9th deployment. Add trust profile chart, trust builder table, AI generator, automated data processing, fix batch upload

Dockerfile

@@ -17,7 +17,7 @@ RUN apt-get update && apt-get install -y \
  && rm -rf /var/lib/apt/lists/*
 
 # Install R packages
-RUN R -e "install.packages(c('relaimpo', 'readxl', 'readr'), repos='http://cran.rstudio.com/')"
+RUN R -e "install.packages(c('relaimpo', 'readxl', 'readr', 'lavaan', 'leaps', 'dplyr', 'tidyr'), repos='http://cran.rstudio.com/')"
 
 # Copy the requirements.txt file, your app script, and the R script into the container.
 COPY requirements.txt /requirements.txt
@@ -25,6 +25,7 @@ COPY app.py /app.py
 COPY process_data.R /process_data.R  
 COPY example_files /example_files
 COPY images /images
+COPY data_source /data_source
 
 # Install Python dependencies from requirements.txt.
 RUN pip3 install --no-cache-dir -r /requirements.txt

app.py

@@ -11,7 +11,35 @@ import io
 import numpy as np
 from itertools import zip_longest
 
+import openai
+from dotenv import load_dotenv
+from openai import OpenAI
+from langchain_openai import ChatOpenAI
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+
 logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Get the OpenAI API key from environment variables
+openai_api_key = os.getenv("OPENAI_API_KEY")
+if not openai_api_key:
+    logger.error("OPENAI_API_KEY is not set.")
+else:
+    logger.info("OpenAI API key loaded.")
+    try:
+        # Initialize OpenAI client with the API key
+        client = OpenAI(api_key=openai_api_key)
+    except Exception as e:
+        logger.error(f"Error initializing OpenAI client: {e}")
+
+max_outputs = 10
+outputs = []
+
+# Global variable to store the selected dataset for AI computation
+selected_dataset_ai = "Volkswagen Customers"  # Default value
 
 
 def plot_model_results(results_df, average_value, title, model_type):
@@ -30,12 +58,12 @@ def plot_model_results(results_df, average_value, title, model_type):
 
     # Define color scheme
     color_map = {
-        "Stability": "#642b93",
-        "Development": "#e9e23e",
-        "Relationship": "#c52121",
-        "Benefit": "#049c54",
-        "Vision": "#f4672a",
-        "Competence": "#2e3094",
+        "Stability": "#375570",
+        "Development": "#E3B05B",
+        "Relationship": "#C63F48",
+        "Benefit": "#418387",
+        "Vision": "#DF8859",
+        "Competence": "#6D93AB",
         "Trust": "#f5918a",
     }
 
@@ -151,6 +179,93 @@ def plot_model_results(results_df, average_value, title, model_type):
     return img
 
 
+def plot_bucket_fullness(driver_df, title):
+    # Determine required trust buckets
+    buckets = [
+        "Stability",
+        "Development",
+        "Relationship",
+        "Benefit",
+        "Vision",
+        "Competence",
+    ]
+
+    # Check if columns are present in df
+    missing_columns = [col for col in buckets if col not in driver_df.columns]
+
+    if missing_columns:
+        logging.warning(
+            f"The following columns are missing in driver_df: {missing_columns}"
+        )
+        return None
+    logging.info("All required columns are present in driver_df.")
+
+    color_map = {
+        "Stability": "#375570",
+        "Development": "#E3B05B",
+        "Relationship": "#C63F48",
+        "Benefit": "#418387",
+        "Vision": "#DF8859",
+        "Competence": "#6D93AB",
+    }
+
+    order = buckets
+
+    # Calculate the percentage of fullness for each column in buckets
+    results_df = (driver_df[buckets].mean()).reset_index()
+    results_df.columns = ["Trust_Bucket", "Fullness_of_Bucket"]
+    results_df["Trust_Bucket"] = pd.Categorical(
+        results_df["Trust_Bucket"], categories=order, ordered=True
+    )
+    results_df.sort_values("Trust_Bucket", inplace=True)
+
+    fig, ax = plt.subplots(figsize=(10, 8))
+
+    ax.bar(
+        results_df["Trust_Bucket"],
+        results_df["Fullness_of_Bucket"],
+        color=[color_map[bucket] for bucket in results_df["Trust_Bucket"]],
+        edgecolor="white",
+        zorder=2,
+    )
+
+    # Adding the percentage values on top of the bars
+    for i, row in enumerate(results_df.itertuples(index=False, name=None)):
+        trust_bucket, fullness_of_bucket = row
+        ax.text(
+            i,
+            fullness_of_bucket + 0.5,  # slightly above the top of the bar
+            f"{fullness_of_bucket:.1f}",
+            ha="center",
+            va="bottom",
+            color="#8c8b8c",
+        )
+
+    y_max = results_df["Fullness_of_Bucket"].max() + 1
+    plt.ylim(0, y_max)
+    plt.ylabel("Fullness")
+    plt.title(title, fontsize=14)
+
+    ax.spines[["top", "right"]].set_color("none")
+
+    # Adding grey dotted lines along the y-axis labels
+    y_ticks = ax.get_yticks()
+    for y_tick in y_ticks:
+        ax.axhline(y=y_tick, color="grey", linestyle="--", linewidth=0.5, zorder=1)
+
+    ax.set_axisbelow(True)
+    plt.tight_layout()
+
+    # Save the figure to a bytes buffer and then to an image
+    img_data = io.BytesIO()
+    plt.savefig(img_data, format="png", facecolor=fig.get_facecolor(), edgecolor="none")
+    img_data.seek(0)
+    img = Image.open(img_data)
+    plt.close(fig)
+
+    return img
+
+
 def call_r_script(
     input_file,
     text_output_path,
@@ -159,10 +274,12 @@ def call_r_script(
     csv_output_path_loyalty,
     csv_output_path_consideration,
     csv_output_path_satisfaction,
+    csv_output_path_trustbuilder,
     nps_present,
     loyalty_present,
     consideration_present,
     satisfaction_present,
+    trustbuilder_present,
 ):
     """
     Call the R script for Shapley regression analysis.
@@ -191,10 +308,12 @@ def call_r_script(
         csv_output_path_loyalty,
         csv_output_path_consideration,
         csv_output_path_satisfaction,
+        csv_output_path_trustbuilder,
         str(nps_present).upper(),  # Convert the boolean to a string ("TRUE" or "FALSE")
         str(loyalty_present).upper(),
         str(consideration_present).upper(),
         str(satisfaction_present).upper(),
+        str(trustbuilder_present).upper(),
     ]
 
     try:
@@ -208,7 +327,7 @@ def call_r_script(
 
 def analyze_excel_single(file_path):
     """
-    Analyzes a single Excel file containing data and generates plots for Trust, NPS, Loyalty and Consideration models.
+    Analyzes a single Excel file containing data and generates plots for Trust, NPS, Loyalty, Consideration, and Satisfaction models.
 
     Args:
         file_path (str): Path to the Excel file.
@@ -234,15 +353,27 @@ def analyze_excel_single(file_path):
         ".txt", "_consideration.csv"
     )
     csv_output_path_satisfaction = text_output_path.replace(".txt", "_satisfaction.csv")
+    csv_output_path_trustbuilder = text_output_path.replace(".txt", "_trustbuilder.csv")
 
-    # Load the dataset (CSV or Excel)
+    # Load the Trust Driver dataset (CSV or Excel)
+    # Trust Driver dataset is mandatory
     df = None
-    if ".xlsx" in file_path:
-        df = pd.read_excel(file_path)
-    elif ".csv" in file_path:
-        df = pd.read_csv(file_path)
-
-    # Step 1: Check for missing columns and handle NPS, Loyalty and Consideration column
+    trustbuilder_present = False
+
+    excel_file = pd.ExcelFile(file_path)
+    # Load the Excel file with the fourth row as the header
+    df = pd.read_excel(file_path, sheet_name="Driver", header=3)
+
+    # Check if the "Builder" sheet is present
+    if "Builder" in excel_file.sheet_names:
+        # Read the "Builder" sheet, making row 6 the header and reading row 7 onwards as data
+        builder_data = pd.read_excel(file_path, sheet_name="Builder", header=5)
+        # Check if the "Builder" sheet contains more than 10 rows
+        trustbuilder_present = len(builder_data) > 10
+    else:
+        trustbuilder_present = False
+
+    # Step 1: Check for missing columns and handle NPS column
     required_columns = [
         "Trust",
         "Stability",
@@ -299,7 +430,8 @@ def analyze_excel_single(file_path):
             "Dataset must contain more than 10 rows after preprocessing.",
         )
 
-    # Step 3: Adjust Shapley regression analysis based on NPS column presence
+    # Step 3: Adjust Shapley regression analysis based on column presence
+    # Handle Trust Driver Analysis and Trust Builder Analysis
     call_r_script(
         file_path,
         text_output_path,
@@ -308,36 +440,46 @@ def analyze_excel_single(file_path):
         csv_output_path_loyalty,
         csv_output_path_consideration,
         csv_output_path_satisfaction,
+        csv_output_path_trustbuilder,
         nps_present,
         loyalty_present,
         consideration_present,
         satisfaction_present,
+        trustbuilder_present,
     )
 
     # Read the output text file
     with open(text_output_path, "r") as file:
         output_text = file.read()
 
+    # Get file name for display
+    file_name = file_path.split("/")[-1]
+
+    # plot how full the trust buckets are
+    title = f"Trust Profile: {file_name}"
+    img_bucketfull = plot_bucket_fullness(df, title)
+
+    # plot trust
     # Get n_samples from output text
     n_samples_trust = output_text.split(": Trust")[1]
     n_samples_trust = n_samples_trust.split("Analysis based on ")[1]
     n_samples_trust = n_samples_trust.split("observations")[0]
 
-    # Get dataset name
-    dataset_name = file_path.split("/")[-1]
-
-    # Plots generation
+    results_df_trust = None
     results_df_trust = pd.read_csv(csv_output_path_trust)
     results_df_trust["Importance_percent"] = results_df_trust["Importance"] * 100
     average_value_trust = results_df_trust["Importance_percent"].mean()
+
     img_trust = plot_model_results(
         results_df_trust,
         average_value_trust,
-        f"TrustLogic® Drivers: {dataset_name}\n(What drives your trust)\nn={n_samples_trust}",
+        f"Trust Drivers: {file_name}",
         "Trust",
     )
 
+    # plot NPS
     img_nps = None
+    results_df_nps = None
     if nps_present:
         # Get n_samples from output text
         n_samples_nps = output_text.split(": NPS")[1]
@@ -350,11 +492,13 @@ def analyze_excel_single(file_path):
         img_nps = plot_model_results(
             results_df_nps,
             average_value_nps,
-            f"TrustLogic® Drivers: {dataset_name}\n(What drives your NPS)\nn={n_samples_nps}",
+            f"NPS Drivers: {file_name}",
             "NPS",
         )
 
+    # plot loyalty
     img_loyalty = None
+    results_df_loyalty = None
     if loyalty_present:
         # Get n_samples from output text
         n_samples_loyalty = output_text.split(": Loyalty")[1]
@@ -369,11 +513,13 @@ def analyze_excel_single(file_path):
         img_loyalty = plot_model_results(
             results_df_loyalty,
             average_value_loyalty,
-            f"TrustLogic® Drivers: {dataset_name}\n(What drives your Loyalty)\nn={n_samples_loyalty}",
+            f"Loyalty Drivers: {file_name}",
             "Loyalty",
         )
 
+    # plot consideration
     img_consideration = None
+    results_df_consideration = None
     if consideration_present:
         # Get n_samples from output text
         n_samples_consideration = output_text.split(": Consideration")[1]
@@ -390,11 +536,13 @@ def analyze_excel_single(file_path):
         img_consideration = plot_model_results(
             results_df_consideration,
             average_value_consideration,
-            f"TrustLogic® Drivers: {dataset_name}\n(What drives your Consideration)\nn={n_samples_consideration}",
+            f"Consideration Drivers: {file_name}",
             "Consideration",
         )
 
+    # plot satisfaction
     img_satisfaction = None
+    results_df_satisfaction = None
     if satisfaction_present:
         # Get n_samples from output text
         n_samples_satisfaction = output_text.split(": Satisfaction")[1]
@@ -411,10 +559,84 @@ def analyze_excel_single(file_path):
         img_satisfaction = plot_model_results(
             results_df_satisfaction,
             average_value_satisfaction,
-            f"TrustLogic® Drivers: {dataset_name}\n(What drives your Satisfaction)\nn={n_samples_satisfaction}",
+            f"Satisfaction Drivers: {file_name}",
             "Satisfaction",
         )
 
+    # plot trust builder table 1 and 2
+    # df_builder = None
+    df_builder_pivot = None
+    if trustbuilder_present:
+        # Create dataframe for trust builder
+        results_df_builder = pd.read_csv(csv_output_path_trustbuilder)
+
+        combined_data = {
+            "Message": results_df_builder["Message"],
+            "Stability": results_df_builder["Stability"].round(0).astype(int),
+            "Development": results_df_builder["Development"].round(0).astype(int),
+            "Relationship": results_df_builder["Relationship"].round(0).astype(int),
+            "Benefit": results_df_builder["Benefit"].round(0).astype(int),
+            "Vision": results_df_builder["Vision"].round(0).astype(int),
+            "Competence": results_df_builder["Competence"].round(0).astype(int),
+        }
+
+        df_builder = pd.DataFrame(combined_data)
+
+        # Create consolidated table
+        # List of bucket columns
+        bucket_columns = [
+            "Stability",
+            "Development",
+            "Relationship",
+            "Benefit",
+            "Vision",
+            "Competence",
+        ]
+
+        # Prepare lists to collect data
+        buckets = []
+        messages = []
+        percentages = []
+
+        # Iterate through each bucket column
+        for bucket in bucket_columns:
+            for index, value in results_df_builder[bucket].items():
+                if value > 0:
+                    buckets.append(bucket)
+                    messages.append(results_df_builder["Message"][index])
+                    percentages.append(int(round(value)))
+
+        # Create the new DataFrame
+        builder_consolidated = {
+            "Trust Driver®": buckets,
+            "Trust Proof Point®": messages,
+            "%": percentages,
+        }
+
+        df_builder_pivot = pd.DataFrame(builder_consolidated)
+
+        # Define the order of the Trust Driver® categories
+        trust_driver_order = [
+            "Stability",
+            "Development",
+            "Relationship",
+            "Benefit",
+            "Vision",
+            "Competence",
+        ]
+
+        # Convert Trust Driver® column to a categorical type with the specified order
+        df_builder_pivot["Trust Driver®"] = pd.Categorical(
+            df_builder_pivot["Trust Driver®"],
+            categories=trust_driver_order,
+            ordered=True,
+        )
+
+        # Sort the DataFrame by 'Trust Driver®' and '%' in descending order within each 'Trust Driver®'
+        df_builder_pivot = df_builder_pivot.sort_values(
+            by=["Trust Driver®", "%"], ascending=[True, False]
+        )
+
     # After processing, ensure to delete the temporary files and directory
     os.remove(csv_output_path_trust)
     if nps_present:
@@ -425,6 +647,8 @@ def analyze_excel_single(file_path):
         os.remove(csv_output_path_consideration)
     if satisfaction_present:
         os.remove(csv_output_path_satisfaction)
+    if trustbuilder_present:
+        os.remove(csv_output_path_trustbuilder)
     os.remove(text_output_path)
     os.rmdir(temp_dir)
 
@@ -453,18 +677,26 @@ def analyze_excel_single(file_path):
         )
 
     return (
+        img_bucketfull,
         img_trust,
         img_nps,
         img_loyalty,
         img_consideration,
         img_satisfaction,
+        # df_builder,
+        df_builder_pivot,
         output_text,
+        results_df_trust,
+        results_df_nps,
+        results_df_loyalty,
+        results_df_consideration,
+        results_df_satisfaction,
     )
 
 
 def batch_file_processing(file_paths):
     """
-    Analyzes all Excel files in a list of file paths and generates plots for Trust, NPS, Loyalty, Consideration, and Satisfaction models.
+    Analyzes all Excel files in a list of file paths and generates plots for all models.
 
     Args:
         file_paths (List[str]): List of paths to the Excel files.
@@ -478,134 +710,168 @@ def batch_file_processing(file_paths):
         str: Summary of the analysis.
     """
 
-    # Process each file
+    img_bucketfull_list = []
     img_trust_list = []
     img_nps_list = []
     img_loyalty_list = []
     img_consideration_list = []
     img_satisfaction_list = []
+    # df_builder_list = []
+    df_builder_pivot_list = []
     output_text_list = []
 
     for file_path in file_paths:
         (
+            img_bucketfull,
             img_trust,
             img_nps,
             img_loyalty,
             img_consideration,
             img_satisfaction,
+            # df_builder,
+            df_builder_pivot,
             output_text,
+            results_df_trust,
+            results_df_nps,
+            results_df_loyalty,
+            results_df_consideration,
+            results_df_satisfaction,
         ) = analyze_excel_single(file_path)
+        img_bucketfull_list.append(img_bucketfull)
         img_trust_list.append(img_trust)
         img_nps_list.append(img_nps)
         img_loyalty_list.append(img_loyalty)
         img_consideration_list.append(img_consideration)
         img_satisfaction_list.append(img_satisfaction)
+        # df_builder_list.append(df_builder)
+        df_builder_pivot_list.append(df_builder_pivot)
         output_text_list.append(output_text)
 
     return (
+        img_bucketfull_list,
         img_trust_list,
         img_nps_list,
         img_loyalty_list,
         img_consideration_list,
         img_satisfaction_list,
+        # df_builder_list,
+        df_builder_pivot_list,
         output_text_list,
     )
 
 
-# Define instruction text and the Gradio app interface as before
-instruction_text = """
-    ## Instructions
-    
-    Please upload an Excel file with your data. The file should contain columns for Trust, Stability, Development, Relationship, Benefit, Vision, Competence, NPS, Loyalty, Consideration and Satisfaction.
-
-    The app will analyze the data, calculate the relative importance of predictors using Shapley values for all models, and display the results in separate plots.
-
-    Note: The analysis may take a few seconds. Please wait for the results to be displayed.
-    """
+def variable_outputs(file_inputs):
 
-max_outputs = 10  # max number of batch datasets allowed in a single upload
-outputs = []
+    # global df_builder_pivot_all
 
+    file_inputs_single = file_inputs
 
-def variable_outputs(file_inputs):
     # Call batch file processing and get analysis results
     (
+        img_bucketfull_list,
         img_trust_list,
         img_nps_list,
         img_loyalty_list,
         img_consideration_list,
         img_satisfaction_list,
+        # df_builder_list,
+        df_builder_pivot_list,
         output_text_list,
-    ) = batch_file_processing(file_inputs)
+    ) = batch_file_processing(file_inputs_single)
+
+    # Update the global variable
+    # df_builder_pivot_all = df_builder_pivot_list
 
     # Get number of datasets uploaded
-    k = len(file_inputs)
+    k = len(file_inputs_single)
 
     # Container for visible plots
     plots_visible = []
 
     # Use zip_longest to iterate over the lists, padding with None
     for row, (
+        img_bucketfull,
         img_trust,
         img_nps,
         img_loyalty,
         img_consideration,
         img_satisfaction,
+        # df_builder,
+        df_builder_pivot,
         output_text,
     ) in enumerate(
         zip_longest(
+            img_bucketfull_list,
             img_trust_list,
             img_nps_list,
             img_loyalty_list,
             img_consideration_list,
             img_satisfaction_list,
+            # df_builder_list,
+            df_builder_pivot_list,
             output_text_list,
         )
     ):
         # Get dataset name
-        dataset_name = file_inputs[row].split("/")[-1]
+        dataset_name = file_inputs_single[row].split("/")[-1]
 
