Based on the article, consumers$$ trust in AI versus human recommendations depends on whether they are focused on the functional and practical aspects of a product $($its utilitarian value$)$ or the experiential and sensory aspects of a product $($its hedonic value$).$ This is referred to as the $$word$-$of$-$machine effect.$$ Summary.

Companies are increasingly turning to AI to make recommendations to

consumers. But sometimes consumers don$$t trust AI$$s advice and want a human$$s

sugestions. To understand when consumers trust one vs$.$ the other, the authors

conducted a series of$...$

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More and more companies are leveraging technological advances in machine learning, natural language processing, and other forms of artificial intelligence to provide relevant and instant

recommendations to consumers. From Amazon to Netflix to REX Real Estate, firms are using AI recommenders to enhance the customer experience. AI recommenders are also increasingly used in the public sector to guide people to essential services. For example, the New York City Department of Social Services uses AI to give citizens recommendations on disability benefits, food assistance, and health insurance.

However, simply offering AI assistance won$$t necessarily lead to more successful transactions. In fact, there are cases when AI$$s suggestions and recommendations are helpful and cases when they might be detrimental. When do consumers trust the word of a machine, and when do they resist it$?$ Our research suggests that the key factor is whether consumers are focused on the functional and practical aspects of a product $($its utilitarian value$)$ or focused on the experiential and sensory aspects of a product $($its hedonic value$).$

In an article in the Journal of Marketing $$ based on data from over $3,000$ people who took part in $10$ experiments $$ we provide evidence supporting for what we call a word$-$of$-$machine effect: the circumstances in which people prefer AI recommenders to

human ones.

The word$-$of$-$machine effect.

The word$-$of$-$machine effect stems from a widespread belief that AI systems are more competent than humans in dispensing advice when utilitarian qualities are desired and are less competent when the hedonic qualities are desired. Importantly, the word$-$of$-$machine effect is based on a lay belief that does not necessarily correspond to the reality. The fact of the matter is humans are not necessarily less competent than AI at assessing and evaluating utilitarian attributes. Vice versa, AI is not necessarily less competent than humans at assessing and

evaluating hedonic attributes. Indeed, AI selects flower arrangements for $1-800-$Flowers and creates new flavors for food companies such as McCormick.

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Nevertheless, our experiments suggest that if someone is focused on utilitarian and functional qualities, then, from a marketer$$s perspective, the word of a machine is more effective than the word of human

recommenders. For someone focused on experiential and sensory qualities, human recommenders are more effective.

For instance, in one of our experiments we assessed the word$-$of$-$ machine effect on people$$s propensity to choose products and people$$s consumption experiences. To do so$,$ we asked over $200$ passersby in $($pre$-$Covid$-19)$ Boston to participate in a blind market test for haircare products. Using leaflets to explain the test, we asked each person to select one of two hair product samples, one recommended by AI and the other by a human. As predicted, when passersby were asked to focus only on utilitarian and functional attributes such as practicality, objective performance, and chemical composition, more people chose the AI$-$recommended sample $(67\%)$ than the one recommended by a person. When passersby were asked to focus only on experiential and sensory attributes such as indulgence, scent, and a spa$-$like

vibe, more people choose the human$-$recommended sample $(58\%)$ than the one recommended by AI$.$

The word$-$of$-$machine effect also emerged in a second field experiment that we conducted in the Italian resort town of Cortina. We first primed people to consider a real estate investment relying only on either its functional and practical qualities or its emotional and sensory$-$based qualities. Then, we asked people to choose one of two selections of house properties: one curated by a human real estate agent and one by an AI

algorithm. When presented with a pitch that focused on practicality, more people $(60\%)$ chose a list of AI$-$curated properties. But more participants $(76\%)$ chose the human$-$curated property list in response to a pitch that appealed to the senses such as enjoyment.

The word$-$of$-$machine effect even extended to product consumption and taste perception. We recruited $144$ participants from the University of Virginia campus and informed them that we were testing chocolate$-$cake rec