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)

Alhashmi, Shaikha F. S.; Salloum, Said A.; Abdallah, Sherief

doi:10.1007/978-3-030-31129-2_36

Cited by 81 publications

(67 citation statements)

References 16 publications

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“…With the final goal to examine the proposed hypotheses, the relationships that the theoretical constructs for the structural model have with each other were assessed via a structural equation model that had SEM-PLS with the maximum likelihood estimation [6,24,25,42,[112][113][114][115]. As appeared in Table 4 and Figure 3, the model had a high predictive power [116], that's, the percentage of the variance within perceived usefulness, perceived ease of use, and e-learning system acceptance are nearly 70%, 70%, and 71%, separately .…”

Section: Structural Model Analysis (Sem)mentioning

confidence: 99%

Investigating a theoretical framework for e-learning technology acceptance

Kurdi

Alshurideh

Salloum

2020

IJECE

Self Cite

126

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E-learning has gained recognition and fame in delivering and distributing educational resources, and the same has become possible with the occurrence of Internet and Web technologies. The research seeks to determine the factors that influence students' acceptance of E-learning and to find out the way these factors determine the students' intention to employ E-learning. A theoretical framework was developed based on the technology acceptance model (TAM). To obtain information from the 270 university students who utilized the E-learning system, a questionnaire was formulated. The results revealed that “social influence, perceived enjoyment, self-efficacy, perceived usefulness, and perceived ease of use” are the strongest and most important predictors in the intention of and students towards E-learning systems. The outcomes offer practical implications for practitioners, lawmakers, and developers in effective E-learning systems implementation to improve ongoing interests and activities of university students in a virtual E-learning atmosphere, valuable recommendations for E-learning practices are given by the research findings, and these may turn out to be as guidelines for the efficient design of E-learning systems.

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Section: Structural Model Analysis (Sem)mentioning

confidence: 99%

Investigating a theoretical framework for e-learning technology acceptance

Kurdi

Alshurideh

Salloum

2020

IJECE

Self Cite

126

View full text Add to dashboard Cite

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“…The structural equation modeling (SEM) approach [88][89][90][91][92][93][94][95][96] was used to test the aforementioned nine hypotheses together. The variance described (R2 value) by each path and every hypothesized connection's path significance in the research model were assessed.…”

Section: Hypothesis Testing and Coefficient Of Determinationmentioning

confidence: 99%

Acceptance of E-learning among university students in UAE: A practical study

AlHamad

2020

IJECE

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The objective of the study is to find out the factors which affect the acceptance of E-learning among students and how the students’ purpose of using E-learning is determined by these factors. The research was based on the Technology Acceptance Model (TAM). The researcher developed a questionnaire to gather data from a sample of 366 university students who actively used the E-learning system. According to the results of the study, subjective norms, perceived usefulness, perceived ease of use, enjoyment, and accessibility” are the vital predictors behind the intention of students for using E-learning systems. This indicates that extended TAM is applicable in the UAE. The results show that policymakers and education developers should take E-learning system seriously since it can be opted not just as a technological solution but also as a learning platform for students belonging to a distant area. The results present practical implications for education developers, policymakers and practitioners in devising useful plans to implement E-learning systems.

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“…As social media websites have attracted millions of users, these websites store a massive number of texts generated by users of these websites [14][15][16][17][18][19][20][21]. Researchers were interested in investigating these metadata for search purposes [17,18,[22][23][24][25]. In this section, a number of research papers that explored the analysis and classification of Twitter metadata were surveyed to investigate different text classification approaches [26] and the text classification results.…”

Section: Literature Reviewmentioning

confidence: 99%