Driving Online Green Product Loyalty Through Website Personalization and Hedonic Quality Enjoyment: Evidence from Vietnamese E-Commerce Consumers
Bibliographic record
Abstract
Amidst the rise of environmental awareness and e-commerce growth in Vietnam, understanding customer loyalty (CL) towards online-purchased environmentally friendly products (EFPs) remains an underexplored yet critical issue.This study examines the influence of website personalization (WP) and hedonic quality enjoyment (ENJ), in conjunction with customer trust (CT) and customer satisfaction (CS), on customer loyalty among Vietnamese e-commerce consumers.This study tested a model integrating these constructs using a quantitative survey of 668 Vietnamese online EFP buyers and Partial Least Squares Structural Equation Modeling (PLS-SEM).Findings reveal that ENJ significantly enhances CS, while WP indirectly influences CS through ENJ and CT, ultimately driving CL.CT and CS positively affect CL, with CS showing a stronger effect.This research offers novel insights into the mediating roles of experiential and trust factors in fostering online EFP loyalty in an emerging market context.It underscores the importance of e-commerce platforms strategically enhancing hedonic experiences and trust to cultivate loyalty among environmentally conscious consumers in Vietnam.These findings contribute to green marketing and e-commerce literature by clarifying indirect personalization mechanisms in a unique cultural setting.These findings provide actionable insights for e-commerce platforms to enhance user experiences and foster sustainable consumption in emerging markets.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.006 | 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".