The Difference of User Perception between Similarity and Dissimilarity Judgments

  • Ming-Xian Sun
  • Chi-Hsien Hsu
  • Ming-Chuen Chuang
Conference paper

DOI: 10.1007/978-3-642-39137-8_8

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8024)
Cite this paper as:
Sun MX., Hsu CH., Chuang MC. (2013) The Difference of User Perception between Similarity and Dissimilarity Judgments. In: Rau P.L.P. (eds) Cross-Cultural Design. Cultural Differences in Everyday Life. CCD 2013. Lecture Notes in Computer Science, vol 8024. Springer, Berlin, Heidelberg

Abstract

The similarity and dissimilarity is a corresponding relationship which is the base of cognitive judgments. The main purpose of this paper is to study the user perception by using similarity judgments. In this study, fifteen innovative products are used as the stimuli which divided into three groups: global, creative and local products. A total of 139 student volunteers participated in the various phases of the study. The feature measures are used to collect data under three different experiments: similarity judgment by random, similarity judgment by order, and dissimilarity judgment by random. In addition, the paper proposed an approach to confirm the effectiveness of collecting data. Then, MDS analysis was used to explore the difference of user perception between similarity and dissimilarity judgments. The results provide designers with a valuable reference for designing innovative products.

Keywords

multidimensional scaling INDSCAL similarity dissimilarity product design 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ming-Xian Sun
    • 1
  • Chi-Hsien Hsu
    • 2
  • Ming-Chuen Chuang
    • 1
  1. 1.Institute of Applied ArtsNational Chiao Tung UniversityHsinchu CityTaiwan
  2. 2.Graduate School of Creative Industry DesignNational Taiwan University of ArtsNew Taipei CityTaiwan

Personalised recommendations