Special Edition · 2019-09-26 · Delhi Ms. Pratibha Gautam Assistant Professor, Computer and...

Volume X | Issue 3 | November 2018 Special Edition | ISSN No. 0975-1874

Special Edition

Chanderprabhu Jain College of Higher

Studies & School of Law

(Approved by Govt. of NCT of Delhi | Affiliated to GGSIP University | Recognised by Bar Council of India)

25 80

CPJ GLOBAL REVIEW

CPJ GLOBAL REVIEW is an Academic Journal that brings together all the academicians and

corporate to provide an insight of management thinking, empirical research studies and management

practices around the globe. This National Journal is devoted to disseminate findings from research work

and exploration of original ideas concerning Business, Management and Technology.

A National Journal of Rishi Aurobindo Educational Society Volume X | Issue 3 | November 2018 Special Edition | ISSN No. 0975-1874

Patrons

Chairman Sh. Subhash Chand Jain Gen. Secretary Dr. Abhishek Jain Director General Sh. Yugank Chaturvedi

Editorial Board

Editor in Chief Prof. J.P. Mohla

Editor Prof. (Dr.) D.C. Agrawal

Co-Editors Ms. Neha Mittal Bhaskar Ms. Ritu Malik Ms. Rekha Jain

Associate Editors Ms. Priyanka Gautam Ms. Anjali Sharma Ms. Vidhi Sood Mr. Arghya Paul

Editorial Advisory Board

Sh. Manoj Sethi, Chairman, CSI Delhi Chapter Prof. (Dr.) Sanjiv Mittal, Professor, USMS, GGSIP University Prof. (Dr.) Amit Prakash Singh, USICT, GGSIP University Prof. (Dr.) Namita Rajput, Principal, Sri Aurobindo College, DU Prof. (Dr.) N.K. Joshi, Dean, Rajasthan Technical University Dr. Suparn Sharma, Associate Professor, SMVD, University, Jammu Prof. (Dr.) Neena Sinha, Dean, USMS, GGSIP University Prof. (Dr.) Sudhir Kumar Jain, Professor, IIT Delhi Prof. (Dr.) Anuradha Jain, Dean, VIPS, GGSIP University Dr. Urvashi Sharma, Associate Professor, FMS, Delhi University Dr. Poonam Khurana, Associate Professor, VIPS, GGSIP University

CHANDERPRABHU JAIN COLLEGE OF HIGHER STUDIES & SCHOOL OF LAW Approved by Govt. of NCT of Delhi | Affiliated to GGSIP University | Recognized by Bar Council of

India Recognized Under Section 2(f) of UGC Act, 1956

EDITORIAL

It gives us immense pleasure to present Special Edition of CPJ Global Review, packed with interdisciplinary research papers shedding new light on contemporary challenges in the domain of Indian business, management & Information Technology. Through critically informed empirical and theoretical investigations, the authors have presented the latest thinking and innovative research on major Management & IT topics impacting profoundly the Management theory and IT practice. CPJ Global Review encourages academicians, researchers and practitioners to share their cross-cultural investigations addressing the challenges in the context of global issues concerning business, management and IT.

From over 35 papers received for presentation in the National Conference “Innovative Realms in Management & Technology" on 16th November, 2018, only twelve papers were selected to by the

subject experts for presentation in this National Conference. The papers presented by the research scholars and academicians have been selected for publication in this special issue, which include

diverse articles, focusing on a plethora of topics.

We are thankful to all members of Editorial Advisory Committee and our Editorial Board

Members, learned reviewers and outstanding contributors, for their continuous and incredible

support in bringing out this special Edition of CPJ Global Review.

Contributions from academicians, professionals and industry practitioners are welcome.

We hope that, this special Edition of CPJ Global Review with all its illuminating features will

serve the intended purpose and will be of immense use for researchers and our revered readers.

Opinion and suggestions from the readers of this special edition are highly solicited for the

continuous growth of the Journal.

Prof. J. P. Mohla Editor in Chief

EDITORIAL

Dear Academicians

CPJ Global Review has completed Nine years of its publication and is entering into the Tenth Year

with this issue. During these years, this Journal has strived hard to maintain high academic standard

and also strict periodicity of annual publication. It is dedicated to the sharing of thoughts and

dissemination of ideas and concepts of modern day with respect to Management, Commerce and

Information Technology and other discipline for stimulating academic eagerness of the researchers.

The Journal has created a landmark path in the academic field and open the door for the industry

Professionals across the country to contribute the articles for Publication after the 3rd National

Conference on "Innovative Reals in Management & Technology" on 16-November-2018.

We believe that learning is a never-ending process and one continues to discover oneself in this

journey. It requires an impetus and environment to thrive and flourish in. Keeping this aim in

mind, the journal seeks to facilitate this learning environment. It is a converted effort to give

academic researcher a platform to present their ideas for an erudite community.

All the papers open up new dimension of research in the identified area such as: NEED TO HOUR:

How would use of Analytics & Dashboards leads to smart Marketing, Scope of Retail Sector and the

Changing Dynamics, Understanding the Modern Kid’s Consumer Psychology, The Telecom Sector &

its Service Quality, Research paper on Big Data and its Security Challenges, Future of Cryptocurrency

Transactions, Factors Influencing Selection of BBA Institute in GGSIPU, Change of technologies

such as NLP Based Social Media Text Classification Using C4.5 Decision Tree Algorithm, Impact of

Data Mining on Big Data Analytics - A Strategic Approach of E-Banking Services and Customer

Satisfaction, Research Papers on A study of Consumer Behavior towards Patanjali Products. This

journal also covers the hot topics like Talent Intelligence, Measurement and Universal Employment

This Issue also talks about the Financial System of Indian Banks and Management of NPA of Banks

and Financial Institutions.

Finally, I extend my sincere thanks to all contributors/authors for sharing their valuable findings

and ideas with us. Further, we wish to encourage more contribution from academicians and

Industry Practitioners to ensure a continued success of the Journal. I welcome contributors that

can demonstrate their research.

Prof. (Dr) D.C. Agrawal Editor

CONTENTS

Title of the Articles Page No.

1. NEED OF HOUR : How would use of Analytics & Dashboards lead to

Smart Marketing

*Prof. (Dr.) D.C. Agrawal **Mr. Ankur Kukreti ***Mr. Gaurangi Mathur

****Mr. Pushkar Raj Kapoor

2. Future of Crypto currency Transactions:

Bitcoins *Dr. Santosh Singhal

3. Understanding the Modern Kid’s Consumer Psychology

*Dr. Zakia Tasmin Rahman **Ms. Rehena Jasmin Rahman

4. The Telecom Sector & Its Service Quality: A Theoretical Glimpse

*Mr. Aditya Singh **Prof (Dr.) Amit Bhardwaj

5. Talent Intelligence, Measurement and Universal Employment

*Mr. Gaurav Kumar Roy **Mr. Ramesh Chandra Panda

***Ms. Swati Bhatia ****Ms. Garima

6. E-Banking Services and Customer Satisfaction: An Empirical Study

*Mr. Raghav Jain

7. Big Data and its Security Challenges

*Mr.Vineet Kumar 8. Scope of Retail Sector and the Changing Dynamics

*Ms. Pooja Khatri

9. Impact of Data Mining on Big Data Analytics: Challenges and Opportunities

*Ms. Pratibha Gautam

10. NLP Based Social Media Text Classification Using C4.5 Decision Tree

Algorithm

*Ms. Priyanka Tomar

11. Factors Influencing Selection of BBA Institute in GGSIPU- Students Perspective

*Ms. Ritu Singh

12. A Study of Consumer Behavior towards Patanjali Products {With

special reference to Meerut (U.P)}

*Ms. Seema Chaudhary **Ms. Sinjia Gupta

1-8

9-14

15-22

23-32

33-36

37-41

42-45

46-53

54-62

63-69

70-77

78-80

v

OUR CONTRIBUTORS

Mr. Aditya Singh Research Scholar, Motherhood University, Roorkee, Uttarakhand.

Prof. (Dr.) D.C. Agrawal Director, Chanderprabhu Jain College of Higher Studies and School of Law

Mr. Gaurav Kumar Roy Assistant Professor Department of Computer Application, Lovely Professional University, Phagwara,

Punjab Ms. Pooja Khatri Assistant Professor, Swami Shraddhanand College, University of

Delhi Ms. Pratibha Gautam Assistant Professor, Computer and Information Science, Vision Institute of Engineering &

Technology, Delhi.

Ms. Priyanka Tomar Assistant Professor IT Department, Chandarprabhu Jain College of Higher Studies & School of Law

Mr. Raghav Jain Assistant Professor, Gitarattan International Business School, Rohini Delhi

Ms. Ritu Singh Assistant Professor, Gitarattan International Business School, Rohini, Delhi.

Dr. Santosh Singhal Associate Professor, Jaipuria School of Business GZB

Ms. Seema Chaudhary Research Scholar, Beacon Institute of Technology, Meerut., (U.P.)

Mr. Vineet Kumar MCA student, GNIM, Punjabi Bagh

Dr. Zakia Tasmin Rahman Assistant Professor, Amity School of Communication, Amity University Uttar Pradesh, Noida Campus

1

NEED OF HOUR:

How would use of Analytics & Dashboards lead to Smart Marketing

Prof. (Dr.) D.C. Agarwal* Ankur Kukreti**

Gaurangi Mathur*** Pushkar Raj Kapoor****

Abstract:

In this paper Author has tried to show that how the use of Analytics & Dashboards helps in elevating the standards of

marketing skills of an organization and would lead to customer retention. By the use of rigorous literature review,

author has identified certain factors to evaluate Analytics level of an organization and has calculated their effect on

smarter marketing. Moreover with the help of self-designed questionnaire author has tried to find out main

parameters for driving Smart marketing, combined impact of each parameter on profitability of the firm and to

measure the impact of Smart marketing on the Organization. Tools and Techniques used was SPSS 24 and Snow Ball Convenience Sampling was used to get the response from the

respondents. Keywords: Analytics & Dashboards, Marketing Skills, Organization, Smarter Marketing

Introduction

Analytics

Analytics is the disclosure, understanding, and

correspondence of important examples in information and

applying those examples towards viable basic leadership. At

the end of the day, analytics can be comprehended as the

connective tissue among information and viable basic

leadership, inside an association. Particularly important in

regions rich with recorded data, analytics depends on the

concurrent utilization of measurements, PC programming

and activities research to evaluate execution.

Data Dashboard

Data dashboard is a data administration apparatus that

outwardly tracks, examines and shows key execution

markers (KPI), measurements and key data focuses to screen

the wellbeing of a business, division or particular process.

They are adaptable to meet the particular needs of an office

and friends. In the background, a dashboard interfaces with

your documents, connections, benefits and API’s, yet at first

glance shows this data as tables, line * Director, CPJ College of Higher Studies & School of Law ** Assistant Professor, Quantum Global Campus, Quantum University, Roorkee, Uttaranchal *** Student B.Com-3rd year, Quantum Global Campus

**** Student B.Com-3rd year, Quantum Global Campus

outlines, bar diagrams and measures. A data dashboard is

the most productive approach to track various data

sources since it gives a focal area to organizations to

screen and dissect execution. Ongoing observing

decreases the long periods of dissecting and long queue

of correspondence that already tested organizations.

Marketing Marketing is the examination and administration of trade

relationships. Marketing is utilized to make, keep and

fulfill the client. With the client as the focal point of its

exercises, it tends to be presumed that marketing is one of

the chief segments of business administration - the other

being innovation.

Marketing Skills In the course of the most recent couple of years, we’ve

burned through a huge number of hours working with many

distinctive marketers. When you invest this much energy

with individuals in a specific job, one of the additionally

fascinating things you can do is endeavor to decide the skills

that make somebody effective in that job. This is especially

fascinating for the field of advertising

CPJ Global Review Vol. X | Issue 3 | November 2018 Special Edition | ISSN No. 0975-1874

2 NEED OF HOUR: How would use of Analytics & Dashboards lead to Smart Marketing

just in light of the fact that showcasing has changed such

a great amount in the most recent decade.

Generally, today’s highly successful marketers:

1. Are revenue-driven marketers

2. Know their customers and markets

3. Create remarkable customer experiences

4. Are great storytellers

5. Test everything and assume nothing

6. Never stop acquiring new marketing tricks

7. Use data to make decisions

8. Enjoy working with technology

9. Manage their work according to a schedule

10. Write very, very well

11. Deliver specific, identifiable results

Literature Review

Jarvinen (2012), contributes to the emerging B2B digital

marketing literature by providing a realistic overview of

the usage, measurement practices, and barriers

surrounding digital marketing in the era of social media.

the results indicate that the advances in digital

measurement tools remain largely unexploited, and the

firms lack the human resources and know-how to make

the most of opportunities provided by the developing

digital environment. The implications of the study

suggest that B2B companies should update their

capabilities with respect to digital marketing usage and

measurement in order to adapt current practices to fit the

characteristics of today’s digital media landscape.

Don O’Sullivan (2012) tells about the widely argued that

an inability to account for marketing’s contribution has

undermined its standing within the firm. They also

explore the effect of ability to measure marketing on

marketing’s stature within the firm, which is

operationalized as chief executive officer satisfaction

with marketing. The empirical results indicate that the

ability to measure marketing performance has a

significant impact on firm performance, profitability,

stock returns, and marketing’s stature within the firm.

Chaffey and Patron (2012), described techniques that can

be used to set up a digital marketing optimization

programme, including a review of how people, process,

measures and tools can be combined. They found that

People and Process are the main barriers in the

application of digital analytics. They concluded with the

statement that Companies should review their structure

and their investment in web analytics and digital

marketing optimization to make sure opportunities are

not falling through the cracks.

Marine and Roig (2015) aimed to highlight the usefulness

of big data analytics to support smart destinations by

studying the online image of Barcelona (a leading smart

city of the world). This paper also works in order to

develop and assess marketing strategies and to improve

branding and positioning policies among tourism and

marketing organizations. It reinforces the ability of cities

such as Barcelona to develop a smart city and destination

concept, as well as a strategy for themselves. Stacy Supak (2015) implements traditional desktop GIS as

well as a free, web-delivered decision-support tool for

tourism planning and marketing to assess ~7.5 million

overnight accommodation reservations made for federal

recreational facilities between 1999 and 2007. Market

profiling derived from local, regional and national customer

origin markets can help any tourism destination, including

national parks and their gateway communities, make smarter

management and marketing decisions.

Joel Jarvinen (2015) proposed that the benefits gained

from marketing performance measurement are

determined by how an organization exploits the metrics

system under specific circumstances. Given the

continuously growing importance of digital marketing in

the industrial sector, this study illustrates how industrial

companies characterized by complex selling processes

can harness Web analytics to demonstrate how digital

marketing activities benefit their businesses. Kari Tanskanen (2015) explores the drivers of buyer and

supplier attractiveness in strategic relationship through 43

interviews in six buyer–supplier dyads. We find

economic and behavior based attractiveness strongly

present in all dyads, while resource and bridging based

attractiveness are emphasized when the strategic intent

has more explorative elements and when the aim is to

leverage the dyadic relationship in developing businesses

outside the dyad. We synthesize our results to a model of

attractiveness in a strategic BSR, which bring forth the

considerations of buyer and supplier attraction.


NEED OF HOUR: How would use of Analytics & Dashboards lead to Smart Marketing 3

Sabrina V. Helm (2015) investigated how ingredient

service brands impact customer preferences on B2B

markets. We specifically assess how ingredients might

impact industrial buyers’ quality perceptions of the end

product. As a result, branded service ingredients offer

host service brands as well as ingredient service brands a

potentially powerful strategy for improving competitive

position in B2B markets. Ebru Genç (2015) examines the integration of

environmental specialists into new product development

teams that are composed of other functional specialists

including marketing, manufacturing, and R&D personnel,

and its impact on SNPD project performance across three

stages: concept development, product development, and

product commercialization. The result of this paper was

that, the integration of the environmental specialist was

more effective on SNPD project performance in the final

stage of the SNPD process when the product was being

launched; this effect is even greater for high-innovative

projects. Gary K. Hunter (2015) accessed two under-researched

issues warrant attention. First, although sales technology

represents a continuous source of change, little is known

about why salespeople commit to technology-induced

changes. Second, knowledge on whether sales force

intelligence norms play a role into translating use of sales

technology to performance gains is remarkably sparse. In

this paper, the result was that finally, sales technology

infusion influences both key outcomes and findings support

the importance of fostering sales force intelligence norms.

Implications of the study are discussed.

Objectives of Study

1. To deduce the main parameters for driving Smart

marketing.

2. To find the combined impact of each parameter

on profitability of the firm.

3. To measure the impact of Smart marketing on the

Organization.

H0= There is no impact of Smart Marketing on

the Organization.

Ha= There is a significant impact of Smart

Marketing on the Organization.

Research Methodology

Data: In this paper author has used primary and

secondary data. Secondary Data: By the use of extensive literature

survey following series of factors affecting Smart

marketing were found:

1. Budget

2. Channels of marketing

3. Technology

4. Ease of access

5. Social Media

6. Human Resource

7. Target Customers

8. Big Data

9. Any other factors

Primary Data: 50 Employees from Marketing and sales

have been selected which were having a sound

experience of purchase and Marketing by the use of

Snow ball convince sampling technique out of which 10

private companies from Roorkee. Based on the responses

of 50 respondents on a self-developed questionnaire

primary data was collected by the author. Out of these 50 Respondents, 27 are males and 23

females. Moreover, all respondents are between an age

group of 20 to 40 years. Reliability of the questionnaire can be proved by

Cronbach’s Alpha

Table 1

Reliability Statstics Cronbach’s

Cronbach’s Alpha Alpha Based on N of Items

Standardized Items

.737 .737 11



Normalization of data can be proved by:

Table 2

Tests of Normality

Kolmogorov-smirnova Shapiro-Wilk

Statistic df sig. Statistic df sig.

Effect_on_Economy .144 50 .011 .961 50 .09

a. Lilliefors Significance Correction

H0 (null hypothesis): Samples data are not significantly

different than a normal population.

Ha (alternative hypothesis): Samples data are significantly

different than a normal population.

Since Significant value of Shapiro-Wilk test>0.05 Null

hypothesis (H0) is rejected and we fail to reject the

Alternative hypothesis (Ha).

Figure 1

It shows that Automatic linear modeling was used in

order to find out the relationship between profitability of

the firm and the factors of Smart Marketing.

Field

(Channels_

transformed)

(Content_

transformed)

(Ease_of_

access_

transformed)

(HR_ transformed)

(Others_

transformed)

(Social_

Media_

transformed) (Target_

Customers_

transformed)

(Technology_

transformed)

Figure 2

Role Action Taken

Change measurement level from

Predictor ordinal to continuous Trim outliers Replace missing values











Replace missing values

Predictor Merge

maximize categories to

association with target





It shows that there are no outliers present in the

independent variables hence the data is perfectly

Normalized and there is no Multi-co-linearity present.

Figure 3 It shows that there is high impact of Ease of access, target

customers and other factors on profitability of firm.

Figure 4 It shows that there is perfect linear relationship between

Profitability of a firm and Smarter Marketing.

Figure 5

It shows that there exist no Skewness and Kurtosis in our

relationship between profitability of firm and Smart

Marketing hence results derived are authentic.

Model Building Summary

Target: Profitability Figure 6 It shows that there exist a linear relationship between

profitability of Firm and Factors supporting Smart

marketing in stepwise deduction. Hence Linear Regression has been applied:

Descriptive Statistics

Mean Std. Deviation N

Profitability 6.6924 .97787 50

Budget 6.7740 1.99180 50

Channels 7.8600 1.41753 50

Technology 6.9200 1.51980 50

Ease of access 6.9420 2.05079 50

Content 7.0560 1.96803 50

Social_Media 7.0040 1.75720 50

HR 7.7320 1.93933 50

Target_

8.4300 .93683 50 Customers

Big_data 2.3300 1.37992 50

Others 7.1500 1.90405 50 This shows that there are no missing variables.



Table 1 Model Summaryb

Model R R Square Adjusted R Std. Error of Change Statistics

Durbin R Square F Change df1 df2 Sig.F Change Square the Estimate Watson

Change

1 .643a .414 .263 .83929 .414 2.752 10 39 .011 2.169 a. Predictors: (Constant), Others, Budget, Big_data, Content, HR, Social_Media, Technology, Target_Customers, Channels,

Ease of access

b. Dependent Variable: Profitability

Table 2 Anovaa

Model Sum of Square df Mean Square F Sig.

1 Regression 19.383 10 1.938 2.752 .011b Residual 27.472 39

Total 46.855 49 .704

a. Dependent Variable: Profitability b. Predictors: (Constant), Others, Budget, Big_data, Content, HR, Social_Media, Technology, Target_Customers, Channels,

Ease of access The value of Durbin Watson test is between 2 and 3 hence results are highly reliable and there exist independence of

error term.

Table 3 Coefficientsa

Model

Unstandardized Coefficients Standardized t Sig.

Correlations Coefficients

B Std. Error Beta Zero-order Partial Part

1 (Constant) 3.225 1.404 2.296 .027

Budget -.094 .071 -.192 -1.332 .190 .100 -.209 -.163

Channels -.073 .111 -.105 -.655 .516 .234 -.104 -.080

Technology .076 .092 .118 .830 .411 .295 .132 .102

Ease_of_access .149 .101 .312 1.474 .149 .475 .230 .181

Content .032 .095 .064 .338 .738 .339 .054 .041

Social_Media .103 .082 .185 1.256 .217 .280 .197 .154

HR .032 .071 .064 .459 .649 .274 .073 .056

Target_Customers .062 .166 .060 .376 .709 .267 .060 .046

Big_data .045 .105 .064 .429 .670 -.091 .069 .053

Others .180 .070 .351 2.556 .015 .493 .379 .313 a. Dependent Variable: Profitability

This Table of coefficients shows the clear picture of Multiple Regression Analysis.



Result and Conclusion Y=a+b1X1+b2X2+b3X3 Finally, the final equation comes out to be-: Profitability of Firm = 3.225+0.141* (Ease of access) +

0.103*(Social Media) + 0.180 *(Other factors) Here rest

other components shows insignificant impact or no

impact on Profitability of the firm. Therefore, we can say that:

1. Smart Marketing has a positive impact on Profitability of the firm.

2. Dashboards and analytic elevates profitability.

3. Profitability of Firm depends upon Ease of access,

Social Media and Other factors

Limitation

1. Results of the study are dependent on the profile

and number of the respondents.

2. Results of the study are dependent on the place

and variables used to conduct the research. References

• https://www.klipfolio.com

• https://en.wikipedia.org

• http://www.exactdrive.com

• https://web.a.ebscohost.com

• https://www.sciencedirect.com

• https://link.springer.com

• http://journals.ama.org

• Clifton, B. (2008) ‘Web analytics: Web traffic data

sources and vendor comparison’. Omega Digital Whitepaper, available at http://www.advanced-

web-metrics.com/docs/web-data-sources.pdf,

accessed 1 February 2012.

• Econsultancy-RedEye2 Conversion Rate

Optimization Report. Published in October 2011.

Google Scholar

• Web Analytics Association. (2011) ‘Outlook

Survey Report’ Published in February. Google

Scholar • Hamel, S. (2009) ‘Online analytics maturity model

(OAMM) paper available at http://immeria.net/

oamm/paper.htm, accessed 1 February 2012. • E-consultancy-Red Eye. (2009) ‘Conversion

Report’. Published in October. Google Scholar • Kaushik, A. (2007) Web Analytics: An Hour a

Day, Wiley, Hoboken, N.J. Google Scholar • Truscott, W. (2003) Six Sigma: Continual

Improvement for Businesses, Butterworth Heinemann, Oxford, UK.Cross Ref Google Scholar

• Decker, S. (2006) ‘Marketing Bullseye 2: Think

Six Sigma Blog post’, 24 July, available at http://decker.typepad.com/welcome/2006/07/ marketing_bulls_1.html, accessed 1 February 2012.

• Gibbins, C., Lee, G. and Patron, M. (2012)

‘RedEye conversion rate optimization dashboard’,

RedEye Whitepaper, January.Google Scholar • Chaffey, D. (2001) ‘Optimising e-marketing

performance — A review of approaches and

tools’, in Proceedings of IBM Workshop on

Business Intelligence and E-marketing. Warwick,

6 December.Google Scholar • Smart Insights. (2010) ‘Introducing RACE=A

practical framework to improve your digital marketing’. Blog post by Dave Chaffey, 15 July, available at http://www.smartinsights.com/blog/ digital-marketing-strategy/race-a-practical-

framework-to-improve-your-digital-marketing/, accessed 1 February 2012.

• Jackson, S. (2009) Cult of Analytics, Butterworth-

Heinemann, Oxford.Google Scholar • Lee, G. (2010) ‘Death of “last click wins”: Media

attribution and the expanding use of media data’,

Journal of Direct, Data and Digital Marketing

Practice, Vol. 12, No. 1, pp. 16–

26.CrossRefGoogle Scholar • Kaplan, R.S. and Norton, D.P. (1993) ‘Putting the

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• Neely, A., Adams, C. and Kennerley, M. (2002) The Performance Prism: The Scorecard for Measuring

and Managing Business Success, Financial Times/ Prentice Hall, Harlow, UK.Google Scholar

Further Reading

• Gibbins, C. (2011) ‘Unlocking the true value of

CRO’, RedEye Whitepaper, March.Google Scholar

• Kaushik, A. (2006) ‘10/90 Rule’, available at http://www.kaushik.net/avinash/the-10-90-rule-for-magnificient-web-analytics-success/, accessed 1 February 2012.

• Nakatani, K. and Chuang, T. (2011) ‘A web

analytics tool selection method: An analytical

hierarchy process approach’, Internet Research,

Vol. 21, No. 2, pp. 171–186.CrossRefGoogle

Scholar

• Sharma, R.S. and Dijaw, V. (2011) ‘Realising the strategic impact of business intelligence tools’, Vine: The Journal of Information and Knowledge

Management Systems, Vol. 41, No. 2, pp. 113–131. CrossRefGoogle Scholar

• Patron, M. (2011) ‘A structured approach to

conversion rate optimization’. Whitepaper

published at Redeye.com, October 2011.Google

Scholar

• Smart Insights. (2011) ‘Your new, new media options’. Smart Insights blog post, by Dave Chaffey, 11 July.Google Scholar

• Web Analytics Association. (2011) ‘Outlook

Survey Report’, Published in February.Google

Scholar

• Web Analytics Association. (2011) ‘Definition of

web analytics’, available at http://www.

webanalyticsassociation.org/?page=aboutus,

accessed 1 February 2012.

• Wilson, R. (2010) ‘Using click stream data to enhance business-to-business website performance’, Journal of Business & Industrial

Marketing, Vol. 25, No. 3, pp. 177–187.

