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International Journal of Information Technology and Language Studies
(IJITLS)
Vol. 3, Issue. 3, (2019). pp. 27-42
International Journal of Information Technology and Language Studies (IJITLS).
http://journals.sfu.ca/ijitls
Implementing Artificial Intelligence in the United
Arab Emirates Healthcare Sector: An Extended
Technology Acceptance Model
Shaikha FS Alhashmi1, Said A. Salloum1, 2, and Chaker Mhamdi3, 4
shaikha.alattar@hotmail.com; ssalloum@sharjah.ac.ae; shaker@buc.edu.om
1 Faculty of Engineering & IT, The British University in Dubai, Dubai, UAE
2 Research Institute of Sciences & Engineering, University of Sharjah, Sharjah, UAE
3 University of Manouba, Tunisia
4 Al Buraimi University College, Oman
Abstract. The United Arab Emirates (UAE) has recently focused on implementing Artificial
Intelligence (AI) projects in the government healthcare sector to help manage chronic diseases and
early detection. However, successful AI implementation depends on adoption and acceptance by
decision-makers, physicians, nurses, and patients. This paper develops and tests a modified
Technology Acceptance Model (TAM) to explore critical success factors (CSFs) for the adoption of AI in
the healthcare sector. The most widely used CSF variables for TAM are Perceived Usefulness (PU),
Perceived Ease of Use (PEU), Attitudes toward Use (ATU) and Behavioral Intention to Use (BIU).
However, a review of 23 qualitative and quantitative studies of TAM literature from 2015 to 2018
suggested that five key external factors should be included in CSF studies using TAM. An extended
model was developed (ETAM) and tested using a qualitative study comprising 53 employees working
in the Dubai IT and healthcare sectors. The study showed that managerial, organizational, operational
and IT infrastructure factors have a positive effect on PU and PEU and, hence, should be included as
CSFs in determining the implementation of AI in the healthcare sector.
Keywords: United Arab Emirates; critical success factors; extended technology acceptance
model; artificial intelligence; healthcare.
1. Introduction
Governments of the UAE are increasingly implementing technology strategies, including the adoption
of AI in the public and business sectors (Government.au). Many initiatives are underway, including the
Dubai Electricity and Water Authority (DEWA). The purpose of this study is to present and test a model to
assist in the successful adoption of AI in the Dubai government health sector, specifically to improve
services for patient monitoring (Dubai Health Authority 2018); however, the CSFs identified in the ETAM
developed and tested herein apply to AI adoption for other types of medical and healthcare services. The
paper proceeds with a literature review that reveals five key external factors that should be included as
CSFs in existing TAMs. It then develops an ETAM and tests its hypotheses using standard methodological
measures for internal consistency and validity and a qualitative survey to assess its effectiveness in
predicting the successful adoption of AI in healthcare. There are many different definitions of AI in the
literature (Ghannajeh et al., 2015; Mokyr, 2018). However, the most relevant for this paper, given the
scope for AI adoption across medical and healthcare practices is the broad conception provided by
(Mijwel, 2015) who uses the term to refer to human intelligence processes that are simulated by
computer systems including learning, reasoning, problem-solving, speech recognition and planning.
Implementing Artificial Intelligence in the United Arab Em irates Healthcare Sector: An Extended Technology
Acceptance Model
28
2. Literature Review
Recently, nobody can deny the role of AI and how it is incorporated in various applications, including
reinforcement learning (Al-Emran, 2015a), robots (Al-Emran, 2015b), NLP (Al-Emran, Zaza, & Shaalan,
2015), data mining (Saa, Al-Emran, & Shaalan, 2019), and internet of things (IoT) (Al-Emran, Malik, & Al-
Kabi, 2020), among others. TAM is a widely used method for determining CSFs for AI implementation in
industries including the healthcare sector (Alharbi & Drew, 2014; Phatthana & Mat, 2011). However, a
literature review of 23 TAM healthcare studies between 2015 and 2018 revealed they ignored criteria
often included in other sectors such as information technology, education, business, and government. See
for example, studies by (Abdullah & Ward, 2016; Al-Emran & Teo, 2019; Noor Al-Qaysi, Mohamad-Nordin,
& Al-Emran, 2018; M. Alshurideh, Salloum, Al Kurdi, & Al-Emran, 2019; Mezhuyev, Al-Emran, Fatehah, &
Hong, 2018; Mezhuyev, Al-Emran, Ismail, Benedicenti, & Chandran, 2019; S. A. S. Salloum & Shaalan,
2018; San & Yee, 2013; Strudwick, 2015). Some of these factors are system quality, computer playfulness,
self-efficacy, content quality, subjective norm, accessibility, enjoyment, and information quality.
