2020).
(3) Applying the integrated theoretical perspective will provide rich insights into the
topic, which no single theoretical model may provide.
Accordingly, this study offers many contributions to the body of knowledge. Apart from
being the first or at least part of the few studies attempting to integrate two theoretical
paradigms (TPB and HBM), we extend these theories with information privacy and personal
norms in explaining the dynamics in MCTA acceptance and resistance in a single study.
Second, we also offer perspectives on the motivating factors of MCTA acceptance and, at the
same time, what could cause citizenry resistance to MCTA. Additionally, the findings from
our research could provide governments, especially those in underrepresented countries,
with full scientific knowledge and rich health information about MCTA acceptance and
resistance. This information helps the policymakers draft effective policy implementation
messages that help MCTA’s acceptance and reduce its rejection in the pandemic context.
The rest of the paper is organized as follows: Section 2 reviews the relevant theoretical
perspectives and then forms hypotheses based on them. Section 3 describes the method used
to gather data for the study, while the analysis and results are reported in Section 4. The last
section concludes by discussing our findings, research implications and limitations of the
current research, which also offers an opportunity for additional investigation on this
important topic.
OIR 2. Theoretical background and hypotheses development
48,1 2.1 Theory of planned behavior (TPB)
The theory of planned behavior (TPB) explains the processes of human behavior (Ajzen,
2011). According to Ajzen (2011), the basic assumption of TPB is that behavior is motivated
by one’s intention, which is also influenced by subjective norms, attitude and perceived
behavioral control. Indeed, TPB offers rich insights into the understanding of human
behavior, is very parsimonious in its assumption, and has shown strong predictive power in
46 most extant studies. Accordingly, TPB has been applied in several prior information systems
studies (research (Fan et al., 2021; Hsieh, 2015; Shmueli, 2021). It is against this background
that we believe that adopting TPB in the current research context will be useful in helping us
understand the factors affecting the acceptance or resistance to the use of MCTA, more so
that we are dealing with the issue of voluntary adoption as recommended by WHO (2020).
According to this theory, an attitude refers to either a negative or positive evaluation of an
object and, in this specific context, MCTA (Ajzen, 2011; Armitage and Conner, 2001). This,
therefore, implies that a positive evaluation of MCTA will go a long way in determining its
eventual acceptance (cf. Zhao et al., 2018), while further reducing resistance to using the
technology (Kim and Park, 2020). This accordingly leads to the formulation of the following
hypotheses:
H1a. Attitude has a positive influence on the intention to use MCTA.
H1b. Attitude has a negative influence on resistance to the use of MCTA.
The TPB construct of subjective norm refers to the influence of significant others on an
individual’s engagement in a particular course of action (Ajzen, 2011; Armitage and Conner,
2001; Fortes and Rita, 2016; Sun et al., 2013; Venkatesh and Bala, 2008). Indeed, human beings
are not only a part of a larger society but also belong to closely knitted groups such as the
nuclear and extended families and peer groups. They are, therefore, consistently subject to
social pressures from others, which could lead to the formation of behavioral responses
towards a specific object. In summary, subjective norm, which is borne out of conformity
pressure or social influence, can promote acceptance decisions related to new technologies
use (Fortes and Rita, 2016; Hsieh, 2015; White Baker et al., 2007; Zhao et al., 2018) and further
reduce resistance to using (Matsuo et al., 2018). Accordingly, we argue that individuals are
subject to social pressures, which primarily inform their choices and decision-making.
Consequently, we expect subjective norms will influence the behavioral responses
(acceptance and resistance) to MCTA (Van Offenbeek et al., 2013). Specifically, we expect
subjective norms to have the opposite influence on adoption intention and resistance to using
MCTA. This leads us to the following set of hypotheses:
H2a. Subjective norms have a positive influence on the intention to use MCTA.
H2b. Subjective norms have a negative influence on resistance to the use of MCTA.
Following previous research, perceived behavioral control reflects the degree to which an
individual perceives that successfully engaging in the behavior is entirely under their
volitional control (Ajzen, 2011; Armitage and Conner, 2001). In context, perceived behavioral
control reflects the degree to which the user believes using an MCTA is easy or difficult.
We argue that the behavioral response to MCTA will be positively affected by adequate
access to the necessary resources needed for the deployment of MCTA, as well as the
willpower to exercise control over it (Ajzen, 2011). Concerning the empirical relationship
between perceived behavioral control and use intentions, several studies (e.g. Hsieh, 2015)
establish a positive link between perceived behavioral control and intention. Additionally,
consistent with earlier research (Ellen et al., 1991; Mani and Chouk, 2017), the current argues
that perceived behavioral control may reduce resistance to using MCTA. We explain that the
individuals may perceive that they would have control over using the MCTA, lowering the Mobile contact
likelihood of resisting it. Accordingly, we formulate the following set of hypotheses. tracing apps
H3a. Perceived behavioral control has a positive influence on the intention to
use MCTA.
H3b. Perceived behavioral control has a negative influence on resistance towards
using MCTA.
47
2.2 Health belief model (HBM)
HBM is one of the most widely adopted theories in the literature used in studying preventive
health behavior (Carpenter, 2010; Huang et al., 2020a, b; Janz and Becker, 1984; Mou et al.,
2016; Rosenstock, 1974; Shmueli, 2021; Sulat et al., 2018) and it has also been recently applied
in the investigation of MCTA acceptance (Guillon and Kergall, 2020; Walrave et al., 2020).
According to influential HBM authors (Janz and Becker, 1984; Rosenstock, 1974; Rosenstock
et al., 1988), factors such as perceived barriers to taking action, perceived disease severity and
perceived disease vulnerability affect a person’s decision to engage in preventive health
behavior. Overall, HBM is an explanatory framework used in understanding “which beliefs
should be targeted in communication campaigns to cause positive health behaviours”
(Carpenter, 2010, p. 661).
Perceived barriers to taking action refer to the underlying circumstances that may hold
back the individual from engaging in preventive health behavior (Janz and Becker, 1984; Jose
et al., 2021; Sulat et al., 2018; Walrave et al., 2020). In our research context, the underlying
circumstances of MCTA may be related to individuals’ forgetfulness to use MCTA,
especially when they are in public spaces, and further borne by the perceived busyness of the
individual. All these issues, therefore, may hinder the individual from engaging in the use of
MCTA. Notably, it has been found by empiricists such as Walrave et al. (2020) that perceived
barriers to taking action negatively influence the adoption intention of MCTA and rather
imply that it exacerbates resistance to the use of MCTA. Considering the above, as well as
empirical findings in numerous other study contexts (Al-Metwali et al., 2021; Carpenter,
2010; Lu et al., 2019; Mou et al., 2016; Sulat et al., 2018), it is reasonable to expect perceived
barriers of taking action to reduce use intention of MCTA while further strengthening the
resistance to use the app. In sum, the above discussion leads us to the following set of
hypotheses:
H4a. Perceived barriers to taking action negatively influence the intention to use MCTA.
H4b. Perceived barriers to taking action positively influence resistance to use MCTA.
Furthermore, regarding perceived disease severity and about COVID-19, this refers to
feelings concerning the serious threat that COVID-19 imposes on the individual’s health
and its related dire consequences such as death and ‘long COVID” (alternatively referred
to as post-COVID syndrome (see also Huang et al., 2020a, b; Janz and Becker, 1984; Sulat
et al., 2018). According to previous research (Carpenter, 2010; Lu et al., 2019; Sulat et al.,
2018; Yu et al., 2021), perceived disease severity has a positive influence on the
individual’s willingness to engage in preventive health behavioral outcomes, and such as
in this case, willingness to adopt MCTA. Based on these findings, it is expected that
perceived disease severity will not only positively lead to a willingness to use MCTA but
also inhibit resistance to using MCTA. Consequently, we formulate the following set of
hypotheses:
H5a. Perceived disease severity positively influences the intention to use MCTA.
H5b. Perceived disease severity negatively influences resistance to the use of MCTA.
OIR For the HBM component of perceived disease vulnerability, scholars such as Zhao et al. (2018:
48,1 245) define it as the “judgement that one will feel that his/her health is being threatened [by
COVID-19]”. In the context of this study, perceived disease vulnerability reflects the
perception of a relatively high risk of contracting COVID-19 disease. Empirically, several
studies have found support that perceived disease vulnerability, also known as perceived
susceptibility to a disease, has a positive influence on individuals’ willingness to engage in
preventive health behaviours (Huang et al., 2020a, b; Janz and Becker, 1984; Lu et al., 2019;
48 Mou et al., 2016). This also implies that perceived disease vulnerability could significantly
lower barriers to MCTA adoption. Based on extant studies (e.g. Walrave et al., 2020), we argue
that individuals who perceive that they are at high risk of contracting the COVID-19 virus will
be more likely to use the MCTA, and by extension, reducing their initial resistance to using
the app. This, thus, leads to the formulation of the following set of hypotheses:
H6a. Perceived disease vulnerability has a positive influence on the intention to
use MCTA.
H6b. Perceived disease vulnerability has a negative influence on the resistance to the use
of MCTA.

