PCA, Clustering & Hypothesis testing - NLB Youngs
##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union# Load the CSV file
NLB_data <- read.csv("~/Desktop/NLB/nlb_data.csv", sep = ";")
# Remove columns 2 to 8 and 101 to 116
NLB_data <- NLB_data %>%
select(-c(2:8, 101:116))
#Remove first row
NLB_data <- NLB_data[-1, ]
head(NLB_data)## status Q1 Q3a Q3b Q3c Q3d Q4a Q4b Q4c Q4d Q5 Q5_4_text Q6a Q6b Q6c Q6d Q6e
## 2 6 7 3 2 5 2 4 3 2 1 2 -2 3 2 1 1 3
## 3 6 9 2 5 1 1 2 1 1 1 1 -2 4 2 1 4 4
## 4 6 9 2 4 4 1 2 2 1 1 2 -2 3 3 1 4 4
## 5 6 7 3 5 5 1 2 2 1 1 1 -2 3 3 2 4 4
## 6 6 7 2 5 5 1 2 1 1 1 1 -2 4 3 3 4 4
## 7 6 9 3 3 1 2 4 3 2 1 2 -2 3 4 1 1 3
## Q6f Q6g Q7 Q8a Q9a Q9b Q9c Q9d Q9e Q9f Q9g Q9h Q31_2a Q31_2b Q31_2c Q31_2d
## 2 3 3 1 5 6 3 5 6 4 7 6 7 7 7 6 6
## 3 4 3 1 7 7 6 3 6 3 7 1 6 6 7 5 7
## 4 4 2 1 7 6 2 3 6 3 7 1 7 7 6 6 6
## 5 3 4 1 7 6 5 6 7 4 7 1 7 7 7 7 7
## 6 4 4 2 -2 7 4 7 4 4 7 4 7 7 7 7 7
## 7 3 4 2 -2 7 3 5 7 7 5 6 5 5 3 7 7
## Q31_2e Q31_2f Q10a Q10b Q10c Q10d Q10e Q11a Q11b Q11c Q11d Q11e Q12a Q12b
## 2 3 6 6 6 6 7 6 5 6 6 7 6 6 7
## 3 7 6 3 7 7 3 6 7 7 7 6 6 6 6
## 4 4 5 6 6 4 6 6 4 7 7 7 7 7 7
## 5 7 7 6 5 4 6 6 3 6 6 7 7 6 7
## 6 7 7 7 4 4 4 4 7 7 7 7 7 7 7
## 7 4 4 7 6 6 5 5 6 7 7 6 6 6 7
## Q12c Q12d Q12e Q13a Q13b Q13c Q13d Q13e Q14a Q14b Q14c Q14d Q14e Q15a Q15b
## 2 7 7 6 5 6 6 7 7 7 6 5 5 6 7 5
## 3 6 6 6 5 7 7 7 7 7 1 1 1 1 2 7
## 4 7 7 7 4 7 7 7 7 6 5 5 5 5 5 7
## 5 7 6 6 1 6 6 7 7 7 4 4 4 4 6 5
## 6 7 7 4 7 7 7 7 7 4 7 7 7 7 4 7
## 7 7 6 6 2 7 7 6 6 5 7 7 7 7 7 6
## Q15c Q15d Q15e Q16a Q16b Q17 Q17_5_text Q18a Q18b Q18c Q18d Q18e Q18e_text
## 2 5 3 3 5 2 2 -2 1 0 1 0 0 -2
## 3 4 1 6 4 4 2 -2 1 1 1 0 0 -2
## 4 7 7 7 4 4 2 -2 1 1 1 1 0 -2
## 5 1 5 5 7 7 2 -2 1 1 1 1 0 -2
## 6 7 7 7 4 4 2 -2 1 1 1 1 0 -2
## 7 5 3 5 3 5 2 -2 1 0 0 0 0 -2
## Q19a Q19b Q19c Q19d Q20 Q21a Q21b Q21c Q21d Q21e Q22 Q23 Q24 Q25 Q25_5_text
## 2 3 5 3 3 2 0 0 1 0 0 2 1 6 1 -2
## 3 5 5 6 7 3 0 0 1 0 0 2 2 5 1 -2
## 4 3 3 3 3 4 -2 -2 -2 -2 -2 2 2 4 1 -2
## 5 5 5 5 6 4 -2 -2 -2 -2 -2 1 2 4 1 -2
## 6 7 7 7 7 1 -1 -1 -1 -1 -1 1 2 4 1 -2
## 7 5 4 6 6 2 1 0 0 0 0 4 2 6 1 -2
## Q26 Q27 Q27_7_text
## 2 2 2 -2
## 3 2 2 -2
## 4 3 7 Unicredit
## 5 2 1 -2
## 6 4 1 -2
## 7 2 1 -21 Dataset overview
- Unit of observation: Each row represents a single respondent from the questionnaire, which was completed by young people aged 18 to 27.
- Sample size: The dataset includes responses from a total of 440 young individuals.
1.1 Variables description
Status: status of the questionnaire (6 = valid, 5 = invalid)
Q1a: Age
Q3: How many times a month do you use the following payment methods on average? (1 = never, 2 = 1-3x month, 3 = 1x week, 4 = many times x week, 5 = everyday)
- Q3a: Cash
- Q3b: Debit or Credit (physical) cards
- Q3c: Phone
- Q3d: Other (PayPal, Stripeβ¦)
Q4: How often do you use cash payment for the following purchases? (1 = never, 2 = 1-3x month, 3 = 1x week, 4 = many times x week, 5 = everyday)
- Q4a: Purchases up to β¬10
- Q4b: Purchases between β¬11 and β¬99
- Q4c: Purchases between β¬100 and β¬1000
- Q4d: Purchases over β¬1000
Q5: How do you usually respond if a merchant does not accept digital payments?
- 1: I prefer to go elsewhere and pay with a digital
payment method.
- 2: I pay in the form available, even if it means
withdrawing cash from an ATM.
- 3: I have not encountered such a situation
yet.
- 4: Other (please specify).
Q6: Which of the following income sources do you have, and how do you receive them? (1 = fully cash, 2 = half cash/half digital, 3 = fully digital, 4 = donβt get income from this source, 5 = donβt want to answer)
- Q6a: Salary or earnings from student work
- Q6b: Pocket Money (from family members)
- Q6c: Gifts (for birthdays, holidays, etc.)
- Q6d: Unreported or occasional work (childcare,
tutoring, etc.)
- Q6e: Government and/or social benefits
- Q6f: Government and other forms of scholarships
(e.g., Zois, corporate)
- Q6g: Returns from investments (stocks, bonds, cryptocurrencies, etc.)
Q7: Do you save money? (This question does NOT include savings from parents or family members.)
- 1: Yes
- 2: No
Q8: In what form do you save money? (Digital vs.Β cash savings) (1 = fully cashβ¦7 = fully digital)
Q9: Various attitudes towards digital and cash payments. (1 = strongly disagreeβ¦7 = strongly agree)
- Q9a: I usually spend money in the form in which I
received it.
- Q9b: I feel concerned about the security of my
personal information when using digital payment methods.
- Q9c: I find digital payments less secure than cash
payments.
- Q9d: I have more confidence in digital payment
methods if they offer features like two-factor authentication.
- Q9e: I feel safe when I carry cash with me.
- Q9f: I prefer to use digital payments because they
are more convenient and save time.
- Q9g: I prefer to use cash to avoid
overspending.
- Q9h: I use cash only when digital payments are not possible.
Q31_2: Please rate the importance of each of the following factors that influence your choice of payment method. (1 = not important at allβ¦7 = extremely important)
- Q31_2a: Ease of use
- Q31_2b: Speed of transaction
- Q31_2c: Ability to use in stores
- Q31_2d: Security of the payment method
- Q31_2e: Features for tracking and budgeting
Q10: How safe do you think the following payment methods are? (1 = strongly not safeβ¦7 = strongly safe)
- Q10a: Cash
- Q10b: (Physical) Debit Card
- Q10c: (Physical) Credit Card
- Q10d: Paying with your phone (Flik, Apple
Payβ¦)
- Q10e: Neobanks (Revolut, N26β¦)
Q11: How easy do you find the following payment methods to use? (1 = strongly not easyβ¦7 = strongly easy)
- Q11a: Cash
- Q11b: (Physical) Debit Card
- Q11c: (Physical) Credit Card
- Q11d: Paying with your phone (Flik, Apple
Payβ¦)
- Q11e: Neobanks (Revolut, N26β¦)
Q12: How accepted do you think the following payment methods are in stores in your environment? (1 = strongly not acceptedβ¦7 = strongly accepted)
- Q12a: Cash
- Q12b: (Physical) Debit Card
- Q12c: (Physical) Credit Card
- Q12d: Paying with your phone (Flik, Apple
Payβ¦)
- Q12e: Neobanks (Revolut, N26β¦)
Q13: How fast do you consider the following payment methods? (1 = strongly not fastβ¦7 = strongly fast)
- Q13a: Cash
- Q13b: (Physical) Debit Card
- Q13c: (Physical) Credit Card
- Q13d: Paying with your phone (Flik, Apple
Payβ¦)
- Q13e: Neobanks (Revolut, N26β¦)
Q14: How do you consider the following payment methods from a privacy perspective? (1 = strongly not privateβ¦7 = strongly private)
- Q14a: Cash
- Q14b: (Physical) Debit Card
- Q14c: (Physical) Credit Card
- Q14d: Paying with your phone (Flik, Apple
Payβ¦)
- Q14e: Neobanks (Revolut, N26β¦)
Q15: How do you consider the following payment methods from a saving check perspective? (1 = I totally donβt have controlβ¦7 = I have total control)
- Q15a: Cash
- Q15b: (Physical) Debit Card
- Q15c: (Physical) Credit Card
- Q15d: Paying with your phone (Flik, Apple
Payβ¦)
- Q15e: Neobanks (Revolut, N26β¦)
Q16: Social influence on payment method choice. (1 = strongly disagreeβ¦7 = strongly agree)
- Q16a: I choose the payment methods my friends
choose.
- Q16b: I choose the payment methods my family members choose.
Q17: How do you most often share expenses among friends?
- 1: With cash
- 2: Through mobile applications (Flik, Revolut,
PayPalβ¦)
- 3: By bank transfer
- 4: I donβt share expenses among friends
- 5: Other (please specify)
Q18: Reasons for preferring digital payments. (0 = Yes, 1 = No)
- Q18a: I can quote the exact sum.
- Q18b: To avoid paying with cash.
- Q18c: Because the process is quick and
convenient.
- Q18d: Because I have my transactions recorded and
it is easier to manage finances.
- Q18e: Other (please specify).
Q19: Concerns about digital payment security. (1 = strongly not concerns, 7 = strongly concerns)
- Q19a: Fraud (e.g., stealing money)
- Q19b: Disclosure of Personal Information
- Q19c: Identity theft
- Q19d: Loss of access due to a hacker attack
Q20: Experience with online fraud.
- 1: Yes, it happened to me.
- 2: Yes, I know people who have had this
happen.
- 3: Yes, it has happened to me and others I
know.
- 4: Iβve never encountered such a situation.
Q21: How did this affect your behavior in further payment habits? (0 = Yes, 1 = No)
- Q21a: I use cash more often in unfamiliar or
suspicious situations (e.g., when traveling).
- Q21b: I use digital payment methods (e.g., virtual
or disposable cards facilitated by neobanks) more often in unfamiliar or
suspicious situations.
- Q21c: I am more cautious with online
payments.
- Q21d: I switched to more secure payment options
(e.g., mobile wallets with authentication).
- Q21e: My behavior hasnβt changed.
Q22: If we had the opportunity, would you switch to digital payments entirely?
- 1: Yes, immediately.
- 2: I would consider it, but I wouldnβt want to give
up cash completely.
- 3: No, I prefer to use cash.
- 4: I donβt know.
Q23: What is your gender?
- 1: Man.
- 2: Woman.
- 3: Another.
- 4: I donβt want to answer.
Q24: What is your highest level of educational attainment?
- 1: Unfinished primary school.
- 2: Completed primary school.
- 3: Completed lower or secondary vocational
education.
- 4: Completed secondary professional or general
education.
- 5: Completed tertiary professional or tertiary professional education (including 1st Bologna level).
- 6: Completed higher university education (including 2nd Bologna level).
- 7: Completed specialization, scientific masterβs degree, PhD.
Q25: What is your current status?
- 1: Student.
- 2: Employed.
- 3: Self-employed.
- 4: Unemployed.
- 5: Other (please specify).
Q26: What is your current net monthly income?
- 1: 0-200 EUR.
- 2: 201-500 EUR.
- 3: 501-800 EUR.
- 4: 801-1300 EUR.
- 5: More than 1300 EUR.
Q27: Which bank do you currently use as your primary bank?
- 1: NLB.
- 2: OTP.
- 3: Intesa Sanpaolo.
- 4: Sparkasse.
- 5: Addiko Bank.
- 6: Workersβ Savings Bank.
- 7: Other (please specify).