Moreover, a review of successful AI implementation in other industry sectors suggested that an extended
model (ETAM) should include five CSF categories: managerial, organizational, operational, strategic, and
IT infrastructure, each of which is discussed in section 3 below. This current study is the first to use this
ETAM model to assess CSF for AI implementation in the healthcare sector including outcomes based on
different user types.
3. Model and Hypotheses
The five factors comprising the ETAM model and its resulting hypotheses used in the study are as
follows.
3.1. Managerial Factors
According to (Costantino, Di Gravio, & Nonino, 2015), managerial factors refer to the influencers within
an organization that impact efficiency and outcomes including the adoption of new technology (Mhamdi,
2017a; S. A. Salloum, Mhamdi, Al-Emran, & Shaalan, 2017; S. A. Salloum, Mhamdi, Al Kurdi, & Shaalan,
2018; Zu’bi, Al-Lozi, Dahiyat, Alshurideh, & Al Majali, 2012). Moreover, the role of management is to build
trust and establish organizational norms in the work environment (Ammari, Al kurdi, Alshurideh, &
Alrowwad, 2017). Trust is important for employees in the health sector because it affects individual
attitudes within a particular organizational culture (Alshurideh, 2016). Establishing certainty in the
validity of assertions made by an individual create shared subjective and social norms and beliefs
whereby the majority of people believe that others in the group will conform to certain behaviors, and
these norms are assumed by management to create social pressures that govern specific practices (S. A.
Al-Mohammadi & Derbel, 2015; E Derbel, 2014; Emira Derbel, 2017a, 2019a; Mhamdi, 2017b; S. A.
Salloum et al., 2017; S.A. Salloum, Al-Emran, Monem, & Shaalan, 2018). Hence, individuals act within
certain guidelines when performing new practices rather than motivated by their own beliefs or emotions
(S. Al-Mohammadi, 2014; Alshurideh, Alhadid, & Al kurdi, 2015). Moreover, sound management is
important in the adoption of AI in healthcare settings, particularly endorsement by medical staff (Bennani
& Oumlil, 2014). As (Alloghani, Hussain, Al-Jumeily, & Abuelma’atti, 2015) show, the perception of trust in
the adoption of AI in medical settings has a positive influence on PU and PEU of new technology,
particularly because AI may replace or augment human actions in medical procedures. PU and PEU have
long been established as key CSFs in the successful adoption of AI (M. T. Alshurideh, Salloum, Al Kurdi,
Monem, & Shaalan, 2019; E Derbel, 2014; Emira Derbel, 2017b, 2019b; S. A. Salloum & Al-Emran, 2018; S.
A. Salloum, Al-Emran, Shaalan, & Tarhini, 2019; S. A. S. Salloum & Shaalan, 2018). Therefore, the role of
management through the creation of trust in effecting the adoption of AI gives rise to the following two
hypotheses in the proposed ETAM model:
H1a: Managerial factors have a positive impact on PU.
H1b: Managerial factors have a positive impact on PEU.
Shaikha FS Alhashm i, Said A. Salloum, and Ch aker Mhamdi
29
3.2. Organizational Factors
In addition to the managerial support for AI in the healthcare sector providers, organizations must be
equipped with the appropriate skill sets and training, which may involve global partnerships with leading
technology providers. A study by (Navimipour & Charband, 2016) showed that appropriate training
programs assist individuals to accept advanced technologies because they enhance their skills
(Alshraideh, Al-Lozi, & Alshurideh, 2017). Relevant expertise within the workplace also increases the
possibility that new technology will be accepted by target users (M Alshurideh, Nicholson, & Xiao, 2012;
Venkatesh, Thong, & Xu, 2016). Since organizational variables, including technological partnerships,
training and local skill sets enable PU and PEU of new technologies, the following hypotheses were
formulated:
H2a: Organizational factors have a positive impact on PU.
H2b: Organizational factors have a positive impact on PEU.
3.3. Operational Factors
Operational factors comprise variables used to evaluate the requirements of a desired service (Al-
dweeri, Obeidat, Al-dwiry, Alshurideh, & Alhorani, 2017; Christensen, Thomas, Calleya, & Nielsen, 2018).
