Study to investigate factors influencing adoption of mobile devices in the health care environment

Abstract

In recent years, the potential use of mobile devices hassignificantly improved the healthcare sector. Despiteclaims of widespread use of mobile devices in healthcare,its adoption is low. Areas of interest for most researchershave focussed on the planning, implementation, technologyand organisational perspectives and individualperspectives. However, adoption of mobile devices formindividual perspective concentrating on individualcharacteristics such as age, gender experience andindividual readiness has received less attention. Thisresearch aims to assess the extent to which the adoptedvariables, self-efficacy and relative advantages impactindividual readiness to adopt mobile devices in healthcare.This research also aims to elaborate how age, gender andexperience operate as mediating factors. The overall aim ofthe research is to develop a health specific conceptualframework for adoption of mobile devices.

For this proposal, an initial conceptual framework isdeveloped from a review of previous literature tounderstand various factors influencing adoption of mobiledevices in healthcare settings. The mixed method approachwill be used to arrive at findings of this study. Aqualitative approach (focus group technique) will be usedto find the determinants that are influencing healthcareprofessionals and patients for adoption of mobile devicesin the healthcare domain. Analysis of qualitative data willbe used to refine the initial conceptual framework drawnfrom the literature. A quantitative approach (web survey)will be used to test the refined conceptual framework.

This research hypothesizes that individual readiness,complexity and social influences are mediated by age,gender and experience for individual intention to adopt

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mobile devices in healthcare. Furthermore, functionalfeatures of mobile devices and compatibility withhealthcare processes will be mediated by age and gender.

This research will contribute to the body of knowledge onadoption of mobile devices in the healthcare context.Further contribution of this research is in improvement ofhealthcare practices and policies.

1.0 BackgroundModern methods of healthcare are associated with mobility,

flexibility, convenience, real time communication and

connectivity; and mobile devices offer all these features

to modernise healthcare (Ben-Zeev et al. 2013; Morón,

Luque & Casilari 2014; Nah, Siau & Sheng 2005; Sarker,

Urbaczewski & Wells 2002). In recent times iPad and iPhone

are popular mobile devices to access high quality health

care services (Castro, D. 2014). The ability to monitor

patients’ health remotely is making mobile devices’ popular

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in health (Kang et al. 2010). Moreover, mobile devices have

made health services economical and convenient (The pulse of

telehealth 2014; Deng, Mo & Liu 2013; Hebert, Korabek & Scott

2006; Wu, Wang & Lin 2007). Further, Mobile devices in

collaboration with other technologies (like skype, FaceTime

and internet technology) are now used to provide real time

monitoring (Slaper & Conkol 2014; Zangbar et al. 2014).

Interpretation of images from remote locations have become

easy for physicians due to high resolution mobile devices

such as iPad (Ramey, Fung & Hassell 2011). Hence, mobile

devices have transformed the healthcare delivery process.

Use of mobile devices in healthcare has enhanced the scope

of health care services and in the near future will make

health services more flexible (Wu, Li & Fu 2011) however,

for adopting mobile devices, both healthcare professionals

(HCPs) and patients feel insecure (Fox 2011; Rogers 2003b;

Slaper & Conkol 2014; Wu, Li & Fu 2011; Wu, Wang & Lin

2007). It attracted researcher’s attention to investigate

the factors which influence HCPs’ and patients’ intention

to adopt mobile devices in healthcare.

1.1 Research aim

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The overall aim of the research is to understand adoption

factors influencing the intention of healthcare

professionals and patients for adoption of mobile devices

in healthcare and build a health specific conceptual

framework for the adoption of mobile devices. The sub aims

of this research are: understanding how self-efficacy and

relative advantages influence individual readiness for

adoption of mobile devices in the healthcare context and

investigate the influence of age, gender and experience as

mediating factors on determinants for adoption of mobile

devices in the healthcare domain.

1.2 Justification of the research

It is evident that adoption of mobile devices in the

healthcare context is low. By understanding adoption of

mobile devices at the individual level may increase the

rate of adoption of mobile devices in healthcare because

the successful use of technology depends upon the

satisfaction and acceptance by the users (Bano & Zowghi

2015; Cellucci, Spil & Wiggins 2014; Zmud 1979). Also,

personal characteristics such as age, gender and experience

are pertinent to adoption of technology in the healthcare

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because they can ensure the long term profitability and

reduction in organisational cost (Boulos et al. 2011; Lim

et al. 2011; Xue et al. 2012). Moreover, this research

will also provide avenues for future research in the domain

of healthcare for adoption of mobile devices. Also, this

research will contribute to the theoretical knowledge and

practices for the adoption of mobile devices in healthcare.

Therefore, this research is focussed on understanding the

adoption pattern of mobile devices in healthcare and on

developing a rigorous (credible, reliable, valid)

conceptual framework.

1.3 Scope of the research

The scope of the research is limited to Queensland,

Australia with the main activities happening in Brisbane

and Toowoomba however, the findings of the research will be

beneficial for countries also. The healthcare system is

well developed in Australia as people are already using

health services by means of mobile devices. It is

difficult to study the factors in developing countries

where the use of mobile devices for health purposes is not

on a large scale. Further, this research will understand

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HCPs’ and patient’s intention of for adoption of mobile

devices but intention may differ from actual final

behaviour performance.

2.0 Literature review

In this research the meaning of individual level of

adoption of mobile devices in the healthcare refers to

adoption of mobile devices by HCPs and patients and not by

organisation. Literature indicates that individual level of

adoption of mobile devices is influenced by planning,

implementation, technology and organisational perspectives

(Brown III et al. 2013; Kay 2011; West 2012; Wu, Li & Fu

2011; Wu, Wang & Lin 2007; Yangil & Chen 2007). For

example, Brown III et al. (2013) discusses that individual

adoption of mobile device in the healthcare depend upon

technology aspect. Small size of mobile devices, low

resolution screens, slow operating system, and variable

connectivity may be problematic to use these devices in the

healthcare environment. Further, in a global survey

conducted by world health organisation (WHO) among 114

nations in 2011 resulted that priority, budgetary

restrictions, staff shortage privacy and data security

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influencing adoption of mobile devices in healthcare which

indicates towards organisational and implementation

perspectives (West 2012). Moreover, Wu, Li and Fu (2011)

explore factors influencing individual intention for

adoption of mobile devices from organizations, ,

technology, system services, and individual psychological

states perspectives. In conclusion, adoption of mobile

devices by individual in the healthcare system is

influenced by many factors.

Theory of planned behaviour (TPB) and diffusion of

innovation theory (DOI) appear to be appropriate for this

research. Various researchers have used TPB to predict

individual behaviour for adoption of technology (Casper

2007; Cheng & Chu 2014; Perkins et al. 2007; Wu, Li & Fu

2011). Casper (2007) has used TPB in the field of health

care to compare mental health professionals intention to

use technology as the assessment tool. Additionally,

Perkins et al. (2007) used TPB to predict clinicians’

behaviour towards clinical practices in mental health

services. Hence, TPB appears acceptable in the domain of

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healthcare for adoption of mobile devices. All the

variables of TPB are represented in figure 1 below.

Figure 1: TPB Adapted from(Ajzen 1988)

TPB is able to explain fundamental predictors for adoption

of technology from individual perspective however TPB is

unsuccessful to explain adoption of technology from

technology and organisational perspective (Ghodeswar &

Vaidyanathan 2007; Looney, Akbulut & Poston 2008; Moseley

2000) which are important for adoption of mobile devices in

healthcare (Wu, Li & Fu 2011). Diffusion of innovation

(DOI) theory has the potential to cover these aspects.

Figure 2: DOI Adapted from (Rogers 2003a)

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DOI theory has the potential to explain operational aspect

of technology in clinical processes (Ghodeswar &

Vaidyanathan 2007). Similar to TPB, DOI theory is also used

by various researchers in the field of health care for the

adoption of technology (Brown et al. 2007; Lei-Shih, Oi-Man

& Goodson 2008). Roger, the originator of the theory has

the opinion that DOI is applicable in various fields

(Rogers 2004). Based on these arguments the second guiding

theory selected for this research is DOI theory.

This research is based on individual level of adoption of

mobile devices in the healthcare. Therefore, both

fundamental aspects and operational aspects of technology

in healthcare are important perspectives in this research.

Kwon and Zmud (1987) also think that adoption of technology

can be studied more effectively by combining DOI with other

theories. Hence, both DOI and TPB are supplements of each

other in this current research.

2.2 Gap in literature

Globally the impact of mobile devices services has improved

the healthcare environment and it will also continue to

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take centre stage in the future also (Lim et al. 2011;

Newswire 2014; Slaper & Conkol 2014; Wu, Li & Fu 2011), but

adoption of mobile devices in healthcare is low

(Christensen & Remler 2009; Kay 2011; Wu, Li & Fu 2011).

Previous literature has explored factors influencing

individuals’ adoption of mobile devices from technical,

organisational, implementation and planning, and individual

perspectives (Boulos et al. 2011; Wu, Li & Fu 2011; Yu et

al. 2006). However, the influence of personal factors (age,

gender, experience and individual readiness) on intention

to adopt mobile devices in the healthcare has yet received

less attention. It is evident from literature that age,

gender and experience act as mediating variables for

adoption of technology (Leventhal 2008; Lim et al. 2011;

Venkatesh & Morris 2000; Xue et al. 2012). Wu, Li and Fu

(2011) also agree that personal factors such as gender

differences play a mediating role for adoption of mobile

devices in healthcare. In addition, individual readiness

for adoption is salient in this study because individual

readiness to adopt technology can ensure long term

profitability and reduction in organisational cost being

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an advantage for an organisation (Caison et al. 2008;

Kuang-Ming, Chung-Feng & Chen-Chung 2013; Thong, Hong & Tam

2006). Previous studies are lacking in the area of the

influences of self-efficacy, relative advantage on

individual readiness for adoption of technology.

