Task-technology fit model: Modelling and assessing the nurses’ satisfaction with health information system using AI prediction models

References

• Nengomasha C, Abankwah R, Uutoni W, et al. Health information systems in Namibia. Inf Learn Sci. 2018;119(7/8):358–376.
   Google Scholar
• Serrano A, Garcia-Guzman J, Xydopoulos G, et al. Analysis of barriers to the deployment of health information systems: a stakeholder perspective. Inf Syst Front. 2020;22:455–474.
   Web of Science ®Google Scholar
• Nsouli R, Vlachopoulos D. Attitudes of nursing faculty members toward technology and e-learning in Lebanon. BMC Nurs. 2020;20(1):1–15.
   Web of Science ®Google Scholar
• Chirchir LK, Aruasa WK, Cheb SK. Perceived usefulness and ease of use as mediators of the effect of health information systems on user performance. Eur J Comput Sci Inf Technol. 2019;7(1):22–37.
   Google Scholar
• Alhendawi K, Suhaimi A. The assessment of information system effectiveness in e-learning, e-commerce and egovernment contexts: a critical review of the literature. J Theor Appl Inf Technol. 2017;95(18): 4897–4912.
   Google Scholar
• Goodhue D, Thompson R. Task-technology fit and individual performance. Manage Inf Syst Q. 1995;19(2):213–236.
   Google Scholar
• Alhendawi K, Al-Janabi A. An intelligent expert system for management information system failure diagnosis. In International Conference on Intelligent Computing & Optimization. Springer, Cham; 2018. p. 257–266.
   Google Scholar
• Sombat P, Chaiyasoonthorn W, Chaveesuk S. The acceptance model of hospital information systems in Thailand: A conceptual framework extending TAM. In 2018 5th International Conference on Industrial Engineering and Applications (ICIEA) IEEE; 2018. p. 89–94.
   Google Scholar
• D’Ambra J, Wilson C. Explaining perceived performance of the World Wide Web: uncertainty and the task-technology fit model. Internet Res. 2004;14(4):294–310.
   Web of Science ®Google Scholar
• Aghazadeh S, Aliyev A, Ebrahimnezhad M. Study the effect of hospital information systems (HIS) on communication improvement and service quality among nursing staff. Life Sci J. 2013;10(5):307–310.
   Google Scholar
• Nykanen P, Brender J, Talmon J, et al. Guideline for good evaluation practice in health informatics (GEP-HI). Int J Med Inform. 2011;80(12):815–827.
   PubMed Web of Science ®Google Scholar
• Sockolow PS, Crawford PR, Lehmann HP. Health services research evaluation principles. Broadening a general framework for evaluating health information technology. Methods Inf Med. 2012;51(2):122–130.
   PubMed Web of Science ®Google Scholar
• Lu CH, Hsiao JL, Chen RF. Factors determining nurse acceptance of hospital information systems. Comput Inform Nurs. 2012;30(5):257–264.
   PubMed Web of Science ®Google Scholar
• Ebnehoseini Z, Tabesh H, Jangi MJ, et al. Investigating evaluation frameworks for electronic health record: a literature review. Open Access Maced J Med Sci. 2021;9(E):8–25.
   Google Scholar
• Spies R, Grobbelaar S, Botha A. A scoping review of the application of the task-technology fit theory. In Conference on e-Business, e-Services and e-Society, Springer, Cham; 2020. p. 397–408.
   Google Scholar
• Melchor-Ferrer E, Buendía-Carrillo D. Financial information management for university departments, using open-source software. Int J Inf Manag. 2014;34:191–199.
   Web of Science ®Google Scholar
• Huang H, Shih PC, Zhu Y, et al. An integrated model for medical expense system optimization during diagnosis process based on artificial intelligence algorithm. J Comb Optim. 2022;44:2515–2532.
   PubMed Web of Science ®Google Scholar
• Wang SL, Lin HI. Integrating TTF and IDT to evaluate user intention of big data analytics in mobile cloud healthcare system. Behav Inf Technol. 2019;38(9):974–985.
   Web of Science ®Google Scholar
• Ali SB, Romero J, Morrison K, et al. Focus section health IT usability: applying a task-technology fit model to adapt an electronic patient portal for patient work. Appl Clin Inform. 2018;9:174–184.
   PubMed Web of Science ®Google Scholar
• Justino M, Tengeh RM. Task-technology fit perspective of the use of m-commerce by retail businesses. Entrepreneur Sustain Issues. 2022;9(4):320.
   Web of Science ®Google Scholar
• Osang F. Open educational resources (OERs) development in Nigeria: determining task technology Fit (TTF) impact on faculty usage, satisfaction and performance. Int J Hum Technol Interact. 2019;3(2):35–46.
   Google Scholar
• Al Thobaity A, Alshammari F. Nurses on the frontline against the COVID-19 pandemic: an integrative review. Dubai Med J. 2020;3(3):87–92.
   Google Scholar
• Kutney-Lee A, Carthon J, Sloane D, et al. Electronic health record usability: associations with nurse and patient outcomes in hospitals. Med Care. 2021;59(7):625.
   PubMed Web of Science ®Google Scholar
• Şenol Çelik S, Hikmet N, Ünal I. Health information technology in nursing: views and attitudes of nurse managers. Hacettepe J Health Admin. 2017;20(4):397–407.
   Google Scholar
• Almarzouqi A, Aburayya A, Salloum S. Determinants of intention to use medical smartwatch-based dual-stage SEM-ANN analysis. Inf Med Unlocked. 2022;28:100859.
