Abstract
This research examines how anchoring bias affects managers’ multi-dimensional evaluations of supplier performance, supplier selection, and the effectiveness of two debiasing techniques. We consider the supplier past performance in one performance dimension as the anchor and investigate whether and how this anchor would have a knock-on effects on evaluating a supplier’s performance in other dimensions. We conducted two online experimental studies (Study 1, sample size = 104 and Study 2, sample size = 408). Study 1 adopts a 2 x 1 (high anchor vs. low anchor) between-subjects factorial experimental design, and Study 2 is a 3 (debiasing: no, consider-the-opposite, mental-mapping) x 2 (high anchor vs. low anchor) between-subjects factorial design. The results from Studies 1 and 2 suggest that when a supplier has received a low evaluation score in one dimension in the past, participants assign the same supplier lower scores in the other dimensions compared to a supplier that has obtained a high score in the past. We also find that anchoring has a knock-on effect on how likely participants are to choose the same supplier in the future. Our findings highlight the asymmetric effectiveness of ‘consider-the-opposite’ and ‘mental-mapping’ debiasing techniques. This research is the first study that examines how anchoring bias managers’ evaluations in a multi-dimensional setting and its knock-on effects. It also explores the effectiveness of two low-cost debiasing techniques. A crucial practical implication is that suppliers’ exceptionally good or disappointing past performance affects the judgement of supply managers. Hence, managers should use consider-the-opposite or mental-mapping techniques to debias the effect of high and low anchors, respectively.
Original language | English |
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Article number | e0303700 |
Pages (from-to) | 1-20 |
Number of pages | 20 |
Journal | PLoS ONE |
Volume | 19 |
Issue number | 5 |
Early online date | 16 May 2024 |
DOIs | |
Publication status | Published - 16 May 2024 |
Keywords
- Adult
- Bias
- Female
- Humans
- Male