2.3 TPB and HBM integration

Although the two theories (TPB and HBM) differ in many respects: scope, parsimony and
explanatory power, several shared parameters make their integration a good fit for our study.
First, the two theories relate to human or social cognition (Bish et al., 2000). For instance, while
the TPB construct of attitude involves a person’s stance, which could be either positive or
negative following a cognitive appraisal of an event or situation (Ajzen, 2011; Armitage and
Conner, 2001), the perceived barriers to taking action, perceived disease severity and the
perceived disease vulnerability constructs in HBM are all perception based. Second, the TPB
and HBM theories are ideal for social inoculation studies. Social inoculation is a technique
used to change attitudes, which involves gradually exposing subjects (people) to the dummy
version of a stronger future threat that may befall them (Evans and Getz, 2003; McGuire,
1964; Traberg et al., 2022) and in this context, exposing the citizenry to viewing videos of
people who have died or suffered from COVID-19 pandemic. Third, TPB and HBM
integration have been applied in many research, particularly in health-related studies
(Barattucci et al., 2022; Bish et al., 2000; Gerend and Shepherd, 2012; Huang et al., 2020a,
2020b; McClenahan et al., 2007; Taylor et al., 2006). In most of these studies, the utility and
efficiency of TPB and HBM integration have been remarkably symbiotic. For instance, in
using TPB and HBM to predict testicular self-examination behavior, McClenahan et al. (2007)
found that both models were similar in the quality of results they produced. Given these
similarities, we believe that integrating the two theories is justified, and their application in
the current study will produce more valid results.

2.4 Contextual factors (personal norm and information privacy)

The personal norm has been defined as the “feelings of moral obligation to perform or refrain
from specific actions” (Schwartz and Howard, 1981, p. 191). Roos and Hahn (2017), while
referring to previous research, noted that personal norms “represent an individual’s own
moral obligation or responsibility to perform, or not to perform a behaviour, beyond
perceived social pressure” (p. 115). Based on the above studies, we argue that individuals who
score high on personal norms will be more likely to use and less likely to resist using the
MCTA because they will perceive the app as technology that will protect them from
contracting COVID-19 (Alsaad and Al-Okaily, 2021). Indeed, research on driving safety has
found that personal norms, which Kim (2018) refers to as personal moral norms, inhibit
texting while driving. Similarly, the studies by Maity et al. (2019) and Udo et al. (2016) logically Mobile contact
established that personal norm have a negative influence on intentions to engage in digital tracing apps
piracy. Moreover, other studies have found support for the positive implication of personal
norms in engendering acceptable and desirable behaviors in society (Ateş, 2020; Juraskova
et al., 2012; Maity et al., 2019; Roos and Hahn, 2017, 2019) and further reinforces our
expectation that personal norms will be critically related to acceptance and resistance to the
use of MCTA. Based on the above review, the following hypotheses are proposed:
49
H7a. Personal norms have a positive influence on the intention to use MCTA.
H7b. Personal norms have a negative influence on resistance to using MCTA.
Information privacy: Consistent with extant studies (Mani and Chouk, 2019; B
elanger and
Crossler, 2011; Mutimukwe et al., 2020), information privacy is defined as the high level of
concern individuals have for privacy and the desire to protect their personal data. In the
context of MCTA and largely considering its novelty as well as the extremely difficult times
the world is currently going through, people are bound to be even more concerned about how
their personal information will be processed, stored and managed by a third-party (Rowe,
2020). Some experts, including international organizations such as Amnesty International,
have warned that using MCTA can considerably violate individuals’ privacy (Litan and
Lowy, 2020). Indeed, multiple studies in the literature suggest that the issue of information
privacy poses a significant barrier to digital health adoption, including the use of MCTA
(Dhagarra et al., 2020; Duan and Deng, 2021; Fortes and Rita, 2016; Fox, 2020; Sergueeva et al.,
2020; Tomczyk et al., 2021) and consequently leading to either a low adoption rate (cf. Li et al.,
2021) or resistance of MCTA (Chan and Saqib, 2021). For instance, Dhagarra et al. (2020)
specifically found that information privacy negatively influences the use intentions of digital
healthcare services. Meanwhile, Chan and Saqib (2021) also found that information privacy is
associated with individuals’ unwillingness to download and use MCTA. It is argued that
when members of the general public perceive that their privacy is more likely to be infringed
upon by adopting MCTA, acceptance of MCTA is expected to be low (Fox et al., 2021) and
therefore exacerbating the use resistance of MCTA. Nonetheless, it might also be interesting
to know whether the current COVID-19 situation provides an extraordinary context for
assessing if individuals might be willing to tradeoff their concerns over privacy with the
expected ingrained benefits of MCTA, which scholars refer to as the privacy dilemma (Fox,
2020; Fox et al., 2021). Simultaneously, there are preliminary findings within the US and
Germany that the decision to adopt MCTA may be seriously hampered by citizens’ concern
over privacy (Kaptchuk et al., 2020; Trang et al., 2020). Taken together, the authors formulate
the following set of hypotheses:
H8a. Information privacy has a negative influence on the intention to use MCTA.
H8b. Information privacy has a positive influence on resistance to the use of MCTA.
In summary, the research hypotheses are captured in our proposed model in Figure 1.

3. Methodology
3.1 Empirical setting, data collection procedure and sample
The empirical setting for the study was Ghana, which was selected because, according to a
section of the media, the country’s government has developed an app-termed as GH
COVID-19 Tracker app-that will enable them to trace members of the public that might have
had close contact with COVID-19 patients (CGTN Africa, 2020; ITU News, 2020).
Furthermore, Ghana is also one of the first countries to come out of national lockdown
after only three weeks of strict lockdown in two of its most prominent regions, i.e. Greater
OIR Theory of Planned Behavior Health Belief Model
48,1 Perceived disease
Subjective norms severity

Intention to use
Perceived barriers
Attitude of taking action

50 Resistance to use
Perceived Perceived disease
behavioral control vulnerability

Contextual issues Controls

• Personal norms *Gender *Age *Educational
Figure 1.
• Information privacy Attainment
Proposed MCTA
theoretical model
Source(s): Author’s own, 2023

Accra and Kumasi regions and effective (digital) contact tracing is critical in order not to
overwhelm the country’s relatively weak health sector. As of August 8, 2021, the current
pandemic has resulted in about 107,000 confirmed cases in the country but with 854 deaths
(WHO dashboard). While the current fatality rate in Ghana is understandably lower than in
other developing nations such as India, Brazil and South Africa, there is genuine concern that
the actual number of cases could have exceeded the reported cases, especially because of
limited testing capacities in the country. Moreover, places such as Ghana cannot afford to
have a higher number of cases due to its relatively weak healthcare system and the
persistence of poverty among several households in the country reliant on daily income for
their survival. Additionally, the impact of the current crisis, especially in Africa and Ghana,
could be very devastating for citizens due to the lack of social safety nets. In addition to the
above, far more important is the worrying concern regarding the notion that countries such
as Ghana might be able to achieve herd immunity against COVID-19 until about 2024 since
these countries are currently lacking sufficient supplies of vaccines. All these, we believe,
make Ghana an adequate case for the current study.
In this study, the data collection was done using online surveys, which is congruent with
related research (Alsaad and Al-Okaily, 2021; Duan and Deng, 2021). Moreover, due to ethical
considerations, it was imperative to use online surveys. The online survey was created using
Google Forms to facilitate the data collection process; we solicited support from individuals
online and group administrators who assisted by sharing the survey link within their
extended network and group members. Therefore, the current study relied on snowball
sampling. At the end of the online survey exercises, we recruited 232 respondents. Out of the
232 received responses, 38 were discarded due to incomplete responses, and thus, 194 were
used for the analysis. Details about the research respondents can be found in Table 1.

3.2 Measures
In this study, we used existing and validated measurement scales. Still, we were also acutely
aware that in these extraordinary times, it is important not to overburden respondents with too
many questions, and thus, where possible, we used shortened scales. To avoid common method,
bias due to the context-specific modifications to the scales used, we revalidated the scales.
Specifically, the entire questionnaire was handed over to fifteen academics comprising five (5)
Attribute Categorization Frequency
Mobile contact
tracing apps
Gender Female 74
Male 117
Prefer not to say 3
Age 18–25 46
26–30 47
31–39 72 51
40–50 24
Above 50 5
Educational attainment Basic/Primary school –
High School 14
HND/Diploma 29 Table 1.
Bachelor degree 99 Characteristics of
Postgraduate 52 research
Source(s): Author’s own, 2023 respondents (N 5 194)

Ph.D. students and ten (10) academics from the marketing and management discipline as pre-
testing before dissemination. Comments and recommendations ranging from sentence structure,
wording and consistency were noted and amended accordingly. This revalidation process helped
to ensure that the clarity, face and content validity of the entire questionnaire was guaranteed.
A list of the measures and their main sources in the literature can be found in Appendix A.
Finally, we controlled the influence of extraneous factors and particular demographics (see also
Figure 1) since they could play a role in decisions regarding the acceptance of MCTA.

