Communities of Practice versus Organizational Climate: Which One Matters More to Dispersed Collaboration in the Front End of Innovation?<sup>*</sup>
Bibliographic record
Abstract
Dispersed collaboration provides many benefits such as members' closeness to local cultures and markets and reachability of talent worldwide. Hence, it is no surprise that dispersed collaboration is frequently being used by product development teams. A necessary but not sufficient condition for innovation performance is the sharing of tacit, non‐codified and explicit, codified knowledge by the team. Situated learning theory, however, predicts that tacit knowledge sharing will be largely prevented by “decontextualization.” Therefore, increasing usage of dispersed collaboration will decrease levels of tacit knowledge—crucial to innovation and organizational performance—in the business unit. This research investigates the moderating role of mechanisms believed to enable tacit knowledge transfer in the front end of innovation. Using data from 116 business units, the moderating role of communities of practice and organizational climate on the relationship between the proficiency of dispersed collaboration and front end of innovation performance is investigated. Encouragement of communities of practice is found to moderate the relationship between proficiency of dispersed collaboration and front end of innovation performance on the business unit level. More specifically, proficiency of dispersed collaboration is not related at all to front end of innovation performance in business units with low support for communities of practice; but a positive relationship exists in business units with high support for communities of practice. This study does not provide support for the moderating effect of organizational climate on the relationship between proficiency in dispersed collaboration and front end of innovation performance. However, supportiveness of climate has a significant direct effect on front end of innovation performance. The findings of this study suggest that managers should simultaneously invest in increasing proficiency in dispersed collaboration and supporting communities of practice. Either one by itself is insufficient. Because of its significant direct effect, managers should also nurture an open climate favoring risk taking, trust, and open interaction.
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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 | 0.007 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".