Examining the impact of advertising, firm-generated content, and user-generated content on customers’ propensity to buy food online
Bibliographic record
Abstract
Abstract This study examines the impact of advertising, firm-generated content (FGC), and user-generated content (UGC) on the customer's propensity to buy food online. An online survey questionnaire was developed and administered to users of food aggregators. The collected responses were analyzed using partial least squares structural equation modeling to examine the relationships between advertising, user-generated content (UGC), firm-generated content (FGC), and customers’ propensity to buy. The findings reveal that both advertising and firm-generated content (FGC) influence customers' propensity to buy. However, user-generated content (UGC) does not appear to have a significant impact. Additionally, advertising does not directly influence the creation of UGC, suggesting that customers may not produce content purely based on advertising, and other factors likely play a role in encouraging UGC. This study offers valuable insights into the varying impacts of different marketing communication channels on consumer behavior within the food aggregator context. By differentiating the effects of advertising, FGC, and UGC, the research deepens our understanding of how these channels influence customers' propensity to buy. The implications of these findings can help marketing managers better understand the flow of communication across these mediums.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".