# The Effect of Countermeasure Readability on Security Intentions
## 5 Results
### Research Model Results  
In this study, a PLS-SEM analysis tested all posed hypotheses. Its results are visually displayed in Figure 3.  
The main focus of this work was to check whether CR influences SI. To assess this accurately, PMT was enhanced with a readability section and applied to the cybersecurity context. The primary endogenous (or dependent) variable was security intentions. Ultimately, the model explained 58% of its variance (\(R^{2}=0.577\)).  
\(R^{2}\) indicates the model's predictive power [73]. \(R^{2}=0.577\) falls well within standards for behavioral studies [74; 75] and is considered a moderate effect size by [PERSON] et al. [61]. Moreover, it was higher than the \(R^{2}\) [PERSON] and [PERSON] [10] (\(R^{2}=0.461\)) and [PERSON] et al. [12] (\(R^{2}=0.28\)) achieved.  
Besides security intentions, other \(R^{2}\) values were deemed weak (RE) or very weak (PS, PV, and RC). This indicates that the variance in those variables was not well explained by the constructs anteceding them. CR, on the other hand, did receive a high \(R^{2}\) value, with \(R^{2}=0.552\). Finally, \(57\%\) (\(R^{2}=0.573\)) of the variance in self-efficacy was explained by countermeasure readability and countermeasure awareness.  
Figure 3 corresponds with the structural, or inner, model of the PLS-SEM model. After having analyzed the outer model in Section 4.3, the structural model was analyzed. The path coefficients the PLS algorithms produced can be interpreted as regular beta coefficients.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **This Text Is Easy to Read** & **This Text Is Easy to Understand** \\ \hline \(M\) & 5.47 (‘hard’) & 5.95 (‘easy’) & 5.36 (‘hard’) & 5.84 (‘easy’) \\ \(SD\) & 1.29 (‘hard’) & 1.06 (‘easy’) & 1.33 (‘hard’) & 1.17 (‘easy’) \\ \(df\) & \multicolumn{2}{c}{350} & \multicolumn{2}{c}{350} \\ \(t\) & \multicolumn{2}{c}{3.9} & \multicolumn{2}{c}{3.6} \\ \(p\) & \(<\)0.001 & \multicolumn{2}{c}{\(<\)0.001} \\ Skewness & \(-1.28\) & \multicolumn{2}{c}{\(-1.19\)} \\ Kurtosis & \(1.40\) & \multicolumn{2}{c}{0.84} \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(t\)-Test results comparing readability metrics with perceived readability and corresponding descriptive statistics.  
Figure 3: The research model results. Note: \({}^{*}p<0.05\); \({}^{**}p<0.01\); \({}^{***}p<0.001\); \(ns\)—not significant.  
Meaning in this case, that if SE changes by one standard deviation, SI changes by 0.567 (\(\beta=0.567\)) standard deviations [76].  
Significance and confidence intervals were estimated for path coefficients using the bootstrapping method. This resulted in six hypothesized relationships being confirmed through significance.  
Countermeasure awareness was found to be a strong significant positive predictor of countermeasure readability (\(\beta=0.758\), \(p<0.001\); H2). Countermeasure readability positively predicted self-efficacy (\(\beta=0.322\), \(p<0.05\); H5). Countermeasure awareness was a strong positive predictor for both self-efficacy (\(\beta=0.487\), \(p<0.01\); H7) and response efficacy (\(\beta=0.547\), \(p<0.01\); H9).  
Self-efficacy was found to be a strong significant positive predictor of security intentions (\(\beta=0.575\), \(p<0.0001\); H12). Lastly, for the variable subjective norm a significant positive relationship (\(\beta=0.296\), \(p<0.05\)) with security intentions was established, supporting H15. Table 4 displays the findings of the PLS-SEM analysis of the structural model concerning the hypotheses.