#a. Are they more successful? (No)
ss1<-lm(scale(baza1$A6_3)~scale(B1_10),glob)
summary(ss1)
##
## Call:
## lm(formula = scale(baza1$A6_3) ~ scale(B1_10), data = glob)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.406 -0.985 0.057 1.100 1.435
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.00373 0.05602 -0.07 0.947
## scale(B1_10) -0.11169 0.05630 -1.98 0.048 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.996 on 314 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.0124, Adjusted R-squared: 0.00923
## F-statistic: 3.94 on 1 and 314 DF, p-value: 0.0482
#b. Active attitude toward changes (measured by introduced changes)? (More active companies perceive globalization as a chance)
ss1<-lm(scale(baza1$zmiany)~scale(B1_11),glob)
summary(ss1)
##
## Call:
## lm(formula = scale(baza1$zmiany) ~ scale(B1_11), data = glob)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2594 -0.6035 -0.0265 0.5654 2.6723
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.00787 0.05288 0.15 0.88
## scale(B1_11) 0.35590 0.05297 6.72 8.8e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.934 on 310 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.127, Adjusted R-squared: 0.124
## F-statistic: 45.1 on 1 and 310 DF, p-value: 8.77e-11
#c. Attitudes (Companies with moderate attitude are more concerned about negative consequences of globalization, but they also undertake more actions. Progressive and conservative companies see threats coming from globalization similarly. Conservative companies perceive globalization as a positive phenomenon less often).
ss1<-lm(scale(C2_6)~scale(B1_10)*postawa,glob)
summary(ss1)
##
## Call:
## lm(formula = scale(C2_6) ~ scale(B1_10) * postawa, data = glob)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.372 -0.804 -0.183 0.728 2.260
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0754 0.0894 0.84 0.400
## scale(B1_10) 0.2146 0.0946 2.27 0.024 *
## postawareal 0.0662 0.1397 0.47 0.636
## postawakonserw -0.2621 0.1329 -1.97 0.049 *
## scale(B1_10):postawareal -0.0368 0.1405 -0.26 0.794
## scale(B1_10):postawakonserw -0.1371 0.1352 -1.01 0.311
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.983 on 302 degrees of freedom
## (13 observations deleted due to missingness)
## Multiple R-squared: 0.0497, Adjusted R-squared: 0.034
## F-statistic: 3.16 on 5 and 302 DF, p-value: 0.00853
ggplot(glob,aes(x=B1_10,y=C2_6,colour=postawa))+geom_point(position="jitter")+geom_smooth(method="lm",se=F)
## Warning: Removed 6 rows containing missing values (stat_smooth).
## Warning: Removed 1 rows containing missing values (stat_smooth).
## Warning: Removed 3 rows containing missing values (stat_smooth).
## Warning: Removed 1 rows containing missing values (stat_smooth).
## Warning: Removed 11 rows containing missing values (geom_point).
ss1<-glm(C1_2~scale(B1_11)*postawa,glob,family="poisson")
summary(ss1)
##
## Call:
## glm(formula = C1_2 ~ scale(B1_11) * postawa, family = "poisson",
## data = glob)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.157 -0.739 -0.628 0.516 2.303
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.6797 0.0671 10.13 < 2e-16 ***
## scale(B1_11) 0.2569 0.0697 3.69 0.00023 ***
## postawareal 0.0515 0.1017 0.51 0.61257
## postawakonserw -0.0972 0.1020 -0.95 0.34063
## scale(B1_11):postawareal -0.0878 0.1063 -0.83 0.40843
## scale(B1_11):postawakonserw -0.2558 0.1027 -2.49 0.01275 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 290.12 on 307 degrees of freedom
## Residual deviance: 267.16 on 302 degrees of freedom
## (13 observations deleted due to missingness)
## AIC: 1028
##
## Number of Fisher Scoring iterations: 5
ggplot(glob,aes(x=B1_11,y=C1_2,colour=postawa))+geom_point(position="jitter")+geom_smooth(method="lm",se=F)
