SuGyaan 1 - SSIMV. Ramana Rao examined various critical issues involved in digitalization of financial services in the areas of convergence of financial services and technology, various

Volume: X, Issue - I, Jan - June, 2018

SuGyaan 1

CONTENTS

Title Page #

ARTICLES

Impact of Voluntary & Involuntary Factors Affecting Employee Retention: A Study on Select I.T. Companies in Hyderabad Region.................................... 5 – T. Sharanya & Dr. Ilyas Ur Rahman

Use and Objectives of Usage of Social Media as a Promotional Tool by the Micro Small and Medium Enterprises According to their Classification in Hyderabad, Telangana ............................................................ 13 – Madhusudan Kumar Kota & Dr. M. Anil Ramesh

Fintech Services in India: Issues and Challenges ............................................ 26 – Dr. S. V. Ramana Rao

Image Retrieval Using Color Features .............................................................. 35 – Venkataramana Battula, B. Sandhya & A.V. Krishna Prasad

Digitalization of Indian Economy and its Impact ............................................... 45 – Dr. D. Indira, Dr. K. V. S. Raju

Business Process and Re-Engineering – With ERP ......................................... 51 – Srinivasa Rao. Budde & Dr. Y.V. Rao

Copyright: Siva Sivani Institute of Management, Secunderabad, India. SuGyaan is a bi-annual publication of the Siva Sivani Institute of Management, NH-7, Kompally, Secunderabad- 500 100.

All efforts are made to ensure correctness of the published information. However, Siva Sivani Institute of management is not responsible for any errors caused due to oversight or otherwise. The views expressed in this publication are purely personal judgments of the authors and do not reflect the views of Siva Sivani Institute of Management. All efforts are made to ensure that published information is free from copyright violations. However, authors are personally responsible for any copyright violation.


SuGyaan 2EDITORIAL : JAN-JUNE, 2018

SPECIAL ISSUE ON “DIGITIZATION OF BUSINESS & ANALYTICSIt gives me immense pleasure in presenting before you SUGYAAN, Volume–X, Issue–I, Jan-June 2018, Management Journal of Siva Sivani Institute of Management. In its ninth year of existence Sugyaan has received a tremendous response from its readers and contributors. Our sincere gratitude to the readers, authors and reviewers for their continuous support and encouragement. This is a special issue on “Digitization of Business & Analytics”. In our continuous effort to contribute to the cause of nation building by promoting quality research through thought provoking ideas in the form of research papers, articles, case studies, and book reviews in the journal. Current Issue of Sugyaan have included six research papers from the discipline of Social Media, Digitization, Fintech Services, Image analysis and Business Process and Re-Engineering etc., First research paper titled “Impact of Voluntary & Involuntary Factors Affecting Employee Retention: A Study on Select I.T. Companies in Hyderabad Region” by T. Sharanya & Dr. Ilyas Ur Rahman examined the impact of factors such as freedom of work, decision making, fringe benefits, higher education, career development programs etc., on employee retention related issues in select IT companies in Hyderabad. They concluded that these factors significantly contributing to intention of employees to stay with the company in the long run. Second Research Paper titled “Use and Objectives of Usage of Social Media as a Promotional Tool by the Micro Small and Medium Enterprises According to their Classification in Hyderabad, Telangana”, by Madhusudan Kumar Kota & Dr. M. Anil Ramesh studied the MSMEs use of social

media as a marketing tool for promoting their products in terms of advertising, awareness, market research, exploring new customers etc., They concluded that majority of the MSMEs are using face book and whats app as promotional tools than compared with Linden, You tube, Instagram etc., Third research paper titled “Fintech Services in India: Issues and Challenges”, by Dr. S. V. Ramana Rao examined various critical issues involved in digitalization of financial services in the areas of convergence of financial services and technology, various fin tech segments like credit, investment management, payment services, personal financial planning etc., trends and challenges. Fourth research paper titled “Image Retrieval Using Color Features”, by Venkataramana Battula, B Sandhya & A.V.Krishna Prasad examined various image reduction techniques and addressed image mining techniques and issues. The paper mainly focus on image extraction, clustering techniques used in image analysis. Fifth research paper titled “ Digitalization of Indian Economy and its Impact”, by Dr. D. Indira, Dr. K. V. S. Raju, studied various digital initiatives of Central Govt. like SWAYAM, MyGov, DBT, E-Hospital, E-Trade, GeM etc., and examined the problems and prospectus of digital economy. Sixth research paper titled “Business Process and Re-Engineering – With ERP”, by Srinivasa Rao. Budde & Dr.Y.V.Rao studied various ERP packages with modules used in the industry and a detailed comparison was drawn across various firms. The last section of the article examined the various phases of business re-engineering and implementation of ERP packages in the company.


SuGyaan 3

IMPACT OF VOLUNTARY & INVOLUNTARY FACTORS AFFECTING EMPLOYEE RETENTION: A STUDY ON SELECT IT COMPANIES IN HYDERABAD REGION

* T.Sharanya ** Dr. Ilyas Ur Rahman

ABSTRACTArticle explores the description of voluntary & involuntary factors of employee retention in Hyderabad Region. The paper also explains need and importance to retain the valuable human resources with relevant retention concepts. The main objective of this paper is to find out & how voluntary factors such as freedom of work, decision making, fringe benefits etc, and involuntary factors like higher education, career development programs will have impact on employee retention in IT companies in Hyderabad. The primary data was collected from IT employees with a well-structured questionnaire. The researcher absorbed random sampling method and the sample size is 100 respondents from Hyderabad region has been taken for the study. The perception of the employees relating to factors on employee retention has been analysed with Cronbach alpha test, Factor Analysis and Correlation. From the research it was established that voluntary and Involuntary factors directly and significantly influences intention of employee to stay back for the companies.

Keywords: voluntary factors, involuntary factors, employee retention..

JEL Classification Code: M5, M50, M51

IntroductionEmployee retention is a process in which the employees are motivated to remain with the organization for the maximum period of time or until the completion of the project. Employee retention is valuable for the organization as well as the employee. Employees attitudes now a days are different. They are not satisfies if don’t have good opportunities in hand. As soon as they feel dissatisfied with the current employer or the job, they switch over to the next company for the job. It is the responsibility of the employer to retain their best employees. If they don’t, they would be left with no good employees. A good employer should know how to attract and retain its employees. Retention involves five major things: Compensation, Support, Relationship, Environment, Growth etc Effective employee retention is a systematic effort by employers to create and foster an environment that encourages current

* Asst. Professor, Dept. of MBA, CMR CET, Hyderabad, Mobile: 9985408941 [email protected]

** Principal, Shadan Institute of Management, Hyderabad.


SuGyaan 4employees to remain employed by having policies and practices in place that address their diverse needs. A strong retention strategy becomes a powerful recruitment tool. Retention of key employees is critical to the long-term health and success of any organization. It is a known fact that retaining your best employees ensures customer satisfaction, increased productivity, satisfied colleagues and reporting staff, effective succession planning and deeply imbedded organizational knowledge and learning. Employee retention matters as organizational issues such as training timing time and investment; lost knowledge insecure employees and a costly candidate search are involved. Hence failing to retain key employee is a costly proposition for an organization. Various estimates suggest that losing a middle manager in most organization’s costs up to five times of his salary. Intelligent employers always realize the importance of retaining the best talent. Retaining talent has never been so important in the Indian scenario; however, things have changed in recent years. In prominent Indian metros at least, there is no dearth of opportunities for the best in the business, or even for the second or the third best. Retention of key employees and treating attrition troubles has never been so important to company.

Review of LiteratureZhang, (2016) study on Interpersonal relationship amid the different departments has a significant impact on employee turnover intention. When an organization or a department have intricate interpersonal relationship, there are many sections or small groups, it may be complicated for employees to deal with the relationship with coworkers and managers, or the workers are to spend a lot of energy to have relationships within the organization or the department, they are rather likely to leave the job.

Al-Emadi, Schwabenland and Wei (2015), retaining employees is more important than just focusing on hiring them. Employee retention is one of the biggest challenges affecting organisations (Das, Nandialath, & Mohan, 2013, p. 15). In addition, turnover is a concern in organisations worldwide (Amah 2009, p. 2). Turnover drains the operating costs of organisations and has significant implications with regards to the loss of human capital and interruptions in organisational activities (Takawiara, Coetzee, & Schreuder, 2014, p. 2).

Pillay, Buitendach & Kanengoni, (2014) study examined that IT industry is the world’s fastest growing industry ever since the beginning of the 21st century. According to Simons, & Buitendach, the number of employees within the South African IT sector increased from 50 000 in 2005 to 180 000 in 2010. Furthermore, it was predicted that by 2015 approximately 100 000 new jobs would be created in South Africa.

Objectives:• To identify the factors causing employee Retention in the organization.


SuGyaan 5• To study factors causing voluntary and non voluntary Retention in IT companies.

Hypothesis:

• Ho 1: There is no significant relation between the Voluntary factors and employee retention.

• Ho 2: There is no a significant relation between the Involuntary factors and employee retention.

MethodologyA survey was conducted by administering a structured questionnaire on organization’s Voluntary and non voluntary factors to 120 employees of Top 10 IT companies of Hyderabad city out of which only 100 respondents response has been collected. Close-ended questions on 5 point likert scale were given to respondents from which the respondents had to select the suitable choice (ranging from Excellent, Good, Satisfactory, poor and very poor).

FINDINGSTable 1 indicates the percentage of the sample drawn based on gender. 75% of respondents were male and, 25 % were female. It was found that there were various position in terms of designation, Among that total percentage of Top level is 12% out which 8% are having 8-12 years of experience, 4% are having 12-15 years of experience. Total percentage of middle level is 62% of which 13% are having 6-8 years of experience, 49% are having 3yrs to 5 years of experience. Total percentage of lower level is 26% out of which 26% are having1year to 2 years of experience.

