investigation of factors affecting healthcare organization’s adoption of telemedicine

8/13/2019 Investigation of factors affecting healthcare organizations adoption of telemedicine

1/10

Investigation of Factors Affecting Healthcare Organizations Adoption of

Telemedicine Technology

Paul Jen-Hwa Hu

University of South Florida

Patrick Y.K. Chau

University of Hong KongOlivia R. Liu Sheng

University of Arizona

Abstract

Recent advances in information and biomedicine

technology have significantly increased the technical

feasibility, clinical viability and economic

affordability of telemedicine-assisted service

collaboration and delivery. The ultimate success of

telemedicine in an adopting organization requires the

organizations proper addressing both technological

and managerial challenges. Based on Tornatzky and

Fleischers framework, we developed and empirically

evaluated a research model for healthcare

organizations adoption of telemedicine technology,

using a survey study that involved public healthcare

organizations in Hong Kong. Results of our

exploratory study suggested that the research model

exhibited reasonable significance and classification

accuracy and that collective attitude of medical staff

and perceived service risks were the two most

significant factors in organizational adoption of

telemedicine technology. Furthermore, several

implications for telemedicine management emergedfrom our study and are discussed as well

1. Introduction

Telemedicine is essentially about use of

information and biomedicine technology to support,

facilitate or improve collaboration and delivery of

healthcare services among geographically dispersed

parties, including physicians and patients [1].

Healthcare organizations have become increasingly

aware of and knowledgeable about telemedicine in

recent years. In effect, many organizations have

exhibited considerable interest in adopting

telemedicine technology to support practices of

member physicians or extend services of the

organization, as manifested by a fast growing

number of telemedicine programs established around

the world.

The ultimate success of telemedicine requires an

adopting organization to address both technological

and managerial challenges effectively [2]. In this

exploratory study, we investigated the decision

factors important for healthcare organizations

adoption of telemedicine technology. We took a

factor modeling approach and specifically employed

the organizational adoption framework proposed by

Tornatzky and Fleischer [3], who suggested that an

organizations adoption of a technological innovation

should take into account several essential contexts,

including the environmental, the organizational and

the technological. This framework conceptually

describes organizational innovation adoption

phenomena and, at the same time, provides a

necessary foundation upon which relevant adoption

factors can be identified within the respective

contexts. Anchoring our analysis of telemedicine

technology adoption by healthcare organizations

within this framework, we identified relevant

adoption factors jointly leading to the development

of a research model, which was evaluated using a

survey study that involved public healthcare

organizations in Hong Kong. Results of the study

provided a desirable point of departure forsubsequent research of organizational adoption of

telemedicine technology in health care.

The organization of the remainder of the paper is

as follows. Section 2 reviews previous telemedicine

research and relevant prior innovation/technology

adoption studies, highlighting our motivation.

Section 3 describes our overall research framework

and the resulting research model, together with the

specific hypotheses to be tested by the study. Section

4 details our research approach, design and data

collection methods, followed by discussion of data

analysis results in Section 5. The paper concludes in

Section 6 with some implications for telemedicinemanagement, readily derived from our findings.

2. Literature Review and Motivation

Broadly, technology adoption can be understood

as an organizations decision to acquire a specific

technology and make it available to target users for

their task performance. Contrary to a common

dichotomous view, we considered technology

Proceedings of the 33rd Hawaii International Conference on System Sciences - 2000

0-7695-0493-0/00 $10.00 (c) 2000 IEEE 1


2/10


3/10

be determined by combination of the organizational,

technological and environmental contexts.

In response to the growing significance of

telemedicine and limited previous research in

organizational adoption, we investigated adoption oftelemedicine technology by healthcare organizations

in Hong Kong. We proposed to develop a research

model that included factors important for

organizational technology adoption and empirically

to examine the model, using a survey study targeting

public healthcare organizations in Hong Kong.

Detailed descriptions of the research framework and

model follow.

3. Research Framework and Model

In this study, we employed the organizational

innovation adoption framework suggested byTornatzky and Fleischer [3] for several reasons.

First, the framework is largely consistent with and

supported by results of most previous research,

including Brancheau and Wetherbe [13], Fichman

[14], Zmud [15], Bretschneider [16], Copper and

Zmud [17], Kimberly and Evanisko [18]. In addition,

the framework appeared to encompass most of the

important adoption issues identified in a previously

conducted case study [9] as well as from our pre-

study interviews with clinical managing physicians

from multiple organizations.

