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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)

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1058))

Abstract

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.

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References

  1. ValueStrat: How can Artificial intelligence transform the healthcare sector in UAE (2018). Accessed 27 Oct 2018

    Google Scholar 

  2. Government.ae, “No Title,” UAE Strateg. Artif. Intell. Gov. https://www.government.ae/en/about-the-uae/strategies-initiatives-and-awards/federal-governments-strategies-and-plans/uae-strategy-for-artificial-intelligence. Accessed 2018

  3. Mokyr, J.: The past and the future of innovation: some lessons from economic history. Explor. Econ. Hist. 69, 13–26 (2018)

    Article  Google Scholar 

  4. Swarup, “No Title,” Artif. Intell. Int. J. Comput. Corp. Res., vol. 2(4) (2012)

    Google Scholar 

  5. Mijwel, M.M.: History of artificial intelligence. Comput. Sci. Coll. Sci., 1–6 (2015)

    Google Scholar 

  6. Costantino, F., Di Gravio, G., Nonino, F.: Project selection in project portfolio management: an artificial neural network model based on critical success factors. Int. J. Proj. Manag. 33(8), 1744–1754 (2015)

    Article  Google Scholar 

  7. Mezhuyev, V., Al-Emran, M., Fatehah, M., Hong, N.C.: Factors affecting the metamodelling acceptance: a case study from software development companies in Malaysia. IEEE Access 6, 49476–49485 (2018)

    Article  Google Scholar 

  8. Navimipour, N.J., Charband, Y.: Knowledge sharing mechanisms and techniques in project teams: Literature review, classification, and current trends. Comput. Human Behav. 62, 730–742 (2016)

    Article  Google Scholar 

  9. Venkatesh, V., Thong, J.Y.L., Xu, X.: Unified theory of acceptance and use of technology: a synthesis and the road ahead. J. Assoc. Inf. Syst. 17(5), 328–376 (2016)

    Google Scholar 

  10. Christensen, L.B.R., Thomas, G., Calleya, J., Nielsen, U.D.: The effect of operational factors on container ship fuel performance. In: Proceedings of Full Scale Ship Performance, The Royal Institution of Naval Architects (2018)

    Google Scholar 

  11. Bennani, A.-E., Oumlil, R.: 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, 1–10 (2014)

    Google Scholar 

  12. Who, X.: Extending TAM: success factors of mobile marketing’. Am. Acad. Sch. Res. J. 1(1), 1–5 (2011)

    Google Scholar 

  13. Zare, S.: Identifying and Prioritizing Supply Chain Management Strategic Factors Based on Integrated BSC-AHP Approach (2017)

    Google Scholar 

  14. Dahiya, D., Mathew, S.K.: IT assets, IT infrastructure performance and IT capability: a framework for e-government. Transform. Gov. People, Process Policy 10(3), 411–433 (2016)

    Google Scholar 

  15. Safdari, R., Saeedi, M.G., Valinejadi, A., Bouraghi, H., Shahnavazi, H.: Technology acceptance model in health care centers of Iran. Int. J. Comput. Sci. Netw. Secur. 17(1), 42 (2017)

    Google Scholar 

  16. Alloghani, M., Hussain, A., Al-Jumeily, D., Abuelma’atti, O.: 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 (2015)

    Google Scholar 

  17. Phatthana, W., Mat, N.K.N.: 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 (2011)

    Google Scholar 

  18. Salloum, S.A., Al-Emran, M.: Factors affecting the adoption of E-payment systems by university students: extending the TAM with trust. Int. J. Electron. Bus. 14(4), 371–390 (2018)

    Article  Google Scholar 

  19. Alshurideh, M., Salloum, S.A., Al Kurdi, B., Al-Emran, M.: Factors affecting the social networks acceptance: an empirical study using PLS-SEM approach. In: 8th International Conference on Software and Computer Applications (2019)

    Google Scholar 

  20. Alharbi, S., Drew, S.: Using the technology acceptance model in understanding academics’ behavioural intention to use learning management systems. Int. J. Adv. Comput. Sci. Appl. 5(1), 143–155 (2014)

