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Understanding the acceptance of mobile health services: A comparison and integration of alternative models


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The advancement of mobile technology and the increasing importance of health promote the boom in mobile health services (MHS) around the world. Although there have been several studies investigating the health technology acceptance behavior from a variety of theoretical perspectives, they have not provided a unified understanding. To fill this research gap, this paper: (1) reviews the health technology acceptance literature and discusses three prominent models (e.g., the technology acceptance model, the theory of planned behavior or the unified theory of use and acceptance of technology, and the protection motivation theory), (2) empirically compares the three models, and (3) formulates and empirically validates the unified model in the context of mobile health services. In the unified model of health technology acceptance, we propose that users' intention to use mobile health services is determined by five key factors: performance expectancy, effort expectancy, social influence, facilitating conditions, and threat appraisals. The results show that the unified model outperforms the three alternative models by significantly improving the R-squares. Finally, the implications for theory and practice are put forward.
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Journal of Electronic Commerce Research, VOL 14, NO 2, 2013
Page 183
Yongqiang Sun
School of Information Management
Wuhan University
299 Bayi Road, Wuhan, Hubei, China
Nan Wang
USTC-CityU Joint Advanced Research Center
University of Science and Technology of China
166 Ren’ai Road, Suzhou, Jiangsu, China
Xitong Guo
School of Management
Harbin Institute of Technology
92 West Da-Zhi Street, Harbin, Heilongjiang, China
Zeyu Peng
School of Business
East China University of Science and Technology
130 Meilong Road, Shanghai, China
The advancement of mobile technology and the increasing importance of health promote the boom in mobile
health services (MHS) around the world. Although there have been several studies investigating the health
technology acceptance behavior from a variety of theoretical perspectives, they have not provided a unified
understanding. To fill this research gap, this paper: (1) reviews the health technology acceptance literature and
discusses three prominent models (e.g., the technology acceptance model, the theory of planned behavior or the
unified theory of use and acceptance of technology, and the protection motivation theory), (2) empirically compares
the three models, and (3) formulates and empirically validates the unified model in the context of mobile health
services. In the unified model of health technology acceptance, we propose that users’ intention to use mobile health
services is determined by five key factors: performance expectancy, effort expectancy, social influence, facilitating
conditions, and threat appraisals. The results show that the unified model outperforms the three alternative models
by significantly improving the R-squares. Finally, the implications for theory and practice are put forward.
Keywords: mobile health; technology acceptance model (TAM); protection motivation theory (PMT); theory of
planned behavior (TPB); the unified theory of use and acceptance of technology (UTAUT)
1. Introduction
The advancement of wireless networks and mobile devices has driven the emergence of mobile health services
(MHS) which can be defined as a variety of healthcare services, including health consulting, hospital registering,
and location-based services delivered through mobile communications and network technologies [Istepanian et al.
2006; Ivatury et al. 2009]. Compared to the previous electronic health services which are based on the desktop
computer and wired network, MHS enables users to access to health services more conveniently. For example, when
a user suddenly suffers a heart attack in the suburbs where the wired network is not available, s/he can press an SOS
button on the customized mobile device for MHS, and then the emergency center of the hospital will receive the
message, identify the location of the user, and arrange for the aid.
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The most important issue for mobile health service providers is to attract and keep their users, precipitating the
understanding on users’ mobile health service adoption behavior as a critical issue for researchers on this specific
research area. However, despite numerous previous studies investigating the electronic health technology adoption
behavior, most of these studies view this phenomenon from the perspective of professionals or physicians [e.g.,
Bhattacherjee et al. 2007; Chau et al. 2002; Klein 2007], focusing on the technologies used in the diagnosis process
[Romanow et al. 2012] such as electronic medical records [e.g., Hennington et al. 2007] and computerized physician
order entry (COPE) systems [e.g., Bhattacherjee et al. 2007]. In contrast, the studies on health technology adoption
behavior from the perspective of patients or consumers are relatively rare. This lack is a mismatch for the increasing
prevalence of health technology or services for consumers who receive medical care [Or et al. 2009]. Thus, our
study fills the gap by examining the health technology acceptance behavior from the perspective of consumers rather
than that of professionals.
Among the limited empirical studies on consumers’ health technology adoption behavior, most studies view this
issue from the technology acceptance theories. For example, Akter et al. [2010] investigate how users’ perceptions
of mobile health service quality influence their intentions to adopt the services from the information systems success
model [DeLone et al. 2003]. Cocosila and Archer [2010] purport that users’ intentions to adopt MHS are determined
by their extrinsic motivation (e.g., the extent to which the services are useful) and intrinsic motivation (e.g., the
extent to which the services are enjoyable) from the motivational model [Venkatesh et al. 2002]. Hung and Jen
[2010] posit behavioral intention to be the result of perceived usefulness and perceived ease of use by drawing on
the technology acceptance model (TAM) [Davis 1989].
Although these studies treating consumer health technology acceptance behavior as a special case of technology
acceptance tell part of the whole story, these studies do not shed light on how usersdecision making processes
differ when the technology is for healthcare rather than for other objectives. According to Nutbeam [1998], health
behavior is defined as “any activity undertaken by an individual, regardless of actual or perceived health status, for
the purpose of promoting, protecting or maintaining health, whether or not such behavior is objectively effective
towards that end” (p. 355). Regarding the adoption of health services as an activity to promote, protect or maintain
health, health technology acceptance behavior should be considered health behavior [Laugesen et al. 2011;
Scammon et al. 2011]. Therefore, a better understanding of the health technology acceptance behavior should be
seen not only from a technology acceptance perspective but also as a health behavior perspective.
Treating health technology acceptance behavior as health behavior, a variety of health behavior theories can be
used to explain this phenomenon. Among these theories, the protection motivation theory (PMT) is most widely
used. This theory argues that individuals’ evaluations on the severity and the vulnerability of the potential threats
(i.e., threat appraisals) and the extent to which they can cope with the threats by conducting certain health behavior
(i.e., coping appraisals) will determine their intentions to perform the health behavior [Rogers 1983]. Here, the
health technology acceptance behavior is regarded as a behavior to cope with the potential threats to health.
Both the technology acceptance and the health behavior theories can be used to explain the health technology
acceptance behavior. The question that follows then is whether or not one stream of theories can outperform another
stream of theories in predicting the health technology acceptance behavior, and whether it is possible to integrate
these two theoretical streams to formulate a unified model. Therefore, the research objective of the study can be
clearly stated as: to compare and integrate the alternative models to explain the health technology acceptance
The remainder of the paper is organized as follows. The technology acceptance theories and the health behavior
theories are first reviewed, after which the differences and the similarities between these theories are articulated and
a unified model to integrate these theories is proposed. Next, the methods and procedures to collect the data are
shown and the data analysis results are reported. Finally, the limitations, theoretical and practical implications of the
study are discussed.
2. Literature Review and Theoretical Background
2.1. Technology Acceptance Theories
Technology acceptance is regarded as one of the most important research areas in the information systems (IS)
research [Venkatesh et al. 2003]. It engages in understanding the variety of factors that determine users’ intentions to
adopt a technology and their actual technology usage behaviors. An overview of the literature relevant to the
technology acceptance behavior is portrayed in Figure 1.
