This project was to identify the impact of gaming on the productivity of a student.

1. 1
A research report on
IMPACT OF VIDEO GAMING ON PRODUCTIVITY
Submitted in partial fulfilment of course objectives for the subject
Business Research Methods
Submitted to Dr. Juhi Gahlot Sarkar
by:
GROUP 6
Akash Kapur 180103019
Amarnadh Reddy Chundu 180103027
Ambika Singh 180103028
Anjali Kathuria 180103033
Arshil Haider Rizvi 180101130
Saksh Sethi 180101128

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ACKNOWLEDGEMENT
“It is not possible to prepare a project report without the assistance and encouragement of
other people. This one is certainly no exception”
On the very outset of this report, we would like to extend sincere and heartfelt obligation
towards all the personages who have helped us in this endeavour. Without their active
guidance, help, cooperation and encouragement, we would not have made headway in the
project.
We are extremely thankful and pay gratitude to our faculty Dr. Juhi Gahlot Sarkar for her
valuable guidance and support on completion of this project in its presently.
We extend our gratitude to Institute of Management Technology, Ghaziabad for giving us the
opportunity to do this project.
At last but not least we extend our gratitude to all of our friends at IMT who helped in our
project directly or indirectly.

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INDEX
S NO TOPICS PAGE NO
1 Introduction and Theoretical Background 4
2 Research Problem 4
3 Conceptual Model 5
4 Research Objectives 6
5 Hypothesis 6
6 Participants 7
7 Data Collection 7
8 Research Questions 7
9 Statistical Analysis 8
10 Shapiro Wilk Test 8
11 Factor Analysis 9
12 Regression Analysis 11
13 Limitations 13
14 Future scope 13

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INTRODUCTION AND THEORETICAL BACKGROUND
As with any other innovation in society, the introduction of video games brought the question
"What are the negative effects or consequences?" Smyth (2007) notes that there seems to be an
increased interest in research in the area of video gaming to answer this question. And there
does indeed seem to be much research on the topic in recent years. In overviewing the research,
one main concern seems to be whether the playing of video games impacts productivity in a
negative or positive way and what those consequences are.
Smyth (2007) suggested that complex games may lead to academic success by engaging
players in problem-solving, critical thinking, and creativity. Skoric et al. (2009) found that
while game addiction leads to negative academic performance, moderate engagement in
gaming can lead to improved performance in an academic setting. They found a positive
correlation between game play and English test scores, which suggests that gaming can actually
lead to better test scores. North Carolina State University is even experimenting with a
synchronous online graduate course that integrates video game design with science curriculum
(Annetta, Murray, Laird, Bohr & Park, 2008)
To sum up this overview of the recent literature on the relationship between the usage of video
games and productivity, Anderson and Dill (2000, pg 17) quite aptly state the predicament in
researching this topic: "There is no definitive answer to the question of whether video games
disrupt academic performance." As the literature review shows, much has been said to support
every aspect of the topic, both positive and negative. The present study seeks to answer the
question: Does playing video games have an impact on productivity as measured by the amount
of time playing and academic performance, punctuality, social behaviour.
HYPOTHESIS
This proposal's hypothesis is that as time spent on playing video games increases, the
productivity will decrease.
RESEARCH PROBLEM
Does gaming have an impact on the productivity of students?

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CONCEPTUAL MODEL
A study conducted by Wood, Griffiths, and Parke (2007) included open-ended questions that
encouraged participants to report different feelings about playing video games. Some of the
negative consequences indirectly related to school performance, in that participants, reported
often missing lectures, skipping homework, etc. One study done by Anand (2007) found a
negative correlation between the amount of time spent playing video games and the GPA and
SAT scores of students. This means that GPA and SAT scores decreased as time spent playing
increased. However, Anand (2007) did recognize the limitation of using SAT scores because
they represent a one-time standardized score. Using GPA is more credible because it represents
a continuous measurement of school performance.
Through exploratory research using the secondary data available from the research papers and
focus group decisions, we identified the factors that determine the quantitative measures of
productivity in student life. The main factors we identified to determine the productivity are
academic performance, punctuality and social behaviour. To assess the academic performance
of a student we took the measure of GPA and to evaluate the student’s cognitive ability and
decision making under stressful situations we considered the CAT percentile of the students.
To determine the punctuality and social behaviour of a student we took the measures of
attendance and time spent with their friends.
The variables considered in doing this research are
Time spent on
Gaming
Productivity of a
Student
Academic
Performance
GPA
CAT/XAT
Percentile
Punctuality
Attendance
percentage
Social Behaviour
Time spent with
Friends

