# Improving User Experience with Recommender Systems by
## 4 Analysis and Results  
The data were analyzed using methods appropriate for the variable types involved, as follows: \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Recommendation Message** & \multicolumn{2}{c}{**Recommender System**} \\ \hline \multirow{3}{*}{Sufficiency} & The information provided was sufficient for me to make a decision to accept & \multirow{3}{*}{Ease of Use} & The recommender system was easy to use \\ \cline{3-3}  & the recommendation & & \\ \cline{3-3}  & & & \\ \hline \multirow{3}{*}{Transparency} & I understood why this recommendation was made to me & \multirow{3}{*}{Confidence} & I am convinced of the suggestions recommended to me by the system \\ \cline{3-3}  & & & \\ \hline \multirow{3}{*}{Confidence} & I am convinced of the recommendation made to me & \multirow{3}{*}{Trust} & The recommender system can be trusted \\ \cline{3-3}  & & & \\ \hline \multirow{3}{*}{Intention to accept recommendation} & \multirow{3}{*}{I would accept the next recommendation} & Satisfaction & I am satisfied with the recommender system \\ \cline{3-3}  & & & \\ \cline{3-3}  & & & \\ \cline{3-3}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Measurement items (self-reported). Figure 4: Example of Likert-scale question. 1. For the self-reported model, we used cumulative logistic regression with random intercept for modeling the probability of having lower values. We used cumulative logistic regression because we treated the dependent variables as ordinal variables. 2. For the behavioral model, we used an approach appropriate for the type of dependent variable as follows: 1. For decision-making time, we used linear regression with a random intercept because the distribution of time was roughly normal; 2. For recommendation acceptance, we used logistic regression with a random intercept because the behavioral decision (to accept or request details) was binary. In all three cases, we used a random intercept model to account for the repeated-measures design; more precisely, the model allowed the intercept to vary by participant to account for the unmeasured participant-specific characteristics that were not correlated with the independent variable but had an effect on the DV (but were not measured). In the following sections, the results from the analyses corresponding to each of the study's three research questions are presented. RQ1. What Is the Effect of Message Design (Characteristics) on a User's Beliefs about System-Generated Recommendations? Information specificity impacted information sufficiency (problem specificity effect H1: b = 0.3039, \(p\) < 0.0001; solution specificity effect H3: b = 0.3714, \(p\) < 0.0001) and information transparency (problem specificity effect H2: b = 0.1814, \(p\) < 0.0001; solution specificity effect H4: b = 0.1499, \(p\) < 0.0011). On the other hand, the remaining hypotheses corresponding to RQ1 were not supported, i.e., those regarding the effect of text styling on information sufficiency (H5: b = 0.0254, \(p\) = 0.8754) and information transparency (H6: b = 0.1729, \(p\) = 0.2697) and the effect of information sequence on information sufficiency (H7: b = -0.0357, \(p\) = 0.6756) and information transparency (H8: b = 0.0608, \(p\) = 0.4865); in addition, situational complexity was not observed to have a significant effect on information sufficiency (H9: b = 0.0672, \(p\) = 0.6804) or information transparency (H10: b = 0.191, \(p\) = 0.2258). RQ2. What Is the Effect of Message Design (Characteristics) on a User's Beliefs Regarding the Case of Use and Usefulness of the RS? The relationship between text styling (bolding) and perceived ease of use was not supported (H11: b = 0.1327, \(p\) = 0.5836), and information sequence was not found to significantly impact perceived ease of use (H12: b = 0.0486, \(p\) = 0.7519). On the other hand, the effects of information sufficiency on usefulness (H13: b = 1.6193, \(p\) < 0.0001) and information transparency on ease of use (H14: b = 1.3461, \(p\) < 0.0001) were shown to be significant. RQ3.