A time series projection model of online seasonal demand for American wine and potential disruption in the supply channels due to COVID-19
Bibliographic record
Abstract
Purpose This study statistically examines the shifting distribution channels in the American wine industry based on the growth trajectory of sales, seasonality and disruption due to consumers switching to online platforms. The purpose of this paper is to design a model that will have general applicability beyond the wine industry. Design/methodology/approach The research uses regression-based additive decomposition of time series data to predict the trajectory of the market share for the digital distribution channel. The study develops a statistical prediction model using time series data between 2007 and 2020, inclusive, sourced from US Annual Wine Reports and Bureau of Alcohol, Tobacco and Firearms databases. Findings The results show an increasing trajectory of wine sales through the online distribution channel with predictable seasonality. The disruptive effects of consumer switching behavior point to a steady increase in sales due both to increasing demand and accelerating switching. Nevertheless, the model shows that bricks and mortar purchases will remain strong and continue to account for the bulk of wine sales. COVID-19 has caused a step function increase in online sales but this should moderate after the crisis subsides and can be tested further. Originality/value This study is original in developing a model for an industry where bricks and mortar sales are growing and are expected to remain strong while there is still identifiable switching to online sales. The wine industry presents a classic case of accelerating switching behavior where there is still a strong franchise for in-store purchases. The model should have general applicability to distribution channels beyond the wine industry where steady growth, marked seasonality and disruptive consumer switching are in evidence.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".