NLB Project

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1 Introduction of data

#install.packages("readxl")
library(readxl)

## Warning: package 'readxl' was built under R version 4.4.2

library(dplyr)

## Warning: package 'dplyr' was built under R version 4.4.2

## 
## 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

mydata <- read_excel("./Survey.xlsx")

mydata <- as.data.frame(mydata)
mydata$ID <- seq(1, nrow(mydata))
mydata <- mydata %>%
  filter(!ID %in% 1)
mydata$ID <- seq(1, nrow(mydata))
head(mydata)

##   Q1 Q21 Q22a Q23a Q23b Q23c Q23d Q23e Q23f Q24 Q25 Q26 Q27a Q27b Q27c Q28 Q29a
## 1  1   6    3    1    1    1    1    1    1   2   3   1    1    1    0   2    3
## 2  1   5    4    1    1    1    1    1    1   1   4   1    1    0    0   2    4
## 3  1   6    4    2    1    1    1    2    2   2   3   2    0    0    1   1    4
## 4  1   6    5    1    1    1    1    1    1   1   4   1    0    0    1   2    5
## 5  1   6    5    1    1    1    1    1    1   1   4   1    0    0    1   2    5
## 6  1   6    5    2    1    1    1    1    1   4   3   1    0    1    0   1    4
##   Q29b Q29c Q29d Q30a Q30b Q30c Q30d Q30e Q31a Q31b Support and guidance
## 1    3    4    5    3    3    3    3    3    4    4                    3
## 2    2    2    1    3    3    3    3    3    5    2                    2
## 3    4    2    5    4    4    4    4    1    2    4                    5
## 4    2    1    1    2    2    4    1    3    5    1                    4
## 5    2    2    2    5    5    5    5    5    5    2                    3
## 6    1    1    4    3    5    5    2    4    4    5                    5
##   Confidence Security Accesibility Clarity Speed
## 1          4        5            3       4     4
## 2          4        4            5       4     5
## 3          4        4            5       5     5
## 4          4        5            3       4     5
## 5          4        4            5       5     5
## 6          4        5            4       5     4
##   Poslovalnica_podpora in usmerjanje Mobilna aplikacija_podpora in usmerjanje
## 1                                  4                                        4
## 2                                  4                                        3
## 3                                  5                                        2
## 4                                  5                                        3
## 5                                  4                                        3
## 6                                  4                                        2
##   Poslovalnica_brezkrbnost Mobilna aplikacija_brezkrbnost Poslovalnica_varnost
## 1                        3                              3                    4
## 2                        4                              4                    4
## 3                        5                              2                    5
## 4                        5                              4                    5
## 5                        4                              4                    4
## 6                        4                              3                    4
##   Mobilna aplikacija_varnost Poslovalnica_dostopnost
## 1                          3                       3
## 2                          4                       3
## 3                          3                       5
## 4                          4                       5
## 5                          3                       4
## 6                          3                       5
##   Mobilna aplikacija_dostopnost Poslovalnica_jasnost Mobilna aplikacija_jasnost
## 1                             3                    4                          4
## 2                             5                    5                          4
## 3                             2                    5                          2
## 4                             3                    5                          4
## 5                             5                    4                          4
## 6                             2                    4                          2
##   Poslovalnica_hitrost Mobilna aplikacija_hitrost                    Q40 Q41
## 1                    3                          4                     -1   2
## 2                    3                          5                     -1   2
## 3                    3                          4                     -1   2
## 4                    2                          5                     -1   2
## 5                    2                          4                     -1   2
## 6                    1                          5 Stay humble, only cash   2
##    Q42 Q43a Q43b Q43c Q43d Q43e Q43f Q43g Q43h Q44 Q45 Q45_13_text Q46 Q47 Q48
## 1 2000    1    0    0    0    0    0    0    0   2   3          -2   2   2   2
## 2 1998    0    0    0    1    0    0    0    0   6   3          -2   2   3   4
## 3 2001    1    0    0    0    0    0    0    0   2   1          -2   2   2   3
## 4 1994    0    0    0    1    0    0    0    0   6  12          -2   5   6   5
## 5 2000    1    1    0    0    0    0    0    0   2   1          -2   2   3   6
## 6 2004    1    0    0    0    0    0    0    0   3   1          -2   1   8   4
##   ID
## 1  1
## 2  2
## 3  3
## 4  4
## 5  5
## 6  6

mydata[c(2:9,11, 17:45,48,58)] <- mydata[c(2:9,11, 17:45,48,58)] %>% mutate_all(as.numeric)
library(dplyr)
mydata$BankF <- case_when(
  mydata$Q45 == 1 ~ 1,   
  mydata$Q45 == 2 ~ 2,  
  mydata$Q45 == 3 ~ 2,  
  mydata$Q45 == 4 ~ 2,  
  mydata$Q45 == 5 ~ 2, 
  mydata$Q45 == 6 ~ 2,  
  mydata$Q45 == 7 ~ 2,  
  mydata$Q45 == 8 ~ 2,  
  mydata$Q45 == 9 ~ 2, 
  mydata$Q45 == 10 ~ 2,  
  mydata$Q45 == 11 ~ 2,  
  mydata$Q45 == 12 ~ 2,  
  mydata$Q45 == 13 ~ 2,  
  TRUE ~ 0)

mydata$Q26 <- factor(mydata$Q26, 
                         levels = c(1, 2), 
                         labels = c("Da","Ne"))
mydata$Q27a <- factor(mydata$Q27a, 
                         levels = c(1, 0), 
                         labels = c("V mobilni aplikaciji","Ne"))
mydata$Q27b <- factor(mydata$Q27b, 
                         levels = c(1, 0), 
                         labels = c("V poslovalnici","Ne"))
mydata$Q27c <- factor(mydata$Q27c, 
                         levels = c(1, 0), 
                         labels = c("Nisem upiorabljal/a","Ne"))
mydata$Q28 <- factor(mydata$Q28, 
                         levels = c(1, 2), 
                         labels = c("V poslovalnici","V mobilni aplikaciji"))

mydata$Q41 <- factor(mydata$Q41, 
                         levels = c(1, 2, 3), 
                         labels = c("Female","Male", "I don't want to answer"))

mydata$Q43a <- factor(mydata$Q43a, 
                         levels = c(1, 0), 
                         labels = c("Selected","Not selected"))

mydata$Q43b <- factor(mydata$Q43b, 
                         levels = c(1, 0), 
                         labels = c("Selected","Not selected"))

mydata$Q43c <- factor(mydata$Q43c, 
                         levels = c(1, 0), 
                         labels = c("Selected","Not selected"))

mydata$Q43d <- factor(mydata$Q43d, 
                         levels = c(1, 0), 
                         labels = c("Selected","Not selected"))

mydata$Q43e <- factor(mydata$Q43e, 
                         levels = c(1, 0), 
                         labels = c("Selected","Not selected"))

mydata$Q43f <- factor(mydata$Q43f, 
                         levels = c(1, 0), 
                         labels = c("Selected","Not selected"))

mydata$Q43g <- factor(mydata$Q43g, 
                         levels = c(1, 0), 
                         labels = c("Selected","Not selected"))

mydata$Q43h <- factor(mydata$Q43h, 
                         levels = c(1, 0), 
                         labels = c("Selected","Not selected"))

mydata$Q44 <- factor(mydata$Q44, 
                         levels = c(1, 2, 3, 4, 5, 6), 
                         labels = c("Less than 1.000 habitants",
                       "1.000 – 5.000 habitants",
                       "5.001 – 20.000 habitants",
                       "20.001 – 50.000 habitants",
                       "50.001 – 100.000 habitants",
                       "More than 100.000 habitants"))

mydata$Q45 <- factor(mydata$Q45, 
                         levels = c(3, 5, 1, 9, 12, 7, 10, 4, 11, 6), 
                         labels = c("OTP banka d.d.","Banka Intesa Sanpaolo d.d.", "Nova Ljubljanska Banka d.d. (NLB)", "Gorenjska Banka d.d.", "Delavska Hranilnica d.d.", "Revolut", "Deželna Banka Slovenije d.d.", "Banka Sparkasse d.d.", "Addiko Bank d.d.", "UniCredit Banka Slovenija d.d."))

mydata$Q46 <- factor(mydata$Q46, 
                         levels = c(1, 2, 3, 5, 6, 4), 
                         labels = c("Študent/-ka","Redno zaposlen/-a", "Upokojen/-a", "Samozaposlen/-a", "Delno zaposlen/-a", "Brezposeln/-a"))

mydata$Q47 <- factor(mydata$Q47, 
                         levels = c(1, 2, 3, 4, 5, 6, 7, 8), 
                         labels = c("Pod 1.000€","1.000€ - 1.500€","1.501€ - 2.000€","2.001€ - 3.000€","3.001€ - 5.000€","5.001€ - 10.000€","Above 10.000€", "I don't want to answer"))

mydata$Q48 <- factor(mydata$Q48, 
                           levels = c(2, 3, 4, 5, 6, 7),  
                           labels = c(
                             "Dokončana osnovna šola", 
                             "Dokončana nižja ali srednja poklicna izobrazba", 
                             "Dokončana srednja strokovna ali splošna izobrazba", 
                             "Dokončana višješolska strokovna ali visokošolska strokovna izobrazba (tudi 1. bolonjska stopnja)", 
                             "Dokončana visokošolska strokovna univerzitetna izobrazba (tudi 2. bolonjska stopnja)", 
                             "Dokončana specializacija, znanstveni magisterij, doktorat"
                           ))
mydata$BankF <- factor(mydata$BankF, 
                         levels = c(1, 2), 
                         labels = c("NLB","Other"))

library(dplyr)

mydataNLB <- mydata %>%
  filter(BankF == "NLB")
mydata2 <- mydata

summary(mydata[,2:45])

