When do people prefer to be asked or told? The interplay between participative/directive advising style and expertise superiority in recommendation acceptance.
Bibliographic record
Abstract
Conventional wisdom in the advisor-advisee literature predominantly condemns directive advising as detrimental and praises participative advising. However, such theoretical predictions seem inconsistent with existing findings. Our research aimed to reconcile this inconsistency by developing a balanced framework grounded in expectation states theory. We propose that the effect of advising style (i.e., participative vs. directive advising) on recommendation acceptance depends critically on advisors' expertise superiority relative to advisees. Across three studies conducted in different advisor-advisee contexts (i.e., doctor-patient, hairdresser-customer, and lawyer-client), we demonstrate that while participative advising is more acceptable when advisors' expertise superiority is lower, directive advising can be equally effective when advisors' expertise superiority is higher. This pattern emerges because expertise superiority shapes advisees' desired participation, creating different participation expectation validation scenarios under participative versus directive advising. Our research suggests that directive advising can be as effective as participative advising in certain situations, offering novel insights into the contingent effectiveness of participative versus directive advising for recommendation acceptance in advisory relationships. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".