CrossRefGoogle Scholar


9

Future of Crypto currency Transactions: Bitcoins

Dr. Santosh Singhal*

Abstract:

Bitcoin is a disseminated currency which has opened an innovative segment in the era of electronic financial

transactions. Bitcoins has stopped the mandatory intervention of third party in the financial transactions which has

bothered many times due to fraud or in terms of annual/late charges. This article tries to explore different phases of

Bitcoin like how to acquire, sources, how to mine, in what ways Bitcoin can be used in financial transactions and also

discussed bitter and better side of Bitcoin. Keywords: Bitcoin, Distributed, Financial Transaction

Introduction

Today is the era of web. From the beginning of internet, it

has a very large electronic network. As the popularity of the

web is increasing day by day, menace of cybercrime also

goes on increasing. In last few years India has registered

107% of CAGR (Common Annual Growth Rate) in the

number of Cyber Crimes. Security in the financial

transactions is a major issue because daily financial

transactions take place in bulk. In today’s time, cyber

criminals are so smart and working in collaborative manner

which makes cybercrime as serious issue across the globe.

These types of people do several types of crime like online

gambling, financial crime, web jacking, cyber pornography,

cyber defamation, virus/warm, email spoofing, data diddling

etc. Digital transactions become important because people

don’t want to stand in queue of banks for withdrawal of

money; even people don’t want to carry cash with them

because of the fear of theft in their mind. Day by day people

prefer to use electronic cash. People like to buy everything

on the internet and try to pay money electronically. Again,

there is a fear of cyber financial crime. Bitcoins are the way

to provide facility to pay electronically. It is system of

electronic cash that allows payment from one party to

another without intervention of any third party or financial

institution.

Protocol that Support Electronic Money Electronic Money: It is a file that the people can use to

pay for things over the internet and can receive with the

assurance that it is real money. The seller must know that * Associate Professor, Jaipuria School of Business GZB

the file is not forged and it is not copied and sent to the

seller and the customer retains a copy of the file to spend

again. The file is not forged, it is confirmed by the bank by a

cryptography policy. Bank also keeps a database of all the

valid money that has been issued so that it can verify to one

that the file that it has received represents real money and

can be credited to one’s account. Main objective is that bank

ensures that any unauthorized person should not steal others

money or manufactures the money.

Protocol: There are three participants the customer, the

seller and the bank involved in online transfer of the

money. Let us assume that only one “money” file exists.

The customer may decide to transfer the money to the

seller which will redeem the file from the bank and ships

the goods to the customers. The customer may scratch the

transaction. i. e. the customer can ask the bank to place

money in the customer’s account hence bank should

make money no longer spendable.

Problem with E-money and Need of Bitcoin

When we make any electronic payment, there is the need of

trustworthy third party that may be bank or any financial

institution. While the system works perfectly for all the

situations but there are some weaknesses with the system.

For example, customer may try to copy the money file,


10 Future of Crypto currency Transactions: Bitcoins

and use it to pay several times. He can also pay and then

cancel the money and can take the goods for free. Financial

organizations cannot avoid intervening disputes. The cost of

mediation rises the cost of financial transactions. Moreover,

there is risk of fraud in this process. There is the need of a

payment system which has no mediator bank so that above

mentioned problems can be resolved.

There is a strong necessity of proper behavior of the

participants otherwise abnormal things may take place.

Similarly, the bank must also behave properly. It must

make sure that two sellers should not redeem same

money file and it should also take care that same money

should not be cancelled and redeemed. Bank should also

be careful for preventing double spending problem

(Spending same money more than once). Seller or Store

should also be careful.

It should not deliver the things until it has been given

valid money. To overcome the above said problems,

crypto currency is used.

Cryptocurrency: It is a peer to peer digital exchange

system in which cryptography is used to generate and

distribute currency units. In this process, distribution,

verification and transaction information maintenance is

done without central authority.

The first fully decentralize cryptocurrency is Bitcoin.

What is Bitcoin

Bitcoin is digital currency that was released as software

in 2009. Bitcoin is a type of currency which is

decentralized in nature. It does not depend on any bank or

financial institution however it uses cryptographic tools

for its creation and management. It is a kind of chain of

digital signature.

Each owner of the coin transfers it to next through digitally

signing the hash of the preceding transaction & public key

of the succeeding owner and adding these values to the

culmination of the coin. Bitcoins are controlled by no one. It

is a distributed virtual money and a peer to peer system in

which the coins are produced by miners. The main

components are the transactions and blocks.

Block is a data structure containing the transaction data.

Blocks are produced by Bitcoin sappers via solving

cryptographic puzzles of control hardness (proofs of work).

The proof of work consists of finding a cryptographic hash

value for a block of transactions which starts with a certain

number of leading zero bits (Initially when Bitcoin was

introduced it was 32 and currently it is 67 zero bits) [5]. Hash of the preceding block is involved into the new

block, which result in a chain of blocks or blockchain.

We can determine the integrity of a given data by comparing

the execution output of SHA-256 algorithm called hash with

an already identified and predicted hash value.

A Hash algorithm converts a large volume of data into a

fixed-length hash. Same data will always produce same

hash but any slight modification in data wills entirely

modify the hash. Bitcoin uses Elliptic Curve Digital Signature Algorithms

(ECDSA-Cryptographic Algorithm) to make sure only

rightful owners have the access to funds.

How we can get Bitcoins

• We have to install Bitcoin wallet to our computer

or mobile phone.

• As we install Bitcoin wallet, it generates first

Bitcoin address and we can generate more

whenever we need it.

• We can share our Bitcoin address within our

networks so that they can pay us or receive

payment from us.

• Bitcoin address should be used only once. [9]

What is Wallet and its Classification?

Wallets are used to store essential credentials used for the

Bitcoins. For the transaction of Bitcoin, public key

cryptography is used. Wallets are the application used to preserve digital

information like public/private key and so on. Broadly

wallets are classified into two categories: ·

• Software Wallets: They help in network connection,

spending Bitcoins, and to verify possession by the

stored credentials of the Bitcoin.


Future of Crypto currency Transactions: Bitcoins 11

They further classified into two categories:

(a) Full Clients: Verification on the native

replica of the block chain is used to be done

by Full Clients.

(b) Lightweight Clients: Instead of having a native

replica of the block chain, Lightweight Clients

refer to Full Clients for sending and receiving

transaction. This makes the lightweight Clients

faster and would be able to work in less power,

low bandwidth devices. One of the best

examples is smart phones.

• Online Wallets: These wallets are very much

similar to the software wallets but for storage of

the credentials. Instead of storing credentials in

the native system which is used for retrieving

funds, they are deposited in the online wallets [7]. There are an amount of exchanges and wallets which provide

Bitcoins. Some are developed exchanges for institutional traders,

while others are simpler wallet services with a more limited

buying and selling capabilities. Following is a list of exchanges

which provide Bitcoins [12]:

Exchange About Based

Coinbase operates one of the

Coinbase most popular wallets and is a

USA simple way to buy Bitcoin. $5

bonus on sign up.

LocalBitcoins. Local Bitcoins matches buyers

Finland and sellers online and in- com person, locally worldwide.

BitQuick Bit Quick claims to be one of

USA the fastest ways you can buy

Bitcoin.

BitBargain Bit bargain has a vast range of

UK different payment options for

UK buyers.

CoinCorner Coin Corner allow purchases Isle of with credit and debit cards for Man

verified users.

Bittylicious A peer-to-peer platform for

UK individuals to buy, sell or trade

Bitcoin and altcoins Bitfinex is a trading platform

Bitfinex for Bitcoin, Litecoin. It allows

USA margin trading and margin

funding.

Xapo Xapo is known for its ease of

USA use and Bitcoin cold-storage

vault.

Blockchain Blockchain is a dispersed ledger that holds the record of

all transactions that have occupied place. If no one keeps

record of the transactions held with Bitcoins, no-one

would be able to keep track of who has paid Bitcoins and

to whom. All the transactions made during a stipulated

time is known as block. Bitcoin’s minors approve these

transactions and write these transactions into the ledger

(Blockchain). Blockchain may be used to know about any transaction

taken place between any Bitcoin address at any pint on

the network. 8. Technology behind Bitcoin Transaction

Management and Working of Bitcoin: When a new block

of transactions is generated, it will add to the blockchain,

hence there exists a long chain of transactions that have

been ever taken place in Bitcoin network. An updated copy of the block chain is provided to

everyone who is the member of Bitcoin network. When a

block is created, Miners take the information about this

block, and compute a hash for this information. SHA-256

Algorithm is used to calculate hash of this information.

Hash is unique for the data. If a person tries to edit the information in any transaction,

its hash value will be different. Only information of block

is not used to compute hash, but the hash of last block

kept in the blockchain is also used to compute the hash.

Because hash value of a block is totally related with the

current transaction and the previous transaction, it

behaves as a security seal of the transaction. If somebody wants to temper with the data, everyone in

the network will be able to know about this. If someone

tries to make a fraud by changing a block that is already

stored in the blockchain, the block’s hash value will

change. If someone wants to check the block’s

authenticity, he may find the hash of the block which can

be different from the previously stored value. In this way

block can be spotted as fake.

Working of Bitcoin When a Bitcoin is sent, it generates a transaction message

and assigns new owner’s public ECDSA key.



Each Bitcoin is associated with public ECDSA key of its

current owner. A new transaction is broadcasted over

Bitcoin network to inform everyone that new owner of

these coins is the owner of the new key. Bitcoin kiosks

are the machines which are connected to the internet and

allow depositing cash in the exchange of Bitcoins given

as paper receipts or by moving money to a public key on

the blockchain. Whenever a Bitcoin is sent, it attaches the

new owner’s public key and signs it with the sender’s

private key.

The sender’s signature on the message authenticates that

the message is trustworthy and transaction history is kept

by everybody so it can be easily verified. It uses Public

Key Cryptography to encrypt and decrypt the data. If a

message is encrypted by public key (Pk), then the private

key (Sk) will be used to decrypt it.

Or if a message is encrypted using the private key (Sk),

then the public key (Pk) will be used to decrypt it. Public

Key can be shared with everyone but private key needs to

be kept secret.

Bitcoin Mining

Bitcoins exist because it gets mined into existence.

Bitcoin Mining helps to add transactions to the block and

to release new Bitcoin.

This process includes compiling current transactions into

the block and trying to solve a computationally difficult

puzzle. The first participant that solves the puzzle takes

chance to solve next puzzle and claim the reward. The

reward contains transaction fees and newly released

Bitcoin.

The verification of transactions which confirms transaction

amounts and whether the payer owns the currency they are

trying to spend while ensuring that the currency units are not

spent twice is called mining. In the Bitcoin mining process,

users create new Bitcoin currency and transaction is

broadcasted over the network. All the computers running the

software in the network compete to solve cryptographic

puzzles which contain data from several transactions.

The difficulty level of the puzzle is directly proportional

to the number of Bitcoin miners are present.

• Who are Bitcoin Miners: Anyone who has internet

connection and have appropriate hardware can

join into mining. Bitcoin mining is decentralize,

so, if there is a difference about whether a block

should be involved or not in block chain, the

conclusion is effectively made through simple

majority consensus, i.e. more than half of the

mining power agree.

• If a person or an organization has control of

greater than half of the Bitcoin network’s mining

power, then they have power to corrupt the

blockchain, this is called 51% attack.

• Why do people mine the Bitcoins: Every time a

person creates hash, He gets reward of 25 Bitcoins

in today’s scenario. That’s why the people are

interested to mine the Bitcoins. The blockchain is

updated and the transactions keep on working.

• Minors cannot interfere with the transaction data

in a block. But they must change the data that

they are using to create a different hash. This is

done by using a random piece of data which is

called ‘nonce’. If the hash computed is not in the

required format, the nonce is changed and the

whole thing is hashed again. It may take many

attempts to find a nonce that works fine. ·

• Block Reward: Block reward is the amount of new

Bitcoin released with each mined block. This reward

is halved every 2,10,000 blocks. The block reward

was 50 Bitcoin in 2009 and 25 Bitcoin in 2014.

• Transaction Fees: As block reward halve after a

certain period, they are approaching to zero, the

miners will not be interested to mine Bitcoin for

the block reward. So block rewards are replaced

by transaction fees.

What can we buy with Bitcoins?

We can buy anything with Bitcoins provided that the

supplier accepts Bitcoins as money and he has installed

Bitcoin wallets to his Computer/Handheld device. Examples of things that we can buy with Bitcoins starts

from Guns, Drugs, Services of Pub in Australia, Dinner in

Japan, by pointing your phone at a sign next to the cash


Future of Crypto currency Transactions: Bitcoins 13

register you can get a slice of pizza for 0.00339 Bitcoin

and anything which can be bought electronically. Many

renowned companies like Dell, Microsoft, WordPress,

Dominos, Overstock.com <http://overstock.com/>,

Subway, TigerDirect, Namecheap, CheapAir.com,

Wikipedia <https://www.wikipedia.org/>, The Internet

Archive and many more are there in the list [9]. Bitcoins

can be changed into real currency via a Bitcoin exchange

like MtGox.com.

Advantages of Bitcoins

These are not printed like any other currency. Hence

printing cost is saved. · World or global currency is one that

is accepted for all trade throughout the world. Some of the

world’s currencies -- the U.S. dollar <https://www.

thebalance.com/the-u-s-dollar-3305729>, the euro <https://

www.thebalance.com/what-is-the-euro-3305928> and the

yen <https://www.thebalance.com/yen-carry-trade-

explained-pros-cons-how-it-is-today-3305971> -- are

accepted for most international transactions. Of these, by far,

the U.S. dollar is the most widely used. That’s also not an

official global currency.

Bitcoins can be considered as international currency so

that there is no need of conversion of currency. · These

are directly managed by software programs. · Bitcoins

can solve problem of black money. · Bitcoins are used for

paying money electronically. · People can buy

Bitcoins. · People can purchase goods against Bitcoins.

Dark Side of Bitcoin

Ransom through Bitcoins: On Friday, 12th May 2017

WannaCry ransomware attack was started and affected more

than 230,000 computers in over 150 countries. The worst-hit

countries are reported to be Russia, Ukraine, India and

Taiwan, but parts of Britain’s National Health Service

(NHS), Spain’s Telefónica, FedEx, Deutsche Bahn, and

LATAM Airlines were hit along with many others countries & companies worldwide [15]. Basically, ransomware is a

category of cyber ware which is deliberately designed to

extract money from a victim. Intension of this cyber-attack

is to ransom money from the patsy (victim) to undo the

changes made by the Trojan virus in victim’s computer. These changes include:”

• To stop access of the victim to his/her data, data

will be encrypted by the Trojan virus

• Stopping normal access of the victim in his/her

computer.

Basic problem in Bitcoin system is that there is no track

record of the people who commits crimes which can be

perceived strongly by Wanna Cry ransomware attack

• Theft or Loss of Bitcoins: Bitcoin can be spent

only in the custody of associated private key.

Bitcoin will be considered as lost in the case of

stealing, damaging of private keys or signature

forgeries [13].

• Malware Attacks: Malware attack on the Bitcoin

is growing day by day through stealing private

keys. The online wallet service myBitcoin.com

recently lost $1.3 million worth of users’ coins

due to malware. However, several solutions like

threshold cryptography and super wallet are

introduced for the malware attack [13].

• Inadvertent (accidental) Loss: Bitcoins can be lost

because of the loss of wallet file due to system

failure or human error. For example, bitomat, the

third largest Bitcoin exchange, recently lost about

$200K worth of Bitcoins (at the exchange rate at the

time) due to the loss of its private wallet? le - the

cause was later identified to be human error, as the

developer hosted the wallet on non-persistent cloud

storage. Backups, Pseudo-random keys, Encryption,

Offline (single-)password-based encryption, online

(multi-)password-based encryption, and trusted

paths are certain mechanism which can be used in

the case of accidental loss [14].

• Scalability: Bitcoin suffers from several scalability

issues, among which we note the following:

Data Retention and Communication Failures, Linear Transaction History, Delayed Transaction

Confirmation and Dynamically Growing Private

Key Storage.

Conclusion

Now a day’s digital currency has been transforming into

Bitcoins till certain extent. Researchers have been worked

on how to make financial transactions more secure through

Bitcoin but still working of Bitcoins are beneath the



surface. It is well understood that working with Bitcoins

is meek (simple), flexible then too without intervention of

third party but not ready to subvert the digital money.

Although life will become simpler but needs to be more

cautious while using Bitcoins.

• https://www.kaspersky.com/resource-center/

threats/ransomware?ignoreredirects=true&gclid=

CJWEhYXn-NMCFVIfaAodhP8Apg. • https://en.wikipedia.org/wiki/WannaCry_

ransomware_attack.

References • https://www.theguardian.com/technology/2017/

• Satoshi Nakamato “Bitcoin: A Peer-to-Peer may/12/nhs-ransomware-cyber-attack-what-is-

wanacrypt0r-20. Electronic Cash System”,October, 2008

• Nikola Bozic+, *, Guy Pujolle*, Stefano Secci “A

Tutorial on Blockchain and Applications to

Secure Network Control-Planes”

• S. Singh, N. Singh “Blockchain: Future of Financial and Cyber Security”

• https://dazeinfo.com/2015/01/06/cyber-crimes-in-

india-growth-2011-2015-study/

• Cyber Crime costs projected to reach $2 trillion by

2019. http://www.forbes.com/

sites/stevemorgan/2016/01/17/cyber-crime-c o s t - p

r o j e c t e d - t o - r e a c h - 2 - t r i l l i o n - b y - 2019/#768e4f293bb0

• P.Sharma, D. Doshi,M.M.Prajapati “Cybercrime: Internal Security threat”

• S. Quamara,A.K.Singh,“Bitcoins and Secure

Financial Transaction Processing, Recent

Advances”

• F. Tshorsh, B. Scheuermann,”Bitcoin and Beyond: A Technical Survey on decentralized Digital

Currenies”

• https://Bitcoin.org/en/how-it-works

• http://www.coindesk.com/information/how- Bitcoin-mining-works/

• h t t p : / / w w w. i n v e s t o p e d i a . c o m / a r t i

c l e s / investing/043014/what-Bitcoin-mining.asp

• http://www.coindesk.com/information/how-can-i-

buy-Bitcoins/

• Simon Barber 1, Xavier Boyen 1, Elaine Shi 2?

and Ersin Uzun “Bitter to Better— How to Make

Bitcoin a Better Currency”.


15

Understanding the Modern Kid’s Consumer Psychology

Dr. Zakia Tasmin Rahman* Ms. Rehena Jasmin Rahman**

Abstract: Kids consumerism has changed the entire business scenario. The kids in the family are getting importance and parents

go by the preferences of the children. Nowadays children were considered to be good decision-makers in the purchase

of goods and services. For example buying decision for electronic gadgets, home appliances, planning of vacations,

etc. mostly the elders in the family go by the decision of the children. Children are becoming tech savvy and are highly exposed to all kind of media including the digital media. Information

technology has changed the child psychology. The new age media has helped in bringing a revolution in kid’s

consumerism. The increasing kid’s consumerism is the influence of social media and smarter gadgets which the

children from a very tender age understand the nuances of decision making in buying goods and services. In fact, the

kids are overpowering the parents and elders’ decision making in buying goods and services. Keywords: Kids, Consumerism, Smarter Gadgets, New Age Media, Decision Making & Goods & Services

Introduction Child is the father of man, has proved in the marketing

and business world too. Here are some points to prove the

statement. Pester Power: Today’s kids have more autonomy and

decision-making power within the family than in

previous generations, so it follows that kids are vocal

about what they want their parents to buy. “Pester power”

refers to children’s ability to nag their parents into

purchasing items they may not otherwise buy. Marketing

to children is all about creating pester power, because

advertisers know what a powerful force it can be. According to the marketing industry book Kidfluence,

pestering or nagging can be divided into two categories—

”persistence” and “importance.” Persistence nagging (a

plea, that is repeated over and over again) is not as

effective as the more sophisticated “importance nagging.”

This latter method appeals to parents’ desire to provide

the best for their children, and plays on any guilt they

may have about not having enough time for their kids.

The Marriage of Psychology and Marketing To effectively market to children, advertisers need to know

what makes kids tick. With the help of well-paid researchers

and psychologists, advertisers now have access to in-depth

knowledge about children’s developmental, emotional and

social needs at different ages. Using research that analyzes

children’s behavior, fantasy lives, artwork, even their

dreams, companies are able to craft sophisticated marketing

strategies to reach young people. For example, in the late

1990s the advertising firm Saatchi and Saatchi hired cultural

anthropologists to study children engaging with digital

technology at home in order to figure out how best to engage

them with brands and products.

The issue of using child psychologists to help marketers

target kids gained widespread public attention in 1999, when

a group of U.S. mental health professionals issued a public

letter to the American Psychological Association (APA)

urging them to declare the practice unethical. Although the

APA did not outright ban psychologists from engaging in

this practice, as a result, the recommendations of their final

report in 2004 included that the APA “undertake efforts to

help psychologists weigh the potential ethical challenges

involved in professional efforts to more effectively advertise

to children, particularly those children who are too young to

comprehend the persuasive intent of television

commercials.”

* Assistant Professor, Amity School of Communication, Amity University Uttar Pradesh, Noida Campus1 ** J.B. Law College, Gauhati University, Assam


16


Building Brand Name Loyalty

Canadian author Naomi Klein tracked the birth of

“brand” marketing in her 2000 book No Logo. According

to Klein, the mid-1980s saw the birth of a new kind of

corporation—Nike, Calvin Klein, Tommy Hilfiger, to

name a few—which changed their primary corporate

focus from producing products to creating an image for

their brand name. By moving their manufacturing

operations to countries with cheap labor, they freed up

money to create their powerful marketing messages. It

has been a tremendously profitable formula, and has led

to the creation of some of the wealthiest and powerful

multi-national corporations the world has seen.

Marketers plant the seeds of brand recognition in very

young children, in the hopes that the seeds will grow into

lifetime relationships. According to the Center for a New

American Dream, babies as young as six months of age

can form mental images of corporate logos and mascots.

Brand loyalties can be established as early as age two,

and by the time children head off to school most can

recognize hundreds of brand logos.

While fast food, toy and clothing companies have been

cultivating brand recognition in children for years, adult-

oriented businesses such as banks and automakers are

now getting in on the act.

Magazines such as Time, Sports Illustrated, Vogue and

People have all launched kid and teen editions—which

boast ads for adult related products such as minivans,

hotels and airlines.

ten viral marketing campaigns (as of 2008) relied heavily

on YouTube, Hotmail and Facebook to reach hundreds of

millions of viewers—and this was before Twitter became

a mainstay of social media. For example, when Burger

King re-launched its ‘Subservient Chicken’ TV

commercial online in 2004, it attracted 15 million hits

within the first five days and more than 450 million hits

over the next few years. [4]

Commercialization in Education School used to be a place where children were protected

from the advertising and consumer messages that

permeated their world—but not anymore. Budget

shortfalls are forcing school boards to allow corporations’

access to students in exchange for badly needed cash,

computers and educational materials. Corporations realize the power of the school environment

for promoting their name and products. A school setting

delivers a captive youth audience and implies the

endorsement of teachers and the educational system.

Marketers are eagerly exploiting this medium in a

number of ways, including:

• Sponsored educational materials: for example, a

Kraft “healthy eating” kit to teach about Canada’s

Food Guide (using Kraft products); or forestry

company Canfor’s primary lesson plans that make

its business focus seem like environmental

management rather than logging.

• Supplying schools with technology in exchange

for high company visibility.

Buzz or Street Marketing

The challenge for marketers is to cut through the intense

advertising clutter in young people’s lives. Many

companies are using “buzz marketing”—a new twist on

the tried-and-true “word of mouth” method. The idea is

to find the coolest kids in a community and have them

use or wear your product in order to create a buzz around

it. Buzz, or “street marketing,” as it’s also called, can

help a company to successfully connect with the savvy

and elusive teen market by using trendsetters to give their

products “cool” status.

Buzz marketing is particularly well-suited to the Internet,

where young people in particular use social networking

platforms to spread the word about music, clothes and

other products. It should come as no surprise that the top

• Exclusive deals with fast food or soft drink

companies to offer their products in a school or

district. • Advertising posted in classrooms, school buses,

on computers, etc. in exchange for funds. • Contests and incentive programs: for example, the

Pizza Hut reading incentives program Book It! in

which children receive certificates for free pizza if

they achieve a monthly reading goal; or

Campbell’s Labels for Education project, in

which Campbell provides educational resources

for schools in exchange for soup labels collected

by students. • Sponsoring school events: The Canadian company


Understanding the Modern Kid’s Consumer Psychology 17

Showbiz brings moveable video dance parties into

schools to showcase various sponsors’ products. In addition, companies are also recognizing the advantages

of developing positive brand associations through

facilitating school field trips. In the U.S., the highly

successful company Field Trip Factory delivers children to

companies for “real-world lessons on everything from

nutrition to health care.” For example, students may visit a

car dealership to learn about car safety. This is seen as a

win-win situation by many educators and retailers because it

lets children have hands-on experiences outside their

classrooms, while building positive associations between

companies, students and their parents and teachers.

Need of the Study

The study is needed for exploring the various brands

which are marketed by targeting the kids as consumers.

This will help the marketers in advertising and promoting

their brands through various media by understanding the

psychology of the kids. The findings of such study will

be helpful to business, planners of business, promotional

activities in-charges, advertisers, strategy makers, policy

makers, executives, marketers, students, academicians

and researchers who are directly or indirectly associated

with advertising and promotions of the various brands

where kids are associated.

Objectives of the Research are to

1. Study the advertisements and promotional

activities of brands which are focused on children

2. Find out the importance of children’s decision

making in buying goods and services

3. Know adaptability of developed technologies by

the children To find out preferences of the kids or the children amongst

the various available products under different brand names,

various surveys are taken into consideration and analyzed.

The surveys which are considered for the study are recent

with detailed information of the products under different

brands. Kind of media that are used for advertisements and

promotional activities are also taken into account.

Children are given preference by the elders in the family

to make the buying decisions of any product or services

which are in need. It was also observed that the kids of

the current generation are inquisitive towards the

technological developments. Their preferences are mostly

inclined towards those products which user-friendly,

time-saving and which has multiple features. These days’

kids are well exposed by media and are enlightened to

take right decision in buying goods and services.

Scope and Utility

Kids or children, parents and elders in the family are

taken into consideration. The study will help the advertisers and promoters of brands,

researchers to understand the buying habits of the people in

terms of various goods and services that are provided under

various brand names. It will help the multinationals, public

and private enterprises to explore and understand the market

from the children’s perspective. Marketers, advertisers,

stakeholders and business establishments will be benefitted.

The kids and elders in the family which comprises the

consumers will also get the benefit of having various brands

options of their choice which produces a particular product.

They will get access to a variety of products with different

names.

