4.1. Connection between cyber knowledge and awareness The connection between previous cyber knowledge and level of cyber security awareness was analyzed controlling for respon- dent country of residence and gender. Three steps were applied in the multiple hierarchical regression. In the first step, we entered country as dummy variables (, d_Poland and d_Slovenia), with Turkey as the comparison country. Gender was included in the second step, and in the last step, we entered the different knowledge variables. Table 2 shows the results of the multiple hierarchical regression analyses. In the first step, respondent country type explained 7.4% of variance in awareness of cyber-attack (R2 = 0.74, F(3,454) = 13.03, p < .01). While Turkey had a significant and positive connection to awareness (α = 2.654, p < .01), both (β = −.139, p < .01) and Poland (β = −.315, p < .01) were negatively associated with cyber- attack awareness. That is, levels of awareness in and Poland were lower compared to other countries. Entering gender into the regression analysis added a significant contribution of 3% to the level of awareness (R2 = 0.10, F (4,453) = 13.72, p < .01). Based on the direction of the coefficient, males were found to have more awareness of cyber-attacks compared to females. The last regres- sion step indicated that cyber knowledge added another 16% to total variance over netizens country of residence and the gender of respondents (R2 = .26, F(8,449) = 20.92, p < .01). Education awareness (Edu_awareness) was positively associated with aware- ness (β = .143, p < .01), meaning that respondents who felt that their current education influenced their awareness to cyber- security also felt higher awareness toward this hazard. Understanding the differences between http and https protocol (Recognition) was associated with higher amount of cyber-attack awareness (β = 0.11, p < .05). Lastly, extensive knowledge of different aspects of computer usage and applications was also positively connected to more awareness of cyber-attacks (β = 0.28, p < .01). Therefore, the results suggest that knowledge of the cyber world and security problems is associated with more awareness of the phenomenon of cyber-attacks, supporting the first hypothesis. Models 2 to 7 focused on the netizen country of residence as a moderator in the connection between cyber knowledge and cyber awareness. The interaction was significant for two variables: recognition and computer knowledge. For the recog- nition variable, the interaction among respondents was significant (β = .125, p < .05), meaning that respondents who recognize differences between http and https protocol and live in exhibited a higher awareness of cyber-attacks com- pared to people living outside with the same knowledge. The opposite was found with respondents with no recognition of differences between http and https protocol. The group that lives in was characterized by the lowest awareness compared to all other groups and students who live outside. The interaction was also found to be significant for Polish respondents but in a negative direction (β = −.199, p < .01). That is, students who live outside Poland had higher aware- ness of cyber-security problems. However, this awareness was lower and was similar to Polish respondents who could not differentiate between http and https protocol. The interaction was not significant in the case of Slovenian respondents. Even so, since the gender distribution of the Slovenian subjects diverged from the general population, this might have affected the results. Figures 2 and 3 illustrate the results of the inter- actions between recognition, countries, and awareness.1 For computer knowledge, the interaction for students was also found significant and positive (β = .456, p < .05), meaning that students with higher knowledge of computer usages who live in had higher awareness of cyber- attacks compared to those living outside with the same knowledge. The opposite was true for students who had less computer knowledge. The group that lives in was characterized by the lowest awareness compared to all other groups and students who live outside. In the case of Poland, the interaction was close to signifi- cant but in the negative direction (β = −.414 p < .10), meaning that students who live outside of Poland exhibited higher awareness compared to those who live in Poland. However, this awareness was lower and was similar to Polish respon- dents when examining those with less computer knowledge. Figures 4 and 5 illustrate the results of the interaction between recognition, countries, and awareness. To aid us in interpreting the results, we classified the continues variables, i.e. computer knowledge, into two categories based on the median (3.28). As such, we had two groups: those with com- puter knowledge (1) and without computer knowledge (0). 4.2. Connection between country, awareness, and behaviors Our next analysis aimed to measure whether awareness is con- nected to cyber user protective habits. Therefore, multivariate hierarchical regressions were conducted with behavior variables as the dependent variables. In the first step, we entered country as dummy variables (d_Israel, d_Poland and d_Slovenia) with Turkey as the comparison country. Gender was included in the second step, and in the last step, we entered the awareness variables. Table 3 shows the result of the multiple hierarchical regression analysis of the protection variables in Model 8 and the information provided by the respondents in Model 9. We also conducted a regression analysis with the behaviors that respon- dents engage in when finishing working on their computer. However, none of the stages in the regression was found to be significant. Model 8 presents the results of the regression analysis con- ducted with the behaviors that respondents used to protect their computer from cyber-attacks. The first step indicated that the three countries made a significant contribution to the overall variances (R2 = .155, p < .01). The three countries were nega- tively associated with the protection behaviors. That is, Israeli (β = −.409, P < .01), Polish (β = −.294, P < .01), and Slovenian respondents (β = −.012, P < .05) employed less protective measures compared to respondents in other countries. The exception was Turkish respondents, who used more protective behaviors compared to respondents in other countries (a = 5.693, p < .01). Even so, when we entered the rest of the variables, the connection between Slovenian respondents and protection failed to be significant. Gender, entered in the second step, was not significantly associated with protection behaviors, meaning that no differences exist between males and females concerning protection activities. However, entering awareness in the last step made a unique contribution to the overall variance of protection behaviors (Chg. R2 = 5.3%, p < .01). Awareness was found to be positively connected to pro- tection behaviors (β = .243, p < .01), indicating that higher awareness of cyber security resulted in extensive behaviors aimed to protect devices. As such, this finding supported the third hypothesis. We also conducted an interaction analysis between awareness and each of the countries. However, the interaction failed to be significant and was not reported in the regression model (β = .038, p > .05, for Israel; β = .056, p > .05 for Poland and β = −.069, p > .05 for Slovenia). Model 9 shows the results of the regression analysis between awareness and readiness to provide information. The findings indicate that respondent country was the sole variable that sig- nificantly contributed to overall variance of the readiness to pro- vide information (R2 = .107, p < .01). The results show that readiness to provide information on the Internet was higher for respondents who live in Israel (β = .309, p < .01), Poland (β = .322, p < .01), and Slovenia (β = .096, p < .05) compared to those who live outside the respondent’s country. Turkey was also positively associated with willingness to provide information (a = 1.793, p < .01), meaning that even in Turkey people are willing to disclose information on the web. As such, we can assume that people today feel more secure providing information online – even when they are more aware of the cyber-attack potential. Gender was not significant, indicating that both male and female users are ready to provide the same amount of information. Surprisingly, aware- ness of the cyber-attack problem was not associated with readiness to disclose information (β = .008, p > .05). The interaction between country and awareness was not significant, and so was not reported in the regression model (β = −.161, p > .05, for Israel; β = .056, p > .05 for Poland and β = .242, p > .05 for Slovenia). Lastly, we tested if awareness also served as a mediator between knowledge and protection. Based on Baron and Kenny’s model (1986), a connection was found between respondent cyber knowledge and protection variables. The first step of the regression analysis shows that knowledge was positively connected to protection (c = .233, p < .01). In the second step, cyber knowledge was positively and directly related with cyber security awareness (a = .317, p < .01) and awareness was positively connected to protec- tion (b = .300, p < .01). However, when the indirection effect was measured, the connection between knowledge and protection decreased (c ́ = .153, p < .01), while the connection between awareness and protection was positive and significant (b = .233, p < .01). These results thus support our last hypothesis, that awareness (partially) med- iates the connection between knowledge and cyber protec- tion behaviors. That is, subjects with more device usage.