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Market Research

Samsung Mobiles

Final Report

Faculty Guide:

Prof Sridhar Telidevara

Submitted by:

GROUP AD1Debdripta Sengupta

Harsh Mohan

Hirangi Pandya

Mohit Sethi

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EXECUTIVE SUMMARY

DETERMINATION OF THE CUSTOMER PERCEPTION, USER PREFERENCES AND BUYING BEHAVIOUR TOWARDS SAMSUNG

SMARTPHONES IN MANIPAL

GROUP AD1

March 26, 2014

Smartphones have increasingly become a necessary part of the lives of the modern man. Owing to the large market potential many competitors exist in the market and the players are increasing on monthly basis. As many as 990 million smartphones were sold in 2013 [1], which is an indicator of the market size and potential. New companies are also finding this market lucrative enough to enter and compete with the big names. They are offering almost same features in the devices to the customers at a much lower price. They are operating on cost differentiation in the market.

Samsung smartphones is the leading company in terms of both revenue and sales currently in India. The company is primarily concerned about the entry of these new players in the market and the effects it will have on Samsung. As Samsung has already established itself as a brand which delivers high quality smartphones in the premium segment. The entry of the new players pose a threat to the company and Samsung wants to understand the market and customers and their perceptions.

The main objective of this research project is to determine the customer perception, user preferences and buying behaviour towards Samsung smartphones in Manipal. Manipal was specifically chosen as it has a very wide range of audience in terms of where they come from. The research had some clear objectives such as (1) to list the factors influencing the buying behaviour of the customer towards smartphones (2) To identify the opportunities for Samsung smartphones in Manipal and (3) To analyse the changing needs and preferences of the Samsung smartphone users. We used stratified proportionate random sampling and determined that the sample size of 100 would be appropriate for our research.

The research was divided in three phases namely exploratory research, conclusive research and finding and analysis. During the exploratory phase secondary research, focus group discussions and in depth interview were conducted. The main idea was to familiarise with the topic of research and to know what the target population who will be a part of the research. The outputs of this stage were the pilot questionnaire which was further modified to the final questionnaire after dry running the same on a small set of test audience.

Conclusive research consisted of collecting and collating the data relevant for our research. We mainly used two formats for collecting the data which were online and face and face interview. Finally after collecting the data SPSS tool was used to analyse the findings and

1IT for Business T.A. Pai Management Institute, Manipal

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come out with the final conclusion. Off the many variables identified during the interviews the analysis of data concluded that only 6 variables were relevant and the same can be clubbed and formed two factors which were significant enough to help us analyse the findings comprehensively

Based on our findings and analysis of data we have concluded that design and Design and Brand Loyalty are the two major factors which contribute to the consumer perception and their buying behaviour.


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1. INTRODUCTION

The advanced development of technology, smartphones have become a necessity for most people. With this increase in demand, more and more companies are manufacturing smartphones. India is one the fastest growing markets of smartphones in the world. Smartphone sales in India increased by 166.8% in 2014. Some of the companies selling smartphones in India are Samsung, Apple, Micromax, Karbonn, etc. Our project focuses on customer perception towards Samsung smartphones.

Samsung was founded by Lee Byung-Chull in 1938. It was first established as a trading company. It is a South Korean multinational conglomerate company that is headquartered in Seoul. Today it operates in more than 88 countries and employees more than 3, 70,000 personnel. The first phone that Samsung launched was SH-700, which was launched in 1993. In 2009, it launched its first ever smartphone, I7500. Today it offers more than 40 models of smartphones. With a market share of 31.5 % and a whopping revenue of Rs. 11,328 crore, Samsung leads the Indian markets.

Samsung has a 13% market share in smartphones in India. Other companies like Apple, Micromax, carbon having a market share of 1%, 10% and 7% respectively. Micromax and Karbonn offer similar features at lower prices than Samsung. Thus these are the major competitors of Samsung in India.

