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• W861813477 abstract "This design research builds on the idea to combine the strengths of traditional survey research with a more practice-oriented benchmarking approach. We present selfsurvey.org, 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." @default.
• W861813477 created "2016-06-24" @default.
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• W861813477 date "2015-01-01" @default.
• W861813477 modified "2023-09-23" @default.
• W861813477 title "SELFSURVEY.ORG: A PLATFORM FOR PREDICTION- BASED BENCHMARKING AND FEEDBACK-ENABLED SURVEY RESEARCH" @default.
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• W861813477 doi "https://doi.org/10.18151/7217532" @default.
• W861813477 hasPublicationYear "2015" @default.
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