         # Based on the number of files uploaded, determine the content of each textbox
         plots = [
+            gr.Markdown(
+                "<span style='font-size:20px; font-weight:bold;'>1) Trust Profile</span>",
+                visible=True,
+            ),
+            gr.Markdown(
+                "This analysis shows you show strongly you are trusted in each of the six Trust Buckets®. You can also see this for any competitor.",
+                visible=True,
+            ),
+            gr.Image(
+                value=img_bucketfull,
+                type="pil",
+                label="Trust Profile",
+                visible=True,
+            ),
+            gr.Markdown(
+                "<span style='font-size:20px; font-weight:bold;'>2) Trust and KPI Drivers</span>",
+                visible=True,
+            ),
+            gr.Markdown(
+                "This analysis shows you which of the TrustLogic® dimensions are most effective in building more trust and improving your KPIs. "
+                + "Here we display Trust and NPS, but in the full version you can include up to four KPIs (e.g. CSAT, Consideration, Loyalty). ​"
+                + "<br>The Trust Buckets® extending to the right are the more important ones. We show how they over and under-index. "
+                + "The average driver impact is 16.7% (100% divided by 6 trust dimensions). The higher the % above average, the more important. "
+                + "That means that you need to ‘fill’ these Trust Buckets® with the right attributes and messages.",
+                visible=True,
+            ),
             gr.Image(
                 value=img_trust,
                 type="pil",
-                # label=f"{dataset_name}: Trust Drivers",
                 label="Trust Drivers",
                 visible=True,
             ),
             gr.Image(
                 value=img_nps,
                 type="pil",
-                # label=f"{dataset_name}: NPS Drivers",
                 label="NPS Drivers",
                 visible=True,
             ),
             gr.Image(
                 value=img_loyalty,
                 type="pil",
-                # label=f"{dataset_name}: Loyalty Drivers",
-                label="Loyalty Drivers",
                 visible=True,
             ),
             gr.Image(
                 value=img_consideration,
                 type="pil",
-                # label=f"{dataset_name}: Consideration Drivers",
-                label="Consideration Drivers",
                 visible=True,
             ),
             gr.Image(
                 value=img_satisfaction,
                 type="pil",
-                # label=f"{dataset_name}: Satisfaction Drivers",
-                label="Satisfaction Drivers",
                 visible=True,
             ),
             gr.Textbox(
                 value=output_text,
-                # label=f"{dataset_name}: Analysis Summary",
-                label="Analysis Summary",
                 visible=False,
             ),
         ]
@@ -613,13 +879,76 @@ def variable_outputs(file_inputs):
         # add current plots to container
         plots_visible += plots
 
+        # if isinstance(df_builder, pd.DataFrame) and isinstance(
+        #     df_builder_pivot, pd.DataFrame
+        # ):
+        if isinstance(df_builder_pivot, pd.DataFrame):
+            # logging.debug(f"df_builder: {df_builder}")
+            logging.debug(f"df_builder_pivot: {df_builder_pivot}")
+
+            markdown_5 = gr.Markdown(
+                "<span style='font-size:20px; font-weight:bold;'>3) Proof Points</span>",
+                visible=True,
+            )
+
+            markdown_6 = gr.Markdown(
+                "These are the reasons to trust and recommend. They can be your brand values, features, attributes, programmes and messages. "
+                + "<br>In the first table, use the little arrow in each column to toggle the most to least effective proof points to fill each Trust Bucket®. Your focus is only on the Trust Bucket® with the highest driver impact. "
+                + "<br>In the second table you see the top scoring proof points ordered by Trust Bucket®. "
+                + "<br>Note: Even if Trust Buckets for Customers and Prospects overlap, the most effective statements are very different. This provides clear guidance for acquisition versus loyalty activities.",
+                visible=True,
+            )
+
+            # table_builder_1 = gr.Dataframe(
+            #     value=df_builder,
+            #     headers=list(df_builder.columns),
+            #     interactive=False,
+            #     label=f"{dataset_name}",
+            #     visible=True,
+            #     height=800,
+            #     wrap=True,
+            # )
+
+            table_builder_2 = gr.Dataframe(
+                value=df_builder_pivot,
+                headers=list(df_builder_pivot.columns),
+                interactive=False,
+                label=f"{dataset_name}",
+                visible=True,
+                height=800,
+                wrap=True,
+            )
+
+            # add builder tables to container
+            plots_visible.append(markdown_5)
+            plots_visible.append(markdown_6)
+            # plots_visible.append(table_builder_1)
+            plots_visible.append(table_builder_2)
+        else:
+            # otherwise, add invisible tables
+            empty_markdown = gr.Markdown("", visible=False)
+            empty_table = gr.Dataframe(value=None, label="", visible=False)
+            plots_visible.append(gr.Markdown("", visible=False))
+            plots_visible.append(gr.Markdown("", visible=False))
+            # plots_visible.append(empty_table)
+            plots_visible.append(gr.Dataframe(value=None, label="", visible=False))
+
     plots_invisible = [
+        gr.Markdown("", visible=False),
+        gr.Markdown("", visible=False),
+        gr.Image(label="Trust Buckets", visible=False),
+        gr.Markdown("", visible=False),
+        gr.Markdown("", visible=False),
         gr.Image(label="Trust Drivers", visible=False),
         gr.Image(label="NPS Drivers", visible=False),
         gr.Image(label="Loyalty Drivers", visible=False),
         gr.Image(label="Consideration Drivers", visible=False),
         gr.Image(label="Satisfaction Drivers", visible=False),
         gr.Textbox(label="Analysis Summary", visible=False),
+        gr.Markdown("", visible=False),
+        gr.Markdown("", visible=False),
+        # gr.Dataframe(value=None, label=" ", visible=False),
+        gr.Dataframe(value=None, label=" ", visible=False),
     ]
 
     return plots_visible + plots_invisible * (max_outputs - k)
@@ -630,6 +959,28 @@ def reset_outputs():
     outputs = []
 
     # Create fixed dummy components
+    markdown_1 = gr.Markdown(
+        "<span style='font-size:20px; font-weight:bold;'>1) Trust Profile</span>",
+        visible=True,
+    )
+    markdown_2 = gr.Markdown(
+        "This analysis shows you show strongly you are trusted in each of the six Trust Buckets®. You can also see this for any competitor.",
+        visible=True,
+    )
+    buckets_plot = gr.Image(value=None, label="Trust Buckets", visible=True)
+
+    markdown_3 = gr.Markdown(
+        "<span style='font-size:20px; font-weight:bold;'>2) Trust and KPI Drivers</span>",
+        visible=True,
+    )
+    markdown_4 = gr.Markdown(
+        "This analysis shows you which of the TrustLogic® dimensions are most effective in building more trust and improving your KPIs. "
+        + "Here we display Trust and NPS, but in the full version you can include up to four KPIs (e.g. CSAT, Consideration, Loyalty). ​"
+        + "<br>The Trust Buckets® extending to the right are the more important ones. We show how they over and under-index. "
+        + "The average driver impact is 16.7% (100% divided by 6 trust dimensions). The higher the % above average, the more important. "
+        + "That means that you need to ‘fill’ these Trust Buckets® with the right attributes and messages.",
+        visible=True,
+    )
     trust_plot = gr.Image(value=None, label="Trust Drivers", visible=True)
     nps_plot = gr.Image(value=None, label="NPS Drivers", visible=True)
     loyalty_plot = gr.Image(value=None, label="Loyalty Drivers", visible=True)
@@ -638,85 +989,561 @@ def reset_outputs():
     )
     satisfaction_plot = gr.Image(value=None, label="Satisfaction Drivers", visible=True)
     summary_text = gr.Textbox(value=None, label="Analysis Summary", visible=False)
+
+    markdown_5 = gr.Markdown(
+        "<span style='font-size:20px; font-weight:bold;'>3) Proof Points</span>",
+        visible=True,
+    )
+    markdown_6 = gr.Markdown(
+        "These are the reasons to trust and recommend. They can be your brand values, features, attributes, programmes and messages. "
+        + "<br>In the first table, use the little arrow in each column to toggle the most to least effective proof points to fill each Trust Bucket®. Your focus is only on the Trust Bucket® with the highest driver impact. "
+        + "<br>In the second table you see the top scoring proof points ordered by Trust Bucket®. "
+        + "<br>Note: Even if Trust Buckets for Customers and Prospects overlap, the most effective statements are very different. This provides clear guidance for acquisition versus loyalty activities.",
+        visible=True,
+    )
+
+    # df_builder = gr.Dataframe(value=None, label="", visible=True)
+    df_builder_pivot = gr.Dataframe(value=None, label="", visible=True)
+
+    outputs.append(markdown_1)
+    outputs.append(markdown_2)
+    outputs.append(buckets_plot)
+    outputs.append(markdown_3)
+    outputs.append(markdown_4)
     outputs.append(trust_plot)
     outputs.append(nps_plot)
     outputs.append(loyalty_plot)
     outputs.append(consideration_plot)
     outputs.append(satisfaction_plot)
     outputs.append(summary_text)
+    outputs.append(markdown_5)
+    outputs.append(markdown_6)
+    # outputs.append(df_builder)
+    outputs.append(df_builder_pivot)
 
     # invisible from second set onwards
     for i in range(1, max_outputs):
-        trust_plot = gr.Image(value=None, label="Trust Drivers", visible=False)
-        nps_plot = gr.Image(value=None, label="NPS Drivers", visible=False)
-        loyalty_plot = gr.Image(value=None, label="Loyalty Drivers", visible=False)
-        consideration_plot = gr.Image(
-            value=None, label="Consideration Drivers", visible=False
-        )
-        satisfaction_plot = gr.Image(
-            value=None, label="Satisfaction Drivers", visible=False
-        )
-        summary_text = gr.Textbox(value=None, label="Analysis Summary", visible=False)
-        outputs.append(trust_plot)
-        outputs.append(nps_plot)
-        outputs.append(loyalty_plot)
-        outputs.append(consideration_plot)
-        outputs.append(satisfaction_plot)
-        outputs.append(summary_text)
+        markdown_empty = gr.Markdown("", visible=False)
+        plot_empty = gr.Image(value=None, label="", visible=False)
+        df_empty = gr.Dataframe(value=None, label="", visible=False)
+        text_empty = gr.Textbox(value=None, label="", visible=False)
+
+        outputs.append(gr.Markdown("", visible=False))
+        outputs.append(gr.Markdown("", visible=False))
+        outputs.append(gr.Image(value=None, label="", visible=False))
+        outputs.append(gr.Markdown("", visible=False))
+        outputs.append(gr.Markdown("", visible=False))
+        outputs.append(gr.Image(value=None, label="", visible=False))
+        outputs.append(gr.Image(value=None, label="", visible=False))
+        outputs.append(gr.Image(value=None, label="", visible=False))
+        outputs.append(gr.Image(value=None, label="", visible=False))
+        outputs.append(gr.Image(value=None, label="", visible=False))
+        outputs.append(gr.Textbox(value=None, label="", visible=False))
+        outputs.append(gr.Markdown("", visible=False))
+        outputs.append(gr.Markdown("", visible=False))
+        # outputs.append(df_empty)
+        outputs.append(gr.Dataframe(value=None, label="", visible=False))
 
     return outputs
 
 
+def data_processing(file_path):
+    """
+    Processes a single CSV file and generates required outputs.
+
+    Args:
+        file_path (str): Path to the CSV file.
+
+    Returns:
+        tuple: Contains processed data and results (customize based on your needs).
+    """
+    # Load the first two rows to get the column names
+    header_df = pd.read_csv(file_path, header=None, nrows=2)
+
+    # Fill NaN values in the rows with an empty string
+    header_df.iloc[0] = header_df.iloc[0].fillna("")
+    header_df.iloc[1] = header_df.iloc[1].fillna("")
+
+    # Merge the two rows to create column names
+    merged_columns = header_df.iloc[0] + " " + header_df.iloc[1]
+
+    # Load the rest of the DataFrame using the merged column names
+    df = pd.read_csv(file_path, skiprows=2, names=merged_columns)
+
+    # For any value in all columns that contain " - " (rating),
+    # split and only take the first part (in digit format)
+    def split_value(val):
+        if isinstance(val, str) and " - " in val:
+            return val.split(" - ")[0]
+        return val
+
+    # Apply the function to all elements of the DataFrame
+    df = df.applymap(split_value)
+
+    # Convert the columns from the third column onwards to numeric
+    df.iloc[:, 2:] = df.iloc[:, 2:].apply(pd.to_numeric, errors="coerce")
+
+    # Search for the text in the column names
+    search_text = "how likely are you to buy another".lower()
+    col_index = [i for i, col in enumerate(df.columns) if search_text in col.lower()]
+
+    if col_index:
+        col_index = col_index[0]  # Assuming there is only one matching column
+
+        # Define the mapping dictionary for reverse replacement
+        replace_map = {1: 5, 2: 4, 4: 2, 5: 1}
+
+        # Replace values in the specified column
+        df.iloc[:, col_index] = df.iloc[:, col_index].replace(replace_map)
+
+    column_mapping = {
+        "Did you own a": "Q1",
+        "your age": "Q2",
+        "How likely are you to recommend buying a": "NPS",
+        "level of trust": "Trust",
+        "buy another": "Loyalty",
+        "consider buying": "Consideration",
+        "Has built a strong and stable foundation": "Stability",
+        "Will develop well in the future": "Development",
+        "Relates well to people like me": "Relationship",
+        "Is valuable to our lives": "Benefit",
+        "Has vision and values I find appealing": "Vision",
+        "Has what it takes to succeed": "Competence",
+    }
+
+    # Create a list to hold the labels
+    list_labels = []
+
+    # Loop through each column in merged_columns
+    for col in merged_columns:
+        label = None
+        for key, value in column_mapping.items():
+            if key.lower() in col.lower():
+                label = value
+                break
+        if label:
+            list_labels.append(label)
+
+    # Determine the difference between the lengths of list_labels and merged_columns
+    difference = len(merged_columns) - len(list_labels)
+
+    # TRUST STATEMENTS TB1 - TB37 populate to the rest of columns
+    # Append the next values ("TB1", "TB2", ...) until list_labels matches the length of merged_columns
+    for i in range(difference):
+        list_labels.append(f"TB{i + 1}")
+
+    # Add list_labels as the first row after the column names
+    df_labels = pd.DataFrame([list_labels], columns=df.columns)
+
+    # Concatenate header_df, df_labels, and df
+    header_df.columns = df.columns  # Ensure header_df has the same columns as df
+
+    # Create a DataFrame with 2 rows of NaNs
+    nan_rows = pd.DataFrame(np.nan, index=range(2), columns=df.columns)
+
+    # Pad 2 rows of NaNs, followed by survey questions to make it the same format as the input excel file
+    df = pd.concat([nan_rows, header_df, df_labels, df]).reset_index(drop=True)
+
+    # Make list labels the column names
+    df.columns = list_labels
+
+    # Remove columns beyond TB37
+    max_tb_label = 37
+    tb_columns = [col for col in df.columns if col.startswith("TB")]
+    tb_columns_to_keep = {f"TB{i + 1}" for i in range(max_tb_label)}
+    tb_columns_to_drop = [col for col in tb_columns if col not in tb_columns_to_keep]
+    df.drop(columns=tb_columns_to_drop, inplace=True)
+
+    # Take snippets from df as drivers
+    kpis = [
+        "Trust",
+        "NPS",
+        "Loyalty",
+        "Consideration",
+        "Satisfaction",
+    ]
+
+    drivers = [
+        "Stability",
+        "Development",
+        "Relationship",
+        "Benefit",
+        "Vision",
+        "Competence",
+    ]
+
+    # Create an empty list to store the selected columns
+    selected_columns = []
+
+    # Check each item in kpis and drivers and search in df.columns
+    for kpi in kpis:
+        for col in df.columns:
+            if pd.notna(col) and kpi.lower() in col.lower():
+                selected_columns.append(col)
+
+    for driver in drivers:
+        for col in df.columns:
+            if pd.notna(col) and driver.lower() in col.lower():
+                selected_columns.append(col)
+
+    # Extract the selected columns into a new DataFrame df_drivers
+    df_drivers = df[selected_columns].iloc[4:].reset_index(drop=True)
+
+    # Create a DataFrame with 2 rows of NaNs
+    nan_rows = pd.DataFrame(np.nan, index=range(2), columns=df_drivers.columns)
+
+    # Pad 3 rows of NaNs to make it the same format as the input excel file
+    df_drivers = pd.concat([nan_rows, df_drivers]).reset_index(drop=True)
+
+    # Get dataset name
+    dataset_name = file_path.split("/")[-1]
+    dataset_name = dataset_name.split(".")[0]
+
+    # Save processed df as an Excel file
+    processed_file_path = f"./example_files/{dataset_name}.xlsx"
+    with pd.ExcelWriter(processed_file_path) as writer:
+        df_drivers.to_excel(writer, sheet_name="Driver", index=False)
+        df.to_excel(writer, sheet_name="Builder", index=False)
+
+    # outputs = variable_outputs([processed_file_path])
+    return processed_file_path
+
+
 def process_examples(file_name):
     file_path = f"example_files/{file_name[0]}"
-    outputs = variable_outputs([file_path])
+    file_path = [file_path]
+    outputs = variable_outputs(file_path)
+
     return outputs
 
