Toward a better understanding of Organic Products Consumption: The Key Role of Consumers’ Involvement
Bibliographic record
Abstract
Food consumption is at the base of human needs and represents an important part of our everyday life (Gottschalk & Leistner, 2013). Furthermore, the organic food market is probably the most promising sector of the global food industry (Tarkiainen & Sundqvist, 2009). However, the gap, often mentioned in the literature, between positive attitude towards organic products and actual behavior regarding these products still represents a major issue. Thus, in this paper, we propose to address this issue using an extended version of the Theory of Planned Behavior to explain parts of this gap and to highlight the crucial role played by consumers’ involvement as a moderator of all relationships in this robust model. To this end, a structural equation modeling was performed and the statistical analysis of a sample of 1327 French consumers supports our organic products buying behavior model. Involvement is a moderator, especially for high-involvement consumers. In particular, the results show that the perceived behavioral control doesn’t have a direct effect on consumers’ buying behavior, but the inclusion of the moderating role of involvement impacts positively this relation for low-involvement consumers. This article concludes with several relevant suggestions in order to help managers to address this attitude-behavior gap. For instance, as increasing consumers’ involvement with organic products seems to decrease the gap between intention to buy organic products and the actual behavior, we suggest to promote organic consumption through increasing consumers’ involvement with organic products. A short-term strategy could be to enhance involvement by stressing the personal benefits of organic products, while in long-term involvement, and thus organic consumption, can be promoted by emphasizing social and environmental importance of organic consumption. Additional suggestions to develop future researches in the area are also provided in the paper.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".