DO CONSTRUCTS OF TECHNOLOGY ACCEPTANCE MODEL

PREDICT THE ICT APPROPRIATION BY PHYSICIANS AND

NURSES IN HEALTHCARE PUBLIC CENTRES IN AGADIR,

SOUTH OF MOROCCO?

Az-Eddine Bennani

Reims Management School, and Université de Technologie de Compiègne, Reims, France

Rachid Oumlil

Université Cady Ayyad, Faculté des Sciences Juridiques, Economiques Et Sociales, Marrakech, Morocco

Keywords: Prediction of ICT appropriation, Public healthcare centers in Morocco, Physicians, Nurses, Technology

Acceptance Model, Perceived Usefulness, Perceived Ease of use, Attitude.

Abstract: This communication explores the constructs of Technology Acceptance Model (TAM) and examines if they

do predict the Information and communication technology (ICT) appropriation by physicians and nurses

working in healthcare public centers in Agadir City, South of Morocco. The study revealed that Attitude,

Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) influence positively the intention of ICT

appropriation. Also Perceived Usefulness is still to be a major determinant of the intention of ICT

appropriation by these practitioners.

1 INTRODUCTION

Information and communication technology (ICT)

leads to some significant changes of information

management. It allows high-quality and efficient

care delivery (Wu et al., 2009) and contributes to

organizational expenses reduction (Scott, 2007).

Thus, the hospital, as a healthcare organization, is

more and more concerned by ICT appropriation by

practitioners, especially physicians and nurses. It is

defined as the process by which people consider

technologies in their practices (DeSanctis and Poole,

1994) and it is also seen, as the ultimate goal of the

usage process (Proulx, 2001).

This communication shows the progress of an

ongoing research project in Morocco. It concerns ten

health pubic centers located in Agadir city, south of

Morocco. It attempts to answer the question: Do

constructs of Technology Acceptance Model (TAM)

predict the ICT appropriation by healthcare

practitioners in health public centres in Morocco? It

examines Attitude, Perceived Usefulness and

Perceived Ease of Use variables that might affect the

intention of the appropriation of ICT by physicians

and nurses working for these centres.

The first paragraph will review the literature on

the adoption theories, will present the Technology

Acceptance Model and its extension, and will

examine the research work on the prediction of ICT

appropriation in healthcare context. The second one

will introduce the hypothesis and will propose a

research model, the third will show the methodology

retained. Before concluding and recommending

managerial implications, the outcomes will be

presented and discussed in the fourth paragraph.

2 LITERATURE REVUE

According to Sally and Indrit (2007), “Many

competing theoretical models co-exist in the

innovation acceptance and adoption literature...,

most of these models attempt build theories to

explain how and why innovations or technologies

are adopted and predict the level of acceptance and

adoption”.

241

Oumlil R. and Bennani A. (2010).

DO CONSTRUCTS OF TECHNOLOGY ACCEPTANCE MODEL PREDICT THE ICT APPROPRIATION BY PHYSICIANS AND NURSES IN

HEALTHCARE PUBLIC CENTRES IN AGADIR, SOUTH OF MOROCCO?.

In Proceedings of the Third International Conference on Health Informatics, pages 241-249

DOI: 10.5220/0002714502410249

 SciTePress

Research in Information Systems studies how

and why individuals adopt ICT (Venkatech et al.,

2003). One of the streams that it developed referred

to adoption theories to study the acceptance and

predict the use of ICT (Kukafka et al., 2003). The

following sub-paragraphs review briefly adoption

theories and models considered as a basis to build up

a framework for predicting ICT appropriation. Prior

presenting researches in the healthcare context, they

introduce Innovation Diffusion Theory (IDT),

Theory of Reasoned Action (TRA), Theory of

Planned Behavior (TPB), and Theory of

Interpersonal Behavior (TIB); and describes

Technology Acceptance Model (TAM) and its

extension.

2.1 Adoption Theories

2.1.1 Innovation Diffusion Theory

Rogers (1983) has developed a theory called the

Innovation Diffusion Theory (IDT) in order to study

and understand the diffusion of a given innovation

within a social group. Compeau and Higgins (1991)

noted that this theory can be also applied to

individual reactions vis-à-vis the decision of

adoption of an innovation, especially computing

technology. In 1995, Rogers considered that five

factors affect the innovation adoption: 1) Relative

advantage 2) Compatibility 3) Complexity 4)

Trialability and 5) Observability. Chau and Tam

(1997) stated that it provides a solid basis to develop

a conceptual ICT appropriation models, but it does

not explain clearly, neither adoption behaviors nor

individual reluctance.