According to Tornatzky and Fleischer [3],

technology adoption that takes place in an

organization is influenced by factors pertaining to the

technological context, the organizational context, and

the external environment. The technological context

essentially describes the technology to be adopted

and can be in part depicted by its important

attributes. The anticipated results of technology use

are another locus of the technological context and

often have significant effects on an adopting

organization, within its existing organizational and

environmental contexts. Thus, the technological

context can be jointly described by important

technology attributes and the anticipated results of

technology use. We replaced Tornatzky and

Fleischers [3] organizational context withorganizational readiness, which refers to the

availability of the conditions needed for an

organizations adopting telemedicine technology

[20]. An organization usually has considerable

influence on its internal condition with respect to a

particular technology adoption. However, desirable

changes to and cultivation of organizational

readiness may require considerable time or resourcesand are often subject to various existing constraints,

internal and external. The external environment

defines the external world in which an organization

operates. In most cases, an organization has limited

influence or control over its external environment

and thus needs to take the context as it is, striving for

an optimal fit with and rapid adaptability to the

context.

Choice of the research framework provided a

foundation upon which specific factors essential for

adoption of telemedicine technology by healthcare

organizations were identified. Figure 1 depicts our

research model. As shown, the technological contextincludes both technology attributes and anticipated

results of technology use. Perceived ease of use [19]

is an important technology attribute. As a group,

physicians are not particularly known for

technological competence and have a tendency to

consider technology as tools for supporting their

practices. Thus, a technology that is difficult to use

or operate is not likely to be well received by

physicians. In turn, as the organization proceeds in

the adoption process, this concern may represent a

backward pressure on the adoption decision making.

The organization consequently needs to properly

evaluate a technologys ease of use as perceived by

physicians. Effects of perceived ease of use havebeen inconsistent in previous research, suggesting its

significance on technology adoption might be

technology-specific or generally of a moderate level.

T e c h n o l o g i c a l C o n t e x tT ech no lo gy A tt r ib ute s: R es u lts o f T ec hn olo gy U se :- P erc e iv ed E as e o f U se - P erc e iv ed B en ef i ts

- P e rc ei ve d T e ch n ol og y S af et y - P e rc ei ve d R i sk s

O r g a n i z a t i o n a l R e a d i n e s s- C o l l e c t i v e A t t i t u d e o f M e d i c a l S t a f f

E x t e r n a l E n v i r o n m e n t- S e r v i c e N e e d s

T e c h n o l o g y

A d o p t i o n

Figure 1: Research Model for OrganizationalAdoption of Telemedicine Technology


0-7695-0493-0/00 $10.00 (c) 2000 IEEE 3


4/10

Thus, we hypothesized that perceived ease of use has

a positive effect on an organizations likelihood of

adopting telemedicine technology.

H1: Higher levels of perceived ease of use have a

positive effect on an organizations likelihood ofadopting telemedicine technology.

Perceived technology safety may be another

important technology attribute. Broadly, most

telemedicine technologies have as yet to mature, as

indicated by limited evidence of their clinical

efficacy. To a great extent, physicians are cautious

about the safety of the technology used in their

providing or delivering needed care and services.

This paramount safety concern of physicians can be

summarized by the first principle of their practice:

Do no harm! Accordingly, we posited that perceived

technology safety has a positive effect ontelemedicine technology adoption by healthcare

organizations.

H2: Higher levels of perceived technology safety

have a positive effect on an organizations

likelihood of adopting telemedicine technology.

Perceived service benefits and perceived service

risks are two essential aspects of anticipated results

of technology use. Support for perceived benefits as

a crucial technology adoption factor has been strong

[20]. In this study, perceived service benefits were

largely comparable to Rogerss [12] relative

advantages and refers to the degree to which

telemedicine technology is perceived as being betterthan or superior to existing service arrangements for

patient care and services. Accordingly, we

hypothesized that perceived service benefits have a

positive effect on organizational adoption of

telemedicine technology. On the other hand,

healthcare organizations are concerned with the

service risks that might result from the use of an

innovation, including technology, protocol,

procedure and treatment plan. Understandably, an

organization may have concerns about the potential

risks of telemedicine in such service areas as

physician-patient relationships, patient privacy and

service effectiveness. As such, we postulated thatperceived service risks have negative effects on

technology adoption.