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Figure 1: An Overview of Technology Acceptance Theories
The key theories used to explain the technology acceptance behavior and the relationships between these
theories are shown in Figure 1. Among these theories, Davis’ [1989] technology acceptance model (TAM) is most
influential. This theory states that users’ intention to adopt a new technology is determined by two key beliefs,
namely, perceived usefulness and perceived ease of use. Perceived usefulness (PU) refers to “the degree to which a
person believes that using a particular system would enhance his or her job performance” [Davis 1989, p.320], while
perceived ease of use (PEOU) refers to “the degree to which a person believes that using a particular system would
be free of effort” [Davis 1989, p. 320]. This theory is derived from a more general theory to explain individual
behavior, namely, the theory of reasoned action (TRA) [Fishbein et al. 1975], which argues that individual
behavioral intention is determined by two key factors: attitude, which describes “an individual’s positive or negative
feelings (evaluative affect) about performing the target behavior” [Fishbein et al. 1975, p. 216] and subjective norm,
which captures “the person’s perception that most people who are important to him think he should or should not
perform the behavior in question” [Fishbein et al. 1975, p. 302]. Attitude is formed based on an individual’s beliefs
about consequences of particular behavior (e.g., behavioral beliefs), and subjective norm is formed based on an
individual’s perceptions of social normative pressures (e.g., normative beliefs). The two important factors in TAM
can actually be regarded as two beliefs resulting in attitude. Obviously, subjective norm is not considered in TAM.
To fill this gap, Venkatesh and Davis [2000] extended TAM by including subjective norm as another important
determinant of intention in the case of mandatory settings (e.g., TAM2). Thus, TAM2, in some sense, is equivalent
to TRA.
The unified theory of acceptance and use of technology (UTAUT) is another widely used theory to explain
technology acceptance [e.g., Fetscherin et al. 2008; Yu 2012; Zhou 2012]. In UTAUT, Venkatesh et al. [2003] -
based on the comparison of the eight prominent theories - further extended TAM2 by reframing the concepts used in
previous studies and including facilitating conditions as an additional predictor of intention. Specifically, perceived
usefulness, perceived ease of use, and subjective norm are respectively represented by three new terms, namely,
performance expectancy, effort expectancy, and social influence.
In greater detail, performance expectancy refers to “the degree to which an individual believes that using the
system will help him or her to attain gains in job performance” [Venkatesh et al. 2003, p. 447]. Effort expectancy
Theory of Reasoned
Action (TRA)
Technology Acceptance
Model (TAM)
Unified Theory of
Technology Acceptance
and use of Technology
Theory of Planned
Behavior (TPB)
IS Success Model
Innovation Diffusion
Task-Technology Fit
+ Perceived Behavioral
Control (PBC)
Key Beliefs Formulating
Antecedents of PU
and PEOU
+ Subjective Norm (SN)
+ Facilitating Conditions
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refers to “the degree of ease associated with the use of the system” [Venkatesh et al. 2003, p. 450]. Social influence
is defined as “the degree to which an individual perceives that important others believe he or she should use the new
system[Venkatesh et al. 2003, p. 451]. The added construct - facilitating conditions - is defined as “the degree to
which an individual believes that an organizational and technical infrastructure exists to support use of the system”
[Venkatesh et al. 2003, p. 453]. This definition is very similar to the concept of perceived behavioral control (PBC)
in the theory of planned behavior (TPB) [Ajzen 1991], which is an extension of TRA by including PBC. Therefore,
UTAUT and TPB are equivalent regarding performance expectancy and effort expectancy as two components of
attitude [Benbasat et al. 2007].
In addition to the relevant TAM theories, there are several other theories used to explain the technology
acceptance behavior including the IS success model, innovation diffusion theory (IDT), and task - technology fit
theory (TTFT). Specifically, DeLone and McLean’s [2003] IS success model suggests that the technology
acceptance behavior is determined by information quality, system quality, and service quality. The innovation
diffusion theory argues that the adoption of innovation is determined by five factors: relative advantage,
compatibility, triability, observability, and complexity
Table 1: Previous Studies on Health Information Technology Acceptance
User Type
Key Conclusion
[Hu et al. 1999]
PU is a significant determinant of attitude and
intention, but PEOU is not.
[Chau et al. 2001]
and IDT
Compatibility has a positive effect on PU.
PU has positive effects on attitude and behavioral
Attitude and PBC have positive effects on behavioral
intention but SN does not.
[Chau et al. 2002]
TAM is more appropriate than TPB for examining
technology acceptance by individual professionals.
[Yi et al. 2006]
PU has a positive effect on behavioral intention, but
PEOU does not.
PBC and SN have
positive impacts on behavioral
Result demonstrability and image have positive effects
on PU and PEOU.
[Bhattacherjee et
al. 2007]
PU has a positive effect on intention, but PEOU does
Perceived compatibility has a positive effect on PU.
[Liang et al. 2010]
Performance expectancy and facilitating conditions
have significant impacts on IT use,
while effort
expectancy and SN do not.
[Moores 2012]
TAM and IS
success model
PU and PEOU have significant impacts on attitude
towards technology adoption.
Information quality has a significant impact on PU but
insignificant effect on PEOU.
[Hung et al. 2012]
Attitude, SN, PBC have positive effects on usage
PU and PEOU have positive effects on attitude.
[Kim et al. 2007]
PU has a positive effect on satisfaction, while PEOU
does not.
[Klein 2007]
PU has a positive effect on behavioral intention.
[Akter et al. 2010]
IS success
Service quality has three dimensions, namely, platform
quality, interaction quality, and outcome quality.
Service quality has positive effects on satisfaction and
behavioral intention.
[Rogers 1995]. The task technology fit theory postulates that when the task characteristic and technology
characteristic are a good fit, an individual’s technology utilization and his/her performance will increase [Goodhue
et al. 1995]. These three theories are often used to explain the antecedents of TAM factors or TPB factors. For
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example, Wixom and Todd [2005] and Celik and Yimaz [2011] argue for information quality and system quality as
the antecedents of PU and PEOU. Dishaw and Strong [1999] propose task-technology fit as the antecedent of PU
and PEOU. Lau et al. [2001] suggest factors in the innovation diffusion theory, including compatibility and relative
advantage, as the antecedents of attitude.
Most previous empirical studies on health information technology (HIT) acceptance are built upon the
technology acceptance theories (as shown in Table 1).
As shown in Table 1, most of these studies focus on investigating the professionals’ technology acceptance
rather than the patients’ technology acceptance. One of the interesting findings of these studies is that perceived ease
of use has no significant impact on behavioral intention, because professionals may exhibit considerable competence
and adaptability to new technologies [Hu et al. 1999]. However, when examining the patients’ technology
acceptance behavior, the conclusion may not hold true, requiring further empirical examination. Further, among
these studies, TAM and TPB are regarded as two of the most influential theories on technology acceptance behavior,
and thus we compare these two models in our study.
2.2. Health Behavior Theories
To differentiate the acceptance behavior of health information technology from other technologies, researchers
need to pay attention to adapting the model specifically to the health care context [Holden et al. 2010]. Therefore,
despite the technology acceptance theories, the health behavior theories also need to be taken into account.
There are four major theories used to explain health behavior: health belief model (HBM), protection
motivation theory (PMT), subjective expected utility theory (SEU), and theory of reasoned action (TRA) (see the
review by Weinstein [1993]). HBM [e.g., Becker 1974] believes that a person makes a decision on whether or not to
take a health-related action based on his/her evaluations on the perceived threat of not taking the action and the net
benefits of taking the action. Specifically, perceived threat is assessed according to perceived susceptibility (i.e.,
one’s opinion of chances of getting a condition) and perceived severity (i.e., one’s opinion of how serious a
condition and its consequences are). Net benefits are calculated based on perceived benefits (i.e., one’s belief in the
efficacy of the advised action to solve the threat) and perceived barriers (i.e., one’s opinion of the tangible and
psychological costs of the advised action). PMT [e.g., Rogers 1975] proposes a series of factors similar to HBM to
explain health behavior. Specifically, PMT uses perceived vulnerability, perceived severity, response efficacy, and
response costs to represent perceived susceptibility, perceived severity, perceived benefits, and perceived barriers in
HBM. It includes a new factor, self-efficacy, to capture the degree to which one has the ability to perform the
advised action [Bandura 1977]. Further, PMT classifies these factors into two categories according to individuals’
decision making stages: the threat appraisals, including perceived vulnerability and perceived severity, and the
coping appraisals, including response efficacy, response costs, and self-efficacy. PMT is generally regarded as a
better theory than is HBM for explaining health behavior [Prentice-Dunn et al. 1986].
