Gilbert Maiga and Flavia Namagembe.pdf - School of Computing ...

8
A User Based Model for Adoption of
Telemedicine by Pediatricians in Resource
Constrained Environments
Gilbert Maiga 21 and Flavia Namagembe 22
Telemedicine has become an important form of Information Technology enabled
delivery and decision support for health care professionals. However, the need by
health care professionals to adopt and use telemedicine faces challenges including lack
of full understanding of why and what motivates the user to use the applications. In
the past decade, two signifi cant models - the technology acceptance model and the
task technology fi t theory - have been used to predict and explain user acceptance and
utilization of information technology. While each of the two models offers signifi cant
explanatory power, the combination of both has been shown to be superior to the
individual models when accounting for technology acceptance. However, currently
no studies are reported that use the combined models to explain the adoption and
use of telemedicine. In this position paper, a conceptual model that uses the superior
explanatory power of the combined TAM and TTF model is presented in an attempt
to predict the user acceptance and utilization of telemedicine among pediatricians
in Uganda. The study using the integrated model, is expected to provide better
understanding of choices about using telemedicine among health care professionals
in Uganda.
Categories and Subject descriptors: H.1.1 [Models and Principles]: Systems and
Information theory – General Systems Theory; J.3 [Life and Medical Sciences]: Medical
Information Systems
Keywords: Telemedicine, e-health, Pediatric care, Technology Acceptance Model,
Task-Technology Fit, User based model, Resource Constrained Environments
1 Introduction
Recent Information Technology [IT] developments have expanded into areas
that can be broadly characterized by their technology applications and targeted
users. To excel, most businesses continue to rely on, and indeed accelerate, heavy
investment in IT [Chau and Hu 2001]. Concurrently, various IT applications
designed to support highly specialized tasks and services by individual professionals
21 Department of Information Technology, Makerere University, Email: gmaiga@cit.
mak.ac.ug
22 ibid, fnamagembe@cit.mak.ac.ug
112

Part 2: Information System Tracking 113
have also proliferated. In the context of health care, Telemedicine technology has
become an important form of IT-enabled delivery and decision support for health
care professionals. In East Africa, where up to 80 percent of the population is rural
based, health care service delivery faces challenges associated with constraints of
remote settings characterized by poor road infrastructure, inadequate health care
facilities and professionals, long distances to the health facility, and the fragmented
nature of health care systems [Standing and Standing 2008]. This has consequently
resulted in many people dying every year, due to curable diseases for lack of health
care services within their reach. In these remote environments, telemedicine offers
the promise of overcoming these constraints, leading to seamless and secure access
to improved, effi cient and quality health care services at a decreased cost [Burney
et.al. 2010].
In Uganda the infant mortality rate is still abnormally high, with as many as
137 children out of 1,000 live births dying before their fi fth birthday. On the
other hand, 76 infants out of every 1,000 live births die before their fi rst birthday
[Mukasa 2008]. The Children who reside in rural and medically underserved regions
experience disparities in access because there are relatively fewer pediatric specialty
and subspecialty services available, and those services that are available are typically
distant from their rural residence. Moreover, for children with Special Health Care
Needs [CSHCN] living in rural communities, obtaining specialty or subspecialty
care is especially challenging because these children require more frequent routine
and urgent medical assessment. Building good health technological infrastructure
to support the application of telemedicine in pediatrics will offer health benefi ts to
children with special needs residing mostly in the rural, underserved areas of the
country like improved access to health care and improved quality of life, thereby
facilitating care that is more accessible, family-centered, and coordinated [Marcin
et.al. 2004].
Although the potential of telemedicine in health care is highly recognized, its
widespread adoption and use has not been fully realized for health care in remote
environments as yet [Standing and Standing 2008]. A leading cause of such poor
outcomes is the lack of models to predict and explain the technology adoption
for telemedicine and health care Zhang et.al. [2010] as well as the lack of trust,
privacy and perceived risk associated with technology [Jarvenpaa and Tractinsky
1999]. Furthermore, most research in telemedicine has focused on technology
developments and clinical applications essential to its success Chau and Hu [2002],
ignoring managerial challenges, including user technology acceptance [Perednia
and Allen 1995]. The above challenges have created a need to increase our very
limited understanding of why and how consumers use telemedicine applications.
Theoretically, a number of technology adoption models have been developed
to explain and predict user behaviors and intentions [Ajzen & Fishbein 1980;
Davism1989; Venkatesh et.al. 2003]. These models have been well tested, validated
and proven to be reliable when used to predict user acceptance of technology for

114 Strengthening the Role of ICT in Development Volume VIII
business organizations, corporations and students. Among the most commonly
used of these models to date are Technology Acceptance Model [TAM] and Task
Technology Fit [TTF] [Usori 2010].
Thus this study regards the health care professional’s use of telemedicine
application as a technology adoption process and uses the two most signifi cant
technology adoption models found in MIS, namely Technology Acceptance
Model [TAM] and Task Technology Fit [TTF] in exploring this adoption
process. As far as known, the two models have never been combined to predict
pediatrician’s adoption to telemedicine more so in remote rural based health
settings characterized by poor physical infrastructure and electricity, inadequate
health care facilities and few health care workers. The rest of this paper discusses
different models that explain technology adoption and comparisons, telemedicine
in Uganda, research questions, methodology and a conceptual framework for the
proposed TAM/TTF.
2. Telemedicine And Health care Delivery
Telemedicine literally means healing at a distance. A broader defi nition from
[WHO 2010] defi nes Telemedicine as the delivery of health care services, where
distance is a critical factor, by all health care professionals using information and
communication technologies for the exchange of valid information for diagnosis,
treatment and prevention of disease and injuries, research and evaluation, and for
the continuing education of health care providers, all in the interests of advancing
the health of individuals and their communities. Related to telemedicine is
“telehealth,” that encompasses a broader defi nition of remote health care that
does not always involve clinical services. Videoconferencing, transmission of
still images, e-health including patient portals, remote monitoring of vital signs,
continuing medical education and nursing call centers are all considered a part of
telemedicine and telehealth [ATA 2011].
Telemedicine encompasses services provided for the patient whose components
involve different providers and consumers that include: i] specialist referral
services typically that involves a specialist assisting a general practitioner in
rendering a diagnosis; ii] Patient consultations using telecommunications to
provide medical data, which may include audio, still or live images, between a
patient and a health professional for use in rendering a diagnosis and treatment
plan; iii] Remote patient monitoring using devices to remotely collect and send
data to a monitoring station for interpretation; iv] Medical education that provides
continuing medical education credits for health professionals and special medical
education seminars for targeted groups in remote locations; v] Consumer medical
and health information including the use of the Internet for consumers to obtain
specialized health information and on-line discussion groups to provide peer-topeer
support [ATA 2011].

