Post on 18-Feb-2017
EyeTrackingdataClasspresentationforthePhDCourse:AppliedStatisticsfortheSocialSciences
HugoGuyader,PhDCandidateinMarketingDepartmentofManagement&Engineering(IEI)
DivisionofBusinessAdministration(FEK)
2015-12-02
HugoGuyader—EyeTrackingdata
StudyPurpose
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How can retailers attract consumers’ visual attention and increase sales of eco-friendly products through in-store practices?
HugoGuyader—EyeTrackingdata
Hypotheses
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H1 Looking at eco-friendly products impacts the green premium. H2 Priming consumers to buy green products will increase the search for green products. H3 An “eco-servicescape” impacts visual attention on eco-friendly products. H4 Displaying Point-of-Purchase (PoP) information about green consumption increases the visual attention on eco-friendly products. H5 Shoppers use green price tags to find the green products. H6 Greenwashing practices reduce visual attention on eco-friendly products.
HugoGuyader—EyeTrackingdata
Experimentaldesign
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• Eye-tracking participants: 66 students (mean age: 23). • Instructions: to buy coffee and fabric softener. • Mock up store: products (classic, eco-friendly/organic, Fair-Trade),
price tags (signalling green products) and PoP information from ICA Maxi
• Eco-servicescape: various items evoking the countryside or agriculture were displayed around the shelves
• 2 experimental conditions: a control group (46%) and a primed group. The priming was: “The person you do the shopping for is sustainable-oriented and prefers to eat organic food.”
• Post-experiment survey collecting socio-demographics and self-reported “greenness”.
HugoGuyader—EyeTrackingdata
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HugoGuyader—EyeTrackingdata
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HugoGuyader—EyeTrackingdata
Variables
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๏ Visual attention data:- Eco-friendly Products: Sum of dwell times on eco-friendly, organic, or fair-trade products.- Green Price Tags: Sum of dwell times on price tags for eco-friendly coffee products. - Greenwashing: Sum of dwell times on each fabric softener with a misleading color packaging.- PoP Information: Sum of dwell times on the information displays about eco- friendly coffee.
- Eco-Servicescape: Sum of dwell times on each element (posters, plants, carpet, and so on).
๏ Decision data:- Green Premium: According to the participant’s product choice (classic, ecological, or fair-trade), we calculated the green premium and transformed it into a categorical variable (median split).- Self-reported “greenness” using Roberts (1995) socially responsible consumer behavior scale (24 items).
HugoGuyader—EyeTrackingdata
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Analyses5 outliers (pairwise deletion): too small nbr of fixations/saccades ‣ analyses for visual attention data based on 61 subjects
➡ chi-square tests, logistic regression, Kruskal-Wallis (K-W) tests
‣ K-W’s H statistic shows whether the ranked data in between-groups is significantly different
‣ non-parametric test because distribution of eye tracking data violates statistical assumptions of normality
HugoGuyader—EyeTrackingdata
DirectLogisticRegression
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Assess the relationship between • visual attention on eco-friendly products
- transformed into a ratio variable: the dwell time for eco-friendly products on the dwell time for all products displayed
• green premium - transformed into a dichotomous variable, with a median split between high and low green premiums
hypothesis1
HugoGuyader—EyeTrackingdata
DirectLogisticRegression
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model statistically significant X2 (1, n = 61) = 61.04, p = .000
R2 = 63.2% (Cox & Snell) and 84.3% (Nagelkerke) 86.9%of cases correctly classified
hypothesis1
participants who looked more at green products were 247 times more likely to pay a green premium
HugoGuyader—EyeTrackingdata
Manipulationcheck
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“greenness”: median split on 24-items scale (α = .886) comparison between primed & control groups • chi-square test for
independence:
the primed group had a significantly higher greenness than the control group
X2 (1, n = 66) = 3.911, p = .048
hypothesis2
HugoGuyader—EyeTrackingdata
Kruskal-WallisTest
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difference in visual attention (= dwell time) on eco-friendly products (= “green” products) between the primed and control groups • K-W test statistically significant
H (1) = 17.420, p = .000 • mean rank for visual attention on green products:
40.03 for the primed group (52.5%) 21.03 for the control group (47.5%)
• effect size: small (29.03% of variance accounted for) • the primed group looked longer at green products
hypothesis2
HugoGuyader—EyeTrackingdata
Kruskal-WallisTest
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difference in visual attention on green products, depending on whether the participants looked “long enough” at the eco-servicescape (= whether they saw it or not) • median split on eco-servicescape at 550 ms • K-W test statistically insignificant
H (1) = .854, p = .355 • mean rank for visual attention on green products:
29.00 for the group (52.5%) that noticed the eco-servicescape 33.21 for the group (47.5%) that didn't
• opposite direction than predicted!