Table 1. Demographic variables (sample size 100)

DEMOGRAPHIC ANALYSIS

Category Total %in totalAge No of respondents Percentage

21-25 36 3626-30 56 5631-35 5 536-40 3 3

Gender No of respondents PercentageMale 75 75

Female 25 25Work Experience No of respondents Percentage

0-2 26 263-5 49 49


SuGyaan 6

6-8 13 13 8-12 8 812-15 4 4

Designation No of respondents Percentage3D Designer 1 1

ADF Developer 2 2Assosiate Software Engineer 10 10

Assistant Manager 1 1Automation Engineer 1 1

Data Analyst 2 2Deputy Manager Developer 2 2

Executive 1 1Engineer Financial Analyst 1 1

Floor Incharge 1 1HR 3 3

Manager 3 3Managing Director 1 1Network Engineer 1 1

Officer 1 1Programmer 4 4QA Analyst 1 1

Quality control 1 1Research associate 1 1Senior consultant 1 1Senior engineer 51 51

Senior Software Engineer 1 1Supervisor 1 1

System analyst 1 1Team lead 5 5

Trainee 1 1Unix admin 1 1

Table 2. Factor Analysis

KMO and Bartlett’s TestKaiser-Meyer-Olkin Measure of Sampling Adequacy. .824Bartlett’s Test of Sphericity Approx.

Chi-Square766.314

Df 190Sig. .000


SuGyaan 7Table 3. Rotated Component Matrix

Rotated Component Matrixa

Component1 2 3 4 5 6

VAR00001 .598 .051 .057 -.391 -.095 .242VAR00002 .075 .786 .043 -.013 .116 -.138VAR00003 .612 .041 .126 -.229 -.173 -.188VAR00004 -.273 .596 -.059 .272 -.004 .408VAR00005 -.460 .546 .315 .194 -.203 .067VAR00006 -.245 .477 -.056 .285 .393 .347VAR00007 .063 -.248 .322 -.300 .646 .252VAR00008 .137 .286 .100 -.027 .775 -.177VAR00009 -.146 .434 .581 -.204 .163 .356VAR00010 -.314 -.006 .035 .175 -.053 .761VAR00011 .508 .002 .097 .144 .254 -.136VAR00012 .752 -.074 .012 .129 .177 -.197VAR00013 -.062 .086 .048 .783 -.083 .126VAR00014 .023 .162 .586 .610 -.084 .180VAR00015 .051 -.054 .853 .120 .215 -.133VAR00016 .734 .049 .192 -.013 .077 -.036VAR00017 .714 -.091 -.114 -.178 -.167 -.180VAR00018 .761 -.262 .026 .028 .177 -.170VAR00019 .688 -.026 -.209 -.171 -.040 -.143VAR00020 .791 -.259 -.235 .051 .059 .056

Table 2, Table 3 presents the result of Kaiser-Meyer-Olkin Measure of Sampling Adequacy test. Since the score .824 hence the data is valid. In this analysis around twenty factors took for the impact of factors towards retention in the IT sector in Hyderabad region, these all factors have impact towards retention from these decision making, freedom of work have more impact on employee retention according to rotated component matrix and as well as Component Score Coefficient Matrix.

Table 4. Cronbach’s Alpha Reliability Test

Reliability

Reliability StatisticsCronbach’s Alpha Cronbach’s Alpha Based on

Standardized ItemsN of Items

.571 .612 20


SuGyaan 8Table 4 shows that the result of Cronbach’s Alpha Based Standardized items. Since the score .571 hence the data is valid, and all voluntary and Non voluntary factors have interrelation among them.

Table 5 & 6. Mean, Standard Deviation & Correlation

CORRELATION BETWEEN VOLUNTARY FACTORS AND EMPLOYEE RETENTION

Descriptive StatisticsMean Std. Deviation N

Voluntary Factors 3.1500 1.13264 100Employee Retention 1.6770 .81681 100

CorrelationsVOLUNTARY

FACTORSEMPLOYEE RETENTION

VOLUNTARY FACTORS Pearson Correlation 1 .752**Sig. (2-tailed) .000N 100 100

EMPLOYEE RETENTION Pearson Correlation .752** 1Sig. (2-tailed) .000N 100 100

**. Correlation is significant at the 0.01 level (2-tailed).

Table 5 explains about Mean and Standard deviation of Voluntary Factors and Employee Retention. Table 6 explains about correlation between Voluntary Factors and employee retention and here Pearson Correlation is .752 so there is a positive correlation between Voluntary Factors and employee retention HenceHo1 null hypothesis is rejected.

Table 7 & 8. Mean, Standard Deviation & Correlation

CORRELATION BETWEEN IN VOLUNTARY FACTORS AND EMPLOYEE RETENTION

CorrelationsMean Std. Deviation N

IN VOLUNTARY FACTORS 1.7200 .87709 100EMPLOYEE RETENTION 1.6000 .77850 100


SuGyaan 9

CorrelationsIN VOLUNTARY

FACTORSEMPLOYEE RETENTION

IN VOLUNTARY FACTORS Pearson Correlation 1 .967**Sig. (2-tailed) .000

N 100 100EMPLOYEE RETENTION Pearson Correlation .967** 1

Sig. (2-tailed) .000N 100 100

**. Correlation is significant at the 0.01 level (2-tailed).

Table 7 present Mean and Standard deviation of In Voluntary Factors and Employee Retention. Table 8 depicts correlation between Involuntary Factors and employee retention and here Pearson Correlation is .967 so there is a positive correlation between Involuntary Factors and employee retention Hence Ho1 null hypothesis is Rejected.

FindingsOrganization must help employees to acquire new skills so that organization goals could be matched up with individual goals by providing required training, An organization should be aware of the needs of the employees before it can launch its retention plans for them, HR must take certain measures to reduce employee stress level by organizing seminars on stress reduction, yoga, one day camps, picnics etc.

Employee retention tools such as job rotation, employee engagement must be introduced in the organization in order to equip employees with the trait of multitasking, HR must design an attractive compensation package according to the job description of the employees, If you are to conduct an exit interview, it is far better to get a neutral third party to conduct them. This might help to improve the reliability of exit interviews.

Conclusion The main aim of this study was how IT companies Voluntary and In voluntary Factors have impact on employees retention. The study found that IT companies in Hyderabad taking measures to retain existing employees. This study can help the top-management encourage voluntary aspects in IT companies that may better develop their employee’s retention, Hence Companies meet their goals and objectives.


SuGyaan 10

References1. Dalton, D.R., & Mesch, D.J. (1990). The impact of flexible scheduling on employee attendance and turnover. Administrative Science Quarterly, 35,

2. AGENDA CONSULTING, (2005), PeopleCount Voluntary Sector, Benchmarks for Human Resources in the UK Voluntary and Community Sector

3. Trevor CO, Gerhart B, Boudreau JW (1997). Voluntary turnover and job performance: curvilinear and the moderating influences of salary growth and promotions. Journal of Applied. Psychology., 82(1): 44-61.

4. Maertz, C.P., Jr., & Campion, M.A. (1998). 25 years of voluntary turnover research: A review and critique. International Review of Industrial and Organizational Psychology, 13, 49- 81.

5. Lee, T. W.; and Mowday, R. T. (1987). “Voluntarily Leaving an Organization: An Empirical Investigation of Stress and Mowdays Model of Turnover”, Academy of Management Journal, pp. 721-743.

6. Stauss, B., Chojnacki, K., Decker, A., Hoffman, F. (2001). “Retention effects of a customer club”, International Journal of Service Industry Management, Vol. 12 No.1, pp.7-19.

7. Davies, D., Taylor,R., Savery, C (2001). “The role of appraisal, remuneration and training in improving staff relations in the Western Australian accommodation industry: A comparative study”. Journal of European Training, 25 (6/7). 366-373.

MJSSIM X (I),1,2018


SuGyaan 11

USE AND OBJECTIVES OF USAGE OF SOCIAL MEDIA AS A PROMOTIONAL TOOL BY THE MICRO SMALL

AND MEDIUM ENTERPRISES ACCORDING TO THEIR CLASSIFICATION IN HYDERABAD, TELANGANA

Dr. M. Anil Ramesh* Madhusudan Kumar Kota**

ABSTRACTA decade ago the promotional tools are very limited and traditional media like print, television and radio was the option for most of the firms. But costly nature of the traditional media alienated small firms in particular Micro, Small and Medium Enterprises (henceforth called as MSMEs). Eventually marketing has become one of their primary challenge. With the evolution of internet, affordable smartphones, cheap data plans, quick adoption of social media by the Indians, and the affordable cost of social media provided an opportunity for the MSMEs to aggressively push their marketing activities by using social media. In this study a sample size of 210 MSME were studied with an objective of understanding the use of social media and the objectives of usage of social media. It was found that Facebook and WhatsApp are the most favourite social media platforms of the MSMEs.

Key Words: Social media; MSME, Promotional tools

JEL Classification Code: M3, M30, M31, M37,

IntroductionA decade ago the availability of promotional tools for business was very limited. Traditional media like print, television and radio were only options. Traditional media was expensive, and only large firms were able to afford. As small firms run on tight budgets, they had to rely on word of mouth from the customers to promote their products and services. The entry of internet gave the small firms an opportunity to choose media that are effective and affordable to the small firms. Increase in mobile usage, cheap data plans, better connectivity propelled the emergence of digital media and ‘new media’ called social media.

Social media is built on the foundations of many-to-many communication model, unlike one-to-many communication model on which traditional media was built (McCann 2015). Consumers are not only using this new media for researching the products and services but

* Director, Siva Sivani Institute of Management, Kompally, Secunderabad.

** Research Scholar, PP.MAN:56, Rayalaseema University, Kurnool. Email id: [email protected]


SuGyaan 12have also started to engage with the firms (Constantinides 2014). Social media changed the marketing ecosystem. Social media has empowered the customers as they are now more informed, interactive and influential. The beauty of social media is that it is highly interactive, measurable and is mostly unpaid.