2.1 Methods of Conducting Telemedicine
Part 2: Information System Tracking 115
Regardless of the purpose, there are two main methods of conducting telemedicine;
Real-time and Store and forward. The choice of method depends on what information
needs to be transmitted, the availability of the appropriate telecommunications
resources and the urgency of the reply [Smith et.al. 2005]. Real-time telemedicine
or Active telemedicine allows participants to send and receive information almost
instantly with negligible delay. A common example of real-time telemedicine is
a discussion about a patient over the telephone. Videoconferencing is another
example although it requires more expensive equipment. Store and forward or
Passive telemedicine applications in contrast is where information is encapsulated
and then transmitted to the recipient for subsequent reply. This method is generally
cheaper and more convenient. Examples include correspondence via E-mail, fax
or the post. A common example of pre-recorded telemedicine is teleradiology, in
which a digital X-ray image is transmitted to a radiologist for reporting [Smith
et.al. 2005].
2.2 Telemedicine in Uganda
Uganda, like most developing nations, still lags behind in the fi eld of health
care. Like many other developing countries, Uganda has a shortage of medical
personnel, especially specialists such as pathologists, dermatologists, radiologists
and cardiologists among others [Ekeland et.al. 2010]. The average doctor to patient
ratio is devastatingly 1:20,000 compared to 1:500 in developed countries [Mugyenyi
2007]. This problem is worsened by the poor infrastructure that makes it hard
for patients to move to places where health services are provided [Mbarika et.al.
2007]. Access to health care is severely limited by nearly nonexistent infrastructure,
unmanageable travel to clinics, and relatively high cost of health care services.
This resulted from the wars and political instability the country has experienced
within the last 30 or so years. Currently, the cost of consultation consumes about
15 to 20 percent of an individual’s average monthly income. These challenges
contribute to limited accessibility to treatment, allowing patients’ diseases to
advance to a stage that is harder to diagnose, more diffi cult to treat, and too often
fatal [Mbarika et.al. 2007].
In response to the urgent need for increased health care accessibility, ICITD
[Southern University] teamed up with Texas Telehealth Technologies to conduct
a baseline survey in an effort to establish the state of telemedicine in Uganda and
use the fi ndings to guide them in developing appropriate strategies for telemedicine
in Uganda. Nine hospitals and a medical research institute in Kampala, Jinja,
Mukono and Luwero districts were visited during the study. The survey used
semi-structured interviews with hospital directors, administrators and technical
personnel who have been involved or are utilizing telemedicine in their service
delivery. The fi ndings suggests that Telemedicine is still in infancy state and a
lot more still needs to be done to develop the telemedicine capabilities, develop

116 Strengthening the Role of ICT in Development Volume VIII
human capacity, and design an appropriate technology that is sustainable within
the limited resources available [Mbarika et.al. 2007].
The ministry of health, in a bid to improve e-health in Uganda has been
involved in a number telemedicine projects, the fi rst one being the Telemedicine
pilot project between the University Teaching Hospital of Mulago and
Mengo Hospital in Kampala. The leading aim of this project was to show
how telecommunication and information technology applications such as
telemedicine can help overcome some of the serious shortages in health care
services in developing countries. The pilot project was also part of a strategy
to provide specialist care in surgery, pediatrics, obstetrics, gynecology, and
internal medicine in regional referral hospitals whose medical teams can afford
only one or two specialists.
It is estimated that 50 percent of all 800 doctors are in Kampala, while 60 percent
of nurses are in rural areas. With a high maternal mortality rate ranging from 500
to 2,000 deaths per 100,000 births and an infant mortality rate of 97 per 1,000,
the need to improve medical delivery and to optimize limited medical resources
is essential [Elotu 2000]. The ministry of health also supported the launching of
the UNC Project-Uganda which was established to support sustainable delivery of
compassionate and competent health care to infants, children, and adolescents in
Uganda; to improve the medical knowledge of the Ugandan health care workforce
through in-country training and a physician exchange program; and to provide
advanced medical equipment, medications, and services necessary for the delivery
of compassionate and competent pediatric care in Uganda [Hughes 2010].
The Ugandan government also launched a project to equip health centers
across the country with techno labs that will facilitate diagnosis and prescription
of treatment for patients without them having to come to a particular health
center [Lyazi 2008]. The project under the Uganda Communication Development
Fund [UCDF] of the Uganda Communications Commission was to see health
centers across the country fi tted with computers, digital cameras, scanners and
other gadgets to allow doctors to diagnose and prescribe treatment to patients in
other health centers.
This project was also meant to enable Ugandan patients with ailments for
which treatment was not possible locally to be able to receive diagnosis and
treatment prescription from Indian doctors [Lyazi 2008]. With an increase in
the use of ICT in Uganda especially computers, Internet, mobile phones, digital
camera, digital Televisions which play a big role in processing, dissemination
and storage of information, as well as the interest by the-government to invest
in e-health, telemedicine in Uganda is likely to improve. Given the right policies,
organisation, resources and institutions, ICTs can be powerful tools in the hands
of those working to improve health [Daly 2003].

