Relating microprocesses to macro‐outcomes in qualitative strategy process and practice research
Bibliographic record
Abstract
Research Summary : A common challenge among qualitative Strategy Process and Strategy‐as‐Practice scholars concerns the need to link micro‐level processes and practices to organizational‐level outcomes in order to make their research more managerially relevant. In this methodological article, we explore and evaluate different ways of addressing this challenge. We draw on a corpus of qualitative process and practice studies to develop and illustrate three micro–macro linking strategies associated with these perspectives: correlation, progression, and instantiation. The strengths and weaknesses of the different linking strategies are discussed, and opportunities for complementarity, combination, and development are proposed. The article reveals the distinctive but complementary contributions of Strategy Process and Strategy‐as‐Practice strands of scholarship to understanding how microprocesses affect macro‐outcomes. Managerial Summary : Managers engage in a variety of strategic management processes and practices in order to develop and implement better strategies, achieve commitment to them from organization members, and ultimately improve organizational outcomes such as financial performance and competitive advantage. Qualitative research on these processes and practices is valuable because it can capture the detail and richness of strategic management as it is practiced in real organizations over time. Yet, it may not always be easy to see how this kind of research can derive useful knowledge about how these processes and practices actually affect outcomes. This article addresses this issue, identifying three methodological approaches (correlation; progression; instantiation) that can help scholars and managers understand these linkages, outlining their strengths and limitations.
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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 | 0.187 | 0.265 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".