Tackling wicked problems in strategic management with systems thinking
Bibliographic record
Abstract
Strategy scholars are increasingly attempting to tackle complex global social and environmental issues (i.e. wicked problems); yet, many strategy scholars approach these wicked problems in the same way they approach business problems—by building causal models that seek to optimize some form of organizational success. Strategy scholars seek to reduce complexity, focusing on the significant variables that explain the salient outcomes. This approach to wicked problems, ironically, divorces firms from the very social-ecological context that makes the problem “wicked.” In this essay, we argue that strategy research into wicked problems can benefit from systems thinking, which deviates radically from the reductionist approach to analysis taken by many strategy scholars. We review some of the basic tenets of systems thinking and describe their differences from reductionist thinking. Furthermore, we ask strategy scholars to widen their theoretical lens by (1) investigating co-evolutionary dynamics rather than focusing primarily on static models, (2) advancing processual insights rather than favoring causal identification, and (3) recognizing tipping points and transformative change rather than assuming linear monotonic changes.
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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| 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".