THE ACCEPTANCE OF WIRELESS HEALTHCARE FOR

INDIVIDUALS

An Integrative View

Ing-Long Wu

, Jhao-Yin Li

, Chu-Ying Fu

, and Shwu-Ming Wu

Department of Information Management, National Chung Cheng University, Chia-Yi, Taiwan

Institute of Information Science, Academia Sinica, Taipei, Taiwan

Department of Information Management, Wu Feng Institute of Technology, Chia-Yi, Taiwan

Department of Human Resource Development, National Kaohsiung University of Applied Science, Kaohsiung, Taiwan

Keywords: Mobile Healthcare, TAM, TPB, Wireless Service, Personal Innovativeness

Abstract: A recent report showed that the adoption rate of mobile healthcare is relatively low. Thus, a study for how

healthcare professionals adopt mobile services to support their work is imperative in practice. An integration

of TAM and TPB has considered both technological and organizational aspects in a complementary manner.

However, while mobile healthcare is considered as an emerging technology with wireless features and often

used in a voluntary motive. The service provision for pervasive usage and individual psychological state are

critical in determining the system use. Accordingly, perceived service availability and personal

innovativeness in IT are the major drivers for the components of TAM and TPB. This study thus proposed

such a research framework for integrating these relevant components from a broader perspective.

Empirical research is further conducted for examining its practical validity.

1 INTRODUCTION

It is only quite recently that a surge of mainstream

popularity has motivated researchers to acknowledge

the value of mobile healthcare. Hospitals are the

places closely related to people’s health and medical

professionals are responsible for patients’ health and

life. Medical professionals always carefully concern

the risk or uncertainty in using new technologies for

helping medical treatments. Hence, medical

professionals usually tend to adopt new technologies

later until they have been growing more mature and

safe in their use. However, wireless technologies can

be widely applied in many ways to help medical

professionals complete their work safely and

efficiently, such as electronic patient record and

real-time monitoring system for heart rate variability.

This creates a great need and importance for mobile

healthcare in the hospitals. However, a recent survey

by the Taiwan government in 2007 indicated a quite

low adoption rate of mobile healthcare.

As technology acceptance model (TAM) focuses

more on technological aspect for its parsimony and

high explained power, however, it lacks considering

the effects of individual and organizational factors in

the adoption process. Theory of planned behavior

(TPB) is indicated with a consideration of these two

factors. Moreover, while mobile devices with

wireless features are portable for personal use and

provide instant supports for medical activities at

anytime and from anywhere, the quality of system

services, such as real-time service availability, is the

major concern for medical professionals to determine

the system use. Many studies have indicated the

same concern for understanding the adoption of

mobile devices, namely perceived service availability

(PSA) (DeLone and McLean, 2003; Venkatesh et al.,

2003; Hong and Tam, 2006). In addition, while

mobile healthcare is an emerging technology for

personal use and often used in a voluntary motive,

the psychological state of medical personnel

specifically plays a critical role in determining the

system use. Many researchers have considered the

same concern for identifying individual difference in

adopting a new technology, namely personal

innovativeness (Thong, 1999; Gallivan, 2003; Tayor,

2007). It is defined as personal innovativeness in IT

(PIIT) for IT context (Agarwal and Prasad, 1998; Yi

et al., 2006).

In sum, while many studies have proposed a

unified model primarily based on TAM and TPB for

various IS settings (Venkatesh et al., 2003; Wu and

124

Wu I., Li J., Fu C. and Wu S. (2010).

THE ACCEPTANCE OF WIRELESS HEALTHCARE FOR INDIVIDUALS - An Integrative View.

In Proceedings of the 12th International Conference on Enterprise Information Systems - Human-Computer Interaction, pages 124-130

DOI: 10.5220/0002864701240130

 SciTePress

Chen, 2005), we first integrated TAM and TPB in a

complementary manner. Moreover, for the

importance of PSA and PIIT in the particular mobile

healthcare, an enhancement of the unified view with

the two antecedents may positively increase the

explained power of a proposed model. Furthermore,

empirical examination is conducted for examining its

practical validity.