    Google Scholar 

  21. Emad, H., El-Bakry, H.M., Asem, A.: A modified technology acceptance model for health informatics (2016)

    Google Scholar 

  22. Baharom, F., Khorma, O.T., Mohd, H., Bashayreh, M.G.: Developing an extended technology acceptance model: doctors’ acceptance of electronic medical records in Jordan. In: ICOCI (2011)

    Google Scholar 

  23. Fayad, R., Paper, D.: The technology acceptance model e-commerce extension: a conceptual framework. Procedia Econ. Financ. 26, 1000–1006 (2015)

    Article  Google Scholar 

  24. Helia, V.N., Indira Asri, V., Kusrini, E., Miranda, E.: Modified technology acceptance model for hospital information system evaluation–a case study (2018)

    Google Scholar 

  25. Teeroovengadum, V., Heeraman, N., Jugurnath, B.: Examining the antecedents of ICT adoption in education using an extended technology acceptance model (TAM). Int. J. Educ. Dev. using ICT 13(3), 4–23 (2017)

    Google Scholar 

  26. Ringle, C.M., Wende, S., Will, A.: SmartPLS 2.0 (Beta). Hamburg (2005). http://www.smartpls.de

  27. Al-Emran, M., Mezhuyev, V., Kamaludin, A.: 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 (2018)

    Google Scholar 

  28. Salloum, S.A.S., Shaalan, K.: Investigating students’ acceptance of E-learning system in higher educational environments in the UAE: applying the extended Technology Acceptance Model (TAM). The British University in Dubai (2018)

    Google Scholar 

  29. Salloum, S.A., Al-Emran, M., Shaalan, K., Tarhini, A.: Factors affecting the E-learning acceptance: a case study from UAE. Educ. Inf. Technol. 24(1), 509–530 (2019)

    Article  Google Scholar 

  30. Chin, W.W.: The partial least squares approach to structural equation modeling. Mod. Methods Bus. Res. 295(2), 295–336 (1998)

    Google Scholar 

  31. Hair Jr., J.F., Hult, G.T.M., Ringle, C., Sarstedt, M.: A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications, Thousand Oaks (2016)

    MATH  Google Scholar 

  32. Dreheeb, A.E., Basir, N., Fabil, N.: Impact of system quality on users’ satisfaction in continuation of the use of e-Learning system. Int. J. e-Education, e-Business, e-Management e-Learning 6(1), 13 (2016)

    Google Scholar 

  33. Senapathi, M., Srinivasan, A.: An empirical investigation of the factors affecting agile usage. In: Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering, p. 10 (2014)

    Google Scholar 

  34. Milošević, I., Živković, D., Manasijević, D., Nikolić, D.: The effects of the intended behavior of students in the use of M-learning. Comput. Hum. Behav. 51, 207–215 (2015)

    Article  Google Scholar 

  35. Al-Emran, M., Salloum, S.A.: Students’ attitudes towards the use of mobile technologies in e-Evaluation. Int. J. Interact. Mob. Technol. 11(5), 195–202 (2017)

    Article  Google Scholar 

  36. Salloum, S.A., Al-Emran, M., Shaalan, K., Tarhini, A.: Factors affecting the E-learning acceptance: a case study from UAE. Educ. Inf. Technol. 24, 1–22 (2018)

    Google Scholar 

  37. Ma, W., Yuen, A.: 11. E-learning system acceptance and usage pattern. Technol. Accept. Educ. Res. Issues, 201 (2011)

    Google Scholar 

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Acknowledgment

This work is a part of a dissertation submitted in fulfilment of MSc Informatics (Knowledge & Data Management) Faculty of Engineering & Information Technology at The British University in Dubai.

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Correspondence to Said A. Salloum .

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Alhashmi, S.F.S., Salloum, S.A., Abdallah, S. (2020). 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: Hassanien, A., Shaalan, K., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019. AISI 2019. Advances in Intelligent Systems and Computing, vol 1058. Springer, Cham. https://doi.org/10.1007/978-3-030-31129-2_36

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