Intention vs. Perception: Understanding the Differences in Physicians’ Attitudes Toward Mobile Health Applications

Abstract

The current state of mobile technology demonstrated that dissemination of mobile health (mHealth) practices is dramatically increasing. However, the success of mobile health technologies does not depend on only the technology but the actual use as well. Understanding the perception and intention about mobile health use is important in order to utilize and adopt the mobile technologies in practice effectively. This chapter provided an overview on two different physician groups (mHealth application users and nonusers) revealing the differences in their attitudes (intentions of users and perceptions of nonusers) toward actual use of mHealth applications. The study employed a secondary research approach. A survey data collected from 137 mHealth user physicians and 122 nonuser physicians were used. A research model was tested in both groups, and the statistical findings were interpreted to identify the differences between the groups. Considering significant and nonsignificant factors influencing in each group, a number of suggestions were outlined in this chapter for developers, managers, and authorities.

Full text

153© Springer International Publishing AG, part of Springer Nature 2018
E. Sezgin et al. (eds.), Current and Emerging mHealth Technologies,
https://doi.org/10.1007/978-3-319-73135-3_10
Chapter 10
Intention vs. Perception: Understanding
theDifferences inPhysicians’ Attitudes
Toward Mobile Health Applications
Emre Sezgin, SevgiÖzkanYildirim, andSonerYildirim
10.1 Introduction
Mobile health (mHealth) has becoming a signicant element for healthcare delivery.
As such, the investments and researches on mHealth have been rapidly increasing. A
number of international associations pointed out the growing market of healthcare
services with the digital era, and most of them anticipated the growth in telemedicine
and remote healthcare services in high numbers for the following decades.
McKinsey’s report in 2015 underlined that mobile device (tablet and smartphone)
market may expand 1.1–1.3 times by 2018. The value created by the expansion may
reach to hundreds of billions of dollars, and this growth will affect health and medi-
cal services the most (Atluri etal. 2015). On the other side, the 2015 OECD Digital
Economy Outlook report presented that “the global mHealth market may reach $23
billion in 2017, with Europe accounting for $6.9 billion and Asia-Pacic for $6.8
billion, ahead of the North American market of $ 6.5 billion” (OECD 2015). The
growth was not only triggered the investments but also the reduction of the costs of
healthcare delivery. By 2017, mHealth use in the European Union was reported to
have potential to save €99 billion in healthcare spending (OECD 2015). Furthermore,
global reports presented that in 2025, the use of the mobile Internet, as well as appli-
cations, was estimated to have an economic impact around 3.7–10.8 trillion dollars
E. Sezgin ()
The Research Institute, Nationwide Children’s Hospital, Columbus, OH, USA
e-mail: esezgin1@gmail.com
S. Ö. Yildirim
School of Informatics, Middle East Technical University, Ankara, Turkey
e-mail: sevgiozk@metu.edu.tr
S. Yildirim
Department of Computer Education and Instructional Technology,
Middle East Technical University, Cankaya, Ankara, Turkey
e-mail: soner@metu.edu.tr
esezgin1@gmail.com