These factors generally refer to the human-technology interface where perceived enjoyment
of user-interaction is a key CSF in the acceptance of new technology (Alghizzawi, Ghani, et al., 2018;
Alghizzawi, Salloum, & Habes, 2018; Bennani & Oumlil, 2014; M. Habes, Salloum, Alghizzawi, & Alshibly,
2018; Mohammed Habes, Salloum, Alghizzawi, & Mhamdi, 2019; S. A. Salloum, Al-Emran, & Shaalan, 2018;
Who, 2011). Further, perceived levels of enjoyment are related to the amount of perceived effort required
by AI users (Alshurideh, 2010; Who, 2011), particularly in the healthcare sector. Since operational factors
influence PU and PEU, the following hypotheses were formulated:
H3a: Operational factors have a positive impact on PU.
H3b: Operational factors have a positive impact on PEU.
3.4. Strategic Factors
Strategic factors comprise the way an organization assists all stakeholders to achieve success
(ELSamen & Alshurideh, 2012; Zare, 2017). In the healthcare sector, these factors comprise user
satisfaction which is the most important CSF in AI adoption because it impacts PU and PEU. Satisfaction
refers to the level in which doctors and project managers are comfortable with the adoption of new
technology. Although perceived levels of enjoyment and required skill sets and expertise may overlap
with user satisfaction as a variable, its CSFs across relevant stakeholders in an organization must be
measured. Since user satisfaction is a key variable in measuring CSF for AI in terms of its impact on PU
and PEU measured by different stakeholders, the following hypotheses were formulated:
H4a: Strategic factors have a positive impact on PU.
H4b: Strategic factors have a positive impact on PEU.
3.5. IT Infrastructure Factors
IT infrastructure variables determine the physical aspects of hardware systems within an organization
(Alkalha, Al-Zu’bi, Al-Dmour, Alshurideh, & Masa’deh, 2012; Dahiya & Mathew, 2016). They comprise
system, content and information quality (Al Dmour, Alshurideh, & Shishan, 2014). System quality
determines the variables of availability, usability, adaptability, and reliability (Shannak et al., 2012).
Several studies showed that system quality is a CSF for the adaption of AI in the healthcare sector.
Further, in the case of AI adoption, the impact of the overall design appeal of the technology is important.
Content quality refers to the depth of AI functionality and its ability to meet new service needs (Solano-
Lorente, Martínez-Caro, & Cegarra-Navarro, 2013). There is a positive relationship between the content
Implementing Artificial Intelligence in the United Arab Em irates Healthcare Sector: An Extended Technology
Acceptance Model
30
quality of AI and PU in the healthcare sector (Fathema, Shannon, & Ross, 2015; Solano-Lorente et al.,
2013) and PEU. Information quality refers to the way that AI projects in the healthcare domain process
information for patient monitoring and management (Basak, Gumussoy, & Calisir, 2015). It must present
data regarding patient treatment and management of health conditions in a timely, comprehensive and
easily comprehensible manner (Alshurideh, 2014; San & Yee, 2013). Information quality can also refer to
user perceptions concerning the quality of information produced by AI (Altamony, Alshurideh, & Obeidat,
2012; Solano-Lorente et al., 2013). Therefore, information quality refers to the level at which the
physicians and patients receive precise and well-timed data based on AI systems, and this affects PU and
PEU (Bashiri, Ghazisaeedi, Safdari, Shahmoradi, & Ehtesham, 2017). Hence, based on the literature review
regarding IT infrastructure, two hypotheses can be formulated:
H5a: Infrastructure factors have a positive impact on PU.
H5b: Infrastructure factors have a positive impact on PEU.
3.6. TAM Constructs
The TAM model explains the general elements of acceptance that illustrates user behavior, including
beliefs regarding PU and PEU.
3.6.1 Perceived Ease of use (PEU)
The PEU of any given system is defined as the level of technology used having the perception of proper
use of the defined technology. PEU has a significant direct or indirect relationship with BIU, which is a key
CSF of successful AI implementation (Alloghani et al., 2015; Phatthana & Mat, 2011). BIU is measured by
various factors. In the healthcare sector, PEU is specifically related to the physician’s perceptions of
technology (Phatthana & Mat, 2011). Hence, the following hypotheses can be formulated:
H2a1: PEU positively affects the BIU of AI projects in the healthcare sector.
H2a2: PEU positively affects the perceived usefulness to implement AI projects in the healthcare sector.
H2a3: PEU positively affects attitudes toward the implementation of AI projects in the healthcare
sector.
