ArticlePDF Available

Implementing Artificial Intelligence in the United Arab Emirates Healthcare Sector: An Extended Technology Acceptance Model

Authors:

Abstract and Figures

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.
Content may be subject to copyright.
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
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= 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
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.
References
Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-
Learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56,
238–256. https://doi.org/10.1016/j.chb.2015.11.036
Al-dweeri, R. M., Obeidat, Z. M., Al-dwiry, M. A., Alshurideh, M. T., & Alhorani, A. M. (2017). The impact of
e-service quality and e-loyalty on online shopping: moderating effect of e-satisfaction and e-trust.
International Journal of Marketing Studies, 9(2), 92.
Al-Emran, M. (2015a). Hierarchical Reinforcement Learning: A Survey. International Journal of Computing
Implementing Artificial Intelligence in the United Arab Em irates Healthcare Sector: An Extended Technology
Acceptance Model
38
and Digital Systems, 4(2), 137–143.
Al-Emran, M. (2015b). Speeding Up the Learning in A Robot Simulator. International Journal of Computing
and Network Technology, 3(3).
Al-Emran, M., Alkhoudary, Y. A., Mezhuyev, V., & Al-Emran, M. (2019). Students and Educators Attitudes
towards the use of M-Learning: Gender and Smartphone ownership Differences. International Journal
of Interactive Mobile Technologies (IJIM), 13(1), 127–135.
Al-Emran, M., Malik, S. I., & Al-Kabi, M. N. (2020). A Survey of Internet of Things (IoT) in Education:
Opportunities and Challenges. In Toward Social Internet of Things (SIoT): Enabling Technologies,
Architectures and Applications (pp. 197–209). Springer.
Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018). PLS-SEM in Information Systems Research: A
Comprehensive Methodological Reference. In 4th International Conference on Advanced Intelligent
Systems and Informatics (AISI 2018) (pp. 644–653). Springer.
Al-Emran, M., & Salloum, S. A. (2017). Students’ Attitudes Towards the Use of Mobile Technologies in e-
Evaluation. International Journal of Interactive Mobile Technologies (IJIM), 11(5), 195–202.
https://doi.org/10.3991/ijim.v11i5.6879
Al-Emran, M., & Teo, T. (2019). Do knowledge acquisition and knowledge sharing really affect e-learning
adoption? An empirical study. Education and Information Technologies.
https://doi.org/10.1007/s10639-019-10062-w
Al-Emran, M., Zaza, S., & Shaalan, K. (2015). Parsing modern standard Arabic using Treebank resources. In
2015 International Conference on Information and Communication Technology Research, ICTRC 2015.
https://doi.org/10.1109/ICTRC.2015.7156426
Al-Maroof, R. S., Salloum, S. A., AlHamadand, A. Q. M., & Shaalan, K. (2019). A Unified Model for the Use and
Acceptance of Stickers in Social Media Messaging. In International Conference on Advanced Intelligent
Systems and Informatics (pp. 370–381). Springer.
Al-Mohammadi, S. (2014). Integrating Reading and Writing in ELT. In Focusing on EFL Reading: Theory
and Practice (pp. 260–274). Cambridge Scholars Publishing.
Al-Mohammadi, S. A., & Derbel, E. (2015). To Whom Do We Write?: Audience in EFL Composition Classes.
In Methodologies for effective writing instruction in EFL and ESL classrooms (pp. 197–208). IGI Global.
Al-Qaysi, N., Mohamad-Nordin, N., & Al-Emran, M. (2019a). An Empirical Investigation of Students’
Attitudes Towards the Use of Social Media in Omani Higher Education. In International Conference on
Advanced Intelligent Systems and Informatics (pp. 350–359). Springer.
Al-Qaysi, N., Mohamad-Nordin, N., & Al-Emran, M. (2019b). What leads to social learning? Students’
attitudes towards using social media applications in Omani higher education. Education and
Information Technologies.
Al-Qaysi, N., Mohamad-Nordin, N., Al-Emran, M., & Al-Sharafi, M. A. (2019). Understanding the differences
in students’ attitudes towards social media use: A case study from Oman. In 2019 IEEE Student
Conference on Research and Development (SCOReD) (pp. 176–179). IEEE.
Al-Qaysi, Noor, Mohamad-Nordin, N., & Al-Emran, M. (2018). A Systematic Review of Social Media
Acceptance from the Perspective of Educational and Information Systems Theories and Models. Journal
of Educational Computing Research. https://doi.org/https://doi.org/10.1177/0735633118817879
Al-Shibly, M. S., Alghizzawi, M., Habes, M., & Salloum, S. A. (2019). The Impact of De-marketing in Reducing
Jordanian Youth Consumption of Energy Drinks. In International Conference on Advanced Intelligent
Systems and Informatics (pp. 427–437). Springer.
Al Dmour, H., Alshurideh, M., & Shishan, F. (2014). The influence of mobile application quality and
attributes on the continuance intention of mobile shopping. Life Science Journal, 11(10), 172–181.
Alghizzawi, M., Ghani, M. A., Som, A. P. M., Ahmad, M. F., Amin, A., Bakar, N. A., … Habes, M. (2018). The
Impact of Smartphone Adoption on Marketing Therapeutic Tourist Sites in Jordan. International
Journal of Engineering & Technology, 7(4.34), 91–96.
Alghizzawi, M., Habes, M., & Salloum, S. A. (2019). The Relationship Between Digital Media and Marketing
Medical Tourism Destinations in Jordan: Facebook Perspective. In International Conference on
Advanced Intelligent Systems and Informatics (pp. 438–448). Springer.
Alghizzawi, M., Salloum, S. A., & Habes, M. (2018). The role of social media in tourism marketing in Jordan.
International Journal of Information Technology and Language Studies, 2(3).
Alharbi, S., & Drew, S. (2014). Using the technology acceptance model in understanding academics’
behavioural intention to use learning management systems. International Journal of Advanced
Shaikha FS Alhashm i, Said A. Salloum, and Ch aker Mhamdi
39
Computer Science and Applications, 5(1), 143–155.
