Data Analysis: Model, Findings, and Implications

This document provides an analysis of data related to the intention to buy Nintendo Switch, including the development and justification of a model, discussion and summary of findings, and managerial implications. It also discusses the limitations of the research and provides suggestions for future research.

Data Analysis
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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
H1- There is significant relationship between intention to buy Nintendo switch and resilience
Hypothesis 2
H0- There is no significant relationship between intention to buy Nintendo switch and anxiety
H1- There is no significant relationship between intention to buy Nintendo switch and anxiety
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. Another main construct is goals which Chinese people want to
achieve. Also, it means difficulty to achieve goals faced by people during covid 19. It relate with
theory of consumer behaviour. Moreover, on basis of it theory can be build. Thus, people
intention can be measured with help of interview.
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.
Statistics
gender education age
N Valid 305 305 305
Missing 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
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gender
Frequency Percent Valid Percent Cumulative
Percent
Valid
1.00 182 59.7 59.7 59.7
2.00 123 40.3 40.3 100.0
Total 305 100.0 100.0
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.
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education
Frequency Percent Valid Percent Cumulative
Percent
Valid
.00 2 .7 .7 .7
1.00 69 22.6 22.6 23.3
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
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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.
age
Frequency Percent Valid Percent Cumulative
Percent
Valid
.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.
Correlation-
Correlations
reslienceavg anxietyavg
reslienceavg
Pearson Correlation 1 -.015
Sig. (2-tailed) .793
N 305 305
anxietyavg
Pearson Correlation -.015 1
Sig. (2-tailed) .793
N 305 305
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Interpretation- It can be interpreted from table that significance value obtained is P= -.015
which is less than P = 0.05. So, it means that there is no relation between anxiety and resilience.
The people do not feel nervous of covid 19 and are ready to face obstacle to attain goals.
Correlations
bordemavg socializescale
bordemavg
Pearson Correlation 1 .376**
Sig. (2-tailed) .000
N 305 305
socializescale
Pearson Correlation .376** 1
Sig. (2-tailed) .000
N 305 305
**. Correlation is significant at the 0.01 level (2-tailed).
Interpretation- It is found that significance value obtained is P= .376 that is less than P = 0.05.
It means there is no relation in boredom and socialize. People are having strong intention to
socialize even when they do nothing.
Regression
Correlations
reslienceavg intentiontobuysw
itchscale
Pearson Correlation reslienceavg 1.000 .198
intentiontobuyswitchscale .198 1.000
Sig. (1-tailed) reslienceavg . .000
intentiontobuyswitchscale .000 .
N reslienceavg 305 305
intentiontobuyswitchscale 305 305
Model Summary
Model R Change Statistics
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R
Square
Adjusted R
Square
Std. Error of
the Estimate
R Square
Change
F
Change
df1 df2 Sig. F
Change
1 .198a .039 .036 .95680 .039 12.415 1 303 .000
a. Predictors: (Constant), intentiontobuyswitchscale
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 11.366 1 11.366 12.415 .000b
Residual 277.384 303 .915
Total 288.749 304
a. Dependent Variable: reslienceavg
b. Predictors: (Constant), intentiontobuyswitchscale
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 4.195 .259 16.220 .000
intentiontobuyswitchscale .175 .050 .198 3.524 .000
a. Dependent Variable: reslienceavg
Interpretation- it can be interpreted from table that significance value obtained is 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 resilience. The Chinese people can achieve goal in spite of
buying Nintendo game during covid 19. They can easily overcome obstacles and accomplish
their goals.
Regression
Correlations
anxietyavg intentiontobuysw
itchscale
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Pearson Correlation anxietyavg 1.000 .349
intentiontobuyswitchscale .349 1.000
Sig. (1-tailed) anxietyavg . .000
intentiontobuyswitchscale .000 .
N anxietyavg 305 305
intentiontobuyswitchscale 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.
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 people intention of buy game is they feel nervous and anxious.
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So, to overcome nervousness they buy game. With that they are able to enjoy and it reduces
stress. The game helps in some entertainment as well.
PART D Discuss the managerial implications of findings
It can be summarised that there is no relation between anxiety and resilience. The people
are ready to face challenge during covid 19 and achieve goals even after buying Nintendo game.
It means they do not see game as obstacle in their goals. Besides, people are having strong
intention to socialize even 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
relationship between intention of buy Nintendo switch and resilience. This means people can
achieve goals by playing game. Hence, there is no intention of buying game with goal. For
overcoming anxiety as well Chinese people buy Nintendo game. 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.
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 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.
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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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