Comparative Analysis of Smartphone and Non-smartphone users

1. SUBMITTED TO
Dr. SHESADEV NAYAK
Professors & Associate Dean,
School of Management
SUBMITTED TO-
Dr. SHESADEV NAYAK
Professors & Associate Dean,
School of Management
SUBMITTED BY
KIRAN JAISWAL
MBA 2
Roll No. –
SUBMITTED BY-
KIRAN JAISWAL
MBA 2nd
SEM
02PG19010005

2. TABLE OF CONTENTS
1. Executive summary
2. Introduction
3. Objective
4. Research Methodology
5. Sample
6. Questionnaire and Responses
7. Data analysis and hypothesis
8. Observation
9. Conclusion
10. References

3. EXECUTIVE SUMMARY
A Smartphone is a mobile electronic device which runs an advanced
operating system that is open to installing new applications, is always
connected to the internet, and which provides very diverse
functionality to the consumer.
Unit sales of smart phones have been growing faster than the overall
market for cell phones. This report finds that this trend will continue,
based upon increased user demand. This demand is primarily driven
by greater competition for mobile applications that add capabilities.
New and prospective Smartphone buyers are drawn to new mobile
applications, even though the median number of applications
downloaded for all platforms, including the Apple iPhone, is
relatively modest—below five applications per user for each platform.
After a period of slower growth caused by uncertain economic
conditions, the Smartphone market will grow over 70% annually over
the next five years. The greatest upside would come in the way of a
mandate by the Chinese government directing manufacturers to use a
Linux platform, such as Google’s Android, for all Smartphones. The
greatest threat to continued Smartphone growth would be from a
crisis, where Smartphone security—or the lack thereof—is to blame.
According to our research, cell phone users are not very concerned
with security, and many do not even take basic steps to protect the
data on their phones.

4. INTRODUCTION
Cell phones generally get a bad reputation when it comes to
education, seen as a distraction to the learning environment. Students
text message, play games, or surf the web when they are supposed to
be learning or studying. But with the development of phones that have
the capacity to run programs designed as study tools and student
resources, cell phones can actually help students learn, rather than
distract from the learning environment.
The spread of Smartphone is the key. Smartphones have many
computer-like features, many with touch-screens and other very
interactive features. Smartphones have download able “apps” that are
provided by either the cell phone provider, or by other companies that
have developed versions of their product especially for Smartphones,
such as Microsoft and Amazon.
A Smartphone is a cellular phone with better, faster and enhanced
operating abilities and performance which earlier was restricted to
Personal Digital Assistant (PDAs) and portable computers only.
Smartphones are best known for their fast processing speed, high
speed internet connectivity and the availability of numerous utility
features. It is these features which set it apart from the other feature
phones available today in the market. Globally, Smartphone Market
grew 80.23% in 2020. The Smartphones segment showed an
impressive growth of 76.42% in India.

5. OBJECTIVES
The aim of this research process is to compare smart phone and non
smart phone users. The various objectives of this problem are as
follows:
 Analyze the preference for smart phones for different age
groups and across genders.
 Analyze the impact of level of education on usage of smart
phones.
 Analyze the satisfaction among the smart phone and non smart
phone users.
 Analyze the reasons why users are preferring smart phones.

6. RESEARCH METHODOLOGY
Data Collection:
The data, which is collected for the purpose of study, is divided into 2 bases:
Primary Source: The primary data comprises information survey of
“Comparative analysis of Smartphones and Non-Smartphones users”. The data
has been collected directly from respondent with the help of structured
questionnaires.
Secondary Source: The secondary data was collected from internet and
references from Library.
The research method used is descriptive research for our problem statement.
Sample Size and Design:
 A sample of 27 people was taken on the basis of convenience. They were
contacted on the basis of random sampling.
Research Period:
 Research work is only carried for 1 weeks.
Research Instrument:
 This work is carried out through self-administered questionnaires. The
questions included were open ended, dichotomous and offered multiple
choices.
Sample
Sample Size : 27
Sample Frame : Raigarh
Sample Unit : Raigarh
Constraints : Time & No of respondents
Survey : Questionnaire

7. Questionnaire and ResponsesQuestionnaire and Responses

8. Multitasking: EXCLUDING VOICE CALLS, how often do you use your
Smartphone while simultaneously doing these activities? (Different situations
are provided and you are required to one of the four options which are
OFTEN, SOMETIMES, SELDOM, NEVER)
Graph-
0
2
4
6
8
10
12
14
Listening
Music
Walking Watching TV Shopping Playing
Computer
Games
While talking
on the phone
(ie., using
APP, advance
feature
OFTEN
SOMETIMES
SELDOM
NEVER
OFTEN SOMETIMES SELDOM NEVER
12 8 4 3
10 10 4 3
8 7 5 7
8 5 8 6
7 5 2 13
11 7 6 3
1. Listening Music
2. Walking
3. Watching TV
4. Shopping
5. Playing Computer Games
6. While talking on the phone (ie.,
using APP, advance feature

