NORTH SOUTH UNIVERSITY

SCHOOL OF BUSINESS

MARKETING RESEARCH (MKT470)

RESEARCH ON: TRUTH IN ADVERTISEMENTS: SOCIAL MEDIA USAGE AND THE BELIEVABILITY AMONG YOUNG CONSUMERS

SUBMITTED BY

Nowshin Ahsan #121 0251 030Rifah Tasnia #121 0250 030Tanvir Ahmed #121 0041 030Wasif Mustayeen Chowdhury #121 0349 030

SUBMITTED TO

Syed Kamrul Islam

Senior Lecturer

School of Business

Date: 12-8-14

Syed Kamrul Islam

Senior Lecturer

School of Business Administration

North South University

Dhaka

Subject: Submission of Research Report on “Truth in Advertisement: Media Usage

and Believability among Young Consumers.”

Dear Sir,

We are very pleased to submit you our research report on “Truth in Advertisement:

Media Usage and Believability among Young Consumers.” The theoretical

knowledge is of no worth if it is not applied in reality. So it has been a great pleasure

for us to have the opportunity to apply our academic knowledge in practical field. The

report is prepared on the basis of the theoretical and practical learning from the

Marketing Research (MKT470) course of summer 2014 conducted by you.

We tried our level best to put careful effort for the preparation of this report. As

students it is usual that inadequacy or error may arise and it may lack professionalism

in some cases. For any unintentional inadequacy in the report, your sympathetic

consideration would be highly appreciated. In addition, we will enthusiastically

welcome any clarification and suggestion about any view and conception

disseminated in the report. We truly appreciate your patience and support.

We sincerely expect that you would be kind enough to accept our report for

evaluation and oblige thereby.

Acknowledgement

First & foremost, we are grateful for this opportunity & would like to express our

profound gratitude & appreciation to Syed Kamrul Islam sir, Senior Lecturer of North

South University, Dhaka for generous support during our Marketing Research

(MKT470) course of summer 2014.

We would also like to express our heartiest gratitude to SKI sir for his tremendous

help & encouragement with our study. We are highly indebted to him for his valuable

advice & intellectual guidance throughout the period of our study. His comments &

suggestions were very stimulating & developed our ideas to accomplish this report.

We would like to acknowledge the contribution of all our surveys for their help by

providing information for this study. The study would not have been possible without

their support & co-operation.

TABLE OF CONTENT

TOPIC PAGE NO.

EXECUTIVE SUMMARY 01

TELECOMMUNICATION

INDUSTRY IN BANGLADESH

02

TELECOM WAR IN

BANGLADESH

03

RESEARCH OBJECTIVES 04

SCOPES & LIMITATIONS 05

RESEARCH METHODOLOGY 06

HYPOTHESIS 07

PRIMARY DATA ANALYSIS 08

AGE 08

GENDER 09

OCCUPATION 10

EDUCATION 11

DEPARTMENT 12

INCOME 13

HYPOTHESIS TESTING 14

ONE SAMPLE T-TEST 14

PAIRED SAMPLE T-TEST 15

INDIVIDUAL SAMPLE TEST

(1)

16

INDIVIDUAL SAMPLE TEST

(2)

18

CROSSTAB & CHI SQUARE 20

ONE WAY ANOVA 22

CORRELATION ANALYSIS 24

LINEAR REGRESSION 25

FINDINGS 27

RECOMMENDATIONS 28

CONCLUSION 29

REFERENCES

Introduction

Our research topic is “Truth in advertisement of telecom companies on social media”

The report we are working on focuses on the level of truth portrayed by telecom

advertisements on social media. It is a known fact that social media is becoming an

very important source of promotion, because as each day pass by more people are

logging into social mediums and are thus exposed to vast promotions from various

brands of various products. Various telecom companies have opened ‘pages’ (on

Facebook) or ‘accounts’ (in Twitter) for raising brand awareness or promoting of their

products on social media. It is also known that markets try to make their promotions

and advertisements as lucrative as possible so that they can retain their customer base

for a long period of time. But the main questions that arises are: Are the promotions

1005 genuine? That does the viewer’s really believe the promotions of the telecom

companies on social media? Are they really portraying the truth? Do the expectation

and performance level match after complying with the offers?

