A Leap Forward Path Model of Niche Based on Brand Ecological Theory
Bibliographic record
Abstract
Based on the theory of brand ecology and evolutionary economics, this paper constructs a model of the transition path of brand niche and clarifies the driving mechanism of the transition of brand niche from the perspective of consumer cognition, that is, brand entrepreneurs under the leading role of heterogeneous human capital Bring out the innovative spirit of brand entrepreneurs through the three elements of property rights, system, and culture, and follow satisfactory decisions to search for consumer cognition to form value co-creation (value niche).Relying on the mobile phone industry, a consumer cognitive niche system has been constructed and the importance of various factors affecting the industry niche has been identified.Specifically, the mobile phone brand niche system is composed of 12 factors at the product level, brand level, and industry level.Among them, the key factors mainly include "quality perception, performance perception, functional innovation, brand reputation, and technological change."The high factor weight is a key factor that affects consumer brand perception and a key indicator that determines the transition of the ecological niche.Taking the Apple mobile phone as a specific case, the niche at each time point was measured and the niche transition was proved to be a historical process, and the comprehensive evaluation scores of the niche of 6 mobile phones were calculated and ranked.The results of the study show that the overall trend is rising first and then stabilizing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".