A framework for building and maintain trust in remote and virtual teams
Bibliographic record
Abstract
Trust is an important concept in assessing and measuring business behaviour from an organisational performance and culture lens, and has become a source of competitive advantage for organisations especially within the knowledge economy. Studies show that organizations with a high level of trust have increased employee morale, more productive workers, and lower staff turnover. Most organisations factor and measure trust as part of keeping a pulse on their organisational culture and design their initiatives around building and maintaining trust. While it is not impossible to build trust virtually, it certainly is harder and requires a different set of considerations. There has been a big shift by organizations catering for more remote and flexible work conditions over the past decade with the “virtual team” becoming the norm. The recent impacts of the COVID-19 pandemic have forced most, if not all, organizations to move in that direction faster than planned. With this movement to more remote working conditions, that are likely to have longer-term impacts, companies will be faced with challenges that virtual teams typically face in establishing and maintaining trust. This paper sought to highlight a framework that organisations, with remote and virtual teams, can use as a guideline to build and maintain trust. The framework suggests that trust is reliant on components from three key areas, namely 1) Foundational, 2) Organisational and 3) Individual. Components related to external aspects that contribute to trust, such as laws, reputation and society, have not been factored in. It is acknowledged that this will play a role in organisational and team trust but has been excluded from the scope of this research.
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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".