# Generative Adversarial Networks in Business and Social Science
## 4 Statistics on GAN Publications in Economics
### Marketing and Publicity  
Table 6 is obtained with respect to the field of Marketing and Publicity.  
A comparative analysis is conducted in [201] on consumer preferences for a real fashion product versus a synthetically generated product created using CycleGAN. It was observed that the creation of new designs using GAN results in a higher perceived value of the product, which potentially generates co-creation value for customers, thereby enhancing their engagement and encouraging purchasing behaviour regardless of whether they are aware of the use of technology. The study suggests that using GAN technology can provide significant advantages by expanding the scope and scale of the product design process and by increasing perceived consumer value.  
In [202], deepfakes are introduced into the marketing literature by proposing a typology, a conceptual framework, and a research agenda based on balanced centrality. This aims to guide future research on deepfakes in marketing studies, thereby allowing both companies and customers to benefit from deepfakes while also safeguarding against potential risks.  
The study presented by [203] proposes a mechanism to automate the generation of textual ads and combines a GAN model with reinforcement learning to optimise the generation of advertising texts.  
Along the same lines as the aforementioned articles, Ref. [204] constructs a general framework to better understand consumers' responses to all forms of advertising manipulation; the researchers are aided by digital and automatic tools that enable advertisers to automate many advertising processes and to produce increasingly sophisticated synthetic ads. These same authors continue by analysing the impact of this increasingly pervasive trend of ad automation. In Ref. [205], the authors show that these AI-based tools, in the same way as GANs, may bring potentially drastic changes to the way ads are conceived, produced, edited, and directed. They also examine the associated ethical issues.  
\begin{table}
\begin{tabular}{l l} \hline
**Year** & **Title** & **Authors** \\ \hline
2022 & Generative adversarial networks for data augmentation and transfer in credit card fraud detection. \\  & Mapping the technological landscape of emerging industry \\
2022 & value chain through a patent lens: An integrated framework \\  & with deep learning. \\
2022 & Responsible cognitive digital clones as decision-makers: a design science research study. \\  & An innovative machine learning model for supply chain \\  & management. \\ \hline \end{tabular}
\end{table}
Table 5: WOS publications in Management.  
Another important aspect in this field is customer privacy. [PERSON]. [206] demonstrate that recent advances in machine learning enable companies to transfer a generative model instead of using real data. They show the effectiveness of GANs for preserving the desired characteristics of the original data, which offers advantages both in terms of privacy and scalability. GANs outperform benchmark models in solving marketing problems and alleviate the logistical and computational burden for data providers since they only need to train one GAN model that can solve various marketing problems. This approach is advantageous in terms of both volume and speed.  
To influence the aesthetic design process, [PERSON]. [207] propose a model that combines a probabilistic variational autoencoder (VAE) with adversarial components from a GAN and a supervised learning component. The model is tested in the automotive sector and demonstrates a significant improvement compared to conventional machine learning models and neural networks.  
Lastly in this section, the work by [208] introduces an innovative approach to user modelling that preserves privacy for digital marketing campaigns by combining learning techniques and GANs. This allows users to retain control of their personal data while enabling marketing professionals to identify suitable behaviours for their campaigns.