2 Data manipulation
#Remove all questionnaires with non valid status
library(dplyr)
NLB_data <- NLB_data %>%
filter(!status == 5)
#Remove status column
NLB_data <- NLB_data[ , -1]
#Remove all under 18/over 27
library(dplyr)
NLB_data <- NLB_data %>%
filter(!Q1 %in% c(1, 12))
# Rename columns
colnames(NLB_data) <- c("Age", "Cash_Use", "Card_Use", "Phone_Use", "OtherPay_Use",
"Cash_Up10", "Cash_11_99", "Cash_100_1000", "Cash_Over1000",
"NoDigital_Response", "NoDigital_Response_Text", "Income_StudJobSalary",
"Income_PocketMoney", "Income_Gifts", "Income_Occasional", "Income_Subsidy",
"Income_Scholarship", "Income_Investments", "Save_Money",
"Save_Form", "Spend_SameForm", "Concern_Security", "LessSecure_Digital",
"Trust_2FA", "Safe_CashCarry", "Prefer_Digital_Convenience",
"Prefer_Cash_Control", "Use_Cash_IfNoDigital", "Importance_Ease", "Importance_Speed",
"Importance_Availability", "Importance_Security", "Importance_TrackingBudgeting",
"Importance_Privacy", "Safe_Cash", "Safe_DebitCard", "Safe_CreditCard", "Safe_PhonePay",
"Safe_Neobank", "Easy_Cash", "Easy_DebitCard", "Easy_CreditCard", "Easy_PhonePay",
"Easy_Neobank", "Accept_Cash", "Accept_DebitCard", "Accept_CreditCard",
"Accept_PhonePay", "Accept_Neobank", "Fast_Cash", "Fast_DebitCard",
"Fast_CreditCard", "Fast_PhonePay", "Fast_Neobank", "Private_Cash",
"Private_DebitCard", "Private_CreditCard", "Private_PhonePay",
"Private_Neobank", "Control_Cash", "Control_DebitCard", "Control_CreditCard",
"Control_PhonePay", "Control_Neobank", "Social_Friends", "Social_Family",
"Expense_Sharing", "Expense_Sharing_Text", "Reason_ExactSum", "Reason_NoCash",
"Reason_Convenient", "Reason_TrackFinances", "Reason_Other", "Reason_Other_Text",
"Concern_Fraud", "Concern_PersonalInfo", "Concern_IDTheft",
"Concern_Hacker", "OnlineFraud_Exp", "Behavior_MoreCash",
"Behavior_SecureDigital", "Behavior_Cautious", "Behavior_SecureOption",
"Behavior_NoChange", "Switch_Digital", "Gender", "Education",
"Status_Employment", "Status_Employment_Text", "Income_Level", "Primary_Bank", "Primary_Bank_Text")2.1 Factoring
# Q1
NLB_data$AgeF <- factor(NLB_data$Age,
levels = c(2:11),
labels = c(18:27))
#Q3a
NLB_data$Cash_UseF <- factor(NLB_data$Cash_Use,
levels = c(1, 2, 3, 4, 5),
labels = c("Never", "1-3 monthly", "1 per week", "Several times a week", "Daily"))
#Q3b
NLB_data$Card_UseF <- factor(NLB_data$Card_Use,
levels = c(1, 2, 3, 4, 5),
labels = c("Never", "1-3 monthly", "1 per week", "Several times a week", "Daily"))
#Q3c
NLB_data$Phone_UseF <- factor(NLB_data$Phone_Use,
levels = c(1, 2, 3, 4, 5),
labels = c("Never", "1-3 monthly", "1 per week", "Several times a week", "Daily"))
#Q3d
NLB_data$OtherPay_UseF <- factor(NLB_data$OtherPay_Use,
levels = c(1, 2, 3, 4, 5),
labels = c("Never", "1-3 monthly", "1 per week", "Several times a week", "Daily"))
#Q4a
NLB_data$Cash_Up10F <- factor(NLB_data$Cash_Up10,
levels = c(1, 2, 3, 4, 5),
labels = c("Never", "Less than half", "Half", "More than half", "Always"))
#Q4b
NLB_data$Cash_11_99F <- factor(NLB_data$Cash_11_99,
levels = c(1, 2, 3, 4, 5),
labels = c("Never", "Less than half", "Half", "More than half", "Always"))
#Q4c
NLB_data$Cash_100_1000F <- factor(NLB_data$Cash_100_1000,
levels = c(1, 2, 3, 4, 5),
labels = c("Never", "Less than half", "Half", "More than half", "Always"))
#Q4d
NLB_data$Cash_Over1000F <- factor(NLB_data$Cash_Over1000,
levels = c(1, 2, 3, 4, 5),
labels = c("Never", "Less than half", "Half", "More than half", "Always"))
# Q5
NLB_data$NoDigital_ResponseF <- factor(NLB_data$NoDigital_Response,
levels = c(1, 2, 3, 4),
labels = c("Pay digital elsewhere", "Pay as available", "Never occurred", "Other"))
#Q6a
NLB_data$Income_StudJobSalaryF <- factor(NLB_data$Income_StudJobSalary,
levels = c(1, 2, 3, 4, 5),
labels = c("Cash", "Cash&Digitally", "Digitally", "Not using", "Don't want to answer"))
#Q6b
NLB_data$Income_PocketMoneyF <- factor(NLB_data$Income_PocketMoney,
levels = c(1, 2, 3, 4, 5),
labels = c("Cash", "Cash&Digitally", "Digitally", "Not using", "Don't want to answer"))
#Q6c
NLB_data$Income_GiftsF <- factor(NLB_data$Income_Gifts,
levels = c(1, 2, 3, 4, 5),
labels = c("Cash", "Cash&Digitally", "Digitally", "Not using", "Don't want to answer"))
#Q6d
NLB_data$Income_OccasionalF <- factor(NLB_data$Income_Occasional,
levels = c(1, 2, 3, 4, 5),
labels = c("Cash", "Cash&Digitally", "Digitally", "Not using", "Don't want to answer"))
#Q6e
NLB_data$Income_SubsidyF <- factor(NLB_data$Income_Subsidy,
levels = c(1, 2, 3, 4, 5),
labels = c("Cash", "Cash&Digitally", "Digitally", "Not using", "Don't want to answer"))
#Q6f
NLB_data$Income_ScholarshipF <- factor(NLB_data$Income_Scholarship,
levels = c(1, 2, 3, 4, 5),
labels = c("Cash", "Cash&Digitally", "Digitally", "Not using", "Don't want to answer"))
# Q7
NLB_data$Income_InvestmentsF <- factor(NLB_data$Income_Investments,
levels = c(1, 2, 3, 4, 5),
labels = c("Cash", "Cash&Digitally", "Digitally", "Not using", "Don't want to answer"))
# Q17
NLB_data$Expense_SharingF <- factor(NLB_data$Expense_Sharing,
levels = c(1, 2, 3, 4, 5),
labels = c("Cash", "Mobile apps", "Bank transfer", "Don't share", "Other"))
# Q20
NLB_data$OnlineFraud_ExpF <- factor(NLB_data$OnlineFraud_Exp,
levels = c(1, 2, 3, 4),
labels = c("Yes - me", "Yes - others", "Yes - both", "No"))
# Q22
NLB_data$Switch_DigitalF <- factor(NLB_data$Switch_Digital,
levels = c(1, 2, 3, 4),
labels = c("Fully digital", "Balance digital-cash", "Cash", "Don't know"))
# Q23
NLB_data$GenderF <- factor(NLB_data$Gender,
levels = c(1, 2, 3, 4),
labels = c("Man", "Woman", "Other", "Don't want to answer"))
# Q24
NLB_data$EducationF <- factor(NLB_data$Education,
levels = c(1, 2, 3, 4, 5, 6, 7),
labels = c("Unfinished primary", "Primary school", "Vocational education", "High School", "Bachelor Degree", "Master Degree", "PhD" ))
# Q25
NLB_data$Status_EmploymentF <- factor(NLB_data$Status_Employment,
levels = c(1, 2, 3, 4, 5),
labels = c("Student", "Employed", "Self-employed", "Unemployed", "Other"))
# Q26
NLB_data$Income_LevelF <- factor(NLB_data$Income_Level,
levels = c(1, 2, 3, 4, 5),
labels = c("0-200 EUR", "201-500 EUR", "501-800 EUR", "801-1300 EUR", "Above 1300 EUR"))
# Q27
NLB_data$Primary_BankF <- factor(NLB_data$Primary_Bank,
levels = c(1, 2, 3, 4, 5, 6, 7),
labels = c("NLB", "OTP", "Intesa Sanpaolo", "Sparkasse", "Addiko Bank", "Delavska Hranilnica", "Other"))## AgeF Cash_UseF Card_UseF Phone_UseF OtherPay_UseF
## 1 23 1 per week 1-3 monthly Daily 1-3 monthly
## 2 25 1-3 monthly Daily Never Never
## 3 25 1-3 monthly Several times a week Several times a week Never
## 4 23 1 per week Daily Daily Never
## 5 23 1-3 monthly Daily Daily Never
## 6 25 1 per week 1 per week Never 1-3 monthly
## Cash_Up10F Cash_11_99F Cash_100_1000F Cash_Over1000F
## 1 More than half Half Less than half Never
## 2 Less than half Never Never Never
## 3 Less than half Less than half Never Never
## 4 Less than half Less than half Never Never
## 5 Less than half Never Never Never
## 6 More than half Half Less than half Never
## NoDigital_ResponseF Income_StudJobSalaryF Income_PocketMoneyF
## 1 Pay as available Digitally Cash&Digitally
## 2 Pay digital elsewhere Not using Cash&Digitally
## 3 Pay as available Digitally Digitally
## 4 Pay digital elsewhere Digitally Digitally
## 5 Pay digital elsewhere Not using Digitally
## 6 Pay as available Digitally Not using
## Income_GiftsF Income_OccasionalF Income_SubsidyF Income_ScholarshipF
## 1 Cash Cash Digitally Digitally
## 2 Cash Not using Not using Not using
## 3 Cash Not using Not using Not using
## 4 Cash&Digitally Not using Not using Digitally
## 5 Digitally Not using Not using Not using
## 6 Cash Cash Digitally Digitally
## Income_InvestmentsF Expense_SharingF OnlineFraud_ExpF Switch_DigitalF
## 1 Digitally Mobile apps Yes - others Balance digital-cash
## 2 Digitally Mobile apps Yes - both Balance digital-cash
## 3 Cash&Digitally Mobile apps No Balance digital-cash
## 4 Not using Mobile apps No Fully digital
## 5 Not using Mobile apps Yes - me Fully digital
## 6 Not using Mobile apps Yes - others Don't know
## GenderF EducationF Status_EmploymentF Income_LevelF Primary_BankF
## 1 Man Master Degree Student 201-500 EUR OTP
## 2 Woman Bachelor Degree Student 201-500 EUR OTP
## 3 Woman High School Student 501-800 EUR Other
## 4 Woman High School Student 201-500 EUR NLB
## 5 Woman High School Student 801-1300 EUR NLB
## 6 Woman Master Degree Student 201-500 EUR NLB3 Descriptive statistics
3.1 Numerical data
library(dplyr)
# Convert character columns in specified ranges to numeric
NLB_data <- NLB_data %>%
mutate(across(c(20:66, 75:78),
~ ifelse(!is.na(.), as.numeric(.), .)))# Select specific numerical data for summary
NLBdata_Likert <- NLB_data[, c("Save_Form", "Spend_SameForm", "Concern_Security", "LessSecure_Digital",
"Trust_2FA", "Safe_CashCarry", "Prefer_Digital_Convenience",
"Prefer_Cash_Control", "Use_Cash_IfNoDigital", "Importance_Ease",
"Importance_Speed", "Importance_Availability", "Importance_Security",
"Importance_TrackingBudgeting", "Importance_Privacy", "Safe_Cash",
"Safe_DebitCard", "Safe_CreditCard", "Safe_PhonePay", "Safe_Neobank",
"Easy_Cash", "Easy_DebitCard", "Easy_CreditCard", "Easy_PhonePay",
"Easy_Neobank", "Accept_Cash", "Accept_DebitCard", "Accept_CreditCard",
"Accept_PhonePay", "Accept_Neobank", "Fast_Cash", "Fast_DebitCard",
"Fast_CreditCard", "Fast_PhonePay", "Fast_Neobank", "Private_Cash",
"Private_DebitCard", "Private_CreditCard", "Private_PhonePay",
"Private_Neobank", "Control_Cash", "Control_DebitCard", "Control_CreditCard",
"Control_PhonePay", "Control_Neobank", "Social_Friends", "Social_Family",
"Concern_Fraud", "Concern_PersonalInfo", "Concern_IDTheft", "Concern_Hacker")]
summary(NLBdata_Likert)## Save_Form Spend_SameForm Concern_Security LessSecure_Digital
## Min. :-2.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.: 1.000 1st Qu.:4.000 1st Qu.:1.000 1st Qu.:2.000
## Median : 4.000 Median :6.000 Median :3.000 Median :4.000
## Mean : 3.395 Mean :5.316 Mean :3.115 Mean :3.655
## 3rd Qu.: 7.000 3rd Qu.:7.000 3rd Qu.:4.000 3rd Qu.:5.000
## Max. : 7.000 Max. :7.000 Max. :7.000 Max. :7.000
## Trust_2FA Safe_CashCarry Prefer_Digital_Convenience Prefer_Cash_Control
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:6.000 1st Qu.:1.000
## Median :6.000 Median :4.000 Median :7.000 Median :4.000
## Mean :5.359 Mean :4.135 Mean :6.046 Mean :3.441
## 3rd Qu.:7.000 3rd Qu.:5.000 3rd Qu.:7.000 3rd Qu.:5.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## Use_Cash_IfNoDigital Importance_Ease Importance_Speed Importance_Availability
## Min. :1.000 Min. :1.000 Min. :1 Min. :1.000
## 1st Qu.:5.000 1st Qu.:6.000 1st Qu.:6 1st Qu.:6.000
## Median :6.000 Median :7.000 Median :7 Median :7.000
## Mean :5.704 Mean :6.105 Mean :6 Mean :6.168
## 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7 Max. :7.000
## Importance_Security Importance_TrackingBudgeting Importance_Privacy
## Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:5.000 1st Qu.:4.000 1st Qu.:4.000
## Median :7.000 Median :5.000 Median :6.000
## Mean :6.076 Mean :5.201 Mean :5.658
## 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.000
## Safe_Cash Safe_DebitCard Safe_CreditCard Safe_PhonePay
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :6.000 Median :5.000 Median :5.000 Median :5.000
## Mean :5.503 Mean :5.138 Mean :4.947 Mean :5.174
## 3rd Qu.:7.000 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## Safe_Neobank Easy_Cash Easy_DebitCard Easy_CreditCard Easy_PhonePay
## Min. :1.000 Min. :1.000 Min. :3.000 Min. :3.00 Min. :1.00
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:5.000 1st Qu.:5.00 1st Qu.:7.00
## Median :5.000 Median :5.000 Median :7.000 Median :7.00 Median :7.00
## Mean :4.826 Mean :5.102 Mean :6.148 Mean :6.02 Mean :6.48
## 3rd Qu.:6.000 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.00 3rd Qu.:7.00
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.00 Max. :7.00
## Easy_Neobank Accept_Cash Accept_DebitCard Accept_CreditCard
## Min. :1.000 Min. :1.000 Min. :3.000 Min. :2.000
## 1st Qu.:4.000 1st Qu.:6.000 1st Qu.:6.000 1st Qu.:6.000
## Median :6.000 Median :7.000 Median :7.000 Median :7.000
## Mean :5.592 Mean :6.227 Mean :6.484 Mean :6.336
## 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## Accept_PhonePay Accept_Neobank Fast_Cash Fast_DebitCard
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:5.00 1st Qu.:4.000 1st Qu.:2.750 1st Qu.:5.000
## Median :7.00 Median :5.000 Median :4.000 Median :7.000
## Mean :6.03 Mean :5.076 Mean :4.076 Mean :6.174
## 3rd Qu.:7.00 3rd Qu.:7.000 3rd Qu.:6.000 3rd Qu.:7.000
## Max. :7.00 Max. :7.000 Max. :7.000 Max. :7.000
## Fast_CreditCard Fast_PhonePay Fast_Neobank Private_Cash
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:5.000 1st Qu.:7.000 1st Qu.:4.000 1st Qu.:4.000
## Median :7.000 Median :7.000 Median :6.000 Median :7.000
## Mean :6.122 Mean :6.526 Mean :5.684 Mean :5.681
## 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## Private_DebitCard Private_CreditCard Private_PhonePay Private_Neobank
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:4.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :4.457 Mean :4.457 Mean :4.395 Mean :4.484
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## Control_Cash Control_DebitCard Control_CreditCard Control_PhonePay
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :5.000 Median :5.000 Median :5.000
## Mean :4.852 Mean :5.155 Mean :5.007 Mean :5.161
## 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## Control_Neobank Social_Friends Social_Family Concern_Fraud
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :-1.000
## 1st Qu.:4.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.: 4.000
## Median :5.000 Median :3.000 Median :4.000 Median : 5.000
## Mean :5.115 Mean :3.125 Mean :3.339 Mean : 4.507
## 3rd Qu.:7.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.: 6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. : 7.000
## Concern_PersonalInfo Concern_IDTheft Concern_Hacker
## Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.: 4.000 1st Qu.: 3.000 1st Qu.: 4.000
## Median : 4.000 Median : 4.000 Median : 5.000
## Mean : 4.457 Mean : 4.273 Mean : 4.849
## 3rd Qu.: 6.000 3rd Qu.: 6.000 3rd Qu.: 6.000
## Max. : 7.000 Max. : 7.000 Max. : 7.0003.1.1 Summary of key insights - Numerical data
3.1.1.1 Trust and Security Preferences
Trust in Security Measures: The data reveals significant variation in usersβ trust in different security measures, particularly 2FA (Two-Factor Authentication). The median value for
Trust_2FAis 6, indicating that most users place high trust in two-factor authentication systems.Security Concerns: Users show notable concern regarding personal information security, with
Concern_PersonalInfohaving a mean value of 4.457. This suggests that there is a moderate level of concern regarding the security of personal data. Interestingly, concerns related to fraud (Concern_Fraud) and digital theft (Concern_IDTheft) also follow similar patterns, with medians around 5, highlighting a general apprehension about fraud and security breaches in the digital space.
