E – Learning: An Effectiveness Analysis

International Journal of Advanced Scientific Research & Development (IJASRD)

ISSN: 2394 - 8906

www.ijasrd.org, Volume 02, Issue 01 (Jan Mar 2015), PP 99 107

2015, IJASRD. All Rights Reserved 99 | P a g e

E Learning: An Effectiveness Analysis Dr. P. Raja 1, R. Satheesh Kumar 2

ABSTRACT: Internet advertising whose goal is to drive customers to your website or

location(s), or to make a call regarding your products or services. While traditional offline

advertising is used by many companies to drive customers to their websites, many

businesses are trying online ads (such as banners, pay per-click ads, pay per-call ads and

pop-ups) in e-newsletters, on compatible websites, on search engines and in online

versions of newspapers and magazines as a way of reaching people who use the internet

for shopping or to gather information. Advertisements in internet are more attractive for

users. Organizations use this opportunity to promote their products. User expecting the

advertisement should have less animation, this helps the page to load faster and makes the

browsing comfortable. Banner advertisements can be followed to reach the advertisements

among more users.

KEYWORDS: E- Advertising, Search Engine, Kinds of Advertising, Satisfaction Level.

While online advertising is still new to many, our can take heart in the fact that the

same design and content requirements and guidelines translate. Well from traditional

advertising to online ads. In fact, your newspaper print ads can simply be duplicated in the

online version of the publication you're advertising in as long as you include a link to your

website. color, fonts, the size of your ad(s) and your message will all play the same critical

role in getting your ads noticed and, more important, responded to. No one wants to have to

wade through too much text to understand an ad's message. So present your message

concisely and clearly, and relate it to an emotion or a situation shared by the consumers

you're trying to reach. Sending advertisements by e-mail is another method of using the

internet as an advertising vehicle. The use of mass direct e-mail, in which businesses send

unsolicited mail message to a list of e-mail accounts, has fallen out of favour and in many

cases breaks new laws designed to crack down on spamming.

An online newsletter sent out by e-mail is a more sophisticated way in which to reach

actual and potential customers. An increasing numbers of businesses have supplemented

their general customer satisfaction surveys with queries concerning customer feelings about

being put on a direct mailing list. Online surveys are also a way to build up an e-mail address

mailing list that can be used to send out company information relatively inexpensively, When

the is well done, the newsletter or promotional piece will include hypertext links to the

company's web page and will encourage the reader to pass the newsletter on to other

interested parties.

1 Assistant Professor, PG & Research Department of Commerce, Government Arts College

(Autonomous), Salem, Tamil Nadu, India. 2 Ph.D., Research Scholar (P.T), PG & Research Department of Commerce, Government Arts College

(Autonomous), Salem, Tamil Nadu, India.

E Learning: An Effectiveness Analysis


Information Technology in India

The Indian Information Technology industry accounts tor a 5.19% of the country's

GDP and export earnings as of 2011, while providing employment to a significant number of

its tertiary sector workforce. More than 2.5 million people are employed in the sector either

directly or indirectly, making a one of the biggest job creators in India and a mainstay of the

national economy, in 2011-13, annual revenues from IT-BPO sector is estimated to have

grown over US$76 billion compared to China with $35.76 billion and Philippines with

%8.85 billion. India's outsourcing inclustry is expected to increase to US%225 billion by

2020.

The most prominent IT hub is IT capital Bangalore. The other emerging destinations

are Chennai, Hyderabad, Trichy Coimbatore, Kolkata, Pune, Mumbai, Ahmedabad, NCR and

Kochi, Technically proficient immigrants from India sought jobs if the western world from

the 195os onwards as Indias education system produced more engineers than its industry

could absorb. Indias growing stature in the information Age enabled it to form close ties

with both the United States of America and the European Union, However, the recent global

financial crises has deeply impacted the Indian IT companies as well as global companies.

As a result hiring has dropped sharply and employees are looking at different sectors like the

financial service, telecommunications, and manufacturing industries, which have been

growing phenomenally over the last few years.

India's IT Services industry was born in Mumbai in 1967 with the establishment of

Tata Group in partnership with Burroughs. The First software export zone SEEPZ was set up

her way back in 1973, the old avatar of the modern day IT park More than 80 percent of the

country's software exports happened out of SEEPZ, Mumbai in 80s.

Each year India produces roughly 500,000 engineers in the country, out of them only

25% to 30% possessed both technical competency and English languages skills although 12%

of India's population can speak in English. India developed a number of outsourcing

companies specializing in customer support via internet or telephone connections. By 20011,

India also has a total of 37,160.000 telephone lines in use, a total of 506,040,000 mobiles

phone connections, a total of 81,000,000 interest users comprising 7.0% of the country's

population, and 7,570,000 people in the country have access to broadband internet making it

the 12th largest country in the world in terms of broadband internet users. Total fixed-line

and wireless subscribers reached 543.20 million as of November, 2009.

