Consumer involvement and knowledge influence on wine choice cue utilisation
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the utilisation of product choice cues in a retail environment and the impact of consumer involvement on this utilisation. It further investigates the impact of product knowledge on product choice cue utilisation and its moderating role on the impact of consumer involvement. Design/methodology/approach The case of wine as an exemplary product category is considered, given the importance and variability of choice cues that have been found to affect product choice. Analysis is conducted on survey data from a sample of wine consumers in Ontario, Canada. Product choice cues are grouped into extrinsic, intrinsic and marketing mix. The importance of how these cues are influenced from different dimensions of consumer involvement is illustrated. Findings The results show that product knowledge has a positive impact on intrinsic product cue utilisation and further moderates this relationship improving the predictability of the hypothesised model. Implications for theory and practice are also discussed. Practical implications From an industry viewpoint, the focus in the past has mostly been on using packaging to attract attention/create awareness, create an image of desirability, etc., but not nearly as much on the functionality aspects thereof; for example alternative smaller packaging sizes to the standard 750 ml wine bottle. Originality/value The study uses a multi-dimensional approach to measure the impact of enduring involvement on utilisation of product choice cues.
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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 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".