3.1.1.2 Preferences for Cash and Digital Payments
Preference for Digital Convenience: The
Prefer_Digital_Conveniencevariable shows that most participants favor digital payment solutions for their ease of use, with a high median score of 7. This aligns with the growing trend toward digitalization of financial transactions.Preference for Cash Control: In contrast, a significant portion of users still values cash control, with the
Prefer_Cash_Controlvariable showing a mean of 3.441 and a median of 4, pointing towards a preference for having tangible control over their spending.Willingness to Use Cash in the Absence of Digital Payments: Interestingly, the
Use_Cash_IfNoDigitalvariable has a mean of 5.704, indicating that while most users prefer digital methods, they are still willing to revert to cash if necessary.
3.1.1.3 Digital Payment Security
Security of Digital Payment Methods: There is a noticeable difference in the perception of security between various digital payment methods.
Safe_CashCarryhas a median of 4, while methods likeSafe_DebitCardandSafe_CreditCardhave medians of 5, suggesting that while users feel relatively secure with traditional methods, there is more trust in newer forms of payment like digital wallets.Cash vs.Β Digital Payment Security: Itβs clear from the data that traditional cash-based payments (e.g.,
Safe_CashCarry) are generally perceived as safer, but usersβ trust in digital solutions like debit and credit cards is growing. However, thereβs still a gap when it comes to mobile payment methods, with mobile solutions such asSafe_PhonePayscoring lower in perceived safety compared to physical cards.
3.1.1.4 Payment Speed
- Preference for Fast Payments: In terms of speed,
digital payments are preferred for their efficiency.
Fast_Cashhas a mean of 4.076, suggesting a moderate preference for faster cash-based transactions. Conversely, digital payments such asFast_CreditCardandFast_PhonePayare rated highly, with means over 6. This reflects the growing demand for quick transactions in todayβs fast-paced world.
3.1.1.6 Conclusion
In summary, the findings highlight a clear dichotomy between the traditional and digital worlds in usersβ preferences for financial transactions. While thereβs a strong trust in traditional security measures and cash-based transactions, there is also a growing acceptance of digital solutions, albeit with a more cautious approach toward security. Usersβ social circles influence their preferences and decisions, underscoring the importance of peer-driven advice in financial matters. The research points toward a future where the convergence of digital convenience and robust security measures will drive the evolution of financial decision-making.
3.2 Categorical data
# Select specific categorical data for summary
categorical_columns <- c("AgeF", "Cash_UseF", "Card_UseF", "Phone_UseF", "OtherPay_UseF",
"Cash_Up10F", "Cash_11_99F", "Cash_100_1000F", "Cash_Over1000F",
"NoDigital_ResponseF", "Income_StudJobSalaryF", "Income_PocketMoneyF",
"Income_GiftsF", "Income_OccasionalF", "Income_SubsidyF", "Income_ScholarshipF",
"Income_InvestmentsF", "Expense_SharingF", "OnlineFraud_ExpF", "Switch_DigitalF",
"GenderF", "EducationF", "Status_EmploymentF", "Income_LevelF", "Primary_BankF")
# Use summary to view frequency counts for categorical columns
summary(NLB_data[, categorical_columns])## AgeF Cash_UseF Card_UseF
## 23 :64 Never : 23 Never : 22
## 22 :44 1-3 monthly :133 1-3 monthly : 62
## 25 :43 1 per week : 66 1 per week : 27
## 20 :40 Several times a week: 65 Several times a week:125
## 24 :30 Daily : 17 Daily : 68
## 21 :23
## (Other):60
## Phone_UseF OtherPay_UseF Cash_Up10F
## Never : 81 Never :180 Never : 34
## 1-3 monthly : 24 1-3 monthly : 96 Less than half:169
## 1 per week : 20 1 per week : 15 Half : 55
## Several times a week: 71 Several times a week: 12 More than half: 31
## Daily :108 Daily : 1 Always : 15
##
##
## Cash_11_99F Cash_100_1000F Cash_Over1000F
## Never : 88 Never :197 Never :252
## Less than half:153 Less than half: 68 Less than half: 21
## Half : 37 Half : 16 Half : 8
## More than half: 18 More than half: 17 More than half: 8
## Always : 8 Always : 6 Always : 15
##
##
## NoDigital_ResponseF Income_StudJobSalaryF
## Pay digital elsewhere: 52 Cash : 10
## Pay as available :195 Cash&Digitally : 43
## Never occurred : 42 Digitally :217
## Other : 15 Not using : 34
## Don't want to answer: 0
##
##
## Income_PocketMoneyF Income_GiftsF
## Cash : 76 Cash :251
## Cash&Digitally : 63 Cash&Digitally : 36
## Digitally : 57 Digitally : 3
## Not using :107 Not using : 13
## Don't want to answer: 1 Don't want to answer: 1
##
##
## Income_OccasionalF Income_SubsidyF
## Cash : 88 Cash : 1
## Cash&Digitally : 17 Cash&Digitally : 1
## Digitally : 3 Digitally : 62
## Not using :187 Not using :235
## Don't want to answer: 9 Don't want to answer: 5
##
##
## Income_ScholarshipF Income_InvestmentsF
## Cash : 0 Cash : 2
## Cash&Digitally : 2 Cash&Digitally : 2
## Digitally :137 Digitally : 93
## Not using :164 Not using :203
## Don't want to answer: 1 Don't want to answer: 4
##
##
## Expense_SharingF OnlineFraud_ExpF Switch_DigitalF
## Cash : 28 Yes - me : 22 Fully digital : 78
## Mobile apps :250 Yes - others: 96 Balance digital-cash:171
## Bank transfer: 14 Yes - both : 11 Cash : 44
## Don't share : 8 No :172 Don't know : 10
## Other : 4 NA's : 3 NA's : 1
##
##
## GenderF EducationF Status_EmploymentF
## Man :120 Unfinished primary : 0 Student :236
## Woman :182 Primary school : 8 Employed : 49
## Other : 1 Vocational education: 2 Self-employed: 7
## Don't want to answer: 1 High School :132 Unemployed : 3
## Bachelor Degree : 99 Other : 9
## Master Degree : 61
## PhD : 2
## Income_LevelF Primary_BankF
## 0-200 EUR :48 NLB :121
## 201-500 EUR :91 OTP :109
## 501-800 EUR :58 Intesa Sanpaolo : 17
## 801-1300 EUR :48 Sparkasse : 7
## Above 1300 EUR:57 Addiko Bank : 8
## NA's : 2 Delavska Hranilnica: 19
## Other : 233.2.1 Summary of key insights - Categorical data
3.2.1.1 Payment Frequency and Method Preferences
Cash Usage:
A large proportion of participants (133 respondents) reported using cash 1-3 times a month, while 66 participants use cash 1 per week, and 17 use it daily. Interestingly, the group that never uses cash for payments is smaller (23 respondents), suggesting that cash remains a popular choice for most users.Card Usage:
For card payments, the most common frequency is several times a week (125 participants), followed by daily usage (68 participants), highlighting that many users rely heavily on cards for transactions. Only 22 respondents never use cards for payments.Phone Payments:
Mobile payments, as expected, have a high daily usage rate with 108 respondents using their phones for payments daily. The group that never uses phone payments is notably large (81 respondents), which could reflect concerns around mobile payment security or simply a preference for other methods.
3.2.1.2 Cash Spend and Usage Distribution
- Frequency of Cash Usage in Different Ranges:
The frequency of cash spending also exhibits a wide range of behavior:- Less than 10 EUR: Only 34 respondents report never spending this amount, with a large number using it less than half the time.
- Cash between 11 EUR to 99 EUR: Most respondents report less than half of their cash spending falling in this range, with a few respondents reporting always spending this amount.
- Cash Usage Above 1000 EUR: The highest usage category is never spending above 1000 EUR, with 252 respondents reporting so.
- Other Payment Methods:
Digital payment methods like bank transfers and mobile apps have seen a higher level of use, with 250 respondents reporting using mobile apps and 14 respondents using bank transfers.
3.2.1.3 Income Sources and Digital vs.Β Cash Payments
- Income and Payment Preferences:
Participants reported diverse income sources:- Cash remains the predominant source of income for many respondents, particularly for PocketMoney and Gifts, where 251 and 36 participants respectively rely on cash.
- Digitally received income is also quite common, particularly for Scholarships (137 respondents) and Investments (93 respondents), suggesting that digital income sources are more prevalent in these categories.
- Responses to Digital Payments:
Most respondents (195 participants) report paying digitally when available, but a significant portion (52 respondents) specifically never pay digitally elsewhere.
3.2.1.4 Spending Habits and Financial Control
Sharing Expenses:
Mobile apps are commonly used for expense-sharing, with 250 respondents using them for this purpose. A smaller group of 28 respondents still use cash for sharing expenses, while 8 respondents do not share their expenses at all.Switch to Digital Payments:
There is a notable shift toward digital payments, with 78 participants fully digital and 171 participants balancing digital and cash. However, 44 respondents still primarily use cash for their payments.
3.2.1.5 Demographic Breakdown
Gender Distribution:
The gender distribution in the sample is 120 males and 182 females, with a small portion of participants marking their gender as other or not wanting to answer.Educational Background:
A significant number of participants have completed high school (132 respondents), with 99 respondents holding a bachelorβs degree, and a smaller portion possessing a masterβs degree (61 respondents). The educational distribution indicates a predominantly young, educated sample, which could influence financial decisions and preferences.Employment Status:
The majority of the participants are students (236 respondents), followed by employed individuals (49 respondents). The data suggests that many participants may be financially dependent, influencing their preferences toward cash, digital payments, and financial control.Income Levels:
Students and young individuals are more likely to report lower-income brackets (e.g., 0-200 EUR), with a median income level appearing to be low overall, particularly for scholarships and casual income sources. However, a small portion of individuals report incomes above 1300 EUR, particularly for those engaged in digital or investment-based income.Primary Bank Usage:
NLB and OTP are the two most used banks among respondents, with 121 and 109 participants, respectively, favoring these institutions. Other banks like Intesa Sanpaolo and Sparkasse are used by significantly fewer respondents, indicating a reliance on a few major banks for digital transactions.