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RESEARCH OBJECTIVES
1. To determine the dependence of time spent on gaming and academic performance
2. To determine the dependence of time spent on gaming and punctuality
3. To determine the dependence of time spent on gaming and social behaviour
HYPOTHESIS
For each of the research objective the following hypotheses were framed
Hypothesis 1
H0: There is no significant dependence on the time spent on gaming and CAT score (µ1=µ2)
H1: There is a significant dependence on the time spent on gaming and CAT score (µ1≠µ2)
Hypothesis 2
H0: There is no significant dependence on the time spent on gaming and GPA (µ1=µ2)
H1: There is a significant dependence on the time spent on gaming and GPA (µ1≠µ2)
Hypothesis 3
H0: There is no significant dependence on the time spent on gaming and Attendance
percentage (µ1=µ2)
H1: There is a significant dependence on the time spent on gaming and Attendance
percentage (µ1≠µ2)
Hypothesis 4
H0: There is no significant dependence on the time spent on gaming and time spent with
friends (µ1=µ2)
H1: There is a significant dependence on the time spent on gaming and time spent with
friends (µ1≠µ2)
Hypothesis 5
H0: There is no significant dependence on the time spent on gaming and productivity of a
gender (µ1=µ2)
H1: There is a significant dependence on the time spent on gaming and productivity of a
gender (µ1≠µ2)

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METHOD
Participants
Participants in this research study were 126 students from Institute of Management
Technology, Ghaziabad. The rationale behind choosing the sample population from IMT was,
it represents students from diverse educational, geographical and cultural backgrounds. We
assume the findings from our sample population represents the entire population. The ages of
the participants ranged from 21 to 25. There were 90 males (71%) and 36 females (29%). Our
sample population has a mix of students who had prior work experience and recent graduates.
The participants in this study were management students from different streams of marketing,
finance and operations.
Data Collection
The responses from our sample population were collected by floating a google form. The
respondents were asked questions on their Academic performance, punctuality and social
behaviour.
Research Questions
1. Age?
2. Gender?
3. Do you play video games?
4. How many hours do you play video games per week?

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5. How many hours do you study per week?
6. Has your playing of video games negatively affected your grades?
7. CAT/XAT Percentile?
8. Latest GPA?
9. Has your playing of video games negatively affected your punctuality?
10. Attendance in the last term?
11. Do you believe that playing video games affects the way people act?
12. Does your playing video games interfere with your time with your family?
13. How many hours do you spend with your friends per week?
14. Since playing video games I am (complete the statement)
STATISTICAL ANALYSIS
The data that was collected was analysed to test whether the underlying distribution is normal.
Shapiro-Wilk test of normality is conducted in SPSS to determine the normal distribution of
the data. The P value of GPA, Time spent on gaming is greater than 0.05 (p>0.05) which
signifies that data is normally distributed. The P value of CAT score, time spent with friends,
attendance percentage is less than 0.05 (p<0.05) which means the data is not perfectly normally
distributed but is approximately normally distributed.

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The visual inspection of the histograms and normal Q-Q plots showed that GPA and time spent
on gaming is approximately normally distributed.
The data was further analysed using a series of descriptive statistics, factor analysis, correlation
analysis and one-way ANOVAs. The independent variable being studied was the time spent on
gaming and the dependent variable being studied were academic performance measured by the
GPA and CAT scores, the dependent variable punctuality was measured by the attendance
percentage, the dependent variable social behaviour was measured by the amount of time spent
with their friends per week.
After measuring the components that determine Productivity using an exploratory research,
confirmatory factor analysis was done to know if there is any correlation among rhea
components that explains the productivity.
The data was then analysed using factor analysis to know the inter correlation of factors in
explaining the under lying variables. For this the scores of CAT and GPA are clustered into a
group that explains the underlying factor Academic performance and Attendance percentage,
time spent with friends are clustered to explain the factor punctuality. The factor analysis was
run on SPSS.

