Business Research Methodology and Data Collection

This document provides an in-depth analysis of the methodology and data collection techniques used in business research. It covers topics such as demographic characteristics of respondents, normality and distribution of data, preliminary analysis, hypothesis testing, and interpretation of findings. The document also includes tables and statistics for better understanding. Suitable for students studying business research or related subjects.

Business Research
Methodology and Data
Collection
1

Table of Contents
2. Analyse demographic characteristics of respondents..............................................................3
3. Normality and distribution of data...........................................................................................7
4. Preliminary analysis.................................................................................................................8
5. Hypothesis testing..................................................................................................................10
6. Interpretation findings...........................................................................................................18
REFERENCES..............................................................................................................................21
2

2. Analyse demographic characteristics of respondents
Table 1
EDUCATION
Frequency Percent Valid Percent Cumulative
Percent
Valid
Vocational/High School 18 3.6 3.6 3.6
HND 5 1.0 1.0 4.6
University Degree 319 63.8 63.8 68.4
Post graduate 158 31.6 31.6 100.0
Total 500 100.0 100.0
3

Table 2
GENDER
Frequency Percent Valid Percent Cumulative
Percent
Valid
Male 270 54.0 54.0 54.0
Female 230 46.0 46.0 100.0
Total 500 100.0 100.0
4

Table 3
AGE
Frequency Percent Valid Percent Cumulative
Percent
Valid
18-27 373 74.6 74.6 74.6
28-37 111 22.2 22.2 96.8
38-47 11 2.2 2.2 99.0
48-57 5 1.0 1.0 100.0
Total 500 100.0 100.0
5

Table 4
AVUPW
Frequency Percent Valid Percent Cumulative
Percent
Valid
Two 173 34.6 34.6 34.6
Four 112 22.4 22.4 57.0
Six 40 8.0 8.0 65.0
Eight 23 4.6 4.6 69.6
Other 152 30.4 30.4 100.0
Total 500 100.0 100.0
6

3. Normality and distribution of data
Table 5
Descriptive Statistics
N Maximum Mean Std. Deviation Skewness
Statistic Statistic Statistic Std. Error Statistic Statistic
Using uber taxi enhances my
self confidence 500 5 3.29 .053 1.181 -.386
Using uber taxi makes me a
worthy person 500 8 2.77 .054 1.217 .299
Using uber services makes
my friends value me 500 22 2.83 .065 1.460 4.544
Using uber taxi is prestiguos 500 7 3.41 .052 1.160 -.274
I trust that uber services is
safer 500 5 3.63 .053 1.177 -.656
I trust that uber services is
robbery- free 500 5 3.34 .050 1.122 -.242
I trust that uber services
drivers are carful when
driving
500 7 3.64 .046 1.034 -.417
I trust that uber mobile
application is safe to use 500 9 3.66 .046 1.026 -.341
I trust that uber taxi drivers
will not run away with my
belongings
500 5 3.42 .050 1.109 -.409
Using uber services helps
me get closer to important
people
500 5 2.65 .049 1.104 .187
Using services connects
socially me to peopls 500 5 2.84 .047 1.043 -.059
Using uber services helps
me develop relationships
with others
500 5 2.91 .051 1.139 .022
7

I feel loved when use Uber
services 500 5 2.90 .051 1.130 -.103
I am greeted and welcomed
by an Uber driver anytime I
pick Uber services
500 44 3.57 .094 2.095 14.370
Descriptive Statistics
Skewness Kurtosis
Std. Error Statistic Std. Error
I will use uber services more often .109 .243 .218
GENDER .109 -1.982 .218
EDUCATION .109 3.019 .218
AGE .109 5.059 .218
AVUPW .109 -1.564 .218
YWUEXP .109 -.645 .218
Valid N (listwise)
4. Preliminary analysis
a. Reliability analysis
Case Processing Summary
N %
Cases
Valid 499 99.8
Excludeda 1 .2
Total 500 100.0
a. Listwise deletion based on all variables in the
procedure.
8

Table 6
Reliability Statistics
Cronbach's
Alpha
Cronbach's
Alpha Based on
Standardized
Items
N of Items
.912 .919 37
b. Convergent validity
Statistics
AVUPW
N Valid 500
Missing 0
Mean 2.74
Median 2.00
Std. Deviation 1.675
Range 4
Minimum 1
Maximum 5
Table 7
AVUPW
Frequency Percent Valid Percent Cumulative
Percent
Valid
Two 173 34.6 34.6 34.6
Four 112 22.4 22.4 57.0
Six 40 8.0 8.0 65.0
Eight 23 4.6 4.6 69.6
Other 152 30.4 30.4 100.0
Total 500 100.0 100.0
9

c) Discriminant validity
factor
loading
square
factor
loading
1-square
factor
loading
λ λ square ɛ
0.737 0.543169 0.456831 N 7
0.727 0.528529 0.471471
0.617 0.380689 0.619311 AVE 0.56279
0.731 0.534361 0.465639
0.819 0.670761 0.329239 CR 4.06047
0.786 0.617796 0.382204
0.815 0.664225 0.335775 DV 0.750193
10