Alhashmi, S. F. S., Salloum, S. A., & Abdallah, S. (2019). Critical Success Factors for Implementing Artificial
Intelligence (AI) Projects in Dubai Government United Arab Emirates (UAE) Health Sector: Applying
the Extended Technology Acceptance Model (TAM). In International Conference on Advanced Intelligent
Systems and Informatics (pp. 393–405). Springer.
Alkalha, Z., Al-Zu’bi, Z., Al-Dmour, H., Alshurideh, M., & Masa’deh, R. (2012). Investigating the effects of
human resource policies on organizational performance: An empirical study on commercial banks
operating in Jordan. European Journal of Economics, Finance and Administrative Sciences, 51(1), 44–64.
Alloghani, M., Hussain, A., Al-Jumeily, D., & Abuelma’atti, O. (2015). Technology Acceptance Model for the
Use of M-Health Services among health related users in UAE. In 2015 International Conference on
Developments of E-Systems Engineering (DeSE) (pp. 213–217). IEEE.
Alomari, K. M., AlHamad, A. Q., & Salloum, S. (n.d.). Prediction of the Digital Game Rating Systems based on
the ESRB.
Alshraideh, A., Al-Lozi, M., & Alshurideh, M. (2017). The Impact of Training Strategy on Organizational
Loyalty via the Mediating Variables of Organizational Satisfaction and Organizational Performance: An
Empirical Study on Jordanian Agricultural Credit Corporation Staff. Journal of Social Sciences (COES&RJ-
JSS), 6, 383–394.
ALSHURIDEH, M. (2010). Customer Service Retention–A Behavioural Perspective of the UK Mobile
Market. Durham University.
Alshurideh, M., Salloum, S. A., Al Kurdi, B., & Al-Emran, M. (2019). Factors affecting the Social Networks
Acceptance: An Empirical Study using PLS-SEM Approach. In 8th International Conference on Software
and Computer Applications (pp. 414–418). Penang, Malaysia: ACM.
https://doi.org/10.1145/3316615.3316720
Alshurideh, M. T., Salloum, S. A., Al Kurdi, B., Monem, A. A., & Shaalan, K. (2019). Understanding the
Quality Determinants that Influence the Intention to Use the Mobile Learning Platforms: A Practical
Study. International Journal of Interactive Mobile Technologies (IJIM), 13(11), 157–183.
Alshurideh, M, Alhadid, A., & Al kurdi, B. (2015). The effect of internal marketing on organizational
citizenship behavior an applicable study on the University of Jordan employees. International Journal
of Marketing Studies, 7(1), 138.
Alshurideh, M, Nicholson, M., & Xiao, S. (2012). The Effect of Previous Experience on Mobile Subscribers’
Repeat Purchase Behaviour. European Journal of Social Sciences, 30(3), 366–376.
Alshurideh, Muhammad. (2014). The Factors Predicting Students’ Satisfaction with Universities’
Healthcare Clinics’ Services: A Case-Study from the Jordanian Higher Education Sector. Dirasat:
Administrative Sciences, 41(2), 451–464.
Alshurideh, Muhammad. (2016). Scope of Customer Retention Problem in the Mobile Phone Sector: A
Theoretical Perspective. Journal of Marketing and Consumer Research, 20, 64–69.
Alshurideh, Muhammad. (2018). Pharmaceutical Promotion Tools Effect on Physician’s Adoption of
Medicine Prescribing: Evidence from Jordan. Modern Applied Science, 12(11).
Alshurideh, Muhammad, Al Kurdi, B., & Salloum, S. A. (2019). Examining the Main Mobile Learning System
Drivers’ Effects: A Mix Empirical Examination of Both the Expectation-Confirmation Model (ECM) and
the Technology Acceptance Model (TAM). In International Conference on Advanced Intelligent Systems
and Informatics (pp. 406–417). Springer.
Altamony, H., Alshurideh, M., & Obeidat, B. (2012). Information systems for competitive advantage:
Implementation of an organisational strategic management process. In Proceedings of the 18th IBIMA
conference on innovation and sustainable economic competitive advantage: From regional development
to world economic, Istanbul, Turkey, 9th-10th May.
Ammari, G., Al kurdi, B., Alshurideh, M., & Alrowwad, A. (2017). Investigating the impact of
communication satisfaction on organizational commitment: a practical approach to increase
employees’ loyalty. International Journal of Marketing Studies, 9(2), 113–133.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and
recommended two-step approach. Psychological Bulletin, 103(3), 411.
Baharom, F., Khorma, O. T., Mohd, H., & Bashayreh, M. G. (2011). Developing an extended technology
acceptance model: doctors’ acceptance of electronic medical records in Jordan. ICOCI.
Basak, E., Gumussoy, C. A., & Calisir, F. (2015). Examining the factors affecting PDA acceptance among
physicians: an extended technology acceptance model. Journal of Healthcare Engineering, 6(3), 399–
Implementing Artificial Intelligence in the United Arab Em irates Healthcare Sector: An Extended Technology
Acceptance Model
40
418.
Bashiri, A., Ghazisaeedi, M., Safdari, R., Shahmoradi, L., & Ehtesham, H. (2017). Improving the prediction of
survival in cancer patients by using machine learning techniques: experience of gene expression data:
a narrative review. Iranian Journal of Public Health, 46(2), 165.
Bennani, A.-E., & Oumlil, R. (2014). The Acceptance of ICT by Geriatricians reinforces the value of care for
seniors in Morocco. IBIMA Publ. J. African Res. Bus. Technol. J. African Res. Bus. Technol, 2014(2014).
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods
for Business Research, 295(2), 295–336.
Christensen, L. B. R., Thomas, G., Calleya, J., & Nielsen, U. D. (2018). The Effect of Operational Factors on
Container Ship Fuel Performance. In Proceedings of Full Scale Ship Performance. The Royal Institution
of Naval Architects.
