Harnessing the Power of Self-Organization in an Online Community During Organizational Crisis1
Bibliographic record
Abstract
Organizational crisis management has traditionally favored a centralized plan-and-control approach. This study explores the possibility for an orderly crisis management process to arise unintentionally from decen-tralized and spontaneous actions in an online community (i.e., self-organization). Based on complex adaptive systems theory, a multilevel model is developed to account for the logical relation between individual-level actions and interactions in an online community and an organizational-level orderly and rational crisis management process, as described by the organizational crisis management literature. We apply this multilevel model to an analysis of 89,596 posts from an online community that was deeply embedded in an earthquake-induced organizational crisis. Results indicate that fluctuation of message content themes in this online community served to energize continuous input from ordinary organization members. These input actualized new possibilities offered by the technology platform for crisis management actions (i.e., actualized IT affordances). Concatenation of immediate impacts of message content themes and actualized IT affordances formed feedback loops that moderated the crisis management activities toward an efficient trajectory. Our findings challenge the traditional assumption that macro-level order requires micro-level order-seeking behaviors. They suggest the viability of self-organization as a new source of organizational order that complements the traditional centralized plan-and-control approach. Theoretical and empirical implications for harnessing the power of ordinary organization members connected by today’s technology platforms are discussed.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".