Positioning for optimal distinctiveness: How firms manage competitive and institutional pressures under dynamic and complex environment
Bibliographic record
Abstract
Abstract Research Summary How firms strategically balance legitimacy and distinctiveness has garnered significant attention but reflects inconsistent perspectives. This inconsistency may stem from the inherent complexity of optimal distinctiveness (OD), which are sensitive to both the context and temporality. This article explores dynamic changes in institutional and competitive pressures and how they co‐evolve with different OD strategies. Through an exploratory, multi‐case study, we propose a pressure‐response model to uncover how firms dynamically pursue OD in response to different combinations of pressures. Furthermore, our findings reveal the mechanisms that drive the dynamic interactions between distinctiveness and legitimacy across different OD strategies. In essence, this study contributes to the OD research agenda by providing insights into the evolution of OD strategies, addressing the how and why behind their development. Managerial Summary Can enterprises effectively balance their needs for legitimacy and distinctiveness by achieving an optimal level of similarity and differentiation from their competitors? This article demonstrates that, in the face of multiple pressures with varying intensities, enterprises continuously adapt their strategic choices to achieve optimal distinctiveness (OD). As institutional and competitive pressures gradually intensify, an enterprise's OD strategies may transition from isomorphic and balancing approaches toward deviation. However, when accumulated inertia hinders the enterprise's ability to respond to emerging pressures, adjustments to the OD strategy may become necessary. Therefore, this study offers entrepreneurs a practical guide on how to dynamically maintain their enterprise's OD by selecting appropriate strategies based on the specific circumstances at hand.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".