The Key Role of Consumers’ Involvement: The Case of Organic Food Consumption
Bibliographic record
Abstract
This paper aims to provide a better understanding of conditions that influence the gap between positive attitude and intention towards organic food products and actual behaviour regarding these products. Thus, we propose an extended version of the Theory of Planned Behaviour (TPB) to explain parts of this gap and we highlight the crucial role played by consumers’ involvement as a moderator. A structural equation modelling was performed, and the sta-tistical analysis of a sample of 1327 French consumers supports our organic food products buying behaviour model. The results showed that the difference between the means of actual behaviour was highly different between low- and high-involvement consumers. More specifically, high-involvement consumers express more positive attitudes towards buying organic food products, perceive higher subjective norms and behavioural control, they have higher behavioural intention, and buy organic food products more frequently. Additionally, the results indicated that, com-pared to low-involvement consumers, high-involvement consumers regard organic food products as more attractive, healthier, tastier, and with higher value. We proposed some marketing strategies to help managers to better promote the organic food products market and, in turn, increase their revenues. For example, marketers therefore have a vested interest in increasing consumer involvement, and, among other things, they can do so by educating them (i.e., high-lighting the benefits of consuming organic foods). Moreover, since high-involvement customers have positive atti-tude-intention and behaviour, they can be allies for marketers through their influence (social norms). Thus, we suggest the use of digital influencers to endorse organic food.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".