3.2.1.6 Conclusion
The categorical analysis highlights the increasing shift towards digital payments, especially with mobile phones and cards. However, cash remains relevant for many respondents, particularly for daily purchases and income sources. Participantsβ educational backgrounds and employment statuses suggest that students and younger individuals, particularly those with lower incomes, are more likely to engage with digital payment systems and seek financial control through cash or digital combinations.
Furthermore, the reliance on social platforms and digital methods for expense sharing is evident, with mobile apps dominating as the preferred platform for dividing costs among peers. Financial preferences, particularly between cash and digital methods, are influenced by age, income level, and bank affiliations, pointing to the growing but cautious adoption of digital financial solutions among younger, student populations.
4 PCA Creation
NLB_PCASafety <- NLB_data[ , c("Safe_DebitCard", "Safe_CreditCard", "Safe_PhonePay", "Safe_Neobank")]
NLB_PCAEase <- NLB_data[ , c("Easy_DebitCard", "Easy_CreditCard", "Easy_PhonePay", "Easy_Neobank")]
NLB_PCAAvailability <- NLB_data[ , c("Accept_DebitCard", "Accept_CreditCard", "Accept_PhonePay", "Accept_Neobank")]
NLB_PCASpeed <- NLB_data[ , c("Fast_DebitCard", "Fast_CreditCard", "Fast_PhonePay", "Fast_Neobank")]
NLB_PCAPrivacy <- NLB_data[ , c("Private_DebitCard", "Private_CreditCard", "Private_PhonePay", "Private_Neobank")]
NLB_PCAControl <- NLB_data[ , c("Control_DebitCard", "Control_CreditCard", "Control_PhonePay", "Control_Neobank")]
library(pastecs)##
## Attaching package: 'pastecs'## The following objects are masked from 'package:dplyr':
##
## first, last## Safe_DebitCard Safe_CreditCard Safe_PhonePay Safe_Neobank
## median 5.00 5.00 5.00 5.00
## mean 5.14 4.95 5.17 4.83
## SE.mean 0.07 0.08 0.09 0.09
## CI.mean.0.95 0.14 0.15 0.17 0.17
## var 1.58 1.85 2.28 2.30
## std.dev 1.26 1.36 1.51 1.52
## coef.var 0.25 0.28 0.29 0.31## Easy_DebitCard Easy_CreditCard Easy_PhonePay Easy_Neobank
## median 7.00 7.00 7.00 6.00
## mean 6.15 6.02 6.48 5.59
## SE.mean 0.06 0.07 0.06 0.09
## CI.mean.0.95 0.13 0.14 0.12 0.17
## var 1.26 1.45 1.22 2.31
## std.dev 1.12 1.20 1.10 1.52
## coef.var 0.18 0.20 0.17 0.27## Accept_DebitCard Accept_CreditCard Accept_PhonePay Accept_Neobank
## median 7.00 7.00 7.00 5.00
## mean 6.48 6.34 6.03 5.08
## SE.mean 0.05 0.06 0.07 0.10
## CI.mean.0.95 0.11 0.13 0.14 0.19
## var 0.88 1.24 1.51 2.81
## std.dev 0.94 1.11 1.23 1.68
## coef.var 0.14 0.18 0.20 0.33## Fast_DebitCard Fast_CreditCard Fast_PhonePay Fast_Neobank
## median 7.00 7.00 7.00 6.00
## mean 6.17 6.12 6.53 5.68
## SE.mean 0.07 0.07 0.06 0.08
## CI.mean.0.95 0.13 0.14 0.12 0.17
## var 1.34 1.47 1.08 2.18
## std.dev 1.16 1.21 1.04 1.48
## coef.var 0.19 0.20 0.16 0.26## Private_DebitCard Private_CreditCard Private_PhonePay
## median 4.00 4.00 4.00
## mean 4.46 4.46 4.39
## SE.mean 0.10 0.10 0.10
## CI.mean.0.95 0.20 0.20 0.20
## var 3.01 2.99 3.22
## std.dev 1.73 1.73 1.80
## coef.var 0.39 0.39 0.41
## Private_Neobank
## median 4.00
## mean 4.48
## SE.mean 0.09
## CI.mean.0.95 0.19
## var 2.71
## std.dev 1.64
## coef.var 0.37## Control_DebitCard Control_CreditCard Control_PhonePay
## median 5.00 5.00 5.00
## mean 5.15 5.01 5.16
## SE.mean 0.10 0.10 0.11
## CI.mean.0.95 0.19 0.20 0.21
## var 2.92 3.03 3.38
## std.dev 1.71 1.74 1.84
## coef.var 0.33 0.35 0.36
## Control_Neobank
## median 5.00
## mean 5.12
## SE.mean 0.10
## CI.mean.0.95 0.19
## var 2.84
## std.dev 1.69
## coef.var 0.33library(FactoMineR)
components12 <- PCA(NLB_PCAAvailability,
scale.unit = TRUE,
graph = FALSE,
ncp = 1)NLB_data$PCASafety <- components10$ind$coord[ , 1]
NLB_data$PCAEase <- components11$ind$coord[ , 1]
NLB_data$PCAAvailability <- components12$ind$coord[ , 1]
NLB_data$PCASpeed <- components13$ind$coord[ , 1]
NLB_data$PCAPrivacy <- components14$ind$coord[ , 1]
NLB_data$PCAControl <- components15$ind$coord[ , 1]
head(NLB_data)## Age Cash_Use Card_Use Phone_Use OtherPay_Use Cash_Up10 Cash_11_99
## 1 7 3 2 5 2 4 3
## 2 9 2 5 1 1 2 1
## 3 9 2 4 4 1 2 2
## 4 7 3 5 5 1 2 2
## 5 7 2 5 5 1 2 1
## 6 9 3 3 1 2 4 3
## Cash_100_1000 Cash_Over1000 NoDigital_Response NoDigital_Response_Text
## 1 2 1 2 -2
## 2 1 1 1 -2
## 3 1 1 2 -2
## 4 1 1 1 -2
## 5 1 1 1 -2
## 6 2 1 2 -2
## Income_StudJobSalary Income_PocketMoney Income_Gifts Income_Occasional
## 1 3 2 1 1
## 2 4 2 1 4
## 3 3 3 1 4
## 4 3 3 2 4
## 5 4 3 3 4
## 6 3 4 1 1
## Income_Subsidy Income_Scholarship Income_Investments Save_Money Save_Form
## 1 3 3 3 1 5
## 2 4 4 3 1 7
## 3 4 4 2 1 7
## 4 4 3 4 1 7
## 5 4 4 4 2 -2
## 6 3 3 4 2 -2
## Spend_SameForm Concern_Security LessSecure_Digital Trust_2FA Safe_CashCarry
## 1 6 3 5 6 4
## 2 7 6 3 6 3
## 3 6 2 3 6 3
## 4 6 5 6 7 4
## 5 7 4 7 4 4
## 6 7 3 5 7 7
## Prefer_Digital_Convenience Prefer_Cash_Control Use_Cash_IfNoDigital
## 1 7 6 7
## 2 7 1 6
## 3 7 1 7
## 4 7 1 7
## 5 7 4 7
## 6 5 6 5
## Importance_Ease Importance_Speed Importance_Availability Importance_Security
## 1 7 7 6 6
## 2 6 7 5 7
## 3 7 6 6 6
## 4 7 7 7 7
## 5 7 7 7 7
## 6 5 3 7 7
## Importance_TrackingBudgeting Importance_Privacy Safe_Cash Safe_DebitCard
## 1 3 6 6 6
## 2 7 6 3 7
## 3 4 5 6 6
## 4 7 7 6 5
## 5 7 7 7 4
## 6 4 4 7 6
## Safe_CreditCard Safe_PhonePay Safe_Neobank Easy_Cash Easy_DebitCard
## 1 6 7 6 5 6
## 2 7 3 6 7 7
## 3 4 6 6 4 7
## 4 4 6 6 3 6
## 5 4 4 4 7 7
## 6 6 5 5 6 7
## Easy_CreditCard Easy_PhonePay Easy_Neobank Accept_Cash Accept_DebitCard
## 1 6 7 6 6 7
## 2 7 6 6 6 6
## 3 7 7 7 7 7
## 4 6 7 7 6 7
## 5 7 7 7 7 7
## 6 7 6 6 6 7
## Accept_CreditCard Accept_PhonePay Accept_Neobank Fast_Cash Fast_DebitCard
## 1 7 7 6 5 6
## 2 6 6 6 5 7
## 3 7 7 7 4 7
## 4 7 6 6 1 6
## 5 7 7 4 7 7
## 6 7 6 6 2 7
## Fast_CreditCard Fast_PhonePay Fast_Neobank Private_Cash Private_DebitCard
## 1 6 7 7 7 6
## 2 7 7 7 7 1
## 3 7 7 7 6 5
## 4 6 7 7 7 4
## 5 7 7 7 4 7
## 6 7 6 6 5 7
## Private_CreditCard Private_PhonePay Private_Neobank Control_Cash
## 1 5 5 6 7
## 2 1 1 1 2
## 3 5 5 5 5
## 4 4 4 4 6
## 5 7 7 7 4
## 6 7 7 7 7
## Control_DebitCard Control_CreditCard Control_PhonePay Control_Neobank
## 1 5 5 3 3
## 2 7 4 1 6
## 3 7 7 7 7
## 4 5 1 5 5
## 5 7 7 7 7
## 6 6 5 3 5
## Social_Friends Social_Family Expense_Sharing Expense_Sharing_Text
## 1 5 2 2 -2
## 2 4 4 2 -2
## 3 4 4 2 -2
## 4 7 7 2 -2
## 5 4 4 2 -2
## 6 3 5 2 -2
## Reason_ExactSum Reason_NoCash Reason_Convenient Reason_TrackFinances
## 1 1 0 1 0
## 2 1 1 1 0
## 3 1 1 1 1
## 4 1 1 1 1
## 5 1 1 1 1
## 6 1 0 0 0
## Reason_Other Reason_Other_Text Concern_Fraud Concern_PersonalInfo
## 1 0 -2 3 5
## 2 0 -2 5 5
## 3 0 -2 3 3
## 4 0 -2 5 5
## 5 0 -2 7 7
## 6 0 -2 5 4
## Concern_IDTheft Concern_Hacker OnlineFraud_Exp Behavior_MoreCash
## 1 3 3 2 0
## 2 6 7 3 0
## 3 3 3 4 -2
## 4 5 6 4 -2
## 5 7 7 1 -1
## 6 6 6 2 1
## Behavior_SecureDigital Behavior_Cautious Behavior_SecureOption
## 1 0 1 0
## 2 0 1 0
## 3 -2 -2 -2
## 4 -2 -2 -2
## 5 -1 -1 -1
## 6 0 0 0
## Behavior_NoChange Switch_Digital Gender Education Status_Employment
## 1 0 2 1 6 1
## 2 0 2 2 5 1
## 3 -2 2 2 4 1
## 4 -2 1 2 4 1
## 5 -1 1 2 4 1
## 6 0 4 2 6 1
## Status_Employment_Text Income_Level Primary_Bank Primary_Bank_Text AgeF
## 1 -2 2 2 -2 23
## 2 -2 2 2 -2 25
## 3 -2 3 7 Unicredit 25
## 4 -2 2 1 -2 23
## 5 -2 4 1 -2 23
## 6 -2 2 1 -2 25
## Cash_UseF Card_UseF Phone_UseF OtherPay_UseF
## 1 1 per week 1-3 monthly Daily 1-3 monthly
## 2 1-3 monthly Daily Never Never
## 3 1-3 monthly Several times a week Several times a week Never
## 4 1 per week Daily Daily Never
## 5 1-3 monthly Daily Daily Never
## 6 1 per week 1 per week Never 1-3 monthly
## Cash_Up10F Cash_11_99F Cash_100_1000F Cash_Over1000F
## 1 More than half Half Less than half Never
## 2 Less than half Never Never Never
## 3 Less than half Less than half Never Never
## 4 Less than half Less than half Never Never
## 5 Less than half Never Never Never
## 6 More than half Half Less than half Never
## NoDigital_ResponseF Income_StudJobSalaryF Income_PocketMoneyF
## 1 Pay as available Digitally Cash&Digitally
## 2 Pay digital elsewhere Not using Cash&Digitally
## 3 Pay as available Digitally Digitally
## 4 Pay digital elsewhere Digitally Digitally
## 5 Pay digital elsewhere Not using Digitally
## 6 Pay as available Digitally Not using
## Income_GiftsF Income_OccasionalF Income_SubsidyF Income_ScholarshipF
## 1 Cash Cash Digitally Digitally
## 2 Cash Not using Not using Not using
## 3 Cash Not using Not using Not using
## 4 Cash&Digitally Not using Not using Digitally
## 5 Digitally Not using Not using Not using
## 6 Cash Cash Digitally Digitally
## Income_InvestmentsF Expense_SharingF OnlineFraud_ExpF Switch_DigitalF
## 1 Digitally Mobile apps Yes - others Balance digital-cash
## 2 Digitally Mobile apps Yes - both Balance digital-cash
## 3 Cash&Digitally Mobile apps No Balance digital-cash
## 4 Not using Mobile apps No Fully digital
## 5 Not using Mobile apps Yes - me Fully digital
## 6 Not using Mobile apps Yes - others Don't know
## GenderF EducationF Status_EmploymentF Income_LevelF Primary_BankF
## 1 Man Master Degree Student 201-500 EUR OTP
## 2 Woman Bachelor Degree Student 201-500 EUR OTP
## 3 Woman High School Student 501-800 EUR Other
## 4 Woman High School Student 201-500 EUR NLB
## 5 Woman High School Student 801-1300 EUR NLB
## 6 Woman Master Degree Student 201-500 EUR NLB
## PCASafety PCAEase PCAAvailability PCASpeed PCAPrivacy PCAControl
## 1 -1.7204789 0.1593095 -1.2385549 0.3545114 -1.2168529 -1.2370388
## 2 -1.1689414 0.9246044 0.2539360 1.3676446 3.9978412 -0.6158432
## 3 -0.6604604 1.4565407 -1.4619345 1.3676446 -0.6395533 2.1738887
## 4 -0.2295463 0.3880241 -0.8265122 0.3545114 0.5197953 -1.2939568
## 5 1.4772851 1.4565407 -0.7917959 1.3676446 -2.9582505 2.1738887
## 6 -0.7464178 0.9246044 -0.8265122 0.7468479 -2.9582505 -0.36661275 Clustering
NLB_CluStd <- as.data.frame(scale(NLB_data[c("PCASafety", "PCAEase", "PCAAvailability", "PCASpeed", "PCAPrivacy", "PCAControl")]))NLB_CluStd$Dissimilarity <- sqrt(NLB_CluStd$PCASafety^2 + NLB_CluStd$PCAEase^2 + NLB_CluStd$PCAAvailability^2 + NLB_CluStd$PCASpeed^2 +NLB_CluStd$PCAPrivacy^2 + NLB_CluStd$PCAControl^2)## Loading required package: ggplot2## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBaDistances <- get_dist(NLB_CluStd,
method = "euclidian")
fviz_dist(Distances,
gradient = list(low = "#230078", # NLB INDIGO BLUE
mid = "#A7A8AA", # NLB LIGHT GRAY
high = "white")) 
NLB_CluStd <- NLB_CluStd %>% rename(Security = PCASafety,
`Ease of use` = PCAEase,
Availability = PCAAvailability,
Speed = PCASpeed,
Privacy = PCAPrivacy,
`Spending Control` = PCAControl)## $hopkins_stat
## [1] 0.6785538
##
## $plot
## NULLlibrary(dplyr)
library(factoextra)
WARD <- NLB_CluStd %>%
get_dist(method = "euclidean") %>%
hclust(method = "ward.D2")
WARD##
## Call:
## hclust(d = ., method = "ward.D2")
##
## Cluster method : ward.D2
## Distance : euclidean
## Number of objects: 304## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## βΉ The deprecated feature was likely used in the factoextra package.