Furthermore, research on adoption of mobile devices in

Australian healthcare settings has received less attention

than it has in Asia and America; therefore, this research

is focussing on three main gaps in the literature: 1) how

age, gender and experience as mediating variables influence

the determinants which influence adoption of mobile devices

in healthcare 2) the influence of self-efficacy and

relative advantages on individual readiness 3) different

healthcare contexts (Australia) compared with previous

studies (America and Asia).

2.3 Research questions

This research on adoption of mobile devices at the

individual level in the healthcare domain is focussed on

the following research questions:

RQ1: What factors constitute a conceptual framework for

adoption of mobile devices in healthcare environment?

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RQ2: How self-efficacy and relative advantages influence

individual readiness for adoption of mobile devices in

healthcare?

RQ3: What is the role of age, gender, and experience as

mediating factors on the determinants of adoption of mobile

devices in healthcare?

2.4 Conceptual framework

In this proposal the initial conceptual framework is

developed from previous literature following six steps.

All these steps and their detailed explanation for the

development of the conceptual framework are attached in

Appendix 4.

Operationalization of constructs and formulation of

hypothesis: Construct operationalization is a practical

step to achieve clear and practical meaning of the

constructs chosen in the hypothetical framework. It

includes an agreed definition of the constructs from

literature, clear meaning of the constructs which is also

suitable for the research and identifying key concepts

properties describing constructs (Ayers & Olander 2013;

Bhattacherjee et al. 2007). These all are helpful to

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understand constructs and apply them in the existing

research context. Each construct considered for the current

study is operationalized and hypothesised below.

Intention: (Ajzen 1988, p. 42) expresses intention as ‘the

measure of the likelihood to perform the behavior’. In this research

intention is the individual perception towards adoption of

mobile devices in healthcare. User intention is a good

indicator of how a system will be accepted. Intention to

use technology may vary because of many factors (Tiong et

al. 2006). Therefore, intention to adopt mobile devices is

a dependent variable and is included in this research.

Figure 3 depicts all the independent variables assumed

which are influencing individual intention for adoption of

mobile devices in healthcare.

1. Individual readiness: Individual readiness to adopt

technology in this research refers to the individual

propensity to embrace and use mobile devices in health

care. Baig (2010) indicates that individual readiness to

adoption of technology is influenced by individual’s

positive and negative feelings. Positive feelings allow

individuals to adopt technology while negative feelings

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distract from adopting technology. The positive feelings

are influenced by advantages offered by the intended

technology and negative feelings create inconvenience which

make individuals less confident to adopt technology (Caison

et al. 2008). Thus, individual readiness to adopt

technology can be influenced by the individual’s self-

efficacy and relative advantages of technology. Individual

readiness construct is important in this research because

the current study is seeking to understand adoption

behaviour at an individual level and individual readiness

plays a major role to adopt technology (Caison et al.

2008).

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Intention to adopt mobile

devices in healthcar

e

Dependent Variable

= Direct influence=Mediating influence=Age=Gender=Experience

Social influences

Self-efficacy

Complexity

Relative advantages

Age Gender

Experienceexperienc

e

Compatibility with healthcare process

Functional features

Independe

nt Variables

(1, 2)

(1,2)(1, 2, 3)

(1, 2,

3)(1, 2, 3)

Individual readiness

(+)

(+)

Several studies result relative advantages and self-

efficacy influencing individual intention for adoption of

technology in the healthcare context (Wu, Li & Fu 2011; Wu,

Wang & Lin 2007; Yangil & Chen 2007). However, research is

lacking in the area of influence of relative advantages and

self-efficacy on individual readiness for adoption of

mobile devices in healthcare. Thus, in the conceptual

framework, influence of relative advantages and self-

efficacy on individual readiness is considered for adoption

of mobile devices.

Age, gender and experience have a mediating effect on

individual readiness for adoption of technology (Caison et

al. 2008). Usually young adults consider themselves more

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= Direct influence=Mediating influence=Age=Gender=Experience

Age Gender

Experienceexperienc

e(1)

(2)

(3)(1) (2)

(3)

Moderating variablesFigure 3: Version 2 conceptual framework for adoption of

mobile devices in healthcare literature

tech- savvy (Lim et al. 2011; Xue et al. 2012) and this

affect is more obvious in men as compared to women.

Durndell and Haag (2002) suggest that men have more

internet experience then women which can make them more

self-efficacious. Lim et al. (2011) studies in this area

explore how women have less confidence in their ability to

adopt technology as compared to men (Lim et al. 2011). Men

are more task oriented and they want to see the benefits

which technology can provide to do their office work is

more (as it can be useful for them to achieve their goal)

(Taylor & Todd 1995; Xue et al. 2012). Overall, young

experienced men are more self-efficacious as compared to

women so their individual readiness for the adoption of

mobile devices in healthcare will be greater than women.

Moreover, Caison et al. (2008) results shows that young

male medical students have more curiosity for adoption of

technology. Therefore, the mediating effect of age, gender

and experience on individual readiness can be studied and

hypothesized for this research.

H1: The influences of individual readiness on intention to

adopt mobile devices in healthcare will be moderated by

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age, gender and experience, such that the effect will be

stronger for young, and particularly experienced males.

a. Self- efficacy: Self- efficacy in current research

is defined as the individual’s confidence in his own

capabilities and strength to use mobile devices in the

healthcare environment. Self-efficacy is the individual’s

confidence on his skills to perform the behaviour. In TPB

self-efficacy is termed as perceived behaviour control.

Self-efficacy is defined by Bandura as ‘people’s perception

about their capability to execute a task’ (Bandura 1986).

Thus, the meaning of self- efficacy remains the same

whether it is denoted by some researcher in terms of

perceived behaviour control. Many researchers have

demonstrated self- efficacy as an important construct for

adoption of technology especially in mobile computing and

wireless technology in healthcare (Lim et al. 2011; Wu,

Wang & Lin 2007; Yangil & Chen 2007). Hence, self-efficacy

is considered as an important construct in this research.

Previous studies have investigated the direct positive

influence of self-efficacy on individual intention for

adoption of technology (Ajzen 2005; Wu, Wang & Lin 2007;

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Yangil & Chen 2007). This research will investigate the

positive impact of self-efficacy on individual readiness

for adoption of mobile devices in healthcare.

H1a: Individual readiness for adoption of mobile devices in

healthcare will be positively influenced by self-efficacy

of the individual to use these devices in healthcare.

b. Relative advantages: Relative advantages in this

research refer to benefits of mobile devices in the

healthcare processes. Rogers (2003b, p. 239)outlines

relative advantage as ‘the degree to which an innovation is perceived as

better than the idea it supersedes’. It can be measured using various

parameters such as cost, features and compatibility. This

means benefits offered by innovation positively impact the

individual to adopt technology (Wang et al. 2010; Wu, Li &

Fu 2011). In the technology acceptance model (TAM) the

term ‘perceived usefulness’ is used in place of relative

advantages (Davis 1985). Many researchers have studied the

direct influence of relative advantage on individual

intention for adoption of technology (Daim, Basoglu &

Topacan 2013; Gagnon et al. 2012; Kim 2009) This research

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is investigating the influence of relative advantages on

individual readiness as is hypothesised below.

H1b: Individual readiness for adoption of mobile devices

in healthcare will be positively influenced by relative

advantages offered by mobile devices in the healthcare

process.

2. Functional features: In this research functional

features refer to the general features of mobile devices

which can influence individual intention for adoption of

mobile devices in healthcare. In the healthcare environment

the most important concern is a person’s health and adopted

technology should have good supporting features with

healthcare process (Wu, Li & Fu 2011). Ghodeswar and

Vaidyanathan (2007) think technology features are

responsible for performance of technology. Therefore,

whatever technology is used it should have good supporting

features within healthcare settings. Mobile devices

features such as size of the screen, image quality, battery

life and layout effect (Kargin & Basoglu 2006; Lu et al.

2005) may influence HCP and patient intention for adoption

of technology because individuals use health services on19

mobile device screen and both the sensory and functional

needs of the individual should be satisfied (Massey, Khatri

& Montoya-Weiss 2007). Therefore, it important to study the

impact of functional features of mobile devices and hence

is included in this research.

It is assumed that functional features of mobile devices

are mediated by age and gender. For older people negative

attribution dominantly affect mind and body which makes

them disinterested in technology use (Thimm, Rademacher &

Kruse 1998). For example, poor eyesight in old age affects

viewing ability (Leventhal 2008). Elderly people may prefer

mobile devices with bigger screen size, good image quality

and touch screen (Boulos et al. 2011). Further, new mobile

devices have many features which may be difficult for old

people to handle (Boulos et al. 2011). Besides age, gender

differences may also affect reaction to mobile device

features. Generally, women have more health complications

than men (Lim et al. 2011) which can increase their

difficulty to operate the mobile device functional

featuring. Thus, it can be inferred that the mediating

effect of age and gender on mobile devices functional

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features will impact more on older women as compare to

older men.

H2: The influence of mobile device feature’s on intention

to adopt mobile devices in healthcare will be moderated by

age and gender such that the effect will be stronger for

older women.