The economic effect of the technologically inclined services sector in India

accounting for 40% of the country's GDP and 30% of export earnings as of 2006, while

employing only 25% of its workforce is summarized by Sharma (2006).

Today, Bangalore is known as the Silicon Valley of India and contributes 33% of

Indian IT Exports. India's second and third largest software companies are head-quartered in

Bangalore, as are many of the global SEI-CMM Level 5 Companies, Next to Bangalore

Hyderabad plays an important role in IT. Lot of companies was developed in Hyderabad, in

the last few years.

The word software" had been coined as a prank by at least 1953, but did not appear

in print until the 1960s. Before this time, computers were programmed either by customers,

or the few commercial computer vendors of the time, such as UNIVAC and IBM. The first



company founded to provide software products and services was Computer Usage Company

in 1955. The software industry expanded in the early 1960s, almost immediately after

computers were first sold in mass-produced quantities. Universities, government, and

business customers created a demand for software. Many of these programs were written in-

house by full-time staff programmers. Some were distributed freely between users of a

particular machine for no change. Others were done on a commercial basis, and other firms

such as Computer Sciences Corporation (founded in 1959) started to grow. The computer-

makers started bundling operating systems software and programming environments with

their machines. Page and most other websites you visit, as they are the primary revenue

driver for the internet.

From banner ads (including rich media banners) to search Engine Optimization

(SEO), social networking, email Marketing, online classified ads, site takeovers, and even

SPAM, online advertising is one of the fastest growing ways to reach are audience

Research Methodology

The research design adopted for this study is Descriptive research. Descriptive

method was adopted because it deals with description of the state of affairs as it exists at

present. The data collection used for this study is interview schedule questionnaire. The

sampling technique used in this study is judgmental sampling. My Actual samples Size is 100

and Statistical Tools used like: Percentage Analysis, Chi Square Analysis, ANOVA Table.

Data Analysis & Interpretation:

Table 3.1 period of internet usage

Factors No. of respondents Percentage (%)

Morning 33 33

Afternoon 15 15

Evening 12 12

Night 40 40

Total 100 100

Sources: Primary data

Inference:

From the above table it is inferred that 19% of the respondents are using internet

in morning, 30% in afternoon, 12% in evening and 40% in night.

0

50

100

150

200

250

Mor

ning

Afte

rnoo

n

Even

ing

Nigh

t

Tota

l

Percentage (%)

No.of respondents



Table 3.2 Hours spent in internet

Hours No. of respondents Percentage (%)

Less than 5 hrs 20 20

5-10 hrs 50 50

10-15 hrs 22 22

Don't know 8 8

Total 100 100


Inference:

From the above table it is inferred that 28% of respondents are using less than 5 hrs,

50% using 5-10 hrs, 20% using 10-15 hrs and 8% of respondents do not know about hours

spending for internet.

Hours Spent in internet

20

50

228

100

Less than 5 hrs

5-10 hrs

10-15 hrs

Don't know

Total

Table 3.3 Location of internet Usage

Places No. of respondents Percentage (%)

Office 20 20

College 50 50

Home 22 22

Net Centers 8 8

Total 100 100


Inference:

The above table infers that 38% choose the Office, 18% choose college, 20%

choose home and 24% choose net centers as their locations for browsing internet.

0

50

100

150

200

Office College Home Net

Centres

Total

Location of internet Usage

Percentage (%)

No.of respondents



Table 3.4 Choice of search engine

Search engine No. of respondents Percentage (%)

Google 20 20

Yahoo 50 50

Others 22 22

Total 100 100


Inference:

The above table shows that 63% of respondents are using Google, 30% of respondents

are using yahoo and 7% of respondents are using other search engines.

Choice of Search Engine

20

50

22

100

20

50

22

100

0

20

40

60

80

100

120

Google Yahoo Others Total

No.of respondents

Percentage (%)

Table 3.5 Places where E-Advertisement are seen

Locations No. of respondents Percentage (%)

Search engine 15 15

Social network 45 45

Web portals 25 25

Others 15 15

Total 100 100


Inference:

The above table indicates that 16% of respondents see advertisement in search engine,

44% seeing in social network, 27% of respondents in web portals, 13% of respondents see

advertisement internet in other areas of internet.