3.6.2 Perceived Usefulness (PU)
PU refers to the level in which users expect new technology to improve job performance (Alharbi &
Drew, 2014) and, in the healthcare sector, it is the measure by which AI improves a physician’s
performance (Alloghani et al., 2015). Moreover, the physician’s perceptions determine the extent to
which it will be implemented (Emad, El-Bakry, & Asem, 2016). Hence, the following hypotheses can be
formulated:
H2b1: PU positively affects attitudes toward the implementation of AI projects in the healthcare sector.
H2b2: PEU positively affects the behavioral intention to implement AI projects in the healthcare sector.
3.6.3 Attitude Toward Use (ATU)
ATU refers to the extent people have positive or negative feelings toward an object or event, and it is
significantly associated with BIU (Baharom, Khorma, Mohd, & Bashayreh, 2011). The identification of
these attitudes helps in understanding the technology use (Al-Emran, Alkhoudary, Mezhuyev, & Al-
Emran, 2019; N. Al-Qaysi, Mohamad-Nordin, & Al-Emran, 2019b, 2019a; N. Al-Qaysi, Mohamad-Nordin,
Al-Emran, & Al-Sharafi, 2019; Malik & Al-Emran, 2018). In the current study, a physician’s ATU refers to
the level of positive or negative feelings they have toward the implementation of AI projects in the
healthcare sector, which in turn, affects adoption and use. Therefore, the following hypothesis can be
formulated:
Shaikha FS Alhashm i, Said A. Salloum, and Ch aker Mhamdi
31
H2c: ATU positively affects the behavioral intention to implement AI projects in the healthcare sector.
3.6.4 Behavioral Intention to Use (BIU)
A physician’s intention to implement AI projects in the healthcare sector can define the proposed levels
of BUI to use a particular system. Various studies have also shown that BIU has a direct and significant
influence on the actual system use of AI projects in the healthcare sector (Fayad & Paper, 2015); (Helia,
Indira Asri, Kusrini, & Miranda, 2018). Hence, the following hypothesis can be formulated:
H2d: BIU positively affects AI system choice for implementation in the healthcare sector.
3.6.5 Actual System Use (ASU)
ASU refers to the period that the technology system is utilized after being accepted and adopted by the
respective subject (Teeroovengadum, Heeraman, & Jugurnath, 2017). In the healthcare sector, it refers to
the period of use after implementation by IT and health staff. However, this construct is dependent on
other TAM constructs, particularly BIU. Hence, the following hypothesis was formulated:
H2e: ASU is positively affected by BIU in the implementation of AI projects in the healthcare sector.
Figure 1. Extended TAM Model.
4. Research Methodology
4.1. Sample description
The population targeted by this study comprised of both IT and health staff. In total, 53 surveyors were
considered to take part in this study. The 13 health centers in Dubai that physicians selected for the study
were picked from include: Al Barsha Health Center, Nad Al Hamar Health Center, Al Safa Health Center, Al
Badaa Health Center, Al Mankhool Health Center, Al Lusaily Health Center, Al Khawaneej Health Center, Al
Towar Health Center, Nad Al Sheba Health Center, Al Mamzar Health Center, Al Mizhar Health Center,
Implementing Artificial Intelligence in the United Arab Em irates Healthcare Sector: An Extended Technology
Acceptance Model
32
Family Gathering, and Za'abeel Health Center. Each of the health centers in the list above was represented
by ten physicians in this research. The sample population was selected based on the availability of the
physicians as the tight schedules of the research participants were put into consideration. The selection of
the sample population was conducted using a purposive sampling method. According to (Tongco, 2007),
purposive sampling is a non-probability sampling technique that the researcher has to rely on
individual’s personal judgment while selecting the members of the sample population to take part in the
study. This sampling technique is known for coming up with a population that aims at providing clear
information to serve a given purpose. That is why only health and IT staff from registered healthcare
centers in Dubai were involved in answering the survey questionnaire. Besides, the accuracy level
required for this research is 90%, according to the confidence level calculator I will need 53 responses in
order to get all the answers that allow this accuracy percentage.
4.2.