Literature Review

Large number of research studies has been conducted on

effect of advertisements and promotional activities both

in India and abroad. The studies of various researchers

have covered numerous kinds of goods and services. The

literature available on selected topic reveals research

studies on consumer behavior in general and kids and

elders in families in particular. An attempt is made to

review some selected works on kid’s consumerism. Ramrayka (2015) stated in her article that each year, the

world’s food and beverage companies spend billions on

marketing and advertising their products to children and

teenagers. The overwhelming majority of these products are

high in calories, added sugar, saturated fat and sodium – fast food, fizzy drinks, sweets and chocolate to name

just a few. Ask your child to recall a food advert and

chances are that it won’t be one for apples or broccoli.US

fast food restaurants alone spent $4.6bn on advertising to

children and teensin 2012. According to Fast Food Facts

2013, children under six saw almost three adverts for fast

foods every day, while 12-17-year-olds saw almost five

adverts a day. The article highlighted that, Dr. Emma Boyland, a

psychologist at the University of Liverpool who specializes


18


in the effects of food marketing on children’s diets, says

the next challenge is to tackle promotion to children via

advergaming and social media.

YTV Kids and Tweens Report (2008) stated that industry

spending on advertising to children has exploded over the

past two decades. In the United States alone, companies

spent over $17 billion doing this in 2009 – more than double

what was spent in 1992.Parents today are willing to buy

more for their kids because trends such as smaller family

size, dual incomes and postponing having children until later

in life mean that families have more disposable income. As

well, guilt can play a role in spending decisions as time-

stressed parents substitute material goods for time spent with

their kids. While stating the media usage habit of the

children the research study, Advertising to Children and

Teens, (2014) stated that the average American child age 8

or older spends more than seven hours a day with screen

media, watching TV, using the computer, playing video

games, and using hand-held devices (Rideout et al., 2010).

Even much younger children, age 2-8, spend nearly two

hours a day with screen media (Common Sense Media,

2013). And through virtually all these media, children are

exposed to advertising. Banner ads are still used to market to

children and teens. Indeed, a study by Yale University’s

Rudd Center for Food Policy and Obesity calculated that

more than three billion “display advertisements” for food

and beverages were viewed on children’s websites between

July 2009 and June 2010 (Ustjanauskas et al., 2013).


Sample Units

Kids or children, parents and elders in the families are

taken into consideration

Kids from 5 to 12 years

Sample size

100 Kids or children

100 Elders

Parents, grand-parents, uncles and aunts, family friends,

relatives, neighbors, seniors and elders

Sample area –Delhi/NCR

Noida sector- 41, 44, 125, 126

Delhi – Maharani Bagh, Ashram, Bhogal, Lajpat Nagar

Phase 1 and 2, South Extension

The questionnaire was given to the elders and interview

method was used for the kids based on the questionnaire.

85 elder respondents filled the questionnaire and 15

respondents amongst the elders are interviewed based on

the questionnaire. All the questions in the questionnaire were analyzed

using coding, tabulation and percentage method.

Findings and Analysis

The study is based on the brands which are related to the

kid’s preferences. There are several product and service

categories which kids give their preferences to the

parents and elders in the family. And consequently, kid’s

preferences become the ultimate decision of the family to

a product or a service. Table 1: Product Categories which Kids helps Parents

and Elders to Choose

Sr. no. Product Categories

1. FMCG Products

2. Home Appliances

3. Automobiles

4. Smarter Electrical Gadgets

5. Properties Table 1 shows the various product categories which kids

prefer their parents and elders to buy. On the other hand

the parents and elders in the family do not take the

purchasing decision independently but includes kids to

give their choices too. Table 2: Services Categories which Kids helps Parents

and Elders to Choose

Sr. no. Services

1. Aviation

2. Hospitality & Tourism

3. Holiday Destinations – Domestic &

International

4. Customer Care Services of Brands

5. Restaurants

6. Salons and Parlors

7. Amusements Parks Table 2 shows the various service categories which kids



prefer their parents and elders to buy. Also the parents

and elders in the family do not take the purchasing

decision independently but includes kids or children to

give their choices too.

Table 3: Responses of Parents when asked whether

they Prefer Children’s Decision in Buying Goods and

Services

No. of Respondents Responses in Percentage

Yes No

100 85 15 Table 3 shows the responses given by the parents and elders

in the family when asked about their kid’s contribution in

purchase of various goods and services decision making of

the family. It shows that 85% of the adults in the families

give preference to their kid’s decision making. There are

responses from 15% respondents which is negative. Table 4: Advertisements of Products and Responses of

the Respondents

Sr. no. Advertisements Responses of the Respondents in

Percent

1. Social media 85%

2. Electronic Ads

83% (Television/Radio)

3. Print Ads (Newspapers/

80% Magazines)

4. Retailer advertising 78%

5. Outdoor advertising or

75% out-of-home (OOH)

advertising

6. Consumer/Product

70% advertising

7. Display advertising 68%

8. Celebrity Endorsements 65%

9. Mobile billboards 65%

10. Comparative advertising 60%

11. Cell Phone & Mobile

56% Advertising

12. Posters and leaflets 56%

13. Trade advertising 55% Table No. 4 shows the various ways of advertisements done

by the marketers to attract the consumers. Social media

advertisements are highly preferred by the consumers.

Social media is preferred by 85% of the respondents. It is

followed by Electronic Ads (Television/Radio) which

was preferred by 83% of the respondents. Print

advertisements are preferred by 80% of the respondents.

Retailer advertisements are considered to be essential;

hence it is opted by 78% of the respondents. Outdoor

advertising is preferred by 75% of the respondents. It is

followed by consumer/product advertising which was

preferred by 70% of the respondents. Display advertising

was opted by 68% of the respondents. Celebrity

endorsement and mobile billboards were preferred by

65% of the respondents. Comparative advertisements

were preferred by 60% of the respondents. It is followed

by cell phone & mobile advertising and posters and

leaflets which was preferred by 56% of the respondents.

Trade advertising is preferred by 55% of the respondents.

Table 5: Promotional Activities of Products and

Responses of the Respondents

Sr. Responses

Promotional Activities of the

no. Respondents

in Percent

1. Premiums or Free gifts offer 95%

2. Discount/Price-off deals/

92% Rebates

3. Combo offers 90%

4. Coupons 87%

5. Reward Plan on purchases/ 87%

Sweepstakes offer

6. Fairs, trade shows and

86% exhibitions

7. Consumer contests with prizes 85%

8. Free Trials 83%

9. Point-of-Purchase (POP)

80% display and demonstrations

Table No. 5 shows the various ways of promotional

activities done by the marketers to attract the consumers. It is considered that promotional tools are more effective

for invoking consumer response. Trial packs, combo

offers and free gifts are comparatively much more

effective in generating more sales. Premiums or Free gifts offer was preferred by 95% of the

respondents. It is followed by Discount/Price-off deals/

Rebates which were preferred by 92% of the respondents.


20


Combo offers was opted by 90% of the respondents.

Coupons and Rewards plan on purchases/Sweepstakes offer

were preferred by 87% of the respondents. Next preferred

option is Fairs, trade shows and exhibitions which were

selected by 86% of the respondents. It is followed by

Consumer contests with prizes and Free trials which was

preferred by 85% and 83% of the respondents. Point-of-

Purchase (POP) display and demonstrations was the option

selected by 80% of the respondents.

Consumers showed a positive perception regarding sales

promotion. Therefore, it can be concluded that various

techniques of sales promotion can help a company to

increase sales and organization’s profitability, which cannot

be ignored. This way, achieving competitive advantages

over its competitors is possible only by offering right

promotional tools in context with product characteristics.

Table 6: Various Media for Advertisements and

Promotional Activities

Sr. no. Media for Advertisements & Promotional

Activities

1. Television

2. Newspapers/Magazines

3. Internet

4. Social Media

5. Radio

6. Any Other

Table No. 6 shows the various mass media that are used

by the marketers for advertisements and promotional

activities of the various brands of products. Television is

the most preferred medium for advertisements and

promotional activities for the consumer durables.

Television is followed by print media which attracts the

consumers. Internet and social media are also largely

preferred by the consumers for information. Fifth stands

radio for providing information to the consumers. Word

of mouth and peer preferences also increase the sales of

the products.

Both print and electronic media are attracting the

consumers of products and services.

The Internet

The Internet is an extremely desirable medium for

marketers wanting to target children:

• It’s part of youth culture. This generation of young

people is growing up with the Internet as a daily

and routine part of their lives.

• Parents generally do not understand the extent to

which kids are being marketed to online.

• Kids are often online alone, without parental

supervision.

• Unlike broadcasting media, which have codes

regarding advertising to kids, the Internet is

unregulated.

• Sophisticated technologies make it easy to collect

information from young people for marketing

research, and to target individual children with

personalized advertising.

• By creating engaging, interactive environments

based on products and brand names, companies

can build brand loyalties from an early age.

The main ways that companies market to young

people online include:

• Relationship building through ads that attempt to

connect with consumers by building personal

relationships between them and the brand.

• Viral ads that are designed to be passed along to

friends.

• Behavioral targeting, where ads are sent to

individuals based on personal information that has

been posted or collected.

• Endorsements by online “influencers” who are

paid to recommend a product in what looks like a

genuine way.

Table 7: Some Important Ways of Promotions and

Advertisements

Sr. no. Media for Advertisements & Promotional

Activities

1. Product placement and embedded ad¬vertising

2. Cross-promotions

3. Online advertising

4. Advergaming

5. Branded websites

6. Viral marketing

7. Online TV ads

8. Downloadable branded items

9. Premium offers to encourage product

purchases



10. Social-media marketing

11. Mobile advertising

12. Location-based mobile marketing

13. Mobile advergames

14. Integrated marketing campaigns Table 7 shows some important ways of advertisements

and promotions. These are better understood by the kids

and children as they have keen interest in the digital and

new age media.

Conclusion

It was found out from the study that due to increasing

reach and development of the various modes of mass

media like Cable television, Radio which includes

various FM Channels, Newspapers, magazines, various

internet sites, call center services and various

advertisements and promotional activities make the kids

aware and gives the right information about the various

available products under different brands in the market. Kids are becoming very tech savvy and show interests in

every new technology that comes in the market. They

follow with all the latest updates that come in every

media. The kids are more keen and alert about the

information that comes up than the adult consumers. The

kids give more time and effort to learn about the new

products, technologies that come up in the market. Their

inclination towards learning is more than the adults. The

kids or the children are becoming good and right decision

makers. Henceforth, the seniors most of the time depend

on the buying decision making of the kids than their own

decision making. Even the marketers of the products also target the kids to

convince the parents and elders in the family to buy a

product or service under a specific brand. This is the reason

in most of the advertisements and promotional activities that

we come across various media see kids or children speaking

about the products. The quality, features and attributes of the

products and services are flawlessly explained by the kids

with great confidence like that of an adult. In fact, the kids

give the information and educate the seniors about what to

purchase and what not to purchase.

Advertisements and promotional activities help in the

decision making of the kids. The kids can convince parents

and elders in the purchase of goods and services. Through

advertisements and promotional activities detailed

information of the available products and services are

given which is quite helpful in understanding the features

and attributes of the product. Recommendations

1. It is necessary to save the kids from the

consumerism trap.

2. Children should be taught by the parents and elders

to differentiate between necessity and luxury.

3. They should be taught to understand the value of

money and to spend hard earned money fruitfully.

4. They should understand the importance of saving.

5. Market is flooded with goods and services, but kids

should be taught to make right buying decision

which should not led them to bankruptcy. Limitations

1. An increased focus on materialism and the prizing

of possessions has produced narcissistic children

who grow up to be adults who never learn the

intrinsic rewards of social belonging and

interdependence.

2. Consumerism leads children to make undue

demands from parents and elders in the family.

3. Consumerism and too much focus of the

marketers on the kids make them extravagant.

4. The children are becoming spendthrift.

5. The children cannot differentiate between

necessity and luxury.

6. It leads to increase peer pressure on those kids

whose parents and elders cannot afford to buy

those things which are considered fashionable,

up-to-date and modern.

References

• Highbarger, J. (June 9, 2015). The Impact of

Consumerism on Children. Retrieved from: http://

rethinkingprosperity.org/1941/

• Tesseras, L. (1 May, 2013). Marketing to Kids.


22


Retrieved from:https://www.marketingweek. com/2013/05/01/marketing-to-kids/

• Ramrayka, L. (25 February, 2014).Brands continue

to target fast food marketing at kids. children’s

rights and businessGuardian sustainable business. Retrieved from:https://www.theguardian.com/

sustainable-business/brands-increase-fast-food-

marketing-kids

• YTV Kids and Tweens Report. (2008). How

Marketers Target Kids?, Marketing and Consumerism. Retrieved from: http://mediasmarts.

ca/digital-media-literacy/media-issues/marketing-

consumerism/how-marketers-target-kids

• (2014). Advertising to Children and Teens: Current Practices. A Common Sense Media Research Brief.

• Bushra, A., Irum, A. and Nahee, (2015). Impact of

Television Advertisement on Consumer Buying Behavior: The Moderating Role of Religiosity in the Context of Pakistan. International Interdisciplinary

Journal of Scholarly Research (IIJSR)

• Shallu and Sangeeta, (2013). Impact of Promotional Activities on Consumer Buying Behavior: A

Study of Cosmetic Industry. International Journal

of Commerce, Business and Management.

• Church, A. H., Nwankpa, N. N. and Vivian, A. O.,

(2015). Influence of Facebook Advertisement on

the Buying Behavior of Students of a Nigerian

University. International Journal of Research

Humanities and Social Sciences

• Ioanas, E. and Stoica, I., (2014). Social Media and

its Impact on Consumers Behavior. International

Journal of Economic Practices and Theories,

Special Issue on Marketing and Business

Development

• Chakrabortty, F. R, Hossian, M, Farhad, A. H and

Islam, J., (2013). Analyzing the Effects of

Sales Promotion and Advertising on Consumer’s

Purchase Behavior. International Journal of

Innovative Research & Development

• Sundarapandiyan, N., Duraiarasi, B., Babu, S.

and Prabakaran, K., (2015). A Research on the

Influence of Media Advertisements in the

Purchasing Decisions of Generation Y in Penang

Malaysia. International Journal of Sciences: Basic

and Applied Research • Syed, H.T, Bilal, H.H. and Lanja, A, (2014). A

Study on Customer Perception of Youth Towards

Branded Fashion Apparels in Jalandhar City.

ELK Asia Pacific Journal of Marketing and Retail

Management • Abdurrahman, I and Mehmet, Y. F, (2015). Effects

of Brand on Consumer Preferences: A Study on Turkmenistan. Eurasian Journal of Business and

Economics • O’Guine, T.C, Allen, C.T &Semenik, R. J, (2006).

Advertising and Integrated Brand Promotion.

Akash Press, India


23

The Telecom Sector & It’s Service Quality:

A Theoretical Glimpse

Aditya Singh* Prof. (Dr.) Amit Bhardwaj**

Abstract:

After two decades of formulation of the National Telecom Policy (NTP) in 1999 The Indian telecom industry is

passing through complex times. The mobile phone has transformed into a persuasive medium to deliver information

services spanning various usage areas such as governance, commerce, agriculture, education and health. Standing

tall by being the second-largest telecommunications market in the world next to china, the industry has now billion

plus subscribers. Comprising of major sectors like telephony, internet and broadcastingthe industry is contributing

significantly in country’s GDP and job growth. Lowest tariffs, Mobile portability and various services have resulted in

a dynamic and hyper competitive market. With a ferocious battle taking place between the established biggies and the

new entrants the companies that will survive the competition will be the ones being able to differentiate themselves on

the basis of service quality. In today’s competitive environment customer occupies the central place in business &

retaining a satisfied customer can pay more as compared to attracting new one. Considering India at the cusp of a

data revolution and telecom being the backbonethis study examines the impact of service quality and service value on

customer bonding. The study aims to provide a better understanding and analyse the gap in service quality of Telecom

sector in terms of customers’ expectations and perceptions using the SERVQUAL and other models. Keywords: Service Quality, Telecom, Telecommunication Industry, Customer Satisfaction, India, Perception

and Expectation, Cellular, Customer, Gap Analysis, SERVQUAL

Introduction

The Telecommunications Industry of India is one of the vast

and leading industries in the world connecting different parts

of the country through various modes like telephone, radio,

television, satellite and internet. The Telecom Regulatory

Authority of India [TRAI] governs this industry by

providing a regulatory framework and favourable

environment for its efficient operation. With a cut-throat

competition the services offered by this industry are easily

accessible at affordable prices to the customers of urban and

rural areas of India. The common man has benefited from

lower prices and access to communication services at the

same time has an access to wide variety of choices. The

importance of services has fuelled the entire ecosystem

resulting in an indispensable focus on service quality and

relationships among several dimensions.

Service quality today is indispensable for any business

enterprise. For a business to survive building a customer * Research Schollar, Motherhood University, Roorkee, Uttarakhand. ** Director, Beacon Institute of Technology, Meerut (U.P.)

satisfaction and brand loyalty is a must and no business

can flourish without meeting the needs of its customers.

Attracting a new customer is difficult and costly as

compared to retaining an existing one which is directly

proportional to Customer Satisfaction and to ensure a

high level of customer satisfaction, organisation must

first know customers “expectations and how can they

meet such expectations. That are what organisations do:

they serve people’s needs. Service quality cements a

healthy relationship between customers & organisation.

A two-way flow of value it implies that the customer

benefits and derives real value from the relationship

which translate into value for the organisation in the form

of increased profits and sustainability. The paper

examines theoretical perspectives on Services & Service

Quality in telecommunication industry specifically as a

means to achieve customer satisfaction.


The Telecom Sector & It’s Service Quality: A Theoretical Glimpse 24

A huge amount of research has been conducted on different

aspects of service quality over last few years, leading to a

sound conceptual base for both practitioners and researchers.

The study attempts to analyse the gap in service quality of

Telecom sector in terms of customers‟ expectations and

perceptions regarding mobile phone services. It is important

to explore and map the differences which indicate that there

is scope for betterment on behalf of the cell phone service

providers. In view of the inflexible competition in the global

business arena where businesses have to survive and grow

on the basis of volume instead of margin, service quality

will constitute an essential plank of service marketing. Gap

reduction in customer expectations will have to be the main

focus of Telecom companies if they are to compete in this

hyper competitive market. To this end, telecom companies

should continually evaluate and revaluate how customers

perceive their services and to implement appropriate

corrective action for retaining the existing customers and

getting new customers. This paper will provide a firm

understanding of Service Quality.

Literature Review

Services

Service is an activity to fulfil someone’s need in the

market. It can be experienced but cannot be touched or

seen. It is an action of doing something for someone or

something. They are experiences that are consumed at the

point of purchase, and cannot be owned. Services

perishes quickly and includes deeds, processes and

performances. On the other hand, we can say services can

compute as all economic activities whose output is not a

physical product or whose construction is generally

consumed at the time it is produced and which provides

added value that are essentially intangible.

Some basic definitions of service as defined by Management Gurus are:

“A service is any activity or benefit that one party can

offer to another which is essentially intangible and does

not result in the ownership of anything.” By Kotler,

Armstrong, Saunders and Wong2

“Services are economic activities that create value and

provide benefits for customers at specific times and

places as a result of bringing about a desired change in or

on behalf of the recipient of the service.” By Christopher

Lovelock2

“Services are the production of essentially intangible

benefits and experience, either alone or as part of a

tangible product through some form of exchange, with

the intention of satisfying the needs, wants and desires of

the consumers.” By C. Bhattacharjee

Wilson et al. (2008) defines services with following

distinctive characteristics:

• Intangibility: Services cannot be touched, seen

smelled or tasted

• Inseparability: Services are produced and

consumed simultaneously. At the point where

service is provided both provider and consumer

are present and cannot be separated.

• Heterogeneity: The quality of services varies as

they are produced by people at different time

interval. It’s difficult to reproduce them.

• Perishability: Services can’t be stored and they

can neither be returned or resold. Service Quality The term `Service Quality’ is an association of two

different words; `service’ and `quality’. Service means

“any activity or benefit that one party can offer to another

that is essentially intangible and does not result in the

ownership of anything.” Quality has come to be acknowledged as a strategic tool to

accomplish functional productivity and better presentation

of business. Service quality’ means the ability of a service

provider to satisfy customer in an efficient manner through

which he can better the performance of business.

Service quality is required for creating customer satisfaction

and is connected to customer perceptions and customer

expectations. Service quality is a result of customer

comparison between them expectations about the service

and their perceptions about the service company argues

Oliver (1997). This implies that if the perceptions exceed the

expectations the service will be considered excellent, if the

expectations equal the perceptions the service will be

considered good and if the expectations are not met the

service will be considered bad.

For service providers, the pursuit of service quality is

essential for competitiveness and gaining momentum.

According to business administration perspective, service

quality is an achievement in customer service.

Mathematically, it is a comparison of perceived



expectations (E) of a service with perceived performance

(P) thus, giving rise to the equationSQ=P-E.

In the service sector too „quality‟ is an important element

for the success of business. It is because of the realization

of its positive link with profits, increased market share,

customer satisfaction. Several earlier studies and authors

pointed out that quality concept in service is different

from the concept prevalent in the goods sector. The

reasons for such a treatment are inherent features of

services like intangibility, inseparability from the

provider, heterogeneous etc. Hence there is a distinct

frame work for quality explication and measurement.

Fig 1: Service Quality, Service Parameters & Customer Satisfaction (Source: A Study of Mobile Services from

Customers‟ Perspective)

Service Quality in the Sight of Various Authors

The term `Service quality’ is harder to define and judge.

Number of authors tried to define it and give definitions

in different point of views. According to Philip Kotler and Gary Armstrong “service

quality is the ability of a service firm to hang on to its

customer. That is, in their opinion customer retention is

the best measure of service quality”. The Service Quality as perceived by its customer has two

dimensions - technical or outcome dimension and the

function or process related dimension suggested

Christian Gronroos. Service quality is “the delivery of excellent or superior

service relative to customer expectation”, as defined by A.

Parasuraman, Valarie A. Zeitham1 and L. Berry. They

have conducted extensive research into service quality

and identified ten criteria in evaluating service quality.

According to them it is the overall judgement of

excellence of service or the difference between one’s

expectation and actual services performed. According to Ladhari, 2009 Service quality not just

provides a company a competitive edge but is also an

important factor to sustain growth. Wisniewski &

Wisniewski, 2005 suggested that consumer expect

qualitative services which creates pressure on businesses

to better understand and evaluate service quality. Service

Quality is important factor related to cost and customer

satisfaction (Howat et al. 2008, Chen 2008). Churchill and Suprenant (1982) consider that service

quality is involved in subjective cognition. In other words,

consumers decide it subconsciously instead of by an

objective judgment. The concept ‘service quality’ is not an independent term,

means, its formation depends upon several factors related

to service and service firms. These factors are grouped

into five broad dimensions including reliability,

responsiveness, assurance, empathy and tangibility. So, from the above discussions it is clear that the service

quality is a difficult concept to define in a single

definition. This idea seems to mention several different

areas, namely, quality of the output, quality ofthe process,

quality of the delivery system and quality as a universal

philosophy of the firm.

Measuring Service Quality

Various tools and techniques are used to measure service

quality and as a result scales like The Gap model,

SERVQUAL, SERVPERF, SERVCON have been widely

accepted and used out of the various service quality

models. Most studies focus on SERVQUAL however it

has been criticized on both methodological and

theoretical grounds. The GAP Model

A customer’s expectation of a particular service is

determined by factors like direction personal needs and past

experiences. The expected service and the perceived service

sometimes may not be equal, thus leaving a gap. The service

quality model or the „GAP model‟ developed



in 1985, highlights the main requirements for delivering

high service quality. It identifies five „gaps‟ that cause

unsuccessful delivery. Proposed by Parasuraman,

Zeithaml & Berry 1988.

Fig 2: The Gap Model

Gap1-4 are Provider’s Gap and Gap 5 is customer’s gap. The five Gaps can be defined as.

Gap 1: Customer Expectations – Management

Perceptions Gap

Gap 2: Management Perceptions - Service Quality

Specifications Gap

Gap 3: Service Quality Specifications - Service Delivery

Gap

Gap 4: Service Delivery - External Communications Gap

Gap 5: Expected Service - Perceived Service Gap (or the Service Performance Gap)

The SERVQUAL Model

Parasuraman et al. conducted exploratory investigation in

order to define service quality. They found irrespective of

the type of services consumer use similar criteria. 10

dimensions were identified and labelled as “Service Quality

Determinants”. This most widely used model originally

consisted of 97 items and ten dimensions which were later

reduced to 22 items measuring 5 dimensions.

Although SERVQUAL has been widely used most of the

SERVQUAL related problems centre on the use of

difference scores and the measurement of expectations.

SERVQUAL was built on the theory that Service quality is

best measured as the gap between customers expectation and

the performance they perceive. Major widespread concern of

the model is the 5-dimension configuration of the scale and

the appropriateness of operationalizing service quality as

expectation- performance gap.

Fig 3: The Extended SERVQUAL Model Van Dyke, Kappelman, and Prybutok (1997) challenge

theoretically, pointing out that there is no cognitive evidence

that respondents’ reason in this manner, and

methodologically, regarding the calculation and validation



of difference scores. Even SERVQUAL’s creators,

Parasuraman, Zeithaml, and Berry found performance

scores operationally superior to difference scores. The SERVPERF Model Since the gap theory of service quality proposed by

Parasurman et al. was supported by little empirical or

theoretical evidence, a “Performance based” service

quality measurement scale, SERVPERF was developed

by Cronin and Taylor 1992.

SERVPERF maintains only the perception of service quality.

While SERVQUAL doesn’t study the service quality,

SERVPERF provides analysis on the service quality.

Superiority of SERVPERF has been demonstrated in

numerous studies however wide acceptability and use of

SERVQUAL suggests consensus regarding its superiority

has not been yet reached. (Brady et al. 2002)

Fig 4: The SERVPER Model

The SERVCON Model The rise of customer demand in terms of convenience of

service exchange has lead researchers to construct a

Conceptual model. The model conceptualizes service

convenience as a second-order, five-dimensional

construct that reflects consumers‟ perceived time and

effort in purchasing or using a service. The SERVCON scale, is a comprehensive instrument for

measuring service convenience.

The five dimensions are independent within a

nomological network that illustrates distinct antecedent

and consequent effects, and the results reinforce the

multidimensional representation, offering insight into the

distinctive relationships between each service

convenience dimension. As an applicative tool

SERVCON scale should be implemented fully and

determine how individual dimensions‟ influence

customers‟ perceptions and behaviour.

While SERVQUAL identifies service quality by comparing the perception of services received with the expectations,



Dimensions of Service Quality

The above models and concepts reveal one fact that, the

concept `service quality’ is not an independent term,

means, its formation depends upon several factors related

to service and service firms.

Taking into consideration the various models of service

quality the Service Dimensions were initially classified

into ten categories i.e. The Long List. Comprising of

Reliability, Responsiveness, Competence, Access,

Courtesy, Communication, Credibility, Security,

Understanding/Knowing the customer, Tangibles.

These factors were then assembled into five broad

proportions that is reliability, responsiveness, assurance,

empathy and tangibility.