Quite often we see that in mass promotions and also nowadays also spotted on social

media promotions that the “*CONDITIONS APPLY” portion is mentioned on the

promotions but many customers fail to notice this statement because it is stated

generally at the bottom and also in very small fonts that makes customers fail to

notice this. This phenomenon as created a buzz all over the world and people comply

with offers without noticing this statement, and later ends up dissatisfied. This

phenomenon has raised questions on the believability factors of promotions as well.

We know that everyday new companies are appearing and in order to grab customer

attention quickly they may provide falsified facts and information to win customers,

which is completely unethical. Many customers have the intellect to catch is falsified

information and thus ward them off, but there are quite a number of customers who

really believe in the provided information contents and eventually grab their attention

and they comply with the offer. Thus eventually they are dissatisfied because they

comply with offer that provide falsified information. It’s up to the customers to

recognize the promotions and survive in the world of the evolving social media.

TELECOM INDUSTRY IN BANGLADESH

The liberalization of Bangladesh’s telecommunications sector began with small steps

in 1989 with the issuance of a license to a private operator for the provision of inter

alia cellular mobile services to compete with the previous monopoly provider of

telecommunications services the Bangladesh Telegraph and Telephone Board

(BTTB). Significant changes in the number of fixed and mobile services deployed in

Bangladesh occurred in the late 1990s and the number of services in operation have

subsequently grown exponentially in the past five years.

The incentives both from government and public sectors have helped to grow this

sector. It is now one of the biggest sector of Bangladesh. The telecom sector in

Bangladesh has seen growth in mobile penetration that has exceeded all expectations

with over 65.1millions subscribers as of September 2010 versus 4million in 2004.

(Bangladesh Telecom Sector Challenges & Opportunities, 2010). According to other

studies the number of mobile phone subscribers in Bangladesh as of February 2009

was 45.21 million (Bangladesh Telecom Regulatory Commission, n.d.) rising to 99.87

million at the end of March 2013 (Telecompaper, n.d.) and it is projected to grow

more over the years.

As a populous country, its huge market has attracted many foreign investors to invest

in this sector. The rapid growth in mobile telephony has undoubtedly had a

transformative impact on the economy in terms of aggregate investment as foreign

companies like Telenor, Axiata, Orascom etc. have invested in our economy through

Grameenphone, Robi, Banglalink etc. respectively. This also has increased

restrictions and regulations for foreign companies trying to enter the market to control

competition and economic overture.

There are 6 mobile phone operators competing in Bangladesh. These are:

1. Airtel Bangladesh Ltd. Branded as Airtel, formerly known as Warid

Telecom

2. Grameenphone/Telenor Bangladesh Ltd.: Branded as Grameenphone

3. Orascom Telecom Ltd.: Branded as Banglalink

4. Pacific Bangladesh Telephone Ltd.: Branded as Citycell

5. Axiata Bangladesh Ltd: Branded as Robi

6. Teletalk Bangladesh Ltd.: Branded as Teletalk

Due to the emphasis of developing a “Digital Bangladesh” greater internet penetration

is of particular relevance to the telecom companies operating in BD. As a

consequence to keep up with the competition in lieu of the materialization of the idea

of a “Digital Bangladesh” the telecom companies are introducing 3G networks. 3G

networks are expected to significantly enhance user experience of existing data

services, with the introduction of video and other high bandwidth services by the

carriers. There has been substantial benefits from greater connectivity, generated from

this market competition and introduction of 3G network, in terms of social cohesion.

One of the key areas where operators in the industry are focused in is rising

importance of convergence and its impact on customer spending patterns.

TELECOM WAR IN BANGLADESH

As mentioned earlier the telecom industry of Bangladesh is in growing stage and it is

going to attain maturity stage soon. And to reap the benefits from this opportunity the

telecom companies are always on their toes grab the first opportunity to provide

services different from their competitors. With an aim to being the best network

providers in Digital Bangladesh the companies are always coming up with new

service packages as well as focusing on making internet accessible to their customer

with better speed and applications. The latest materialization of this aim is the wide

spread launch of 3G network by the telecom companies of Bangladesh.

According to a recent study by Webable, a digital marketing company, the growth

rate of subscription of Faceook in BD is 5% and is projected to reach 10million by the

end of November

2014. So the

importance of

grabbing the

customers through

these social media

has caught the

attention of the

telecom companies. So they are becoming very active on these social media sites and

they are coming up with different packages so that the customers can access these

social media sites through them and they can promote their services to their

customers. This way they not only attract more customers but also generate more

sales and get one step ahead of their competitors.

