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A real-time network-based approach for analysing best–worst data types

Author

Listed:
  • Ákos Münnich

    (University of Debrecen)

  • Emese Vargáné Karsai

    (DataExpert Services Ltd)

  • Jenő Nagy

    (DataExpert Services Ltd
    Institute of Aquatic Ecology, Centre for Ecological Research
    University of Debrecen)

Abstract

Best–worst scaling is a widespread approach in market research used for collecting data on the needs and preferences of people. However, the current preparation of its design and the analysis of the data depends on complex statistical methods. One of the most commonly used models for estimating individual preference probabilities is the hierarchical Bayes model, which can only be applied after the data collection phase. This type of calculation needs more infrastructural background and a large sample to provide accurate estimations. Here, we introduce a new application that enables fast calculations and individual-level real-time estimations, which also has a great potential to ask additional questions depending on the respondent’s answers during live interviews. Our network-based approach (integrating the PageRank algorithm) works well for online surveys, and it supports our dynamic and adaptive, real-time evaluation (DART) of best–worst data types, and results in more relevant decision making in marketing.

Suggested Citation

  • Ákos Münnich & Emese Vargáné Karsai & Jenő Nagy, 2022. "A real-time network-based approach for analysing best–worst data types," SN Business & Economics, Springer, vol. 2(1), pages 1-24, January.
    • Handle: RePEc:spr:snbeco:v:2:y:2022:i:1:d:10.1007_s43546-021-00181-3
    DOI: 10.1007/s43546-021-00181-3
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    References listed on IDEAS

    as
    1. Flynn, Terry N. & Louviere, Jordan J. & Peters, Tim J. & Coast, Joanna, 2007. "Best-worst scaling: What it can do for health care research and how to do it," Journal of Health Economics, Elsevier, vol. 26(1), pages 171-189, January.
    2. Stan Lipovetsky, 2018. "MaxDiff Choice Probability Estimations on Aggregate and Individual Level," International Journal of Business Analytics (IJBAN), IGI Global, vol. 5(1), pages 55-69, January.
    3. Louviere, Jordan & Lings, Ian & Islam, Towhidul & Gudergan, Siegfried & Flynn, Terry, 2013. "An introduction to the application of (case 1) best–worst scaling in marketing research," International Journal of Research in Marketing, Elsevier, vol. 30(3), pages 292-303.
    4. Louviere,Jordan J. & Hensher,David A. & Swait,Joffre D. With contributions by-Name:Adamowicz,Wiktor, 2000. "Stated Choice Methods," Cambridge Books, Cambridge University Press, number 9780521788304.
    5. Hensher,David A. & Rose,John M. & Greene,William H., 2015. "Applied Choice Analysis," Cambridge Books, Cambridge University Press, number 9781107465923.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Adaptive; Decision making; Entropy; Maximum difference; PageRank; Segmentation;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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