TASK-TECHNOLOGY FIT: INTELLIGENT EVALUATION MODEL FOR USER SATISFACTION WITH HEALTH INFORMATION SYSTEMS
DOI:
https://doi.org/10.15282/ijsecs.11.2.2025.9.0141Keywords:
Health Information System (HIS), Artificial Neural Network (ANN), Effectiveness, Prediction, RapidMinerAbstract
This study presents a predictive model aimed at investigating the level of satisfaction with healthcare information platforms in Gaza, focusing on medical professionals such as doctors, pharmacists, nurses, and laboratory technicians. Addressing the need for reliable assessment tools for HIS performance, the research adopts the Task-Technology Fit (TTF) framework and employs artificial neural networks to model user satisfaction. The dataset comprises 150 records, each including four input variables, technology characteristics, task characteristics, task-technology fit, and security; and one output variable representing user satisfaction. The model was developed and tested using RapidMiner, achieving a prediction accuracy of 86.7%. These findings suggest that the proposed approach offers a viable method for assessing HIS effectiveness. This study is among the first in the Gaza Strip to incorporate security as an integral component within the TTF model using ANN, thus contributing empirical insights into the design and evaluation of context specific HIS systems. The research recommends further validation of the model across different electronic or online management information systems to confirm its generalizability.
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