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The eigen values of the first two components are greater than one and hence the academic
performance as an underlying factor is explained by the two components, GPA and CAT score.
The scree plot visualises the eigen values that were described earlier. There is a sudden drop
from component 2 to 3 and 3 to 4 which means that the components chosen were not correlated
enough to explain the underlying factors.
The reason that the components failed to explain the underlying factors is because of the
inadequate sample size. This is confirmed from the KMO and Barret’s test. The Kaiser-Meyer-
Olikin measure of sampling adequacy is a statistic that explains the proportion of variance in
the variables that might be caused because of the underlying factors. Generally, the test statistic
value close to 1 is considered to be good, which indicates that a factor analysis is useful. In our
case the statistic value is 0.472 which means that factor analysis is not useful for our variables.

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After the factor analysis, one-way Multiple linear regressions were done to determine the
correlation between the independent variable time spent on gaming to the dependent variables
CAT score, GPA, Time spent with friends, Attendance percentage.
Linear regression analysis on CAT scores vs Time spent on gaming
The R square value is 0, which implies that no variation in the CAT scores can be explained
by the time spent on Gaming.
CAT score = -0.06*Time spent on Gaming + 94.731
Linear regression analysis on GPA vs Time spent on Gaming
The R square value is 0, which implies that no variation in the GPA can be explained by the
time spent on Gaming
GPA = -0.06*Time spent on Gaming + 6.856

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Linear regression analysis on Attendance percentage vs Time spent on Gaming
The R square value is 0.017, which implies that only 10% of the variation in attendance
percentage can be explained by the time spent on Gaming
Attendance percentage = 0.204*Time spent on Gaming + 78.024
Linear regression analysis on Time spent with Friends vs Time spent on Gaming
The R square value is 0, which implies that no variation in the time spent with friends can be
explained by the time spent on Gaming
Time spent with friends = -0.0.63*Time spent on Gaming + 12.839
All the results have shown that there is no statistical significance between the time spent on
gaming and the productivity of a student.

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LIMITATIONS
1. Study was limited to IMT Ghaziabad students hence the findings of the study cannot
be generalized.
2. Equal number of samples could not be selected for each age group. Also equal
number of samples could not be selected based on gender.
3. Random sampling technique could not be used to select samples.
4. The current research methodology does not take into consideration the type of game
people play and the purpose of gaming.
FUTURE SCOPE
Our work has provided an understanding of how to study the issue of how video games affect
worker productivity levels. With more time and more resources, future work could determine
with finality the veracity of our hypothesis. Future studies using our methodology should give
subjects a better idea of how much work remains in their transcription task. We chose to not
tell subjects how much work they had left to do; we feared that subjects who knew they were
nearly done would perform differently than subjects who did not. We wanted to create
conditions like a workplace, where work does not end after the first hour so chose not to inform
subjects on their progress during the study. In retrospect, information about how many entries
remain could reduce subject dropout rates. While many subjects will quit regardless of this
change a progress bar might convince subjects who would otherwise have quit to continue
onward, seeing how close they were to finishing and becoming eligible for the incentive. Any
future study constructed along the same lines as ours should be constructed in such a way that
all the data considered is for complete passes through the study. Three tactics come to mind
that will satisfy this constraint. The first option is to remove from the study any subjects who
do quit partway through. This requires a much larger sample population than we were able to
obtain, because the drop-out rate will cull out a large percentage of the subject pool. Also, if
our hypothesis is correct, and the subjects who choose to continue do so because they have not
yet begun to lose effectiveness. At that point, the only data being considered comes from people
who have not yet begun to lose productivity as of the end of the study. A more reliable solution
to the problem of subjects dropping out is to not allow subjects to quit. This will require more
draconian measures and/or greater resources to encourage participation, while trying to
maintain the integrity of the data. In order to attract subjects for such a study, the incentive,
monetary or otherwise, for each subject to participate in the study will have to be significant.
Also, enforcing a policy of ‘no quitting’ will likely require that the study be taken off the

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Internet and into a more controlled setting, such as a specific computer lab at a scheduled time.
This will make the study less accessible to some subjects who would otherwise be able to
participate from home or work at their convenience. Although these two alternatives were
outside the limits of our time and resources, a company could implement either plan using its
own employees as a testing base; the study would be a normal work break. A company also
might have the financial resources to pay subjects for their time, which will most likely result
in both a reduced dropout rate and a larger sample size. We also can add few more parameters
of research-
1. Improvement of cognitive ability.
2. Decision making ability
3. Planning and resource management
4. Pattern Recognition