 
+def process_datasets(file_inputs):
+    """
+    Processes uploaded datasets and calls appropriate functions based on file type.
+
+    Args:
+        file_inputs (List[UploadFile]): List of uploaded files.
+
+    Returns:
+        List[gr.Blocks]: List of Gradio output components.
+    """
+    outputs_list = []
+
+    for file_input in file_inputs:
+        file_path = file_input.name
+        file_extension = os.path.splitext(file_path)[-1].lower()
+
+        if file_extension == ".xlsx":
+            outputs_list.append(file_path)
+
+        elif file_extension == ".csv":
+            processed_file_path = data_processing(file_path)
+            outputs_list.append(processed_file_path)
+
+    outputs = variable_outputs(outputs_list)
+
+    return outputs
+
+
+def chatbot_response(message):
+
+    global selected_dataset_ai
+    # global df_builder_pivot_all
+
+    # Load the selected dataset
+    dataset_file_path = f"example_files/{selected_dataset_ai}.xlsx"
+
+    # Run the Trust Builder analysis
+    try:
+        (
+            img_bucketfull,
+            img_trust,
+            img_nps,
+            img_loyalty,
+            img_consideration,
+            img_satisfaction,
+            # df_builder,
+            df_builder_pivot,
+            output_text,
+            results_df_trust,
+            results_df_nps,
+            results_df_loyalty,
+            results_df_consideration,
+            results_df_satisfaction,
+        ) = analyze_excel_single(dataset_file_path)
+
+        if df_builder_pivot is not None:
+            qualified_bucket_names_list = []
+
+            # Remove buckets with values below 18%
+            qualified_bucket_names_trust = results_df_trust[
+                results_df_trust["Importance_percent"] >= 18
+            ]["Predictor"].tolist()
+            qualified_bucket_names_list.append(qualified_bucket_names_trust)
+
+            if results_df_nps is not None:
+                qualified_bucket_names_nps = results_df_nps[
+                    results_df_nps["Importance_percent"] >= 18
+                ]["Predictor"].tolist()
+                qualified_bucket_names_list.append(qualified_bucket_names_nps)
+
+            if results_df_loyalty is not None:
+                qualified_bucket_names_loyalty = results_df_loyalty[
+                    results_df_loyalty["Importance_percent"] >= 18
+                ]["Predictor"].tolist()
+                qualified_bucket_names_list.append(qualified_bucket_names_loyalty)
+
+            if results_df_consideration is not None:
+                qualified_bucket_names_consideration = results_df_consideration[
+                    results_df_consideration["Importance_percent"] >= 18
+                ]["Predictor"].tolist()
+                qualified_bucket_names_list.append(qualified_bucket_names_consideration)
+
+            if results_df_satisfaction is not None:
+                qualified_bucket_names_satisfaction = results_df_satisfaction[
+                    results_df_satisfaction["Importance_percent"] >= 18
+                ]["Predictor"].tolist()
+                qualified_bucket_names_list.append(qualified_bucket_names_satisfaction)
+
+            # Flatten the list of lists and convert to a set to remove duplicates
+            qualified_bucket_names_flat = [
+                item for sublist in qualified_bucket_names_list for item in sublist
+            ]
+            qualified_bucket_names_unique = list(set(qualified_bucket_names_flat))
+
+            # Filter df_builder_pivot to include only statements where "Trust Driver" is in qualified_bucket_names_unique
+            df_builder_pivot = df_builder_pivot[
+                df_builder_pivot["Trust Driver®"].isin(qualified_bucket_names_unique)
+            ]
+
+            # Remove statements with values below 18%
+            df_builder_pivot = df_builder_pivot[df_builder_pivot["%"] >= 18]
+            df_builder_pivot_str = df_builder_pivot.to_string(index=False)
+        else:
+            df_builder_pivot_str = "Trust Builder information is not available."
+
+    except FileNotFoundError:
+        df_builder_pivot_str = "Dataset not found."
+    except Exception as e:
+        df_builder_pivot_str = f"An error occurred during analysis: {e}"
+
+    # Define knowledge base
+    knowledge = None
+
+    # Define the path to the .md file
+    knowledge_file_path = "./data_source/time_to_rethink_trust_book.md"
+
+    # Read the content of the file into a variable
+    with open(knowledge_file_path, "r", encoding="utf-8") as file:
+        knowledge = file.read()
+
+    # Create the prompt template
+    prompt_message = f"""
+    You are an expert copywriter that generates content based on the instruction from the user request.
+    
+    USER_REQUEST: {message}
+    
+    Equip yourself with domain knowledge in the field of Trust Analysis with the knowledge base.
+    KNOWLEDGE_BASE: {knowledge}
+    
+    The user has selected the dataset: {selected_dataset_ai}.
+    
+    The user already computes his/her Trust Analysis and the result is displayed as DATAFRAME_PROOF_POINT: {df_builder_pivot_str}.
+    
+    There are 3 columns in DATAFRAME_PROOF_POINT: Trust Driver, Trust Proof Point, and %.
+    Trust Driver: contains Trust indicators/buckets. 
+    Trust Buckets: contains 6 unique Trust Buckets: Stability, Development, Relationship, Benefit, Vision, and Competence.
+    Trust Proof Point: contains Trust statements/messages associated with its Trust indicator/bucket.
+    %: contains the percentage of how strong the Trust statements/messages contribute to their respective Trust indicators/buckets.
+    The higher the % value is, the more important the Trust Proof Points are.
+    
+    Here's how you need to generate your response:
+    1. If not explicitly mentioned, the user's default company name is Volkswagen. 
+    2. First, mention which dataset is selected. 
+    3. If DATAFRAME_PROOF_POINT is None or empty: 
+        - Respond to the user by saying Trust Builder information is not given and you will reply based on general knowledge.
+        - Generate your response to the user prompt based on KNOWLEDGE_BASE and general knowledge.
+    4. If DATAFRAME_PROOF_POINT is not None or empty:
+        - For each Trust Bucket Filter in DATAFRAME_PROOF_POINT, select Trust Proof Points related to that Trust Bucket that have values 18% and above. They are considered as top scoring statements. 
+        - Display only the top scoring statements with values of 18% and above. 
+        - Then, respond to the user prompt based on these top scoring statements.
+    
+    You must adhere to generating the exact type of sales content required by the user based on USER_REQUEST. 
+    Use KNOWLEDGE_BASE as a reference in terms of definitions and examples.
+    The sales content must be accurate, factual, and precise, based on the top scoring statements. Avoid making up new information.
+    
+    YOUR RESPONSE: 
+    """
+
+    llm = ChatOpenAI(model="gpt-4o", temperature=0.0)
+    response = llm.invoke(prompt_message)
+    return response.content
+
+
+def read_ai_dataset_selection():
+    global selected_dataset_ai
+    return selected_dataset_ai
+
+
+def update_ai_dataset_selection(selection):
+    global selected_dataset_ai
+    selected_dataset_ai = selection
+    return selection
+
+
 with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column():
-            # title = gr.Markdown("# TrustLogic & NPS Driver Analysis (Batch Version)")
-            # description = gr.Markdown(instruction_text)
-
-            with gr.Row():
-                dataset = gr.Dataset(
-                    components=[gr.Textbox(visible=False)],
-                    label="Upload example datasets to calculate the Trust Drivers",
-                    type="values",
-                    samples=[
-                        # ["HSBC.xlsx"],
-                        # ["CBA.xlsx"],
-                        # ["Red Cross.xlsx"],
-                        # ["Health Insurance.xlsx"],
-                        # ["WV.xlsx"],
-                        # ["Care.xlsx"],
-                        # ["BUPA.xlsx"],
-                    ],
-                )
-
-            with gr.Row():
-                # set file upload widget
-                file_inputs = gr.Files(label="Excel/CSV Datasets")
-
-            with gr.Row():
-                # set clear and submit butttons
-                clear_button = gr.ClearButton(file_inputs)
-                submit_button = gr.Button("Submit", variant="primary")
+    # with gr.Column():
+    #     gr.Markdown(
+    #         "<span style='font-size:20px; font-weight:bold;'>Click 'Volkswagen Customers' or 'Volkswagen Prospects' to see the full results and play with the TrustAI.</span>",
+    #         visible=True,
+    #     )
+    #     gr.Markdown(
+    #         "Our calculator will conduct the driver analysis from the underlying Excel file and display the results. "
+    #         + "Scroll down to view them and interact with them. "
+    #         + "In the full version you can link your survey directly to our calculator or export your data as CSV and drag & drop it into our calculator.",
+    #         visible=True,
+    #     )
+
+    with gr.Column():
+        # with gr.Row():
+        #     vw_customers_btn = gr.Button("Volkswagen Customers")
+        #     vw_prospects_btn = gr.Button("Volkswagen Prospects")
+
+        # with gr.Row():
+        #     gr.Markdown(
+        #         "<span style='font-size:20px; font-weight:bold;'>Click any of the examples below to see top-line driver results in different categories.</span>",
+        #         visible=True,
+        #     )
+
+        # with gr.Row():
+        #     hsbc_btn = gr.Button("HSBC")
+        #     cba_btn = gr.Button("Commonwealth Bank")
+        #     bupa_btn = gr.Button("BUPA")
+        #     health_insurance_btn = gr.Button("GMHBA")
+        #     care_btn = gr.Button("CARE")
+        #     red_cross_btn = gr.Button("Red Cross")
+
+        with gr.Row():
+            # set file upload widget
+            file_inputs = gr.Files(label="Dataset")
+
+        with gr.Row():
+            # set clear and submit butttons
+            clear_button = gr.ClearButton(file_inputs)
+            submit_button = gr.Button("Submit", variant="primary")
 
         with gr.Column():
             # set default output widgets
             outputs = reset_outputs()
 
         # function for submit button click
-        submit_button.click(fn=variable_outputs, inputs=file_inputs, outputs=outputs)
+        submit_button.click(fn=process_datasets, inputs=file_inputs, outputs=outputs)
 
         # function for clear button click
         # this only handles the outputs. Input reset is handled at button definition
         clear_button.click(fn=reset_outputs, inputs=[], outputs=outputs)
 
-        # function for example files
-        # dataset.click(fn=process_examples, inputs=dataset, outputs=outputs)
+        # # Create gr.State components to store file names as lists
+        # vw_customers_state = gr.State(value=["Volkswagen Customers.xlsx"])
+        # vw_prospects_state = gr.State(value=["Volkswagen Prospects.xlsx"])
+        # hsbc_state = gr.State(value=["HSBC.xlsx"])
+        # cba_state = gr.State(value=["Commonwealth Bank.xlsx"])
+        # bupa_state = gr.State(value=["BUPA.xlsx"])
+        # health_insurance_state = gr.State(value=["GMHBA.xlsx"])
+        # care_state = gr.State(value=["CARE.xlsx"])
+        # red_cross_state = gr.State(value=["Red Cross.xlsx"])
+
+        # vw_customers_btn.click(
+        #     fn=process_examples,
+        #     inputs=[vw_customers_state],
+        #     outputs=outputs,
+        # )
+        # vw_prospects_btn.click(
+        #     fn=process_examples,
+        #     inputs=[vw_prospects_state],
+        #     outputs=outputs,
+        # )
+        # hsbc_btn.click(
+        #     fn=process_examples,
+        #     inputs=[hsbc_state],
+        #     outputs=outputs,
+        # )
+        # cba_btn.click(
+        #     fn=process_examples,
+        #     inputs=[cba_state],
+        #     outputs=outputs,
+        # )
+        # bupa_btn.click(
+        #     fn=process_examples,
+        #     inputs=[bupa_state],
+        #     outputs=outputs,
+        # )
+        # health_insurance_btn.click(
+        #     fn=process_examples,
+        #     inputs=[health_insurance_state],
+        #     outputs=outputs,
+        # )
+        # care_btn.click(
+        #     fn=process_examples,
+        #     inputs=[care_state],
+        #     outputs=outputs,
+        # )
+        # red_cross_btn.click(
+        #     fn=process_examples,
+        #     inputs=[red_cross_state],
+        #     outputs=outputs,
+        # )
+
+    with gr.Column():
+        gr.Markdown(
+            "<span style='font-size:20px; font-weight:bold;'>4) Instant Insight-2-Action</span>",
+            visible=True,
+        )
+        gr.Markdown(
+            "With <b>TrustAI</b> you go straight from insight to implementation ideas. "
+            + "Select <b>the dataset you want to use.</b> ",
+            visible=True,
+        )
+
+        radio = gr.Radio(
+            choices=["Volkswagen Customers", "Volkswagen Prospects"],
+            label="Select a dataset you want to use for the TrustAI",
+            value=read_ai_dataset_selection(),  # Initialize with the current selection
+            visible=True,
+        )
+
+        gr.Markdown(
+            "Tell TrustAI what you want to generate and <b>create trust-enhanced ideas using the top trust and KPI proof points.</b> "
+            + "<br><br>Or copy and paste this <b>sample prompt</b> to the AI input field below: <br>"
+            + "<i>''Write a letter to get reader to want to come to showroom''</i>",
+            visible=True,
+        )
+
+        radio.change(fn=update_ai_dataset_selection, inputs=radio, outputs=[])
+
+        # Text input box for the user to enter their prompt
+        prompt_input = gr.Textbox(
+            lines=2,
+            value="",
+            label="Enter your prompt",
+            visible=True,
+        )
+
+        # with gr.Column():
+        gr.Markdown(
+            "Click <b>'Submit'</b> and our TrustAI will generate responses based on your input prompt.",
+            visible=True,
+        )
+
+        # Submit button
+        submit_button = gr.Button("Submit")
+        # Output display box to show the response
+        output_display = gr.Markdown(label="Response")
+
+        # Connect the submit button to the chatbot_response function
+        submit_button.click(
+            fn=chatbot_response, inputs=prompt_input, outputs=output_display
+        )
 
 
 demo.launch(server_name="0.0.0.0")

data_source/time_to_rethink_trust_book.md

@@ -0,0 +1,207 @@
+# The Six Buckets of Trust®
+
+Our TrustLogic® approach is to think of trust as six elements. To simplify this, we’ve placed those elements into six buckets, which we refer to as The Six Buckets of Trust®. These six buckets are the fundamental elements that underpin trust.
+
+- Vision trust is about your bigger vision, mission and values.
+
+- Development trust is leadership, relevance.
+
+- Benefit trust is the benefits you bring to your relationships.
+
+- Competence trust is the different competencies you can bring to your vision.
+
+- Stability trust is building a strong and stable foundation.
+
+- Relationship trust is how you relate to people.
+
+To remember them easily, think of them as part of a trust story:
+
+- Stability/Development: where do you come from and how do you go forward?
+
+- Relationship/Benefit: what’s it like to work with you and what do I get out of it?
+
+- Vision/Competence: what do you strive for and what do you have to get there?
+
+
+## Definitions of the 6 Trust Buckets
+
+### Stability Trust
+
+Stability trust is, ‘Why can I trust you to have built a strong and stable foundation? What have you achieved in the past?’
+
+From birth, we live in relationships or, that is, a network of social relations. A strong bond and foundation is crucial and fundamental for every stage of development – it provides us with safety and stability. We feel protected and through this stability we get stronger and evolve. We express stability through continuity, long-term bonding, longevity and past achievements.
+
+#### Key words
+- Track record
+- Longevity
+- Size
+- Staff numbers
+- Wins
+- Headline clients
+
+#### Ask yourself
+- What’s your track record?
+- How long have you been around?
+- What turnover do you have?
+- How many staff?
+- What big successes?
+- Headline clients?
+
+#### Examples
+- We have succeeded even in the most challenging times for over 140 years (even 20 years is fine with all the crises that have happened).
+- We have over 12,000 staff globally and 532 locally.
+- Our longest serving staff member, Julie in accounts, has been with us for more than 30 years.
+- Just in the past decade we have won XYZ awards 7 times.
+- Key clients include (for example) ABC which has been a client since we incorporated them 74 years ago.
+
+### Development Trust
+
+Development trust is, ‘Why can I trust you to develop well into the future, to be a leader and stay relevant to me?
+
+Today’s markets have grown increasingly complex and are developing faster than ever. As a result, companies and brands need to adapt, develop and change to grow, maintain and secure the trust that customers put in them. Similarly, we need to trust for future development. We are curious and interested about new things; we want to experience them and make new discoveries. How do we handle new things, how do we progress; how innovative are we? How do we show that we are excited about the future and what actions do we take? A stable foundation is the basis for the willingness to innovate. How do we show our clients and colleagues that we want to continually challenge the way we work for the better–and that they should be part of those changes?
+
+#### Key words
+- Invest
+- Forefront
+- Cutting edge
+- Curious
+- Forward-looking
+- Forward-thinking
+- Future
+- Trends
+- New
+- Develop
+
+#### Ask yourself
+- What does your firm and your team invest into? (Even cyber security is good).
+- What on-the-job and other training and development is happening?
+- How do you choose and nurture young talent?
+- Do you read or follow any cutting-edge things, write or present on them?
+
+#### Examples
+- Don’t ask Juan about his weekend. He read the update on XYZ.
+- During the next 3 years we’re investing $5m into cyber security.
+- As an employer of choice we get the best talent and nurture them actively.
+- Christina in my team is thinking about doing an MBA on the side. Any suggestions?
+
+### Relationship Trust
+
+Relationship trust is about ‘Why can I trust you to relate well to people like me? What quality does this relationship have and how do you show that you invest into it?’
+
+The interpersonal relationship (or relationship between organisation and person) is hugely important. How good
+are we at interacting with other people? To be sensitive, to listen carefully, to exchange ideas, to be empathetic and to show social awareness? What kind of quality does this relationship have? How can I show that I invest in the
+relationship? That I value and respect the people around me.
+
+#### Key words
+- Together/We
+- My team and I
+- Inspire
+- Exchange
+- Support
+- Invest into
+- More than clients
+
+#### Ask yourself
+- What quality of relationship do you want to be trusted for?
+- What activities do you do with/for clients beyond the immediate work?
+- What relationships have grown out of your work over time?
+- Do you speak enough about your and the client’s team by name?
+
+#### Examples
+- Many clients have become friends over time.
+- We invest in client secondments to get to know our clients better.
+- We care about our clients as people as much as organisations.
+- Bring surprising things to meetings (even on Zoom).
+- Phrase things more in human/colloquial terms.
+
+### Benefit Trust
+
+Benefit trust is, ‘What benefit do I get from this relationship with you? What is valuable to me about this?’
+
+What is the benefit of engaging with each other? What kind of added value do we as people, employees, clients and the company receive? How do you measure productivity, growth and profit? What are the emotional benefits? We want the relationship to be beneficial for all participants, for us and our customers or business partners at the same time – both financially and emotionally. This usually leads into conflicts, as sometimes different motivations and needs collide. For example, we want to run a financially successful business operation, at the same time we want to offer the best service and quality to our customers and business partners. Benefit trust deals with the challenge to find solutions, compromises and takes the tension between needs and motivations into account.What is the benefit of engaging with each other? What kind of added value do we as people, employees, clients and the company receive? How do you measure productivity, growth and profit? What are the emotional benefits? We want the relationship to be
+beneficial for all participants, for us and our customers or business partners at the same time – both financially
+and emotionally. This usually leads into conflicts, as sometimes different motivations and needs collide. For example, we want to run a financially successful business operation, at the same time we want to offer the best
+service and quality to our customers and business partners. Benefit trust deals with the challenge to find solutions, compromises and takes the tension between needs and motivations into account.
+
+#### Key words
+- Succeed
+- Progress
+- Grow
+- Win
+- Benefit
+- Value
+- Share
+
+#### Ask yourself
+- What value do clients get from you? Operational, technical or human?
+- How do you help the team to grow? On a global or regional level, what benefit does that provide?
+- Have you considered other aspects like being proactive (rather than responsive)? Fun, stimulation, new ideas,
+clarity, re-evaluation?
+
+#### Examples
+- Enjoying working with us is as important as the technical expertise, because the best outcomes are achieved if both are in balance.
+- Unparalleled access to connections/opportunities/insights.
+- Our unique ‘value mash’ approach ensures we continuously improve the quality while working at the best possible efficiency.
+
+### Vision Trust
+
+Vision trust is about your bigger vision, mission and values. This is more about your purpose and role in society than business goals – ‘What kind of vision and values can I trust you for and how does it manifest itself?’
+
+Without clear direction, values, and goals, we won´t take action. We need a challenging direction and ambitions which encourage us to achieve something in which we believe. The same goes for being trusted as a visionary. You need a clear and appealing vision and to be trusted for that vision. Only the one who courageously takes the lead can gain the others’ trust. What are we dreaming of? How do we want our ideal future to look like? What vision of the future do we have to offer? What moral compass and values do we have, and use as fundamental to our lives?
+
+#### Key words
+- Values
+- Pro bono
+- Volunteering
+- Supporting
+- Participating
+- Charity
+- Philanthropy
+- Serve
+
+#### Ask yourself
+- What causes do your firm/team/you support and how?
+- Why do you espouse those values and causes?
+- Why are you in this business? How does this connect with your values?
+
+#### Examples
+- John serves on the board of Youth Hand Up and personally mentors disadvantaged youths.
+- Robert is an avid guitar player and supports the XYZ festival.
+- Having grown up in a family that strongly emphasised XYZ, today Chris is the backbone of the cause.
+- As a firm, we support ABC. In our practice we specifically support XYZ.
+
+### Competence Trust
+
+Competence trust is, ‘What competencies can I trust you to have to fulfill on your vision and to succeed?’
+
+We can´t succeed without proper tools, equipment and staying power. The vision can only succeed and be
+realised if we have the competences for it. Competence trust guarantees an anchor in reality and brings us
+back down to earth. Therefore, we have to ask ourselves which skills will help us to achieve our goals. What
+strengths? Which techniques, skills and competences do we have and can we be trusted for? And, of course, how
+does this manifest itself? But also, what competences do we credit to our customers? How can we help them to
+manage their lives? What exactly is the added value we provide to them?
+
+#### Key words
+- Expertise
+- Calibre
+- Understanding
+- Synthesising
+- Know how/who
+- Sought after
+- Creativity
+- People understanding
+
+#### Ask yourself
+- What competencies do you (and your team/firm) have? Think beyond the technical.
+- Publications, speaking, presenting, panel invites.
+- What qualities have you honed from growing up?
+- What awards have you won?
+- What high-profile landmark projects have you done and what calibre of clients do you work with?
+- Don’t forget your team and colleagues. Their competence trust can be equally important.
+
+#### Examples
+- I have grown up in a family of entrepreneurs and thus inherently know the business imperative.
+- In many of our projects the ability to read and navigate politics is as important as the technical expertise.
+- We have won XYZ awards 5 years in a row.
+- I lecture on ABC at XYZ University. While sharing my expertise, it also keeps me up to date with the latest trends.
+