2. 3 Issues of Telemedicine Adoption in Uganda
Part 2: Information System Tracking 117
According to [Mbarika et.al. 2007], telemedicine in Uganda is still in its infancy
state and a lot more still needs to be done to develop the telemedicine capabilities,
develop human capacity, and design an appropriate technology that is sustainable
within the limited resources available. The Ugandan health care professional is
increasingly using mobile technologies and e-mail with very limited use of video
conferencing and high end telemedicine technologies. There have been several
new researches and developments in this space. Saroj and Indra [2008] argue that
mobile phones are becoming an important ICT tool not only in urban regions,
but also in remote and rural areas. The liberalization of the telecommunications
sector in Uganda has opened up space for new entrants with new and innovative
technological solutions. It is therefore highly anticipated that these service
providers will alter their energies and pay special attention to mobile health care
since this has already been done in other developing countries such as India, where
some of the telecommunication service providers are originating [Isabalija 2011].
Despite these developments, the adoption and use of telemedicine in Uganda
still remains a challenge [Isabalija 2011]. Many systems have either failed to kickstart
or they have stopped working in their infancy. Part of the causes of these
failures, are the gaps in the implementation frameworks. As Oladosu et.al. [2009]
argue that practicable solutions need to be tailored towards existing success stories
and local conditions where the telemedicine strategy is being established and that
systems such as e-health require contextual considerations in implementation
and sustainability. According to [Isabalija 2011], the most hideous challenges
for telemedicine adoption, implementation and sustainability in Uganda were
identifi ed as lack of telemedicine skilled staff, inadequate training, lack of policy,
and resistance to change by members of staff. Further to this [Nazvia 2011] had
identifi ed resources as a big problem for telemedicine sustainability. Most of the
health resources in Uganda come from private donors and when they pull out, the
systems also collapse. [Isabalija 2011] highlights a case study in Nsambya Hospital
where the telemedicine project was started with help from partners of St. Raphael
Hospital in Milan in Italy. The project worked well until when St. Raphael
Hospital withdrew their support. Since then, Nsambya has failed to sustain it.
2.4 Related Adoption Models for predicting Technology Adoption
A number of theories are used in IS research [Wade 2009]. However, the interest
of the study lies in the theories that are about Technology adoption. Below are
some of the most commonly used theories;
2.4.1 The Theory of Reason Action
The Theory of Reasoned Action [TRA], fi rst developed in the late 1960s by Martin
Fishbein and revised and expanded by Fishbein and Icek Azjen in the decades that
followed, is a theory that focuses on a person’s intention to behave in a certain
way. An intention is a plan or a likelihood that someone will behave in a particular

118 Strengthening the Role of ICT in Development Volume VIII
way in specifi c situations — whether or not they actually do so. To understand
behavioral intent, which is seen as the main determinant of behavior, the TRA
looks at a person’s attitudes towards that behavior as well as the subjective norms
of infl uential people and groups that could infl uence those attitudes. Attitude
refers to personal beliefs about the positive or negative value associated with a
health behavior and its outcomes [Montano and Kaspryzk 2008].
Subjective norm refers to a person’s beliefs that most of his/her important
others think he should or should not perform the behavior in question [Ajzen
and Fishbein 1980]. Subjective norms are internalization and identifi cation.
Internalization refers to the process by which, when a technology user perceives
that an important person like the user's manager thinks that the user should use
this new technology, the user will incorporate this person's beliefs into his/her
own belief structure.
Fig1. Theory of Reasoned action [ Fishbein & Ajzein 1975]
2.4.2 Theory of Planned Behavior
The theory of planned behavior is an extension of the theory of reasoned action
[Ajzen and Fishbein 1980; Fishbein and Ajzen 1975] made necessary by the
original model limitations in dealing with behaviors over which people have
incomplete volitional control [Ajzen 1991]. TPB posits that individual behavior
is driven by behavioral intentions where behavioral intentions are a function of
an individual’s attitude toward the behavior, the subjective norms surrounding
the performance of the behavior, and the individual’s perception of the ease with
which the behavior can be performed [behavioral control]. Attitude toward the
behavior is defi ned as the individual’s positive or negative feelings about performing
a behavior. It is determined through an assessment of one’s beliefs regarding the
consequences arising from a behavior and an evaluation of the desirability of these
consequences.
Subjective norm is defi ned as an individual’s perception of whether people
important to the individual think the behavior should be performed. The

Part 2: Information System Tracking 119
contribution of the opinion of any given referent is weighted by the motivation
that an individual has to comply with the wishes of that referent. Behavioral control
is defi ned as one’s perception of the diffi culty of performing a behavior. TPB
views the control that people have over their behavior as lying on a continuum
from behaviors that are easily performed to those requiring considerable effort,
resources, etc. Although Ajzen has suggested that the link between behavior and
behavioral control outlined in the model should be between behavior and actual
behavioral control rather than perceived behavioral control, the diffi culty of
assessing actual control has led to the use of perceived control as a proxy [Eagly
and Chaiken 1993].
Fig 2. Theory of Planned Behavior [Icek 1991]
2.4.3 Diffusion of Innovation Theory.
DOI theory sees innovations as being communicated through certain channels
over time and within a particular social system [Rogers 1995]. Individuals are seen
as possessing different degrees of willingness to adopt innovations and thus it is
generally observed that the portion of the population adopting an innovation
is approximately normally distributed over time [Rogers 1995]. Breaking this
normal distribution into segments leads to the segregation of individuals into
the following fi ve categories of individual innovativeness [from earliest to latest
adopters]: innovators, early adopters, early majority, late majority, laggards
[Rogers 1995]. Members of each category typically possess certain distinguishing
characteristics which include; i) innovators - venturesome, educated, multiple
information sources, ii) early adopters - social leaders, popular, educated, iii) early
majority - deliberate, many informal social contacts, iv) late majority - skeptical,
traditional, lower socio-economic status, v) laggards - neighbors and friends are
main info sources, fear of debt. Based on the above, the DOI theory clearly spells
out that workers do not embrace innovations like a new technology at the same

