When Consumers Trust AI Recommendations

Amazon recently launched a Personal Shopper service that uses a combination of AI technology and human stylists to curate clothing for Prime members. Based on a style profile generated by the customer, a human stylist chooses pieces from an AI-mined database of thousands of brands. Subscription services like Stitch Fix and Thread are taking similar approaches to personalized styling, using AI to assist human recommendations.

More and more companies are leveraging technological advances in AI, machine learning, and natural language processing to provide recommendations to consumers. As these companies evaluate AI-based assistance, one critical question must be asked: When do consumers trust the “word of machine,” and when do they resist it?

A new Journal of Marketing study explores reasons behind the preference of recommendation source (AI vs. human). The key factor in deciding how to incorporate AI recommenders is whether consumers are focused on the functional and practical aspects of a product (its utilitarian value) or on the experiential and sensory aspects of a product (its hedonic value).

Relying on data from over 3,000 study participants, our research team provides evidence supporting a word-of-machine effect. We define this effect 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. The word-of-machine effect stems from a widespread belief that AI systems are more competent than humans at dispensing advice when functional and practical qualities (utilitarian) are desired and less competent when the desired qualities are experiential and sensory-based (hedonic). Consequently, the importance of utilitarian attributes determine preference for AI recommenders over human ones, while the importance of hedonic attributes determine resistance to AI recommenders over human ones.

In actuality, humans are not necessarily more competent at evaluating hedonic products and AI is not necessarily more competent at evaluating utilitarian products. In fact, we can find examples in the marketplace today that challenge this belief. For instance, AI selects flower arrangements for 1-800-Flowers and creates new flavors for food companies such as McCormick, Starbucks, and Coca-Cola. Still, by and large, this widespread belief exists.

We first tested the word-of-machine effect using experiments designed to assess people’s tendency to choose products based on consumption experiences and recommendation source. We found that when presented with instructions to choose products based solely on utilitarian/functional attributes, more participants chose AI-recommended products. When asked to only consider hedonic/experiential attributes, a higher percentage of participants chose human recommenders. The word-of-machine effect also extended to product consumption and taste perception. In one study, we presented participants with two chocolate cakes, one created with the ingredients selected by an AI chocolatier and one created with ingredients selected by a human chocolatier. Participants ate one of the two cakes and rated it on two hedonic/experiential features (indulgent taste and aromas, pleasantness to the senses) and two utilitarian/functional attributes (beneficial chemical properties, healthiness). The AI-recommended cake was rated as less tasty but healthier than the cake recommended by the human, even though they were identical in appearance and actual ingredients.

When utilitarian features are most important, we found that the word-of-machine effect was more distinct. In one study, participants were asked to imagine buying a winter coat and rate how important utilitarian/functional attributes (e.g., breathability) and hedonic/experiential attributes (e.g., fabric type) were in their decision making. 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.

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. These results suggest that companies whose customers are known to be satisfied with “one size fits all” recommendations (i.e., not in need of a high level of customization) may rely on AI-systems. However, companies whose customers are known to desire personalized recommendations should rely on humans.

Although there is a clear correlation between utilitarian attributes and consumer trust in AI recommenders, companies selling products that promise more sensorial experiences (e.g., fragrances, food, wine) may still use AI to engage customers. In fact, we found that 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.

Overall, the word-of-machine effect has important implications as the development and adoption of AI, machine learning, and natural language processing challenges managers and policy-makers to harness these transformative technologies. The digital marketplace is crowded and consumer attention span is short. Understanding the conditions under which consumers trust, and do not trust, AI advice will give companies a competitive advantage in this space.