example_files/Volkswagen Consumers.csv

@@ -0,0 +1,75 @@
+Did you own a Volkswagen in the last 5 years?,What’s your age?,How likely are you to recommend buying a Volkswagen to friends and family?,What’s your level of trust in Volkswagen?,"If you owned a VW in the last 5 years, how likely are you to buy another Volkswagen?","On a scale of 1 to 5, please tell us how much you agree with the following statements about Volkswagen.",,,,,,"On a scale of 1 to 5, please indicate how important the following statements are to you in trusting Volkswagen.",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+Response,Response,Response,Response,Response,Has built a strong and stable foundation.,Will develop well in the future.,Relates well to people like me.,Is valuable to our lives.,Has vision and values I find appealing.,Has what it takes to succeed,Employees are provided with extensive continuous training.,Produce almost 9 million cars per year.,"Employ almost 700,000 people and provide over 1.8 m families with work.",We are one of the longest-established car companies.,Building great and affordable cars is our foundation.,We spend over Euro 15 billion a year on research & development.,"Every year we are granted well over 2,000 new patents.",We have strong succession planning and nurture our best talent globally.,We are at the forefront of technology to deliver better cars and driving experiences.,We strongly focus on keeping and nurturing our team and have a 99.5% retention rate.,"At any stage we train over 15,000 apprentices.","According to Auto Institute 2022, we are the most innovative car company.","We don't just look at cars, we look at your future mobility.","If someone leaves us, for 20 years they can come back into the same position.",We work with our unions in our restructuring and future plans.,Our beginnings are a unique combination of investors and unions and today 9 of our 20 board members are staff representatives.,"We offer 22 weeks of paid parental leave, special leave for miscarriages and stillbirths and a 'Career with Children' project.",We work continuously with our customers to understand their needs and desires.,Our Compass 2.0 Program aims to progress diversity and inclusion even further.,We are committed to zero-emission manufacturing and cars.,"We aim to create lasting values, offer good working conditions, and conserve resources and our all environment.",We have a clear 'Way to Zero Emissions' roadmap for the next decades.,Our employees and Volkswagen support refugees in many countries.,At every level we offer our customers great value for money cars through our brands ranging from Porsche to Skoda.,Our brands are ranked No 2 and 5 in the reliability rankings.,The interior designs and sizes are well-considered for customers' changing needs.,We put a lot of emphasis on the interior experience and two of our cars have been ranked in the top 10.,After service and repair quality and price are as important to us as the initial purchase.,"From everyday to luxury, we offer customers amazing brands and models including Volkswagen, ŠKODA, SEAT, CUPRA, Audi, Lamborghini, Bentley, Porsche and Ducati.",We bring together the world's best talent in many disciplines to create your cars.,Our employees are provided with extensive continuous training.,We have learned from our mistakes in the Diesel Affair and we have made fundamental changes.,Our technology and manufacturing capabilities are second to none.,"Oliver Blume, our group CEO, started with Audi in 1994, became the CEO for Porsche in 2015 and our Group CEO in 2022.","In every discipline, we are creative at heart because that's how the future is being shaped.","From the latest science in metals to AI, we have the leading competencies.",At the heart of our decision-making is the long-term quality of life for all of us.
+Yes,18-34,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,4,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,3,3,5 - Strongly Agree,4,4,4,4,4,5 - Strongly Agree,4,4,5 - Strongly Agree,4,5 - Strongly Agree,4,3,4,4,5 - Strongly Agree,3,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree
+Yes,18-34,4,5 - Trust fully,4 - Not very likely,4,5 - Strongly Agree,3,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,4,3,3,4,4,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,3,3,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,3,5 - Strongly Agree,4,3,4,3,3,4,5 - Strongly Agree,5 - Strongly Agree
+Yes,18-34,4,5 - Trust fully,1 - Very likely,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,4,5 - Strongly Agree,4,4,4,4,4,4,4,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,4,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4
+Yes,18-34,1 - Not likely at all,2,4 - Not very likely,3,5 - Strongly Agree,3,4,2,3,3,3,2,2,3,4,3,3,3,2,3,1 - Disagree,3,3,3,3,1 - Disagree,2,3,3,3,3,2,3,2,2,3,4,2,4,3,2,1 - Disagree,2,2,3,2
+Yes,18-34,4,5 - Trust fully,2 - Somewhat likely,4,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,4,3,4,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4
+Yes,18-34,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,3,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,4,3,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,4,4,4,3
+Yes,18-34,5 - Highly likely,2,1 - Very likely,3,2,3,3,2,2,2,4,3,2,2,2,3,4,3,2,3,4,3,3,3,3,2,3,4,2,2,2,4,2,2,4,2,3,3,2,4,2,4,4,2,3,3
+Yes,18-34,4,3 - Neither trust nor distrust,2 - Somewhat likely,5 - Strongly Agree,4,2,5 - Strongly Agree,5 - Strongly Agree,4,4,3,4,4,4,3,5 - Strongly Agree,4,3,3,5 - Strongly Agree,4,5 - Strongly Agree,3,4,4,3,3,4,4,3,2,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,4,4,4,3,4,3,5 - Strongly Agree,3,3,4
+Yes,18-34,3,4,3,4,4,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
+Yes,18-34,5 - Highly likely,5 - Trust fully,2 - Somewhat likely,4,5 - Strongly Agree,5 - Strongly Agree,4,4,4,3,4,4,4,4,4,4,4,4,3,4,4,4,4,3,4,4,5 - Strongly Agree,4,4,4,4,4,4,4,3,5 - Strongly Agree,4,3,4,4,4,4,4,4,3,4
+Yes,18-34,3,3 - Neither trust nor distrust,4 - Not very likely,3,4,3,2,5 - Strongly Agree,3,3,3,5 - Strongly Agree,2,4,3,3,3,3,4,3,3,3,3,3,4,5 - Strongly Agree,2,3,3,4,3,2,3,4,2,4,4,3,3,4,3,4,4,2,4,3
+Yes,18-34,2,2,2 - Somewhat likely,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2
+Yes,18-34,4,3 - Neither trust nor distrust,3,3,3,5 - Strongly Agree,4,2,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,4,2,4,3,5 - Strongly Agree,4,2,5 - Strongly Agree,5 - Strongly Agree,4,2,4,4,4,3,4,3,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,3,5 - Strongly Agree,3,5 - Strongly Agree,4,3,3,3,4
+Yes,18-34,4,4,1 - Very likely,4,4,5 - Strongly Agree,4,3,5 - Strongly Agree,5 - Strongly Agree,4,2,5 - Strongly Agree,4,2,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,4,3,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,2,4,4,4,3,4,4,4,5 - Strongly Agree,4,4,4,5 - Strongly Agree,5 - Strongly Agree,2,5 - Strongly Agree,4,3,3
+Yes,18-34,5 - Highly likely,4,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,4,1 - Disagree,3,4,5 - Strongly Agree,3,4,4,5 - Strongly Agree,4,4,3,3,4,3,4,3,5 - Strongly Agree,3,3,4,3,4,4,3,3,3,4,4,3,5 - Strongly Agree,4,4,4,4,4,4
+Yes,18-34,4,4,2 - Somewhat likely,3,4,4,3,4,4,3,5 - Strongly Agree,4,4,4,3,4,4,4,4,3,4,4,4,3,5 - Strongly Agree,4,3,3,3,4,3,4,4,5 - Strongly Agree,3,3,2,3,3,4,3,4,4,3,4,4
+Yes,18-34,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree
+Yes,18-34,2,2,2 - Somewhat likely,2,3,3,2,4,2,2,2,2,3,2,2,2,2,2,3,3,3,3,3,3,3,2,2,4,3,2,2,3,2,3,4,3,2,2,2,2,3,2,3,2,3,2
+Yes,18-34,1 - Not likely at all,2,1 - Very likely,5 - Strongly Agree,3,2,3,2,2,3,1 - Disagree,3,1 - Disagree,2,1 - Disagree,1 - Disagree,2,3,2,3,1 - Disagree,3,1 - Disagree,4,4,5 - Strongly Agree,2,1 - Disagree,5 - Strongly Agree,2,5 - Strongly Agree,1 - Disagree,2,4,3,3,3,5 - Strongly Agree,1 - Disagree,3,3,3,4,2,4,5 - Strongly Agree
+Yes,18-34,4,4,5 - Not likely at all,4,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,4,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,4,4,3,5 - Strongly Agree,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,4,4,5 - Strongly Agree
+Yes,18-34,1 - Not likely at all,4,4 - Not very likely,4,4,3,3,3,4,4,3,4,3,4,5 - Strongly Agree,3,3,3,2,2,2,5 - Strongly Agree,2,3,3,3,3,4,4,4,4,4,3,1 - Disagree,3,3,5 - Strongly Agree,3,4,2,4,5 - Strongly Agree,4,4,3,3
+Yes,18-34,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree
+Yes,18-34,3,2,2 - Somewhat likely,3,4,3,3,3,3,4,4,4,4,4,4,3,4,4,3,4,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,3,4,4,4,4,3,4,4,4
+Yes,18-34,5 - Highly likely,4,5 - Not likely at all,5 - Strongly Agree,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,3,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,4,3,5 - Strongly Agree,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,3,4,4,3,3,4
+Yes,18-34,4,4,3,4,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,3,3,4,4,3,4,5 - Strongly Agree,4,3,3,4,3,4,2,4,3,4,2,4,4,3,3,4,3,2,4,1 - Disagree,4,2,4,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,3
+Yes,18-34,4,3 - Neither trust nor distrust,3,4,4,3,3,4,3,3,3,3,3,2,2,4,4,3,4,4,4,2,3,2,3,4,3,3,3,4,2,4,3,3,2,2,3,4,3,3,4,3,3,3,4,4
+Yes,18-34,4,4,1 - Very likely,5 - Strongly Agree,4,4,5 - Strongly Agree,4,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,5 - Strongly Agree,4
+Yes,18-34,5 - Highly likely,5 - Trust fully,1 - Very likely,4,4,5 - Strongly Agree,4,4,4,5 - Strongly Agree,4,4,4,4,4,4,4,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,3,4,4,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,4,4,4,4,4,4,4,4,4,4
+Yes,18-34,3,2,4 - Not very likely,3,4,4,4,4,4,5 - Strongly Agree,5 - Strongly Agree,4,4,4,3,4,5 - Strongly Agree,4,5 - Strongly Agree,3,4,4,4,4,4,4,3,4,4,4,4,4,4,4,5 - Strongly Agree,4,3,4,3,4,4,4,4,4,4,3
+Yes,18-34,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree
+Yes,18-34,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,4,3,5 - Strongly Agree,4,3,3,2,3,5 - Strongly Agree,5 - Strongly Agree,3,4,5 - Strongly Agree,4,3,4,3,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,3,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,2,3,3,4,3,5 - Strongly Agree,1 - Disagree,5 - Strongly Agree
+Yes,35-54,3,2,1 - Very likely,5 - Strongly Agree,4,3,5 - Strongly Agree,1 - Disagree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,3,2,5 - Strongly Agree,3,3,1 - Disagree,5 - Strongly Agree,3,5 - Strongly Agree,3,2,2,1 - Disagree,3,1 - Disagree,5 - Strongly Agree,4,3,1 - Disagree,1 - Disagree,2,1 - Disagree,3,4,2,2,3,2,1 - Disagree,4,3
+Yes,35-54,3,2,3,1 - Disagree,2,3,5 - Strongly Agree,2,4,3,2,1 - Disagree,5 - Strongly Agree,1 - Disagree,2,5 - Strongly Agree,3,1 - Disagree,2,3,3,3,2,3,2,5 - Strongly Agree,5 - Strongly Agree,3,4,5 - Strongly Agree,5 - Strongly Agree,4,1 - Disagree,2,3,3,3,4,3,2,3,1 - Disagree,2,3,3,2
+Yes,35-54,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree
+Yes,35-54,3,3 - Neither trust nor distrust,2 - Somewhat likely,4,3,4,5 - Strongly Agree,4,2,4,4,4,5 - Strongly Agree,4,2,3,3,4,3,4,3,2,2,5 - Strongly Agree,2,3,3,3,3,4,2,3,4,3,3,4,4,3,3,4,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,2,3
+Yes,35-54,4,5 - Trust fully,5 - Not likely at all,4,5 - Strongly Agree,4,4,5 - Strongly Agree,3,3,2,5 - Strongly Agree,3,3,2,4,5 - Strongly Agree,3,5 - Strongly Agree,2,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,2,3,4,4,5 - Strongly Agree,4,3,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,4,2,3,3,4,4,5 - Strongly Agree,3,4,4
+Yes,35-54,4,4,5 - Not likely at all,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,4,3,4,3,4,4,3,3,4,5 - Strongly Agree,4,4,4,5 - Strongly Agree,4,4,4,5 - Strongly Agree,4,4,3,5 - Strongly Agree,4,5 - Strongly Agree,3,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,3,4,3,3
+Yes,35-54,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,3,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,3,3,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,3,4,3,3,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,4,5 - Strongly Agree,3,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,3,4,5 - Strongly Agree
+Yes,35-54,5 - Highly likely,4,2 - Somewhat likely,5 - Strongly Agree,4,5 - Strongly Agree,4,4,5 - Strongly Agree,4,3,4,4,5 - Strongly Agree,4,4,4,5 - Strongly Agree,3,4,3,4,5 - Strongly Agree,4,3,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,3,4,4,5 - Strongly Agree
+Yes,35-54,5 - Highly likely,5 - Trust fully,5 - Not likely at all,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,3,5 - Strongly Agree,5 - Strongly Agree,3,3,5 - Strongly Agree,4,2,5 - Strongly Agree,2,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3
+Yes,35-54,4,4,3,4,4,3,4,5 - Strongly Agree,4,4,5 - Strongly Agree,4,4,4,4,4,3,4,4,5 - Strongly Agree,3,4,4,4,4,4,3,5 - Strongly Agree,4,4,4,4,4,5 - Strongly Agree,4,4,4,4,4,4,4,4,3,5 - Strongly Agree,3,3
+Yes,35-54,5 - Highly likely,5 - Trust fully,1 - Very likely,4,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,4,4,4,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,4,5 - Strongly Agree
+Yes,35-54,2,4,2 - Somewhat likely,4,4,3,3,2,4,3,2,3,3,3,2,2,4,4,3,2,2,2,4,2,3,5 - Strongly Agree,2,2,5 - Strongly Agree,2,4,3,3,4,2,3,2,1 - Disagree,3,3,3,2,1 - Disagree,1 - Disagree,4,3
+Yes,35-54,3,3 - Neither trust nor distrust,4 - Not very likely,3,3,3,2,4,3,3,3,4,3,3,3,3,4,3,4,4,2,3,3,3,2,4,3,3,3,3,3,3,5 - Strongly Agree,3,2,3,3,3,4,3,3,3,2,3,3,3
+Yes,35-54,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,2,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,3,4,3,5 - Strongly Agree,3,5 - Strongly Agree,4,3,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,1 - Disagree,5 - Strongly Agree,4,2,4,3,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,1 - Disagree
+Yes,35-54,3,0 - Do not trust at all,4 - Not very likely,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,3,3,1 - Disagree,1 - Disagree,1 - Disagree,3,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,2,1 - Disagree,3,3,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,3,4,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,3,1 - Disagree,1 - Disagree,3,1 - Disagree,1 - Disagree,1 - Disagree
+Yes,35-54,3,4,3,3,4,4,4,3,4,4,4,3,3,4,4,3,3,3,2,3,5 - Strongly Agree,3,3,3,5 - Strongly Agree,4,3,3,4,4,3,4,4,4,4,4,3,4,4,3,3,3,4,3,4,4
+Yes,35-54,4,4,2 - Somewhat likely,4,4,3,3,3,4,4,3,3,4,4,3,3,4,4,3,2,2,3,3,1 - Disagree,2,1 - Disagree,3,3,1 - Disagree,2,1 - Disagree,3,3,4,3,3,3,3,3,3,4,4,3,3,3,3
+Yes,35-54,4,4,1 - Very likely,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
+Yes,35-54,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree
+Yes,35-54,5 - Highly likely,5 - Trust fully,5 - Not likely at all,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,4,5 - Strongly Agree,3,5 - Strongly Agree,4,3,2,3,1 - Disagree,3,4,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,4,2,5 - Strongly Agree,3,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,2,4,3,2,3
+Yes,35-54,4,5 - Trust fully,4 - Not very likely,4,5 - Strongly Agree,3,4,3,5 - Strongly Agree,3,3,5 - Strongly Agree,4,5 - Strongly Agree,2,3,2,2,3,3,3,3,3,3,4,4,3,2,5 - Strongly Agree,3,2,2,5 - Strongly Agree,2,2,2,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,3,3,2,5 - Strongly Agree
+Yes,35-54,4,4,2 - Somewhat likely,4,4,4,4,3,3,3,4,3,2,4,3,4,4,4,4,4,4,3,2,2,3,4,3,4,4,3,4,4,3,4,4,3,3,2,4,3,3,4,4,5 - Strongly Agree,3,3
+Yes,35-54,3,3 - Neither trust nor distrust,3,4,4,3,2,3,3,3,4,3,4,4,5 - Strongly Agree,4,3,4,3,3,4,4,2,3,4,4,3,5 - Strongly Agree,2,3,3,3,4,4,3,3,3,3,3,3,4,3,3,4,3,5 - Strongly Agree
+Yes,35-54,4,4,3,4,4,4,4,4,4,3,3,3,3,4,3,3,3,4,3,3,4,3,3,3,3,3,3,3,3,3,3,3,3,4,4,3,3,4,3,3,3,3,3,3,3,3
+Yes,35-54,4,4,2 - Somewhat likely,4,4,5 - Strongly Agree,4,4,4,3,3,3,3,4,4,4,4,3,3,4,4,4,3,4,3,4,4,4,3,3,4,4,3,4,4,4,3,4,4,4,4,3,4,4,4,4
+Yes,35-54,4,5 - Trust fully,2 - Somewhat likely,4,5 - Strongly Agree,3,4,4,4,4,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,4,4,5 - Strongly Agree,4,4,4,4,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree
+Yes,35-54,3,1,2 - Somewhat likely,4,2,1 - Disagree,3,3,1 - Disagree,3,2,3,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,4,1 - Disagree,1 - Disagree,4,1 - Disagree,3,1 - Disagree,1 - Disagree,2,1 - Disagree,1 - Disagree,1 - Disagree,4,3,3,3,1 - Disagree,2,4,2,1 - Disagree,1 - Disagree,1 - Disagree,2,1 - Disagree,3,4,3,4,3
+Yes,35-54,4,4,4 - Not very likely,3,4,4,3,3,3,3,3,3,3,3,3,3,4,4,4,3,3,3,3,4,4,3,3,4,3,3,3,4,3,4,4,4,3,3,4,3,3,3,3,4,4,4
+Yes,35-54,4,4,2 - Somewhat likely,4,4,4,4,4,4,4,4,5 - Strongly Agree,4,4,4,3,4,4,3,4,4,4,3,4,4,3,4,4,4,4,4,3,4,4,4,4,4,4,4,3,4,3,3,4,3,4
+Yes,35-54,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree
+Yes,35-54,5 - Highly likely,4,1 - Very likely,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
+Yes,35-54,4,4,1 - Very likely,4,4,4,3,4,4,4,2,2,4,3,4,2,4,3,4,4,4,3,4,3,4,4,4,5 - Strongly Agree,3,4,4,3,4,3,4,4,4,3,3,3,3,4,4,3,4,5 - Strongly Agree
+Yes,35-54,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,3,5 - Strongly Agree
+Yes,55-74,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree
+Yes,55-74,4,4,5 - Not likely at all,4,5 - Strongly Agree,5 - Strongly Agree,4,3,3,5 - Strongly Agree,3,3,4,5 - Strongly Agree,3,3,5 - Strongly Agree,4,5 - Strongly Agree,3,5 - Strongly Agree,4,3,3,4,5 - Strongly Agree,4,3,4,4,3,4,5 - Strongly Agree,2,3,2,3,5 - Strongly Agree,4,4,3,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,3
+Yes,55-74,2,2,4 - Not very likely,3,3,1 - Disagree,1 - Disagree,4,2,4,4,4,4,2,3,4,2,2,3,3,1 - Disagree,1 - Disagree,4,3,2,3,1 - Disagree,2,3,1 - Disagree,3,4,1 - Disagree,4,3,3,1 - Disagree,4,2,4,1 - Disagree,2,4,4,2,2
+Yes,55-74,3,3 - Neither trust nor distrust,3,3,3,2,3,3,3,3,4,3,3,3,3,3,3,3,3,4,3,3,3,3,3,3,3,3,3,3,2,3,3,2,3,2,2,4,3,3,3,3,3,3,3,3
+Yes,55-74,4,5 - Trust fully,1 - Very likely,4,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree
+Yes,55-74,5 - Highly likely,4,5 - Not likely at all,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,4,4,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4
+Yes,55-74,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree
+Yes,55-74,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,4,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,4
+Yes,55-74,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,3,4,3,1 - Disagree,5 - Strongly Agree,5 - Strongly Agree,4,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,3,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,4

