## 3 Results
### Validation Results
#### 3.2.2 Evaluating the Practicability of Data Augmentation
Although the augmented datasets related to the original dataset (Real (original) + Synthesized (CTGAN), Real (original) + Synthesized (TVAE)), which yielded averaged correlation coefficients of approximately 0.850, achieved relatively higher concordance with the original dataset, the values are presumed to be the contribution of the original dataset, not the synthesized dataset generated using each DGNN. Among the augmented data based on the seed data, the augmented data composed of both the seed and synthesized data generated by the CopulaGAN achieved the highest predictive performance and relatively higher concordance. dataset, if marketers aim to achieve better predictive performance than the current levels, augmenting or synthesizing the dataset could prove effective. Furthermore, the predictive performance of the synthesized data approximated or surpassed that of the augmented data. The synthesized data generated by CTGAN obtained the highest predictive performance, surpassing that of the augmented data (\"Real (seed) + Synthesized (CTGAN)\" and \"Real (original) + Synthesized (CTGAN)\"). A similar trend was observed in datasets related to CopulaGAN and partially in those related to TVAE, indicating that synthesized data can effectively emulate real data as a \"clone\" of the original dataset. In particular, GAN-based synthesizers (CTGAN and CopulaGAN) were superior to the TVAE synthesizer. The distinctiveness of the network architecture between the GAN-based and VAE-based synthesizers might have caused differences in their predictive performance. The mode-specific normalization module is a unique function in GAN-based synthesizers. It might function effectively when the synthesizers train the data. Given that it adjusted complicated data composed of conditional and continuous columns to an appropriate kernel function using a variational Gaussian mixture model (VGM), ideal data might have been generated based on a probabilistic distribution. However, as this is one assumption, we must investigate this phenomenon further in the following research. Data cloning techniques using synthetic data generated using DGNNs have proven successful in computer vision tasks such as image, movie, and audio recognition [46; 47; 48; 49]. The use of synthesized data as cloned real data may help in addressing security-related concerns in consumer survey research. Respondents often hesitate to disclose personal information regarding wealth and health owing to privacy issues [50], which could impact their willingness to participate in marketing activities. Marketers face challenges in obtaining such sensitive information and may incur higher costs compared with conventional consumer surveys. However, informing respondents in advance that \"cloned\" survey data synthesized by DGNNs will be used for analysis might alleviate concerns regarding data usage and participation. As synthesized data do not contain specified personal information, even in the event of a data breach, respondents' privacy remains protected. Data synthesis techniques can be integrated into the management of highly secure marketing research monitoring databases, such as those containing information on individuals with high incomes or specific medical conditions. This strategy is a promising privacy preserving strategy [51] in the marketing fields. Blockchain technology plays a pivotal role in privacy and security preservation. Joint technologies combining blockchain [52; 53] and DGNN-synthesized data [54; 55] have offered robust security measures, facilitating the access and utilization of customer databases and thereby enhancing the execution of marketing strategies. In this way, the present study may offer the possibility to propose promising applications because the predictive performances of synthesized data were better than those of the original data. However, this suggests that there may be discrepancies in the predictive results between synthesized and original data.