1.1. Manipal

Manipal is a university town located in Karnataka, India. Manipal has a population of 20,000 where number of students is 17000 and the other 3000 are office-goers, academicians, doctors, etc. Manipal had the highest density of mobile phone users in India in 2006. 98% of the population had mobile phones.

Manipal is home to Manipal University which has 19 colleges. Major education institutions in Manipal include T. A. Pai Management Institute, KMC, Manipal Institue of Technology, etc. Thus majority of population of Manipal consists of students. Since students from all over the country come to Manipal, it has a very diverse population in terms of age, culture, educational background, etc.

1.2. Rationale for the Project

This project will determine the factors influencing the buying behaviour and perceptions of Samsung Smartphone users in Manipal. It will help us identify the opportunities for Samsung smartphones in Manipal.

With the analysis of this factors and opportunities, we can decide how to sustain or improve the sale of Samsung smartphones in Manipal.


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2. OBJECTIVES AND SCOPE

2.1. Management Objective

To sustain and improve the sales growth of the Samsung smartphones in Manipal through thorough data analysis of the consumer awareness/ perception/ brand association/ TOM/ intention to buy.

2.2. Research Objectives

1. To list the factors influencing the buying behaviour of the customer towards smartphones

2. To identify the factors responsible for the sales of Samsung smartphones

3. To identify the opportunities for Samsung smartphones in Manipal

4. To analyse the changing needs and preferences of the Samsung smartphone users

2.3. Research Questions

1. What factors satisfy/dissatisfy the users of Samsung smartphone?

2. What motivates the switching behaviour?

3. Does brand perception influence decision making?

2.4. Scope

The research study is limited to Manipal region where most population is students. People from the age of 18-40 years will be surveyed.

A stratified proportionate random sampling method is used where the sample is divided into two stratas. The first strata is students and the second strata is non-students. The second strata will include office-goers, doctors of KMC, academicians of TAPMI, MIT, etc. and local shop-keepers selling mobile phones.

3. DEFINITION OF CONSTRUCTS

For our research we used both Exploratory and Descriptive (Conclusive) Research to meet our objectives. Exploratory research helped us to identify the factors which influence the buying behaviour and the perception of customers towards smartphones. Descriptive Research was used to compare how those factors made a person choose the brand of the


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smartphone they own or intend to own and if there is any difference in the preferences of the Samsung smartphone users and non-Samsung smartphone users.3.1. Secondary Research

In this we searched about the market share of various brands of smartphones, variants of smartphones offered by them and compared them with Samsung smartphone to find out the current market position for Samsung smartphones. Also customer expectations, and their views about the smartphone they own and what factors influenced their buying behaviour and compared it with Samsung smartphone owner views and preferences.

3.2. Focus- group discussion and In-depth interview

In order to know the perception of customers and the factors which influence the buying behaviour of a customer towards smartphones two focus-group discussion (One in MIT and one in TAPMI) were conducted with 8-10 members selected through stratified proportionate simple random sampling (basically for students) and in-depth interviews for non-students consisting of doctors, academicians, retailers.

The main focus areas were: Factors they look for in a smartphone On what factors one remain loyal to their brand What factors influences switching of brand Attitude and response towards Samsung smartphones

3.3. FGD findings

During the FGD and the in-depth interview phases, our research identified various variables which were needed to be included in the questionnaire. Few of the variables which were identified during our FGD are as follows:

1. Screen Size2. Convenience-carrying3. Screen glass4. Water/dust resistant5. Advertisements6. Promotions7. Mode of buying (online/stores)8. Competitors9. Age specific models10. Awareness11. Brand switching12. Customer care services


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13. Brand 14. Price sensitivity15. Battery life16. Technical specifications17. Brand loyalty18. Brand association19. Recommendations20. Packaging21. Warranty

Along with these, there are several other factors which popped up during these stages and

had to be incorporated in the questionnaire. These can be classified into three broad

categories:

Parameterso Brando Design

o Price Brands

o Samsung

o Appleo Micromax

o Karbonno Nokia

o Sony Features and services

o Battery life

o Customer careo Technical specifications

All the factors listed above were discussed during the two FGDs conducted in our research process.

