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Investigating shopping choice preferences in Helsinki and Tampere urban regions
Ari HyvönenJames CulleySimo Syrman
Aims of the research
• Investigating specialty shopping preferences• The goal is to study the spatial dependencies of the
choice orientation
Research outline
• Choice orientations– Principal component analysis (PCA) for questionnaire data
• Choice orientation and socio-economic variables– MANOVA to study the relationship
• Spatial dependency– PCA and cluster analysis to classify urban structure
Choice orientation - PCA
• Data from ”Shopping centers as a part of sustainable consumption and urban structure” –project– Total of 24 000 questionnaires were send out to a random
sample of house holds in Helsinki metropolitan area (HMA) and Tampere region in 2009
• Total response rate was 26,2 % (6294)– HMA 4582 and Tampere 1712
• PCA for the 28 questions concerning choice orientation– 7 orthogonally rotated components– Explains 64.7 % of the variance
The choice orientations
1. Entertainment & auxiliary services
2. Quality & selection
3. Price
4. Ease of access
5. Access by car
6. Shopping environment
7. Customer service
Dependency between socio-economic variables and the components• MANOVA
– Socio-economic variables:• Age• Family structure• Education• Sex• Income
– Results were statistically significant due to large sample size.– But the amount counted were small
• Partial eta squared around 0.05
• Conclusion– Not a meaningful relationship between socio-economic situation of
house holds and choice orientation
Urban segments
• Grid data (250 x 250 m)– Distance to nearest shopping center – Population density– Number of flats– Square meters per person– Number of workplaces
• PCA with Varimax -rotation– 3 components
• Cluster analysis– CLARA– Silhouette value
• Result: 7 clusters
Depedency
• Discriminant analysis (DA)– Responders were classified by the basis of the urban
segmentation grid– The aim of DA was to verify and study the classification of the
responders– Results:
• The most significant component was the access by car– Price and Entertainment & auxiliary service were also significant
• But in practice not meaningful
Tampere region
• Same analysis as for HMA– Discovered choice orientations were slightly different
• Latent structure of the PCA
– Most likely cause is the differences in public transportation network
• Discriminant analysis revealed only one statistically significant component– The public transportation component