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)

Recently, the government of United Arab of Emirates (UAE) is focusing on Artificial Intelligence (AI) strategy for future projects that will serve various sectors. Health care sector is one of the significant sectors they are focusing on and the planned (AI) projects of it is aiming to minimize chronic and early prediction of dangerous diseases affecting human beings. Nevertheless, project success depends on the adoption and acceptance by the physicians, nurses, decision makers and patients. The main purpose of this paper is to explore out the critical success factors assist in implementing artificial intelligence projects in the health sector. Besides, the founded gap for this topic was explored as there is no enough sharing of multiple success factors that assist in implementing artificial intelligence projects in the health sector precisely. A modified proposed model for this research was developed by using the extended TAM model and the most widely used factors. Data of this study was collected through survey from employees working in the health and IT sectors in UAE and total number of participants is 53 employees. The outcome of this questionnaire illustrated that managerial, organizational, operational and IT infrastructure factors have a positive impact on (AI) projects perceived ease of use and perceived usefulness.

[1]  N. Basir,et al.  Impact of System Quality on Users’ Satisfaction in Continuation of the Use of e-Learning System , 2016 .

[2]  Steve Drew,et al.  Using the Technology Acceptance Model in Understanding Academics' Behavioural Intention to Use Learning Management Systems , 2014 .

[3]  V. N. Helia,et al.  Modified technology acceptance model for hospital information system evaluation – a case study , 2018 .

[4]  Fauziah Baharom,et al.  Developing an extended technology acceptance model: Doctor's acceptance of electronic medical records in Jordan , 2011 .

[5]  Dragan Manasijevic,et al.  The effects of the intended behavior of students in the use of M-learning , 2015, Comput. Hum. Behav..

[6]  David Paper,et al.  The Technology Acceptance Model E-Commerce Extension: A Conceptual Framework , 2015 .

[7]  Khaled Shaalan,et al.  Factors affecting the E-learning acceptance: A case study from UAE , 2018, Education and Information Technologies.

[8]  Viraiyan Teeroovengadum,et al.  Examining the antecedents of ICT adoption in education using an Extended Technology Acceptance Model (TAM) , 2017 .

[9]  Will W. K. Ma,et al.  E-Learning System Acceptance and Usage Pattern , 2011 .

[10]  Arthur Tatnall Editorial for EAIT issue 1, 2019 , 2018, Education and Information Technologies.

[11]  Said A. Salloum,et al.  Factors affecting the adoption of e-payment systems by university students: extending the TAM with trust , 2018, Int. J. Electron. Bus..

[12]  Vitaliy Mezhuyev,et al.  PLS-SEM in Information Systems Research: A Comprehensive Methodological Reference , 2018, AISI.

[13]  Saji K. Mathew,et al.  IT assets, IT infrastructure performance and IT capability: a framework for e-government , 2016 .

[14]  Giulio Di Gravio,et al.  Project selection in project portfolio management: An artificial neural network model based on critical success factors , 2015 .

[15]  Said A. Salloum,et al.  Students' Attitudes Towards the Use of Mobile Technologies in e-Evaluation , 2017, Int. J. Interact. Mob. Technol..

[16]  S. Zare Identifying and Prioritizing Supply Chain Management Strategic Factors Based on Integrated BSC-AHP Approach , 2017 .

[17]  Marko Sarstedt,et al.  Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research , 2014 .

[18]  A. Bennani,et al.  The Acceptance of ICT by Geriatricians reinforces the value of care for seniors in Morocco , 2014 .

[19]  Muhammad Alshurideh,et al.  Factors affecting the Social Networks Acceptance: An Empirical Study using PLS-SEM Approach , 2019, ICSCA.

[20]  Vitaliy Mezhuyev,et al.  Factors Affecting the Metamodelling Acceptance: A Case Study From Software Development Companies in Malaysia , 2018, IEEE Access.

[21]  Nima Jafari Navimipour,et al.  Knowledge sharing mechanisms and techniques in project teams: Literature review, classification, and current trends , 2016, Comput. Hum. Behav..

[22]  J. Mokyr,et al.  The past and the future of innovation: Some lessons from economic history , 2018, Explorations in Economic History.

[23]  Ananth Srinivasan,et al.  An empirical investigation of the factors affecting agile usage , 2014, EASE '14.

[24]  Dhiya Al-Jumeily,et al.  Technology Acceptance Model for the Use of M-Health Services among Health Related Users in UAE , 2015, 2015 International Conference on Developments of E-Systems Engineering (DeSE).

[25]  Viswanath Venkatesh,et al.  Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead , 2016, J. Assoc. Inf. Syst..