Impact of Anxiety and Resilience on Nintendo Switch Purchase Intention

This report presents a data analysis of factors influencing the purchase intention of the Nintendo Switch, focusing on a study of Chinese consumers. The analysis explores the relationships between variables such as gender, education, age, anxiety, resilience, and socialization, using descriptive statistics, correlation, and regression methods. Findings indicate that anxiety significantly impacts purchase intention, while resilience does not show a significant correlation. The study also highlights the importance of socialization in the context of gaming during the COVID-19 pandemic. Limitations include the use of specific factors and the lack of qualitative research, and suggestions for future research involve incorporating qualitative techniques and in-depth interviews to gain deeper insights into consumer behavior and purchase intentions. The report concludes with managerial implications, emphasizing the role of anxiety in purchase decisions and the potential for Nintendo to capitalize on social interaction within the gaming community.

Data Analysis
1

Table of Contents
PART B Develop and justify model 3
PART C discuss and summarise findings 3
PART D Discuss the managerial implications of findings 11
PART E Limitation of research and suggestions for future research 11
REFERENCES 13
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PART B Develop and justify model
Hypothesis 1
H0- There is no significant relationship between intention to buy Nintendo switch and resilience
in male
H1- There is significant relationship between intention to buy Nintendo switch and resilience in
female
Hypothesis 2
H0- There is no significant relationship between intention to buy Nintendo switch and anxiety in
male
H1- There is no significant relationship between intention to buy Nintendo switch and anxiety in
female
Hypothesis 3
H0- There is no significant relationship between intention to buy Nintendo switch and socialize
in male
H1- There is significant relationship between intention to buy Nintendo switch and socialize in
female
Hypothesis 4
H0- There is no significant relationship between intention to buy Nintendo switch and boredom
in male
H1- There is significant relationship between intention to buy Nintendo switch and boredom in
female
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Hypothesis 5
These both hypothesis is taken to relate purchase intention with people behaviour.
There are various constructs in these. Here, first is anxiety of people during covid 19 that
how they feel during lock down. In this the sign of coefficients is that it will state relationship
between anxiety and intention to buy game in Chinese people during covid 19. Alongside,
another sign of coefficient is that relationship between resilience and intention to buy Nintendo
game during covid 19. These two coefficient indicate purchase intention with behavior of
Chinese people.
PART C discuss and summarise findings
Justification of methods used-
There are various methods which has been used in data analysis. It is defined as below
Descriptive- this method is used to find out characteristics of population of present study. With
that it has been easy to find out how many people belong to what age group, their education
level, gender, etc. of Chinese consumers.
Correlation- The method is used to find out how two variables are related to each other or what
is degree of association between the two (Knyazev and et.al., 2021). Thus, through that the
relation is determined between boredom, resilience, anxiety and socialise of Chinese consumers.
Regression – This method is applied in study to find out relationship between dependent
variable on independent variable. Besides, it enables in testing of hypothesis in study.
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Statistics
gender education age
N
Valid 305 305 305
Missin
g 0 0 0
Mean 1.4033 2.3672 2.4918
Std. Error of Mean .02814 .05696 .05005
Median 1.0000 2.0000 2.0000
Std. Deviation .49136 .99484 .87402
gender
Frequency Percent Valid
Percent
Cumulative
Percent
Vali
d
1.00 182 59.7 59.7 59.7
2.00 123 40.3 40.3 100.0
Total 305 100.0 100.0
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Interpretation- From above table it can be stated that out of 305, 182 are male and 123 are
female. So, the proportion of male is more than female in this study. Besides, mean is 1.4 which
shows most sample are male.
education
Frequency Percent Valid
Percent
Cumulative
Percent
Vali
d
.00 2 .7 .7 .7
1.00 69 22.6 22.6 23.3
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2.00 89 29.2 29.2 52.5
3.00 105 34.4 34.4 86.9
4.00 40 13.1 13.1 100.0
Total 305 100.0 100.0
Interpretation- It can be analysed from graph that 69 respondents have studied till high school
diploma. Whereas 89 have studied in some college and having no degree. 105 is studying in
bachelor’s degree since 4 years and 40 are having master degree or Phd. Moreover, mean value
is 2.46 which state most sample are having bachelor degree.
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age
Frequency Percent Valid
Percent
Cumulative
Percent
Vali
d
.00 2 .7 .7 .7
1.00 11 3.6 3.6 4.3
2.00 170 55.7 55.7 60.0
3.00 93 30.5 30.5 90.5
4.00 18 5.9 5.9 96.4
5.00 9 3.0 3.0 99.3
6.00 1 .3 .3 99.7
7.00 1 .3 .3 100.0
Total 305 100.0 100.0
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Interpretation- It can be interpreted from data that 11 people are of age below 18 years, 170 are
of age between 18 – 24 and 93 of 25- 34. Also, 18 are of age 35- 44 and only 9 are of age 45- 54.
There are only one respondent who is of age 55- 64 and 1 of 65- 74. Also, mean value is 2.4 that
means most people are of age 18 – 24.
Regression –
Correlations
gender resilience anxiety
Pearson Correlation
gender 1.000 -.011 .180
resilience -.011 1.000 -.015
anxiety .180 -.015 1.000
Sig. (1-tailed) gender . .426 .001
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resilience .426 . .397
anxiety .001 .397 .
N
gender 305 305 305
resilience 305 305 305
anxiety 305 305 305
Model Summaryb
Model R R
Square
Adjusted R
Square
Std. Error
of the
Estimate