Survey Structure
As aforementioned above, this particular research study relied on a questionnaire survey tool to collect
data from the participants. After being developed, the questionnaire survey was uploaded online, and
respondents were provided with the link. Generally, the questionnaire was structured in such a way that
it captured all the items that could provide precise data concerning critical success factors for
implementing Artificial Intelligence projects in the healthcare sector. Structurally, the survey tool was
segmented into six different parts. Part A only comprised of demographic information regarding the
research respondents. Part one only comprised of demographic information regarding the research
respondents. Part two addressed managerial factors as Part three captured prompts on operational
factors. Strategic factors, IT infrastructure factors and organizational factors were placed in parts four,
five and six respectively. In all the parts, there were at least three questions that asked participants on
their perceptions about the factors. In total, the questionnaire survey utilized in this research study has
26 items. Again, the survey employed used a 5-point Likert scale with multiple choices structured as
follow: 1-strongly disagree, 2-disagree, 3-neutral, 4-agree, and 5-strongly agree.
5. Findings and Discussion
5.1. Measurement Model Analysis
Partial Least Squares-Structural Modelling (PLS-SEM) was used to analyze the measurement and
structural models (Chin 1998) using industry-standard SmartPLS software V. 3.2.6 (Ringle, Wende & Will
2005). We followed the guiding principles provided by (Al-Emran, Mezhuyev, & Kamaludin, 2018) for
employing PLS-SEM in the IS domain. The measurement model (Outer Model) describes the relationship
between the indicators, while the structural model (Inner Model) describes the relationship between the
latent constructs. PLS-SEM was employed with the highest probability model to measure the proposed
model (Anderson & Gerbing, 1988). To measure reliability and convergent validity, various
measurements were carried out including Factor Loadings, Average Variance Extracted and Composite
Reliability. Factor loadings were used to determine the weight and correlation value of all questionnaire
variables as perceived indicators. A larger load value can signify factor dimensionality. Reliability is
measured using Composite Reliability (CR). An accurate value is provided by CR using factor loadings
employed by the formula. The average extent of variance in the specified variable defining the latent
construct is known as the Average Variance Extracted (AVE). A discriminate validity value of more than
one factor means that the convergence of each factor can be assessed using AVE. As shown in Table 1, the
experimental outcome for questionnaire reliability and convergent validity was more than the standard
value for the reliability and convergent validity. Table 1 presents a summary of the reliability and validity
of the questionnaire, together with the analytical outcomes for each factor by depicting the variable
attained from the questionnaire.
5.2.1 Assessment of the Structural (Outer) Model
5.2.1.1 Convergent validity
Factor loadings, variance extracted and reliability (including Cronbach’s Alpha and CR) were used as
indicators (Hair, Black, Babin, Anderson & Tatham 1998) to determine the comparative degree of
convergent validity. For every construct, the reliability coefficient and CR exceeded 0.7, which suggests
Shaikha FS Alhashm i, Said A. Salloum, and Ch aker Mhamdi
33
internal consistency between construct measurements (Hair et. al. 1998). Cronbach’s alpha value was
more than the standard value of 0.7 (Gefen et. al. 2000; Nunnally & Bernstein 1978), as shown in Table 1
while composite reliabilities of constructs ranged from 0.774 to 0.864. Regarding variance extracted, all
AVE values ranged from 0.668 to 0.820, which accounted for a minimum of 50% variance (Falk & Miller
1992). Hence, convergent validity was achieved by assessing the constructs using appropriate measures.
5.2.1.2 Discriminate validity
Table 2 shows that all AVE values were more than the squared correlation between constructs in the
measurement model; and hence, criteria for discriminate validity were fulfilled (Fornell & Larcker 1981;
Hair et al. 1998). The constructs should normally show at least 50% measurement variance when the AVE
is higher than 0.5. To assess discriminate values, SmartPLS was utilized. The loadings and cross-loadings
are given in Table 3. It is evident from the detailed assessment of the loadings and cross-loadings that
there is widespread loading of all the measurement items on their own latent constructs rather than
dependent loading on other constructs. The AVE analysis is given in Table 2, where the bold diagonal
items represent the square root of the AVE scores. Conversely, the off-load diagonal elements represent
correlations among the constructs. Table 2 clearly shows that AVE values ranged between 0.824 and
0.946, which is more than the proposed value of 0.5, and suggests that for each construct, the AVE is
greater than any correlations between them. This demonstrates a higher variance of all constructs within
their own measures instead of variance with other constructs in the model, and this improves
discriminate validity.
Constructs Items Factor
Loading
Cronbach’s
Alpha
CR AVE
Actual Use
AU1 0.863
0.894 0.822 0.710
AU2 0.966
AU3
0.873
AU4 0.965
Intention to use Social
Networks
IU1
0.825
0.892 0.774 0.690
IU 2 0.899
IU 3 0.789
IU 4
0
.