2.1.2 Theory of Reasoned Action

In 1975, Fishbien and Ajzen defined relationships

between beliefs, attitudes, social norms, intentions

and behavior in what they called Theory of

Reasoned Action (TRA). It stated that an

individual's actual behavior is determined by the

person's intention to perform the behavior, and this

intention is influenced jointly by the individual's

attitude and subjective norms. Attitude is determined

by relevant beliefs about the results of performing

the behavior and the evaluation of the desirability of

those results.

2.1.3 Theory of Planned Behavior

According to the Theory of Planned Behavior (TPB)

(Ajzen, 1991) which is an extension of TRA, the

behavior is determined by the intention; this one is

predicted by three factors: attitude towards the

behavior, subjective norms, and perceived

behavioral control. It introduced a third construct of

intention called Perceived Behavior Control (PBC).

This theory attempted to overcome the problem of

volitional control found in TRA and postulates that

attitude, subjective norms, and PBC are direct

determinants of intentions that have a positive

impact on appropriation behavior.

2.1.4 Theory of Interpersonal Behavior

The Theory of Interpersonal Behavior (TIB) is

founded by Triandis on 1980. It is based on the

psychological model to understand ICT

appropriation by individuals and integrated the

majority of the variables presented in the above

mentioned theories. The TIB postulated that a

behavior has three determinants: Intention, Habits

and Facilitating Conditions. The intention itself

involves four determinants: Social factors, Perceived

consequences (cognitive dimension of the practice),

Personal affection (emotional dimension) and

Convictions.

2.2 Technology Acceptance Model and

its Extension

2.2.1 Technology Acceptance Model

The literature in Information Systems area showed

that a large number of models were drawn from the

TRA. For instance contrary to IDT that focused on

innovation itself, TRA examines perceptions of the

ICT appropriation by individuals (Moore and

Benbasat, 1991). One of these models, moreover,

simplest, easiest and more used by the researchers in

this area is the Technology Acceptance Model

(TAM) (Figure 1 in Appendix). Since it was

developed by Davis in 1989, it represents the most

powerful models to establish the variables which

influence the acceptance of ICT. This model is

proved to be the more successful in the prediction

and the explanation of the appropriation of this

technology (Adams and al, 1992; Igbaria, 1993;

Chang, 1998). The TAM suggests that the real use of

a technology can be determined by behavioural

intention. This later is influenced by individual

attitude. Behavioural intention is modelled as a

function of the attitude and usefulness and

determines the actual use. Attitude is defined as the

positive or negative feelings towards ICT. Within

this model, Davis (1989) defines the two constructs

that are of primary relevance to computer acceptance

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242

Figure 1: Technology Acceptance Model (Davis et al.,

1989).

behaviors: (1) Perceived Usefulness (PU) means

degree to which a person believes that using a

particular system would enhance his/her job

performance; (2) Perceived Ease of Use (PEOU)

designs degree to which a person believes that using

a particular system would be free of effort. These

two basic constructs undergo the effect of external

factors (individual, organisational or technical) that

influence positively or negatively intention of ICT

appropriation by individual. Thus, there is a need

for the extension of the original technology

acceptance model.

2.2.2 Extended Technology Acceptance

Model

Many changes to the original TAM model have been

made allowing new models. The most well-known

ones are presented in the following.

In 1995 Taylor and Todd developed a model

called “the Augmented TAM” which is an extension

of the original TAM. They aimed to understand the

behavior of experienced and inexperienced users.

The results revealed that their model can be used to

predict ICT appropriation for inexperienced users,

and the perceived usefulness was the strongest

predictor of intention of the ICT appropriation for

the inexperienced users. Whereas, perceived

behavioral control was the predictor of experienced

users’ intention. They also found a strong link

between behavioral intention and behavior for

experienced users.

The objective of Igbaria, Parasuraman, and

Baroudi’s study (1996) was to examine the influence

of three motivators 1) perceived usefulness, 2)

perceived enjoyment/fun, and 3) social pressure on

ICT appropriation by individual. Their results

showed the importance of perceived usefulness,

perceived enjoyment, and social pressure in

mediating the relationships of antecedent variables

and perceived complexity on microcomputer

appropriation. They explained the relationships

between perceived usefulness, perceived enjoyment,

social pressure, and microcomputer usage, and

revealed that perceived complexity is an important

variable in linking skills, organizational support, and

organizational usage with perceived usefulness,

perceived enjoyment, and social pressure.

