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Food decisions: Causes of biases

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Page 1: Food decisions: Causes of biases

| | Department Health Sciences and Technology (D-HEST) Consumer Behavior CB

Prof. Dr. Michael Siegrist

Food decisions: Causes of biases

| | Department Health Sciences and Technology (D-HEST) Consumer Behavior CB

Experiential system Analytic system

1. Holistic 1. Analytic

2. Affective: pleasure-pain oriented 2. Logical: reason oriented (what is sensible)

3. Associationistic connections 3. Logical connections

4. Behavior mediated by ÒvibesÓ from past experiences

4. Behavior mediated by conscious appraisal of events

5. Encodes reality in concrete images, metaphors, and narratives

5. Encodes reality in abstract symbols, words, and numbers

6. More rapid processing: oriented toward immediate action

6. Slower processing: oriented toward delayed action

7. Self-evidently valid: Òexperiencing is believingÓ 7. Requires justification via logic and evidence

Two Modes of Thinking

Slovic et al. 2004

16.10.16 Prof. Dr. Michael Siegrist 2

Page 2: Food decisions: Causes of biases

| | Department Health Sciences and Technology (D-HEST) Consumer Behavior CB

In the food domain various heuristics help us to make quick decisions

Heuristics may result in biased decisions

The importance of heuristics

16.10.16 Prof. Dr. Michael Siegrist 3

| | Department Health Sciences and Technology (D-HEST) Consumer Behavior CB

People often rely on heuristics to make

judgments or decisions (e.g., availability,

representativeness, affect heuristic)

A general feature of heuristic judgment is

attribute substitution (Kahneman and

Frederick, 2005)

A target attribute (not readily accessible) is

substituted by an heuristic attribute (which

comes easier to mind)

Heuristics

16.10.16 Prof. Dr. Michael Siegrist 4

Page 3: Food decisions: Causes of biases

| | Department Health Sciences and Technology (D-HEST) Consumer Behavior CB

Affect heuristic

16.10.16 Prof. Dr. Michael Siegrist 5

| | Department Health Sciences and Technology (D-HEST) Consumer Behavior CB

S�tterlin & Siegrist (2015)

16.10.16 Prof. Dr. Michael Siegrist 6

Page 4: Food decisions: Causes of biases

| | Department Health Sciences and Technology (D-HEST) Consumer Behavior CB

Affect evoked by the nutrition information and perceived healthiness of breakfast cereals using the label Òfruit sugarÓ vs.

ÒsugarÓ in the nutrition table.

Fruit sugar Sugar

M (SD) [N] M (SD) [N]

Affect evoked by

the information 43.81 (20.05) [127] 36.14 (18.72) [124]

Perceived

healthiness 39.61 (21.52) [127] 32.51 (19.32) [124]

Note. Ratings of affect could range from 0 (very negative) to

100 (very positive). Ratings of perceived healthiness could range from 0 (not healthy at all) to 100 (very healthy).

Experimental Manipulation

Perceived Healthiness

Experimental Manipulation

Perceived Healthiness

Affect

a

b

7.10* (2.58)

1.31 (1.85)

0.75* (0.05)

7.67

* (2

.45)

16.10.16 Prof. Dr. Michael Siegrist 7

| | Department Health Sciences and Technology (D-HEST) Consumer Behavior CB

Natural is better heuristic

16.10.16 Prof. Dr. Michael Siegrist 8

Page 5: Food decisions: Causes of biases

| | Department Health Sciences and Technology (D-HEST) Consumer Behavior CB

Factors that Influence Perception of Naturalness

Rozin, 2005

16.10.16 Prof. Dr. Michael Siegrist 9

| | Department Health Sciences and Technology (D-HEST) Consumer Behavior CB

Factors that Influence Perception of Naturalness

Evans et al., 2013

0 10 20 30 40 50 60 70 80 90 100

Orange juice

Orange juice with 2% fruit powder

Orange juice with 4% fruit powder

Orange juice with 6% fruit powder

Naturalness

16.10.16 Prof. Dr. Michael Siegrist 10

Page 6: Food decisions: Causes of biases

| | Department Health Sciences and Technology (D-HEST) Consumer Behavior CB

With E-Number (M,

SD) (N=121)

