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TitleProduct Personality
LanguageEnglish
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Total Pages223
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Page 111

111 Development of a Product Personality Scale

Table 6.6

Statistics of the six groups of items in the concept product personality scale
Attractive
Casual
Charming
Cheerful
Cute
Easy-going
Energetic
Feminine
Flexible
Friendly
Happy
Informal
Nice
Open
Pleasant
Popular
Pretty
Relaxed
Romantic
Sensitive
Sweet
Terrific
Young

Aggressive
Dominant
Excessive
Obtrusive
Showy


Bourgeois
Businesslike
Calm
Consistent
Decent
Honest
Inconspicuous
Mature
Modest
Precise
Predictable
Reliable
Sensible
Serious
Well-groomed


Careless
Chaotic
Childish
Corny
Creepy
Immature
Odd
Pathetic
Silly
Unreliable
Untidy


Conspicuous
Eccentric
Exuberant
Funny
Idiosyncratic
Interesting
Lively
Provocative
Tough
Wild





Aloof
Boring
Cheerless
Insular
Masculine
Old-fashioned
Reticent
Strict
Unattractive
Uninteresting


α = .96 α = .83 α = .88 α = .84 α = .91 α = .89
ri = .51** ri = .53** ri = .32** ri = .34** ri = .50** ri = .45**

** Correlation coefficient significantly differs from zero, p < .001.


Three items fall outside of these groups: vulnerable (“kwetsbaar”), intelligent
(“intelligent”), and annoying (“vervelend”). The position of these characteristics changes
from solution to solution. They do not consistently appear together with the same items.
This means that the way they are used to describe products is not similar for all product
classes and respondents. Since we cannot be sure what we would be measuring if we
included these three items in the scale, we chose to delete them from the set.

Stage 2: Reducing the item pool

Six large groups of items, which seem internally consistent, appear in the data.
We could reduce the amount of 78 items by selecting one item from each of these
groups, leaving only 6 items. However, this would not do justice to the broad scope of
meaning represented by the items in each group. The diversity of the set of items caused
the cluster analysis to format clusters at a very general level. We thus set out to cluster
analyze the six groups again, individually. Ward’s algorithm was used in all six analyses.

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