Telecom companies are always creating new packages so that their customers can

access facebook or Whatsapp for free or for a low price as seen in the advertisements.

This is done to gain a competitive edge in the telecom war.

RESEARCH OBJECTIVE

For the research, our broad objective would be to figure out how effective are the

promotional strategies of the telecom companies in the social media sites. We tried to

scrutinize through our research that whether the telecom companies have been

successful in conducting their promotional strategies in terms of making them

believable for their customers and the subsequent believability pattern of the

customers.

In addition, some of our specific objectives are as follows:

Finding out the demographic pattern of the customers in terms of their age,

level of study, income/ allowance etc. to link with our study.

Whether the customers have access to internet in the first place.

The level of activity and time contribution of the customers on internet and

social media sites.

Their exposure to the advertisements of the telecom brand pages.

Their believability of the advertisements.

Effect of peripheral tools in the advertisements in persuasion of generating

believability among customers.

Assessing the success or failure of the advertisements of the telecom

companies among its target consumers.

SCOPES AND LIMITATIONS

Our study has two folds.

On one hand, we tried to determine the demography of the customers of the

target market of the telecom companies targeted via social media sites and the

accessibility and the usage pattern of internet by the customers.

On the other hand, we tried to find out the effectiveness of the advertisements

of the telecom companies of BD in social media sites to generate sales among

young consumers.

The limitations of our study are:

We had small sample size. Therefore our findings may not be very valid. But

our results are still valid; it is just less likely to occur than if we had enough

resources to conduct a questionnaire with a larger sample.

We had a narrow time frame to conduct the research, and so maybe we could

not produce as quality of a project as could be possible with a longer time

frame.

As in any project, there might be errors in our questionnaire or the procedures

used to implement it, which can lead us to skewed data or false info. Errors in

the finding may also come from the respondents’ part, where they might not

have actually told the truth, but just put a random answer just to get them done

with the survey.

RESEARCH METHODOLOGY

Methodology refers to a simple set of working methods or procedures and underlies a

particular study relative to the systematic method. We followed all fundamental steps

of a standard research process to conduct this research. This research has been

conducted in order to determine whether customers have access to internet and are

exposed to the advertisements of the telecom companies and the subsequent

believability of the advertisements.

For our primary data, in order to answer the research objectives, we considered a

sample size of 100 respondents from different universities.

We chose probability sampling method to get better and accurate results for our

research. Under probability sampling method we conducted simple random sampling

as well as snowball sampling. By using this technique in making our sample, we tried

to ensure that each element in the population have an equal chance of being included

in the sample as well as we get relevant respondents for the survey. It is done to

reduce any kind of biasness that can hamper our research.

Respondents answered a survey questionnaire which was structured by using Likert

format, multiple choice questions, and open ended questions. Data gathered for this

research have been then computed for interpretation.

Along with primary data, we have also used secondary resources that contained

published articles and literatures to support the survey results. The descriptive method

has been used for this research. The descriptive method means gathering information

about the present existing condition. The aim of using descriptive research is to verify

formulated hypotheses that refer to the present situation in order to elucidate it. It is a

quick, practical and reliable research approach. Moreover, this method allowed

enough flexibility in research with scope of analyzing important new issues and

questions which arose during the time interval of the study. In this study, the

descriptive research method was employed to identify the role and significance of

marketing research behind the product’s success. We chose this research method

considering the advantages that allowed us to use qualitative or quantitative data or

both.

HYPOTHESIS

Due to competition the telecom companies have introduced advanced technologies to

grab attention of customers. They have introduced 3G in the market and made its use

easily accessible to their customers. As a result customers can access the social media

sites more. Due to the up rise in the subscription of social media sites by customers

the telecom companies are ensuring their presence in these social media sites and

constantly promoting their product offers via them. Some advertisements are believed

by the customers some are not. And due to unaccountability of these advertisements

there is a chance of fraudulence through these advertisements.

Based on these insights in order to validate our research considered the following

three hypotheses (H):

H1: Consumers have access to internet as well as they are exposed to the

advertisements of the telecom companies and they believe the advertisements.

H2: Consumers have access to internet but they are not exposed to advertisements of

the telecom companies.

H3: Consumers have access to internet as well as they are exposed to the

advertisements of the telecom companies but they do not believe the advertisements.

In order to confirm any of our above mentioned hypotheses, we ran the following

tests using SPSS: Frequency, Crosstabs, Chi-Square Tests, T-test, Correlation and

Regression.