No E-Number (M, SD)

(N=123)

t-value

(E 100) Curcumin, natural food color (orange-yellow)

65.10 (32.26) 75.89 (26.42) 2.86**

(E 220) Sulfur dioxide, blocks browning reactions in dry fruits

39.55 (33.29) 46.72 (33.93) 1.66*

(E 620) Glutamic acid, flavor enhancer

30.62 (29.11) 37.89 (33.58) 1.81*

Food Additives: The Symbolic Power of

E-Numbers

16.10.16 Prof. Dr. Michael Siegrist 11

As how artificial or natural do you evaluate the following food additives?

(0=artificial, 100=natural)

** p < .01; * p < .05, one-tailed

| | Department Health Sciences and Technology (D-HEST) Consumer Behavior CB

The production of beef (red meat) is important for agriculture. Most meat is produced in intensive animal husbandry. This production method has

negative impacts on the environment and it is associated with animal suffering. Heavy consumption of red meat results in a significantly higher risk

to get colon cancer.

By means of biotechnology in-vitro meat is to be produced. In doing so, red meat is produced in the laboratory. This production method is more

environmentally friendly and it is associated with less animal suffering compared with traditional meat production. Heavy consumption of in-vitro

meat results in a significantly higher risk to get colon cancer. The risk is

comparable with the one of the consumption of red meat from traditional meat production

Risk of Traditional Versus

Cultured Meat

16.10.16 Prof. Dr. Michael Siegrist 12

Page 7: Food decisions: Causes of biases

| | Department Health Sciences and Technology (D-HEST) Consumer Behavior CB

Traditional Meat

Cultured Meat t-value

How artificial (0) or natural (100) do you assess this meat?

62.65 (25.45) 17.96 (21.46) 15.06*

How unacceptable (0) or acceptable (100) do you perceive

the risk associated with this meat?

59.35 (25.87) 25.18 (26.38) 10.40*

Risk of Traditional Versus Cultured Meat

16.10.16 Prof. Dr. Michael Siegrist 13

* p < .001

| | Department Health Sciences and Technology (D-HEST) Consumer Behavior CB

Risk of Traditional Versus Cultured Meat

16.10.16 Prof. Dr. Michael Siegrist 14

Experimental Manipulation

Acceptability of Risk

Experimental Manipulation

Acceptability of Risk

Naturalness

a

b

-34.17* (3.28)

-44.69* (2.97)

-4.83 (3.66)

AcAc

-4.83 (3.66)

Page 8: Food decisions: Causes of biases

| | Department Health Sciences and Technology (D-HEST) Consumer Behavior CB

Participants were invited to a

sensory experiment

Evaluate a Òmousse au

chocolatÓ

Three groups

No information

Natural Vanilla

Artificial Vanillin

Impact of Symbolic Information:

Sensory Experience

16.10.16 Prof. Dr. Michael Siegrist 15

| | Department Health Sciences and Technology (D-HEST) Consumer Behavior CB

The Same Product, but Different Information

Condition 1: Control

(n = 47)

Condition 2: Natural vanilla

(n = 47)

Condition 3: Artificial vanilla

(n = 45)

M SD M SD M SD

Expectation 76.09 13.77 74.67 13.97 69.5 17.40

Liking (hedonic rating) 68.04ab

18.47 72.58a 16.64 61.64

b 23.07

Willingness to buy 4.00ab

1.22 4.40a .86 3.55

b 1.33

Note. Means with different letters are significantly different (p<.05)