Agarwal and Prasad (1998) developed a

modified TAM model that focused on the

perceptions of technology usefulness and ease of

use. It is based on the idea that personal

innovativeness positively moderates the relationship

between the perceptions of relative advantage, ease

of use, compatibility and the intention of ICT

appropriation. Moreover, Agarwal and Karahanna

(1998) have added other causal links between

compatibility, perceived usefulness and ease use.

Their results support the indirect effect of

compatibility on the individual attitude through the

perceived usefulness an ease of Use. They showed

that compatibility is an important factor in the

contemporary technological environment.

Compeau, Higgins, and Huff (1999) developed a

conceptual model to study the influences of self-

efficacy, performance and personal outcome

expectations, affect, and anxiety on ICT

appropriation. Their results reveal that the

relationship between personal outcome expectations

and affect was not supported. Furthermore, they

confirmed a negative relationship between personal

outcome expectations and use, and found that self-

efficacy explains a total of 18% of the variance in an

individual ICT appropriation.

In 2000 Venkatesh and Davis extended the

original TAM to TAM2 by introducing social and

cognitive constructs. Their objective was to explain

PU and appropriation intention in terms of social

influence processes (Subjective Norm,

Voluntariness, and Image) and cognitive

instrumental processes (Job Relevance, Output

Quality, Result Demonstrability and PEOU). Their

results showed that the TAM2 explained up to 60%

of the variance in perceived usefulness as a

determinant of individual appropriation intention.

In 2003, Venkatesh et al. Synthesized acceptance

models to propose the Unified Theory of Acceptance

and Use of Technology (UTAUT). It is formulated

with four constructs of intentions and usage:

performance expectancy, effort expectancy, social

influence, and facilitating conditions, moderated by:

gender, age, experience, and voluntariness of use.

UTAUT identifies three constructs as determinants

of intention of appropriation: performance expected

from use, expected efforts and social influence. It

DO CONSTRUCTS OF TECHNOLOGY ACCEPTANCE MODEL PREDICT THE ICT APPROPRIATION BY

PHYSICIANS AND NURSES IN HEALTHCARE PUBLIC CENTRES IN AGADIR, SOUTH OF MOROCCO?

243

accounts for 70% of variance in appropriation

intention, and enables a much more sophisticated

analysis of appropriation behaviors.

2.3 Prediction of ICT Application in

Healthcare Context

TAM is widely used by researchers to predict the

appropriation of ICT by individuals, particularly in

the healthcare context. This subparagraph presents a

summary of the literature review in this context.

Hu et al. (1999) applied the original TAM model

to study the appropriation of telemedicine- an ICT

application- by physicians. The outcomes showed

that perceived usefulness and attitude have a large

influence on the intention of physicians to

appropriate telemedicine. Thus, they concluded the

limited utility of this model to predict and explain

ICT appropriation by individuals in healthcare

context. Subsequently, in addition to the original

model, Chau and Hu (2001, 2002) used an integrated

model derived from TAM and TPB to evaluate

decisions of ICT appropriation based on the

technology’s compatibility. Their results revealed

that PU and Attitude influence positively

telemedicine appropriation by physicians, whereas

PEOU, subjective norms and perceived behavioral

control have no effect on this appropriation. They

concluded that TAM model seemed to be the most

appropriate model to predict and explain ICT

appropriation by physicians.

To study factors influencing ICT appropriation

by physicians, Croteau and Vieru (2002) proposed a

conceptual model combining the original TAM and

the Innovation diffusion theory. They used a sample

of 390 physicians to test it. A group of 250 were

working for a large urban institution, and another

group of 140 for rural institutions. The outcomes

showed that perceived effort and PEOU have a

significant effect on the intention of ICT

appropriation by physicians for both groups; PU is

found to be the most significant factor of this

intention.

Referring to TAM2, Chismar and Wiley-Patton

(2003) considered cognitive instrumental and social

influence to find out the variable contributing to the

appropriation intention of Internet-based

applications by pediatricians in Hawaii. Their

outcomes revealed that the primary factors of ICT

appropriation by pediatricians relate to their

usefulness and job relevance. Still, ease of use and

social influence do have no influence on this

intention. They conclude that TAM2 model

explained at least 40 % of physicians’ intention to

appropriate internet-based applications.

