The impact of reference effects on online purchase intention of agricultural products
Bibliographic record
Abstract
Abstract Purpose The purpose of this paper is to analyze the impact of reference effects on online purchase intention (OPI) of agricultural products in B2C context and to examine how consumers' food safety consciousness (FSC) moderates that impact. Design/methodology/approach An empirical survey was used to test the hypotheses. Data were collected from a total of 297 online consumers in China. A structural equation modeling is utilized to assess the relationships proposed in the research model. Findings The findings of this study show that reference effects have a significant impact on OPI of agricultural products. Both perceived value (PV) and perceived risk (PR) play a mediating role in the relations between reference effects and OPI, but the mediating effect of the PV is found to be significantly greater than that of the PR. Consumers' FSC significantly and positively moderates the impact of reference effects on OPI, meaning that the more attention consumers pay to food safety, the greater the impact of reference effects on OPI will become. Research limitations/implications First, this study mainly analyzes the positive impact of reference effects on OPI. Future research could discuss the negative impact of reference effects and compare the differences between them. Second, this study only takes the PV and PR as mediators into the research model. Future research could consider adding trust, attitude, and other variables and further explore and clarify the influencing mechanism between reference effects and OPI. Third, this study examines the moderating role of consumers' FSC but does not fully discuss the moderating role of product categories. Further research could compare the influence of reference effects among multiple product categories. Practical implications This study provides valuable insights for agricultural enterprises and online vendors that reference effects are one of the most important factors to influence OPI. It suggests to agricultural enterprises and online vendors that reference effects can be used as a new instrument to influence consumers' online purchase decisions. Originality/value This study for the first time defines reference effects in an online setting and introduces the perspective of reference effects to establish a theoretical model to explain consumers' OPI of agricultural products. The study reveals the influencing mechanism of reference effects on OPI and thus enriches the theory of online purchase behavior.
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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 | 0.012 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".