## Warning: Removed 5 rows containing missing values (stat_smooth).
## Warning: Removed 1 rows containing missing values (stat_smooth).
## Warning: Removed 4 rows containing missing values (stat_smooth).
## Warning: Removed 2 rows containing missing values (stat_smooth).
## Warning: Removed 12 rows containing missing values (geom_point).
ss1<-lm(scale(C2_6)~scale(B1_11)*postawa,glob)
summary(ss1)
##
## Call:
## lm(formula = scale(C2_6) ~ scale(B1_11) * postawa, data = glob)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.557 -0.769 -0.114 0.705 2.385
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.00984 0.08604 -0.11 0.909
## scale(B1_11) 0.44531 0.08581 5.19 3.9e-07 ***
## postawareal 0.15620 0.13319 1.17 0.242
## postawakonserw -0.13812 0.12952 -1.07 0.287
## scale(B1_11):postawareal -0.09291 0.13450 -0.69 0.490
## scale(B1_11):postawakonserw -0.29450 0.12814 -2.30 0.022 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.934 on 298 degrees of freedom
## (17 observations deleted due to missingness)
## Multiple R-squared: 0.139, Adjusted R-squared: 0.125
## F-statistic: 9.65 on 5 and 298 DF, p-value: 1.48e-08
ggplot(glob,aes(x=B1_11,y=C2_6,colour=postawa))+geom_point(position="jitter")+geom_smooth(method="lm",se=F)
## Warning: Removed 7 rows containing missing values (stat_smooth).
## Warning: Removed 2 rows containing missing values (stat_smooth).
## Warning: Removed 5 rows containing missing values (stat_smooth).
## Warning: Removed 2 rows containing missing values (stat_smooth).
## Warning: Removed 16 rows containing missing values (geom_point).
ss1<-lm(scale(C2_7)~scale(B1_11)*postawa,glob)
summary(ss1)
##
## Call:
## lm(formula = scale(C2_7) ~ scale(B1_11) * postawa, data = glob)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.749 -0.718 -0.149 0.730 2.289
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1774 0.0841 2.11 0.036 *
## scale(B1_11) 0.3389 0.0845 4.01 7.6e-05 ***
## postawareal -0.0307 0.1312 -0.23 0.815
## postawakonserw -0.5122 0.1269 -4.04 6.8e-05 ***
## scale(B1_11):postawareal -0.1318 0.1328 -0.99 0.322
## scale(B1_11):postawakonserw -0.1599 0.1262 -1.27 0.206
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.93 on 305 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.14, Adjusted R-squared: 0.126
## F-statistic: 9.94 on 5 and 305 DF, p-value: 8.09e-09
ggplot(glob,aes(x=B1_11,y=C2_7,colour=postawa))+geom_point(position="jitter")+geom_smooth(method="lm",se=F)
## Warning: Removed 3 rows containing missing values (stat_smooth).
## Warning: Removed 1 rows containing missing values (stat_smooth).
## Warning: Removed 3 rows containing missing values (stat_smooth).
## Warning: Removed 2 rows containing missing values (stat_smooth).
## Warning: Removed 9 rows containing missing values (geom_point).
ss1<-lm(scale(D1_3)~scale(B1_11)*postawa,glob)
summary(ss1)
##
## Call:
## lm(formula = scale(D1_3) ~ scale(B1_11) * postawa, data = glob)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.569 -0.598 0.162 0.678 2.070
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0204 0.0833 0.25 0.807
## scale(B1_11) 0.4451 0.0837 5.32 2e-07 ***
## postawareal 0.2944 0.1300 2.26 0.024 *
## postawakonserw -0.3226 0.1257 -2.57 0.011 *
## scale(B1_11):postawareal -0.2445 0.1316 -1.86 0.064 .
## scale(B1_11):postawakonserw -0.2072 0.1250 -1.66 0.098 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.921 on 305 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.178, Adjusted R-squared: 0.164
## F-statistic: 13.2 on 5 and 305 DF, p-value: 1.23e-11
ggplot(glob,aes(x=B1_11,y=D1_3,colour=postawa))+geom_point(position="jitter")+geom_smooth(method="lm",se=F)