To explain the intention of patient to use e-health

solutions, Wilson and Lankton (2004), tested three

theoretical models of ICT acceptance (TAM,

motivational model and integrated model)

among

patients who had recently registered for access to

provider-delivered

e-health. An online questionnaire

was administered to 1750 to measure

perceptual

constructs from these models (intrinsic

motivation,

perceived ease of use, perceived usefulness/extrinsic

motivation, and behavioral intention to use e-health).

Only 163 were completed representing 9% of the

total population. Results confirmed that the three

tested models performed well in predicting

patients'

intention to appropriate to e-health.

Banderker and Van Belle (2006) conducted a

qualitative study in two public hospitals in Western

Cape, South Africa, to study the influencing factors

of the mobile appropriation by physicians. They

concluded that Work importance, Utility, Fit task-

work, Demonstrability, Self-efficacy and

Characteristics of the technology influence

positively the intention of physicians to appropriate

ICTs. Moreover, physicians having technical skills

intend more to appropriate technologies.

Schaper and Pervan (2007) examined the ICT

appropriation by the Australian therapists. To

develop their conceptual model, they referred to

literature review on adoption of technologies by

health care actors, to Unified Theory of Acceptance

Use of Technology, and to generic structure of

acceptance proposed Chau and Hu in 2002. Results

of their analysis confirmed that the influence of

compatibility, attitude, and self-efficacy on the

intention of therapists to appropriation ICT is larger

than the one of perceived effort, and social

influence. They also noted the importance of

motivation, individual engagement and the variables

moderating (age, experience, and skill) to explain

this intention.

In 2007 Wu et al., aimed to present a conceptual

framework for assessing the medical professional

behavioral intention to adopt Mobile Healthcare

Systems which defining the healthcare information

processing systems, including all relevant medical

professional participants and the use of new

technology to deliver healthcare services and

exchange healthcare information via mobile devices

anytime and everywhere. Technology acceptance

model (TAM) and the innovation diffusion theory

(IDT) serve as the theoretical basis to develop their

research model. Conformation factor analysis was

performed to test the reliability and validity of the

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measurement model, and the structural equation

modeling technique was used to evaluate the causal

model. Their results indicated that compatibility,

perceived usefulness and perceived ease of use

significantly affected healthcare professional

appropriation intention. Furthermore, Mobile

Healthcare Systems (MHS) self-efficacy had strong

indirect impact on healthcare professional

appropriation intention through the mediators of

perceived usefulness and perceived ease of use.

However, the hypotheses for technical support and

training effects on the perceived usefulness and

perceived ease of use were not supported.

Wu et al., (2008), tried to understand factors that

might influence healthcare professional’s

appropriation to an adverse event reporting system.

Their conceptual model integrated, besides to TAM

constructs, trust and management support as

determinants of intention appropriation. The

outcomes indicated that perceived usefulness,

perceived ease of use, subjective norm, and trust had

a significant effect on a professional’s intention to

appropriate an adverse event reporting system.

Recently, Aggelidis and Chatzoglou (2009)

attempted to test the applicability and effectiveness

of technology acceptance models in health care area

in Greece, and examined factors affecting the

intention of public health institutions’ personnel to

appropriate ICTs. They have extended the original

TAM by include the following exogenous

constructs: anxiety, self-efficacy, facilitating

condition, training and social influence. Their results

indicated that perceived usefulness, ease of use,

social influence, attitude, facilitating conditions and

self-efficacy significantly affect hospital personnel

behavioral intention. Training has a strong indirect

impact on behavioral intention through the

mediators of facilitating condition and ease of use.

3 HYPOTHESES AND

RESEARCH MODEL

Research work reviewed in the previous paragraph

and by a prior exploratory study conducted by the

author (Bennani et al., 2008) inspired the

hypothetical research model suggested in this

communication (Figure 2). Compared to the

traditional TAM model, the interrelationships

between PU, PEOU constructs and the Attitude

variable have been modified to stress the importance

of the later in the proposed model in order to explain

the intention of ICT appropriation by physicians and

nurses.

Figure 2: Hypothetical research model.

Roger’s innovation diffusion theory provided a

context in which ICT appropriation could be

explored, but it didn’t explain this appropriation

totally. However, it offered a basis for the

development of this model.

Theories of reasoned action, planned behavior

and interpersonal behavior examined individual

behavior, and concluded that ICT appropriation, as

behavior, can be depicted by Intention variable.

This later is defined like an intermediate between

attitude and the behavior (Fishbein and Ajzen,1975).