120 Strengthening the Role of ICT in Development Volume VIII
time. They go through a number of stages and there’s need to understand their
behavior before any changes can be brought in.
Fig. 3 Diffusion of Innovation [Rogers 1995]
2.4.4 Technology Acceptance Model
An individual’s intentional or voluntary use of a technology is referred to as
technology acceptance [Davis et.al. 1989]. Although other predictive models
exist, TAM is the most widely recognized model of behavioral intention in
the information systems literature [Ma and Liu 2004]. The TAM was derived
from the theory of reasoned action [TRA]. The TRA is a very general model
of behavior that suggests beliefs infl uence attitudes, which determine intentions,
and that intentions dictate behavior [Ajzen and Fishbein 1980]. However, TAM
suggests that an intention to accept technology is determined directly by attitude,
perceived usefulness, and perceived ease of use. Additionally, perceived usefulness
infl uences behavioral intention indirectly through attitudes, while perceived
ease of use infl uences behavioral intention indirectly through both attitudes and
perceived usefulness [Davis et.al. 1989]. Perceived usefulness has been defi ned as
an individual’s perception that the utilization of a particular technology will be
advantageous in an organizational setting over a current practice. Perceived ease of

Part 2: Information System Tracking 121
use is the perception by an individual that the utilization of the new technology
will be relatively painless or effortless [Davis et al. 1989].
Fig 4: Technology Acceptance Model [Davis et.al 1989; Venkatesh
et.al. 2003]
TAM has proven to be a reliable and robust model through rigorous empirical
testing [Yarbrough and Smith 2007]. Empirical tests of TAM prove the model to
be applicable for individuals of all levels of IT competency [Lai and Li 2005; Yu
et.al. 2005], genders, and ages [Lai and Li 2004]. Furthermore, the TAM has been
successful at predicting acceptance of a wide variety of technologies [King and He
2006; Ma and Liu 2004]. In general, the model has done a good job of predicting
variance in technology acceptance in a wide variety of contexts for different types
of users.
2.4.5 Task Technology Fit Model
Task-Technology Fit [TTF] model is widely used for the prediction and
explanation of technology Utilization [Usori 2010]. It was developed by Goodhue
and Thompson in 1995 to evaluate IT as well as predict and explain its use from
the perspective of task; unlike TAM where the emphasis was on using beliefs -
“perceived usefulness” and “perceived ease of use” to predict and explain users’
acceptance of IT [Usori, 2010]. TTF theory holds that IT is more likely to have a
positive impact on individual performance and be used if the capabilities of the IT
match the tasks that the user must perform. Goodhue and Thompson, [1995] found
the TTF measure, in conjunction with utilization, to be a signifi cant predictor of
user reports of improved job performance and effectiveness that was attributable
to their use of the system under investigation. Since the initial work, TTF has been
applied in the context of a diverse range of information systems including e-health,
electronic commerce systems and combined with or used as an extension of other
models related to IS outcomes such as the technology acceptance model [TAM].
The TTF measure presented by [Goodhue and Thompson 1995] has undergone
numerous modifi cations to suit the purposes of the particular study.

122 Strengthening the Role of ICT in Development Volume VIII
Fig. 5 Task Technology Fit [Goodhue and Thomson 1995]
2.4.6 Integration of the TAM and the Task Technology Fit model
During the past decade, two signifi cant models of information technology [IT]
utilization behavior have emerged in the MIS literature. These two models, the
technology acceptance model [TAM] and the task technology fi t model [TTF],
provide a much needed theoretical basis for exploring the factors that explain
Technology utilization and its link with user performance [Dishaw and Strong
1998]. TAM focuses on attitudes toward using a particular IT which users develop
based on perceived usefulness and ease of use of the IT. TTF focuses on the match
between user task needs and the available functionality of the IT. According to
[Dishaw and Strong 1998], the general argument for combining the models is that
they capture two different aspects of users’ choices to utilize IT.
TAM and the attitude/behavior models on which it is based, assume that
users’ beliefs and attitudes toward a particular technology largely determine
whether users exhibit the behavior of utilizing the IT. Critics note that
users regularly utilize IT that they do not like because it improves their job
performance. TTF models take a decidedly rational approach by assuming
that users choose to use IT that provides benefi ts, such as improved job
performance, regardless of their attitude toward the IT. Both aspects, attitude
toward the IT and rationally determined expected consequences from using
the IT, are likely to affect users’ choices to utilize IT. Therefore, while each of
these models offers signifi cant explanatory power, it is logical to expect that a
model that incorporates both TAM and TTF will be more effective or superior
to the individual models in its explanation and prediction of the adoption and
utilization process for an IT system by the user [Usoro 2010].

