Abstract
Companies are increasingly adopting Artificial Intelligence (AI) today. Recently however debates started over the risk of human cognitive biases being replicated (and scaled) by AI. Research on biases in AI predicting consumer choice is incipient and focuses on observable biases. We provide a short synthesis of cognitive biases and their potential risk of being replicated in AI-based choice prediction. We also discuss for the first time the risk of unobservable biases, which affect choice indirectly, through other biases. We exemplify this by looking at looking at three prevalent, most frequently investigated biases in consumer behaviour: extremeness aversion, regret aversion and cognitive regulatory focus (prevention- versus promotion-focus). Based on a sample of 1747 respondents, through partial least squares structural equation modelling and significance testing, we show that regret aversion (unobservable bias) significantly reduces extremeness aversion (observable bias) and mediates the influence of cognitive regulatory focus (unobservable bias).
Highlights
Artificial Intelligence (AI) is increasingly being adopted today in business and commerce
We provide a short synthesis of cognitive biases and their potential risk of being replicated in AI-based choice prediction
Based on a sample of 1747 respondents, through partial least squares structural equation modelling and significance testing, we show that regret aversion significantly reduces extremeness aversion and mediates the influence of cognitive regulatory focus
Summary
AI is increasingly being adopted today in business and commerce. Hopes that it can help predict better consumer choice, to the benefit of both companies and consumers, are high. There is currently little to no research on whether human cognitive biases can be traced through (big) data, and on what happens when some biases influence choice differently through other biases than on their own – multiplicative effects. Regret aversion mediates the relationship between regulatory focus and extremeness aversion; in other others, prevention regulatory focus combined with regret aversion reduces extremeness aversion To test these hypotheses, we use the results from a comprehensive survey of 1747 respondents, which we analyse through partial least squares structural equation modelling (PLS-SEM) and statistical significance testing. We show the results, focused on (a) how choice from a set of low-medium-high alternatives is influenced by regret aversion, (b) how choice is influenced by consumer cognitive regulatory focus (prevention-focused or promotion-focused cognition), and (c) the mediation effect of regret aversion on the relationship between cognitive regulatory focus and choice and extremeness aversion
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