Abstract
The study sought to examine the task-technology fit of ChatGPT for healthcare services. The research model was based on an integration of the task-technology fit (TTF) model and the technology acceptance model 2 (TAM2). The study used a quantitative research design. Data were collected from 265 health workers who have used ChatGPT in the performance of their work. Data analysis was done using structural equation modelling. Six of the seven hypotheses were accepted. The results show significant positive relationships between task characteristics and task-technology fit, task-technology fit and ChatGPT usage, ChatGPT usage and performance impacts, social influence and perceived usefulness, perceived usefulness and usage intention, as well as usage intention and ChatGPT usage. There was an insignificant positive relationship between task-technology fit and performance impacts.
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