154
per year (Manyika etal. 2013). For instance, potential value gain was estimated to be
10–20% cost reduction only in chronic disease treatment via telemedicine.
Considering the current developments and estimations, the worldwide dissemination
and use of mobile health technologies have constantly been increasing. Similarly, use
of mobile technologies and applications by healthcare providers has also increased
(PwC Health Research Institute 2014; Ventola 2014). In that regard, the mobile
application markets (App Stores) presented over thousands of applications related to
healthcare services that are used for checking tests, keeping records, and taking
assistance in diagnoses. These applications aimed to assist physicians or patients to
manage and maintain healthcare-related data by enabling storing, recording, and
accessing information (Hao etal. 2013; Martínez-Pérez etal. 2013).
On the other side, these reports demonstrated that the mHealth technologies have
penetrated to many different segments, and they have been offered to different user
groups in the market (e.g., patients, physicians, nurses). These groups were expected
to use mHealth applications in checking, controlling, and maintaining personal
healthcare or to deliver the services. However, it should be noted that the success of
these technologies does not solely depend on the technological innovations itself.
The perceptions about mHealth and the intention to use these new technologies are
important elements in order to utilize them in practice effectively. In that regard, not
only the mHealth users’ intentions but also the perception of potential users should
be considered, and the assessment of user behavior is an important input for the
success of mHealth use.
10.1.1 Background Information onAssessment
Individuals’ behaviors and attitudes toward information technologies have been
investigated for a long time (King and He 2006; Rondan-Cataluña etal. 2015). The
concept was employed for assessment of technology acceptance in the early 1990s,
and the studies in technology acceptance gained interests (Davis 1989; Wood and
Bandura 1989; Ajzen 1991; Venkatesh and Davis 2000; Venkatesh etal. 2003). One
of the leading theories was proposed by Davis (1989) as the technology acceptance
model (TAM). TAM is used to determine factors inuencing behaviors of users
toward technolo gies. The model argues that the actual use of technologies is
inuenced by perceived ease of use (PEOU) and perceived usefulness (PU). Thus,
PEOU and PU were main contributors to individuals’ attitude and behavioral
intention (BI). In the latter studies, TAM has been modied involving other
constructs to assess effects of different factors about different technologies (Bagozzi
and Warshaw 1992; Venkatesh and Davis 2000). In the literature, there have been a
number of studies about the healthcare technologies successfully using TAM theory
(Holden and Karsh 2010). Furthermore, the studies employed an integrated or
modied TAM to keep up with changing user needs and healthcare technologies.
However, a major drawback of TAM was pointed out as the difculty in the
generalization of results and inconsistency in relationships between constructs
(Venkatesh etal. 2003; Legris etal. 2003; Sun and Zhang 2006). Following TAM,
E. Sezgin et al.
esezgin1@gmail.com

155
the unied theory of acceptance and use of technology (UTAUT) was proposed as a
new integrated theory, which aims to assess the likelihood of success of new
technologies and determine drivers of acceptance (Venkatesh etal. 2003). In 2012,
Venkatesh, Thong, and Xu (2012) proposed UTAUT 2, which was an updated
UTAUT including hedonic motivation, price value, and habit as additional exogenous
variables inuencing behavioral intention. Similar to TAM, UTAUT has been
successfully implemented in a number of studies (Schaper and Pervan 2007b;
Chang etal. 2007; Aggelidis and Chatzoglou 2009; Kijsanayotin etal. 2009; Pynoo
etal. 2012; Dünnebeil etal. 2012). In addition to that, the theory of planned behavior
(TPB) and innovation diffusion theory (IDT) have also been used in behavioral
researches in healthcare delivery (Sezgin and Özkan-Yildirim 2014).
10.1.2 Aim oftheStudy
This chapter investigated the intentions and perceptions of physicians toward
mHealth applications considering two different perspectives of physicians. In that
regard, following a secondary research methodology, ndings of previous researches
about mHealth application use and adoption were employed to provide a comparison
between two physician groups. Authors believe this comparison would be a valuable
asset providing a distinct overview, which would be used in planning new health
application development, management, and promotion.
10.2 Methodology
The chapter employed a secondary research method, which focuses on the synthesis
of previous researches (Sezgin etal. 2017; Sezgin etal. 2016). In order to provide a
comparative overview, the ndings of these researches were discussed revealing the
similarities and differences in mHealth application adoption by two user groups.
The detailed methodology and research procedure of these researches were given in
this section.
The researches that were held in this study were reported ndings for intentions
and perceptions toward mHealth application by the user and nonuser physicians. In
these researches, similar research and testing procedures were employed which
helped to present a common ground for the comparison. In both researches, to
understand the inuencing factors to use mHealth apps, a systematic method was
followed. At the rst phase, a literature research was conducted to identify researches
about mHealth. It also helped to understand the behavioral theories in the domain as
well as to gather constructs for assessing adoption and acceptance of mobile health
information systems by the physicians. Following that, the conceptual model was
developed, and hypotheses were formulated. In both researches, the same model
was used, and the data collection was completed by employing a structured survey
(questionnaire). Convenience sampling was used as the data collection method, and
10 Intention vs. Perception: Understanding theDifferences inPhysicians’ Attitudes…
esezgin1@gmail.com

