The effect of Human versus Algorithm based recommendations and the framing objectivity of the recommendation on the perceived trustworthiness of a fitness application

Past research shows that people tend to be averse when it comes to the usage of algorithms. This is unfortunate since research also shows that algorithms often tend to outperform human beings in several tasks. This study investigates the effect of the use of algorithms in the context of fitness applications. Specifically focusing on the relationship between the recommendation source and the users’ intention to follow the fitness app recommendations. In this research we try to overcome people’s negative perception towards algorithms by using different forms of recommendations. Based on previous research we expect that the perceived trustworthiness plays an important role in people’s intention to follow a recommendation. An online survey was conducted in which the recommendation source (human coach vs. AI coach) and recommendation framing (subjective vs. objective) were manipulated, and their relationship with the perceived source trustworthiness and the intention to follow the recommendation was examined. A set of exploratory analyses were performed that found a higher perceived trustworthiness for an objectively framed recommendation as opposed to a subjectively framed recommendation coming from an AI coach. This adds new insights in the algorithm appreciation literature. Future research should elaborate on this finding.