Antecedents and consequences of satisfaction regarding apparel bought online during the COVID-19 pandemic
Bibliographic record
Abstract
Purpose Although well documented for physical stores, consumer motives for buying apparel online have been poorly investigated. Drawing on the social exchange theory (SET), the authors tested a framework that relates time savings, effort savings and money savings to satisfaction, e-loyalty and e-word-of-mouth (e-WOM). Design/methodology/approach A cross-sectional, web-based survey was conducted in Canada during the coronavirus (COVID-19) pandemic. Data were collected from 247 participants who made online clothing purchases and analysed using partial least-squares structural equation modelling. The reliability and validity of the measurement model were assessed, and the path coefficients of the structural model were estimated. Findings Money savings have a strong effect on e-satisfaction, which in turn determines e-loyalty and e-WOM. Time savings have also been found to influence e-satisfaction, whereas effort savings have no influence. Finally, the results indicate that e-satisfaction competitively mediates the relationship between money savings and both e-loyalty and e-WOM. Originality/value Utilising the SET, this study contributes to deepening the knowledge of online clothing purchase in the context of the COVID-19 pandemic. The authors provide a comprehensive view of the mechanisms through which time savings and money savings are the strongest drivers of customer satisfaction, which in turn influence customer loyalty and e-WOM when buying clothes online.
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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 | 0.011 |
| 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".