PRIMARY DATA ANALYSIS

Descriptive Test Analysis

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation

AGE 100 1 3 1.68 .548

gender 100 1 2 1.23 .423

occupation 100 1 3 1.09 .351

Income 100 1 6 1.87 1.160

education 100 1 3 1.97 .223

dept 100 1 6 1.77 1.644

Valid N

(listwise)100

AGE

Frequency Percent Valid Percent Cumulative

Percent

Valid

18-20 36 36.0 36.0 36.0

21-23 60 60.0 60.0 96.0

24-26 4 4.0 4.0 100.0

Total 100 100.0 100.0

Interpretation: Among the total respondents of 100, majority of them belongs to 21-

23 age range which is the young adults of the society. Second majority goes to the age

range of 18-20. There were respondents who fall under the age range of 24-26.

Gender

Frequency Percent Valid

Percent

Cumulative

Percent

Valid

male 77 77.0 77.0 77.0

female 23 23.0 23.0 100.0

Total 100 100.0 100.0

Interpretation: Among 100 samples 77 were male and 23 were female.

Occupation

Frequency Percent Valid

Percent

Cumulative

Percent

Valid

student 93 93.0 93.0 93.0

privtate

tutor5 5.0 5.0 98.0

intern 2 2.0 2.0 100.0

Total 100 100.0 100.0

Interpretation: Among 100 samples 93 of them are student, 5 are private tutor, 2

were intern.

Education

Frequency Percent Valid

Percent

Cumulative

Percent

Valid

hsc 4 4.0 4.0 4.0

undergrad 95 95.0 95.0 99.0

graduate 1 1.0 1.0 100.0

Total 100 100.0 100.0

Interpretation: Among our 100 samples 4 are HSC student, 95 are undergrad student

& 1 is graduate student.

Department

Frequency Percent Valid

Percent

Cumulative

Percent

Valid

bba 81 81.0 81.0 81.0

economic 1 1.0 1.0 82.0

arch 1 1.0 1.0 83.0

engrr 12 12.0 12.0 95.0

others 5 5.0 5.0 100.0

Total 100 100.0 100.0

Interpretation: Among our 100 samples 81 are from BBA department, 1 from

Economics department, 1 from archi department, 12 from engrr department, 5 from

other department.

Income

Frequency Percent Valid

Percent

Cumulative

Percent

Valid 2000-5000 55 55.0 55.0 55.0

5001-8000 19 19.0 19.0 74.0

8001-10000 12 12.0 12.0 86.0

10000+ 13 13.0 13.0 99.0

6 1 1.0 1.0 100.0

Total 100 100.0 100.0

Interpretation: Among 100 candidates 55 of them get 2000-5000 tk, 19 candidates

get 5001-8000 tk, 12 of them get 8000-10,000 tk, 13 of them get 10,000+ tk.

Hypothesis Testing

One Sample T-test

Q: Education level of the respondents

Q: The provided information in graphical presentations play an important factor in the

believability of the promotions.

H0: Educational level of respondents does not have any influence on the believability

of the information provided in graphical presentations.

H1: Educational level of respondents have influence on the believability of the

information provided in graphical presentations.

One-Sample Statistics

N Mean Std.

Deviation

Std. Error

Mean

education 100 1.97 .223 .022

graphical_presentatio

n98 3.93 .933 .094

One-Sample Test

Test Value = 1.5

t df Sig. (2-

tailed)

Mean

Difference

95% Confidence

Interval of the

Difference

Lower Upper

education 21.104 99 .000 .470 .43 .51

graphical_presentation 25.759 97 .000 2.429 2.24 2.62

Interpretation: One sample T-test shows that the significance level is .000 which

is less than .05. so P<0.05. So we can say that there is a significant influence of

Educational level of respondents on the believability of the information provided in

graphical presentations. W e c a n r e j e c t t h e H 0 a n d m a y n o t r e j e c t H 1

Paired Sample T-Test

Q: Age of the respondents

Q: The level of truth in the advertisements offers portrayed and the actual service

experience after subscribing to the advertised offer on a scale of 1-6.

H0: There is no difference between age groups and the level of truth into

advertisement offered

H1: There is difference between age groups and the level of truth into advertisement

offered.

A paired-samples t-test was conducted to compare the level of truth with age group

of 18-20, 21-23, 24-26.

Paired Samples Statistics

Mean N Std.