H3: Higher levels of perceived service benefits

resulting from use of telemedicine technology

have a positive effect on an organizations

likelihood of adopting the technology.

H4:Higher levels of perceived service risks resulting

from use of telemedicine technology have a

negative effect on an organizations likelihoodof adopting the technology.

In most cases, physicians are arguably the most

important users of telemedicine technology. Based

on findings from a previously conducted case study

[9] as well as comments made by clinical managing

physicians in pre-study interviews, the attitude of

medical staff toward telemedicine and its enabled

services is important to organizational technology

adoption. The bottom-line is that my staff would

use the technology, commented the chief-of-service

of a surgery department where a previously acquired

computer-based patient record system had seldombeen used by his fellow surgeons. Previous research

has also suggested that attitudes of key personnel are

an important factor for organizational technology

adoption [21-22]. Specifically, attitude assessment

should proceed at a collective rather than an

individual level. Thus, we hypothesized that the

collective attitude of medical staff has a positive

effect on organizational adoption of telemedicine

technology.

H5: Stronger levels of collective attitude of medical

staff toward telemedicine and its enabled

services have a positive effect on an

organizations likelihood of adopting

telemedicine technology.

Service needs are an important factor for the

external environment. Healthcare organizations

purpose is to provide services to those in need and

thus they need to explore and evaluate alternatives

when existing delivery arrangements cannot meet

service demands, measured by service volume or

quality. In their investigation of organizational

adoption of computer-aided software engineering

(CASE) technology, Rai and Yakuni [23] concluded

that needs-pulled factors were important to

organizational adoption decision. Accordingly, we

posited that service needs have a positive effect ontelemedicine technology adoption by healthcare

organizations.

H6: Higher levels of service needs have a positive

effect on an organizations likelihood of

adopting telemedicine technology.


0-7695-0493-0/00 $10.00 (c) 2000 IEEE 4


5/10

4. Research Approach, Design and Data

Collection

This section describes our research approach,target organizations, instrument development, and

data collection methods.

4.1 Research Approach We took a factor modeling approach, aimed at

advancing the general understanding of telemedicine

technology adoption by healthcare organizations. As

Kimberly and Evanisko [18] commented,

concentration of the research focus can help to

identify and isolate factors that clarify the nature of

phenomena in a particular sector and, at the very

least, can suggest hypotheses that can be generalized

beyond that sector and tested in other sectors, eventhough the finding may have limited direct

applicability. With the factor modeling approach

taken, we identified factors that may affect

organizational adoption of telemedicine technology

and empirically evaluated their significance,

generating results that will be needed for subsequent

confirmatory studies and research model

development or refinement.

We took a key informant approach to data

collection. Specifically, we used responses of clinical

managing physicians contacted at participating

organizations (including clinical departments) to

assess technology adoption that had taken place intheir respective organizations. The particular target

informants included hospital executive officers,

clinical departments chiefs-of-service, and care

center directors. Use of key informants to obtain

information about the investigated organizations is

justifiable and common [21,24]; its application in our

study was appropriate and desirable for several

reasons. First, key informants presumably have a

fairly comprehensive understanding of the external

environment and the internal condition of the their

respective organization. By and large, our target

informants had better knowledge about the overall

(big) picture and therefore were considered to bemore qualified information providers than individual

physicians. At the same time, key informants were

physicians themselves and therefore were able to

analyze and evaluate technology adoption from the

perspective of care providers as well. The dual role

of target informants as administrator/manager and

clinician was essential and desirable for our

examination of telemedicine technology adoption

taking place at the organizational level.

The dependent variable was organizationaltechnology adoption, which was measured using a

continuum consisting of 7 distinct and consecutive

current adoption levels that approximate the

likelihood of telemedicine technology adoption by

individual organizations. That is, the probability of

an organizations adopting telemedicine technology

will increase as its current adoption stage moves up

the adoption continuum. For instance, an

organization that has already submitted a formal

adoption proposal currently under an external

funding agencys review is more likely to adopt

telemedicine technology than organizations that have

thought about potential adoption but decided not topursue it at present. Use of the adoption continuum

not only described organizational technology

adoption in increasing detail but also permitted

dichotomous classification in data analysis. This

process-oriented treatment is intuitive and logical

because technology adoption taking place in an

organization usually progresses through several

distinct but consecutive latent stages before reaching

an observable state, including actual technology

acquisition and use. In this light, absence of

observable adoption manifestations does not

necessarily suggest that an organization has decided

not to adopt a particular technology. On the contrary,

the technology adoption under consideration mayhave been steadily progressing through the necessary

intermediary latent stages and soon become

observable.