example_files/Volkswagen Customers.csv

@@ -0,0 +1,75 @@
+Did you own a Volkswagen in the last 5 years?,What’s your age?,How likely are you to recommend buying a Volkswagen to friends and family?,What’s your level of trust in Volkswagen?,"If you owned a VW in the last 5 years, how likely are you to buy another Volkswagen?","On a scale of 1 to 5, please tell us how much you agree with the following statements about Volkswagen.",,,,,,"On a scale of 1 to 5, please indicate how important the following statements are to you in trusting Volkswagen.",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+Response,Response,Response,Response,Response,Has built a strong and stable foundation.,Will develop well in the future.,Relates well to people like me.,Is valuable to our lives.,Has vision and values I find appealing.,Has what it takes to succeed,Employees are provided with extensive continuous training.,Produce almost 9 million cars per year.,"Employ almost 700,000 people and provide over 1.8 m families with work.",We are one of the longest-established car companies.,Building great and affordable cars is our foundation.,We spend over Euro 15 billion a year on research & development.,"Every year we are granted well over 2,000 new patents.",We have strong succession planning and nurture our best talent globally.,We are at the forefront of technology to deliver better cars and driving experiences.,We strongly focus on keeping and nurturing our team and have a 99.5% retention rate.,"At any stage we train over 15,000 apprentices.","According to Auto Institute 2022, we are the most innovative car company.","We don't just look at cars, we look at your future mobility.","If someone leaves us, for 20 years they can come back into the same position.",We work with our unions in our restructuring and future plans.,Our beginnings are a unique combination of investors and unions and today 9 of our 20 board members are staff representatives.,"We offer 22 weeks of paid parental leave, special leave for miscarriages and stillbirths and a 'Career with Children' project.",We work continuously with our customers to understand their needs and desires.,Our Compass 2.0 Program aims to progress diversity and inclusion even further.,We are committed to zero-emission manufacturing and cars.,"We aim to create lasting values, offer good working conditions, and conserve resources and our all environment.",We have a clear 'Way to Zero Emissions' roadmap for the next decades.,Our employees and Volkswagen support refugees in many countries.,At every level we offer our customers great value for money cars through our brands ranging from Porsche to Skoda.,Our brands are ranked No 2 and 5 in the reliability rankings.,The interior designs and sizes are well-considered for customers' changing needs.,We put a lot of emphasis on the interior experience and two of our cars have been ranked in the top 10.,After service and repair quality and price are as important to us as the initial purchase.,"From everyday to luxury, we offer customers amazing brands and models including Volkswagen, ŠKODA, SEAT, CUPRA, Audi, Lamborghini, Bentley, Porsche and Ducati.",We bring together the world's best talent in many disciplines to create your cars.,Our employees are provided with extensive continuous training.,We have learned from our mistakes in the Diesel Affair and we have made fundamental changes.,Our technology and manufacturing capabilities are second to none.,"Oliver Blume, our group CEO, started with Audi in 1994, became the CEO for Porsche in 2015 and our Group CEO in 2022.","In every discipline, we are creative at heart because that's how the future is being shaped.","From the latest science in metals to AI, we have the leading competencies.",At the heart of our decision-making is the long-term quality of life for all of us.
+Yes,18-34,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,4,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,3,3,5 - Strongly Agree,4,4,4,4,4,5 - Strongly Agree,4,4,5 - Strongly Agree,4,5 - Strongly Agree,4,3,4,4,5 - Strongly Agree,3,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree
+Yes,18-34,4,5 - Trust fully,4 - Not very likely,4,5 - Strongly Agree,3,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,4,3,3,4,4,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,3,3,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,3,5 - Strongly Agree,4,3,4,3,3,4,5 - Strongly Agree,5 - Strongly Agree
+Yes,18-34,4,5 - Trust fully,1 - Very likely,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,4,5 - Strongly Agree,4,4,4,4,4,4,4,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,4,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4
+Yes,18-34,1 - Not likely at all,2,4 - Not very likely,3,5 - Strongly Agree,3,4,2,3,3,3,2,2,3,4,3,3,3,2,3,1 - Disagree,3,3,3,3,1 - Disagree,2,3,3,3,3,2,3,2,2,3,4,2,4,3,2,1 - Disagree,2,2,3,2
+Yes,18-34,4,5 - Trust fully,2 - Somewhat likely,4,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,4,3,4,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4
+Yes,18-34,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,3,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,4,3,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,4,4,4,3
+Yes,18-34,5 - Highly likely,2,1 - Very likely,3,2,3,3,2,2,2,4,3,2,2,2,3,4,3,2,3,4,3,3,3,3,2,3,4,2,2,2,4,2,2,4,2,3,3,2,4,2,4,4,2,3,3
+Yes,18-34,4,3 - Neither trust nor distrust,2 - Somewhat likely,5 - Strongly Agree,4,2,5 - Strongly Agree,5 - Strongly Agree,4,4,3,4,4,4,3,5 - Strongly Agree,4,3,3,5 - Strongly Agree,4,5 - Strongly Agree,3,4,4,3,3,4,4,3,2,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,4,4,4,3,4,3,5 - Strongly Agree,3,3,4
+Yes,18-34,3,4,3,4,4,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
+Yes,18-34,5 - Highly likely,5 - Trust fully,2 - Somewhat likely,4,5 - Strongly Agree,5 - Strongly Agree,4,4,4,3,4,4,4,4,4,4,4,4,3,4,4,4,4,3,4,4,5 - Strongly Agree,4,4,4,4,4,4,4,3,5 - Strongly Agree,4,3,4,4,4,4,4,4,3,4
+Yes,18-34,3,3 - Neither trust nor distrust,4 - Not very likely,3,4,3,2,5 - Strongly Agree,3,3,3,5 - Strongly Agree,2,4,3,3,3,3,4,3,3,3,3,3,4,5 - Strongly Agree,2,3,3,4,3,2,3,4,2,4,4,3,3,4,3,4,4,2,4,3
+Yes,18-34,2,2,2 - Somewhat likely,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2
+Yes,18-34,4,3 - Neither trust nor distrust,3,3,3,5 - Strongly Agree,4,2,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,4,2,4,3,5 - Strongly Agree,4,2,5 - Strongly Agree,5 - Strongly Agree,4,2,4,4,4,3,4,3,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,3,5 - Strongly Agree,3,5 - Strongly Agree,4,3,3,3,4
+Yes,18-34,4,4,1 - Very likely,4,4,5 - Strongly Agree,4,3,5 - Strongly Agree,5 - Strongly Agree,4,2,5 - Strongly Agree,4,2,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,4,3,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,2,4,4,4,3,4,4,4,5 - Strongly Agree,4,4,4,5 - Strongly Agree,5 - Strongly Agree,2,5 - Strongly Agree,4,3,3
+Yes,18-34,5 - Highly likely,4,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,4,1 - Disagree,3,4,5 - Strongly Agree,3,4,4,5 - Strongly Agree,4,4,3,3,4,3,4,3,5 - Strongly Agree,3,3,4,3,4,4,3,3,3,4,4,3,5 - Strongly Agree,4,4,4,4,4,4
+Yes,18-34,4,4,2 - Somewhat likely,3,4,4,3,4,4,3,5 - Strongly Agree,4,4,4,3,4,4,4,4,3,4,4,4,3,5 - Strongly Agree,4,3,3,3,4,3,4,4,5 - Strongly Agree,3,3,2,3,3,4,3,4,4,3,4,4
+Yes,18-34,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree
+Yes,18-34,2,2,2 - Somewhat likely,2,3,3,2,4,2,2,2,2,3,2,2,2,2,2,3,3,3,3,3,3,3,2,2,4,3,2,2,3,2,3,4,3,2,2,2,2,3,2,3,2,3,2
+Yes,18-34,1 - Not likely at all,2,1 - Very likely,5 - Strongly Agree,3,2,3,2,2,3,1 - Disagree,3,1 - Disagree,2,1 - Disagree,1 - Disagree,2,3,2,3,1 - Disagree,3,1 - Disagree,4,4,5 - Strongly Agree,2,1 - Disagree,5 - Strongly Agree,2,5 - Strongly Agree,1 - Disagree,2,4,3,3,3,5 - Strongly Agree,1 - Disagree,3,3,3,4,2,4,5 - Strongly Agree
+Yes,18-34,4,4,5 - Not likely at all,4,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,4,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,4,4,3,5 - Strongly Agree,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,4,4,5 - Strongly Agree
+Yes,18-34,1 - Not likely at all,4,4 - Not very likely,4,4,3,3,3,4,4,3,4,3,4,5 - Strongly Agree,3,3,3,2,2,2,5 - Strongly Agree,2,3,3,3,3,4,4,4,4,4,3,1 - Disagree,3,3,5 - Strongly Agree,3,4,2,4,5 - Strongly Agree,4,4,3,3
+Yes,18-34,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree
+Yes,18-34,3,2,2 - Somewhat likely,3,4,3,3,3,3,4,4,4,4,4,4,3,4,4,3,4,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,3,4,4,4,4,3,4,4,4
+Yes,18-34,5 - Highly likely,4,5 - Not likely at all,5 - Strongly Agree,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,3,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,4,3,5 - Strongly Agree,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,3,4,4,3,3,4
+Yes,18-34,4,4,3,4,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,3,3,4,4,3,4,5 - Strongly Agree,4,3,3,4,3,4,2,4,3,4,2,4,4,3,3,4,3,2,4,1 - Disagree,4,2,4,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,3
+Yes,18-34,4,3 - Neither trust nor distrust,3,4,4,3,3,4,3,3,3,3,3,2,2,4,4,3,4,4,4,2,3,2,3,4,3,3,3,4,2,4,3,3,2,2,3,4,3,3,4,3,3,3,4,4
+Yes,18-34,4,4,1 - Very likely,5 - Strongly Agree,4,4,5 - Strongly Agree,4,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,5 - Strongly Agree,4
+Yes,18-34,5 - Highly likely,5 - Trust fully,1 - Very likely,4,4,5 - Strongly Agree,4,4,4,5 - Strongly Agree,4,4,4,4,4,4,4,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,3,4,4,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,4,4,4,4,4,4,4,4,4,4
+Yes,18-34,3,2,4 - Not very likely,3,4,4,4,4,4,5 - Strongly Agree,5 - Strongly Agree,4,4,4,3,4,5 - Strongly Agree,4,5 - Strongly Agree,3,4,4,4,4,4,4,3,4,4,4,4,4,4,4,5 - Strongly Agree,4,3,4,3,4,4,4,4,4,4,3
+Yes,18-34,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree
+Yes,18-34,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,4,3,5 - Strongly Agree,4,3,3,2,3,5 - Strongly Agree,5 - Strongly Agree,3,4,5 - Strongly Agree,4,3,4,3,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,3,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,2,3,3,4,3,5 - Strongly Agree,1 - Disagree,5 - Strongly Agree
+Yes,35-54,3,2,1 - Very likely,5 - Strongly Agree,4,3,5 - Strongly Agree,1 - Disagree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,3,2,5 - Strongly Agree,3,3,1 - Disagree,5 - Strongly Agree,3,5 - Strongly Agree,3,2,2,1 - Disagree,3,1 - Disagree,5 - Strongly Agree,4,3,1 - Disagree,1 - Disagree,2,1 - Disagree,3,4,2,2,3,2,1 - Disagree,4,3
+Yes,35-54,3,2,3,1 - Disagree,2,3,5 - Strongly Agree,2,4,3,2,1 - Disagree,5 - Strongly Agree,1 - Disagree,2,5 - Strongly Agree,3,1 - Disagree,2,3,3,3,2,3,2,5 - Strongly Agree,5 - Strongly Agree,3,4,5 - Strongly Agree,5 - Strongly Agree,4,1 - Disagree,2,3,3,3,4,3,2,3,1 - Disagree,2,3,3,2
+Yes,35-54,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree
+Yes,35-54,3,3 - Neither trust nor distrust,2 - Somewhat likely,4,3,4,5 - Strongly Agree,4,2,4,4,4,5 - Strongly Agree,4,2,3,3,4,3,4,3,2,2,5 - Strongly Agree,2,3,3,3,3,4,2,3,4,3,3,4,4,3,3,4,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,2,3
+Yes,35-54,4,5 - Trust fully,5 - Not likely at all,4,5 - Strongly Agree,4,4,5 - Strongly Agree,3,3,2,5 - Strongly Agree,3,3,2,4,5 - Strongly Agree,3,5 - Strongly Agree,2,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,2,3,4,4,5 - Strongly Agree,4,3,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,4,2,3,3,4,4,5 - Strongly Agree,3,4,4
+Yes,35-54,4,4,5 - Not likely at all,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,4,3,4,3,4,4,3,3,4,5 - Strongly Agree,4,4,4,5 - Strongly Agree,4,4,4,5 - Strongly Agree,4,4,3,5 - Strongly Agree,4,5 - Strongly Agree,3,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,3,4,3,3
+Yes,35-54,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,3,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,3,3,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,3,4,3,3,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,4,5 - Strongly Agree,3,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,3,4,5 - Strongly Agree
+Yes,35-54,5 - Highly likely,4,2 - Somewhat likely,5 - Strongly Agree,4,5 - Strongly Agree,4,4,5 - Strongly Agree,4,3,4,4,5 - Strongly Agree,4,4,4,5 - Strongly Agree,3,4,3,4,5 - Strongly Agree,4,3,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,3,4,4,5 - Strongly Agree
+Yes,35-54,5 - Highly likely,5 - Trust fully,5 - Not likely at all,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,3,5 - Strongly Agree,5 - Strongly Agree,3,3,5 - Strongly Agree,4,2,5 - Strongly Agree,2,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3
+Yes,35-54,4,4,3,4,4,3,4,5 - Strongly Agree,4,4,5 - Strongly Agree,4,4,4,4,4,3,4,4,5 - Strongly Agree,3,4,4,4,4,4,3,5 - Strongly Agree,4,4,4,4,4,5 - Strongly Agree,4,4,4,4,4,4,4,4,3,5 - Strongly Agree,3,3
+Yes,35-54,5 - Highly likely,5 - Trust fully,1 - Very likely,4,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,4,4,4,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,4,5 - Strongly Agree
+Yes,35-54,2,4,2 - Somewhat likely,4,4,3,3,2,4,3,2,3,3,3,2,2,4,4,3,2,2,2,4,2,3,5 - Strongly Agree,2,2,5 - Strongly Agree,2,4,3,3,4,2,3,2,1 - Disagree,3,3,3,2,1 - Disagree,1 - Disagree,4,3
+Yes,35-54,3,3 - Neither trust nor distrust,4 - Not very likely,3,3,3,2,4,3,3,3,4,3,3,3,3,4,3,4,4,2,3,3,3,2,4,3,3,3,3,3,3,5 - Strongly Agree,3,2,3,3,3,4,3,3,3,2,3,3,3
+Yes,35-54,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,2,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,3,4,3,5 - Strongly Agree,3,5 - Strongly Agree,4,3,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,1 - Disagree,5 - Strongly Agree,4,2,4,3,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,1 - Disagree
+Yes,35-54,3,0 - Do not trust at all,4 - Not very likely,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,3,3,1 - Disagree,1 - Disagree,1 - Disagree,3,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,2,1 - Disagree,3,3,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,3,4,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,3,1 - Disagree,1 - Disagree,3,1 - Disagree,1 - Disagree,1 - Disagree
+Yes,35-54,3,4,3,3,4,4,4,3,4,4,4,3,3,4,4,3,3,3,2,3,5 - Strongly Agree,3,3,3,5 - Strongly Agree,4,3,3,4,4,3,4,4,4,4,4,3,4,4,3,3,3,4,3,4,4
+Yes,35-54,4,4,2 - Somewhat likely,4,4,3,3,3,4,4,3,3,4,4,3,3,4,4,3,2,2,3,3,1 - Disagree,2,1 - Disagree,3,3,1 - Disagree,2,1 - Disagree,3,3,4,3,3,3,3,3,3,4,4,3,3,3,3
+Yes,35-54,4,4,1 - Very likely,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
+Yes,35-54,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree
+Yes,35-54,5 - Highly likely,5 - Trust fully,5 - Not likely at all,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,4,5 - Strongly Agree,3,5 - Strongly Agree,4,3,2,3,1 - Disagree,3,4,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,4,2,5 - Strongly Agree,3,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,2,4,3,2,3
+Yes,35-54,4,5 - Trust fully,4 - Not very likely,4,5 - Strongly Agree,3,4,3,5 - Strongly Agree,3,3,5 - Strongly Agree,4,5 - Strongly Agree,2,3,2,2,3,3,3,3,3,3,4,4,3,2,5 - Strongly Agree,3,2,2,5 - Strongly Agree,2,2,2,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,3,3,2,5 - Strongly Agree
+Yes,35-54,4,4,2 - Somewhat likely,4,4,4,4,3,3,3,4,3,2,4,3,4,4,4,4,4,4,3,2,2,3,4,3,4,4,3,4,4,3,4,4,3,3,2,4,3,3,4,4,5 - Strongly Agree,3,3
+Yes,35-54,3,3 - Neither trust nor distrust,3,4,4,3,2,3,3,3,4,3,4,4,5 - Strongly Agree,4,3,4,3,3,4,4,2,3,4,4,3,5 - Strongly Agree,2,3,3,3,4,4,3,3,3,3,3,3,4,3,3,4,3,5 - Strongly Agree
+Yes,35-54,4,4,3,4,4,4,4,4,4,3,3,3,3,4,3,3,3,4,3,3,4,3,3,3,3,3,3,3,3,3,3,3,3,4,4,3,3,4,3,3,3,3,3,3,3,3
+Yes,35-54,4,4,2 - Somewhat likely,4,4,5 - Strongly Agree,4,4,4,3,3,3,3,4,4,4,4,3,3,4,4,4,3,4,3,4,4,4,3,3,4,4,3,4,4,4,3,4,4,4,4,3,4,4,4,4
+Yes,35-54,4,5 - Trust fully,2 - Somewhat likely,4,5 - Strongly Agree,3,4,4,4,4,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,4,4,5 - Strongly Agree,4,4,4,4,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree
+Yes,35-54,3,1,2 - Somewhat likely,4,2,1 - Disagree,3,3,1 - Disagree,3,2,3,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,4,1 - Disagree,1 - Disagree,4,1 - Disagree,3,1 - Disagree,1 - Disagree,2,1 - Disagree,1 - Disagree,1 - Disagree,4,3,3,3,1 - Disagree,2,4,2,1 - Disagree,1 - Disagree,1 - Disagree,2,1 - Disagree,3,4,3,4,3
+Yes,35-54,4,4,4 - Not very likely,3,4,4,3,3,3,3,3,3,3,3,3,3,4,4,4,3,3,3,3,4,4,3,3,4,3,3,3,4,3,4,4,4,3,3,4,3,3,3,3,4,4,4
+Yes,35-54,4,4,2 - Somewhat likely,4,4,4,4,4,4,4,4,5 - Strongly Agree,4,4,4,3,4,4,3,4,4,4,3,4,4,3,4,4,4,4,4,3,4,4,4,4,4,4,4,3,4,3,3,4,3,4
+Yes,35-54,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree
+Yes,35-54,5 - Highly likely,4,1 - Very likely,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
+Yes,35-54,4,4,1 - Very likely,4,4,4,3,4,4,4,2,2,4,3,4,2,4,3,4,4,4,3,4,3,4,4,4,5 - Strongly Agree,3,4,4,3,4,3,4,4,4,3,3,3,3,4,4,3,4,5 - Strongly Agree
+Yes,35-54,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,3,5 - Strongly Agree
+Yes,55-74,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree
+Yes,55-74,4,4,5 - Not likely at all,4,5 - Strongly Agree,5 - Strongly Agree,4,3,3,5 - Strongly Agree,3,3,4,5 - Strongly Agree,3,3,5 - Strongly Agree,4,5 - Strongly Agree,3,5 - Strongly Agree,4,3,3,4,5 - Strongly Agree,4,3,4,4,3,4,5 - Strongly Agree,2,3,2,3,5 - Strongly Agree,4,4,3,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,3
+Yes,55-74,2,2,4 - Not very likely,3,3,1 - Disagree,1 - Disagree,4,2,4,4,4,4,2,3,4,2,2,3,3,1 - Disagree,1 - Disagree,4,3,2,3,1 - Disagree,2,3,1 - Disagree,3,4,1 - Disagree,4,3,3,1 - Disagree,4,2,4,1 - Disagree,2,4,4,2,2
+Yes,55-74,3,3 - Neither trust nor distrust,3,3,3,2,3,3,3,3,4,3,3,3,3,3,3,3,3,4,3,3,3,3,3,3,3,3,3,3,2,3,3,2,3,2,2,4,3,3,3,3,3,3,3,3
+Yes,55-74,4,5 - Trust fully,1 - Very likely,4,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree
+Yes,55-74,5 - Highly likely,4,5 - Not likely at all,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,4,4,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4
+Yes,55-74,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree
+Yes,55-74,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,4,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,4
+Yes,55-74,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,3,4,3,1 - Disagree,5 - Strongly Agree,5 - Strongly Agree,4,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,3,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,4

