diff --git a/Scripts/interaction_plots_presi.R b/Scripts/interaction_plots_presi.R
index 0ab7a740a6d05476f9cb0b34fc2db2a8861a3045..967a8618aa6ea87bd8eac3d1e6cd6dd69ec75e33 100644
--- a/Scripts/interaction_plots_presi.R
+++ b/Scripts/interaction_plots_presi.R
@@ -1,58 +1,58 @@
-# Create Interaction Term Plot for Presentation
-
-
-####
-create_interaction_term_plot <- function(ols_summary, treatment_labels, ord, unit, down, up) {
-  alpha <- 0.1
-  z_value <- qnorm(1 - alpha / 2)
-  
-  plot_data <- summary(ols_summary)
-  plot_data <- as.data.frame(plot_data$coefficients)
-  plot_data$ME <- z_value * plot_data$`Std. Error`
-  plot_data <- rownames_to_column(plot_data, "Coefficient")
-  
-  plot_data <- plot_data %>% filter(str_detect(Coefficient, "Treatment"))
-  
-  plot_data$Coefficient <- treatment_labels
-  
-  plot <- ggplot(data = plot_data) +
-    geom_bar(aes(x = factor(Coefficient, levels=c(ord)), y = Estimate, fill = Coefficient), stat = "identity", position = 'dodge', width = 0.5, alpha = 0.7) +
-    geom_errorbar(aes(x = Coefficient, ymin = Estimate - ME, ymax = Estimate + ME), width = 0.3, position = position_dodge(0.8)) +
-    scale_x_discrete(guide = guide_axis(angle = 0)) +
-    guides(fill = "none") +
-    coord_cartesian(ylim=c(down, up)) +
-    xlab("Treatment Group") +
-    ylab(paste0(unit))
-  
-  return(plot)
-}
-
-
-case_A_labels <- c("Treated", "Voluntary Treated")
-case_C_labels <- c("No Info 2", "Text 1", "Text 2", "Video 1", "Video 2")
-case_C_labels_re <- c("Text 1", "Text 2", "Video 1", "Video 2", "No Info 2")
-
-plot_interview_A <- create_interaction_term_plot(ols_time_spent_control_A, case_A_labels, case_A_labels,
-                                                 "Interview Time in Seconds", -250, 380)
-plot_interview_C <- create_interaction_term_plot(ols_time_spent_control_C, case_C_labels, case_C_labels_re,
-                                                 "Interview Time in Seconds", -250, 380)
-
-plot_cc_A <- create_interaction_term_plot(ols_time_cc_control_A, case_A_labels, case_A_labels,
-                                          "Mean Choice Card Time in Seconds", -5, 5)
-plot_cc_C <- create_interaction_term_plot(ols_time_cc_control_C, case_C_labels, case_C_labels_re,
-                                          "Mean Choice Card Time in Seconds", -5, 5)
-
-plot_mani_A <- create_interaction_term_plot(ols_percentage_correct_control_A, case_A_labels, case_A_labels,
-                                            "Percentage of Correct Quiz Statements", -5, 15)
-plot_mani_C <- create_interaction_term_plot(ols_percentage_correct_control_C, case_C_labels, case_C_labels_re,
-                                            "Percentage of Correct Quiz Statements", -5, 15)
-
-plot_cons_A <- create_interaction_term_plot(conseq_model_control_A, case_A_labels, case_A_labels,
-                                            "Consequentiality Score", -0.5, 0.8)
-plot_cons_C <- create_interaction_term_plot(conseq_model_control_C, case_C_labels, case_C_labels_re,
-                                            "Consequentiality Score", -0.5, 0.8)
-
-plot_opt_A <- create_interaction_term_plot(ols_opt_out_control_A, case_A_labels, case_A_labels,
-                                           "Number of Opt-out Choices", -1.5, 1)
-plot_opt_C <- create_interaction_term_plot(ols_opt_out_control_C, case_C_labels, case_C_labels_re,
-                                           "Number of Opt-out Choices", -1.5, 1)
+# Create Interaction Term Plot for Presentation
+
+
+####
