# Research on Effective Advertising Types in Virtual Environment
## 6 Discussion  
The purpose of this study was to derive effective advertisement types by analyzing visual attention and advertisement effects according to the advertisement types applied in VR. The advertisement types were defined as avatar costumes, products, and wall posters. To verify the user response to the advertisement, visual attention was measured by developing EVAMS, which measures the number of fixations and fixation time. To \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**ANOVA**} \\ \hline \multicolumn{5}{c}{**Advertisement Recall**} \\ \hline  & **Sum of Squares** & _df_ & **Mean Square** & **F** & **Sig.** \\ \hline Between groups & 6.532 & 2 & 3.266 & 3.451 & 0.036 ** \\ \hline Within groups & 82.334 & 87 & 0.946 & & \\ \hline Total & 88.866 & 89 & & & \\ \hline
**Type of Advertisement** & **Mean** & **SD** & **Min** & **Max** \\ \hline Avatar costume & 5.10 & 0.82 & 3.33 & 6.00 \\ \hline Product & 4.47 & 1.02 & 2.00 & 6.00 \\ \hline Wall poster & 4.62 & 1.06 & 2.33 & 6.00 \\ \hline \hline \end{tabular}  
* \(p<0.05\).  
\end{table} 
Table 6: One-way ANOVA of advertisement recall by ad types. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Number of Fixations** & **Fixation Time** \\ \hline Advertisement attention & 0.206 * & 0.085 \\ \hline Advertisement recall & 0.314 *** & 0.239 ** \\ \hline \({}^{*}p<0.10\), **\(p<0.05\), ***\(p<0.01\). & & \\ \hline \hline \end{tabular}  
* \(p<0.10\), ***\(p<0.05\), ***\(p<0.01\).  
\end{table} 
Table 7: Correlation between visual attention and ad effectiveness. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**ANOVA**} \\ \hline  & \multicolumn{4}{c}{**Advertisement Attention**} \\ \hline  & **Sum of Squares** & _df_ & **Mean Square** & **F** & **Sig.** \\ \hline Between groups & 2.888 & 2 & 1.444 & 3.721 & 0.028 ** \\ \hline Within groups & 33.759 & 87 & 0.388 & & \\ \hline Total & 36.647 & 89 & & & \\ \hline
**Type of Advertisement** & **Mean** & **SD** & **Min** & **Max** \\ \hline Avatar costume & 4.23 & 0.59 & 2.67 & 5.00 \\ \hline Product & 3.79 & 0.59 & 2.67 & 5.00 \\ \hline Wall poster & 4.02 & 0.69 & 2.17 & 5.00 \\ \hline \hline \end{tabular}  
* \(p<0.05\).  
\end{table} 
Table 5: One-way ANOVA of advertisement attention by ad types. analyze the participants' subjective advertisement effects through the survey, advertisement attention and advertisement recall were investigated. The results are as follows. As a result of analyzing visual attention according to advertisement type in VR through EVAMS, there was a significant difference between the averages in the number of fixations and fixation time according to advertisement type. With regard to the number of fixations and fixation time in VR, avatar costumes, wall posters, and products were observed to be the highest. By analyzing the subjects' subjective advertisement effects through a survey, both advertisement attention and advertisement recall were derived in the following order: avatar costumes, wall posters, and products. It was observed that the avatar costume evoked the highest visual attention, followed by the wall poster and product. This supports the results of previous studies showing that the more salient and conspicuous the type of advertisement related to the direct behavior of users or people, the higher the attention and recall of the ad [41; 42]. However, in the case of the wall poster and product, the subjects felt that the wall poster and product were the backgrounds, as in the creative placement of [PERSON] and [PERSON] (1996) [16]. The results of the correlation analysis, which was conducted to identify the connection between quantitative and qualitative analyses of user responses to advertisement types, showed significant correlations between the number of fixations and advertisement attention and between fixation times and advertisement recall. For the number of fixations and advertisement attention, the correlation coefficient was positive at 0.206 but low.