Fig 6 TAM and TTF models combined [Dishaw et al. 2002]
Part 2: Information System Tracking 123
According to Dishaw and Strong [1999], a weakness of TAM for understanding
IT utilizations is its lack of task focus. IT is a tool by which users accomplish
organizational tasks. The lack of task focus in evaluating IT and its acceptance,
use, and performance contributes to the mixed results in IT evaluations. While
TAM’s usefulness concept implicitly includes task; that is to say usefulness means
useful for something-more explicit inclusion of task characteristics may provide a
better model of IT utilization. The task technology fi t perspective addresses this
problem through its ability to support a task is expressed by the formal construct
known as task technology fi t [TTF], which implies matching of the capabilities
of the technology to the demands of the task. TTF posits that IT will be used if,
and only if, the functions available to the user support [fi t] the activities of the
user [see Fig. 5]. Rational, experienced users will choose those tools and methods
that enable them to complete the task with the greatest net benefi t. Information
technology that does not offer suffi cient advantage will not be used [Dishaw and
Strong 1999]. The study will use TAM which is the most commonly used model
in health IT [Holden and Karsh 2011] and TTF which will provide a fi t between
the capabilities of the IT and the tasks that the user has to perform.

124 Strengthening the Role of ICT in Development Volume VIII
2.5 A User Based model for predicting Technology Adoption for Health
Care
Health care organizations have heavily invested in IT to support their service
delivery and Telemedicine is becoming widespread. The proliferation of
information technology [IT] in supporting highly specialized tasks and services has
made it increasingly important to understand the factors essential to technology
acceptance by individuals [Holden and Karsh 2010]. The authors posit that “Health
information technology [IT] research often focuses on IT design and implementation
but perhaps not enough on how clinician end users react to already implemented IT.”
Literally, little attention has been paid to the capabilities of new technologies like
Telemedicine, the factors affecting pediatrician’s decisions to adopt telemedicine
and actually make use of it. Telemedicine has great potential to improve the quality
of health care delivery, but these improvements will occur only if health care
professionals successfully make full use of it. According to Yen et.al. [2010], not
every worker is willing to adopt to the new technology to accomplish their tasks.
Why some users have a higher intention to adopt to technology while others are
less willing to become technology users is a question in the current study. In
addressing the issue of predicting pediatrician’s adoption to telemedicine, both
Technology Acceptance Model [TAM] and Task Technology Fit [TTF] model are
important theoretical bases in the Information Systems fi eld.
These two popular models have been used in considerable quantities of researches
to understand determinants of user acceptance to information technology [Yen
et.al. 2010]. Applying both of them in the context of telemedicine will pose a
signifi cant improvement in understanding pediatricians' intention to adopt to
telemedicine, which will be determined by the fi t between the characteristics of
the task and technology as well as user’s perceived ease of use and usefulness. It
should be noted, however, that both these models were not specifi cally developed
for health care context and some of their core concepts appear not to be relevant
to health care investigators.

Fig7. Proposed User Based Model for Telemedicine Adoption
Technology
characteristics
Task
characteristics
H6
H5
ORIGINAL TAM/TTF INTEGRATED
Task Technology Fit
[TTF]
Fig7. Proposed User Based Model for Telemedicine Adoption
H4
Part 2: Information System Tracking 125
H3
Perceived
Privacy [PP]
Perceived ease
of Use
[PEOU]
H7
Perceived
Usefulness
[PU]
H1
Perceived Trust
[PT]
Description of variables/constructs from the conceptual Framework
Technology characteristics
H10
H11
H8
H9
Intension to use
the technology
Various technology characteristics like the ability to easily adopt to this technology,
easy to learn technology, not complex for the users and rate of technology
adaptability will play a big role in infl uencing technology adoption. We assume
that pediatricians will only adopt a technology which is easy to learn and use than
a technology which is complex to learn and use. They will look at the time it will
take them to learn this technology as determinant. Users will be less interested in
a technology that is going to take them ages to learn and however much it seems
useful in accomplishing their tasks, they will be less interested in adopting this
technology.
Tasks Characteristics
The nature of characteristics to be performed by the available technology will also
affect adoptability. If pediatricians fi nd out that the tasks to be performed, like
treating children from a distance, will improve child health care delivery basing
on the available technology, adoptability will dissolve easily.
H2
H10
H13

126 Strengthening the Role of ICT in Development Volume VIII
Perceived Ease of Use
PEOU of this website has a positive association with user’s perceived usefulness.
This will mean that the higher the familiarity with a particular technology, the
higher the perceptions the of health professional’s ability to adopt and use the
technology. The more the health workers’ utilization experiences of telemedicine
technology, the higher the health workers’ PEOU and hence intention to adapt
to the technology.
Perceived Usefulness
According to the above model, a user’s “PEOU” of a technology will have a
positive association with user’s perceived usefulness “PU.” If a user of a given
technology fi nds out that a technology is easy to use, they will in the long run
attach usefulness to this technology. And when a user attaches usefulness to a
given technology, it will lead to a reduction in a user's perceived lack of trust.
Perceived Trust
In the new model, we introduce a new variable perceived Trust-which is also a
potentially limiting factor in user acceptance of telemedicine. Trust is identifi ed
as a signifi cant factor in organizational settings .Medicine operates on trust:
Patients trust their doctors; doctors trust their staff; and patients, doctors, nurses,
and hospitals must trust each other [Mayer et.al. 1995]. As practicing medicine
becomes ever more complex involving the use of technology to communicate,
and collaborate between practitioners over long distances in order to improve the
impact on, trust is very important. Doctors increasingly must trust doctors. As
trust is the lubricant of commerce, collaborative telemedicine [Walsum, 2003]. .
It is upon that background that we introduce a new variable trust in our proposed
model as a determinant of technology adoption among pediatricians.
Perceived Privacy
The study also introduces the variable of perceived privacy. Doctors are concerned
about the technologies used in transferring the patient’s data before, during and
after any telemedicine encounter [Nabeel 2004]. Protection of the data is important
as any security breaches in the system may lead to legal issues. Thus pediatricians
will use the technology to perform a desired task if they are sure that the privacy
of patients is not going to be violated.
Hands on Education
Another variable that this study proposes to introduce is the hands-on education.
Doctors who have been involved in hands-on educational interventions are most
likely going to fi nd the telemedicine technology very easy to use and hence have
no problem in adapting to it as compared to those without this knowledge.