3.3 Common method of bias assessment

To proactively counter the possibility of common method bias (CMB) in the study, we
considered recommendations from several past pieces of literature, and we provided general
information about MCTA. Relatedly, in the header section of the online questionnaire,
respondents were informed about the broader objective of the study. Still, for good reasons,
we still need to provide details regarding the constructs or the relationships being
investigated. Furthermore, we informed respondents that only their aggregated information
would be used in the study while assuring them that their information would only be used for
scientific analysis. Similarly, we told respondents that there were no right or wrong answers
to the questions asked, and that participation was voluntary. This study also used different
response anchors ranging from very unlikely to very likely, very low to very high and
strongly disagree to strongly agree. Besides this, we also examined evidence about CMB by
employing Harman’s factor technique, and our finding revealed the most dominant factor’s
variation to be about 23.75%. Several factors equally emerged from the analysis whose
eigenvalues exceeded 1. Furthermore, we used the full collinearity approach as an additional
benchmark for evaluating the presence of CMB; our results based on Kock’s (2015)
recommendation revealed that none of the VIF values, at the manifest item or construct level,
exceeded the conservative figure of 3.3. Thus, we conclude that CMB does not pose any risk to
the interpretation of research results.

3.4 Analytical approach

Statistically, our research objective is to maximize explained variance in the targeted
construct(s) and, in this context, use intentions and resistance to use MCTA, which according
to researchers, notably Hair et al. (2020), is more suited for component-based SEM, which is a
reason we further employed ADANCO software (Henseler, 2017).
OIR 4. Results
48,1 4.1 Measurement model assessment
Before assessing the structural model, we evaluated the quality criteria of the outer model.
Considering this, we followed the usual recommendations in the literature (Hair et al., 2020) by
assessing first factor loadings, and our results indicate that all factor loadings are above 0.707
except for PST 3, which has a loading of 0.652 (Table 2) and are all statistically significant at
p < 0.001. We then assessed the Cronbach’s alpha (CA) coefficient, composite reliability (CR) and
52 average variance extracted (AVE) scores. The results supported the convergent validity of
measurement variables (Table 2); in particular, the scores for CA, CR and AVE all met the
conservative thresholds used in evaluating them in the literature (for details, see Hair et al., 2020).
Furthermore, we followed the recommendations by Fornell and Larcker (1981) and
Henseler et al. (2015) to assess whether the measurement variables are conceptually distinct,
i.e. test for discriminant validity. Our results based on Fornell and Larcker’s approach,

Constructs/items Loading Mean Var CA CR AVE

Intention to use 0.914 0.960 0.921

USE1 0.957 3.278 1.601
USE2 0.962 3.335 1.561
Attitude 0.860 0.935 0.877
ATTI 0.933 3.289 1.222
ATT2 0.940 3.608 1.193
Perceived disease severity 0.798 0.886 0.727
PDS1 0.933 3.345 1.429
PDS2 0.940 3.438 1.678
PDS3 0.652 3.711 1.388
Perceived disease vulnerability 0.842 0.903 0.757
PDV1 0.875 3.227 1.482
PDV2 0.895 3.443 1.419
PDV3 0.840 3.103 1.399
Perceived barriers of taking action 0.853 0.932 0.872
PBT1 0.933 2.964 1.486
PBT2 0.935 3.041 1.470
Information Privacy 0.877 0.918 0.789
IPC 1 0.878 3.345 1.429
IPC 2 0.858 3.438 1.678
IPC 3 0.927 3.711 1.388
Personal norms 0.905 0.940 0.840
PNM1 0.885 3.412 1.321
PNM2 0.952 3.516 1.153
PNM3 0.912 3.598 1.309
Subjective norms 0.903 0.939 0.837
SBN1 0.892 3.010 1.347
SBN2 0.932 3.258 1.436
SBN3 0.919 3.268 1.348
Perceived behavioral control 0.737 0.847 0.649
PBC1 0.825 3.727 0.873
PBC2 0.812 3.407 1.103
PBC3 0.779 3.407 1.217
Resistance to use 0.834 0.900 0.750
Table 2.
Measurement model’s RES1 0.886 2.381 0.963
descriptive statistics RES2 0.883 2.237 1.021
and convergent RES3 0.827 2.392 1.006
validity assessment Note(s): Var. (Variance); Cronbach’s alpha (CA); Composite reliability (CR); Average variance extracted (AVE)
criteria Source(s): Author’s own, 2023
support discriminant validity (Panel A, Table 3). We also have support from the heterotrait– Mobile contact
monotrait (HTMT) correlational approach (Henseler et al., 2015), especially as none of the tracing apps
HTMT values exceeded 0.9 (see Panel B in Table 3) (see also Benitez et al., 2020; Franke and
Sarstedt, 2019). Moreover, based on HTMT inference, we find that none of the values included
one (see also Benitez et al., 2020; Osakwe, 2019); it can be concluded, therefore, that there is
evidence for discriminant validity. Additionally, although unreported due to space
constraints, visual inspection of the cross-loadings showed that all loadings were loaded
primarily into their given constructs, as no significant cross-loadings were clear. Taken 53
together, our measurement model meets all reasonable quality criteria, and thus, we can
subsequently move to the assessment of the structural model.

4.2 Structural model assessment

Based on the recommendations of researchers such as Benitez et al. (2020), we examined the
fit indices for both the estimated and saturated models. Results generally indicate adequate
model fit (see Table 4), especially as the standardized root mean squared residual (SRMR),
unweighted least squares (euclidean) distance (dULS) and the geodesic distance (dG) values
were within reported acceptable limits in the literature (see Benitez et al., 2020 for details). In
other words, Table 4 implies that our model cannot be rejected and further indicates that the
proposed theoretical model is potentially useful for accounting for the impact of the TPB and

Construct 1 2 3 4 5 6 7 8 9 10

Panel A: Fornell-Larcker criterion

1. Perceived severity 0.727
2. Perceived 0.181 0.757
vulnerability
3. Attitude 0.038 0.109 0.877
4. Subjective norms 0.004 0.075 0.226 0.837
5. Personal norms 0.034 0.125 0.515 0.166 0.840
6. Resistance use 0.001 0.029 0.054 0.034 0.020 0.789
7. Information Privacy 0.000 0.032 0.188 0.031 0.164 0.000 0.750
8. Barriers to taking 0.002 0.000 0.006 0.007 0.000 0.023 0.094 0.872
action
9. Intention to use 0.041 0.091 0.635 0.133 0.330 0.017 0.181 0.000 0.921
10. Perceived behavioral 0.034 0.129 0.160 0.061 0.169 0.147 0.015 0.000 0.0730 0.649
control
Panel B: HTMT criterion
1. Perceived severity
2. Perceived 0.529
vulnerability
3. Attitude 0.242 0.384
4. Subjective norms 0.058 0.303 0.537
5. Personal norms 0.224 0.405 0.807 0.451
6. Resistance use 0.032 0.165 0.248 0.176 0.131
7. Information Privacy 0.005 0.205 0.507 0.204 0.460 0.003
8. Barriers to taking 0.058 0.020 0.093 0.103 0.018 0.153 0.349
action
9. Intention to use 0.237 0.336 0.899 0.394 0.624 0.128 0.484 0.018
10. Perceived behavioral 0.237 0.436 0.493 0.272 0.498 0.456 0.145 0.037 0.317
control Table 3.
Note(s): The diagonal elements in Panel A indicates square roots of AVEs. CV stands for control variable Discriminant validity
Source(s): Author’s own, 2023 results
OIR HBM constructs and information privacy and personal norms on use intentions and
48,1 resistance to using MCTA.
Next, we assessed the coefficient of determination (R2) and significance of the path coefficients
based on bootstrapping procedure of 4,999 resampled. The R2 indicated that the model could
explain 65.1% of use intentions. Further, the model explains 35.2% variation in resistance to
using MCTA. According to Table 5, of the proposed predictors of use intentions of MCTA, only
attitude toward MCTA emerged as the most positive and significant predictor of use intentions of
54 MCTA and thus providing statistical support for H1a. Amongst the most important predictors of
resistance to the use of MCTA were attitude, perceived barriers to taking action and personal

Saturated model Estimated model Conclusion

Discrepancy Value HI95 HI99 Value HI95 HI99

SRMR 0.057 0.045 0.069 0.057 0.046 0.070 Supported

dULS 1.503 0.958 2.230 1.514 0.975 2.295 Supported
dG 0.846 1.294 1.809 0.848 1.290 1.812 Supported
Table 4. Note(s): Standardized root mean squared residual (SRMR); Euclidean distance (dULS) and the Geodesic
Results of the overall distance (dG)
model fit statistics Source(s): Author’s own, 2023