Costantino, F., Di Gravio, G., & Nonino, F. (2015). Project selection in project portfolio management: An
artificial neural network model based on critical success factors. International Journal of Project
Management, 33(8), 1744–1754.
Dahiya, D., & Mathew, S. K. (2016). IT assets, IT infrastructure performance and IT capability: a
framework for e-government. Transforming Government: People, Process and Policy, 10(3), 411–433.
Derbel, E. (2014). Constructing Afro-Caribbean Identity through Memory and Language in Grace Nichols’s
I Is Along Memoried Woman. In Selim. Y.F and Mohamed,E (Eds). Who Defines Me: Negotiating Identity
in Language and Literature, Pp 63-76. UK: Cambridge Scholars Publishing.
Derbel, Emira. (2017a). Iranian Women in the Memoir: Comparing Reading Lolita in Tehran and Persepolis
(1) and (2). Cambridge Scholars Publishing.
Derbel, Emira. (2017b). The African Novel: The Ongoing Battle against Literary and National Neo-
Colonialism. International Journal of Information Technology and Language Studies, 1(1).
Derbel, Emira. (2019a). Feminist Graphic Narratives: The Ongoing Game of Eluding Censorship.
Mediterranean Journal of Social Sciences, 10(1), 49.
Derbel, Emira. (2019b). Teaching Literature through Comics: An Innovative Pedagogical Tool.
International Journal of Applied Linguistics and English Literature, 8(1), 54–61.
ELSamen, A., & Alshurideh, M. (2012). The impact of internal marketing on internal service quality: A case
study in a Jordanian pharmaceutical company. International Journal of Business and Management,
7(19), 84.
Emad, H., El-Bakry, H. M., & Asem, A. (2016). A Modified Technology Acceptance Model for Health
Informatics.
Falk, R. F., & Miller, N. B. (1992). A primer for soft modeling. University of Akron Press.
Fathema, N., Shannon, D., & Ross, M. (2015). Expanding The Technology Acceptance Model ( TAM ) to
Examine Faculty Use of Learning Management Systems ( LMSs ) In Higher Education Institutions.
MERLOT Journal of Online Learning and Teaching, 11(2), 210–232.
https://doi.org/10.12720/joams.4.2.92-97
Fayad, R., & Paper, D. (2015). The technology acceptance model e-commerce extension: a conceptual
framework. Procedia Economics and Finance, 26, 1000–1006.
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models With Unobservable Variables
and Measurement Error. Journal of Marketing Research, 18(1), 39–50.
https://doi.org/10.2307/3151312
Gefen, D., Straub, D., & Boudreau, M.-C. (2000). Structural equation modeling and regression: Guidelines
for research practice. Communications of the Association for Information Systems, 4(1), 1–77.
https://doi.org/10.1.1.25.781
Ghannajeh, A., AlShurideh, M., Zu’bi, M., Abuhamad, A., Rumman, G., Suifan, T., & Akhorshaideh, A. (2015).
A Qualitative Analysis of Product Innovation in Jordan’s Pharmaceutical Sector. European Scientific
Journal, 11(4), 474–503.
Habes, M., Salloum, S. A., Alghizzawi, M., & Alshibly, M. S. (2018). The role of modern media technology in
improving collaborative learning of students in Jordanian universities. International Journal of
Information Technology and Language Studies, 2(3), 71–82.
Habes, Mohammed, Salloum, S. A., Alghizzawi, M., & Mhamdi, C. (2019). The Relation Between Social
Media and Students’ Academic Performance in Jordan: YouTube Perspective. In International
Conference on Advanced Intelligent Systems and Informatics (pp. 382–392). Springer.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data analysis (Vol.
Shaikha FS Alhashm i, Said A. Salloum, and Ch aker Mhamdi
41
5). Prentice hall Upper Saddle River, NJ.
Helia, V. N., Indira Asri, V., Kusrini, E., & Miranda, S. (2018). Modified technology acceptance model for
hospital information system evaluation–a case study.
Ma, W., & Yuen, A. (2011). 11. E-LEARNING SYSTEM ACCEPTANCE AND USAGE PATTERN. Technology
Acceptance in Education: Research and Issues, 201.
Malik, S. I., & Al-Emran, M. (2018). Social Factors Influence on Career Choices for Female Computer
Science Students. International Journal of Emerging Technologies in Learning (IJET), 13(5), 56–70.
Mezhuyev, V., Al-Emran, M., Fatehah, M., & Hong, N. C. (2018). Factors affecting the Metamodelling
Acceptance: A Case Study from Software Development Companies in Malaysia. IEEE Access, 6, 49476–
49485.
Mezhuyev, V., Al-Emran, M., Ismail, M. A., Benedicenti, L., & Chandran, D. A. (2019). The acceptance of
search-based software engineering techniques: An empirical evaluation using the technology
acceptance model. IEEE Access. https://doi.org/10.1109/access.2019.2917913
Mhamdi, C. (2017a). Interpreting Games: Meaning Creation in the Context of Temporality and
Interactivity. Mediterranean Journal of Social Sciences, 8(4).
Mhamdi, C. (2017b). What Can Video Add to the Learning Experience? Challenges and Opportunities.
International Journal of Information Technology and Language Studies, 1(1), 17–24.
Mijwel, M. M. (2015). History of Artificial Intelligence. Computer Science, College of Science, Pp.1-6.
Milošević, I., Živković, D., Manasijević, D., & Nikolić, D. (2015). The effects of the intended behavior of
students in the use of M-learning. Computers in Human Behavior, 51, 207–215.
Mokyr, J. (2018). The British industrial revolution: an economic perspective. Routledge.
Navimipour, N. J., & Charband, Y. (2016). Knowledge sharing mechanisms and techniques in project
teams: Literature review, classification, and current trends. Computers in Human Behavior, 62, 730–
742.
Nunnally, J. C., & Bernstein, I. H. (1978). Psychometric theory.
Phatthana, W., & Mat, N. K. N. (2011). The Application of Technology Acceptance Model (TAM) on health
tourism e-purchase intention predictors in Thailand. In 2010 International Conference on Business and
Economics Research (Vol. 1, pp. 196–199).
