The Double Molotov Cocktail of Brexit and COVID-19: Can Contact Intensity Help Explain Levels of Trust and Belief in the Future between Companies?
Bibliographic record
Abstract
Can contact intensity help explain levels of trust and belief in the future among companies? This question is particularly important in times of exogenous shocks such as Brexit and COVID-19 when various sectors frequently experience a contraction of business activity. Putnam’s theory can help explain cooperation and long-term resilience among companies when business conditions radically change. Trustworthy companies can be named ‘hard-riders’, as they are good at creating social relationships and rewarding their trading partners through social recognition and continued cooperation. With their capacity for contact intensity, hard riders receive an extra social benefit to reinforce trust-based cooperation. Survey responses from 193 participants in our new database (DanComTrust) on British and Danish small and medium-sized enterprises (SMEs) show that there is a significant effect from both contact intensity and trust intensity on belief in the future when tested individually in logit models. However, when both variables are included, all effects from contact intensity disappear and only trust intensity remains significant, indicating that the effect from contact intensity works through trust. These results suggest that a high level of contact intensity will increase the trust between cooperating companies, resulting in a greater belief in the future. This insight is relevant for maintaining and building future resilience between companies and their trading partners within and outside the EU.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".