## Please report the issue at <https://github.com/kassambara/factoextra/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
library(factoextra)
library(NbClust)
fviz_nbclust(NLB_CluStd, kmeans, method = "wss") +
labs(subtitle = "Elbow Method")
## K-means clustering with 4 clusters of sizes 63, 93, 103, 45
##
## Cluster means:
## Security Ease of use Availability Speed Privacy Spending Control
## 1 0.4866563 0.4709095 -0.15616090 0.24994550 0.8603929 -0.8773218
## 2 -0.7517767 0.7902180 -0.57596893 0.69484422 -0.8043929 0.5618051
## 3 0.1430845 -0.5409715 0.04180835 -0.07174967 -0.0235824 0.1264165
## 4 0.5448485 -1.0541669 1.31326640 -1.62170805 0.5118394 -0.2221667
## Dissimilarity
## 1 2.469170
## 2 2.324214
## 3 1.698921
## 4 3.443997
##
## Clustering vector:
## [1] 2 1 2 3 2 2 3 3 3 3 2 3 1 3 2 2 1 3 3 2 1 2 2 4 3 3 3 2 2 4 1 4 3 3 3 2 2
## [38] 4 2 2 1 2 4 3 3 4 1 3 3 2 4 3 2 2 1 3 2 2 1 3 2 4 1 1 1 1 2 3 3 2 4 1 3 2
## [75] 2 1 2 1 3 3 1 4 3 1 1 3 3 3 2 3 4 2 1 1 2 2 3 2 4 1 1 2 2 2 3 4 1 2 2 1 3
## [112] 1 1 1 2 3 1 4 2 3 2 1 4 4 1 2 3 2 2 3 1 1 4 2 3 3 4 1 1 4 3 3 2 2 2 4 2 2
## [149] 1 3 2 3 3 1 3 3 2 3 3 2 2 3 2 4 3 3 2 1 4 4 4 2 4 1 2 4 1 1 3 2 3 3 2 3 1
## [186] 3 3 3 4 1 4 3 2 3 4 3 3 3 1 3 1 4 1 4 2 4 4 4 4 3 3 3 2 1 2 3 1 3 2 3 1 3
## [223] 3 3 3 4 2 2 4 2 2 1 2 3 3 2 2 2 2 3 2 3 1 1 3 3 2 2 2 2 2 2 2 4 2 1 3 3 1
## [260] 1 1 3 2 2 3 2 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 2 1 1 1 1 4 1 4 4 2 2 1 2 3
## [297] 3 4 1 4 3 4 2 4
##
## Within cluster sum of squares by cluster:
## [1] 301.0014 275.2221 329.2284 299.6181
## (between_SS / total_SS = 40.2 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"library(factoextra)
# Define the NLB colors
NLB_colors <- c("#230078", "#84BD00", "#FA7800", "#63666A", "#A7A8AA", "black", "orange")
fviz_cluster(Clustering,
palette = NLB_colors, # Use NLB colors for clusters
repel = FALSE,
ggtheme = theme_bw(), # Black and white theme
data = NLB_CluStd)
## Security Ease of use Availability Speed Privacy Spending Control
## 1 0.4866563 0.4709095 -0.15616090 0.24994550 0.8603929 -0.8773218
## 2 -0.7517767 0.7902180 -0.57596893 0.69484422 -0.8043929 0.5618051
## 3 0.1430845 -0.5409715 0.04180835 -0.07174967 -0.0235824 0.1264165
## 4 0.5448485 -1.0541669 1.31326640 -1.62170805 0.5118394 -0.2221667
## Dissimilarity
## 1 2.469170
## 2 2.324214
## 3 1.698921
## 4 3.443997##
## Attaching package: 'tidyr'## The following object is masked from 'package:pastecs':
##
## extractFigure <- pivot_longer(Figure, cols = c("Security", "Ease of use", "Availability", "Speed", "Privacy", "Spending Control"))
Figure$Group <- factor(Figure$id,
levels = c(1, 2, 3, 4, 5),
labels = c("1", "2", "3", "4", "5"))
Figure$ImeF <- factor(Figure$name,
levels = c("Security", "Ease of use", "Availability", "Speed", "Privacy", "Spending Control"),
labels = c("Security", "Ease of use", "Availability", "Speed", "Privacy", "Spending Control"))
library(ggplot2)
ggplot(Figure, aes(x = ImeF, y = value)) +
geom_hline(yintercept = 0) +
theme_bw() +
geom_point(aes(shape = Group, col = Group), size = 3) +
geom_line(aes(group = id), linewidth = 1) +
ylab("Averages") +
xlab("Cluster variables") +
scale_color_manual(values = NLB_colors) + # Use NLB colors for points and lines
ylim(-3, 3) +
theme(axis.text.x = element_text(angle = 45, vjust = 0.50, size = 10))
NLB_CluStd$Group <- Clustering$cluster
fit <- aov(cbind(`Security`, `Ease of use`, `Availability`, `Speed`, `Privacy`, `Spending Control`) ~ as.factor(Group),
data = NLB_CluStd)
summary(fit)## Response Security :
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(Group) 3 82.949 27.6495 37.695 < 2.2e-16 ***
## Residuals 300 220.051 0.7335
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response Ease of use :
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(Group) 3 152.19 50.731 100.92 < 2.2e-16 ***
## Residuals 300 150.81 0.503
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response Availability :
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(Group) 3 110.18 36.726 57.14 < 2.2e-16 ***
## Residuals 300 192.82 0.643
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response Speed :
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(Group) 3 167.71 55.905 123.97 < 2.2e-16 ***
## Residuals 300 135.29 0.451
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response Privacy :
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(Group) 3 118.66 39.553 64.37 < 2.2e-16 ***
## Residuals 300 184.34 0.614
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response Spending Control :
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(Group) 3 81.711 27.2370 36.925 < 2.2e-16 ***
## Residuals 300 221.289 0.7376
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 16 Criterion validity (significant descriptors)
6.1 Frequency of Mobile Payment Usage per Month
NLB_data$Phone_Use_merged <- ifelse(
NLB_data$Phone_Use == 1, "never",
ifelse(NLB_data$Phone_Use %in% c(2, 3), "irregular basis", "regular basis")
)
# Convert the merged column to a factor with levels in the desired order
NLB_data$Phone_Use_merged <- factor(
NLB_data$Phone_Use_merged,
levels = c("never", "irregular basis", "regular basis")
)##
## Pearson's Chi-squared test
##
## data: NLB_data$Phone_Use_merged and as.factor(NLB_data$Group)
## X-squared = 19.394, df = 6, p-value = 0.0035476.2 Frequency of Cash Usage for Purchases Up to 10 EUR
NLB_data$Cash_Up10_merged <- ifelse(
NLB_data$Cash_Up10 %in% c(1, 2), "less than half",
ifelse(NLB_data$Cash_Up10 == 3, "half", "more than half")
)
# Convert the merged column to a factor with levels in the desired order
NLB_data$Cash_Up10_merged <- factor(
NLB_data$Cash_Up10_merged,
levels = c("less than half", "half", "more than half")
)##
## Pearson's Chi-squared test
##
## data: NLB_data$Cash_Up10_merged and as.factor(NLB_data$Group)
## X-squared = 20.107, df = 6, p-value = 0.002657 Demographics
7.1 Demographics - significant :)
7.1.1 Frequency of Mobile Payment Usage per Month
# Calculate frequency by Mobile payment usage
Phone_freq <- NLB_data %>%
group_by(Group, Phone_Use_merged) %>%
summarise(Count = n(), .groups = 'drop') %>%
group_by(Group) %>%
mutate(Percentage = Count / sum(Count) * 100)
# Plot the frequency by Mobile payment usage
ggplot(Phone_freq, aes(x = Group, y = Percentage, fill = Phone_Use_merged)) +
geom_bar(stat = "identity", position = "stack") +
labs(
title = "Distribution of Mobile Payment Usage Across Youngs (18-27 y.o.)",
x = "Group",
y = "Percentage(%)",
fill = "Frequency of Mobile Payment Usage"
) +
scale_fill_manual(values = NLB_colors) + # Use NLB colors for the fill
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis labels if needed
##
## Attaching package: 'kableExtra'## The following object is masked from 'package:dplyr':
##
## group_rows| Group | never | irregular basis | regular basis |
|---|---|---|---|
| 1 | 38.1 | 14.3 | 47.6 |
| 2 | 18.3 | 17.2 | 64.5 |
| 3 | 22.3 | 8.7 | 68.9 |
| 4 | 37.8 | 22.2 | 40.0 |
7.1.2 Frequency of Cash Usage for Purchases Up to 10 EUR
# Calculate frequency by cash usage for purchases up to 10 EUR
Purchases_freq <- NLB_data %>%
group_by(Group, Cash_Up10_merged) %>%
summarise(Count = n(), .groups = 'drop') %>%
group_by(Group) %>%
mutate(Percentage = Count / sum(Count) * 100)
# Plot the frequency by cash usage for purchases up to 10 EUR
ggplot(Purchases_freq, aes(x = Group, y = Percentage, fill = Cash_Up10_merged)) +
geom_bar(stat = "identity", position = "stack") +
labs(
title = "Distribution of Cash Payment for Purchases up to 10 EUR Among Youngs (18-27 y.o.)",
x = "Group",
y = "Percentage(%)",
fill = "Number of Cash Payments (Up to 10 EUR)"
) +
scale_fill_manual(values = NLB_colors) + # Use NLB colors for the fill
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis labels if needed
| Group | less than half | half | more than half |
|---|---|---|---|
| 1 | 65.1 | 23.8 | 11.1 |
| 2 | 72.0 | 8.6 | 19.4 |
| 3 | 68.9 | 23.3 | 7.8 |
| 4 | 53.3 | 17.8 | 28.9 |
7.2 Demographics - not significant :(
7.2.1 Frequency of Card Payment Usage per Month
NLB_data$Card_Use_merged <- ifelse(
NLB_data$Card_Use == 1, "never",
ifelse(NLB_data$Card_Use %in% c(2, 3), "irregular basis", "regular basis"))
# Convert the merged column to a factor with levels in the desired order
NLB_data$Card_Use_merged <- factor(
NLB_data$Card_Use_merged,
levels = c("never", "irregular basis", "regular basis"))
# Calculate frequency by Mobile payment usage
Card_freq <- NLB_data %>%
group_by(Group, Card_Use_merged) %>%
summarise(Count = n(), .groups = 'drop') %>%
group_by(Group) %>%
mutate(Percentage = Count / sum(Count) * 100)
# Plot the frequency by Mobile payment usage
ggplot(Card_freq, aes(x = Group, y = Percentage, fill = Card_Use_merged)) +
geom_bar(stat = "identity", position = "stack") +
labs(
title = "Distribution of Card Payment Usage Across Youngs (18-27 y.o.)",
x = "Group",
y = "Percentage(%)",
fill = "Frequency of Card Payment Usage"
) +
scale_fill_manual(values = NLB_colors) + # Use NLB colors for the fill
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis labels if needed
| Group | never | irregular basis | regular basis |
|---|---|---|---|
| 1 | 6.3 | 28.6 | 65.1 |
| 2 | 10.8 | 28.0 | 61.3 |
| 3 | 4.9 | 32.0 | 63.1 |
| 4 | 6.7 | 26.7 | 66.7 |
7.2.2 Frequency of Other Payment (PayPal, Stripe, etc.) Usage per Month
NLB_data$OtherPay_Use_merged <- ifelse(
NLB_data$OtherPay_Use == 1, "never",
ifelse(NLB_data$OtherPay_Use %in% c(2, 3), "irregular basis", "regular basis"))
# Convert the merged column to a factor with levels in the desired order
NLB_data$OtherPay_Use_merged <- factor(
NLB_data$OtherPay_Use_merged,
levels = c("never", "irregular basis", "regular basis"))
# Calculate frequency by Mobile payment usage
OtherPay_freq <- NLB_data %>%
group_by(Group, OtherPay_Use_merged) %>%
summarise(Count = n(), .groups = 'drop') %>%
group_by(Group) %>%
mutate(Percentage = Count / sum(Count) * 100)
# Plot the frequency by Mobile payment usage
ggplot(OtherPay_freq, aes(x = Group, y = Percentage, fill = OtherPay_Use_merged)) +
geom_bar(stat = "identity", position = "stack") +
labs(
title = "Distribution of Other Payment (PayPal, Stripe, etc.) Usage Across Youngs (18-27 y.o.)",
x = "Group",
y = "Percentage(%)",
fill = "Frequency of Other Payment Usage"
) +
scale_fill_manual(values = NLB_colors) + # Use NLB colors for the fill
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis labels if needed
| Group | never | irregular basis | regular basis |
|---|---|---|---|
| 1 | 73.0 | 25.4 | 1.6 |
| 2 | 57.0 | 38.7 | 4.3 |
| 3 | 50.5 | 42.7 | 6.8 |
| 4 | 64.4 | 33.3 | 2.2 |
7.2.3 Frequency of Cash Usage for Purchases 11-99 EUR
# Merge cash usage for purchases between 11-99 EUR
NLB_data$Cash_11_99_merged <- ifelse(
NLB_data$Cash_11_99 %in% c(1, 2), "less than half",
ifelse(NLB_data$Cash_11_99 == 3, "half", "more than half")
)
# Convert the merged column to a factor with levels in the desired order
NLB_data$Cash_11_99_merged <- factor(
NLB_data$Cash_11_99_merged,
levels = c("less than half", "half", "more than half")
)
# Calculate frequency by cash usage for purchases between 11-99 EUR
Purchases1199_freq <- NLB_data %>%
group_by(Group, Cash_11_99_merged) %>%
summarise(Count = n(), .groups = 'drop') %>%
group_by(Group) %>%
mutate(Percentage = Count / sum(Count) * 100)
