Darden School of Business and Boston University Release Research on Word-of-Machine Effect: Why People Prefer AI Recommenders to Human Ones

More and more companies are leveraging technological advances in AI, machine learning, and natural language processing to provide relevant and in-the-moment recommendations to consumers.

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Provider:

PR Newswire

Retrieved on:

Tuesday, December 1, 2020

Information

Tags:

Articles, Communication design, Recommender system, Academic disciplines, Artificial intelligence, Advertising, Science and technology

Geotags:

Virginia

Organisation:

DARDEN SCHOOL OF BUSINESS, JOURNAL OF MARKETING, HARVARD BUSINESS REVIEW, BOSTON UNIVERSITY

Summary

Key Points:

More and more companies are leveraging technological advances in AI, machine learning, and natural language processing to provide relevant and in-the-moment recommendations to consumers.
As a consequence, consumers prefer AI recommenders over human ones when a product or service has utilitarian characteristics.
Conversely, consumers prefer human recommenders over AI ones when a product or service has hedonic characteristics.
Understanding the conditions under which consumers trust, and do not trust, AI advice will give companies a competitive advantage in this space.

Darden School of Business and Boston University Release Research on Word-of-Machine Effect: Why People Prefer AI Recommenders to Human Ones

CHARLOTTESVILLE, Va., Dec. 1, 2020 /PRNewswire/ -- In an article in the Journal of Marketing—based on data from over 3,000 people who took part in 10 experiments—and in a follow-up article in Harvard Business Review, Chiara Longoni (Boston University) and Luca Cian (Darden School of Business) provide evidence supporting what they call a word-of-machine effect: the circumstances in which people prefer AI recommenders to human ones.

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

The word-of-machine effect stems from a (false) lay belief for which people believe that AI systems are more competent than humans in dispensing advice when functional and practical qualities (utilitarian) are desired and less competent when the desired qualities are experiential and sensory-based (hedonic). As a consequence, consumers prefer AI recommenders over human ones when a product or service has utilitarian characteristics. Conversely, consumers prefer human recommenders over AI ones when a product or service has hedonic characteristics.

There are also interventions that managers might deploy to correct the lay belief underlying the word-of-machine effect. Companies selling more sensorial products (e.g., fragrances) may still use AI to engage customers. Customers embrace AI as long as it works in partnership with humans. Also, asking customers to consider the opposite viewpoint of what they initially believe about AI competence successfully reduces the effect.

Overall, the word-of-machine effect has important implications as rapid development and adoption of applications of AI, machine learning, and natural language processing challenge 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.

For more information, please contact Chiara Longoni ([email protected])

article in Harvard Business Review

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SOURCE Darden School of Business