##       Q21             Q22a            Q23a            Q23b      
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:4.000   1st Qu.:4.000   1st Qu.:1.000   1st Qu.:1.000  
##  Median :5.000   Median :5.000   Median :1.000   Median :1.000  
##  Mean   :4.502   Mean   :4.325   Mean   :1.381   Mean   :1.192  
##  3rd Qu.:5.000   3rd Qu.:5.000   3rd Qu.:2.000   3rd Qu.:1.000  
##  Max.   :6.000   Max.   :5.000   Max.   :2.000   Max.   :2.000  
##       Q23c            Q23d            Q23e            Q23f      
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000  
##  Median :1.000   Median :1.000   Median :1.000   Median :2.000  
##  Mean   :1.283   Mean   :1.181   Mean   :1.355   Mean   :1.543  
##  3rd Qu.:2.000   3rd Qu.:1.000   3rd Qu.:2.000   3rd Qu.:2.000  
##  Max.   :2.000   Max.   :2.000   Max.   :2.000   Max.   :2.000  
##      Q24                 Q25        Q26                        Q27a    
##  Length:265         Min.   :1.000   Da:141   V mobilni aplikaciji:106  
##  Class :character   1st Qu.:3.000   Ne:124   Ne                  :159  
##  Mode  :character   Median :4.000                                      
##                     Mean   :3.498                                      
##                     3rd Qu.:4.000                                      
##                     Max.   :5.000                                      
##              Q27b                      Q27c                       Q28     
##  V poslovalnici: 67   Nisem upiorabljal/a:114   V poslovalnici      : 96  
##  Ne            :198   Ne                 :151   V mobilni aplikaciji:169  
##                                                                           
##                                                                           
##                                                                           
##                                                                           
##       Q29a            Q29b            Q29c            Q29d           Q30a      
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.00   Min.   :1.000  
##  1st Qu.:3.000   1st Qu.:2.000   1st Qu.:1.000   1st Qu.:2.00   1st Qu.:2.000  
##  Median :4.000   Median :3.000   Median :2.000   Median :3.00   Median :3.000  
##  Mean   :3.438   Mean   :2.728   Mean   :2.596   Mean   :2.97   Mean   :3.072  
##  3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:4.00   3rd Qu.:4.000  
##  Max.   :5.000   Max.   :5.000   Max.   :5.000   Max.   :5.00   Max.   :5.000  
##       Q30b            Q30c            Q30d            Q30e      
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:3.000   1st Qu.:3.000   1st Qu.:2.000   1st Qu.:2.000  
##  Median :4.000   Median :4.000   Median :3.000   Median :3.000  
##  Mean   :3.377   Mean   :3.426   Mean   :3.117   Mean   :2.932  
##  3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:4.000  
##  Max.   :5.000   Max.   :5.000   Max.   :5.000   Max.   :5.000  
##       Q31a            Q31b       Support and guidance   Confidence   
##  Min.   :1.000   Min.   :1.000   Min.   :1.00         Min.   :1.000  
##  1st Qu.:4.000   1st Qu.:2.000   1st Qu.:3.00         1st Qu.:4.000  
##  Median :4.000   Median :2.000   Median :4.00         Median :4.000  
##  Mean   :4.128   Mean   :2.506   Mean   :3.83         Mean   :4.075  
##  3rd Qu.:5.000   3rd Qu.:3.000   3rd Qu.:5.00         3rd Qu.:5.000  
##  Max.   :5.000   Max.   :5.000   Max.   :5.00         Max.   :5.000  
##     Security      Accesibility      Clarity          Speed      
##  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 :5.000   Median :4.000   Median :4.000   Median :4.000  
##  Mean   :4.264   Mean   :4.042   Mean   :4.023   Mean   :4.219  
##  3rd Qu.:5.000   3rd Qu.:5.000   3rd Qu.:5.000   3rd Qu.:5.000  
##  Max.   :5.000   Max.   :5.000   Max.   :5.000   Max.   :5.000  
##  Poslovalnica_podpora in usmerjanje Mobilna aplikacija_podpora in usmerjanje
##  Min.   :1.000                      Min.   :1.000                           
##  1st Qu.:3.000                      1st Qu.:3.000                           
##  Median :4.000                      Median :3.000                           
##  Mean   :3.811                      Mean   :3.125                           
##  3rd Qu.:4.000                      3rd Qu.:4.000                           
##  Max.   :5.000                      Max.   :5.000                           
##  Poslovalnica_brezkrbnost Mobilna aplikacija_brezkrbnost Poslovalnica_varnost
##  Min.   :1.000            Min.   :1.000                  Min.   :1.000       
##  1st Qu.:3.000            1st Qu.:3.000                  1st Qu.:4.000       
##  Median :4.000            Median :4.000                  Median :4.000       
##  Mean   :3.921            Mean   :3.551                  Mean   :4.211       
##  3rd Qu.:5.000            3rd Qu.:4.000                  3rd Qu.:5.000       
##  Max.   :5.000            Max.   :5.000                  Max.   :5.000       
##  Mobilna aplikacija_varnost Poslovalnica_dostopnost
##  Min.   :1.000              Min.   :1.000          
##  1st Qu.:3.000              1st Qu.:3.000          
##  Median :4.000              Median :4.000          
##  Mean   :3.698              Mean   :3.785          
##  3rd Qu.:4.000              3rd Qu.:5.000          
##  Max.   :5.000              Max.   :5.000          
##  Mobilna aplikacija_dostopnost Poslovalnica_jasnost Mobilna aplikacija_jasnost
##  Min.   :1.000                 Min.   :1.000        Min.   :1.000             
##  1st Qu.:3.000                 1st Qu.:3.000        1st Qu.:3.000             
##  Median :4.000                 Median :4.000        Median :3.000             
##  Mean   :3.792                 Mean   :3.913        Mean   :3.347             
##  3rd Qu.:4.000                 3rd Qu.:5.000        3rd Qu.:4.000             
##  Max.   :5.000                 Max.   :5.000        Max.   :5.000             
##  Poslovalnica_hitrost Mobilna aplikacija_hitrost
##  Min.   :1.000        Min.   :1.0               
##  1st Qu.:2.000        1st Qu.:4.0               
##  Median :3.000        Median :4.0               
##  Mean   :2.891        Mean   :4.2               
##  3rd Qu.:4.000        3rd Qu.:5.0               
##  Max.   :5.000        Max.   :5.0

Survey Questions:

Q1: Ali uporabljate mobilno aplikacijo banke, kjer imate odprt primarni račun (npr. KlikIn, mBank@Net in podobno)?
[1 - Da, 2 - Ne]

Q21: Kako pogosto uporabljate mobilno bančno aplikacijo?
[1 - Manj kot enkrat na mesec, 2 - Enkrat na mesec, 3 - Dva do trikrat na mesec, 4 - Enkrat na teden, 5 - Večkrat na teden, 6 - Vsak dan]

Q22: V kolikšni meri se strinjate z naslednjo trditvijo?
[1 - Sploh se ne strinjam, 2 - Se ne strinjam, 3 - Niti se ne strinjam, niti se strinjam, 4 - Se strinjam, 5 - Popolnoma se strinjam]

    Q22a: Zavedam se, da lahko v mobilni aplikaciji opravim več storitev kot zgolj pregled stanja na računu in opravljanje transakcij.

Q23: Ali ste vedeli, da lahko naslednje storitve opravite v mobilni aplikaciji?
[1 - Da, 2 - Ne]

• Q23a: Sklenitev potrošniškega kredita.
• Q23b: Spremembo limita na računu.
• Q23c: Naročilo nove kartice.
• Q23d: Sprememba limita na plačilni kartici.
• Q23e: Odprtje varčevalnega računa.
• Q23f: Sklenitev depozita.

Q24: Kje ste prvič izvedeli za možnost uporabe naprednih storitev v mobilni bančni aplikaciji?
[1 - V mobilni bančni aplikaciji, 2 - V poslovalnici banke, 3 - v oglasih, 4 - Preko družbenih omrežij, 5 - Preko prijateljev/družine, 6 - V tej anketi, 7 - Drugo]

Q25: Kako enostavno je najti napredne storitve v vaši mobilni bančni aplikaciji?
[1 - Zelo težko, 2 - Težko, 3 - Niti težko, niti enostavno, 4 - Enostavno, 5 - Zelo enostavno]

Q26: Ste v svoji aplikaciji kdaj opazili promocije ali obvestila o naprednih storitvah?
[1 - Da, 2 - Ne]

Q27: Kje ste v zadnjem letu izvajali napredne storitve?
[Izbira več možnih odgovorov]

• Q27a: V mobilni aplikaciji.
• Q27b: V poslovalnici.
• Q27c: Nisem jih uporabljal/a.

Q28: Če bi naslednji teden uporabili napredno storitev, kje bi to storili?
[1 - V mobilni aplikaciji, 2 - V poslovalnici]

Q29: V kolikšni meri naslednji dejavniki vplivajo na vašo odločitev, da ne uporabljate naprednih storitev mobilne banke?
[1 - Sploh se ne strinjam, 2 - Se ne strinjam, 3 - Niti se ne strinjam, niti se strinjam, 4 - Se strinjam, 5 - Popolnoma se strinjam]

• Q29a: Pomanjkanje potrebe po uporabi naprednih storitev.
• Q29b: Težave pri iskanju naprednih storitev v aplikaciji.
• Q29c: Zaskrbljenost glede varnosti.
• Q29d: Direktni stik z uslužbenci v poslovalnici.

Q30: Pogosteje bi uporabljal/a napredne storitve v mobilni bančni aplikaciji.
[1 - Sploh se ne strinjam, 2 - Se ne strinjam, 3 - Niti se ne strinjam, niti se strinjam, 4 - Se strinjam, 5 - Popolnoma se strinjam]

• Q30a: če bi bilo napredne storitve lažje najti v aplikaciji.
• Q30b: če bi aplikacija ponujala navodila po korakih.
• Q30c: če bi aplikacija zmanjšala potreben čas za opravljanje te storitve.
• Q30d: če bi aplikacija ponujala personalizirana priporočila.
• Q30e: če bi aplikacija imela izboljšan dizajn.

Q31: V kolikšni meri se strinjate z naslednjima trditvama?

• Q31a: Možnost uporabe naprednih storitev v mobilni aplikaciji je zelo koristna.
• Q31b: Zaskrbljenost glede varnosti me odvrača od uporabe naprednih funkcij v mobilni aplikaciji.

Q40: Imate še kakšen komentar ali priporočilo glede uporabe mobilne banke?

Q41: Spol
[1 - Ženska, 2 - Moški, 3 - Ne želim odgovoriti]

Q42: Prosimo, vpišite leto rojstva.
[Odprto besedilo]

Q43: S kom živite v istem gospodinjstvu?
[Izbira več možnih odgovorov]

• Q43a: S starši
• Q43b: S sorojenci
• Q43c: S partnerko/jem
• Q43d: Z otroki
• Q43e: Živim sam/a

2 Clustering

mydata_clu_std <- as.data.frame(scale(mydata[c(3,11,26,27)]))
library(factoextra) 

## Warning: package 'factoextra' was built under R version 4.4.2

## Loading required package: ggplot2

## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa

get_clust_tendency(mydata_clu_std, 
                   n = nrow(mydata_clu_std) - 1,
                   graph = FALSE) 

## $hopkins_stat
## [1] 0.5586369
## 
## $plot
## NULL

colnames(mydata) [3] <- "Awareness"
colnames(mydata) [11] <- "Ease"
colnames(mydata) [26] <- "Value"
colnames(mydata) [27] <- "Trust"

I changed the name of the variables. We are creating clusters on 4 cluster variables: “Awareness”, “Ease”, “Value”, “Trust”.