156
an online survey tool was employed. Non-mHealth application user physicians
(n=122) and mHealth application user physicians (n=137) participated in the
survey. Conrmatory factor analysis and structural equation modeling (SEM) were
used in the analysis of quantitative data. Figure10.1 provided an outline of the
research processes.
The following constructs were used in the model, and they were tested in both
researches in order to understand perception (of nonusers) and intention (of users)
toward mHealth applications.
• Behavioral intention (BI): The act of deciding to use a particular technology
(Venkatesh etal. 2003).
• Performance expectancy (PE): Personal beliefs using technology would increase
the job performance (Venkatesh etal. 2003).
• Effort expectancy (EE): Personal beliefs using technology would be free of effort
(Venkatesh etal. 2003).
• Compatibility (CO): The perception about the use of technology is consistent
with users’ needs, experiences, and values (Rogers 1995).
• Mobile self-efcacy (MS): Perceptions about personal abilities to use the tech-
nology to fulll healthcare task and duties on mobile devices (Schaper and
Pervan 2007b).
Fig. 10.1 Flow of the research processes
E. Sezgin et al.
esezgin1@gmail.com

157
• Technical support and training (TT): The perception and the need for support and
training to gain knowledge about the technology (Venkatesh etal. 2003).
• Perceived service availability (PS): The perception about the technology which
is able to support “pervasive and timely usage” (Venkatesh etal. 2003).
• Personal innovativeness in IT (PI): The state of a person’s willingness to take a
risk in trying a new technology or innovation (Agarwal and Prasad 1998).
• Social inuence (SI): The degree of social perceptions about technology’s desir-
ability (Venkatesh etal. 2003).
• Mobile anxiety (MA): The apprehension when using or having the possibility to
use mobile devices and applications (Schaper and Pervan 2007b).
• Result demonstrability (RD): Tangibility or the level of observability of the
results in using technology (Venkatesh and Davis 2000).
• Habit (HB): Repetitiveness and routine act of behavior in using the technology
(Gagnon etal. 2003).
10.3 Comparison ofUser andNonuser Physicians
In this section, the signicant and nonsignicant factors of mHealth application use
were outlined. Figures10.2 and 10.3 presented the research model used for each
group outlining signicant (continuous line) and nonsignicant (dashed line)
relationships. Research model testing resulted differently for each group regarding
signicant relations as well as the implications. In this section, a comparison of
factors inuencing these different groups was given.
Signicant and nonsignicant relationships for both groups were given in
Table10.1. The researches reported that PE and PI inuenced BI for users and EE
and TT inuenced BI for nonusers. This nding revealed that mHealth application
user physicians would perceive their job performance and their willingness to try
new technologies inuential their intention to use mHealth applications (Chau and
Hu 2002). On the other side, the perception of nonusers depended on the ease of
using mHealth, and the support they were receiving would affect their intention to
use mHealth applications (Chang etal. 2007).
The behavioral intention was inuenced by perceived service availability and
mobile anxiety in both groups. Thus, there was a common perception regarding
reachable and accessible mHealth applications in practice (Becker etal. 2014).
Furthermore, compatibility inuenced performance expectancy, and mobile self-
efcacy inuences effort expectancy for both groups. Here, job performance was
perceived to be related to compatible systems by nonusers similar to users, such as
mHealth with hospital systems. In addition to that, the ease of mHealth use was
perceived to be related with personal competency for both groups. However, their
indirect inuence on behavioral intention can be observed differently in each group
due to the signicant impact of PE and EE.Thus, compatibility was rather inuential
on BI over PE for user physicians, and mobile self-efcacy was on BI over EE for
10 Intention vs. Perception: Understanding theDifferences inPhysicians’ Attitudes…
esezgin1@gmail.com

158
nonusers. That impact would be related to perceived job performance of user physi-
cians since they observe the relation of compatibility and job performance. For non-
users, the expected ease of using mHealth applications could be perceived to be
related to personal competency (Schaper and Pervan 2007a).
On the other side, the direct effect of CO, HB, MS, and SI was not inuential on
BI for both groups. Here, there was a consensus of physicians about direct impact
on BI.Even though CO and MS had an indirect effect, they were not perceived to
have a signicant inuence on BI as well as HB and SI.As explained in the previous
section, these factors might have seen rather less relevant or non-applicable by the
physicians considering the current state of mHealth application use in health
institutions (Gagnon etal. 2015).
Social Influence
Compatibility
Technical Support and
Training
Perceived Service
Availability
Result Demonstrability
Performance Expectancy
Behavioral Intention
Effort Expectancy
Personal Innovativeness
Mobile Anxiety
Mobile Self-efficacy
Habit
Fig. 10.2 Research model for mHealth user physicians
E. Sezgin et al.
esezgin1@gmail.com

