Organizational size and knowledge flow: a proposed theoretical link
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This paper seeks to present a theory clarifying the negative relationship between organizational unit size and knowledge flows referred to as Gita's Rule. Design/methodology/approach This paper draws from the literature and develops a grounded theory. Various applications and propositions are suggested through this theoretical lens. Findings It is suggested that, as the size of an organizational unit increases, the effectiveness of internal knowledge flows dramatically diminishes and the degree of intra‐organizational knowledge sharing decreases. Research limitations/implications It is proposed that 150 employees represents a general breaking point, after which knowledge sharing reduces due largely to increased complexity in the formal structure, weaker interpersonal relationships and lower trust, decreased connective efficacy, and less effective communication. Practical implications The research points to the key dimension of organizational size that must be considered when developing models and reviewing case studies. Originality/value The research reported in this paper is among the first to explicitly tackle the issue of how knowledge flows are affected by organizational size. A theory is developed and several research propositions are introduced for future studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it