3. Complexity: Complexity in this research refers to the

degree to which HCPs and patients experiences difficulty in

using mobile devices for health care services. Rogers

(2003b, p. 257) states complexity as ‘the degree to which an

innovation is perceived as relatively difficult to understand and use.’ In the

context of adoption of mobile devices in healthcare, if an

individual has to struggle with use of technology in health

process, then individual’s intention for adoption of mobile

devices will be weak. Many researchers proposed complexity

as one of the major factors for adoption of technology in

healthcare, with antonyms like simple/ easy to

use/perceived easiness, perceived ease of use (Daim,

Basoglu & Topacan 2013). Complexity can be an important

factor for adoption of mobile devices in healthcare

environments as if HCPs and patients find it difficult to21

operate these devices in healthcare process; their

intention for adoption will be low. Thus, complexity

negatively affects adoption of mobile devices and is

included in this research.

Age, gender and experience have a mediating effect on

complexity. In the healthcare domain various other

technologies are also used (Baig 2010) and users

experience, age and gender with the use of those

technologies can influence individual intention for

adoption of technology (Venkatesh et al. 2003). Lim et al.

(2011) recognises that gender has a moderating effect for

adoption of technology and this effect is more prevalent in

females (Taylor & Todd 1995).Venkatesh and Morris (2000)

describes that complexity to use technology is mediated by

gender differences and prior experience with technology.

Women who do not have prior experience with technology may

feel more uncomfortable in using technology (Lim et al.

2011). Therefore, it can be hypothesised that complexity of

operation of mobile devices in the healthcare process is

moderated by age, gender and experience and this influence

is more predominant in women.

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H3: The influence of complexity on intention to adopt

mobile devices in healthcare will be moderated by age,

gender and experience such that the effect will be greater

for older inexperienced women.

4. Social influences: Social influences in this research

are defined as the influences of people on individuals for

adoption of mobile devices in healthcare services. Goswami

and Chandra (2013, p. 68) define social influences as the

‘influence of the people associated with the individual’. In the theory of

reasoned action (TRA), TAM2, TPB, C-TAM-TPB the term social

norm is used and has the same meaning as social influences.

according to Ajzen (1991, p. 188) subjective norm refers

to ‘the perceived social pressure to perform behaviour’.

Social influences are the influences of peers on the

individual (Morris, Venkatesh & Ackerman 2005). Social

influences have a great influence on individual adoption of

innovation (Rogers 2003b) and is considered one of the

essential constructs in the present research setting.

Social influences are moderately affected by age, gender

and experience. It is observed that women are easily

influenced by the people with whom they interact (Morris,23

Venkatesh & Ackerman 2005). Further, women prefer more

social interactions and are more socially active as

compared to men and as a result they are more involved in

the socialization process and more likely to listen and

follow the opinions of their friends and co-workers

(Hodgson & Watson 1987). It has been observed that elderly

people who have little experience in technology usage, rely

more on their friends (Lim et al. 2011; Morris & Venkatesh

2000; Venkatesh et al. 2003). Thus, it is concluded that

social influences are moderated by age, gender and

experiences and this leads to the following hypothesis.

H4: The influence of social influence on intention to adopt

mobile devices in healthcare will be moderated by age,

gender and experience such that the effect will be stronger

for women, particularly older women who have less

experience with technology.

5. Compatibility with healthcare processes: Compatibility

of clinical practices in this research refers to the

consistency of mobile devices with clinical processes.

defines compatibility as ‘the degree to which an innovation

is perceived as consistent with the existing values, past24

experiences, and needs of potential adopters’. Both HCPs

and patients show less interest for adoption of new

technology (Rogers 2003b; Wu, Li & Fu 2011). However, if

the technology is compatible with HCP’s work process,

they like to adopt it (Xue et al. 2012). Additionally, in

the health care environment new technology is adopted if it

supports the existing system and adds newness to the system

and mobile devices have that potential (Daniel Castro, Ben

miller & Nager 2014). Therefore, compatibility construct is

added in the conceptual framework.

Age and gender have a mediating effect on compatibility.

Generally elderly people are resistant to technology change

but if technology is suitable with their work process it

changes their mindset to adopt technology (Xue et al.

2012). Further, men’s ability to perform mental rotation

tasks is better compared to women. Rotation tasks are the

asks of reposition of 2D or 3D objects (Roberts & Bell

2000). For the use of health services in mobile devices,

individuals have to interact with various 2D and 3D

objects on the screen with which older females may feel

more difficulty than men because they are considered to be

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less tech-savvy (Xue et al. 2012). Therefore, it is

considered that age and gender have a mediating effect on

compatibility with clinical process for adoption of mobile

devices.

H5: The influence of compatibility on intention to adopt

mobile devices in healthcare will be moderated by age and

gender such that the effect will be stronger for older

women.

2.0 Research methodology

This research is using a mixed method which provides

strength to research by overcoming the drawbacks of both

qualitative and quantitative approaches (Klassen et al.

2012; Onwuegbuzie & Johnson 2006); and heighten the

validity of research (Greene, Caracelli & Graham 1989).

Venkatesh, Brown and Bala (2013) suggest that selection of

mix method depends upon the research questions, purpose and

context. The aim of the research is to develop a conceptual

framework; and the nature of the research questions is

exploratory and confirmatory which gives support for a

mixed methodology in this research. Further, mixed method

is a vital method for research whose aims are either of26

these: developmental, completeness, complementarity,

expansion, confirmation, compensation and diversity; and

having research questions that are exploratory and

confirmatory (Onwuegbuzie & Collins 2007; Venkatesh, Brown

& Bala 2013). Moreover, Creswell et al. (2011) have a

notion that mixed method is suitable for problems which are

influenced by a number of levels such as individual,

organisation and policies. Based on above discussion, this

research will use a sequential mix method approach with the

qualitative method followed by quantitative method.

3.1 Research philosophy

Research philosophy is a belief about the nature of reality

(ontology), values (axiology) and how knowledge is obtained

by the researcher (epistemology) (Saunders, Lewis &

Thornhill 2012). Saunders, Lewis and Thornhill (2012, p.

127) define ‘Research philosophy is an over-arching term relating to the

development of knowledge and the nature of that knowledge’. This

statement shows that research philosophy can be selected

considering three aspects: epistemology, ontology and

axiology. Epistemology is concerned with the question: ‘how

we know? What we know? ‘, ontology refers to the ‘nature of

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reality’ and axiology refers to belief about values of

research (what is good? what is right? what is important?).

Saunders, Lewis and Thornhill (2012) claim that selection

of research philosophy is based upon the research

question(s), research methodology and how a researcher

understands the research process. Depending upon these

arguments, pragmatism is the underlying research philosophy

in this research (Ethridge 2004; Fisher 2004; Saunders,

Lewis & Thornhill 2012). Pragmatists can use either or both

subjective or objective epistemology and ontology (Feilzer

2010; Saunders, Lewis & Thornhill 2012). Also, pragmatism

is applicable for mixed method and multilevel research

(Denscombe 2008). Morgan (2007) thinks that pragmatism is

suitable for research which supports transferability of

results. This research will use mixed methodology and work

with the holistic view of qualitative and quantitative

research. Therefore, pragmatic research philosophy is a

suitable philosophy. A comparison of pragmatism with

interpretivism, positivism and realism is given in Appendix

12.

3.2 Research approaches

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This research will use both qualitative and quantitative

approaches sequentially to achieve research objectives.

Qualitative approach is preferable when research has to

determine motivation, perception or beliefs (Milena,

Dainora & Alin 2008). Creswell et al. (2011) signify that

qualitative research helps to obtain information for those

processes which evolve over time and the use of mobile

devices such as iPad in healthcare is an emerging idea

which is progressing with the passage of time. Thus, in

this research context a qualitative approach is useful to

determine constructs and hypotheses and formulate a

conceptual framework. On the other hand, quantitative

research aims at testing of hypotheses (Yilmaz 2013) and

hence, in this research it will provide validation for the

conceptual framework developed in qualitative research.

In qualitative and quantitative research design focus group

and web survey techniques will be used respectively. The

objective of qualitative research is to determine

constructs, develop hypotheses and develop a conceptual

framework. Focus groups can provide valuable information to

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achieve that aim because in focus groups the responses come

after a relaxed, comfortable and enjoyable discussion (Baig

2010; Kai-Wen 2014) . As a result, more and more

information can be collected (Hussey & Hussey 1997;

Kitzinger 1994). In contrast, quantitative research aims at

testing hypotheses and validating conceptual framework

which can be achieved if researcher is able to collect

large number of responses. Cavana, Delahaye and Sekeran

(2001) have the idea that web survey is an effective

techniques to get more responses in less time and cost in

the new area of research. To increase the response rate in

this study respondents will be contacted through email and

personally prior to being sent the survey questionnaire

(Kaplowitz, Hadlock & Levine 2004). In conclusion, in this

research focus groups and web survey are the appropriate

research techniques in qualitative and quantitative

research design correspondingly.

3.3 Population, sampling and sample size

In this research purposive (convenience) sampling is

considered most appropriate techniques. It is the suitable

techniques for the researchers who have to select

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participants deliberately based on a certain criteria and

who can provide good information (Cavana, Delahaye &

Sekeran 2001; Guarte & Barrios 2006; Teddlie & Yu 2007). In

the healthcare domain not all the HCPs can participate in

this research due to their workload (Wu, Li & Fu 2011).

Also, it is difficult to receive responses from all the

patients. Therefore, purposive (convenience) sampling is

the best technique to choose participants who are

available conveniently and are willing to participate

(Onwuegbuzie & Collins 2007).

Selecting sample size for both qualitative and quantitative

research is a critical task (Onwuegbuzie & Collins 2007).

Kotrlik and Higgins (2001) think that sample size selection

depends upon target population. Further, Onwuegbuzie and

Collins (2007) suggest that sample size depends upon

research design. In qualitative research design, a small

sample is sufficient to collect information (Johnson &

Christensen 2010). Various researchers suggest different

sample sizes (such as 6- 9, 6-10, 6-12, 8-10) for focus

groups data collection, (Christensen & Remler 2009;

Johnson & Christensen 2010). In this study 6-8 respondents

31

will be selected per focus group and 6-9 sessions of focus

groups will be held depending upon when researcher reach at

saturation point. Hence, in total 36-81 participants will

be needed in this research to conduct the focus groups

discussions.