SEU and TRA are considered as more general theories on health behavior. SEU [e.g., Ronis 1992] postulates
that an individual’s behavior is determined by his/her evaluation of the expected utilities of alternative behaviors and
the utilities of these behaviors. Similarly, TRA [e.g., Fishbein et al. 1975] articulates that individual behaviors are
determined by attitude (i.e., the sum of the expected values of the behavioral consequences), and subjective norm
(i.e., an individual’s perception of whether people important to the individual think the behavior should be
performed). SEU and TRA can be used to explain general individual behaviors but are not limited to health
As shown in Figure 2, all four health behavior theories are associated with the two fundamental principles:
expectancy value theory and cost benefit analysis [Weinstein 1993]. For example, in PMT, perceived severity
and perceived vulnerability are derived from the expectancy value theory, and the response efficacy and response
costs are derived from the cost benefit analysis.
Regarding PMT’s advantage over HBM and SEU for including the factor self-efficacy and that TRA is superior
to other theories for including social influence (e.g., subjective norm), we take PMT and TRA (or its extension TPB)
as the major theories for understanding health behavior.
Although PMT has been widely used as a theory to explain the adoption of health technology or services in
health psychology (see the meta-analysis by Floyd et al. [2000]), in the literature its application in the mobile health
context is rarely found. In our study, we thus examine whether PMT is an appropriate theory to explain the mobile
health service adoption behavior and compare the predictive powers of PMT and technology acceptance theories.
2.3. Rationality for Theoretical Integration
Regarding mobile health service adoption behavior as both technology acceptance behavior and health behavior,
a comprehensive understanding of the issue requires the integration of these two theoretical perspectives. To
formulate a unified theory on health technology acceptance, the three prominent theories (e.g., TAM, TPB or
UTAUT, and PMT) are compared in Table 2.
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Figure 2: An Overview of Health Behavior Theories
As evident in Table 2, several different terms used in different theories share similar meanings. For example,
perceived usefulness in TAM is similar to response efficacy in PMT, reflecting the degree to which using mobile
health services can reduce the potential threats to health; self-efficacy and response cost in PMT can be respectively
regarded as perceived internal and external behavioral control (PBC) in TPB or facilitating conditions in UTAUT.
Further, perceived usefulness and perceived ease of use in TAM reflect TPB attitude. Thus, to formulate a unified
theory, we need to deal with the conceptual overlaps in different theories.
Following the framework of UTAUT [Venkatesh et al. 2003], perceived usefulness or response efficacy can be
seen as performance expectancy; perceived ease of use can be captured by effort expectancy; subjective norm can be
represented by social influence. Self-efficacy and response cost can be interpreted by facilitating conditions. Further,
we adapt the term, threat appraisals, in PMT to capture perceived vulnerability and perceived severity [Rogers 1975].
Through this reconceptualization in the unified model of health technology acceptance, five factors are taken as the
determinants of health technology acceptance: performance expectancy, effort expectancy, social influence,
facilitating conditions, and threat appraisals.
Table 2: A Comparison of the Three Prominent Theories
Unified Model
Performance Expectancy
Perceived Usefulness /
Response Efficacy
Effort Expectancy
Perceived Ease of Use
Social Influence
Subjective Norm
Facilitating Conditions
Self-Efficacy (Perceived
Internal Behavioral
Response Cost (Perceived
External Behavioral
Threat Appraisals
Perceived Vulnerability
Perceived Severity
Through the comparisons of the three prominent models, we can see that TAM captures only the components of
Expectancy Value
Benefit Cost
Subjective Norm
Response Cost
Response Efficacy
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performance and effort expectancy. TPB or UTAUT captures the first four components but does not consider the
component of threat appraisals. In contrast, PMT does not include the components of effort expectancy and social
influence. Therefore, the integrated model (see Figure 3) is expected to have greater predictive power than any of the
alternative models.
Figure 3: Research Model
3. Hypotheses
3.1. Performance Expectancy
Performance expectancy describes users’ opinions of the effectiveness of using a technology [Venkatesh et al.
2003]. Within our research context where mobile health services (MHS) are the target technology, its effectiveness
can be captured by the extent to which it can help users to reduce the health-relevant threat, and thus response
efficacy in the PMT theory is treated as a proxy of performance expectancy. When users consider that using MHS
can enable them to reduce the threats to health, they will be more likely to adopt this technology. This positive
relationship can also be supported according to the positive effect of performance expectancy on behavioral
intention in UTAUT [Venkatesh et al. 2003] and the positive effect of response efficacy on behavioral intention in
PMT [Rogers 1975].We thus propose:
H1: Response efficacy is positively associated with MHS adoption intention.
3.2. Effort Expectancy
Response Cost
Ease of Use
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Effort expectancy describes users’ opinion of the effort associated with the use of a technology [Venkatesh et al.
2003]. It can be represented by perceived ease of use in TAM. In previous studies on the professionals’ technology
acceptance behaviors, because most professionals have adequate competence to learn and operate the technology,
perceived ease of use is found to have insignificant impact on behavioral intention [e.g., Hu et al. 1999]. However,
within our research context regarding elderly users not being skilled users of new technologies, the elderly will not
be likely to try a new technology which is perceived to be complex. In this situation, perceived ease of use should be
a significant predictor of behavioral intention. This is supported by UTAUT, which argues that age moderates the
relationship between perceived ease of use and intention, such that this relationship will be more salient for older
users than for younger users [Venkatesh et al. 2003]. Therefore, we propose that:
H2: Perceived ease of use is positively associated with MHS adoption intention.
3.3. Social Influence
Social influence captures how users’ decision making is affected by significant others’ perceptions. It can be
reflected by the subjective norm in TPB. Previous studies on professionals’ health technology acceptance behavior
have found that social influence does not play an important role [e.g., Chau et al. 2001] because most professionals
are confident in their own decision making and are not concerned about others’ opinions. However, in our study,
because most elderly users tend to have their decision making reliant on others’ suggestions, social influence should
play a more important role. The UTAUT also states that the relationship between social influence and behavioral
intention is stronger for older users [Venkatesh et al. 2003]. Thus, we propose that:
H3: Subjective norm is positively associated with MHS adoption intention.
3.4. Facilitating Conditions
Facilitating conditions describes the potential conditions that constrain or facilitate performing the behavior.
This is similar to the concept of perceived behavioral control in TPB. According to TPB, perceived behavioral
control is derived from two sources: external and the internal control [Ajzen 1991]. External control stresses the
extent to which individuals have adequate external resources to perform a behavior, while internal control focuses
on the extent to which individuals have the ability to undertake the behavior [Pavlou et al. 2006; Yang et al. 2009].
Within our research context, response cost is regarded to be associated with external control because it is
relevant to the resources (especially the money and effort) spent for learning and using the MHS. If users need to
spend considerable money to pay for the services or much effort to learn the technology (i.e., high response cost),
they may be unlikely to use the technology, indicating a negative relationship between response cost and adoption
intention [e.g., Rogers 1975]. Further, for elderly users who care more about the value of expenditures, response cost
should be an important factor influencing their decision making. For example, according to our initial interview with
some of the elderly users, they gave raising their grandsons priority over other issues. Therefore, we propose that:
H4: Response cost is negatively associated with MHS adoption intention.
The internal control in our study refers to users’ ability to learn and use mobile health services. When users are
confident in their ability to use the technology, they will be more likely to use that technology. This relationship has
been well established in previous studies on technology acceptance [e.g, Venkatesh et al. 2003]. Within our research
context, lack of competence to use the technology may become a major barrier inhibiting the elderly users’ new
technology acceptance. As suggested by Morris et al. [2005], the relationship between perceived behavioral control
and behavioral intention is stronger for older users. Thus, we propose that:
H5: Self-efficacy is positively associated with MHS adoption intention.