Deviation

Std. Error

Mean

Pair 1

AGE 1.68 100 .548 .055

truth_leve

l3.19 100 1.022 .102

3.19. The standard deviation for the age is .548 and for the truth level, also .102. The

number of participants in each condition (N) is 100..

Paired Samples Correlations

N Correlatio

n

Sig.

Pair 1AGE &

truth_level100 -.215 .032

Paired Samples Test

Paired Differences t df Sig.

(2-

tailed)

Mean Std.

Deviatio

n

Std.

Error

Mean

95%

Confidence

Interval of the

Difference

Lower Upper

Pair 1

AGE -

truth_leve

l

-

1.5101.259 .126 -1.760 -1.260

-

11.99

1

99 .000

There was a significant difference in the scores for IV level 1 (M= 1.68 , SD=.548)

and truth level (M= 3.19, SD= 1.022) conditions; t(99) = -11.991, p = 0.000

 The Sig. (2-Tailed) value in our example is 0.000. This value is less than .05.

Because of this, we can conclude that there is a statistically significant difference

between the mean for the age and truth level. Since our Paired Samples Statistics box

revealed that the Mean number of age was greater than the Mean for truth level.

So, we may reject the H0 and we may not reject the H1.

Individual Sample Test (1)

Q: Gender of the respondents

Q: How frequently do you log into social media sites on a scale of 1-5?

H0: There is no difference in male and female respondents regarding the frequency of

log into social media sites.

H1: There is significant difference in male and female respondents regarding the

frequency of log into social media sites.

An independent-samples t-test was conducted to compare the frequency of logging

into social media sites in male and female respondents.

Group Statistics

gender N Mean Std. Deviation Std. Error Mean

Frequencymale 77 3.78 1.392 .159

female 23 3.91 1.411 .294

In the Group Statistics box, the mean for male is 3.78. The mean for female is 3.91.

The standard deviation for male is 1.392 and for female is 1.411. The number of

participants in each condition (N) is 100.

Independent Samples Test

Levene's

Test for

Equality

of

Variance

s

t-test for Equality of Means

F Sig. t df Sig.

(2-

tailed

)

Mean

Differenc

e

Std.

Error

Differenc

e

95%

Confidence

Interval of

the

Difference

Lowe

r

Uppe

r

Frequenc

y

Equal

variance

s

assume

d

.02

3

.88

1

-.40

398 .688 -.134 .332 -.792 .525

Equal

variance

s not

assume

d

-.40

0

35.76

5.691 -.134 .334 -.812 .544

There was no significant difference in the scores for male respondents(M= 3.78 , SD=

1.392) and female respondents (M= 3.91, SD= 1.411) conditions; t(98)= 0.403, p =

0.688

The Sig. (2-Tailed) value in our example is 0.688. This value is greater than 0.05.

Because of this, we can conclude that there is no statistically significant difference

between the mean number of logging into social sites for the male and female groups.

Since our Group Statistics box revealed that the Mean for the female group was

greater than the Mean for the male group. We can conclude that male were not able to

differentiate significantly than female in logging into social sites. So, we may not

reject the H0.

Individual Sample Test (2)

Q: Gender of the respondents

Q: Extent of believability of the advertisements

H0: There is no difference in male and female respondents between the extent of

believability the advertisements.

H1: There is statistically significant difference between male and female respondents

in the extent of believability the advertisements.

An independent-samples t-test was conducted to compare the extent of believability

of the advertisements in male and female respondents.

Group Statistics

gender N Mean Std.

Deviation

Std. Error

Mean

Believabilit

y

male 77 3.13 1.174 .134

female 23 2.70 1.105 .230

In the Group Statistics box, the mean for male is 3.13. The mean for female is 2.70.

The standard deviation for male is 1.174 and for female is 1.105. The number of

participants in each condition (N) is 100.

Independent Samples Test

Levene's

Test for

Equality

of

Varianc

es

t-test for Equality of Means

F Sig

.

t df Sig.

(2-

taile

d)

Mean

Differen

ce

Std.

Error

Differen

ce

95%

Confidence

Interval of

the

Difference

Low

er

Uppe

r

Believabili

ty

Equal

varianc

es

assume

d

.00

9

.92

4

1.57

798 .118 .434 .275 -.112 .981

Equal

varianc

es not

assume

d

1.63

0

38.07

1.111 .434 .266 -.105 .974

There was no significant difference in the scores for male respondents(M= 3.13 , SD=

1.392) and female respondents (M= 2.70, SD= 1.105) conditions; t(98)= 1.577, p

= .118

The Sig. (2-Tailed) value in our example is 0.118. This value is greater than 0.05.