Ringle, C. M., Wende, S., & Will, A. (2005). SmartPLS 2.0 (Beta). Hamburg. Available in Http://Www.
Smartpls. De.
Saa, A. A., Al-Emran, M., & Shaalan, K. (2019). Factors Affecting Students’ Performance in Higher
Education: A Systematic Review of Predictive Data Mining Techniques. Technology, Knowledge and
Learning. https://doi.org/10.1007/s10758-019-09408-7
Salloum, S. A., & Al-Emran, M. (2018). Factors affecting the adoption of E-payment systems by university
students: Extending the TAM with trust. International Journal of Electronic Business, 14(4), 371–390.
Salloum, S. A., Al-Emran, M., Khalaf, R., Habes, M., & Shaalan, K. (2019). An Innovative Study of E-Payment
Systems Adoption in Higher Education: Theoretical Constructs and Empirical Analysis. International
Journal of Interactive Mobile Technologies, 13(6). https://doi.org/10.3991/ijim.v13i06.9875
Salloum, S. A., Al-Emran, M., & Shaalan, K. (2018). The Impact of Knowledge Sharing on Information
Systems: A Review. In International Conference on Knowledge Management in Organizations (pp. 94–
106). Slovakia: Springer.
Salloum, S. A., Al-Emran, M., Shaalan, K., & Tarhini, A. (2019). Factors affecting the E-learning acceptance:
A case study from UAE. Education and Information Technologies, 24(1), 509–530.
https://doi.org/https://doi.org/10.1007/s10639-018-9786-3
Salloum, S. A., Alhamad, A. Q. M., Al-Emran, M., Monem, A. A., & Shaalan, K. (2019). Exploring Students’
Acceptance of E-Learning Through the Development of a Comprehensive Technology Acceptance
Model. IEEE Access, 7, 128445–128462. https://doi.org/10.1109/access.2019.2939467
Salloum, S. A., Mhamdi, C., Al-Emran, M., & Shaalan, K. (2017). Analysis and Classification of Arabic
Newspapers’ Facebook Pages using Text Mining Techniques. International Journal of Information
Technology and Language Studies, 1(2), 8–17.
Salloum, S. A., Mhamdi, C., Al Kurdi, B., & Shaalan, K. (2018). Factors affecting the Adoption and
Meaningful Use of Social Media: A Structural Equation Modeling Approach. International Journal of
Information Technology and Language Studies, 2(3), 96–109.
Salloum, S. A. S., & Shaalan, K. (2018). Investigating students’ acceptance of E-learning system in Higher
Educational Environments in the UAE: Applying the Extended Technology Acceptance Model (TAM).
Implementing Artificial Intelligence in the United Arab Em irates Healthcare Sector: An Extended Technology
Acceptance Model
42
The British University in Dubai.
Salloum, S.A., Al-Emran, M., Monem, A. A., & Shaalan, K. (2018). Using text mining techniques for
extracting information from research articles. In Studies in Computational Intelligence (Vol. 740).
Springer. https://doi.org/10.1007/978-3-319-67056-0_18
Salloum, Said A, Al-Emran, M., Habes, M., Alghizzawi, M., Ghani, M. A., & Shaalan, K. (2019). Understanding
the Impact of Social Media Practices on E-Learning Systems Acceptance. In International Conference on
Advanced Intelligent Systems and Informatics (pp. 360–369). Springer.
Salloum, Said A, Al-Emran, M., Shaalan, K., & Tarhini, A. (2018). Factors affecting the E-learning
acceptance: A case study from UAE. Education and Information Technologies, 1–22.
San, A. N. C., & Yee, C. J. (2013). The modified technology acceptance model for private clinical physicians:
A case study in Malaysia, Penang. International Journal of Academic Research in Business and Social
Sciences, 3(2), 380.
Shannak, R., Masa’deh, R., Al-Zu’bi, Z., Obeidat, B., Alshurideh, M., & Altamony, H. (2012). A theoretical
perspective on the relationship between knowledge management systems, customer knowledge
management, and firm competitive advantage. European Journal of Social Sciences, 32(4), 520–532.
Solano-Lorente, M., Martínez-Caro, E., & Cegarra-Navarro, J. G. (2013). Designing a Framework to Develop
eLoyalty for Online Healthcare Services. Electronic Journal of Knowledge Management, 11(1).
Strudwick, G. (2015). Predicting nurses’ use of healthcare technology using the technology acceptance
model: an integrative review. CIN: Computers, Informatics, Nursing, 33(5), 189–198.
Teeroovengadum, V., Heeraman, N., & Jugurnath, B. (2017). Examining the antecedents of ICT adoption in
education using an extended technology acceptance model (TAM). International Journal of Education
and Development Using ICT, 13(3).
Tongco, M. D. C. (2007). Purposive sampling as a tool for informant selection. Ethnobotany Research and
Applications, 5, 147–158.
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2016). Unified theory of acceptance and use of technology: A
synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328–376.
Who, X. (2011). Extending TAM: success factors of mobile marketing’. American Academic & Scholarly
Research Journal, 1(1), 1–5.
Zare, S. (2017). Identifying and Prioritizing Supply Chain Management Strategic Factors Based on
Integrated BSC-AHP Approach.
Zu’bi, Z., Al-Lozi, M., Dahiyat, S., Alshurideh, M., & Al Majali, A. (2012). Examining the effects of quality
management practices on product variety. European Journal of Economics, Finance and Administrative
Sciences, 51(1), 123–139.
... These extensions of TAM align with the Unified Theory of Acceptance and Use of Technology2 (UTAUT) models [34]. Researchers have considered constructs such as professional rank, price value [35], privacy, use resistance, anxiety, facilitating conditions [35,36], experience [12,18], support [33,37], social influence [36,38], computer skills [12], perceived risk [35,36], managerial, organisational [32], strategic and technical infrastructure [18,39] on the acceptance of technology in several studies. Since the HIS holds plenty of sensitive data, healthcare practitioners are highly concerned about data protection mechanisms. ...
