Embed Size (px)
Citation preview
Explaining experimental changes in consumer
behaviour in realistic settings using observational data
Adriaan Kole, René de Wijk, Daniëlla Stijnen, Anna Maaskant
Wageningen UR: Food & Bio based Research
Consumer Sciences & Intelligent Systems group
Restaurant of the Future
A normal university cafetaria?
Look inside…
Diners being observed
A multifunctional research facility
• Restaurant for 200 persons• Grand café• Research kitchen• Sensory laboratory• Mood rooms• Mind room
• 45 video cameras• 7 video analysis workstations• 3 km of cabling
Restaurant of the Future in the press
Laboratories for sensory and physiological research
• Measure physiological and emotional responses
• Study effects of food odor, taste and texture
The Restaurant; designed to study the effects of changing
environments on consumer behaviour
• Light• Odor• Temperature• Buffet lay out• Price• Assortment…. Etc,
Observational research
• Observe food selection and consumption
• Study effects of environmental and social variables
The behavioral approach: Food choice in Restaurant
300 registered consumers:
Age (yrs) 40.88
% Males 51%
BMI 23.76
NeophobiaScore 26.56
Education High
Some results
� We know the nutritional content of the products,
we know who is buying what:
� Clusters of consumers based on nutritional intake
� Consistency of repeated lunch selections
� Lunch composition; the effect of soup on the
other lunch selections
Cluster analysis
Three clusters of consumers
1 2 3
Lunch energy from carbohydrates (%) 31 42 53
Lunch energy from protein (%) 16 19 19
Lunch energy from fat (%) 52 37 24
Relatively healthy food choice
carbohydrates : fat : protein 50%:25%:25%
Cluster analysis
Three clusters of consumers
1 2 3
Lunch energy from carbohydrates (%) 31 42 53
Lunch energy from protein (%) 16 19 19
Lunch energy from fat (%) 52 37 24
Need more carbohydrates and less fat
Cluster analysis
Three clusters of consumers
1 2 3
Lunch energy from carbohydrates (%) 31 42 53
Lunch energy from protein (%) 16 19 19
Lunch energy from fat (%) 52 37 24
Needs MUCH more carbohydrates and MUCH less fat
Cluster analysis
Three lusters of consumers
1 2 3
Lunch energy from carbohydrates (%) 31 42 53
Lunch energy from protein (%) 16 19 19
Lunch energy from fat (%) 52 37 24
Basis for health intervention:
how to move consumers from cluster 1 to 3.
Consistency calculated for:
� Nutrients: fat, carbohydrates & proteins
� Energy
� Price
� Weight
� Number of lunch items
� Location of lunch items
� Type of lunch items
80
90
100
Sna
ck
Bre
ad
Sou
p
Sal
ad
San
dwic
h filli
ng
Drin
ksS
andw
ich
Des
sert
sH
ot m
eals
But
ter
Fruit
Type of food/drink
Co
ns
iste
nc
y o
f re
pe
at
pu
rch
as
e (
%)
0
10
20
30
40
50
60
70
Ra
te o
f re
pe
at
pu
rch
as
e (
%)
Consistency
Rate
Some lunch items attract fewer, but loyal consumers
80
90
100
Sna
ck
Bre
ad
Sou
p
Sal
ad
San
dwic
h filli
ng
Drin
ksS
andw
ich
Des
sert
sH
ot m
eals
But
ter
Fruit
Type of food/drink
Co
ns
iste
nc
y o
f re
pe
at
pu
rch
as
e (
%)
0
10
20
30
40
50
60
70
Ra
te o
f re
pe
at
pu
rch
as
e (
%)
Consistency
Rate
… while other lunch items attract more, but less
loyal consumers
Most consumers are consistent with regard to their
choice of buffets and ……
0
5
10
15
20
25
30
35
40
45
50
50 51-60 61-70 71-80 81-90 91-99 100
Consistency of repeat purchases (%)
Nu
mb
er
of
co
ns
um
ers
(%
of
tota
l)
… with regard to lunch price, weight & number of items
0
0.2
0.4
0.6
0.8
1
1.2
Pric
eW
eight
No.
produ
cts
Con
sist
ency
buffe
t
Con
sist
ency
food/d
rink
Ene
rgy
in lu
nchP
rote
ins
FatS
atura
ted fa
tTra
ns fa
t
Uns
atura
ted fa
t
Multi
ple u
nsat
urate
df Fat
Car
bohyd
rate
s
Mono/d
isac
char
ides
Die
tary
fiber N
aCo
eff
icie
nt
of
va
ria
tio
n (
st.
de
v/m
ea
n)
Consumers are not consistent with regard to the
nutritional composition of their lunches.