9. How often are you consuming different types of information on your
Smartphone?
Graph-
0
5
10
15
20
25
OFTEN
SOMETIMES
SELDOM
NEVER
OFTEN SOMETIMES SELDOM NEVER
13 10 3 1
16 7 3 1
18 5 2 2
17 6 3 1
18 6 21 1
7 15 3 2
17 7 2 1
12 11 3 1
16 8 2 1
17 8 1 1
9 7 4 7
1. Text message
2. Reading e-mail
3. Searching for specific information
4. Talking on the phone
5. Viewing content on social network
6. Weather forecasts
7. Communicating with friends on social
networks
8. News
9. Listening to music
10. Watching Video (Youtube, etc)
11. Games

10. DATA ANALYSIS AND HYPOTHESIS
Data Cleaning:
 The observations who don’t use smart phones have been removed from the
Data.
 Ordinal Scale data has been re-coded as given below:
Never=1, Seldom=2, Sometimes=3, Often=4
Multiple Regression Analysis:
Dependent Variable: Communication with friends on Social Network
Independent Variable: Playing Games, Viewing content on social network,
Listening to music, Reading e-mail, News, Weather forecasts, Watching Video
(Youtube), Searching for specific information, Talking on the phone
Ho [Null Hypothesis]: Communicating with friends on Social Network does not
depend on Playing Games, Viewing content on social network, Listening to music,
Reading e-mail, News, Weather forecasts, Watching Video (Youtube), Searching for
specific information, Talking on the phone
H1 [Alternate Hypothesis]: Communicating with friends on Social Network depends
on Playing Games, Viewing content on social network, Listening to music, Reading e-
mail, News, Weather forecasts, Watching Video (Youtube), Searching for specific
information, Talking on the phone
Output:
Model Summaryb
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .779a
.606 .370 .451
a. Predictors: (Constant), Games, Viewing content on social network,
Listening to music, Reading e-mail, News, Weather forecasts, Watching Video
(Youtube), Searching for specific information, Talking on the phone
b. Dependent Variable: Communicating with friends on social networks
The R-Square of .606 indicates that 60.6% of the independent variables are explained
by the dependent variable.

11. ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 4.706 9 .523 2.569 .051a
Residual 3.054 15 .204
Total 7.760 24
a. Predictors: (Constant), Games, Viewing content on social network, Listening to music, Reading e-mail,
News, Weather forecasts, Watching Video (Youtube), Searching for specific information, Talking on the
phone
b. Dependent Variable: Communicating with friends on social networks
Conclusion: The p-value of 0.51 > 0.5 states that we have to accept the Null
Hypothesis.
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) .292 1.006 .290 .776
Reading e-mail .099 .193 .114 .515 .614
Searching for specific
information
.024 .258 .032 .092 .928
Talking on the phone -.114 .362 -.129 -.315 .757
Viewing content on social
network
.502 .304 .492 1.653 .119
Weather forecasts .097 .190 .123 .507 .619
News .319 .226 .362 1.413 .178
Listening to music .058 .203 .058 .284 .780
Watching Video (Youtube) -.055 .278 -.046 -.199 .845
Games .029 .091 .062 .324 .751
a. Dependent Variable: Communicating with friends on social networks
The Regression Model can be written as:
Communicating with friends on social network= .292 + 0.99 x Reading Email + 0.24
x Searching Information -.114 x Talking on Phone + .502 x viewing content on social
network +.097 x Weather Forecasts +.319 x News +.058 x Listening to music -.055 x
watching video(Youtube) + .029 x Games

12. ANOVA:
Dependent Variable: “ Reading Emails on Smart Phone” & “Searching for
Specific Information on smart phone”
Independent Variable: Different Education groups
Ho [Null Hypothesis]: There is no significant difference of Reading Emails on
Smart Phone among the different Education Groups.
Ha [Alternate Hypothesis]: There is a significant difference of Reading Emails on
Smart Phone among the different Education Groups.
Ho [Null Hypothesis]: There is no significant difference of Searching for specific
information on Smart Phone among the different Education Groups.
Ha [Alternate Hypothesis]: There is a significant difference of Searching for
specific information on Smart Phone among the different Education Groups.
Output:
ANOVA
Sum of Squares df Mean Square F Sig.
Reading e-mail Between Groups .266 3 .089 .188 .903
Within Groups 9.894 21 .471
Total 10.160 24
Searching for specific
information
Between Groups 1.399 3 .466 .777 .520
Within Groups 12.601 21 .600
Total 14.000 24
Conclusion:
The p-value of Reading Email .903 > 0.5 & Searching for specific information .520
>0.5 indicates that we have to accept the Null Hypothesis. Hence there is no
significant difference of Reading Emails & Searching for specific information on
Smart Phone among the different Education Groups.