Reliability

Reliability is defined as the ability to perform the

promised service dependably and accurately. In broad

sense reliability means, service firms’ promises about

delivery, service provisions, problem resolutions and

pricing. Customers like to do business with those firms,

who keep their promises. So, it is a crucial element in the

service quality perception by the customer and his

faithfulness. Hence the service organizations need to be

aware of customer need of reliability. In the case of

telecom services, the reliability dimension includes -

regularity, attitude towards complaints, keep customers

informed, consistency, procedures and more.

Responsiveness

Responsiveness is the desire to help customers and to

provide service. This dimension focuses the customer

requests, questions, complaints and problems. It also

focuses on punctuality, presence, professional

commitment etc., of the employees or staff. It can be

calculated on the length of time customers wait for

assistance, answers to questions etc. The conditions of

responsiveness can be improved by continuously view

the process of service delivery and employee’s attitude

towards requests of customers.

Assurance

The third dimension of service quality is the Assurance

dimension. It can be defined as employee’s knowledge,

courtesy and the ability of the firm and its employees to

inspire trust and confidence in their customers.

Empathy Another dimension of service quality is the Empathy

dimension. It is defined as the caring, individualized attention provided to the customers by

their service firms. This dimension tries to convey the

meaning through personalized or individualized services

that customers are unique and special to the firm. The

focus of this dimension is on variety of services that

satisfies different needs of customers, individualized or

personalized services etc. In this case the service

providers need to know customer’s personal needs or

wants and preferences. Tangibility The fifth dimension of service quality is the Tangibility

which is defined as the appearance of physical facilities,

equipment’s, communication materials and technology.

All these provide enough hints to customers about the

quality of service of the firm. Also, this dimension

enhances the image of the firm. Hence tangibility

dimension is very main to company and they need to

invest heavily in arranging physical facilities.

• Providing services as promised.

• Dependability on handling

customers’ service problems.

Reliability • Performing services correctly

the first time.

• providing sevices at the

promised time.

• Maintaining error-free records.

• Keeping customers informed

about when services will be

performed.

Responsiveness • Prompt service to customers.

• Willingness to help customers.

• Readiness to respond to

customers’ requests.

• Employees who instill

confidence in customers.

• Making customers feel safe in

Assurance

their transactions.

• Employees who are consistently

courteous.

• Employees who have the

knowledge to answer customer

questions.



• Giving customers individual

attention.

• Employees who deel with

Empathy

customers in a caring fashion.

• Having the customer’s best

interest at heart.

• Employees who understand the

needs of their customers.

• Convenient business hours.

• Modern equipment.

• Visually appealing facilities.

Tangibles • Employees who have a neat,

professional appearance.

• Visually appealing materials

associated with the service. Fig: 5 SERVQUAL dimensions and 22 Factors for

assessing Service Quality

The Telecom Sector in India

Telecommunication has emerged as an economic miracle

transforming the lives of millions and shaping the socio-

economic development of Digital India. One of the prime

support services needed for rapid growth and modernisation

the major milestones can be marked by development of

wireless, mobile and broadband communication leading to

the Next Generation Networks and Satellites and the Tower

and resource sharing economies.

A 165-year-old sector the Tele-density of Indian telecom

industry (wireless plus wire line) has grown from a low

of3.60% in March 2001 to 84% in March 2016. Future

technologies like mobility, analytics, cloud, Internet of

Things (IoT) and Machine to Machine (M2M) will play a

key role. With the telecom industry undergoing a

transformational change in last decade the mobile operators

have successfully adopted innovative models to sustain

growth. According to an estimate the telecom industry

contributed to 6.5% of India’s GDP while providing direct

and indirect employment to four million people in 2015.

One of the fastest growing sectors with annual growth

rate (CAGR) of 7.3 percent in the last decade.

Apart from this classification of Telecom industry based

on ownership into public and private sector the Indian

telecom Market can be split into three segments:

1. Mobile (Wireless): Comprises establishments

operating and maintaining switching and

transmission facilities to provide direct

communications via airwaves

2. 2. Fixed-line (wireline): Consists of companies

that operate and maintain switching and

transmission facilities to provide direct

communications through landlines, microwave or

a combination of landlines and satellite link-ups.

3. 3. Internet services: Includes Internet Service

Providers (ISPs) that offer broadband internet

connections through consumer and corporate

channels A surge in the subscriber base has necessitated network

expansion covering a wider area, thereby creating a need

for significant investment in telecom infrastructure. To

curb costs and focus on core operations, telecom

companies have been segregating their tower assets into

separate companies. Inspired by the success seen by

Indian players in towers business, most of the operators

around the world are replicating the model.



With 70 per cent of the population staying in rural areas, the

rural market would be a key growth driver in the coming

years. The country has a strong telecommunication

infrastructure and in terms of telecommunication ratings,

India ranks ahead of its peers in the West and Asia.

Competition among telecom companies is extremely high

and the pace of transformation of the industry over the

course of last year has been astonishing, even by its own

standards. Jio’s entry in the market in September 2016 has

shifted the battle from voice to the data front. It has given

rise to a consolidation wave that has swept the industry due

to escalated levels of competition.

Therefore, telecom companies should continually assess

and reassess how customers perceive them services and

implement appropriate corrective action for retaining the

existing customers and getting new customers.

Telecom Services

Keeping in view the service quality models and service

expectations the service in the telecom industry can be

comprehensively classified as shown in the figure below

and are an important determinant in selecting the Service

provider.




Service quality can be measured by soft measures and

hard measures. Soft measures are those that cannot easily

be observed and must be collected by talking to

customers, employees or others. Under this method the

telecom can measure quality in the following ways.

• Customer Satisfaction Surveys: Under this method

individual customers or corporate customer may be

asked to rate their specific and overall impression of

service delivery. For this a questionnaire or

interview schedule can be used. Besides this, focus

group interviews and other market research

techniques can be utilized for this purpose.

• Internal Performance Analysis: In this method

employee surveys are conducted to determine

perception of the quality of service delivered to

customers on specific dimensions. Also, feedback

from quality circles, performance evaluation

reports, customer retention levels etc., provide

information to monitor quality of services. Hard measures of service quality This method includes

those characteristics and activities that can be counted,

timed or measured through audits. Various techniques

can be used to make changes such as: Quality Function Deployment (QFD). The relationship

between service quality and customer satisfaction has

received considerable attention in academic literature.

The results of most research studies have indicated that

the service quality and customer satisfaction are indeed

unconventional but are closely related that and a rise in

one is likely to result in an increase in another setup.

Conclusion

In the increasing competitive market, especially telecom

market, the focus on service quality is essential to service

firms for their survival and success. The management of

service quality helps the management to maintain

consistency in service delivery and to meet changing

customer expectations more efficiently and effectively.

Also, it provides some benefits to firms such as, service

differentiation from competitors, better image, higher

profitability, increased customer satisfaction, increase

customer retention and loyalty, staff morale, productivity

etc. Hence measurement of service quality is an inevitable

task to the service firms, especially telecom companies.

References

• Cottle, D, (1990), Client-centred service: How to

keep them coming back for more, New York:

Wiley

• Cronin J. Joseph Jr. & Steven Taylor (1994): SERVPERF vs. SERVQUAL, Reconciling

Performance based & Perceptions minus

Expectations, Measurement of Service Quality,

Journal of Marketing January, Pg. 126 – 127.

• Deloitte, Indian tower industry: The future is

data 2017 report

• Edvardsson, B., 1996 “Making Service Quality

Improvement Work”, Managing Service Quality,

Volume 6(1), P. 49-52

• Ibid., p. 118

• Kathleen Seiders & Glenn B. Voss & Andrea L. Godfrey & Dhruv Grewal SERVCON:

development and validation of a multidimensional

service convenience scale, Springer Publications

• Kotler, Philip and Armstrong, Gary (2006).

Principles of marketing, New Delhi: Prentice Hall Inc., p. 263

• Kurtz, David L. and Clow, Kanneth E. (2002).

Services marketing, Singapore: John Wiley & sons P. Ltd, p. 106

• Nitin Seth and S.G. Deshmukh, Service quality

models: a review, Emeralds Insight

• Oliver RL, 1993, “A conceptual model of service

quality and service satisfaction: Compatible goals,

different concepts, Advances in Service

Marketing management, Volume 2, P. 65-85

• Oliver, R.L, 1997, Satisfaction: A Behavioural

Perspective on the Consumer, Mc Graw Hill, New

York



• Olu Ojo The Relationship Between Service Services Marketing, Mumbai: Himalaya Publishing

Quality and Customer Satisfaction in the House., p. 259

Telecommunication Industry,BRAND. Broad

Research in Accounting, Negotiation, and • White, C., & Yu, Y. T, (2005), “Satisfaction

DistributionISSN 2067-8177, Volume 1, Issue 1, emotions and consumer behavioural intentions”,

2010 Journal of Services Marketing, Volume 19(6/7), P.

411-421

• Parasuraman A, Zeithmal V.A and Berry L.L, 1985, “A conceptual model of service quality and • Zeithaml V.A, Berry LL, Parasuraman A,

its implications of future research”, Journal of 1993, “The nature and determinants of customer

Marketing, Volume 4, P. 45-50 expectations of service”, Journal of the academy of

• Parasuraman A. & Valarie A. Zeithaml and

Marketing Service, Volume 21, P. 1-12

Leonard Berry 1985 – SERVQUAL – A Multiple • Zeithaml, V. A., & Bitner, M. J, (2003), Service

Item Scale for Customer Perceptions of Service marketing: Integrating customer focus across the

quality”, Journal of Retailing, Volume 64(1), P. firm (3rd ed.). Boston, MA: McGraw-Hill/Irwin

1240 p.117

• Parasuraman, A., Zeithml, V. A. and Berry, L. • Zeithaml, V. A., 2009, “Service Quality,

L., 1990, “Delivering Quality Service: Balancing Profitability, and the Economic Worth of

Customer Perception and Expectations”, The Free Customers: What We Know and What We Need to

Press, New York, P. 226 Learn”, Journal of Academy of Marketing Science,

• Rajkumar Paulrajan and Harish

Volume 28(1), P.67-85

Rajkumar1Service Quality and Customers • Zeithaml, V.A., Parasuraman, A., Berry, L.L.,

Preference of Cellular Mobile Service Providers 1990, Delivering Quality Service, The Free Press,

Journal of Technology Management & New York

Innovation2011, Volume 6, Issue 1 • Zeithmal V.A and Bitner M J, 2000, Services

• Siew-Phaik Loke1, Ayankunle Adegbite Taiwo2, Marketing: Integrating Customer Focus across the

Hanisah Mat Salim1, and Alan G. Downe2, firm, McGraw-Hill, New York

Service Quality and Customer Satisfaction in

a Telecommunication Service Provider, 2011 • Zeithmal V.A, Berry L.L, Parasuraman A, 1988,

International Conference on Financial Management “Communication and Control Processes in the

and Economics Singapore Delivery of Service Quality”, Journal of Marketing,

Volume 52, P. 35-48

• Taylor, S., Baker, T., 1994, “An assessment of

the relationship between service quality

andcustomer satisfaction in the formation of

consumers‟ purchase intentions”, Journal of

Retailing, Volume 70 (2), P.163–178

• Tornow, W.W, Wiley, J.W, 1991, “Service quality

and management practices: a look at employee

attitude, customer satisfaction, and bottom-line

consequence”, Human Resource Planning,

Volume 14 (2), P. 105–115

• Venugopal, Vasanti and V.N., Raghu (2001).


33

Talent Intelligence, Measurement and Universal Employment

Gaurav Kumar Roy* Ramesh Chandra Panda**

Ms. Swati Bhatia*** Ms. Garima****

Abstract:

With the pace of the modern era, it may be a cliché of modern business for a company or organization to narrate that

“people are our greatest asset”. But, this is one truism which business tycoons and leaders should take seriously. Up

to 70% of any company’s worth and significance lies on the skills and experience, delivered by their employees. It

tends to a myth, however, business executives and Human Resource (HR) departments have very little insight as to

how to cultivate these assets and skills for a better business and to gain fruitful outcome. My paper, here expresses

with full gesture the importance of talent and how talent-intelligence with universal employment and the right

measurement of talents in human and their respective idea can bring next generation job and hiring mechanism as

well as employment flavor to market trend. Keywords: Employment, Talent Measurement, Company, Skills, Assets, Talent, Intelligence, Employment

Introduction

The terminology is quiet exciting right! Much has been

said regarding fine ways of measuring talent and different

tests and tools to use, however there is very little written

and published which explores the way of making it real

and simple for talents to opt, seek and get opportunity to

showcase their talent in the real-world which will fall

impact on the bottom liners and hiring managers. In

today’s knowledge-based economy, organizations derive

up to 70 percent of their value from the skills, experience,

and performance of their employees.1 Making Data

Timely and Intuitive Yet most companies have little

visibility into how well this large talent asset is managed.

Without a unified approach to talent data, real workforce

intelligence is not readily available to provide key

insights into individual and business performance. --------

------------------------------------------------------------ Corresponding Author: Ramesh Chandra Panda, School of

Mechanical Engineering, Lingayas Vidyapeeth, Faridabad,

India. E-mail Id: [email protected] Orcid Id: https://orcid.org/0000-0002-0801-7961 How to cite this article: Panda RC. Talent Intelligence,

Measurement and Universal Employment.

Talent measurement using online portals and websites

can enhance better selection processes. As Charles

Darwin quoted, “It is not the strongest of the species that

survives, nor the most intelligent that survives. It is the

one that is most adaptable to change”; for which job

seekers accelerated time to full productivity making

employees smarter to work. and employees must go with the flow and trending

opportunities and make them like a shining nail in their

particular skill-set. This will result in improved job

performance, Talent Intelligence and these types of tools

shows leaders how to transform the value and impact of

talent measurement in their respective companies and

organizations. The main catch in to extract out the

expected talent from the crowd of both hard working and

smart working employees with efficiency understanding

which is done by human resource. But when an artificial

talent detection system (such as measuring project rating,

a live-projects or new idea-oriented projects, innovative

research methodology, customer satisfaction rating and

feedback, recommendations and endorsements on

employees by external agents) starts implementing within

different organizations and companies, it can bring a

revolutionary change for the organization. Competencies

are the essential ingredients of success at work, distilled

* Assistant Professor Department of Computer Application, Lovely Professional University, Phagwara, Punjab, India. ** Assistant Professor, Mechanical Department (Mechatronics), Lingayas Vidyapeeth, Faridabad, Haryana, India. *** Assistant Professor, SCM (Management), Lingayas Vidyapeeth, Faridabad, Haryana, India. **** Assistant Professor, Computer Science Department, Lingayas Vidyapeeth, Faridabad, Haryana, India.


34 Talent Intelligence, Measurement and Universal Employment

into their observable skills and behaviours. Competencies

might describe management skills that contribute to

better leadership, such as resourcefulness, courage, or

decision quality. They can also include key functional or

technical skills. [2]

How Stuffs Work Actually

As I started my own startup, and the innovation was all

about universal employment methodology as modules

incorporated within my startup-idea, I found talent

analytics a major component in extracting out the skilled

employees from within a pool. For all entrepreneurship

workshop or courses, there should have to be a Talent

Analytics section, based on which cofounders and other

human resource should analyze the critical skills and co-

related that with the passion of an employee/candidate.

There are things which come in bundle like: knowledge,

skills, abilities and other characteristics which is

abbreviated as KASO’s in one of the research papers .

The idea behind competencies to spot the talent gained

popularity in the year 1970s with the work of the noted

American psychologists David McClelland. According to

his study and say, he says that we have to think about how

competent employees or general job-seekers are in terms of

knowledge and skills, but also in terms of their broader

behavior, motives, targets and attitude to grab things.

The challenges that most company faces is the system of

opting talent through talent intelligence which is not just that

intelligent to precisely perform the task. Having good talent

intelligence and an accurate understanding of skills,

expertise and qualities of a pool of employees or human

power – is essential for the people decisions that every

business makes. If they want to avoid randomly hiring and

promoting people, in the long run - all companies need to

evaluate and gauge individual skills and talents.

So, all of the things come with proper talent measurement – as how organizations go about gathering and using

information about the talents of their people and cultivate

them to extract out refined skills and employees for their

organization. But as this critical task is often taken for

granted, not well understood or undertaken that limit its

value to firms.

Talent Measurement: is the use of a multi-disciplinary

methods, techniques and tools for gathering and using

information about individuals and their talents. Most of the

organization rely on the intuition of that organizations’

leaders and conducting simple on-trend interviews, while

other organizations conduct online tests. But that one test

may not completely showcase his/her talent but according to

company’s requirement and job profile, he might be very

good at that work/task. So there must be talent measuring

criteria or system universal for all organizations.

Recruiting new talented people by measuring the right

amount of skills and to keep your company competitive with

the fast evolving business can be a hard work, tedious task

too. Going through over a huge pile of spreadsheets,

database, searching job portals, emailing them; over more

than 3900 companies and choosing the right employee is

quiet a challenging task. These kinds of tasks can be made

smart using talent intelligence systems, which keys record of

each, measure and analyze your employees’ capability and

pick up the right human resource for your organization’s

work. Some newly evolved software that does these types of

work for you are Taleo.

Why Usual Talent Measurement Is Not Working

Old Fashioned Talent Measurement:

According to my research work, I’ve encountered with 5

notable points which every organization is looking at and

the issues they are facing in measuring the appropriate

talent lies in any one or more of these below mentioned

categories –

• It’s kind of hard to know that work

• The implementation portion gets overlooked

• Measuring talents is quiet complex from human

perspective

• Measuring the methodology not always meets the

business / organization’s need

• Business lacks expertise

What needs to be done

Now, lets’ walk with me and see what I see. Imagine a

world of yours where you can see or identify the fastest

growing skills within your reach, where the employees,

talents consciously know the fact that the demand is

accelerating at the fastest rate and act accordingly.


Talent Intelligence, Measurement and Universal Employment 35

That details of like-minded people of your company or

organizations or surroundings gets fed to a system, which

eventually calculates out for you or your organization the

potential skills’ gap within your company, and help

recruiting the right employee and development strategy

(for both freshers and experienced employees) & to close

those gaps. You can imagine a system or an intelligence automation

technique which can compare skills of your organization

to that of your rivals or competitors’ and to identify

potential competitive advantages and disadvantages.

Think about the system or technique using which you can

identify the right talent and skills you need, to take

advantages of future opportunities, key in over the entire

globe to search for the surplus of the talent you or your

organization are hunting for, and revive and refine your

global workforce strategy accordingly to take advantage

of these surplus. The intelligence which guides you based

on your hiring needs, you can start to pin point the talent

and skills from among the pool of employees available. Talent Intelligence or such kind of systems can also give

you or your organization the better understanding of how

those employees are interested in working for your

organizations; either through contract , or independent

work like freelance or full time job etc. The moment all

these criteria and modules are set to their right position,

you can easily measure the effectiveness of your

workforce and your strength of your projects, your

company is working for; efficiently measure the strategy

of workforce by valuating talent inflows and outflows

relative to your company and its associated competitions.

Universal Employment

According to me, what I see is, there are a lot of people

suffering from this problem of unemployment. Not because

they lack study, marks or skills, but because they did not

follow their passion as their profession, their focus as their

ultimate goal for bread and butter. During their student age,

they lack counseling of education to tell them what best suits

their focus, and what subject they should choose in order to

directly or indirectly reach their goal.

I find various students engaged in multiple activities such

as playing guitar, singing, beat-boxing, drummer, indoor

and outdoor sports, arts (both physical as well as digital

i.e. modeling) and out of these 2 or 3 they are

passionate to make guitar playing or making beat boxing

their profession, or art using colored-powder or 3D art by

drawing in white paper. But these talents did not find stage

or spot light to express them. And because of this reason,

their focus become a choice, a hobby or a secondary option

for life, and look, the primary focus becomes the

engineering, doctorate, general study, diploma in any subject

which their parents forces or what he thinks some-what okay.

This eventually reduces the man power and hence leads to

lowering the country’s talent.

As Confucius had a great quote which says, “Find

Occupation or job you like or you are passionate about, and

you’ll never work a day.” This is why, I’ve come up with a

startup idea of creating a platform where they can focus on

their passion equally with a degree or whatever study he or

she wants to do, and in parallel showcase their talent in that

website via video, audio recording, showcasing their arts

and skills, 360o paranoiac viewing or by writing as well.

There are various other ways of showcasing their talents as

well. But why in this platform?

This is because unlike LinkedIn and Naukri.com which

only focus professionals, my idea based platform will

focus on both professional as well as passion-oriented

man-power and skills who are roaming empty-handed

because they have skills but no platform to showcase.

Here, people from Bollywood, Hollywood, artists and

developers, professional managers and directors, actors’ & producers, investors from various organizations and

firms will join to recruit the best skill-sets and man-

power, which will benefit the students, employees as well

as the delegates and professionals to hire without a doubt. Moreover, there will be a freelancing option. Now let us

discuss how it will work. In this platform of talent

showcasing and getting employed, the main formula will be

– people with different skills can register for this platform to

create a account and connect with talents, those people will

vary from job seekers to fresher’s, CEOs and Managers, to

Actors and Directors, singer and guitarists to Youtubers and

Professors. So the next thing will be hiring, for a guitarist

who wants to make a career with his passion, can find a

senior guitarist or a band where he can join based on the

skills and genres or the seniors may even guide them to

follow the path to join them later. Similar is applicable for a

student with his/her professor via this platform. Now, it is

worth noting that even normal people who does nothing or is

a broker or a person who let


36 Talent Intelligence, Measurement and Universal Employment

suppose lead a big organization or team or business can

also join, and he may provide some work to someone,

who is a job seeker and registered in this platform for

work. As a freelancer, he will do his work and get paid &

ratings and this rating will eventually help that guy to get

a better job based on the previous comments, feedbacks

and ratings. At the same time, he can earn also which

eventually reduces the unemployment problem. Now a

guitarist is not getting such direct big exposure, but the

one such platform can provide such environment.

There are other things such as project showcasing, based on

which HRs and other senior professionals and managers can

hire a particular employee. Even if any customer gets

benefited from his project, he can share the feedback and

ratings there also, which will make his project searches

appear in the top list. All these will eventually reduces the

work of a person who is hiring any particular individual.

Here the hiring will not be totally based on degree name,

marks, CGPAs and institutes’ tag-name, here hiring of any

employee will be based totally on his working skills, his

non-corporate working, his work done on off-field or as

freelancer and how much passionate he is for his work. This

will reduce the pressure to monitor every employee of an

organization and headache of CEOs and managers as well.

I’m saying this because, that time the dedication of

employee will not be for getting experience or salary, but for

his work which he will do as passion, and hence the

outcome will be tremendously good.

TAM (Total Addressable Market): is also popularly

termed as total available market is a reference unit used

for referencing the revenue opportunity available for any

particular product or service. TAM also helps in

prioritizing the various business opportunities we can get

by serving the potential product opportunity. My above

mentioned business product model also has a huge crowd,

audience and the most important users – of different

stream, different field and having different taste. So

eventually the total Addressable Market is also huge

which can make business profit model a comfortable one.

Business Point of This Idea

So, the plan is to make a free version for those who want

moderate features of this online product such as connecting

and getting feeds and notifications and freelance work and

showcase his/her ideas and project (under copyright or

patent). The next is the professional edition, which will be

having some monthly cost in order to access more facilities

and features such as, machine learning capabilities to whom

you must connect, use of recommendation engine for better

job suggestion, counseling for students for opting better

courses, subjects and opportunities based on some

predetermined results and consequences, and frequent

opportunities (either freelancing or job offers) based on

geographical locations or choices. The third is the premium

edition, which is a lot better version with more additional

features and functionalities such as live chat-bot for any

query related to your work, job profile and job description,

and other minute details will be answered by the chat-bot

based on the past analysis of chats, blogs and forums

available on that topic and many other awesome features

which are not yet to reveal. This premium edition with cost

little more than the profession edition and this is one way of

earning. Other than that, companies that get tie up for

hackathons and other certification courses through our portal

will be given additional features to showcase their students’

talents and skills for better job opportunities, which will be

eventually calculated by the talent intelligence apps.

Conclusion and Future Work

As a conclusion I want to say that the future of employment

and hiring criteria will not only be a written or oral interview

or HR Round, rather there will be a profile check where

recruiters will look for number of views (popularity) and

rating of projects (as a freelancer or indie developer) and the

depth of project work with uniqueness and calculations of

how many achievements you get on any particular field.

References

• Using Talent Intelligence for Meaningful

Performance Management Oracle white paper

“Talent Intelligence: Key to U.S. Business Success,” July 2012

• Competencies: building blocks of job performance,

Page-4 Precision talent intelligence, ,Korn Ferri Institute,

• Korn Ferry Institute White-paper on Precision

Talennt Intelligence.

• Wikipedia for different terminologies

• Building the foundation of Modern Talent Management – by Professor Shlomo Ben-Hur


37

E-Banking Services and Customer Satisfaction: An Empirical Study

Raghav Jain*

Abstract:

Purpose: The purpose of the study is to identify the various factors which influence the account holders of different banks

towards usage of E-Banking facilities and to find out the impact of E-Banking facilities on customer satisfaction.

Design/Methodology: Data was collected by distributing questionnaires to various account holders residing in Delhi

NCR area. To study the impact of various factors of usage of E-Banking services which lead to customer satisfaction,

multiple regression was used. Analysis was done on the basis of 170 respondents.

Findings: The customer satisfaction is very complex behavior which is affected by various factors like time saving,

convenience, affordability/cost, reliability and safety etc. as depicted in the study. The output of multiple regression

reflected that all the factors are important for the satisfaction of the customers. Therefore the entire hypotheses were

accepted. Keywords : E-Banking, Customer Satisfaction, Global Access and Convenience

Introduction

Banking Industry in India

India’s banking sector is sufficiently capitalized and well

regulated by Reserve Bank of India. The economic

development of any country is totally dependent upon its

financial market and baking system. The Indian banking

system consists of 27 public Sector banks, 26 private

banks, 46 foreign banks, 56 regional and rural banks,

1574 urban cooperative banks and more than 90000 rural

cooperative banks Indian banking industry has recently

witnessed the roll of innovations in its banking system.

Online Banking is an innovation of new age banking

system. It is used to transfer funds, paying off bills,

checking balances, purchase of investments etc.

Meaning of E-Banking

E-Banking is also termed as online banking, internet

banking or virtual banking. It is an electronic payment

system that enables customers of any bank to use most of

the services offered by banks to its customer at their ease

(sitting at home). E-Banking services include checking of

account balances, transactions, fund transfers, investment

purchase and sales, credit facility, bill payments etc. by

sitting at home. It is contrast to branch banking. Online

banking was first introduced in early 1980s in New York,

US. Due to various positive and influencing factors it was

introduced in UK in 1983. In 1990s, various banks

(worldwide) saw the rising popularity of the internet

banking, slowly and steadily it marked presence in

various countries.

