The “ICON” archetype
Bibliographic record
Abstract
Purpose This paper aims to investigate the relationship between a firm's “ICON” archetype, turbulence in its operating environment and its performance. Design/methodology/approach A questionnaire‐based survey of 258 marketing managers in South Africa used a modified ICON scale to identify archetypes, assess perceived turbulence, and measure performance with respect to profitability, market share and growth rate. Findings The archetype to which a firm conforms depends to some extent on its perception of environmental turbulence, and has an influence on all aspects of its performance. “Isolate” firms tend to under‐perform on all measures; “shapers” exhibit significantly higher rates of growth. Research limitations/implications The limitations are associated with mail surveys, single‐respondent bias, and subjective assessment of performance. The study nevertheless demonstrates the validity and usefulness of the ICON matrix and scale, and sets directions for further investigation. Practical implications Offers a simple yet powerful way for marketing managers and planners to identify their firm's ICON archetype, and illustrates the impact it can have on performance. Originality/value A managerially useful adaptation of the original ICON scale is applied beyond the conventional setting of North America or Europe, in a challenging managerial environment.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".