INTEGRATIVE LEADERSHIP DYNAMICS: FUSING ADAPTIVE STRATEGIES WITH URSIFORM RESILIENCE IN ORGANISATIONAL DECISION-MAKING
Bibliographic record
Abstract
The organisational landscape of the 21st century is characterized by systemic volatility and institutional disruption, requiring leadership frameworks that combine agility with resilience. This study assesses the effectiveness of integrative leadership dynamics, and adaptive strategies with ursiform resilience and how it enhance decision-making processes with a focus on selected telecommunication sector in Nigeria. The research employs a mixed-methods approach that incorporates both descriptive statistics and regression modeling. Through comparative case analyses, the study shows that leaders who incorporate these two dimensions consistently outperform their peers in crisis management and institutional renewal, achieving recovery from disruptions faster. This paper introduces integrative leadership dynamics, a new paradigm that merges adaptive strategies based on Heifetz’s Adaptive Leadership Theory with ursiform resilience, a metaphor representing bear-like endurance, behavioral flexibility, and regenerative capacity. By integrating adaptability and resilience strategic, this model addresses significant shortcomings in traditional leadership approaches that struggle with modern complexities. Key findings highlight a synergistic decision-making culture: adaptive tactics facilitate real-time improvisation and scenario prediction, while ursiform traits promote psychosocial stability and resource sustainability in challenging situations. Organizations that embrace this hybrid approach demonstrate an enhanced ability to balance agility with systemic coherence, turning turbulence into strategic opportunities. Leaders who integrate adaptive and resilience principles exhibited superior crisis management, utilizing real-time improvisation while maintaining systemic coherence. The study recommends institutionalizing integrative leadership through experiential training programs, resilience metrics and scenario-based simulations to foster adaptive fluency. By combining theoretical rigor with empirical validation, this framework provides a practical roadmap for organizations navigating ongoing disruption, transforming volatility into strategic renewal while ensuring ethical and operational resilience in both developing and globalized contexts.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".