In quantitative research design a large sample is needed to

generalise the results. Roscope (1975) cited in (Baig

2010) clarifies that in the quantitative research

paradigm, if data is divided into different themes then a

sample of 30 responses is enough for each theme. Currently

the conceptual framework drawn from the literature uses 4

themes, therefore 120 participants are required, but in

this research respondents will be selected based on number

of constructs and 25 responses are considered enough for

each construct. Therefore, in total 200 participants are

required for 8 constructs. However, the final sample size

will be decided after qualitative data analysis.

3.4 Data collection and analysis techniques

This research is using both primary as well as secondary

data. The main aim of secondary data collection is to

become familiar with the factors which influence adoption

32

of technology in healthcare and create an initial

conceptual framework. Alternatively, the main goal of

primary data collection is to answer research questions on

which this research is based. Thus both primary and

secondary data is important to achieve the overall aim of

the research. For Secondary data, (already collected for

this research) information is retrieved from articles

published in peer reviewed journals, books, reports and

magazines. Primary data will be collected and analysed in

two phases: 1) focus group 2) web survey.

Phase1: Focus group data collection and analysis

The first phase of primary data collection will be

completed in two steps: 1) pre-focus group, pilot focus

group and 2) focus group data collection. The first step

will provide experience for conducting actual focus group

interviews (Baig 2010). Pre-focus group and pilot focus

group are similar to focus groups, but the number of

participants remains less compared to the actual focus

group (Baig 2010). For the pre-focus and pilot study 4-5

participants will be selected. Each session will be held

for approximately 90 minutes. For focus group data

33

collection a semi-structured approach will be used which

encourages participants to contribute as much as possible

(Saunders, Lewis & Thornhill 2012). The objective and

description of the appropriate participants will be clearly

stated so that the findings of the research will be

reliable and on track.

Focus groups discussions will be recorded (both audio and

video) and transcribed. This transcribed file will be

converted into Microsoft word version 10. Further it will

be edited by the researcher to eliminate the information

identifying participants and will be uploaded into Nvivo to

analyse the data for repeated items, themes and categories.

Data analysis is a process of converting raw data into

intelligent information (Zikmund 2010).

Data obtained from focus group discussions will be analysed

to improve constructs, hypothesises and the version 2 of

the conceptual framework developed from literature. For

analysing qualitative data, content analysis techniques

will be used. It is a technique of analysing written,

verbal or visual communication messages (Cole 1988). The

34

benefit of content analysis is that words can be

categorised into smaller categories (Elo & Kyngäs 2008).

Hence, this phase will provide refinement to the conceptual

framework developed from literature and help to design a

survey questionnaire.

Phase2: Web survey data collection and analysis

In the second phase of primary data collection, cross-

sectional, web survey technique will be used. The aim of

this phase is to test the hypothesis generalise the

conceptual framework drawn from phase1 data collection. Web

survey is a cost effective method to collect information in

short duration of time (Fan & Yan 2010). The survey

questionnaire will be uploaded to the USQ website and

responses will be collected in the USQ database. The survey

questionnaire will require approximately one hour. Data

will be collected using a five point Likert scale.

In the survey questionnaire descriptive and inferential

analysis will be conducted using SPSS (AMOS) software.

Descriptive analysis of data will be helpful for

summarizing and simplifying data which will provide

descriptive validity. Inferential analysis will be used to

35

understand meanings and implications which can be attained

using statistical techniques (such as t- test, Chi Square

test, regression and structural equation modelling) to get integrative

validity (Baig 2010). A rough draft of the sample of the

survey questionnaire is attached in Appendix7: this will be

modified after phase 1 data collection and analysis.

3.5 Reliability and validity

Reliability means that the measured outcome should remain

consistent while repeating the same experiment many times.

(Cavana, Delahaye & Sekeran 2001; Neuman 2003). The

qualitative phase is a subjective phase and it is difficult

to get the same response each time the test is repeated

with the same individual (Bashir, Afzal & Azeem 2008)

however, the responses will be analysed to sift out the

same central ideas or themes. Reliability of quantitative

data will be ensured by using a standard reliability test

(Cronbach’s alpha test).

Validity of research ensures the quality of research. Poor

sampling and inaccurate and misleading responses may

challenge the validity of research (Hussey & Hussey 1997).

In qualitative approach four types of validity will be

36

ensured in this research: 1) descriptive validity: refers

to validity of factual accuracy of the data collected which

will be ensured by providing actual transcribed data. 2)

Interpretive validity: refers to respondents own word

validity which will be achieved by providing direct quote

supported by researcher’s interpretation. 3) Internal

validity: refers to uniformity of procedure which will be

confirmed by same set of questions and audio-video

recording of discussions. 4) External/ theme: refers to

theme and construct validity which will be achieved by

reviewing the items by researcher, discussing with

supervisors and consulting literature (Baig 2010;

Onwuegbuzie & Collins 2007; Onwuegbuzie & Johnson 2006).

In the quantitative approach the validation process is

followed to check the validity of the survey instrument and

will be conducted in three stages (Cavana, Delahaye &

Sekeran 2001). First stage: initial validation will be

conducted to check completeness, wording, and

appropriateness of the instrument which is called face

validity (Zikmund 2010). Face validity will be conducted

using pre-testing among PhD students and staff members.

37

Second stage: item validity which is the degree that a

measure covers the domain of interest, will be tested

through a pilot study (Zikmund 2010). Third stage: two

types of construct validity, namely convergent and

discriminant validity will be conducted. A correlation

coefficient helps to measure both types of validity. Both

will ensure that the items are measured with a higher

degree of correlation. However, the value of convergent

validity should not be too high as compared to discriminant

validity. As a rule of thumb when the value of convergent

validity is higher than 0.75 its discriminant validity is

interrogated (Zikmund 2010) which shows that two related

constructs are separate identities.

4.0 Outcomes and contributions of research

It is anticipated that individual readiness, complexity and

social influences are mediated by age, gender and

experience for individual intention for adoption of

technology; and general features of mobile devices and

compatibility with healthcare processes will be mediated by

age and gender.

38

This research will provide significant contributions in

theory, practice and policies. The conceptual model

developed in this research will be the first of its type

for adoption of mobile devices in healthcare at the

individual level. This model will serve as a pathway for

the healthcare and information technology domain to design

information communication tools for healthcare. Moreover,

this study will contribute to policy makers in healthcare.

They can redesign policies for the use of technology which

can fit in the healthcare environment (with users and

technology context). Also, the results from this research

can be transferable to the other part of Australian

healthcare settings.

4.1 Limitations and future research

This study is based more on intention rather than actual

behaviour as actual behaviour may change prior to intention

performance. This drawback can be overcome by studying the

actual use of mobile devices in healthcare. Further, policy

makers and management people are not included in this study

which can provide additional information about adoption of

mobile devices in the healthcare system. It is therefore

39

important to include those people in future research. In

addition to it, future research can be conducted for

adoption of mobile devices in a particular application such

as tele radiology and tele dermatology.

40

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Appendix 1: Summary of selected readings for adoption of technology in healthcareS.No.

Subject/ Data Source

Author

Technology used

Factor Future Research/ Limitations

Method/Theory

1. Medicaldirectors

(Gagnon etal.2005)CanadaQuebec

Telehealth

Theorganisationalfactors whichaffect adoptionof telehealthare: size andlocation ofhospital,availability ofnecessaryequipment andhuman resources,financialconstraints andphysicians’resistance.

Factorsinfluencingadoption oftelehealth atindividual andprofessionallevels are notidentified.

(Literaturereviewandsurvey)/InstitutionalTheory

2. Clinicians

(Tionget al.2006)-Australia

Wirelesstechnology

Five major themeswhich affectadoption ofwirelesstechnology inAustralianhealthcaresetting areperformanceexpectancy,effortexpectancy,facilitatingconditions,social influencesand mediatingfactors.

Theme obtainedwill befurtherverified usingsurveyinstrument forfutureresearch.

Qualitative/UTAUT

3. Physicians,nurses,administrators and

(Peddle2007)

Telehealth

Technicalproblems, staffturnover,workload,financialconstraints,inter

Futureresearch canbe conductedconsideringhealth careproviders asfocal point.

Qualitative /structuration

58

administrativeassistants

organizationaltrust,Lack ofsubstantivepolicy andpolicymakingbodies andprivacy,confidentialityand liability arethe berries fortelehealthadoption.

4. Medicaldoctors andnurses

(Yangil &Chen2007)-USA

Smartphone

Adoption of smartmobile isinfluenced byusefulness andease of use andorganisation sizeandobservability.Further, Healthcareprofessionals donot perceiveindividualfactors thatinfluence theirattitude towardsusing smartphone.

Longitudinalstudy can beconducted forbetterreliability ofresults.

Quantitative/TAM+DOI

5. Healthcareprofessionals(medicaldirectorsandchiefofinformationsystem)

(Wu,Wang &Lin2007)-Taiwan

Mobilecomputing

The factors whichaffect adoptionof mobilecomputing inhealthcare arecompatibility, PUand PEOU.Further technicalsupport andtraining have nodirect impact onadoption ofmobile computingin healthcare.

Further studycan beconducted toexplore otherfactors suchas privacy andsecurityissue, systemandinformationquality,limitations ofmobile devicesfor adoptionof mobilecomputing inhealthcare.