It shows desire, wish, and determination or will to

emit a behavior. Dodds, Monroe and Grewall

(1991), specified that intention of ICT appropriation

represents the probability that a potential user will

intend to use technology. Referring to Technology

Acceptance Model, Intention is influenced by

individual Attitude, Perceived Usefulness (PU) and

Perceived Ease Of Use. Three hypotheses are tested

in this communication.

Attitude

The first hypothesis is:

Attitu

: The attitude influences positively the

intention of the appropriation of ICT by physician

and nurses working in health public centers

According to Triandis (1980) Attitude indicates

“the affect”, it is defined as the feeling of pleasure,

gaiety and dissatisfaction that associates individual

to a given behavior. For Ajzen (1975), attitude is “a

predisposition to answer an object in a favorable or

unfavorable way”. It expresses the positive or

negative feelings about performing the ICT

appropriation (Ajzen and Fishbein, 1980, Davis,

1989). Several studies noted the positive influence

of attitude on intention (Davis et al. 1989; Jackson et

al. 1997; Karahana et al. 1999). In healthcare

context, Chau and Hu (2001, 2002) concluded that

Attitude influences positively the intention to

appropriate telemedicine, moreover, Schaper and

Pervan (2007) confirmed the positive impact of

DO CONSTRUCTS OF TECHNOLOGY ACCEPTANCE MODEL PREDICT THE ICT APPROPRIATION BY

PHYSICIANS AND NURSES IN HEALTHCARE PUBLIC CENTRES IN AGADIR, SOUTH OF MOROCCO?

245

Attitude on ICT appropriation by therapists.

Recently, Aggelidis and Chatzoglou (2009)

indicated that Attitude affect positively hospital

personnel ICT appropriation intention.

Perceived Usefulness (PU)

The second hypothesis is:

: The Perceived usefulness influences

positively the intention for the appropriation of ICT

by physician and nurses working in health public

centers

Perceived usefulness (PU) is one of the prior

belief constructs developed by TAM. It is defined as

“the degree to which a person believes that using a

particular system would enhance his or her job

performance” (Davis et al., 1989). It represents a

theoretical substitute of “the relative advantage”

developed in the IDT. According to Roger (1995),

relative advantage means the degree to which using

the innovation is perceived as being better than

using its precursor. PU is proved to be an important

determinant of ICT appropriation intention (Taylor

and Todd , 1995 ; Igbaria et al., 1996 Venkatesh and

Davis, 2000; Gefen et al., 2003, Venkatesh et al,

2003). Within the healthcare area, perceived

usefulness is a major determinant of physicians’

intention to appropriate ICTs. This result is

supported by Hu et al., 1999; Chau and Hu 2001,

2002; Croteau and Vieru, 2002; Chismar and Wiley-

Patton, 2002; Schaper and Pervan, 2007; Wu et al.,

2007; Aggelidis and Chatzoglou, 2009.

Perceived Ease Of Use (PEOU)

The third hypothesis is:

PEOU

: The Perceived ease of use influences

positively the intention for the appropriation of ICT

by physician and nurses working in health public

centers

Perceived ease of use (PEOU) refers to “the

degree to which a person believes that using a

particular system would be free of effort” (Davis et

al., 1989). It indicates degree to which user finds

that the use of a technology is relatively deprived of

effort. Technologies perceived as being easier to use

and less complicated are more likely to be

appropriated by potential users. Compared to

perceived usefulness, perceived ease of use is

considered the second important determinant of a

user’s ICT appropriation (Davis, 1989; Taylor and

Todd, 1995; Igbaria et al., 1996; Agarwal and

Prasad, 1998; Venkatesh and Davis, 2000;

Venkatesh et al., 2003). In healthcare context,

whereas some researchers found, that, PEOU does

not have a significant direct effect on users’

appropriation intention (Chau and Hu 2001, 2002;

Croteau and Vieru, 2002; Chismar and Wiley-

Patton, 2002; Schaper and Pervan, 2007; Wu et al.,

2007; Aggelidis and Chatzoglou, 2009), Others

confirmed that it influences positively the ICT

appropriation (Croteau and Vieru, 2002; Wu et al.

2007; Wu et al., 2008; Aggelidis and Chatzoglou,

2009).

