Engineering emotional product identities in high-luxury vehicles
Bibliographic record
Abstract
This paper aims to describe one avenue of a programme of research into brand identity and its relationship to engineering product concepts at Bentley Motors Limited. We start with a review of the automotive market place, showing that it has become ‘commoditised’; functional product properties have reached a level of technical parity and distributional saturation and thus branding and style have become the new ‘attractive product qualities’. We will discuss how within the high-luxury and ‘pinnacle’ automotive markets, brand associations - personal beliefs, values and emotions, and brand identity, as expressed through the lineage of product design - are especially salient in creating differentiation and commercial advantage. This results in automakers’ seeking brand-focused design and engineering strategies in order to promote brand identities through multi-sensory product property stimuli. We respond to this background with one of a series of studies into the lineage of product properties and vehicle features at Bentley Motors. Drawing upon contemporary research from cognitive science and marketing into concept identity recognition and categorisation, quantitative and qualitative data from focus groups is analysed to propose diagnostic scales of ‘typicality’ for vehicle properties. From this we demonstrate that the identification and diagnosis of ‘Bentleyness’, the perceived fit between the brand and its products, varies and is weighted for different product features.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".