Relations B Std. Err T-value p-value Supported Cohen’s f2 R2

ATT → USE 0.832 0.075 11.099 0.000 U 0.815 USE 5 65.06%

ATT → RES 0.415 0.092 4.523 0.000 U 0.110 RES 5 35.21%
PNM→ USE 0.004 0.068 0.063 0.475 x 0.000
PNM→ RES 0.178 0.095 1.871 0.031 U 0.021
PBT→ USE 0.046 0.048 0.978 0.164 x 0.005
PBT→ RES 0.334 0.067 4.998 0.000 U 0.152
PDS→ USE 0.052 0.048 1.081 0.126 x 0.006
PDS → RES 0.098 0.085 1.147 0.140 x 0.011
PDV → USE 0.031 0.055 0.563 0.287 x 0.002
PDV → RES 0.074 0.080 0.925 0.177 x 0.005
PBC → USE 0.071 0.051 1.403 0.080 x 0.009
PBC → RES 0.112 0.0816 1.375 0.085 x 0.012
SBN → USE 0.005 0.043 0.123 0.451 x 0.000
SBN → RES 0.032 0.078 0.409 0.341 x 0.001
IPC → USE 0.045 0.054 0.831 0.203 x 0.004
IPC → RES 0.054 0.075 0.723 0.235 x 0.003
Control variables
Age→ USE 0.009 0.051 0.173 0.862 x 0.000
Age → RES 0.028 0.076 0.366 0.714 x 0.001
Edu → USE 0.039 0.039 0.054 0.475 x 0.003
Edu → RES 0.081 0.076 1.074 0.283 x 0.006
Gen → USE 0.055 0.032 1.740 0.082 x 0.008
Gen → RES 0.091 0.095 0.960 0.337 x 0.012
Note(s): ATT-Attitude; USE –Intention to use; PBT – Perceived barriers of taking action; PDS – Perceived
disease severity; PDV – Perceived disease vulnerability; PBC –Perceived behavioral control; IPC – Information
Privacy; SBN-Subjective norms; RES – Use resistance; Edu-Education; Gen – Gender. Control variables were
Table 5. based on a two-tailed t-test, while hypothesized relationships were derived using a one-tailed t-test. f2 5 effect
Results of size. Bootstrap analysis with 4,999 subsamples used to generate t-values. U 5 Supported, x 5 not supported
hypothesized paths Source(s): Author’s own, 2023
norms. Thus, our model also provides statistical support for H1b, H4b and H7b. Surprisingly, we Mobile contact
find no statistical support for the influence of subjective norms, perceived behavioral control, tracing apps
perceived severity, perceived vulnerability and information privacy on either intention to use or
resistance to MCTA within the study context. Significantly, attitude was the most influential
predictor of intention to use and resistance to using MCTA (Table 5).

5. Discussion and conclusions 55

5.1 Discussion of findings
In this study, we analyzed individuals’ acceptance of and resistance to MCTA. First, we
analyzed the relationship between the three TPB constructs (attitude, subjective norm and
perceived behavioral control) and intention to use MCTA. Second, we analyzed the
relationship between the same TPB variables with our second-dependent variable resistance
to using MCTA. Third, we analyzed the relationship between HBM constructs (perceived
disease severity, perceived barriers to taking action and perceived disease vulnerability) to
use. Fourth, we assessed the relationship of the HBM variables with resistance to the use of
MCTA. Again, we examined the relationship between contextual variables (personal norms
and information privacy) and intention to use. Finally, we evaluated the relationship between
contextual variables (personal norms and information privacy) and resistance to using
MCTA. Structural validation of these integrated models shows that the variance explained
(R2 5 65.06%) for the acceptance of MCTA was superior to the variance explained
(R2 5 35.21%) for the resistance of MCTA. This implies that the proposed integrated model
could predict intention to use better than resistance to use MCTA.
Our results confirm that the relationship between attitude and intention to use MCTA was
positive and significant. This finding lends credence to the results of previous studies (Abbas
and Mohtar, 2016; Birkmeyer et al., 2021; Yuen et al., 2020). For instance, Yuen et al. (2020) found
that attitude was the most important predictor of public acceptance of autonomous vehicles. In
contrast, Abbas and Mohtar’s (2016) results show that attitude strongly influences customers’
resistance to innovation. As such, in the current study, our results have demonstrated that
attitude does not only positively affect acceptance but also negatively influences resistance to
use MCTA. Contrary to expectation, our results indicate that perceived behavioral control
contributes to neither acceptance nor resistance to using MCTA. Thus, this result alienates
previous research findings (AlBar and Hoque, 2019; Cobelli et al., 2021). For instance, in AlBar
and Hoque’s study on E-health services in the Kingdom of Saudi Arabia, it was found that
perceived behavioral control is not directly related to behavioral intent. Consequently,
acceptance or resistance to the use of MCTA does not depend on perceived behavioral control,
probably due to the nature of the COVID-19 pandemic. This means that given the pandemic
context, the public was not concerned about whether they had control over the use of the
MCTA. Again, surprisingly, our results also show that subjective norms do not influence
acceptance or resistance to the use of MCTA. This result coincides with recent research findings
on adopting innovation (Cobelli et al., 2021; Ifinedo, 2018). For instance, the current research
evidence bears some semblance to the study by Ifinedo (2018), which shows in the Canadian
telehealth context that subjective norm is an insignificant predictor of behavioral intent.
However, the result deviates from extant studies on the influence of subjective norms on
acceptance and resistance of new technology (Fortes and Rita, 2016; Hsieh, 2015; Matsuo et al.,
2018; Van Offenbeek et al., 2013; White Baker et al., 2007; Zhao et al., 2018).
The analysis of the HBM model indicates that perceived disease severity and perceived
disease vulnerability have no influence on the intention to use or resistance to using MCTA.
This finding, which deviates from previous studies (Carpenter, 2010; Lu et al., 2019; Sulat
et al., 2018; Yu et al., 2021) means that individuals are not moved by the severity and highly
infectious nature of COVID-19 in their decision to accept or resist the use of MCTA. However,
OIR in line with our expectation, the results demonstrated that the perceived barrier to taking
48,1 influences individuals’ resistance to using the MCTA. The result is consistent with findings
from previous studies (Sulat et al., 2018). Additionally, we found that the perceived barrier to
taking action has no influence on behavioral intention to use MCTA. These results contradict
findings from previous studies (Walrave et al., 2020).
The results confirm our prediction that personal norms will negatively affect the intention
to use MCTA, but surprisingly reject our hypothesis that personal norms will positively
56 influence the intention to adopt MCTA. The negative influence of personal norms on
resistance to using MCTA corroborates with previous studies (Kim, 2018; Maity et al., 2019;
Udo et al., 2016), whereas the insignificant positive association between personal norms and
intention to use MCTA contrast with previous research (Ateş, 2020; Juraskova et al., 2012;
Maity et al., 2019; Roos and Hahn, 2017, 2019). Accordingly, our study implies that in deciding
to use MCTA, particularly in a pandemic period, one’s moral obligation does not matter. Still,
the same moral obligation plays a seminal role in resistance to using MCTA.
Meanwhile, the negative influence of information privacy on the intention to use MCTA and
the positive influence of information privacy on the resistance to use MCTA within the context
under study were confirmed, although they were statistically insignificant. This research finding
deviates from the previous research works on users’ acceptance of health technologies literature
(Dhagarra et al., 2020; Fox and Connolly, 2018), including an additional study on MCTA showing
the significant negative influence of information privacy on behavioral intent (Kaptchuk et al.,
2020). However, within the context of health technology, the current investigation is not alone in
reporting the minimal influence of information privacy on attitudinal and behavioral responses
(cf. Sergueeva et al., 2020). Besides, a recent systematic review of the literature on health
information technology acceptance reveals that the relationship between privacy and user
acceptance is inconclusive (for details, see Kavandi and Jaana, 2020). Our findings lend to Fox
et al. (2021) indicate information privacy as one of the precursors of MCTA acceptance or
rejection; this study, domiciled in a less digitalized prone environment, does not automatically
rule out the significance of information privacy as a forebear in the adoption/Rejection of MCTA.
We argue that a clear invasion of individuals’ privacy is of paramount concern to individuals
devoid of geographical location. Consequently, this finding ignites the call by governments,
specifically within the developing economy to address information privacy concerns by mapping
a well-cut-out campaign with privacy concerns and protection for the citizenry whiles
championing the significance of information disclosures, personal privacy rights, transparency
and vivid explanations of the use of MCTA. Whiles we concur with previous research works on
privacy concerns resulting in mixed findings towards the adoption of emerging digital
technology (e.g. Conger et al., 2013; Jamil and da Silva, 2021; Majumdar and Bose, 2015; Xu and
Gupta, 2009). In the context of efficient implementation of MCTA, our research implies that given
the minimal impact of information privacy on public attitudes and use intentions of MCTA,
individuals, especially in Ghana, may be willing to share their personal information without
considerable concerns about their information privacy.