example_files/Volkswagen Prospects.csv

@@ -0,0 +1,129 @@
+Did you own a Volkswagen in the last 5 years?,What’s your age?,How likely are you to recommend buying a Volkswagen to friends and family?,What’s your level of trust in Volkswagen?,"If you have never owned a VW, how likely are you to consider buying a Volkswagen?","On a scale of 1 to 5, please tell us how much you agree with the following statements about Volkswagen.",,,,,,"On a scale of 1 to 5, please indicate how important the following statements are to you in trusting Volkswagen.",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,RID
+Response,Response,Response,Response,Response,Has built a strong and stable foundation.,Will develop well in the future.,Relates well to people like me.,Is valuable to our lives.,Has vision and values I find appealing.,Has what it takes to succeed,Employees are provided with extensive continuous training.,Produce almost 9 million cars per year.,"Employ almost 700,000 people and provide over 1.8 m families with work.",We are one of the longest-established car companies.,Building great and affordable cars is our foundation.,We spend over Euro 15 billion a year on research & development.,"Every year we are granted well over 2,000 new patents.",We have strong succession planning and nurture our best talent globally.,We are at the forefront of technology to deliver better cars and driving experiences.,We strongly focus on keeping and nurturing our team and have a 99.5% retention rate.,"At any stage we train over 15,000 apprentices.","According to Auto Institute 2022, we are the most innovative car company.","We don't just look at cars, we look at your future mobility.","If someone leaves us, for 20 years they can come back into the same position.",We work with our unions in our restructuring and future plans.,Our beginnings are a unique combination of investors and unions and today 9 of our 20 board members are staff representatives.,"We offer 22 weeks of paid parental leave, special leave for miscarriages and stillbirths and a 'Career with Children' project.",We work continuously with our customers to understand their needs and desires.,Our Compass 2.0 Program aims to progress diversity and inclusion even further.,We are committed to zero-emission manufacturing and cars.,"We aim to create lasting values, offer good working conditions, and conserve resources and our all environment.",We have a clear 'Way to Zero Emissions' roadmap for the next decades.,Our employees and Volkswagen support refugees in many countries.,At every level we offer our customers great value for money cars through our brands ranging from Porsche to Skoda.,Our brands are ranked No 2 and 5 in the reliability rankings.,The interior designs and sizes are well-considered for customers' changing needs.,We put a lot of emphasis on the interior experience and two of our cars have been ranked in the top 10.,After service and repair quality and price are as important to us as the initial purchase.,"From everyday to luxury, we offer customers amazing brands and models including Volkswagen, ŠKODA, SEAT, CUPRA, Audi, Lamborghini, Bentley, Porsche and Ducati.",We bring together the world's best talent in many disciplines to create your cars.,Our employees are provided with extensive continuous training.,We have learned from our mistakes in the Diesel Affair and we have made fundamental changes.,Our technology and manufacturing capabilities are second to none.,"Oliver Blume, our group CEO, started with Audi in 1994, became the CEO for Porsche in 2015 and our Group CEO in 2022.","In every discipline, we are creative at heart because that's how the future is being shaped.","From the latest science in metals to AI, we have the leading competencies.",At the heart of our decision-making is the long-term quality of life for all of us.,Open-Ended Response
+No,18-34,3,5 - Trust fully,2 - Somewhat likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,3,5 - Strongly Agree,5 - Strongly Agree,3,3,3,5 - Strongly Agree,3,3,3,3,3,3,3,5 - Strongly Agree,3,3,5 - Strongly Agree,3,3,5 - Strongly Agree,3,3,3,5 - Strongly Agree,3,5 - Strongly Agree,3,5 - Strongly Agree,3,5 - Strongly Agree,66444d4d-816f-8c05-1e10-70baf8fc942f
+No,18-34,4,4,2 - Somewhat likely,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,4,4,4,4,4,4,4,4,4,4,4,4,4,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,66444d5f-0e59-b44a-a9f7-220c38e0cb84
+No,18-34,3,3 - Neither trust nor distrust,3,3,3,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,3,3,3,4,4,3,3,66444d20-3bdb-7e12-7a35-3cd5212c2a29
+No,18-34,3,3 - Neither trust nor distrust,2 - Somewhat likely,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,66444d48-2e05-5b6c-ffdf-5502e6041ef4
+No,18-34,4,3 - Neither trust nor distrust,2 - Somewhat likely,4,5 - Strongly Agree,5 - Strongly Agree,4,4,3,5 - Strongly Agree,5 - Strongly Agree,4,4,4,3,5 - Strongly Agree,3,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,2,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,4,4,5 - Strongly Agree,4,4,4,4,4,5 - Strongly Agree,66444d53-548c-8e20-ac79-99d0a1598e4a
+No,18-34,3,2,2 - Somewhat likely,3,3,4,3,4,3,3,4,4,4,5 - Strongly Agree,3,2,4,4,5 - Strongly Agree,3,4,5 - Strongly Agree,4,2,4,4,2,4,4,5 - Strongly Agree,3,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,2,3,3,4,3,4,4,3,66444d38-72a6-020f-a01b-aa87e56cc239
+No,18-34,3,3 - Neither trust nor distrust,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,66444d44-e82b-8eb6-b3bd-8c7e0ad614a3
+No,18-34,3,3 - Neither trust nor distrust,3,4,4,3,3,4,4,3,2,4,3,3,4,3,4,3,4,3,2,3,3,2,4,5 - Strongly Agree,3,3,2,2,4,4,3,3,3,4,3,3,3,4,2,3,4,2,4,4,66444d4b-6109-bf15-258a-f507a7c9d6db
+No,18-34,4,3 - Neither trust nor distrust,1 - Very likely,3,5 - Strongly Agree,5 - Strongly Agree,3,4,4,4,2,3,3,2,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,3,4,2,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,2,2,5 - Strongly Agree,5 - Strongly Agree,3,2,3,5 - Strongly Agree,5 - Strongly Agree,4,1 - Disagree,4,3,2,3,2,2,5 - Strongly Agree,4,5 - Strongly Agree,66444d53-73b8-3eb0-0515-59b06b1aecda
+No,18-34,5 - Highly likely,5 - Trust fully,5 - Not likely at all,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,66444d4e-b421-105f-4c6c-78670ba97025
+No,18-34,3,3 - Neither trust nor distrust,3,4,4,3,3,3,4,3,4,3,4,4,3,2,3,4,3,3,3,3,3,3,3,4,4,3,4,3,2,3,3,4,3,3,3,3,4,3,3,2,3,4,3,3,66444d4b-ee80-3990-5e3a-022e73ef894f
+No,18-34,3,4,2 - Somewhat likely,3,3,3,3,3,3,3,3,3,3,3,3,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,66444d58-f862-61e2-0bd0-40ae4045aa49
+No,18-34,1 - Not likely at all,2,3,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,66444d6e-d69b-b23c-ff03-c36c4bf1a041
+No,18-34,1 - Not likely at all,0 - Do not trust at all,5 - Not likely at all,1 - Disagree,3,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,2,2,3,3,2,2,2,3,2,2,3,3,3,3,3,2,2,2,2,2,2,3,2,3,2,2,3,2,2,2,4,2,2,2,2,3,2,66444d56-7bbb-3c76-1850-237b052f3817
+No,18-34,5 - Highly likely,3 - Neither trust nor distrust,2 - Somewhat likely,3,4,3,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,66444d41-d5e5-198c-50c3-f0ecfb0187f5
+No,18-34,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,66444d4d-9301-3dd7-7e9e-4c3f9ff40e2e
+No,18-34,3,3 - Neither trust nor distrust,3,5 - Strongly Agree,4,4,4,4,4,3,5 - Strongly Agree,4,5 - Strongly Agree,4,4,4,4,4,4,4,4,4,3,4,3,4,4,3,4,4,3,4,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,3,5 - Strongly Agree,4,4,3,4,3,3,4,4,66444666-0d63-8021-10f3-0cc88e86d455
+No,18-34,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,3,4,4,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,4,3,4,5 - Strongly Agree,3,4,5 - Strongly Agree,3,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,3,3,5 - Strongly Agree,4,3,5 - Strongly Agree,3,5 - Strongly Agree,3,3,3,3,3,4,3,4,5 - Strongly Agree,3,5 - Strongly Agree,664446c0-5551-e5d3-31ae-e77dbf92e6ce
+No,18-34,4,4,2 - Somewhat likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,4,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,3,3,5 - Strongly Agree,4,4,4,4,4,4,3,3,4,4,4,4,4,4,5 - Strongly Agree,4,4,3,3,3,4,5 - Strongly Agree,4,4,4,3,4,6644469a-afa8-8e15-6f16-c781b1561b54
+No,18-34,4,3 - Neither trust nor distrust,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,66444677-6451-2ad0-7a18-1ad1e3308bfc
+No,18-34,3,2,3,3,3,3,4,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,4,3,4,4,3,4,4,4,4,4,4,5 - Strongly Agree,3,3,3,3,3,5 - Strongly Agree,4,3,4,4,5 - Strongly Agree,2,4,5 - Strongly Agree,4,4,3,4,3,6644468f-e5b2-81f9-4a80-113920a1406d
+No,18-34,3,4,1 - Very likely,3,4,5 - Strongly Agree,3,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,4,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,3,4,3,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,4,5 - Strongly Agree,3,3,5 - Strongly Agree,4,4,5 - Strongly Agree,6644469e-e092-2a17-e64b-211a96601572
+No,18-34,3,2,3,3,3,3,3,3,3,3,3,3,3,3,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,3,3,3,664446ac-adc5-1d38-4057-3f6c8a88d6d2
+No,18-34,4,3 - Neither trust nor distrust,4 - Not very likely,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,4,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,664446ab-ad2b-cbb3-7ee8-e637db3154a6
+No,18-34,4,4,2 - Somewhat likely,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,5 - Strongly Agree,4,4,4,66444674-5ea9-5c0a-8acb-e708319b8d14
+No,18-34,3,3 - Neither trust nor distrust,2 - Somewhat likely,3,3,2,3,4,4,4,3,4,4,5 - Strongly Agree,4,3,3,5 - Strongly Agree,4,4,4,5 - Strongly Agree,3,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,3,4,4,3,4,3,4,4,4,4,4,4,3,3,4,5 - Strongly Agree,4,6644468f-05bf-52d4-d273-8437f1551d2a
+No,35-54,1 - Not likely at all,3 - Neither trust nor distrust,5 - Not likely at all,3,4,2,3,4,4,4,2,4,4,4,3,2,3,3,4,3,3,3,2,3,2,4,3,3,4,3,3,2,2,3,4,4,3,3,2,4,3,2,3,4,3,4,664446a7-809f-bc1e-b646-856759dd0578
+No,35-54,5 - Highly likely,4,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,4,4,4,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,664446ac-7670-1927-8a92-5cf95cab6581
+No,35-54,3,3 - Neither trust nor distrust,3,3,3,3,3,3,3,4,3,3,4,4,3,3,3,4,4,4,3,3,3,3,2,3,4,2,3,3,3,3,4,4,3,3,3,3,3,4,2,3,3,3,3,3,66442afd-52ac-af6f-364a-56288cb47c39
+No,35-54,4,4,1 - Very likely,4,4,5 - Strongly Agree,4,4,4,4,4,4,4,4,3,4,4,4,4,3,4,4,3,4,3,3,4,3,3,3,3,3,4,4,4,4,4,4,3,4,4,3,4,4,4,4,66442b2a-d35a-dd2d-63ac-6910459cba6d
+No,35-54,3,3 - Neither trust nor distrust,3,3,3,3,3,4,3,4,5 - Strongly Agree,3,5 - Strongly Agree,4,5 - Strongly Agree,3,4,4,3,4,3,4,3,3,4,4,3,2,5 - Strongly Agree,4,4,3,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,3,4,2,3,3,3,66442b15-9934-17de-6aec-d26d6d987583
+No,35-54,3,3 - Neither trust nor distrust,3,2,4,3,3,3,3,4,3,4,4,4,4,3,4,3,4,4,4,4,4,4,4,4,5 - Strongly Agree,4,4,4,4,3,4,4,4,4,5 - Strongly Agree,4,4,4,4,3,4,5 - Strongly Agree,4,4,66442b31-7881-aeb9-c12e-1dec2c90c795
+No,35-54,1 - Not likely at all,3 - Neither trust nor distrust,5 - Not likely at all,4,3,3,3,3,3,3,3,4,2,3,3,3,3,2,3,3,4,3,4,3,3,3,4,3,3,3,4,2,3,3,3,3,4,4,3,4,3,4,3,3,4,2,66442b0d-9adf-c081-adf5-13cbcb5c22db
+No,35-54,3,4,2 - Somewhat likely,4,4,3,4,3,4,4,3,4,5 - Strongly Agree,4,4,4,4,4,5 - Strongly Agree,4,4,4,5 - Strongly Agree,4,4,3,4,2,3,4,3,2,3,5 - Strongly Agree,3,4,4,4,4,4,3,5 - Strongly Agree,4,4,4,4,66442b30-d5f4-9330-807c-ee016fe22299
+No,35-54,4,4,1 - Very likely,4,4,4,4,4,4,3,3,3,3,3,3,3,3,3,3,3,3,3,4,3,4,3,4,4,4,3,3,3,3,3,4,4,4,5 - Strongly Agree,3,4,3,3,3,3,4,3,66442ae7-d880-a757-ea77-8e047ed7c732
+No,35-54,1 - Not likely at all,3 - Neither trust nor distrust,4 - Not very likely,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,66442b14-60fd-c335-0aa4-f5695dd8c704
+No,35-54,1 - Not likely at all,0 - Do not trust at all,5 - Not likely at all,1 - Disagree,2,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,5 - Strongly Agree,3,4,5 - Strongly Agree,3,1 - Disagree,3,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,4,4,5 - Strongly Agree,5 - Strongly Agree,3,4,5 - Strongly Agree,4,1 - Disagree,3,3,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,3,3,4,4,66442b13-6c57-85a7-e8e1-3a0722e8e139
+No,35-54,4,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,4,4,3,5 - Strongly Agree,5 - Strongly Agree,3,4,4,5 - Strongly Agree,4,4,4,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,3,3,4,3,3,3,3,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,4,3,5 - Strongly Agree,3,4,4,5 - Strongly Agree,66442b10-b804-8b33-e142-97a2bb103972
+No,35-54,1 - Not likely at all,0 - Do not trust at all,5 - Not likely at all,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,2,1 - Disagree,1 - Disagree,3,1 - Disagree,1 - Disagree,2,2,2,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,2,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,2,2,2,1 - Disagree,1 - Disagree,1 - Disagree,2,2,1 - Disagree,1 - Disagree,2,66442b2a-4808-2c4a-3461-4957d35e0b1a
+No,35-54,3,3 - Neither trust nor distrust,3,3,3,2,3,3,3,3,3,3,3,4,3,3,4,3,2,3,3,3,2,3,3,3,4,4,3,3,3,3,4,3,3,3,3,3,3,3,3,3,3,3,3,3,66442b08-de02-175d-ec62-97f8f543ec2b
+No,35-54,3,3 - Neither trust nor distrust,3,3,3,3,3,3,3,3,3,3,5 - Strongly Agree,5 - Strongly Agree,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,5 - Strongly Agree,5 - Strongly Agree,3,3,5 - Strongly Agree,3,3,3,3,3,3,3,3,66442b33-f66a-789a-e3c0-b8f1341a9c51
+No,35-54,3,3 - Neither trust nor distrust,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,66442b29-4383-69ff-c405-bb51e0a0d369
+No,35-54,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,3,5 - Strongly Agree,3,4,3,5 - Strongly Agree,4,5 - Strongly Agree,2,4,5 - Strongly Agree,2,4,5 - Strongly Agree,4,3,3,5 - Strongly Agree,2,4,5 - Strongly Agree,5 - Strongly Agree,3,4,3,5 - Strongly Agree,4,3,4,4,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,2,5 - Strongly Agree,4,4,4,3,3,4,66442b2c-5d0c-f17b-4947-9c6d0fff5aa0
+No,35-54,4,4,2 - Somewhat likely,5 - Strongly Agree,5 - Strongly Agree,4,4,4,5 - Strongly Agree,4,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,3,4,4,4,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,66442b3b-4ef0-4b31-444e-3458d8482210
+No,35-54,2,3 - Neither trust nor distrust,2 - Somewhat likely,4,3,3,2,3,2,3,3,3,3,4,3,2,2,3,3,2,3,3,3,3,3,3,4,3,4,3,3,2,3,3,4,3,3,4,2,4,3,3,3,3,2,2,66442b2f-1629-e390-7983-1c974994d29b
+No,35-54,1 - Not likely at all,0 - Do not trust at all,5 - Not likely at all,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,3,3,3,1 - Disagree,3,1 - Disagree,3,3,1 - Disagree,3,3,3,1 - Disagree,3,3,3,3,3,3,3,3,3,3,1 - Disagree,1 - Disagree,3,3,3,3,3,3,3,3,3,3,3,3,66442b06-d4cb-8843-045f-9f4aec0f0672
+No,35-54,1 - Not likely at all,0 - Do not trust at all,4 - Not very likely,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,66442b06-259a-fa99-f5d7-a7f2704c0d9c
+No,35-54,4,4,2 - Somewhat likely,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,5 - Strongly Agree,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,66442b0c-b0c9-2e22-1bf8-93df7772c947
+No,35-54,5 - Highly likely,5 - Trust fully,2 - Somewhat likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,66442b0d-e33e-2eff-a993-77bd22157f14
+No,35-54,3,4,2 - Somewhat likely,5 - Strongly Agree,4,3,3,4,3,4,4,3,4,4,3,4,4,4,4,3,4,4,3,4,4,3,4,4,4,4,4,2,3,4,4,4,4,4,4,4,3,4,4,4,3,3,66442170-ce98-70bc-4cc2-dd5207e2f520
+No,35-54,1 - Not likely at all,0 - Do not trust at all,5 - Not likely at all,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,66442175-3f41-90fb-af28-fe5dc94e413e
+No,35-54,5 - Highly likely,4,3,3,4,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,3,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,3,4,4,3,5 - Strongly Agree,4,3,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,4,3,5 - Strongly Agree,3,3,4,4,5 - Strongly Agree,3,3,4,6644218e-24c2-63b9-1d1e-c55df6605ab6