3.5. Threat Appraisals
The motivation theory postulates that any individual behavior is driven by the needs [Deci et al. 1985].
However, all of the aforementioned factors have focused mainly on what should be considered when an individual
has identified certain needs to adopt a new technology, while how these needs are generated is not taken into
consideration. According to PMT, a health behavior is induced not only by threat appraisals that assess the
probability and severity of the threats (i.e., needs) but also by coping appraisals that assess how one responds to the
situation [Rogers 1975]. Thus, beyond the aforementioned factors that capture only coping appraisals, the threat
appraisals should also be considered.
According to PMT, threat appraisals include two constructs: perceived vulnerability and perceived severity.
Perceived vulnerability refers to the probability that one will experience harm, while perceived severity refers to the
degree of harm from unhealthy behavior [Rogers 1975]. When users consider that they are more likely to suffer a
health-relevant threat (i.e., high perceived vulnerability) and/or the harm of the threat is serious (i.e., high perceived
severity), they will tend to adopt the health technology that can avoid or reduce the threat [Rogers 1975].
Accordingly, we propose that:
H6: Perceived vulnerability is positively associated with MHS adoption intention.
H7: Perceived severity is positively associated with MHS adoption intention.
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4. Methodology
4.1. Research Setting and Subjects
To test our proposed research model and hypotheses, a field survey was conducted with subjects who were the
customers of a large company providing mobile health services (MHS) targeting elderly consumers in Harbin, China.
This company is one of the biggest companies in China providing integrated health services for the elderly, and it is
also the only company that obtains the ISO9001 certification in China to provide home healthcare services for the
elderly, indicating that the target company is an appropriate site for collecting data. As to the subjects, elderly
consumers were selected as the target subjects because the target company takes elderly consumers as target
consumers, and this consumer group accounts for a vast proportion of the whole consumers of health services.
However, this sampling strategy also limits the external validity of the research result and should be regarded as a
limitation of the study.
The MHS was initiated by the target company in collaboration with the Harbin government and was first
released to the market in February 2010. The target consumers of this service were the elderly of one million
families in 500 communities in Harbin. When the study was conducted in June and July 2011, it was still not largely
adopted by these potential consumers.
The characteristics of the MHS provided by this company can be described as follows. First, the consulting and
assistance center provides the elderly with emergency aid through real time positioning techniques and refined daily
information and consulting services. Second, the customized terminal is designed with larger font sizes, bigger
buttons, and higher volume, as well as radio and other multimedia entertainment functions. Third, remote
positioning services, with the help of Location Based Services (LBS) or Global Positioning System (GPS), can
enable relatives of the elderly to achieve real-time positioning when the elderly are in an emergency. Additionally, it
can facilitate the elderly to search for transportation routes by themselves.
The fees for the service were charged by two major components: the payment for the mobile terminal (about
159 USD) and the monthly fee for the services (about 3 USD per month). The fees for the service were relatively
low because this project was supported by the government’s provision of certain financial subsidies to the company.
4.2. Operationalization of Constructs
As most of the measures for the constructs in the research model are available in prior studies, we adapted these
measures as the basis for developing our measures. To fit with our research context, we first stated the possible
problems that might be encountered by users, including (1) getting lost when not being familiar with the traffic, (2)
suddenly requiring emergency aid, and (3) having little knowledge about self-care. Then, in relation to these
problems, we asked the subjects to evaluate perceived vulnerability and perceived severity. These two constructs
were measured with the adjusted items of threat susceptibility and threat severity from Johnston and Warkentin
We then introduced the characteristics of the mobile health services provided by the target company, including:
(1) the consulting and assistance center providing real time emergency aid services, (2) the customized terminal
designed in terms of older people’s usage habit, and (3) the remote positioning services based on the Location Based
Services (LBS) and Global Positioning System (GPS). The pricing strategies and payment modes were also
introduced. Next, the subjects were asked to answer the questions on response efficacy, self-efficacy, and response
cost. The measures for response efficacy and self-efficacy were adapted from Johnston and Warkentin [2010]. The
measures for response cost were developed in terms of the research context. To ensure the comprehensiveness of
cost, response cost was measured with a formative construct with three dimensions: one dimension captures
monetary cost, as suggested by Lee and Larsen [2009] (e.g., “Mobile health services are expensive to purchase”),
while the other two dimensions capture learning cost (e.g., “I have to spend effort on learning how to use mobile
health services”) and switching cost (e.g., “Using mobile health services will change my prior life style”).
Further, other questions on TAM and TPB factors and adoption intention were asked. Perceived usefulness and
perceived ease of use were measured with the items adapted from Bhattacherjee et al. [2007]. The measures for TPB
factors, including attitude, subjective norm, and perceived behavioral control, were adapted from Kim [2009]. The
measures for the dependent variable, that is, intention to adopt MHS, were adapted from Johnston and Warkentin
[2010]. A seven-point Likert scale was used for all items (see the Appendix A).
4.3. Data Collection Procedure
The target company helped to approach the potential consumers of the services and to collect the data through
community service centers. As the company was also engaged in other businesses targeting older people, it had good
relationships with many community service centers. In the community service centers, the company routinely
provided training for its customers, about every 3 months. We requested the senior manager of the company to
evenly distribute 250 questionnaires to the managers of its 12 service centers. These service centers were in charge
of 372 communities among the total 500, representing a wide range of the geographical variety in Harbin. During
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the routine training and interaction, the company provided new product promotions when customers visited its
service centers. In the company, each employee in the service centers was in charge of a specific group of customers.
The manager in the service center randomly asked the employees in the center to distribute the survey to his/her
customers. Further, to encourage the participation, we also provided ten eggs as the incentives for participation
because older people always visited the service center when they went to the grocery shop; thus, using eggs as the
incentive was consistent with their needs and could promote participation.
We believe this sampling and survey procedure is appropriate, given the characteristics of the elderly, who
cannot be as easily accessed as ordinary people. The employees in service centers, in contrast, had established good
relationships with their customers during their long time cooperation and could easily explain the survey to their
customers using familiar approaches. All of this ensured the success of the data collection.
Among the 250 distributed questionnaires, 212 were returned with a response rate of 84.8%. The high response
rate was due to the good relationships between the employees in the service centers and the potential consumers.
After removing the incomplete cases and outliers, 204 valid responses were obtained. Among these subjects, female
subjects occupy 46.6%, and over 80% of the subjects are over 40 years of age. The education level for 52.9% of the
subjects is high school or below; approximately 51.5% have fewer than two years of computer experience, and about
70% of subjects have more than two years of mobile device usage experience.
5. Data Analysis and Results
Partial Least Squares (PLS) was used to test the research model because of the several advantages of this
technique. First, as a second-generation structural equation modeling technique, it can estimate the loadings (and
weights) of indicators on constructs (hence, assessing construct validity) and the causal relationships among
constructs in multistage models [Fornell et al. 1982; Gefen et al. 2011; Hair et al. 2011]. Second, in comparison with
covariance-based structural equation modeling, PLS is robust with fewer statistical identification issues; moreover, it
is most suitable for models with formative constructs and relatively small samples [Hair et al. 2011], which is the
case in our study. Additionally, whereas covariance-based structural equation modeling is regarded as being more
appropriate for theory confirmation, PLS does provide a good approximation of covariance-based structural
equation modeling in terms of final estimates [Gefen et al. 2011; Hair et al. 2011]. Based on the above
considerations, PLS was chosen for the current study.
The data analysis was conducted in two stages. In the first stage, the reliability and validity of the constructs
were assessed to ensure the appropriateness of the measurement model; in the second stage, the structural model was
assessed, and hypotheses were tested [Hair et al. 1998].