5.2 Implications for research

Our study provides several implications and contributions for other researchers. A primary
theoretical contribution is integrating TPB, HBM and contextual factors (personal norms and
information privacy) to examine the individual determinants of acceptance and resistance
to MCTAs in a pandemic situation. Reflecting on the works of Horvath (2019), as in the case of
this study, obvious fusion assists researchers in creating an inferential connection between
elements of a component of different theories, producing a new supposition synthesized from
them, which this study has sought to achieve. Accordingly, we provide theoretical insights
for future researchers that may assist in encouraging individuals’ intention to use the MCTA.
 Second, most studies on new technology explored adoption or acceptance at the expense of Mobile contact
factors contributing to the resistance to innovation (Claudy et al., 2015), thereby creating a tracing apps
vacuum in innovation resistance research. Hence, by incorporating or studying resistance in
addition to the acceptance of MCTA, we have heeded various calls, which advise the research
community to give some attention to the resistance component of new technology studies. By
extension, we have reduced the research deficit in innovation resistance literature.
Third, our findings advance the body of knowledge because unlike extant research (e.g.
Hsieh, 2015), this investigation shows that perceived behavioral control and subjective norm, 57
while directly contributing to positive influence on attitudes towards MCTA, are not enough
to lead to use or reject intentions of MCTA directly. Likewise, the influence of perceived
severity and perceived vulnerability as a basic concept of the HBM model on gauging the use
and rejection intent of MCTA was disputed. These findings, however, are divergent from the
prior research findings (Birkmeyer et al., 2021), which found that perceived disease threat
positively affects attitude toward mobile health.
Further, it is worth noting that our study findings differ from previous research (Walrave
et al., 2020) in two ways: The first relates to the differences in the geographic scope. Second, our
findings revealed that the most salient predictors of intention to resist MCTA were attitude,
perceived barriers to taking action and personal norms, which were quite disparate with
previous research findings in telehealth studies (Kim et al., 2021; Williams et al., 2021; Saqlain
et al., 2020). These findings open a debate to broaden the horizon of acceptance/rejection of
MCTA in the study context, considering a myriad of factors. It is therefore not gainsaying that
by including contextually relevant factors in the study, such as personal norms and
information privacy, of which the latter factor seems to be more influential in the study, the
current article advances existing research on the integrated theoretical model adopted to map
out the intent to un(use) MCTA which could support the manual contact tracing efforts in the
fight against the spread of SARS-CoV-2 and its associated COVID-19 pandemic.
Finally, this is the first work to gauge public attitudes and intentions to un(use) MCTA
based on an integrated theoretical model. This study, therefore, contributes to the burgeoning
literature on four different theoretical perspectives; thus TPB, HBM, (personal norm and
information privacy-contextual relevant cases) by validating the theoretical model in the
novel but the surreal context of COVID-19 pandemic of voluntary adoption/rejection of
MCTA, which conforms to WHO’s a provisional recommendation to nations.

5.3 Policy implications

Given that our research particularly shows that attitude is critically related to use intentions
and resistance to the use of MCTA, health policymakers, along with other important
stakeholders such as the media and other opinion-molders of the society, have an important
role to play in this regard. Importantly, these stakeholders may need to redouble their efforts
in ways that can motivate members of the public to have a strong and positive attitude
toward the use of MCTA and complimentary COVID-19 mitigation measures. We should also
note that attitudes can only be strongly influenced when the public is well aware of the
potential benefits of using MCTA; thus, providing reasonable information using the media is
imperative. In summary, our study implies that it is imperative for national governments
across the world, especially those who will soon rollout MCTA, to design well-tailored
messaging through the media that can resonate well with opinion-molders of the society who,
in turn, can influence those who look up to them for advice and other forms of guidance.

5.4 Limitations and future research

The major limitation of this study is that, even though the proposed theoretical model can be
applied in other contexts, the findings are impacted by the fact that the experimental dataset
OIR used in testing the model was from a single country. As such, this calls for an additional
48,1 examination of the proposed theoretical model in other national contexts, especially because
this will help to enhance the understanding of this model. Moreover, it will also be good for
future research to extend the proposed theoretical model to other areas, such as vaccine
acceptance or hesitancy. There is also room for improvement of the current model by
including relevant situational influences, such as institutional distrust and cultural factors
that may serve as boundary conditions in several of the proposed relationships in the model.
58 The above suggestions could assist in resolving the inconclusive findings in the study area.

Acknowledgments
The authors dedicate the current study to our able-bodied medical personnel, security
agencies and related individuals worldwide who are firmly dedicated to fighting against our
common but invisible enemy of ours called the SARS-CoV-2 virus. The authors are also
indebted to all the research participants for their time.
Michael Adu Kwarteng and Alex Ntsiful acknowledge support from the following projects:
(1) Horizon Europe (HORIZON) 101071300 – “Sustainable Horizons - European
Universities designing the horizons of sustainability (SHEs).”
(2) Research Project No. IGA/FaME/2023/009 Performance and profitability of the
company: the effects of digitization and consumer behavior and
(3) Research Project FSR FORD 5-6/2021-23/FaME/003: “Emerging technologies,
consumer well-being, and enterprises in the post-pandemic world: the implications on
Heath and transportation sectors in the Czech Republic.”
Alex Ntsiful also acknowledges support from the following projects:
(1) IGA/FaME/ 2022/003: A study on budgetary control and budgetary technology in
local government of developed and emerging economies and
(2) Research Project No. IGA/FaME/2021/003 entitled “Consumer behavior and
performance management of firms in a competitive digital world.”
Declaration of Competing Interests: The authors declare no competing interests.