159
10.4 Suggestions
The previous section outlined the ndings of intention and perception to use
mHealth applications and implications. Considering both groups, in this section, a
number of elements were outlined in order to be considered in application
development and managerial processes in the common ground. Becker etal. (2014)
provided psychological, clinical, technological, and regulatory viewpoints to outline
the state of the mHealth. In this section, these viewpoints were used to categorize
the elements in suggestions.
Social Influence
Compatibility
Technical Support and
Training
Perceived Service
Availability
Result Demonstrability
Performance Expectancy
Behavioral Intention
Effort Expectancy
Personal Innovativeness
Mobile Anxiety
Mobile Self-efficacy
Habit
Fig. 10.3 Research model for nonuser physician
10 Intention vs. Perception: Understanding theDifferences inPhysicians’ Attitudes…
esezgin1@gmail.com

160
10.4.1 Psychological Perspective
Today, more than 75% of world population are able to access mobile communica-
tion services (Becker etal. 2014). In the largest countries, such as the USA and
China, more than 27 thousand medical applications were available on Android and
iOS market (Xu and Liu 2015). However, literature provided that mHealth applica-
tions were underutilized in practice, and it has created no dramatic change in both
organizational culture of health institutions and health behavior (Becker etal.
2014). In that regard, collaboration has been a need among application developers,
physicians, and researchers who have expertise in behavior and attitudes. In this
study, the signicance of perception in job performance, ease of mHealth use, per-
sonal perspectives in new technologies, and potential of anxiety were revealed for
both groups. Thus, the following elements should be considered for mHealth
applications.
Table 10.1 Signicant and nonsignicant relations for mHealth user physicians and nonuser
physician
User physicians
Nonuser
Physicians
Sig. Non-sig. Sig. Non-sig.
PS→BI ✓ ✓
MA→BI ✓ ✓
CO→PE ✓ ✓
MS→EE ✓ ✓
CO→BI X X
HB→BI X X
MS→BI X X
SI→BI X X
PI →EE X X
PS→EE X X
TT→EE X X
TT→PE X X
PE→BI ✓X
PI →BI ✓X
PI →PE ✓X
RD→PE ✓X
PS→EE ✓X
EE→BI X ✓
TT→BI X ✓
HB→EE X ✓
RD→EE X ✓
CO→EE X ✓
MA→EE X ✓
E. Sezgin et al.
esezgin1@gmail.com

161
Focusing on the job performance and providing simple applications Since the
workload is high and quick access to the information is a need, physicians rather
prefer less exhausting assistive services in practice. Thus, they expect effort-free
and useful, to-the-point applications in healthcare services. The simplicity of the
application and providing quick and relevant information are valuable features in
use (Gagnon etal. 2015).
Promotional activities for new mHealth applications There is a potential interest of
physicians toward new technologies. Utilizing this feature, mHealth applications
could be promoted among physicians for encouraging active use and creating a
positive perception in healthcare services. Thus, instead of basic training or seminars
at the initial stage, the promotional activities, such as meetings or activities including
social interactions, would attract both users and potential users toward using
mHealth applications in practice. Alternatively, key characters in the organizations,
such as “opinion leaders,” would be assistive to disseminate the use of mHealth
applications, which would also impact the organizational culture and mHealth use
“etiquette” in the long term (Hao etal. 2013).
The next level training. Following the promotional activities, training would help
physicians to use mHealth in completing daily tasks. It could be provided as on-the-
job training and in-action implementations. It is especially benecial for new users
in order to eliminate the risk of resistance and reduce potential anxiety in use by
familiarizing the new users to the mHealth applications. In addition to that, it would
reduce the possible risks as errors in multitasking (Wu etal. 2005; Varshney 2014).
10.4.2 Clinical Perspective
In the current state, literature and the study demonstrated that simple features of
mobile technologies work effectively in clinical practice, especially in developing
countries, such as communication applications and SMS (Free etal. 2013; Källander
etal. 2013; Becker etal. 2014).
Collaboration is the core The study provided that there is a social bond among
healthcare providers (i.e., physicians, nurses, technicians). Thus, collaboration
among healthcare providers has been a must, and the applications should be
developed regarding collaboration of the core of the operations. In that regard, easy
sharing methods and collaborative working tools would be benecial in mHealth
applications.
Providing continuous services The service availability was perceived to be an
important factor for the physicians. In that regard, one of the major benets of
communication applications was their service availability and providing access to
10 Intention vs. Perception: Understanding theDifferences inPhysicians’ Attitudes…
esezgin1@gmail.com

