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Digital health is a rapidly growing domain of technologies utilizing algorithms to track and make recommendations about health, mental well-being, and fitness. This shift towards the widespread adoption of these algorithmic systems is partly due to algorithms increasing the accuracy and consistency of judgments relative to human beings (Dawes et al., 1989; Grove, Zald, Lebow, Snitz, & Nelson, 2000). Individuals generally are more likely to trust algorithm recommendations over judgments made by other people (Dietvorst, Simmons, & Massey, 2015; Logg, Minson, & Moore, 2019; Prahl & Van Swol, 2017). Previous literature also shows that personifying technologies affect the belief, trust, and capability of those technologies (Darling, Nandy, & Breazeal, 2015; Waytz, Cacioppo, & Epley, 2010). Though there is consistency in the literature about how judgments are made when advised by algorithms generally, there is a need to understand how perceptions of algorithm's recommendations change when viewed in a health context and how judgment about these recommendations subsequently may affect future health behavior. No previous studies have linked these judgments on recommendations by algorithms with subsequent behavior change. What weight do we place on health algorithms' advice versus a medical expert's? Does making a health algorithm more human-like increase our willingness to trust and believe and then act upon the said algorithm's recommendations? This first study seeks to understand whether making algorithms more human-like will increase willingness to trust and believe in recommendations given by these algorithms and how that compares to the trust in a human expert. |