Part 2: Information System Tracking 127
According to the proposed TAM posits the hypotheses below need to be tested:
Hypothesis 1 [H1]: Perceived ease of use is positively related to intention to use
telemedicine technology.
Hypothesis 2 [H2]: Perceived usefulness is positively related to the intention to
use telemedicine technology.
According to TTF posits the hypotheses below need to be tested:
Task-technology fi t model posits that, besides the user-held beliefs about the system,
the chief determinant of the user adoption and utilization of an IT system is the
extent to which the system offers functionalities correspondent with the tasks
the user wants performed [Goodhue, 1995]. The positive relationship between
TTF and the intention to use the actual system has been confi rmed in numerous
researches [Dishaw and Strong 1999; Klopping and McKinney 2004].
This research also expects that the users will be more inclined to use a
technology that provides functionalities that match the health tasks of the user.
Hence on TTF side, this research proposes the following hypotheses:
Hypothesis 3 [H3]: Task-technology fi t is positively related to perceived
ease of use of telemedicine technology.
Hypothesis 4 [H4]: Task-technology fi t is positively related to perceived
usefulness of telemedicine technology.
Hypothesis 5 [H5]: The characteristics of the available technology have an
association with the fi t between telemedicine technology and the health task.
Hypothesis 6 [H6]: The tasks to be carried out by the health worker have an
association between the fi t between task and the technology.
Hypothesis 7[H7]. The user’s perceived ease of use has a relationship with
perceived usefulness.
Hypothesis 8 [H8]. The user privacy concerns have an association with
perceived usefulness.
Hypothesis 9[H9]: Perceived trust of using a technology to carry out a given
task has an association with intention to use the technology.
Hypothesis 10 [H10]: Task-technology fi t has an association with the user’s
perceived trust of adopting telemedicine technology to conduct a given task
Hypothesis 11 [H11]: There is a fi t a fi t between a user's privacy concerns
with regard to the task to be performed by the technology.
Hypothesis 12 [H12]: Perceived ease of use is strongly associated with hands
on educational interventions.
Hypothesis 13 [H13]: Perceived usefulness has an association with the
doctor’s intention to adopt to telemedicine technology.
It should also be noted that task-technology fi t is positively related to the
intention to use telemedicine technology.

128 Strengthening the Role of ICT in Development Volume VIII
4 Research Method And Design
A fi eld study research design shall be used to collect data required to test the
hypothesis and answer the research questions. The items to be used to operationalize
the constructs being investigated are adopted from relevant prior research, with
necessary validation and wording changes tailored to telemedicine and the targeted
pediatric context. These are the perceived usefulness (PU) and perceived ease of
use (PEOU) as adapted from Davis [1989]. All items are to be measured on a fi vepoint
Likert-type scale with anchors from ‘‘strongly agree’’ to ‘‘strongly disagree’’.
To ensure desired balance and randomness in the questionnaire, half of the items
are to be worded with proper negation and all items randomly sequenced on the
questionnaire in order to reduce the potential ceiling or fl oor effect that induces
monotonous responses for items designed to measure a given construct.
Pre-tests - The adopted instrument shall be examined to ensure face and content
validity and reliability within the targeted context. First pediatricians are to be
asked to assess content validity. Using the same pediatricians, the instrument shall
be evaluated for wording clarity. The instrument’s reliability is to be evaluated
using Cronbach’s alpha.
Sample size - What size of the sample shall be required to make precise generalizations
about Telemedicine adoption with confi dence? The sample size addresses issues
of precision (how close our estimate is to the true population characteristics) and
confi dence (how certain we are that our estimate will really hold true for the
population). Sample size is based on time available, budget and necessary degree of
precision. The sample size needed is a function of the confi dence interval of (+/-
5%), a confi dence level of 95%) and the population size. Sample size was determined
using the following formula [Bartlet et al., 2001; Krejcie and Morgan, 1970].
S = .......................................... (Equation
1)
Where: S = Sample Size; Z = Z Value (e.g. 1.96 for 95% confi dence interval);
X = Percentage picking a choice, expressed as decimal (0.5 used for sample size
needed); C = Confi dence interval, expressed as decimal (0.05) +/- 5 used for
sample size needed).
Correction for fi nite population
New S = ........................................ (Equation
2)
Where: P = Population
Based on a population of at least 400 (four hundred) registered and practicing
doctors in Uganda, this study requires at a minimum, a sample size of 169 medical
doctors as respondents. Equation 2 is based on large sample size theory. The actual
study provides for more respondents than the minimum required sample size.

Part 2: Information System Tracking 129
Study Administration - The study shall target pediatricians practicing in public
and private hospitals and clinics in Uganda. Choice of the target pediatricians shall
be made based on their present or probable future involvement in telemedicine
technology. The decision on the particular medical specialty/subspecialty areas
to be included shall be based on their frequent inclusion by prior telemedicine
research and documented clinical application results.
Data Analysis Results and Model Testing - Reliability and Validity of the Research
Constructs - The factors (constructs) to be investigated shall be evaluated for
reliability, convergent and discriminant validity. Reliability shall be examined
using Cronbach’s alpha values. Factor analysis is to be conducted to examine
measurement convergent and discriminant validity. Convergent validity is
considered satisfactory when items load high on their respective constructs
(i.e. factors). The models shall be tested for goodness-of-fi t measures namely:
Chi-square/d.f.; goodness-of-fi t index (GFI); and the adjusted goodness-of-fi t
index (AGFI).
5 Conclusions & Future Work
Extending TAM with TTF constructs will provide a better explanation for the
variance in IT utilization than either TAM or TTF models alone. Integrated TAM/
TTF model combines an attribute/behavior model [TAM] with models of task
technology fi t. In the integrated model, TTF constructs directly affect IT utilization
and indirectly affect IT utilization through TAM’s primary explanatory variables,
perceived usefulness and perceived ease of use. The new variables introduced -
Privacy, Trust and Hands on Education - also provide a much more analytical
reasoning of the pediatrician’s intention to adopt telemedicine. The integrated
TAM/TTF model holds much promise for helping researchers and practitioners
better understand why individuals, in this context health workers, choose to use
IT for particular tasks. Such understanding is especially important to IT managers
who are investing in tools for information users and IT professionals. It should
also help technology developers understand how technology characteristics and
their fi t with task characteristics lead to user choices in respect of using the tool.
This position paper presents a model to explain adoption of telemedicine in
remote and resource constrained environments. What remains is to collect data to
be used for evaluating the proposed model and for its subsequent validation with
pediatricians.

