# Enhancing User Acceptance of an AI Agent's Recommendation in Information-Sharing Environments
## 3 The Empirical Study
### Results
\begin{table}
\begin{tabular}{l c} \hline \hline
**UI Factor** & **Effect Size** \\ \hline Visualization & 0.323 \\ Paid agent & 0.079 \\ Explainable AI & 0.003 \\ Agent provider & 0.025 \\ Performance feedback & 0.025 \\ Decision time limit & 0.017 \\ Motivating & 0.092 \\ Nudge & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The effect sizes of the main factors. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Source** & **Sum of Squares** & **d.f.** & **Mean Square** & **F** & **Sig.** \\ \hline Corrected model & 3.927 & 36 & 0.109 & 1.196 & 0.318 \\ Intercept & 16.000 & 1 & 16.000 & 175.481 & <0.001 \\ Visualization \(\times\) performances feedback & 0.063 & 1 & 0.063 & 0.685 & 0.415 \\ Visualization \(\times\) explainableAI & 0.063 & 1 & 0.063 & 0.685 & 0.415 \\ Visualization \(\times\) nudge & 0.016 & 1 & 0.016 & 0.171 & 0.682 \\ Visualization \(\times\) motivating & 0.028 & 1 & 0.028 & 0.305 & 0.586 \\ Visualization \(\times\) agent operator & 0.000 & 1 & 0.000 & 0.000 & 1.000 \\ Visualization \(\times\) paid agent & 0.002 & 1 & 0.002 & 0.019 & 0.891 \\ Visualization \(\times\) decision time limit & 0.016 & 1 & 0.016 & 0.171 & 0.682 \\ Explainableai \(\times\) performance feedback & 0.062 & 1 & 0.062 & 0.685 & 0.415 \\ Performance feedback \(\times\) nudge & 0.016 & 1 & 0.016 & 0.171 & 0.682 \\ Performance feedback \(\times\) motivating & 0.063 & 1 & 0.063 & 0.685 & 0.415 \\ Agent operator \(\times\) performance feedback & 0.444 & 1 & 0.444 & 4.874 & 0.036 \\ Paid agent \(\times\) performance feedback & 0.016 & 1 & 0.016 & 0.171 & 0.682 \\ Performance feedback \(\times\) decision time limit & 0.141 & 1 & 0.141 & 1.542 & 0.225 \\ Explainableai \(\times\) nudge & 0.016 & 1 & 0.016 & 0.171 & 0.682 \\ Explainableai \(\times\) motivating & 0.111 & 1 & 0.111 & 1.219 & 0.279 \\ Explainableai \(\times\) agent operator & 0.250 & 1 & 0.250 & 2.742 & 0.109 \\ Paid agent \(\times\) explainableai & 0.085 & 1 & 0.085 & 0.933 & 0.343 \\ Explainableai \(\times\) decision time limit & 0.016 & 1 & 0.016 & 0.171 & 0.682 \\ Motivating \(\times\) nudge & 0.016 & 1 & 0.016 & 0.171 & 0.682 \\ Agent operator \(\times\) nudge & 0.016 & 1 & 0.016 & 0.171 & 0.682 \\ Paid agent \(\times\) nudge & 0.063 & 1 & 0.063 & 0.685 & 0.415 \\ Decision time limit \(\times\) nudge & 0.028 & 1 & 0.028 & 0.305 & 0.586 \\ Agent operator \(\times\) motivating & 0.007 & 1 & 0.007 & 0.076 & 0.785 \\ Paid agent \(\times\) motivating & 0.002 & 1 & 0.002 & 0.019 & 0.891 \\ Decision time limit \(\times\) motivating & 0.002 & 1 & 0.002 & 0.019 & 0.891 \\ Paid agent \(\times\) agent operator & 0.391 & 1 & 0.391 & 4.284 & 0.048 \\ Agent operator \(\times\) decision time limit & 0.016 & 1 & 0.016 & 0.171 & 0.682 \\ Paid agent \(\times\) decision time limit & 0.174 & 1 & 0.174 & 1.904 & 0.179 \\ Error & 2.462 & 27 & 0.091 & & \\ Total & 22.389 & 64 & & & \\ Corrected total & 6.389 & 63 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ANOVA analysis (main effect and two-factors interactions). result. Note: Although the \(p\)-value does not indicate the size of an effect (or the size of the difference in a comparison), \(p\)-values below 0.01 constitute strong evidence against the null value, \(p\)-values between 0.01 and 0.05 constitute moderate evidence, and \(p\)-values between 0.05 and 0.10 constitute weak evidence [103].