Optimal distinctiveness: Broadening the interface between institutional theory and strategic management
Bibliographic record
Abstract
Research summary : A ttaining optimal distinctiveness—positive stakeholder perceptions about a firm's strategic position that reconciles competing demands for differentiation and conformity—has been an important focal point for scholarship at the interface of strategic management and institutional theory. We provide a comprehensive review of this literature and situate studies on optimal distinctiveness in the broader scholarly effort to integrate institutional theory into strategic management. Our review finds that much extant research on firm‐level optimal distinctiveness is grounded in the strategic balance perspective that conceptualizes conformity and competitive differentiation as a trade‐off along a single organizational attribute. We argue for a renewed research agenda that draws on recent developments in institutional theory to conceptualize organizational environments as more multiplex, fragmented, and dynamic, and discuss its implications for core strategic management topics . Managerial summary : T his article aims to provide managers with a more comprehensive and contemporary view of how firms can become optimally distinct—being different enough from peer firms to be competitive, but similar enough to peers to be recognizable. We aim to equip managers with an understanding of firms as complex, multidimensional entities, and encourage them to identify and orchestrate various types of strategic resources to reconcile conformity versus differentiation tensions, address the multiplicity of stakeholder expectations, and aptly modify their positioning strategies in order to succeed in dynamic environments . Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.032 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".