Quantitative/ TAM

6. Mediumsize

(Kim &Garris

Mobilewireles

Results show thatperceived

The result canbe generalize

Quantitative

59

Koreanuniversityemployees

on2008)-Korea

stechnology

ubiquity andperceivedreachabilitysignificantlyaffect anindividual’sintention to usemobile wirelesstechnology.

by includingindividualsform differentcountries andcultures.

/ TAM

7. HIMSSanalyticsdatabase

(Furukawa etal.2008)-US

Healthinformationtechnology(HIT)

Technology,geographiclocation of thehospital andpatient safetyare the factorswhich impedeadoption oftechnology in UShospitals.

For adoptionof technologyhealthapplicationbeing used arenot consideredin thisresearch.

Meta-analysis(systematicliteraturereview)

8. LiteratureReview

(Wanget al.2010)

Hometelemonitoring

Cognitive,physicallimitations,health status,diseaseconditions,technologyliteracy, andperceivedusefulness oftechnology arethe factors whichinfluence olderpeople foradoption oftechnology.Presence ofCollaborators,access totechnology,cultural andsocietal factorsare affectingolder peoplecollaborators foradoption oftechnology.Policy relatingto technology andeconomic factors

Futureresearch canexplore effectof futurechangingpolicies ontechnologyadoption.

Meta-analysis/

60

related tocontext are alsoaffectingadoption rate.

9. HCPs (Singhet al.2010)Georgia

telehealth

Collaborationbetween districtand localcommunity affectthe adoption oftelehealth inrural area.

------------------

QualitativeLongitudinalstudy(casestudy)

10.HCPs’ (Hafeez-BaigAbdul& Raj2010)-Australia

Wirelesshandhelddevices

Clinicalpractices andcompatibility,influence theuse of wirelesshandheld devicesin theAustralianhealthcareenvironment whilesocialdemographics actas a mediator.

Actual use ofany wirelesshand handledevices andapplicationsis not testedin thisresearch.

Mixmethodology/TAM

11.Hospital’sprofessionals

(Wu,Li &Fu2011)

Adoption ofmobilehealthcare

Perceived serviceavailability(PSA) andpersonalinnovativeness inIT (PIIT) are themain issues foradoption oftechnology inhealthcare.

-Furtherresearch canbe conductedwhen usersbecome moreaware ofmobilehealthcare(experience).-Also casestudy can bedone toobserve theusefulness ofthe researchframework.

Quantitative/ TAM+TPB

12.(Sanders etal.2012)

Adoption oftelehealth andtelecarefromtheprospective of

Participantsthink thatspecial skillsare necessary tooperateequipment. Alsothey werereluctant to makechange inexisting

The group ofthe people whowas taken inthis researchwas very smallso thisresearch couldnot fullyexamine thecontextual and

Quantitative/TAM/TPB

61

thepeoplewhodeclined toparticipate orwithdraw fromtrial

services. organisationalfactors

13.Ruralhealthoffices

(Singhet al.2012)-NewYork

Adoption ofelectronichealthrecord

The barriers foradoption ofelectronicmedical record(EMR)in rural andurban health caresetting is thesize oforganisation

Low responserate andsurveyinstrument didnot providedetailedguidance torespondents asrespondentwere alreadyfamiliar withelectronichealth record.

Quantitative(Survey)/Notbasedon anytheory

14.Systematicliteraturereviewfromvariousdatabases

(Gagnon etal.2012)

informationcommunicationtechnology(ICT)

Perceived ease ofuse , perceivedusefulness,design issues,technicalconcerns,familiarity withICT, and time arethe factors whichare influenceHCPs’ foradoption of ICT.

Only HCPs areconsidered inthis researchotherstakeholders(e.g.patients) canbe consideredfor futureresearch.

Meta-analysis(systematicliteraturereview)/

15.106Zealandoptometrists

(Heidarian &Mason2013)- NewZealand

HIT The potentialbarriers for HITadoption inoptometry aretechnologyupgrades, cost,lack of time forimplementation,and training.

Futureresearch canbe conductedto evaluatehow technologyis affectingthe qualityand efficiencyof care.

Mixmethodology(Semi-structuredinterviewandsurvey) /

16.Seniorprofes

(Zinszer et

HIT Barriers for HITadoption are:

End users suchas nurses,

Qualitative

62

sionals andadministrators

al.2013)

lack of nationalvision andleadership,insufficientinvestment, andpoorconceptualizationof the priorityareas forimplementing HITin public health.

doctors,public healthInspectors,epidemiologists, analysts,and clericalstaff can beincluded forfutureresearch.

(casestudy)/

17.Diabetic andobesitypatients

(Daim,Basoglu &Topacan2013)-Turkey

Wirelessserviceprotocol usedforobesityanddiabeticpatients ineHealth

Service quality,compatibility,quality ofsupport,informationquality, usagetime, image,accessibility,and ease of useare significantdeterminantsOf usefulness;and responsetime,compatibility,social influence,understandability, and self-efficacy aresignificantantecedents ofease of use inthe e-healthinformationservice adoption.

Only 32%respondentswere obsessedand three werediabetic. Soit isdifficult tosay thatsample sizewasappropriate.

Mixmethodology(interview,analytichierarchystudy,pilotstudy ,experimentalstudy)/ TAM

18.Malaysia

(Saad,Alias&Ismail2013)

telehealth(MyHealthPortal)

Adoption oftelehealth may beaffected byperceivedusefulness,perceived ease ofuse, trust andattitude.

This study isin datacollectionphase

Sequentialmixmethod(Semi-structuredinterview,questionnaire)/TAM+U&G

63

19.Middleagedandolderpeople

(Deng,Mo &Liu2013)-china

mHealth Behaviorintention to usemobile healthservices for theolder users’depend uponperceived value,attitude,perceivedbehavior control,technologyanxiety, andself-actualizationneeds positivelyaffect adoptionof m healthservices.

Furtherresearch caninclude seniorcitizens whohavedisabilitiesand are lesssociallyactive.

Quantitative/ValueAttitudeBehaviourModel+TPB

20. ITmanager

(Thomas, Yao& Guo2014)China

mobileandInternettechnologies

Experience andSecurityare the factorsthat motivatehealthcare ITmanagers toinnovate servicesusing mobile andInternettechnologies

Furtherresearch canbe conductedto find outadditionalfactors whichaffect ITmanagerattitudestowardsupportinginnovation.

Quantitative(Survey)/

21.Report (DanielCastro, Benmiller&Nager2014)-US

Telehealth

Right policiesare essential fortelehealthimplementation

------------------

TAM

22.Palliativecareclinicians

(Bradford etal.2014)-RoyalChildren’sHospitalBrisba

Telehealth

According toclinician’sperspectives themajor factors forhome telehealthadoption programare: Technology,Individual andService.

A small sampleis used inthis studyandinterviewer ispreviouslyknown tointerviewees.

Qualitative/

64

neQueenslandAustralia

65

Appendix 2: Summary of theories/models used for technology acceptance in healthcare

Name/ Year/ Author

Independent Construct

Dependent Construct

Level of analysis

Origination

TechnologyAcceptance Model(TAM)Davis (1986);Davis (1989)

Perceivedusefulness,Perceived easeof use

Behavioralintention touse,Systemusage

Individual

InformationSystems,TechnologyAdoption

Innovationdiffusion theory(DOI)Lazarsfeld et.al. (1949);Rogers (1962);Rogers andShoemaker (1971);Rogers (1995)

Compatibilityof Technology,Complexity ofTechnology,RelativeAdvantage(Perceived Needfor Technology)

ImplementationSuccessorTechnologyadoption

Group,Firm,Industry,Society

Anthropology/Sociology/Education/Communication/MarketingandManagement/Geography/Economics

Theory ofReasoned Action(Fishbein (1967),Ajzen andFishbein (1973),Fishbein andAjzen (1975)

Attitude towardbehaviour,Subjective norm

Behavioralintention,Behavior

Individual

Socialpsychology

Theory of PlannedBehaviour Ajzen(1985), Ajzen(1991)

Attitude towardbehaviour,Subjectivenorm, PerceivedbehaviourControl

Behavioralintention,Behavior

Individual

Socialpsychology

Social andcognitive theory(Albert Bandura)(Bandura 1986)

Personalfactors,Behavior,Environment

Learning, Changeinbehavior

Individualandgroup

Psychology

Technology-Organization-EnvironmentFramework(Tornatzky and

TechnologicalContextOrganizationalContextEnvironmental

TechnologyAdoption(orLikeliho

Firm/Organization

Organizational Psychology

66

Fleisher1990ework)

Context od ofAdoption,Intention toAdopt,ExtentofAdoption)

Task technologyFit (TTF)(Goodhue &Thompson 1995;Zigurs & Buckland1998)

Taskcharacteristics, Technologycharacteristics

Individualperformance,Systemutilization

Individual

InformationSystems

Unified theory ofacceptance anduse of technology(UTAUT)(Venkatesh et al.2003)

Performanceexpectancy,Effortexpectancy,Socialinfluence,Facilitatingconditions,Gender, Age,Experience,Voluntarinessof use

Behavioralintention, Usagebehavior

Individual

InformationSystems,TechnologyAdoption

67

Appendix 3: Factors affecting adoption of technology in healthcare from integrative perspective

I. (Tiong et al. 2006) Australia1. Performance Expectancy

a. Benefit to work b. Increased efficiency c. Mobility d. Consistency e. Reduce work time cycle

2. Social Influencesa. Credibilityb. Management Decisions c. Commitmentsd. Collegiality e. Culture

3. Effort Expectancya. Comfortable b. Technology Awareness c. User Friendliness d. Learning Times e. Familiarity

4. Facilitating conditionsa. Trainingb. Resources c. Cost Effectiveness d. Infrastructure e. Positiveness of Existing Systems

