## 4 Results
### Hypotheses Testing
#### 4.2.3 Effect of Emotional Response (H4 to H6)  
We hypothesized that emotional response would mediate the relationship between question type and information disclosure such as question sensitivity positively influencing emotional response (H4a) and emotional response negatively influencing disclosure (H4b). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Nudge Comparison**} & \multicolumn{2}{c}{**Question**} & \multirow{2}{*}{**Estimate**} & \multirow{2}{*}{**StdErr**} & \multirow{2}{*}{**DF**} & \multirow{2}{*}{**t-Value**} & **One-Tail** & **Hypothesis** \\  & & & & & & & \\ \hline \multirow{2}{*}{Sensitivity signal: Present vs. Absent} & Low & 1.47 & 0.80 & 1774 & 1.84 & 0.0334 & H2a \\  & High & \(-\)1.03 & 0.85 & 1774 & \(-\)1.21 & 0.1142 & H2b \\ \hline \multirow{2}{*}{Social proof: Low vs. High} & Low & \(-\)1.29 & 1.15 & 1772 & \(-\)1.13 & 0.1301 & H3 \\  & High & \(-\)1.32 & 1.5 & 1770 & \(-\)1.14 & 0.1269 & H3 \\ \hline \multirow{2}{*}{Social proof: Low vs. None} & Low & \(-\)0.75 & 0.73 & 1772 & \(-\)1.03 & 0.1519 & - \\  & High & \(-\)0.38 & 0.82 & 1772 & \(-\)0.47 & 0.6418 & - \\ \hline \multirow{2}{*}{Social proof: High vs. None} & Low & 1.14 & 1.17 & 1772 & 0.98 & 0.1637 & - \\  & High & \(-\)1.67 & 1.23 & 1772 & \(-\)1.36 & 0.3248 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Logistic Regressions: Effect of Nudges on Response Rate Per Question Sensitivity. The results of the linear regression show that emotional response tends to decrease when high-sensitivity questions are asked compared to low-sensitivity questions (estimate = \(-0.10\)), but this result is not statistically significant (\(p\)-value = 0.3226). Thus, H4a is not supported. The result of the logistic regression on the effect of emotional response on information disclosure (estimate = 0.01) is not statistically significant (\(p\)-value = 0.1948). Therefore, H4b is not supported. We then tested the effect of the information disclosure nudges on emotional response. We hypothesized that when a low sensitivity signal is present (vs. absent) for less sensitive questions, the emotional response decreases (H5a) and that when a high sensitivity signal is present (vs. absent) for more sensitive questions, the emotional response increases (H5b). Moreover, we hypothesized that social proof moderates the relationship between question sensitivity and emotional response, such that greater social proof leads to lower emotional response and less social proof leads to higher emotional response (H6). Table 10 summarizes these results. The above results show that emotional response to low-sensitivity questions decreases (estimate = \(-0.13\)) when the low-sensitivity signal is present compared to when absent, however, this result is not statistically significant (\(p\)-value = 0.1948). Thus, H5a is not supported. The results show that, for high-sensitivity questions, the emotional response decreases (estimate = \(-0.03\)) when high sensitivity signal is present compared to when absent. This result is marginally significant with a \(p\)-value of 0.0511. Since the results are contrary to the hypothesis, H5b is not supported. For the social proof nudge, the results show that emotional response decreases when the social proof is low compared to when the social proof is high for both question sensitivity levels (estimates = \(-0.12\) and \(-0.11\)). These results are not statistically significant (\(p\)-values = 0.1717 and 0.1651). Thus, H6 is not supported. The comparison between the two social proof levels when present vs. when absent (no social proof) was also tested. The only significant result is the decrease in the emotional response (estimate = \(-0.59\)) to low-sensitivity questions when the low social proof is present compared to when it is absent (\(p\)-value = 0.0006). The other comparisons' results were not significant. The interaction between the effect of the two nudges on emotional response was also tested, but also found to be not significant. The validated research model is shown Figure 4.