5.1. Assessment of the Measurement Model
Reflective constructs and formative constructs were assessed using different approaches. For the reflective
constructs, the reliability, convergent validity, and discriminant validity were examined. Composite reliability (CR)
and average variance extracted (AVE) were used to assess the reliability of reflective constructs [Fornell et al. 1981;
Hsu et al. 2008]. With the exception of perceived behavior control, all other constructs were with adequate CR
(ranged from .880 to .901) and AVE (ranged from .695 to .747), considerably above the suggested value of .70
and .50 [Fornell et al. 1981; Hsu et al. 2008]. After a careful check of the construct, perceived behavioral control, it
was found that the loading of the second item was only .415 (t=1.564). After removing this item, the CR and AV E
increased to .891 and .804, respectively, thus satisfying the criteria for reliability (see Table 3).
The convergent validity of reflective constructs was assessed by seeing if the loadings on the expected
constructs were high enough [Anderson et al. 1988; Jiang et al. 2002]. As shown in the Appendix, all item loadings
were higher than 0.70 and significant (p<.001), suggesting good convergent validity of constructs. The discriminant
validity can be assessed by testing whether the square roots of AVEs are greater than the correlations [Fornell et al.
1981]. As shown in Table 3, all correlations were below the square roots of AVEs, indicating that the constructs had
good discriminant validity.
The formative construct response cost in our study can be assessed by checking its item weights and
loadings, which, respectively, represent the relative and absolute importance of these items [Cenfetelli et al. 2009].
As shown in Table 1, the weights for RC1 and RC3 were significant, while RC2 was not. However, the item loading
for RC2 was 0.516 (t=3.790), suggesting that although RC2 was not as important as RC1 and RC2, it was still with
important absolute value [Cenfetelli et al. 2009]. In accordance with Cenfetelli and Bassellier [2009], RC2 was still
included in the analysis in order to keep the completeness of formative constructs.
Because our data were collected from a single source at the same time point, common method variance might
be a concern [Podsakoff et al. 2003]. We used the method suggested by Liang et al. [2007] to examine this issue.
The results showed that the trait factors (e.g., the proposed constructs) explained 72.5% of the variance, while the
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method factors explained only less than 1% of the variance, indicating that common method bias was not a threat to
the present study.
Table 3: Correlations and Discriminant Validity
Note: AI=Intention to adopt, ATTD=Attitude, SN=Subjective norm, PBC=Perceived behavioral control, PU=Perceived
Usefulness, PEOU=Perceived Ease of Use, PV=Perceived vulnerability, PS=Perceived severity, RE=Response efficacy,
SE=Self-efficacy, RC=Response cost. Diagonal elements denote the square root of AVE. As response cost is taken as a formative
construct, the CR and AVE for this construct is not available.
5.2. Assessment of the Structural Model
The structural model was assessed by checking the significance of path coefficients (β) between different
factors. As illustrated in Figure 4, the results showed that except for the relationship between perceived severity and
adoption intention, all other proposed relationships were significant.
Specifically, the results indicated that response efficacy had a significant positive effect on intention (β=.297,
t=6.097), lending support to H1. Perceived ease of use was found to have a significant effect on intention (β=.150,
t=2.217), and thus H2 was supported. Subjective norm was found to be significantly related to intention (β=.192,
t=2.934), supporting H3. Two factors of facilitating conditions, self-efficacy and response cost, were found to have
significant positive (β=.161, t=3.000) and negative (β=-.111, t=2.243) impacts on intention, respectively. For the two
factors of threat appraisals, perceived vulnerability had a significant effect on intention (β=.120, t=2.104), while
perceived severity did not (β=-.042, t=0.825). These factors fully explained 43.6% of the variance of intention.
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Figure 4: PLS Results
With regards to whether or not the proposed research model can be seen as an improvement over the alternative
models, the PLS results for the three alternative models (e.g., TAM, TPB and PMT) are shown in Table 4. The
results indicate that TAM and TPB, respectively, explain 32.6% and 32.7% of the total variance of intention, while
the PMT explains 38.8% of the total variance of intention. Although PMT has greater R-square than the other two
alternatives, it is still less powerful than the proposed unified model, which explains 4.8% more variance, validating
the value of the theoretical integration.
6. Discussion and Implications
6.1. Key Findings
This study based on theories of technology acceptance and health behavior develops a unified model to explain
the health information technology acceptance behavior. There are three key findings of the study.
First, through the comparisons between the three alternative models and the unified model, this study shows that
the unified model outperforms its competing models by providing higher R-squares. Specifically, TAM and TPB,
which focus on the general technology acceptance issues, are with weaker predictive powers than is PMT, which
stresses health behavior. All of these three models are not as effective as the unified model, indicating that a
comprehensive understanding of health technology acceptance behavior should consider both technology acceptance
behavior as well as health behavior.
Response Cost
Ease of Use
Note: nsp>.1, *p<.05, **p<.01, ***p<.001
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Table 4: PLS Results for the Three Alternative Models
Independent Variables
Perceived Usefulness (PU)
Perceived Ease of Use (PEOU)
Attitude (ATTD)
Subjective Norm (SN)
Perceived Behavioral Control (PBC)
Perceived Vulnerability (PV)
Perceived Severity (PS)
Response Efficacy (RE)
Response Cost (RC)
Self-Efficacy (SE)
Note: nsp>.1, *p<.05, **p<.01, ***p<.001.
Second, the results show that the factors relevant to coping appraisals are more important than are the factors
associated with threat appraisals in predicting health technology acceptance. Specifically, response efficacy is found
to be the most influential factor, followed by subjective norm, self-efficacy, and perceived ease of use. All of these
factors are relevant to the coping stage. However, the factors relevant to threat appraisals have only relatively weak
(e.g., perceived vulnerability) or no (e.g., perceived severity) effects on behavioral intention. This is consistent with
the meta-analysis results of Floyd et al. [2000], suggesting that coping appraisals should be paid more attention in
the study on health behavior.
Third, this study reveals that subjective norm and perceived ease of use have significant impacts on adoption
intention. This is contrary to the findings in previous studies on professionals’ technology acceptance behavior [e.g.,
Chau et al. 2002; Hu et al. 1999]. This can be explained by the distinctions between professional users and consumer
users. Because professional users generally have adequate competency to learn and use a new technology, they will
tend to rely on their own judgment in their decision making, and the technology complexity will not be a barrier
inhibiting their technology acceptance [Hu et al. 1999]. However, for consumer users, especially the elderly users in
our study who are not so skilled in learning and using a new technology, the ease of use of the technology and others’
opinions becomes important. This indicates that effort expectancy and social influence should be taken into account
when investigating consumer health technology acceptance.
It is worth noting that perceived severity is found to have insignificant impact on adoption intention, suggesting
that the effect size of perceived severity is limited. According to previous literature, coping appraisals can achieve
medium effect size, while threat appraisals can achieve only small effect size [Floyd et al. 2000]. The meta-analysis
also indicates that perceived vulnerability often has stronger impact than does perceived severity [Milne et al. 2000].
In this case, when more influential predictors exist, the impact of perceived severity becomes unobservable. This is
induced by its low relative value rather than its absolute value [Petter et al. 2007]. A good indicator of its absolute
impact on intention is its correlation with intention. As shown in Table 3, the correlation between perceived severity
and intention is 0.227, suggesting that its absolute value is significant at the significant level of p<.01.
6.2. Theoretical Implications
The study can contribute to the health information technology literature in several ways. First, this study
provides a unified model of health technology acceptance by considering both the technology acceptance and health
behavior theories. It amends the previous studies that investigate health technology acceptance behavior solely based
on the general technology acceptance theories without considering the distinctions between health technology and
other technologies [e.g., Hu et al. 1999]. In this paper, the acceptance of health technology is regarded as both a
technology acceptance behavior and a health behavior, providing a more comprehensive understanding on this
specific technology acceptance behavior. It can be regarded as a response to the call that encourages developing a
technology acceptance model adapted to the health care context [Holden et al. 2010]. It also suggests future research
on health technology acceptance to draw upon the theoretical underpinnings from these two research streams.
