| Literature DB >> 32003493 |
Mark Pilling1, Natasha Clarke1, Rachel Pechey1, Gareth J Hollands1, Theresa M Marteau1.
Abstract
AIM: To estimate the effects of wine glass size on volume of wine sold in bars and restaurants.Entities:
Keywords: Alcohol; choice architecture; glass size; mega-analysis; multiple treatment reversal design; portion size; purchasing; replication; sales; wine
Mesh:
Year: 2020 PMID: 32003493 PMCID: PMC7496108 DOI: 10.1111/add.14998
Source DB: PubMed Journal: Addiction ISSN: 0965-2140 Impact factor: 6.526
Establishment and data set characteristics
| Data set | Establishment | Setting | Study date | Glass sizes (ml) | Sales by‐the‐glass (%) | Days |
|---|---|---|---|---|---|---|
| 1 | I | Bar | 2015 | 250(C) 300(A) 370 (B) | 93 | 112 |
| 2 | III | Bar | 2016 | 300(C) 510(B) | 88 | 98 |
| 3 | II | Bar | 2016 | 300(C) 370(A) 510 (B) | 88 | 123 |
| 4 | II | Bar | 2018 | 290(C) 350(A) 450 (B) | 90 | 125 |
| 5 | IV | Bar | 2018 | 290(C) 350(A) 450 (B) | 91 | 136 |
| 6 | I | Restaurant | 2015 | 250(C) 300(A) 370 (B) | 63 | 112 |
| 7 | V | Restaurant | 2017 | 290(C) 350(A) 450 (B) | 66 | 126 |
| 8 | V | Restaurant | 2018 | 290(C) 350(A) 450 (B) | 67 | 189 |
See Supporting information, Table S1 for details on each design, using this ABC key.
Regression models assessing the impact of wine glass size on log volume of daily wine sold for (i) bars‐only, (ii) restaurants‐only and (iii) overall.a
| Bar estimate [ | %change (95% CI) | Restaurant estimate [ | %change (95% CI) | Overall estimate [ | %change (95% CI) | |
|---|---|---|---|---|---|---|
| Modelling of the mean log wine volume | ||||||
| (intercept) | 2.876 [< 0.001] | NA | 4.814 [< 0.001] | NA | 4.054 [< 0.001] | NA |
| Glass size 250 ml | 0.058 [0.408] | 6.0% (−7.6 to 21.6) | −0.101 [0.068] | −9.6% (−19.0 to 0.7) | −0.040 [0.326] | −4.1% (−11.9 to 4.3) |
| 300 ml | Ref | Ref | Ref | |||
| 370 ml | −0.013 [0.728] | −0.5% (−8.1 to 6.1) | 0.071 [0.013] | 7.3% (1.5 to 13.5) | 0.042 [0.062] | 4.3% (−0.2to 9.1) |
| 450 ml | 0.010 [0.850] | 1.0% (−9.1 to 12.2) | 0.009 [0.784] | 0.9% (−5.5 to 7.7) | 0.005 [0.867] | 5.0% (−5.2 to 6.5) |
| 510 ml | −0.004 [0.926] | −0.4% (−9.4 to 9.4) | NA | NA | 0.028 [0.537] | 2.8% (−5.9 to 12.4) |
| Busyness level (log) | 0.923 [p < 0.001] | 151.7% (126.4 to 180.0) | 0.685 [< 0.001] | 100.3% (76.9 to 126.9) | 0.711 [< 0.001] | 103.5% (89.7 to 118.3) |
| Day: Monday | Ref | Ref | Ref | |||
| Tuesday | 0.018 [0.779] | 1.8% (−10.2 to 15.5) | 0.023 [0.671] | 2.3% (−7.9 to 13.7) | 0.050 [0.242] | 5.1% (−3.3 to 14.3) |