Literature Review

Singhal D. and Padhmanabhan (2008) identified 5

major features of internet banking which leads to

customer satisfaction. Study revealed that more than 50%

of the respondents agreed that internet banking is

convenient and flexible. Tiwari R. et.al (2007) scrutinizes the strategic relevance

of mobile financial services (MFS) to the competitive

position of the firm concerned. Authors represented five

propositions about the role of innovative business

* Assistant Professor, Gitarattan International Business School, Rohini Delhi


38 E-Banking Services and Customer Satisfaction: An Empirical Study

solutions in the banking sector and recommended that a

large scale empirical study to test these propositions be

conducted in future.

Liao et al. (1999) studied the relationship between

innovation attributes and online banking adoption

Daniel (1999) examined online like culture of innovation,

market share, organization restrictions and customer

acceptance from the perspective of internet managers in

companies.

Mole (1998) examined behavioral issues pertaining to

online banking such as satisfaction, word of mouth;

repurchase intentions, price sensitivity, propensity to

complain and switching barriers.

Lee M.C. (2009) studied and summarized the various

benefits of online banking facility to form a positive

factor named perceived benefit. In addition, extracting

from perceived risk theory, five specific risk facets –

performance, financial, social, security/privacy and time

risk – are synthesized with perceived benefit as well as

integrated with the technology acceptance model (TAM)

and theory of planned behavior (TPB) model to propose a

theoretical model to explain customers’ intention to use

online banking.

Problem Statement

The advancements in technology have brought the mobile

and internet banking services to the fore. The banking

sector is laying greater emphasis on providing improved

services to their clients and also upgrading their

technology infrastructure, in order to enhance the

customer’s overall experience as well as give banks a

competitive edge. The purpose of the study is to identify

and measure the impact of various dimensions of e-

banking on overall customer satisfaction.

Objectives of the Study

• To study the dimensions of satisfaction of bank

account holders with respect of E-Banking

services.

• To study the impact of Online Banking services

on customer satisfaction.

Hypothesis of the Study

Hypothesis framed for the purpose of the study were as

follows:

Hypothesis 1: There is a significant relationship between

Security and Customer satisfaction Hypothesis 2: There is a significant relationship between

Reliability and Customer Satisfaction Hypothesis 3: There as a significant relationship between

Cost/affordability and Customer satisfaction Hypothesis 4: There is a significant relationship between

Time Saving factor and Customer Satisfaction Hypothesis 5: There is a significant relationship between

Global access and convenience and Customer Satisfaction Research Design

The nature of the study is exploratory. An organized and

systematic study was conducted to arrive at the desired

objectives. Respondents of the study were the account

holders of both public sector and private sector banks.

Only those respondents were considered for the study

whose age was more than 18 years. Regression model

was used as a statistical technique. This study is

important to identify the dimension of satisfaction factor

of account holders who are having their bank accounts.

Data Collection and Sampling

The secondary data was used through a structured

questionnaire. Data was collected from account holders

of different banks in Delhi NCR region. The total of 250

questionnaire were distributed where 170 were used for

the study and rest were not taken due incompleteness of

answers given by respondents. The questionnaire used for the study was self constructed

and was divided into two sections. First section of the

questionnaire was framed to get the demographic details

of the respondents like Name, Age, Gender, Nationality

and Income. Second section reflected the items of impact

of E-Banking which leads to customer satisfaction in

Delhi NCR area. For this 5-point Likert scale was used.


E-Banking Services and Customer Satisfaction: An Empirical Study 39

Convenience sampling technique was used to collect the

data from various respondents. The respondents for the

purpose of this research were only those having bank

account and age of more than 18 years.

Statistical tool used

IBM SPSS 21 (Statistical Package for the Social Sciences)

was used for data analysis. Regression analysis was used to

study the impact of service quality on tourist satisfaction.

For reliability the Cronbach’s Alpha was calculated and

KMO and Bartlett’s Test were used to check the adequacy.

Data Analysis and

Interpretation Reliability Test Cronbach’s Alpha test was applied to check the reliability

of the questionnaire. The value of Cronbach’s Alpha is

found .855 in Security part, 0.350 in Reliability part,

0.436 in Cost/Affordability part, 0.841 in Time

factor/Saving part,0.853 in Global Access and

Convenience part, and 0.833 in customer Satisfaction of

the questionnaire, which is well above than 0.6, which

consider the instrument to reliable for the study (except

Cost & Reliability factor). The overall questionnaire

showed the .831 as reliability. Cronbach’s Alpha

coefficient shows high reliability and consistency among

statements used and for the whole questionnaire. Table: 1 Reliability Statistics

Reliability Statistics Cronbach’s

Cronbach’s A l p h a

No of Based on Alpha Standardized Items

Items

Security .855 .856 7

Reliability .350 .314 5

Cost/ .436 .453

6

Affordability

Time Factor .841 .844

7

(Saving)

Global .853 .853

10 Access and

Convenience Customer

.833 .833

1 Satisfaction

Full

.831 .835

36 Questionnaire

Mean of Independent Variables:

The mean value for each of the independent variable

contributing to customer satisfaction is shown in table 2. The highest mean score of 4.5 is explained by Global

Access and Convenience reflecting as the most important

factor for customer satisfaction while using e-banking

services by different account holders of various banks (public and private both)

Table2: Mean Scores

Dimensions Mean Rank

Security 4.1 3

Reliability 3.2 5

Cost/Affordability 3.7 4

Time Factor (Saving) 4.6 1

Global Access and Convenience 4.5 2

Customer Satisfaction 4.29

Regression Analysis

The results of correlation analysis showed that there is a

positive relationship between all the variables and have

the significant impact of p<0.05. Thus it was essential to

study the regression to study the impact independent

variables on dependent variable. Regression analysis was

conducted to test the hypothesis. Table 3 shows in detail

the summary of regression analysis: Table3: Regression Table, Dependent Variable: Customer Satisfaction

Independent Dependent Variable Tourist Hypothesis

Variable Satisfaction Accepted

R

R F- Sig

square value

Security 0.725 .525 70.67 0.00 Accepted

Reliability 0.34 0.115 17 0.00 Accepted

Cost/

0.504 0.254 19.278 0.00 Accepted Affordability

Time Factor 0.69 0.47 53.672 0.00 Accepted

(Saving)

Global 0.838 0.702 74.79 0.00 Accepted Access &

Convenience


40 E-Banking Services and Customer Satisfaction: An Empirical Study

Hypothesis 1 Findings and Conclusion

The table reflects that Global Access and Convenience has

very strong positive relationship with tourist satisfaction.

The value of R square is explained by 70.2 % variance in

customer satisfaction is resulted by Convenience.

Significance value is 0.00 which is less than the p (0.05) and

the value reflects that the hypothesis is accepted.

Hypothesis 2

The data above shows that Time Saving factor is the

moderator predictor of customer satisfaction while using

e-banking facility. The regression results reflected

moderate positive relationship with customer satisfaction.

The value of R square is explained by 47% variance in

customer satisfaction which reflects strong positive

predictor of customer satisfaction. Significance value is

0.00 which is less than the p (0.05) value reflects that the

hypothesis is accepted.

Hypothesis 3

The table above reflects that Security is the significant

predictor of customer satisfaction. The value of R square

is explained by 52.5 % variance in customer satisfaction

which shows strong significant predictor of customer

satisfaction. Significance value is 0.00 which is less than

the p (0.05) value reflects that the hypothesis is accepted.

Hypothesis 4

The table above reflects that Cost/Affordability is the low

predictor of customer satisfaction. The value of R square

is explained by 25.40 % variance in customer satisfaction

which shows low predictor of customer satisfaction.

Significance value is 0.00 which is less than the p (0.05)

value reflects that the hypothesis is accepted.

Hypothesis 5

The data above shows that Reliability is the least predictor

of customer satisfaction. The regression results reflected has

positive low relationship with customer satisfaction. The

value of R square is explained by 11.5 % variance in tourist

satisfaction which shows least predictor of customer

satisfaction. Significance value is 0.00 which is less than the

p (0.05) value reflects that the hypothesis is accepted.

From the above study I found that the dimensions of E-

Banking have varying impact on the overall customer

satisfaction of account holders of various banks. The

highest contribution in the overall customer satisfaction

was Global Access & convenience and Security.

Limitations

There are certain limitations in this study. Due to the

various limitations of time, resources and bandwidth to

conduct the research, convenience sampling was used.

Future studies may employee simple probability

sampling approach to generalize the results. Further

studies can be replicated with the large sample size.

References

• https://www.ibef.org/industry/banking-india.aspx

• h t t p s : / / e n . w i k i p e d i a . o rg / w i k i / O

n l i n e _ banking#Features

• Ahasanul Haque et al (2009). Issues of E-Banking

Transaction: An Empirical Investigation on

Malaysian

• Customers Perception. Journal of applied Sciences.

(Retrived from www.ebsco.com on 20 March2009)

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Internet Banking, Survey (Retrieved from http://

www.itwire.com/content/view/4570/53 on 20

March 2009)

• Mishra A. K. (NK) (2009). Internet Banking in

India-Part I. Retrieved from http://www.

banknetindia.com/banking/ibkg.htm on 18 March

2009

• Srivastva Saurabh (2009). Internet Banking - A Global Way to Bank, Retrieved from http://www.

indianmba.com/Faculty_Column/FC908/fc908.

html on 18 March 2009

• Singhal D. (2008). A study on customer perception

towards internet Banking: Identifying major

contributing factors, The Journal of Nepalese

Business Studies, Vol5,1, pageno. 101-111.


E-Banking Services and Customer Satisfaction: An Empirical Study

• Tiwari, R., S. Buse, and C. Herstatt (2007):

“Mobile Services in Banking Sector: The Role of

Innovative Business Solutions in Generating Competitive Advantage”, in: Proceedings of the International Research Conference on Quality,

Innovation and Knowledge Management, New

Delhi, pp. 886-894.

• Mukharjee A. and Nath P. (2003): A model of

trust in online relationship banking, International

journal of Bank Marketing, 21/1, Pg 5-15

41


42

Big Data and its Security Challenges

Vineet Kumar*

Abstract:

“Big Data” provides futuristic techniques and mechanisms to store, distribute, capture, manage and examine petabyte or

larger-sized datasets with high-velocity and different shapes. Big data can be structured, unstructured or semi-structured,

resulting in inability of ordinary data management methods. Data is produced from various different sources and can arrive

in the system at various rates. In order to action these large amounts of data in a reasonable and efficient way, parallelism

is used. Big Data is a data whose scale, diversity, and complexity require new architecture, techniques, algorithms, and

analytics to manage it and extract value and hidden knowledge from it. Hadoop is the core platform for structuring Big Data,

and solves the problem of making it useful for analytics purposes.

Keywords: Bigdata, Hadoop Framework, HDFS, Map Reduce, Hadoop Component

Introduction

1. Big Data: immensely large datasets that are tough to deal with using Relational Databases Storage/Cost,

Search/Performance, Analytics and Visualization.

There’s no particular method defined to determine

whether the particular size of data comes under the

category of big data or no and also data continues to

change over time, most analysts and practitioners

currently refer to data sets from 30-50 terabytes (10 12 or

1000 gigabytes per terabyte) to multiple petabytes (1015

or 1000 terabytes per petabyte) as big data.

streams into your enterprise in order to maximize

its value. 4. Veracity of Data: Veracity means accuracy of data.

Data is uncertain due to the inconsistency and in

completeness. Veracity means anxiety or accuracy

of data. Data is uncertain due to the inconsistency

and in completeness. Veracity means anxiety or

accuracy of data. Data is uncertain due to the

inconsistency and in completeness.

2. 4 V’s of Big Data 2. Challenges with Big Data Security

1. Volume of Data: The amount of data is known as

volume. Volume of data stored in enterprise

repositories have grown from megabytes and

gigabytes to petabytes 40 Zetta bytes of data will

be created by 2020 which is 300 times from2005.

2. Variety of Data: Different types of data and

sources of data. Data variety exploded from

structured and legacy data stored in enterprise

repositories to unstructured, semi structured,

audio, video, XML etc.

3. Velocity of Data: Velocity refers to the speed of

data processing. For time-sensitive processes such

as catching fraud, big data must be used as it

* MCA Student, GNIM, Punjabi Bagh

1. Heterogeneity and Incompleteness: When

humans consume information, a great deal of

heterogeneity is comfortably tolerated. In fact, the

richness of natural language can provide valuable

depth.

However, machine analysis algorithms expect

homogeneous data, and cannot understand nuance.

In consequence, data must be carefully structured

as a first step in (or prior to) data analysis.

Computer systems work most efficiently if they

can store multiple items that are all identical in

size and structure. 2. Privacy: Privacy of data is one bigger problem


Big Data and its Security Challenges 43

with big data. In some countries, there are tough

laws about the data privacy, for example in USA

there are tough law for health records, but for

others it is less forceful. For example in social

media we cannot get the private posts of users for

sentiment analysis.

3. Scale: As the name says Big Data is having huge

size of data sets. Managing with large data sets is a

big problem from decades. In previous years, this

problem was solved by the processors getting faster

but now data quantity is becoming large and

processors are static. World is moving towards the

Cloud technology, due to this shift data is generated

in a very high rate. This high rate of increasing data

is becoming a challenging problem to the data

analysts. Hard disks are used to store the Data. They are slower I/O performance.

4. Human Collaborations: In spite of the

tremendous advances made in computational

analysis, there remain many patterns that humans

can easily detect but computer algorithms have a

hard time finding. Ideally, analytics for Big Data

will not be all computational rather it will be

designed explicitly to have a human in the loop. The new sub-field of visual analytics is

attempting to do this, at least with respect to the

modelling and analysis phase in the pipeline.

3. Hadoop Architecture

projects which belong to the category of infrastructure for

distributed computing. Hadoop mainly consists of:

1. File System (The Hadoop File System)

2. Programming Paradigm (Map Reduce)

Hadoop was developed by Google’s MapReduce that is a

software framework where an application break down

into various parts. The Current Apache Hadoop

ecosystem consists of the Hadoop Kernel, MapReduce,

HDFS and numbers of various components like Apache

Hive, Base and Zookeeper. HDFS and MapReduce. 3. (i) Hadoop Distributed File System

Hadoop develop with a distributed File System called HDFS,

HDFS stands for Hadoop Distributed File System. The

Hadoop Distributed File System is a versatile, clustered way

to handling files in a big data environment. HDFS is not the

final terminal for files. It is a kind of data service that offers

a different set of capabilities required when data volumes

and velocity are high. Because the data is written once and

then read many times. HDFS is a good choice for supporting

big data analysis.

HDFS follow the Master Slave and Architecture.

(a) Name Node

Hadoop is an open source project hosted by Apache

Software Foundation. It consists of many small sub

It is centrally placed node, which contains

information about Hadoop file system. The main

task of name node is that it records all the metadata


44 Big Data and its Security Challenges

& attributes and specific locations of files & data

blocks in the data nodes. Name node acts as the

master node as it stores all the information about

the system and provides information which is

newly added, modified and removed from data

nodes.

(b) Data Node

It works as slave node. Hadoop environment may

contain more than one data nodes based on

capacity and performance. A data node performs

two main tasks storing a block in HDFS and acts

as the platform for running jobs.

3. ( ii) Map Reduce Framework

MapReduce is defined as a programming model for

processing and generating large sets of data. There are

two phases in MapReduce, the “Map” phase and the

“Reduce” phase. The system splits the input data into

multiple chunks, each of which is assigned a map task

that can process the data in parallel. Each map task reads

the input as a set of (key, value) pairs and produces a

transformed set of (key, value) pairs as the output. The

framework shuffles and sorts outputs of the map tasks,

sending the intermediate (key, value) pairs to reduce task,

which groups them into final results.

Map- the function takes key/value pairs as input and

generates an intermediate set of key/value pairs

Reduce- the function which merges all the intermediate

values associated with the same intermediate key

4. Security Issues and Challenges: Challenges

Related to Characteristics of Big Data

• Data Volume: storage is the very first issue that

comes in as we thing about the volume. As data

volume increases so the amount of space required

to store data efficiently also increases. Not only that the huge volumes of data needs to

be retrieved at a fast speed to extract results from

them. Networking, bandwidth, cost of storing like

in-house versus cloud storing are other areas to be

looked after [1].

With the increase in volume of data the value of data

records tends to decrease in proportion to age,

type, richness and quality [2]. The advent of

social networking sites have led to production of

data of the order of terabytes every day. Such

volumes of data are difficult to be handled using

existing traditional databases [2]. • Data Velocity: Computer systems are creating

more and more data, both operational and

analytical at increasing speeds and the number of

consumers of that data are growing. People want

all of the data and they want it as soon as possible

leading to what is trending as high-velocity data.

High velocity data can mean millions of rows of

data per second. Traditional database systems are

not capable enough of performing analytics on

such volumes of data and that is constantly in

motion. Data generated by both devices and

actions of human beings like log files, website

clickstream data like in E-commerce; twitter feeds

can’t be collected because the state of the art

technology can’t handle that data [2].

• Data Variety: Big data comes in many a form

like messages, updates and images in social media

sites, GPS signals from sensors and cell phones

and a whole lot more.

Many of these sources of big data are virtually new

or rather as old as the networking sites themselves,

like the information from social networks, Facebook, launched in 2004 and Twitter in 2006. Smart phones and other mobiles devices can be

bracketed in the same category. As these devices

are ubiquitous the traditional databases that store

most corporate information until recently are

found to be ill suited to these data. Much of these

data are unstructured and unwieldy and noisy

which requires rigorous technique for decision

making based on the data. Better algorithms to

analyze them are an issue too [5]. • Data Value: Data are stored by different

organizations to gain insights from them and use

them for analytics for business intelligence. This

storing produces a gap between the business

leaders and the IT professionals. The business

leaders are concerned with adding value to their

business and obtaining profits from it. More the

data more are the insights. This however doesn’t


Big Data and its Security Challenges

go well with the IT professionals as they have to deal with the technicalities related to storing and processing the huge amounts of data

Conclusion

I have entered an era of Big Data. The paper describes the

concept of Big Data along with 3 Vs, Volume, Velocity and

variety of Big Data and also, I’ll discuss some of the

security issues that comes in when we work on big data. I

discuss some basic concept of Architecture of big data

Hadoop along with security problems. Hadoop which is an

open source software used for processing of Big Data.

Reference

• Review Paper on Big Data and Hadoop

Harshawardhan S. Bhosale1, Prof. Davendra P.

Gadekar2

• International Journal of Computer Engineering &

Technology (IJCET) Ms. Gurpreet Kaur, Ms.

Manpreet Kaur

• h t t p : / / w w w. i a e m e . c o m / M a s t e r A d

m i n / u p l o a d f o l d e r / I J C E T _ 0 7 _ 0 4

_ 0 0 2 - 2 / IJCET_07_04_002-2.pdf

• Goes, Paulo B. (2014). "Design science research in

top information systems journals". MIS Quarterly: Management Information Systems.

• Marr, Bernard (6 March 2014). "Big Data: The 5 Vs Everyone Must Know".

• boyd, dana; Crawford, Kate (21 September 2011).

"Six Provocations for Big Data". Social Science Research Network: A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society. doi:10.2139/ssrn.1926431.

• "Data, data everywhere". The Economist. 25

February 2010. Retrieved 9 December 2012.

45


46

Scope of Retail Sector and the Changing Dynamics

Pooja Khatri*

Abstract:

This is an exploratory paper which attempts to focus on the current scenario and future potential of the retail industry

in India. It aims to throw light on the changing trends of the retail sector. Growth in per capita GDP, a boom in

consumption expenditure, FDI reforms, GST implementation, government’s numerous initiatives, a rise in middle-

income groups etc. indicate a possible boom in the retail industry of India.

Online retail which was growing at a rate of 130% in 2014 had a growth rate of just 26% in 2017. Multiple reasons

like increased smartphone penetration, increased internet penetration, large young population, government’s

initiatives for promoting electronic payments, FDI inflows in electronic ventures etc. make us rightly believe in studies

which say that Indian e-commerce market will be second largest e-commerce market by 2034.

But that does not mean that physical retail stores are out of business or that the importance of physical stores is

declining. A number of factors have been deliberated upon because of which retailers and customers still prefer

physical stores. Some new concepts like omnichannel retail, fluid shopping, and pop-up stores have been discussed

which are the attempts of online retailers to supplement the online-only business model and gain more visibility

among potential customers of their brands.

Keywords: Retail, Online Retail, E-Commerce, Physical Stores and Consumers

Introduction

According to AT Kearney Report 2017, India has taken over

China to be ranked at the top on the Global Retail

Development Index. This comes with expectations of strong

economic growth. Asian Development Bank expects India to

grow at 7.3% in 2018 and 7.65% in 2019. India also scores

high on market attractiveness. There are a lot of reforms

introduced in the retail sector along with rising incomes,

changing lifestyles and increased digital connectivity. The

retail sector is broadly divided into online retail and the

traditional brick and mortar retail. Both of them see huge

potential in the Indian economy. According to the India

Brand Equity Foundation, Indian retail market is expected to

grow at 12% per annum. This paper highlights certain

reasons which have made the Indian retail sector very

attractive for investments by both domestic and international

investors. After focusing on factors contributing to positive

sentiments of both overall retail and online retail in India;

this paper also attempts to study that physical stores are not

yet out of business.

* Assistant Professor, Swami Shraddhanand College, University of Delhi

There are various challenges of e-commerce faced by

online retailers like high customer acquisition costs,

lower visibility among prospective customers, last-mile

logistics, etc. because of which many big online retailers

are eyeing brick and mortar. Consumers also have a

preference for physical stores because they provide

instant delivery, ease of payment, touch and feel

experience, easy grievance redressal mechanism. Online retail growth was just 26% in 2017 as compared

to 39% in 2016. Many e-tailers like Myntra, Nykaa,

Google are already opting for physical presence in the

market. We attempt to study the reasons why physical

stores are still dominant in the retail sector and still a

preference for both retailers and consumers.


• To study the potential of the retail sector in India.


Scope of Retail Sector and the Changing Dynamics 47

• To understand the scope of e-commerce in India.

• To understand why retailers prefer physical stores

for their brands.

• To understand why consumers still have a

preference for physical retail stores.

• To understand the new emerging concepts in the

retail sector that attempts to integrate both online

platform and physical stores.

Organization of Paper The paper is structured in the following sections: section

1 introduces to the scope of the retail sector in India;

section 2 provides a review of the literature on different

aspects of e-commerce, and physical retail; section 3

presents the analytical description of our subject matter.

Finally, it has been concluded by summarizing the study.

Review of Literature

• David M. Szymanski, et al (2000) explores that

e-satisfaction for consumers depend on product

offerings, product information, site design, and

financial security. Out of these, site design and

financial security are the most dominant factors.

• Charles Steinfield, et al (2002) conduct case

studies of ten B2B and B2C US companies to

conclude that a ‘click and mortar’ concept is

emerging where each channel has spillover effects

for another channel. It lists the sources of synergy,

management strategies to achieve synergies and

various synergy benefits.

• Pui-Mun Lee (2002) through a primary research

over 524 respondents conclude that problematic

issues of online shopping can be resolved by same

online retailers via opening up of offline stores.

• Joel E. Collier (2006) using the Likert scale and

structural equation modeling explores that

customers perception of quality and satisfaction

with online purchases depend upon product

delivery, interaction with the website and ability

to address problems. Out of all these product

delivery being the dominant factor.

• Neil F. Doherty, et al (2010) provides an extensive

literature review about internet retailing published

over the last twenty years and establishes that virtual

merchants are not yet dominating the market. It

also brings about that electronic intermediaries

are becoming important but physical stores are

not yet out of business. • Erik Brynjolfsson, et al (2013) conducts an

exploratory as well as empirical research to

determine how competition varies across products

for traditional stores as well as internet retail. It

highlights successful strategies for omnichannel

retailing. It also brings out that internet retailers

face lesser competition from brick and mortar

while selling niche products and high competition

while selling mainstream products. • Kacen, et al (2013) conduct a web-based as well as

a paper-based survey of 22 shoppers to bring out

many disadvantages of online buying like shipping

charges, lack of social experience, exchange refund

policy, lack of salespeople, post-purchase service

and uncertainty about getting the right item. • Kumar Vinay (2013) studies different aspects of

perceived risk of online consumer behavior. The

study establishes that there are six different types

of perceived risks in internet shopping which are

functional risk, physical risk, financial risk, social

risk, psychological risk, and time risk. All of these

can vary across people, products, and situations. It

also puts forward that highest perceived risk

occurs in case of apparel due to the difficulty to

feel and see the texture of the item online. • Xua Liu, et al (2013) does an exploratory study

to find reasons behind the purchase behavior of

luxury goods. It establishes that lack of trust is the

main reason why luxury shoppers avoid shopping

online. Retailers should make efforts to build the

trust factor stronger. Also, retailers should adopt a

hybrid strategy because of the need of shoppers to

touch and feel the product. • Jayakrishnan S Nair (2015) brings about the high

scope of e-commerce activities in India. He also says

that along with many opportunities; there are a

number of challenges like logistics, poor internet

speed, customer loyalty, a mental barrier of touch

and feel, tax regime etc. in the field of online retail

in India. To truly capitalize on the opportunity of e-

commerce, India needs to improve its physical


48 Scope of Retail Sector and the Changing Dynamics

infrastructure, operational environment, and

policy framework.

• AlaguBalaraman (2016) believes that a hybrid

strategy which blurs the line between physical

stores and e-commerce is in trend now. It can lead

to a delightful buying experience where one form

of the retail act as a gateway to another.

Scope and Methodology

The study looks into various reasons because of which

physical retail stores are not yet dead both for retailers and

consumers. It highlights various weaknesses of e-commerce

despite its huge potential in the Indian market. This study is

primarily based on secondary data. A number of industry

reports, journals, news articles, national and international

reports have been used for analysis.

Analysis

Scope of Retail Industry

India is the fastest growing economy of the world growing

at 8.2% in the first quarter of 2018-2019. Along with strong

GDP growth is the booming consumption expenditure which

is expected to reach the US $ 3600 billion by 2020 from

US$ 1824 billion in 2017. It signals towards increasing

income, evolving consumption patterns and changing

lifestyles of the Indian economy. It is clear that the Indian

retail sector is one such sector which can highly benefit from

the changing statistics of the economy. India already tops

Global Retail Development Index 2017. As per Department

of Industrial and Promotion (DIPP), Indian retail has

received FDI equity flows of US $ 1.42 billion from April

2000 to June 2018. Government’s initiatives related to FDI,

indirect taxes, e-commerce, local sourcing requirements

have made the retail sector very attractive for both national

and international players. Some of the reasons contributing

to the huge potential for the retail sector in India are

discussed here.

• GDP per capita growth of 5.88% in 2016 indicates

an increase in the purchasing power of consumers. It

will lead to a change in consumer preferences for

non-essential products and increased aspirations or

international brands. Purchasing power is growing in

products like jewelry, shoes, watches, beverages,

cosmetics, and apparels.