2.1 Finding optimal number of clusters

library(factoextra)
library(NbClust)

fviz_nbclust(mydata_clu_std, kmeans, method = "wss") +
  labs(subtitle = "Elbow method")

fviz_nbclust(mydata_clu_std, kmeans, method = "silhouette")+
  labs(subtitle = "Silhouette analysis")

library(dplyr)
library(factoextra)
WARD <- mydata_clu_std %>%
  get_dist(method = "euclidean") %>%  
  hclust(method = "ward.D2")          

WARD

## 
## Call:
## hclust(d = ., method = "ward.D2")
## 
## Cluster method   : ward.D2 
## Distance         : euclidean 
## Number of objects: 265

library(factoextra)
fviz_dend(WARD)

## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## ℹ 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.

fviz_dend(WARD,
          k = 5,
          cex = 0.5,
          palette = "jama",
          color_labels_by_k = TRUE,
          rect = TRUE)

We decided to go with 5 clusters.

2.2 Editing clusters

library(factoextra)
library(ggplot2)

Clustering <- kmeans(mydata_clu_std, 
                     centers = 5, 
                     nstart = 25) 

Clustering

## K-means clustering with 5 clusters of sizes 27, 74, 76, 32, 56
## 
## Cluster means:
##         Q22a        Q25       Q31a       Q31b
## 1 -2.2987082 -0.6636458 -0.1920661  0.4967113
## 2  0.1713700 -0.7658139  0.1806157 -0.3039786
## 3  0.5063700  0.9025907  0.5693474 -0.7926587
## 4 -0.0860382 -0.5414867 -1.9278200  0.2004560
## 5  0.2438008  0.4164170  0.1828583  1.1234050
## 
## Clustering vector:
##   [1] 1 3 4 3 3 5 2 5 3 1 4 4 5 2 4 1 2 2 5 5 3 5 4 3 3 1 5 2 2 2 5 2 3 3 3 5 3
##  [38] 4 3 2 3 5 3 3 2 3 3 3 3 3 2 3 5 5 2 2 5 3 5 5 5 3 3 2 3 2 2 2 5 5 5 5 3 2
##  [75] 4 2 5 4 3 5 5 1 3 2 3 2 1 5 3 2 2 1 1 1 2 2 5 3 2 1 3 1 4 3 2 3 1 2 1 3 3
## [112] 5 2 2 1 2 5 1 3 2 5 2 2 4 5 1 2 2 1 2 5 2 5 3 5 1 5 4 4 2 3 1 1 4 2 4 2 3
## [149] 3 4 3 4 2 3 2 5 1 4 1 3 3 3 3 2 2 3 2 3 2 5 3 4 4 3 2 2 3 2 3 3 3 5 3 2 2
## [186] 4 2 2 2 3 1 3 5 4 4 2 2 3 3 5 4 4 2 5 2 4 3 3 5 5 2 1 3 2 5 2 5 2 5 5 4 5
## [223] 3 2 3 2 3 5 3 2 5 3 5 1 1 4 1 5 3 2 4 2 5 2 3 2 3 2 5 5 2 3 3 5 5 2 2 3 4
## [260] 4 3 3 5 4 4
## 
## Within cluster sum of squares by cluster:
## [1]  95.65988  92.49211  79.70569 116.86933  85.02220
##  (between_SS / total_SS =  55.5 %)
## 
## Available components:
## 
## [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
## [6] "betweenss"    "size"         "iter"         "ifault"

library(factoextra)
fviz_cluster(Clustering, 
             palette = "Set1", 
             repel = TRUE,
             ggtheme = theme_bw(),
             data = mydata_clu_std)

mydata$Dissimilarity <- sqrt(
  mydata_clu_std$Q22a^2 +
  mydata_clu_std$Q25^2 +
  mydata_clu_std$Q31a^2 +
  mydata_clu_std$Q31b^2 
)

head(mydata[order(-mydata$Dissimilarity), c("ID", "Dissimilarity")], 10) 

##      ID Dissimilarity
## 191 191      6.365406
## 11   11      4.823598
## 202 202      4.716886
## 93   93      4.390156
## 241 241      4.281977
## 38   38      4.229806
## 234 234      4.116562
## 115 115      3.993249
## 103 103      3.861159
## 10   10      3.798713

mydata <- mydata %>%
  filter(!ID %in% c(191))

mydata$ID <- seq(1, nrow(mydata))

mydata_clu_std <- as.data.frame(scale(mydata[c(3,11,26,27)])) 

library(factoextra) 
get_clust_tendency(mydata_clu_std,
                   n = nrow(mydata_clu_std) - 1,
                   graph = FALSE) 

## $hopkins_stat
## [1] 0.5736077
## 
## $plot
## NULL

fviz_dend(WARD,
          k = 5,
          cex = 0.5,
          palette = "jama",
          color_labels_by_k = TRUE,
          rect = TRUE)

library(factoextra)
library(ggplot2)

Clustering <- kmeans(mydata_clu_std, 
                     centers = 5, 
                     nstart = 25) 

Clustering

## K-means clustering with 5 clusters of sizes 56, 74, 76, 32, 26
## 
## Cluster means:
##    Awareness       Ease      Value      Trust
## 1  0.2355188  0.4112907  0.1731763  1.1381089
## 2  0.1611116 -0.7879947  0.1708796 -0.2980480
## 3  0.5052525  0.9044778  0.5689982 -0.7897317
## 4 -0.1033202 -0.5604314 -1.9884698  0.2094871
## 5 -2.3155481 -0.5971993 -0.0752230  0.4475952
## 
## Clustering vector:
##   [1] 5 3 4 3 3 1 2 1 3 5 4 4 1 2 4 5 2 2 1 1 3 1 4 3 3 5 1 2 2 2 1 2 3 3 3 1 3
##  [38] 4 3 2 3 1 3 3 2 3 3 3 3 3 2 3 1 1 2 2 1 3 1 1 1 3 3 2 3 2 2 2 1 1 1 1 3 2
##  [75] 4 2 1 4 3 1 1 5 3 2 3 2 5 1 3 2 2 5 5 5 2 2 1 3 2 5 3 5 4 3 2 3 5 2 5 3 3
## [112] 1 2 2 5 2 1 5 3 2 1 2 2 4 1 5 2 2 5 2 1 2 1 3 1 5 1 4 4 2 3 5 5 4 2 4 2 3
## [149] 3 4 3 4 2 3 2 1 5 4 5 3 3 3 3 2 2 3 2 3 2 1 3 4 4 3 2 2 3 2 3 3 3 1 3 2 2
## [186] 4 2 2 2 3 3 1 4 4 2 2 3 3 1 4 4 2 1 2 4 3 3 1 1 2 5 3 2 1 2 1 2 1 1 4 1 3
## [223] 2 3 2 3 1 3 2 1 3 1 5 5 4 5 1 3 2 4 2 1 2 3 2 3 2 1 1 2 3 3 1 1 2 2 3 4 4
## [260] 3 3 1 4 4
## 
## Within cluster sum of squares by cluster:
## [1]  88.14381  95.70238  82.80060 120.50690  75.50678
##  (between_SS / total_SS =  56.0 %)
## 
## Available components:
## 
## [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
## [6] "betweenss"    "size"         "iter"         "ifault"

library(factoextra)
fviz_cluster(Clustering, 
             palette = "Set1", 
             repel = TRUE,
             ggtheme = theme_bw(),
             data = mydata_clu_std)

mydata <- mydata %>%
  filter(!ID %in% c(11))

mydata$ID <- seq(1, nrow(mydata))

mydata_clu_std <- as.data.frame(scale(mydata[c(3,11,26,27)])) 

library(factoextra)
library(ggplot2)

Clustering <- kmeans(mydata_clu_std, 
                     centers = 5, 
                     nstart = 25) 

Clustering

## K-means clustering with 5 clusters of sizes 76, 54, 75, 32, 26
## 
## Cluster means:
##     Awareness       Ease       Value      Trust
## 1  0.16017846 -0.8533305  0.11948655 -0.3059896
## 2  0.21326412  0.3776779  0.22118546  1.1460396
## 3  0.49918810  0.9111497  0.59543745 -0.7892907
## 4 -0.03067026 -0.2443742 -1.97805346  0.2824627
## 5 -2.31336479 -0.6176055 -0.09173426  0.4433489
## 
## Clustering vector:
##   [1] 5 3 4 3 3 2 1 2 3 5 4 2 1 1 5 1 1 2 2 3 2 4 3 3 5 2 1 1 1 2 1 3 3 3 2 3 4
##  [38] 3 1 3 2 3 3 1 3 3 3 3 3 1 3 2 2 1 1 2 3 2 2 2 3 3 1 3 1 1 1 2 2 2 2 3 1 4
##  [75] 1 2 4 4 2 2 5 3 1 3 1 5 2 3 1 1 5 5 5 1 1 2 3 1 5 3 5 4 3 1 3 5 1 5 3 3 2
## [112] 1 1 5 1 2 5 3 1 2 1 1 4 4 5 1 1 5 1 2 1 2 3 2 5 2 4 4 1 3 5 5 4 1 4 1 3 3
## [149] 4 3 1 1 3 1 2 5 4 5 3 3 3 3 1 1 3 1 3 1 2 3 4 4 3 1 1 3 1 3 3 3 2 3 1 1 4
## [186] 1 1 1 3 3 2 4 4 1 1 3 3 2 4 4 1 2 1 4 3 3 4 2 1 5 3 1 2 1 2 1 2 2 4 2 3 1
## [223] 3 1 3 2 3 1 2 3 2 5 5 4 5 2 3 1 4 1 2 1 3 1 3 1 2 2 1 3 3 2 2 1 1 3 4 4 3
## [260] 3 2 4 4
## 
## Within cluster sum of squares by cluster:
## [1] 109.86189  82.00624  80.53778 111.18358  76.83579
##  (between_SS / total_SS =  56.1 %)
## 
## Available components:
## 
## [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
## [6] "betweenss"    "size"         "iter"         "ifault"

library(factoextra)
fviz_cluster(Clustering, 
             palette = "Set1", 
             repel = TRUE,
             ggtheme = theme_bw(),
             data = mydata_clu_std)

mydata <- mydata %>%
  filter(!ID %in% c(86,200,143,239,37))

mydata$ID <- seq(1, nrow(mydata))

mydata_clu_std <- as.data.frame(scale(mydata[c(3,11,26,27)])) 

library(factoextra)
library(ggplot2)