162
the service time and location independent. Here, the benets of communication
applications could be embraced in a broader extend to include healthcare-specic
services providing signicant functions available.
10.4.3 Technological Perspective
The study provided that the technological infrastructure of healthcare institutions
include the Internet and local area computer network within the institutions. Each
hospital uses a medical health record system to keep the track and to report the
operations. In that regard, a couple of issues should be considered for mHealth
application use.
Compatibility and interoperability of applications Compatibility of mHealth appli-
cations with the healthcare systems would inuence physicians’ working routines
and the job performance as well. The current state of mHealth showed that the
technology is still evolving and incompatible mHealth applications exist (Becker
etal. 2014). Thus, the development of a mobile-compatible healthcare service
platform for institutions is as important as developing mHealth application itself.
In addition to that, the communication among the systems is also crucial for services.
Interoperable systems would also boost the development and use of mHealth
applications in healthcare services.
Providing demonstrable results The ability to demonstrate the medical results, cal-
culations, problems, or processes was perceived important by the physicians. Hence,
the mHealth technology being provided should grant the ability to display and share
high-quality visual medical contents. In that regard, increasing visual quality as
well processing speed in medical contents would be valuable in healthcare
delivery.
Focusing on infrastructure Technological infrastructure, especially the communi-
cation network, is important for timely delivery of healthcare services (Sezgin and
Özkan-Yildirim 2016). However, the reliability could be an issue, and uninterrupted
service could not be provided for the developing countries (Varshney 2014). Thus,
developing an interoperable and compatible platform does also rely on a reliable
infrastructure. It is suggested to develop a contingency plan and ad hoc solution
maps for unexpected infrastructural issues (such as electricity cuts, network loss,
hardware and software malfunctions).
E. Sezgin et al.
esezgin1@gmail.com

163
10.4.4 Regulatory Perspective
Laws and regulations regarding mHealth technologies and applications are at the
initial stage (Barton 2012; Becker etal. 2014). In developing countries, it was
estimated to adapt the regulations in the long term. In that regard, the following
points would be considered in mHealth application development.
Acting with the laws and regulations about mHealth Even though the current state
of regulations is in the development phase, the need for laws and regulations is
increasing considering the number of available mHealth applications in the market.
These applications were commercially available and enabled users to share con-
dential information with the third parties. Thus, for security and privacy of informa-
tion, regulatory acts were required by the authorities. In the study, the physicians
have also stated their expectations on regulations about mHealth applications.
Standards for applications This study reported that some mHealth applications
were following international standards in medical practice while providing content
in healthcare. However, the market was crowded with many other unregulated and
unstandardized applications being available for the end users. Considering the
current trajectory, mHealth applications following the standards were found more
reliable by the physicians. Thus, considering international standards in the
developmental phase would help to build the reliability and credibility of the
mHealth applications. In addition to that, providing the procedures for implementing
international standards at national level application development would also be
recommended to the authorities.
Considering the four perspectives, the current stage of mHealth would be an
opportunity for developers to anticipate the trajectory of the transformation in
healthcare services and to release their applications in the market on time. In that
regard, the potential of change in organizational culture and its evolution around
mHealth applications and technologies should be considered in long-term strategic
plans.
10.5 Conclusion
In this chapter, a comparative assessment of mHealth application adoption by the
physicians was reported. Considering the intentions and perceptions of physicians,
several suggestions were outlined. The suggestions in this chapter would be helpful
for better understanding the characteristics of two different groups of physicians.
The ndings would guide developers and authorities to understand user needs.
Thus, it would be a valuable input in the mHealth application and healthcare policy
development.
10 Intention vs. Perception: Understanding theDifferences inPhysicians’ Attitudes…
esezgin1@gmail.com

