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. The word-of-machine effect also emerged in a second field experiment that we conducted in the Italian resort town of Cortina. 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.
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. 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. To do so, we asked over 200 passersby in (pre-Covid-19) Boston to participate in a blind market test for haircare products. For someone focused on experiential and sensory qualities, human recommenders are more effective.įor 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. 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. Insight CenterĮxamining the challenges and opportunities that lie ahead. Indeed, AI selects flower arrangements for 1-800-Flowers and creates new flavors for food companies such as McCormick. Vice versa, AI is not necessarily less competent than humans at assessing and evaluating hedonic attributes.
The fact of the matter is humans are not necessarily less competent than AI at assessing and evaluating utilitarian attributes. Importantly, the word-of-machine effect is based on a lay belief that does not necessarily correspond to the reality. 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. 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. 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 fact, there are cases when AI’s suggestions and recommendations are helpful and cases when they might be detrimental. However, simply offering AI assistance won’t necessarily lead to more successful transactions. For example, the New York City Department of Social Services uses AI to give citizens recommendations on disability benefits, food assistance, and health insurance. AI recommenders are also increasingly used in the public sector to guide people to essential services. From Amazon to Netflix to REX Real Estate, firms are using AI recommenders to enhance the customer experience. 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.
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