+create_interaction_term_plot <- function(ols_summary, treatment_labels, ord, unit, down, up) {
+  alpha <- 0.1
+  z_value <- qnorm(1 - alpha / 2)
+  
+  plot_data <- summary(ols_summary)
+  plot_data <- as.data.frame(plot_data$coefficients)
+  plot_data$ME <- z_value * plot_data$`Std. Error`
+  plot_data <- rownames_to_column(plot_data, "Coefficient")
+  
+  plot_data <- plot_data %>% filter(str_detect(Coefficient, "Treatment"))
+  
+  plot_data$Coefficient <- treatment_labels
+  
+  plot <- ggplot(data = plot_data) +
+    geom_bar(aes(x = factor(Coefficient, levels=c(ord)), y = Estimate, fill = Coefficient), stat = "identity", position = 'dodge', width = 0.5, alpha = 0.7) +
+    geom_errorbar(aes(x = Coefficient, ymin = Estimate - ME, ymax = Estimate + ME), width = 0.3, position = position_dodge(0.8)) +
+    scale_x_discrete(guide = guide_axis(angle = 0)) +
+    guides(fill = "none") +
+    coord_cartesian(ylim=c(down, up)) +
+    xlab("Treatment Group") +
+    ylab(paste0(unit))
+  
+  return(plot)
+}
+
+
+case_A_labels <- c("Treated", "Voluntary Treated")
+case_C_labels <- c("No Info 2", "Text 1", "Text 2", "Video 1", "Video 2")
+case_C_labels_re <- c("Text 1", "Text 2", "Video 1", "Video 2", "No Info 2")
+
+plot_interview_A <- create_interaction_term_plot(ols_time_spent_control_A, case_A_labels, case_A_labels,
+                                                 "Interview Time in Seconds", -250, 380)
+plot_interview_C <- create_interaction_term_plot(ols_time_spent_control_C, case_C_labels, case_C_labels_re,
+                                                 "Interview Time in Seconds", -250, 380)
+
+plot_cc_A <- create_interaction_term_plot(ols_time_cc_control_A, case_A_labels, case_A_labels,
+                                          "Mean Choice Card Time in Seconds", -5, 5)
+plot_cc_C <- create_interaction_term_plot(ols_time_cc_control_C, case_C_labels, case_C_labels_re,
+                                          "Mean Choice Card Time in Seconds", -5, 5)
+
+plot_mani_A <- create_interaction_term_plot(ols_percentage_correct_control_A, case_A_labels, case_A_labels,
+                                            "Percentage of Correct Quiz Statements", -5, 15)
+plot_mani_C <- create_interaction_term_plot(ols_percentage_correct_control_C, case_C_labels, case_C_labels_re,
+                                            "Percentage of Correct Quiz Statements", -5, 15)
+
+plot_cons_A <- create_interaction_term_plot(conseq_model_control_A, case_A_labels, case_A_labels,
+                                            "Consequentiality Score", -0.5, 0.8)
+plot_cons_C <- create_interaction_term_plot(conseq_model_control_C, case_C_labels, case_C_labels_re,
+                                            "Consequentiality Score", -0.5, 0.8)
+
+plot_opt_A <- create_interaction_term_plot(ols_opt_out_control_A, case_A_labels, case_A_labels,
+                                           "Number of Status Quo Choices", -1.5, 1)
+plot_opt_C <- create_interaction_term_plot(ols_opt_out_control_C, case_C_labels, case_C_labels_re,
+                                           "Number of Status Quo Choices", -1.5, 1)
diff --git a/project_start.qmd b/project_start.qmd
index ebf9a2404ccbd594c13613d9ec54d6434ae4ad6f..844420d2e7105926c1b5cf30efaad2f09484a29d 100644
--- a/project_start.qmd
+++ b/project_start.qmd
@@ -148,9 +148,9 @@ To what extent do you agree or disagree with the following statements?
 -   Timings: We saved the net interview time and the mean Choice Card time.-\> **Survey engagement**
 -   **Consequentiality**:
 
