On the heterogeneity and equifinality of knowledge transfer in small innovative organizations
Bibliographic record
Abstract
Purpose To date, it remains unclear whether the experiences of large corporations with regard to knowledge transfer and process formalization can be successfully replicated in small companies. In this paper, the authors seek to contribute to the specialized literature on internal knowledge transfer processes and their degree of formalization in the context of small-sized innovative firms. Design/methodology/approach The authors adopt a multiple case study approach to perform an in-depth comparative analysis of processes deployed to transfer knowledge internally and their degree of formalization, relying on rich narratives shared by informants during the data gathering stage. This sample is composed of five small innovators operating in the software industry in Quebec and Ontario. Findings The authors identify seven knowledge transfer processes in our sample, namely communities of practice, within project teams, across project teams, non-project related meetings, in-house exchanges with clients, technological devices, and playful activities. Uncovering a high cross-case variation in terms of process formalization, the findings imply that the degree of formalization of intra-firm knowledge transfer processes has no direct bearing on the innovative success of small software companies. Originality/value The study sheds new light on the topic of heterogeneity of small organizations from the perspective of knowledge transfer endeavors and provides empirical evidence in support of equifinality for a subset of small-sized innovators from the software sector.
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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 | 0.023 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".