From the data analysis perspective, use of the

adoption continuum is pragmatic and desirable. As

described, the consecutive adoption levels can

represent or indicate the likelihood of technology

adoption and thus are appropriate for our hypothesis

testing. As such, the logistic regression technique can

be applied to classify the dependent variable using

some appropriate adoption stage threshold.

Furthermore, the adoption continuum is effective in

coping with existing real-world constraints. To agreat extent, telemedicine developments in Hong

Kong are largely in an early phase and actual

technology use currently is not widespread but is

expected to grow rapidly. Thus, use of the adoption

continuum allowed our investigation of current


0-7695-0493-0/00 $10.00 (c) 2000 IEEE 5


6/10

adoption stages of various organizations in spite of

their overall limited technology use and, at the same

time, enabled the identification of potential adoption

barriers.

4.2 Target Organizations We targeted public healthcare organizations in

Hong Kong, including hospitals, rehabilitation care

centers and specialized clinics. Clinical departments

were considered as independent units of analysis

because of their autonomy and specialization. The

decision to concentrate on public healthcare

organizations was made primarily because of the

relative likelihood of their adopting the technology.

To a certain extent, public healthcare organizations

are more likely to adopt telemedicine technology

than their counterparts in the private sector for

several reasons [9]. First, public healthcareorganizations represent the major care providers in

Hong Kong and thus have greater service needs that

may be effectively addressed by telemedicine than

private care providers. Secondary and tertiary care,

in particular, have considerable under-addressed

service demands. Second, public organizations have

relatively greater access to the resources necessary

for adopting new technologies, compared with

private clinics and hospitals. Third, public

organizations have reasonable technical support,

from both in-house technology bases and the Health

Authority (HA), which probably has the most

sophisticated technology capability in the Hong

Kong healthcare system. Thus, these organizations

tend to be more technologically ready for

telemedicine than private clinics and hospitals.

4.3 Instrument Development The development of our survey instrument

proceeded as follows. First, we reviewed relevant

prior research to identify and select appropriate

candidate measurement inventories, which were then

supplemented with additional items derived based on

findings of pre-study interviews and discussion with

a focus group that consisted of 3 chiefs-of-service

from different specialty areas. The resulting

preliminary question items were examined by thesame focus group, which evaluated their content

validity at face value. Based on group feedback,

several minor modifications, including wording

choice, were made to enhance the question items

communicability in the healthcare context.

The question items were subsequently tested using

a card sorting method [25] that involved one chief-

of-service, one hospital medical executive and onedirector of a long-term care center. None of these

pre-test physicians had participated in the focus

group and, like the focus group physicians, they were

excluded from the subsequent formal study. The

question items were printed on 8x6 index cards,

which were shuffled and presented randomly to the

pre-test physicians, each of whom was asked to sort

the cards into appropriate categories. Results from

the card-sorting test were largely satisfactory; the

physicians were able to categorize the question items

correctly with an accuracy rate of 83 percent or

better.

A 7-point Likert scale was used for all questionitems except the one that measured the dependent

variable, with 1 being strongly agree and 7 being

strongly disagree. To ensure desired balance of the

items in the questionnaire, half of the question items

were properly negated to invite the attention of

respondents who, as a result, might become

increasingly alert to manipulated question items. In

addition, all the question items were arranged

randomly to minimize the potential ceiling (floor)

effect that could induce monotonous responses to

question items designed to measure a particular

underlying construct. To anchor the responses

properly [26], we provided in the questionnaire an

explicit working definition of telemedicine andincluded in each packet selected general references

of telemedicine and some example technologies.

The dependent variable, current adoption level,

was defined at 7 distinct and consecutive levels, each

of which provides a necessary foundation for the

level above it. For data analysis purposes, we

considered an organization as an adopter if it has at

least submitted a formal technology adoption

proposal that was currently under external review.

