Consumers may trust AI recommendations if they perceive the technology as being accurate and unbiased. Factors that can influence trust in AI include the transparency of the decision-making process, the credibility of the source of the recommendations, and the perceived alignment of the AI’s recommendations with the consumer’s own goals and values. On the other hand, consumers may resist AI recommendations if they perceive the technology as being untrustworthy or if the recommendations conflict with their own preferences or beliefs. Additionally, if the recommendations are too personal or too invasive, it may cause discomfort for some consumers.
The key factor in determining 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 experiential and sensory value) (its hedonic value).
Researchers from Boston University and the University of Virginia published a new paper in the Journal of Marketing that examines how consumers respond to AI recommenders when they are focused on the functional and practical aspects of a product (its utilitarian value) versus the experiential and sensory aspects of a product (its experiential and sensory value) (its hedonic value).
The study, forthcoming in the Journal of Marketing, is titled “Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The ‘Word-of-Machine’ Effect” and is authored by Chiara Longoni and Luca Cian.
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.
Longoni
To provide recommendations to customers, more and more businesses are leveraging technological advances in AI, machine learning, and natural language processing. As these businesses assess AI-based assistance, one critical question must be addressed: When do consumers trust the “word of machine,” and when do they reject it?
A new Journal of Marketing study investigates the factors that influence recommendation source preference (AI vs. human). The key factor in determining 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 experiential and sensory value) (its hedonic value).
The research team provides evidence supporting a word-of-machine effect, defined as the phenomenon by which trade-offs between utilitarian and hedonic aspects of a product determine preference for, or resistance to, AI recommenders, using data from over 3,000 study participants.
The word-of-machine effect stems from a widespread belief that AI systems are more competent than humans at providing advice when functional and practical qualities (utilitarian) are desired, but less competent when experiential and sensory-based qualities are desired (hedonic). As a result, the importance or salience of utilitarian attributes determines preference for AI recommenders over human ones, whereas hedonic attributes determine resistance to AI recommenders over human ones.
The researchers tested the word-of-machine effect using experiments designed to assess people’s tendency to choose products based on consumer experiences and recommendation source. Longoni explains that “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 was more noticeable when utilitarian features were most important. Participants in one study were asked to imagine purchasing a winter coat and rate the importance of utilitarian/functional attributes (e.g., breathability) and hedonic/experiential attributes (e.g., fabric type) in their decision-making. The higher the utilitarian/functional features were rated, the higher the preference for AI over human assistance, and the higher the hedonic/experiential features were rated, the higher the preference for human assistance 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.
Despite the fact that there is a clear relationship 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. People, in fact, welcome AI recommendations as long as AI collaborates with humans. When AI plays an assistive role, rather than replacing human intelligence, the AI-human hybrid recommender outperforms a human-only assistant.
Overall, the word-of-machine effect has significant implications as the development and adoption of AI, machine learning, and natural language processing confront managers and policymakers with the challenge of harnessing these transformative technologies. As Cian puts it, “Consumer attention spans are short, and the digital marketplace is crowded. Understanding the circumstances under which consumers trust and distrust AI advice will provide businesses with a competitive advantage in this space.”