16.10.16 Prof. Dr. Michael Siegrist 16

Page 9: Food decisions: Causes of biases

| | Department Health Sciences and Technology (D-HEST) Consumer Behavior CB

Self-made foods taste better heuristic

16.10.16 Prof. Dr. Michael Siegrist 17

| | Department Health Sciences and Technology (D-HEST) Consumer Behavior CB

Participants 60 students (32 females, 28 males, age: M = 24, SD = 3)

recruited in cafeterias and libraries

asked to participate Òin a taste testÓ

Procedure Self-prepared: Participant mixed all ingredients

together (recipe)

Other-prepared: RA mixed everything together

(but participant saw the recipe)

Milkshake 120 g of raspberries, 100 ml of milk (2.5% fat),

80 ml of cream, and 10 g of sugar 444 kcal

Study: Self prepared vs other-prepared

Dohle, Rall, Siegrist (2014) Food Quality and Preference

Prof. Dr. Michael Siegrist 18 16.10.16

Page 10: Food decisions: Causes of biases

| | Department Health Sciences and Technology (D-HEST) Consumer Behavior CB

0

50

100

150

200

250

self-prepared other-prepared

Co

nsu

mp

tio

n (

g)

Consumption

Note. Error bars indicate the standard errors of the means. p = .032 (one-tailed). Dohle, Rall, Siegrist (2014)

Food Quality and Preference

Prof. Dr. Michael Siegrist 19 16.10.16

Participants in the self-prepared

group

- liked the milkshake better

- perceived it as more natural

compared with participants in the

other-prepared group

| | Department Health Sciences and Technology (D-HEST) Consumer Behavior CB

Food disgust is a food-rejection emotion

Protects against the ingestion of potentially harmful

agents

Disgust may also pose a problem for the acceptance of

novel foods, however

Disgust Heuristic

16.10.16 Prof. Dr. Michael Siegrist 20

Source: http://blogs.cfr.org/patrick/2013/05/16/theres-

a-fly-in-my-soup-can-insects-satisfy-world-food-

needs/

Page 11: Food decisions: Causes of biases

| | Department Health Sciences and Technology (D-HEST) Consumer Behavior CB

High value protein

Favourable fatty acid composition

High vitamin and mineral content

Low production requirements

(little water, space)

Efficient feed conversation

Small environmental

food print

16.10.16 Prof. Dr. Michael Siegrist 21

Insects as ÔMini-LivestockÕ (De Foliart, 1995)

| | Department Health Sciences and Technology (D-HEST) Consumer Behavior CB

Survey results suggest that introducing insects in a processed

form may result in an increased

acceptance of food

Experiment

Control group: Tortilla chips

Experimental group: Tortilla chips with

cricket flour

Results suggest that participants in the

experimental group are more willing to eat

unprocessed foods compared with

participants in the control group

16.10.16 Prof. Dr. Michael Siegrist 22

Acceptance of Insects

Page 12: Food decisions: Causes of biases

| | Department Health Sciences and Technology (D-HEST) Consumer Behavior CB

Consumers decisions are often the result of the experiential

system Heuristics like the affect heuristic play an important role

Consumers value naturalness

Risks associated with novel foods are less acceptable compared

with the same risks of traditional foods

Self-preparation of foods influences liking

Disgust is an important barrier for the acceptance of novel foods

There are large differences between experts and laypeople

regarding risk prioritization of food hazards

Conclusions

16.10.16 Prof. Dr. Michael Siegrist 23

| | Department Health Sciences and Technology (D-HEST) Consumer Behavior CB

S�tterlin B, Siegrist M (2015) Simply adding the word "fruit" makes sugar

healthier: The misleading effect of symbolic information on the perceived

healthiness of food. Appetite 95:252-261.

Rozin P (2005) The meaning of "natural". Psychological Science

16:652-658.

Dohle, S., Rall, S. & Siegrist, M. (2014). I cooked it myself: Preparing

food increases liking and consumption. Food Quality and Preference,

33(1), 14-16.

Hartmann, C. & Siegrist, M. (2016). Becoming an insectivore: Results of

an experiment. Food Quality and Preference, 51, 118Ð122.

Literatur

16.10.16 Prof. Dr. Michael Siegrist 24