4 RESEARCH METHOD

Physicians and nurses working for the health public

centers located in the city of Agadir represent the

target population for this study. They correspond to

a total of 145 individuals, divided into 37 physicians

and 108 nurses. The data collection process took

place from the beginning of June 2008 and lasted six

months, till December, 2008. A set of questionnaires

were handed on to major or physician chief of each

one of the ten centers, who dealt with their

distribution to the various actors of the process of

patient care. One week later, a first revival is

ensured to get the feed-back from respondents, and

to provide explanation whenever it is needed. The

total completed questionnaires were retained for

being studied. Data processing was performed using

SPSS software package. The response rate achieved

was quite high (76.55%), with 111 replies. As shown

in Table 1, the breakdown of these replies consisted

of 30 physicians and 81 nurses. 64.9% are females

and only 35.1% corresponded to males. Almost the

half of the respondents (45.9%) are older than 40,

42.3% are aged between 25 and 40, whereas, only

11.7% who are less than 25.

Table 1: breakdown and frequency of the replies.

Frequency Frequency

(percentage)

Activity Physicians 30 27%

Nurses 81 73 %

Gender Male 39 35.1%

Female 72 64.9%

Age Less than 25

years

13 11.7%

Between 25

and 40 years

47 42.3%

More than 40

years

51 45.9%

5 RESULTS AND DISCUSSION

A confirmatory analysis is used to check the content

reliability of the hypothetical model constructs. For

this issue, the Cronbach’s coefficient (α) was

HEALTHINF 2010 - International Conference on Health Informatics

246

Table 2: Reliability of variables.

Variable Cronbach’s

alpha (α)

Interpretation

Attitude α=0.818

A very good coherence

between items of

“Attitude”

Perceived

Usefulness

α=0,904

A very good coherence

between items of

“Perceived Usefulness”

Perceived Ease

of Use

α=0,845

A very good coherence

between items of

“Perceived Ease of Use”

Intention α=0.813

A very good coherence

between items of

“Intention”

calculated. As exposed in Table 2 in the Appendix, a

very good coherence is shown between items of

Attitude (α=0.818), Perceived Usefulness (α=904),

Perceived Ease of Use (α=0,845) and Intention

(α=0.813). The four constructs are retained as their

reliability coefficient being higher than the

recommended threshold (0.7). Furthermore a

correlation between intention, as a dependent

variable, and Attitude, Perceived Usefulness and

Ease Of Use as independent constructs is calculated

to test the three initial hypotheses: H

Attitu

, H

and

PEOU

. Results reveal that Attitude PU and PEOU

showed a significant positive correlation with the

Intention of ICT appropriation construct (Table 3 in

Appendix). Moreover linear regression results show

that only two constructs of these three independents

variables (PU and PEOU) determine ICT

appropriation (Table 4).

Table 3: Correlation between the three independent

variables and the Intention of ICT appropriation construct.



Attitude PU PEOU

Pearson

Correlation

0.373 0.570 0.410

P-

Value

0.000 0.000 0.000

The findings are consistent with Chau and Hu

2001, 2002; Croteau and Vieru, 2002; Chismar and

Wiley-Patton, 2003; Schaper and Pervan, 2007; Wu

et al., 2007; Aggelidis and Chatzoglou, 2009.

Contrary to some authors (Chau and Hu 2001, 2002;

Croteau and Vieru, 2002; Chismar and Wiley-

Patton, 2002; Schaper and Pervan, 2007; Wu et al.,

2007; Aggelidis and Chatzoglou, 2009), results of

this communication reveal that PEOU influence

positively the intention of ICT appropriation by

physician and nurses.

Table 4: Regression results.

Model

R, R

Coefficients t Sig.

Std.

Error

0.617

0.380

(Constant) -,012 ,083 -,143 ,887

Perceived

Usefulness

,492 ,087 5,655 ,000

Perceived

Ease of Use

,226 ,087 2,591 ,011

6 CONCLUSIONS AND

MANAGERIAL IMPLICATION

Using constructs of the TAM model, this

communication attempts to explain ICT

appropriation by physicians and nurses working in

healthcare public centers in Agadir, south of

Morocco. It concludes that the PU, PEOU and

Attitude, considered together, explain these

appropriation in healthcare context, but still

insufficient and it needs to be extended and

completed by other external variables such as culture

and scientific level of the physicians and nurses

working in health public centres in Morocco. Also,

the outcomes confirm that Perceived Usefulness is

still to be a major determinant of ICT appropriation

by healthcare actors, especially by physicians and

nurses. Finally, the authors suggest that the proposed

model could help healthcare managers, and Chief

Information Officers to identify success factors of

ICT appropriation in healthcare organizations in

order to consider them and develop more appropriate

information system solutions for these organizations.