Second, this study proposes a model to understand the acceptance of consumer health technology rather than
professional health technology, identifying the distinctions between the acceptance behavior of these two types of
users. Most previous studies on health technology acceptance focus on understanding the factors enabling or
inhibiting the physicians or professionals’ health technology adoption [e.g., Bhattacherjee et al. 2007] but pay less
attention on the technology acceptance behavior of consumers [Or et al. 2009]. Regarding the increasing importance
of consumer health technologies, this study provides certain initial exploration of this new type of technology
acceptance behavior. Specifically, the study shows that subjective norm and self-efficacy, which are identified as
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insignificant in previous studies on professional health technology acceptance [e.g., Hu et al. 1999], become
significant in the context of consumer health technology. This suggests that future research should take the
distinctions between professional and consumer health technologies into account when proposing research models.
Third, this study also extends the traditional protection motivation theory (PMT) by pointing out the role of
social influence and effort expectancy. Traditional PMT takes response efficacy, self-efficacy, response cost,
perceived vulnerability, and perceived severity as five key determinants of health behavior [Floyd et al. 2000].
However, several other important factors associated with the technology acceptance process have been neglected.
Specifically, although PMT has recognized the role of effectiveness of technology (e.g., response efficacy), it does
not take into consideration the effort relevant to technology use (i.e., effort expectancy or perceived ease of use).
Further, PMT assumes that users make decisions based on their own evaluations, ignoring the fact that that users’
decision making can also be affected by social influence. Therefore, an improvement of the traditional PMT is to
include social influence and effort expectancy in the model.
6.3. Practical Implications
Several practical implications can be derived from the study. First, response efficacy is found to be the most
important factor in the mobile health service adoption, and thus service providers should try their best to improve
their service quality in order to attract more consumers. Second, since social influence can positively affect user
behavior, service providers should carry out certain promotion strategies to obtain early adopters and then expand
the consumer scale through the social influence (e.g., word of mouth). Third, perceived ease of use and self-efficacy
are two important factors influencing user behavior; for this reason service providers should adopt a user-centric
service design method to ensure not only that the services can be easily learned and used, but also that service
providers provide trainings for the potential consumers. Fourth, regarding the negative effect of response cost on
adoption intention, service providers should set an appropriate service price, one which would be accepted by
potential users. This can be achieved through a marketing survey. Finally, as perceived vulnerability has significant
impact on adoption intention, service providers should identify the target market by analyzing not only the threats
that the services could reduce but also the people who are more likely to experience the threats.
6.4. Limitations
Despite the theoretical and practical implications of the study, our findings should be interpreted in the light of
the limitations. First, elderly users were taken as the sample in the study because elderly users account for a vast
proportion of the whole MHS users. With the increase of people’s health concerns, MHS providers should also begin
to target other populations as potential customers. Thus, the conclusions of the current study should be applied with
caution when the sampling population changes (e.g., the role of self-efficacy may not be as salient for college
students). Second, because the study was conducted in China, which has a collectivistic culture, applying the
conclusions to other cultural societies should be further examined in future research. Finally, while the explanatory
power of the model (43.6% for intention) was acceptable, it could potentially be enhanced through the inclusion of
additional factors in future research.
7. Conclusion
Previous literature on health technology acceptance focuses on professional health technology and relies heavily
on the general technology acceptance theories; however, the consumer health technology acceptance behavior is less
discussed. In this study concerning consumer health technology acceptance behavior as both a technology
acceptance behavior as well as a health behavior, we propose a unified model that integrates the technology
acceptance theories and health behavior theories. Through comparing the technology acceptance model, the theory
of planned behavior, the protection motivation theory, and the unified model, the superiority of the unified model is
confirmed. This study suggests that future research on health technology acceptance should consider the distinctions
between professional and consumer health technology and take both the technology acceptance theories and the
health behavior theories into account.
This study was partially supported by the Hong Kong Scholars Program and the National Science Foundation of
China Grant (Project No. 71201118, 71101037, 71201058), the Fundamental Research Funds for the Central
Universities (Project No. 274130), and Wuhan University Academic Development Plan for Scholars after 1970s
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APPENDIX A. Measures, Loadings and Weights
a This item was removed from the analysis due to the low loading.
Intention to Adopt (AI): Adapted from [Johnston et al. 2010]
AI1. I intend to use mobile health services in the next 3 months.
AI2. I predict I will use mobile health service in the next 3 months.
AI3. I plan to use mobile health services in the next 3 months.
Attitude (ATTD): Adapted from [Kim 2009]
ATTD1. Using mobile health services is a bad/good idea.
ATTD2. Using mobile health services is a foolish/wise idea.
ATTD3. I dislike/like the idea of using mobile health services.
ATTD4. Using mobile health services is unpleasant/pleasant.
Subjective Norm (SN): Adapted from [Kim 2009]
SN1. People who influence my behavior think that I should use mobile health services.
SN2. People who are important to me think that I should use mobile health services.
Perceived Behavioral Control (PBC): Adapted from [Kim 2009]
PBC1. I have control over using mobile health services.
aPBC2. I have the resources necessary to use mobile health services.
PBC3. I have the knowledge necessary to use mobile health services.
Perceived Usefulness: Adapted from [Bhattacherjee et al. 2007]
PU1. Using mobile health services will improve my life quality.
PU2. Using mobile health services will make my life more convenient.
PU3. Using mobile health services will make me more effective in my life.
PU4. Overall, I find mobile health services to be useful in my life.
Perceived Ease of Use: Adapted from [Bhattacherjee et al. 2007]
PEOU1. Learning to operate mobile health services will be easy for me.
PEOU2. I can easily become skillful at using mobile health services.
PEOU3. I can get mobile health services to do what I want them to do.
PEOU4. Overall, mobile health services are easy to use.
Perceived Vulnerability (PV): Adjusted according to Threat Susceptibility in [Johnston et al. 2010]
Please answer the following questions in terms of these problems: (1) getting lost for not
being familiar with the traffic; (2) suddenly requiring emergency aid; (3) having little
knowledge about self-care.
PV1. I am at risk for suffering the stated problems.
PV2. It is likely that I will suffer the stated problems.
PV3. It is possible for me to suffer the stated problems.
Perceived Severity (PS): Adjusted according to Threat Severity in [Johnston et al. 2010]
Please answer the following questions in terms of these problems: (1) getting lost for not
being familiar with the traffic; (2) suddenly requiring emergency aid; (3) having little
knowledge about self-care.
PS1. If I suffered the stated problems, it would be severe.
PS2. If I suffered the stated problems, it would be serious.
PS3. If I suffered the stated problems, it would be significant.
Response Efficacy (RE): Adapted from [Johnston et al. 2010]
RE1. Mobile health services work for solving these problems.
RE2. Mobile health services are effective for solving these problems.
RE3. When using mobile health services, solving these problems is more likely to be
Self-Efficacy (SE): Adapted from [Johnston et al. 2010; Lee et al. 2009]
SE1. It is easy for me to use mobile health services.
SE2. I have the capability to use mobile health services.
SE3. I am able to use mobile health services without much effort.
Response Cost (RC): Adapted from [Lee et al. 2009]
RC1. Mobile health services are expensive to purchase.
RC2. I have to spend effort on learning how to use mobile health services.
RC3. Using mobile health services will change my life style.
... The research on user behavior intention is mainly from the perspective of technology or social psychology. The main theories of this kind of research are TRA (theory of reasoned action), TPB (theory of planned behavior), TAM (technology acceptance model), TAM2 (technology acceptance model 2), UTAUT (unified theory of technology acceptance and use of technology), IDT (innovation diffusion theory), TFT (test/technology fit ) (Sun, Wang, Guo, & Peng, 2013). TRA believes that people's behavior is often made after rational thinking, predicted and control. ...