Because of this, we can conclude that there is no statistically significant difference

between the mean number of believability of advertisements for the male and female

groups. Since our Group Statistics box revealed that the Mean for the female group

was greater than the Mean for the male group. We can conclude that male were not

able to differentiate significantly than female’s believability of the advertisements. So,

we may not reject the H0.

CROSSTAB & CHI SQUARE TESTS

Q: Gender of the respondents

Q: Access to internet

H0: There is no difference between male and female in having internet access.

H1: There is significant difference between male and female in having internet access

Case Processing Summary

Cases

Valid Missing Total

N Percent N Percent N Percent

gender *

Internet_Access100 100.0% 0 0.0% 100 100.0%

gender * Internet_Access Crosstabulation

Internet_Access Total

yes no sometimes

gender male Count 75 1 1 77

% within gender 97.4% 1.3% 1.3% 100.0%

% within

Internet_Access78.1% 50.0% 50.0% 77.0%

% of Total 75.0% 1.0% 1.0% 77.0%

female

Count 21 1 1 23

% within gender 91.3% 4.3% 4.3% 100.0%

% within

Internet_Access21.9% 50.0% 50.0% 23.0%

% of Total 21.0% 1.0% 1.0% 23.0%

Total

Count 96 2 2 100

% within gender 96.0% 2.0% 2.0% 100.0%

% within

Internet_Access100.0% 100.0% 100.0% 100.0%

% of Total 96.0% 2.0% 2.0% 100.0%

This table allows us to understand that almost both males and females have internet

access.

Chi-Square Tests

Value df Asymp. Sig.

(2-sided)

Pearson Chi-

Square1.715a 2 .424

Likelihood Ratio 1.448 2 .485

N of Valid Cases 100

a. 4 cells (66.7%) have expected count less than 5. The

minimum expected count is .46.

In this table we can see in "Pearson Chi-Square" row that, x= 1.715 and  p = .424.

This tells us that there is no statistically significant association between Gender and

internet access; that is, both Males and Females got internet access. So we may not

reject the null hypothesis.

It can be easier to visualize data than read tables. The clustered bar chart option

allows a relevant graph to be produced that highlights the group categories and the

frequency of counts in these groups.

.

One Way Anova Test

Q: Age of the respondents

Q: level of trust when the ad message is endorsed by a celebrity.

H0: There is no difference in the level of trust when the ad message is endorsed by a

celebrity between people of different ages.

H1: There is significant difference in the level of trust when the ad message is

endorsed by a celebrity between people of different ages.

Descriptives

Endorsement

N Mean Std.

Deviation

Std.

Error

95% Confidence

Interval for Mean

Minimum Maximum

Lower

Bound

Upper

Bound

18-20 36 3.06 1.068 .178 2.69 3.42 1 5

21-23 60 2.78 1.136 .147 2.49 3.08 1 5

24-26 4 3.00 1.414 .707 .75 5.25 1 4

Total 100 2.89 1.118 .112 2.67 3.11 1 5

In this Descriptive Statistics box, the mean for the age group 18-20 is 3.06. The mean

for the age group 21-23 is 2.78 and the mean for the age group 24-26 is 3.00. The

standard deviation for the age group of 18-20 is 1.1068, the standard deviation for the

age group of 21-23 is 1.136 and the standard deviation for the age group of 24-26 is

1.414.  The number of participants in each condition (N) is 100.

Test of Homogeneity of Variances

Levene

Statistic

df1 df2 Sig.

.195 2 97 .823

ANOVA

Endorsement

Sum of

Squares

df Mean Square F Sig.

Between

Groups1.718 2 .859 .682 .508

Within Groups 122.072 97 1.258

Total 123.790 99

The Sig. value in our example is .508. This value is more than .05. Because of this,

we can conclude that there was a no significant effect of age on trust on celebrity

endorsement at the p>.05 level for the three conditions [F(2, 97) =.682, p =.508]. So,

we may not reject the null hypothesis.

Post Hoc Tests:

Multiple Comparisons

Dependent Variable: endorsement

Tukey HSD

(I)

AGE

(J)

AGE

Mean Difference (I-

J)

Std.

Error

Sig. 95% Confidence Interval

Lower

Bound

Upper

Bound

18-20

21-23 .272 .237 .485 -.29 .84

24-26 .056.

