Embrace the Unexpected: How Organizations Foster Participatory Improvisation with Customers
Bibliographic record
Abstract
This study explores how organizations, together with their customers, solve problems in the face of disruptions, a process I call “participatory improvisation.” Drawing on interviews, ethnographic observations, and archival data collected from an underground restaurant, Secret Kitchen, and the theory of interaction order, I develop a process model of participatory improvisation with a two-part structure. First, I find that an organization must lay the foundation for participatory improvisation by establishing alternative conventions (e.g., expect and embrace the unexpected). Second, these conventions facilitate mutual face work by both the organization and customers in response to disruptions, thereby protecting interactions from breakdowns. When alternative conventions are not established, participatory improvisation may be ineffective, and interactions may be severely threatened. These findings contribute to the literature on organizational improvisation by uncovering how organizations can foster participatory improvisation and how it unfolds in situ. They also reveal an alternate way for customer-facing organizations to achieve their goals beyond routinization. Funding: This work was supported by the Ewing Marion Kauffman Foundation Dissertation Grant.
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".