Research Project: Globalization's Impact on Tesco: Regression Analysis

This research project investigates the impact of globalization on Tesco, focusing on its effects on the company's vulnerability to market variations. The project utilizes data analysis and regression techniques to identify significant variables influencing both the overall impact of globalization and the impact on Tesco's vulnerability. The analysis includes descriptive statistics, correlations, and regression models, considering factors like gender, age group, Tesco-related experience, outsourcing ability, and geographic patterns. The findings reveal that gender, Tesco-related experience, and outsourcing ability significantly affect the overall impact, while the vulnerability model incorporates gender, age group, Tesco-related experience, outsourcing ability, and geographic pattern. The model explains 76.4% of the variation in the dependent variable, indicating a good fit. The research provides insights into how globalization affects Tesco's operations and market resilience, with the average person viewing the impact on vulnerability as neutral.

Running head: RESEARCH PROJECT
RESEARCH PROJECT
Name of the Student
Name of the University
Author Note

1RESEARCH PROJECT
Lo2 – data analysis
Overall impact variable is constructed by taking the average values of the all impact variables
without taking the Impact to vulnerability as this variable is negatively related to the positive
impacts. This is because the question is how much a person is agreed with the fact that
globalization makes the industries more vulnerable towards the market variations.
Regression analysis of the overall impact of globalization of Tesco:
Descriptive Statistics
Mean
Std.
Deviation N
Overall_impact 2.1433 .46658 50
Gender 1.66 .745 50
Age_group 2.46 .973 50
Tesco_realted_age 2.64 .964 50
factor 1.58 .883 50
Outsourcing_abilit
y
1.92 1.383 50
geographic_patter
n
3.10 .735 50
Correlations
Overall_im
pact Gender
Age_gro
up
Tesco_real
ted_age factor
Outsourcin
g_ability
geographic
_pattern
Pearson
Correlation
Overall_impac
t
1.000 -.551 -.448 -.473 -.156 -.219 -.142
Gender -.551 1.000 .670 .508 .337 .567 .510
Age_group -.448 .670 1.000 .680 .277 .513 .391

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Tesco_realted
_age
-.473 .508 .680 1.000 .490 .590 .138
factor -.156 .337 .277 .490 1.000 .273 .035
Outsourcing_a
bility
-.219 .567 .513 .590 .273 1.000 .610
geographic_pa
ttern
-.142 .510 .391 .138 .035 .610 1.000
Sig. (1-tailed) Overall_impac
t
. .000 .001 .000 .139 .063 .163
Gender .000 . .000 .000 .008 .000 .000
Age_group .001 .000 . .000 .026 .000 .003
Tesco_realted
_age
.000 .000 .000 . .000 .000 .169
factor .139 .008 .026 .000 . .028 .406
Outsourcing_a
bility
.063 .000 .000 .000 .028 . .000
geographic_pa
ttern
.163 .000 .003 .169 .406 .000 .
N Overall_impac
t
50 50 50 50 50 50 50
Gender 50 50 50 50 50 50 50

3RESEARCH PROJECT
Age_group 50 50 50 50 50 50 50
Tesco_realted
_age
50 50 50 50 50 50 50
factor 50 50 50 50 50 50 50
Outsourcing_a
bility
50 50 50 50 50 50 50
geographic_pa
ttern
50 50 50 50 50 50 50
Variables Entered/Removeda
Model
Variables
Entered
Variables
Removed Method
1 geographic_p
attern, factor,
Age_group,
Outsourcing_
ability,
Gender,
Tesco_realted
_ageb
. Enter
2 . geographic_p
attern
Backward
(criterion:
Probability of
F-to-remove
>= .100).
3 . Age_group Backward
(criterion:
Probability of
F-to-remove
>= .100).

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4 . factor Backward
(criterion:
Probability of
F-to-remove
>= .100).
a. Dependent Variable: Overall_impact
b. All requested variables entered.
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
1 .657a .431 .352 .37555
2 .657b .431 .367 .37128
3 .655c .429 .379 .36780
4 .638d .408 .369 .37064
a. Predictors: (Constant), geographic_pattern, factor,
Age_group, Outsourcing_ability, Gender,
Tesco_realted_age
b. Predictors: (Constant), factor, Age_group,
Outsourcing_ability, Gender, Tesco_realted_age
c. Predictors: (Constant), factor, Outsourcing_ability,
Gender, Tesco_realted_age
d. Predictors: (Constant), Outsourcing_ability, Gender,
Tesco_realted_age
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) 2.972 .340 8.751 .000
Gender -.368 .109 -.588 -3.374 .002
Age_group .037 .092 .077 .400 .691
Tesco_realted_age -.247 .101 -.510 -2.441 .019
factor .095 .072 .180 1.327 .191
Outsourcing_abilit
y
.113 .065 .334 1.733 .090
geographic_patter
n
-.007 .110 -.011 -.066 .948
2 (Constant) 2.953 .171 17.312 .000
Gender -.370 .105 -.591 -3.532 .001

