A Platform for Prediction-Based Benchmarking and Feedback-Enabled Survey Research

Till J. Winkler, Marko Sarstedt, Marian Keil, Paul Rost

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    This design research builds on the idea to combine the strengths of traditional survey research with a more practice-oriented benchmarking approach. We present, an online survey platform that allows providing instant and respondent-specific feedback based on a scientifically grounded research model and a structural equation model-based prediction technique. Based on the partial least squares analysis results of a training dataset, selfsurvey employs a scoring algorithm to derive respondent-specific predicted scores, compares these with the observed scores, and provides visualized and text-based outputs. Our evaluation of selfsurvey in the context of a maturity benchmarking study provides an indication for the perceived usefulness of this artifact and its underlying scoring algorithm. We argue that this prediction-based approach, which goes far beyond the functionality of common univariate benchmarking tools, can be used for a wide range of survey studies and help increase the perceived relevance of academic survey studies to practice.
    Original languageEnglish
    Title of host publicationECIS 2015 Proceedings
    EditorsJörg Becker, Jan vom Brocke, Marco De Marco
    Number of pages15
    Place of PublicationAtlanta, GA
    PublisherAssociation for Information Systems. AIS Electronic Library (AISeL)
    Publication date2015
    Article number204
    ISBN (Print)9783000502842
    Publication statusPublished - 2015
    EventThe 23rd European Conference on Information Systems (ECIS) 2015 - Westfälische Wilhelms-Universität Münster, Münster, Germany
    Duration: 26 May 201529 May 2015
    Conference number: 23


    ConferenceThe 23rd European Conference on Information Systems (ECIS) 2015
    LocationWestfälische Wilhelms-Universität Münster
    Internet address
    SeriesProceedings of the European Conference on Information Systems


    • Survey research
    • Structural equation models
    • Partial least squares
    • Multivariate prediction
    • Benchmarking
    • Design research

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