| Wednesday | 0.071 [0.246] | 7.3% (4.8 to 21.0) | 0.205 [< 0.001] | 22.8% (9.5 to 37.6) | 0.179 [< 0.001] | 19.6% (10.1 to 30.0) |
| Thursday | 0.115 [0.071] | 12.2% (9.5 to 27.2) | 0.150 [0.011] | 16.2% (3.5 to 30.4) | 0.180 [< 0.001] | 19.7% (10.0 to 30.1) |
| Friday | 0.145 [0.044] | 15.6% (0.4 to 33.1) | 0.411 [< 0.001] | 50.7% (35.4 to 67.8) | 0.380 [< 0.001] | 46.2% (34.5 to 58.9) |
| Saturday | 0.149 [0.079] | 16.1% (1.7 to 37.1) | 0.455 [< 0.001] | 57.6% (36.5 to 82.0) | 0.438 [< 0.001] | 54.9% (40.0 to 71.5) |
| Sunday | −0.054 [0.353] | −5.3% (15.5 to 6.2) | −0.129 [0.042] | −12.1 (−22.4 to −0.5) | −0.047 [0.276] | −4.6% (−12.4 to 3.8) |
| Year: 2015 | Ref | NA | Ref | |||
| 2016 | 0.329 [< 0.001] | 39.0% (25.0 to 54.5) | Ref | 0.313 [< 0.001] | 36.7% (25.6 to 48.8) | |
| 2017 | NA | 0.089 [0.041] | 9.3% (0.4 to 19.0) | 0.135 [< 0.001] | 14.5% (6.6 to 23.0) | |
| 2018 | 0.136 [0.007] | 14.6% (3.8 to 26.6) | 0.028 [0.480] | 2.9% (−4.9 to 11.2) | 0.089 [0.004] | 9.3% (3.0 to 16.0) |
| Temperature (C) | −0.016 [< 0.001] | −1.5% (−2.2 to −1.0) | −0.007 [0.010] | 0.7% (−1.2 to −0.2) | −0.009 [< 0.001] | −0.9% (−1.3 to −1.0) |
| Bank holiday, no | Ref | Ref | Ref | |||
| Yes | −0.069 [0.429] | −6.6% (−21.2 to 10.7) | 0.053 [0.526] | 5.4% (−10.4 to 24.0) | 0.016 [0.741] | 1.6% (−7.5 to 11.6) |
| School holiday, no | Ref | Ref | Ref | |||
| Yes | −0.007 [0.840] | −0.7% (−7.2 to 6.2) | −0.058 [0.039] | −5.6% (−10.6 to −0.3) | −0.021 [0.369] | −2.0% (−6.3 to 2.5) |
| World Cup or UEFA football | ||||||
| No | Ref | Ref | Ref | |||
| Yes | −0.120 [0.005] | −11.3% (−18.3 to −3.7) | −0.075 [0.194] | −7.3% (−17.2 to 3.9) | −0.088 [0.009] | −8.5% (−14.3 to −2.2) |
| England football, no | Ref | Ref | Ref | |||
| Yes | −0.241 [0.035] | −21.4% (−37.2 to −1.7) | 0.119 [0.248] | 12.6% (−7.9 to 37.8) | −0.076 [0.409] | −7.3% (−22.6 to 11.0) |
| Setting, bars | NA | NA | Ref | |||
| Restaurants | NA | NA | 0.676 [< 0.001] | 96.6% (87.5 to 106.2) | ||
P‐value < 0.1;
P‐value < 0.05;
P‐value < 0.01;
P‐value < 0.001.
The outcome is the daily volume of wine sold on the natural log scale. Parameter 95% confidence intervals (CI) and P‐values, respectively, appear in parenthesis and in square brackets. Ref = reference category; NA = not applicable; UEFA = Union of European Football Associations.
Figure 1Predicted percentage change (with 95% confidence interval) in daily wine volume sales from 300‐ml glasses in (i) bars and (ii) restaurants. The red line indicates zero change compared to sales with 300‐ml glasses. [Colour figure can be viewed at wileyonlinelibrary.com]