Measuring trust in organisational research: Review and recommendations
Bibliographic record
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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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 | 0.082 | 0.235 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".