Analysing the determinants to accept a virtual assistant and use cases among cancer patients: a mixed methods study (Preprint)

Abstract

BACKGROUND Technological progress in artificial intelligence has led to the increasing popularity of virtual assistants, i.e., embodied conversational agents that allow chatting with a technical system in a natural language. However, only little comprehensive research is conducted about patients' perceptions and possible applications of virtual assistant in healthcare with serious illnesses. Therefore, acceptance remains laggard. OBJECTIVE This research aims to investigate the key acceptance factors and value-adding use cases of a virtual assistant for patients diagnosed with cancer. METHODS Qualitative interviews with eight former patients and four doctors of a Dutch radiotherapy institute were conducted to establish an appropriate theoretical acceptance model and gain insights into possible use cases. The unified theory of acceptance and use of technology (UTAUT) was used to systematise perceptions and was inductively modified as a result of the interviews. The subsequent research model was triangulated via an online survey with 127 respondents diagnosed with cancer. A structural equation model was used to determine the relevance of acceptance factors. Through a multigroup analysis, differences between sample subgroups were compared. RESULTS During the interviews, support was found for the four factors of the UTAUT. Additionally, three factors, self-efficacy, trust, and resistance to change, were added as an extension of the UTAUT. Participants found a virtual assistant helpful in receiving information about logistic questions, treatment procedures, side effects, or scheduling appointments. The quantitative study found that the constructs performance expectancy (ß=.399), effort expectancy (ß=.258), social influence (ß=.114), and trust (ß=.210) significantly influenced behavioural intention to use a virtual assistant, explaining 80% of its variance. Self-efficacy (ß=.792) acts as antecedent of effort expectancy. Facilitating conditions and resistance to change were not found to have a significant relationship with user intention. No different relevance of factors was found between subgroups of age, gender, and experience. However, descriptive statistics indicate a higher intention of younger patients to use a virtual assistant. CONCLUSIONS Performance and effort expectancy are the leading determinants of virtual assistant acceptance. The latter is dependent on a patient’s self-efficacy, highlighting the need for close involvement during the development and introduction. The high relevance of trust indicates the need for a reliable, secure service that should be promoted as such. Social influence suggests using doctors in endorsing the abovementioned characteristics. CLINICALTRIAL N/A