Inciting consumers to buy fairly‐traded products: a field experiment
Bibliographic record
Abstract
Purpose Research on fairly‐traded products has shown that changing consumers' attitudes may not be the best strategy to bring consumers to purchase these products. The objective of this study is to examine a different, non‐cognitive approach based on the utilization of behavioral influence strategies. Design/methodology/approach A field experiment was conducted involving 168 consumers. The experiment took place in the context of a commercial stand in which fairly‐traded products such as coffee, sugar, chocolate, and so on, were sold. Three factors were manipulated: the concreteness of the information provided to visitors; the provision of information about the popularity of fairly‐traded products among relevant others; and the possibility of receiving concrete feedback from a producer. Findings The paper finds that, contrary to what was expected, abstract information led to a greater amount of money spent on average by visitors. In addition, knowing that fairly‐traded products were popular among relevant others had a significant impact on money spent only when feedback was not offered to the participants. Research limitations/implications A field experiment does not offer a high degree of control over nuisance variables. The application of the manipulations and the randomization of participants in this study were therefore not optimal. Practical implications Managers involved in the marketing of fairly‐traded products who communicate with potential buyers using concrete messages should make sure that consumers are attentive to their messages. These messages should inform consumers that fairly‐traded products are purchased by relevant others. Originality/value The paper provides useful information on how to influence consumers' attitudes to purchasing fairly‐traded products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".