Thus, as we defined them, adopters also included

organizations that had already implemented

telemedicine technology and used it for clinical

purposes or had located and secured the necessaryfinancial resources and technology source.

Organizations whose current adoption stages had not

reached the particular adoption threshold were then

considered as non-adopters in our data analysis.

We chose proposal submission for external review as


0-7695-0493-0/00 $10.00 (c) 2000 IEEE 6


7/10

the minimum adoption criterion because it was

observable, signaling that formal adoption had

begun. Furthermore, the distinction between proposal

submission for external review and its succeeding

stage (i.e., have or about to complete an adoptionplan) was more defined and explicit than that

between the succeeding stage and its precursor (i.e.,

have designed a task force or individual to

investigate potential adoption).

4.4 Data Collection We collected the data using a self-administered

questionnaire survey. Contact information for the

target informants was obtained from an internal

medical staff directory published by the HA. Before

distributing the questionnaires, each target

respondent was informed by a faxed introductory

letter that briefly stated the studys purpose and itsanticipated results and significance. Questionnaire

packets were sent by postal mail. Each contained a

cover letter that explicitly stated the purpose and

intended use of the data to be collected, endorsement

letters from the Hong Kong Telemedicine

Association (HKTA) and the IT division of the HA,

selected general references of telemedicine and

sample technologies, the questionnaire and a self-

addressed stamped envelop. Use of the HA internal

medical staff directory allowed coding and tracking

for individual informants, enabling the identification

of non-respondents to be contacted in subsequent

follow-up procedures.

Each target informant was given approximately 2

weeks to complete the questionnaire, dated from the

estimated packet arrival. A reminder letter was faxed

to all target respondents a week after their estimated

receipt of the questionnaire. A second reminder letter

was faxed each respondents secretary 2 or 3 days

before the specified response time window expired,

asking her to remind the subject to complete the

questionnaire and return it using the stamped return

envelop provided. Reminders and additional

questionnaires were sent by mail to those who failed

to return the completed questionnaire within the

initial response period. Late respondents were given

another 10 days to complete the questionnaires andtheir secretaries were telephoned to inform them

about the incoming questionnaires and ask them

again to remind the subjects to complete the

questionnaires. A second reminder and another

questionnaire were faxed to the target respondents

who had not yet responded at the end of the extended

response period. Finally, a terminating response

window of one week was indicated in a faxed

reminder to the remaining non-respondents, whowere asked a final time to mail in their completed

questionnaires.

5. Data Analysis Results

In this section, we highlight data analysis findings

in terms of respondent profile, measurement

reliability and construct validity, and logistic

regression results, described as follows.

5.1 Respondent Profile Of the 188 questionnaires distributed, 113 were

completed and returned, showing a 60.1 percentresponse rate. Among them, nine were partially

completed and therefore were excluded from the

subsequent data analysis, making the effective

response rate 50%. As a group, the responding

organizations had an average of 34.8 physicians or

specialists and employed 142.1 nurses and 34.3

technicians. Most of our respondents were male

(84.1%), held the post of chief-of-service (66.4%),

and had received their basic medical education in

Hong Kong (81.4%). On the average, respondents

were 43.5 years of age and had had 17.7 years of

post-internship clinical practice.

From the perspective of medical specialty areas,distribution of the responding organizations was

fairly balanced. A total of 18 specialty areas were

represented by the collected data, which showed

relatively higher levels of participation from Internal

Medicine, Pediatrics, Radiotherapy and Oncology,

Surgery, Obstetrics and Gynecology, and Pathology

than other specialty areas. Primary care, long-term

care and rehabilitating care were also included and

accounted for 3.5, 0.9 and 6.2 percent of responses,

respectively.

A total of 62 responses were completed and

returned within the initial response window,

accounting for 65.5 percent of the effective

responses. These respondents were considered earlyrespondents, whereas the remaining ones were late

respondents. Comparative analysis of the early and

late respondents suggested no significant differences

in their respective organizations, as measured by the


0-7695-0493-0/00 $10.00 (c) 2000 IEEE 7


8/10

number of physicians, nurses and technicians.

Similarly, these two respondent groups were largely

comparable in several areas, including age, post-

internship clinical experience, and the respective

distribution of gender, post, and country wheremedical school was attended. Jointly, findings from

the comparative analysis suggested little or reduced

non-response bias.