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Age_group .035 .088 .074 .401 .691
Tesco_realted_age -.244 .090 -.504 -2.724 .009
factor .095 .071 .180 1.347 .185
Outsourcing_abilit
y
.110 .052 .327 2.134 .038
3 (Constant) 2.964 .167 17.792 .000
Gender -.349 .089 -.557 -3.895 .000
Tesco_realted_age -.225 .076 -.466 -2.971 .005
factor .090 .069 .171 1.309 .197
Outsourcing_abilit
y
.110 .051 .325 2.145 .037
4 (Constant) 2.988 .167 17.908 .000
Gender -.333 .089 -.531 -3.722 .001
Tesco_realted_age -.187 .071 -.386 -2.652 .011
Outsourcing_abilit
y
.105 .051 .310 2.037 .047
a. Dependent Variable: Overall_impact
Hence, the significant variables that significantly effects the impacts of globalization of
Tesco are Gender of person, the number of years the person related with Tesco and the
outsourcing ability of Tesco as obtained by using the backward interpolation regression in
SPSS with rejection significance level of 0.1.
Descriptive statistics of vulnerability:
Descriptive Statistics
N
Minimu
m
Maximu
m Mean
Std.
Deviation
Impact_Vulnerabil
ity
50 0 4 1.76 1.333
Valid N (listwise) 50
Hence, by on an average globalization affects vulnerability of organization by market
conditions by 1.76, which is very close to 2 or the average people think that the relation
between globalization of Tesco and its vulnerability to market condition variation is neutral.
Regression analysis to find the model of impact vulnerability:

6RESEARCH PROJECT
Correlations
Impact_Vu
lnerability Gender
Age_gro
up
Tesco_real
ted_age factor
Outsourcin
g_ability
geographic
_pattern
Pearson
Correlation
Impact_Vulner
ability
1.000 .655 .700 .598 .277 .808 .649
Gender .655 1.000 .670 .508 .337 .567 .510
Age_group .700 .670 1.000 .680 .277 .513 .391
Tesco_realted_
age
.598 .508 .680 1.000 .490 .590 .138
factor .277 .337 .277 .490 1.000 .273 .035
Outsourcing_a
bility
.808 .567 .513 .590 .273 1.000 .610
geographic_pa
ttern
.649 .510 .391 .138 .035 .610 1.000
Sig. (1-tailed) Impact_Vulner
ability
. .000 .000 .000 .026 .000 .000
Gender .000 . .000 .000 .008 .000 .000
Age_group .000 .000 . .000 .026 .000 .003

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Tesco_realted_
age
.000 .000 .000 . .000 .000 .169
factor .026 .008 .026 .000 . .028 .406
Outsourcing_a
bility
.000 .000 .000 .000 .028 . .000
geographic_pa
ttern
.000 .000 .003 .169 .406 .000 .
N Impact_Vulner
ability
50 50 50 50 50 50 50
Gender 50 50 50 50 50 50 50
Age_group 50 50 50 50 50 50 50
Tesco_realted_
age
50 50 50 50 50 50 50
factor 50 50 50 50 50 50 50
Outsourcing_a
bility
50 50 50 50 50 50 50
geographic_pa
ttern
50 50 50 50 50 50 50
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate

8RESEARCH PROJECT
1 .891a .793 .764 .647
a. Predictors: (Constant), geographic_pattern, factor,
Age_group, Outsourcing_ability, Gender,
Tesco_realted_age
ANOVAa
Model
Sum of
Squares df Mean Square F Sig.
1 Regression 69.107 6 11.518 27.496 .000b
Residual 18.013 43 .419
Total 87.120 49
a. Dependent Variable: Impact_Vulnerability
b. Predictors: (Constant), geographic_pattern, factor, Age_group,
Outsourcing_ability, Gender, Tesco_realted_age
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) -1.776 .585 -3.035 .004
Gender .081 .188 .045 .429 .670
Age_group .414 .158 .302 2.620 .012
Tesco_realted_age .094 .174 .068 .541 .592
factor .023 .123 .015 .185 .854
Outsourcing_abilit
y
.429 .112 .445 3.826 .000
geographic_patter
n
.411 .189 .227 2.176 .035
a. Dependent Variable: Impact_Vulnerability
Hence, the regression model of impact to vulnerability is
Impact to vulnerability = 0.81*Gender + 0.414*Age_group + 0.094*Tesco_realted_age +
0.023*factor + 0.429*Outsourcing_ability + 0.411*geographic_pattern
The model explains 76.4% of variation in the dependent model and hence this is a good fitted
model with significant variables.

9RESEARCH PROJECT