References
Abbas, M. and Mohtar, S.B. (2016), “Factors influencing consumer resistance to innovation:
relationship between relative advantage, attitude towards existing product, social influence and
self-efficacy”, Researchers World, Vol. 7 No. 1, p. 70.
Ajzen, I. (2011), “The theory of planned behaviour: reactions and reflections”, Psychology and Health,
Vol. 26 No. 9, pp. 1113-1127.
AlBar, A.M. and Hoque, M.R. (2019), “Patient acceptance of e-health services in Saudi Arabia:
an integrative perspective”, Telemedicine and e-Health, Vol. 25 No. 9, pp. 847-852.
Al-Metwali, B.Z., Al-Jumaili, A.A., Al-Alag, Z.A. and Sorofman, B. (2021), “Exploring the acceptance of
COVID-19 vaccine among healthcare workers and general population using health belief
model”, Journal of Evaluation in Clinical Practice, Vol. 27 No. 5, pp. 1112-1122, doi: 10.1111/
jep.13581.
Alsaad, A. and Al-Okaily, M. (2021), “Acceptance of protection technology in a time of fear: the case of
Covid-19 exposure detection apps”, Information Technology and People, Vol. 35 No. 3, pp. 1116-
1135, doi: 10.1108/ITP-10-2020-0719.
Armitage, C.J. and Conner, M. (2001), “Efficacy of the theory of planned behavior: a metaanalytic
review”, British Journal of Social Psychology, Vol. 40, pp. 471-499.
Ateş, H. (2020), “Merging theory of planned behavior and value identity personal norm model to
explain pro-environmental behaviors”, Sustainable Production and Consumption, Vol. 24,
pp. 169-180.
Barattucci, M., Pagliaro, S., Ballone, C., Teresi, M., Consoli, C., Garofalo, A., Giorgio, A.D. and Ramaci, Mobile contact
T. (2022), “Trust in science as a possible mediator between different antecedents and COVID-19
booster vaccination intention: an integration of health belief model (HBM) and theory of tracing apps
planned behavior (TPB)”, Vaccines, Vol. 10 No. 7, p. 1099.
B
elanger, F. and Crossler, R.E. (2011), “Privacy in the digital age: a review of information privacy
research in information systems”, MIS Quarterly, Vol. 35 No. 4, pp. 1017-1041.
Benitez, J., Henseler, J., Castillo, A. and Schuberth, F. (2020), “How to perform and report an impactful
analysis using partial least squares: guidelines for confirmatory and explanatory IS research”, 59
Information and Management, Vol. 57 No. 2, 103168.
Birkmeyer, S., Wirtz, B.W. and Langer, P.F. (2021), “Determinants of mHealth success: an empirical
investigation of the user perspective”, International Journal of Information Management,
Vol. 59, 102351.
Bish, A., Sutton, S. and Golombok, S. (2000), “Predicting uptake of a routine cervical smear test:
a comparison of the health belief model and the theory of planned behaviour”, Psychology and
Health, Vol. 15 No. 1, pp. 35-50.
Braithwaite, I., Callender, T., Bullock, M. and Aldridge, R.W. (2020), “Automated and partly automated
contact tracing: a systematic review to inform the control of COVID-19”, The Lancet Digital
Health, Vol. 2 No. 11, pp. e607-e621.
Carpenter, C.J. (2010), “A meta-analysis of the effectiveness of health belief model variables in
predicting behavior”, Health Communication, Vol. 25 No. 8, pp. 661-669.
CGTN Africa (2020), “Ghana develops app to help track COVID-19 patients”, available at: https://
africa.cgtn.com/2020/04/24/ghana-develops-app-to-help-track-covid-19-patients/
Chan, E.Y. and Saqib, N.U. (2021), “Privacy concerns can explain unwillingness to download and use
contact tracing apps when COVID-19 concerns are high”, Computers in Human Behavior,
Vol. 119, 106718.
Chatzidakis, A., Kastanakis, M. and Stathopoulou, A. (2016), “Socio-cognitive determinants of consumers’
support for the fair trade movement”, Journal of Business Ethics, Vol. 133 No. 1, pp. 95-109.
Claudy, M.C., Garcia, R. and O’Driscoll, A. (2015), “Consumer resistance to innovation—a behavioral
reasoning perspective”, Journal of the Academy of Marketing Science, Vol. 43 No. 4, pp. 528-544.
Cobelli, N., Cassia, F. and Burro, R. (2021), “Factors affecting the choices of adoption/non-adoption of
future technologies during coronavirus pandemic”, Technological Forecasting and Social
Change, Vol. 169, 120814.
Conger, S., Pratt, J.H. and Loch, K.D. (2013), “Personal information privacy and emerging
technologies”, Information Systems Journal, Vol. 23 No. 5, pp. 401-417.
Cooper, S., van Rooyen, H. and Wiysonge, C.S. (2021), “COVID-19 vaccine hesitancy in South Africa:
how can we maximize uptake of COVID-19 vaccines?”, Expert Review of Vaccines, Vol. 20 No. 8,
pp. 921-933, doi: 10.1080/14760584.2021.1949291.
Deng, Z. (2013), “Understanding public users’ adoption of mobile health service”, International Journal
of Mobile Communications, Vol. 11 No. 4, pp. 351-373.
Dhagarra, D., Goswami, M. and Kumar, G. (2020), “Impact of trust and privacy concerns on
technology acceptance in healthcare: an Indian perspective”, International Journal of Medical
Informatics, Vol. 141, 104164.
Duan, S.X. and Deng, H. (2021), “Hybrid analysis for understanding contact tracing apps adoption”,
Industrial Management and Data Systems, Vol. 121 No. 7, pp. 1599-1616.
Ellen, P.S., Bearden, W.O. and Sharma, S. (1991), “Resistance to technological innovations:
examination of the role of self-efficacy and performance satisfaction”, Journal of the
Academy of Marketing Science, Vol. 19 No. 4, pp. 297-307.
Evans, R.I. and Getz, J.G. (2003), “Social inoculation”, in Encyclopedia of Primary Prevention and
Health.
OIR Fan, C.W., Chen, I.H., Ko, N.Y., Yen, C.F., Lin, C.Y., Griffiths, M.D. and Pakpour, A.H. (2021), “Extended
theory of planned behavior in explaining the intention to COVID-19 vaccination uptake among
48,1 mainland Chinese university students: an online survey study”, Human Vaccines and
Immunotherapeutics, Vol. 17 No. 10, pp. 3413-3420, doi: 10.1080/21645515.2021.1933687.
Fornell, C.G. and Larcker, D.F. (1981), “Evaluating structural equation models with unobservable
variables and measurement error”, Journal of Marketing Research, Vol. 18 No. 1, pp. 39-50.
Fortes, N. and Rita, P. (2016), “Privacy concerns and online purchasing behaviour: towards an
60 integrated model”, European Research on Management and Business Economics, Vol. 22 No. 3,
pp. 167-176.
Fox, G. (2020), “‘To protect my health or to protect my health privacy?’ A mixed-methods
investigation of the privacy paradox”, Journal of the Association for Information Science and
Technology, Vol. 71 No. 9, pp. 1015-1029, doi: 10.1002/asi.24369.
Fox, G., Clohessy, T., van der Werff, L., Rosati, P. and Lynn, T. (2021), “Exploring the competing
influences of privacy concerns and positive beliefs on citizen acceptance of contact tracing
mobile applications”, Computers in Human Behavior, Vol. 121, 106806.
Fox, G. and Connolly, R. (2018), “Mobile health technology adoption across generations: narrowing the
digital divide”, Information Systems Journal, Vol. 28 No. 6, pp. 995-1019.
Franke, G. and Sarstedt, M. (2019), “Heuristics versus statistics in discriminant validity testing: a
comparison of four procedures”, Internet Research, Vol. 29 No. 3, pp. 430-447.
Garrett, P.M., White, J.P., Lewandowsky, S., Kashima, Y., Perfors, A., Little, D.R., Geard, N., Mitchell,
L., Tomko, M. and Dennis, S. (2021), “The acceptability and uptake of smartphone tracking for
COVID-19 in Australia”, Plos One, Vol. 16, e0244827.
Gerend, M.A. and Shepherd, J.E. (2012), “Predicting human papillomavirus vaccine uptake in young
adult women: comparing the health belief model and theory of planned behavior”, Annals of
Behavioral Medicine, Vol. 44 No. 2, pp. 171-180.
Guillon, M. and Kergall, P. (2020), “Attitudes and opinions on quarantine and support for a contact-
tracing application in France during the COVID-19 outbreak”, Public Health, Vol. 188, pp. 21-31.
Hair, J.F., Howard, M.C. and Nitzl, C. (2020), “Assessing measurement model quality in PLS-SEM using
confirmatory composite analysis”, Journal of Business Research, Vol. 109, pp. 101-110.
Hassandoust, F., Akhlaghpour, S. and Johnston, A.C. (2021), “Individuals’ privacy concerns and
adoption of contact tracing mobile applications in a pandemic: a situational privacy calculus
perspective”, Journal of the American Medical Informatics Association, Vol. 28 No. 3,
pp. 463-471.
Henseler, J. (2017), Adanco 2.0. 1-User Manual, Composite Modeling GmbH & Company, Kleve.
Henseler, J., Ringle, C.M. and Sarstedt, M. (2015), “A new criterion for assessing discriminant validity
in variance-based structural equation modelling”, Journal of the Academy of Marketing Science,
Vol. 43 No. 1, pp. 115-135.
Horvath, I. (2019), July, “Combining unmergeables: a methodological framework for axiomatic fusion
of qualitative design theories”, Proceedings of the Design Society: International Conference on
Engineering Design, Cambridge University Press, Vol. 1, No. 1, pp. 3591-3600.
Hsieh, P.J. (2015), “Physicians’ acceptance of electronic medical records exchange: an extension of the
decomposed TPB model with institutional trust and perceived risk”, International Journal of
Medical Informatics, Vol. 84 No. 1, pp. 1-14.
Huang, X., Dai, S. and Xu, H. (2020a), “Predicting tourists’ health risk preventative behaviour and
travelling satisfaction in Tibet: combining the theory of planned behaviour and health belief
model”, Tourism Management Perspectives, Vol. 33, 100589.
Huang, Y., Sun, M. and Sui, Y. (2020b), “How digital contact tracing slowed COVID-19 in East Asia”,
Harvard Business Review, Vol. 15 No. 04, available at: https://hbr.org/2020/04/how-digital-
contact-tracing-slowed-covid-19-in-east-asia
Ifinedo, P. (2018), “Empirical study of Nova Scotia nurses’ adoption of healthcare information systems: Mobile contact
implications for management and policy-making”, International Journal of Health Policy and
Management, Vol. 7 No. 4, p. 317. tracing apps
ITU News (2020), “Ghana launches COVID-19 tracker app”, available at: https://news.itu.int/ghana-
launches-covid-19-tracker-app/
Jamil, G.L. and da Silva, A.R. (2021), “Emerging technologies in a modern competitive scenario:
understanding the panorama for security and privacy requirements”, in Handbook of Research
on Digital Transformation and Challenges to Data Security and Privacy, pp. 1-16. 61
Janz, N.K. and Becker, M.H. (1984), “The health belief model a decade later”, Health Education
Quarterly, Vol. illl, pp. 1-47.
Joo, J. and Shin, M.M. (2020), “Resolving the tension between full utilization of contact tracing app
services and user stress as an effort to control the COVID-19 pandemic”, Service Business,
Vol. 14, pp. 461-478.
Jose, R., Narendran, M., Bindu, A., Beevi, N., Manju, L. and Benny, P.V. (2021), “Public perception and
preparedness for the pandemic COVID 19: a health belief model approach”, Clinical
Epidemiology and Global Health, Vol. 9, pp. 41-46.
Juraskova, I., O’Brien, M., Mullan, B., Bari, R., Laidsaar-Powell, R. and McCaffery, K. (2012), “HPV
vaccination and the effect of information framing on intentions and behaviour: an application of
the theory of planned behaviour and moral norm”, International Journal of Behavioral Medicine,
Vol. 19, pp. 518-525, 2012.
Kaptchuk, G., Goldstein, D., Hargittai, E., Hofman, J. and Redmiles, E. (2020), “How good is good
enough for COVID19 apps? The influence of benefits, accuracy, and privacy on willingness to
adopt”, available at: https://arxiv.org/pdf/2005.04343.pdf
Kavandi, H. and Jaana, M. (2020), “Factors that affect health information technology adoption by
seniors: a systematic review”, Health and Social Care in the Community, Vol. 28 No. 6,
pp. 1827-1842.
Kim, H.S. (2018), “The role of legal and moral norms to regulate the behavior of texting while
driving”, Transportation Research Part F, Traffic Psychology and Behaviour, Vol. 52,
pp. 21-31.
Kim, H. (2021), “COVID-19 apps as a digital intervention policy: a longitudinal panel data analysis
in South Korea”, Health Policy, Vol. 125 No. 11, pp. 1430-1440, doi: 10.1016/j.healthpol.2021.
07.003.
Kim, J. and Park, E. (2020), “Understanding social resistance to determine the future of Internet of
Things (IoT) services”, Behaviour and Information Technology, Vol. 41 No. 3, pp. 547-557,