... Ifinedo (2012) integrates PMT and TPB to study employees' acceptance of information system security policies and finds that self-efficacy, attitudes, subjective norms, response effectiveness, and perceived vulnerability positively affect employees' intention to conduct information system security policies. Sun et al. (2013) have also studied the influencing factors of behavioral intention of users to mobile health services that was based on PMT and TAM. ...
... In the situation of mobile health, the response cost is mainly the time and money that people need to spend to learn and use mobile health services. People may be less willing to use mobile health if they feel that they have to put in more effort to learn this emerging technology or spend more money to use health services (R. W. Sun et al., 2013). Conversely, if fewer resources are needed to implement protective measures, people may be willing to adopt such measures (Pechmann, Zhao, Goldberg, & Reibling, 2003;Workman et al., 2008). ...
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Purpose: This paper aims to explore the factors influencing users' behavioral intentions in adopting mobile health services in China by using UTAUT (Unified Theory of Acceptance and Use of Technology) model and PMT (Protection Motivation Theory) theory. Accordingly, a unified model combining technology acceptance theory and health behavior theory is developed. Design/methodology/approach: Based on the UTAUT model as the basic theoretical framework, this paper proposes hypotheses and research models, and discusses various factors that influence the behavior intention of Chinese users to adopt mobile health services from the perspective of ordinary users. This study uses the questionnaire survey method to collect relevant samples in Zaozhuang City, Shandong Province, China, using online questionnaires and on-site distribution. Findings: It is expected that finding from the study would be beneficial to medical institutions and service providers develop mobile health products that are more suitable for the Chinese market and Chinese users and provide basis and data support for the formulation of regulatory policies by government departments, and contribute to the informatization of health services. Originality/value: This study proposes an extended theoretical model based on UTAUT model and integrating PMT theory to explore the factors affecting users' willingness to adopt mobile health in China from technology acceptance theory and health behavior theory. Therefore, the extended model has greater prediction ability. It is an extension of UTAUT's relevant dimensions in the study of mobile health users' adoption intention.
... Halaweh (2017), Sun et al. (2013), Katta and Patro (2017) ST1 I feel that my private information is secure when I use COD for e-commerce transactions. ...
... This research establishes that perceived trust, privacy issues avoidance, and perceived safety from financial risks are the major factors behind the formation of perceived security. The finding of this study allied with the previous findings that perceived security is a major factor that leads to the ACOD in online shopping (Nguyen et al., 2020;Halaweh, 2017;Sun et al., 2013;Salloum and Al Emran, 2018). This exhibits that with the development of technology e-shopping apps and payment systems are getting more user-friendly. ...
... The results of a review study [83] also showed that most of the participants use the Internet "websites" to get health information. Currently, with the increasing growth of the volume of information, the number of websites that provide health information to users is increasing [83,84]. Obtaining health information can result in selfconfidence in personal health management and a higher level of health literacy. ...
... On the other hand, information is available on the "Internet" 24 h a day, and people are not exposed to judgments or questions when searching for personal and confidential health issues that people avoid declaring in face-to-face visits. Another important point is that the "Internet" can provide the possibility of creating interaction and receiving emotional support for people [83,84]. ...
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Introduction People need health information to maintain their health. Despite the variety of sources and tools for providing health information, there is little evidence about Iranian people's preferences in using these sources and tools. The objective of this study was to identify the preferred health information sources, tools, and methods for presenting health information in these tools. Methods This national survey was conducted among a sample of 4000 Iranian people between April and September 2021. The data was collected using a valid and reliable questionnaire (α = 0.86) consisting of four sections: participants' demographic information, current sources of obtaining health information, preferred information technology (IT) tools for accessing health information, and the method of presenting this information. Linear regression was used to investigate the relationship between demographic factors and other questions. Results The participants received health information mostly from the "Internet" (3.62), "family or friends" (3.43), "social networks" (3.41), "specific websites" (3.41), and "mobile apps" (3.27). "Social networks" (3.67), Internet "websites" (3.56), and "mobile apps" (3.50) were the most suitable tools for receiving health information. The participants preferred the presentation of health information in the form of "Images" (3.85), "educational videos" (3.69), and "texts" (3.53). Age, education, and marital status had a significant relationship with most of the preferred information sources, tools, and information presentation methods ( p < 0.05). Conclusion The results of this study showed that Iranian people are more active information seekers than passive ones compared to a decade ago. The preferred sources and tools identified in this research can be used by healthcare planners and policy-makers in Iran and other developing countries to design and develop IT interventions that meet people's needs. Improving access to the Internet, social networks, and mobile apps and providing health information via images, educational videos, and texts on these platforms enhance access to the information people need.
... Our study measured the unified theory of acceptance and use of technology (UTAUT) among healthcare professionals in low-resource healthcare settings. Several studies have already applied the UTAUT model to predict mHealth adoption [58][59][60][61][62][63]. The CFA validation findings of the seven-factor measurement model (PE, EE, FCs, SI, SC, BI, and MA) based on the 25 valid items (9 items with a low loading on the constructs in the model were removed) showed a strong correlation between PE and EE, PE and FCs, PE and SI, PE and SC, and PE and BI. ...
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This study was conducted with objectives to measure and validate the unified theory of the acceptance and use of technology (UTAUT) model as well as to identify the predictors of mobile health (mHealth) technology adoption among healthcare professionals in limited-resource settings. A cross-sectional survey was conducted at the six public and private hospitals in the two districts (Lodhran and Multan) of Punjab, Pakistan. The participants of the study comprised healthcare professionals (registered doctors and nurses) working in the participating hospitals. The findings of the seven-factor measurement model showed that behavioral intention (BI) to mHealth adoption is significantly influenced by performance expectancy (β = 0.504, CR = 5.064, p < 0.05) and self-concept (β = 0.860, CR = 5.968, p < 0.05) about mHealth technologies. The findings of the structural equation model (SEM) showed that the model is acceptable (χ 2 (df = 259) = 3.207; p = 0.000; CFI = 0.891, IFI = 0.892, TLI = 0.874, RMSEA = 0.084). This study suggests that the adoption of mHealth can significantly help in improving people's access to quality healthcare resources and services as well as help in reducing costs and improving healthcare services. This study is significant in terms of identifying the predictors that play a determining role in the adoption of mHealth among healthcare professionals. This study presents an evidence-based model that provides an insight to policymakers, health organizations, governments, and political leaders in terms of facilitating, promoting, and implementing mHealth adoption plans in low-resource settings, which can significantly reduce health disparities and have a direct impact on health promotion.
... The reason behind this finding may be that all the respondents of the present study were experienced mobile phone users and not afraid to use mobile phone technology. As level of experience increases, its negative effects may decrease (Venkatesh & Bala, 2008). When people positively perceive and feel secure about technology usage, they are relaxed and less anxious about using it, because they believe they can learn and cope with the issues related to the technology (Mac Callum et al., 2014). ...
... Chi et al. (2011) showed that consumer subjective norms positively affected behavioral intention to purchase smartphones. Sun et al. (2013) demonstrated that subjective norm was positively related to the adoption intention of mobile health services. ...
Conference Paper
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Nowadays, when many family structures are child-centered, it can be seen that the seemingly mother or father decision maker in the purchasing process of some products and services is actually children. Especially in recent years, the impact of children on their parents' purchasing behavior has been the subject of research by experts. In the current study, the Planned Behavior Theory (PDT), one of the theories frequently used in researching consumer behavior, was included and the effect of Generation Alpha, which has just begun to find a place in the literature, on their parents' purchasing behavior was analyzed. Partial Least Squares Structural Equation Modeling (EKK-SEM), which is frequently used in examining the relationships between multiple variables, was used in the analysis process of the research. Additionally, analyses were carried out with Smart PLS 4, one of the analysis programs used together with EKK-YEM. In the study examining the purchasing behavior of Generation Alpha parents, as a result of the analysis of the data, it was seen that Generation Alpha children had a limited effect on their parents' purchasing behavior.
