AI 딥러닝을 이용한 구조방정식모형의 설명력 향상에 관한 탐색적 연구
A Study on the Explanatory Power of Structural Equation Models using AI Deep Learning
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구조방정식모형(SEM)에 인공지능 딥러닝 기술(ANN)을 접목한 SEM-ANN 2단계 접근방법이 경영학 연구에 적용되어 연구되었다. 그러나 선행연구는 성능평가 방식의 차이로 인하여 SEM과 ANN의 연구결과를 비교하기 어렵고, 표본 크기에 따른 성능 변화를 제시하는 못하는 한계가 있다. 따라서 본 연구에서는 SEM과 ANN 분석에 동일한 성능평가 방식을 사용하여 연구결과를 비교하였고, 선행연구에 비해 상대적으로 큰 1632개의 표본을 수집하여 분석하였다. 연구결과는 다음과 같다. 첫째, SEM-ANN 2단계 접근으로 R²는 향상되었지만, 그 향상 효과는 작았다. 둘째, 훈련된 ANN 모델을 새로운 데이터에 적용하여 얻는 R²로 연구모델의 일반화 성능을 규명하였다. 셋째, 전체 데이터 1632개에서 136개, 307개, 660개를 각각 샘플링하여 하위 데이터셋을 만들고, 표본 크기에 따른 R² 변화를 비교하였다. 그 결과 모든 하위 데이터셋에서 R²가 향상되는 것을 확인하였다. 넷째, 표본 크기가 클수록 ANN 모델의 R²가 향상됨을 확인하였다.
Abstract
The SEM-ANN two-step approach, which combines artificial intelligence deep learning technology (ANN) with structural equation modeling (SEM), was applied to management research. However, previous studies have limitations in that it is difficult to compare the research results of SEM and ANN due to differences in performance evaluation methods and cannot present performance changes according to sample size. Therefore, in this study, we compared research results using the same performance evaluation method for SEM and ANN analysis, and collected and analyzed 1,632 samples, which are relatively large compared to previous studies. The research results are as follows. First, R² was improved with the SEM-ANN two-step approach, but the improvement effect was small. Second, the generalization performance of the research model was investigated using R² obtained by applying the trained ANN model to new data. Third, sub-datasets were created by sampling 136, 307, and 660 of the total data of 1632, respectively, and the change in R² according to sample size was compared. As a result, it was confirmed that R² improved in all sub-datasets. Fourth, it was confirmed that the larger the sample size, the better the R² of the ANN model.
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