Bibliographic record
Abstract
Purpose – The purpose of this paper is to trace the emergence of knowledge-centric innovative enterprises that function in a disaggregated and dispersed form and further contemplate the economic and managerial rationale behind this strategy. A constant challenge to large organizations as well as those pursuing the intent to grow bigger is how to sustain the innovative dynamism. Design/methodology/approach – The authors review the evolution of disaggregated and dispersed enterprises and discuss the changing cost structures for transactions, integration and coordination in the global knowledge economy. They elaborate the benefits of scale reduction and dispersed operations with examples. Findings – Their review of the extant practices suggests that managers are finding value in disaggregating the firm operations. Disaggregation enhances the firm agility and responsiveness and helps the firm exploit the fleeting opportunities without incurring the opportunity cost or risking high investment. Practical implications – Corporations need to become nimble, and their structure should be networked and permeable with significant industry actors. Integration would be imprudent if there is huge sunk cost due to uncertainty in business. Scale reduction and disaggregation, and operating in a dispersed mode – like a shoaling form – would help the companies exploit the fleeting opportunities without incurring the opportunity cost and risking high investment. Originality/value – In addition to reviewing the rise of disaggregated enterprises, we explore the economic and managerial rationale of the disaggregation strategy, and discuss the learning and innovation, investment and cost-related advantages that stem from the disaggregated form of organization.
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.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".