Strategic ambiguity: a systematic review, a typology and a dynamic capability view
Bibliographic record
Abstract
Purpose While strategic ambiguity has increasingly been used as a communication practice in response to crises such as the COVID-19 pandemic and global conflicts, its proactive role in shaping organizations remains underexamined. Moreover, a comprehensive investigation into its antecedents, moderators, mechanisms, and outcomes – aligned with specific strategic ambiguity aims – is still lacking. We investigate how organizations deploy strategic ambiguity to shape their environment and identify the factors that affect the effectiveness of strategic ambiguity in achieving diverse strategic aims. Design/methodology/approach We conducted a systematic literature review (SLR) of 22 empirical studies on strategic ambiguity in organizational communication. We analyzed articles using the Gioia method to identify its key components – antecedents, mechanisms, moderators, and outcomes – based on the pursued aim. Findings We reframe strategic ambiguity as a dynamic capability and, building on this, we introduce a novel typology of strategic ambiguity based on two key dimensions: organizational flexibility (centralized vs decentralized) and environmental responsiveness (proactive vs reactive). Four distinct aims of strategic ambiguity, each with specific antecedents, mechanisms, moderators, and outcomes, emerge: (1) collaboration and engagement, (2) flexibility and adaptability, (3) control and influence and (4) reputation and legal protection. Originality/value We reframe the understanding of strategic ambiguity by positioning it as a dynamic capability rather than merely a strategic communication practice. By introducing a typology that outlines antecedents, mechanisms, moderators, and outcomes for each specific aim, we offer a structured framework for comprehensively understanding and leveraging strategic ambiguity.
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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 | 0.024 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.038 | 0.037 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".