Bibliographic record
Abstract
Identifying market evolution is a necessary step in persistence analysis of marketing input–output relationships. Using the advertising–sales relationship to represent general marketing input–output dynamics, the authors theoretically distinguish two types of market evolution: (1) intrinsic evolution, in which sales evolve independent of advertising and temporary advertising can generate persistent effects, and (2) induced evolution, in which sales evolution is supported by sustained advertising budgets in an intrinsic-stationary market and there are no real persistent effects of temporary advertising. The proposed intrinsic market evolution test can identify intrinsic-evolving and intrinsic-stationary markets. The authors analyze five major budgeting implications and provide methods to quantify temporary and sustained budgeting. In general, in an intrinsic-evolving market, budgeting can be short-term focused, whereas in an intrinsic-stationary market, the focus should be on sustained budgeting. Percentage budgeting at a sufficient level can create induced evolution. Contrary to conventional wisdom, temporary, intensive advertising campaigns are often not necessary. Empirical illustrations demonstrate the two types of evolutions and the relationships between budgeting methods and sales performance.
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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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".