## 5 Discussion and Conclusions  
To the best of our knowledge, this is the first study to apply deep generative models to data consisting of two different modalities in a management science study. We demonstrated that data augmentation by synthesizing different modalities of data using deep generative models might contribute to yielding good predictive performances in detecting whether candidates are veteran nurses. Moreover, we found out that the number of epochs plays a crucial role in improving predictive performance rather than the number of generated sample sizes. This suggests that our approach could apply synthesized data to business fields by anonymizing individual information because the real data might be replaceable with synthesized data, even if the synthesized data were small and limited in size. As for selecting appropriate deep generative models, because all deep generative models learned through sufficient epochs might yield good performance, an approach to synthesizing data derived from different sourced modalities might be effective for management science issues. In particular, we confirmed that TVAE outperformed the GAN-based synthesizers regarding the stability of the generated data quality through all epochs. Thus, the TVAE is the better synthesizer in validated deep generative models in our study, regarding predictive performances, computation time, and data quality. However, there are some concerns regarding the application of such models. Considering that our results are consistent with previous studies [38; 39], the GAN-based synthesizers are expected to require longer computation durations than the TVAE across almost all epochs. Meanwhile, the TVAE generated no variance data for several variables during the shorter epochs (5-30 epochs). This outcome implies that the TVAE has a computational time advantage over the CTGAN; however, it requires sufficient epochs to attain satisfactory data quality. Moreover, although CopulaGAN is the GAN-based synthesizer modifying the CTGAN  
Figure 8: Computation times. to grasp dependencies between features, there were a few differentiations among deep generative models. This suggests that the usage of the other GAN-based synthesizers, as well as CopulaGAN, might also be appropriate to replicate the correlation structures of data. Regardless of the model type, the predictive performance improved proportionally to the epochs and not the sample sizes generated. This outcome indicates that sufficient epochs play a crucial role in improving predictive performance. This suggests that a priory setting of sufficient epochs must be required to yield good performance using synthesized data generated using deep generative models. In this way, our study contributes to applying different modal-based-synthesized data generated using deep generative models to management science issues. However, our study has several limitations and concerns. Most brain science studies are conducted with a small number of participants. However, [PERSON] et al. [40] pointed out that experiments with a small sample size have low statistical power and reproducibility of an experiment. Executing the experiment with a larger sample size might have been desirable in our study. Although our results may be effective under certain conditions, they were derived from only one case of predicting skilled nurses. There are a lot of issues in management science contexts to solve: social networks [41] and multiple source data fusion for management decision-making systems in the data transformation era [42]. Therefore, our results should be applied with caution. This study may have attained better predictive performance because we generated and predicted data in a well-designed experiment. Given the few cases in which data based on well-designed experiments can be obtained in the management science field, the extent to which our results can be applied is still being determined. Summarily, trials using various types of multimodal seed data are required.