## Warning: Removed 3 rows containing missing values (stat_smooth).
## Warning: Removed 1 rows containing missing values (stat_smooth).
## Warning: Removed 3 rows containing missing values (stat_smooth).
## Warning: Removed 2 rows containing missing values (stat_smooth).
## Warning: Removed 9 rows containing missing values (geom_point).
#d. Globalization as a chance or a threat vs. introducing changes (excluding entering new markets) . We can create 4 groups – scared fighters, scared losers, optimistic fighters, and optimistic losers? Which strategy results in a bigger success? (Companies more optimistic about globalization undertake more changes [both when we use the complex indicator, and single items]. Companies which recently entered a new market feel more negative about globalization of competition, but negative convictions about globalization of competition have not motivated to any other changes. These companies are more reluctant to introduce changes).
summary(lm(baza1$zmiany~scale(B1_11)*zagrozenie,glob))
##
## Call:
## lm(formula = baza1$zmiany ~ scale(B1_11) * zagrozenie, data = glob)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9256 -0.5871 -0.0333 0.5379 2.4953
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1097 0.0685 1.60 0.11
## scale(B1_11) 0.3862 0.0645 5.98 6.1e-09 ***
## zagrozenieTRUE -0.1574 0.0962 -1.64 0.10
## scale(B1_11):zagrozenieTRUE -0.1168 0.0970 -1.20 0.23
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.828 on 308 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.139, Adjusted R-squared: 0.13
## F-statistic: 16.6 on 3 and 308 DF, p-value: 5.33e-10
ggplot(glob,aes(x=B1_11,y=baza1$zmiany,colour=zagrozenie))+geom_point(position="jitter")+geom_smooth(method="lm",se=F)
## Warning: Removed 4 rows containing missing values (stat_smooth).
## Warning: Removed 1 rows containing missing values (stat_smooth).
## Warning: Removed 4 rows containing missing values (stat_smooth).
## Warning: Removed 9 rows containing missing values (geom_point).
summary(lm(baza1$zmiany~scale(B1_10)*zagrozenie,glob))
##
## Call:
## lm(formula = baza1$zmiany ~ scale(B1_10) * zagrozenie, data = glob)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2954 -0.6018 0.0136 0.5282 2.7113
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.274 0.121 2.27 0.024 *
## scale(B1_10) 0.290 0.113 2.56 0.011 *
## zagrozenieTRUE -0.336 0.211 -1.59 0.112
## scale(B1_10):zagrozenieTRUE -0.206 0.218 -0.94 0.345
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.884 on 313 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.0212, Adjusted R-squared: 0.0118
## F-statistic: 2.26 on 3 and 313 DF, p-value: 0.0818
summary(lm(C1_2~scale(B1_11)*zagrozenie,glob))
##
## Call:
## lm(formula = C1_2 ~ scale(B1_11) * zagrozenie, data = glob)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.487 -1.097 -0.565 0.903 3.743
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.0417 0.1205 16.94 < 2e-16 ***
## scale(B1_11) 0.3935 0.1129 3.49 0.00056 ***
## zagrozenieTRUE -0.0173 0.1690 -0.10 0.91853
## scale(B1_11):zagrozenieTRUE -0.1853 0.1696 -1.09 0.27535
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.45 on 305 degrees of freedom
## (12 observations deleted due to missingness)
## Multiple R-squared: 0.0479, Adjusted R-squared: 0.0385
## F-statistic: 5.12 on 3 and 305 DF, p-value: 0.00182
ggplot(glob,aes(x=B1_11,y=C1_2,colour=zagrozenie))+geom_point(position="jitter")+geom_smooth(method="lm",se=F,formula=y~exp(x))