855
Perceived Ease of use
PEU1
0.889
0.775 0.788 0.668
PEU2
0.746
PEU3 0.919
PEU4
0.847
Perceived Usefulness
PU1
0.955
0.819 0.845 0.766
PU2
0.959
PU3 0.822
PU4
0.817
Perceived Playfulness
PP1
0.7
66
0.713 0.864 0.820
PP2
0.888
PP3 0.811
PP4
0.936
Table 1. Convergent validity results conforming acceptable values (Factor loading, Cronbach’s Alpha, CR 0.70 & AV E >
0.5).
Implementing Artificial Intelligence in the United Arab Em irates Healthcare Sector: An Extended Technology
Acceptance Model
34
Actual
Use
Intention to use
Social Networks
Perceived
Ease of use
Perceived
Usefulness
Perceived
Playfulness
Actual Use 0.946
Intention to use
Social Networks
0.583 0.835
Perceived
Ease of use
0.357 0.651 0.847
Perceived
Usefulness 0.444 0.523 0.212 0.881
Perceived
Playfulness
0.143 0.425 0.356 0.513 0.824
Table 2. Fornell-Larcker Scale.
Actual
Use
Intention
to use
Social
Networks
Perceived
Ease of
use
Perceived
Usefulness
Perceived
Playfulness
AU1 0.863 0.123 0.576 0171 0.470
AU2 0.966 0.525 0.343 0.433 0.353
AU3
0.873
0
.390
0.181
0.18
4
0.
288
AU4
0.965
0.
2
52
0.422
0.382
0.326
IU1
0.252
0.825
0.256
0.224
0.379
IU 2 0.394 0.899 0.138 0.228 0.332
IU 3
0.444
0.789
0.461
0.182
0.425
IU 4
0.346
0.855
0.178
0.533
0.524
PEU1
0.275
0.142
0.889
0.644
0.448
PEU2 0.423 0.423 0.746 0.342 0.420
PEU3 0.431 0.474 0.919 0.299 0.439
PEU4
0.527
0.346
0.847
0.422
0.483
PU1
0.546
0.334
0.117
0.955
0.432
PU2 0.314 0.257 0.322 0.959 0.396
PU3 0.389 0.245 0.212 0.822 0.426
PU4
0.377
0.286
0.272
0.817
0.428
PP1
0.356
0.454
0.353
0.22
2
0.766
PP2
0.
245
0.376
0.273
0.372
0.888
PP3 0.322 0.413 0.216 0.463 0.811
PP4
0.334
0.146
0.525
0.545
0.936
Table 3. Cross-loading results.
5.2.2 Assessment of the Structural (Inner) Model
5.2.2.1 Coefficient of Determination
The coefficient of determination (R2 value) measure is the standard method used to examine a
structural (Inner) model. Using this coefficient, the predictive accuracy of the model is determined by a
process involving the squared correlation among a given endogenous construct’s actual and predicted
values. The coefficient denotes an exogenous latent variable’s combined impact on an endogenous latent
variable. The squared correlation of the actual and predicted values of the variables is given by the
coefficient. Hence, the coefficient also shows the degree of variance in the endogenous constructs
explained by every exogenous construct recognized with it. Chin (1998) suggests that coefficient values
exceeding 0.67 are strong, values from 0.33 to 0.67 are considered direct, those from 0.19 to 0.33 are
weak, and those less than 0.19 are inadmissible. As Figure 2 shows, 63% of external factor constructs in
the model have positive values, and therefore, it has moderate predictive power. Further, Table 4 and
Figure 2 show that the constructs of Intention to use Social Networks and Actual Use have high predictive
power since their R2 values are approximately 61% and 63% respectively.
Shaikha FS Alhashm i, Said A. Salloum, and Ch aker Mhamdi
35
Constructs
R
2
Results
Intention to use Social Networks 0.613 High
Actual Use 0.627 High
Table 4. R2 of the endogenous latent variables.
5.2.2.2 Structural Model Analysis
To analyze the various hypothesized associations, the structural equation modeling was used (see
Table 5). (Al-Emran & Salloum, 2017; Milošević, Živković, Manasijević, & Nikolić, 2015) stated that the
values of fit indices that were computed showed that there was a suitable fit for the structural model to
the data for the given research model. As per the opinion of (Milošević et al., 2015), this study
recommends the intended values of fit indices, there is fitting structural model fit to the data for the
research model (Al-Maroof, Salloum, AlHamadand, & Shaalan, 2019; Al-Shibly, Alghizzawi, Habes, &
Salloum, 2019; Alghizzawi, Habes, & Salloum, 2019; Alhashmi, Salloum, & Abdallah, 2019; Alomari,
AlHamad, & Salloum, n.d.; Muhammad Alshurideh, 2018; Muhammad Alshurideh, Al Kurdi, & Salloum,
2019; Mohammed Habes et al., 2019; S. A. Salloum, Al-Emran, Khalaf, Habes, & Shaalan, 2019; S. A.