164
It should be noted that this study has also extended the literature regarding
researches investigating users and nonusers’ behaviors in healthcare technologies
(Cheung etal. 2013; Bidmon etal. 2014; Sims etal. 2014). However, further studies,
employing qualitative designs, would be resourceful to achieve in-depth understanding
in physician intentions and perceptions toward mHealth application use.
References
Agarwal R, Prasad J(1998) A conceptual and operational denition of personal innovativeness in
the domain of information technology. Inf Syst Res 9:204–215
Aggelidis VP, Chatzoglou PD (2009) Using a modied technology acceptance model in hospitals.
Int JMed Inform 78:115–126
Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50:179–211
Atluri V, Rao S, Rajah T etal (2015) Unlocking digital health: opportunities for the mobile value
chain 1:1–8. https://healthcare.mckinsey.com/sites/default/les/Healthcare_WhitePaper_screen_
April17.pdf
Bagozzi RP, Warshaw PR (1992) Development and test of a theory of technological learning and
usage. Hum Relations 45:659–686
Barton AJ (2012) The regulation of mobile health applications. BMC Med 10:46. https://doi.
org/10.1186/1741-7015-10-46
Becker S, Miron-Shatz T, Schumacher N etal (2014) mHealth 2.0: experiences, possibilities, and
perspectives. JMIR mHealth uHealth 2:e24. https://doi.org/10.2196/mhealth.3328
Bidmon S, Terlutter R, Rottl J(2014) What explains usage of mobile physician-rating apps results
from a web-based questionnaire. JMed Internet Res 16:1–22. https://doi.org/10.2196/jmir.3122
Chang I-C, Hwang H-G, Hung W-F, Li Y-C (2007) Physicians’ acceptance of pharmacokinetics-
based clinical decision support systems. Expert Syst Appl 33:296–303. https://doi.org/10.1016/j.
eswa.2006.05.001
Chau PYKP, PJ H (2002) Investigating healthcare professionals’ decisions to accept telemedicine
technology: an empirical test of competing theories. Inf Manag 39:297–311
Cheung CS, Tong EL, Cheung NT etal (2013) Factors associated with adoption of the electronic
health record system among primary care physicians. JMIR Med informatics 1:e1. https://doi.
org/10.2196/medinform.2766
Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information
technology. Manag Inf Syst 13:319–340
Dünnebeil S, Sunyaev A, Blohm I etal (2012) Determinants of physicians’ technology accep-
tance for e-health in ambulatory care. Int JMed Inform 81:1–15. https://doi.org/10.1016/j.
ijmedinf.2012.02.002
Free C, Phillips G, Watson L etal (2013) The effectiveness of mobile-health technologies to
improve health care service delivery processes: a systematic review and meta-analysis. PLoS
Med 10:e1001363. https://doi.org/10.1371/journal.pmed.1001363
Gagnon M-P, Godin G, Gagné C etal (2003) An adaptation of the theory of interpersonal behav-
iour to the study of telemedicine adoption by physicians. Int JMed Inform 71:103–115. https://
doi.org/10.1016/S1386-5056(03)00094-7
Gagnon M-P, Ngangue P, Payne-Gagnon J, Desmartis M (2015) M-health adoption by health-
care professionals: a systematic review. JAm Med Informatics Assoc 23:1–10. https://doi.
org/10.1093/jamia/ocv052
Hao H, Padman R, Telang R (2013) Physician’s usage of mobile clinical applications in a com-
munity hospital: a longitudinal analysis of adoption behavior. UK Acad. Inf. Syst. Conf. Proc.
2013
E. Sezgin et al.
esezgin1@gmail.com