--- To what extent do you believe that the decisions you make will have an impact on how the green spaces in your neighbourhood are designed in the future?
+- To what extent do you believe that the decisions you make will have an impact on how the green spaces in your neighborhood are designed in the future?
 
--- To what extent do you believe that the decisions you make will affect whether you have to pay a contribution for urban greening in the future?
+- To what extent do you believe that the decisions you make will affect whether you have to pay a contribution for urban greening in the future?
 :::
 
 ## Methods (1) {auto-animate="true"}
@@ -337,7 +337,7 @@ htmlreg(l=list(conseq_model_A, conseq_model_control_A, conseq_model_C, conseq_mo
 :::
 :::
 
-## OLS: Opt-out
+## OLS: Status quo
 
 ::: panel-tabset
 ### Plot
@@ -352,27 +352,16 @@ ggpubr::ggarrange(plot_opt_A, plot_opt_C)
 ```{r, results='asis'}
 htmlreg(l=list(ols_opt_out_A, ols_opt_out_control_A, ols_opt_out_C, ols_opt_out_control_C),
        custom.model.names = c("Case A", "with Controls", "Case B", "with Controls"),
-       custom.header = list("Dependent variable: Number of opt-out choices" = 1:4),
+       custom.header = list("Dependent variable: Number of status quo choices" = 1:4),
        custom.coef.map = list_ols,  stars = c(0.01, 0.05, 0.1), float.pos="tb",
        custom.note = "%stars. Standard errors in parentheses.",
        label = "tab:optout", single.row = TRUE,
-       caption = "Results of OLS on number of opt-out choices.")
+       caption = "Results of OLS on number of status quo choices.")
 ```
 :::
 :::
 
-## MXL: Split Samples
 
-```{r}
-ggplot(data=mxl_melt_info, aes(x=Coefficent, y=abs(value), fill=variable)) +
-  geom_bar(stat="identity",  position='dodge', width = 0.9) +
-  geom_errorbar(aes(x=Coefficent, ymin=abs(value)-ME, ymax=abs(value)+ME), width=0.3, position=position_dodge(0.8)) +
-  ylab("Absolute Value") +
-  xlab("Coefficient") +
-  scale_x_discrete(guide = guide_axis(angle = 45)) +
-  scale_fill_brewer(palette = "Set2", labels = c("Treated", "Optional Treatment", "Not Treated"), name="Treatment") +
-  theme(legend.position = c(0.85, 0.8)) 
-```
 
 ## MXL: Effects on stated preferences
 
@@ -469,12 +458,28 @@ htmlreg(c(case_C_cols_NR[1], remGOF(case_C_cols_NR[2:8])),
 
 ::: incremental
  
-- Respondents that voluntary access information do engage differently in the survey
+- Respondents that voluntary access information do engage more in the survey & have a higher consequentiality score
+
+- Voluntary information access is negatively correlated with number of status quo choices
+
+- Higher willingness to pay values in groups that voluntary access information
 
 :::
 
 ## Conclusion
 
+::: incremental
+
+- Obligatory and voluntary information treatments increase information recall and willingness to pay for naturalness of and proximity to urban green spaces
+
+- Exogenous treatments do not affect consequentiality
+
+- Voluntary information access is correlated with increased consequentiality, higher survey engagement and higher willingness to pay
+
+- Obligatory information treatment is more effective
+
+:::
+
 ## Appendix
 
 Information provision (Video) Link to the video: https://idiv.limequery.com/upload/surveys/682191/files/urban-heat-island-effekt.mp4
@@ -488,23 +493,23 @@ Information provision (Video) Link to the video: https://idiv.limequery.com/uplo
 ![](Grafics/sum_b_2.png){width="300"}
 
 
-## Socio Demografics {.smaller}
+<!-- ## Socio Demografics {.smaller} -->
 
-::: {style="font-size: 50%;"}
-::: panel-tabset
-### Case A
+<!-- ::: {style="font-size: 50%;"} -->
+<!-- ::: panel-tabset -->
+<!-- ### Case A -->
 
-```{r}
-kableExtra::kable(treatment_socio_A)
-```
+<!-- ```{r} -->
+<!-- kableExtra::kable(treatment_socio_A) -->
+<!-- ``` -->
 
