The Search for Feedback: Matching Personal Values with Organizational Support
Bibliographic record
Abstract
The present study introduced and examined a theoretical framework, based on person-organization fit theory, to explain how the feedback environments leaders create impact the way their employees value feedback and the extent to which they will look and ask for feedback in the workplace. A sample of 408 employed participants were recruited through multiple online recruitment services originating from various locations mainly including Canada (17.9%) and the United States of America (74.8%). Participants’ average age was 36.2, 33.8% males and 65.7% females, and average salary was $65 000 (M salary = $64 628). The majority indicated a full-time work status (78.2%), and 66.2% reported working in a non-management role. Job roles spanned industries including education, healthcare, retail, government, restaurant-hospitality, information technology, and business finance. Participants completed an online self-report questionnaire assessing perceptions of their feedback environment, feedback orientation, person-organization fit, work engagement, and feedback-seeking. Analyses revealed that the feedback practices leaders engage in can actually predict how useful their employees see feedback and how able and likely they are to apply it to their work and seek it more often. Both a supportive feedback environment and strong feedback orientation positively predicted that employees would feel their values, needs, and abilities are being met by what their organizations expect and that this perceived fit would predict increased work engagement. These findings suggest that leaders have a real opportunity to influence how their employees see the value in feedback by the practices they choose to engage in and that these actions can predict how strongly employees feel they fit within their organization and how engaged they are in their work. Results help to clarify that leaders play a role in how often their employees will ask for evaluative and developmental feedback through the meaning they help their employees ascribe to it.
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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 | 0.004 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".