... Moreover, decisions on budget and resource allocation promote a favourable approach towards digitalisation within the institutions. For instance, a study on the application of Artificial Intelligence (AI) in the government healthcare sector in the United Arab Emirates (UAE) tested an extended TAM model that incorporates aspects such as managerial, operational, strategic, and IT infrastructure [39]. Introducing a system can affect the organisation's existing workflow. ...
... At present, all hospital units and wards are not occupied by the HIS [10]. Thus, purposive sampling was utilised [47], with a criterion that participants had at least one year of expertise in HIS, in line with a previous study sampling method conducted in Indonesia and the UAE on TAM [23,39]. ...
Article
Full-text available
The deployment of Health Information Systems (HIS) in Sri Lanka has been low in adoption compared to developed countries. There has been a dearth of studies to identify the factors that improve the adoption of HIS in developing countries. Thus, this study investigates the factors influencing the acceptance of HIS among public healthcare staff. A survey was administered among 170 medical professionals, including nurses and doctors. Partial Least Squares Structural Equation Modelling (PLS-SEM) was applied to the dataset with 5000 bootstrap subsamples. The research model was developed based on the prior literature and by extending the Technology Acceptance Model (TAM) to the context of public healthcare. A positive relationship was observed between the actual use of HIS and constructs such as perceived usefulness, perceived ease of use, attitude, behavioural intention, prior use of HIS by supervisors, computer anxiety and facilitating conditions. These findings confirm the applicability of the proposed extended TAM in the public healthcare system of a developing country. Furthermore, HIS practitioners and policymakers in the healthcare sector would find these results valuable. Supplementary Information The online version contains supplementary material available at 10.1186/s12913-024-12173-8.
... 4,5,13 This highlights the importance of integrating AI-related terminology and applications into the nursing educational programs and the continuous education efforts for practicing nurses; which may extend to other healthcare workers as well. 26 Nurses need to be introduced to the imminent transformation of nursing practice as AI and other technology applications are becoming part of their daily work for nurses. Nurses' roles and functions are being assisted by the different applications of technology including AI. ...
... 7,12 These conservative attitudes are particularly pronounced among female and older nurses, suggesting that demographic factors play a crucial role in shaping perceptions of technological advancements. 7,26,30 Conservative attitudes toward AI may stem from various concerns, including fears about job displacement, doubts about AI's reliability and accuracy, and ethical worries about patient privacy and the depersonalization of care. 7,26 Addressing these concerns through targeted education about the benefits and ethical use of AI, and providing empirical evidence of AI's positive impacts on patient outcomes and workflow efficiency could be vital strategies for fostering more receptive attitudes. ...
... 7,26,30 Conservative attitudes toward AI may stem from various concerns, including fears about job displacement, doubts about AI's reliability and accuracy, and ethical worries about patient privacy and the depersonalization of care. 7,26 Addressing these concerns through targeted education about the benefits and ethical use of AI, and providing empirical evidence of AI's positive impacts on patient outcomes and workflow efficiency could be vital strategies for fostering more receptive attitudes. 7,26,30 The current study results about the conservative attitudes and concerns toward AI technology come in contrary to the results reported by Kwak et al., 31 as they examined the attitudes of student nurses toward AI technology and resulted in the positive willingness of the students to work with AI-integrated applications in nursing practice with ease. ...
Article
Full-text available
Introduction Worldwide, healthcare systems aim to achieve the best possible quality of care at an affordable cost while ensuring broad access for all populations. The use of artificial intelligence (AI) in healthcare holds promise to address these challenges through the integration of real-world data-driven insights into patient care processes. This study aims to assess nurses’ awareness and attitudes toward AI-integrated tools used in clinical practice. Methods A descriptive cross-sectional design captured nurses’ responses at three governmental hospitals in Saudi Arabia by using an online questionnaire administered over 4 months. The study involved 220 registered nurses with a minimum of one year of clinical experience, selected through a convenience sampling method. The online survey consisted of three sections: demographic information, an assessment of nurses’ AI knowledge, and the general attitudes toward the AI scale. Results Nurses displayed “moderate” levels of awareness toward AI technology, with 70.9% having basic information about AI and only 58.2% (128 nurses) were considered “aware” of AI as they dealt with one of its healthcare applications. Nurses expressed openness to AI integration (M = 3.51) on one side, but also had some concerns about AI. Nurses expressed conservative attitudes toward AI, with significant differences observed based on gender (χ² = 4.67, p < 0.05). Female nurses exhibited a higher proportion of negative attitudes compared to male nurses. Significant differences were also found based on age (χ² = 9.31, p < 0.05), with younger nurses demonstrating more positive attitudes toward AI compared to their older counterparts. Educational background yields significant differences (χ² = 6.70, p < 0.05), with nurses holding undergraduate degrees exhibiting the highest positive attitudes. However, years of nursing experience did not reveal significant variations in attitudes. Conclusion Healthcare and nursing administrators need to work on increasing the nurses’ awareness of AI applications and emphasize the importance of integrating such technology into the systems in use. Moreover, addressing nurses’ concerns about AI's control and discomfort is crucial, especially considering generational differences, with younger nurses often having more positive attitudes toward technology. Change management strategies may help overcome any hindrances.
... Public acceptance of AI services is related to the government's capacity as a provider (Gesk & Leyer, 2022), where trust and the practical applications of AI are significant factors shaping public opinion and engagement with the technology. Despite of uniformity acceptance tool for AI technology, however the recent studies revealed that the modified-extended Davis' TAM' model is the most preference models for explaining the factors that influence the acceptance of various artificial intelligence technologies across different user groups, settings, and (Agustini et al., 2023;Alhashmi et al., 2019;Aziz et al., 2020;Darayseh, 2023;Hercheui & Mech, 2021;Jimenez et al., 2021;Kim, 2024;Na et al., 2022;Sohn & Kwon, 2020;Wang et al., 2023;C. Zhang et al., 2023) . ...