13. ANOVA:
Dependent Variable: “ Reading News” & “Watching videos (Youtube)”
Independent Variable: Different Age groups
Ho [Null Hypothesis]: There is no significant difference of Reading News on Smart
Phone among the different Age Groups.
Ha [Alternate Hypothesis]: There is a significant difference of Reading News on
Smart Phone among the different Age Groups.
Ho [Null Hypothesis]: There is no significant difference of Watching Videos on
Smart Phone among the different Age Groups.
Ha [Alternate Hypothesis]: There is a significant difference of Watching Videos on
Smart Phone among the different Age Groups.
Output:
ANOVA
Sum of Squares df Mean Square F Sig.
News Between Groups .583 3 .194 .434 .731
Within Groups 9.417 21 .448
Total 10.000 24
Watching Video (Youtube) Between Groups .507 3 .169 .719 .552
Within Groups 4.933 21 .235
Total 5.440 24
Conclusion:
The p-value of Reading News .731 > 0.5 & Watching Videos .552 >0.5 indicates that
we have to accept the Null Hypothesis. Hence there is no significant difference of
Reading News & Watching Videos on Smart Phone among the different Age Groups.

14. ANOVA:
Dependent Variable: “ Listening to Music” & “Talking on Phone”
Independent Variable: Gender
Ho [Null Hypothesis]: There is no significant difference of Listening to Music
between Male & Female.
Ha [Alternate Hypothesis]: There is a significant difference of Listening to Music
between Male & Female.
Ho [Null Hypothesis]: There is no significant difference of Talking on phone
between Male & Female.
Ha [Alternate Hypothesis]: There is a significant difference of Talking on phone
between Male & Female.
Output:
ANOVA
Sum of Squares df Mean Square F Sig.
Listening to music Between Groups .006 1 .006 .018 .893
Within Groups 7.994 23 .348
Total 8.000 24
Talking on the phone Between Groups .103 1 .103 .238 .630
Within Groups 9.897 23 .430
Total 10.000 24
Conclusion:
The p-value of Listening to Music .893 > 0.5 & Talking on Phone .630 >0.5 indicates
that we have to accept the Null Hypothesis. Hence there is no significant difference of
Listening to Music & Talking on pone between Male & Female respondents.

15. OBSERVATIONS
Following observations could be made after surveying 74 people about the usage of
Smartphones
1. 48.1% of the males and 51.9% of the females use smartphones.
2. People falling in the age group of 22-25 use smartphones the most with
apercentage of 59.3% followed by people in the age group of 18-21 with
14.8%.
3. Graduates are frequent users of smartphones with 40.7% falling in Bachelor’s
Degree. Category and 33.3% belonging to Master’s Degree.
4. Smartphone users is 85.2% & primary mobile phone users is 7.4%
5. Students & working in corporate sector mostly tend to use it for school or work
related tasks, listening to music, e-mailing, chatting, gaming and texting (These
are all cases excluding voice calls).

16. CONCLUSION
The smartphone market is rapidly changing, with constant product introductions,
quickly evolving technology and designs, short product life cycles, aggressive pricing,
rapid imitation of product and technological advancements, a highly price sensitive
consumers. The smartphone market consists of all firms throughout the world that
manufacture and sell smartphones to consumers. No one firm in the market has
sufficient market share to control prices, resulting is strong rivalry and competitive
pricing. The barriers to entry are high due to the existence of patents, high fixed costs
and economies of scale, regulation, and brand loyalty. The individual market
participants engage in attempts at product differentiation, some being more successful
than others.
Inter market effects are also significant in the smartphone market. Multiple other
markets have an effect on the smartphone market: from the suppliers, to the industrial
designers, to the distributors, to the retailers, to the network service providers.
Because of its rapid change, the smartphone market is likely to be significantly
different in as short a time as 1-5 years.
REFERENCES
1) http://www.ideals.illinois.edu/bitstream/handle/2142/18484/Cromar,%20Scott%20-
%20U.S.%20Smartphone%20Market%20Report.pdf
2) https://sites.google.com/site/itec1301smartphoneswotanalysis/home/weaknesses
3) http://my.opera.com/fukefan/blog/2012/04/06/survey-report-smartphone-
usersobsessive-fiddlers