5.2 Measurement Reliability Use of multiple items to measure a specific

construct requires examination of the reliability or

internal consistency among the measurements [27].

We evaluated measurement reliability using the

Chronbach alpha derived from the question items for

the same construct. As shown in Table 1, all

investigated constructs exhibited an alpha value of

close to or greater than 0.7, a common reliabilitythreshold for exploratory research [28]. Thus, data

analysis results suggested that the measurements

used in the study encompassed reasonable reliability.Factor Cronbachs Alpha

Perceived Benefits (5 Items) 0.75

Perceived Risks (4 Items) 0.80

Service Needs (2 Items) 0.85

Collective Attitude of Medical Staff (3 Items ) 0.78

Ease of Use (2 Items) 0.70

Perceived Technology Safety (2 Items) 0.68

Table 1: Analysis of Measurement Reliability

5.3 Convergent and Discriminant Validity

Construct validity of the survey instrument wasevaluated in terms of convergent and discriminant

validity [27]. Specifically, we performed inter-item

correlation analysis and factor analysis. Based on the

analysis results, correlation coefficients were

considerably higher among question items designed

to measure the same construct than among those

intended for different constructs. The observed

higher levels of correlation among measurements for

the same than different constructs suggested that our

instrument exhibited adequate convergent and

discriminant validity.

A principal component factor analysis was also

performed, using varimax rotation method with

Kaiser normalization. A total of 6 components were

extracted, precisely matching the number of

constructs included in our research model. Question

items designed for the same construct exhibited

prominently and distinctively higher factor loadings

on a single component than on others, suggesting

satisfactory convergent and discriminant validity of

the measurements. Jointly, results of correlation

coefficient analysis and factor analysis suggested that

our instrument encompassed adequate construct

validity, as manifested by satisfactory measurement

convergent and discriminant validity.

5.4 Logistic Regression Results Logistic regression was used to evaluate our

research model and hypotheses. Choice of this

particular data analysis technique was based

primarily on its flexibility in assumption

requirements and our intended dichotomous analysis

of the dependent variable. Specifically, we examined

the significance and classification accuracy of the

research model. Regression results showed that the

research model was not significantly different from a

perfect model that can classify all responding

organizations to the correct adopter or non-adopter

category, as indicated by its goodness-of-fit statisticwith chi-square being 73.17 and significance being

0.8551. The classification accuracy or discriminant

power of the model was also examined. Judged from

the adoption threshold used in the study, our data

consisted of 19 adopters and 75 non-respondents.

Thus, random guesswork in theory would result in a

classification accuracy of 67.74%; that is, (19/94)2+

(75/94)2 = 0.6774. On the other hand, the

classification accuracy accomplished by the research

model was 84.04, considerably higher than that of

random choice. The observed superiority suggested

that the research model encompassed desirable

classification accuracy or discriminant power.

Support for individual hypotheses was examined

using the respective regression coefficients and their

significance. As summarized in Table 2, collective

attitude of medical staff, perceived service risks and

perceived ease of use were found to have significant

effects on organizational technology adoption, with

p-values being 0.005, 0.006 and 0.013 respectively.

However, perceived service benefits, service needs

and perceived technology safety appeared to be

insignificant in determining whether or not a

healthcare organization would adopt telemedicine

technology.


0-7695-0493-0/00 $10.00 (c) 2000 IEEE 8


9/10

Factor Coefficient Wald Statistic Significance

Perceived Service Benefits -0.42 1.66 0.1975

Perceived Service Risks -0.99 7.72 0.0055

Service Needs 0.46 2.30 0.1292

Collective Attitude of Medical Staff 1.48 8.00 0.0047

Perceived Ease of Use -0.71 6.12 0.0133

Perceived Technology Safety 0.38 1.37 0.2421

Table 2: Logistic Regression Results

6. Implications for Telemedicine

Management

Several implications for telemedicine

management can be readily drawn from our study

findings. First, attitudes of medical staff toward

telemedicine and its enabled services are essential to

technology adoption and thus management may need

to ensure that a favorable collective attitude has been

cultivated and solidified before proceed with actual

technology acquisition and implementation. In our

study, collective attitude of medical staff toward

telemedicine and its enabled services was the mostsignificant factor differentiating adopters and non-

adopters. This may in part have resulted from the

professional nature of health care, in which

physicians often have relatively autonomous

decision making and are able to preserve individual

independence in determining whether or not to adopt

a technology. Compared with end-users in a

business-organization context, physicians appeared

to have more influence on adoption decisions that

may affect their practices, making their attitude

assessment and management an increasingly

important issue in organizational technology

adoption. Our study results suggested that perceived service

risks were the second most significant factor for

organizational adoption of telemedicine technology.

