1. A New Approach to Measure Preferences of Users in Built Environments: Integrating Cognitive Mapping and Utility Models
Benedict Dellaert
Erasmus University Rotterdam
Theo Arentze
Oliver Horeni
Harry Timmermans
Eindhoven University of Technology

2. Why study users preferences an decisions?
Consumer research increasingly important for real estate decision making
to predict choice behavior
to improve product design and services
to communicate more effectively

3. Example – preferences shopping locations
Which type?
Which attributes important?
Why important?
Centre of small city
Centre of large city
Centre at the edge of city

4. Example – preferences housing locations
City centre
– which attributes?
– why important?
Outside centre
– which attributes?
– why important?
Country side
– which attributes?
– why important?

5. Measuring mental representations
Traditional Laddering (face to face, qualitative)
Gutman 1982, Reynolds and Gutman 1988
Meaning Structure Method
Coolen and Hoekstra 2001
Association Pattern Technique / Hard laddering
Audenaert et al. 1998, Ter Hofstede et al. 1999
CNET (Causal Network Elicitation)
Arentze et al 2008; Horeni et al. 2014
Attribute-Benefit surveys
Ashok et al. 2002, Chandukala et al. 2002, Dellaert and Stremersch 2005, Luo, Kannan and Ratchford 2008

6. Example - Choosing a city-trip destination
Tourist destination choice
Attributes
Benefits
Utilities

7. Problem and objective Cognitive mapping methods offer rich information
which attributes are important for which reasons
But, the information is qualitative
no quantitative information about the importance of attributes and values of preferences
Purpose of the present study
extending means-end chains analysis
with a method to estimate importance weights for attributes directly from mental representations

8. Theory and model
Cognitive activation of components in mental representation is based on utility and cognitive cost framework
Akin to information search theory
Additional components are included only if expected gain of evaluating alternatives exceeds the cognitive costs

9. Model of activation Basic building block is a D–A–B chain
Decision alternative
Attribute
Benefit
Value of including a D–A–B chain
Gain: reduced risk of making incorrect decision
Costs: mental effort required for activation in evaluation

10. Parametrization of the model
SDA
SAB α c
Decision
alternative
Dispersion
Cognitive cost
(threshold) Si
Strength of influence of one node on another α
Utility of benefit in decision D
Dispersion across the range of the variable
SDA x SAB x α x D
Gain of adopting a given chain in the MR c
Gain has to be greater than a threshold of cognitive costs to include D-A-B chain in mental representation.

11. Utility model of activation
Net utility of activating a DAB chain in situation n
Need to standardize the dispersion (scale) per attribute across alternatives
Probability of activating a DAB chain follows binary logit specification

12. Data collection – choice task
Mental representation of decision where to shop, when to shop, and which transportation to use

13. Data collection – survey method
Representative sample from a Dutch online panel, 1070 responses, collected over two waves
Average age 43.1 years, 55% female, and 35% had earned a bachelor’s degree or higher
Minimum frequency for each of the AB and DA links set to 30 (approx. 5% occurrence probability)
108 AB links and 41 DA links meet this criterion
Implies: 196 observations per individual of yes/no occurrence for DAB chain
Also observed chosen shopping alternative

14. Results Some illustrative DAB chains
Shopping location (e.g., Down town) (D) – Crowdedness in the store (A) – Shopping pleasure (B)
Transportation mode (e.g., Car) (D) – Number of bags to carry (A-S) – Ease of Travel (B)

15. Link strength DA (example))
Results - estimation
Link strength DA (example))
Attributes
Decision alternatives
Value
t-value
available time to shop
timing of shopping 1
shopping location
0.90
-5.51
weather
transport mode
0.84
-8.39
0.82
-9.55
number of bags to carry
0.78
-14.41
0.75
-15.55
durability of bought products
required time to shop
departure time flexibility of the transport mode
0.81

16. Link strength AB (example))
Results - estimation
Link strength AB (example))
Benefits
Attributes
Value
t-value
travel pleasure
number of bags to carry
1.24
8.22
departure time flexibility of the TM
1.23
7.58
crowdedness on the way
1.21
4.39
travel time
1.09
2.89
capacity of the transport mode
1.01
0.22
weather 1
accessibility of the store
0.99
-0.07
ease of shopping
available time to shop
1.20
8.06
1.02
0.78
crowdedness in the store
0.67
required time to shop
opening hours

17. Benefit activation and cognitive costs (example)
Results - estimation
Benefit activation and cognitive costs (example)
Benefits
Value
t-value
travel pleasure
1.94*
33.83
ease of shopping
3.66*
61.21
shopping comfort
3.43*
36.01
shopping pleasure
3.26*
33.01
attractivity of the shopping environment
2.50*
18.11
diversity in product choice
4.41*
46.40
shopping success
3.62*
39.71
…..
Cognitive costs
2.29*
57.18
Individual error component
0.72*
38.59

18. Results – deriving attribute utilities
Attribute utility values and willingness to travel (example)
Attribute
Value
(utility)
(minutes travel time)
crowdedness in the store
-72.99
40.0
crowdedness on the way
-67.27
36.9
departure time flexibility of the TM
54.70
30.0
accessibility of the store
50.01
27.4
simplicity of the travel route
46.81
25.7
time to find a parking lot
-37.27
20.4
atmosphere in the shopping location
37.00
20.3
familiarity with the shopping location
35.39
19.4

19. Results – deriving attribute utilities
Attribute and benefit utility impact rankings

20. Conclusions
This study has shown how the utility of attributes can be derived from individuals’ means–end chains of decisions
This bridges the gap between
qualitative insights on the basis of means–end chain or laddering research
quantitative insights based on conjoint choice experiments or discrete choice models
The new method most useful under market conditions in which
supply is relatively complex, highly stable over time, and narrow in terms of the number alternatives that any given customer will consider

21. Conclusions The proposed approach based on means–end chains provides
a viable way to generate in-depth insights about consumer attribute consideration
that can be quantified in terms of consumer decision utility
and used as input for discrete choice experiments

22. Thank you!