• Consumption expenditure accounts for near 70% of

GDP which indicates that spending patterns are

evolving for a major section of the economy. As per

a study by the Boston consulting group, India is

expected to become the third largest consumer

economy in the world by 2025 reaching the US $400

billion in consumption. • The share of the middle-income class is increasing

which has led to more brand consciousness and

luxury purchases. Consumers are becoming more

educated and thus prefer to try new products. • FDI reforms to allow 100% FDI in single-brand

retail trade and relaxation provided for sourcing

requirements are an attraction for FDI in retail. • Implementation of the Goods and Services Tax (GST)

on 1st July 2017 has provided many benefits to the

retail sector. It has replaced a number of indirect

taxes and made the system less complex than what it

was earlier. GST also allows claiming of credit for

input taxes. It also allows some rebates for

entrepreneurs and startups. In short, GST has made

the retail sector more organized and transparent. • Government’s Digital India initiative is making it

easy for doing business in India. It is expected to

lead to less paperwork and quick and efficient

performance of services. Along with it, steps have

been taken to make India the cash-less economy thus

raising more hopes for the e-commerce sector. • Access to credit has been made easier because of the

various steps are taken up by the government under

its financial inclusion plans. This tends to increase

consumption expenditure further. • Growing trends toward nuclear families and rapid

urbanization may mean more demand for durable

goods and other household goods and services. • There is very high potential for organized retail as it

constitutes just 7% of the total retail sector in FY17.

Indian retail sector is highly unorganized despite

huge transformations that have happened in the last

two decades. A shift to organized retail is on the rise

because of customers demand for a superior

shopping experience. As per Anarock Property consultants, Indian retail sector



is expected to grow to$1.3 trillion in 2020 from just

$ 672 billion in 2017. There is also huge potential for

online retail which is relatively a new concept in India.

Scope of e-commerce

According to Forrester’s predictions 2018 India report,

the growth rate of online retail was above 100% between

2013 and 2015. It was just 26% in 2017. Due to many

factors, it is expected to grow in leaps and bounds in near

future. As per India Brand equity foundation, the Indian

e-commerce industry is expected to surpass the US and

become the second largest e-commerce market by 2034.

Factors like availability of affordable smartphones,

increase in the number of internet users, a boom in digital

payments, increased investments by venture capitalists,

mergers and acquisitions, expansion of e-commerce into

non-metropolitan cities, changing lifestyles, ongoing

digital transformation, analytics-driven customer

engagement etc. will support the growth of this sector.

The e-commerce market is expected to grow to the US

$ 200 billion by 2026 from just US $ 39 billion in 2017.

• Smartphone penetration in India has reached 36%

in 2017 making India the second biggest mobile

market of the world from just 299 million users in 2017, it is expected that this figure may go up to

442 million by 2022. This will boost online retail

because a major portion of online transactions is

carried out through mobile phones only.

• Rising internet penetration is also expected to

contribute to the growth of e-commerce. Internet

users are expected to increase from 481 million in

2017 to 829 million by 2021. Introduction of

Jio in 2016 has done a lot to improve internet

connectivity. Indian now ranks number one in data

consumption compared to 155th rank before Jio

launch. Also, the cost of data has come down to less

than Rs. 15/GB from around Rs. 250/GB. It is to

note that monthly mobile data consumption has

increased more than 15 times after Jio launch.

Internet penetration in urban India stands at 64.84 % and at 20.64% in rural areas as of Dec 2017.

• There is a continuous growth momentum in the

share of card payments and payments via Unified

Payment Services. This is being aided by the

adoption of apps like Paytm, PhonePe, and

Google Pay. Paytm has also launched Paytm

payment bank with zero charges on online

transactions and free virtual debit card. • Indian startups related to e-commerce are also

very attractive to foreign investors. E-commerce

industry in India received the US $ 2.1 billion in

2017 only via private equity and venture capital

deals by firms like Softbank, Sequoia capital, Alibaba, Tencent holdings, DST global etc. Indian

firms like Flipkart, Snapdeal, Ola, Zomato, Swiggy

and Paytm are receiving the most of foreign funds

and are thus expected to fuel the industry further.

Walmart has already taken a 77% stake in Flipkart. • Major online players like Amazon, Flipkart, Ola,

Swiggy are making large investments by

themselves to improve logistics and payment

system to fuel future growth. • The Indian government is also taking favorable steps

in this direction. The government has already

allowed 100% FDI in e-commerce under

‘marketplace’ model and also in food retail. The

government has allocated the US $ 1.24 billion to

Bharat net project in the 2018-19 union budget to

provide broadband services to over 1.5 lack gram

panchayats. Government is working on e-commerce

policy to regulate and make the industry more

transparent. Initiatives like startup India, skill India,

innovation fund and digital India are to contribute to

the growth of e-commerce environment. Seeing

these efforts of government, Google is planning to enter into e-commerce

space in India by November 2018. • Google and TATA trust are working together



on ‘Internet Saathi’ project to improve internet

penetration among the women of rural India. This

will expand e-commerce to non-metropolitan

areas of India. • India’s youth that is around a quarter of its

population constitutes the largest share of digital

shoppers who prefer joyful shopping online.

• The online stores are accessible for 24 hours and

also deliver products home. E-retailers prefer

online platform because of no need of maintaining

sophisticated showrooms and less operating costs.

Retailers’ Preference for Brick and Mortar

All these reasons provide us with a satisfactory belief

about why e-commerce has a bright future in Indian

territory. But it comes with its own limitations. Because

of which there is a new trend on the rise where large

online retailers are opting for physical presence in the

traditional marketplace through opening up of brick and

mortar stores. According to Forrester’s report, the online

retail share was just 2% of the total retail market in India.

There are many factors because which customers in India

prefer to shop from physical stores instead of online

stores. Also, there are many factors which work against

online retailers in Indian market forcing players like

Amazon, Zivame, Nykaa, Teabox, MI, Myntra, Lime

road, Pepperfry etc. to eye brick and mortar.

E-commerce sales as a percentage of total retail sales

in selected countries in 2017 Source: https://www.

statista.com

• The biggest benefit of operating online is the low

operating costs but in India, the cost of online

customer acquisition is very high. There is tough

competition to bag a position on the first few pages

of search engines. Online space is highly crowded

with a number of startups coming online every other

day. Also, there is a high price competition in e-

commerce. Indian customers are price sensitive and

heavy discounts thus act as an attractive force. Once

such heavy discounts are no more available on a

platform, online shoppers tend to shift to another

platform or altogether give up online shopping. Also

to enhance brand awareness, online retailers have to

shed a lot of money on advertisements like Flipkart

has come up with many TV commercials. • Brands like Myntra and MI have come up with

physical stores to enhance their visibility in the

market. Storefront advertisements attract many

potential customers and also help to instantly

convert them to actual buyers. • One major challenge for the growth of online

business in India is related to logistics. The most

complex part is the last mile delivery. Indian market

is fragmented and divided into many territories and

localities. Companies find it a difficult task to

deliver the courier at right time and in good

condition to the consumers. Reaching far spread

geographical areas is still a big challenge even after

the adoption of strategies like Flipkart has launched

F quick which allows people to sign up as freelance

last mile delivery executives. Amazon India’s kirana

network is also one such strategy that attempts to

boost the last mile deliveries. • There are also roadblocks in the way customers

prefer to pay for their purchases. Cash on delivery

is still a favorite mode of payment for a typical

online customer. But this method increases the

risk of theft, fraud, and reconciliation. • There are taxation related complications related to

e-commerce. Latest news in this regard as

published in economic times on 19th Sept 18 says

that online retailers have to register themselves

for GST in all states. Also, they need to collect

tax at source. There are still no clear guidelines

related to GST for online retailers.



Consumers’ Preference for Brick and Mortar

Future group’s Kishore Biyani plans to add 500 fashion

big bazaar and 100 big bazaar stores in the coming years

in the physical retail market. He strongly believes that

online competition is no big threat to physical retailers

due to high operational costs of e-commerce and a very

small market share. He says that physical stores can

never entirely lose to online business. Even in USA

physical retail constitutes around 89% of total retail and

in China, this figure is around 82%. Online retail in India

just constitutes around 2% of total retail. Consumer

perceptions related to e-commerce plays a prominent role

in the recent trend of e-marketers opting for physical

presence. According to a research conducted by Google,

Bain & company and venture fund Omidyar network, in

2017-18, there were 50 million active online shoppers but

at the same time, there were 54 million online shopping

dropouts. These dropouts were either first time online

shoppers or found the online user interface complex.

• The largest factor contributing to consumer’s

preference for offline retail is the feeling of touch

and feel. Customers want to physically touch and

feel the products before making a purchase. It is

because of this that online retail penetration for

clothing was just 3% in 2017.

• Customers also have preference for a more guided

customer experience in physical stores which is

completely absent in online purchases.

• Customers feel that payment mechanism available

on websites may risk their financial privacy and

make them prone to frauds. If customers opt for COD then it becomes difficult for online retailers

to manage cash.

• Online retail may provide customers with a dull

interface or maybe with a very complicated

interface on its websites.

• Sometimes extra delivery charges act as a

deterrent in making online purchases.

• Lack of close interaction with retailers on online

platforms reduces the confidence of the refund/

return policy or complaint redressal mechanism.

• Offline purchases allow instant delivery

enhancing the chances of an accurate order in

perfect condition.

• In-store experience still attracts a large section of

Indian population. The usage of internet is also

highly skewed towards urban areas and young

population. Thus online platforms are still out of

reach for a large section of Indian population. It seems that physical retail stores are not out of business yet

and will not be dead in near future also because both online

and offline platforms of shopping have got their own

strengths and weaknesses. Because of these reasons, there

are new trends coming up in the retail industry like

omnichannel retailing, fluid shopping, pop-up stores, in-

store returns, experience center, guide shops, etc. Omni-

channel retailing is when retailers aim to provide seamless

experience whether shopping online or at any traditional

retail store. It provides integrated customer experience on all

the platforms which a customer can use for shopping. It is a

step further of multi-channel retailing because of its focus on

integration. In India, for example, Zivame (online lingerie

retailer), Adidas and pepperfry are amongst many who are

integrating more than one platform for shopping. Zivame

has opened stores in Bangalore which allows to make

educated choices and provides a touch and feel experience to

complement their online stores. Although such attempts of

integration come up with its own IT challenges. Fluid

shopping is one such idea which blurs the lines between a

number of shopping channels.

Source: http://newsonscreen.com/google-may-open-

up-offline-stores-in-india-soon/

There is a trend of pop-up stores. These are small temporary

retail outlets majorly used for the launch of some products.

Google has come up with these kinds of stores in Mumbai

and NCR to sell its android phones, laptops and chrome

book. These are less costly to set up than a full-fledged brick

and mortar shop but still help to create a dynamic shopping

experience. It helps to overcome the lack of



touch and feel psychology that is prevalent in the online

mode of business. Experience centers are also like pop-up

stores which tend to provide a direct customer experience

to the shoppers. Google has already set-up ‘dark rooms’

in an attempt to demonstrate their pixel 2 smartphone’

low light image features. Nykaa, an online cosmetics

player is also to open around 30 stores soon to let

customers directly experience its products. BOPIS or

‘Buy Online Pickup-in stores’ is a new concept emerging

to reduce shipping costs of online retailers. Omni-channel

retailers are encouraging customers to order their

products on an online platform and then themselves pick

the merchandise from nearby physical stores. Refund and

return mechanism are also being increasingly aided by

such physical stores only. Some other brands are aiming

for full-fledged physical presence in the market.

Source: http://www.businessfortnight.com/myntra-plans-

offline-stores-pvt-brands/

Myntra is to come offline with its cosmetics products

under ‘Myntra beauty brand’. Myntra has already opened

stores of its private brand Roadster and franchisee outlets

of Spain’s mango brand. Other brands like the furniture

seller urban ladder, cosmetics retailer Nykaa, and

eyewear Lenskart are already operating physical stores in

different parts of the country. Thus in spite of the huge

potential of e-commerce, the importance of brick and

mortar retail has not declined. In fact, it is the strength of

physical stores that many large online players are eying

brick and mortar. Physical stores attempt to complement

the online retail and help to overcome weaknesses of the

online platform for the customers.

Conclusion

The study has focused on the huge potential of the retail

industry in India because of the changing dynamics of the

economy and steps initiated by the government to boost

the growth of the Indian retail sector. Online retail has a

small share in overall retail business but it has a number

of advantages. A large section of Indian population

prefers to make online purchases. E-commerce in India

attracts huge FDI and our government is committed to

regulating this sector further for its enhanced growth.

According to India brand equity, the Indian e-commerce

market will become the world’s second-largest market by

2034. Despite such positive efforts and customer

experiences, there are a number of limitations for both

online sellers and online shoppers. There is a trend for

large online retailers to establish physical retail stores in

the market for enhanced visibility and many other

benefits. Physical stores of various forms tend to

complement the presence of online retailers. New

concepts such as pop-up stores, omnichannel retail,

experience centers, etc. are now common in the retail

industry and provide scope for further research.

References

• Charles Steinfield, Thomas Adelaar, Ying-Ju Lai, ‘Integrating Brick and Mortar locations with

E-commerce: Understanding synergy

opportunities’ (2002).

• David M. Szymanski, Richard T. Hise, ‘E-

satisfaction: an initial examination’ (2000).

• Erik Brynjolfsson, Yu Jeffrey Hu, Mohammed S.

Rahman, ‘Competing in the age of Omnichannel

Retailing’ (2013).

• Jayakrishnan S Nair, ‘E-retailing in India:

Opportunities and Challenges’ (2015).

• Joel E. Collier and Carol C. Bienstock, ‘How Do Customers Judge Quality in an E-tailor’ (2006).

• Kacen, Hess and Chiang, ‘Bricks or clicks?

consumer attitudes toward traditional stores and

online stores’ (2013).

• kumar Vinay, ‘A Study on Perceived risk in

online consumer behavior of youth: An Indian

perspective’ (2013).

• Pui -mun lee, ‘Behavioural model of online

purchasers in an e-commerce environment’ (2002).


Scope of Retail Sector and the Changing Dynamics

• Xua Liu, Alvin C Burns, Yingjian Hou,

‘Comparing online and in-store shopping

behavior towards luxury goods’ (2013).

• http://in.fashionnetwork.com/news/Future-group-

sees-huge-potential-for-physical-retail-in-

India,896046.html#.W6wrsntKjIU

• www.businessfortnight.com/myntra-plans-offline-

stores-pvt-brands/

• www.digit.in/mobile-phones/google-to-launch-

offline-retail-stores-in-India-by-late-2018-report-

38806.html • www.firstpost.com/tech/news-analysis/indian-e-

commerce-players-are-expanding-to-brick-and-

mortar-stores-3702333.html • www.forbes.com/sites/rganatra/2018/01/18/22-

retail-industry-predictions-for-brick-and-mortar-

stores-in-2018/#24fdb6a27000 • www.forrester.com/report/The+State+Of+Indias+

Online+Retail+Market+In+2017/-/E-RES138071#

• www.ibef.org/industry/retail-india.aspx

• www.livemint.com/Opinion/m/The-future-of-

retail.html

• w w w . l i v e m i n t . c o m / C o m p a n i e s /

ozsQtBsIEpyWqmHdPQKDbK/Ecommerce-

firms-turning-to-offline-stores-for-customer-conn.

html • w w w . l i v e m i n t . c o m /

Industry/7ahYCsvi9A99lznYTi0VFP/Online-

retail-sales-in-India-seen-growing-to-327-billion-

t.html • www.statista.com/statistics/255083/online-sales-

as-share-of-total-retail-sales-in-selected-countries/

• www. thehindubusinessline . com/catalyst/ why-

ecommerce-is-eyeing-brickandmortar/

article9353994.ece

53


54

Impact of Data Mining on Big Data Analytics:

Challenges and Opportunities

Pratibha Gautam*

Abstract:

“Big data” has become a highlighted buzzword since last year; “big data mining” has almost immediately followed

up as an emerging, interrelated research area. Big Data concerns large-volume, complex, growing data sets with

multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity,

Data mining techniques are providing great aid in the area of Big Data analytics, since dealing with Big Data are big

challenges for the applications. Big Data analytics is the ability of extracting useful information from such huge

datasets. This paper presents different approaches used for Big Data Analysis by using Data Mining techniques. Big

Data is now rapidly expanding in all science and engineering domains, including physical, biological and biomedical

sciences. This paper provides an overview of big data mining and discusses the related challenges and the new

opportunities. We hope our effort will help reshape the subject area of today’s data mining technology toward solving

tomorrow’s bigger challenges emerging in accordance with big data. The basic objective of this paper is to explore

the potential impact of big data challenges, open research issues, and various tools associated with it. As a result, this

paper provides a platform to explore big data at numerous stages. Additionally, it opens a new horizon for

researchers to develop the solution, based on the challenges and open research issues.

Keywords: Data Mining, Big Data, Big Data Mining, Big Data Analytics

Introduction

The era of petabyte has come and almost gone, leaving us to

confront the exabytes era now. Technology revolution has

been facilitating millions of people by generating

tremendous data via ever-increased use of a variety of digital

devices and especially remote sensors that generate

continuous streams of digital data, resulting in what has

been called as “big data”. It has been a confirmed

phenomenon that enormous amounts of data have been

being continually generated at • miraculous and ever

increasing scales. In 2010, Google estimated that every two

days at that time the world generated as much data as the

sum it generated up to 2003. Regardless of the very recent

“Big Data Executive Survey 2013” by New Vantage

Partners that states “It’s about variety, not volume”, many

people would still believe the issue with big data is scale or

volume. Big data sure involves a great variety of data forms:

text, images, videos, sounds, and whatever that may come

into the play, and their arbitrary combinations. Big

data frequently comes in the form of streams of a variety of

types. Time is an integral dimension of data streams, which

implies that the data must be processed in a timely or real-

time manner. Besides, the current major consumers of big

data, corporate businesses, are especially interested in “a big

data environment that can accelerate the time-to-answer

critical business questions that demonstrate business values”.

The time dimension of bid data naturally leads to yet another

key characteristic of big data – speed or velocity. The era of

Big Data has arrived. Every day, 2.5 quintillion bytes of data

are created and 90% of the data in the world today were

produced within the past two years. Our capability for data

generation has never been so powerful and enormous ever

since the invention of the Information Technology in the

early 19th century. The theme of this paper is to provide a study on the issue of

big data analytics and data mining, its challenges and the

* Assistant Professor, Computer and Information Science, Vision Institute of Engineering & Technology, Delhi


Impact of Data Mining on Big Data Analytics: Challenges and Opportunities 55

opportunities. We also point to a few research topics that

are either promising or much needed for solving the big

data and big data mining problems. In order to make our

discussion logical and smooth, we will start with a review

of some essential and relevant concepts, including data

mining, big data, big data mining, and the some platforms

related to big data and big data mining. Data Mining Data mining attempts to implement basic processes that

facilitate the extraction of meaningful information and

knowledge from unstructured data. Data mining extracts

patterns, changes, associations and anomalies from large

data sets. The objective of data mining is to identify valid,

novel, potentially useful, and understandable correlations

and patterns in existing data. The two "high-level" primary goals of data mining, in

practice, are prediction and description. 1. Prediction involves using some variables or fields in

the database to predict unknown or future values of

other variables of interest. 2. Description focuses on finding human-interpretable

patterns describing the data. (A) Steps in Data Mining: The following steps are

usually followed in data mining. These steps are iterative,

with the process moving backward whenever needed.

1. Develop an understanding of the application,

relevant prior knowledge, and the end user’s goals.

2. Create a target data set to be used for discovery.

3. Clean and pre-process data.

4. Reduce the number of variables and find invariant

representations of data if possible.

5. Choose the data mining task (classification,

regression, clustering, etc.)

6. Choose the data mining algorithm.

7. Search for patterns of interest.

8. Interpret the pattern mined.

9. Consolidate knowledge discovered and prepare a

report.

(B) Data Mining Process: Data Mining is an iterative

process that uses a variety of data analysis tools to discover

patterns and relationships in data. Data mining is an

interactive and iterative process involving data pre-

processing], search for patterns, knowledge evaluation, and

possible refinement of the process based on input from

domain experts or feedback from one of the steps. The pre-

processing of the data is a time-consuming, but critical, first

steps in the data mining process. It is often domain and

application dependent; however, several techniques

developed in the context of one application or domain can

be applied to other applications and domains as well.

Big Data We are living in an interesting era – the era of big data, full

of challenges and opportunities. Organizations have already

started to deal with petabyte-scale collections of data; and

they are about to face the exabyte scale of big data and the

accompanying benefits and challenges. Big data is playing a

crucial role in the future in all things of our lives and our

societies. For example, governments have now started

mining the data of social media networks and blogs, and

online-transactions and other sources of information to

recognize the need for government facilities, the suspicious

organizational groups, and to predict future events. Even,

service providers start to track their customers’ purchases

made through online, instore, and on-phone, and customers’

behaviors through recorded streams of online clicks, as well

as product reviews and ranking, for improving their

marketing efforts, predicting new growth points of profits,

and increasing customer satisfaction. The mismatch between

the demands of the big data management and the capabilities

that current DBMSs can provide has reached the historically

high peak. The three Vs (volume, variety, and velocity) of big data

each implies one distinct aspect of critical deficiencies of

today’s DBMSs. Gigantic volume requires equally great

scalability and massive parallelism that are beyond the

capability of today’s DBMSs; the great variety of data

types of big data particularly unfits the restriction of the

closed processing architecture of current database

systems and the speed/velocity request of big data

processing asks for commensurate real-time efficiency

which again is far beyond where current DBMSs could

reach. The limited availability of current DBMSs defeats

the velocity request of big data from yet another angle.

To overcome this scalability challenge of big data, several


56 Impact of Data Mining on Big Data Analytics: Challenges and Opportunities

attempts have been made on exploiting massive parallel

processing architectures. The first such attempt was made

by Google. Google created a programming model named

MapReduce that was coupled with the GFS (Google File

System), a distributed file system where the data can be

easily partitioned over thousands of nodes in a cluster.

Later, Yahoo and other big companies created an Apache

open-source version of Google’s MapReduce framework,

called Hadoop MapReduce.

• Variety makes big data really big. Big data comes

from a great variety of sources and generally has

in three types: structured, semi structured and

unstructured. Structured data inserts a data

warehouse already tagged and easily sorted but

unstructured data is random and difficult to

analyze. Semi-structured data does not conform to

fixed fields but contains tags to separate data

elements.

• Volume or the size of data now is larger than

terabytes and petabytes. The grand scale and rise

of data outstrips traditional store and analysis

techniques.

• Velocity is required not only for big data, but also

all processes. For time limited processes, big data

should be used as it streams into the organization

in order to maximize its value.

During in the intensity of this information, another

component is the verification of data flow. It is difficult to

control large data so data security must be provided. In

addition, after producing and processing of big data, it

should create a plus value for the organization and Industry.

Data Mining & Big Data Big data and data mining are two different things. Both of

them relate to the use of large data sets to handle the

collection or reporting of data that serves businesses or other

recipients. However, the two terms are used for two different

elements of this kind of operation. Big data is a term for a

large data set. Big data sets are those that outgrow the

simple kind of database and data handling architectures that

were used in earlier times, when big data was more

expensive and less feasible. For example, sets of data that

are too large to be easily handled in a Microsoft Excel

spreadsheet could be referred to as big data sets. Data

mining refers to the activity of going through big data sets to

look for relevant or pertinent information. This type of

activity is really a good example of the old axiom "looking

for a needle in a haystack." The idea is that businesses

collect massive sets of data that may be homogeneous or

automatically collected. Decision-makers need access to

smaller, more specific pieces of data from those large sets.

They use data mining to uncover the pieces of information

that will inform leadership and help chart the course for a

business.

Big Data Analytics via Data Mining

Data analytics refers to the Business Intelligence &

Analytics technologies that are grounded mostly in data

mining and statistical analysis. Most of these techniques

rely on the mature commercial technologies of relational

DBMS, data warehousing, ETL, OLAP, and BPM .Since the late 1980s, various data mining algorithms

have been developed by researchers from the artificial

intelligence, algorithm, and database communities. In the

IEEE 2006 International Conference on Data Mining

(ICDM), the 10 most influential data mining algorithms

were identified based on expert nominations, citation

counts, and a community survey. In ranked order, they

are C4.5, k-means, Support Vector Machine, Apriori, EM

(expectation maximization), PageRank, AdaBoost, Naïve

Bayes, and CART Algorithms. These algorithms cover

classification, clustering, regression, association analysis,

and network analysis. Most of these popular data mining

algorithms have been incorporated in commercial and

open source data mining systems. Other advances such as

neural networks for classification/prediction and

clustering and genetic algorithms for optimization and

machine learning have all contributed to the success of

data mining in different applications.



Two other data analytics approaches commonly taught in

business school, that are use in statistical theories and

models, multivariate statistical analysis covers analytical

techniques such as regression, factor analysis, clustering,

and discriminant analysis that have been used successfully

in various business applications. Developed in the

management science community, optimization techniques

and heuristic search are also suitable Most of these

techniques can be found in business school curricula.

Due to the success achieved collectively by the data mining

and statistical analysis community, data analytics continues

to be an active area of research. Statistical machine learning

often based on well-grounded mathematical models and

powerful algorithms, techniques such as Bayesian networks,

Hidden Markov models, support vector machine,

reinforcement learning, and ensemble models, have been

applied to data, text, and web analytics applications. Other

new data analytics techniques explore and leverage unique

data characteristics, from sequential/ temporal mining and

spatial mining, to data mining for high-speed data streams

and sensor data.

Increased privacy concerns in various e-commerce, e-

government, and healthcare applications have caused

privacy preserving data mining to become an emerging

area of research. Many of these methods are data-driven,

relying on various anonymization techniques, while

others are process driven, defining how data can be

accessed and used. In addition to active academic research on data analytics,

industry research and development has also generated much

excitement, especially with respect to big data analytics for

semi-structured content. Unlike the structured data that can

be handled repeatedly through a RDBMS, semi-structured

data may call for ad hoc and one-time extraction, parsing,

processing, indexing, and analytics in a scalable and

distributed MapReduce or Hadoop environment.

MapReduce has been hailed as a revolutionary new platform

for large scale, massively parallel data access. Inspired in

part by MapReduce, Hadoop provides a Java based software

framework for distributed processing of data intensive

transformation and analytics. The top three commercial

database suppliers— Oracle, IBM, and Microsoft— have all

adopted Hadoop.

Big Data Analytics is the application that enables

organizations to analyze large sets of data to discover

patters and other useful information with the help of data

mining tools. Due to the significant contribution of Big

Data Analytics, the amount of data was exponentially

increased within the past decade i. e 2005-15. The technological advances in storage, processing, and

analysis of Big Data include:

• The rapidly decreasing cost of storage and CPU

power in recent years.

• The flexibility and cost-effectiveness of

datacenters and cloud computing for elastic

computation and storage.

