Navigating storms together: team involvement in crisis decision-making and strategic ambidexterity in Chinese firms
Bibliographic record
Abstract
Purpose This study examines how team involvement in crisis decision-making within Chinese firms impacts the development of strategic ambidexterity in response strategies. It demonstrates how team-based decision-making fosters the integration of explorative and exploitative strategies, the moderating role of threat from the crisis and the influence of ambidexterity on ultimate decision quality. Design/methodology/approach A quantitative survey was conducted with 287 business executives and senior managers from various Chinese industries. Respondents reflected on recent crisis situations, evaluating team involvement, crisis-induced threat level and the characteristics of their crisis response strategies. Ordinary least squares (OLS) regression was used to test the proposed hypotheses. Findings Team involvement in crisis decision-making significantly supports the development of an ambidextrous recovery strategy that balances exploration and exploitation. This relationship strengthens under conditions of higher threat from the crisis, highlighting the increasing value of collaborative decision-making in severe crises. Ambidextrous strategies are linked to higher quality assessments of decision outcomes. Originality/value This study contributes to crisis management and strategic ambidexterity research by showing how team involvement fosters ambidextrous strategies, particularly under severe crisis-induced threat conditions. The findings offer practical insights for managers, emphasizing the importance of team-based decision-making and balanced strategic approaches during crises. The study provides a culturally specific view of these phenomena within the context of Chinese firms, extending theoretical understanding and offering actionable guidance for practitioners.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".