... Research suggests that effort expectancy has been identified as an important factor that directly affects the intention of users to use clinical decision support systems (Sun et al., 2013). Users generally feel connected to simple and easy-to-use technologies (Shareef et al., 2017). ...
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Purpose Despite technological advancements to enhance patient health, the risks of not discovering the correct interactions and trends in digital health are high. Hence, a careful policy is required for health coverage tailored to needs and capacity. Therefore, this study aims to explore the adoption of a cognitive computing decision support system (CCDSS) in the assessment of health-care policymaking and validates it by extending the unified theory of acceptance and use of technology model. Design/methodology/approach A survey was conducted to collect data from different stakeholders, referred to as the 4Ps, namely, patients, providers, payors and policymakers. Structural equation modelling and one-way ANOVA were used to analyse the data. Findings The result reveals that the behavioural insight of policymakers towards the assessment of health-care policymaking is based on automatic and reflective systems. Investments in CCDSS for policymaking assessment have the potential to produce rational outcomes. CCDSS, built with quality procedures, can validate whether breastfeeding-supporting policies are mother-friendly. Research limitations/implications Health-care policies are used by lawmakers to safeguard and improve public health, but it has always been a challenge. With the adoption of CCDSS, the overall goal of health-care policymaking can achieve better quality standards and improve the design of policymaking. Originality/value This study drew attention to how CCDSS as a technology enabler can drive health-care policymaking assessment for each stage and how the technology enabler can help the 4Ps of health-care gain insight into the benefits and potential value of CCDSS by demonstrating the breastfeeding supporting policy.
Background: Due to the fact that in the Iranian health information seeking literature, a questionnaire to examine the factors affecting the online health information seeking behavior of students and their health beliefs, was not found, the present study designed a questionnaire for this purpose, and approved its validity and reliability using exploratory and confirmatory factors analysis. Materials and Methods: The statistical population of the present study consisted of all students studying in all educational levels in public and private universities of Ahvaz during year 2019, the total number of which was 46054 people. According to Cochran's formula, 600 students were selected for the research sample. The instrument used in the present study was a questionnaire. The questionnaire was a combination of 36-item questionnaire base on Mo, Sheen and Cohen and Deng, Liu and Hinz research questionnaires. The validity of the questionnaire was assessed by three methods: the use of scientific articles, factor analysis and its reliability calculated by use of Cronbach's alpha coefficient. Data analysis was performed using SPSS and LISREL software. Results: By performing explanatory factor analysis, the variables affecting online search for health information were categorized and renamed into ten factors: Trust in quality and efficiency, Useful intention, Self-efficacy, Cognitive values, Perceived severity, Perceived anxiety, Perceived sensitivity, Perceived barriers, Individual health beliefs, and Social values. Then these factors were investigated using confirmatory factor analysis. Conclusion: Examination of the standardized coefficients in the obtained structural model showed that almost all factor loads have the desired value.
Online health communities have emerged with the development of information and communication technology. Patients with chronic diseases can consult doctors in OHCs to achieve more efficient self-management. This study aims to explore the effects of OHCs acceptance on patient self-management based on the Unified Theory of Acceptance and Use of Technology. We conducted an online questionnaire survey of patients who have participated in OHCs and used partial least squares to analyze the data collected. The results show that performance expectancy and social influence positively affect patients’ continuous intention and health self-management behavior in OHCs. Moreover, the continuous intention has positively affected self-management behavior. However, effort expectancy has no significant positive effects on continuous intention and self-management behavior. Finally, the implications of theoretical and practical implications are discussed.
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Presents an integrative theoretical framework to explain and to predict psychological changes achieved by different modes of treatment. This theory states that psychological procedures, whatever their form, alter the level and strength of self-efficacy. It is hypothesized that expectations of personal efficacy determine whether coping behavior will be initiated, how much effort will be expended, and how long it will be sustained in the face of obstacles and aversive experiences. Persistence in activities that are subjectively threatening but in fact relatively safe produces, through experiences of mastery, further enhancement of self-efficacy and corresponding reductions in defensive behavior. In the proposed model, expectations of personal efficacy are derived from 4 principal sources of information: performance accomplishments, vicarious experience, verbal persuasion, and physiological states. Factors influencing the cognitive processing of efficacy information arise from enactive, vicarious, exhortative, and emotive sources. The differential power of diverse therapeutic procedures is analyzed in terms of the postulated cognitive mechanism of operation. Findings are reported from microanalyses of enactive, vicarious, and emotive modes of treatment that support the hypothesized relationship between perceived self-efficacy and behavioral changes. (21/2 p ref)
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The 2010 Patient Protection and Affordable Care Act is intended to transform the U.S. health care system. Its success will require the transformation of consumers' views about health and their willingness to participate in healthful behaviors. Focusing on three barriers to consumers' engagement in healthful behaviors, the authors review the research literature and suggest opportunities for further research. Using a social marketing perspective, they suggest actions for health care providers, marketers, and policy makers to help overcome these barriers.
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Information technology executives strive to align the actions of end users with the desired security posture of management and of the firm through persuasive communication. In many cases, some element of fear is incorporated within these communications. However, within the context of computer security and information assurance, it is not yet clear how these fear-inducing arguments, known as fear appeals, will ultimately impact the actions of end users. The purpose of this study is to investigate the influence of fear appeals on the compliance of end users with recommendations to enact specific individual computer security actions toward the mitigation of threats. An examination was performed that culminated in the development and testing of a conceptual model representing an infusion of technology adoption and fear appeal theories. Results of the study suggest that fear appeals do impact end user behavioral intentions to comply with recommended individual acts of security, but the impact is not uniform across all end users. It is determined in part by perceptions of self-efficacy, response efficacy, threat severity, and social influence. The findings of this research contribute to information systems security research, human computer interaction, and organizational communication by revealing a new paradigm in which IT users form perceptions of the technology, not on the basis of performance gains, but on the basis of utility for threat mitigation.
I: Background.- 1. An Introduction.- 2. Conceptualizations of Intrinsic Motivation and Self-Determination.- II: Self-Determination Theory.- 3. Cognitive Evaluation Theory: Perceived Causality and Perceived Competence.- 4. Cognitive Evaluation Theory: Interpersonal Communication and Intrapersonal Regulation.- 5. Toward an Organismic Integration Theory: Motivation and Development.- 6. Causality Orientations Theory: Personality Influences on Motivation.- III: Alternative Approaches.- 7. Operant and Attributional Theories.- 8. Information-Processing Theories.- IV: Applications and Implications.- 9. Education.- 10. Psychotherapy.- 11. Work.- 12. Sports.- References.- Author Index.
The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addition to the known problems related to sample size and power, is that it may indicate an increasing correspondence between the hypothesized model and the observed data as both the measurement properties and the relationship between constructs decline. Further, and contrary to common assertion, the risk of making a Type II error can be substantial even when the sample size is large. Moreover, the present testing methods are unable to assess a model's explanatory power. To overcome these problems, the authors develop and apply a testing system based on measures of shared variance within the structural model, measurement model, and overall model.
Fast advances in the wireless technology and the intensive penetration of cell phones have motivated banks to spend large budget on building mobile banking systems, but the adoption rate of mobile banking is still underused than expected. Therefore, research to enrich current knowledge about what affects individuals to use mobile banking is required. Consequently, this study employs the Unified Theory of Acceptance and Use of Technology (UTAUT) to investigate what impacts people to adopt mobile banking. Through sampling 441 respondents, this study empirically concluded that individual intention to adopt mobile banking was significantly influenced by social influence, perceived financial cost, performance expectancy, and perceived credibility, in their order of influencing strength. The behavior was considerably affected by individual intention and facilitating conditions. As for moderating effects of gender and age, this study discovered that gender significantly moderated the effects of performance expectancy and perceived financial cost on behavioral intention, and the age considerably moderated the effects of facilitating conditions and perceived self-efficacy on actual adoption behavior.