Measuring trust in organisational research: Review and recommendations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Although the organisational literature is increasingly converging on common definitions and theoretical conceptualisations of trust, it is unclear whether the same is true for the measures used to operationalise trust. In this paper, we review the organisational literature to assess the degree of sophistication and convergence across studies in how trust has been measured. Our analysis of 171 papers published over 48 years revealed that the state of the art of trust measurement is rudimentary and highly fragmented. In particular, we identified a total of 129 different measures of trust. Moreover, in only 24 instances were we able to verify that a previously developed and validated measure of trust had been replicated verbatim, and 11 of these replications were by the same authors who originated the measure. In addition to the limited degree of replication, the measurement of trust in the organisational literature is characterised by weak evidence in support of construct validity and limited consensus on operational dimensions. What makes these findings even more surprising is that our review also identified several measures of trust that have been carefully developed and thoroughly validated. We profile those measures with strong measurement properties and discuss their trade-offs. We also present a framework for measuring trust that provides guidance to researchers for selecting or developing a measure of trust and propose an agenda for future research with an emphasis on resolving enduring debates in the literature.
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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 | 0.038 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it