Article
Full-text available
Artificial intelligence (AI) technology in healthcare has the potential to reach mass targets by providing rapid and free-cost services. The paper tries to reveal determinant factors on AI acceptance using the extended Technology Acceptance Model and testing correlations among organizational capability and self-efficacy as external variables to user's acceptance on the classic TAM variables. The study utilized a structural equation model for quantitative tests. It is revealed that using advanced technology in rural areas goes beyond simply developing a system that meets technological standards per se. Building local organization capability is a must when it gives a multiplier positive impact on other variables. The better the organizational capability, the higher the self-efficacy of the village users, leading to higher acceptance and increased use of the AI application at the village level. These results reaffirm the strength of TAM as a foundational framework for understanding user acceptance of technology in diverse settings and validate its continued relevance in contemporary research.
... Alhashmi et al. (2019) [25] performed research on the use of artificial intelligence (AI) in the UAE healthcare business. The UAE is aggressively emphasising AI integration to improve its government healthcare services. ...
Article
Full-text available
Background: Leveraging healthcare technology improves human development and well-being. However, adoption is frequently delayed by behavioural and psychological barriers, such as perceived usefulness, trust, and organisational readiness. This review examines the suitability of the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technology Acceptance Model (TAM) frameworks in healthcare settings, focusing on behavioural, educational, and psychological factors that influence technology adoption. Methods: A total of 20 peer-reviewed articles from 2019 to 2024 were examined. Results: The review identified significant organisational and psychological obstacles, including a lack of trust, inadequate training, and organisational support. While the UTAUT provided a more comprehensive viewpoint, it needed to be modified to include context-specific factors, including trust, facilitating circumstances, and educational interventions. Meanwhile, the TAM’s emphasis on perceived usefulness and ease of use was shown to be insufficient for dealing with complex healthcare situations. Conclusions: Interventions targeting stakeholders’ organisational and psychological preparation and educational strategies are essential to overcoming resistance and enhancing trust. Future research should look into integrative frameworks incorporating behavioural, psychological, and instructional tactics to improve the use of technology in healthcare.
... 2. (Alhashmi et al., 2019) • The objective of the study was exploring critical success factors for the adoption of AI in the healthcare sector. ...
Article
Artificial Intelligence (AI) has the potential to transform the healthcare ecosystem, but further research is needed to understand how it can enhance healthcare capabilities. This study analyzes the literature on AI and healthcare capability using the PRISMA approach, applying specific search keywords and inclusion/exclusion criteria. The findings indicate that AI benefits the healthcare ecosystem, significantly influences health outcomes, and transforms medical practices. However, there is limited literature and a lack of understanding regarding how AI enhances healthcare capabilities. Most studies date from 2019, suggesting that COVID-19 has accelerated the adoption of AI systems in healthcare. This research contributes theoretically by developing a framework that clarifies AI’s role in enhancing healthcare capabilities, serving as a foundational model for future studies. It identifies critical gaps in the literature, especially in the Global South, and encourages exploration in under-researched areas where healthcare professionals can benefit from AI. Additionally, it bridges the gap between AI and healthcare, enriching interdisciplinary dialogue relevant to emerging economies facing financial constraints. Practically, the study provides actionable insights for healthcare practitioners and policymakers in the Global South on leveraging AI to improve service delivery. It sets the stage for empirical research, promoting the testing and refinement of the proposed framework in resource-limited contexts, while raising awareness among healthcare staff, managers, and technology developers about AI’s role in healthcare.
... Data serves as the foundation of AI, with the quality, quantity, and accessibility of data posing notable challenges [1,6,8]. AI systems need vast amounts of high-quality data to function effectively [2,[35][36][37][38]. Organizations often struggle with data silos, where data is fragmented across various systems and departments, impeding comprehensive data aggregation and analysis. ...
Article
Full-text available
This research paper investigates the key factors influencing AI acceptance, focusing on elements such as technological readiness, perceived usefulness, and ease of use, along with the organizational and societal impacts. It identifies the significant obstacles to AI adoption, including ethical concerns, data privacy issues, and the potential for job displacement. The study also explores the importance of trust and transparency in promoting AI acceptance, highlighting the necessity for explainable AI (XAI) to build user confidence. Strategies for enhancing AI acceptance are examined, emphasizing the need for robust regulatory frameworks, ongoing education, and skill development to mitigate resistance and boost user engagement. The research stresses the importance of a user-centric approach in AI system design and implementation, taking into account end-user needs and concerns. Additionally, it underscores the value of collaboration between industry, academia, and policymakers in fostering an environment conducive to AI innovation and acceptance. By offering a thorough analysis of the factors affecting AI acceptance and the associated challenges, this paper provides valuable insights and actionable strategies for stakeholders aiming to navigate the complex landscape of AI integration effectively.
... Data serves as the foundation of AI, with the quality, quantity, and accessibility of data posing notable challenges [1,6,8]. AI systems need vast amounts of high-quality data to function effectively [2,[35][36][37][38]. Organizations often struggle with data silos, where data is fragmented across various systems and departments, impeding comprehensive data aggregation and analysis. ...
Article
Full-text available
This research paper investigates the key factors influencing AI acceptance, focusing on elements such as technological readiness, perceived usefulness, and ease of use, along with the organizational and societal impacts. It identifies the significant obstacles to AI adoption, including ethical concerns, data privacy issues, and the potential for job displacement. The study also explores the importance of trust and transparency in promoting AI acceptance, highlighting the necessity for explainable AI (XAI) to build user confidence. Strategies for enhancing AI acceptance are examined, emphasizing the need for robust regulatory frameworks, ongoing education, and skill development to mitigate resistance and boost user engagement. The research stresses the importance of a user-centric approach in AI system design and implementation, taking into account end-user needs and concerns. Additionally, it underscores the value of collaboration between industry, academia, and policymakers in fostering an environment conducive to AI innovation and acceptance. By offering a thorough analysis of the factors affecting AI acceptance and the associated challenges, this paper provides valuable insights and actionable strategies for stakeholders aiming to navigate the complex landscape of AI integration effectively.
