PACIS 2020 Proceedings


While social desirability is a long-standing problem in information systems research and which is difficult to measure in questionnaires, we propose a more objective measure of social desirability based on electroencephalographic data. Using a novel machine learning approach analyzing specific fine-graded electroencephalographic sub-bands, we achieve an accuracy of over 75% on completely unseen evaluation data, which is a methodological landmark in measuring social desirability. Our results have theoretical, methodological and practical implications.



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