Research Study Finds Links Between AI Recommendations And Consumer Approval

The University of America Marketing Association researchers have recently uncovered notational study that can highlight the significance of the pivotal factors that are often considered while integrating AI based recommendation systems for consumers that are focused primarily on functional and practical aspects of a product (often considered as the utilitarian value) when compared to the experimental and sensory aspects of the product (often considered as the hedonic value). The team focuses their attention on the following study considering that the number of firms attaining to AI-based leveraging models is increasing day by day along with machine learning and natural language processing tools.

The team focused their findings on data obtained from over 3,000 participants that solely expressed their preferences for recommendation sources when compared to AI based systems and recommendations based on manual assumptions. This phenomenon is defined as word-of-machine effects – which essentially is the trade-off between the utilitarian and hedonic aspects of a product that the consumer has to account for. The AI-based system points out the difference between utilitarian attributed at a larger scale when compared to pointers from the human based recommendation systems. The research team tested the “word-of-machine” effect using a set of experiments that were bound to assess people’s tendency toward choosing products based on consumption patterns and recommendations sources.

The team found that when consumers were presented with consumption-based experiences and recommendation sources the latter tend to choose products based solely on utilitarian/functional attributed rather than AI-based recommendations. When asked to choose a product based on hedonic/experimental attributes, a higher number of consumers choose products that were based on human recommenders. Previous iterations of the study in the same universe were used which quoted that a clear correlation is based between utilitarian attributes and consumer trust in AI recommenders where firms were more focused on selling products based on sensorial experiences.

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