Attitudes towards m-wine purchasing A cross-country Study
Bibliographic record
Abstract
The objective of this paper is to focus on exploring consumer perceptions regarding m-wine purchasing (buying wine through mobile commerce) between different countries. This topic is of major importance nowadays especially based on the research firm Gartner Inc statement that predicts that m-commerce (mobile commerce) will soon overtake e-commerce (Gartner, 2011). Furthermore, like other industries, the wine industry began using the Internet in the 1990s but the early adopters were constrained by complicated wine shipping regulations, security concerns by customers, among other things (Bruwer & Wood, 2005; Gebauer & Ginsburg, 2003; Quinton & Harridge-March, 2003; Thach, 2009). Following Lockshin & Corsi’s suggestions (2012), we are investigating one of the areas with the greatest research needs: m-wine purchasing. To answer our research question, a quantitative study examined mobile phone ownership, wine purchasing and consumption, and wine purchasing via mobile phones across six countries each of which varies in terms of wine consumption levels (Trade Data and Analysis, 2011), Internet penetration (International Telecommunications Union, 2013; United States Census Bureau, 2012) and mobile phone usage (Adobe, 2013; ComScore, 2013 ; Kaplan, 2012).This research involved 3317 respondents from six countries, including France, Germany, Greece, South Africa, the U.S.A and Canada. Data was collected between October 1 and December 15, 2013, using both personal and online questionnaires. The online survey resided on a landing page designed using responsive web design (e.g. adaptable to all screen sizes and devices). The questionnaire was structured into three sections: (1) use of mobile phone (2) wine purchasing and consumption and (3) wine and mobile
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".