Governing Within Semirigid Limits: Navigating the Centralization–Decentralization Paradox in Blockchain‐Based Platforms
Bibliographic record
Abstract
ABSTRACT Blockchain‐based platforms can facilitate data sharing and coordination in interorganizational ecosystems by enabling secure, tamper‐evident recordkeeping and streamlined, trust‐minimized transactions across organizational boundaries. However, their decentralized architecture may conflict with the centralized control exercised by platform sponsors, giving rise to a centralization–decentralization paradox. This study explores how this paradox unfolds in a large, blockchain‐based logistics platform that was ultimately discontinued. Through an in‐depth, longitudinal case study, we identify three interrelated governance contradictions—regarding ownership, trust, and growth—that triggered destabilizing oscillations between centralized and decentralized governance modes. We introduce the concept of semirigid limits to capture the bounded flexibility within which governance can be made and adapted under such paradoxical conditions. Our findings show that the centralization–decentralization paradox is especially difficult to navigate when strategic boundary conditions—here, industry competition, fragmented coordination, and high interdependencies—are present. Our study contributes to the paradox and governance literature by theorizing how governance contradictions emerge and persist and by identifying the mechanisms that constrain alignment and adaptation. We also offer guidance for managers in regard to addressing the competing demands of centralization and decentralization in interorganizational platforms.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".