doi: 10.1080/0144929X.2020.1827033.
Kim, M.J., Hall, C.M. and Bonn, M. (2021), “Does international travel frequency affect covid-19
biosecurity behavior in the United States?”, International Journal of Environmental Research
and Public Health, Vol. 18 No. 8, p. 4111.
Kock, N. (2015), “Common method bias in PLS-SEM: a full collinearity assessment approach”,
International Journal of E-Collaboration, Vol. 11 No. 4, pp. 1-10.
Li, T., Cobb, C., Baviskar, S., Agarwal, Y., Li, B., Bauer, L. and Hong, J.I. (2021), “What makes people
install a COVID-19 contact-tracing app? Understanding the influence of app design and
individual difference on contact-tracing app adoption intention”, Pervasive and Mobile
Computing, Vol. 75, 101439.
Lin, J., Carter, L. and Liu, D.E. (2021), “Privacy concerns and digital government: exploring citizen
willingness to adopt the COVIDSafe app”, European Journal of Information Systems, Vol. 30
No. 40, pp. 389-402.
Litan, R.E. and Lowy, M. (2020), Freedom and Privacy in the Time of Coronavirus, The Brookings
Institution, available at: https://www.brookings.edu/research/freedom-and-privacy-in-the-time-
of-coronavirus/
OIR Lu, J., Luo, M., Yee, A.Z.H., Sheldenkar, A., Lau, J. and Lwin, M.O. (2019), “Do superstitious beliefs
affect influenza vaccine uptake through shaping health beliefs?”, Vaccine, Vol. 37 No. 8,
48,1 pp. 1046-1052.
Lu, X., L. Reynolds, T., Jo, E., Hong, H., Page, X., Chen, Y. and A. Epstein, D. (2021), May, “Comparing
perspectives around human and technology support for contact tracing”, Proceedings of the
2021 CHI Conference on Human Factors in Computing Systems, pp. 1-15.
Maity, M., Bagchi, K., Shah, A. and Misra, A. (2019), “Explaining normative behavior in information
62 technology use”, Information Technology and People, Vol. 32 No. 1, pp. 94-117.
Majumdar, A. and Bose, I. (2015), December, “Privacy calculus theory and its applicability for
emerging technologies”, in Workshop on E-Business, Springer, Cham, pp. 191-195.
Mani, Z. and Chouk, I. (2017), “Drivers of consumers’ resistance to smart products”, Journal of
Marketing Management, Vol. 33 Nos 1-2, pp. 76-97.
Mani, Z. and Chouk, I. (2019), “Impact of privacy concerns on resistance to smart services: does the
‘big brother effect’ matter?”, Journal of Marketing Management, Vol. 35 Nos 15-16,
pp. 1460-1479.
Matsuo, M., Minami, C. and Matsuyama, T. (2018), “Social influence on innovation resistance in
internet banking services”, Journal of Retailing and Consumer Services, Vol. 45, pp. 42-51.
McClenahan, C., Shevlin, M., Adamson, G., Bennett, C. and O’Neill, B. (2007), “Testicular
self-examination: a test of the health belief model and the theory of planned behaviour”,
Health Education Research, Vol. 22 No. 2, pp. 272-284.
McGuire, W.J. (1964), “Inducing resistance to persuasion: some contemporary approaches”, Advances
in Experimental Social Psychology, Vol. 1, pp. 191-229.
Mou, J., Shin, D.H. and Cohen, J. (2016), “Health beliefs and the valence framework in health
information seeking behaviors”, Information Technology and People, Vol. 29 No. 4, pp. 876-900.
Munzert, S., Selb, P., Gohdes, A., Stoetzer, L.K. and Lowe, L. (2021), “Tracking and promoting the
usage of a COVID-19 contact tracing app”, Nature Human Behaviour, Vol. 5, pp. 247-255.
Mutimukwe, C., Kolkowska, E. and Gr€onlund,  A. (2020), “Information privacy in e-service: effect of
organizational privacy assurances on individual privacy concerns, perceptions, trust and
self-disclosure behavior”, Government Information Quarterly, Vol. 37 No. 1, 101413.
Ntsiful, A., Kwarteng, M.A. and Inegbedion, H.E. (2022), “How health-related messaging increase
intentions to download and use mobile contact (COVID-19) tracing apps: preliminary findings”,
Cogent Social Sciences, Vol. 8 No. 1, 2035912.
Oldeweme, A., M€artins, J., Westmattelmann, D. and Schewe, G. (2021), “The role of transparency, trust,
and social influence on uncertainty reduction in times of pandemics: empirical study on the
adoption of COVID-19 tracing apps”, Journal of Medical Internet Research, Vol. 23 No. 2, e25893.
Osakwe, C.N. (2019), “Understanding customer-perceived quality in informal stores”, Journal of
Services Marketing, Vol. 33 No. 2, pp. 133-147.
Otu, A., Osifo-Dawodu, E., Atuhebwe, P., Agogo, E. and Ebenso, B. (2021), “Beyond vaccine hesitancy:
time for Africa to expand vaccine manufacturing capacity amidst growing COVID-19 vaccine
nationalism”, The Lancet Microbe, Vol. 2 No. 8, pp. e347-e348, doi: 10.1016/S2666-5247(21)
00126-9.
Reuters (2021), “Vaccine hesitancy slows Africa’s COVID-19 inoculation drive”, available at: https://
www.reuters.com/world/africa/vaccine-hesitancy-slows-africas-covid-19-inoculation-drive-2021-
05-04/(accessed 01 August 2021).
Roos, D. and Hahn, R. (2017), “Does shared consumption affect consumers’ values, attitudes, and
norms? A panel study”, Journal of Business Research, Vol. 77, pp. 113-123.
Roos, D. and Hahn, R. (2019), “Understanding collaborative consumption: an extension of the theory of
planned behavior with value-based personal norms”, Journal of Business Ethics, Vol. 158,
pp. 679-697.
Rosenstock, I.M. (1974), “Historical origins of the health belief model”, Health Education Monographs, Mobile contact
Vol. 2 No. 4, pp. 328-335.
tracing apps
Rosenstock, I.M., Strecher, V.J. and Becker, M.H. (1988), “Social learning theory and the health belief
model”, Health Education Quarterly, Vol. 15 No. 2, pp. 175-183.
Rowe, F. (2020), “Contact tracing apps and values dilemmas: a privacy paradox in a neo-liberal world”,
International Journal of Information Management, Vol. 55, 102178, doi: 10.1016/j.ijinfomgt.2020.
102178.
63
Saqlain, M., Munir, M.M., Rehman, S.U., Gulzar, A., Naz, S., Ahmed, Z., Tahir, A.H. and Mashhood, M.
(2020), “Knowledge, attitude, practice and perceived barriers among healthcare workers
regarding COVID-19: a cross-sectional survey from Pakistan”, Journal of Hospital Infection,
Vol. 105 No. 3, pp. 419-423.
Saw, Y.E., Tan, E.Y., Liu, J.S. and Liu, J.C. (2021), “Predicting public uptake of digital contact tracing
during the COVID-19 pandemic: results from a nationwide survey in Singapore”, Journal of
Medical Internet Research, Vol. 23 No. 2, e24730.
Schwartz, S.H. and Howard, J.A. (1981), “A normative decision-making model of altruism”, in Altruism
and Helping Behavior, pp. 189-211.
Sergueeva, K., Shaw, N. and Lee, S.H.M. (2020), “Understanding the barriers and factors associated
with consumer adoption of wearable technology devices in managing personal health”,
Canadian Journal of Administrative Sciences, Vol. 37, pp. 45-60.
Shmueli, L. (2021), “Predicting intention to receive COVID-19 vaccine among the general population
using the health belief model and the theory of planned behavior model”, BMC Public Health,
Vol. 21, p. 804, doi: 10.1186/s12889-021-10816-7.
Sisay, O.B. (2021), The Challenge of Covid-19 Vaccine Hesitancy in Africa, Tony Blair Institute for
Global Change, available at: https://institute.global/advisory/challenge-covid-19-vaccine-
hesitancy-africa
Sulat, J.S., Prabandari, Y.S., Sanusi, R., Hapsari, E.D. and Santoso, B. (2018), “The validity of health
belief model variables in predicting behavioral change: a scoping review”, Health Education,
Vol. 118 No. 6, pp. 499-512.
Sun, Y., Wang, N., Guo, X. and Peng, Z. (2013), “Understanding the acceptance of mobile health
services: a comparison and integration of alternative models”, Journal of Electronic Commerce
Research, Vol. 14 No. 2, pp. 183-200.
Taylor, D., Bury, M., Campling, N., Carter, S., Garfied, S., Newbould, J. and Rennie, T. (2006), A Review of
the Use of the Health Belief Model (HBM), the Theory of Reasoned Action (TRA), the Theory of
Planned Behaviour (TPB) and the Trans-theoretical Model (TTM) to Study and Predict Health
Related Behaviour Change, National Institute for Health and Clinical Excellence, London, pp. 1-215.
The Economist Intelligence Unit [EIU] (2021), “Africa faces major obstacles to accessing Covid
vaccines”, available at: https://www.eiu.com/n/africa-faces-major-obstacles-to-accessing-covid-
vaccines/
The New York Times (2021), “Africa’s Covid crisis deepens, but vaccines are still far off”, available at:
https://www.nytimes.com/interactive/2021/07/16/world/africa/africa-vaccination-rollout.html
(accessed 01 August 2021).
Tomczyk, S., Barth, S., Schmidt, S. and Muehlan, H. (2021), “Utilizing health behavior change and
technology acceptance models to predict the adoption of COVID-19 contact tracing apps: cross-
sectional survey study”, Journal of Medical Internet Research, Vol. 23 No. 5, e25447.
Traberg, C.S., Roozenbeek, J. and van der Linden, S. (2022), “Psychological inoculation against
misinformation: current evidence and future directions”, The ANNALS of the American
Academy of Political and Social Science, Vol. 700 No. 1, pp. 136-151.
Trang, S., Trenz, M., Weiger, W.H., Tarafdar, M. and Cheung, C.M.K. (2020), “One app to trace them
all? Examining app specifications for mass acceptance of contact-tracing apps”, European
Journal of Information Systems, Vol. 29 No. 4, pp. 415-428, doi: 10.1080/0960085X.2020.1784046.
OIR Udo, G., Bagchi, K. and Maity, M. (2016), “Exploring factors affecting digital piracy using the norm
activation and UTAUT models: the role of national culture”, Journal of Business Ethics,
48,1 Vol. 135, pp. 517-541.
van Offenbeek, M., Boonstra, A. and Seo, D. (2013), “Towards integrating acceptance and resistance
research: evidence from a telecare case study”, European Journal of Information Systems,
Vol. 22 No. 4, pp. 434-454.
Venkatesh, V. and Bala, H. (2008), “Technology acceptance model 3 and a research agenda on
64 interventions”, Decision Sciences, Vol. 39 No. 2, pp. 273-315.
Viktor, V.W., Sebastian, B., Edouard, B., Alan, P.M., Marcel, S., Theresa, S., Carmela, T., Effy, V. and
Nicola, L. (2020), “A research agenda for digital proximity tracing apps”, Swiss Medical Weekly,
Vol. 150, w20324, doi: 10.4414/smw.2020.20324.
Villius Zetterholm, M., Lin, Y. and Jokela, P. (2021), July, “Digital contact tracing applications
during COVID-19: a scoping review about public acceptance”, Informatics, Vol. 8 No. 3,
p. 48, MDPI.
Walrave, M., Waeterloos, C. and Ponnet, K. (2020), “Adoption of a contact tracing app for containing
COVID-19: a health belief model approach”, JMIR Public Health and Surveillance, Vol. 6 No. 3,
e20572.
White Baker, E., Al-Gahtani, S.S. and Hubona, G.S. (2007), “The effects of gender and age on new
technology implementation in a developing country: testing the theory of planned behavior
(TPB)”, Information Technology and People, Vol. 20 No. 4, pp. 352-375.
Williams, S.N., Armitage, C.J., Tampe, T. and Dienes, K. (2021), “Public attitudes towards COVID-19
contact tracing apps: a UK-based focus group study”, Health Expectations, Vol. 24 No. 2,
pp. 377-385.
World Health Organization [WHO] (2020), “Ethical considerations to guide the use of digital proximity
tracking technologies for COVID-19 contact tracing: interim guidance”, available at: https://
apps.who.int/iris/handle/10665/332200
Xu, H. and Gupta, S. (2009), “The effects of privacy concerns and personal innovativeness on potential
and experienced customers’ adoption of location-based services”, Electronic Markets, Vol. 19
No. 2, pp. 137-149.
Yu, Y., Lau, J.T., She, R., Chen, X.i., Li, L. and Chen, X. (2021), “Prevalence and associated factors of
intention of COVID-19 vaccination among healthcare workers in China: application of the
Health Belief Model”, Human Vaccines and Immunotherapeutics, Vol. 19, pp. 1-9.
Yuen, K.F., Chua, G., Wang, X., Ma, F. and Li, K.X. (2020), “Understanding public acceptance of
autonomous vehicles using the theory of planned behaviour”, International Journal of
Environmental Research and Public Health, Vol. 17 No. 12, p. 4419.
Zhao, Y., Ni, Q. and Zhou, R. (2018), “What factors influence the mobile health service adoption?
A meta-analysis and the moderating role of age”, International Journal of Information
Management, Vol. 43, pp. 342-350.