591.995 -1.35 1.46

21-2318-20 -.272 .237 .485 -.84 .29

24-26 -.217 .579 .926 -1.60 1.16

24-2618-20 -.056 .591 .995 -1.46 1.35

21-23 .217 .579 .926 -1.16 1.60

Looking at the Sig. column in our example, we can see that most of the values are

greater than .05. However, there are two values that are 0.995. These values

correspond with the comparison between the age group 18-20 and the age group 24-

26. For this reason, we can conclude that the age group of 18-20 and 24-26 are

significantly different in terms of truth level celebrity endorsement in ads However,

the other condition comparisons are not significantly different from one another. This

means that the age group of 21-23 and 24-26 are not significantly different. It also

means that the age group of 21-23 and 18-20 are not significantly different. 

A one-way between subjects ANOVA was conducted to compare the age group with

the trust in ad message when the ad is endorsed by a celebrity , our age groups are 18-

20, 21-23 and 24-26. There was a significant effect age group on words remembered

at the p>.05 level for the three conditions [F(2, 97) = .682, p = .508]. Post hoc

comparisons using the Tukey HSD test indicated that the mean score for the age

group 18-20 (M = 3.06, SD = 1.608) was significantly different than the no sugar

condition (M = 2.78, SD = 1.136). However, the a little sugar condition  (M = 3.00,

SD = 1.414) did not significantly differ from the age group of 18-20 and the age

group of 21-23 conditions. 

Correlation Analysis

Correlation helps us figure out the association between any two variables. In order

to analyze our collected data, we tried to assume hypothesis for correlation using

the following questions:

Q: Frequency of the advertisements from the subscribed pages or groups

Q: The extent you believe the advertisements

H0: The frequency of the advertisements from the subscribed pages or groups does

not have effect on the believability of respondents on the advertisements

H1: The frequency of the advertisements from the subscribed pages or groups have

effect on the believability of respondents on the advertisements

Correlations

subscribed_pages Believability

subscribed_pages

Pearson Correlation 1 .254*

Sig. (2-tailed) .011

N 100 100

Believability

Pearson Correlation .254* 1

Sig. (2-tailed) .011

N 100 100

*. Correlation is significant at the 0.05 level (2-tailed).

we can see that the Pearson correlation coefficient, r, is 0.254, and P =0.011 that this

is statistically significant (p < 0.05). 

Interpretation:

A Pearson product-moment correlation was run to determine the relationship

between the frequency of the advertisements from subscribed pages and the

believability of the respondents on the advertisements. There was a medium but still

strong, positive correlation between the frequency of the advertisements from

subscribed pages and the believability of the respondents on the advertisements

which was statistically significant (r=o.254,n =100,p<0.05). So we can say that we

may reject the null hypothesis.

LINEAR REGRESSION

Linear regression is the next step up after correlation. It is used when we want

to predict the value of a variable based on the value of another variable. The

variable we want to predict is called the dependent variable (or sometimes, the

outcome variable). The variable we are using to predict the other variable's value is

called the independent variable (or sometimes, the predictor variable).

Q: Gender of the respondents

Q: the level of trust when the ad message is endorsed by a celebrity.

H0: Gender of the respondents does not have any impact on the trust level when the

ad message is endorsed by a celebrity.

H1: Gender of the respondents have impact on the trust level when the ad message is

endorsed by a celebrity.

Descriptive Statistics

Mean Std.

Deviation

N

endorsement 2.89 1.118 100

gender 1.23 .423 100

Correlations

endorsement gender

Pearson

Correlation

endorsement 1.000 -.053

gender -.053 1.000

Sig. (1-tailed)endorsement . .301

gender .301 .

Nendorsement 100 100

gender 100 100

Variables Entered/Removeda

Model Variables

Entered

Variables

Removed

Method

1 genderb . Enter

a. Dependent Variable: endorsement

b. All requested variables entered.

Model Summary

Model R R Square Adjusted R

Square

Std. Error of

the Estimate

1 .053a .003 -.007 1.122

a. Predictors: (Constant), gender

ANOVAa

Model Sum of

Squares

df Mean Square F Sig.

1

Regression .344 1 .344 .273 .602b

Residual 123.446 98 1.260

Total 123.790 99

a. Dependent Variable: endorsement

b. Predictors: (Constant), gender

Coefficientsa

Model Unstandardized

Coefficients

Standardized

Coefficients

t Sig. 95.0% Confidence

Interval for B

B Std.

