“Preference for Human and Non-human Agent in Random Events: Effect of Probability and Outcome Valence” by TaeWoo Kim, Joseph Goodman and Adam Duhachek

“Preference for Human and Non-human Agent in Random Events: Effect of Probability and Outcome Valence” by TaeWoo Kim, Joseph Goodman and Adam Duhachek

Prior research on illusion of control has shown that individuals prefer to make their own choices in a random event (e.g., preferring to choose a lottery number on their own vs. to have it chosen by someone else), believing that they can control the outcome. In the current research, we introduce a novel framework in which we compare human agents with a previously unexamined novel agent in this literature, namely, an artificial agent (e.g., A.I., algorithms, robots). We hypothesize that people will perceive a greater illusion of control in a moderate probability event (i.e., 50% chance of winning) when a human (vs. an artificial agent) is involved. In the case of a high probability event (i.e., 90% chance of winning), we hypothesize that people will feel stronger certainty for the outcome when an artificial (vs. human) agent is involved because artificial agents are perceived as more likely to translate 90% into a real outcome, whereas human agents are considered to be relatively more error-prone, thus making the low probability negative outcome (i.e., 10% chance of losing) loom larger compared to an artificial agent. In support of these hypotheses, we found that individuals would prefer a human (vs. artificial) agent in a random event (e.g., as an agent who throws the dice) when the probability of the positive outcome is moderate (e.g., 50% chance of winning $100 in a card game) (Study 1). In high probability events (e.g., 90% chance of winning $100 in a card game), however, we found that consumers feel a greater sense of control when an artificial (vs. human) agent is involved in a random event (Studies 2 and 3). We currently seek more evidence of the hypothesized effect in various probabilistic consumer contexts – for example, in receiving medical treatment (e.g., 50% chance of recovery from a disease) or in choosing products with given probabilities of positive or negative outcomes (e.g., 70% chance of a satisfactory restaurant experience).

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