130 Strengthening the Role of ICT in Development Volume VIII
References
Abel, U., Shoyelu, S., Kuofie, M. (2010). Task-technology fit and technology acceptance models
applicability to e-tourism. Journal of Economic Development, Management, IT, Finance and
Marketing 2, 1-32.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision
Processes. 50, 179-211.
Ajzen, I., Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice-
Hall, Englewood Cliffs: NJ.
Allen, A., Hayes, J., Sadisvan, R., Williamson, S.K., Wittman, C. (1995) A pilot study of the
physician acceptance of technology. Journal of Telemedicine and Telecare 1, 34-37.
American Telemedicine Association. (2011). Quality healthcare through communications
technology. Retrieved on 2 February 2012, from http://www.americantelemed.org
Bolognesi, N. (2006). The Promise and Problems of Telemedicine 2006. Presented at the 11th
annual conference of the International Society for Telemedicine and Health, Cape Town,
South Africa.
Breen, G.M., Thomas, T. H., Ortiz, W.J. (2010). Information technology adoption in rural
health clinics: a theoretical analysis. Journal of Information Technology Impact 10, 1-14.
Burney, A.S.M., Nadeem, M. and Zain, A. (2010). Information and communication technology
in healthcare management systems: prospects for developing countries. International
Journal of Computer Application (0975-8887), 4, 2.
Burney S. M. A. (2008). Inductive and deductive research approach. http://www.drburney.
net. Retrieved April 15, 2012, from http://www.drburney.net/INDUCTIVE%20&%20
DEDUCTIVE%20RESEARCH%20APPROACH%2006032008.pdf
Chau, P.Y.K., Hu, P.J.H. (2002). Investigating healthcare professionals’ decision to accept
telemedicine technology: an empirical test of competing theories. Information and
Management 39, 297-311.
Chismar, G.W., Sonja, W. P. (2002). Annual symposium proceedings. Test of the technology
acceptance model for the internet in pediatrics. Honolulu, Hawai’i.
Collins, A.J. 2007. A typology of mixed methods sampling designs in social science research.
The Qualitative Report 281-316.
Creswell, J.W., and Tashakkori, A. (2007). Developing publishable mixed methods manuscript.
Journal of Mixed Methods Research 1, 107-111.
Dally, J. (2003). Information and communications technology applied to the Millennium
Development Goals, Available: http://topics.developmentgateway.org/ict/sdm/
previewDocument.do~activeDocumentId=840982
Davis, F.D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information
technology.MIS Quarterly 13, 319-340.
Dishaw, M.T., and Strong, D.M. (1998). Extending the technology acceptance model with task
technology constructs. Information & Management 36, 9-21.
Dongsheng,L., Hao,T., Bei,W. and Shimin, H. (2011). Integrating TTF and TAM perspectives
to explain mobile knowledge work adoption. Journal of Convergence Information
Technology 6, 50-63.

Part 2: Information System Tracking 131
Eagly, A. H., and Chaiken, S. (1993). The psychology of attitudes. Fort Worth: Harcourt Brace
Jovanovich College Publishers.
Edworthy, S.M. (2004). Telemedicine in developing countries: may have more impact than in
developed countries. Clinical Review 323, 525-226.
Ekeland, A. G., Bowes, A., and Flottorp, S. (2010). Effectiveness of telemedicine: a systematic
review of reviews. International Journal of Medical Informatics 79, 736-771.
Elotu, J. (2010, November Wednesday). IT brings telemedicine in Uganda. Retrieved on 5
January 2012, from internet society: http://www.isoc.org.
Gammon D., Johannessen L.K., Sorensent., Wynn R., and Whitten P. (2008). An overview and
analysis of theories employed in telemedicine studies. Methods of Information in Medicine
47, 260-290.
Godwin, Ju. (2001). Privacy and security concerns as a major barrier for e-commerce: a survey
study. Information Management & Computer Security. 9, 165-174.
Hennington, A.H., Janz, B.D. (2007). Information systems and healthcare XVI: physician
adoption of electronic medical records: applying the UTAUT model in a healthcare
context. Communications of the Association for Information Systems 19, 60-80.
Holden and Karsh. (2010). The technology acceptance model: its past and its future in health
care. J Biomed Inform 43,1, 159, February 2010. Doi:10.1016/J.Jbi.2009.07.002.
Houghton, G. B., Marshall, S.P., and Kim, V.W. (2003).Trust, trait theory, and collaboration
in telemedicine: a circumplex perspective. Proceedings of the 36th Hawaii International
Conference on System Sciences.
Isabalija, S., Mayoka, K.G., Rwashana, A. S. and Mbarika, V. W. (2011). Factors affecting
adoption, implementation and sustainability of telemedicine information systems in
Uganda. Journal of Health Informatics in Developing Countries 1, 299 – 316.
Kobusinge, G. (2009). ICT framework for food security. Kampala: Makerere University.
Krejcie, R. and Morgan, D. (1970). Determining sample size for research activities.
Educational and Psychological Measurement 30, 607-610.
Lai, S.V., and Li, H., (2004), Technology acceptance model for internet banking: an invariance
analysis. Information & Management. Article in press. Accepted 21January 2004.
Lyazi, B. (2008). Telemedicine and e-health. Uganda introduces telemedicine program with help
from India. Retrieved on 16 March 2012 from http://www.telemedicinealerts.com
Ma, Q., and Liu, L. (2004). The technology acceptance model: A meta-analysis of empirical
findings. Journal of Organizational and End User Computing 16, 59-74.
Mbarika, V.M.O (2010). Telemedicine in Uganda. Retrieved in January 2012 from http://
www.icitd.theictacademy.net
Mercin, J.P., Ellis, J., Mawis, R., Nagrampa, E., Nesbitt, T.S., and Diamond, R.J. (2004).
Satisfaction and impact of providing specialty care through telemedicine to primary care.
Pediatrics. 113, 1-6.
Montano, D. E., and Kasprzyk, D. (2008). Theory of reasoned action, theory of planned
behavior, and the integrated behavioral model. In K. Glanz, B. K. Rimer, and K.
Viswanath, Health Behavior and Health Education: Theory, Research, and Practice (pp. 67-
96). San Francisco: Jossey-Bass.

