Do people reason when they accept tricky offers? A case of approach and avoidance motivated reasoning

- Deceptive offer acceptance supports confirmation bias against cheating detection.
- Offer acceptance or rejection is a case of approach/avoidance motivated reasoning.
- Three experiments with a thematic version of the Wason Selection Task were carried out.
- Confirmatory card selection (acceptance) was enhanced when the offer was linked to a need.
- Falsification predominates with a cheating warning and information on alternatives.

Tricky or deceptive sales offers are often accepted by people. But how do we explain the acceptance of such disadvantageous offers? We propose that buyers represent offers in terms of costs and benefits in a conditional goal-related mental rule along the lines of “if a cost is paid, a benefit is obtained”. This representation would be influenced by BAS (approach) and BIS (avoidance) motivational systems. Behavioural approach system (BAS) would trigger seeking evidence confirming the offer and would lead us to accept it. Behavioural inhibition system (BIS), on the contrary, seeks disconfirming evidence, leading to rejection of the offer. Activation of approach and avoidance motivation would be influenced by internal variables and the context. We carried out three experiments where participants were told to check if a deceptive conditional offer was a cheat in a thematic Wason Selection Task. Experiment 1 showed that participants preferred confirmatory selection when the benefit was linked to a human need. In Experiment 2, warning information indicating cheating was introduced, while in Experiment 3 information about product alternatives was added to the instructions. We found a weakening of confirmatory card selection in Experiment 2, and a clear preference towards falsifying selection in Experiment 3. These results support the role of approach and avoidance motivated reasoning in buying/selling exchanges. Implications of these results are discussed in comparison with cheater detection algorithm and conformation bias predictions.