Modeling the relationship between perceived values, e-satisfaction, and e-loyalty
Bibliographic record
Abstract
Perceived value, E-satisfaction, and E-loyalty are widely discussed in the practitioner literate and considered as critical factors for the success of E-commerce.Those constructs still contribute to the significant impacts on cross-border E-commerce, which is a part of E-commerce.However, Cross-border Ecommerce, particularly for Sino-Thai Cross-border E-commerce, as an emerging market, does not draw enough attention from scholars.Hence, the lack of theoretical and empirical researches leads to few or limited support or guide for suppliers and governments to tackle this complex issue.The study aims to develop and empirically examine the interrelationships between Perceived Value (FV, PDV, EV & SV), E-satisfaction, and E-loyalty in Sino-Thai cross border e-commerce based on China's customers.Meanwhile, it attempts to manifest the mediation impacts on the associations between Perceived Value (FV, PDV, EV & SV) and E-loyalty through E-satisfaction.The questionnaire lasted over 3 months in 2019 for data collection and was conducted with 381respondents who had shopping experience in the platforms of Sino-Thai Cross-border E-commerce, by using self-administrated questionnaires.Confirmed factor analysis and structural equational model were performed in Amos 24 to test the hypotheses and analyze the collected data.The empirical findings elucidate that perceived functional value, procedural value, and social value except for emotional value, significantly and positively impact on e-loyalty through e-satisfaction.Moreover, the findings stress that the full mediating effect of e-satisfaction on the relationships between FV, PDV, SV, and E-loyalty as well.In light of this, the findings of this study make an effort on the development of the model based on those 3 constructs in Cross-border E-commerce and offer strategic insights for the entrepreneurs and governments in this field.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".