The MSMEs sector emerged as one of the fastest growing sectors in Indian economy which contributed 37.5 per cent to GDP and employment to 11.7 crore Indians. Usage of social media will help the MSMEs to interact, engage and establish relationships with the customers (Agnihotri et al., 2012). Small firms are generally slow in adoption of new technologies, and social media is no exception. The approached the usage of social media by MSMEs are varied. According to Anderson (cited in Constantinides 2014) and Parise and Guinan (2008), the passive approach is employing social media as a source to seek information about the market, customer and the competition. The active approach is employing social media as a communication tool, acquire and retain customers. (McKinsey 2007 a, b)

Social media created a level playing field for the MSMEs and placed them at par with large firms. Opportunity to MSMEs to promote their products and services. MSMEs cannot depend on the traditional promotional tools, and it is imperative that MSMEs adopt social media as a promotional tool.

This paper studied the various social media used by the MSMEs to promote their business. The paper also explored the objectives of the MSMEs to use social media.

Review of LiteraturePenumaka (2009) rightly called Indian MSMEs as the steroids for the Indian economy as MSME significantly contribute to the exports, employment, alleviating poverty and augment to regional and social equity. The MSMEs sector emerged as one of the fastest growing sectors in Indian economy with 5 crore MSMEs contributing 37.5 per cent to GDP and generating employment for 11.7 crore Indians.(KPMG, 2017)

MSMEs are classified into Micro, Small and Medium enterprises depending on the level of capital invested.

Table 1: MSME classification based on Capital invested

MSME Classification Manufacturing Service

Micro Investment ≤ 25 lakhs rupees Investment ≤ 10 lakhs rupeesSmall Investment >25 lakhs but < 5 crores rupees Investment >10 lakhs but < 2 crores rupeesMedium Investment > 5 crores but < 10 crores rupees Investment > 2 crores but <5 crores rupees

Source: Ministry of Micro, Small and Medium Enterprises, Government of India


SuGyaan 13According to Morris (2002), small firms are operating in an atmosphere of uncertainty, lack of forecasting, blurring boundaries and mindset that is stuck in traditional management. Firms’ competition landscape is influenced by forces like change, complexity, chaos and contradictions (Hitt and Reed, 2000). These forces have an overbearing impact on marketing.

Characteristically small firms are different from large firms (McCartan-Quinn and Carson, 2003) and small firms are more flexible, innovative and has cost advantages on their side. (Walsh and Lipinski, 2009). Marketing power, capital and managerial resources are significant disadvantages for small businesses (Motwani et al., 1998). Notwithstanding their size small businesses carryout promotional activities in some form or other. Small businesses can not garner the benefit of marketing due to lack of marketing knowledge, poor cash flows, the small size of operation, strategic and tactical problems related to the customers (O’Dwyer, Gilmore &Carson, 2009). noted that “small firms face marketing challenges Romano and Ratnatunga (1995) which can and will ultimately determine their future”(pp.9-30).

Small businesses can derive global position by engaging in innovative marketing practices which is a critical component in small business’s profitability, long-term growth and survival (Gilmore et al., 2001). Alves, H., Fernandes, C., & Raposo, M. (2016) argued that small firms should adopt social media for marketing activities like branding, market research, customer relationship management, service provision and sales promotions. According to Kaplan and Haenlein (2009) “Social media is a group of internet based applications that build on the ideological and technological foundations of Web 2.0 and that allow the creation and exchange of the user-generated content”(p.61).

According to We Are Social (2018) out of total Indian population of 1347 million (134.7 crores); 462 million (46.2 crores) are internet users; 250 million (25 crores) are active social media users; 844 million (84.4 crores) are unique mobile users, and 230 million (23 crores) are using social media on mobile. With customers moving towards social media it is imperative that MSMEs adopt social media as a promotional tool.

The 2018 Social Media Marketing Industry Report published by Stelzner, M. (2018), of Social Media Examiner, listed the major reasons behind small businesses using social media an Increase exposure, generate leads, develop loyal fans, provide marketplace insights, provide marketplace insights, improve sales, grow business partnership and Increase thought leadership. According to Stelzner, M. (2018), the most important social media platforms for business are Facebook (67%), LinkedIn (12%), Instagram (10%), Twitter (5%), YouTube (4%) and Pinterest (2%). According to Dua, H. S. (2017) easy business visibility; more and higher rate of conversions; lower cost of marketing of products and services; better customer experiences; higher customer loyalty and brand authority and increase in inbound website traffic are the major advantages of using social media by the small businesses.


SuGyaan 14

Research ObjectivesThe present study intends to

• Study various social media that are used as a promotional tool by the MSMEs according to their classification.

• Study the relationship between MSME classification and social media usage.

• Study the influence of MSME classification on the objectives social media usage of MSME owners.

Hypothesis:H1: There is a significant relationship between MSME Classification and the objective: Advertise and promote product and services.

H2: There is a significant relationship between MSME Classification and the objective: Create awareness of our brand.

H3: There is a significant relationship between MSME Classification and the objective: Conduct market research.

H4: There is a significant relationship between MSME Classification and the objective: Get referrals( through likes, shares, followers, and Word of mouth).

H5: There is a significant relationship between MSME Classification and the objective: Reach new customers.

H6: There is a significant relationship between MSME Classification and the objective: Develop existing customer’s relationship with better engagement.

H7: There is a significant relationship between MSME Classification and the objective: Quick communication with customers.

H8: There is a significant relationship between MSME Classification and the objective: Conduct customer service activities.

H9: There is a significant relationship between MSME Classification and the objective: Receive customer feedback on existing products/services directly and quickly.

H10: There is a significant relationship between MSME Classification and the objective: Receive customer feedback on existing products/services directly and quickly.

Research MethodologyAn empirical study was conducted to test the mentioned research objective. In order to understand the influence of MSME classification on the social media usage by the MSME


SuGyaan 15owners, the data collected was subjected to computation for descriptive statistics, F-values, ANOVA and post-hoc tests.

Reliability StatisticsA 10 -item scale that was developed by Ainin, S. et al. (2015) to measure social media usage was adopted in this study. The responses are obtained on a five-point Likert scale (5 = strongly agree and 1= strongly disagree). The reliability coefficient above 0.70 is considered as acceptable; the results have yielded high alpha coefficient of 0.921, suggesting that the scale is internally homogenous and highly reliable.

Research ToolThe research questionnaire of this study consists of questions on the preference of social media used for promotion by the MSME and a ten-scale questionnaire on objectives for use of social media. The ten elements of the scale are:

Table 2: Elements of the scale

Usage1 Advertise and promote product and services

Usage2 Create awareness of our brand

Usage3 Conduct market research

Usage4 Get referrals (through likes, shares, followers, and Word of mouth)

Usage5 Reach new customers

Usage6 Develop existing customer’s relationship with better engagement

Usage7 Quick communication with customers

Usage8 Conduct customer service activities

Usage9 Receive customer feedback on existing products/services directly and quickly

Usage10 Receive customer feedback on new/future product/services

Research Survey A total of 270 MSME owners were met for administering the questionnaire, and 210 MSME owners responded by filling the questionnaire with a response rate of 77 per cent. Among the MSME owners, 171 MSME owners were using social media which is 81 per cent of the total respondents. All the respondent were MSME owners.

IV. ResultsThe respondents were derived from the various spectrum of MSMEs: 44 per cent were from Micro, 43 per cent were from Small and 12 per cent of the respondents were from medium enterprises.

81 per cent (171 MSMEs) of the respondents were using social media as a promotional tool.


SuGyaan 16Figure 1: Usage of Social media by the MSMEs

Figure 1 reflects, nearly 90 per cent of the respondents chose Facebook and WhatsApp as their preferred social media; followed by 34 per cent chose to LinkedIn; 24 per cent preferred Twitter; 22 per cent preferred YouTube; 19 per cent preferred Google Plus; 15 per cent preferred Instagram, and 4 per cent preferred Pinterest.

Table 3 Preferred social media platform according to MSME Classification

MSM

E C

lass

ifica

tion

Face

book

% Wha

tsA

pp

% Link

edIn

% Twitt

er

% YouT

ube

% Goo

gle

Plus

% Inst

agra

m

% Pint

eres

t

%

Micro 70 46 69 45 25 43 22 55 19 50 14 44 12 48 4 57Small 65 42 67 44 23 40 12 30 15 39 13 41 11 44 3 43Medium 18 12 17 11 10 17 6 15 4 11 5 16 2 8 0 0Total 153 100 153 100 58 100 40 100 38 100 32 100 25 100 7 100

Table 3, it is observed that respondents from Micro enterprises are at the forefront of using social media as a promotional tool.

Facebook is used by the majority of the respondents from Micro enterprises (46%), closely followed by respondents from small enterprises (42%) and followed by respondents from medium enterprises (12%);

WhatsApp is used equally by both the respondents from micro enterprises (45%) and respondents from small enterprises (45%) followed by the respondents from medium enterprises (11%).

LinkedIn is used the majority of the respondents from micro enterprises (43%); followed by the respondents from small enterprises (40%) and followed by the response from medium enterprises (17%).


SuGyaan 17Twitter is used by a large majority of respondents from Micro enterprises (55%), followed by respondents from small enterprises (30%) and respondents from medium enterprises (15%)

YouTube is used by a large majority of respondents from Micro enterprises (50%), followed by respondents from small enterprises (39%) and respondents from medium enterprises (11%).

Google Plus is used by a majority of the respondents from micro enterprises (44%), followed by respondents from small enterprises (41%) and respondents from medium enterprises (16%).

Instagram is used by a majority of the respondents from micro enterprises (18%), followed by respondents from small enterprises (44%) and respondents from medium enterprises (8%).

Pinterest is used by a large majority of the respondents from micro enterprises (57%), followed by respondents from small enterprises (43%) and was ignored by respondents from medium enterprises.

Results of ANOVA

An analysis of variance was conducted to determine whether there are any significant differences in objectives for usage of social media(Usage) by MSME Classification. All the assumptions of ANOVA were examined.

Table 4: Mean, Standard Deviation and Analysis of Variance

Item

Varia

ble

N Mea

n

Std.

Dev

iatio

n

Std.