Salloum, Alhamad, Al-Emran, Monem, & Shaalan, 2019; S. A. S. Salloum & Shaalan, 2018; Said A Salloum et
al., 2019; Said A Salloum, Al-Emran, Shaalan, & Tarhini, 2018) (see Fig. 2). It can be seen in Table 4 that all
the values were in the given range. In addition to it, few direct hypotheses also showed support (Ma &
Yuen, 2011). The resulting path coefficients of the suggested research model are shown in Figure 2.
Generally, the data supported sixteen out of seventeen hypotheses. All endogenous variables were
verified in the model (PU, PEOU, AT, BI, and AU). Based on the data analysis hypotheses H1a, H2a, H3a,
H3b, H4a, H5a, H5b, H6, H7, H8, H9, H10, H11, and H12 were supported by the empirical data, while H4b
was rejected. The results showed that Perceived Usefulness significantly influenced Managerial factor (β=
0.312, P<0.05), organizational factors (β= 0.189, P<0.05), Operational factors supporting (β= 0.147,
P<0.05), Strategic factors (β= 0.206, P<0.05), IT Infrastructure factor (β= 0.527, P<0.001) and Perceived
Ease of Use (β= 0.242, P<0.001), hypothesis H1a, H2a, H3a, H4a, H5a and H6 respectively. Perceived
Usefulness and Perceived Ease of Use were determined to be significant in affecting Attitude towards use
(β= 0.116, P<0.001) and (β= 0.256, P<0.001) supporting hypotheses H7 and H8. Perceived Usefulness and
Perceived Ease of Use were determined to be significant in affecting Behavioral intention to use (β=
0.502, P<0.001) and (β= 0.105, P<0.01) supporting hypotheses H9 and H10. Furthermore, Perceived Ease
of Use was significantly influenced by four exogenous factors: Managerial factor (β= 0.163, P < P<0.05),
organizational factors (β= 0.139, P<0.05), Operational factors (β= 0.255, P<0.05), and IT Infrastructure
factor (β= -0.605, P<0.001) which support hypotheses H1b, H2b, H3b, H4b and H5b. The relationship
between Strategic factors and Perceived Ease of Use (β= 0.033, P=0.696) is statistically not significant,
and Hypotheses H4b is generally not supported. Finally, the relationship between Attitude towards use
and Behavioral intention to use (β= 0.697, P<0.001) is statistically significant, and Hypotheses H11 is
generally supported, and the relationship between Behavioral intention to use and Actual System Use (β=
0.901, P<0.001) is statistically also significant, and Hypotheses H12 supported. A summary of the
hypotheses testing results is shown in Table 5.
Hyp. Relationship Path t-value p-value Direction Decision
H1a Managerial factor ->
Perceived Usefulness
0.312 3.235 0.015 Positive Supported
H1b Managerial factor ->
Perceived Ease of Use
0.163 2.163 0.031 Positive Supported
H2a Organizational factors ->
Perceived Usefulness
0.189 2.499 0.013 Positive Supported
H2b
Organizational
-
> Perceived
Ease of Use
0.139
4.405
0.035
Positive
Supported
H3a Operational factors ->
Perceived Usefulness
0.147 1.873 0.042 Positive Supported
H3b Operational factors ->
Perceived Ease of Use
0.255 1.604 0.046 Positive Supported
H4a Strategic factors ->
Perceived Usefulness
0.206 3.129 0.002 Positive Supported
H4b Strategic factors ->
Perceived Ease of Use
0.033 0.391 0.696 Positive Not supported
H5a
IT Infrastructure factor
-
>
Perceived Usefulness
0.527
7.428
0.000
Positive
Supported
Implementing Artificial Intelligence in the United Arab Em irates Healthcare Sector: An Extended Technology
Acceptance Model
36
H5b IT Infrastructure factor ->
Perceived Ease of Use
0.605 7.197 0.000 Positive Supported
H6
Perceived Ease of Use
-
>
Perceived U
sefulness
0.242
3.268
0.001
Positive
Supported
H7 Perceived Usefulness ->
Attitude towards use
0.116 4.737 0.004 Positive Supported
H8 Perceived Ease of Use ->
Attitude towards use
0.256 6.883 0.008 Positive Supported
H9 Perceived Usefulness ->
Behavior
al intention to use
0.502 7.036 0.005 Positive Supported
H10 Perceived Ease of Use ->
Behavioral intention to use
0.105 7.379 0.006 Positive Supported
H11
Attitude towards use
-
>
Behavioral intention to use
0.697
17.897
0.000
Positi
ve
Supported
H12 Behavioral intention to use
-> Actual System Use
0.901 77.891 0.000 Positive Supported
Table 5. Results of structural Model.