Change Statistics Durbin-
WatsonR Square
Change
F
Change
df1 df2 Sig. F
Change
1 .180a .032 .026 .485 .032 5.061 2 302 .007 1.870
a. Predictors: (Constant), anxiety, resilience
b. Dependent Variable: gender
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 2.380 2 1.190 5.061 .007b
Residual 71.017 302 .235
Total 73.397 304
a. Dependent Variable: gender
b. Predictors: (Constant), anxiety, resilience
Interpretation- It can be interpreted from table that significance value obtained is P= .007
which is less than P = 0.05. So, it means that there is no relation between anxiety and resilience
with gender. The males and females anxiety and resilience are not associated with intention to
buy Nintendo.
Regression
Correlations
intentiontobuyni
ntendo
resilience
Pearson Correlation intentiontobuynintendo 1.000 .008
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resilience .008 1.000
Sig. (1-tailed) intentiontobuynintendo . .445
resilience .445 .
N intentiontobuynintendo 304 304
resilience 304 304
Model Summaryb
Model R R
Square
Adjusted R
Square
Std. Error
of the
Estimate
Change Statistics Durbin-
WatsonR Square
Change
F
Change
df1 df2 Sig. F
Change
1 .008a .000 -.003 1.102 .000 .019 1 302 .889 1.810
a. Predictors: (Constant), resilience
b. Dependent Variable: intentiontobuynintendo
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression .024 1 .024 .019 .889b
Residual 366.873 302 1.215
Total 366.896 303
a. Dependent Variable: intentiontobuynintendo
b. Predictors: (Constant), resilience
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 5.040 .337 14.972 .000
resilience .009 .065 .008 .139 .889
a. Dependent Variable: intentiontobuynintendo
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Interpretation- It can be interpreted from table that significance value obtained is P= .889
which is more than P= 0.05. It means that null hypothesis is accepted. So, resilience does not led
to intention to buy Nintendo in male and female.
Regression
Correlations
anxietyav
g
intentiontobuyswitchscal
e
Pearson
Correlatio
n
anxietyavg 1.000 .349
intentiontobuyswitchscal
e .349 1.000
Sig. (1-
tailed)
anxietyavg . .000
intentiontobuyswitchscal
e .000 .
N
anxietyavg 305 305
intentiontobuyswitchscal
e 305 305
Model Summary
Model R R
Square
Adjusted
R
Square
Std.
Error of
the
Estimate
Change Statistics
R Square
Change
F
Change
df1 df2 Sig. F
Change
1 .349a .122 .119 1.32366 .122 42.142 1 303 .000
a. Predictors: (Constant), intentiontobuyswitchscale
ANOVAa
Model Sum of
Squares
df Mean
Square
F Sig.
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1
Regression 73.835 1 73.835 42.142 .000b
Residual 530.879 303 1.752
Total 604.714 304
a. Dependent Variable: anxietyavg
b. Predictors: (Constant), intentiontobuyswitchscale
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 1.950 .358 5.450 .000
intentiontobuyswitchscale .447 .069 .349 6.492 .000
a. Dependent Variable: anxietyavg
Interpretation – It is analysed that significance value P= .000 which is less than P= 0.05. It
means that null hypothesis is rejected. So, there is relationship between intention of buy
Nintendo switch and anxiety. The males shows more anxiety than female to buy game.
PART D Discuss the managerial implications of findings
It can be summarised that males are having more anxiety than females. Also, there is no
relation between anxiety and resilience. It means there is no relation of people goals with
intention of buying Nintendo game. Besides, males are having strong intention to socialize than
females during covid 19 when they do nothing (Munch 2017). Thus, this is also intention to buy
Nintendo game during covid 19. Through that, they are able to interact and communicate with
others. So, by playing game they socialize with others. It is concluded that there is no
relationship between intention of buy Nintendo switch and resilience in males and females.
Hence, there is no intention of buying game with goal. When people feel anxious they buy game.
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Therefore, anxiety is intention to purchase Nintendo. It enables them to overcome anxiety. Thus,
there is relation between anxiety and resilience of people in intention to buy game.
PART E Limitation of research and suggestions for future research
In doing this study it was found that there are certain limitation of research which is
described as below :
 The first is that for measuring consumer intention the use of factors such as anxiety,
resilience, socialize, etc. were used. So, they are no appropriate as compared to research
aim. Besides, it does not relate with consumer intention.
 There was no literature or theories explained in study regarding consumer intention and
behaviour. So, it becomes difficult to relate literature with findings and find out key
constructs in research.
 The data should have been gathered through observation research method. This means
qualitative technique was not used to observe ongoing behaviour of Chinese people.
Thus, there was no reliable insights collected in it (Parmar and et.al., 2018).
Thus, to overcome limitations there is need to provide some suggestions for future
research. It is as follows :
 There should have qualitative research technique used as well in study so that more
reliable insight is obtained about purchase intention of people in buying Nintendo game.
In that, more secondary data and info is collected.
 There can be interview done to gain insight behind what is intention of people to
purchase Nintendo game. Also, this can be easy to analyse behaviour of people.
Moreover, it gives an in depth overview of people intention to buy game.
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REFERENCES
Books and journals
Knyazev, S. and et.al., 2021. Epidemiological data analysis of viral quasispecies in the next-
generation sequencing era. Briefings in bioinformatics, 22(1), pp.96-108.
Munch, E., 2017. A user’s guide to topological data analysis. Journal of Learning
Analytics, 4(2), pp.47-61.
Parmar, C., and et.al., 2018. Data analysis strategies in medical imaging. Clinical cancer
research, 24(15), pp.3492-3499.
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