A Theory of Digital Firm-Designed Markets: Defying Knowledge Constraints with Crowds and Marketplaces
Bibliographic record
Abstract
In this paper, we investigate the ways in which new forms of organization enabled by digital technologies, such as crowdsourcing and digital marketplaces, are allowing firms to circumvent and defy traditional knowledge constraints. This is part of the broader question of when and why these forms of organization are more efficient relative to alternatives, given that some firms simultaneously utilize crowdsourcing, marketplaces, and traditional forms of organization. We observe that an important cluster of these new organizational forms are able to circumvent knowledge constraints, because they combine elements of market and hierarchical organization in firm-designed hybrid arrangements. We further categorize these firm-designed markets into one-sided market arrangements (crowds) and two-sided market arrangements (marketplaces). To explain their efficiency relative to hierarchies and relative to each other, we take a knowledge-based perspective and review ways in which firm-designed markets reduce or remove both first-order (known unknown) and second-order (unknown unknown) knowledge constraints compared with hierarchies. Our argument hinges on the notion that firm-designed markets provide semidirected and undirected search and generativity mechanisms that allow firms to go beyond what is possible with centrally directed search.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".