0
0.2
0.4
0.6
0.8
1
1.2
Pric
eW
eight
No.
produ
cts
Con
sist
ency
buffe
t
Con
sist
ency
food/d
rink
Ene
rgy
in lu
nchP
rote
ins
FatS
atura
ted fa
tTra
ns fa
t
Uns
atura
ted fa
t
Multi
ple u
nsat
urate
df Fat
Car
bohyd
rate
s
Mono/d
isac
char
ides
Die
tary
fiber N
aCo
eff
icie
nt
of
va
ria
tio
n (
st.
de
v/m
ea
n)
Summary
Consumers pay relatively little attention to their nutritional
needs in terms of energy and nutritional composition.
They rely more on factors such as portion size and habitual
routes along buffets.
Some results: consumption patterns
� Lunch composition; the effect of soup on the
other lunch selections. � Hypothesis: Soup is supposed to be relatively satiating. Hence, we
expect fewer calories on a soup tray compared to a non-soup tray.
Some background information
Soup and non-soup consumers are similar with
regard to their personal characteristics.
Soup and non-soup lunches are similar with regard
to energy from macro-nutrients.
What else is in the lunch?(Percent energy from product categories)
Non-soup lunches contain more bread, sandwich
fillings and snacks
No Soup SoupSandwich fillings 6.3 2.5Sandwiches 6.8 3.9Butter 1.9 1.1Bread 19.0 10.6Dessert 9.9 3.7Drinks 23.8 23.9Fruit 0.8 0.4Green Salad 4.6 1.6Meal Salad 4.6 2.9Snack 14.8 11.6Soup 0.0 34.1Hot meal 7.1 3.2
What else is in the lunch?: Total energy
Over 200 lunches, the difference adds up to 10000 calories, or
approximately less 3 LB bodyweight if everything else is
constant(1 LB bodyweight = appr. 3500 cals).
1867 kJoules 1677 kJoules
Lunch
Without soup With soup
Conclusions of the soup case
� Results support the satiating properties of soup.
� However, if soup eaters consume fewer calories,
why are they not thinner…..
� …. Or are they compensating on other meals?
Routine buffets: Faster visits result in more purchases
% visits resulting
in purchase
Time of visit
w.o.purchase
(s)
Bread 86 5,4
Fruits & Juices 91 2,6
Sandwich Fillings 75 4,0
Salads 51 5,0
Soups 72 9,1
Sandwiches 37 7,4
Desserts 65 3,7
Snacks 62 11,5
r= -0.8, sig.
Routine buffets:
% visits resulting
in purchase
Time of visit
w.o.purchase
(s)
Bread 86 5,4
Fruits & Juices 91 2,6
Sandwich Fillings 75 4,0
Salads 51 5,0
Soups 72 9,1
Sandwiches 37 7,4
Desserts 65 3,7
Snacks 62 11,5
Interventions should focus on less habitual foods which are thoroughly inspected first.
Tracing walking patterns
Challenge
Observe the walking patterns of visitors
to learn their attention patterns during
choosing behaviour
Solution
LED-based tracking solution
Camera’s can observe individuals, even
when camera’s do not overlap
Preliminary result
Individual tracks showing browsing
behaviour and choosing times
Aggregated tracks showing hot zones
Bread
BreadSoups
Sandwiches
Coffee & Tea
Desserts
Lemonades
Cash register
Sn
ac
ks
Soups
Bre
ad
Sandwich
Fillings
Juices
Juices
Sa
lad
sS
an
dw
ich
es
Le
mo
na
de
s
Coffee & Tea
De
ss
erts
Cash register
Entrance
The Standard
Route
Tracking results: a possible basis for interventions
� Food choice behaviour is not random
� Food choice behaviour may be partly related to
proximity of buffets
� Combinations of buffets suggest equivalence of
choices (e.g., snacks vs sandwiches)
Summary 2
1. Restaurant well-suited for longitudinal studies on:
1. (variations in) consumer preferences/food choice
2. Repeat purchasing of specific products
3. Effects of behavioral and/or environmental
interventions with regard to food choice
0.00
0.20
0.40
0.60
0.80
1.00
New Healthy Welfare-
Friendly
Selected chicken product
Re
lati
ve
tim
e d
uri
ng
me
nu
se
lec
tio
n (
0-1
.0)
ColaCucumber
DrinbBuffetFrenchFriesFriedOnionKetchup
LettuceMayoOnionReadHealthyReadNew
ReadWelfare-friendlySelectedChickenTomatoVegetablesWater
Interventions: the Effect of product information on routing
0.00
0.20
0.40
0.60
0.80
1.00
New Healthy Welfare-
Friendly
Selected chicken product
Re
lati
ve
tim
e d
uri
ng
me
nu
se
lec
tio
n (
0-1
.0)
ColaCucumber
DrinbBuffetFrenchFriesFriedOnionKetchup
LettuceMayoOnionReadHealthyReadNew
ReadWelfare-friendlySelectedChickenTomatoVegetablesWater
Order of food choice varies with the type of chicken product.
