Managerial Empathy Facilitates Egocentric Predictions of Consumer Preferences
Bibliographic record
Abstract
Common wisdom suggests that managerial empathy (i.e., the mental process of taking a consumer perspective) helps executives separate their personal consumption preferences from those of consumers, thereby preventing egocentric preference predictions. The results of the present investigation, however, show exactly the opposite. First, the authors find that managerial empathy ironically accelerates self-reference in predictions of consumer preferences. Second, managers’ self-referential tendencies increase with empathy because taking a consumer perspective activates managers’ private consumer identity and, thus, their personal consumption preferences. Third, empathic managers’ self-referential preference predictions make them less likely to use market research results. Fourth, the findings imply that when explicitly instructed to do so, managers are capable of suppressing their private consumer identity in the process of perspective taking, which helps them reduce self-referential preference predictions. To support their conclusions, the authors present four empirical studies with 480 experienced marketing managers and show that incautiously taking the perspective of consumers causes self-referential decisions in four contexts: product development, communication management, pricing, and celebrity endorsement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".