Clustering <- kmeans(mydata_clu_std, 
                     centers = 5, 
                     nstart = 25) 

Clustering

## K-means clustering with 5 clusters of sizes 79, 25, 50, 30, 74
## 
## Cluster means:
##     Awareness       Ease      Value      Trust
## 1  0.50108668  0.8823227  0.6219599 -0.7289837
## 2 -2.38619015 -0.5895733 -0.1589148  0.5303956
## 3  0.14549250  0.3470049  0.1111776  1.2357670
## 4  0.08869192 -0.3574300 -1.9775371  0.1497189
## 5  0.13693951 -0.8323177  0.1162874 -0.2966229
## 
## Clustering vector:
##   [1] 2 1 4 1 1 3 5 3 1 2 4 3 5 4 2 5 5 3 1 1 3 4 1 1 2 3 5 5 5 3 5 1 1 1 3 1 1
##  [38] 5 1 3 1 1 5 1 1 1 1 1 5 1 3 1 5 5 3 1 3 3 3 1 1 5 1 5 5 5 3 3 3 3 1 5 4 5
##  [75] 3 4 4 3 3 2 1 5 1 5 3 1 5 5 2 2 2 5 5 3 1 5 2 1 2 4 1 5 1 2 5 2 1 1 3 5 5
## [112] 2 5 3 2 1 5 3 5 5 4 4 2 5 5 2 5 3 5 3 1 3 2 1 4 4 5 1 2 2 5 4 5 1 1 4 1 4
## [149] 5 1 5 3 2 4 2 1 1 1 1 5 5 1 5 1 5 3 1 4 4 1 5 5 1 5 1 1 1 3 1 5 5 4 5 5 5
## [186] 1 1 3 4 4 5 5 1 1 3 4 5 3 5 4 1 1 4 3 5 2 1 5 3 5 3 5 3 3 4 3 1 5 1 5 1 3
## [223] 1 5 3 1 3 2 2 4 2 3 1 5 5 3 5 1 5 1 5 3 3 5 1 1 1 3 5 5 1 4 4 1 1 3 4 4
## 
## Within cluster sum of squares by cluster:
## [1]  95.40383  77.28224  80.33349  91.09260 105.03046
##  (between_SS / total_SS =  56.3 %)
## 
## Available components:
## 
## [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
## [6] "betweenss"    "size"         "iter"         "ifault"

library(factoextra)
fviz_cluster(Clustering, 
             palette = "Set1", 
             repel = TRUE,
             ggtheme = theme_bw(),
             data = mydata_clu_std)

mydata <- mydata %>%
  filter(!ID %in% c(90,155,85,146,100))

mydata$ID <- seq(1, nrow(mydata))

mydata_clu_std <- as.data.frame(scale(mydata[c(3,11,26,27)])) 

library(factoextra)
library(ggplot2)

Clustering <- kmeans(mydata_clu_std, 
                     centers = 5, 
                     nstart = 25) 

Clustering

## K-means clustering with 5 clusters of sizes 23, 76, 49, 79, 26
## 
## Cluster means:
##     Awareness       Ease       Value       Trust
## 1 -2.41967781 -0.6566175 -0.16063729  0.44471611
## 2  0.11993807 -0.8977034  0.06023898 -0.26926030
## 3  0.11446989  0.3155981  0.08097431  1.31250594
## 4  0.49980397  0.8895872  0.61326842 -0.71180559
## 5  0.05552915 -0.0928475 -2.04997890  0.08289087
## 
## Clustering vector:
##   [1] 1 4 5 4 4 3 2 3 4 1 5 3 2 2 1 2 2 3 4 4 3 5 4 4 1 3 2 2 2 3 2 4 4 4 3 4 4
##  [38] 2 4 3 4 4 2 4 4 4 4 4 2 4 3 4 2 2 3 4 3 3 3 4 4 2 4 2 2 2 3 3 3 3 4 2 5 2
##  [75] 3 5 5 3 3 1 4 2 4 2 4 2 2 1 1 2 2 3 4 2 1 4 1 4 2 4 1 2 1 4 4 3 2 2 1 2 3
## [112] 1 4 2 3 2 2 5 5 1 2 2 1 2 3 2 3 4 3 1 4 5 5 2 4 1 1 2 5 2 4 4 4 2 2 4 2 3
## [149] 1 5 4 4 4 4 2 2 4 2 4 2 3 4 5 5 4 2 2 4 2 4 4 4 3 4 2 2 5 2 2 2 4 4 3 5 5
## [186] 2 2 4 4 3 5 2 3 2 5 4 4 5 3 2 1 4 2 3 2 3 2 3 3 5 3 4 2 4 2 4 3 4 2 3 4 3
## [223] 1 1 5 1 3 4 2 2 3 2 4 2 4 2 3 3 2 4 4 4 3 2 2 4 5 5 4 4 3 5 5
## 
## Within cluster sum of squares by cluster:
## [1]  70.16428 124.58392  80.49920  99.56269  63.35325
##  (between_SS / total_SS =  56.5 %)
## 
## Available components:
## 
## [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
## [6] "betweenss"    "size"         "iter"         "ifault"

library(factoextra)
fviz_cluster(Clustering, 
             palette = "Set1", 
             repel = TRUE,
             ggtheme = theme_bw(),
             data = mydata_clu_std)

mydata <- mydata %>%
  filter(!ID %in% c(193,190,164))

mydata$ID <- seq(1, nrow(mydata))

mydata_clu_std <- as.data.frame(scale(mydata[c(3,11,26,27)])) 

library(factoextra)
library(ggplot2)

Clustering <- kmeans(mydata_clu_std, 
                     centers = 5, 
                     nstart = 25) 

Clustering

## K-means clustering with 5 clusters of sizes 75, 26, 79, 23, 47
## 
## Cluster means:
##     Awareness       Ease       Value      Trust
## 1  0.13714948 -0.8797902  0.08082592 -0.2392861
## 2  0.01602031 -0.1291655 -2.03520230 -0.0309770
## 3  0.50815392  0.9082966  0.62028545 -0.7001278
## 4 -2.40486418 -0.6499943 -0.16676336  0.4696909
## 5  0.09499952  0.2667449  0.03587920  1.3459376
## 
## Clustering vector:
##   [1] 4 3 2 3 3 5 1 5 3 4 2 5 1 2 4 1 1 5 3 3 5 2 3 3 4 5 1 1 1 5 1 3 3 3 5 3 3
##  [38] 1 3 5 3 3 1 3 3 3 3 3 1 3 5 3 1 1 5 3 5 5 5 3 3 1 3 1 1 1 5 5 5 5 3 1 2 1
##  [75] 5 2 2 5 5 4 3 1 3 1 3 1 1 4 4 1 1 5 3 1 4 3 4 3 1 3 4 1 4 3 3 5 1 1 4 1 5
## [112] 4 3 1 5 1 1 2 2 4 1 1 4 1 5 1 5 3 5 4 3 2 2 1 3 4 4 1 2 1 3 3 3 1 1 3 1 5
## [149] 4 2 3 3 3 3 1 1 3 1 3 1 5 3 2 3 1 1 3 1 3 3 3 5 3 1 1 2 1 1 1 3 3 5 2 2 1
## [186] 1 3 3 2 1 1 2 3 3 2 5 1 4 3 1 5 1 5 1 5 5 2 5 3 1 3 1 3 5 3 1 5 3 5 4 4 2
## [223] 4 5 3 1 1 5 1 3 1 3 1 5 5 1 3 3 3 5 1 1 3 2 2 3 3 5 2 2
## 
## Within cluster sum of squares by cluster:
## [1] 121.45870  62.95169 101.43697  71.50918  74.12297
##  (between_SS / total_SS =  56.7 %)
## 
## Available components:
## 
## [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
## [6] "betweenss"    "size"         "iter"         "ifault"

library(factoextra)
fviz_cluster(Clustering, 
             palette = "Set1", 
             repel = TRUE,
             ggtheme = theme_bw(),
             data = mydata_clu_std)

mydata <- mydata %>%
  filter(!ID %in% c(220,10,109))

mydata$ID <- seq(1, nrow(mydata))

mydata_clu_std <- as.data.frame(scale(mydata[c(3,11,26,27)])) 

library(factoextra)
library(ggplot2)

Clustering <- kmeans(mydata_clu_std, 
                     centers = 5, 
                     nstart = 25) 

Clustering

## K-means clustering with 5 clusters of sizes 48, 75, 79, 20, 25
## 
## Cluster means:
##     Awareness       Ease        Value       Trust
## 1  0.07755914  0.2561182 -0.003027745  1.38856900
## 2  0.10677233 -0.9230788  0.077510269 -0.22238704
## 3  0.50068099  0.9055025  0.618119866 -0.69048033
## 4 -2.49544837 -0.4992393 -0.152598342  0.31410430
## 5 -0.05502378 -0.1845070 -2.057897640 -0.06825693
## 
## Clustering vector:
##   [1] 4 3 5 3 3 1 2 1 3 5 1 2 5 4 2 2 1 3 3 1 5 3 3 4 1 2 2 2 1 2 3 3 3 1 3 3 2
##  [38] 3 1 3 3 2 3 3 3 3 3 2 3 1 3 2 2 1 3 1 1 1 3 3 2 3 2 2 2 1 1 1 1 3 2 5 2 1
##  [75] 5 5 1 1 4 3 2 3 2 3 2 2 4 4 2 2 1 3 2 4 3 4 3 2 3 4 2 4 3 3 1 2 2 2 1 4 3
## [112] 2 1 2 2 5 5 4 2 2 4 2 1 2 1 3 1 4 3 5 5 2 3 4 4 2 5 2 3 3 3 2 2 3 2 1 4 5
## [149] 3 3 3 3 2 2 3 2 3 2 1 3 5 3 2 2 3 2 3 3 3 1 3 2 2 5 2 2 2 3 3 1 5 5 2 2 3
## [186] 3 5 2 2 5 3 3 1 1 2 4 3 2 1 2 1 2 1 1 5 1 3 2 3 2 3 1 3 2 1 3 1 4 5 4 1 3
## [223] 2 2 1 2 3 2 3 2 1 1 2 3 3 3 1 2 2 3 5 5 3 3 1 5 5
## 
## Within cluster sum of squares by cluster:
## [1]  81.67665 127.65628 106.19954  49.86444  62.38244
##  (between_SS / total_SS =  56.5 %)
## 
## Available components:
## 
## [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
## [6] "betweenss"    "size"         "iter"         "ifault"

library(factoextra)
fviz_cluster(Clustering, 
             palette = "Set1", 
             repel = TRUE,
             ggtheme = theme_bw(),
             data = mydata_clu_std)

