The Impact of Online Reviews on Sustainable Product Adoption in the Food Industry: A Serial Mediation Effect of Consumer Trust and Perceived Value
Bibliographic record
Abstract
This research analyzes the effect of online reviews on the adoption of organic goods in the context of a sustainable product through the serial mediation of consumer trust and perceived value. In marketing, online reviews are an important aspect of newly emerging product and service strategies as they affect customer behavior and perception regarding the quality, credibility, and value of a product. Primary data was collected from 290 respondents in Pakistan using a structured questionnaire. According to the findings of the study, it was found that online reviews are profoundly consequential to the sustained adoption of organic food products, with consumer trust and perceived value as the main mediating variables. Online trust increases consumers’ confidence in the product, which enhances the perceived value and adoption rate of the product. The mediation analysis also corroborated that the collective moderating effect of trust and perceived value cuts across the positive effects produced by the online reviews in a consumer’s decision. The outcomes provide useful information for marketers who operate within the organic food sector, particularly on how consumer trust can be enhanced, and product value can be articulated via online reviews to promote sustainable consumption practices.
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 | 0.004 | 0.039 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".