## 5 Results
### Structural Model Results and Hypotheses Testing  
Respondents answered Likert-scale questions on which we applied one-sample t-tests. The results are presented in Table 2 showing the t-values for each correlation between the variables. For each correlation, we are interested primarily in three values: Direct effects \(\beta\), t-value, and \(p\)-value. The t is interested in being greater than 0, and \(p<=0.05\). In the case where we have an indirect effect in BI, then for each correlation, we are interested primarily in three values: specific indirect effects \(\beta\), t-value, and \(p\)-value. Table 2 summarizes the results of the Bootstrapping estimation with 500 resamples and Figure 6 the structural model result. The model explains 72.4% of behavioral intention to use the technology's (BI). \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{1}{c}{**Path**} & \multicolumn{1}{c}{**Direct Effects \(\beta\)**} & \multicolumn{1}{c}{**t-Value**} & \multicolumn{1}{c}{**R\({}^{2}\)**} & \multicolumn{1}{c}{**Hypothesis**} \\ \multicolumn{1}{c}{**Behavioral Intention (BI)**} & & & & 0.724 & \\ H1: Perceived Vulnerability (PV) & \(-0.100\) & 3.193 & & Not Supported \\ H2: Perceived Severity (PS) & 0.046 & 1.261 & & Rejected \\ H3: Self-Efficacy (SE) & 0.103 & 3.273 & & Supported \\ H4: Perceived Response Efficacy (PRE) & 0.532 & 13.735 & & Supported \\ H5: Perceived Response Cost (PRC) & \(-0.007\) & 0.275 & & Supported \\ H6: Innovativeness (Innov) & 0.347 & 9.676 & & Supported \\ \hline \multicolumn{1}{c}{**Perceived Vulnerability (PV)**} & & & & \\ H7: Privacy Concerns (PC) & 0.583 & 16.356 & 0.340 & Supported \\ \hline \multicolumn{1}{c}{**Perceived Severity (PS)**} & & & 0.208 & \\ H8: Privacy Concerns (PC) & 0.456 & 10.463 & & Supported \\ \hline \multicolumn{1}{c}{**Self-Efficacy (SE)**} & & & & 0.059 & \\ H9: Privacy Concerns (PC) & 0.242 & 5.921 & & Supported \\ \hline \multicolumn{1}{c}{**Perceived Response Efficacy (PRE)**} & & & 0.649 & \\ H10: Privacy Concerns (PC) & 0.073 & 2.673 & & Supported \\ H12: Trust in Technology (TT) & 0.784 & 35.674 & & Supported \\ \hline \multicolumn{1}{c}{**Perceived Response Cost (PRC)**} & & & & \\ H11: Privacy Concerns (PC) & 0.213 & 4.904 & & Supported \\ H13: Trust in Technology (TT) & 0.063 & 1.442 & 0.056 & Rejected \\ \hline \hline \end{tabular} Note: All effects are significant at \(p<0.001\) except H1 and H3 with \(p=0.001\).  
\end{table} 
Table 2: Direct effects, total indirect, and total effects \(\beta\) of determinants of intention to use BBCA technology. Figure 5: Discriminant validity (diagonal values show AVE square root). Hypothesis 1 suggests that there is a positive relationship between Perceived Vulnerability (PV) and intention to use BBCA technology. No support was found for this relationship (b = \(-0.100\); \(p\) > 0.01). Thus, Perceived Vulnerability (PV) works as an inhibitor in intention to use BBCA technology. This finding is in consistence with prior studies that argue [63] that the potential harm to users' privacy posed by the misuse of biometric data is a topic of debate and it frequently discourages large-scale adoption of biometric systems. Hypothesis 2 suggests a positive relationship between perceived severity and intention to use BBCA technology. This hypothesis is not supported (b = 0.046; \(p\) > 0.01). This result is consistent with some prior research works [95], but not all [48]. Thus, this evidence provides mixed findings on perceived severity. Hypothesis 3 proposes a positive relationship between self efficacy and intention to use BBCA technology. Self-efficacy, as hypothesized, contributes positively to intention to use BBCA technology. This is consistent with prior studies that argue that people who have a high degree of self-efficacy have a greater sense of self-belief in their ability to mobilize motivation, and courses of action required to complete a mission successfully [93].