2 LITERATURE REVIEW AND

HYPOTHESES DEVELOPMENT

Based on the above discussion, Figure 1 provides a

pictorial depiction of this research framework. The

followings discuss the theoretical bases and

development of relevant hypotheses.

Perceived

Ease of use

Personal

Innovativeness

in IT

Perceived

Behavioral Control

Behavioral

Intention

Subjective

No rm

H1 1

H1 2

Attitude

Perceived

Service

Availability

Perceived

Us ef u l ne ss

H10

Technology Acceptance Model

Theory of Planned Behavior

Figure 1: Research model.

2.1 Mobile Healthcare

Recent report has shown the importance of health

monitoring systems that can reduce the number of

readmissions for the patients suffering from many

chronic health problems (Toledo et al. in press). The

systems can also help in keeping track of patients

with one or more cognitive disabilities, such as the

stray prevention system for the elderly with dementia

(Lin et al. in press). Specifically, examples for

mobile healthcare include an implementation of

infrared and radio-based locator badges (Stanford,

2002), long-term health monitoring by wearable

devices (Jovanov et al. 2002), wireless sensor for

blood oxygen saturation monitoring (Asada, et al.

2003). Moreover, the use of wireless technologies

can also reduce long-term cost of healthcare and

result in an increased productivity of healthcare

providers. (Varshney, 2003).

2.2 TAM and TPB

TAM is designed for modeling user acceptance of

information technology (Davis et al., 1989). This

model hypothesizes these relationships, actual use

toward certain technology directly influenced by a

person’s behavioral intention to use (BI) and in turn,

behavioral intention to use determined by perceived

usefulness (PU) and attitude toward the technology.

Furthermore, perceived usefulness and attitude (ATT)

are affected by perceived ease of use (PEOU). TPB

differs from TAM by adding two components,

subjective norm (SN) and perceived behavioral

control (PBC). SN refers to the perceived social

pressure to perform or not to perform the behavior.

PBC refers to people’s perception of ease or

difficulty in performing the behavior of interest.

Consequently, Behavioral intention to use is jointly

determined by a person's attitude, subjective norm,

and perceived behavioral control toward the

behavior.

Some researchers have applied TAM and TPB

concepts on the use of telemedicine technologies for

healthcare professionals, such as WAP-based

telemedicine systems (Chau and Hu, 2002; Yi, et al.,

2006). The following discusses the development of

relevant hypotheses.

Based on TPB, there are three direct antecedents,

attitude, perceived behavioral control, and subjective

norm, for determining behavioral intention to use.

We can argue that three hypotheses are thus proposed

for them.

H1. Attitude has a positive effect on behavioral

intention to use mobile healthcare.

H2. Perceived behavioral control has a positive effect

on behavioral intention to use mobile healthcare.

H3. Subjective norm has a positive effect on

behavioral intention to use mobile healthcare.

Next, according to the TAM structure, as discussed

previously, we can argue that four hypotheses are

thus proposed for them.

H4. Perceived usefulness has a positive effect on

behavioral intention to use mobile healthcare.

H5. Perceived usefulness has a positive effect on

attitude toward using mobile healthcare.

H6. Perceived ease of use has a positive effect on

attitude toward using mobile healthcare.

H7. Perceived ease of use has a positive effect on

perceived usefulness for mobile healthcare.

THE ACCEPTANCE OF WIRELESS HEALTHCARE FOR INDIVIDUALS - An Integrative View

125

1.3 PIIT and IT Adoption

Personal Innovativeness represents the degree to

which an individual is willing to take a risk by trying

out an innovation (Flynn and Goldsmith 1993).

Personal innovativeness can be classified into five

types: innovators, early adopters, early majority, late

majority, and laggards. Personal innovativeness with

its application in information technology was first

termed personal innovativeness in IT (PIIT)

(Agarwal and Prasad, 1998, 1999). The following

presents the development of relevant hypotheses.

