Bibliographic record
Abstract
Purpose – Computer-mediated communication systems (CMCSs) have become the standard for supporting virtual teamwork. However, interpersonal trust formation though CMCSs is impaired due to limited media richness of the communication channels. The aim of this paper is to identify trust forming cues that occur naturally in face-to-face environments and are suitable to include in CMCSs design, to facilitate greater trust in virtual teams. Design/methodology/approach – To select cues that had a strong effect on fostering trust behaviour, a non-participatory ethnographic study was conducted. Two student teams at the University of Waterloo were observed for 6-12 months. Researchers identified mechanisms used for building trust and bridging team developmental barriers. Findings – The paper identifies five trust tokens that were effective in developing trust and bridging team developmental barriers: expertise, recommendations, social capital, willingness to help/benevolence, and validation of information. These behavioural cues, or behavioural trust tokens, which are present in face-to-face collaborations, carry important trust supporting information that leads to increased trust, improved collaboration, and knowledge integration. These tokens have the potential to improve CMCSs by supplementing the cues necessary for trust formation in virtual environments. Practical implications – This study identifies important mechanisms used for fostering trust behaviour in face-to-face collaborations that have the potential to be included in the design of CMCSs (via interface design objects) and have implications for interface designers, team managers, and researchers in the field of teamwork. Originality/value – This work presents the first ethnographic study of trust between team members for the purpose of providing improved computer support for virtual collaboration via redesigned interface components.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".