+No,35-54,4,4,2 - Somewhat likely,4,4,4,3,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,4,3,5 - Strongly Agree,5 - Strongly Agree,3,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,3,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,4,4,4,3,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,3,4,4,6644217d-e213-8f97-5999-566364b2ada1
+No,35-54,4,4,4 - Not very likely,3,4,4,3,3,3,4,3,4,4,4,4,3,3,4,4,4,3,4,3,4,4,4,4,4,3,3,3,4,3,3,4,3,4,4,3,4,3,4,3,4,4,4,66442161-524e-4d9d-1b74-50a16518bbe7
+No,35-54,4,5 - Trust fully,1 - Very likely,4,4,4,4,4,4,4,3,3,4,4,3,1 - Disagree,4,4,4,4,4,4,3,4,4,4,4,3,4,4,4,1 - Disagree,4,3,3,4,3,4,4,4,3,4,3,3,4,4,664420e0-a850-3d27-08f1-b04f28643922
+No,35-54,4,3 - Neither trust nor distrust,2 - Somewhat likely,5 - Strongly Agree,2,2,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,2,3,3,3,4,3,2,4,2,4,3,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,3,4,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,3,3,5 - Strongly Agree,4,4,3,6644215c-9e7e-d34f-5555-d53efee17d7a
+No,35-54,4,4,2 - Somewhat likely,4,4,4,4,4,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,4,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,4,66442171-b39d-645a-358b-0d2e13bb8d2a
+No,35-54,3,5 - Trust fully,2 - Somewhat likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,3,3,5 - Strongly Agree,4,3,3,3,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,5 - Strongly Agree,3,4,3,3,3,3,3,3,3,6644214e-2239-7ce8-6cdd-3fe78e56d73c
+No,35-54,1 - Not likely at all,3 - Neither trust nor distrust,5 - Not likely at all,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,1 - Disagree,4,5 - Strongly Agree,3,3,3,5 - Strongly Agree,2,5 - Strongly Agree,3,3,4,3,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,4,4,5 - Strongly Agree,3,5 - Strongly Agree,4,4,4,5 - Strongly Agree,3,2,5 - Strongly Agree,66442118-d792-3bcc-9c92-b2b6f3d55ed3
+No,35-54,2,2,4 - Not very likely,4,3,2,2,3,3,4,2,3,4,4,3,3,3,3,3,4,3,2,3,3,3,3,3,3,3,3,2,2,3,2,2,3,4,3,3,3,4,4,4,3,3,3,66442183-a575-a7b1-f644-08f609c4b253
+No,35-54,3,3 - Neither trust nor distrust,3,4,4,4,3,3,3,3,3,3,4,3,3,3,3,4,3,3,4,3,4,4,3,4,4,4,4,4,4,4,3,4,4,4,3,3,3,4,3,4,3,3,3,3,6644216a-e0d7-6099-4dc3-f844868da704
+No,35-54,5 - Highly likely,4,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,4,3,4,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,4,3,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,3,5 - Strongly Agree,4,3,4,4,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,3,5 - Strongly Agree,5 - Strongly Agree,4,66442181-f336-11b3-c624-c561f8a20c9a
+No,35-54,1 - Not likely at all,3 - Neither trust nor distrust,4 - Not very likely,3,1 - Disagree,5 - Strongly Agree,1 - Disagree,4,3,2,1 - Disagree,2,2,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,5 - Strongly Agree,1 - Disagree,1 - Disagree,5 - Strongly Agree,3,4,5 - Strongly Agree,1 - Disagree,5 - Strongly Agree,3,4,5 - Strongly Agree,5 - Strongly Agree,2,5 - Strongly Agree,4,1 - Disagree,3,5 - Strongly Agree,4,1 - Disagree,4,4,3,5 - Strongly Agree,1 - Disagree,2,4,3,6644215d-7709-ea32-eb6c-a23cdfeee78c
+No,35-54,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,6644215e-f740-9447-b15f-38afd72b00bf
+No,35-54,3,4,2 - Somewhat likely,4,4,3,3,3,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,4,5 - Strongly Agree,66442185-78d7-2ff7-c9aa-67c78b61fab1
+No,35-54,5 - Highly likely,4,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,66442173-febd-ca22-fbd4-0257d82525d4
+No,35-54,3,5 - Trust fully,2 - Somewhat likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,66442129-5c13-19d3-3591-903791c8b67c
+No,35-54,5 - Highly likely,5 - Trust fully,1 - Very likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,66442149-13ea-2297-7e90-1d32a0e204bc
+No,35-54,2,3 - Neither trust nor distrust,3,3,3,2,3,3,3,3,3,4,3,3,3,3,3,3,3,3,3,2,2,3,3,3,3,3,3,2,3,3,3,3,3,3,3,3,3,2,3,3,3,2,3,3,66442154-c38e-9f7e-be89-7f8030a9b3ed
+No,35-54,3,3 - Neither trust nor distrust,3,4,4,4,4,3,3,3,3,4,3,3,4,3,4,3,3,3,4,3,3,5 - Strongly Agree,4,3,3,3,3,3,4,3,3,5 - Strongly Agree,3,4,4,3,3,3,4,4,3,3,4,3,66442189-f730-61a9-9298-70c94d33265e
+No,35-54,3,3 - Neither trust nor distrust,4 - Not very likely,4,3,2,2,3,3,3,3,3,3,3,2,3,4,2,3,3,3,3,3,3,3,3,2,3,3,3,3,3,3,3,3,3,2,3,3,4,3,3,3,4,3,3,6644215b-c8a5-b6b5-056a-1482dee6386b
+No,55-74,2,2,3,3,3,2,2,3,2,4,3,4,4,4,4,3,3,3,4,3,4,3,3,4,4,4,3,4,3,3,3,4,4,3,3,4,4,3,3,4,2,4,3,3,4,4,66442177-53c5-b94a-cd4a-5a367fce8f4c
+No,55-74,4,4,2 - Somewhat likely,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,6644217e-f612-5f0e-b572-1c06663549a0
+No,55-74,3,3 - Neither trust nor distrust,3,4,3,3,4,3,3,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,3,4,4,4,4,5 - Strongly Agree,4,4,4,5 - Strongly Agree,5 - Strongly Agree,66442189-f6d4-90b9-ec84-f9c984ee0330
+No,55-74,4,4,1 - Very likely,5 - Strongly Agree,4,4,4,3,5 - Strongly Agree,4,4,3,4,5 - Strongly Agree,3,4,3,4,4,3,4,4,3,4,3,4,4,3,4,4,4,3,4,5 - Strongly Agree,4,5 - Strongly Agree,4,4,4,4,3,5 - Strongly Agree,3,3,4,4,6644216b-63dc-18b2-b229-2a53be2127eb
+No,55-74,2,2,2 - Somewhat likely,2,3,4,3,3,3,2,1 - Disagree,4,4,3,5 - Strongly Agree,4,3,2,1 - Disagree,1 - Disagree,4,3,4,1 - Disagree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,2,3,5 - Strongly Agree,3,2,2,5 - Strongly Agree,4,3,2,2,2,5 - Strongly Agree,4,5 - Strongly Agree,1 - Disagree,2,3,66442195-90bf-a747-1b54-a08651b60263
+No,55-74,3,3 - Neither trust nor distrust,3,3,3,3,3,3,3,3,3,3,3,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,6644217c-575d-f2cc-aade-93351df1451e
+No,55-74,1 - Not likely at all,0 - Do not trust at all,5 - Not likely at all,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,6644215c-743a-9a38-305c-e4e13d67af38
+No,55-74,3,4,3,4,4,4,3,4,3,4,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,4,4,4,4,4,4,4,3,4,4,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,4,4,4,4,4,66442173-cc6c-65b8-d872-83f9494ee8ee
+No,55-74,2,4,1 - Very likely,4,4,4,4,4,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,4,4,4,4,4,4,4,5 - Strongly Agree,4,4,4,5 - Strongly Agree,4,4,4,4,4,4,4,4,5 - Strongly Agree,4,4,5 - Strongly Agree,4,4,4,4,4,4,4,4,66442176-7c46-c979-3752-509863bbf6d4
+No,55-74,1 - Not likely at all,3 - Neither trust nor distrust,3,2,2,1 - Disagree,1 - Disagree,1 - Disagree,3,3,3,3,3,3,3,2,3,3,3,3,3,3,2,3,3,3,3,3,3,3,3,2,3,3,3,3,3,3,3,3,2,3,2,3,3,3,6644218a-1b8b-6c4b-daca-49b9703fbf69
+No,55-74,1 - Not likely at all,3 - Neither trust nor distrust,4 - Not very likely,3,4,3,4,4,4,4,4,4,3,4,2,2,4,4,4,4,2,4,4,4,4,4,4,4,4,4,4,4,4,4,2,4,4,4,4,4,4,4,2,4,4,4,66442160-abb1-e42e-03cd-fc8e88351d87
+No,55-74,5 - Highly likely,5 - Trust fully,2 - Somewhat likely,5 - Strongly Agree,4,3,3,4,4,4,4,4,5 - Strongly Agree,3,4,4,4,3,4,4,3,4,5 - Strongly Agree,4,4,5 - Strongly Agree,4,4,1 - Disagree,5 - Strongly Agree,2,3,4,5 - Strongly Agree,4,4,5 - Strongly Agree,4,4,5 - Strongly Agree,3,4,3,4,3,4,66442179-47d7-6b0f-1d45-70627a4326e3
+No,55-74,2,2,4 - Not very likely,2,2,2,2,2,2,3,3,3,2,3,2,3,3,3,3,3,2,3,3,3,3,3,3,2,3,2,3,3,3,3,3,3,2,3,3,3,2,3,3,3,3,3,66442155-424b-fcb9-3bb2-82c40e1b03df
+No,55-74,1 - Not likely at all,1,5 - Not likely at all,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,6644217a-6894-8a90-fbf5-c75487643248
+No,55-74,1 - Not likely at all,0 - Do not trust at all,1 - Very likely,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,66442153-ca01-3c50-a630-0f68a422b8bc
+No,55-74,2,3 - Neither trust nor distrust,4 - Not very likely,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,6643f652-6a97-1806-677d-bd277a982965
+No,55-74,1 - Not likely at all,3 - Neither trust nor distrust,5 - Not likely at all,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,6643f63d-0d0b-3afb-69f8-072db4670fdc
+No,55-74,1 - Not likely at all,5 - Trust fully,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,6643f6b6-001b-02d1-3ec3-a2428868fc0a
+No,55-74,4,4,2 - Somewhat likely,4,4,4,3,3,4,3,3,3,4,4,3,3,3,4,3,3,4,3,3,3,3,3,4,3,4,4,3,3,3,4,4,4,3,4,3,3,3,4,3,3,4,3,6643f69f-82ef-bfb8-2499-9b29fe054e44
+No,55-74,2,1,4 - Not very likely,1 - Disagree,1 - Disagree,3,3,4,2,2,2,4,2,2,2,2,2,2,2,3,2,2,1 - Disagree,2,3,2,2,2,1 - Disagree,2,2,2,2,1 - Disagree,2,1 - Disagree,2,2,2,2,2,2,1 - Disagree,2,3,3,6643f6b4-5d81-1aac-e30c-b2a183338c9e
+No,55-74,1 - Not likely at all,3 - Neither trust nor distrust,3,3,3,1 - Disagree,1 - Disagree,3,5 - Strongly Agree,3,3,3,3,3,3,3,3,4,4,3,4,3,3,3,3,3,5 - Strongly Agree,3,3,5 - Strongly Agree,3,5 - Strongly Agree,3,3,3,3,3,3,3,3,3,3,5 - Strongly Agree,3,3,5 - Strongly Agree,6643f69d-3582-98d4-55a4-5b95000f5afc
+No,55-74,3,4,2 - Somewhat likely,4,3,3,3,3,4,4,3,3,4,4,2,3,3,4,4,4,3,3,3,4,2,4,5 - Strongly Agree,2,3,4,3,3,3,3,3,4,4,3,3,4,4,4,2,3,2,4,6643f6d2-ee3d-8dd9-ce07-9053ecf2472d
+No,55-74,4,4,2 - Somewhat likely,5 - Strongly Agree,4,4,3,4,5 - Strongly Agree,3,4,4,5 - Strongly Agree,5 - Strongly Agree,4,4,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,3,4,4,3,4,4,4,3,3,3,4,3,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,4,3,3,4,3,5 - Strongly Agree,3,5 - Strongly Agree,6643f67b-a407-4ac1-ce0c-84eca8693c0a
+No,55-74,1 - Not likely at all,1,5 - Not likely at all,2,3,2,1 - Disagree,3,3,3,3,3,4,2,3,3,3,2,3,3,3,3,3,1 - Disagree,2,2,3,1 - Disagree,1 - Disagree,3,2,1 - Disagree,3,3,3,2,3,3,3,4,3,2,2,2,3,3,6643f6d1-19ee-aded-e5d2-84127c26c4a8
+No,55-74,4,4,1 - Very likely,4,3,4,3,4,4,4,4,4,5 - Strongly Agree,3,4,4,3,5 - Strongly Agree,4,4,4,3,4,3,3,3,4,3,4,4,4,3,3,4,4,4,4,3,4,4,3,3,4,3,3,4,6643f6a7-7b73-c3bc-1090-54820b7d0117
+No,55-74,3,2,3,4,3,3,3,3,3,3,5 - Strongly Agree,3,5 - Strongly Agree,1 - Disagree,1 - Disagree,1 - Disagree,2,1 - Disagree,5 - Strongly Agree,5 - Strongly Agree,1 - Disagree,2,3,3,3,3,2,3,1 - Disagree,1 - Disagree,1 - Disagree,3,1 - Disagree,3,1 - Disagree,1 - Disagree,5 - Strongly Agree,3,3,1 - Disagree,2,1 - Disagree,3,3,3,1 - Disagree,6643f6b7-bb53-15c7-3466-d4d07e20e27f
+No,55-74,3,4,3,4,4,4,4,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,4,4,5 - Strongly Agree,5 - Strongly Agree,4,4,4,4,4,4,4,4,4,4,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,5 - Strongly Agree,4,4,3,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,6643f6d9-5129-efa0-a4cc-fb7192b016df
+No,55-74,2,3 - Neither trust nor distrust,5 - Not likely at all,3,4,1 - Disagree,1 - Disagree,2,4,3,3,3,3,4,3,3,3,3,3,3,3,3,3,3,3,4,3,3,3,3,2,3,3,3,3,3,4,3,3,3,3,3,3,3,3,3,6643f69b-f42e-eaee-1e65-2ce0e1600920
+No,55-74,2,2,2 - Somewhat likely,2,1 - Disagree,1 - Disagree,1 - Disagree,2,2,2,2,3,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,6643f6d1-6146-493e-e782-bcf81e1720be
+No,55-74,1 - Not likely at all,3 - Neither trust nor distrust,5 - Not likely at all,4,3,2,3,4,3,4,3,4,4,4,3,4,3,4,4,4,4,2,4,3,4,5 - Strongly Agree,3,3,5 - Strongly Agree,4,3,4,3,5 - Strongly Agree,3,4,3,3,4,4,2,3,4,4,3,3,6643f6c3-ec7d-e57c-22e6-438abd6ac039
+No,55-74,3,2,3,3,3,3,3,3,3,3,4,4,4,3,3,3,3,4,3,3,3,4,4,2,4,3,3,4,3,4,3,4,3,3,3,3,3,3,3,3,3,4,3,4,4,3,6643f6cd-59b5-6178-a812-5eaa140ded44
+No,55-74,5 - Highly likely,5 - Trust fully,5 - Not likely at all,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,6643f686-b59c-8c56-f876-613fa9adccdb
+No,55-74,3,3 - Neither trust nor distrust,4 - Not very likely,4,3,3,3,3,3,3,3,3,3,3,3,4,3,3,3,3,3,3,3,3,3,4,3,3,2,3,2,3,3,3,3,3,4,3,3,3,3,3,3,4,3,3,6643f6a2-f395-d7f0-57fc-8cbba07c634a
+No,55-74,2,3 - Neither trust nor distrust,3,3,4,3,3,4,3,4,4,4,4,4,2,3,3,4,3,3,4,4,2,3,3,4,4,3,4,4,4,3,4,4,4,4,4,4,3,4,2,3,3,3,4,3,6643f699-4ead-df9f-1fa3-b8863877ffe8
+No,55-74,4,4,3,4,4,4,3,3,4,4,4,4,5 - Strongly Agree,4,4,3,3,4,4,3,3,3,3,5 - Strongly Agree,3,3,4,2,4,4,4,3,4,4,4,4,4,4,3,4,4,4,3,3,3,4,6643f671-5ac1-7d13-a457-3a3caa621e23
+No,55-74,3,4,4 - Not very likely,4,4,3,4,4,4,3,4,3,4,4,4,3,4,4,3,4,3,4,3,4,3,3,4,3,4,3,3,3,3,4,4,4,4,3,4,3,3,4,3,4,4,4,6643f654-3a2a-9270-0ef6-b3b3d1165881
+No,55-74,3,3 - Neither trust nor distrust,2 - Somewhat likely,3,3,2,2,2,2,3,2,4,3,2,3,2,2,3,3,3,3,2,1 - Disagree,2,2,2,2,3,2,3,4,3,3,3,2,2,3,1 - Disagree,3,4,2,3,2,2,3,3,6643f6d3-c9c7-ab8e-2d68-aa0f267416fc
+No,55-74,5 - Highly likely,5 - Trust fully,2 - Somewhat likely,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,6643f6a0-7d1d-f9df-1e7b-63d346451c9a
+No,55-74,4,4,1 - Very likely,5 - Strongly Agree,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,6643f6cb-c487-fff2-18d1-3e3ab75c3b44
+No,55-74,3,3 - Neither trust nor distrust,4 - Not very likely,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,3,3,3,3,6643f628-b928-3bbf-64b1-46b506807adc
+No,55-74,1 - Not likely at all,3 - Neither trust nor distrust,5 - Not likely at all,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,4,3,4,4,3,5 - Strongly Agree,5 - Strongly Agree,3,4,3,4,4,4,4,4,4,4,4,4,3,4,3,3,3,5 - Strongly Agree,4,3,4,4,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,4,4,6643f648-b3f7-a3e7-1fe2-0f2badb6ba2c
+No,55-74,1 - Not likely at all,3 - Neither trust nor distrust,3,3,3,2,3,3,3,3,3,3,3,3,3,2,2,3,3,3,3,3,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,6643f6cb-77cf-e344-2db6-9a4a26d6efff
+No,55-74,3,3 - Neither trust nor distrust,3,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,6643f698-eb15-a067-c245-bed0220996dc
+No,55-74,3,3 - Neither trust nor distrust,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,6643f68f-e992-cc55-6163-f979aaf3ed7f
+No,55-74,3,3 - Neither trust nor distrust,1 - Very likely,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,6643f66f-ebd2-254c-482b-47258a45795e
+No,55-74,2,4,3,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,4,4,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,4,4,4,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,3,4,5 - Strongly Agree,4,5 - Strongly Agree,4,6643f6ce-25ba-3a08-49eb-e8d6ec3e05ab
+No,55-74,3,3 - Neither trust nor distrust,4 - Not very likely,3,3,2,3,3,4,4,3,4,5 - Strongly Agree,4,3,4,3,5 - Strongly Agree,5 - Strongly Agree,4,3,5 - Strongly Agree,5 - Strongly Agree,4,3,5 - Strongly Agree,3,2,3,5 - Strongly Agree,4,3,4,4,3,4,4,4,3,3,4,4,2,4,4,3,6643f65c-f3d5-dc63-6559-8efb2fe7a5b1
+No,55-74,3,3 - Neither trust nor distrust,4 - Not very likely,4,3,3,3,3,3,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,3,4,5 - Strongly Agree,5 - Strongly Agree,4,4,5 - Strongly Agree,4,5 - Strongly Agree,4,5 - Strongly Agree,4,4,4,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,4,3,4,5 - Strongly Agree,3,4,4,5 - Strongly Agree,3,4,3,4,6643f638-bb27-8fd5-af98-7491de4c81ff
+No,55-74,1 - Not likely at all,3 - Neither trust nor distrust,5 - Not likely at all,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,6643f636-54ef-a921-2edb-96a74b4d68a1
+No,55-74,1 - Not likely at all,3 - Neither trust nor distrust,3,3,3,2,3,3,3,4,4,4,3,3,4,3,4,4,4,3,3,3,4,3,3,4,4,3,3,3,3,3,4,4,4,4,5 - Strongly Agree,4,4,3,4,4,3,3,4,4,6643f69d-85c8-4481-df43-ce607d1ed4aa
+No,55-74,1 - Not likely at all,0 - Do not trust at all,1 - Very likely,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,3,3,2,2,4,1 - Disagree,2,1 - Disagree,3,1 - Disagree,2,3,2,2,1 - Disagree,2,2,1 - Disagree,3,2,1 - Disagree,2,2,2,2,3,1 - Disagree,1 - Disagree,2,2,2,3,2,2,2,3,3,6643f64f-53c1-a4b3-90dc-f7a852a08fa0
+No,55-74,3,4,2 - Somewhat likely,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,6643f624-dc40-b07e-ea82-36bd1b90244f
+No,75+,1 - Not likely at all,5 - Trust fully,5 - Not likely at all,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,3,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,5 - Strongly Agree,4,5 - Strongly Agree,4,4,5 - Strongly Agree,6643f632-d6c8-caf9-e52a-41c884f5905c
+No,75+,2,3 - Neither trust nor distrust,3,3,3,3,3,3,3,4,3,3,3,3,3,3,3,4,2,3,2,2,2,3,3,3,3,3,4,2,3,3,3,2,4,3,3,3,3,3,3,4,3,3,3,3,6643f655-6ef8-ff32-bb61-b76d312e1751
+No,75+,3,3 - Neither trust nor distrust,3,3,3,4,3,3,4,4,4,3,4,4,4,3,3,3,5 - Strongly Agree,4,4,3,3,3,3,4,4,2,4,4,4,3,4,3,4,4,3,3,4,5 - Strongly Agree,3,4,3,3,4,4,6643f615-29f5-4550-a5a4-e612fcdbd605
+No,75+,1 - Not likely at all,0 - Do not trust at all,5 - Not likely at all,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,1 - Disagree,6643f61f-63ce-2974-fae3-34f0d64c79c4

