Impact of product description and involvement on purchase intention in cross-border e-commerce
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the impact of product description and involvement on purchase intention in a cross-border e-commerce (CBEC) setting from a psychological perspective. Design/methodology/approach This study proposes a research model of purchase intention in CBEC based on the involvement theory and commitment-involvement theory. The research model was tested using the covariance-based structural equation modeling technique. Data were collected from consumers on a popular CBEC platform in China. Findings A high-quality product description has no significant positive effect on purchase intention, but it has significant positive effects on product cognitive involvement, product affective involvement, platform enduring involvement and platform situational involvement. In addition, product affective involvement, platform enduring involvement and platform situational involvement all have significant positive effect on purchase intention, but this effect is not significant in the relationship between product cognitive involvement and purchase intention. Practical implications This study calls for sellers to optimize product descriptions on CBEC platforms in order to attract more buyers and generate more profits. Originality/value This study integrates two theories of involvement into the research model in the CBEC context. Based on this model, the authors analyzed how product description affects purchase intention under the joint influence of two involvement factors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it