Places where E-Advertisement are seen

0

50

100Search engine

Social network

Web portals Others

Total No.of respondents

Percentage (%)



Table 3.6 Satisfaction level on E-advertisement

Level of satisfaction No. of respondents Percentage (%)

Highly satisfied 10 10

Satisfied 14 14

Neutral 17 17

Highly dissatisfied 33 33

Dissatisfied 26 26

Total 100 100


Inference:

From the above table it is inferred that 9% of the respondents are highly satisfied,

15% of the respondents are satisfied, 12% of the respondents are neutral, 30% of the

respondents are highly dissatisfied and 34% of the respondents are dissatisfied with the

advertisements displaying internet.

Level of Satisfaction

Highly satisfied

Satisfied

Neutral

Highly dissatisfied

Dissatisfied

Total

Table 3.7 Respondents do click pop ups.

Level of satisfaction No. of respondents Percentage (%)

Yes 50 50

No 22 22

Sometimes 28 28

Total 100 100


Inference:

The above table show that 28% of the respondents will click pop-up advertisement

and 50% will not click pop up advertisement and 22% will click pop up sometimes.

Respondents do click popoups

50

22

28

100128

Yes

No

Sometimes

Total



Table 3.8 Advertisements disturbs browsing

Level of agree No. of respondents Percentage (%)

Strongly agree 54 54

Agree 21 21

Disagree 15 15

Strongly Disagree 10 10

Total 100 100


Inference:

The above table indicates that 54% of the respondent strongly agree, 22% of the

respondents agree, 15% respondents disagree and 10% of the respondents strongly

disagree regarding disturbance of advertisement while browsing.

Advertisement disturbs browsing

54

21

1510

100

Strongly agree Agree Disagree Strongly Disagree Total

Table 3.9 Opinion about search engine advertisement

Opinion No. of respondents Percentage (%)

Excellent 30 30

Good 38 38

Better 19 19

Bad 13 13

Total 100 100


Inference:

The above table indicates that, 30% says search engine advertisement as excellent

and 38% of respondents says as good and 19% says as better and 13% of respondents say

band as their opinion about search engine advertisement.

Excelle

nt

Good

Bett

er

Bad

Tota

l

No.of respondents 0

20

40

60

80

100

Opinion about search engine advertisement

No.of respondents

Percentage (%)



Table 3.10: Kind of Advertisement easy to follow

Advertisement No. of respondents Percentage (%)

Pop ups 7 7

Classifieds 22 22

E-mail 18 18

Banner 53 53

Total 100 100


Inference:

The above table indicates that 7% of the respondents are easy to follow pop ups,

22% follow classifieds, 18% follow E-mail and 53% follow Banner advertisements.

Kind of Advertisement easy to follow

7

22 18

53

100

-20

0

20

40

60

80

100

120

0 1 2 3 4 5 6

No.of respondents

Conclusion

The efficiency of banks is not always reflected only by the size of its balance but also

the level of return on its assets. The NPAs do not generate interest incomes for bank but

at the same time banks are required to provide provisions for NPAs from their current

profits. By studying and analyzing the NPAs of OPBs and NPBs it can be concluded

that NPBs are having less NPAs compare to OBPs and by introduction of prudential forms

for asset management in banking sector leads to decrease of NPAs of OPBs. Banks can

efficiently manage their NABs if they follow the rules strictly. Sanctioning the advances

without biases the NPAs will be eradicated and Banking sector will boom and through

this India's GDP will rise and economy will grow.

References

Kothari, C.R., Research, Methodology Methods and Techniques, New Delhi, New Age

international (P) Ltd., Publishers, Second Edition, 2006.

Gupta, S.P., Statistical Methods, New Delhi, Sultan Chand and Sons Publishers, Thirty Forth Edition.

2007.

Philip Kotlar, Marketing Management" III Edition.

Hamid Nazerzadeh, Internet advertising: Optimization and economic aspects?

Malthouse, Edward C, Calder, Bobby J, Journal of Advertising, September 22, 2009.



Author(s) Profile:

Dr. P. Raja, Assistant Professor, PG & Research Department of

Commerce in Government Arts College, Salem. He is a passionate academician,

research supervisor in Commerce, with more than 20 years of accomplished

experience in teaching and research. He published over 50 national and

international referred journals & 3 most selling books. He produce 60 M.Phil.

Research Scholars in the field of Commerce and 600 master degree students are

finished their research work & 8 Ph.D., Research Scholars doing their research under his

guideline.

Mr. R. Satheesh Kumar, Part Time Ph.D., Research Scholar in Government

Arts College, Salem, She has more than 10 years of experience teaching. She

published 5 national/international referred journals in the field of human

resource management, finance, and marketing. Currently she perusing her

Ph.D., (P.T.) under the Supervision Dr. P. Raja.

Documents

E – Learning: An Effectiveness Analysis