SUM λ SUM
λsquare
SUM ɛ
(error
variance)
5.232 3.93953 3.06047
5. Hypothesis testing
H1: Consumer need for prestige has a positive effect on riders’ continuous usage intentions
Table 9
Correlations
I will continue to
use uber service
Using uber taxi
is prestiguos
Pearson Correlation
I will continue to use uber
service 1.000 .321
Using uber taxi is prestiguos .321 1.000
Sig. (1-tailed)
I will continue to use uber
service . .000
Using uber taxi is prestiguos .000 .
N
I will continue to use uber
service 500 500
Using uber taxi is prestiguos 500 500
Model Summaryb
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square
Change
F Change df1
1 .321a .103 .101 .920 .103 57.092 1
Model Summaryb
Model Change Statistics Durbin-Watson
df2 Sig. F Change
1 498a .000 1.831
a. Predictors: (Constant), Using uber taxi is prestiguos
b. Dependent Variable: I will continue to use uber service
11

ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 48.284 1 48.284 57.092 .000b
Residual 421.164 498 .846
Total 469.448 499
a. Dependent Variable: I will continue to use uber service
b. Predictors: (Constant), Using uber taxi is prestiguos
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 2.736 .128 21.384 .000
Using uber taxi is prestiguos .268 .035 .321 7.556 .000
a. Dependent Variable: I will continue to use uber service
H2: Trust has a positive effect on riders’ continuous usage intentions
Table 10
Correlations
I will encourage
others to use
uber service
I trust that uber
services is safer
Pearson Correlation
I will encourage others to use
uber service 1.000 .382
I trust that uber services is
safer .382 1.000
Sig. (1-tailed)
I will encourage others to use
uber service . .000
I trust that uber services is
safer .000 .
12

N
I will encourage others to use
uber service 500 500
I trust that uber services is
safer 500 500
Model Summaryb
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square
Change
F Change df1
1 .382a .146 .144 .897 .146 84.845 1
Model Summaryb
Model Change Statistics Durbin-Watson
df2 Sig. F Change
1 498a .000 1.972
a. Predictors: (Constant), I trust that uber services is safer
b. Dependent Variable: I will encourage others to use uber service
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 68.307 1 68.307 84.845 .000b
Residual 400.931 498 .805
Total 469.238 499
a. Dependent Variable: I will encourage others to use uber service
b. Predictors: (Constant), I trust that uber services is safer
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 2.578 .130 19.815 .000
13

I trust that uber services is
safer .314 .034 .382 9.211 .000
a. Dependent Variable: I will encourage others to use uber service
H3: Customer return investment has a positive effect on riders’ continuous usage
intentions
Table 11
Correlations
I will use uber
services more
often
Uber service is
cheaper
Pearson Correlation
I will use uber services more
often 1.000 .322
Uber service is cheaper .322 1.000
Sig. (1-tailed)
I will use uber services more
often . .000
Uber service is cheaper .000 .
N
I will use uber services more
often 499 499
Uber service is cheaper 499 499
Model Summaryb
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square
Change
F Change df1
1 .322a .104 .102 .963 .104 57.491 1
Model Summaryb
Model Change Statistics Durbin-Watson
df2 Sig. F Change
1 497a .000 1.890
a. Predictors: (Constant), Uber service is cheaper
14

b. Dependent Variable: I will use uber services more often
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 53.276 1 53.276 57.491 .000b
Residual 460.564 497 .927
Total 513.840 498
a. Dependent Variable: I will use uber services more often
b. Predictors: (Constant), Uber service is cheaper
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 2.664 .131 20.401 .000
Uber service is cheaper .286 .038 .322 7.582 .000
a. Dependent Variable: I will use uber services more often
H4: Convenience has a positive effect on riders’ continuous usage intentions
Table 12
Correlations
I will continue to
use uber service
Uber service is
always
accessible
Pearson Correlation
I will continue to use uber
service 1.000 .496
Uber service is always
accessible .496 1.000
Sig. (1-tailed) I will continue to use uber
service
. .000
15

Uber service is always
accessible .000 .
N
I will continue to use uber
service 500 500
Uber service is always
accessible 500 500
Model Summaryb
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square
Change
F Change df1
1 .496a .246 .245 .843 .246 162.739 1
Model Summaryb
Model Change Statistics Durbin-Watson
df2 Sig. F Change
1 498a .000 1.830
a. Predictors: (Constant), Uber service is always accessible
b. Dependent Variable: I will continue to use uber service
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 115.624 1 115.624 162.739 .000b
Residual 353.824 498 .710
Total 469.448 499
a. Dependent Variable: I will continue to use uber service
b. Predictors: (Constant), Uber service is always accessible
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
16