Err

or

95% Confidence Interval for

Mean

Min

imum

Max

imum

AN

OVA

Low

er B

ound

Upp

er B

ound

Sum

of

Squa

res

df Mea

n Sq

uare

F Sig.

Usage 1 Micro 77 4.60 .568 .065 4.47 4.73 3 5

1.644 2 .822 1.998 .139Small 75 4.44 .663 .077 4.29 4.59 3 5

Medium 19 4.32 .820 .188 3.92 4.71 3 5

Total 171 4.50 .645 .049 4.40 4.59 3 5

Usage 2 Micro 77 4.66 .553 .063 4.54 4.79 3 5

1.717 2 .858 2.109 .125Small 75 4.51 .645 .074 4.36 4.65 3 5

Medium 19 4.37 .895 .205 3.94 4.80 3 5

Total 171 4.56 .642 .049 4.46 4.66 3 5


SuGyaan 18

Usage 3 Micro 77 3.96 1.117 .127 3.71 4.21 1 5

2.370 2 1.185 .880 .417Small 75 3.83 1.143 .132 3.56 4.09 1 5

Medium 19 3.58 1.387 .318 2.91 4.25 1 5

Total 171 3.86 1.160 .089 3.68 4.03 1 5

Usage 4 Micro 77 4.48 .788 .090 4.30 4.66 1 5

0.033 2 .016 0.029 .971Small 75 4.51 .685 .079 4.35 4.66 3 5

Medium 19 4.47 .772 .177 4.10 4.85 3 5

Total 171 4.49 .739 .056 4.38 4.60 1 5

Usage 5 Micro 77 4.57 .696 .079 4.41 4.73 2 5

0.443 2 .221 0.442 .644Small 75 4.49 .705 .081 4.33 4.66 3 5

Medium 19 4.42 .769 .176 4.05 4.79 3 5

Total 171 4.52 .706 .054 4.41 4.63 2 5

Usage 6 Micro 77 4.47 .771 .088 4.29 4.64 2 5

0.249 2 .125 0.210 .811Small 75 4.39 .769 .089 4.21 4.56 2 5

Medium 19 4.42 .769 .176 4.05 4.79 3 5

Total 171 4.43 .766 .059 4.31 4.54 2 5

Usage7 Micro 77 4.51 .754 .086 4.34 4.68 2 5

4.139 2 2.069 3.281 .040Small 75 4.48 .742 .086 4.31 4.65 2 5

Medium 19 4.00 1.106 .254 3.47 4.53 1 5

Total 171 4.44 .805 .062 4.32 4.56 1 5

Usage8 Micro 77 4.31 .877 .100 4.11 4.51 1 5

5.534 2 2.767 3.365 .037Small 75 4.31 .822 .095 4.12 4.50 2 5

Medium 19 3.74 1.284 .295 3.12 4.36 1 5

Total 171 4.25 .919 .070 4.11 4.38 1 5

Usage 9 Micro 77 4.38 .844 .096 4.19 4.57 1 5

1.255 2 .628 0.820 .442Small 75 4.27 .859 .099 4.07 4.46 2 5

Medium 19 4.11 1.049 .241 3.60 4.61 2 5

Total 171 4.30 .874 .067 4.17 4.43 1 5

Usage10 Micro 77 4.40 .815 .093 4.22 4.59 1 5

1.874 2 .937 1.251 .289Small 75 4.32 .857 .099 4.12 4.52 2 5

Medium 19 4.05 1.079 .247 3.53 4.57 2 5

Total 171 4.33 .867 .066 4.20 4.46 1 5

Note: Significance at 0.05 level

About objectives for using social media to “advertise and promote product and services” (Usage1) it is quite clear from table 4 that the respondents from micro enterprises (mean =4.60) have scored better than the respondents from small (mean=4.44) and medium enterprises (mean = 4.32). Interestingly the F value presented in the table suggests that the variations presented in their mean scores are not statistically significant. In other words, the objectives for usage of social media to “advertise and promote product and services” is uniform across all the three categories of micro, small and medium enterprises. Hence the null hypothesis is accepted.


SuGyaan 19About objectives, for using social media to “Create awareness of our brand” (Usage2), it is quite clear from the table that the respondents from micro enterprises (mean =4.66) have scored better than the respondents from small (mean=4.51) and medium enterprises (mean = 4.37). Interestingly the F value presented in the table suggests that the variations presented in their mean scores are not statistically significant. In other words, the objectives for usage of social media to “Create awareness of our brand” is uniform across all the three categories of micro, small and medium enterprises. Hence the null hypothesis is accepted.

About objectives for using social media to “Conduct market research” (Usage 3), it is quite clear from the table that the respondents from micro enterprises (mean =3.96) have scored better than the respondents from small (mean=3.83) and medium enterprises (mean = 3.58). Interestingly the F value presented in the table suggests that the variations presented in their mean scores are not statistically significant. In other words, the objectives for usage of social media to “Conduct market research” is uniform across all the three categories of micro, small and medium enterprises. Hence the null hypothesis is accepted.

With regard to objectives for using social media to “Get referrals through likes, shares, followers, and Word of mouth” (Usage4) it is quite clear from the table that the respondents from small enterprises (mean =4.51) have scored better than the respondents from micro (mean=4.48) and medium enterprises (mean = 4.47). Interestingly the F value presented in the table suggests that the variations presented in their mean scores are not statistically significant. In other words, the objectives for usage of social media to “Get referrals (through likes, shares, followers, and Word of mouth)” is uniform across all the three categories of micro, small and medium enterprises. Hence the null hypothesis is accepted.

About objectives for using social media to “Reach new customers” (Usage5) it is quite clear from the table that the respondents from micro enterprises (mean =4.57) have scored better than the respondents from small (mean=4.39) and medium enterprises (mean = 4.42). Interestingly the F value presented in the table suggests that the variations presented in their mean scores are not statistically significant. In other words, the objectives for usage of social media to “Reach new customers” is uniform across all the three categories of micro, small and medium enterprises. Hence the null hypothesis is accepted.

About objectives for using social media to “Develop existing customer’s relationship with better engagement” (Usage6) it is quite clear from the table that the respondents from micro enterprises (mean =4.47) have scored better than the respondents from small (mean=4.39) and medium enterprises (mean = 4.42). Interestingly the F value presented in the table suggests that the variations presented in their mean scores are not statistically significant. In other words, the objectives for usage of social media to “Develop existing customer’s relationship with better engagement” is uniform across all the three categories of micro, small and medium enterprises. Hence the null hypothesis is accepted.


SuGyaan 20About objectives for using social media to “Quick communication with customers” (Usage7) it is quite clear from the table that the respondents from micro enterprises (mean =4.51) have scored better than the respondents from small (mean=4.48) and medium enterprises (mean = 4.00). Interestingly the F value presented in the table suggests that the variations presented in their mean scores are statistically significant. In other words, the objectives for usage of social media for “Quick communication with customers” is not uniform across all the three categories of micro, small and medium enterprises. Hence the alternate hypothesis is accepted.

About objectives for using social media to “Conduct customer service activities” (Usage8) it is quite clear from the table that the respondents from micro enterprises (mean =4.31) and the respondents from small (mean=4.31) have scored better than the respondents from medium enterprises (mean = 3.74). The F value presented in the table suggests that the variations presented in their mean scores are statistically significant. In other words, the objectives for usage of social media to “Conduct customer service activities” is not uniform across all the three categories of micro, small and medium enterprises. Hence the alternate hypothesis is accepted.

With regard to objectives for using social media to “Receive customer feedback on existing products/services directly and quickly” (Usage9) it is quite clear from the table that the respondents from micro enterprises (mean =4.38) have scored better than the respondents from small (mean=4.27) and medium enterprises (mean = 4.11). Interestingly the F value presented in the table suggests that the variations presented in their mean scores are not statistically significant. In other words, the objectives for usage of social media to “Receive customer feedback on existing products/services directly and quickly” is uniform across all the three categories of micro, small and medium enterprises. Hence the null hypothesis is accepted.

With regard to objectives for using social media to “Receive customer feedback on new/future product/services” (Usage10) it is quite clear from the table that the respondents from micro enterprises (mean =4.40) have scored better than the respondents from small (mean=4.32) and medium enterprises (mean = 4.05). Interestingly the F value presented in the table suggests that the variations presented in their mean scores are not statistically significant. In other words, the objectives for usage of social media to “Receive customer feedback on new/future product/services” is uniform across all the three categories of micro, small and medium enterprises. Hence the null hypothesis is accepted.


SuGyaan 21

Table 5 Tukey Post-hoc test results

Tukey HSD

Dependent Variable

Mean Difference

(I-J)

Std. ErrorLower Bound

Sig.Upper Bound

95% Confidence Interval

Usage_7 Micro Small .026 .129 .977 -.28 .33Medium .506* .203 .036 .03 .99

Small Micro -.026 .129 .977 -.33 .28Medium .480 .204 .051 .00 .96

Medium Micro -.506* .203 .036 -.99 -.03Small -.480 .204 .051 -.96 .00

Usage_8 Micro Small .005 .147 .999 -.34 .35Medium .575* .232 .038 .03 1.12

Small Micro -.005 .147 .999 -.35 .34Medium .570* .233 .041 .02 1.12

Medium Micro -.575* .232 .038 -1.12 -.03Small -.570* .233 .041 -1.12 -.02

To further examine the differences among the variables, t-tests were calculated between each pair of measurements. Tukey pairwise comparisons were conducted for all significant effects and results are depicted in table 5.For the main effect of MSME Classification, the mean of ‘Quick communication with customers’(Usage7) for medium (M =4.00, SD = 1.106) was smaller than for micro (M = 4.51, SD = 1.106). For the main effect of MSME Classification, the mean of ‘Conduct customer service activities ‘(Usage8) for medium (M = 3.74, SD = 0.877) was significantly smaller than for micro (M = 4.31, SD = 3.38). No other significant effects were found.