3.4. In-depth interview findings

Key constructs arising from interview were:

Price Brand Loyalty Technical specifications Design Value for money


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Battery life After sales service Switching behaviour Customer care Recommend to others

3.5. Questionnaire design

All the factors which are discussed in FGD and in-depth interview stages are combined to prepare a questionnaire. Before doing the final research with the final questionnaire, a pilot questionnaire was prepared and was tested with around 15-20 respondents to understand the internal consistency of the responses and to achieve higher degree of reliability from the final questionnaire. We identified few minor changes to be made in the pilot questionnaire and prepared the final questionnaire. The final questionnaire contained 21 questions. The questions were open ended, with few were Likert scale variables while others were dichotomous responses.

3.5.1. Pilot Questionnaire

Number of people: 15-20

Location: TAPMI and Tiger Circle, Manipal

Learnings: Accuracy of questions, add different options in the questions, decrease the number of questions

3.5.2. Final Questionnaire

Design: Based on the factors identified in FGD, interview and pilot testing

Type: Likert scales, multiple choice, dichotomous

Mode of data collection: Online, face-to-face collection

3.6. Sampling design

Sampling Methodology: Stratified Proportionate Simple Random Sampling

Target: Students (Strata 1) & Non-students (Strata 2)

Sample Size:

Total Size = 100

Manipal population = 20000 with population of students = 17000 and non-students = 3000

Therefore, the proportion of students = 0.85

Proportion of non-students = 0.15


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Size of strata 1 = 85

Size of strata 2 = 15

3.7. Conclusive research

3.7.1. Modes of data collection

Online questionnaire floated in TAPMI and MIT exclusively for the student population

Face-to-face collection in the KMC Hospital region to attract highest number of respondents at the smallest time. This method is mainly targeted for the non-students.

Face-to-face collection from the customers and retailers of mobile stores in Manipal. This will help to gather responses from both students and non-students.

3.7.2. Analysis Factor Analysis using SPSS

o To group similar respondents with similar characteristics together into distinct groups

o To study the profile characteristics of respondents

Discriminant Analysis using SPSSo To identify the factors which discriminate between the buyers and non-buyers

of Samsung smartphones

4. FACTS AND FINDINGS

The Likert scale variables which are included in the questionnaire are:I. Service

II. DesignIII. Technical specificationsIV. Agreeing to switchV. LoyaltyVI. Customer care

VII. BatteryVIII. Price

IX. Security featuresX. Intention to useXI. Recommend others

In order to test the internal consistency of the responses of all the 11 independent variables, a reliability test analysis is run to generate the Cronbach’s alpha. The following table describes that 99 valid responses are processed without any exclusions.


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Case Processing SummaryN %

Cases Valid 99 100.0

Excludeda 0 .0

Total 99 100.0

a. List wise deletion based on all variables in the procedure.

The desired value of Cronbach’s Alpha is more than 0.7. To achieve that the number of independent variables is reduced to 6 and we get a Cronbach’s Alpha of 0.727 as shown below.

Reliability Statistics

Cronbach's Alpha N of Items

.727 6

The following table gives the details of the 6 independent Likert scale variables with which the final reliability test is run. These variables generate a Cronbach’s Alpha of 0.727. The model can be further strengthened by improving the Cronbach’s Alpha to 0.792 if the variable Design is removed. However, that would decrease the number of independent variables to 5.

Item-Total Statistics

Scale Mean if Item

Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha if

Item Deleted

Design 15.90 9.806 .052 .792

Technical spec. 16.45 8.863 .249 .746

Use_Fault(Loyalty) 16.43 7.207 .548 .662

Price 16.19 7.769 .524 .673

Intention to use 16.03 6.662 .739 .600

Recommend Others 16.11 6.508 .714 .603

4.1. Factor Analysis

Factor Analysis is a data reduction tool. In our market research analysis, the factor analysis is conducted to identify the linear combination of independent variables called factor which is not directly observable but can be inferred from the input variables. This will address our


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management objective of identifying the factors which influence the consumer awareness and buying behaviour.

