Transcending Knowledge Differences in Cross-Functional Teams
Bibliographic record
Abstract
Knowledge differences impede the work of cross-functional teams by making knowledge integration difficult, especially when the teams are faced with novelty. One approach in the literature for overcoming these difficulties, which we refer to as the traverse approach, is for team members to identify, elaborate, and then explicitly confront the differences and dependencies across the knowledge boundaries. This approach emphasizes deep dialogue and requires significant resources and time. In an exploratory in-depth longitudinal study of three quite different cross-functional teams, we found that the teams were able to cogenerate a solution without needing to identify, elaborate, and confront differences and dependencies between the specialty areas. Our analysis of the extensive team data collected over time surfaced practices that minimized members' differences during the problem-solving process. We suggest that these practices helped the team to transcend knowledge differences rather than traverse them. Characteristic of these practices is that they avoided interpersonal conflict, fostered the rapid cocreation of intermediate scaffolds, encouraged continued creative engagement and flexibility to repeatedly modify solution ideas, and fostered personal responsibility for translating personal knowledge to collective knowledge. The contrast between these two approaches to knowledge integration—traverse versus transcend—suggests the need for more nuanced theorizing about the use of boundary objects, the nature of dialogue, and the role of organizational embeddedness in understanding how knowledge differences are integrated.
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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 | 0.018 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".