REFERENCES

Adams, D.A., R.R. Nelson & P.A. Todd, 1992. Perceives

Usefulness, Ease of Use, and Usage of Information

Technology: A Replication. MIS Quarterly, Vol. 16,

No. 2, pp. 227-247

Agarwal, R., Karahanna, E., 1998. On the multi-

dimensional nature of compatibility beliefs in

technology acceptance. In: Proceedings of the DIGIT

Conference,

Agarwal, R. and Prasad, J., 1998. The antecedents and

consequents of user perceptions in information

DO CONSTRUCTS OF TECHNOLOGY ACCEPTANCE MODEL PREDICT THE ICT APPROPRIATION BY

PHYSICIANS AND NURSES IN HEALTHCARE PUBLIC CENTRES IN AGADIR, SOUTH OF MOROCCO?

247

technology adoption. Decision Support Systems(22):

pp.15 -29

Aggelidis, V.P., Chatzoglou, P.D., 2009. Using a modified

technology acceptance model in hospitals.

International journal of medical informatics, 7 8, 115–

126

Ajzen, I., 1991. The theory of planned behavior.

Organisational Behavior and Human Decision

Process, vol. 52, no. 2, pp. 179-211.

Banderker, N. and Van Belle, J.P. 2006. Mobile

technology adoption by doctors in public healthcare in

South Africa. Proceedings of 2006 ECIS Goteborg

14th, 12-14 June 2006, Gothenburg, Sweden, 1-13,

ISBN 91-631-9894-0.

Bennani, A., Belalia, M., Oumlil, R. 2008. As a human

factor, the attitude of healthcare practitioners is the

primary step for the e-health. First outcome of an

ongoing study in Morocco. Communication of

IBIMA, Volume 3.

Chang, MI K., 1998.Predicting Unethical Behavior: A

Comparison of the Theory of Reasoned Action and the

Theory of Planned Behavior. Journal of Business

Ethics, Vol. 17, pp. 18225-1834

Chau, P.Y.K. Hu, P.J. 2002. Investigating healthcare

professionals’ decisions to accept telemedicine

technology: an empirical test of competing theories.

Information & Management 39 (4) pp.297–311.

Chau, P.Y.K. Hu, P.J. 2001. Information technology

acceptance by professionals: a model comparison

approach. Decision Sciences 23 (4) pp.699–719.

Chau. P. Y.K, and Tam. K. Y. 1997. Factors Affecting the

Adoption of Open Systems: An Exploratory Study.

MIS Quarterly (21:1). pp. 1-24

Chismar, W.G., Wiley-Patton, S., 2003. Does the extended

technology acceptance model applies to physicians. in:

Proceedings of the 36th Hawaii International

Congress on System Sciences, IEEE Computer

Society, Big Island, Hawaii.

Compeau, D., Higgins, C., and Huff, S. 1999. Social

Cognitive Theory and Individual Reactions to

Computing Technology: A Longitudinal Study. MIS

Quarterly, Vol23, No.2, pp.145-158.

Compeau, D. and Higgins, C,. 1991. A Social Cognitive

Theory Perspective on Individual Reactions to

Computing Technology. Proceedings of the 12th ICIS,

New-York, pp.187-198.

Croteau A-M., and Vieru D. 2002. Telemedicine Adoption

by Different Groups of Physicians, Proceedings of the

35th Hawaii International Conference on System

Sciences

Davis, F. D., 1989. Perceived usefulness, perceived ease

of use, and user acceptance in information technology.

MIS Quarterly, vol. 13, no. 3, pp. 319-340.

Davis F.D., Bagozzi R., Warshaw P.R., 1989. User

acceptance of computer technology : a comparison of

two theoretical models. Management Science, Vol. 35,

N° 8, August, pp. 982-1003.

DeSanctis, G. et Poole, M.S. 1994. Capturing complexity

in advanced technology use: Adaptive Structuration

theory. Organisation Science, 5 (2), pp 121-146

Dodds, W.B., K.B. Monroe, and D.Grewal. 1991. Effects

of Price, Brand, and Store Information on Buyers'

Product Evaluations. Journal of Marketing Research,

Vol. 28 (August), pp.307-319.

Fishbein, M. and Ajzen. I.,1975. Belief, Attitude, Intention

and Behaviour: An Introduction to Theory and

Research. Addison-Wesley, Reading, MA

Gefen, D., Karahanna, E., Straub, D., 2003. Trust and

TAM in online shopping: an integrated model”, MIS

Quarterly, 17 (1), pp.51–90

Hu, P.J. , Chau, P.Y.K., Liu Sheng, O.R., Tam, K.Y.,

1999. Examining the technology acceptance model

using physician acceptance of telemedicine

technology. Journal of Management Information

Systems 16 (2) pp.91–112.