ConclusionThe primary objective of the study was to study various social media that are used as a promotional tool by the MSMEs and also to study the influence of MSME classification on the objectives of social media usage of MSME owners. Facebook(89.5%) and WhatsApp (89.5%) are the most preferred social media by the MSME owners followed by LinkedIn (33.9%), Twitter (23%), YouTube (22.2%). Google Plus (18.7%), Instagram (14.6%) and Pinterest (4.1%). Micro enterprises have aggressively adopted social media as promotional tool followed by small enterprises and medium enterprises. Coming to the MSME classification wise usage of social media, micro enterprises are at the fore front in using all the social media than small and medium enterprises. Interestingly there is a significant relationship between MSME Classification and the objective: Quick communication with customers and there is a significant relationship between MSME Classification and the objective: Conduct customer service activities. The main effects are between micro and medium enterprises. No significant relationships were observed for rest


SuGyaan 22of the objectives. Finally, it is concluded that MSMEs are using social media as a promotional tool and are also clear about the objectives of using social media as a promotional tool. Further, the study was done in the city of Hyderabad, and the study can be extended to other cities. However, further research needed to be done to understand whether the usage of social media is yielding any financial benefits to the MSMEs.

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Influencing customer satisfaction in B2B sales. Industrial Marketing Management. 53. 10.1016/j.indmarman.2015.09.003.

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8. KPMG Report (2017), A Study By Kpmg In India And Google January 2017. (n.d.). Retrieved from https://assets.kpmg.com/content/dam/kpmg/in/pdf/2017/01/Impact-of-internet-and-d.

9. Madhusudan Kumar Kota. (2018). Influence of MSME Classification on the Antecedents of Social Media Usage – an Empirical Study Conducted on Select Micro, Small and Medium Enterprises of Hyderabad, Telangana. Int J Recent Sci Res. 9(4), pp. 26377-26383.

10. Margaret McCann, Alexis Barlow, (2015). “Use and measurement of social media for SMEs”, Journal of Small Business and Enterprise Development, Vol. 22 Issue: 2, pp.273-287, https://doi.org/10.1108/JSBED-08-2012-0096

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12. McKinsey, (2007a), How businesses are using Web 2.0: A McKinsey global survey, The McKinsey Quarterly


SuGyaan 2313. McKinsey, (2007b), How companies can make the most of user-generated content, The

McKinsey Quarterly, August 200714. McKinsey, (2008a), Building the Web 2.0 Enterprise: McKinsey Global Survey Results,

The McKinsey Quarterly, July 2008.15. McKinsey, (2008b), The next step in Open Innovation, The McKinsey Quarterly, June

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edge. London: Sage Publications.17. Morris, Michael & Schindehutte, Minet & W. LaForge, Raymond. (2002).

Entrepreneurial Marketing: A Construct for Integrating Emerging Entrepreneurship and Marketing Perspectives. Journal of Marketing Theory & Practice. 10. 19. 10.1080/10696679.2002.11501922.

18. Motwani, J.A., Jiang, J.J. and Kumar, A. (1998), “A comparative analysis of manufacturing practices of small vs large Western Michigan organizations”, Industrial Management & Data Systems, Vol. 98 No. 1, pp. 8-11.

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MJSSIM X (I), 2, 2018


SuGyaan 24

FINTECH SERVICES IN INDIA: ISSUES AND CHALLENGES

* Dr. S.V. Ramana Rao

ABSTRACTIndia is in the process of digitization across sectors of the economy due to rise in the usage of internet and smartphones. A report released jointly by BCG and Facebook titled “ENCASHING ON DIGITAL: Financial Services in 2020” reported that the first 100 million ‘Digital Indians’ were majorly men, millennials and metro based. These people are not confined merely to searing and social networking but also engaged in transactions like online shopping and banking activities. Financial inclusion can be accomplished by use of technology which helps to reach masses. Internationally technology is driving a wave of innovation in finance which is changing the way services are delivered and products are customized. Usage of artificial intelligence and machine learning is helping the institutions to provide services at cheaper rates, easy reach to customers, innovative and efficient. Among the developing countries India is surpassing Kenya and Philippines who are pioneers in financial services innovation and experimentation. Fintech is an emerging innovation in financial services sector. The services offered by Fintech companies are complementing to each other and at the same time posing a challenge to traditional banking services. Combining technology with financial services is vital for digital economy.

As per The Boston Consulting Group (BCG) and Facebook report on “ENCASHING ON DIGITAL: Financial Services in 2020”, by 2020 Digitization plays huge role in financial services and estimated that there will be over 150 million urban banking users actively have access to online banking services which is 4 times increase from 2016. Keeping in view of growth potential the present study focuses on understanding Fintech services, growth and development especially in the Indian context, types of Fintech services, challenges and opportunities by using secondary data sources. Financial services industry is highly influenced by Digitization among all the industries like consumer electronics, travel and home appliances. The financial sector is facing a challenge in handling the well informed millennial customers. Fintech is no longer viewed as a disruptor rather collaborating to face the digital world challenges.

Key Words: Fintech, B2B Models, Customer Loyalty

JEL Classification Code: O0, O32, O320, O33

* Professor and Area Chair-Finance, Siva Sivani Institute of Management, Secunderabad.


SuGyaan 25

IntroductionConventional financial services around the world have experiencing radical changes due to technology and innovation. Digital economy can be built through convergence of technology and financial services. India is in the process of transformation as the government initiatives like linking financial services to Aadhaar cards, encouraging public to do cashless transactions especially post demonetization and roll out of GST and filing of GST returns help the country to move into digital economy. India ranked second in the growth rate of Fintech adaptation among digitally active consumers across the globe (Prakash Mally, 2017). Though Fintech services are new to India but lot of attention has been drawn. Technology has been a key enabler for the growth of a digital economy. Adoption of technology by Indian banks and Financial services providers facilitate in enhancing customers reach and services by improving operational effectiveness. Fintech companies are helping to create new markets, new products and services to serve economically viable manner. Fintech companies are complementing and at the same time challenging the traditional banking and financial services institutions as these firms are equipped with advanced data analytics capabilities. This sector potential can be assessed by looking into the funding pattern both at global and domestic level. There are 1400 deals worth of $17 billion in 2016 alone happened at global level and in India $270 million worth of deals which helped India to be in the top ten Fintech markets globally1.

Banking and financial services sector in India was operating through brick and mortar with labour intensive, paper based and manual processes. Fintech firms are data driven and trying to plug the inefficiencies in the existing system and reduction in cost resulting into greater penetration in the market especially in financial services and insurance segments.

Concept of FintechTechnology has been considered as business enabler by the Traditional banks and Financial Institutions. Fintech Technology (Fintech) companies are transforming the role by using the digital technologies and creating new business segments. Fintech is the application of technology to offer new financial products and services in an innovative manner economically. FinTech is increasingly becoming an important focus area for all the key stakeholders in India’s Financial Services industry – Regulators, Traditional Banks, NBFCs, Payment Banks, Investors, Payment Service Providers, Broking and Wealth Management Companies, Insurance providers and pure-play Fintech players. In the Indian Fintech eco system Fin Tech companies are complementing the traditional banking and financial services industry rather than disintermediating them. Traditionally Indian financial services industry based on brick and mortar branch banking, labour intensive, manual and paper

1 https://www2.deloitte.com/content/dam/Deloitte/in/Documents/financial-services/in-fs-fintech-india-ready-for-breakout-noexp.pdf.


SuGyaan 26based process. The emergence of technology and digitization process and its application extensively reduced the inefficiencies in the traditional system. Fintech business model is making use of data extensively to serve customers in an economical way for acquiring the customers too. Fintech business models do help to penetrate in financial services and insurance products market.

Fintech – Convergence of Financial services and Technology

Source :Reprinted from Fintech in India | Ready for breakout, July 2017, page no.7 retrieved from https://www2.deloitte.com/content/dam/Deloitte/in/Documents/financial-services/in-fs-fintech-india-ready-for-breakout-noexp.pdf.

Purpose of the Study: It is understood from the review of available literature that Fintech services in the Indian context still in the nascent stage and not much research was happened. Most of the literature is in the form of reports prepared by the various consulting firms especially focused on the issues and challenges, growth for Fintech services and types of services offered by the companies etc. hence the present study has been carried out with the following objectives.

1.The objective of the present study is to analyse Fintech ecosystem in India.

2. To understand the issues and challenges with special reference to Indian context.

Research MethodologyAs study is descriptive in nature secondary data has been used to accomplish the study objectives. Secondary data has been collected from various sources like consulting firms like Deloitte, KPMG, BCG etc reports on Fintech services. Different newspapers articles and magazines articles etc are also used in the study.

Transforming Financial ServicesTechnological innovation considered to be most powerful force would address current challenges. It has the power to remake incumbent businesses from the inside out and to reshape and reveal customer preferences. Technology gave birth to many Fintech start- ups which are transforming the financial landscape as competitors and as partners. Computerization of banks changed the way accounts are maintained and introduction of cards, net banking


SuGyaan 27changed the payment system. Banking structure, processes, procedures are undergoing radical changes due to innovation and technology. The conventional role of banks including commercial banks are at times are challenged by the Fintech firms. According to a 2015 report by Pricewaterhouse Coopers(PWC), India’s unbanked population was at 233 million (Kumar Abhishek, 2017).Large unbanked and unserved Indian population due to limited access to customers, small scale of operations, higher transaction costs and limited number of customers resulted into explore of multiple innovations to provide banking and its allied services using technology especially Fintech firms. Due to challenges to fulfilling KYC norms many banks and NBFCs stayed away to serve the customers but now Aadhaar authentication framework providing an opportunity to the newly launched payments and small banks to reach out to unbanked population of the country2. According to a report by Nasscom jointly with Akamai estimates that internet users in India will grow to 730 million by 2020 of 75% will be rural population which will help to grow Fintech organisations presence3. Banks and Fintech firms co-exist and gain from each other by serving the customers. Fintech organizations are using innovative technology in lending and payment process. These firms use machine learning algorithms and alternative data points such as social media footprints, call records, shopping histories, and payments to utility service providers to increase efficiency and provide greater access to credit (Kumar Abhishek, 2017). The processing of loan approvals has become faster too. In India, Fintech services disruption is indeed large due to with its innovative products and potential for scalable, low-cost solutions, as the agent of disruptive change in the sector. Fintech is expected to dramatically change the supply of financial services in India which basically helps in to fill gaps in the market, improve efficiencies and collaborate with existing players through innovative B2B models4.