hypothesis3
HugoGuyader—EyeTrackingdata
Kruskal-WallisTest
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difference in visual attention on green products, depending on whether the participants looked “long enough” at the eco-servicescape (= whether they saw it or not) • cut-off value on eco-servicescape at 385 ms • K-W test statistically insignificant
H (1) = .196, p = .658 • mean rank for visual attention on green products:
30.19 for the group (60.7%) that noticed the eco-servicescape 32.25 for the group (39.3%) that didn’t
• still opposite direction than predicted!
hypothesis3
HugoGuyader—EyeTrackingdata
Kruskal-WallisTest
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difference in visual attention on green products, depending on whether the participants looked “long enough” at the eco-servicescape (= whether they saw it or not) • cut-off value on eco-servicescape at 782 ms • K-W test statistically insignificant
H (1) = .036, p = .850 • mean rank for visual attention on green products:
31.50 for the group (42.6%) that noticed the eco-servicescape 30.63 for the group (57.4%) that didn't
• same direction than predicted!
hypothesis3
HugoGuyader—EyeTrackingdata
Kruskal-WallisTest
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difference in visual attention on eco-friendly coffee, depending on whether participants noticed PoP info displays • K-W test statistically significant
H (1) = 5.076, p = .024 • mean rank for visual attention on green coffee:
35.88 for the group (52.5%) that noticed the displays 25.62 for the group (47.5%) that didn't
• effect size: small (8.46% of variance accounted for) • Participants who saw the PoP information display
also looked longer at the eco-friendly coffee products
hypothesis4
HugoGuyader—EyeTrackingdata
Kruskal-WallisTest
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difference in visual attention on eco-friendly coffee, depending on whether participants noticed green-colored price-tags • cut-off value on green price-tags at 200 ms • K-W test statistically significant
H (1) = 9.862, p = .002 • mean rank for visual attention on green coffee:
35.08 for the group (24.6%) that noticed green price-tags 18.50 for the group (75.4%) that didn't
• effect size: small (16.44% of variance accounted for) • Participants who looked at the green price-tags looked longer at
the eco-friendly coffee products.
hypothesis5
HugoGuyader—EyeTrackingdata
Kruskal-WallisTest
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difference in visual attention on eco-friendly fabric softener (pink), depending on whether participants looked at the misleading green-colored softener • cut-off value on green price-tags at 666 ms • K-W test statistically significant
H (1) = 5.441, p = .020 • mean rank for visual attention on eco-friendly softeners:
34.43 for the group (29.5%) that looked at the “greenwashed” softeners 22.81 for the group (70.5%) that didn't
• effect size: small (9.06% of variance accounted for) • Participants who looked longer at the “greenwashed” softeners were
more likely to miss the true eco-friendly softener.
hypothesis6
HugoGuyader—EyeTrackingdata
Findings
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Retailers can influence consumers’ green behavior by influencing their purchase intentions (priming), displaying relevant information (PoP info display), orienting them inside the store (green pricetags), offering a “true” green product assortment (greenwashing).
➡As a consequence of visual attention on eco-friendly products, consumers’ green premium increased.
Greenwashing practices distract consumers from finding the eco-friendly products they look for.
hypotheses supported: 5/6
HugoGuyader—EyeTrackingdata
AlternativeAnalyses
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• All of these were preliminary analyses… Decisions yet have to be taken.
✦ Multiple Regressions (Pearson or Spearman’s Rho) ✦ T-test & ANOVAs ✦ PLS ✦ Multinomial regression
➡It all depends on what raw data is used to represent the theoretical concept… Some things work, others don't.
HugoGuyader—EyeTrackingdata
Priming
VisualA.en0on:Eco-friendlyProducts GreenPremium
VisualA.en0on:Greenpricetags
VisualA.en0on:Informa0on
VisualA.en0on:Eco-Servicescape
VisualA.en0on:Greenwashing
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H3
?
H2
H5
H4
H1
H6
HugoGuyader—EyeTrackingdata
Priming
VisualA.en0on:Eco-friendlyProducts GreenPremium
VisualA.en0on:Greenpricetags
VisualA.en0on:Informa0on
VisualA.en0on:Eco-Servicescape
VisualA.en0on:Greenwashing
coffeesoBener
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HugoGuyader—EyeTrackingdata
Priming
VisualA.en0on:Eco-friendlyProducts GreenPremium
VisualA.en0on:Greenpricetags
VisualA.en0on:Informa0on
VisualA.en0on:Eco-Servicescape
VisualA.en0on:Greenwashing
coffeesoBener
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+
H3
?
H2
H5
H4
H1
H6