Another study indicated that when consumers wanted recommendations matched to their unique preferences, they resisted AI recommenders and preferred human recommenders regardless of hedonic or utilitarian preferences.
companies whose customers are known to desire personalized recommendations should rely on humans.
The more utilitarian/functional features were highly rated, the greater the preference for AI over human assistance, and the more hedonic/experiential features were highly rated, the greater the preference for human over AI assistance.
Researchers from Boston University and University of Virginia published a new paper in the Journal of Marketing that examines how consumers respond to AI recommenders when focused on the functional and practical aspects of a product (its utilitarian value) versus the experiential and sensory aspects of a product (its hedonic value).
In fact, people embrace AI's recommendations as long as AI works in partnership with humans. When AI plays an assistive role, "augmenting" human intelligence rather than replacing it, the AI-human hybrid recommender performs as well as a human-only assistant.
Relying on data from over 3,000 study participants, the research team provides evidence supporting a word-of-machine effect, defined as the phenomenon by which the trade-offs between utilitarian and hedonic aspects of a product determine the preference for, or resistance to, AI recommenders
Consequently, the importance or salience of utilitarian attributes determine preference for AI recommenders over human ones, while the importance or salience of hedonic attributes determine resistance to AI recommenders over human ones.
When asked to only consider hedonic/experiential attributes, a higher percentage of participants chose human recommenders
When utilitarian features are most important, the word-of-machine effect was more distinct.
When do consumers trust the "word of machine," and when do they resist it?