## Warning: Removed 6 rows containing missing values (stat_smooth).
## Warning: Removed 2 rows containing missing values (stat_smooth).
## Warning: Removed 4 rows containing missing values (stat_smooth).
## Warning: Removed 12 rows containing missing values (geom_point).
summary(lm(C2_6~scale(B1_11)*zagrozenie,glob))
##
## Call:
## lm(formula = C2_6 ~ scale(B1_11) * zagrozenie, data = glob)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.884 -0.938 -0.407 1.116 3.593
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.3493 0.1070 21.96 < 2e-16 ***
## scale(B1_11) 0.4725 0.1003 4.71 3.8e-06 ***
## zagrozenieTRUE 0.0762 0.1504 0.51 0.61
## scale(B1_11):zagrozenieTRUE -0.0705 0.1511 -0.47 0.64
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.28 on 301 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.112, Adjusted R-squared: 0.103
## F-statistic: 12.7 on 3 and 301 DF, p-value: 7.9e-08
ggplot(glob,aes(x=B1_11,y=C2_6,colour=zagrozenie))+geom_point(position="jitter")+geom_smooth(method="lm",se=F)
## Warning: Removed 7 rows containing missing values (stat_smooth).
## Warning: Removed 5 rows containing missing values (stat_smooth).
## Warning: Removed 4 rows containing missing values (stat_smooth).
## Warning: Removed 16 rows containing missing values (geom_point).
summary(lm(C2_6~scale(B1_10)+scale(B1_11),glob))
##
## Call:
## lm(formula = C2_6 ~ scale(B1_10) + scale(B1_11), data = glob)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.011 -0.970 -0.320 0.989 3.680
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.3804 0.0730 32.59 < 2e-16 ***
## scale(B1_10) 0.1301 0.0758 1.72 0.087 .
## scale(B1_11) 0.4163 0.0754 5.52 7.2e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.28 on 302 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.119, Adjusted R-squared: 0.114
## F-statistic: 20.5 on 2 and 302 DF, p-value: 4.55e-09
ggplot(glob,aes(x=B1_10,y=C2_6))+geom_point(position="jitter")+geom_smooth(method="lm",se=F)
## Warning: Removed 11 rows containing missing values (stat_smooth).
## Warning: Removed 11 rows containing missing values (geom_point).
summary(lm(C2_7~scale(B1_11)*zagrozenie,glob))
##
## Call:
## lm(formula = C2_7 ~ scale(B1_11) * zagrozenie, data = glob)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.341 -0.904 -0.271 1.096 2.582
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.7083 0.1019 26.59 < 2e-16 ***
## scale(B1_11) 0.5589 0.0959 5.83 1.4e-08 ***
## zagrozenieTRUE -0.2085 0.1430 -1.46 0.146
## scale(B1_11):zagrozenieTRUE -0.3703 0.1441 -2.57 0.011 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.23 on 308 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.108, Adjusted R-squared: 0.0988
## F-statistic: 12.4 on 3 and 308 DF, p-value: 1.17e-07
ggplot(glob,aes(x=B1_11,y=C2_7,colour=zagrozenie))+geom_point(position="jitter")+geom_smooth(method="lm",se=F)
## Warning: Removed 4 rows containing missing values (stat_smooth).
## Warning: Removed 1 rows containing missing values (stat_smooth).
## Warning: Removed 4 rows containing missing values (stat_smooth).
## Warning: Removed 9 rows containing missing values (geom_point).
summary(lm(D1_3~scale(B1_11)*zagrozenie,glob))
##
## Call:
## lm(formula = D1_3 ~ scale(B1_11) * zagrozenie, data = glob)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.025 -0.713 0.287 0.975 2.357
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.4809 0.0969 35.94 < 2e-16 ***
## scale(B1_11) 0.4205 0.0912 4.61 5.9e-06 ***
## zagrozenieTRUE 0.0926 0.1360 0.68 0.50
## scale(B1_11):zagrozenieTRUE -0.0217 0.1371 -0.16 0.87
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.17 on 308 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.117, Adjusted R-squared: 0.108
## F-statistic: 13.6 on 3 and 308 DF, p-value: 2.41e-08
ggplot(glob,aes(x=B1_11,y=D1_3,colour=zagrozenie))+geom_point(position="jitter")+geom_smooth(method="lm",se=F)
## Warning: Removed 4 rows containing missing values (stat_smooth).
## Warning: Removed 1 rows containing missing values (stat_smooth).
## Warning: Removed 4 rows containing missing values (stat_smooth).
## Warning: Removed 9 rows containing missing values (geom_point).
#e. Attitude toward globalization and diversity of information sources. (Companies with positive beliefs about globalization of competition have more diverse sources of information. But – do global companies have more diverse sources of information? (Yes, they do) (No, they don't :( )).
