Journal of the Association for Information Systems · 2020 · 10 citations · 0 references
Social SciencesPsychologySocial PsychologyArtificial IntelligenceMachine LearningSocial CognitionElectroencephalogram AnalysisElectroencephalogram Signal ProcessingElectroencephalographyNeural Data AnalysisNeural NetworksSocial EvaluationEeg Feature ExtractionEeg Signal PreprocessingEeg Signal ProcessingElectroencephalogram ClassificationElectroencephalogram Signal AnalysisElectroencephalography Feature ExtractionMachine Learning For ElectroencephalogramResearch Information Systems
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.