One study analyzed individual’s Internet anxiety, an

attitude toward the Internet use, when they may

experience perceived unreliability, risk, and

vulnerability from using it and suggested a

relationship between PIIT and the Internet anxiety

(Thatcher et al., 2007). Additional study discussed

the relationship between software developers’

innovativeness and their attitude toward a new

development process (Gallivan, 2003). We can posit

that one hypothesis is thus proposed for this.

H8. Personal innovativeness in IT has a positive

effect on attitude toward using mobile healthcare.

In the study of Yi et al. (2006) in terms of the

acceptance of PDA by medical professionals, the

PIIT is posited as a direct antecedent of perceived

behavioral control toward the behavior of adopting

PDA. Another study proposed PIIT as a determinant

of computer self-efficacy with respect to the use of

Window or Lotus software (Agarwal et al., 2000).

Moreover, computer self-efficacy has been indicated

as a determinant of perceived behavioral

control(Taylor and Todd, 1995). Therefore, the PIIT

has the indirect impact on perceived behavioral

control toward the behavior. We can argue that one

hypothesis is thus proposed for this.

H9. Personal innovativeness in IT has a positive

effect on perceived behavior control toward using

mobile healthcare.

A prior study indicated that PIIT is a significant

antecedent of perceived easy of use in terms of

knowledge workers in using IT (Lewis et al., 2003).

Another study indicated that higher PIIT leads to

higher perceived ease of use for financial service

software (Walczuch et al., 2007). We can assume that

one hypothesis is thus proposed for this.

H10. Personal innovativeness in IT has a positive

effect on perceived ease of use for mobile

healthcare.

1.4 PSA and IT Adoption

The use of mobile healthcare is closely related to the

patients’ health and life. Healthcare professionals are

usually hesitant to adopt a new system service, such

as mobile healthcare, while it is still in the early

development stage of its regular use. Among these

concerns regarding the mobile service, there is a

specific perception that relates to the unique features

of this service with wireless devices and its particular

usage context for patient safety. Perceived service

availability (PSA) refers to the degree to which an

innovation is perceived as being able to support

pervasive and timely usage. The following discusses

the development of relevant hypotheses. Many

studies have generally highlighted the importance of

service provision in determining the personal use of a

new mobile service (Islam and Fayad, 2003; Hong

and Tam, 2006). Specifically, one study indicated

that PSA is expected to have a direct effect on

perceived usefulness of a mobile data service (Hong

and Tam, 2006). We can posit that one hypothesis is

thus proposed for this.

H11. Perceived service availability has a positive

effect on perceived usefulness for mobile

healthcare.

The same study, as discussed above, also argued that

PSA has the direct influence on perceived ease of use

for the mobile data service (Hong and Tam, 2006).

The other study proposed that facilitating conditions

is a determinant of perceived ease of use for a new

technology (Venkatesh, 2000). Facilitating

conditions are defined as the provision factors in the

user environment to support the use of a new

technology (Venkatesh et al., 2003). We can argue

that one hypothesis is thus proposed for this.

H12. Perceived service availability has a positive

effect on perceived ease of use for mobile

healthcare.

3 RESEARCH DESIGN

3.1 Instrument

3.1.1 Basic Information

This part collects basic information about

organizational characteristics including hospital type

and bed size as well as respondent characteristics

including position, gender, working experience,

education level, and age.

ICEIS 2010 - 12th International Conference on Enterprise Information Systems

126

3.1.2 TAM and TPB Constructs

The measuring items for TAM constructs, including

PU, PEOU, ATT, and BI, were adapted from the

measurement developed by Venkatesh and Davis

(1996; 2000). They contain 4 items, 4 items, 4 items,

and 3 items respectively. The measuring items for

TPB constructs, including PBC and SN, were

adapted from the measurement developed by Taylor

and Todd (1995). They contain 3 items and 3 items

respectively.

