Strategy by Doing and Product-Market Performance: A Contingency View
Bibliographic record
Abstract
The strategy-by-doing perspective argues that firms operating in highly dynamic environments can benefit from taking strategic actions in lieu of advance planning because such actions have learning effects that help the firm keep pace with changes in the environment. The implicit assumption is that strategy by doing is effective in dynamic environments but likely not in stable environments. This study challenges this notion and expands the purview of the strategy-by-doing perspective. We first argue that strategy by doing is generally an effective strategy due to the organizational learning it facilitates. We next discuss how environmental dynamism is multidimensional, encompassing both market and technological dynamism. The positive effects of strategy by doing on product-market performance are amplified in highly dynamic environments that feature high levels of both market and technological dynamism. We go on to argue that stable environments are also suitable for strategy by doing, where it can facilitate opportunity creation. However, strategy by doing may hinder performance in mixed environments where one form of dynamism is present and the other is not. Focusing on strategy by doing in the form of product changes, our analysis of 4,000 firms over a period of 20 years shows support for our arguments about environmental contingencies affecting the relationship between strategy by doing and performance. We discuss how these findings have implications for theory and practice.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".