5. Other Mediating Factorsa. Formal Systems b. Right Practice c. Clinical data protection d. Knowing Limitations e. Availability of new technology

68

II. (Furukawa et al. 2008)6. Technology7. Geographic location8. Patient safety

III. (Hafeez-Baig Abdul & Raj 2010)9. Clinical practices10. Compatibility

IV. (Singh et al. 2012)13 Financial constraints14 Return on investment issues15 Initial data entry labour intensive16 Initial loss of productivity17 Training burden for physicians

V. (Davis 1985; Gagnon et al. 2012)18 Perceived usefulness 19 Perceived ease of use

VI. (Heidarian & Mason 2013) New Zealand healthcare setting

20 Technology updates21 Cost 22 Lack of time 23 Equipment integration

VII. (Zinszer et al. 2013) Canadian Health care setting24 Lack of national vision 25 Lack of leadership26 Insufficient investment27 Poor conceptualization of the priority areas

VIII. (Gagnon et al. 2005) Canada28. Hospital’s size and location29. Availability of the necessary equipment and human

resources30. Training31. Lack of resources32. Financial constraints 33. Physicians’ resistance34. Technology quality35. Performance

69

36. Ease of use37. Conviviality

IX. (Peddle 2007) Canada38. Technical problems39. Staff turnover40. Workload41. Financial constraints42. Inter-organizational trust43. Lack of substantive policy and policymaking

bodies44. Privacy45. Confidentiality 46. Liability

X. (Michael J. Ackerman et al. 2010)47. Lack of technology integration48. Interoperability,49. Standardization50. Limited financing 51. Lack of data standards52. Cultural barriers53. Usability54. Ease of use55. Security56. Privacy57. Trust

XI. (Wang et al. 2010)58. Cognitive, physical limitation59. Health status60. Disease conditions61. Technology literacy62. Perceived Usefulness of technology

XII. (Kluge 2011) not added in summary63. Liability64. Interoperability65. Legacy

XIII. (Brewster et al. 2014)66. Staff-Patient interaction

70

67. Credibility68. Negative impact of service change69. Autonomy70. Technical issues

XIV. (Daim, Basoglu & Topacan 2013)71 Usefulness72 Quality of Services73 Compatibility With User’s Life Style74 Quality of Support75 Quality of Information76 Image of Technology77 Usages Time78 Accessibility79 Easy to Use

XV. (Cimperman et al. 2013) 80. Security 81. Usability

XVI. (Daniel Castro, Ben miller & Nager 2014)82. Standard of care83. State licensing policies84. Compatibility 85. Interoperabiliy

XVII. (Bradford et al. 2014) Australia86. Technical ( setting up equipment, username and

password )87. Individual (culture, linguistic and social

variations)88. Service factors (Lack of staff)

XVIII. (Wu, Wang & Lin 2007)89. Device Size90. Access Procedure91. Ease of use92. Attitude93. Self- efficacy94. Compatibility95. Technical Support and Training

XIX. (Yangil & Chen 2007)71

96 Attitude of individual (healthcare professional) 97 Perceived usefulness98 Perceived ease of use99 Self-efficacy100 Organisation size101 Observability

XX. (Kim & Garrison 2008)102 Cognitive Influence Process

a. Job relevanceb. Perceived ease of usec. Perceived usefulness

103 Technological Influence Processa. Ubiquityb. Reachabilityc.

XXI. (Wu, Li & Fu 2011)104 Personal innovativeness105 Availability

XXII. (Goswami & Chandra 2013)106 Interface clarity107 User friendliness108 Social influences109 Support of product and services provided110 Learning predisposition

XXIII. (Deng, Mo & Liu 2013)111 Physical condition112 Resistance to change113 Technology anxiety114 Self-actualization needs

72

Appendix 4: Steps followed for development of conceptual frameworkStep1: Thorough study of literature for adoption of technology in healthcare

In step 1 thorough study of literature is conducted tobecome familiar with the topic of the research. Severalkeywords are used to search the literature. Some of themare: adoption of technology, factors for adoption oftechnology, technology and healthcare, mobile devices inhealthcare, user intention for technology use, telehealth,mHealth and eHealth. Databases used to search literatureare: EBSCOhost MegaFILE Complete which covers topic onvarious fields such as arts, biological and physicalsciences, business, education, engineering and spatialsciences, maths computing, and nursing. Other secondarysources used for this research are web of sciences, Medlinelibrary and Wily online library, USQ library, googlescholar etc. A list of all the research articles read forthis research is attached in Appendix 1.

Step2: Identification of factors influencing adoption of technology in healthcare.

In this phase various factors influencing adoption oftechnology are identified. This step provides a broad viewof all the factors which can influence adoption oftechnology in healthcare environment. Factors identified inthis step are related to adoption of various technologiessuch as EHR, wireless technology, HIT, telehealth, hometele monitoring, eHealth mobile computing and mobile health(Daim, Basoglu & Topacan 2013; Deng, Mo & Liu 2013;Heidarian & Mason 2013; Singh et al. 2012; Wang et al.2010; Zinszer et al. 2013). After completing step2 it isidentified that some studies are conducted for adoption oftechnology in the healthcare from an individual andorganisational level. (Appendix 3 attached in research

73

proposal shows a list factors found from various researcharticles.) So, step3 is followed to separate out factors atindividual level and organisational level of adoption oftechnology.

Step3: Identification of factors influencing adoption of technology in healthcare at individual level

This step is followed to understand factors influencingindividual level of adoption of technology in thehealthcare. Therefore, factors are put into two categories.The scrutiny of factors is accomplished based on whetherthe study is conducted from organisational perspective orindividual perspective. For example, the studies which areconducted for adoption of technology from management peopleperspective, implementation of an application in healthcareand policies are put in the category of organisation levelof adoption of technology (Gagnon et al. 2005; Peddle 2007;Zinszer et al. 2013). Further the studies which areconducted for adoption of technology in the healthcare byHCPs, patients are considered at individual level ofadoption (Bradford et al. 2014; Deng, Mo & Liu 2013; Gagnonet al. 2003; Wang et al. 2010). In this step main focus isto select the factors which influence HCPs and patients foradoption of technology. There are, numerous factorsaffecting adoption of technology in healthcare atindividual level but it is not necessary that all thefactors found in literature at this stage may influenceadoption of mobile devices in healthcare. So there isfurther need to choose factors appropriate for adoption ofmobile devices in the healthcare which is explained in nextstep.

Step 4: Selection of factors appropriate for adoption for mobile devices in healthcare at individual level

The motive of this step is to select most appropriatefactors influencing adoption of mobile devices inhealthcare. Research articles selected for this criterion

74

are: mobile computing, mobile wireless devices, andwireless devices (Goswami & Chandra 2013; Wu, Wang & Lin2007; Yangil & Chen 2007). Mobile computing or wirelesstechnology articles are selected as these are related tomobile devices and are appropriate articles to assumeconstructs for conceptual framework. Again, a list ofseveral factors is found which influence adoption of mobiledevices in health care. It is a difficult decision tochoose which factors can be selected and which can bediscarded. A justification for the construct selected inconceptual framework is given in 2.4.1 article.

Step 5: Selection of themes to cover factors which may influence adoption of mobile devices in healthcare at individual level.

As explained in step 4 decision of selection of importantfactors from a long list of factors is a difficultsituation. To solve this problem, themes are decided whichcan cover a long list of factors influencing adoption ofmobile devices. In this step, Gagnon et al. (2012)literature review articles appear to be very helpfulwherein various factors for adoption of technology by HCPsare covered under four themes: 1) factors related to ICT 2)individual factor or HCPs characteristics 3) Humanenvironment 4) organisational environment. From individualpoint of adoption of mobile devices all the factors arecovering under first three themes with modification suchas: 1) factors related to mobile 2) individual factor orHCP characteristics 3) Human environment therefore,initially three themes are considered for the conceptualframework. However, three themes are unable to coverfactors which are related to use of mobile devices inhealthcare process context. Therefore, there is a need tofurther extend these themes.

Step 6: Final selection of factors and theme.

75

Initial selection of three themes is not providing muchclarity to the conceptual framework. Therefore, themes areselected and finalise according to use of mobile devices inhealthcare context. Final selection of theme and factorsare fixed by giving special attention for: healthcare,mobile devices from individual context. Four categories ofthemes selected for conceptual framework as follows:

1. Individual characteristics (individual readiness,self-efficacy, age, gender and experience)

2. Factors related to mobile devices (general features ofmobile devices)

3. Human environment (social influences)4. Factors related to use of mobile devices in healthcare

process (complexity, relative advantages andcompatibility with health practices)

Based on these five themes initial conceptual framework isdrawn which is shown in figure 4 below:

1. Individual characteristics

Individual characteristics in this research refer to age,gender, experience, individual readiness and self-efficacyfor adoption of mobile devices in the healthcareenvironment. Every individual have differentcharacteristics which may influences their intention foradoption of technology (Agarwal & Prasad 1999). Inhealthcare domain, HCPs and patients have different levelof exposure to technology which can further leads to theirstrong or weak intention to adopt technology in healthcare.Therefore, individual characteristic is important incontext with adoption of mobile devices in the healthcare.