The following matrix shows the correlation between the independent variables. An

independent variable has a correlation coefficient of 1 with its own value.

Correlation Matrix

Design

Technical

spec.

Use_Fault

(Loyalty) Price

Intention

to use

Recommend

Others

Correlation Design 1.000 .077 .037 .016 -.007 .068

Technical spec. .077 1.000 .360 .061 .169 .184

Use_Fault(Loyalty) .037 .360 1.000 .324 .550 .478

Price .016 .061 .324 1.000 .657 .600

Intention to use -.007 .169 .550 .657 1.000 .849

Recommend

Others

.068 .184 .478 .600 .849 1.000

Sig. (1-tailed) Design .226 .358 .439 .471 .251

Technical spec. .226 .000 .273 .047 .034

Use_Fault(Loyalty) .358 .000 .001 .000 .000

Price .439 .273 .001 .000 .000

Intention to use .471 .047 .000 .000 .000

Recommend

Others

.251 .034 .000 .000 .000


Descriptive Statistics

Mean Std. Deviationa Analysis Na Missing N

Design 3.53 .774 99 0

Technical spec. 2.97 .788 99 0

Use_Fault(Loyalty) 2.99 .898 99 0

Price 3.23 .780 99 0

Intention to use 3.39 .855 99 0

Recommend Others 3.31 .911 99 0

a. For each variable, missing values are replaced with the variable mean.

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In order to establish the strength of factor analysis, we establish the reliability and validity of

the obtained reduction. Value of KMO statistics greater than 0.5 denotes factor analysis could

be used for the given set of data. The p-value of 0.000 which is less than 0.05 indicates the

rejection of the hypothesis that the correlation matrix of the variables is insignificant.

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .726

Bartlett's Test of Sphericity Approx. Chi-Square 227.647

df 15

Sig. .000

Communalities of the 6 variables are found out. It indicates how much of each variable is

accounted for by the underlying factors taken together.

Communalities

Initial Extraction

Design 1.000 .290

Technical spec. 1.000 .691

Use_Fault(Loyalty) 1.000 .600

Price 1.000 .673

Intention to use 1.000 .883

Recommend Others 1.000 .812

Extraction Method: Principal Component Analysis.

The percentage of variance explained by each of the factor is computed by using eigenvalues

as shown in the table below. There are two factors with eigenvalues greater than 1. As shown

in the following table, the percentage of variance explained by first factor is 47.368% and

that for second factor is 65.824%


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Total Variance Explained

Component

Initial Eigenvalues

Extraction Sums of Squared

Loadings

Rotation Sums of Squared

Loadings

Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 2.842 47.368 47.368 2.842 47.368 47.368 2.735 45.579 45.579

2 1.107 18.456 65.824 1.107 18.456 65.824 1.215 20.245 65.824

3 .965 16.079 81.904

4 .546 9.094 90.997

5 .403 6.722 97.719

6 .137 2.281 100.000

Extraction Method: Principal Component Analysis.

The Scree Plot plots the eigenvalues generated for each of the 6 components of which 2 have

eigenvalues more than 1. Thus, we consider two components from the 6 independent

variables.


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The component matrix and the rotated component matrix shown below provide the

correlation coefficient between the factor score and the 6 independent variables.

Component Matrixa

Component

1 2

Design .064 .535

Technical spec. .340 .759

Use_Fault(Loyalty) .708 .313

Price .756 -.318

Intention to use .923 -.175

Recommend Others .892 -.126

Extraction Method: Principal Component

Analysis.

a. 2 components extracted.

Rotated Component Matrixa

Component

1 2

Design -.071 .534

Technical spec. .141 .819


Price .812 -.120

Intention to use .938 .060

Recommend Others .895 .100


Analysis.

Rotation Method: Varimax with Kaiser

Normalization.

a. Rotation converged in 3 iterations.


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Component Transformation Matrix

Component 1 2

1 .969 .249

2 -.249 .969

Extraction Method: Principal

Component Analysis.