Averages <- Clustering$centers
Averages

##     Awareness       Ease        Value       Trust
## 1  0.07755914  0.2561182 -0.003027745  1.38856900
## 2  0.10677233 -0.9230788  0.077510269 -0.22238704
## 3  0.50068099  0.9055025  0.618119866 -0.69048033
## 4 -2.49544837 -0.4992393 -0.152598342  0.31410430
## 5 -0.05502378 -0.1845070 -2.057897640 -0.06825693

2.3 Cluster Profile Plot

Figure <- as.data.frame(Averages)
Figure$ID <- 1:nrow(Figure)

library(tidyr)
Figure <- pivot_longer(Figure, cols = c("Awareness", "Ease", "Value", "Trust"))



Figure$Group <- factor(Figure$ID, 
                       levels = c(1, 2, 3, 4,5), 
                       labels = c("1", "2", "3", "4","5"))


Figure$NameF <- factor(Figure$name, 
                       levels = c("Awareness", "Ease", "Value", "Trust"), 
                       labels = c("Awareness", "Ease", "Value", "Trust"))


library(ggplot2)
ggplot(Figure, aes(x = NameF, y = value)) +
  geom_hline(yintercept = 0) +
  theme_bw() +
  geom_point(aes(shape = Group, col = Group), size = 5, alpha = 0.4) +
  geom_line(aes(group = ID), linewidth = 1.5) +
  ylab("Averages") +
  xlab("Cluster variables")+
  ylim(-2.5, 2.5) +
  theme(axis.text.x = element_text(angle = 45, vjust = 0.50, size = 12)) +
  scale_color_manual(values = c("1" = "#230078", "2" = "#84BD00", "3" = "#FA7800", "4" = "#63666A", "5" = "#A7A8AA"))

mydata$Group <- Clustering$cluster
fit <- aov(cbind(Awareness, Ease, Value, Trust) ~ as.factor(Group), 
             data = mydata)

summary(fit)

##  Response Awareness :
##                   Df Sum Sq Mean Sq F value    Pr(>F)    
## as.factor(Group)   4 80.074 20.0186  87.691 < 2.2e-16 ***
## Residuals        242 55.245  0.2283                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response Ease :
##                   Df Sum Sq Mean Sq F value    Pr(>F)    
## as.factor(Group)   4 86.860 21.7150  76.879 < 2.2e-16 ***
## Residuals        242 68.354  0.2825                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response Value :
##                   Df Sum Sq Mean Sq F value    Pr(>F)    
## as.factor(Group)   4 71.857 17.9643  76.009 < 2.2e-16 ***
## Residuals        242 57.195  0.2363                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response Trust :
##                   Df Sum Sq Mean Sq F value    Pr(>F)    
## as.factor(Group)   4 172.02  43.004  74.816 < 2.2e-16 ***
## Residuals        242 139.10   0.575                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

There is significant difference in variables across clusters, all p-values are smaller than 0.001.

mydata$Q42 <- as.numeric(as.character(mydata$Q42))
current_year <- as.numeric(format(Sys.Date(), "%Y"))
mydata$Age <- current_year - mydata$Q42

Changed the year of birth into age in number of years.

aggregate(mydata$Age, 
          by = list(mydata$Group), 
          FUN = median)

##   Group.1    x
## 1       1 27.0
## 2       2 29.0
## 3       3 27.0
## 4       4 23.5
## 5       5 35.0

3 Differences across clusters and validity tests

library(rstatix)

## Warning: package 'rstatix' was built under R version 4.4.2

## 
## Attaching package: 'rstatix'

## The following object is masked from 'package:stats':
## 
##     filter

mydata %>%
  group_by(Group) %>%
  shapiro_test(Age)

## # A tibble: 5 × 4
##   Group variable statistic            p
##   <int> <chr>        <dbl>        <dbl>
## 1     1 Age          0.804 0.00000163  
## 2     2 Age          0.863 0.000000887 
## 3     3 Age          0.832 0.0000000502
## 4     4 Age          0.728 0.0000868   
## 5     5 Age          0.897 0.0162

kruskal.test(Age ~ Group, 
             data = mydata)

## 
##  Kruskal-Wallis rank sum test
## 
## data:  Age by Group
## Kruskal-Wallis chi-squared = 4.5423, df = 4, p-value = 0.3376

We can not reject H0 at p=0.3376.

No significant difference in Age between groups.

aggregate(mydata$Age, 
          by = list(mydata$Group), 
          FUN = median)

##   Group.1    x
## 1       1 27.0
## 2       2 29.0
## 3       3 27.0
## 4       4 23.5
## 5       5 35.0

aggregate(mydata$Age, 
          by = list(mydata$Group), 
          FUN = mean)

##   Group.1        x
## 1       1 34.27083
## 2       2 36.09333
## 3       3 33.40506
## 4       4 32.10000
## 5       5 38.48000

Age does not differ statistically across segments, however here are means and medians of age in years across segments.

library(dplyr)

mydata$UsageFrequency <- case_when(
  mydata$Q21 %in% 1:4 ~ "Occasional Usage",
  mydata$Q21 %in% 5:6 ~ "Frequent Usage"
)

DifUsage <- chisq.test(mydata$UsageFrequency, mydata$Group)

DifUsage

## 
##  Pearson's Chi-squared test
## 
## data:  mydata$UsageFrequency and mydata$Group
## X-squared = 12.12, df = 4, p-value = 0.01648

We reject H0 at p=0.017.

There is significant association between usage of the mobile app across groups.

library(dplyr)
library(ggplot2)

usage_table <- table(mydata$Group, mydata$UsageFrequency)

usage_proportions <- prop.table(usage_table, margin = 1)

usage_df <- as.data.frame(as.table(usage_proportions))

most_frequent_group <- usage_df %>%
  group_by(Var2) %>%  
  slice_max(Freq, n = 1)

print("Most Frequent Groups for Each Usage Category:")

## [1] "Most Frequent Groups for Each Usage Category:"

print(most_frequent_group)

## # A tibble: 2 × 3
## # Groups:   Var2 [2]
##   Var1  Var2              Freq
##   <fct> <fct>            <dbl>
## 1 3     Frequent Usage   0.785
## 2 5     Occasional Usage 0.52

ggplot(usage_df, aes(x = Var1, y = Freq * 100, fill = Var2)) +
  geom_bar(stat = "identity", position = "stack") +
  labs(
    x = "Group",
    y = "Proportion (%)",
    fill = "Usage Category",
    title = "Usage Proportions Across Groups"
  ) +
    scale_fill_manual(
    values = c("Frequent Usage" = "#230078", "Occasional Usage" = "#84BD00"))  +
  theme_minimal() +
  theme(
    legend.position = "bottom",
    axis.text.x = element_text(angle = 45, hjust = 1)  
  )

Here we see which group/segment has most frequent useres etc.

DifWhere <- chisq.test(mydata$Q28, mydata$Group)

DifWhere

## 
##  Pearson's Chi-squared test
## 
## data:  mydata$Q28 and mydata$Group
## X-squared = 26.786, df = 4, p-value = 2.196e-05

We can reject H0 at p<0.001.

Significant association between in Where individuals would carry out advanced service next week if they had to and group.

library(dplyr)
library(ggplot2)

service_table <- table(mydata$Group, mydata$Q28)

service_proportions <- prop.table(service_table, margin = 1)

service_df <- as.data.frame(as.table(service_proportions))

service_df$Var2 <- recode(service_df$Var2, 
                          `1` = "In Branch", 
                          `2` = "In Mobile App")


most_frequent_service <- service_df %>%
  group_by(Var2) %>%  
  slice_max(Freq, n = 1)  

print("Most Frequent Groups for Each Service Option:")

## [1] "Most Frequent Groups for Each Service Option:"

print(most_frequent_service)

## # A tibble: 2 × 3
## # Groups:   Var2 [2]
##   Var1  Var2                  Freq
##   <fct> <fct>                <dbl>
## 1 4     V poslovalnici       0.55 
## 2 3     V mobilni aplikaciji 0.873

ggplot(service_df, aes(x = Var1, y = Freq * 100, fill = Var2)) +
  geom_bar(stat = "identity", position = "stack") +
  labs(
    x = "Group",
    y = "Proportion (%)",
    fill = "Service Option",
    title = "Preference for Advanced Service Location by Group"
  ) +
  scale_fill_manual(
    values = c("V poslovalnici" = "#230078", "V mobilni aplikaciji" = "#84BD00"),
    labels = c("V poslovalnici" = "In branch", "V mobilni aplikaciji" = "In the mobile app"))  +
  theme_minimal() +
  theme(
    legend.position = "bottom",  # Move legend below the plot
    axis.text.x = element_text(angle = 45, hjust = 1)  # Rotate x-axis labels for better readability
  )

Here we see the distribution of the latter across groups, used for describing the groups/segments.

DifNotify <- chisq.test(mydata$Q26, mydata$Group)

DifNotify

## 
##  Pearson's Chi-squared test
## 
## data:  mydata$Q26 and mydata$Group
## X-squared = 24.965, df = 4, p-value = 5.113e-05

We can reject H0 at p<0.001.