132 Strengthening the Role of ICT in Development Volume VIII
Mugyenyi, P. (2007). Health care in Uganda: Challenges and opportunities. OHSU Seminar
proceedings of February 2007. McGill University.
Mukasa, H. (2008). Uganda: Infant mortality rate still high. Kampala: New Vision. Retrieved 7
October 2011, from www.newvision.co.ug
Oladosu, J. B., Sunday, O., and Mathew, O. A. (2009). Framework for a context-aware
mobile e-health service discovery infrastructure for rural/suburban healthcare. Journal of
Theoretical and Applied Information Technology 6, 081 – 091.
Omry, Z., Lupiana, D., Mtenzi, F., and Wu, B. (2010). Analysis of the challenges affecting
e-healthcare adoption in developing countries. International Journal of Information Studies
2, 38-50.
Onwegbuzie, R.B. (2004). Mixed methods of research, a research paradigm whose time has
come. American Journal of Education. 14-20.
Paul, D.l., and McDaniel, R.R. (2004). A field study of the effect of interpersonal trust on
virtual collaborative relationship performance. MIS Quarterly 28, 2, 183–227.
Perednia, D.A., and Allen, A. (1995). Telemedicine technology and clinical applications.
Journal of the American Medical Association 273, 483-488.
Mayer , R., Davis, J., and Schoorman, F.(1995). An integrative model of organizational trust,
Academy of Management Review, Academy of Management, Ada, OH, 1995 July, 709-
734.
Rogers, E.M. (1995). Diffusion of innovations. 4th Edition. New York: The Free Press.
Simon S.R., Kaushal, R,, Cleary, P.D., Jenter, C.A., Volk, L.A., and Orav E.J. (2007). Physicians
and electronic health records: a statewide survey. Arch intern med 167, 507–512.
Smith, A. C., Bensink, M. M., Armfield, N. N., Stillman, J. J., and Cattery, L. L. (2005).
Telemedicine and rural health care applications. Journal of Postgraduate Medicine 51, 286-
293.
Soroj, M., and Indra, P.S. (2008). mHealth: a developing country perspective. Making the
eHealth Connection. Bellagio, Italy 13 July - 8 August.
Spens, K.M. and Kovács, G. (2006). A content analysis of research approaches in logistics
research. International Journal of Physical Distribution & Logistics Management 36,5, 374 -
390
Spratt, C.W. and Bernadette,R.R.. (2004). Mixed research methods. Commonwealth of Learning
352, 1-55.
Standing, S., and Standing, C. (2008). Mobile technology and healthcare: adoption issues and
systematic problems. International Journal of Electronic Healthcare 4, 221 – 235.
Zhang, H., Cocosila, M. and Archer, N. (2010). Factors of adoption of mobile information
technology by homecare nurses: a technology acceptance model 2 approach. Cin-computers
Informatics Nursing 28, 1, 49-56.
Vakantesh, V., Kuechler, W. (2004). Design science research in information systems. 20 January
2004, last updated 30 September 2011. URL http://desrist.org/desrist.
Venkatesh, V., and Davis, F.D. (2000). A theoretical extension of the technology acceptance
model: four longitudinal field studies. Management Science 46, 186-204.

Part 2: Information System Tracking 133
Venkatesh, V., Morris, M.G., Davis, G.B., and Davis, F.D. (2003). User acceptance of
information technology: toward a unified view. MIS Quarterly 27, 425–78.
Wootton, R. (2008). Telemedicine support for the developing world. Journal of Telemedicine
and Telecare, 109-114.
World Health Organisation. (2010). Global observatory for e-health series. Telemedicine
Opportunities and Developments in Member States 2, 1-96.
Yarbrough, A.K. 2007. Technology acceptance among physicians: a new take on TAM. Medical
Care Research and Review 64, 650-672.
Yen, C.D., Chin, S.W., Cheng, F., and Huang, W.Y. (2010). Determinants of users’ intention to
adopt wireless technology: an empirical study by integrating TTF with TAM. Computers
in Human Behavior 26, 906-915
Zhang, H., Cocosila, M., and Archer, N. (2010). Factors of adoption of mobile information
technology by homecare nurses: a TAM2 approach. Computers, Informatics, Nursing 28, 1,
49-56. Doi: 10.1097/NCN.0b013e3181c0474a