Error

Beta Lower

Bound

Upper

Bound

1(Constant) 3.062 .347 8.830 .000 2.374 3.750

gender -.139 .267 -.053 -.523 .602 -.669 .390

a. Dependent Variable: endorsement

Interpretation:

A linear regression test was computed to analyze the relationship between the Gender

and endorsement. In the first table, we have the model summary. Here R= .053, which

represents simple correlation. It indicates a moderate degree of correlation. The R

Square value indicates how much of the dependent variable can be explained by the

independent variable.

In the next table ANOVA, the regression variable predicts the outcome variable

significantly

well. This is because the P = 0.602, which is more than 0.05 and therefore there is no

statistically significant relationship between the two variables .Finally the Coefficients

table provides us with information on each predictor variable. We can see that the

constant and the variable contribute significantly to the model (bylooking at the Sig.

column).

FINDINGS

(1) From an individual sample test it was determined that gender does not have any

influence on logging into social media sites. An argument of men logging into social

media sites or vice versa will prove invalid, thus this states that anyone can log into

social media sites, regardless of their gender.

(2) From another individual sample test it was concluded that gender does not have

any influence on the extent of believability of promotions. It’s not that when an

advertisement is shown to a man and a woman, one will believe and the other won’t.

Both men and women goes through the same mental process when they view a

promotion. They interpreted the promotions according to their own mentality.

(3) From a one sample t test we can conclude that educational level of respondents

have an effect on the believability of the information provided in graphical

presentations. People who are more educated tends to comprehend graphical

presentations much better and interpreted them better than people who are enrolled in

lower education levels.

(4) From a paired sample t test, it is concluded, there is difference between age groups

and the level of truth into advertisement offered. As people grow their mentality

develops and has a much better comprehension of the world around. Thus they can

also comprehend and understand advertisements better and thus can analyze the

contents and judge whether the advertisements are true or not.

(5) From the chi square test we have concluded that access of internet is not defined

by the gender of the respondents. In the present day each and every one has access to

the internet regardless the gender.

(6) There is no significant effect of age on trust on celebrity endorsement, people of

all age has fixed perception of a celebrity and what he does, so thus age has no effect.

(7) From a correlation analysis it was concluded that the frequency of the

advertisements from the subscribed pages has effect on the believability of

respondents on the advertisements. The more a person is exposed to promotions the

more he/she believes in the promotions.

(8) From the linear regression test it was found that gender has an effect on brand

endorsement by celebrities. Male and female both tend to perceive promotions

differently when celebrities do endorsements.

RECOMMENDATIONS

(1) Telecom company marketers should come up campaigns that promotes customers

to use the internet via cell phones, in this way they can introduce more internet-based

packages and apply promotions for them accordingly.

(2) For widespread awareness and promotions of packages marketers should promote

their products on social media as more people are logging into the internet everyday.

(3) Marketers should be more ethical in their promotions, so that they don’t deceive

their cutomers because in the modern era of marketing companies need to bulid strong

profitable relations with customers in order to succed in the modern era, if customers

are deceived than customers will be dissatisfied they will move on to their

competitor’s brand and loose share.

(4) Advertisements should be made in such a way that people of almost age groups

can understand. As we now that in the current generation, people of almost all age

groups own a cell phone. So in order for them to comply with the offers they need to

understand the promotions.

(5) We have found out from tests that customers tend to believe adds more when they

are frequently shown, so social media sites shold post more advertisements in a fixed

period of time.

(6)The promotions should be made in such a way that people from all education level

can understand. Promotions should use simle, precise and clear information and

language where necessary.

CONCLUSION

Through our research, we have been able to find out that gender does not have any

influence on logging into social media sites rather educational level of respondents

have an effect on the believability of the information provided in graphical

presentations i.e. advertisements of the telecom companies. We also found out that

difference in age group has an effect on believing the advertisements and also the

frequency of the advertisements from has effect on the believability of respondents on

the advertisements. Ultimately most of the people do not see the advertisements and

those who see do not believe them and thus don’t comply with the offers.

So we would like to recommend that telecom marketers should come up and

encourage internet usage on cellphones and through that untapped sector promote

their brand vigorously. They should go for more ethical promotions or else they will

lose customers. Alongside that the advertisements should be understood by customers

of all ages. Promotions should be clear and easily understood.

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