On the risk of studying practices in isolation: Linking what, who, and how in strategy research
Bibliographic record
Abstract
This article challenges the recent focus on practices as stand-alone phenomena, as exemplified by the so-called “Practice-Based View of Strategy” proposed by Bromiley and Rau. While the goal of “Practice-Based View of Strategy” points to the potential for standard practices to generate performance differentials (in contrast to the resource-based view), it marginalizes well-known insights from practice theory more widely. In particular, by limiting its focus to practices, that is, “what” practices are used, it underplays the implications of “who” is engaged in the practices and “how” the practices are carried out. In examining practices in isolation, the “Practice-Based View of Strategy” carries the serious risk of misattributing performance differentials. In this article, we offer an integrative practice perspective on strategy and performance that should aid scholars in generating more precise and contextually sensitive theories about the enactment and impact of practices as well as about critical factors shaping differences in practice outcomes.
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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 | 0.164 | 0.213 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.012 | 0.137 |
| Scholarly communication | 0.031 | 0.054 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 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".