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DISE: Dynamic Intelligent Survey Engine

  • Christian Schlereth
  • Bernd Skiera
Chapter

Abstract

Knowledge about consumers’ preferences is of utmost importance for many marketing decisions, but transactional data are frequently unavailable. Therefore, marketing researchers have developed ground-breaking methods that build upon stated preference data to measure consumers’ preferences; these methods include self-explicated methods, ratingbased conjoint analysis, and choice-based conjoint analysis. This article describes DISE (Dynamic Intelligent Survey Engine), which aims to enhance research involving the measurement of consumer preferences. DISE is an extendable, web-based survey engine that supports the construction of technically sophisticated surveys and that limits the effort that researchers must invest to develop new preference methods. We discuss the overall architecture of DISE, discuss how to implement and include new data collection methods, and finally outline how these new methods can be employed in surveys, using an illustrative example. We conclude this article with an invitation to researchers to join in the development of DISE.

Web-Based Survey Survey Platform Conjoint Analysis Preference Measurement 

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Literature

  1. [1]
    Arsanjani, A./Zhang, L.-J./Ellis, M./Allam, A./Channabasavaiah, K. (2007): Design an SOA Solution Using a Reference Architecture, http://www.ibm.com/developerworks/library/ar-archtemp/.
  2. [2]
    Berbner, R./Grollius, T./Repp, N./Heckmann, O./Ortner, E./Steinmetz, R. (2005): An Approach for the Management of Service-oriented Architecture (SoA) based Application Systems, Paper presented to the Enterprise Modelling and Information Systems Architectures (EMISA), Klagenfurt, Austria, 2005.Google Scholar
  3. [3]
    Brazell, J. D./Diener, C. G./Karniouchina, E./Moore, W. L./Séverin, V./Uldry, P.-F. (2006): The No- Choice Option and Dual Response Choice Designs, Marketing Letters, Vol. 17, 4, pp. 255–268.CrossRefGoogle Scholar
  4. [4]
    Flynn, T. N./Louviere, J. J./Peters, T. J./Coast, J. (2007): Best-Worst Scaling: What it can do for Health Care Research and How to do it, Journal of Health Economics, Vol. 26, 1, pp. 171–189.CrossRefGoogle Scholar
  5. [5]
    Fritz, M./Schlereth, C./Figge, S. (2011): Empirical Evaluation of Fair-Use Flat Rate Strategies for Mobile Internet, " Business & Information Systems Engineering, Vol. 3, 5, pp. 269–277.CrossRefGoogle Scholar
  6. [6]
    Gamma, E./Helm, R./Johnson, R./Vlissides, J. M./Larman, C. (2005): Design Patterns: Elements of Reusable Object-Oriented Software, Boston: Addison Wesley.Google Scholar
  7. [7]
    Gensler, S./Hinz, O./Skiera, B./Theysohn, S. (2011): Willingness-to-Pay Estimation with Choice- Based Conjoint-Analysis: Addressing Extreme Response Behavior with Individually Adapted Designs, Working Paper.Google Scholar
  8. [8]
    Green, P. E./Carmone, F. J./Smith, S. M. (1989): Multidimensional Scaling, Concepts and Applications.Google Scholar
  9. [9]
    Green, P. E./Rao, V. (1971): Conjoint Measurement for Quantifying Judgemental Data,” Journal of Marketing Research, Vol. 8, pp. 355–363.CrossRefGoogle Scholar
  10. [10]
    Johnson, E. J./Payne, J. W./Schkade, D. A./Bettman, J. R. (1989): Monitoring Information Processing and Decisions: The Mouselab System,” Fuqua School of Business, Technical Report.Google Scholar
  11. [11]
    Louviere, J./Islam, T. (2008): A Comparison of Importance Weights and Willingness-to-pay Measures Derived from Choice-based Conjoint, Constant Sum Scales and Best-worst Scaling,” Journal of Business Research, Vol. 61, pp. 903–911.CrossRefGoogle Scholar