# Plot the frequency by cash usage for purchases between 11-99 EUR
ggplot(Purchases1199_freq, aes(x = Group, y = Percentage, fill = Cash_11_99_merged)) +
geom_bar(stat = "identity", position = "stack") +
labs(
title = "Distribution of Cash Payment for Purchases Between 11-99 EUR Among Youngs (18-27 y.o.)",
x = "Group",
y = "Percentage(%)",
fill = "Number of Cash Payments (11-99 EUR)"
) +
scale_fill_manual(values = NLB_colors) + # Use NLB colors for the fill
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis labels if needed
| Group | less than half | half | more than half |
|---|---|---|---|
| 1 | 79.4 | 15.9 | 4.8 |
| 2 | 76.3 | 12.9 | 10.8 |
| 3 | 87.4 | 6.8 | 5.8 |
| 4 | 66.7 | 17.8 | 15.6 |
7.2.4 Frequency of Cash Usage for Purchases 100-1000 EUR
# Merge cash usage for purchases over 1000 EUR
NLB_data$Cash_Over1000_merged <- ifelse(
NLB_data$Cash_Over1000 %in% c(1, 2), "less than half",
ifelse(NLB_data$Cash_Over1000 == 3, "half", "more than half")
)
# Convert the merged column to a factor with levels in the desired order
NLB_data$Cash_Over1000_merged <- factor(
NLB_data$Cash_Over1000_merged,
levels = c("less than half", "half", "more than half")
)
# Calculate frequency by cash usage for purchases over 1000 EUR
PurchasesOver1000_freq <- NLB_data %>%
group_by(Group, Cash_Over1000_merged) %>%
summarise(Count = n(), .groups = 'drop') %>%
group_by(Group) %>%
mutate(Percentage = Count / sum(Count) * 100)
# Plot the frequency by cash usage for purchases over 1000 EUR
ggplot(PurchasesOver1000_freq, aes(x = Group, y = Percentage, fill = Cash_Over1000_merged)) +
geom_bar(stat = "identity", position = "stack") +
labs(
title = "Distribution of Cash Payment for Purchases Over 1000 EUR Among Youngs (18-27 y.o.)",
x = "Group",
y = "Percentage(%)",
fill = "Number of Cash Payments (Over 1000 EUR)"
) +
scale_fill_manual(values = NLB_colors) + # Use NLB colors for the fill
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis labels if needed
| Group | less than half | half | more than half |
|---|---|---|---|
| 1 | 87.3 | 1.6 | 11.1 |
| 2 | 92.5 | 3.2 | 4.3 |
| 3 | 92.2 | 1.0 | 6.8 |
| 4 | 82.2 | 6.7 | 11.1 |
7.2.5 Age
# Calculate percentage by Group
Age_freq <- NLB_data %>%
group_by(Group, AgeF) %>%
summarise(Count = n(), .groups = 'drop') %>%
group_by(Group) %>%
mutate(Percentage = Count / sum(Count) * 100)
NLB_colors2 <- c("#230078", # Deep Blue
"#3A1A8B", # Purple-blue
"#4D33B1", # Lighter purple-blue
"#5F47D8", # Soft lavender
"#84BD00", # Green (from the original)
"#A7C700", # Soft green
"#98FA00", # Light green
"#FA7800", # Orange (from the original)
"#FF9A33", # Lighter orange
"#FFB266") # Light peachy-orange
# Plot the percentage by Response
ggplot(Age_freq, aes(x = Group, y = Percentage, fill = as.factor(AgeF))) +
geom_bar(stat = "identity", position = "stack") +
labs(
title = "Distribution of Age by Youngs' Clusters (18-27 y.o.)",
x = "Group",
y = "Percentage (%)",
fill = "Age"
) +
scale_fill_manual(values = NLB_colors2) + # Apply the new homogeneous color palette
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis labels if needed
| Group | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 4.8 | 3.2 | 14.3 | 9.5 | 12.7 | 23.8 | 14.3 | 9.5 | 6.3 | 1.6 |
| 2 | 1.1 | 7.5 | 8.6 | 8.6 | 15.1 | 24.7 | 8.6 | 20.4 | 2.2 | 3.2 |
| 3 | 4.9 | 9.7 | 12.6 | 7.8 | 13.6 | 15.5 | 10.7 | 13.6 | 4.9 | 6.8 |
| 4 | 13.3 | 4.4 | 22.2 | 2.2 | 17.8 | 22.2 | 4.4 | 8.9 | 4.4 | 0.0 |
# Convert AgeF from factor to numeric
NLB_data$AgeF <- as.numeric(as.character(NLB_data$AgeF))
# Now aggregate and calculate the mean
Age_means <- aggregate(AgeF ~ Group, data = NLB_data, FUN = mean, na.rm = TRUE)
# Print results
print(Age_means)## Group AgeF
## 1 1 22.47619
## 2 2 22.75269
## 3 3 22.49515
## 4 4 21.622227.2.6 Income
# Calculate frequency by Income (Income_LevelF) and Group
income_freq <- NLB_data %>%
group_by(Group, Income_LevelF) %>%
summarise(Count = n(), .groups = 'drop') %>%
group_by(Group) %>%
mutate(Percentage = Count / sum(Count) * 100)
# Plot the frequency by Income
ggplot(income_freq, aes(x = Group, y = Percentage, fill = Income_LevelF)) +
geom_bar(stat = "identity", position = "stack") +
labs(
title = "Distribution of Income among Youngs (18-27 y.o.)",
x = "Group",
y = "Frequency",
fill = "Income"
) +
scale_fill_manual(values = NLB_colors) + # Apply the previous color palette
theme_minimal() # Using minimal theme
| Group | 0-200 EUR | 201-500 EUR | 501-800 EUR | 801-1300 EUR | Above 1300 EUR |
|---|---|---|---|---|---|
| 1 | 12.7 | 41.3 | 15.9 | 15.9 | 14.3 |
| 2 | 17.2 | 25.8 | 20.4 | 17.2 | 19.4 |
| 3 | 12.6 | 29.1 | 22.3 | 13.6 | 21.4 |
| 4 | 24.4 | 24.4 | 13.3 | 17.8 | 17.8 |
# Create a new column with calculated midpoints of Income Level
NLB_data <- NLB_data %>%
mutate(Income_Mid = case_when(
Income_LevelF == "0-200 EUR" ~ (0 + 200) / 2,
Income_LevelF == "201-500 EUR" ~ (201 + 500) / 2,
Income_LevelF == "501-800 EUR" ~ (501 + 800) / 2,
Income_LevelF == "801-1300 EUR" ~ (800 + 1300) / 2,
Income_LevelF == "Above 1300 EUR" ~ (1300 + 1700) / 2, # Assuming 1300-1700 as range
TRUE ~ NA_real_ # Assign NA for any unexpected values
))
# Print first rows to verify transformation
print(head(NLB_data[, c("Income_LevelF", "Income_Mid")]))## Income_LevelF Income_Mid
## 1 201-500 EUR 350.5
## 2 201-500 EUR 350.5
## 3 501-800 EUR 650.5
## 4 201-500 EUR 350.5
## 5 801-1300 EUR 1050.0
## 6 201-500 EUR 350.5# Compute mean income for each cluster
Income_means <- aggregate(Income_Mid ~ Group, data = NLB_data, FUN = mean, na.rm = TRUE)
# Print results
print(Income_means)## Group Income_Mid
## 1 1 641.5556
## 2 2 711.5215
## 3 3 730.1618
## 4 4 664.96597.2.7 Employment status
# Calculate frequency by Employment status (Status_Employment) and Group
status_freq <- NLB_data %>%
group_by(Group, Status_EmploymentF) %>%
summarise(Count = n(), .groups = 'drop') %>%
group_by(Group) %>%
mutate(Percentage = Count / sum(Count) * 100)
# Plot the frequency by status
ggplot(status_freq, aes(x = Group, y = Percentage, fill = Status_EmploymentF)) +
geom_bar(stat = "identity", position = "stack") +
labs(
title = "Distribution of Employment among Youngs (18-27 y.o.)",
x = "Group",
y = "Frequency",
fill = "Employment status"
) +
scale_fill_manual(values = NLB_colors) + # Apply the NLB colors palette
theme_minimal() # Keep the minimal theme
| Group | Student | Employed | Self-employed | Other | Unemployed |
|---|---|---|---|---|---|
| 1 | 82.5 | 14.3 | 1.6 | 1.6 | 0.0 |
| 2 | 82.8 | 15.1 | 2.2 | 0.0 | 0.0 |
| 3 | 72.8 | 17.5 | 3.9 | 2.9 | 2.9 |
| 4 | 71.1 | 17.8 | 0.0 | 11.1 | 0.0 |
# Reclassify Employment Status
NLB_data <- NLB_data %>%
mutate(Status_EmploymentF = case_when(
Status_EmploymentF == "Employed" ~ "With Job",
Status_EmploymentF == "Self-employed" ~ "With Job",
TRUE ~ Status_EmploymentF # Keep other categories unchanged
))
# Recalculate the frequency by employment status and group
status_freq <- NLB_data %>%
group_by(Group, Status_EmploymentF) %>%
summarise(Count = n(), .groups = 'drop') %>%
group_by(Group) %>%
mutate(Percentage = Count / sum(Count) * 100)| Group | Student | With Job |
|---|---|---|
| 1 | 82.5 | 15.9 |
| 2 | 82.8 | 17.2 |
| 3 | 72.8 | 21.4 |
| 4 | 71.1 | 17.8 |
7.2.8 Education
# Calculate frequency by Education (Q24F) and Group
education_freq <- NLB_data %>%
group_by(Group, EducationF) %>%
summarise(Count = n(), .groups = 'drop') %>%
group_by(Group) %>%
mutate(Percentage = Count / sum(Count) * 100)
# Plot the frequency by Education
ggplot(education_freq, aes(x = Group, y = Percentage, fill = EducationF)) +
geom_bar(stat = "identity", position = "stack") +
labs(
title = "Distribution of Education level among Youngs (18-27 y.o.)",
x = "Group",
y = "Frequency",
fill = "Education level"
) +
scale_fill_manual(values = NLB_colors) + # Apply the NLB colors palette
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis labels if needed
| Group | High School | Bachelor Degree | Master Degree | PhD | Primary school | Vocational education |
|---|---|---|---|---|---|---|
| 1 | 46.0 | 41.3 | 12.7 | 0.0 | 0.0 | 0.0 |
| 2 | 44.1 | 32.3 | 21.5 | 2.2 | 0.0 | 0.0 |
| 3 | 39.8 | 31.1 | 24.3 | 0.0 | 2.9 | 1.9 |
| 4 | 46.7 | 24.4 | 17.8 | 0.0 | 11.1 | 0.0 |
7.2.9 Response
# Calculate frequency by Response
response_freq <- NLB_data %>%
group_by(Group, NoDigital_ResponseF) %>%
summarise(Count = n(), .groups = 'drop') %>%
group_by(Group) %>%
mutate(Percentage = Count / sum(Count) * 100)
# Plot the frequency by Response
ggplot(response_freq, aes(x = Group, y = Percentage, fill = NoDigital_ResponseF)) +
geom_bar(stat = "identity", position = "stack") +
labs(
title = "Responses to Merchants Not Accepting Digital Payments - Youngs (18-27 y.o.)",
x = "Group",
y = "Frequency",
fill = "Response"
) +
scale_fill_manual(values = NLB_colors) + # Apply the NLB colors palette
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis labels if needed
| Group | Pay digital elsewhere | Pay as available | Never occurred | Other |
|---|---|---|---|---|
| 1 | 14.3 | 63.5 | 12.7 | 9.5 |
| 2 | 22.6 | 64.5 | 7.5 | 5.4 |
| 3 | 15.5 | 62.1 | 18.4 | 3.9 |
| 4 | 13.3 | 68.9 | 17.8 | 0.0 |
7.2.10 Banks
# Calculate frequency by Banks
Banks_freq <- NLB_data %>%
group_by(Group, Primary_BankF) %>%
summarise(Count = n(), .groups = 'drop') %>%
group_by(Group) %>%
mutate(Percentage = Count / sum(Count) * 100)
# Plot the frequency by Bank
ggplot(Banks_freq, aes(x = Group, y = Percentage, fill = Primary_BankF)) +
geom_bar(stat = "identity", position = "stack") +
labs(
title = "Distribution of Primary Banks used by Youngs (18-27 y.o.)",
x = "Group",
y = "Frequency",
fill = "Bank"
) +
scale_fill_manual(values = NLB_colors) + # Apply the NLB colors palette
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis labels if needed
| Group | NLB | OTP | Intesa Sanpaolo | Addiko Bank | Delavska Hranilnica | Other | Sparkasse |
|---|---|---|---|---|---|---|---|
| 1 | 39.7 | 36.5 | 9.5 | 1.6 | 3.2 | 9.5 | 0.0 |
| 2 | 31.2 | 36.6 | 7.5 | 2.2 | 5.4 | 11.8 | 5.4 |
| 3 | 48.5 | 34.0 | 2.9 | 2.9 | 7.8 | 2.9 | 1.0 |
| 4 | 37.8 | 37.8 | 2.2 | 4.4 | 8.9 | 6.7 | 2.2 |
7.2.11 Gender
# Calculate frequency by Gender
Gender_freq <- NLB_data %>%
group_by(Group, GenderF) %>%
summarise(Count = n(), .groups = 'drop') %>%
group_by(Group) %>%
mutate(Percentage = Count / sum(Count) * 100)
# Plot the frequency by Bank
ggplot(Gender_freq, aes(x = Group, y = Percentage, fill = GenderF)) +
geom_bar(stat = "identity", position = "stack") +
labs(
title = "Distribution of Gender among Youngs (18-27 y.o.)",
x = "Group",
y = "Frequency",
fill = "Gender"
) +
scale_fill_manual(values = NLB_colors) + # Apply the NLB colors palette
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis labels if needed
| Group | Man | Woman | Other | Donβt want to answer |
|---|---|---|---|---|
| 1 | 33.3 | 65.1 | 1.6 | 0.0 |
| 2 | 40.9 | 58.1 | 0.0 | 1.1 |
| 3 | 40.8 | 59.2 | 0.0 | 0.0 |
| 4 | 42.2 | 57.8 | 0.0 | 0.0 |
8 Cluster description
8.1 Cluster 1: Security-Conscious Users
Group demographics: The average age in this group is 22.5 years. The gender distribution is slightly unbalanced, with 65.1% women and 33.3% men. Most individuals have completed high school education (46%), followed by bachelor degree (41.3 %). The little majority use NLB as their primary bank (39.7%), with an average income of 641.55 EUR.