Igbaria, M., Parasuraman, S., and Baroudi, J.J. 1996. A

motivational model of microcomputer usage. Journal

of Management Information Systems, 13, 127-143

Igbaria, M., 1993. User Acceptance of Microcomputer

Technology: An Empirical Test. Omega Int J. of Mg&

Sci, Vol. 21, No.1, pp. 73-90.

Jackson C.M., Chow S., Leitch R.A., 1997. Toward an

understanding of the behavioral intention to use an

information system. Decision Sciences, Vol. 28, N°

2,pp. 357-389.

Karahana E., Straub D.W., Chervany N.L., 1999.

Information technology adoption across time : across

sectional comparison of pre-adoption and post-

adoption beliefs. MIS Quarterly, Vol 23, N°2, pp. 183-

213

Kukafka R., Johnson S.B., Linfante A., Allegrante J.P.

2003. Grounding a New Information Technology

Implementation Framework in Behavioural Science: a

Systematic Analysis of the Literature on IT Usage.

Journal of Biomedical Informatics, Vol., 36, N° 3,

June, pp. 218-227.

Moore, G., and Benbasat, I.1991. Development of an

instrument to Measure the Perceptions of Adopting

and Information Technology Innovation. Information

Systems Research, (2:3).pp. 192-222

Proulx, S., 2001. Les formes d’appropriation d’une culture

numérique comme enjeu d’une société du savoir. Actes

du colloque COREVI « Gouvernance et usages

d’internet : vers un nouvel environnement normatif,

Montréal

Rogers, E. M., 1983. Diffusion of Innovations. New York:

Free Press.

Rogers, E. M., 1995. “Diffusion of Innovations”.4th ed.

New York, NY: The FreePress

Sally, R. and Indrit, T., 2007, “A Conceptual Framework

and Propositions for the Acceptance of Mobile

Services. Journal of Theoretical and Applied

Electronic Commerce Research, VOL 2 , ISSUE 2,

August, pp.61 - 73

Schaper, L.K., Pervan, G.P. A. 2007. Model of

information and communication technology

acceptance and utilization by occupational therapists.

International Journal of medical Informatics 76

.pp.790–800.

HEALTHINF 2010 - International Conference on Health Informatics

248

Scott, R.E., 2007. E-Records in health—preserving our

future. International Journal of Medical Informatics

76. pp.427–431

Taylor, S., P. A. Todd. 1995. Understanding information

technology usage: A test of competing models.

Information Systems Research. 6, pp.144–176.

Triandis, H.C. 1980. Values, Attitudes and Interpersonal

Behavior. Nebraska Symposium on Motivation 1979:

Beliefs, Attitudes and Vaiues, University of Nebraska

Press, Lincoln, pp.195-259

Venkatesh, V., & Davis, F.D. (2000). A theoretical

extension of the technology acceptance model: four

longitudinal field studies. Management Science, 46,

pp.186-204.

Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.,

2003. User acceptance of information technology:

Toward a unified view. MIS Quarterly, Vol. 27 No. 3,

September, pp. 425-478.

Wilson, E.V., Lankton, N.K. 2004. Research and

publication opportunities at the intersection of IS and

healthcare. Proceedings of the Tenth Americas

Conference on Information Systems, New York, New

York,USA, pp.4608-4613.

Wu, J-H., Wang,S-C., Lin, L-M., 2007. Mobile

computing acceptance factors in the healthcare

industry: A structural equation model. International

journal of medical informatics 7 6 pp.66–77

Wu, J-H., Shen, W-S., Lin, L-M., Greenes, R.A.,

Bates.D.W .2008. Testing the technology acceptance

model for evaluating healthcare professionals’

intention to use an adverse event reporting system.

International Journal for Quality in Health Care

Advance January 25, 2008

Wu, J-H., Chenc, Y-C., Greenese, R.A., 2009. Healthcare

technology management competency and its impacts

on IT–healthcare partnerships development.

International Journal of Medical Informatics. (7 8) pp.

71–82.

DO CONSTRUCTS OF TECHNOLOGY ACCEPTANCE MODEL PREDICT THE ICT APPROPRIATION BY

PHYSICIANS AND NURSES IN HEALTHCARE PUBLIC CENTRES IN AGADIR, SOUTH OF MOROCCO?

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