Chaos, Complexity, and Contingency Theories: A Comparative Analysis and Application to the 21st Century Organization
Bibliographic record
Abstract
Organizations in the 21st century deal with constant changes such as globalization, technological evolutions, regulatory changes, competition, and other unexpected events, among others. These challenges can be viewed and addressed through the lenses of contemporary theories. This paper selected three contemporary theories namely chaos, complexity, and contingency theories, and presented their foundations and characteristics by comparing and contrasting their key concepts. These concepts include nonlinearity, feedback, bifurcation, strange attractors, fractals, and self-organization for chaos theory; nonlinearity, dynamism, feedback, self-organization, emergence, and adaptability for complexity theory; and adaptation, equifinality, effectiveness, and congruency for contingency theory. Examples of studies and organizational applications of these theories were provided, and implications for scholars and organizational leaders were discussed. By explaining notions such as how the capacity of a system could be greater than the sum of the capacities of its subunits, this paper can act as a starting point for anyone seeking to understand the three theories or use them for research or organizational purpose.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".