165
Holden RJ, Karsh B-T (2010) The technology acceptance model: its past and its future in health
care. JBiomed Inform 43:159–172. https://doi.org/10.1016/j.jbi.2009.07.002
Källander K, Tibenderana JK, Akpogheneta OJ etal (2013) Mobile health (mHealth) approaches
and lessons for increased performance and retention of community health Workers in low-
and Middle-Income Countries: a review. JMed Internet Res 15:e17. https://doi.org/10.2196/
jmir.2130
Kijsanayotin B, Pannarunothai S, Speedie SM (2009) Factors inuencing health information tech-
nology adoption in Thailand’s community health centers: applying the UTAUT model. Int
JMed Inform 78:404–416
King WR, He J(2006) A meta-analysis of the technology acceptance model. Inf Manag 43:740–755.
https://doi.org/10.1016/j.im.2006.05.003
Legris P, Ingham J, Collerette P (2003) Why do people use information technology? A criti-
cal review of the technology acceptance model. Inf &amp. Manag 40:191–204. https://doi.
org/10.1016/S0378-7206(01)00143-4
Manyika J, Chui M, Bughin J, Dobbs R (2013) Disruptive technologies: advances that will
transform life, business, and the global economy-executive summary. McKinsey Global
Institute, New York. https://www.mckinsey.com/business-functions/digital-mckinsey/
our-insights/disruptive-technologies
Martínez-Pérez B, de la Torre-Díez I, López-Coronado M (2013) Mobile health applications for
the most prevalent conditions by the World Health Organization: review and analysis. JMed
Internet Res 15:e120. https://doi.org/10.2196/jmir.2600
OECD (2015) OECD digital economy outlook 2015
PwC Health Research Institute (2014) Top health industry issues of 2015
Pynoo B, Devolder P, Duyck W etal (2012) Do hospital physicians’ attitudes change during PACS
implementation? A cross-sectional acceptance study. Int JMed Inform 81:88–97. https://doi.
org/10.1016/j.ijmedinf.2011.10.007
Rogers EM (1995) Diffusion of innovations. Free Press, NY
Rondan-Cataluña FJ, Arenas-Gaitán J, Ramírez-Correa PE (2015) A comparison of the differ-
ent versions of popular technology acceptance models: a non-linear perspective. Kybernetes
44:788–805. https://doi.org/10.1108/JFM-03-2013-0017
Schaper L, Pervan G (2007a) An investigation of factors affecting technology acceptance and use
decisions by Australian allied health therapists. In: Proceedings of the 40th Hawaii interna-
tional conference on system sciences, IEEE, pp141–141
Schaper LK, Pervan GP (2007b) ICT and OTs: a model of information and communication tech-
nology acceptance and utilisation by occupational therapists. Int JMed Inform 76:212–221.
https://doi.org/10.1016/j.ijmedinf.2006.05.028
Sezgin E, Özkan-Yildirim S (2014) A literature review on attitudes of health professionals towards
health information systems: from e-health to m-health. Procedia Technol 16:1317–1326
Sezgin E, Özkan-Yildirim S (2016) A cross-sectional investigation of acceptance of health infor-
mation technology: a nationwide survey of community pharmacists in Turkey. Res Soc Adm
Pharm 12:949–965
Sezgin E, Ozkan-Yildirim S, Yildirim S (2016) Understanding the perception towards using
mHealth applications in practice: physicians’ perspective. Inf Dev:1–19. https://doi.
org/10.1177/0266666916684180
Sezgin E, Ozkan-Yildirim S, Yildirim S (2017) Investigation of Physicians' awareness and use of
mHealth apps: a mixed method study. Health Policy and Technology 6(3):251–267
Sims MH, Fagnano M, Halterman JS, Marc W (2014) Provider impressions of the use of a
mobile crowdsourcing app in medical practice. Heal informatics 22:221–231. https://doi.
org/10.1177/1460458214545896
Sun H, Zhang P (2006) The role of moderating factors in user technology acceptance. Int JHum
Comput Stud 64:53–78. https://doi.org/10.1016/j.ijhcs.2005.04.013
Varshney U (2014) Mobile health: four emerging themes of research. Decis Support Syst 66:20–35.
https://doi.org/10.1016/j.dss.2014.06.001
10 Intention vs. Perception: Understanding theDifferences inPhysicians’ Attitudes…
esezgin1@gmail.com

166
Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: four
longitudinal eld studies. Manag Sci 46:186–204
Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology:
toward a unied view. MIS Q 27:425–478. https://doi.org/10.2307/30036540
Venkatesh V, Thong JYL, Xu X (2012) Consumer acceptance and use of information technology:
extending the unied theory of acceptance and use of technology. MIS Q 36:157–178
Ventola CL (2014) Mobile devices and apps for health care professionals: uses and benets. Pharm
Ther 39:356–364
Wood R, Bandura A (1989) Social cognitive theory of organizational management. Acad Manag
Rev 14:361–384. https://doi.org/10.2307/258173
Wu J, Wang S, Lin L (2005) What drives mobile health care? An empirical evaluation of technol-
ogy acceptance. In: Proceedings of 38th Hawaii international conference on system sciences,
pp1–9
Xu W, Liu Y (2015) mHealthApps: a repository and database of mobile health apps. JMIR Mhealth
Uhealth 3:e28. https://doi.org/10.2196/mhealth.4026
E. Sezgin et al.
esezgin1@gmail.com