3.1.3 PIIT and PSA

The measuring items for PIIT were based on the

recommendations of Agarwal and Prasad (1998) and

Yi et al. (2006), including 3 items. The measuring

items for PSA were adapted from the measurements

developed by Venkatesh (2000) and Hong and Tam

(2006), including 4 items.

3.2 Sample Design

Mobile healthcare is still in an early stage to use and

is considered to be a new technology for medical

professionals. It was assumed that larger hospitals

would be more likely to have this early experience.

We randomly selected 80 hospitals to be the study

sample from the population of 450 hospitals.

Furthermore, physicians and nurses in the hospitals

would be the major respondents since they are the

major users of mobile healthcare. We first sent an

invitation letter to one designated person in each

selected hospital and ask them for the help in

distributing questionnaires to their colleagues,

including physicians and nurses. After that, 10

questionnaires were sent for each hospital through

the designated person. A total of 800 questionnaires

were sent out to the potential respondents. 140 valid

questionnaires were successfully received. The

response rate was 17.5%.

3.3 Scale Validation

PLS is a structural equation modeling (SEM)

technique that employs a nonparametric and

component-based approach for estimation purposes.

This study uses PLS to analyze the measurement

model. PLS is the best analytical tool available to

fit the requirement of small sample size.

Convergent

validity is assessed by three criteria, factor loading

construct reliability, and average variance extracted

(Fornell and Larcker, 1981). Discriminant validity is

assessed by the measure that AVE for a construct

should be larger than the squared correlation between

the construct and other constructs. The testing results

indicate that convergent and discriminant validities

are all in a high acceptable level.

4 STATISTICAL ANALYSIS

PLS was used to examine the structural model. There

are two steps in evaluating the structural model.

First, we needed to estimate standardized path

coefficients and their statistical significance for

testing the hypotheses. PLS does not provide a

significance test or confidence interval estimation.

We re-sampled 1000 times with Bootstrapping

analysis to obtain a stable result for these analyses.

Second, the coefficient of determination (

) for

endogenous variables was calculated to assess the

predictive power of this model. Figure 2 shows the

testing results of the structural model.

Percei ved

Ease of use

Personal

Innovativeness

in IT

Percei ved

Behavioral Control

Behavioral

Intention

Subjective

No r m

0.38*

0.13

0.40*

0.16

Attitude

Percei ved

Servi ce

Availability

Percei ved

Us e f ul n e s s

0.33*

0.60*

0.15

0.28*

0.22*

0.25*

0.23*

0.24*

Technology Acceptance Model

Theory of Planned Behavior

= 0.65

= 0.62

= 0.25

= 0.29

= 0.69

Value on path: Standardized coefficients (β),

: Coefficient of

determination, *: p<0.01

Figure 2: Result of the structure model.

For the components of TPB, attitude, perceived

behavior control and subjective norm are all reported

to be the important antecedents of behavioral

intention to use mobile healthcare (β=0.22, 0.25,

0.23). Therefore, Hypotheses 1, 2, and 3 are all

supported. For the components of TAM, perceived

usefulness has a significant positive impact on

behavioral intention to use (β=0.24). Hypothesis 4 is

thus supported. Perceived usefulness is a significant

determinant of attitude toward using mobile

healthcare (β=0.60). Hypothesis 5 is thus supported.

Perceived ease of use reveals no significance in

influencing attitude (β=0.16). Therefore, Hypothesis

6 is not supported. In contrast, perceived ease of use

plays a critical role in determining perceived

usefulness (β=0.40). Hypothesis 7 is thus supported.

THE ACCEPTANCE OF WIRELESS HEALTHCARE FOR INDIVIDUALS - An Integrative View

127

In a brief summary, the four constructs, attitude,

perceived behavior control and subjective norm, and

perceived usefulness, jointly explain 65% variance in

behavioral intention to use mobile healthcare

(

=0.65).