76Intention

to use mobile

devices in telehealth

Human environment

Factors related to mobile devices mobile devices

Individual characteristics

Individual readiness

Self-efficacy

General features of mobile devices

Age

Gender

Experiences

Mediating variables in this research refer to age, genderand experience for adoption of mobile devices inhealthcare. Morris, Venkatesh and Ackerman (2005) personalcharacteristics such as age, gender and experience ofindividual have moderating effect on intention to usetechnology. There are many reasons to theorise personalcharacteristics as moderating factors. Since the use ofmobile devices in healthcare is an emerging trend anddirect effect of personal characteristics cannot be studiedtherefore mediating effect of personal characteristics willbe studied in this research. Let us take an example ofexperience; there are many individuals who have never triedmobile devices in healthcare their belief will be formedbased on experience of previous use of technology in otherdomain technology (Taylor & Todd 1995). Further, peoplewho have used mobile devices in healthcare their experienceis also coming from past use of mobile devices (Karahanna,Straub & Chervany 1999; Lim et al. 2011). Venkatesh et al.(2003) suggested that validity of eight models alsoincreased by including moderator factors. Sun and Zhang

77

Intention to use mobile

devices in telehealth

Figure4: Version 1 conceptual framework for adoption of mobile devices in healthcare

Human environment

Factors related to use of mobile devices in Healthcare process

Complexity with healthcare process

Compatibility with healthcare process

Relative advantages

Social influences

(2006) also identified age, gender and experience asmoderator variables and their importance in his meta-analysis research. Therefore, age, gender and experienceare studied as moderator factors in current research.

2. Factors related to mobile devicesRefers to those features of mobile devices which caninfluence their use in the health care setting. The factorsconsidered under this theme are the general factors ofmobile devices such as image quality, battery life andscreen size.

3. Factors related to use of mobile devices in healthcareprocess

Refer to those factors of mobile devices which caninfluence use of mobile devices in the healthcareprocesses. This theme is understood from Fichman (1992)which states : characteristics of innovation are thecharacteristics posed by innovation and considered byadopters to adopt innovation. Every innovation has its ownspecific characteristics. For example, people usually wantto see the benefits of innovation. In this research,mobile devices are considered innovation and theiradvantages; complexity to operate, compatibility withhealthcare environment can influence individual intentionfor adoption of these devices in healthcare. Therefore,factors proposed in this theme are: complexity level tooperate mobile devices in healthcare practices, relativeadvantages and compatibility with health care practices.

4. Human environment

Human environment in this research is defined as people whoinfluence an individual for adoption of mobile devices inthe healthcare. They may by peer group, colleagues andrelatives. Human environment play a major role for adoptionof technology. In human environment one factor that is‘social influences’ is considered which affect individualfor adoption of mobile device in healthcare.

78

79

Appendix 5: Research methodologyS. No.

Features Qualitative Quantitative

1 Justification(Why qualitative andquantitative research approaches selected?)

To explore the constructs influencing adoption of mobile devices and to refine conceptual frame work developed from literature.

To test the hypotheses and validate the conceptual framework developed in qualitative study.

2 Research paradigm(What is researcher’s understanding of the truth?)

PragmatismReason: use abductive approach and suitable for mixed method research design. There can be single or many realities (Feilzer 2010; Saunders, Lewis & Thornhill 2012)

3 Sample Size (How many respondents will be selected for research)

Pre-test= 4- 5 respondents,

Pre-test = 4-5 respondents,

Pilot test= 4-5 respondents,

Pilot test= 4-5 respondents,

Total required =36-81 respondents (Christensen & Remler 2009; Johnson & Christensen 2010; Morgan 1997)

25 responses per themeTotal respondents=depend uponthe results of qualitative study Roscope (1997) cited in(Baig 2010)

4 Sample Selection (where to find respondents?)

HCPs and patient in Toowoomba public and private hospitals

HCPs and patient in Toowoomba and Brisbane public and private hospitals.

5 Sample Frame(Who will be the respondents)

The name list of HCPs and Patients selected for data collection

6 Sampling(How respondents will be selected?)

Purposive SamplingReason: HCPs remain are busy and not all the patients can participate due to illness.

7 Research Design(How data will be collected for research?)

Focus groups (semi- structured interview)Reason: focus groups can provide more and more information on new are of research(Baig 2010; Kai-Wen

Web survey(questionnaire)Reason: web survey is aneconomical and less timeconsuming technique to capture user’s opinion on new are of

80

2014) research(Cavana, Delahaye & Sekeran 2001)

8 Data analysis software

N VIVO SPSS(AMOS)

81

Appendix 6: Proposed milestones and time table

S. No. Task/ Activity 2014 2015 2016 2017 Budget

1) Literature review

MarchJune

(4Months)

3) Proposal writing

July-Sept.(3Month

)

5) Proposal revision andsubmission

Oct. Jan.(4Month

)

7) * Proposalpresentation *Feb.

8)

Focus group guide,and ethicsapplicationdevelopment; andediting of proposalon the basis offeedback receivedfrom panel

March-April

9) *Data collection:Qualitative

*May $1300(1Months)

11)

Data analysis:Qualitative

May-June

$40

(2 Month)

13) *Data collection:Quantitative

*July-Aug(2 Months)

15)

Data Analysis:Quantitative

Sept-Oct.(2 Months)

17) Writing Chapters 1, 2& 3

Nov-Dec(2Months)

19) Writing Chapters 4 & Jan-

82

5March(3Months)

21) Dissertation review

April- July(4Months)

23) *Conferenceattendance

*Aug $3000(1 month)

25) Thesis proof reading,printing and binding Aug $600

26) *Dissertationsubmission

*1st Sept.(1 Month)

28) External examination

Oct.-Dec(3Months)

30) Dissertation revision Jan-Feb (2month)

31) Completion date /total

1 March

4940

Appendix 7: Thesis layout

Chapter Title of the ChapterChapter 1 Introduction

Chapter 2 Literature review

Chapter 3 Research methodology

Chapter 4 Focus group data collection

Chapter 5 Focus group data analysis, results discussion and improved conceptual framework

Chapter 6 Quantitative data collection

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Chapter 7 Quantitative data analysis and results discussion, final conceptual framework

Chapter 9 Conclusions, limitations, contributions and future research

84

Appendix 8: constructs and itemis selected in current research

Variables

Definition Items References

Inte

ntio

n

(Ajzen 1988,p. 42)expressesintention as themeasure of thelikelihood toperform thebehavior whichfurther leads tobehaviorperformance.

I1: Assuming that I have theiPhone/ iPad, I intend to useit in healthcare.I2: Whenever possible, I intendto use the iPhone/ iPad inhealthcare.I3: To the extent possible, Iwould use the iPhone/ iPad inhealthcare.I4: I intend to increase my useof the iPhone/ iPad inhealthcare in the future.

(Yangil &Chen 2007)

Indivi

dual

readines

s

Individualreadiness is’individual’sability toembrace and adoptnew technology’.

IR1: I am ready to used mobiledevices in healthcare ifIR2: I am familiar with themIR3: I have knowledge abouttheir applicationsIR4: I know their advantagesIR5: I have resource to useit.

(Honka et al.2011; Wu,Wang & Lin2007; Yangil& Chen 2007)

Self-e

fficacy Self-efficacy

refers toindividual’sbelief on his owncapability forusing mobiledevice inhealthcare.

I am able to use mobile devicesin healthcare as SE1: I am confident about it.SE2: I have ability to use it.SE3: It is under my control touse device.

(Wu, Wang &Lin 2007;Yangil & Chen2007)

Compat

ibil

ity

The degree towhich use ofmobile devices inhealthcare isconsideredcompatible withhealthcareprocess.

Using mobile devices in healthcare wouldCP1: fit the way I like to workin hospital.CP2: compatible with all aspectof hospital work.CP3: fit with current work process of hospital.CP4: fit healthcare functioningprocess

(Daim, Basoglu & Topacan 2013;Daniel Castro, Ben miller & Nager 2014)

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Comp

lexity

The Degree towhich use ofmobile devices inhealthcare is noteffort free.Adapted from(Karahanna,Straub & Chervany1999) withmodifications.

Using mobile devices inhealthcare wouldCX1: be unclear and difficult.CX2: require mental effort.CX3: be frustrating.CX4: not be easy to operate

(Daim, Basoglu & Topacan 2013;Yangil & Chen2007)

Func

tional

feat

ures

The Degree towhich generalfeature of mobiledevices areperceived assuperior to usein healthcare.

I would like to use mobiledevice in healthcare ifGS1: it is light weighted.GS2: image quality is good.GS3: Screen size isappropriate.GS4: its battery life is long.

(Bradford etal. 2014;Brewster etal. 2014;Gagnon et al.2005; Kim &Garrison2008; Wu,Wang & Lin2007)

Relative

advant

ages

The degree towhich use ofmobile devices inhealthcare isperceived asbetter than theprevioustechnology.

Using mobile devices inhealthcare wouldAD1: improve work productivity.AD2: be effective in hospitalsAD3: accomplish hospital taskmore quicklyAD4: increase mobility inhealthcareAD5: reduce work time cycle

(Kim &Garrison2008; Wang etal. 2010)

Soci

al influence

s

Individual beliefabout ‘what willothers think’ ifhe/she will usemobile device inhealthcare.

I would use mobile device inhealthcare becauseSI1: people who are importantto me think so.SI2: people who influence methink so.SI3: my colleagues are usingit.SI4: it is compulsory decisionof management.SI5: people whose opinions arevalued to me prefer it.

(Bradford etal. 2014; Kim& Garrison2008; MichaelJ. Ackermanet al. 2010;Tiong et al.2006)

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Appendix 9: Survey questionnaire

Part I

The aim of this research is to build a conceptual framework foradoption of mobile devices in healthcare. Conceptual framework cancontribute to design mobile communication devices according to

healthcare needs.

For each question you have to click on a button providedthat indicates your intention to use mobile devices inhealthcare.

No-one will be able to identify your personalresponses. Anonymised data will be used for research.

Please click on the box □ to give your opinion. If you change your mind just click on other box and make your new choices.

