# Research on Factors Affecting Solvers' Participation Time in Online Crowdsourcing Contests
## 5 Empirical Results  
The data, descriptive statistics, and correlation results are shown in Tables 2 and 3, respectively. From the descriptive statistics, we observed that the minimum participation time is less than one minute. This is because some solvers have done the task before registration and submission, so they could sign up and submit solutions in a short time. However, the proportion of this behavior is small in the overall sample, which could not affect the overall regression results, and the average participation time is 892.1 min, about 15 h, in line with the actual reality. From the correlation results, we can see that there is a certain correlation between some variables. For this, we tested the variance expansion factor between the variables and the results showed that they are all less than 5, which means there is no multicollinearity between the variables. Regression analysis is feasible among these variables. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable Types** & **Variable Name** & **Definition** \\ \hline Dependent & Participation time & The simple arithmetic means of the time spent by all solvers on solving the task, that is, the average participation time, referred to as participation time, in minutes. \\ \hline \multirow{6}{*}{Task rewards} & Seeker sets the monetary reward for the winner of the task, is measured in RMB. \\ \cline{2-3}  & \multirow{2}{*}{Task duration} & The period between start time and end time of task set by seekers is used as a measurement for project duration, is measured in days. \\ \cline{2-3}  & Description length & The number of characters used in the task description. \\ \cline{2-3}  & \multirow{2}{*}{Description} & The picture or files provided by seekers for task description. \\ \cline{2-3}  & \multirow{2}{*}{Attachment} & The value 1 indicates the task including attachment, otherwise the value is 0. \\ \cline{2-3}  & \multirow{2}{*}{Task Feedback} & The number of communications between seekers and solvers in the task process. \\ \cline{2-3}  & \multirow{2}{*}{Extra Explanation} & The task supplementary explanation added by seekers in the task process. In Taskcn.com, the supplementary explanation is independent of the task description and is displayed in an additional position below the task description. \\ \cline{2-3}  & \multirow{2}{*}{Task Density} & The task density is the number of similar tasks for 7 days before and after the start time for a task. \\ \cline{2-3}  & \multirow{2}{*}{Market Price} & The market price is the average price of other similar tasks for 7 days before and after the start time for a task. \\ \hline \multirow{6}{*}{Control Variables} & Seeker’s Credit & The seeker’s credit in Taskcn.com. \\ \cline{2-3}  & \multirow{2}{*}{Seeker’s Experience} & The number of tasks that seeker released in Taskcn.com. \\ \cline{2-3}  & \multirow{2}{*}{Seeker’s Authentication} & If the seeker has real-name authentication, which is 1, otherwise 0. \\ \cline{2-3}  & \multirow{2}{*}{Time dummy variable} & Use the month as a time dummy variable to control the impact of release time changes on participation time. \\ \cline{2-3}  & & \\ \end{tabular}
\end{table} 
Table 1: Description of the definition of the main variables of the model. We used STATA software to perform stepwise least squares regression on the samples. The final results are shown in Table 4. It should be noted that there are some null value samples in the task description variable, which were rejected during logarithmic processing. Column 1 of Table 4 reports the regression of task rewards and duration to participation time with a significant confidence level of the coefficient. Then, we added the quadratic term of task duration into the regression model and the result is reported in column 2. The sign of one term of task duration is positive and the quadratic term is negative, indicating that there is a significant inverted U-shaped relationship between the task duration and the participation time with the significance level \(p<0.001\). So, Hypothesis 1a and 1b are proved.