Applying a Method for Augmenting Data Mixed from Two Different Sources Using Deep Generative Neural Networks to Management Science
Source abstract
Abstract
Although a multimodal data analysis, comprising physiological and questionnaire survey data, provides better insights into addressing management science concerns, such as challenging the predictions of consumer choice behavior, studies in this field are scarce because of two obstacles: limited sample size and information privacy. This study addresses these challenges by synthesizing multimodal data using deep generative models. We obtained multimodal data by conducting an electroencephalography (EEG) experiment and a questionnaire survey on the prediction of skilled nurses. Subsequently, we validated the effectiveness of the synthesized data compared with real data regarding the similarities between these data and the predictive performance. We confirmed that the synthesized big data were almost equal to the real data using the trained models through sufficient epochs. Conclusively, we demonstrated that synthesizing data using deep generative models might overcome two significant concerns regarding multimodal data utilization, including physiological data. Our approach can contribute to the prevailing combined big data from different modalities, such as physiological and questionnaire survey data, when solving management issues.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Forecasting Techniques and Applications · Time Series Analysis and Forecasting · Music and Audio Processing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Marketing, Faculty of Commerce, University of Marketing and Distribution Sciences, 3-1 Gakuen-Nishimachi, Nishi-ku, Kobe 651-2188, Hyogo, Japan
Department of Nursing, Japanese Red Cross Hiroshima College of Nursing, 1-2 Ajinadai-Higashi, Hatsukaichi 738-0052, Hiroshima, Japan
Graduate School of Comprehensive Scientific Research Program in Health and Welfare, Prefectural University of Hiroshima, 1-1 Gakuencho, Mihara 723-0053, Hiroshima, Japan
Hiroshima Red Cross Hospital Atomic-Bomb Survivors Hospital, 1-9-6 Sendamachi, Naka-ku, Hiroshima 730-8619, Hiroshima, Japan
Hiroshima Office, Survey Research Center Co., Ltd., 2-29 Tatemachi, Naka-ku, Hiroshima 730-0032, Hiroshima, Japan
Faculty of Health and Welfare, Prefectural University of Hiroshima, 1-1 Gakuencho, Mihara 723-0053, Hiroshima, Japan
Hiroshima Business and Management School, Prefectural University of Hiroshima, 1-1-71 Ujina-Higashi, Minami-ku, Hiroshima 734-8558, Hiroshima, Japan
Funding
JSPS KAKENHI
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