Further reading
Amnesty International (2020), “Bahrain, Kuwait and Norway contact tracing apps among most
dangerous for privacy”, available at: https://www.amnesty.org/en/latest/news/2020/06/bahrain-
kuwait-norway-contact-tracing-apps-danger-for-privacy/
Henseler, J. and Dijkstra, T.K. (2020), “ADANCO 2.2. Kleve: composite modeling”, available at: http://
www.compositemodeling.com
Appendix Mobile contact
tracing apps
Construct Indicator Description Adapted from

Attitude towards ATT1 I am happy to download and use COVID-19 contact tracing Sun et al. (2013)
MCTA apps
ATT2 I believe using COVID-19 contact tracing apps is a good idea
Intention to use USE1 It is very likely that I would use COVID-19 contact tracing Same as above 65
apps
USE2 Using COVID-19 contact tracing apps on my mobile phone is
something I would do
Perceived behavioral PBC 1 I should have control over using the COVID-19 contact Same as above
control tracing apps
PBC 2 I have the resources necessary to use the COVID-19 contact
tracing apps
PBC 3 I have the knowledge necessary to use the COVID-19 contact
tracing app
Subjective norms SBN1 I will be happy to use COVID-19 contact tracing apps if Venkatesh and
people who influence my behaviour think that I should use it Bala (2008)
SBN2 If people who are important to me think that the contact
tracing apps is good then I will consider using it
SBN3 Overall, using contact tracing apps will depend heavily on
peers’ and family members’ influence
Personal norms PNM1 I feel that I have an ethical/moral obligation to support the Chatzidakis et al.
use of COVID-19 contact tracing apps (2016)
PNM2 I personally feel I should support the use of the COVID-19
contact tracing apps
PNM3 Supporting the COVID-19 contact tracing app would be the
right thing for me to do
Information privacy IPC 1 I’m concerned about data collected by the COVID-19 contact Mani and Chouk
tracing apps without my permission (2019)
IPC 2 I’m concerned about the use of my personal data without my
consent
IPC 3 In general, I’m concerned about threats to my personal
privacy
Perceived barriers of PBT1 I am concerned that I might forget to use the COVID-19
taking action contact tracing app whenever I go outside of my house/
compound
PBT2 I am concerned that I might be too busy to use the contact Deng (2013)
tracing app
Perceived disease PDS1 If I suffer from COVID-19 disease, it would be severe
severity PDS2 If I suffer from the COVID-19 disease, it would be serious Sun et al. (2013)
PDS3 It will be devastating to be diagnosed with coronavirus
Perceived disease PDV1 I am at risk of been exposed to the COVID-19 disease
vulnerability pandemic
PDV2 It is likely that I will suffer the stated disease if I am not Same as above
careful
PDV3 It is possible for me to suffer the stated disease (COVID-19)
Resistance to use RES1 I have a negative opinion about the use of COVID-19 contract
tracing apps
RES2 I’m not in favor of using the COVID-19 contact tracing apps Mani and Chouk Table A1.
(2019) Constructs and
RES3 I have a bad judgement on the use of the contact tracing apps underlying items with
Source(s): Author’s own, 2023 literature sources
OIR About the authors
Michael Adu Kwarteng is a senior lecturer in the Department of Management and Marketing, Tomas
48,1 Bata University in Zlin, Czech Republic. His research focuses on Internet marketing applications, social
media analytics, enterprise competitive analyses,and preference modelling. He is particularly interested
in deploying data mining techniques in extracting and generating new information to improve business
decision making, especially for marketers. The results of his research have been published in peer-
reviewed scientific journals and presented at international conferences. Michael Adu Kwarteng is the
66 corresponding author and can be contacted at: kwarteng@utb.cz, adukwart@gmail.com
Alex Ntsiful is a PhD student in the Department of Business Administration, Tomas Bata University
in Zlin, Czech Republic. In his doctoral thesis, Alex examines how technology adoption in performance
management practices translates into superior performance in the healthcare context. He holds a Master
of Business Administration and Bachelor of Arts in Psychology degrees, both from the University of
Ghana. He is also a certified Professional in Human Resource (PHR) from the Human Resource
Certification Institute, USA. Given his background, Alex research focuses on people’s attitudes and
behaviour in the workplace, e-performance management, and technology, and the future of work.
Christian Nedu Osakwe has a doctorate degree in Management and Economics with a focus in
Marketing Management. His research interests cover three main areas: marketing capabilities
development of the firm in relation to firm performance, digital adoption both at the firm- and individual-
level, and consumer behaviour with a particular focus on services marketing. His publications appear in
well-known outlets such as Information Development, Journal of Retailing and Consumer Services,
Journal of Services Marketing, Journal of Strategic Marketing, and Total Quality Management and
Business Excellence.
Kwame Simpe Ofori is currently pursuing another doctorate degree in the School of Management
and Economics, University of Electronic Science and Technology of China, Chengdu, China University.
He also works as a Lecturer in the Computer Science Department at Ho Technical University, Ho, Ghana.
His research interests are in the areas of consumer behavior, technology adoption, trust in online
systems and PLS path modelling. His papers have appeared in journals such as Journal of Cyber Security
and Mobility, Total Quality Management and Business Excellence, African Journal of Economic and
Management Studies and Marketing Intelligence and Planning.

For instructions on how to order reprints of this article, please visit our website:
www.emeraldgrouppublishing.com/licensing/reprints.htm
Or contact us for further details: permissions@emeraldinsight.com