-### Case B
+<!-- ### Case B -->
 
-```{r}
-kableExtra::kable(treatment_socio_C)
-```
-:::
-:::
+<!-- ```{r} -->
+<!-- kableExtra::kable(treatment_socio_C) -->
+<!-- ``` -->
+<!-- ::: -->
+<!-- ::: -->
 
 ## NR OLS
 
@@ -526,136 +531,16 @@ htmlreg(l=list(nr_model_treat_A), single.row = TRUE,
 ```
 :::
 
-<!-- ## MXL: WTP space -->
-
-<!-- ::: panel-tabset -->
-
-<!-- ### Scenario A -->
-
-<!-- ```{r} -->
-
-<!-- apollo_modelOutput(mxl_wtp_case_a) -->
-
-<!-- ``` -->
-
-<!-- ### Scenario B -->
-
-<!-- ```{r} -->
-
-<!-- apollo_modelOutput(mxl_wtp_case_b) -->
-
-<!-- ``` -->
-
-<!-- ### Scenario C -->
-
-<!-- ```{r} -->
-
-<!-- apollo_modelOutput(mxl_wtp_case_c) -->
-
-<!-- ``` -->
-
-<!-- ::: -->
-
-<!-- ## MXL: WTP space Graphs -->
-
-<!-- ::: panel-tabset -->
-
-<!-- ### Scenario A -->
-
-<!-- ::: panel-tabset -->
-
-<!-- ### Naturalness -->
-
-<!-- ```{r} -->
-
-<!-- wtp_nat_a -->
-
-<!-- ``` -->
-
-<!-- ### Walking Distance -->
-
-<!-- ```{r} -->
-
-<!-- wtp_wd_a -->
-
-<!-- ``` -->
-
-<!-- ::: -->
-
-<!-- ### Scenario B -->
-
-<!-- ::: panel-tabset -->
-
-<!-- ### Naturalness -->
-
-<!-- ```{r} -->
-
-<!-- wtp_nat_b -->
-
-<!-- ``` -->
-
-<!-- ### Walking Distance -->
-
-<!-- ```{r} -->
-
-<!-- wtp_wd_b -->
-
-<!-- ``` -->
-
-<!-- ::: -->
-
-<!-- ### Scenario C -->
-
-<!-- ::: panel-tabset -->
-
-<!-- ### Naturalness -->
-
-<!-- ```{r} -->
-
-<!-- wtp_nat_c -->
-
-<!-- ``` -->
-
-<!-- ### Walking Distance -->
-
-<!-- ```{r} -->
-
-<!-- wtp_wd_c -->
-
-<!-- ``` -->
-
-<!-- ::: -->
-
-<!-- ::: -->
-
-<!-- ## MXL: WTP space -->
-
-<!-- with NR index -->
-
-<!-- ::: panel-tabset -->
-
-<!-- ### Scenario A -->
-
-<!-- ```{r} -->
-
-<!-- apollo_modelOutput(mxl_wtp_case_a_NR) -->
-
-<!-- ``` -->
-
-<!-- ### Scenario B -->
-
-<!-- ```{r} -->
-
-<!-- apollo_modelOutput(mxl_wtp_case_b_NR) -->
-
-<!-- ``` -->
-
-<!-- ### Scenario C -->
-
-<!-- ```{r} -->
-
-<!-- apollo_modelOutput(mxl_wtp_case_c_NR) -->
+## MXL: Split Samples
 
-<!-- ``` -->
+```{r}
+ggplot(data=mxl_melt_info, aes(x=Coefficent, y=abs(value), fill=variable)) +
+  geom_bar(stat="identity",  position='dodge', width = 0.9) +
+  geom_errorbar(aes(x=Coefficent, ymin=abs(value)-ME, ymax=abs(value)+ME), width=0.3, position=position_dodge(0.8)) +
+  ylab("Absolute Value") +
+  xlab("Coefficient") +
+  scale_x_discrete(guide = guide_axis(angle = 45)) +
+  scale_fill_brewer(palette = "Set2", labels = c("Treated", "Optional Treatment", "Not Treated"), name="Treatment") +
+  theme(legend.position = c(0.85, 0.8)) 
+```
 
-<!-- ::: -->