There is association between groups and whether individuals noticed notifications about advanced services.

library(dplyr)
library(ggplot2)

notification_table <- table(mydata$Group, mydata$Q26)

notification_proportions <- prop.table(notification_table, margin = 1)

notification_df <- as.data.frame(as.table(notification_proportions))

notification_df$Var2 <- recode(notification_df$Var2, 
                               `1` = "Yes", 
                               `2` = "No")

most_frequent_notification <- notification_df %>%
  group_by(Var2) %>%
  slice_max(Freq, n = 1)

print("Most Frequent Groups for Each Notification Response:")

## [1] "Most Frequent Groups for Each Notification Response:"

print(most_frequent_notification)

## # A tibble: 2 × 3
## # Groups:   Var2 [2]
##   Var1  Var2   Freq
##   <fct> <fct> <dbl>
## 1 3     Da    0.747
## 2 4     Ne    0.75

ggplot(notification_df, aes(x = Var1, y = Freq * 100, fill = Var2)) +
  geom_bar(stat = "identity", position = "stack") +
  labs(
    x = "Group",
    y = "Proportion (%)",
    fill = "Notification Noticed",
    title = "Notifications About Advanced Services by Group"
  ) +
  scale_fill_manual(
    values = c("Da" = "#230078", "Ne" = "#84BD00"),
    labels = c("Da" = "Yes", "Ne" = "No"))  +
  theme_minimal() +
  theme(
    legend.position = "bottom",
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

Here we see the distribution of the recievement of notifications across groups, used for describing the groups/segments.

DiffAwareness <- chisq.test(mydata$Q23a, mydata$Group)

DiffAwareness

## 
##  Pearson's Chi-squared test
## 
## data:  mydata$Q23a and mydata$Group
## X-squared = 15.959, df = 4, p-value = 0.003075

We can reject H0 at p=0.004.

There is association between groups and awareness of advanced feature “Getting Consumer Loan”.

DiffAwareness1 <- chisq.test(mydata$Q23e, mydata$Group)

DiffAwareness1

## 
##  Pearson's Chi-squared test
## 
## data:  mydata$Q23e and mydata$Group
## X-squared = 18.035, df = 4, p-value = 0.001215

We can reject H0 at p=0.002.

There is association between groups and awareness of advanced feature “Opening a Saving Account”.

DiffAwareness2 <- chisq.test(mydata$Q23f, mydata$Group)

DiffAwareness2

## 
##  Pearson's Chi-squared test
## 
## data:  mydata$Q23f and mydata$Group
## X-squared = 14.801, df = 4, p-value = 0.005132

We can reject H0 at p=0.006.

There is association between groups and awareness of advanced service “Term Deposits”.

Under here are visual representations of awareness of the mentioned advanced services across groups/segments. This can be used for interpretation of segments and recommendations.

library(dplyr)
library(ggplot2)

visualize_distribution <- function(data, group_col, awareness_col, awareness_label) {
  
  awareness_table <- table(data[[group_col]], data[[awareness_col]])
  
  
  awareness_proportions <- prop.table(awareness_table, margin = 1)
  
 
  awareness_df <- as.data.frame(as.table(awareness_proportions))
  
  
  awareness_df$Var2 <- recode(awareness_df$Var2, 
                              `1` = "Yes", 
                              `2` = "No")
  
  
  ggplot(awareness_df, aes(x = Var1, y = Freq * 100, fill = Var2)) +
    geom_bar(stat = "identity", position = "fill") +
    labs(
      x = "Group",
      y = "Proportion (%)",
      fill = "Awareness",
      title = paste("Awareness of", awareness_label, "by Group")
    ) +
    scale_fill_manual(
    values = c("Yes" = "#230078", "No" = "#84BD00"))  +
    theme_minimal() +
    theme(
      legend.position = "bottom",
      axis.text.x = element_text(angle = 45, hjust = 1)
    )
}


plot_q23a <- visualize_distribution(
  data = mydata,
  group_col = "Group",
  awareness_col = "Q23a",
  awareness_label = "Getting Consumer Loan"
)


plot_q23e <- visualize_distribution(
  data = mydata,
  group_col = "Group",
  awareness_col = "Q23e",
  awareness_label = "Opening a Saving Account"
)

# Plot for Q23f (Term Deposits)
plot_q23f <- visualize_distribution(
  data = mydata,
  group_col = "Group",
  awareness_col = "Q23f",
  awareness_label = "Term Deposits"
)

# Display the plots
print(plot_q23a)

print(plot_q23e)

print(plot_q23f)

4 Interpretation of clusters

Segment 1: Trust Champions

Description: This group scores the highest in trust, while other dimensions (Awareness, Ease, and Value) remain around the average. They are confident in the app’s reliability but may not feel strongly compelled to use advanced services unless trustworthiness is a key selling point.

Key Traits:

• Trust: Very high.
• Awareness, Ease, Value: Around average.
• Barriers: Lack of compelling motivation beyond trust to engage with advanced services.

Targeting Strategy:

• Build on Trust: Emphasize that advanced features are just as reliable and secure as the core app services.
• Highlight Practical Benefits: Demonstrate how advanced features can enhance the app’s utility without compromising trust.
• Introduce Exclusivity: Frame advanced features as “trustworthy tools” available to a select group of valued users.

Segment 2: Cautious Navigators

Description: This group is generally balanced across all dimensions, but their Ease score is slightly below average. They may feel hesitant to adopt advanced features because they perceive them as too complicated or not intuitive.

Key Traits:

• Awareness, Trust, Value: Average.
• Ease: Slightly below average.
• Barriers: Perceived complexity or lack of user-friendliness.

Targeting Strategy:

• Simplify Onboarding: Offer step-by-step tutorials, walkthroughs, or in-app prompts to demystify advanced features.
• Emphasize User-Friendliness: Highlight how easy it is to activate and use advanced features in marketing messages.
• Provide Support: Include accessible help resources or a dedicated support channel for questions about advanced services.

Segment 3: High Performers

Description: This group scores above average across all dimensions, with Ease being their strongest area. However, their Trust score is the smallest and falls below average. They may engage with advanced features if their concerns around trust and reliability are addressed.

Key Traits:

• Ease, Awareness, Value: Above average.
• Trust: Below average.
• Barriers: Lack of trust in the app’s advanced features or uncertainty about their reliability.

Targeting Strategy:

• Address Trust Concerns: Use testimonials, endorsements, and transparent explanations to build confidence in advanced services.
• Promote Ease and Value: Highlight how easy and rewarding it is to use advanced features, leveraging their preference for intuitive tools.
• Demonstrate Reliability: Offer guarantees or show evidence of the app’s security and performance in handling advanced features.

Segment 4: Trust-Driven Skeptics

Description: This group scores below average across most dimensions, but their Trust score is just above average. They may trust the app but are otherwise disengaged due to lack of awareness or motivation.

Key Traits:

• Trust: Slightly above average.
• Awareness, Ease, Value: Below average.
• Barriers: Low awareness and lack of engagement with the app’s features.

Targeting Strategy:

• Raise Awareness: Use in-app notifications, banners, or campaigns to explain the purpose and benefits of advanced features.
• Leverage Trust: Position advanced features as extensions of the app’s reliable core functionality.
• Encourage Exploration: Use small incentives (e.g., rewards, discounts) to motivate them to try advanced features.

Segment 5: Disengaged Low-Engagers

Description: This group scores below average across all dimensions, with the lowest scores in Awareness and Value. They are the most disengaged group and may not understand or see the value in the app’s advanced services.

Key Traits:

• Awareness, Value: Very low.
• Ease, Trust: Below average.
• Barriers: Lack of awareness and a strong sense that advanced features aren’t worth using.

Targeting Strategy:

• Focus on Awareness: Use prominent marketing, notifications, or educational content to ensure this group understands the availability and benefits of advanced services.
• Demonstrate Clear Value: Show how advanced services solve specific problems or improve their app experience.
• Simplify Access: Make it extremely easy for this group to try advanced features, perhaps through a free trial or an opt-in demo mode.

mydata$AgeGroup <- cut(mydata$Age, 
                       breaks = c(18, 30, 40, 50, 60, Inf), 
                       labels = c("18-30", "31-40", "41-50", "51-60", "60+"))

table_age <- table(mydata$AgeGroup, mydata$Q23a)

print(table_age)

##        
##          1  2
##   18-30 74 66
##   31-40 26  2
##   41-50 26 10
##   51-60 18  7
##   60+   14  4

5 Analysis of awareness of advanced services across age groups

library(ggplot2)

prop_table5 <- prop.table(table(mydata$AgeGroup, mydata$Q23a), margin = 1)
prop_df5 <- as.data.frame(as.table(prop_table5))

ggplot(prop_df5, aes(x = Var1, y = Freq, fill = Var2)) +
  geom_bar(stat = "identity", position = "fill") +
  labs(x = "Age Group", 
       y = "Proportion", 
       fill = "Awareness of Advanced Service", 
       title = "Getting a Conmsumer Loan",) +
  scale_fill_manual(
    values = c("1" = "#230078", "2" = "#84BD00"),  
    labels = c("1" = "Yes", "2" = "No")  
  ) +
  theme_minimal() +
  theme(
    legend.position = "bottom",  
    axis.text.x = element_text(angle = 45, hjust = 1)  
  )

library(ggplot2)

prop_table51 <- prop.table(table(mydata$AgeGroup, mydata$Q23b), margin = 1)
prop_df51 <- as.data.frame(as.table(prop_table51))

ggplot(prop_df51, aes(x = Var1, y = Freq, fill = Var2)) +
  geom_bar(stat = "identity", position = "fill") +
  labs(x = "Age Group", 
       y = "Proportion", 
       fill = "Awareness of Advanced Service", 
       title = "Change of the Account Limit",) +
  scale_fill_manual(
    values = c("1" = "#230078", "2" = "#84BD00"),  
    labels = c("1" = "Yes", "2" = "No")  
  ) +
  theme_minimal() +
  theme(
    legend.position = "bottom",  
    axis.text.x = element_text(angle = 45, hjust = 1)  
  )

library(ggplot2)

prop_table52 <- prop.table(table(mydata$AgeGroup, mydata$Q23c), margin = 1)
prop_df52 <- as.data.frame(as.table(prop_table52))

ggplot(prop_df52, aes(x = Var1, y = Freq, fill = Var2)) +
  geom_bar(stat = "identity", position = "fill") +
  labs(x = "Age Group", 
       y = "Proportion", 
       fill = "Awareness of Advanced Service", 
       title = "Ordering a new card",) +
  scale_fill_manual(
    values = c("1" = "#230078", "2" = "#84BD00"),  
    labels = c("1" = "Yes", "2" = "No")  
  ) +
  theme_minimal() +
  theme(
    legend.position = "bottom",  
    axis.text.x = element_text(angle = 45, hjust = 1)  
  )

library(ggplot2)

prop_table53 <- prop.table(table(mydata$AgeGroup, mydata$Q23d), margin = 1)
prop_df53 <- as.data.frame(as.table(prop_table53))