summary(lm(baza1$informacja~scale(B1_10)+scale(B1_11),glob))
##
## Call:
## lm(formula = baza1$informacja ~ scale(B1_10) + scale(B1_11),
## data = glob)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.463 -0.584 0.044 0.576 2.701
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.00799 0.04752 0.17 0.87
## scale(B1_10) 0.00585 0.04929 0.12 0.91
## scale(B1_11) 0.33610 0.04939 6.81 5.2e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.839 on 309 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.14, Adjusted R-squared: 0.135
## F-statistic: 25.2 on 2 and 309 DF, p-value: 7.48e-11
summary(lm(baza1$informacja~scale(B1_11)+global,glob))
##
## Call:
## lm(formula = baza1$informacja ~ scale(B1_11) + global, data = glob)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5672 -0.5554 0.0253 0.5742 2.5905
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0970 0.0640 1.52 0.13
## scale(B1_11) 0.3502 0.0477 7.35 1.8e-12 ***
## globalTRUE -0.1969 0.0956 -2.06 0.04 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.834 on 309 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.152, Adjusted R-squared: 0.146
## F-statistic: 27.6 on 2 and 309 DF, p-value: 9.17e-12
summary(lm(baza1$informacja~scale(B1_11)+global,glob))
##
## Call:
## lm(formula = baza1$informacja ~ scale(B1_11) + global, data = glob)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5672 -0.5554 0.0253 0.5742 2.5905
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0970 0.0640 1.52 0.13
## scale(B1_11) 0.3502 0.0477 7.35 1.8e-12 ***
## globalTRUE -0.1969 0.0956 -2.06 0.04 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.834 on 309 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.152, Adjusted R-squared: 0.146
## F-statistic: 27.6 on 2 and 309 DF, p-value: 9.17e-12
#summary(lm(baza1$informacja~global,glob))
#summary(lm(baza1$informacja~global,glob))
summary(lm(baza1$informacja~scale(B1_11),glob))
##
## Call:
## lm(formula = baza1$informacja ~ scale(B1_11), data = glob)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4738 -0.5827 0.0425 0.5746 2.6936
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.00802 0.04745 0.17 0.87
## scale(B1_11) 0.33766 0.04752 7.10 8.3e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.838 on 310 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.14, Adjusted R-squared: 0.137
## F-statistic: 50.5 on 1 and 310 DF, p-value: 8.3e-12
#f. Companies operating on a global level perceive globalization of competition as a chance more often than other companies. On the other hand, only companies operating on a county level see globalization of competition as negative process more often.
summary(lm(B1_11~global,glob))
##
## Call:
## lm(formula = B1_11 ~ global, data = glob)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.134 -0.571 0.211 0.992 1.248
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.116 0.076 -1.52 0.129
## globalTRUE 0.256 0.113 2.26 0.024 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.993 on 310 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.0162, Adjusted R-squared: 0.0131
## F-statistic: 5.11 on 1 and 310 DF, p-value: 0.0244
summary(lm(B1_10~global,glob))
##
## Call:
## lm(formula = B1_10 ~ global, data = glob)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.560 -1.204 0.440 0.795 1.796
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.2045 0.0997 32.13 <2e-16 ***
## globalTRUE 0.3557 0.1495 2.38 0.018 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.32 on 315 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.0177, Adjusted R-squared: 0.0145
## F-statistic: 5.66 on 1 and 315 DF, p-value: 0.018
#g New technologies importance in company sector moderates the influence of trends perception on introduced changes
ss1<-glm(C1_2~scale(B1_11)*znaczenie,glob,family="poisson")
summary(ss1)