  12. [12]
    Louviere, J./Street, D./Burgess, L./Wasi, N./Islam, T./Marley, A. A. J. (2008): Modeling the Choices of Individual Decision-Makers by Combining Efficient Choice Experiment Designs with Extra Preference Information, Journal of Choice Modelling, Vol. 1, 1, pp. 128–163.CrossRefGoogle Scholar
  13. [13]
    Louviere, J. J./Woodworth, G. (1983): Design and Analysis of Simulated Consumer Choice or Allocation Experiments: An Approach Based on Aggregated Data, Journal of Marketing Research, Vol. 20, 4, pp. 350–367.CrossRefGoogle Scholar
  14. [14]
    Netzer, O./Srinivasan, S. (2011): Adaptive Self-Explication of Multi Attribute Preferences, Journal of Marketing Research, Vol. 48, 1, pp. 140–156.CrossRefGoogle Scholar
  15. [15]
    Park, Y. H./Ding, M./Rao, V. (2008): Eliciting Preference for Complex Products: A Web-Based Upgrading Method, " Journal of Marketing Research, Vol. 45, 5, pp. 562–574.CrossRefGoogle Scholar
  16. [16]
    Schaaf, R./Schlereth, C./Skiera, B./Eckert, J. (2011): A Comparison of Six Self-Explicated Approaches, Working Paper.Google Scholar
  17. [17]
    Schlereth, C. (2010): Optimale Preisgestaltung von internetbasierten Diensten, Hamburg: Verlag Dr. Kovač.Google Scholar
  18. [18]
    Schlereth, C./Skiera, B. (2011): Reduced Dual Response, University of Frankfurt, Working Paper.Google Scholar
  19. [19]
    Schlereth, C./Skiera, B. (2012): Measurement of Consumer Preferences for Bucket Pricing Plans with Different Service Attributes, International Journal of Research in Marketing, forthcoming.Google Scholar
  20. [20]
    Schlereth, C./Skiera, B./Wolk, A. (2010): Augmented Methods of Ranking-Based and Choice-Based Conjoint Analysis to Estimate Willingness to Pay for Services under Two-Part Tariffs, Goethe University Frankfurt, Working Paper.Google Scholar
  21. [21]
    Schlereth, C./Skiera, B./Wolk, A. (2011): Measuring Consumers’ Preferences for Metered Pricing of Services, Journal of Service Research, Vol. 14, 4, pp. 443–459.CrossRefGoogle Scholar
  22. [22]
    Scholz, S. W./Meissner, M./Decker, R. (2010): Measuring Consumer Preferences for Complex Products: A Compositional Approach Based on Paired Comparisons, Journal of Marketing Research, Vol. 47, 4, pp. 685–698.CrossRefGoogle Scholar
  23. [23]
    Srinivasan, V./Park, C. S. (1997): Surprising Robustness of the Self-Explicated Approach to Customer Preference Structure Measurement," JMR, Vol. 34, 2, pp. 286–291.Google Scholar
  24. [24]
    Street, D. J./Burgess, L./Louviere, J. J. (2005): Quick and Easy Choice Sets: Constructing Optimal and Nearly Optimal Stated Choice Experiments, International Journal of Marketing Research, Vol. 22, 4, pp. 459–470.CrossRefGoogle Scholar
  25. [25]
    Toubia, O./Hauser, J. R./Simester, D. I. (2004): Polyhedral Methods for Adaptive Choicebased Conjoint Analysis, Journal of Marketing Research, Vol. 41, 1, pp. 116–131.CrossRefGoogle Scholar
  26. [26]
    Wertenbroch, K./Skiera, B. (2002): Measuring Consumer Willingness to Pay at the Point of Purchase, " Journal of Marketing Research, Vol. 39, 2, pp. 228–241.CrossRefGoogle Scholar
  27. [27]
    Yu, J./Goos, P./Vandebroek, M. (2011): Individually Adapted Sequential Bayesian Conjoint-Choice Designs in the Presence of Consumer Heterogeneity, International Journal of Research in Marketing, Vol. 28, 4, pp. 378–388.CrossRefGoogle Scholar

Copyright information

© Gabler Verlag | Springer Fachmedien Wiesbaden 2012

Authors and Affiliations

  • Christian Schlereth
    • 1
  • Bernd Skiera
    • 1
  1. 1.Goethe University FrankfurtFrankfurtGermany

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