   Google Scholar
• Sebetci Ö. Enhancing end-user satisfaction through technology compatibility: an assessment on health information system. Health Policy Technol. 2018;7(3):265–274.
   Web of Science ®Google Scholar
• Alhendawi K, Suhaimi A. The assessment of information system effectiveness in e-learning, e-commerce and e-government contexts: a critical review of the literature. J Theor Appl Inf Technol. 2017;95(18):4897–4912.
   Google Scholar
• Gürsel G, Zayim N, Gülkesen K, et al. A New approach in the evaluation of hospital information systems. Turkish J Electr Eng Comput Sci. 2014;22:214–222.
   Web of Science ®Google Scholar
• Pourasghar F, Tabrizi JS, Ala A, et al. Satisfaction and perception of emergency department personnel on electronic triage system. Iranian J Med Inf. 2015;4(1):1–5.
   Google Scholar
• Ferdousi R, Arab-Zozani M, Tahamtan I, et al. Attitudes of nurses towards clinical information systems: a systematic review and meta-analysis. Int Nurs Rev. 2021;68(1):59–66.
   PubMed Web of Science ®Google Scholar
• Isaac O, Ramayah T, Mutahar AM. Internet usage and net benefit among employees within government institutions in Yemen: an extension of DeLone. Int J Soft Comput. 2017;12:178–198.
   Google Scholar
• Isaac O, Abdullah Z, Ramayah T, et al. Internet usage, user satisfaction, task-technology fit, and performance impact among public sector employees in Yemen. Int J Inf Learn Technol. 2017;34:210–241.
   Web of Science ®Google Scholar
• Tolentino DA. Quantitative exploration of nurses’ task-technology fit within the electronic health record using regression and data mining: an EHR Workflow Analysis (Doctoral dissertation). The University of Arizona; 2020.
   Google Scholar
• Sombat P, Chaiyasoonthorn W, Chaveesuk S. The extended acceptance model of the use of hospitals information systems in Thailand. Asia-Pacific Soc Sci Rev. 2020;20(1):184–204.
   Google Scholar
• Park C. Exploring a new determinant of task technology fit: content characteristics. J Int Technol Inf Manage. 2019;27(3):100–118.
   Google Scholar
• Sabbir M, Islam M, Das S. Understanding the determinants of online pharmacy adoption: a two-staged SEM-neural network analysis approach. J Sci Technol Policy Manag. 2020;17:666–687.
   Google Scholar
• Payrovnaziri S, Chen Z, Rengifo-Moreno P, et al. Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review. J Am Med Inform Assoc. 2020;27(7):1173–1185.
   PubMed Web of Science ®Google Scholar
• Tompson R, Barclay D, Higgins C. The partial least squares approach to causal modeling: personal computer adoption and use as an illustration. Technol Stud Spec Issue Res Method. 2019;2(2):285–309.
   Google Scholar
• Riana D, Hidayanto AN, Hadianti S, et al. Integrative factors of E-health laboratory adoption: a case of Indonesia. Future Internet. 2021;13(2):26.
   Web of Science ®Google Scholar
• Parkes A. The effect of task–individual–technology fit on user attitude and performance: an experimental investigation. Decis Support Syst. 2013;54(2):997–1009.
   Web of Science ®Google Scholar
• Gunesekera A, Bao Y, Kibelloh M. The role of usability on e-learning user interactions and satisfaction: a literature review. J Syst Inf Technol. 2019.
   Google Scholar
• Al Amin M, Arefin M, Sultana N, et al. Evaluating the customers’ dining attitudes, e-satisfaction and continuance intention toward mobile food ordering apps (MFOAs): evidence from Bangladesh. Eur J Manag Bus Econ. 2021;30(2):211–229.
   Web of Science ®Google Scholar
• Chen L, Chih-Hsuan T, Wan-Chuan C. The relationship between attitude toward using and customer satisfaction With mobile application services: an empirical study from the life insurance industry. J Enterp Inf Manag 2019. 2015;53:194–200.
   Google Scholar
• Jeyaraj A. A meta-regression of task-technology fit in information systems research. Int J Inf Manage. 2022;65:102493.
   Web of Science ®Google Scholar
• Navarro M, Prasetyo Y, Young M, et al. The perceived satisfaction in utilizing learning management system among engineering students during the COVID-19 pandemic: integrating task technology fit and extended technology acceptance model. Sustainability. 2021;13(19):10669.
   Web of Science ®Google Scholar
• Alharthi HM. Predicting physicians’ satisfaction with electronic medical records using artificial neural network modeling. Saudi J Health Sci. 2019;8:105–126.
   Google Scholar
• Arkin F, Aras G, Dogu E. Comparison of artificial neural networks and logistic regression for 30-days survival prediction of cancer patients. Acta Inform Med. 2020;28(2):108.
   PubMedGoogle Scholar
• Hu Y, Gan H. Predictive analysis of hospital HIS system usage satisfaction based on machine learning. Computational and Mathematical Methods in Medicine. 2022;2022:1–11.
   Web of Science ®Google Scholar
• Chen R, Stewart W, Sun J, et al. Recurrent neural networks for early detection of heart failure from longitudinal electronic health record data: implications for temporal modeling with respect to time before diagnosis, data density, data quantity, and data type. Circulation Cardiovasc Qual Outcomes. 2019;12(10):e005114.
   PubMed Web of Science ®Google Scholar
• Kalayou M, Endehabtu B, Tilahun B. The applicability of the modified technology acceptance model (TAM) on the sustainable adoption of eHealth systems in resource-limited settings. J Multidiscip Healthc. 2020;13:18–27.
   Web of Science ®Google Scholar