##
## Call:
## glm(formula = C1_2 ~ scale(B1_11) * znaczenie, family = "poisson",
## data = glob)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.147 -0.610 -0.586 0.556 2.140
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.54633 0.07124 7.67 1.7e-14 ***
## scale(B1_11) 0.00633 0.07119 0.09 0.929
## znaczenieTRUE 0.17999 0.09092 1.98 0.048 *
## scale(B1_11):znaczenieTRUE 0.20350 0.09199 2.21 0.027 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 289.91 on 307 degrees of freedom
## Residual deviance: 266.21 on 304 degrees of freedom
## (13 observations deleted due to missingness)
## AIC: 1024
##
## Number of Fisher Scoring iterations: 5
ss1<-lm(scale(C2_6)~scale(B1_11)*znaczenie,glob)
summary(ss1)
##
## Call:
## lm(formula = scale(C2_6) ~ scale(B1_11) * znaczenie, data = glob)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.493 -0.756 -0.220 0.718 2.438
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0981 0.0892 -1.10 0.272
## scale(B1_11) 0.2027 0.0889 2.28 0.023 *
## znaczenieTRUE 0.1184 0.1155 1.03 0.306
## scale(B1_11):znaczenieTRUE 0.2045 0.1156 1.77 0.078 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.939 on 300 degrees of freedom
## (17 observations deleted due to missingness)
## Multiple R-squared: 0.126, Adjusted R-squared: 0.117
## F-statistic: 14.4 on 3 and 300 DF, p-value: 8.97e-09
ss1<-lm(scale(C2_7)~scale(B1_11)*znaczenie,glob)
summary(ss1)
##
## Call:
## lm(formula = scale(C2_7) ~ scale(B1_11) * znaczenie, data = glob)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.654 -0.813 -0.121 0.652 2.192
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2384 0.0871 -2.74 0.00655 **
## scale(B1_11) 0.1757 0.0874 2.01 0.04530 *
## znaczenieTRUE 0.4000 0.1135 3.52 0.00049 ***
## scale(B1_11):znaczenieTRUE 0.0933 0.1142 0.82 0.41426
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.938 on 307 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.12, Adjusted R-squared: 0.112
## F-statistic: 14 on 3 and 307 DF, p-value: 1.4e-08
ss1<-lm(scale(D1_3)~scale(B1_11)*znaczenie,glob)
summary(ss1)
##
## Call:
## lm(formula = scale(D1_3) ~ scale(B1_11) * znaczenie, data = glob)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.588 -0.694 0.108 0.659 1.921
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2614 0.0859 -3.04 0.00253 **
## scale(B1_11) 0.2294 0.0861 2.66 0.00816 **
## znaczenieTRUE 0.4267 0.1119 3.81 0.00017 ***
## scale(B1_11):znaczenieTRUE 0.1046 0.1125 0.93 0.35350
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.924 on 307 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.163, Adjusted R-squared: 0.155
## F-statistic: 20 on 3 and 307 DF, p-value: 7.41e-12
##### Summary
glob$konserwa<-glob$postawa=="konserw"
glob$B1_11<-scale(glob$B1_11)
glob$interterm<-glob$B1_11*glob$znaczenie
## to jest model do artykułu
modelfirm<-"
skutki=~D1_3+C1_2+C2_6
skutki~B1_11+znaczenie+interterm+konserwa+global
"
seall<-sem(modelfirm,glob)
summary(seall,rsquare=T)
## lavaan (0.5-17) converged normally after 27 iterations
##
## Used Total
## Number of observations 303 321
##
## Estimator ML
## Minimum Function Test Statistic 15.499
## Degrees of freedom 10
## P-value (Chi-square) 0.115
##
## Parameter estimates:
##
## Information Expected
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|)
## Latent variables:
## skutki =~
## D1_3 1.000
## C1_2 0.921 0.170 5.421 0.000
## C2_6 1.059 0.167 6.342 0.000
##
## Regressions:
## skutki ~
## B1_11 0.192 0.077 2.498 0.012
## znaczenie 0.289 0.102 2.837 0.005
## interterm 0.220 0.099 2.221 0.026
## konserwa -0.294 0.103 -2.850 0.004
## global 0.467 0.100 4.668 0.000
##
## Variances:
## D1_3 1.070 0.110
## C1_2 1.802 0.161
## C2_6 1.310 0.131
## skutki 0.184 0.066
##
## R-Square:
##
## D1_3 0.304
## C1_2 0.180
## C2_6 0.286
## skutki 0.605
fitMeasures(seall, c("cfi", "rmsea"))
## cfi rmsea
## 0.966 0.043
semPaths(seall,what="stand",layout="tree2",posCol="darkblue",style="lisrel")
