## 3 Research Methods
### Analysis 2: Ordinal Regression Analysis
\begin{table}
\begin{tabular}{l l l l l l l} \hline  & **Hypothesized Paths** & \(\beta\) & **S.E.** & **C.R.** & & **Std. \(\beta\)** \\ \hline H1: & Expected outcome \(\longrightarrow\) Mobile security practices & 0.229 & 0.036 & 6.425 & \(\ast\) & 0.246 \\ H2: & Security inconvenience \(\longrightarrow\) Mobile security & \(-0.173\) & 0.018 & \(-9.567\) & \(\ast\) & \(-0.366\) \\ H3: & Security inconvenience \(\longrightarrow\) Expected outcome & \(-0.198\) & 0.019 & \(-10.276\) & \(\ast\) & \(-0.392\) \\ H4: & Usage behavior \(\longrightarrow\) Expected outcome & 0.090 & 0.027 & 3.336 & \(\ast\) & 0.122 \\ H5: & Usage behavior \(\longrightarrow\) Security inconvenience & \(-0.239\) & 0.057 & \(-4.148\) & \(\ast\) & \(-0.164\) \\ H6: & Security awareness \(\longrightarrow\) Expected outcome & 0.306 & 0.141 & 3.730 & \(\ast\) & 0.096 \\ H7: & Security awareness \(\longrightarrow\) Security inconvenience & \(-1.199\) & 0.298 & \(-4.720\) & \(\ast\) & \(-0.190\) \\ H8: & Security monitoring \(\longrightarrow\) Expected outcome & \(-0.278\) & 0.097 & \(-3.242\) & \(\ast\) & \(-0.112\) \\ H9: & Security monitoring \(\rightarrow\) Security inconvenience & \(-0.444\) & 0.207 & \(-2.515\) & \(\ast\) & \(-0.091\) \\ H10: & Job characteristic \(\longrightarrow\) Usage behavior & 0.403 & 0.171 & 2.362 & \(\ast\) & 0.096 \\ H11: & Job characteristic \(\longrightarrow\) Security awareness & 0.144 & 0.032 & 4.042 & \(\ast\) & 0.147 \\ H12: & Job characteristic \(\longrightarrow\) Expected outcome & 0.295 & 0.116 & 2.297 & \(\ast\) & 0.085 \\ H13: & Job characteristic \(\longrightarrow\) Security inconvenience & 0.250 & 0.249 & 1.163 & & 0.041 \\ H14: & Security training \(\longrightarrow\) Security awareness & 0.307 & 0.029 & 10.368 & \(\ast\) & 0.348 \\ \hline \end{tabular} \(\ast\) Path is significant at the 0.01 level.  
\end{table} 
Table 4: Path coefficients. likely (reference group: risk4) are 0.478 times lower than the odds of mobile security practice for someone who perceived the risk to be very unlikely (reference group: risk1). \begin{table}
\begin{tabular}{l l c c} \hline \hline  & & **N** & **Percentage** \\ \hline \multirow{3}{*}{Parcceived risk:} & \multirow{3}{*}{incident0: No known past security incident} & 400 & 66.1 \\  & & incident1: Has prior security incident & 205 & 33.9 \\  & & risk1: Very unlikely & 60 & 9.9 \\ \multirow{3}{*}{Perceived risk:} & \multirow{3}{*}{risk2: Unlikely} & 117 & 19.3 \\  & & risk3: Quite unlikely & 170 & 28.1 \\ \multirow{3}{*}{} & \multirow{3}{*}{risk4: Quite likely} & 157 & 26.0 \\  & & risk5: Likely & 76 & 12.6 \\ \multirow{3}{*}{} & \multirow{3}{*}{risk6: Very likely} & 25 & 4.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Data distribution of work-related variables. \begin{table}
\begin{tabular}{l l c} \hline \hline  & **N** & **Percentage** \\ \hline \multirow{3}{*}{Mobile security practice:} & Never & 158 & 26.1 \\  & Seldom & 64 & 10.6 \\  & Sometimes & 85 & 14.0 \\  & Quite frequent & 144 & 23.8 \\  & Frequent & 52 & 8.6 \\  & Very frequent & 102 & 16.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Data distribution of mobile security practice after transformation. Figure 2: Observed path model. Ordinal logistic regression assumes that the effects of independent variables are consistent across different thresholds, which is also known as the proportional odds assumption. For instance, it assumes that the coefficients that describe the relationship between the lowest versus all higher categories of the dependent variable are the same as the coefficients that describe the relationship between the second-lowest category and all higher categories. A test of parallel lines was carried out to test this assumption, and the result is presented in Table 9. The null hypothesis states that the lines of the model are parallel. As shown in Table 9, the test result of the ordinal logistic regression model is non-significant as the significance value is 0.729.