The future of the web? The coordination and <scp>early‐stage</scp> growth of decentralized platforms
Bibliographic record
Abstract
Abstract Research Summary This abductive study investigates how management occurs without managerial authority as part of a previously unseen organizational form—the decentralized platform with an independent market value. Our mixed‐methods study of the cryptocurrency industry draws on fuzzy‐set qualitative comparative analyses (QCA) to analyze archival and interview data and offer new theory on how decentralized platforms coordinate activities to grow in an early‐stage, before network effects kick in. We find that, in the absence of a central authority, platforms coordinate activities with three mechanisms, namely decentralized (a) algorithmic coordination, (b) social coordination, and (c) goal coordination. Our QCA treat these mechanisms as explanatory conditions and, using a representative sample of 20 cryptocurrency platforms, reveal which configurations of decentralized coordination mechanisms nurture, or hinder, early‐stage platform growth. Managerial Summary Firms operate around a managerial hierarchy that distributes tasks, resources, information, and rewards to organizational members who pursue common goals as contract‐bound employees. From 2009, a new organizational form, called the “decentralized platform,” emerged and diffused without relying on hierarchy nor managerial authority—and without having to employ anyone. The most prominent decentralized platform, Bitcoin, has millions of users, thousands of contributors, and a market valuation never achieved before by an organization without a CEO nor shareholders. This study explicates how this unprecedented level of organizational decentralization functions in practice. We foreshadow implications for the digital economy, wherein “Web3” innovations, such as non‐fungible tokens and DAOs, have already shifted the orchestrating role played by platforms in capitalist societies.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".