1
(Constant) 1.947 .139 14.023 .000
Uber service is always
accessible .470 .037 .496 12.757 .000
a. Dependent Variable: I will continue to use uber service
H5: Search benefit has a positive effect on riders’ continuous usage intentions
Table 13
Correlations
I will use uber
services more
often
uber drivers are
easilty available
Pearson Correlation
I will use uber services more
often 1.000 .463
uber drivers are easilty
available .463 1.000
Sig. (1-tailed)
I will use uber services more
often . .000
uber drivers are easilty
available .000 .
N
I will use uber services more
often 500 500
uber drivers are easilty
available 500 500
Model Summaryb
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square
Change
F Change df1
1 .463a .214 .213 .900 .214 135.990 1
Model Summaryb
Model Change Statistics Durbin-Watson
df2 Sig. F Change
1 498a .000 1.862
17

a. Predictors: (Constant), uber drivers are easilty available
b. Dependent Variable: I will use uber services more often
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 110.252 1 110.252 135.990 .000b
Residual 403.748 498 .811
Total 514.000 499
a. Dependent Variable: I will use uber services more often
b. Predictors: (Constant), uber drivers are easilty available
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 2.023 .141 14.344 .000
uber drivers are easilty
available .444 .038 .463 11.661 .000
a. Dependent Variable: I will use uber services more often
6. Interpretation findings
Table 1- It can be interpreted from table that out of 500 respondents 319 people were having
university degree, 158 were post graduate and 18 were studying in high school. Besides that, 5
were having higher national diploma. Thus, it can be stated that most participants were young
and having university degree or post graduate (Tavakol, and Dennick, 2015).
Table 2 – From table it can be analysed that out of 500 sample, 270 were male and 230 were
female. So, it can be stated that proportion of male were more as compared to females.
Table 3 – By analysing table it is evaluated that 373 people belong to age group of 18-27, 111
belong to 28- 37. Moreover, 11 belong to age group of 38-47 and 5 were of age 48 -57. Hence, it
18

is stated that most respondents were young and belong to age group of 18-27. They were those
who uses Uber as taxi service more often.
Table 4- It is analysed that out of 500 samples, the average usage per week of 173 respondent
were 2 times, 112 were of 4 times, 40 were 6 times and 23 were 8 times. Furthermore, average
usage per week of 152 people were other.
Table 5- It is analysed that skewness of most answer are in between -1 o 1 so it means that
distribution is moderately skewed. So, it is bell shaped curve.
Table 6- It can be interpreted from table that Cronbach’s alpha value is .912. So, it shows that
there is high level of internal consistency in scale with specific examples (Bonett, and Wright,
2015). Thus, the data which gathered in it is acceptable.
Table 7 – It is interpreted from table that items on scale show how many times rider takes rides
in a week. The mean in this is 2.74 and SD is 1.675. so, there is no item pairing in it.
Table 9- From above table it can be stated that significant value obtained is P= 0.000 that is less
than P= 0.05. Thus, null hypothesis is rejected. It means that consumer need for prestige does not
have positive effect on rider continuous usage intention. The prestige does not lead to allow
customer to continuous use uber taxi service. Hence, prestige does not result in creating positive
impact on taxi usage by rider. However, if prestige is high then it will not create positive image
in mind of rider.
Table 10 – It is analysed from table that significant value obtained is P= 0.000 that is less than
P= 0.05. Thus, null hypothesis is rejected. It means trust does not have positive effect on rider
continuous usage intention. Here, even if rider thinks that uber is safe and trust it then also it will
not have positive effect on usage intention. Alongside, trust there are various other factors as
well that led to positive affect on continuous use of taxi services.
Table 11- By interpreting data, it is found that significant value obtained is P= 0.000 that is less
than P= 0.05. Thus, null hypothesis is rejected. It means customer return investment does not
have positive effect on rider continuous usage intention. Even if uber services are cheaper the
customer will not use it continuously. There may be loss as well in customer retune and this can
lead to ineffective use of it.
19

Table 12- From above table it can be stated that significant value obtained is P= 0.000 that is less
than P= 0.05. Thus, null hypothesis is rejected. It means that convenience does not have positive
effect on rider continuous usage intention. So, there is no relationship between convenience and
usage intention. Furthermore, even if uber service is easily available there is no positive affect on
using services again (Santos, 2016).
Table 13- It is analysed from table that significant value obtained is P= 0.000 that is less than P=
0.05. Thus, null hypothesis is rejected. It means there is no relationship between search benefit
and continuous usage intention. Therefore, there is no positive effect on rider. So, they will not
use uber service more often even they drivers are available easily.
20

REFERENCES
Books and journals
Bonett, D.G. and Wright, T.A., 2015. Cronbach's alpha reliability: Interval estimation,
hypothesis testing, and sample size planning. Journal of Organizational Behavior, 36(1),
pp.3-15.
Santos, J.R.A., 2016. Cronbach’s alpha: A tool for assessing the reliability of scales. Journal of
extension, 37(2), pp.1-5.
Tavakol, M. and Dennick, R., 2015. Making sense of Cronbach's alpha. International journal of
medical education, 2, p.53.
21