The Wine Sector in the Digital Era: An Empirical Evaluation of E-quality Opportunities
Bibliographic record
Abstract
Wine quality has been viewed as critical to the success of online businesses, with the growing understanding that it is one of the main aspects of customer satisfaction and loyalty. The aim of this research is to analyze the multi item web quality construct in its relationship with loyalty intention and satisfaction. The study aims to contribute to the literature by addressing two hypotheses: 1) Does loyalty moderate the relationship between satisfaction and the multi-item construct of web-quality? 2) Does satisfaction moderate the relationship between loyalty and the multi-item construct of web-quality? In order to answer these research questions a questionnaire and an interview were used on a sample of 2.782 users of the nine wine consortia web sites of Friuli Venezia Giulia between November 2013 and November 2014. Friuli Venezia Giulia is an Italian Region of ancient winemaking traditions situated in Italy’s northeast and borders Slovenia and Austria. The web consortia aim to regulate, promote and support the regional wine tradition in Italy and abroad. During the filed research data was gathered trough a structured questionnaire that was comprised using a multi-item scales which referred to the seven dimensions of web quality derived from the literature review (i.e., usability, design, processing speed, information quality, contact, navigability and content). Two moderated regression analyses were conducted to investigate first the moderating effects of loyalty on the relationship between web quality and satisfaction and secondly the moderating effect of satisfaction the relationship between web quality and loyalty. Specifically the results show that usability, design, contacts, navigability and contents are the five items of e-quality that have the main positive influence on customer satisfaction, instead of the processing speed that has a negative impact; when we consider customer loyalty as a dependent variable; the main effects are presented by all the seven items.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".