The market system for Fintech in India

2 http://ficci.in/sector/3/Add_docs/Financial-Foresights-Jan-2017.pdf. 3 http://www.nasscom.in/sites/default/files/media_pdf/nasscom_akmai_technologies_report_showcase_how_internet_changing_india.pdf. 4 https://www.nathaninc.com/sites/default/files/Fintech%20in%20India.pdf.


SuGyaan 28Source : Reprinted from Fintech in India report prepared by Nathan Associates India, Jan 2017, page no. 7 retrieved from https://www.nathaninc.com/wp-content/uploads/2017/02/Fintech-in-India.pdf. In India Fintech services market serve both to Business to Customer and Business to business. The market is governed by rules and regulations like central government policies, financial inclusion polices, international agreements and RBI regulations etc. Technology and digital infrastructure, skilled workforce, rise of incubators and investors etc are acting are facilitators for the growth of Fintech services in the Indian space.

Fintech segments - Indian contextDeloitte global along with World Economic Forum jointly conducted a research on “Future of Financial Services” mentioned that there are twenty segments under six broad categories. The broad categories are Credit, Payments, Investment Management, Personal Finance Management, Bank tech and InsurTech.

Areas Fintech Segments Brief DescriptionCredit Peer –to-Peer Lending

Crowd funding

Market place for loans

Online lenders-on-book lend-ing by NBFCs

Credit Scoring Platforms

All forms of lending market places including Peer –to-Peer lenders and market places that connect borrowers with both institutional and lenders.

Also includes crowd fudning and equity funding platforms

NBFCs that use alternative scoring and digital channels for acquisition.

Payments M-Wallets and PPIs

Merchant Payment and PoS services

Inetrnational remittance

Crypto Currencies

Services that enable transfer of funds for various use cases Peer -to –Peer, Peer-to-Merchant, Gov-ernment –to- Person etc.

Services targeted at both payees and merchants by enabling requisite payment infrasturcture through mobile or other teachnologies.

Investment Management

Robo Advisors

Discount brokers

Online Finance advisors

Wealth advisory services dekivered through tech-nology governed rules and investment startegies.

Personal Finance Management

Tax filing and Processing

Spend Management and financial planning

Credit scoring services

Tools and services for active management of in-dividual financial profiles. Ex: Spend,investments and credit profiles etc.

Bank tech Big data

Blockchain

Customer onboarding plat-forms

Services that utilize many data points such as fi-nancial transactions, spending patterns to build risk profile of the customer. This provides an al-ternative to traditional underwriting methods that are unable serve people with limited credit data.


SuGyaan 29

Insure Tech Insurance agrregator

IOT,Weables and kinematics

Small business insurance

Usage based insurance

Source : Reprinted from Fintech in India | Ready for breakout 2017, page no. 10 retrieved from https://www2.deloitte.com/content/dam/Deloitte/in/Documents/financial-services/in-fs-fintech-india-ready-for-breakout-noexp.pdf.

Fintech Trends It is estimated that by 2020 Fintech grow to the level of USD 2.4 billion and been projected that 2018 is crucial year to realize the growth potential5. Many banking innovations are welcomed by customers and private players making huge investments which may result into greater access to banking services to masses and wider offering of customer services. The following trends many play catalyst role in Fintech space6.

• Next –gen Chatbots • Machine Learning • Block chain • Smart workflows • Automated personalisation • Open banking • Physical and digital merger • Extended digital coverage • Agile architecture • Security

Financial Institutions + Fintech Collaboration Collaboration between Financial institutions and Fintech firms should bring synergy that the combined entity should be stronger than either individual entities. The collaboration brings innovation mind set, agility (speed to adjust), consumer-centric perspective and an infrastructure built for digital transactions which can bring by Fintech firms and act as collaborators. Indian banking eco system is in the transformation state due to entry of Fintech firms. The traditional financial institutions and banks are adopting advance technology to meet the growing customer demands. Fintech firms and traditional financial institutions collaborate for the long term growth. As per the World Fintech report 2018 from Capgemini and LinkedIn in association with Efma stated that four characteristics are necessary for sustained success across our pillars: People, Finance, Business and Technology7 . The

5 https://yourstory.com/2017/11/top-10-fintech-trends-influence-banking-industry-2018/6 Ibid7 https://thefinancialbrand.com/71050/banking-fintech-collaboration-bigtech-trends.


SuGyaan 30success of collaboration depends on understanding of Financial institutions and Fintech firms of each other’s strengths and weaknesses. The collaboration must result into improving customer experience, reduction in operational costs of serving customers, personalization, speed, contextuality, and seamless delivery at the same time it must protect from competitive forces like Google, Amazon, Facebook and Apple (GAFA) or challenges from Alibaba and Tencent8. Hence the relationship between Fintech firms and financial institutions should be collaboration rather than competition. The biggest challenge is creating a culture and environment to flourish together but the challenge is finding the right talent to facilitate these collaborations.

The following diagram explains about Fintech firm’s areas of collaboration.

Source : Reprinted from Banking + Fintech Collaboration: More Important Than Ever by Jim Marous, retrieved from https://thefinancialbrand.com/71050/banking-fintech-collaboration-bigtech-trends. Key factors leading to success of Fintech companies: The following are the major factors that could help in success of Fintech companies.

• Customer Loyalty • Technology and IT Infrastructure • Innovative use of data • Funding Environment• Value Proposition• Cost of operations

8 Ibid


SuGyaan 31

Challenges to Indian Fintech Sector A report by a consulting firm Deloitte in its report entitled Fintech in India: Ready for Breakout mentioned that Fintech firms can reshape the financial services ecosystem in India. there are many factors such as inefficiencies in the traditional banking system in the country, unbanked and underserved population by the financial institutions, increase in smartphone penetration etc are facilitating in Fintech growth in the country9. In spite of positive factors there are many challenges as Prantik Ray, Professor of finance at XLRI said that insufficient regulations are major obstacle for Fintech growth in India. The two major concern bodies in India like Reserve Bank of India and Securities & Exchange Board of India (SEBI) are yet to provide guidelines for Fintech operations. Majority of regulations are still as per the banking regulatory norms inspite of allowing small banks and payment banks allowed their operations by RBI and many obstacles are remain unresolved to Fintech firms smooth functioning in the country. It’s like asking them to operate with one hand tied behind their backs.10” Ravi Aron, associate professor of information systems at the Johns Hopkins Carey Business School, felt that banks and Fintech firms join hands together but the big banks especially in the public sector have many regulations that are having limited flexibility. Sufficient telecom infrastructure is another challenge for the growth of Fintech business. Belgavi from PWC opinioned that there are three areas which are quite challenging for Fintech growth in India. They are literacy in terms of adoption and security, accessibility of funding to few and last one is weak in creating world class innovative products. “It requires a different mindset and a lot of patience from all stakeholders,” says Belgavi. Regional imbalances especially in terms of financial inclusion in some low income states, gender disparity issues such as restricted internet, cell phones access among women particularly in small towns and villages poses more challenges to reach customers to provide financial services11. Largely Fintech products are meant for urban elite and the rich but the same is being pushed down to customers at low income category which may not work as diversity in the customer group is so huge. The challenge is to innovate at different income levels and be able to customize is the challenge that Fintech firms are experiencing.

Conclusion Technological innovations paving the way for emergence of Fintech firms which are helping to provide services to customers at the most economical way and at the same time facilitating to reach the unserved and underserved customers. These firms are acting as supplements to traditional banking system to provide superior customer experience with a wider reach. FinTech is increasingly becoming an important focus area for all the key stakeholders in

9 https://www.fairobserver.com/region/central_south_asia/india-fintech-whatsapp-tech-news-headlines-today-30980/. 10 Ibid11 https://medium.com/village-capital/the-true-challenges-in-the-fintech-space-in-india-ffd6e7837385.


SuGyaan 32India’s Financial Services industry – Regulators, Traditional Banks, NBFCs, Payment Banks, Investors, Payment Service Providers, Broking and Wealth Management Companies, Insurance providers and pure-play Fintech players. Banks and Fintech firms co-exist and gain from each other by serving the customers. Fintech is expected to dramatically change the supply of financial services in India which basically helps in to fill gaps in the market, improve efficiencies and collaborate with existing players through innovative business models. A research conducted jointly by Deloitte and World Economic Forum identified that there are twenty segments under six broad categories of Fintech services being offered. The broad categories are Credit, Payments, Investment Management, Personal Finance Management, Bank tech and InsurTech. The Fintech eco space may experience different trends like block chain, automated personalization, open banking, agile architecture etc which may transform the entire the financial services industry in India. Fintech firms will be successful if they take care of customer’s loyalty, technology and IT infrastructure, data usage and funding availability.

References 1. Prakash Mallya (Dec 4, 2017), India’s Small Businesses Are Ready To Boom, Thanks To

Fintech, Retrieved from https://www.forbes.com/sites/prakashmallya/2017/12/04/indias-small-business-are-ready-to-boom-thanks-to-fintech/#54326cf42e04.

2. Kumar Abhishek (2017), The FinTech Revolution - Transforming Financial Services, Retrieved from http://ficci.in/sector/3/Add_docs/Financial-Foresights-Jan-2017.pdf.

3. Jim Marous (2018), Banking + Fintech Collaboration: More Important Than Ever, Retrieved from https://thefinancialbrand.com/71050/banking-fintech-collaboration-bigtech-trends/.

4. Financial Innovation and Emerging Markets Opportunities for Growth vs. Risks for Financial Stability–Proceedings retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2136519.

5. Thomas Philippon (2017), The FinTech Opportunity, BIS Working Paper No. 655, retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3015064.