For the construct of PIIT, it indicates no

significant influence to attitude (β=0.15). Therefore,

Hypothesis 8 is not supported. Conversely, it is the

significant determinant of both perceived behavioral

control and perceived ease of use (β=0.28, 0.33).

Hypotheses 9 and 10 are thus supported. In a brief

summary, the three constructs, perceived usefulness,

perceived ease of use, and PIIT, jointly explain 62%

variance of attitude toward using mobile healthcare

(

=0.62). Next, PIIT singly explains 25% variance

of perceived behavioral control with respect to using

mobile healthcare. For the construct of PSA, it

indicates as an important antecedent of perceived

usefulness (β=0.38). Hypothesis 11 is thus supported.

In contrast, it is not a significant influencer of

perceived ease of use (β=0.13). Therefore,

Hypothesis 12 is not supported. In a brief summary,

the two constructs, perceived ease of use and PSA,

together explain 69% variance of perceived

usefulness (

=0.69). Next, the two unique features

for mobile healthcare, PIIT and PSA, jointly explain

29% variance of perceived ease of use (

=0.29).

5 FINDINGS AND DISCUSSIONS

This current model indicates a high explained power

for behavioral intention to use mobile healthcare with

=0.65 while compared the previous studies. This

may be because the four antecedents of behavioral

intention to use, that is, perceived usefulness,

attitude,

perceived behavioral control, and subjective norm,

are all significant in their influence. Among these

antecedents, perceived usefulness particularly plays

the same significant role as the three components of

TPB, attitude, perceived behavioral control, and

subjective norm. Research on the issue of integrating

TAM and TPB has shown mixed results for its

impact on behavioral intention to use (Wu and Chen,

2005). Moreover, perceived usefulness has much

greater influence on attitude toward using mobile

healthcare than perceived ease of use, (β=0.60 vs.

0.16). In a brief summary for the two findings,

perceived usefulness can be recognized as a

particularly important determinant for encouraging

the use of mobile healthcare in the hospitals.

Next, this study importantly indicates that PIIT is not

significant in determining attitude, but significant in

influencing perceived behavioral control. This

finding is particularly new for the adoption of mobile

healthcare while attitude is usually reported as the

major produced effect in the literature. This finding

also explains the above indication for the similar role

of perceived behavioral control in affecting

behavioral intention to use. More specifically, the

reasons to explain this are two-fold. First, while

mobile healthcare has been widely advocated and

recognized for healthcare professionals in the

hospital in order to improve healthcare quality

recently, the major problem for determining the use

of mobile healthcare is not the favorable or

unfavorable trait of healthcare professionals in the

psychological state rather than the externally physical

forces or control factors to encourage their usage,

such as the necessity of using this innovation to treat

patients in certain environments. Second, healthcare

professionals with high PIIT enforce their interest or

psychological state in favor of the use of mobile

healthcare and as a result, they perceive better

control or ease in performing the adoption behavior.

Finally, PSA, as an important determinant of

using new technology, significantly reveals the effect

on perceived usefulness and no effect on perceived

ease of use. This finding is particularly critical for the

use of mobile healthcare while most professionals

still thought that mobile healthcare is a type of

emerging technology for posing a difficulty to use.

This may have an indication for encouraging the

professionals to use this technology in the hospitals.

The reasons for these results may be noted as below.

For the former, mobile healthcare is closely related to

the patient life and its usefulness for healthcare

professional is greatly dependent on whether it can

be regularly operated regardless of the time and

places. For the latter, while the design of user

interface for mobile services has been in a stable and

consistent form for users, PSA may not be an

important determinant of perceived ease of use any

more at the current technological level.

6 CONCLUSIONS

AND SUGGESTIONS

The findings have important implications for both

practitioners and researchers. For the practitioners,

PSA initially drives the forces to determining

behavioral intention to use through two layers of

mediators in terms of TAM beliefs, that is, perceived

usefulness and attitude. This description basically

ICEIS 2010 - 12th International Conference on Enterprise Information Systems

128

relates to a particular technological issue in the

adoption of mobile services. This means that to

effectively encourage medical professionals to use

mobile healthcare, the provided service for pervasive

and timely usage without any difficulty should be

well prepared in the hospitals. Next, the TAM

belief (perceived usefulness) and PIIT have indicated

to be the underlying antecedents in determining

behavioral intention to use through the mediators of

attitude and perceived behavioral control respectively.

