Assessing the Behavioural Intention of Individuals to Use an AI Doctor at the Primary, Secondary, and Tertiary Care Levels
The research has been designed to fit into the framework of AI doctors and used previously validated measurements. Social influence (SI), was derived from Teo (Teo, 2009) and Park et al. (Park, Baek, Ohm, & Chang, 2014). Perceived task technology ft (PTTF) was adapted from Lu and Yang (Lu & Yang, 2014). Performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), high-tech lifestyle (HTLS), behavioural intention to use AI doctors primary level (Primary BI), behavioural intention to use AI doctors secondary level (Secondary BI), behavioural intention to use AI doctors tertiary level (Tertiary BI) which were adapted from Venkatesh et.al (Venkatesh et al., 2012). The last two indicators of performance expectancy “AI will provide up-to-date health info that I need”, and “AI will provide new instructional content that I need” were adapted from Lee and Letho (D. Y. Lee & Lehto, 2013). Privacy concern (PC) was adapted from Martins et al. (Martins, Oliveira, & Popovič, 2014). A five-point Likert scale on a rating of 1 (strongly disagree) to 5 (strongly agree) was used to evaluate each indicator. People who knew more about how AI technology was used in daily life, and believed AI technology to be a significant improvement in healthcare were more at ease with the medical use of AI (Armero et al., 2022). Participants with existing health data collecting experiences with devices, such as a smartwatch, band, bracelet, glasses, or fitness trackers have been chosen as the sample. These high-tech devices can instantly measure and report many things about individuals. These participants have been deliberately chosen because they can get a clear picture of their health, including their real-time heart rate, blood pressure, sleep time and quality, activity tracker, body mass index, and calories spent. Since many features of these people have become measurable, they have been asked how they would like this data to be used by AI technology. The data has been collected from 432 participants.