• The development of new frameworks such as Hadoop, which allow users to take advantage of

these, distributed computing systems storing large

quantities of data through flexible parallel

processing. In this section, we focus on the data mining Process

model for Big Data analytics. The overall model is

divided into 2 Sub-Processes: Data Management and

Analytics, which further broken down into 5 stages. Big Data Phases and Processes

Data Management Analytics

A. Requirements & D. Modeling & Analysis

Recording B. Extraction, Cleaning & E. Interpretation

Annotation C. Integration, Aggregation&

Presentation Table1: Process for Extracting Information from Big

Data Set In short, big data is the asset and data mining is the

"handler" of that is used to provide beneficial results.

Therefore, Data mining correlate with discovering useful

models in massive data sets by itself, machine learning

combine with data mining and statistical methods enabling

machines to understand datasets. With the help of the

different data mining techniques and tools, we can easily

extract required information from the big data set. Data

mining can involve the use of different kinds of software

packages such as analytics tools. It can be automated, or it

can be largely labor-intensive, where individual workers

send specific queries for information to an archive or

database. Generally, data mining refers to operations that



involve relatively sophisticated search operations that

return targeted and specific results. For example, a data

mining tool may look through dozens of years of

accounting information to find a specific column of

expenses or accounts receivable for a specific operating

year. So with the help of data mining techniques, it can

make an easy process to extract the useful and relative

knowledge and information from a big data set.

Conclusionly, we can also says that the analysis of big

data become an easier process with the help of the

different tools and techniques of data mining.

Research Challenges & Opportunities in Data

mining and Big data Analysis

Recent year’s big data has been acquired in several domains

like health care, public administration, retail, biochemistry,

and other interdisciplinary scientific researches. Web-based

applications encounter big data frequently, such as social

computing, internet text and documents, and internet search

indexing. Social computing includes social network analysis,

online communities, recommender systems, reputation

systems, and prediction markets where as internet search

indexing includes ISI, IEEE Xplorer, Scopus, etc.

Considering this advantages of big data it provides a new

opportunities in the knowledge processing tasks for the

upcoming researchers. However opportunities always follow

some challenges.

To handle the challenges we need to know various

computational complexities, information security, and

computational method, to analyze big data. For example,

many statistical methods that perform well for small data

size do not scale to voluminous data. Similarly, many

computational techniques that perform well for small data

face significant challenges in analyzing big data. Various

challenges that the health sector face was being

researched by many researchers.

The challenges of big data analytics and data mining are

classified into the different broad categories namely

6.1 Mining data streams in extremely large database One

important problem is mining data streams in extremely large

databases. Satellite and computer network data [3] can easily

be of this scale. However, today’s data mining technology is

still too slow to handle data of this scale. In addition, data

mining should be a continuous, online process, rather than

an occasional one-shot process.

Organizations that can do this will have a decisive

advantage over ones that do not. Data streams present a

new challenge for data mining researchers. 6.2 Mining complex knowledge from complex data

One important type of complex knowledge is in the form

of graphs. Recent research has touched on the topic of

discovering graphs and structured patterns from large

data, but clearly, more needs to be done. Another form of

complexity is from data that are non independent and

identically distributed. This problem can occur when

mining data from multiple relations. In most domains, the

objects of interest are not independent of each other, and

are not of a single type. We need data mining systems

that can soundly mine the rich structure of relations

among objects. 6.3 Privacy preserving data mining Privacy preserving

data management is an important emerging research area

that emerged in response to two important needs: data

analysis and ensuring the privacy of the data owners.

Privacy preserving data publishing emphasizes the

importance of need for privacy threats in data sharing. A

new approach seeks to protect data without focusing on

the infrastructure level, but at element or aggregate data

type. This type of pervasive security can be achieved by

classifying data and enforcing access control. 6.4 Hetrogeinty and Incompleteness In the past, data

mining techniques have been used to discover unknown

patterns and relationships of interest from structured,

homogeneous, and small datasets. Variety, as one of the

essential characteristics of big data, is resulted from the

phenomenon that there exists nearly unlimited different

sources that generate or contribute to big data. This

phenomenon naturally leads to the great variety or

heterogeneity of big data. The data from different sources

inherently possesses a great many different types and

representation forms, and is greatly interconnected,

interrelated, and delicately and inconsistently represented.

Mining such a dataset, the great challenge is perceivable and

the degree of complexity is not even imaginable before we

deeply get there. Heterogeneity in big data also means that it

is an obligation (rather than an option) to accept and deal

with structured, semi-structured, and even entirely

unstructured data simultaneously. This is especially so in

data-intensive, scientific computation areas .Nevertheless,

though bringing up greater technical challenges, the

heterogeneity feature of big data means a new opportunity



of unveiling, previously impossible, hidden patterns or

knowledge dwelt at the intersections within

heterogeneous big data. As a classic data mining example, we consider a simple

grocery transaction dataset that records only one type of

data, i.e., goods items. Examples insights that might be

mined from this dataset may include, e.g., the famous

association of “beer and diapers” showing a strong

linkage between the two items, and popular items like

milk that are almost always purchased by customers,

showing strong linkage of milk to all other items. In

contrast to that, big data mining must deal with semi-

structured and heterogeneous data. Now we generalize

the aforementioned simple example by extending the

scenario to an online market such as eBay. The dataset

now is a richer network consisting of at least three

different types of objects: items, buyers, and sellers.

Interrelation may broadly exist, e.g., between commodity

items in the form of “bought with”, between sellers and

items in the form of “sell” and “sold by”, between buyers

and items in the form of “buy” or “bought by”, and

between buyers and sellers in the form of “buy from” and

“sold to”. This data network has different types of objects

and relationships. We speculate that existing data mining

techniques would not maximally uncover the hidden

associations and insights in this data network. For a heterogeneous set of big data, trying to construct a

single model would most likely not result in good-enough

mining results; thus constructing specialized, more complex,

multi-model systems is expected . An interesting algorithm

following this spirit is proposed in that first determines

whether the given dataset is truly heterogeneous, and if so, it

then partitions the set into homogeneous subsets and

constructs a specialized model for each homogeneous subset.

Partitioning, as an intuitive approach, would speed up the

process of knowledge discovery from heterogeneous big

data. However, potential patterns and knowledge may miss

the opportunity of being discovered after partitioning if

important relationships crossing distinct homogeneous

regions are not adequately retained. The social community

mining problem has recently received a lot attention from

the researchers. This problem desires “multi-network, user-

dependent, and query based analysis”. It conveys that the

intersections between multiple networks bear potential

knowledge and insights that may not be discovered if a

homogenous model is to be enforced.

However, the degree of the heterogeneity captured does

not reflect the real degree of the inherent heterogeneity

existing in the big data. Mining hidden patterns from

heterogeneous multimedia streams of diverse sources

represents another frontier of data mining research. The

output of this research has broad applicability such as

detection of spreading dangerous diseases and prediction

of traffic patterns and other critical social events. 6.5 Scalability The unprecedented volume/scale of big

data requires commensurately high scalability of its data

management and mining tools. Instead of being timid, we

shall proclaim the extreme scale of big data because more

data bears more potential insights and knowledge that we

have no chance to discover from conventional data. We

are optimistic with the following approaches that, if

exploited properly, may lead to remarkable scalability

required for future data and mining systems to manage

and mine the big data:

1. Cloud computing that has already demonstrated

admirable elasticity, which, combined with

massively parallel computing architectures, bears

the hope of realizing the needed scalability for

dealing with the volume challenge of big data.

2. Advanced user interaction support (either GUI-or

language-based) that facilitates prompt and

effective system-user interaction. Big data mining

straightforwardly implies extremely time-

consuming navigation in a gigantic search and

prompt feedback/interference/guidance from users

must be beneficially exploited to help make early

decisions, adjust search/mining strategies on the

fly, and narrow down to smaller but promising

sub-spaces. 6.6 Speed/Velocity For big data, speed/velocity really

matters. The capability of fast accessing and mining big data

is not just a subjective desire, it is an obligation especially

for data streams– we must finish a processing/ mining task

within a certain period of time, otherwise, the

processing/mining results becomes less valuable or even

worthless. Exemplary applications with real-time requests

include earthquake prediction, stock market prediction and

agent-based autonomous exchange (buying/selling) systems.

Speed is also relevant to scalability – conquering or partially

solving anyone helps the other one.

The speed of data mining depends on two major factors:



data access time and the efficiency of the mining

algorithms themselves. Exploitation of advanced

indexing schemes is the key to the speed issue.

Multidimensional index structures are especially useful

for big data. For example, a combination of R-Tree and

KD-tree and the more recently proposed Fast Bit shall be

considered for big data. Besides, design of new and more

efficient indexing schemes is much desired, but remains

one of the greatest challenges to the research community.

An additional approach to boost the speed of big data

access and mining is through maximally identifying and

exploiting the potential parallelism in the access and

mining algorithms. The elasticity and parallelism support

of cloud computing are the most promising facilities for

boosting the performance and scalability of big data

mining systems.

6.7 Timeliness As the size of the data sets to be processed

increases, it will take more time to analyse. In some

situations results of the analysis is required immediately. For

example, if a fraudulent credit card transaction is suspected,

it should ideally be flagged before the transaction is

completed by preventing the transaction from taking place at

all. Obviously a full analysis of a user’s purchase history is

not likely to be feasible in real time. So we need to develop

partial results in advance so that a small amount of

incremental computation with new data can be used to arrive

at a quick determination. Given a large data set, it is often

necessary to find elements in it that meet a specified

criterion. In the course of data analysis, this sort of search is

likely to occur repeatedly. Scanning the entire data set to

find suitable elements is obviously impractical. In such cases

Index structures are created in advance to permit finding

qualifying elements quickly. The problem is that each index

structure is designed to support only some classes of criteria.

6.8 Security And privacy Challenges for Big Data

Analysis Big data refers to collections of data sets with sizes

outside the ability of commonly used software tools such as

database management tools or traditional data processing

applications to capture, manage, and analyze within an

acceptable elapsed time. Big data sizes are constantly

increasing, ranging from a few dozen terabytes in 2012 to

today many petabytes of data in a single data set. Big data

creates tremendous opportunity for the world economy both

in the field of national security and also in areas ranging

from marketing and credit risk analysis to

medical research and urban planning. The extraordinary

benefits of big data are lessened by concerns over privacy

and data protection. As big data expands the sources of data it can use, the

trust worthiness of each data source needs to be verified

and techniques should be explored in order to identify

maliciously inserted data. Information security is becoming a big data analytics

problem where massive amount of data will be correlated,

analyzed and mined for meaningful patterns. Any

security control used for big data must meet the following

requirements:

• It must not compromise the basic functionality of

the cluster.

• It should scale in the same manner as the cluster.

• It should not compromise essential big data

characteristics.

• It should address a security threat to big data

environments or data stored within the cluster.

Unauthorized release of information, unauthorized

modification of information and denial of resources are

the three categories of security violation. The following

are some of the security threats:

• An unauthorized user may access files and could

execute arbitrary code or carry out further attacks.

• An unauthorized user may eavesdrop/sniff to data

packets being sent to client.

• An unauthorized client may read/write a data

block of a file.

• An unauthorized client may gain access privileges

and may submit a job to a queue or delete or

change priority of the job. The following are some of the methods used for

protecting big data: Using Authentication Methods: Authentication is the

process verifying user or system identity before accessing

the system. Authentication methods such as Kerberos can

be employed for this. Use File Encryption: Encryption ensures confidentiality



and privacy of user information, and it Secures the sensitive

data. Encryption protects data if malicious users or

administrators gain access to data and directly inspect files,

and renders stolen files or copied disk images

unreadable.File layer encryption provides consistent

protection across different platforms regardless of OS/

platform type. Encryption meets our requirements for big

data security. Open source products are available for most

Linux systems; commercial products additionally offer

external key management, and full support. This is a cost

effective way to deal with several data security threats.

Implementing Access Controls: Authorization is a

process of specifying access control privileges for user or

system to enhance security. Use Key Management: File layer encryption is not

effective if an attacker can access encryption keys. Many

big data cluster administrators store keys on local disk

drives because it’s quick and easy, but it’s also insecure

as keys can be collected by the platform

administrator.Use key management service to distribute

keys and certificates and manage different keys for each

group, application, and user. Logging: To detect attacks, diagnose failures, or investigate

unusual behavior, we need a record of activity. Unlike less

scalable data management platforms, big data is a natural fit

for collecting and managing event data. Many web

companies start with big data particularly to manage log

files. It gives us a place to look when something fails, or if

someone thinks you might have been hacked. So to meet the

security requirements, we need to audit the entire system on

a periodic basis.

Use Secure Communication: Implement secure

communication between nodes and between nodes and

applications. This requires an SSL/TLS implementation

that actually protects all network communications rather

than just a subset. Thus the privacy of data is a huge

concern in the context of Big Data.

Conclusion

We are living in the big data era where enormous amounts

of heterogeneous, semistructured and unstructured data are

continually generated at unprecedented scale. Big data

discloses the limitations of existing data mining techniques,

resulted in a series of new challenges related to

big data mining. Big data mining is a promising research

area. In spite of the limited work done on big data mining so

far, we believe that much work is required to overcome its

challenges related to heterogeneity, scalability, speed,

accuracy, trust, privacy. Big data analysis is becoming

indispensable for automatic discovering of intelligence that

is involved in the frequently occurring patterns and hidden

rules. Big data analysis helps companies to take better

decisions, to predict and identify changes and to identify

new opportunities. In this paper we discussed about the

opportunities and challenges related to big data mining and

also Big Data analysis tools like Map Reduce over Hadoop

which helps organizations to better understand their

customers and the marketplace and to take better decisions

and also helps researchers and scientists to extract useful

knowledge out of Big data. In addition to that we introduce

some big data mining tools and how to extract a significant

knowledge from the Big Data set, which will help the

research scholars to choose the best mining tool for their

research work and for big data analytics. Our future work

would primarily focuses on the Big Data analytics approach

discussed above using various data mining techniques.

References

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• Han Hu, Yongyang Nen, Tat Seng Chua, Xuelong Li, Towards Scalable System for Big Data Analytics: A Technology Tutorial, IEEE Access,

Volume 2, Page No 653, June 2014.

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Status, and Forecast to the Future, SIGKDD Explorations, Volume 14, Issue 2, 2012.

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survey, IEEE/IFIP Network Operations &

Management Symposium (NOMS 2010),Osaka

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• Kishor, D., Big Data: The New Challenges in Data



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• Sagiroglu, S. and Sinanc, D., Big Data: A Review, International Conference on Collaboration Technologies and Systems (CTS), pp.42-47, 20-

24, May 2013.

• Richa Gupta, Sunny Gupta and Anuradha Singhal,

Big Data: Overview, IJCTT, Vol 9, Number 5,

March 2014.

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Mining with Big Data, Jan 2014.

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5, May 2014.

• Gantz, J., & Reinsel, D. (2011). The 2011 Digital

Universe Study: Extracting Value from Chaos.

• Sabia and Sheetal Kalra, Application of Big Data: Current Status and Future Scope, IJACTE, Vol 3, Issue 5, 2014.

• James Manyika, Michael Chui, Brad Brown,

Jacques Bhuhin, Richard Dobbs, Charles

Roxburgh and Angela Hungh Byers, Big Data: The next frontier for innovation, competition and

productivity, June 2011.

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International Conference on Collaboration Technologies and Systems (CTS), pp.42-47, 20-24 May 2013

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Issue 4, April 2015


63

NLP Based Social Media Text Classification Using C4.5 Decision

Tree Algorithm

Priyanka Tomar*

Abstract:

In this age of internet and communication technology the social media is one of the most popular platforms. Now in

these days social media is not only used for communication that is also used for business promotion and other

advertisement activities. Therefore different marketing and promotional institutions are utilizing the social media post

and reviews for discovering the consumer’s orientations and sentiments. In this context the proposed work is aimed to

design and develop a social text classification system using the concept of NLP and data mining. In this presented

work a data mining approach is presented by which the sentiment analysis of twitter dataset is presented. In this

context the proposed methodology works in three major phases in first phase the data preprocessing is performed for

improving the quality of learning data. In addition of that the NLP text parser is also used for extracting the NLP

features form the data. In next phase the C4.5 decision tree is employed on data for developing the data model using

the training set and in final phase the testing of the prepared data model is performed. The experimental performance

study is performed on the proposed system demonstrate the proposed technique obtain the higher accuracy and low

resource consumption in terms of time and memory.

Keywords: Social Media, Text Classification, Data Mining, NLP, Supervised Learning, Decision Tree C4.5

Introduction

The data mining and their techniques are used for

processing bulk amount of data. The motive of

processing such huge amount of data is to find the

application centric data patterns. Therefore popularity

and applications of data mining techniques are growing

much rapidly. In a number of different industries i.e.

banking, education, business intelligence and others these

applications are become much popular and helping hands.

On the other side in different kinds of data sources, in

recent years the social media is become one of the most

frequently growing data source. In addition of it is a place

where a significant amount of traffic is available that

consume and generate the data. Analysis of the social

media data can be helpful for various industries to

develop product reviews, consumer’s feedback, and other

business intelligence and strategy development. In this context the proposed work is focused on designing

and development of a social media text classification

technique. That technique evaluate the social media text

post for finding the authors sentiments in three different

class labels i.e. positive, negative and neutral. In this

context the twitter dataset is taken as initial data source

for algorithm training and testing purpose. In order to

learn the pattern supervised learning algorithm decision

C4.5 is used. After the implementation of proposed

technique the results of the technique is also computed

for finding their accuracy and efficiency for processing

the amount of data. in this section the overview of the

proposed concept is provided in next section the overall

methodology and system design is presented.

Proposed Work This section provides the detailed information about the

proposed methodology and the system functional aspects.

In addition of that the proposed algorithm for classifying

the twits according to the author sentiments is also

discussed. A. Methodology

The proposed system for twitter sentiment analysis is

* Assistant Professor IT Department, Chandarprabhu Jain College of Higher Studies & School of Law


64 NLP Based Social Media Text Classification Using C4.5 Decision Tree Algorithm

explained using figure 2.1. In this diagram the required

components are also demonstrated, all these components

are discussed in this section in detail.

Twitter dataset: any machine learning or data mining

model needs some initial data for training and testing

purpose. In this presented work the main aim is to

classify the social media text dataset. Therefore for the

sentiment based text analysis the dataset is extracted from

the online source [1]. This dataset contains user ID, twit,

time stamp and the twit sentiment class labels.

Figure 1 Proposed System Architecture

Data Preprocessing: the aim of the data preprocessing in

data mining is to enhance the quality of data. Therefore

different data processing techniques are applied on data

for reducing the amount of noise, unwanted data from

initial or raw dataset. In addition of that sometimes it is

required to transform the data, map the data to other

formats too. In this presented work we followed two

major steps for improving the quality of data.

1. Removing Stop Words: as we discussed the

proposed work is intended to analyze the text data or

twits. Such kind of data found in unstructured

manner. The unstructured source of information

makes the data processing more complex in nature.

Additionally the size of each instance of twits is also

different in size or length. Therefore first we reduce

the amount of unwanted stop words form the entire

data. the stop words are those words that are utilized

frequently for sentence design but these words are

not much significant for demonstration of

knowledge or domain such as (“is”, “am”, “are”,

“this”, “that”) and more.

2. Removing Special Characters: similarly, the

special characters available in the initial dataset is

also removed in this phase such as (“”, /-%#) and

others.

In order to remove such kinds of unwanted words and

characters two different lists of words and characters are

prepared. Additionally using a find and replace function

the cleaning of data is performed.

NLP Parser: after cleaning of data the remaining data is

used for further training and testing purpose. Therefore

such raw data is needed to be transforming into a

structured format. Therefore Stanford NLP (natural

language processing) parseris used for processing the

remaining data. Using this process the text sentences are

parsed into their part of speech information. Therefore,

this process is sometimes also called as the POS (part of

speech) tagging. For example, we a sentence:

Ram is a Good Boy

Using the NLP parser we extract the following

information from the above sentence.

Noun Verb Article Adjective Noun

Feature Extraction: after parsing entire dataset sentences

into their part of speech information. The transformation of

the unstructured data into structured data is performed.

Therefore a 2D vector is prepared; an example of data

transformation is demonstrated in table 2.1


NLP Based Social Media Text Classification Using C4.5 Decision Tree Algorithm 65

Noun Pronoun Verb Adverb ….

2 1 2 1

Table 2.1 Dataset Transformation Here each instance of the vector contains the count of part of

speech information available in the given sentence.

Training Set: after conversion of the dataset into

structured format the data set is split into two parts based

on 70-30 ratio. First 70% of data randomly selected for

training of the data mining algorithm, this part of data is

termed here as the training set.

Testing Set: the entire dataset is again processed for

finding the 30% of samples which are randomly selected

and used for testing of the trained model. This part of

sample data is known as the testing set.

C4.5 Decision Tree: C4.5 algorithm developed by

Quinlan, 1993) an algorithm that learns the decision-tree

classifiers, it has been observed that C4.5 performs short

in the domain where there is pre-entrance of continuous

attributes compared with the learning tasks with mostly

separate attributes. For instance, a system which looks for

well defined decision tree with 2 levels and then put

comments:

“The accuracy of trees made with T2 is equalized or even

exceed trees of C4.5 upon 8 out of all the datasets, with

the entire except one that have incessant attributes only.”

INPUT: An exploratory data set of data (D) portrayed

with the means of discrete variables.

OUTPUT: A decision tree say T which is constructed by

means of passing investigational data sets.

1. A node (X) is created;

2. Check if the instance falls in the same class.

3. Make node (X) as the leaf node and assign a label

CLASS C;

4. Check IF the attribute list is empty, THEN

5. Make node(X) a leaf node and assign a label of

most customary CLASS;

6. Now choose an attribute which has highest

information gain from the provided attribute List,

and then marked as the test_attribute;

7. Confirming X in the role of the test_attribute;

8. In order to have a recognized value for every test_

attribute for dividing the samples;

9. Generating a fresh twig of tree that is suitable for

test_attribute = atti from node X;

10. Take an assumption that Bi is a group of test_

attribute=attiin the samples;

11. If (Bi = = NULL) THEN

12. Add a new leaf node, with label of the most

common class;

13. ELSE a leaf node is going to be added and

returned by the Generate_decision_tree. Classified Data: the decision tree c4.5 generates the tree

structure using the attributes and values available in

dataset. This tree contains the attributes name in their

intermediate nodes and the values are mounted over the

edges of the tree. Additionally the decisions of the dataset

and corresponding to the tree attributes are listed in the

leaf nodes. That is defined as the trained decision tree

over a given dataset. This trained decision tree is used for

classifying the attributes and data instances provided for

classification. Each data instance in test dataset is

traversed using the trained tree and after traversal the leaf

nodes provide the decisions or prediction class label for

test dataset. That is the final outcome of the proposed

system.

This section provides the complete details about the

proposed working model for classifying the twitter data

using the data mining approach for finding the emotional

class labels. In next section the overall process is

summarized using the algorithm steps.

B. Proposed Algorithm

The proposed algorithm for classifying the user’s

sentiments is provided using the table 2.1.

Input: twitter dataset T Output: emotional class labels C



Process:

1. R_n=readInputData(T)

2. P_n=preProcessData(R_n )

3. for(i=1;i≤n;i++)

a. V_i=NLP.ParseText(P_i )

4. end for

5. [TrainCV,TestCV]=Split(V_n)

6. T_model=C45.Train(TrainCV)

7. C=T_model.Classify(TestCV)

8. Return C

Table 2.1 Proposed Algorithm

Result Analysis

The implementation of the required data model for

classifying the twitter data model is accomplished

successfully. Based on the experimental observations the

results analysis is defined in this section.

A. Accuracy

The accuracy of a machine learning or data mining model

demonstrates the correctness of the implemented system.

the accuracy of such system can be estimated using the

following formula:

Dataset instances Accuracy %

100 84

200 86

400 87

700 89

1000 91

1400 92

1800 94

Table 3.1 Accuracy %

Figure 3.1 accuracy % The accuracy of the proposed system for twitter text

classification is provided using table 3.1 and figure 3.1.

The accuracy of the proposed data model is demonstrated

in Y axis of the diagram and the X axis shows the total

instances provided for classification to the implemented

system. the accuracy of the proposed data model is

reported here in terms of percentage. According to the

computed accuracy the proposed technique outperform

for classifying the sentiment based text additionally the

performance of system increases with the amount of

training samples increases for training of the system.

B. Error Rate

The error rate of the data mining model demonstrates the

misclassification rate of the system. Therefore that can be

computed using the ratio between the misclassified samples

and the total samples to classify. The error rate of the system

can be computed using the following formula.

Dataset instances Error Rate %

100 16



200 14

400 13

700 11

1000 9

1400 8

1800 6

Table 3.2 Error Rate %

Figure 3.2 Error Rate The error rate of the proposed sentiment based text

classification system is demonstrated using figure 3.2 and

table 3.2. The error rate of the proposed system is computed

in terms of percentage. The given diagram includes error

rate percentage in Y axis and the X axis contains the total

instances provided for classification. The misclassification

rate of the proposed system is reduces with the amount of

dataset increases therefore the proposed model enhances the

correctness step by step as the significant amount of training

samples are available for classification.

C. Memory Usage The memory usage of an algorithm provides the

measurement of space complexity of the data model. The

memory consumption of the proposed technique is

measured using the technique of JAVA technology. For

computation of memory usages the following formula

can be used.

Figure 3.3 Memory Usages

Dataset instances Memory usages (KB)

100 25633

200 26615

400 27163

700 27918

1000 28781

1400 29762

1800 30183

Table 3.3 Memory Usages The memory usages of the proposed data mining model

are demonstrated using the table 3.3 where the values of

the experiments are provided in terms of KB (kilobytes).

Additionally the figure 3.3 shows the graphical

representation of the table 3.3 values. In this diagram the

X axis includes the dataset instances provided for

classification and the Y axis contains the corresponding

memory usages of the algorithm. According to the

measured memory usages the memory consumption of

the system increases with the amount of data supplied for

classification and learning.

D. Time Consumption The time consumption of the proposed data model is

computed for finding the time complexity of the proposed

algorithm. That is computed using the following formula:

time consumed=end time-start time



Figure 3.4 Time Consumption

Dataset instances Time consumed (MS)

100 35

200 67

400 126

700 208

1000 315

1400 448

1800 569

Table 3.4 Time Consumption

The time consumption of the proposed sentiment based

text classification data model is defined using figure 3.4

and table 3.4. In this diagram the X axis contains the data

instances used for experimentation and the Y axis

contains the computed time consumed. The time

consumption of the proposed system is measured here in

terms of milliseconds (MS). According to the obtained

results the proposed system time consumption increases

with the amount of data instances increases for

experimentation. But that remains acceptable for

classifying the datasets in low resource consumption.