Group behaviors: regarding mobile payments,
47.6% of people in this group use mobile payment
methods regularly, and 65.1% of payments are made using
card.
Cash payments are used for more than half small
purchases (up to 10 EUR) in only 11.1% of cases, for
medium purchases (11β99 EUR) in 4.8% of cases, and for
large purchases (100-1000 EUR) in 11.1% of cases.
- Key Characteristics:
- Security: This group places significant importance on security (mean: 0.86), reflecting their perception that traditional methods like cash and cards provide safer payment options. Their comfort with traditional methods is likely influenced by their strong sense of security, which aligns with the high mean for security on cards and cash in comparison to mobile payments or neobanks.
- Digital Payments: They exhibit lower usage of mobile payments (38.1% never use), preferring more established and perceived secure methods such as cards (mean: 0.65 for βEase of Useβ of cards, regularly used by 65.1%). Neobanks and mobile wallets are less favored in comparison, likely due to concerns over the security (mean: 0.86) and privacy (mean: 0.51) of newer digital platforms.
- Bank Preferences: Their choice of banks is highly local, with NLB (39.7%) and OTP (36.5%) being the most frequently used. This reflects their reliance on well-known and trusted financial institutions that provide traditional services and emphasize security.
These individuals prioritize security and privacy, particularly when it comes to transactions involving larger sums or unfamiliar methods. While they are open to digital payments, they remain cautious and conservative in their payment preferences, sticking to well-known and reliable forms like cash and debit cards. This groupβs higher reliance on traditional payment methods may reflect broader generational trust issues with new technologies, particularly with respect to data privacy.
8.2 Cluster 2: Tech-Savvy Enthusiasts
Group demographics: The average age in this group is 22.8 years. The gender distribution is slightly unbalanced, with 58.1% women and 40.9% men. Most individuals have completed high school education (44.1%), followed by bachelor degree (32.3%), and 21.5% have a masterβs degree. The primary banks used by this group are OTP (36.6%) and NLB (31.2%), with an average income of 711.52 EUR.
Group behaviors: Regarding mobile payments,
64.5% of people in this group use mobile payment
methods regularly, and 61.3% of payments are made using
card.
Cash payments are used for more than half of small
purchases (up to 10 EUR) in 19.4% of cases, for medium
purchases (11β99 EUR) in 10.8% of cases, and for large
purchases (100β1000 EUR) in 4.3% of cases.
- Key Characteristics:
- Payment Method Preferences: This group shows the highest preference for mobile payments (64.5% use regularly), reflecting their openness to adopting new payment technologies. This is consistent with the high ease of use mean for mobile payments (mean: 0.79), as they perceive these methods as efficient and simple to use.
- Tech Enthusiasm: They value ease of use (mean: 0.79) and speed (mean: 0.69) of transactions, and are less concerned with the traditional norms surrounding payment methods. Their perception of neobanks (mean: 0.71 for Ease of Use) and mobile payments as fast, efficient, and modern speaks to their affinity for cutting-edge technologies.
- Bank Preferences: They utilize a more diverse set of banks, with OTP (36.6%) and NLB (31.2%) being the most common, but their preference for newer, tech-savvy banking solutions likely supports their high engagement with mobile wallets.
The Tech-Savvy Enthusiasts are the early adopters of digital innovations. They prioritize convenience, flexibility, and speed, which digital payment solutions like mobile payments and neobanks deliver. This group is more comfortable embracing new payment systems and frequently uses them to support their on-the-go, fast-paced lifestyles. Their high engagement with mobile payments (64.5% use regularly) showcases their affinity for technology and digital banking solutions.
8.3 Cluster 3: Pragmatic Minimalists
Group demographics: The average age in this group is 22.5 years. The gender distribution is slightly unbalanced, with 59.2% women and 40.8% men. Most individuals have completed high school education (39.8%), followed by bachelor degree (31.1%) and 24.3% have a masterβs degree. The most common bank in this group is NLB (48.5%), with an average income of 730.2 EUR.
Group behaviors: Regarding mobile payments,
68.9% of people in this group use mobile payment
methods regularly, and 63.1% of payments are made using
card.
Cash payments are used for more than half of small
purchases (up to 10 EUR) in 7.8% of cases, for medium
purchases (11β99 EUR) in 5.8% of cases, and for large
purchases (100β1000 EUR) in 6.8% of cases.
- Key Characteristics:
- Payment Method Preferences: This group exhibits a pragmatic approach, preferring traditional payment methods like cash (68.9% use for small purchases, more than half for purchases under 10 EUR) while using cards regularly (63.1%). They have moderate ease of use perceptions for cards (mean: 0.47), reflecting their comfort with these methods, but also demonstrate an openness to newer solutions in a more moderate capacity.
- Efficiency: Their focus is on ease of use (mean: 0.47) and availability (mean: 0.04). This group values functionality over novelty, choosing payment methods that are reliable and widely accepted.
- Bank Preferences: Similar to the other clusters, they show a preference for local banks like NLB (48.5%) and OTP (34%), aligning with their practical approach to financial decisions. They are slightly less engaged with mobile payments than Tech-Savvy Enthusiasts, but they still use cards regularly.
The Pragmatic Minimalists are skeptical of excessive novelty but still open to using digital payment methods that are straightforward and well-accepted. They are not as enthusiastic as the Tech-Savvy Enthusiasts, but they prefer the familiarity and simplicity of physical cards and cash for transactions. This group likely seeks functionality and efficiency without unnecessary complexity.
8.4 Cluster 4: Skeptical Traditionalists
Group demographics: The average age in this group is 21.6 years, the youngest among all clusters. The gender distribution leans toward women (57.8%), with 42.2% men. Most individuals have completed high school education (46.7%), while 24.4% have a bachelor degree, and 17.8% have a masterβs degree. The most popular banks in this group are OTP (37.8%) and NLB (37.8%), with an average income of 665.0 EUR.
Group behaviors: Regarding mobile payments,
40.0% of people in this group use mobile payment
methods regularly, and 66.7% of payments are made using
card.
Cash payments dominate, with small purchases (up to 10
EUR) made for more than half of purchases in 28.9% of
cases, medium purchases (11β99 EUR) in 15.6% of
cases, and large purchases (100β1000 EUR) in 11.1% of
cases.
- Key Characteristics:
- Payment Method Preferences: This group is the most cautious toward mobile and digital payments, with the highest percentage of respondents using cash for smaller purchases (53.3% use cash for purchases under 10 EUR). They are also less likely to use neobanks or mobile payments on a regular basis, with cash usage being their strongest preference (mean: -1.05 for Ease of Use for mobile payments).
- Traditional Preferences: They favor cash for purchases across all ranges, and are more likely to see cash as offering better control over spending (mean: -0.22 for Spending Control). Their perception of traditional payment methods is deeply rooted in trust and familiarity.
- Bank Preferences: Like the others, this group uses NLB and OTP, with a notable proportion using other local banks.
The Skeptical Traditionalists show resistance to change, preferring more familiar, traditional payment methods. Their inclination towards cash and reluctance toward mobile payments or neobanks likely stems from their concerns about security and privacy. They have a strong belief in having control over their spending (mean: -0.22 for Spending Control), which they feel is best achieved with tangible forms of payment like cash or debit cards.
9 Perception map
library(pastecs)
NLB_PCA <- NLB_data[ , c("Safe_Cash", "Safe_DebitCard", "Safe_CreditCard", "Safe_PhonePay", "Safe_Neobank",
"Easy_Cash", "Easy_DebitCard", "Easy_CreditCard", "Easy_PhonePay", "Easy_Neobank",
"Accept_Cash", "Accept_DebitCard", "Accept_CreditCard", "Accept_PhonePay", "Accept_Neobank",
"Fast_Cash", "Fast_DebitCard", "Fast_CreditCard", "Fast_PhonePay", "Fast_Neobank",
"Private_Cash", "Private_DebitCard", "Private_CreditCard", "Private_PhonePay", "Private_Neobank",
"Control_Cash", "Control_DebitCard", "Control_CreditCard", "Control_PhonePay", "Control_Neobank")]library(dplyr)
NLB_PCA <- NLB_PCA %>%
rename(
Cash_Security = Safe_Cash,
Debit_Security = Safe_DebitCard,
Credit_Security = Safe_CreditCard,
Mobile_Security = Safe_PhonePay,
NeoBanks_Security = Safe_Neobank,
Cash_Ease = Easy_Cash,
Debit_Ease = Easy_DebitCard,
Credit_Ease = Easy_CreditCard,
Mobile_Ease = Easy_PhonePay,
NeoBanks_Ease = Easy_Neobank,
Cash_Availability = Accept_Cash,
Debit_Availability = Accept_DebitCard,
Credit_Availability = Accept_CreditCard,
Mobile_Availability = Accept_PhonePay,
NeoBanks_Availability = Accept_Neobank,
Cash_Speed = Fast_Cash,
Debit_Speed = Fast_DebitCard,
Credit_Speed = Fast_CreditCard,
Mobile_Speed = Fast_PhonePay,
NeoBanks_Speed = Fast_Neobank,
Cash_Privacy = Private_Cash,
Debit_Privacy = Private_DebitCard,
Credit_Privacy = Private_CreditCard,
Mobile_Privacy = Private_PhonePay,
NeoBanks_Privacy = Private_Neobank,
Cash_Control = Control_Cash,
Debit_Control = Control_DebitCard,
Credit_Control = Control_CreditCard,
Mobile_Control = Control_PhonePay,
NeoBanks_Control = Control_Neobank)library(tibble)
perceptual <- NLB_PCA %>%
pivot_longer(everything(), names_to = "name", values_to = "score") %>%
separate(name, into = c("Payment method", "Variable"), sep = "_")%>%
pivot_wider(names_from = Variable, values_from = score, values_fn = mean) %>%
column_to_rownames(var = "Payment method")
print(perceptual)## Security Ease Availability Speed Privacy Control
## Cash 5.503289 5.101974 6.226974 4.075658 5.680921 4.851974
## Debit 5.138158 6.148026 6.483553 6.174342 4.457237 5.154605
## Credit 4.947368 6.019737 6.335526 6.121711 4.457237 5.006579
## Mobile 5.174342 6.480263 6.029605 6.526316 4.394737 5.161184
## NeoBanks 4.825658 5.592105 5.075658 5.684211 4.483553 5.115132## Dim.1 Dim.2
## Security -0.7563320 0.534605714
## Ease 0.8659604 0.465478553
## Availability -0.1808968 0.942766082
## Speed 0.9753947 0.204881709
## Privacy -0.9934258 0.026725632
## Control 0.9275822 -0.001610424
9.1 Interpretation of the PCA Biplot (Perception Map)
This Principal Component Analysis (PCA) biplot provides a visual representation of financial payment methods and how they relate to key perceptual factors such as security, privacy, speed, availability, control, and ease of use.