The propensity to resist change can be considerable

in health care, especially when change may bring

about significant service uncertainty or adverse

ramifications to physicians practices. Conceivably,

physicians may be concerned about incorporating

telemedicine in their practices in the light of

potential legal liability and service disputes or

degradation. However, not all perceived service risks

are verifiable and their removal requires evidence-

based information exchange and communication.

Effective means for removing or reducing undue

perceived service risks may include pre-adoption

technology experimentation and trial use as well as

making arrangements for interacting with peers

routinely providing telemedicine-assisted services.

Perceived ease of use of telemedicine technology

was also found to have a significant effect on the

likelihood of an organizations adopting technology.

However, the coefficient obtained from the

regression analysis was negative. This suggests that

an organization currently in an adoption stage close

to reaching actual technology implementation and

use may not consider perceived ease of use as

important actor as would an organization currently

in a primitive adoption stage. The observed

discrepancy is in agreement with some prior

research that has suggested perceived ease of use to

be an insignificant or relatively weak predictor of

technology adoption. In the context of telemedicine,

the finding suggests that an organization highly

anxious about the technologys ease of use may

become less concerned about this particular issue

when it has moved to a more advanced adoption

stage (e.g., beyond formal technology investigationand evaluation). That is, perceived ease of use might

be a relatively less important technology adoption

factor but its significance can be over-appraised by

organizations not familiar with telemedicine or

situated in an early stage of the adoption process.

Undue perceived service risks may result from

knowledge barriers which can be reduced or

removed with detailed technology assessment and

proper communication of evaluation results.

Our study findings also suggested that effects of

perceived service benefits on technology adoption

were not significant. One plausible explanation may

be that telemedicine remains a novelty to many

organizations whose technology adoption or

intention is primarily driven by considerations other

than specific service benefits, including envisioned

professional status enhancement and clinical

technology experimentation or exploration.

Similarly, service needs were not found to be an

important adoption factor, suggesting that

telemedicine should not be viewed as a panacea

for all unmet service needs but rather as facilitating

or supporting some but not all physicians tasks and

services. Desirable sustainability requires an

organization to identify explicitly in its technology

adoption plan the target services of telemedicine that

cannot be satisfactorily addressed by existing servicearrangements. In addition, effects on perceived

technology safety were not significant, either. This

may in part reflect that many organizations tend to

consider technology adoption from the perspective

of service experimentation or clinical trials.

References:

[1] Bashshur, R.L, Sanders, J.H., and Shannon, G.W.(eds.), Telemedicine: Theory and Practice, CharlesThomas, Springfield, IL, 1997.


0-7695-0493-0/00 $10.00 (c) 2000 IEEE 9


10/10

[2] Perednia, D.A., and Allen, A., TelemedicineTechnology and Clinical Applications, Journal of

American Medical Association, Vol.273, No.6, 1995,pp.483-488.

[3] Tornatzky, L.G. and Fleischer, M., The Process ofTechnological Innovation, Lexington Books,

Lexington, MA, 1990.[4] Jutra, A., Teleroentgen Diagnosis by Means of

Videotape Recording, American Journal ofRoentgenology, Vol.82, 1959, pp.1099-1102.

[5] Wittson, C.L., Afflect, D.C., and Johnson, V., Two-

way Television Group Therapy, Mental Hospitals,Vol.12, 1961, pp.22-23.

[6] Mitchell, B.R., Mitchell, J.G., and Disney, A.P.,User Adoption Issues in Renal Telemedicine,

Journal of Telemedicine and Telecare, Vol.2, No.2,1996, pp.81-86.

[7] Mairinger, T., Gable, C., Derwan, P., Mikuz, G., andFerrer-Roca, O., What Do Physicians Think of

Telemedicine? A Survey in Different EuropeanRegions, Journal of Telemedicine and Telecare,Vol.2, No.1, 1996, pp.50-56.

