Quality & Quantity

, Volume 49, Issue 1, pp 385–405 | Cite as

Visual conjoint analysis (VCA): a topology of preferences in multi-attribute decision making

  • Peter Sarlin
  • Shahrokh Nikou
  • József Mezei
  • Harry Bouwman


This paper proposes an approach denoted visual conjoint analysis (VCA). Conjoint analysis is commonly used in marketing to understand consumers’ decision criteria, particularly why consumers prefer and select certain products and their variations. Yet, little efforts have been made to provide visual means for exploring and visualizing preferences and utilities of consumers. In this paper, we propose an approach that enables identifying a low-dimensional topology of consumer profiles and their demographic characteristics. Through a two-step approach, VCA makes use of techniques for (i) data reduction and (ii) dimension reduction in combination with conjoint analysis. It provides a two-dimensional representation (dimension reduction) of a small number of respondent segments (data reduction). This provides means for two key tasks: (i) identifying the topology of multivariate respondent profiles in a lower dimension, focusing on neighborhood relations, and (ii) visual representations of information describing the respondent profiles, as well as the combination of the two tasks. The approach is applied to a real-world case of consumers’ preferences of mobile platform ecosystems.


Conjoint analysis Cluster analysis Data reduction Dimension reduction Visual conjoint analysis 



Shahrokh Nikou gratefully acknowledges financial support from the Nokia Foundation and Foundation for Economic Education.


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Peter Sarlin
    • 1
    • 2
  • Shahrokh Nikou
    • 3
  • József Mezei
    • 3
  • Harry Bouwman
    • 4
    • 5
  1. 1.Centre of Excellence SAFEGoethe University FrankfurtFrankfurtGermany
  2. 2.RiskLab at IAMSRÅbo Akademi University and Arcada University of Applied SciencesTurkuFinland
  3. 3.Department of Information Technologies, Institute for Advanced Management Systems ResearchÅbo Akademi UniversityTurkuFinland
  4. 4.Faculty of Technology, Policy, and ManagementDelft University of TechnologyDelftThe Netherlands
  5. 5.Institute for Advanced Management Systems ResearchÅbo Akademi UniversityTurkuFinland

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