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  4. Personalizing the Customization Experience: A Matching Theory of Mass Customization Interfaces and Cultural Information Processing
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Personalizing the Customization Experience: A Matching Theory of Mass Customization Interfaces and Cultural Information Processing

Journal
Journal of Marketing Research
ISSN
0022-2437
Type
journal article
Date Issued
2019
Author(s)
de Bellis, Emanuel  
;
Hildebrand, Christian  
;
Ito, Kenichi
;
Herrmann, Andreas  
;
Schmitt, Bernd
DOI
10.1177/0022243719867698
Abstract (De)
Mass customization interfaces typically guide consumers through the configuration process in a sequential manner, focusing on one product attribute after the other. What if this standardized customization experience was in fact personalized for consumers based on how they process information? A series of large-scale field and experimental studies, conducted with Western and Asian consumers, shows that matching the interface to consumers’ culture-specific processing style enhances the effectiveness of mass customization. Specifically, presenting the same information isolated (by-attribute) to Western consumers but contextualized (by-alternative) to Asian consumers increases satisfaction with and likelihood to purchase the configured product, along with the amount of money spent on the product. These positive consumer responses emerge because of an increase in “interface fluency”—consumers’ subjective experience of ease when using the interface. The authors advise firms to personalize the customization experience by employing “processing-congruent interfaces” across consumer markets.
Language
English
HSG Classification
contribution to scientific community
HSG Profile Area
Global Center for Customer Insight
Refereed
Yes
Publisher
American Marketing Association
Volume
56
Number
6
Start page
1050
End page
1065
URL
https://www.alexandria.unisg.ch/handle/20.500.14171/99741
Subject(s)

social sciences

Division(s)

ICI - Institute for C...

IBT - Institute of Be...

Eprints ID
257619
File(s)
Thumbnail Image
Name

deBellis2019.pdf

Size

448.44 KB

Format

Adobe PDF

Checksum (MD5)

f3f5e1cd0b8d794eff8a04467843d236

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