[8] Gschwendtner, A., Netzer, T., Mairinger, B., and

Mairinger, T., What Do Students Think AboutTelemedicine? Journal of Telemedicine andTelecare, Vol.3, No.3, 1997, pp.169-171.

[9] Liu Sheng, O. R., Hu, P.J., Wei, C., Higa, K., and Au,G., Adoption and Diffusion of Telemedicine

Technology in Healthcare Organizations: AComparative Case Study in Hong Kong,Journal ofOrganizational Computing and Electronic

Commerce, Vol.8, No.4, 1998, pp.247-75.[10] Niederman, F., Brancheau, J.C., and Wetherbe, J.C.,

Information Systems Issues for the 1990s, MISQuarterly, Vol.15, No.4, December 1991, pp.475-

500.[11] Brancheau, J.C., Janz, B.D., and Wetherbe, J.C.,

Key Issues in Information Systems Management:1994-95 SIM Delphi Results, MIS Quarterly,

Vol.20, No.2, June 1996, pp.225-242.[12] Rogers, E.M., Diffusion of Innovations, 4th Edition,

Free Press, New York, NY, 1995.[13] Brancheau, J.C. and Wetherbe, J.C., The Adoption

of Spreadsheet Software: Testing InnovationDiffusion Theory in the Context of End-UserComputing, Information Systems Research, Vol.1,No.2, 1990, pp.115-143.

[14] Fichman, R.G., Information Technology Diffusion:

A Review of Empirical Research, Proc. of theTwelfth International Conference on Information

Systems, Dallas TX, December 1992, pp.195-206.[15] Zmud, R.W., Diffusion of Modern Software

Practices: Influences of Centralization andFormalization, Management Science, Vol.28, No.12,1982, pp.1421-1431.

[16] Bretschneider, S., Management Information Systems

in Public and Private Organizations: An EmpiricalTest, Public Administration Review, Vol.50, No.5,1990, pp.536-545.

[17] Cooper, R. and Zmud, R., Information Technology

Implementation: A Technological DiffusionApproach, Management Science, Vol.36, No.2,1990, pp.156-172.

[18] Kimberley, J.R. and Evanisko M.J., Organizational

Innovation: The Influence of Individual,

Organizational, and Contextual Factors on HospitalAdoption of Technological and Administrative

Innovations, Academy of Management Journal,Vol.24, No.4, 1981, pp.689-713.

[19] Davis, F.D., A Technology Acceptance Model forEmpirically Testing New End-user Information

Systems: Theory and Result, Doctoral Dissertation,Sloan School of Management, Massachusetts

Institute of Technology, 1986.[20]Iacovou, C.L., Benbasat, I., and Dexter, A.S.,

Electronic Data Interchange and Small

Organizations: Adoption and Impact of Technology,MIS Quarterly, Vol.19, 1995, pp.465-485.

[21]Nickell, G.S. and Seado, P.C., The Impact ofAttitudes and Experience on Small Business

Computer Use,American Journal of Small Business,Vol.10, No.4, 1986, pp.37-47.

[22] Thong, J. and Yap, C.S., CEO Characteristics,Organizational Characteristics and Information

Technology Adoption in Small Business, Omega:The International Journal of Management Science,Vol.23, No.4, 1995, pp.429-442.

[23] Rai, A. and Yakuni, R., A Structural Model forCASE Adoption Behavior, Journal of Management

Information Systems, Vol.13, No.2, 1996, pp.205-234.

[24] Chau, P.Y.K. and Tam, K.Y., Factors Affecting the

Adoption of Open Systems: An Exploratory Study,MIS Quarterly, Vol.21, No.1, March 1997, pp.1-24.

[25] Moore, G.C. and Benbasat, I., Development of anInstrument to Measure the Perception of Adopting an

Information Technology Innovation, InformationSystems Research, Vol.2, No.3, 1991, pp.192-223.

[26] Hufnagel, E.M. and Conca, C., User Response Data:The Potential for Errors and Biases, Information

Systems Research, Vol.5, No.1, 1994, pp.48-73.[27] Straub, D.W., Validating Instruments in MIS

Research, MIS Quarterly, Vol.13, No.2, June 1989,pp.147-169.

[28] Nunnally, J.C. Psychometric Theory, 2nd Edition,McGraw-Hill, New York, 1978


/ $ ( ) IEEE

investigation of factors affecting healthcare organization’s adoption of telemedicine

Documents