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DISE: Dynamic Intelligent Survey Engine

In: Quantitative Marketing and Marketing Management

Author

Listed:
  • Christian Schlereth

    (Goethe University Frankfurt)

  • Bernd Skiera

    (Goethe University Frankfurt)

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.

Suggested Citation

  • Christian Schlereth & Bernd Skiera, 2012. "DISE: Dynamic Intelligent Survey Engine," Springer Books, in: Adamantios Diamantopoulos & Wolfgang Fritz & Lutz Hildebrandt (ed.), Quantitative Marketing and Marketing Management, edition 127, chapter 11, pages 225-243, Springer.
    • Handle: RePEc:spr:sprchp:978-3-8349-3722-3_11
    DOI: 10.1007/978-3-8349-3722-3_11
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    Citations

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    Cited by:

    1. Nicole Stein & Stefan Spinler & Helga Vanthournout & Vered Blass, 2020. "Consumer Perception of Online Attributes in Circular Economy Activities," Sustainability, MDPI, vol. 12(5), pages 1-16, March.
    2. Katharina Keller & Christian Schlereth & Oliver Hinz, 2021. "Sample-based longitudinal discrete choice experiments: preferences for electric vehicles over time," Journal of the Academy of Marketing Science, Springer, vol. 49(3), pages 482-500, May.
    3. Katharina Saunders & Christian Hagist & Alistair McGuire & Christian Schlereth, 2020. "Preferences of a new health care profession. A pilot study with anaesthesia technologist trainees in Germany," WHU Working Paper Series - Economics Group 20-01, WHU - Otto Beisheim School of Management.
    4. Schlereth, Christian & Skiera, Bernd & Schulz, Fabian, 2018. "Why do consumers prefer static instead of dynamic pricing plans? An empirical study for a better understanding of the low preferences for time-variant pricing plans," European Journal of Operational Research, Elsevier, vol. 269(3), pages 1165-1179.
    5. A. Cristina Mihale-Wilson & Jan Zibuschka & Oliver Hinz, 2019. "User preferences and willingness to pay for in-vehicle assistance," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(1), pages 37-53, March.
    6. Kostyra, Daniel S. & Reiner, Jochen & Natter, Martin & Klapper, Daniel, 2016. "Decomposing the effects of online customer reviews on brand, price, and product attributes," International Journal of Research in Marketing, Elsevier, vol. 33(1), pages 11-26.

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