# The Role of Decision-Making Styles in Shaping Cybersecurity Compliance Behavior
## 5 Analysis and Findings  
The varimax rotation principal component analysis was performed to verify the factor loadings of the variables. In this way, the components for the independent and dependent variables were determined. The analysis classified 27 items into seven components. Furthermore, all variables' Cronbach's alpha values varied from 0.77 to 0.91. A reliability coefficient above 0.70 was favored [54]. Thus, this study's Cronbach alpha values for all variable measurements were reliable. Descriptive statistics and interrelations between the constructs were analyzed. The means, standard deviations, and correlations between the variables are indicated in Table 1. Then, regression analyses were performed to test the hypotheses. The first regression analysis was conducted for the whole group, regardless of the participants' decision-making styles. Model 1 (Table 2) shows that perceived severity (\(\beta\) = 0.186; \(p\) < 0.01), perceived vulnerability (\(\beta\) = 0.096; \(p\) < 0.05), self-efficacy (\(\beta\) = 0.133; \(p\) < 0.01), response-efficacy (\(\beta\) = 0.106; \(p\) < 0.05), and security awareness (\(\beta\) = 0.238; \(p\) < 0.01) were positively related to cybersecurity compliance behavior. Nevertheless, there was no support for the relationship between perceived barriers and cybersecurity compliance behavior. Model 1 provided a statistically significant fit to the data (R\({}^{2}\) = 0.356, F = 60.823, \(p\) = 0.000), and 35.6% of the variation in cybersecurity compliance behavior was explained by Model 1. Hence, while H5 was not supported, H1, H2, H3, H4, and H6 were all supported for all participants. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**No** & **Var.** & **Mean** & **Std. Dev.** & **CrAlph** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline
1 & PS & 4.56 & 0.72 & 0.91 & & & & & & \\
2 & PV & 4.28 & 0.80 & 0.89 & 0.713 \({}^{**}\) & & & & & \\
3 & SEEF & 3.31 & 1.04 & 0.90 & 0.262 \({}^{**}\) & 0.352 \({}^{**}\) & & & & \\
4 & REF & 4.23 & 0.76 & 0.87 & 0.655 \({}^{**}\) & 0.749 \({}^{**}\) & 0.370 \({}^{**}\) & & & \\
5 & PB & 2.79 & 0.99 & 0.77 & \(-\)0.016 & 0.040 & 0.117 \({}^{**}\) & 0.050 & & \\
6 & SEAW & 3.83 & 0.88 & 0.81 & 0.420 \({}^{**}\) & 0.513 \({}^{**}\) & 0.521 \({}^{**}\) & 0.582 \({}^{**}\) & 0.109 \({}^{**}\) & \\
7 & CSCB & 3.92 & 0.91 & 0.85 & 0.459 \({}^{**}\) & 0.479 \({}^{**}\) & 0.382 \({}^{**}\) & 0.490 \({}^{**}\) & 0.078 \({}^{*}\) & 0.500 \({}^{**}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Correlations of variables. After an overall look at all participants, regression analyses were performed for subgroups of participants regarding individual decision-making styles. In this study, the data set did not yield sufficient samples for two decision-making styles (spontaneous and avoidant styles). However, the remaining decision-making styles were obtained from [PERSON]'s [32] most well-known taxonomic categorization of decision-making styles and included dependent, intuitive, and rational styles. As can be seen in Model 2 (Table 2), perceived severity (\(\beta\) = 0.200; \(p\) < 0.01), self-efficacy (\(\beta\) = 0.153; \(p\) < 0.01), security awareness (\(\beta\) = 0.198; \(p\) < 0.01), and cybersecurity compliance behaviors were positively correlated. Contrary to Model 1, no support was found for the relationships between perceived vulnerability and response-efficacy variables and cybersecurity compliance behaviors in Model 2. Additionally, in parallel with Model 1, no support was found for the relationship between perceived barriers and cybersecurity compliance behaviors. Model 2 presented a statistically significant fit to the data (R\({}^{2}\) = 0.303, F = 28.875, \(p\) = 0.000) and explained a 30.3% variance in cybersecurity compliance behaviors. Thus, for individuals with rational decision-making styles, H2, H4, and H5 were not supported, whereas H1, H3, and H6 were supported. The regression analysis results for individuals with intuitive decision-making styles are reflected in Model 3.