Social Influence on online wine evaluations at a wine social networking site: Effects of consensus and expertise Omer Gokcekus and Miles Hewstone (Seton Hall University) (Oxford University) AAWE 10th Annual Conference, June 21-25, 2016, Bordeaux, France

Gokcekus & Hewstone, AAWE 2016, Bordeaux “I can certainly see that you know your wine. Most of the guests who stay here wouldn’t know the difference between Bordeaux and Claret.” Basil Fawlty, ‘Fawlty Towers’ Evaluations of wine, like many other forms of evaluation, might sensibly be based on the views of others. (Schamel, 2000; Ashenfelter, 1990; Hodgson, 2008; Reuter, 2009). Social psychologists have documented how and why people are influenced by actions and the beliefs of similar others (Asch, 1956; Cialdini & Goldstein, 2004; Cialdini, Reno & Kallgren, 1990; Sherif, 1936; Nolan et al., 2008).  ‘Normative’ (conforming with the positive expectations of others; Deutsch & Gerard, 1955, p. 629)  ‘Informational’ (accepting information from others as evidence about reality; ibid.) 2

Gokcekus & Hewstone, AAWE 2016, Bordeaux Impact of prior ratings on other group members’ opinions A recent trend in consumer behavior is that, given easy access provided by the internet, consumers have started “… abandoning traditional expert sources in favor of the perspectives of their peers ” Griskevicius et al., 2008, p. 84. (Chevalier & Mayzlin, 2006; Iyengar et al., 2009). Ratings posted by regular wine drinkers belonging to a web-based wine network, where wine evaluations and ratings become available to members once they are posted. Using this naturalistic data we conduct an archival analysis to ascertain the impact of prior ratings on other group members’ opinions. 3

Gokcekus & Hewstone, AAWE 2016, Bordeaux Hypothesis 1: Social Influence 1a: There will be a direct relationship between the wine evaluations to which visitors to the website (respondents) are exposed to and their own subsequent wine evaluations; and 1b: If influence in this context is more normative, this association will be even stronger than the one between respondents’ evaluations and either professionals’ (experts’) evaluations or prices. 4

Gokcekus & Hewstone, AAWE 2016, Bordeaux Hypotheses 2 and 3: Conformity There will be a direct, positive relationship between the uniformity of wine evaluations to which respondents are exposed to and their own subsequent wine evaluations. The impact of the first 3-4 evaluations will be greatest, with diminishing increments per added wine rater. 5

Gokcekus & Hewstone, AAWE 2016, Bordeaux Hypothesis 4: Informational Influence The respondents’ wine evaluations will be more in agreement with the prior evaluations, when the prior evaluations are made by more expert group members. 6

Gokcekus & Hewstone, AAWE 2016, Bordeaux Naturalistic Data for Archival Analysis: Wines and respondents from Cellartracker.com 7 Sample 1: NapaSample 2: Willamette Vintage2008 VarietyCabernet sauvignonPinot noir Wine region (AVA)U.S., California, NapaU.S. Oregon, Willamette Valley Day of determinationNovember 17 th, 2011May 1 st, 2014 Number of wines with at least 10 or more notes

Gokcekus & Hewstone, AAWE 2016, Bordeaux Sample 1: Cabernet Sauvignons from Napa 8 Number of notesAverage score Experts ’ score Average price Nov '11Feb '12April '12Nov '12May '14 Nov '11 Feb '12 April '12 Nov '12 May '14 First - 1 score First- 2 score First- 3 score First – 4 score Nov. '11 Nov. '11 May ‘14 Average Std. Dev Minimum Maximu m Total 3, , , , ,

Gokcekus & Hewstone, AAWE 2016, Bordeaux Sample 2: Pinot Noirs from Willamette Valley 9 Number of notes Average score Experts’ Score Average price Group First - 1 score First- 2 score First- 3 score First – 4 score Average $ Std. Dev $ Minimum $ Maximum $ Total 4,

Regression results: Average wine scores (minus first four group members) as dependent variable: Napa 10 Explanatory variable(1)(2)(3)(4)(5)(6)(7)(8) First-1 score (0.05)* First-2 score (0.05)* First-3 score (0.04)* First-4 score (0.04)* (0.06)* (0.07)* Experts’ score (0.08)* (0.10)* (0.07)* Price (0.003) (0.004) Constant F-statistic R2R Adjusted R No. of observation106 36

Regression results: Average wine scores (minus first four group members) as dependent variable: Willamette Valley 11 Explanatory variable(1)(2)(3)(4)(5)(6)(7)(8) First-1 score (0.04)* First-2 score (0.05)* First-3 score (0.05)* First-4 score (0.04)* (0.04)* (0.04)* Experts’ score (0.07)* (0.06)* (0.06)* Price (0.006)* (0.005)* Constant F-statistic R2R Adjusted R No. of observation103 97

Consensus among these first four members … 12 ｜ Group score i – First-4 score i ｜ = δ 0 + δ 1 *(STD-4 i ) + ν i Napa sample: δ 0 = and δ 1 = Willamette sample, δ 0 = and δ 1 = ________________________________________________ (STD-group i ) = β 0 + β 1 *(STD-4 i ) + ϑ i Napa sample: β 0 = and β 1 = Willamette sample: β 0 = and β 1 = 0.706

When the prior evaluations came from more “expert” group members: Credibility … 13 Explanatory variable(1)(2)(3)(4)(5)(6)(7)(8)(9) First-1 score (0.05)* First-2 score (0.05)* First-3 score (0.04)* First-4 score (0.04)* (0.06)* (0.07)* (0.04)* Experts’ score (0.08)* (0.10)* (0.07)* Price (0.003) (0.004) Inventory*First-4 score (0.001)* Tasting notes*First-4 score (0.008) Members they follow*First-4 score (0.01) Their followers*First-4 score (0.03) Constant F-statistic R2R Adjusted R No. of observation

Gokcekus & Hewstone, AAWE 2016, Bordeaux Fast declining impact of initial evaluations from 2 nd to 6 th influence source (Note. The change in R 2 is divided by % change in group size (percentage-wise normalized)) 14

Gokcekus & Hewstone, AAWE 2016, Bordeaux Main Findings Wine evaluations are subject to social influence; four of our five hypotheses are confirmed: In support of Hypothesis 1a we found a significant direct relationship between the wine evaluations respondents were exposed to and their own subsequent wine evaluations. Variations in the first four group members’ ratings explained a substantial proportion of the variation in subsequent wine evaluations (79% in the Napa sample, and 58% in the Willamette sample). Consistent with Hypothesis 1b, the first four group members’ ratings, regardless of the variations among these ratings, explained the rest of the group’s average rating better than expert ratings. 15

Gokcekus & Hewstone, AAWE 2016, Bordeaux Main Findings Confirming Hypothesis 2, in both sample sets, we found that the more uniform the earlier evaluations were, (a) the closer the subsequent wine evaluations were to the average rating of the earlier evaluations, and (b) the more uniform the subsequent evaluations were. As predicted by Hypothesis 3, in both sample sets, the first 3-4 evaluations by other raters had the greatest impact, with diminishing increments for each added wine rater. Results did not, however, support Hypothesis 4, that wine evaluations would be more in agreement with prior evaluations, when those ratings were made by more expert group members 16