Pursuit of decentralization in blockchain-based systems: An empowerment perspective
Bibliographic record
Abstract
Decentralized autonomous organizations (DAOs) represent an innovation in the design of organizations by creating blockchain-based human-machine systems that are governed based on the collective decisions of their participants. Although this new form of organizing promises to sustain participation and foster decentralized governance, many existing DAOs have failed to achieve the intended degrees of decentralization. This study aims to understand how DAOs can fulfill their potential for decentralization by empowering individuals to participate in governance. Using an abductive approach guided by the empowerment theory, this research identifies three key practices underpinning empowerment in DAOs: promoting autonomy, ensuring transparency, and fostering communication. A configurational approach is used to identify complementarities among these practices that lead to three distinct governance archetypes associated with varying degrees of decentralization. Based on fuzzy-set qualitative comparative analysis (fsQCA) of 30 DAO cases, we introduce “deliberative democracy” as a DAO governance archetype that allows for increasingly decentralized governance. Our findings demonstrate that, although a high degree of autonomy is needed to sustain decentralization, there needs to be sufficient communication among autonomous actors to facilitate the collective management of DAOs. These findings advance the understanding of decentralization in information systems research and highlight the governance mechanisms that foster decentralization in blockchain-based systems.
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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 | 0.007 | 0.007 |
| 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 | 0.009 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.004 |
| 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".