Figure 2. Path coefficient results.
6. Conclusion and Future Studies
6.1. Study Contributions and Discussion
This study proposed that the external CSFs used for the successful adaption of AI projects within the
healthcare sector were managerial, operational, organizational, strategic and IT infrastructure factors,
which have not previously been included in the literature concerning TAMs in the health care sector. As
shown in Table 5, seventeen sub-hypotheses were derived from the literature and the questionnaire,
which were tested by a PLS-SEM methodology to link indicators to the latent constructs. It was found that
PU significantly influenced Managerial and Organizational, Operational, Strategic, and IT Infrastructure
factors and the PEU hypothesis H1a, H2a, H3a, H4a, H5a and H6 respectively. Perceived U and PEU were
determined to be significant in affecting ATU supporting hypotheses H7 and H8. PU and PEU were
Shaikha FS Alhashm i, Said A. Salloum, and Ch aker Mhamdi
37
determined to be significant in affecting BIU (supporting hypotheses H9 and H10). Further, PEU was
significantly influenced by four exogenous factors: managerial, organizational, operational and IT
Infrastructure factors, which support hypotheses H1b, H2b, H3b, and H5b. However, the relation between
PEU and strategic factors, and hence, H4b was not supported at (β= 0.033, P=0.696). Hence, except for
one external factor, all proposed hypotheses sharing positive relations.
The total number of cases dedicated to bootstrapping in the current study involves 300 cases that are
suitable for the sample size. The total outcome of the analyzed 17 hypotheses was provided in Table 5.
The coefficient of determination R2, refers to the values that are part of the variance in the actual variable
and the predictable variables of the endogenous constructs. (Chin, 1998) proposed that an R2 value of
more than 0.67 is high, between 0.33 and 0.67 to be moderate and between 0.19 and 0.33 is a weak area.
According to Table 4 the Endogenous variables’ R2 for actual system use, attitude toward use, Behavioral
intention to use, perceived ease of use and perceived usefulness have resulted between 0.636 and 0.464
and the results are moderate power for all of it. Also, the CR of variables showed the various hypothesized
associations, the model of structural equation was utilized, and the results illustrated that out of the
seventeenth hypothesis all of it were supported except one hypothesis.
The ETAM model, therefore, suggests that project managers and key influencers in the health domain
must consider external factors outlined in the extended model because, apart from strategic factors, these
impact key CSFs including PU, PEU, BIU, ATU, and ASU.
6.2. Limitations and Future Directions
This first, an analyzes used for (TAM) external factors by reviewing 23 literature studies in different
segments such as health care, education and government within the year range of 2015 and 2018. Second,
after discovering most of the factors with the use of (TAM) extension, a new model was initiated to
support this dissertation paper. Third, a proper suitable assessment for the new model was through the
use of a fit approach called PLS-SEM. Moreover, a survey questionnaire employed to gather the required
data and the target audience for this survey were health and IT staff working in different entities and the
total number of participants was 53 employees. A total of 17 hypotheses 16 out of it was supported by
this research, and the findings illustrate a positive relationship between the factors and the variables of
the model. Managerial factors, organizational factors, operational factors and IT infrastructure factors
demonstrated a positive relationship with the Perceived Ease of Use and Perceived Usefulness. However,
the strategic factors illustrated a negative relationship with the Perceived Ease of Use.
Various adaptions have been left for future research to be examined due to time shortage (interview
physicians, doctors, and health staff is usually time and effort consuming, requiring long process and
procedures. It could be significant if future studies consider only government hospitals across the UAE
and propose a unique modified model concerning the physicians' practice process with patients. The
modified (TAM) model in his dissertation can be constructed by focusing on one critical success factor
and study it deeply from different angels.
Acknowledgment
This work is a part of a dissertation submitted in fulfilment of MSc Informatics (Knowledge & Data
Management), Faculty of Engineering & IT, The British University in Dubai.
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