While self-esteem is an important concept for health, psychological well-being, and work success, correctly measuring self-esteem is a long-standing and unresolved problem. Here we propose a more objective measure of self-esteem based on electroencephalographic data. Using a novel machine learning approach analyzing specific fine-graded electroencephalographic sub-bands, we can correctly classify high and low self-esteem with an accuracy of over 79 percent, which represents a methodological landmark for health and information systems research. Our results have theoretical, methodological and practical implications.
Buettner, Ricardo; Sauter, Daniel; Eckert, Isabelle; and Baumgartl, Hermann, "Classifying High and Low Self-Esteem using a Novel Machine Learning Method based on EEG Data" (2021). PACIS 2021 Proceedings. 39.
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