... More importantly, this model has been proven to be applicable in predicting the adoption of AI. For example, Alhashmi et al. [40] used the TAM model to understand the factors influencing AI adoption in the healthcare system in the United Arab Emirates. Similarly, Mohr and Kühl [41] employed the TAM model, focusing on two factors-perceived ease of use (PEU) and perceived usefulness (PU)-to examine AI acceptance among farmers in Germany. ...
... The Technology acceptance model has been used as a practical framework to explore users' level of interaction with emerging digital tools. Recently, TAM has been used in many studies to explore users engagement with AI in various fields such as agriculture (Mohr & Kuhl, 2021), construction (Na et al., 2023), commerce (Wang et al., 2023) and healthcare (Alhashmi et al., 2019). However, there have been very few attempts to explore the engagement level of learners with AI through the lens of TAM in educational settings. ...
Article
Full-text available
Through the last decades, Artificial Intelligence (AI) has revolutionized the field of education and transformed traditional teaching approaches. This study aimed to examine how university students adopt AI tools in their learning processes and the role of digital literacy (DL) in this process through the lens of the Technology Acceptance Model (TAM). In this context, this study measured the impact of DL on university students' acceptance of AI technologies and their intention to use such technologies in the future. The data was collected from university students (N = 154) at a university in Western Türkiye during the fall semester of 2023. Data collection was conducted using two separate online forms; the first form included items adapted from the Digital Literacy Scale developed by Bayrakçı and Narmanlıoğlu (2021) to measure digital literacy levels, while the second form included items adapted from the UTAUT study by Venkatesh et al. (2003). The hypothesis testing results showed that students with higher levels of DL perceived the usefulness and ease of use of AI tools more positively, which positively affected their intention to adopt AI-based tools. The study also found that perceived usefulness and ease of use were important in shaping students' attitudes and behavioural intentions towards AI. When students perceive AI as a valuable tool for learning and find it easy to interact with, they are more willing to use it. This study suggests that DL plays a significant role in the acceptance of AI-based tools among university students, and accordingly, the TAM is a practical and accurate model to explore students’ potential engagement with AI in the learning process.
Article
Full-text available
Artificial intelligence (AI) has transformed healthcare, yet patients’ acceptance of AI-driven medical services remains constrained. Despite its significant potential, patients exhibit reluctance towards this technology. A notable lack of comprehensive research exists that examines the variables driving patients’ resistance to AI. This study explores the variables influencing patients’ resistance to adopt AI technology in healthcare by applying an extended Ram and Sheth Model. More specifically, this research examines the roles of the need for personal contact (NPC), perceived technological dependence (PTD), and general skepticism toward AI (GSAI) in shaping patient resistance to AI integration. For this reason, a sequential mixed-method approach was employed, beginning with semi-structured interviews to identify adaptable factors in healthcare. It then followed with a survey to validate the qualitative findings through Structural Equation Modeling (SEM) via AMOS (version 24). The findings confirm that NPC, PTD, and GSAI significantly contribute to patient resistance to AI in healthcare. Precisely, patients who prefer personal interaction, feel dependent on AI, or are skeptical of AI’s promises are more likely to resist its adoption. The findings highlight the psychological factors driving patient reluctance toward AI in healthcare, offering valuable insights for healthcare administrators. Strategies to balance AI’s efficiency with human interaction, mitigate technological dependence, and foster trust are recommended for successful implementation of AI. This research adds to the theoretical understanding of Innovation Resistance Theory, providing both conceptual insights and practical implications for the effective incorporation of AI in healthcare.
Article
Full-text available
Social learning refers to the learning delivered through social media applications. The examination of students’ attitudes towards using social media applications for learning activities is still not fully understood. For this reason, this research is carried out with the aim of measuring the students’ attitudes towards using social media from the lenses of several attributes, including gender, age, governorate, year of study, social media application, experience, and interest. The population of this study is the students enrolled at eight different universities and colleges located in eight different governorates in Oman. A total of 1307 students took part in this research through the use of an online survey. The results showed that gender, age, governorate, experience, and interest have significant impacts on students’ attitudes. Nevertheless, the study years and social media applications did not expose any significant effect on students’ attitudes. Additionally, WhatsApp was found to be the most predominant application used for educational purposes. Further, the study reported the barriers faced by the students while using social media applications for learning purposes. In response to these barriers, the study also provided a number of suggestions for improving the overall usage of social media in higher educational institutes.
Article
Full-text available
Studying the factors that affect the e-learning adoption is not a new research topic. Nevertheless, exploring the effect of knowledge acquisition and knowledge sharing on e-learning adoption is a relatively new research trend that has not been featured in the existing literature. Thus, this study was conducted to build a new model by extending the technology acceptance model (TAM) with knowledge acquisition and knowledge sharing to examine the e-learning adoption. A total of 403 students enrolled at Al Buraimi University College (BUC) in Oman was surveyed. Using the Partial Least Squares-Structural Equation Modeling (PLS-SEM) to evaluate the proposed model, the results suggested that knowledge acquisition, knowledge sharing, perceived usefulness, and perceived ease of use have significant direct effects on the students’ behavioral intention to adopt e-learning systems. The findings also suggested that knowledge acquisition and knowledge sharing have a significant positive influence on perceived usefulness and perceived ease of use. The evidence from these results provides holistic insights which could assist the policy-makers and educators to better understand the factors affecting the adoption of e-learning systems. The implications for theory and practice, limitations, and future work are also discussed.
Conference Paper
Full-text available
Social media use by university students attracted the interest of several scholars worldwide. However, a limited amount of research articles was published to investigate the students' attitudes towards social media usage in the Arab world universities. As a case study, the core objective of this research is to measure the students' attitudes towards the use of social media at an academic institute in Oman. The study intends to examine the differences in these attitudes from the lenses of several attributes, including gender, age, year of study, and social media application. A total of 198 students took part in the study through an online survey. Although the findings showed no significant differences in attitudes with regard to the aforementioned studied attributes, it is imperative to report that all the participated students were mutually interested in using social media applications in learning practices.
