## 3 Methods
### Stimuli Development
#### 3.2.2 Question Sensitivity (Pre-Test)  
To generate a pool of low and high-sensitivity questions to be used in the lab experiment and control for the relevance of each question to their assigned context, a within-subject online questionnaire was administered on Qualtrics (Provo, UT, USA) and distributed through Amazon Mechanical Turk (Mturk). To build the questionnaire, a pool of 210 questions centered around six contexts (35 questions per context) was generated based on prior research investigating sensitive topics [49; 113] (e.g., in the travel context: \"Are you fully vaccinated against Covid19? Refer to Appendix A for the full list of questions\"). To be eligible to complete the questionnaire, participants had to be located in North America and have a Mturk HIT approval rate of at least 90% (i.e., the proportion of prior completed tasks performed by the user that were approved by Mturk requesters) to ensure the quality of responses. Participants were given 1 USD compensation for the time they took to participate in the study. In total, 400 participants answered the questionnaire. After a meticulous review of the questionnaire data and exclusion of participants that failed one of the attention checks, the final sample for the first phase of this research was 316. The sample included 66% (207 participants) men and 34% (109) women ranging from 18 to over 66 years of age; 22% of participants (70) were from Canada and 78% were from the United States (246). The questionnaire consisted of presenting participants with one of the contexts developed for the lab experiment. Then, participants were asked to rate a group of questions within the given context on two dimensions: the question's sensitivity and relevance to the given context (see Table 1). Each participant was randomly assigned to one context and rated the sensitivity and relevance of all questions (35) for that given context. Each context got between 49 and 57 participants' responses. At the end of the rating of the 35 questions, participants had to answer a few demographic questions. Given that sensitivity can vary, as previously mentioned, in time and through cultures, the sensitivity item was chosen as a pre-test for the lab experiment to ensure that the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Item** & **Scale** & **Source** \\ \hline \multirow{3}{*}{Question sensitivity} & Rank the sensitivity of each question the chatbot asks you & \
\begin{tabular}{c} 7-point Likert scale from \\ “Extremely general” to \\ “extremely sensitive” \\ \end{tabular} & Developed by researchers \\ \hline \multirow{3}{*}{Question relevance} & Rank the relevance of each question to the context &
\begin{tabular}{c} 7-point Likert scale from \\ “Extremely irrelevant” to \\ “extremely relevant” \\ \end{tabular} & Developed by researchers \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Pre-test Variables Operationalization. questions to be asked were perceived by users as low vs. high in sensitivity specifically in the North American context where this study took place. The relevance item was also added to control for relevance. The items were created using 7-point Likert scales. For the question sensitivity item, participants had to rate from 1 (extremely general) to 7 (extremely sensitive) the sensitivity of each question given the context presented. Participants were provided with the definition of information sensitivity used in this research [58]. For the question's relevance item, participants had to rate from 1 (extremely irrelevant) to 7 (extremely relevant) the relevance of each question to the context they were presented with. To narrow down the question pool based on the survey's results, the mean relevance and sensitivity of each question were calculated. Then, all the questions averaging less than four out of seven (4/7) on the relevance axis were eliminated.