This means that both technological (perceived

usefulness) and individual issues (PIIT) are

important for overcoming the impediment of using

mobile healthcare.

For the technological aspect, the design of mobile

healthcare needs to carefully examine the functional

requirements of users and further is able to provide

useful information for helping the decision making of

medical professionals. For the individual aspect,

the hospitals may provide incentives for encouraging

medical professionals to be often kept in an

innovative manner with their regular tasks. This will

improve the willingness of an individual to take a

risk by trying out an innovation. Finally, the TPB

components, attitude, perceived behavioral control,

and subjective norm, involve the relevant

organizational and individual issues for indicating

their impact on the adoption of mobile services.

The hospital, as a type of organization’s form, should

be able to provide some training programs for

increasing the skill level of employees and nurturing

their confidence in facing new technologies.

For the researchers, prior research on information

technology acceptance in general and mobile services

in particular has been focused on the general

components of TAM or TPB. This research has

considered the roles of system services and personal

trait in the innovation acceptance. This is because

mobile healthcare with wireless features is an

emerging technology for medical professionals in

terms of high uncertainty in system services, great

change of their work styles, and real belief of its

usefulness. These considerations are particularly

important for the context of mobile healthcare. This

will provide a new thinking/concept for theoretically

defining the antecedents of behavioral intention to

use in the context of mobile healthcare.

Finally, although this research has produced some

interesting results, a number of limitations may be

inherent in it. Many studies have reported that gender

difference plays a moderating role for the

relationship between attitude, perceived behavioral

control, or subjective norm and behavioral intention

to use. Next, the response rate for this survey is lower

than desirable, despite the various efforts to improve

it. One of the reasons for this may be due to

inexperience of the respondents in using mobile

healthcare and reluctant to answer the questionnaire.

Finally, while medical doctors from larger hospitals

are always quite busy, few questionnaires may have

been completed by subordinates and therefore, the

data may have some biases.

REFERENCES

Agarwal, R. and Prasad, J. (1998). A conceptual and

operational definition of personal innovativeness in

the domain of information technology. Information

Systems Research, 9(2), 204–215.

Agarwal, R. and Prasad, J. (1999). Are individual

differences germane to the acceptance of new

information technologies? Decision Sciences, 30(2),

361–391.

Agarwal, R. and Karahanna, E. (2000). Time flies when

you’re having fun: Cognitive absorption and beliefs

about information technology usage. MIS Quarterly,

24(4), 665–694.

Asada, H., Shaltis, P., Reisner, A., Rhee, S., and

Hutchinson, R. (2003). Mobile monitoring with

wearable photoplethysmographic biosensors. IEEE

Engineering in Medical and Biology Magazine,

28–40.

Chau, P. Y. K. and Hu, P. J. (2002). Investigating healthcare

professionals’ decisions to accept telemedicine

technology: an empirical test of competing theories.

Information & Management, 39, 297-311.

Davis, F. D., Bagozzi, R. P., and Warshaw, P. R. (1989).

User acceptance of computer technology: a

comparison of two theoretical models. Management

Science, 35, 982–1002.

DeLone, W.H., and McLean, E.R. (2003). The DeLone and

McLean model of information systems success: a

ten-year update. Journal of Management Information

Systems, 19(4), 9-30.

Flynn, L R. and Goldsmith, R. E. (1993). A validation of

the goldsmith and hofacker innovativeness scale.

Educational and Psychological Measurement, 53,

1105-1116.

Fornell, C., and Larcker, D.F. Structural equation models

with unobservable variables and measurement error:

Algebra and statistics. Journal of Marketing Research,

18, 3 (1981), 382-388.

