Acquiring market flexibility via niche portfolios
Bibliographic record
Abstract
Purpose This paper seeks to establish that the instability of niche markets, and their predisposition to catastrophic collapse, makes market flexibility a prerequisite for long‐term survival among niche marketers. It describes the two ways by which a niche marketer can acquire this market flexibility and demonstrates the advantages of the second of these two approaches, i.e. the development of a portfolio of separated niches. Design/methodology/approach An in‐depth discussion of niche instability/implosion, and how niche market flexibility can be acquired to increase the survivability of such events, provides the context for a single in‐depth case study of a company employing a systematic niche market flexibility approach. A multi‐method approach was adopted drawing on both interviews and documentary evidence. Findings Planning for flexibility is essential for long‐term survival as a niche marketer. Two broad approaches to achieve this exist – i.e. contingency and portfolio planning – which are not mutually exclusive. The portfolio approach offers specific advantages and examples of its successful applications exist. Research limitations/implications This is a single case study. Practical implications The article has significant implications for practice, as fragmentation of markets and globalisation of production makes niche marketing desirable/essential for many players. Originality/value The area of planning for flexibility using a niche portfolio marketing strategy is under‐researched at present.
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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 | 0.002 | 0.006 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".