Conference Paper
Full-text available
This study aimed to analyze and discover the relation of using digital media sites (Facebook) on promoting medical tourism destinations in Jordan, and its impact on the behavior of tourists through the technologies provided by these means. Away from the traditional methods in marketing, the researchers used the survey methodology for a sample of 560 tourists distributed at central of Jordan in Dead Sea area to realize the study objective, a new framework was suggested to show the impact of Facebook on the behavior of tourists through: demographic variables, Facebook features, advertising, by using the TAM model in adoption of social media technology in tourism marketing for tourist destinations in Jordan. The proposed data were analyzed using the Smart PLS system by modeling structural equations (SEM). The outcome of the study showed that the advantages of Facebook, advertising and demographic variables have a favorable effect on the (PEOU) of the tourist and the PU in the adoption of tourism behavior, in addition to the (PU) and (PEOU) (ATT), which led to the adoption of behavior around therapeutic tourism destinations in Jordan. By determining the impact of Facebook in marketing tourism in Jordan, it would be useful to conduct further research to provide better proposals for marketing tourist therapeutic destinations in Jordan.
Conference Paper
Full-text available
This study aims mainly at analyzing the relationship between social media and students’ academic performance in Jordan in the context of higher education from a YouTube perspective. It intends to explore the benefits this relationship may have in enhancing students; leaning and improving their academic performance. To successfully reach its aims, this study proposes a new model aiming at verifying the relationship of social Bookmarking, YouTube Features, Perceived Usefulness, Use of Social Media, on Jordanian students’ academic performance. To verify the validity of the proposed model, data were analyzed using Smart PLS using structural equations modeling (SEM). Data were collected from Yarmouk University in Jordan covering all the levels of study at the university. An electronic questionnaire was conducted for a target of 360 students who participated in this study. The findings of the study revealed that Social Bookmarking, YouTube Features, Perceived Usefulness, Use of Social Media are important factors to predict students’ academic performance in relation to using social networking media for e-learning purposes in Jordan.
Conference Paper
Full-text available
There have been several longitudinal studies concerning the learners’ acceptance of e-learning systems using the higher educational institutes (HEIs) platforms. Nonetheless, little is known regarding the investigation of the determinants affecting the e-learning acceptance through social media applications in HEIs. In keeping with this, the present study attempts to understand the influence of social media practices (i.e., knowledge sharing, social media features, and motivation and uses) on students’ acceptance of e-learning systems by extending the technology acceptance model (TAM) with these determinants. A total of 410 graduate and undergraduate students enrolled at the British University in Dubai, UAE took part in the study by the medium of questionnaire surveys. The partial least squares-structural equation modeling (PLS-SEM) is employed to analyze the extended model. The empirical data analysis triggered out that social media practices including knowledge sharing, social media features, and motivation and uses have significant positive impacts on both perceived usefulness (PU) and perceived ease of use (PEOU). It is also imperative to report that the acceptance of e-learning systems is significantly influenced by both PU and PEOU. In summary, social media practices play an effective positive role in influencing the acceptance of e-learning systems by students.
Conference Paper
Full-text available
This paper explores the world of energy drinks and its negative effects on the youth and how to de-market it, the study uses a quantitative method to evaluate the impacts of consuming energy drinks among minors and the youth, in this paper we distributed surveys on the targeted sample who are the youth of Jordan and got back some very interesting results, the results of the surveys were analyzed thoroughly using the latest mathematical analyzing methods and software to give out a clear big picture of what is happening in reality with the consumers of energy drinks from the youth. the results that we arrived to showed some trends that are worrying which require immediate actions before the issues go out of hand, and for the sake of countering and solving these issues we made a list of applicable recommendations that we found best at the end of the paper to act as a starting point to apply measures to solve the issues at hand in order to save the society from a danger they are unaware of, and help preserve the health of youth.
Conference Paper
The analysis of fuel performance of ships is a topic of fundamental interest to shipping companies. This paper studies how fuel performance is influenced by operational factors. Noon-report data for six identical container ships and continuous monitoring data for one of them have been used to investigate the effect of trim, time since dry docking, captain, and choice of ship on fuel performance. A performance indicator is defined to allow for comparison of different operating conditions and statistical tools are used to determine the effect of each factor. The results show that (1) operational factors do have a statistically significant effect on fuel performance and (2) different data collection systems may lead to different conclusions.
Conference Paper
Although social media usage in higher education has been frequently examined, little is known regarding the differences in students’ attitudes towards its use in the Omani higher education. Therefore, this study provides an opportunity to advance the understanding of these differences in attitudes by focusing on three different attributes, namely age, gender, and social media application. A total of 169 students enrolled at Sultan Qaboos University in Muscat, Oman took part in the study by the medium of an online survey. The empirical data analysis triggered out that there was a significant difference in attitudes with regard to age groups, in which students aged between 18 and 22 years old are much interested in using social media than the others. Nevertheless, the findings also showed that there was no significant difference in attitudes with respect to social media application and gender. More interesting, the outcomes pointed out that WhatsApp is the most frequent application used by students for educational purposes.
Conference Paper
The combination of two technology model which are the Technology Acceptance Model (TAM) and Use of Gratifications Theory (U&G) to create an integrated model is the first step in predicting the importance of using emotional icons and the level of satisfaction behind this usage. The reason behind using these two theories into one integrated model is that U&G provides specific information and a complete understanding of usage, whereas TAM theory has proved its effectiveness with a variety of technological applications. A self-administered survey was conducted in University of Fujairah with college students to find out the social and cognitive factors that affect the usage of stickers in WhatsApp in the United Arab of Emirates. The hypothesized model is validated empirically using the responses received from an online survey of 372 respondents were analyzed using structural equation modeling (SEM-PLS). The results show that ease of use, perceived usefulness, cognition, hedonic and social integrative significantly affected the intention to use sticker by college students. Moreover, personal integrative had a significant influence on the intention to use sticker in UAE.