Different consumers, different strategies?
Interventions: the Effect of product information on routing
� Citrus odour: more combination meals chosen
� Vanilla odour: more often fish/meat with staple foods
P=0.05P=0.03
P=0.02
20
30
40
50
60
70
80
90
100
% o
f vis
ito
rs
CitrusAroma
VanillaAroma
Bron: FBR/CICS
Intervention: effect ambient aromas on choice
Intervention: Effect of ads on food choice
� Photos of salads decrease demand for desserts.
0.0
10.0
20.0
30.0
40.0
50.0
60.0
NoDessert FruitDessert VanillaDessert
% o
f vis
ito
rs
SaladPhoto
StirrFryPhoto
Bron: FBR/CICS
Intervention: calorie labeling
400
450
500
550
600
Average lunch energy (kCals)
Reference period Information period
Bron: FBR/CICS
About 30% of the consumers eats less calories
... And 70% increases calorie intake
Bron: FBR/CICS
Recuctions: toppings, bread, meals, cold snacks
Increase: salads, warm snacks
Interventions: CO2 labeling
Effect of new technology on salad sales
� Humidifier increases attention for
the salad buffet
� Humidifier decreases salad sales
(food technology phobia)
Bron: FBR/CICS
46
54
59
0
10
20
30
40
50
60
70
Experimental condition
Pe
rce
nta
ge
ap
pro
ac
h p
er
da
y
No humidif ier
Half humidif ier
Full humidif ier
59
50
51
44
46
48
50
52
54
56
58
60
Experimental condition
Perc
en
tag
e c
ho
ice p
er
ap
pro
ach
No humidifier
Half humidifier
Full humidifier
Effect of product on food choice
Eating gesture analysis
Challenge:
observe the eating gestures to learn about
eating habits and emotions (liking/disliking)
Solution
Formalised framework describing which
eating gestures exist
Computer vision software to detect
occurring gestures
Preliminary result
Individual eating gestures and eating
patterns can automatically be detected
Smaller bites result in smaller meals
small large free100
200
300
400
500LS
HS
P < 0.001 P < 0.05
P < 0.05
Inta
ke (
g)
30% reduction in grams eaten when taking small bites compared to large bites
Bron: Dieuwerke Bolhuis. WUR/HV
Eating faster is eating more, esp. with smaller bite sizes
200
300
400
500
600
Short(20 s/100g)
Long(60 s/100g)
a a
bc
LB 6.7 bites/100g
HB 20 bites/100g
Inta
ke
(g
)
Bron: Dieuwerke Bolhuis. WUR/HV
Lab: bite sizes can be reduced by using stronger flavours
Conclusions and discussion
� Effects can be described empirically (this is what we
observe that consumers do): observational measures
� We can influence what people do and observe.
Conclusions and discussion
� Usually psychological theory explain effects based on how
people are and how their brain works.
� If we only observe – we don’t know these things
� How can we bridge the gap between lab and real life.
Conclusions and discussion
� Or do we not need to, since only the effects matter??
![WELCOME [docs.healthandsafetyhub.co.uk]docs.healthandsafetyhub.co.uk/Barhale/Presentations/... · 2012. 10. 3. · 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 ... • 2 RIDDORS in the](https://img.pdfslide.us/doc/110x75/5ff13d7674d2bf43c02b8b77/welcome-docs-docs-2012-10-3-020-040-060-080-100-120-140-160-.jpg)