process_data.R

@@ -1,34 +1,199 @@
 # Load required libraries
+library(relaimpo)
 library(readxl)
 library(readr)
-library(relaimpo)
+library(lavaan)
+library(leaps)
+library(dplyr)
+library(tidyr)
+
+# Logging function
+log_message <- function(message, output_text_file) {
+  cat(message, "\n")
+  write(message, file = output_text_file, append = TRUE)
+}
+
+# Trust Driver analysis function
+trust_driver_analysis <- function(model_formula, data, output_text_file, csv_file) {
+  tryCatch({
+    # Fit linear regression model
+    model <- lm(model_formula, data = data)
+
+    # Calculate relative importance using the lmg method
+    calc_relaimpo <- calc.relimp(model, type = "lmg", rela = TRUE)
+    # Calculate average importance
+    average_importance <- mean(calc_relaimpo$lmg)
+
+    # Open the output text file in append mode to add this model's output
+    file_conn <- file(output_text_file, open = "a")
+    # Capture output to include in the text file
+    full_output <- capture.output({
+      print("Trust Driver Analysis:\n")
+      print(calc_relaimpo)
+      cat("\nAverage Importance: ", average_importance, "\n")
+    })
+    # Write output to text file
+    writeLines(full_output, file_conn)
+    close(file_conn)
 
-# Define function to process each model
-process_model <- function(model_formula, data, output_text_file, csv_file) {
-  # Fit linear regression model
-  model <- lm(model_formula, data = data)
-
-  # Calculate relative importance using the lmg method
-  calc_relaimpo <- calc.relimp(model, type = "lmg", rela = TRUE)
-  # Calculate average importance
-  average_importance <- mean(calc_relaimpo$lmg)
-
-  # Open the output text file in append mode to add this model's output
-  file_conn <- file(output_text_file, open = "a")
-  # Capture output to include in the text file
-  full_output <- capture.output({
-    print(calc_relaimpo)
-    cat("\nAverage Importance: ", average_importance, "\n")
+    # Create data frame of predictor names and their importance
+    results <- data.frame(Predictor = names(calc_relaimpo$lmg), Importance = calc_relaimpo$lmg)
+
+    # Save results to CSV file
+    write.csv(results, file = csv_file, row.names = FALSE)
+  }, error = function(e) {
+    log_message(paste("Error in trust_driver_analysis:", e$message), output_text_file)
   })
-  # Write output to text file
-  writeLines(full_output, file_conn)
-  close(file_conn)
+}
+
+# Trust Builder Analysis function
+trust_builder_analysis <- function(data, data_headers, output_text_file, csv_file) {
+  tryCatch({
+    # Map the questions to column names
+    question_to_column <- setNames(as.list(data_headers[1, ]), as.character(data_headers[2, ]))
+
+    # Number of important statements to be selected
+    p <- 6
+
+    # Define the list of column names
+    bucket_columns <- c("Stability", "Development", "Relationship", "Benefit", "Vision", "Competence")
+
+    # Select columns based on the predefined list
+    bucket <- data %>% select(all_of(bucket_columns))
+
+    # Select all columns from the consumer dataframe that contain "TB" in their names and assign them to the variable TB
+    TB <- data %>% select(contains("TB"))
+
+    # Initialize a matrix with 37 rows and 6 columns, filled with NA values
+    coef <- matrix(NA, ncol = 6, nrow = 37)
+
+    # Initialize an empty list to store the predictors for each bucket column
+    bucket_predictors <- list()
+
+    # Loop over each of the 6 columns
+    for (i in 1:6) {
+      # Extract the i-th column from 'bucket' as a matrix and assign it to 'y'
+      y <- as.matrix(pull(bucket[, i]))
+
+      # Convert 'TB' dataframe to a matrix and assign it to 'x'
+      x <- as.matrix(TB)
+
+      # Perform best subset regression using 'x' as predictors and 'y' as the response variable
+      fit <- regsubsets(x, y, nbest = 1, nvmax = p)
+
+      # Summarize the regression subsets
+      fit_sum <- summary(fit)
+
+      # Store the coefficients of the best model in the i-th column of 'coef' matrix
+      coef[, i] <- fit_sum$outmat[p, ]
+
+      # Print the predictors used in the best model
+      predictors <- names(which(fit_sum$outmat[p, ] == "*"))
+
+      # Append the predictors to the bucket_predictors list
+      bucket_predictors[[bucket_columns[i]]] <- predictors
+    }
+
+    # Create the desired output format as model
+    model_str <- sapply(names(bucket_predictors), function(col) {
+      paste(col, "~", paste(bucket_predictors[[col]], collapse = "+"))
+    })
+
+    # Prepend the Trust x and y to model_str
+    model_str <- c("Trust ~ Stability + Development + Relationship + Benefit + Vision + Competence", model_str)
+
+    # Fit the model using sem() function
+    fit <- sem(model_str, data = data)
+    fit_summary <- summary(fit, standardized = TRUE, fit.measures = TRUE, rsquare = TRUE)
 
-  # Create data frame of predictor names and their importance
-  results <- data.frame(Predictor = names(calc_relaimpo$lmg), Importance = calc_relaimpo$lmg)
+    # Make it percentages
+    output <- fit_summary$pe[fit_summary$pe$op == "~", c("lhs", "rhs", "std.all")]
 
-  # Save results to CSV file
-  write.csv(results, file = csv_file, row.names = FALSE)
+    # Define the function to convert std.all to percentages
+    convert_to_percentage <- function(df) {
+      df %>%
+        group_by(lhs) %>%
+        mutate(abs_std = abs(std.all),
+              sum_abs_std = sum(abs_std),
+              percent_std = (abs_std / sum_abs_std) * 100) %>%
+        select(-abs_std, -sum_abs_std) %>%
+        ungroup()
+    }
+
+    # Convert the estimates to percentages
+    percentage_output <- convert_to_percentage(output)
+
+    # Extract TB column names
+    tb_column_names <- colnames(TB)
+
+    # Convert std.all to a wide format dataframe
+    percentage_output_wide <- percentage_output %>%
+      pivot_wider(names_from = lhs, values_from = percent_std) %>%
+      rename_with(~ gsub("std.all\\.", "", .), starts_with("std.all"))
+
+    # Create a new dataframe with TB columns and percentage estimates
+    result_df <- data.frame(TB = tb_column_names)
+
+    # Merge the result_df with percentage_estimates_wide
+    result_df <- left_join(result_df, percentage_output_wide, by = c("TB" = "rhs"))
+
+    # Fill NA values with 0 to ensure proper representation
+    result_df[is.na(result_df)] <- 0
+
+    # Add corresponding messages of TB as a new column
+    result_df$Message <- sapply(result_df$TB, function(tb_col) question_to_column[[tb_col]])
+
+    # Convert 'TB' column to a factor with the correct order
+    result_df$TB <- factor(result_df$TB, levels = paste0("TB", 1:37))
+
+    # Exclude 'est' and 'Trust' columns and merge rows by 'TB'
+    result_df <- result_df %>%
+      select(-std.all, -Trust) %>%
+      group_by(TB) %>%
+      summarise(across(everything(), ~ if(is.numeric(.)) sum(., na.rm = TRUE) else first(.))) %>%
+      arrange(TB)
+
+    # Reorder columns to have Message as the second column
+    result_df <- result_df %>%
+      select(TB, Message, everything())
+
+    # Open the output text file in append mode to add this model's output
+    file_conn <- file(output_text_file, open = "a")
+
+    # Capture output to include in the text file
+    full_output <- capture.output({
+      print("Trust Builder Analysis:\n")
+      print("Data header mapping:\n")
+      print(question_to_column)
+      print("Buckets:\n")
+      print(bucket)
+      print("Messages:\n")
+      print(TB)
+      print("Coefficients matrix (coef:\n")
+      print(coef)
+      print("Model:\n")
+      cat(model_str, sep = "\n")
+      print("Fit summary:\n")
+      print(fit_summary)
+      print("Output:\n")
+      print(output)
+      print("Output in percentage (%):\n")
+      print(percentage_output)
+      print("result_df:\n")
+      print(result_df)
+    })
+    # Write output to text file
+    writeLines(full_output, file_conn)
+    close(file_conn)
+
+    # Create data frame of predictor names and their importance
+    results <- data.frame(result_df)
+
+    # Save results to CSV file
+    write.csv(results, file = csv_file, row.names = FALSE)
+  }, error = function(e) {
+    log_message(paste("Error in trust_builder_analysis:", e$message), output_text_file)
+  })
 }
 
 # Read command-line arguments
@@ -40,53 +205,84 @@ csv_output_path_nps <- args[4]
 csv_output_path_loyalty <- args[5]
 csv_output_path_consideration <- args[6]
 csv_output_path_satisfaction <- args[7]
-nps_present <- as.logical(tolower(args[8]))  # Expecting "TRUE" or "FALSE" as the argument
-loyalty_present <- as.logical(tolower(args[9]))
-consideration_present <- as.logical(tolower(args[10]))
-satisfaction_present <- as.logical(tolower(args[11]))
+csv_output_path_trustbuilder <- args[8]
+nps_present <- as.logical(tolower(args[9]))  # Expecting "TRUE" or "FALSE" as the argument
+loyalty_present <- as.logical(tolower(args[10]))
+consideration_present <- as.logical(tolower(args[11]))
+satisfaction_present <- as.logical(tolower(args[12]))
+trustbuilder_present <- as.logical(tolower(args[13]))
+
+# Log the starting of the script
+log_message("Starting Trust Driver and Builder Analysis Script.", output_text_file)
 
-# Load the dataset (CSV or Excel)
-data <- NULL
+########## Trust Driver Analysis ######################
+
+# Load the trust driver dataset (CSV)
+data_driver <- NULL
 if (grepl(".xlsx", input_file)) {
-  data <- read_excel(input_file)
-} else if (grepl(".csv", input_file)) {
-  data <- read_csv(input_file)
+  # Load the Excel file with the fourth row as the header
+  data_driver <- read_excel(input_file, sheet = "Driver", skip = 3)
 }
 
 # Process the Trust model
-process_model(Trust ~ Stability + Development + Relationship + Benefit + Vision + Competence, 
-              data,
-              output_text_file,
-              csv_output_path_trust)
+trust_driver_analysis(
+  Trust ~ Stability + Development + Relationship + Benefit + Vision + Competence, 
+  data_driver,
+  output_text_file,
+  csv_output_path_trust)
 
 # Conditionally process the NPS model
 if (nps_present) {
-  process_model(NPS ~ Stability + Development + Relationship + Benefit + Vision + Competence, 
-                data,
-                output_text_file,
-                csv_output_path_nps)
+  trust_driver_analysis(
+    NPS ~ Stability + Development + Relationship + Benefit + Vision + Competence, 
+    data_driver,
+    output_text_file,
+    csv_output_path_nps)
 }
 
 # Conditionally process the Loyalty model
 if (loyalty_present) {
-  process_model(Loyalty ~ Stability + Development + Relationship + Benefit + Vision + Competence, 
-                data,
-                output_text_file,
-                csv_output_path_loyalty)
+  trust_driver_analysis(
+    Loyalty ~ Stability + Development + Relationship + Benefit + Vision + Competence, 
+    data_driver,
+    output_text_file,
+    csv_output_path_loyalty)
 }
 
 # Conditionally process the Consideration model
 if (consideration_present) {
-  process_model(Consideration ~ Stability + Development + Relationship + Benefit + Vision + Competence, 
-                data,
-                output_text_file,
-                csv_output_path_consideration)
+  trust_driver_analysis(
+    Consideration ~ Stability + Development + Relationship + Benefit + Vision + Competence, 
+    data_driver,
+    output_text_file,
+    csv_output_path_consideration)
 }
 
 # Conditionally process the Satisfaction model
 if (satisfaction_present) {
-  process_model(Satisfaction ~ Stability + Development + Relationship + Benefit + Vision + Competence, 
-                data,
-                output_text_file,
-                csv_output_path_satisfaction)
-}
+  trust_driver_analysis(
+    Satisfaction ~ Stability + Development + Relationship + Benefit + Vision + Competence, 
+    data_driver,
+    output_text_file,
+    csv_output_path_satisfaction)
+}
+
+########## Trust Builder Analysis ######################
+
+if (trustbuilder_present) {
+  data_builder <- NULL
+
+  if (grepl(".xlsx", input_file)) {
+    # Read the 4th and 5th rows as header mapping
+    data_builder_headers <- read_excel(input_file, sheet = "Builder", skip = 3, n_max = 2)  
+    # Read the rest of the data, skipping the first 5 rows (to start from row 6)
+    data_builder_rows <- read_excel(input_file, sheet = "Builder", skip = 5)
+  }
+
+  # Process the Builder model
+  trust_builder_analysis(data_builder_rows, data_builder_headers, output_text_file, csv_output_path_trustbuilder)
+
+}
+
+# Log the ending of the script
+log_message("Trust Driver and Builder Analysis Script Completed.", output_text_file)

requirements.txt

@@ -5,3 +5,7 @@ gradio
 Pillow  
 openpyxl  
 numpy
+python-dotenv
+openai
+langchain
+langchain-openai