Revisiting the ‘concentration vs spreading debate’: perceived risk and strategic flexibility in decision-making following an unanticipated environmental market disruption
Bibliographic record
Abstract
Managing risk is an important aspect of owner-managers’ decision-making; however, the impact of major environmental market disruptions remains largely under-researched. Underpinned by an effectuation lens, this study examines decision-making associated with the ‘concentration versus spreading debate’ (focusing on a single or limited number of product-market strategies rather than diversifying across a variety of product-markets). The context features post natural-disaster recovery strategies following a high magnitude earthquake that immediately ended firms’ sales within their local proximity. The study employs a longitudinal qualitative research design involving 16 smaller-sized wine producers in the Canterbury/Waipara Valley cluster of New Zealand, illustrating different degrees of strategic flexibility among owner-managers. Unique insights offer varying ‘how and why’ perspectives into decision-making regarding the extent to which product-market strategies differed across core and augmented product portfolios and geographic markets prior to and following the disruption caused by the unanticipated natural disaster.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".