Consumer intentions towards AI in the healthcare industry

Abstract

Two major challenges for the healthcare industry are to reduce misdiagnosis of patients and to become more efficient in everyday practice. The lack of efficiency became extremely apparent by the pressure on healthcare workers caused by the COVID-19 pandemic. This could be assisted with new technologies. Artificial intelligence (AI) is one technological development that has the potential of improving healthcare by overcoming these two challenges. Hence, more research on AI in healthcare is essential. To successfully implement AI in healthcare it is vital to understand what positively influences individuals to accept and utilise AI in healthcare. Therefore, the present study investigates the following research question: What factors influence consumers’ intention to accept AI within healthcare? Seven potential factors are tested to either directly or indirectly affect consumer intentions. Attitudes and trust are hypothesised to have a direct effect, perceived usefulness is hypothesised to have a direct as well as an indirect effect, and perceived knowledge, anthropomorphism, data transparency, and privacy concerns are hypothesised to have an indirect effect. The purpose of this study is to find out which of these factors have the highest influence on consumer intention to successfully implement a future AI healthcare service. This topic is investigated through a quantitative method with nine hypotheses based on previous literature and theoretical frameworks. A questionnaire was designed and distributed to students at the School of Business, Economics and Law at Gothenburg University to collect data. The analysis supports all hypotheses, except for hypotheses 8 and 9 concerning data transparency and privacy concerns. Further, the proposed research model has a good fit and is consistent with the data as well as in line with previous theories. Perceived usefulness was shown to be the strongest predictor of consumer intention, followed by consumer attitudes. Furthermore, perceived usefulness was the strongest predictor of attitudes, meaning that perceived usefulness additionally affected consumer intention through a mediating variable. Anthropomorphism is the strongest predictor to trust, also affecting consumer intentions indirectly through a mediating variable. Therefore, the mentioned factors are the ones that should be focused on when aiming to increase consumer intention regarding AI-based healthcare. The present thesis contributes to research on the utilisation of AI within healthcare from a consumer perspective, which is an area where there is opportunity for further exploration. By focusing on the predictors in the proposed model consumer intentions to utilise AI in healthcare can be increased.