9.1.1 Axes Interpretation:
- Dim1 (69.2%) and Dim2 (23.9%) together explain 93.1% of the variance, meaning most of the variation in the data can be understood from this two-dimensional representation.
- Dim1 (X-axis) likely captures a contrast between traditional vs.Β modern payment methods, with Cash positioned far to the left and Mobile payments, Credit, and Debit cards toward the right.
- Dim2 (Y-axis) may represent the trade-off between accessibility and digital convenience vs.Β security and privacy concerns.
9.1.2 Key Observations:
- Cash vs.Β Digital Payments
- Cash is located far left, strongly associated with privacy but negatively correlated with factors like speed, control, and availability.
- Credit, Debit, and Mobile Payments are clustered together on the right, indicating that they share common characteristics, particularly ease of use, speed, and control.
- NeoBanks (Fintech-Driven Banking)
- NeoBanks are positioned lower in Dim2, indicating a potential perception of risk or lack of trust compared to traditional banking options.
- They may also be less associated with security and privacy, as they are further away from these vectors.
- Direction and Meaning of Vectors (Arrows):
- Security and Privacy point toward the upper left, aligning with Cash, indicating that users who prioritize these factors tend to prefer cash transactions.
- Speed, Ease of Use, and Control point toward the right, aligning with Mobile and Digital payment methods, suggesting that these are perceived as efficient and user-friendly.
- Availability is pointing upwards, which suggests that people associate availability with banking products that are easily accessible across multiple channels.
9.1.3 Conclusion:
This perception map illustrates the trade-offs in different payment methods: - Cash is viewed as the safest and most private option, but at the cost of convenience and speed. - Digital payment methods (Credit, Debit, Mobile) are associated with efficiency, ease of use, and control, but potentially at the expense of security and privacy. - NeoBanks are viewed as a distinct category, perhaps due to concerns over trust or unfamiliarity compared to traditional banking systems.
This visualization helps us understand consumer preferences when choosing payment methods and their perceived benefits and drawbacks.
10 Hypothesesis
10.1 Majority of young people use cash at least once a month.
Letβs check if assumptions to perform a parametric are met:
n * π> 5 -> 304 * 0.5 = 152 > 5
n(1-π) > 5 -> 304 * 0.5 = 152 > 5
Both assumptions are met, we can proceed with the parametric test - test of population proportion
H0: Ο = 0.5
H1: Ο > 0.5
## [1] 0##
## 1-sample proportions test without continuity correction
##
## data: 281 out of 304, null probability 0.5
## X-squared = 218.96, df = 1, p-value < 2.2e-16
## alternative hypothesis: true p is greater than 0.5
## 95 percent confidence interval:
## 0.8954807 1.0000000
## sample estimates:
## p
## 0.9243421We reject H0 at p<0.001, the proportion of young people who still use cash at least once a month is larger than 50%.
10.3 Most young people use cash for small payments (up to 10 EUR).
To test this, we will do a test of population proportion. Letβs check if assumptions are met.
n * π> 5 -> 304 * 0.5 = 152 > 5
n(1-π) > 5 -> 304 * 0.5 = 152 > 5
Assumptions are met, so we can proceed with the test of population proportion.
H0: π = 0.5
H1: π > 0.5
## [1] 0##
## 1-sample proportions test without continuity correction
##
## data: 270 out of 304, null probability 0.5
## X-squared = 183.21, df = 1, p-value < 2.2e-16
## alternative hypothesis: true p is greater than 0.5
## 95 percent confidence interval:
## 0.8549349 1.0000000
## sample estimates:
## p
## 0.8881579We reject H0 at p<0.001. We have found that more than 50% use cash for their small (less than 10 EUR payments) at least some of the time.
Letβs do the same test, to test how many people use cash for less than half of their small payments.
H0: π = 0.5
H1: π > 0.5
## [1] 0##
## 1-sample proportions test without continuity correction
##
## data: 169 out of 304, null probability 0.5
## X-squared = 3.8026, df = 1, p-value = 0.02559
## alternative hypothesis: true p is greater than 0.5
## 95 percent confidence interval:
## 0.5087589 1.0000000
## sample estimates:
## p
## 0.5559211We reject H0 at p = 0.026. We have found that the proportion of people, who use cash for less than half of their their small payments is greater than 50%.
10.4 Young people who save, mainly save in cash.
NLB_data_save$Save_Form <-ifelse(test = NLB_data_save$Save_Form == -2,
yes = NA,
no = NLB_data_save$Save_Form)## [1] 34NLB_data_save$Save_FormF<- factor(NLB_data_save$Save_Form,
levels = c(1, 2, 3, 4, 5, 6, 7),
labels = c("Fully in cash", "Up to 25% in cash", "25% and 50% in cash", "About 50% in cash, 50% digital", "25% to 50% digital", "Up to 25% digital", "Fully digital"))library(ggplot2)
library(dplyr)
# Calculate percentages
data_percent <- NLB_data_save %>%
count(Save_FormF) %>%
mutate(percentage = (n / sum(n)) * 100)
# Create the bar plot with NLB colors
ggplot(data_percent, aes(x = Save_FormF, y = percentage)) +
geom_bar(stat = "identity", fill = NLB_colors[1], color = "black") + # Apply NLB Indigo Blue color
labs(title = "How do you save?",
x = "Preference",
y = "Percentage") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
We have 233 people who save in our sample. Based on the frequency graph and the calculations, we have found that in our sample, only 34 (14.6%) people who save, save more than 50% in cash. Based on this, we have decided to reject our hypothesis, that young people who save, mostly do so in cash.
10.5 Young people (18β27) mostly use the money in the same form they receive it.
To test this, we will do a test of population proportion. Letβs check if assumptions are met.
n * π> 5 -> 304 * 0.5 = 152 > 5
n(1-π) > 5 -> 304 * 0.5 = 152 > 5
Assumptions are met, so we can proceed with the test of population proportion.
H0: π = 0.5
H1: π > 0.5
## [1] 207##
## 1-sample proportions test without continuity correction
##
## data: 207 out of 304, null probability 0.5
## X-squared = 39.803, df = 1, p-value = 1.405e-10
## alternative hypothesis: true p is greater than 0.5
## 95 percent confidence interval:
## 0.6355172 1.0000000
## sample estimates:
## p
## 0.6809211We reject H0 at p<0.001. We have found that more than 50% of young people somewhat agree, agree or completely agree that they tend to use money in the same form as they receive it.
10.6 The convenience of digital payments is a more important factor influencing adoption among young people (18β27) than security.
## Importance_Ease Importance_Security
## 1 7 6
## 2 6 7
## 3 7 6
## 4 7 7
## 5 7 7
## 6 5 7## 'data.frame': 304 obs. of 2 variables:
## $ Importance_Ease : num 7 6 7 7 7 5 6 6 4 7 ...
## $ Importance_Security: num 6 7 6 7 7 7 5 7 7 7 ...## 'data.frame': 304 obs. of 2 variables:
## $ Importance_Ease : num 7 6 7 7 7 5 6 6 4 7 ...
## $ Importance_Security: num 6 7 6 7 7 7 5 7 7 7 ...## Importance_Ease Importance_Security
## Min. :1.000 Min. :1.000
## 1st Qu.:6.000 1st Qu.:5.000
## Median :7.000 Median :7.000
## Mean :6.105 Mean :6.076
## 3rd Qu.:7.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.000# Calculate the mean of column 29 (Enostavnost)
mean_enostavnost <- mean(NLB_R6H6[[1]], na.rm = TRUE)
print(mean_enostavnost)## [1] 6.105263# Calculate the mean of column 29 (Varnost)
mean_varnost <- mean(NLB_R6H6[[2]], na.rm = TRUE)
print(mean_varnost)## [1] 6.075658##
## Shapiro-Wilk normality test
##
## data: NLB_R6H6$diffs
## W = 0.8771, p-value = 6.971e-15Since the p-value is much smaller than 0.05, we reject the null hypothesis that the differences are normally distributed.
This means the data does not follow a normal distribution, which justifies using a non-parametric test like the Wilcoxon Signed-Rank test instead of a paired t-test.
wilcox.test(
NLB_R6H6[[1]],
NLB_R6H6[[2]],
paired = TRUE,
correct = FALSE,
exact = FALSE,
alternative = "two.sided")##
## Wilcoxon signed rank test
##
## data: NLB_R6H6[[1]] and NLB_R6H6[[2]]
## V = 5908, p-value = 0.537
## alternative hypothesis: true location shift is not equal to 0p-value = 0.537
Since the p-value is greater than 0.05, we fail to reject the null hypothesis.
This indicates that there is no statistically significant difference between the two variables (Enostavnost and Varnost).
In the context of our hypothesis, this means that we do not have enough evidence to say that convenience is a significantly stronger factor than security in influencing digital payment adoption among young people.
The Wilcoxon test results indicate that there is no statistically significant difference between the perceived importance of convenience and security (p = 0.537). This means that we fail to reject the null hypothesis, suggesting that convenience is not significantly more important than security in influencing digital payment adoption among young people (18-27 years old).
Therefore, our data does not support the hypothesis that convenience is a more important factor than security in digital payment adoption. Both factors appear to have similar levels of importance for young users.
10.7 Young people (18β27) mostly prefer a society that predominantly uses digital payments but remain reluctant to completely eliminate cash.
NLB_R4H4$Q22F <- factor(NLB_R4H4$Switch_Digital,
levels = c(1, 2, 3, 4),
labels = c("Fully digital", "Balance digital-cash", "Cash", "Don't know"))library(ggplot2)
library(dplyr)
# Calculate percentages
data_percent <- NLB_R4H4 %>%
count(Q22F) %>%
mutate(percentage = (n / sum(n)) * 100)
# Create the bar plot with percentages
ggplot(data_percent, aes(x = Q22F, y = percentage)) +
geom_bar(stat = "identity", fill = NLB_colors[1], color = "black") + # Apply NLB Blue color
labs(title = "Would you switch to a fully digital society?",
x = "Preference",
y = "Percentage") +
theme_minimal()
## Q22F n percentage
## 1 Fully digital 78 25.657895
## 2 Balance digital-cash 171 56.250000
## 3 Cash 44 14.473684
## 4 Don't know 11 3.618421From the graph, we can clearly see that the largest percentage of our respondents have answered that they would consider switching to a mostly digital society, but the do not want to fully give up cash.
To further test this, we will do a test of population proportion. Letβs check if assumptions are met.
n * π> 5 -> 304 * 0.5 = 152 > 5
n(1-π) > 5 -> 304 * 0.5 = 152 > 5
Assumptions are met, so we can proceed with the test of population proportion.
H0: π = 0.5
H1: π > 0.5
##
## 1-sample proportions test without continuity correction
##
## data: 171 out of 304, null probability 0.5
## X-squared = 4.75, df = 1, p-value = 0.01465
## alternative hypothesis: true p is greater than 0.5
## 95 percent confidence interval:
## 0.5153528 1.0000000
## sample estimates:
## p
## 0.5625We reject H0 at p = 0.015. Population proportion of people who like digital but are not prepared to fully give up cash is larger than 50%.
10.8 Young people (18β27) are motivated to use digital payments when splitting bills with friends due to the simplicity of transferring exact amounts.
## Reason_ExactSum
## 1 1
## 2 1
## 3 1
## 4 1
## 5 1
## 6 1## 'data.frame': 304 obs. of 1 variable:
## $ Reason_ExactSum: chr "1" "1" "1" "1" ...col_name <- colnames(NLB_R8H8)[1]
NLB_R8H8$Reason_ExactSum <- factor(NLB_R8H8$Reason_ExactSum,
levels = c(1, 0, -2),
labels = c("Transfer whole amounts", "Other reasons", "Not applicable"))
library(dplyr)
NLB_R8H8 %>% count (Reason_ExactSum)## Reason_ExactSum n
## 1 Transfer whole amounts 236
## 2 Other reasons 28
## 3 Not applicable 40To test this, we will do a test of population proportion. Letβs check if assumptions are met.
n * π> 5 -> 264 * 0.5 = 132 > 5
n(1-π) > 5 -> 264 * 0.5 = 132 > 5
Assumptions are met, so we can proceed with the test of population proportion.
H0: π = 0.5
H1: π > 0.5
##
## 1-sample proportions test without continuity correction
##
## data: 236 out of 264, null probability 0.5
## X-squared = 163.88, df = 1, p-value < 2.2e-16
## alternative hypothesis: true p is greater than 0.5
## 95 percent confidence interval:
## 0.8586738 1.0000000
## sample estimates:
## p
## 0.8939394Since p-value < 0.05, we reject the null hypothesis Hβ. This means there is strong statistical evidence that the true population proportion is greater than 50%.
3.1.1.5 Social Influence on Financial Decisions
Social_Friendsand 4 forSocial_Family, it indicates that social circles play a critical role in shaping financial behavior, particularly in the areas of trust and digital adoption.