Gallivan, M. J. (2003). The influence of software

developers’ creative style on their attitudes to and

assimilation of a software process innovation.

Information & Management, 40, 443-465.

Hong, S.-J. and Tam, K. Y. (2006). Understanding the

adoption of multipurpose information appliances: the

case of mobile data services. Information Systems

THE ACCEPTANCE OF WIRELESS HEALTHCARE FOR INDIVIDUALS - An Integrative View

129

Research, 17(2), 162-179.

Islam, N. and Fayad, M. (2003). Toward ubiquitous

acceptance of ubiquitous computing. Communications

of ACM, 46(2), 89-92.

Jovanov, E., O’Donnel, A., Morgan, A., Priddy, B., and

Hormigo, R. (2002). Prolonged telemetric monitoring

of heart rate variability using wireless intelligent

sensors and a mobile gateway. The Second Joint IEEE

EMBS/BMES Conference, 1875–1876.

Lewis, W., Agarwal, R., and Sambamurthy, V. (2003).

Sources of influence on beliefs about information

technology use: An empirical study of knowledge

workers. MIS Quarterly, 27(4), 657–678.

Lin, C., Chiu, M., Hsiao, C., Lee, R., and Tsai, Y. (in

press). A wireless healthcare eservice system for

elderly with dementia. IEEE Transactions on IT in

Biomedicine.

Stanford, V. (2002). Using pervasive computing to deliver

elder care. IEEE Pervasive Computing Magazine,

10–13.

Taylor, S. and Todd, P. A. (1995). Understanding

information technology usage: A test of competing

models. Information Systems Research, 6 (2),

144–176.

Taylor, N. J. (2007). Public grid computing participation:

An exploratory study of determinants. Information &

Management, 44, 12–21.

Thatcher, J. B., Loughry, M. L., Lim, J., and McKnight, D.

H. (2007). Internet anxiety: an empirical study of the

effects of personality, beliefs, and social support.

Information & Management, 44, 353–363.

Thong, J. Y. L. (1999). An integrated model of information

systems adoption in small businesses. Journal of

Management Information Systems, 15(4), 187-214.

Toledo, P., Jimenez, S., Pozo, F., Roca, J., Alonso, A., and

Hernandez, C. (in press). A telemedicine experience

for chronic care in COPD. IEEE Transactions on IT

in Biomedicine.

Varshney, U. (2003). Location management for mobile

commerce applications in wireless internet. ACM

Transactions on Internet Technologies, 3(3) 221-232.

Varshney, U. (2007). Pervasive healthcare and wireless

health monitoring. Mobile Network Applications, 12,

113-127.

Venkatesh, V. (2000). Determinants of perceived ease of

use: integrating control, intrinsic motivation, and

emotion into the technology acceptance model.

Information Systems Research, 11(4), 342–365.

Venkatesh, V. and Davis, F. (1996). A model of the

antecedents of perceived ease of use: development

and test. Decision Sciences, 27(3), 451–481.

Venkatesh, V. and Davis, F. (2000). A theoretical

extension of the technology acceptance model: four

longitudinal field studies. Management Science, 46(2),

186–204.

Venkatesh, V., Morris, M., Davis, G., and Davis, F. (2003).

Use acceptance of information technology: toward a

unified view.

MIS Quarterly, 27(3), 425-478.

Walczuch, R., Lemmink, J., and Streukens, S. (2007). The

effect of service employees’ technology readiness on

technology acceptance. Information & Management,

44, 206–215

Wu, I. L. and Chen, J. L. (2005). An extension of trust and

TAM model with TPB in the initial adoption of

on-line tax: an empirical study. International Journal

of Human-Computer Studies, 62, 784-808.

Yi, M. Y., Jackson, J. D., Park, J. S., and Probst, J. C.

(2006). Understanding information technology

acceptance by individual professionals: toward an

integrative view. Information & Management, 43,

350-363.

ICEIS 2010 - 12th International Conference on Enterprise Information Systems

130