SEGMENTS OF MARKETERS BASED ON A PERCEIVED IMPORTANCE OF MARKETING KNOWLEDGE AND SKILLS
Bibliographic record
Abstract
The purpose of this article is to define and empirically verify a range of knowledge and skills which are necessary in order to segment marketers, based on their perceptions of the importance of such marketing knowledge and skills. To empirically verify the importance of marketing knowledge and skills, a 28-item measurement instrument was developed. Responses from 235 marketing vice-presidents, marketing directors, sales directors or company presidents/owners in Slovenia were obtained (an 11.8% total response rate). The results reveal four clusters of marketers: marketing specialists, marketing generalists, non-marketers and marketing generals. General and leadership skills are highly evaluated, together with the knowledge and skills related to competition and the company’s market position. Consistent with the general prejudice about marketing people, creative thinking skills and imagination are very important, especially to marketing generalists in our survey. Yet, the knowledge and skills related to consumer behaviour and marketing communication are less important, especially to marketing generals and non-marketers. Certain implications for marketing education and practice arise from the survey.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".