6. Sanjay Vijayakumar (2015), ‘FinTech’ real threat to Indian financial services industry: Y.M. Deosthalee, retrieved from http://www.thehindu.com/business/Industry/fintech-real-threat-to-indian-financial-services-industry-ym-deosthalee/article7770621.ece.

7. Sundararajan S(2017 ), Top 10 fintech trends that could influence the banking industry in 2018 retrieved from https://yourstory.com/2017/11/top-10-fintech-trends-influence-banking-industry-2018.

MJ SSIM X (I), 3, 2018


SuGyaan 33

IMAGE RETRIEVAL USING COLOR FEATURES

Venkataramana Battula* B Sandhya**

A.V. Krishna Prasad***

ABSTRACTDue to the digitization of data and advances in technology, it has become extremely easy to obtain and store large quantities of data, particularly Multimedia data. Image data plays vital role in every aspect of the systems like business for marketing, hospital for surgery, engineering for construction, Web for publication and so on. Fields ranging from Commercial to Military need to analyze these data in an efficient and fast manner. The need for image mining is high in view of such fast growing amounts of image data. In data mining, one typically works with immense volumes of raw data, which demands effective algorithms to explore the data space. In analogy to data mining, the space of meaningful features for image analysis is also quite vast. Recently, the challenge associated with these problem areas have become more tractable through progress made in machine learning and concerted research effort in manual feature design by domain experts. Presently, tools for mining are few and require human intervention. The approach commonly followed to mine from Images i.e. extract patterns and derive knowledge from large collections of images, deals mainly with identification and extraction of unique features for a particular domain. Feature selection and extraction is the pre-processing step of Image Mining. Obviously this is a critical step in the entire scenario of Image Mining. Though there are various features available, the aim is to identify the best features and thereby extract relevant information from the images. Unique characteristics of image mining then analyze the overall process and discuss the main technology of image. The standard data mining techniques for classification/ clustering are applied on the extracted features, such as color. The features extracted and the techniques used are then evaluated for their contribution to solving the problem. Many different methods for measuring the performance of a system have been created and used by researchers, the most common of which are precision and Recall.

Key Words - Image Mining, Image Mining techniques, Issues, Feature Extraction, Clustering.

JEL Classification Code: Z0, Y8* Assistant Professor, Department Of CSE, MVSREC, Hyderabad, India, [email protected]

**AssociateProfessor,DepartmentofCSE,MVSREC,Hyderabad,India,[email protected]

*** Associate Professor, Department of CSE, MVSREC, Hyderabad, India, [email protected]


SuGyaan 34

IntroductionImage mining is a very important technique which is used to mine knowledge easily from image. It is simply an expansion of data mining in the field of image processing. It handles with the hidden knowledge extraction, image data association and additional patterns which are not clearly accumulated in the images[8]. The most important function of the mining is to generate all significant patterns without prior information of the patterns.

Image mining techniques besides investigating suitable frameworks for image mining, early image miners have attempted to use existing techniques to mine for image information. The techniques frequently used include object recognition, image indexing and retrieval, image classification and clustering, association rules mining, and neural network[6].

Image mining is an interdisciplinary field that integrates techniques like computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. Rule mining has been adopting to huge image data bases. Mining has been done in accordance with the integrated collections of images and its related data. Rule mining technique is exploited to determine relations between structures and functions of human brain. There are two most significant techniques. The first technique is to mine from huge amount of images alone and the second technique is to mine from the integrated collections of images and related alphanumeric data. Image mining can be classified into two kinds. The image processing is one in which, it involves a domain specific application where the focus is in the process of extracting the most relevant image features into a suitable form and the image mining is one in which, it involves general application where the focus is on the process of generating image patterns that may be helpful in the understanding of the interaction between high-level human perception of images and low-level features. So, the latter may be the best one to lead the improvement in the accuracy of images retrieved from image databases. The main intention of image mining is to produce all considerable patterns without any information of the image content, the patterns types are different. They could be classification patterns, description patterns, correlation patterns, temporal patterns and spatial patterns. Image mining handles with all features of huge image databases which comprises of indexing methods, image storages, and image retrieval, all regarding in an image mining system.

The establishment of an image mining system is frequently an intricate process because it implies joining diverse techniques ranging from image retrieval and indexing schemes up to data mining and pattern recognition. Further, it is anticipated that a good quality image mining system provides users with a useful access into the image storage area at the same time it recognizes data patterns and generates knowledge beneath image representation. Such system basically be supposed to bring together the following functions: image


SuGyaan 35storage, image processing, feature extraction, image indexing and retrieval and, pattern and knowledge discovery.

Image Mining TechniquesUsing object models which might be known a priori, an object recognition technique finds objects in actuality from an image. Machine learning and purposeful information extraction can simply be realized when some objects have been identified and recognized through machine. The object recognition problem might be refer to as any supervised labeling problem according to models of known items i. e. given a target image containing a number interesting objects and a collection of labels corresponding to a collection of models known to technique, what is object recognition to assign correct product labels to regions, or a collection of regions, in the image. Image mining requires that images be retrieved according to some requirement specifications. The requirement specifications can be classified into three levels of increasing complexity: (a) Level 1 comprises low level features of such as color, texture, shape or the spatial location of image elements. (b) Level 2 comprises image retrieval by derived or logical features like objects of a given type or individual objects or persons. (c) Level 3 comprises high level features of image.

To further improve image retrieval rate, image data base using a fast and useful indexing scheme is required. A couple of main approaches are usually: reducing dimensionality or indexing high dimensional info.

In supervised classification technique, as input a collection of labeled (Pre-classified) images are given, and here the problem is to label a newly Encountered, yet unlabeled images. Typically, the given Labeled (training) images are used to do the machine learning of the class description which in turn is use to label a new Image. In unsupervised classification (or image clustering), the problem is always to group a given assortment of unlabeled images straight into meaningful clusters based on the image content with not a priori knowledge. Clustering is often more advantage for minimizing the searching time period of images inside database. There are a variety of clustering methods: hierarchal, partitioning, density-based, grid based and fuzzy clustering methods.

Association rule mining generates rules who have support and confidence greater than some user specific minimum support in addition to minimum confidence thresholds. A normal association rule mining algorithm works within two steps. The first step finds all substantial item sets that match the minimum support constraint. The second move generates rules from each of the large item sets that match the minimum confidence constraint.

Neural network Neural Networks are computational systems made up of simple processing units called neurons which are usually organized into layers with fully or partially


SuGyaan 36connections. The main task associated with a neuron is to receive the activation values from its neighbors (the output of other neurons), compute an output based on its weighted input parameters and send that output to its neighbors.

Image mining issues

Image mining research remains in their infancy and many issues continue to be solved. Particularly, for image mining research to progress to a fresh height, the pursuing issues need to be investigated. Issues [11]:

(a) Propose new representation schemes for visual patterns that are able to encode sufficient contextual information to allow for meaningful extraction of useful visual characteristics.

(b) Devise efficient content-based image indexing and retrieval techniques to facilitate fast and effective access in large image repository.

(c) Design semantically powerful query languages for image databases;

(d) Explore new discovery techniques that take into account the unique characteristics of image data;

(e) Central key issue in image mining is how to preprocess image sets so as to represent in form that supports the application of data mining.

(f) Image pattern representation: How can we represent the image pattern such that the contextual information, spatial information, and important image characteristics are retained in the representation scheme?

(g) Image features selection: Which are the important images features to be used in the mining process so that the discovered patterns are meaningful visually?

(g) Image pattern visualization: How to present the mined patterns to the user in a visually-rich environment?

Feature ExtractionFeature Extraction is a method of capturing visual content of images for indexing & retrieval [3]. Visual features (primitive or low-level image features) can be divided into Domain-specific features (eg. fingerprints, human faces) and General features (eg. color, texture, shape). According to the abstraction level, they can be further divided into Pixel-level features (Features calculated at each pixel, e.g. color, location) and Local features (Features calculated over the results of subdivision of the image band on image segmentation or edge detection). On the other hand, all features can be coarsely classified into low-level features and high-level features. Low-level features can be extracted directed from the original images, whereas high-level feature extraction must be based on low-level features. It’s


SuGyaan 37

obvious that, the lower the abstraction level of the features employed, the easier to locate them in the image, yet the more difficult to use them for understanding the meaning of that image, and vice versa. The issue of choosing the features to be extracted should be guided by the following concerns [2]:

The features should carry enough information about the image and should not require any domain-specific knowledge for their extraction. They should be easy to compute in order for the approach to be feasible for a large image collection and rapid retrieval. They should relate well with the human perceptual characteristics since users will finally determine the suitability of the retrieved images. Because of perception subjectivity, there does not exist a single best representation for a feature [8]. One of the features that are used is the color of the Image, since it has very strong correlation with the underlying objects of the Image[4]. Extracted statistical values of the color spaces (RGB & HSV). These values include the first, the second and the third moments. Mean is the first statistical moment. Although the mean of a colored image holds some information about its color features, but it is not enough. In this regard, we used the standard deviation and skewness as well[1].

Clustering and RetrievalClustering is the process of grouping the data into classes or clusters so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters. Dissimilarities are assessed based on the attribute values describing the objects. Often, distance measures are used. Cluster analysis has been widely used in numerous applications, including pattern recognition, data analysis, image processing, and market research. By clustering[5], one can identify dense and sparse regions and, therefore, discover overall distribution patterns and interesting correlation among data attributes. For this purpose k-Means method is used[6].

In retrieval given a query image set of images most similar to the query images are retrieved. This is done in two stages. In first stage identify the cluster to which the query image belongs to. In second stage identify the most similar images within the cluster for a given query image. For this purpose k-nearest-neighbor method is used.

Results & DiscussionsThe study extracted statistical values of the two mentioned color spaces. These values include the first, the second and the third moments. Mean is the first statistical moment. Although the mean of a colored image holds some information about its color features, but it is not enough. In this regard, we used the standard deviation and skewness as well, according to the following equations.


SuGyaan 38Variance

Skewness

Computes the feature vector consisting of 18 real numbers for each image. For each of the features, a normalization step is provided to fit the numbers between 0-1.