Normalization.

Component Score Coefficient Matrix

Component

1 2

Design -.098 .474

Technical spec. -.054 .693


Price .329 -.212

Intention to use .354 -.072

Recommend Others .332 -.032


Analysis.


Normalization.

Component Scores.

The following table identifies which of the independent variables contribute to which of the

two factors. The factor loadings in the rotated component matrix determine which of the

variables would come under which factors. Our analysis shows the factors as follows:

1. Component 1 (Loyalty, Price, Intention to use, Recommend others)

2. Component 2 (Design, Technical Specification)

Based on these factors we labelled the two components as:

1. Brand perception factor

2. Model perception factor

Component 1 Component 2

Independent

variables

Loyalty, Price,

Intention to use,

Recommend others

Design, Technical

Specification


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Labelling the

factors

Brand perception

factor

Model perception

factor

Discriminant

The purpose of discriminant analysis is to identify which customers are likely to buy

Samsung smartphones and which are not going to buy them.

Analysis Case Processing Summary

Unweighted Cases N Percent

Valid 99 100.0

Excluded Missing or out-of-range group

codes

0 .0

At least one missing

discriminating variable

0 .0

Both missing or out-of-range

group codes and at least one

missing discriminating variable

0 .0

Total 0 .0

Total 99 100.0

We examine whether there are any significant differences between groups on each of the

independent variables using group means and ANOVA results data. The Group Statistics and

Tests of Equality of Group Means tables provide this information. If there are no significant

group differences it is not worthwhile proceeding any further with the analysis.

From the generated results shown below, for example, there is a significant group mean

differences for Design, Loyalty

Group Statistics

Own Samsung? Mean Std. Deviation

Valid N (listwise)

Unweighted Weighted

Without Samsung Design 3.18 .931 40 40.000

Technical spec. 2.85 .736 40 40.000


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Use_Fault(Loyalty) 2.65 .736 40 40.000

Price 3.28 .847 40 40.000

Intention to use 3.48 .877 40 40.000

Recommend Others 3.43 .958 40 40.000

With Samsung Design 3.76 .536 59 59.000

Technical spec. 3.05 .818 59 59.000


Price 3.20 .738 59 59.000

Intention to use 3.34 .843 59 59.000


Total Design 3.53 .774 99 99.000

Technical spec. 2.97 .788 99 99.000


Price 3.23 .780 99 99.000

Intention to use 3.39 .855 99 99.000


Tests of Equality of Group Means

Wilks' Lambda F df1 df2 Sig.

Design .860 15.830 1 97 .000

Technical spec. .984 1.556 1 97 .215

Use_Fault(Loyalty) .902 10.559 1 97 .002

Price .998 .199 1 97 .656

Intention to use .994 .601 1 97 .440

Recommend Others .990 1.013 1 97 .317


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Pooled Within-Groups Matrices

Design

Technical

spec.

Use_Fault

(Loyalty) Price

Intention

to use

Recommend

Others

Correlation Design 1.000 .032 -.091 .035 .024 .115

Technical spec. .032 1.000 .340 .068 .181 .199

Use_Fault(Loyalty) -.091 .340 1.000 .356 .607 .540

Price .035 .068 .356 1.000 .656 .599

Intention to use .024 .181 .607 .656 1.000 .848

Recommend

Others

.115 .199 .540 .599 .848 1.000

Box's Test of Equality of Covariance Matrices

Box’s M tests the null hypothesis that the covariance matrices do not differ between groups

formed by the dependent. We want this test not to be significant so that the null hypothesis

that the groups do not differ can be retained.

Log Determinants

Own Samsung? Rank Log Determinant

Without Samsung 6 -5.364

With Samsung 6 -6.698

Pooled within-groups 6 -4.933

The ranks and natural logarithms of determinants printed are

those of the group covariance matrices.

Test Results

Box's M 119.167

F Approx. 5.278

df1 21

df2 25784.923

Sig. .000

Tests null hypothesis of equal

population covariance matrices.


