## 5 Conclusions  
This study investigated web search generation. Unlike prior research studying web search effects or web searchers' characteristics, we investigated the effect of marketing \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **NWM** & **SNM** & **NSP** \\ \hline Elasticity of Web Search Generation & 0.008 & 1.018 & 0.018 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Elasticity of marketing activities for web search generation. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Coefficient Estimates** & \multicolumn{2}{c}{**Standard**} & \multirow{2}{*}{\(p\)-Value**} \\  & & **Error** & & \\ \hline Constant & \(-\)9978.64 *** & 2234.36 & 0.000 \\ \(WSV_{it-1}\) & 0.343 *** & 0.0001 & 0.000 \\ \(NWM_{it}\) & 10.346 *** & 0.0297 & 0.000 \\ \(SNM_{it}\) & 0.565 *** & 0.0006 & 0.000 \\ \(PSP_{i}\) & \(-\)146.90 & 684.75 & 0.830 \\ \(NSP_{i}\) & 0.041 *** & 0.016 & 0.011 \\ Genre (Drama) & 347.24 & 981.74 & 0.724 \\ Genre (Comedy) & 4564.11 *** & 923.80 & 0.000 \\ Genre (Action) & 4844.28 * & 2534.04 & 0.056 \\ Genre (Romance) & 2242.35 * & 1157.86 & 0.053 \\ Genre (Fiction) & 119.85 & 1501.43 & 0.936 \\ Genre (Animation) & 1843.57 & 1627.04 & 0.257 \\ Genre (Thriller) & 245.56 & 1298.53 & 0.850 \\ Genre (Documentary) & 361.42 & 1066.78 & 0.735 \\
3D & 2708.74 & 1827.19 & 0.138 \\ IP & 5670.06 *** & 1349.61 & 0.000 \\ January & 8119.13 *** & 1368.28 & 0.000 \\ March & 3930.59 * & 2001.42 & 0.050 \\ April & 716.79 *** & 1442.36 & 0.619 \\ May & 6172.33 *** & 2116.58 & 0.004 \\ June & 4175.15 & 2585.75 & 0.106 \\ July & 2834.88 * & 1624.05 & 0.081 \\ August & 3361.38 * & 1900.21 & 0.077 \\ September & 3058.37 *** & 1142.65 & 0.007 \\ October & 1460.31 & 1836.80 & 0.427 \\ November & 0.054 & 0.071 & 0.444 \\ December & 0.087 & 0.223 & 0.696 \\ \hline \hline \end{tabular} (We can easily adjust this statement if it is not appropriate; \(p\)-value: * \(<\)0.1, *** \(<\)0.01.)  
\end{table} 
Table 3: Estimation results of AB dynamic panel estimation. activities on the web search volume and examined its dynamics. Our analysis of the data collected from the Chinese film industry revealed two major findings. First, the web search volume was influenced by three marketing activities (news media, social network marketing, and the effect of the stars of the film), and social network marketing had the strongest influence on the web search volume compared with the other two activities. Second, we found that web search generation was dynamic; the previous day's web search volume increased the present-day web search volume [6]. Our two findings contribute to the academic discussion by providing fresh empirical findings related to web search generation. They also provide critical managerial implications to marketers who design marketing activities to launch new products. More specifically, our first findings suggest that understanding the effect of each marketing activity on the volume of web searches enables marketers to improve their marketing activities. Investing their resources in social network marketing rather than in news media or film star promotion is an effective strategy in the film industry. Next, the star's performance, such as his or her acting capabilities, has no impact on web searches, whereas their fans increase the web search volume. This supplements the prior research discussing whether the stars of a movie increase movie demand [6]. Our findings explain that stars do increase movie demand by inducing consumers to search for information about movies, discovering the new role of a film star. All in all, our first findings imply that generating a web search is a more complex task than merely increasing consumers' awareness of a new product [2]. Consumers may search for information not merely to obtain information from others but to share information with others. Our second findings suggest that a web search generated at one time leads to web searching at another time.