THE STRATEGIC MARKETING FUNCTION IN DYNAMIC MARKETS
Bibliographic record
Abstract
Marketing has a glamorous role in the business world. It is at the forefront of the firm-market interaction, it represents the best compensated activity in an organization. Marketing assumes a leadership role in the firm and sets the tone of the strategic plans. The approach and strategies seen in marketing throughout the market lifecycle are influenced by factors and players, which significantly curtail its role and circumscribe its influence. This article examines the evolution, along the market lifecycle, of marketing’s role and spheres of influence, and proposes two predictive mechanisms that allow for the estimation of “tipping points” for the different characteristics that define the marketing profile along the lifecycle. We introduce the big bang model of market dynamics and examine the behavior of the marketing function depending where the firm is relative to sink holes, transient and steady states and how the market is pulsing defines the marketing priorities. The article further identifies the factors circumscribing the functions and freedoms of marketing along the lifecycle in hi-tech, to develop a clear picture of the various roles that marketing plays as the firm proceeds along the lifecycle from incubation to market maturity.
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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 | 0.004 |
| 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 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".