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Summary of Canonical Discriminant Functions

This provides information on each of the discriminate functions (equations) produced. The maximum number of discriminant functions produced is the number of groups minus 1. We are only using two groups here, namely ‘With Samsung’ and ‘Without Samsung’, thus only one function is displayed. The canonical correlation is the multiple correlation between the predictors and the discriminant function. With only one function it provides an index of overall model fi t which is interpreted as being the proportion of variance explained (R2).With a canonical correlation of .575, 33.1% of the variance in the discriminating model can be explained due to the changes in the independent variables.

Eigenvalues

Function Eigenvalue % of Variance Cumulative %

Canonical

Correlation

1 .495a 100.0 100.0 .575

a. First 1 canonical discriminant functions were used in the analysis.

Wilks’ Lambda indicates the significance of the discriminating function. P-value = 0 denotes

discriminant function to be significant.

Wilks' Lambda

Test of Function(s) Wilks' Lambda Chi-square df Sig.

1 .669 37.798 6 .000

The values in the table below denote the relative contribution of the variables in

discriminating the two groups. Considering the absolute values of all the 6 variables, we

notice Design and Loyalty to have the maximum relative contribution in discriminating the

two groups.


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Standardized Canonical Discriminant

Function Coefficients

Function

1

Design .733

Technical spec. -.024

Use_Fault(Loyalty) 1.009

Price .103

Intention to use -.357

Recommend Others -.529

The Pearson coefficient values in the Structure matrix shown below measures the relative

importance of the predictors.Structure Matrix

Function

1

Design .574

Use_Fault(Loyalty) .469

Technical spec. .180



Price -.064

Pooled within-groups correlations between

discriminating variables and standardized

canonical discriminant functions

Variables ordered by absolute size of

correlation within function.


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Canonical Discriminant Function

Coefficients

Function

1

Design 1.016

Technical spec. -.030

Use_Fault(Loyalty) 1.178

Price .131



(Constant) -4.100

Unstandardized coefficients

Functions at Group Centroids

Own Samsung?

Function

1

Without Samsung -.846

With Samsung .573

Unstandardized canonical

discriminant functions evaluated at

group means

Classification Statistics

Classification Processing Summary

Processed 99

Excluded Missing or out-of-range group

codes

0

At least one missing

discriminating variable

0

Used in Output 99


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Prior Probabilities for Groups

Own Samsung? Prior

Cases Used in Analysis

Unweighted Weighted

Without Samsung .500 40 40.000

With Samsung .500 59 59.000

Total 1.000 99 99.000

Separate-Groups Graphs


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Classification Resultsb,c

Own Samsung?

Predicted Group Membership

TotalWithout Samsung With Samsung

Original Count Without Samsung 28 12 40

With Samsung 12 47 59

% Without Samsung 70.0 30.0 100.0

With Samsung 20.3 79.7 100.0

Cross-validateda Count Without Samsung 23 17 40

With Samsung 13 46 59

% Without Samsung 57.5 42.5 100.0

With Samsung 22.0 78.0 100.0


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a. Cross validation is done only for those cases in the analysis. In cross validation, each case is classified by the

functions derived from all cases other than that case.

b. 75.8% of original grouped cases correctly classified.

c. 69.7% of cross-validated grouped cases correctly classified.

The above table shows the hit ratio = 75.8% for the original grouped cases correctly

classified. Thus research on discriminant analysis generates a satisfactory result.

5. CONCLUSION

Our research results has successfully mapped the findings with the management and research

objectives. We were able to identify the variables which influence the buying behaviour of

the consumers. Two important variables which influence the buyers’ perception are brand

loyalty and the design of the smartphone. Also, our factor analysis was able to identify two

major factors which drive the buyers’ perception. We labelled those two factors are Brand

perception factor and Model perception factor. We found out the reasons for the switching

behaviour of customers and the frequency of the switches a customer has made. Our findings

has also ascertained the fact that there are few other competitors like LG, Apple, Nokia etc.

which are readily recalled by the customers when they think of smartphones.