Therefore it becomes more essential for different new

generation application development and marketing

strategy. Therefore the proposed work is intended to

investigate the text analysis methods for classifying the

social media text. In this context a new data model using

data mining techniques and the NLP concept is proposed

for design and implementation. The proposed technique

utilizes the twitter dataset for proposed system design and

implementation. The proposed system works in three main phases. In first

phase the twitter text dataset is preprocessed for cleaning

the noise and unwanted text. In next phase the NLP

parser is implemented for parsing the text data and part of

speech tagging. After POS tagging of text data the

unstructured data is transformed into the structured data

format. After data transformation the C4.5 or J48

decision tree is employed for preparing the decision tree

model. Finally the test dataset is applied on trained

decision tree. During the classification of the test dataset

the performance of proposed data model is measured. The performance of the proposed system found

acceptable due to higher and enhancing accuracy and

error rate. In addition of that for computing and

demonstration of efficiency the time complexity and

space complexity of the algorithm is also computed

which is also acceptable. Therefore the proposed system

can be used for real world application development. In addition of that in near future the following extension

of the work is proposed for design and implementation.

1. The proposed system currently used a traditional

data model in near future a hybrid data model is

proposed for improving the performance of

current system.

2. The proposed system can also be improved using

the ensemble learning approach thus the proposed

model is extended in near future using this

concept also.

Conclusion & Future Work

The social media is one of the leading SaaS platforms in

now in these days. That is not only used for communication

and meetings it is now enabled for finding the targeted

consumers for online business and brand promotions.

References

1. S. M. Mohammad, S. Kiritchenko, and X. Zhu, “NRC-canada: Building the state-of-the-art in

sentiment analysis of tweets”, in Proceedings of

the seventh international workshop on Semantic

Evaluation Exercises, 2013, pp. 321–327.



2. Hariharan, Bharath, LihiZelnik-Manor, ManikVarma, and SvnVishwanathan. "Large scale max-margin multi-label classification with priors." In Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 423-430. 2010.

3. Cetintas, Suleyman, Luo Si, Hans Peter Aagard, Kyle Bowen, and Mariheida Cordova-Sanchez. "Microblogging in a classroom: classifying students' relevant and irrelevant questions in a microblogging-supported classroom." IEEE Transactions on Learning Technologies 4, no. 4 (2011): pp. 292-300.

4. Hong, Liangjie, Ovidiu Dan, and Brian D. Davison, "Predicting popular messages in twitter",

In Proceedings of the 20th international

conference companion on World Wide Web, pp.

57-58. ACM, 2011.

5. Durlak, Joseph A., Roger P. Weissberg, Allison B.

Dymnicki, Rebecca D. Taylor, and Kriston B.

Schellinger, "The impact of enhancing students’

social and emotional learning: A

meta‐analysis of school‐based

universal interventions." Child

development 82, no. 1 (2011): 405-432.

6. Mac Kim, Sunghwan, and Rafael A. Calvo,

"Sentiment analysis in student experiences of

learning", In Educational Data Mining 2010. 2010.

7. Wakefield, Jenny S., Scott J. Warren, and MettaAlsobrook. "Learning and teaching as

communicative actions: A mixed-methods

Twitter study." Knowledge Management & E-

Learning 3, no. 4 (2011): 563.

8. Ghiassi, Manoochehr, James Skinner, and David Zimbra. "Twitter brand sentiment

analysis: A hybrid system using n-gram

analysis and dynamic artificial neural

network." Expert Systems with

applications 40, no. 16 (2013): 6266-

6282.

70

Factors Influencing Selection of BBA Institute in GGSIPU-Students

Perspective

Ritu Singh*

Abstract:

The purpose of this paper is to identify the key factors which influence student’s decision in selecting BBA institute from Guru Gobind Singh Indraprastha University, Delhi. The data was collected from students studying in various BBA institutes affiliated to GGSIPU. Self-designed questionnaire administered electronically and personally for collecting the primary data. Convenience sampling technique was used to select the sample. Data was analysed using SPSS and

presented in the form of tables and charts and interpretations have been drawn using factor analysis technique. The results of the study have led to the identification of nine significant factors in selection of a BBA institute: Brand Image of the Institute, Co-Curricular Activities, Academic Performance, Word of Mouth, Location, Experienced & Educated

Faculty, Infrastructure, Fee Structure and Placement Record of the Institute. This Study will help BBA institutes to

understand the insights of students’ perspective on basis of which they select the institute.

Keywords: BBA Institute, Guru Gobind Singh Indraprastha University, Factor Analysis.

Introduction

Now a days, Indian Market is growing at faster rate. As

existing companies are expanding in size, new start-ups have

also come into the picture. All these companies require

candidates with sound business and operations knowledge.

Due to this, it has been observed a noticeable inclination for

management courses among youngsters for past few years.

Bachelor of Business Administration is one of the career

choice students make for their graduation studies. BBA is a

three-year undergraduate program. This course has appeared

as that one of the professional degrees which has a motive to

teach students the basics of business skills and offer deep

development for the management skills. Through this course,

students possessing substantial interest in becoming

management professionals get to acquire related knowledge

from square one. BBA is an excellent choice among

youngsters because it accepts students from any background.

Guru Gobind Singh Indraprastha University is the first university established in 1998 by Govt. of NCT of Delhi. The University is recognized by University Grants Commission (UGC), India under section 12B of UGC

Act. It is a teaching and affiliating University with the explicit objective of facilitating and promoting studies, research and extension work in emerging areas of higher

education with focus on professional education. In all, the

University has 100 affiliated institutes; of these, 76 are self-

financed and 24 are owned and managed by the Govt. of

NCT of Delhi/Govt. of India. In these affiliated institutions,

80 academic programmes are being conducted with an

intake of 22,000+ students with a total strength of 62,000+.

The BBA programme is divided into six semesters (five

months each) duration. Each semester comprises of five

theory subjects and three practical subjects. In addition to

this, every semester has a paper referred as Personality

Development & Communication Skills equal to the

weightage of one paper. Fifth semester has summer training.

In the sixth semester students have to submit project report,

which allows the Institute to expose the students to subjects

beyond the laid down syllabi and conduct research. This

course run in two-shifts each having intake of 120 each. The

existing reservation policy stipulates 10% seats (24 in

number) as “Management Quota Seats”. Out of the

remaining 90% (108 seats), 85% (184 seats) are reserved for

Delhi candidates and 15% (32 seats) for outside Delhi

candidates. There are more than 10 colleges offering BBA

which are affiliated to GGSIPU i.e. MSI, MAIMS, VIPS,

TIAS, GIBS, RDIAS, BCIPS, DSPSR, SGTBITM, DIRD

etc. As it is trendy degree course among youngsters, students are

* Assistant Professor, Gitarattan International Business School, Rohini, Delhi


Factors Influencing Selection of BBA Institute in GGSIPU- Students Perspective 71

becoming more aware and careful while selecting different

colleges, institutes and universities. Competitive pressure is

increasing day by day which is forcing university & the

educational institutions to look for different measures in

order to compete for students. Therefore, it become more

significant to study the different factors that influence

students’ selection of BBA institute from GGSIPU, Delhi.

Literature Review Mutairi & Saeid (2016) conducted a research on Factors

Affecting Students’ Choice for MBA Program in Kuwait Universities. In this study it was found that students considered alumni feedback and campus visit are the main sources of information followed by friends’ suggestions and university websites. Overseas accreditation was considered as the most used criteria which is followed by other factors like faculty reputation, institution reputation and admission requirements.

Bhuria, V. (2015) in his research on Selection Criteria of

College & Course in Engineering Education-Student

Perception showed that quality of campus placement,

quality of education and quality of faculty are the three

major criteria in the selection of college. Out of three

criteria campus placements was on the priority list. Aguado et.al. (2015) conducted his research on Factors

Affecting the Choice of School and Students’ Level of

Interest towards the Maritime Program. In his research it

was found that Students from public schools have

significantly higher possibility of being influenced by

their parents, siblings, friends and relatives. Moorthy, Mahendran & Saravanan (2014) in their

study on Impact of Choice Factors on Selection of

Engineering Institution in India found that quality of

teaching and value-added services to create good image

of the institute are the priority of the students.

Dubey Pushkar & Sudhir Kumar (2013) in their research

on College of Languages in Taiwan identified 11 different

factors which play a significant role in student’s decision-

making process. The study was carried out in College of

Languages in Taiwan with respect to distance learning

programme. Different factors in order of their importance

was found like advertisement, Famous and experience

faculty, Good infrastructure of an institution, Location of the

institution, communication facilities, Institutions fee

structure, priority to older institute. College hostel and its

facilities, good university results, Good track record of

institution, branch result, Good overall teaching, Regular

theory classes, Regular laboratory classes are given the

highest priority before taking admission in the college by

the students John, S. F & Senith S. (2013) conducted a study in India

on Factor Branding in Selection of Higher Educational Institutions in India measure the influence of branding in Engineering Institutions and the Service, Innovation, Quality, Price, Image and External Exposure. He concluded with his research by pointing that no higher educational institutions will survive in the future if they fail to brand their institution in the right way. Higher educational institutions have to brand their institutions before others brand. Sabir, R. I. (2013) conducted a research in Pakistan to

find out the factors that are related to student’s choice to select university and desired courses among undergraduate engineering and business students. In this research it was found that higher education commission ranking, institutional reputation and employment

prospects was the most important vital factors allied to the selection of desired university and course. Awang et al (2013) studied Students' attitudes and their

academic performance in nationhood education. This study

considered four main factors i.e suitability and interest in

syllabus, being active in class, early preparation and revision,

types of enjoyable activities, interest in teaching aids and

good class attendance. This research showed that students'

perception of their lecturers plays a significant role in

determining their learning outcomes in Malaysian Studies,

while the lecturers are of the opinion that learning

environment is a factor which contributes significantly to the

students' achievement in this subject. The study suggested

teachers to build upon excellent rapport with students and

create an attractive and enjoyable environment to get the

students to dynamically involve in the classroom activities

and learn the Malaysian Studies enthusiastically and

efficiently.

Mehboob Farhan et al (2012) in their research in Pakistan

explored three dimensions of Student Decision choice, one

aspect are the factors internal to student, driving them

internally towards selection decision. The second aspect is

the factors that lie external to student’s domain and

influence by and large to the decisions made by them. The

third important attribute is the social factor like friends,

parents and teachers. Their good or bad word of mouth, right

or wrong could make it very worthwhile for student decision

choice at the end. Three factors which are further

categorized into eleven sub factors, internal factors

(Aspiration, Aptitude, and Career), External factors (Courses,

Cost, Location, Reputation, Promotion,


72 Factors Influencing Selection of BBA Institute in GGSIPU- Students Perspective

and Facilities) and Social factors (parents/friends/teacher). Career & facilities were the most influential factors.

Shumba. A. & Naong. M (2012) conducted a research

on “Factors Influencing Students’ Career Choice and

Aspirations in South Africa” found that the family is a

significant factor in determining their career choice followed by the ability of the student’s awareness to

identify his/her preferred career choice and teachers

influence to find their career choice.

Ng et al. (2008) found that men and women appear to

have similar patterns in the factors affecting their career

choice. They also found that students placed a strong

emphasis on self-development. They observed that the

majority of students aspired to careers reflecting a desire

for career benefits and becoming wealthy.

Ming Kee Sia Joseph (2010) in his research on different

factors that influence student’s behavior in selecting a

college in Malaysia showed two broad factors i.e. “Fixed

College Characteristics” and the “College Effort to

Communicate with Students” that influenced student’s

college choice decision in Malaysia. Fixed College

Characteristics was included location, academic program,

college reputation, educational facilities, cost, and

availability of financial aid and employment opportunities.

In second factor advertising, higher Education Institutions

representatives and campus visit was considered

Sidin et al. (2003) conducted a research in which they

tried to find out the criteria with which students select

their tertiary institutions and it was observed that

academic quality, facilities, campus surroundings,

personal characteristics, income are the important

considerations for students and their families.

Agarwala Tanuja (2008) conducted a research on Factors

influencing career choice of management students in India.

It was observed that student’s choice for their management

career is based on their own “skills, competencies, and

abilities” and “education and training” (intrinsic career

choice factors). It was also found that “father” exerted the

greatest influence on their career choice.

Objective of the Study

To identify the factors influencing choice of BBA

Institute from GGSIP University, Delhi.


• Research type: This study was exploratory in

nature.

• Population: The population for the study was

Current students and Past Students of BBA

institutes affiliated from GGSIPU, Delhi.

• Sample size: 154

• Sampling Technique: Convenience sampling

technique was used to select the sample.

• Technique: Factor Analysis technique was used to

reduce the data and identify significant factors

affecting selection of a BBA institute by students

in GGSIPU

• Data Collection: Primary data was collected using self-reported questionnaire having two sections. The first section had questions on demographic variables like age, education, and marital status followed by second section which was related to different statements based on 5 Point Likert scale. The secondary data was obtained from Journals,

websites and reports to review the existing

literature

• Data analysis Tool: Data analysis has been done

using SPSS as a tool.

Demographic Profile of Respondents Descriptive Statistics, discussed in this study comprises of

the frequency and percentages of profiles of the respondents.

Demographic profiles of respondents according to variable

gender, marital status, age and education.

Table 1: Demographic Profile of Respondents

Gender Frequency Percentage

Male 46 29%

Female 108 70%

Education Frequency Percentage (%)

Graduate 102 66%

Post-Graduate 52 34

Age Group Frequency Percentage (%)

18-25 years 145 94%

26 and above 9 6%

Marital Status Frequency Percentage (%)

Married 8 5%

Unmarried 146 95%



Data Analysis Value of Bartlett’s test of sphericity and the Kaiser-Meyer-Olkin is used to measure the Sample Adequacy. As shown in table 2, the Kaiser-Meyer-Olkin measure was 0.781 which indicates that the collected data were suitable for exploratory factor analysis. Similarly, Bartlett's Test of Sphericity was significant at (p<0.001, p=0.000) which indicated sufficient correlation between the variables. The results of the EFA indicated Nine-factor structure using the criteria of an eigenvalue greater than 1. The extracted factors accounted for 67.728%of the total variance. All factor loadings were generally high, and the lowest loading was 0.667.

Table 2: KMO and Bartlett’s Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .781

Approx. Chi-Square 1509.467

Bartlett's Test of Sphericity Df 465

Sig. .000

Table 3 displays the total variance explained by the

identified nine factors affecting selection of BBA institute in GGSIP University. A total of nine factors

were extracted because their Eigen values were greater than “1”. These nine factors when extracted, were able to

explain 64.240 percent of the variance. The scree plot graphs the eigenvalue against the factor

number. Figure 1 showed the scree graph and it also

indicates that from the ninth factor the line is almost flat,

which means each successive factor is accounting for

smaller and smaller amounts of the total variance.

Table 4 showed a matrix of the factor loadings for each

variable onto each factor which create nine factors from

28 statements.

Table 3

Component Initial Eigenvalues Extraction Sums of Squared Rotation Sums of Squared Loadings

Total % of Cumulative Total % of Cumulative Total % of Cumulative

Variance % Variance % Variance %

1 7.124 22.981 22.981 7.124 22.981 22.981 4.056 13.085 13.085

2 2.394 7.724 30.705 2.394 7.724 30.705 2.951 9.52 22.605

3 1.908 6.155 36.86 1.908 6.155 36.86 2.434 7.85 30.455

4 1.821 5.875 42.734 1.821 5.875 42.734 2.231 7.196 37.652

5 1.693 5.462 48.196 1.693 5.462 48.196 1.821 5.893 43.545

6 1.506 4.858 53.054 1.506 4.858 53.054 1.67 5.386 48.93

7 1.285 4.144 57.198 1.285 4.144 57.198 1.65 5.322 54.252

8 1.108 3.574 60.772 1.108 3.574 60.772 1.608 5.186 59.438

9 1.075 3.468 64.24 1.075 3.468 64.24 1.488 4.801 64.24

10 0.992 3.201 67.441

11 0.937 3.024 70.465

12 0.83 2.677 73.142

13 0.744 2.399 75.541

14 0.682 2.202 77.742

15 0.657 2.12 79.863

16 0.639 2.06 81.922

17 0.601 1.937 83.86

18 0.558 1.801 85.661

19 0.543 1.752 87.413

20 0.48 1.547 88.96

21 0.47 1.517 90.477



22 0.43 1.388 91.865

23 0.378 1.221 93.085

24 0.35 1.13 94.215

25 0.345 1.112 95.327

26 0.314 1.011 96.339

27 0.258 0.833 97.172

28 0.252 0.812 97.984

29 0.209 0.674 99.395

30 0.188 0.605 100

Figure 1: Scree Plot After performing varimax rotation method with Kaiser normalization, Factor 1 comprised of three items, Factor 2 comprised of four items, Factor 3 comprised of three items with the following, Factor 4 comprised of three items, Factor 5 comprised of three items, Factor 6 comprised of three items, Factor 7 comprised of three items, Factor 8 comprised of two items, and the last factor 9 comprised of the 2 items.

Nine factors were successfully found using factor analysis

which influence students’ selection of BBA institute from GGSIPU.

Table 4: Rotated Component Matrix using Varimax

Rotation Method

Statement Component

1 2 3 4 5 6 7 8 9

S1 .824

S2 .679

S3 .853

S4 .766

S5 .882

S6 .677

S7 .679

S8 .729

S9 .707

S10 .742

S11 .778

S12 .843

S13 .784

S14 .815

S15 .776

S16 .795

S17

S18 .718

S19 .759

S20 .709

S21 .673



S22 .819

S23 .836

S24

S25 .809

S26 .770

S27 .794

S28 .828 Table 5 showed the name of extracted nine factors,

statements included in each factor and the percentage of

variance explained by each factor. The first factor shows

the highest percentage of variance explained when it was

extracted. Table 5: Final Factors list which influence selection of

BBA institute from GGSIPU

S. No Factors Statements Variance

Explained %

1 Brand image of S22, S25, S26 13.085 the institute

2 Co- curricular S6, S7, S8, 9.520 activities S9.

3 Academic

S16, S21, S23 7.850 Record

4 Word of mouth S13, S14, S15 7.196

5 Location S1, S2, S3. 5.893

Experienced

6 & Educated S18, S19, S20 5.386

Faculty

7 Infrastructure S10, S11, 5.322 V12.

8 Fee structure S27, S28. 5.186

Co-Curricular

0.815 4 Activities

Academic

0.728 3 Performance

Word of mouth 0.790 3

Location 0.824 3

Experienced &

0.702 3 Educated Faculty

Infra structure 0.734 3

Fees Structure .741 2

Placement Record .729 2

Findings of the Study Factor analysis is used to identify latent constructs or factors.

It is commonly used to reduce variables into a smaller set to

save time and facilitate easier interpretations. The

interpretation of factor analysis is based on rotated factor

loadings, rotated eigenvalues, and scree test. Their fore to

fulfil our objective, we have used factor analysis technique

to find out different factors which are influencing students’

selection of a particular BBA institute from GGSIPU. This

study was done with the population of 154 students of

different IPU affiliated BBA institutes and it is observed and

analysed that students get motivated through number of

factors while selection of a particular institute for their

admission. There are certain factors which are more

important and is on the priority list of the students. Nine

factors have been identified that influence students’ selection

of BBA institute from Guru Gobind Singh Inderprastha

University. These factors are Brand Image of the Institute,

Co-Curricular Activities, Academic Record, Word of Mouth,

Location, Education & Experience of faculties,

Infrastructure, Fee and Placement Record of the Institute.

Reliability Testing Limitations & Scope of the Study A reliable measure should have an alpha value of .70 or

more (Nunnally 1978). Reliability scores of all the constructs were found to exceed the threshold set by

Nunnally, all measures demonstrated good levels of reliability (greater than 0.70). The output of the same is

given in Table 6. Table 6: Reliability Statistics of identified factors

Factor Cronbach's Alpha N of Items

Brand Image 0.845 3

1. The size of the sample was restricted to BBA

institutes of only one university i.e. Guru Gobind

Singh Indraprastha University, Delhi. 2. Effective sample size for conducting research was

154 which was small as compared to total

population. 3. Time factor was another limitation in the research.

4. The scope of the study is limited to GGSIPU.



Recommendations

1. Brand image of the institute is on priority list of the students while choosing institute for their admission. Institutes reputation and ranking majorly influence student’s perspective for their admission. If institute fail to create and maintain its brand image in right way then it would be very difficult for the survival of the institute. Therefore, it is recommended for the institutes to work on its brand image in a positive way.

2. Past Academic Record of the institute is also an

important factor for the student’s selection criteria.

Therefore, institute should focus on the quality of

education which they are providing to their

students.

3. Location is one of the major concerns for the students and their family members because of many factors i.e. distance from home, travelling time, connectivity, safety etc. Therefore, to get more admission, institute should emphasise on the connectivity & safety issue, wherein it can provide institute buses for different route along with safety measures

4. Placement activities &record of the institute is

another very important factor while selecting the

institute. In present scenario, mostly students want to

join good organisation immediately after their BBA

so, they prefer institutes which provide more

industry exposure with good placement records.

5. Experience, education & knowledge of Faculty is another most important criteria while selecting the institute. As quality of education provided by the college is based upon the knowledge and experience of its faculty member, institutes must focus on their faculties as well. They can upgrade their faculty time to time by providing different measures like FDP etc. to get the better results from the students.

6. College infrastructure should be designed in such a

way where all facilities are present in classrooms i.e.

Digitised library, WI-FY access to students, well

equipped computer laboratory, canteen etc.

Scope for Future Research

In view of the above-mentioned limitations, future

studies can also include different institutes, colleges &

universities other than GGSIPU. Sample size can be

large to make the result in more generalised form. Future

researcher also compares different university or institutes

to check the student’s perspective and priority related to

different factors for their selection for admission.

Conclusion It is important for the university and its affiliated institutes to maintain quality by giving weightage to different factors and get more students for admission. The

results and findings of this study may provide university or college administrators with some insight into the factors to focus to grab more attention from student’s side by influencing their choice for selection of the institute.

References • Abdullah AL-Mutairi1 & Muna Saeid (2016), Factors

Affecting Students’ Choice for MBA Program

in Kuwait, International Journal of Business and

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78

A Study of consumer Behavior towards Patanjali Products

Ms. Seema Chaudhary* Ms. Sinjia Gupta**

Abstract:

Patanjali is also being called the Indian Body Shop. This research paper is an attempt to understand the buying

behavior of the consumer for Patanjali products. The fast growing cosmetic industry has focused on organic, herbal

and Ayurvedic products. For purchasing products the consumer go through the various phases that is also known as

process of consumer buying behavior .Patanjali is one of the fastest growing company in today's world. The company

is expected to revenues of Rs 20,000 crore by fiscal year 2020 (IIFL Institutional Equities Report). 150 families were

randomly selected for collecting the data. The Questionnaires which we are given to the respondent provide various

information which includes competitive pricing, quality of product, Brand image of baba Ramdev and good

advertising strategies were the most important reason for popularity of Patanjali product. Questionnaires and

interview technique were used for collecting the information. This study also aims at identifying customer’s perception

towards present and expected products from Patanjali.

Keywords: Consumer, Price, Behavior, Product, Purchase

Introduction

Patanjali Ayurveda started in 2007. The patanjali limited is an Indian FMCG Company. Manufacturing unit and headquarter are located in the industrial area of Haridwar. While the register office is located at Delhi .The Company manufactures mineral and herbal products.

Patanjali declared its annual turnover of the year 2016-2017

to be estimated Rs 10,216 crore (US $ 1.6 billion). Patanjali

Ayurveda home grown firm in business such as food,

consumer packaged goo and healthcare. The vitial principals

that drive the patanjali business are cost effective manner of

production, world class quality with natural ingredients and

plugging back the profits into the business. Baba Ramdev,

price, and quality are the three most important factors

working for growing sales. And the recommendation of

Patanjali is working very well for the brand itself as people

are recommending it to their friends and family members. It

sums that there is more acceptability of the brand amongst older people the

younger people but won’t take time for that change. It has

also positioned itself as swadeshi brand, which has appeal

among category of consumer.


1. To know why consumer, prefer Patanjali products.

2. To study the source of consumer preferences.

3. To study the product expected by the consumer in

future from Patanjali.

* Research Scholar, Beacon Institute of Technology, Meerut., (U.P.)

** Assistant Professor, Beacon Institute of Technology, Meerut., (U.P.)

4. To study the satisfaction level of consumers after

using Patanjali products. Research Methdology This paper is based on primary data and secondary data. Primary data through questionnaire from 150 users of

Patanjali products within Meerut. The sampling method

used in the study was random sampling secondary data through research report, journals, newspapers and

website of Patanjali.

Data Analysis

• The collected data of fig. 1 refers to According to

age groups interest of respondents towards

Patanjali products and we analysis 18-28 age

group prefer maximum Patanjali products.

(fig. 1)


A Study of consumer Behavior towards Patanjali Products 79

• In table 2 respondent divide gender wise male

respondents were 22 and female were 28.

products ,30(20%) respondents prefer cosmetic

product and 15(10%) rspondendents prefer

ayurvedic products

Gender No. of Respondents

Male 66

Female 84

Table 1

• Table 3 refers to the occupation of respondent

among which 55% of the respondents were

employees,20% are student and other 25% are

self employed and others.

•

Fig.3

• Table 4 is referring to the preferences of respondent

towards Patanjali product where 5 factors were taken

and allotted them some ranking. The Ranking

showing the preferences of respondents towards

Patanjali product and their satisfaction level.

Fig. 5 In table 6 respondents give information regarding future product which one they want in future from

Patanjali and we get data through questionnaire is

75(50%) respondents want dry fruits, 60(40%) respondent want clothes and the 15(10%)

respondents want perfumes in the market.

Factor impacting on consumer towards purchasing

Patanjali product

Factors Strongly

Agree Neutral Disagree Strongly

Total Ranking Agree disagree

Reasonable 60 57 15 3 15 150 4

price

Good 75 60 9 6 0 150 1

quality

Baba 69 60 15 3 3 150 3

Ramdev

Swadeshi 75 60 6 6 3 150 2

advertising 15 45 60 15 15 150 5

Table 2

• Table 5 shows that products which consumer

purchase more and we find that out of 150

respondents 105(70%) respondents prefer food

Fig.6

Conclusion The findings in the paper show that 18-28 age group

consumers prefer Patanjali product mainly cosmetic and

other age group consumer mainly prefer food product.

Respondents were showing their interest in more products

and they expected that Patanjali cover all type of product. In

this survey we get that all type occupation people were

preferring Patanjali product. It means Patanjali not affect

only one level of consumer, it covers all level of consumer.

It gives the path that if Patanjali manufacture other products

it will get positive response from consumers.


80 A Study of consumer Behavior towards Patanjali Products

References

• http://www.ibef.org/industry/Indian-consumer-

market.aspx

• http://www.patanjaliayurved.net

• patanjali_ayurved_visit_note_oct_15_EDEL

• http://en.m.wikipedia.org/wiki/patanjali_Ayurved

• Herbal Medicine for Market Potential in India: An

Overview (2008) Sharma, Shankar, Tyagi, Singh, &

Rao

• Kotler, P. (2008). Marketing Management (11th ed).

New Delhi: Pearson Education


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CPJ-CHS & School of Law National Journal Volume X | Issue 3 | November 2018 Special Edition

Special Edition · 2019-09-26 · Delhi Ms. Pratibha Gautam Assistant Professor, Computer and...

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