ggplot(prop_df53, aes(x = Var1, y = Freq, fill = Var2)) +
  geom_bar(stat = "identity", position = "fill") +
  labs(x = "Age Group", 
       y = "Proportion", 
       fill = "Awareness of Advanced Service", 
       title = "Change of the Card Limit",) +
  scale_fill_manual(
    values = c("1" = "#230078", "2" = "#84BD00"),  
    labels = c("1" = "Yes", "2" = "No")  
  ) +
  theme_minimal() +
  theme(
    legend.position = "bottom",  
    axis.text.x = element_text(angle = 45, hjust = 1)  
  )

library(ggplot2)

prop_table54 <- prop.table(table(mydata$AgeGroup, mydata$Q23e), margin = 1)
prop_df54 <- as.data.frame(as.table(prop_table54))

ggplot(prop_df54, aes(x = Var1, y = Freq, fill = Var2)) +
  geom_bar(stat = "identity", position = "fill") +
  labs(x = "Age Group", 
       y = "Proportion", 
       fill = "Awareness of Advanced Service", 
       title = "Opening a Saving Account",) +
  scale_fill_manual(
    values = c("1" = "#230078", "2" = "#84BD00"),  
    labels = c("1" = "Yes", "2" = "No")  
  ) +
  theme_minimal() +
  theme(
    legend.position = "bottom",  
    axis.text.x = element_text(angle = 45, hjust = 1)  
  )

library(ggplot2)

prop_table55 <- prop.table(table(mydata$AgeGroup, mydata$Q23f), margin = 1)
prop_df55 <- as.data.frame(as.table(prop_table55))

ggplot(prop_df55, aes(x = Var1, y = Freq, fill = Var2)) +
  geom_bar(stat = "identity", position = "fill") +
  labs(
    x = "Age Group",
    y = "Proportion",
    fill = "Awareness of Advanced Service",  
    title = "Term Deposits"
  ) +
  scale_fill_manual(
    values = c("1" = "#230078", "2" = "#84BD00"),  
    labels = c("1" = "Yes", "2" = "No")  
  ) +
  theme_minimal() +
  theme(
    legend.position = "bottom",  
    axis.text.x = element_text(angle = 45, hjust = 1)  
  )

set.seed(123)
data <- iris[, -5]  # Removing the species column
kmeans_result <- kmeans(data, centers = 5)

# Get the size of each cluster
kmeans_result$size

## [1] 19  8 38 23 62

6 Percentage Distribution of Primary Bank by Group

table_clusters1 <- table(mydata$Group, mydata$Q45)

prop_table_clusters1 <- prop.table(table_clusters1, margin = 1)

prop_df1 <- as.data.frame(as.table(prop_table_clusters1))

library(ggplot2)
ggplot(prop_df1, aes(x = Var1, y = Freq * 100, fill = Var2)) +  
  geom_bar(stat = "identity", position = "dodge") +
  labs(
    x = "Group", 
    y = "Percentage (%)", 
    fill = "Category", 
    title = "Percentage Distribution of Primary Bank by Group"
  ) +
 scale_fill_manual(values = c("Nova Ljubljanska Banka d.d. (NLB)" = "#230078", "OTP banka d.d." = "#84BD00", "Banka Intesa Sanpaolo d.d." = "#FA7800", "Gorenjska Banka d.d." = "lightblue2", "Delavska Hranilnica d.d." = "darkred", "Revolut" = "cyan", "Deželna Banka Slovenije d.d." = "violet", "Banka Sparkasse d.d." = "magenta", "Addiko Bank d.d." = "salmon", "UniCredit Banka Slovenija d.d." = "yellow3"))

7 Principal Component Analysis

mydata_PCA <- mydata2[c(28:33)]
mydata_PCA2 <- mydata2[c(34:45)]
R <- cor(mydata_PCA)

library(psych)

## 
## Attaching package: 'psych'

## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha

corPlot(R)

library(psych)
cortest.bartlett(R, n = nrow(mydata_PCA))

## $chisq
## [1] 553.3252
## 
## $p.value
## [1] 2.869381e-108
## 
## $df
## [1] 15

library(psych)
KMO(R)

## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = R)
## Overall MSA =  0.74
## MSA for each item = 
## Support and guidance           Confidence             Security 
##                 0.82                 0.70                 0.70 
##         Accesibility              Clarity                Speed 
##                 0.77                 0.79                 0.77

library(FactoMineR)

## Warning: package 'FactoMineR' was built under R version 4.4.2

components <- PCA(mydata_PCA, 
                  scale.unit = TRUE, 
                  graph = FALSE)
library(factoextra)
get_eigenvalue(components)

##       eigenvalue variance.percent cumulative.variance.percent
## Dim.1  2.9970186        49.950310                    49.95031
## Dim.2  1.0382843        17.304739                    67.25505
## Dim.3  0.7337893        12.229822                    79.48487
## Dim.4  0.5971498         9.952497                    89.43737
## Dim.5  0.4276216         7.127027                    96.56440
## Dim.6  0.2061363         3.435605                   100.00000

7.1 How many components to retain?

Eigenvalue of first 2 principal components for standardized variables is bigger than 1.

The first 1 principal components explain more than 40% of the data. We measure evaluation which is subjective, so we measure soft data for which the chosen number of components should explain around 40% of the data.

The last chosen principal component captures more than 5% of total variance of original variables (5).

fviz_eig(components, 
         choice = "eigenvalue", 
         main = "Screeplot",
         ylab = "Eigenvalue",
         xlab = "Principal component",
         addlabels = TRUE)

When looking at the Scree plot the biggest difference between eigenvalues. This is between 1 and 2, so we should choose 1 principal component.

library(psych)
fa.parallel(mydata_PCA,
            sim = FALSE,
            fa = "pc")

## Parallel analysis suggests that the number of factors =  NA  and the number of components =  1

Parallel analysis suggests that we should choose 1 principal component.

Because we need to do the perception map, which has 2 principal components, we will use 2.

library(FactoMineR)
components <- PCA(mydata_PCA, 
                  ncp = 2, 
                  scale.unit = TRUE, 
                  graph = FALSE)

components$var$cor

##                          Dim.1       Dim.2
## Support and guidance 0.6342613 -0.42424621
## Confidence           0.8410214 -0.21848787
## Security             0.8091472 -0.17588683
## Accesibility         0.6469456  0.49579602
## Clarity              0.7638862 -0.09612296
## Speed                0.4802439  0.72427414

loadings <- components$var$cor
library(factoextra)
eigenvalue <- get_eigenvalue(components)[1:2,1 ]
coefficient1 <- loadings[1:6]/sqrt(eigenvalue)[1]
coefficient2 <- loadings[7:12]/sqrt(eigenvalue)[2]

library(tidyverse)

## Warning: package 'tidyverse' was built under R version 4.4.2

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats   1.0.0     ✔ readr     2.1.5
## ✔ lubridate 1.9.3     ✔ stringr   1.5.1
## ✔ purrr     1.0.2     ✔ tibble    3.2.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ psych::%+%()      masks ggplot2::%+%()
## ✖ psych::alpha()    masks ggplot2::alpha()
## ✖ rstatix::filter() masks dplyr::filter(), stats::filter()
## ✖ dplyr::lag()      masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(psych)

mydata_PCAD <- mydata_PCA2 %>% 
  pivot_longer(everything(), names_to = "name", values_to = "score")  %>% 
  separate(name, into = c("retailer", "dimension"), sep = "_")%>% 
  pivot_wider(names_from = retailer, values_from = score, values_fn = mean) %>%
  column_to_rownames(var = "dimension")

mydata_PCA_std <- scale(mydata_PCAD)
poslovalnica1 <- sum(mydata_PCA_std[,1]*coefficient1)
mobilna_banka1 <- sum(mydata_PCA_std[,2]*coefficient1)
poslovalnica2 <- sum(mydata_PCA_std[,1]*coefficient2)
mobilna_banka2 <- sum(mydata_PCA_std[,2]*coefficient2)

7.2 PCA - Biplot

library(factoextra)

p <- fviz_pca_biplot(components, repel = TRUE, invisible = "ind", col.var = "#33006F")
p + 
  annotate("point", x = poslovalnica1, y = poslovalnica2, color = "#84BD00", size = 4, shape = 16) +
  annotate("text", x = poslovalnica1, y = poslovalnica2, label = "Branch", vjust = -1, color = "#84BD00") +
  annotate("point", x = mobilna_banka1, y = mobilna_banka2, color = "#FA7800", size = 4, shape = 16) +
  annotate("text", x = mobilna_banka1, y = mobilna_banka2, label = "Mobile bank", vjust = -1, color = "#FA7800")

Principal component analysis was performed on 6 standardized variables (n = 265).

The KMO measure confirms the appropriateness of the variables, KMO = 0.74, although the data falls into the category “Middling”. The MSA statistics for the individual variables are above 0.50 for all variables.

Based on the component’s loadings, we conclude that PC1 (𝜆1 = 2.997) represents quality, while PC2 (𝜆2 = 1.038) represents the contrast between stability and accessibility.

summary(mydata[4:9])

##       Q23a           Q23b           Q23c            Q23d            Q23e      
##  Min.   :1.00   Min.   :1.00   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:1.00   1st Qu.:1.00   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000  
##  Median :1.00   Median :1.00   Median :1.000   Median :1.000   Median :1.000  
##  Mean   :1.36   Mean   :1.17   Mean   :1.271   Mean   :1.154   Mean   :1.332  
##  3rd Qu.:2.00   3rd Qu.:1.00   3rd Qu.:2.000   3rd Qu.:1.000   3rd Qu.:2.000  
##  Max.   :2.00   Max.   :2.00   Max.   :2.000   Max.   :2.000   Max.   :2.000  
##       Q23f      
##  Min.   :1.000  
##  1st Qu.:1.000  
##  Median :2.000  
##  Mean   :1.518  
##  3rd Qu.:2.000  
##  Max.   :2.000

8 Hypothesis testing

Market Research Hypothesis 1: Users have knowledge about the existence of possible advanced services.

H_0: The number of respondents who answered Yes is equal to the number of respondents who answered No.

H_1: The number of respondents who answered Yes is greater than the number of respondents who answered No.


We reject H_0 at p < 0.001. The proportion of Yes responses is greater than 50%.

binom.test(164, 265, p = 0.5, alternative = "greater")

## 
##  Exact binomial test
## 
## data:  164 and 265
## number of successes = 164, number of trials = 265, p-value = 6.535e-05
## alternative hypothesis: true probability of success is greater than 0.5
## 95 percent confidence interval:
##  0.5670886 1.0000000
## sample estimates:
## probability of success 
##              0.6188679

Limit Change Hypothesis:

H_0: The number of respondents who answered Yes is equal to the number of respondents who answered No.

