Toward a dynamic model of organizational resilience
Bibliographic record
Abstract
Purpose This study aims to examine contemporary research on organizational resilience and then propose an integrated dynamic model to study organizational resilience with a more inclusive concept and future research agenda developed. Design/methodology/approach This conceptual paper uses the literature review method to analyze and categorize current research on organizational resilience, and then based on the analysis of existing organizational resilience studies, this paper proposes an integrated model for a more inclusive and integrated concept of organizational resilience with refined future research directions. Findings A thorough analysis of current organizational resilience research shows that existing studies on organizational resilience have largely focused on isolated dimensions by treating organizational resilience as a state rather than a dynamic capability. This paper proposes that an integrated concept of organizational resilience consists of three dimensions including cognitive, behavioral and contextual resilience, and this dynamic capability should be examined from three different levels, including individual, group and organizational levels to better conceptualize organizational resilience and for better applicability in management practice. Originality/value The past decades have seen increasing interests in organizational resilience both from academic scholars and from management practitioners. However, research on this emerging field remains fragmented, and there is little consensus on the conceptualization of organizational resilience. This study contributes to the literature by thoroughly examining current research on organizational resilience and proposes an integrated dynamic model to study organizational resilience.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".