Facilitating emergence: Complex, adaptive systems theory and the shape of change
Bibliographic record
Abstract
This study used Principal Component Analysis to examine factors that facilitate emergent change in an organization.As organizational life becomes more complex, today's dominant management paradigms no longer suffice.This is particularly true in a health care setting where multiple sources of disease interacting with each other meet with often-competing organizational priorities and accountabilities in a highly complex world.This study identifies new ways of approaching complexity by embracing the capacity of complex systems to find their own form of order and coherence.Based on a review of the literature, interviews with hospital CEOs, and my organization development practice experience in the health care sector, I identified nine constructs of interest: a strategic framework; organizational culture; work structures; CEO and executive team; leadership culture; quality control systems; accountability framework; learning structures; and feedback processes.One hundred and sixty-two senior leaders, managers, and staff at a hospital in Toronto, Canada, who had completed an eight-week leadership program, completed an Emergence Survey © based on the nine constructs of interest.The survey includedLikert items representing the nine constructs, as well as opportunities to provide narrative feedback.In the initial analysis of the survey results, the items taken as a whole would not converge on a clear set of components.It was also clear that the mean for most of the items was very high.I theorized that the size of the sample and possibility that they were a favorably biased convenience sample because they had self-selected as leaders may have contributed to the lack of convergence and high mean.I then theorized three clusters of constructs, based on what appeared to be natural affinities.At that point I facilitated two focus groups with people who were among the survey group.Both focus groups affirmed the importance of each of the factors in improving organizational performance indicators such as patient satisfaction, staff v engagement, and quality.I then completed a principal component analysis of each of the three clusters of constructs.From this analysis, seven components emerged.Five of these, executive engagement, safe-fail culture, collaborative decision-processes, a collaborative quality, and intentional learning processes had reliability >.70; culture of experimentation and purposeful orientation had reliability < .70.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".