Part I1

S.No 1. Intention

1Poor

2Fair

3Good

4VeryGood

5Excellent

1 Assuming that I have mobile devices, I intend to use it in healthcare

2 Whenever possible, I intend to use mobile devices in healthcare

3 To the extent possible, I woulduse the mobile devices in healthcare

4 I intend to increase my use of mobile devices in healthcare in near future

S.No 2. Individual readiness

1Poor

2Fair

3Good

4VeryGood

5Excellent

1 I am ready to use mobile devicesin healthcare if I am familiar

87

with them.2 I am ready to used mobile

devices in healthcare if I haveknowledge about theirapplications which are used inhealthcare

3 I am ready to used mobiledevices in healthcare if I knowtheir advantages

4 I am ready to used mobile devices in healthcare if I have resource to use them.

S.No 3. Functional Features

1Poor

2Fair

3Good

4VeryGood

5Excellent

1 I would like to use mobiledevices in healthcare if it islight weighted.

2 I would like to use mobiledevices in healthcare if itsimage quality is good.

3 I would like to use mobiledevices in healthcare if itsscreen size is appropriate.

4 I would like to use mobiledevices in healthcare if itsbattery life is long.

S.No 4. Complexity

1Poor

2Fair

3Good

4VeryGood

5Excellent

1Using mobile devices healthcarewould be unclear and non-understandable.

2Using mobile devices healthcarewould require a lot of mentaleffort.

3 Using mobile devices inhealthcare would be frustrating

4 Using mobile devices will beeasy in healthcare process.

S.No

5. Social influences 1Poor

2Fair

3Good

4Very

5Excellent

88

Good

1I would use mobile devices in healthcare because people who are important to me think so.

2I would use mobile devices in healthcare because people whose opinions are valued to me preferit.

3I would use mobile device in healthcare because people who influence me think so.

4I would use mobile device in healthcare because it is compulsory decision of management.

5I would use mobile device in healthcare because my colleaguesare using it.

S.No 6. Compatibility

1Poor

2Fair

3Good

4VeryGood

5Excellent

1 Using mobile devices in healthcare would fit the way I like to work in hospital.

2 Using mobile devices in healthcare would be compatible with all aspect of hospital work.

3 Using mobile devices in healthcare would fit with current work process of hospital.

4 Using mobile devices in healthcare would fit healthcare functioning process.

S.No 7. Self- efficacy

1Poor

2Fair

3Good

4VeryGood

5Excellent

5 I am able to use mobile devicesin healthcare as I am confidentabout it.

6 I am able to use mobile devicesin healthcare as I have ability

89

to use it.7 I am able to use mobile devices

in healthcare as It is under mycontrol to use these devices.

S.No 8. Relative advantages

1Poor

2Fair

3Good

4VeryGood

5Excellent

8 Using mobile devices inhealthcare would improve workproductivity.

9 Using mobile devices inhealthcare would be effective inhospital.

10 Using mobile devices inhealthcare would accomplishhealthcare task more quickly.

11 Using mobile devices inhealthcare would increasemobility in healthcare process.

Adapted from (Wu, Li & Fu 2011; Yangil & Chen 2007)

with some modification in original questions.

Part III

Gender: □Female □Male

Age: □below30 years old □30–40 years old □41–50 years old □50 years old or more

Work experience: □below 5years □5–15 year’s □15–25 year’s □25year or more

Status: □Health care professional □Patient □Others please specify----------

Is there any other comments that you would like to add for the

research to consider about?

90

Thank you for your time and effort to complete this survey.Your cooperation is valued and very much appreciated!

91

Appendix 10: Focus group questions

Part I

The aim of this research is to build a conceptual framework foradoption of mobile devices in healthcare. Conceptual framework cancontribute to design mobile communication devices according to

healthcare needs.

Time for this session is approximately 1hour. This session will be recorded (both audio and video) because all

the information provided by you is important to the researcher. No-one will be able to identify your personal responses.

Anonymised data will be used for research. This session is about to know your experiences or perception with

adoption/ use of mobile devices in healthcare. Participation to this session is voluntary; you are free to end

this session at any time.

Part II

1. Can you tell me your intention for adoption of mobile devices in healthcare?

Probe: if I have, to some extent, in coming future.

2. What do you think about your readiness for adoption of mobile devices in healthcare?

Probe: if I am familiar, if I have knowledge, if I have resources.

3. Do you think features of mobile devices can affect their adoptionin healthcare?

Probe: weight, image quality, screen size and battery life.

4. What is your perception about complexity level for the use of mobile devices in healthcare?

Probe: unclear, no understandable, need lot of mental effort,frustrating.

5. Do you think you will be influenced by your social circle for adoption of mobile devices in healthcare?

Probe: friends, relatives or colleagues.

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6. Do you think use of mobile devices in healthcare will be compatible with healthcare process?

Probe: current work process, all work process, my style of working

7. Do you think you are able to use mobile devices in healthcare?

Probe: I am confident, I am able, it is under my control.

8. Can you tell me the advantages of using mobile devices in healthcare?

Probe: productivity, effective, fast.

9. Do you think other there are some other factors which can influence you for adoption of mobile devices in healthcare?

Part III

Gender: □Female □Male

Age: □below30 years old □30–40 years old □41–50 years old □50years old or more

Work experience: □below 5years □5–15 year’s □15–25 year’s □25year or more

Status: □Health care professional □Patient □Others please specify----------

Appendix 11: Topic introduction for unfocused groupMy name is Vasundhara and I am a PhD research student in USQ Toowoomba. I am trying to find out health care professionals and patients intention for adoption of mobiledevices, especially iPad and iPhone.

This discussion is very relaxed and open-ended. Instead of knocking on your door or ringing up and asking questions, it is simply to chat about the subject we’re interested in.So that’s what’s going to happen: I will explain what we’retrying to find out and then I will leave it to you to say whatever you would like to say about that subject.

It is going to be very informal. There are no rules. I am not in charge – I am just going to sit here and listen and

93

take a few notes. I will record the discussion as well – just so I don’t miss any of the things you’re saying. Is that OK with everybody?

The comments you make will be combined with comments made by other people in groups like this that I am conducting invarious public and private hospitals in Toowoomba, and those comments will form the basis of my research. Of course, your comments will be completely anonymous – I willnot use people’s names in any part of my research, and I don’t even need to know what your names are.

Thank you for giving up your time this evening/ morning – obviously, this kind of research depends on people like youbeing prepared to give us your time and your opinions. So thank you.

Okay then let’s get started. I simply want to know you perception about adoption of mobile devices in healthcare. In other words, what do you think if you have to adopt mobile devices like iPad /iPhone? It’s entirely up to you what you’d like to say … how you’d like to tackle the subject. There are no right or wrong answers.

Okay … over to you … leave me right out of it. I’m just here to listen. So … if one of you would like to start, let’s just see how the conversation goes from there …

Adapted from (Randle, Mackay & Dudley 2014) with some minorchanges

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Appendix 12: Comparison of research philosophiesComparison Pragmatism Positivism

Realism Interpretivism

points

Epistemol

ogy (from

where

knowledge

comes?)

Either or

both

subjective

and objective

phenomena.

It focussed

on adductive

approach

(Feilzer

2010)

Applicability

to this

research:

using both

subjective

and objective

observations.

Pure

observational

(subjective)

phenomena that

is free from

researchers own

values

interest,

purpose and

psychological

schemata (Howe

1988)

Focussed on

prior formal

propositions,

quantifiable

measures,

hypothesis

testing and

drawing of

The social

critiques

(Klein Myers

1999) which

leads to

insufficient

and

misunderstand

ing of

data(Saunders

, Lewis &

Thornhill

2012).

Subjective

observation,

social

phenomena

(Ngwenyama &

Lee 1997).

Try to

understand

phenomena

through the

meaning that

people

assign to

them

(deductive

approach)

(Orlikowski

and Baroudi

1991)

95

inferences

(inductive

approach)

(Klein and

Myers 1999).

Ontology

(Nature

of

reality)

Single

(objective)

or multiple

(subjective)

realities

that are used

to answer

research

question(s)

(Saunders,

Lewis &

Thornhill

2012)

Applicability

to this

research : as

different

individuals

can have

varying ideas

Single

(objective) and

independent of

social actor

Single

(objective)

and

constructed

through our

senses when

we experience

reality or

constructed

through our

senses when

we experience

reality and

critically

analyse in

our mind.

Multiple

(subjective)

and socially

constructed

96

about

adoption of

mobile

devices

Axiology

(role of

values)

Important

Research use

both point of

view

(subjective

and objective

)

Applicability

to this

research: is

using both

subjective

and objective

viewpoints in

the form of

focus group

and survey

questionnaire

.

Value free

Researcher is

independent of

data and use

objective view

Value laden

Researcher is

biased by

world,

culture and

upbringing

Value bound

Researcher

is part of

what is

being

researched.

Methods

used

Mixture of

qualitative

and

Generally used

with

quantitative

Qualitative

or

quantitative

Small

sample, in-

depth

97

quantitative

approach

(Denscombe

2008).

Mix method or

multilevel

research

approaches.

Applicability

to this

research: is

using mixed

methodology.

approach but

not

compulsory(Onwu

egbuzie & Leech

2005)

according to

the

suitability

of subject

matter

investigatio

n ,

qualitative(

Howe 1988;

Saunders,

Lewis &

Thornhill

2012)

Implicati

ons

Transferabili

ty (Morgan

2007) and

compatibility

of thesis

(Howe 1988).

Applicability

to this

research:

results

cannot be

generalised

as research

---------- ------------- -------------

--

98

is using

purposive

sampling.

Some

variables

likes social

influences,

self-efficacy

and relative

advantages

can be used

for adoption

of mobile

devices in

other

context.

99

Appendix 13: Health services on mobile devices

Source: (Lim et al. 2011)

100

Adapted from Boulos et al. (2011)

101

i