H_1: The number of respondents who answered Yes is greater than the number of respondents who answered No.


We reject H_0 at p < 0.001. The proportion of Yes responses is greater than 50%.

binom.test(214, 265, p = 0.5, alternative = "greater")

## 
##  Exact binomial test
## 
## data:  214 and 265
## number of successes = 214, number of trials = 265, p-value < 2.2e-16
## alternative hypothesis: true probability of success is greater than 0.5
## 95 percent confidence interval:
##  0.7633183 1.0000000
## sample estimates:
## probability of success 
##              0.8075472

Ordering a New Card Hypothesis:

H_0: The number of respondents who answered Yes is equal to the number of respondents who answered No.

H_1: The number of respondents who answered Yes is greater than the number of respondents who answered No.


We reject H_0 at p < 0.001. The proportion of Yes responses is greater than 50%.

binom.test(190, 265, p = 0.5, alternative = "greater")

## 
##  Exact binomial test
## 
## data:  190 and 265
## number of successes = 190, number of trials = 265, p-value = 5.532e-13
## alternative hypothesis: true probability of success is greater than 0.5
## 95 percent confidence interval:
##  0.667899 1.000000
## sample estimates:
## probability of success 
##              0.7169811

Limit Change on Credit Card Hypothesis:

H_0: The number of respondents who answered Yes is equal to the number of respondents who answered No.

H_1: The number of respondents who answered Yes is greater than the number of respondents who answered No.


We reject H_0 at p < 0.001. The proportion of Yes responses is greater than 50%.

binom.test(217, 265, p = 0.5, alternative = "greater")

## 
##  Exact binomial test
## 
## data:  217 and 265
## number of successes = 217, number of trials = 265, p-value < 2.2e-16
## alternative hypothesis: true probability of success is greater than 0.5
## 95 percent confidence interval:
##  0.7754507 1.0000000
## sample estimates:
## probability of success 
##              0.8188679

Opening a Saving Account Hypothesis:

H_0: The number of respondents who answered Yes is equal to the number of respondents who answered No.

H_1: The number of respondents who answered Yes is greater than the number of respondents who answered No.


We reject H_0 at p < 0.001. The proportion of Yes responses is greater than 50%.

binom.test(171, 265, p = 0.5, alternative = "greater")

## 
##  Exact binomial test
## 
## data:  171 and 265
## number of successes = 171, number of trials = 265, p-value = 1.3e-06
## alternative hypothesis: true probability of success is greater than 0.5
## 95 percent confidence interval:
##  0.5940064 1.0000000
## sample estimates:
## probability of success 
##               0.645283

Term Deposits Hypothesis:

H_0: The number of respondents who answered Yes is equal to the number of respondents who answered No.

H_1: The number of respondents who answered Yes is greater than the number of respondents who answered No.


We reject H_0 at p < 0.001. The proportion of Yes responses is greater than 50%.

binom.test(121, 265, p = 0.5, alternative = "greater")

## 
##  Exact binomial test
## 
## data:  121 and 265
## number of successes = 121, number of trials = 265, p-value = 0.9299
## alternative hypothesis: true probability of success is greater than 0.5
## 95 percent confidence interval:
##  0.4049412 1.0000000
## sample estimates:
## probability of success 
##              0.4566038

Market Research Hypothesis 2: Users have no difficulties finding advanced services in the mobile bank.

H_0: μ = 3.

H_1: μ > 3.


We cannot reject H_0 at p < 0.001. The proportion of Yes responses is not greater than 50%.

shapiro.test(mydata2$Q25)

## 
##  Shapiro-Wilk normality test
## 
## data:  mydata2$Q25
## W = 0.87107, p-value = 3.88e-14

We reject H_0 at p < 0.001. The distribution is not normal.

We need to use a non-parametric test.

wilcox.test(mydata2$Q25,
            mu=3,
            alternative = "greater")

## 
##  Wilcoxon signed rank test with continuity correction
## 
## data:  mydata2$Q25
## V = 10456, p-value = 9.898e-16
## alternative hypothesis: true location is greater than 3

Market Research Hypothesis 3: Users prefer to access advanced services in-app.

H_0: The number of respondents who answered Yes is equal to the number of respondents who answered No.

H_1: The number of respondents who answered Yes is greater than the number of respondents who answered No.


We reject H_0 at p < 0.001. Users have no difficulties finding advanced services in the mobile bank.

binom.test(198, 320, p = 0.5, alternative = "greater")

## 
##  Exact binomial test
## 
## data:  198 and 320
## number of successes = 198, number of trials = 320, p-value = 1.271e-05
## alternative hypothesis: true probability of success is greater than 0.5
## 95 percent confidence interval:
##  0.5718382 1.0000000
## sample estimates:
## probability of success 
##                0.61875

Market Research Hypothesis 4: Users don’t use the advanced services in the mobile bank because they don’t need them.

H_0: μ = 3

H_1: μ > 3


We reject H_0 at p < 0.001. The proportion of Yes responses is greater than 50%.

shapiro.test(mydata2$Q29a)

## 
##  Shapiro-Wilk normality test
## 
## data:  mydata2$Q29a
## W = 0.88792, p-value = 4.419e-13

We reject H_0 at p < 0.001. The distribution is not normal.

We need to use a non-parametric test.

wilcox.test(mydata2$Q29a,
            mu=3,
            alternative = "greater")

## 
##  Wilcoxon signed rank test with continuity correction
## 
## data:  mydata2$Q29a
## V = 13012, p-value = 2.59e-07
## alternative hypothesis: true location is greater than 3

Market Research Hypothesis 5: Users perceive the value of the advanced services offered by mobile banking as high.

H_0: μ = 3

H_1: μ > 3


We reject H_0 at p < 0.001. Users don’t use the advanced services in the mobile bank because they don’t need them.

shapiro.test(mydata2$Q31a)

## 
##  Shapiro-Wilk normality test
## 
## data:  mydata2$Q31a
## W = 0.77981, p-value < 2.2e-16

We reject H_0 at p < 0.001. The distribution is not normal.

We need to use a non-parametric test.

wilcox.test(mydata2$Q31a,
            mu=3,
            alternative = "greater")

## 
##  Wilcoxon signed rank test with continuity correction
## 
## data:  mydata2$Q31a
## V = 27089, p-value < 2.2e-16
## alternative hypothesis: true location is greater than 3

Market Research Hypothesis 6: Users need more detailed video tutorials for advanced services.

H_0: μ = 3

H_1: μ > 3


We reject H_0 at p < 0.001. Users perceive the value of the advanced services offered by mobile banking as high.

shapiro.test(mydata2$Q30b)

## 
##  Shapiro-Wilk normality test
## 
## data:  mydata2$Q30b
## W = 0.88174, p-value = 1.762e-13

We reject H_0 at p < 0.001. The distribution is not normal.

We need to use a non-parametric test.

wilcox.test(mydata2$Q30b,
            mu=3,
            alternative = "greater")

## 
##  Wilcoxon signed rank test with continuity correction
## 
## data:  mydata2$Q30b
## V = 14114, p-value = 3.013e-06
## alternative hypothesis: true location is greater than 3

Additional Market Research Hypothesis:

Users would be more likely to use advanced services if the app was simpler to navigate.

H_0: μ = 3

H_1: μ > 3


We reject H_0 at p < 0.001. Users need more detailed video tutorials for advanced services.

shapiro.test(mydata2$Q30a)

## 
##  Shapiro-Wilk normality test
## 
## data:  mydata2$Q30a
## W = 0.90609, p-value = 8.214e-12

We reject H_0 at p < 0.001. The distribution is not normal.

We need to use a non-parametric test.

wilcox.test(mydata2$Q30a,
            mu=3,
            alternative = "greater")

## 
##  Wilcoxon signed rank test with continuity correction
## 
## data:  mydata2$Q30a
## V = 9042.5, p-value = 0.2652
## alternative hypothesis: true location is greater than 3

Market Research Hypothesis:

Users would be more likely to use advanced services if the app had faster processing times.

H_0: μ = 3

H_1: μ > 3


We cannot reject H_0. We cannot say that users would be more likely to use advanced services if the app was simpler to navigate.

shapiro.test(mydata2$Q30c)

## 
##  Shapiro-Wilk normality test
## 
## data:  mydata2$Q30c
## W = 0.8937, p-value = 1.079e-12

We reject H_0 at p < 0.001. The distribution is not normal.

We need to use a non-parametric test.

wilcox.test(mydata2$Q30c,
            mu=3,
            alternative = "greater")

## 
##  Wilcoxon signed rank test with continuity correction
## 
## data:  mydata2$Q30c
## V = 14359, p-value = 1.577e-07
## alternative hypothesis: true location is greater than 3

Market Research Hypothesis:

Users would be more likely to use advanced services if the app offered personalized recommendations.

H_0: μ = 3

H_1: μ > 3


We reject H_0 at p < 0.001. Users would be more likely to use advanced services if the app had faster processing times.

shapiro.test(mydata2$Q30d)

## 
##  Shapiro-Wilk normality test
## 
## data:  mydata2$Q30d
## W = 0.89455, p-value = 1.233e-12

We reject H_0 at p < 0.001. The distribution is not normal.

We need to use a non-parametric test.

wilcox.test(mydata2$Q30d,
            mu=3,
            alternative = "greater")

## 
##  Wilcoxon signed rank test with continuity correction
## 
## data:  mydata2$Q30d
## V = 11254, p-value = 0.1314
## alternative hypothesis: true location is greater than 3

Market Research Hypothesis:

Users would be more likely to use advanced services if the app had an improved design.

H_0: μ = 3

H_1: μ > 3


We cannot reject H_0. We cannot say that users would be more likely to use advanced services if the app offered personalized recommendations.

shapiro.test(mydata2$Q30e)

## 
##  Shapiro-Wilk normality test
## 
## data:  mydata2$Q30e
## W = 0.91132, p-value = 2.041e-11

We reject H_0 at p < 0.001. The distribution is not normal.

We need to use a non-parametric test.

wilcox.test(mydata2$Q30e,
            mu=3,
            alternative = "greater")

## 
##  Wilcoxon signed rank test with continuity correction
## 
## data:  mydata2$Q30e
## V = 8691, p-value = 0.8457
## alternative hypothesis: true location is greater than 3

We cannot reject H_0. We cannot say that users would be more likely to use advanced services if the app had an improved design.