How employees react to unsolicited and solicited advice in the workplace: Implications for using advice, learning, and performance.
Bibliographic record
Abstract
employees react to unsolicited and solicited advice. Here, we suggest that recipients are likely to attribute self-serving motives to those providing unsolicited advice and prosocial motives to those providing solicited advice. These motives shape the extent to which recipients use advice, learn from it, and perform better as a result of receiving it. In an organizational network study of unsolicited and solicited advice ties (Study 1), an experience-sampling study of daily episodes of receiving unsolicited and solicited advice across two workweeks (Study 2), and an experiment where we manipulated advice solicitation and whether the advisor was a friend or a coworker (Study 3), we found general support for our model. Moderation analyses revealed that recipient reactions were not affected by friendship with the advisor, the number of overlapping advice ties between the advisor and recipient, or the position of the advisor in the social network. By showing how perceptions of the advisor's motive can explain variability in the impact of unsolicited and solicited advice on recipients, this research clarifies the recipient reactions that advisors must navigate if their advice is to have impact at work. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".