6. CHALLENGES AND LEARNINGS

The major challenges faced during the entire research phase are:

1. Time constraint- We had to conduct an exploratory research and then a conclusive

research with a sample of 100 in a period of 1 month. It was a challenge to complete it

within the deadline. However, we completed it with final responses of 99.

2. All relevant aspects and perspectives were not covered during FGD. For example, we

identified new concerns from the customers like overheating of the device, battery

charging time etc. during our FGD. We included these factors later on. Thus, two

FGDs were conducted in order to get an exhaustive list of independent variables.

3. During the in-depth interview, few questions were misinterpreted by the respondents.

Before preparing the questionnaire we rectified our mistake and made the necessary

changes.

4. While measuring the internal consistency of the responses through the reliability

statistic Cronbach’s alpha, our research had to exclude 5 Likert scale variables and


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complete the final conclusive analysis with only 6 Likert scale variables. There was a

trade-off between including more variables in the analysis and maintaining a desired

level of internal consistency.

Appendix

Questionnaire

1. Which of the following age category do you fall into?

18-25

26-40

> 40


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2. Please indicate your gender

Male

Female

3. Which of the following indicate your type of employment

Public employment

Private employment

Self-employment

Student

Unemployed

4. Do you own a smartphone?

Yes

No

5. Which of the mobile brands do you use?

Samsung

Karbonn

Micromax

Apple

Nokia

Sony

LG


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Other ( ) please mention the name

6. Please select the option for the following factors on the Likert scale on the basis of your satisfaction:

Strongly Disagree

Disagree Neither Agree Strongly Agree

Are you happy with the services of your smartphone?

Does the design of the phone satisfy you?

Are you satisfied with the technical specifications of the phone w.r.t. the price and other phones in the same price range?

Will you disown the smartphone for a competitor's product in the same price range?

Are you ready to use your smartphone even if you find any fault in your device?

Are you satisfied with the customer care services of your smartphone?

Are you satisfied with the battery life of the smartphone you


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own?

7. What brands do come to your mind when you think about smartphones?

Samsung

Karbonn

Micromax

Apple

Nokia

Sony

LG


8. When you call to complain or query anything, how satisfied are you with the current brand on the following:

Very satisfied Satisfied Dissatisfied Very dissatisfied Neutral

Overall customer

care service

Ability to get

attendant quickly


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Attitude of the

attendant

Ability to provide

a solution

9. Overall, how satisfied are you with the current brand:

Very satisfied

Satisfied

Dissatisfied

Very dissatisfied

Neutral

10. I intend to continue using my current smartphone for a long time to come

Strongly Agree

Agree

Neutral

Disagree

Strongly Disagree

11. I will encourage friends and relatives to buy the brand I am using

Strongly Agree

Agree

Neutral

Disagree

Strongly Disagree

12. For how long you are using your current smartphone?

<1year

1-2 year


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2-3 year

>3year

13. In past how many times have you switched the brand?

Once

Twice

Thrice

>3 Times

14. What were the reason for your switch?

Poor Battery life

Overheating of the device

Poor Customer Service

Change of Geographic Location

Device stopped working

Better substitute products

Other ( ) please mention

15. Are you having any thoughts about switching your current brand?

Yes

No

16. If yes what are the reasons?

Poor Battery life

Overheating of the device


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Poor Customer Service

Change of Geographic Location

Device stopped working

Better substitute products

Other ( ) please mention

17. Which of the factors among the following help(s) you associate with the Brand of the mobile brand the most?

Company Background

Presence across India

Brand Ambassador

Pricing

Design

Technology

Brand Reliability

Brand Performance

18. Who recommended you to buy Samsung smartphones?

Friends

Family

Advertisement

Online Review


19. Where do you buy smartphones?


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Online

Company Stores

Other stores

20. What is your opinion about the price of the smartphone you own?

Highly Under priced

Under priced

Normal

Overpriced

Highly Overpriced

21. What products do come to your mind when you think about Samsung?

Smartphones

Laptops

Desktops

Television

Others ( ) please mention the same


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