Felt Trust: A Critical Review, Constructive Redirection, and Exploratory Meta-Analysis
Bibliographic record
Abstract
After decades of scholarly focus on studying trust from the trustor's perspective, there has been a rapidly growing interest in understanding trust from the trustee's perspective, with a particular emphasis on felt trust (i.e., a trustee's perception of being trusted by a trustor). The fundamental assumption underlying this trustee-centric perspective is that it complements the dominant trustor-centric perspective and enables a more comprehensive understanding of how trust manifests and operates in the workplace. Unfortunately, our critical review of 121 felt trust studies reported in 87 manuscripts reveals major problems in three areas (conceptualization and measurement, theorizing, and research methods) that prevent this field from reaching its full potential. To remedy this, we build on existing frameworks, best practices, and exemplars from the (felt) trust literature to outline a constructive redirection of the field. We subsequently empirically test the field’s fundamental assumption by meta-analytically exploring the distinctiveness and incremental validity of felt trust beyond other trust concepts. Taken together, our envisioned redirection and meta-analytic findings enable the field of felt trust to live up to its promise and enrich understanding of organizational trust.
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 | 0.313 | 0.637 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.051 | 0.028 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| 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".