Intentions, Intermediaries, and Interaction: Examining the Emergence of Routines
Bibliographic record
Abstract
abstract A thorough understanding of how routines emerge is necessary to derive the performance benefits they yield for organizations. In this paper, we suggest that a routine emerges from interactions between actors, interactions that are enabled by the exchange ofintermediaries. Specifically, intermediaries transmit the intentions of one actor to another and thus potentially align the actions and responses of those actors. If, however, the intermediaries that are exchanged do not clearly transmit the intentions of one actor to another, then a weak routine emerges. Conversely, if intermediaries clearly transmit the intentions, a strong routine emerges in which a given action more often meets with the expected response across iterations. We substantiate our arguments with a field experiment on the towel‐changing routine in a hotel where we manipulated the procedure to exchange towels, which resulted in the emergence of a stronger routine. Our study offers several implications for theoretical and empirical research on routines, including to the burgeoning research on micro‐foundations.
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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 | 0.004 | 0.023 |
| 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 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".