Bibliographic record
Abstract
Trust is a major factor influencing the cohesiveness among virtual team members. While recent research in the fields of information systems and management has examined this construct, there are no existing instruments that measure all the different bases of trust. Drawing on the literature, three different bases of trust applicable to virtual teams have been identified: personality-based, institutional-based, and cognitive trust, with cognitive trust further subdivided into three dimensions: stereotyping, unit grouping, and reputation categorization. This chapter reports on the development of an instrument to capture these three bases of trust. Using exploratory, and thereafter, confirmatory factor analysis, the instrument is validated, and the psychometric properties of the construct(s) are verified in the context of U.S.-Canadian student virtual teams engaged in systems development projects. In addition to confirming the conceptual bases of trust, the instrument validation process found that stereotyping in virtual teams can be of three distinct types: message-based, physical appearance/behavior-based, and technology-based. The development and validation of this instrument should enable future researchers to measure virtual team trust in a broad range of technology and team configurations.Request access from your librarian to read this chapter's full text.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".