Building and Protecting Organizational Trust with External Publics: Canadian Senior Executives' Perspectives
Bibliographic record
Abstract
Background Building and protecting trust has always been challenging, yet critical for organizational success.Analysis This article examines how Canadian organizations recognized as being successful generate trust with their external publics. Using a grounded theory approach, the authors interviewed 10 senior executives from publicly recognized successful Canadian companies.Conclusion and implications Based on their findings, the authors propose eight principles for organizations to follow to build and protect organizational trust with their external publics.Keywords Organizational trust; In-depth interviews; Grounded theory; External publicsContexte La construction et la protection de la confiance ont toujours été difficiles, mais essentielles pour le succès de l’organisation.Analyse Cet article examine comment les organisations canadiennes qui sont reconnues comme réussies instaurent la confiance avec leurs publics externes. En utilisant une approche de la théorie ancrée, dix cadres supérieurs d’entreprises réussies publiquement reconnues ont été interviewés au Canada.Conclusions et implications S’inspirant de leurs découvertes, les auteurs proposent huit principes pour les organisations à suivre afin de construire et de protéger la confiance organisationnelle avec leurs publics externes.Mots clés Confiance organisationnelle: Entretiens approfondis; Théorie ancrée; Public externe
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.023 | 0.012 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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 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".