Table 1

SNO Feature Value Statistical Moment1 0.19628662907220182 Mean of R

2 0.21311960979770378 SD of R

3 1.308215487522458 Skewness Of R

4 0.27657418843188314 Mean of G

5 0.24337601025300162 SD of G

6 1.0945823373899315 Skewness Of G

7 0.39870920617596 Mean of B

8 0.338522370263233 SD of B

9 0.9808919209706068 Skewness Of B

10 0.3614761599255634 Mean of H

11 0.29729271158610443 SD of H

12 0.9028042740190301 Skewness Of H

13 0.3412609703267246 Mean of S

14 0.33189334648289814 SD of S

15 1.0793281862954323 Skewness Of S

16 0.4049482048077853 Mean of V

17 0.33759673485197317 SD of V

18 0.967319788427158 Skewness Of V


SuGyaan 39The following image(Figure 1) 6.jpg is compute color features in Table-1

Figure 1

Here number of clusters are specified as 10, after applying K-Means to Color Features of images will generate image id , their cluster number, centroids of each cluster.

TABLE 2

Clusters of Images

Cluster Number Total Number Of Images in each

Cluster1 1272 593 1144 875 1556 787 1118 1439 7510 51

Here identify the following query image belong to which cluster Computing the distance between query image and existing cluster’s centroids.

Here identify the following query image belong to which cluster

Fig: 2


SuGyaan 40After computing the distance between query image and existing cluster’s centroids in table3

TABLE 3

SNO CLUSTER NUMBER

EUCLIDEAN DISTANCE

1 8.0 0.0252228449522468622 9.0 0.044285003561848833 3.0 0.39902410550016024 0.0 0.41208861964000585 7.0 0.53524459925788966 2.0 0.79484623918031897 4.0 1.0154324123938838 6.0 1.1391030207540139 1.0 1.303607836998315710 5.0 1.8027448693845294

Fig: 3 Query Images

After applying KNN with k=4 Euclidean distance between query image and images within the cluster are in table 4

TABLE 4

S.no Image Distance1 432.0 0.02 418.0 0.0043016891586677353 478.0 0.00780186430815008054 479.0 0.007846366049289991

Return similar images for given query image(7.jpg) are in figure 4

Figure 4


SuGyaan 41

Conclusion The set of 1000 images are clustered into 10 groups using Simple K-Means. The clustering is performed on 18 color features extracted from each image. Given a query image, the cluster/group of images closest to the query image is found. Distance between the cluster’s centroids and the image feature vector is computed to find the cluster to which the image belongs to. And can find the nearest images of the given query image within the cluster using KNN.

At present all the extracted 18 features used for computation. In future reduction in number of features can be pursued to reduce the computation.

References1. Hesamoddin Salehian, Fatemeh Zamani, Mansour Jamzad, “ Fast Content Based Color

Image Retrieval System Based on Texture Analysis of Edge Map”,2011.

2. Barbora Zahradnikova, Sona Duchovicova and Peter Schreiber, “Image Mining: Review and New Challenges”, (IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 6, No. 7, 2015

3. Preeti Chouhan, Mukesh Tiwari, “Feature Extraction Techniques for Image Retrieval Using Data Mining and Image Processing Techniques” , International Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue 5, May 2016

4. Dipesh Patel, Darshan Patel , “Improvement in Performance of Image Retrieval using Various Features in CBIR System”, International Journal of Computer Applications (0975 – 8887) Volume 138 – No.11, March 2016

5. N. Pavithra, K. Sivaranjani, “Content Based Image Retrieval System Data Mining Using Classification Technique”, IJCSMC, Vol. 5, Issue. 7, July 2016, pg.519 – 522

6. Abeer esa a m alomairi, Ghazali sulong, “An overview of content-based image retrieval techniques”, Journal of Theoretical and Applied Information Technology 20th February 2016. Vol.84. No.2.

7. Jayamala K.Patil, “Comparative Analysis of Content Based Image Retrieval using Texture Features for Plant Leaf Diseases” , International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 9 (2016) pp6244-6249 © Research India Publications.

8. Janki Naik, Prof. Sagar Patel, “Techniques and Issues in Image Mining: Survey” , IJSRD - International Journal for Scientific Research & Development| Vol. 1, Issue 2, 2013 | ISSN (online): 2321-0613


SuGyaan 429. A.Hema, E.Annasaro,“A Survey in Need of Image Mining Techniques” ,International

Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, Issue 2, February 2013.

10. A.Kannan,Dr.V.Mohan,Dr.N.Anbazhagan, “Image Clustering and Retrieval using Image Mining Techniques” , IEEE International Conference on Computational Intelligence and Computing Research 2010.

11. JiZhang, Wynne Hsu, Mong Li Lee, “Image Mining: Trends and Developments”, International Workshop on Multimedia Data Mining, 2001.

12. Vaibhavi S. Shukla, “A Survey on Image Mining, its Techniques and Application”, International Journal of Computer Applications (0975 – 8887) Volume 133 – No.9, January 2016.

M J SSIM X (I), 4, 2018


SuGyaan 43

DIGITALIZATION OF INDIAN ECONOMY AND ITS IMPACT

*Dr. D.Indira **Dr. K.V.S. Raju

ABSTRACTThe concept of digital economy is evolving all the time because of its multifaceted and dynamic nature. As shown by the experience of leading countries, digital technologies can be transformational for the development of generating economic and social benefits for people. Indian economy is composed of 93% by informal sector which means they are not a part of the formal business, the economy is highly understated on one side and the majority of the population is not reaping the benefits of financing from banks as well as other social welfare measures from the government. The present study attempts to explore the need and impact of digitalization of Indian economy.

Keywords: Digital Economy, electronic commerce, digital technologies, informal sector, financing

JEL Classification Code: O0, O3, O31, O32, O33, O34

IntroductionDigital Economy refers to an economy that is based on digital technologies. Digital Economy is defined by Oxford dictionary as “ an economy which functions primarily by means of digital technology, especially electronic transactions made using the internet”

According to OECD, the digital economy enables and executes the trade of goods & services through electronic commerce on the internet

Today, every nation wants to be fully digitalized that will empower society in a better manner. The ‘Digital India’ Programme, an initiative of our honorable prime minister Mr.Narendra Modi , will emerge new progressions in every sector and will enable in reducing corruption and transparency in operations which are some of the major roadblocks in the progression of Indian Economy.Hence,an attempt has been made in this paper to understand Digital India-as a campaign where technologies and connectivity will come together to make an impact on all aspects of governance and improve the quality of life of citizens. Global investors and administrators have supported Modi’s Digital India initiative

* Professor, GRIET, Email id:[email protected], Ph no: 9966016412 ** Professor & Dean, GRIET, Email id:[email protected], Ph no: 9949655559


SuGyaan 44

Objective: 1. To understand the initiatives taken by the Government of India towards digitalization of economy

2. To analyze the need for Indian economy to move towards digitalization

Digital IndiaThe Programme contains tasks that target to make government services available to people digitally

Technology is a bridge that connects India’s villages and make them aware to the opportunity of internet and access to information from across the world. Digital India emphasizes on e-governance and seeks to transform India into a digitally empowered society. It is to ensure that government services are available to citizens electronically.It also aims to transform ease of doing business in the country which already has shown significant improvement by moving 32 places from 130 in 2016 to 100 in 2017.

Digital India Initiatives of the GovernmentThe Government of India is establishing state wide area networks across the country spending about 2005 crores over a period of 5 years to connect all state/union territory headquarters upto the block level via district/sub-divisional head quarters in a vertical hierarchical structure with a minimum bandwidth capacity of 2mbps per link

1. Single Window Interface for trade - As a part of the “Ease of doing business”, Government of India has taken up single window project to facilitate across states in India. This would reduce time and cost of doing business

2. Rapid Assessment System - National E-commerce division, has developed a rapid assessment system for continuous feedback for e-services delivered by the government towards system improvement and governance

3. Open data - Open government data is a platform for supporting open data initiative of Government.This portal is to be used by Government of India ministers/departments, their organizations to publish datasets, documents, services, tools and applications collected by them for public use. It intends to increase transparency in the functioning of government

4. National Super Computing Mission - has been envisaged to empower the national academic and R&D institutions, spread across the country, by installing a vast supercomputing grid comprising of more than 70 high-performance computing facilities.

5. A mobile applications store - has been created to facilitate the process of development and deployment of suitable mobile applications for delivery of public services through


SuGyaan 45mobile devices.

6. MeghRaj - In order to utilise and harness the benefits of Cloud Computing, Government of India has embarked upon an ambitious initiative - “GI Cloud” which has been named as ‘MeghRaj’. The focus of this initiative is to accelerate delivery of e-services in the country while optimizing ICT spending of the Government.

7. Jeevan Pramaan - is a biometric enabled digital service for pensioners of Central Government, State Government or any other Government organization. The new service aims to streamline the process of issuing life certificate and make it a hassle-free experience for the pensioners. With this initiative the pensioner’s requirement to physically present him/her in front of the disbursing agency or the certification authority will become a thing of the past benefiting the pensioners in a huge way and cutting down on unnecessary logistical hurdles.

8. HRIDAY - The Ministry of Urban Development, Government of India, launched the National Heritage City Development and Augmentation Yojana (HRIDAY) scheme on 21st January, 2015, with a focus on holistic development of heritage cities. The scheme aims to preserve and revitalise soul of the heritage city to reflect the city’s unique character by encouraging aesthetically appealing, accessible, informative & secured environment.

9. SWAYAM - seeks to bridge the digital divide for students who have hitherto remained untouched by the digital revolution and have not been able to join the mainstream of the knowledge economy.This is done through an indigenous developed IT platform that facilitates hosting of all the courses, taught in classrooms from 9th class till post-graduation to be accessed by anyone, anywhere at any time.

10. MyGov - platform is a unique path breaking initiative which was launched by the Hon’ble Prime Minister of India, Shri Narendra Modi. It is a unique first-of-its-kind participatory governance initiative involving the common citizen at large. The idea of MyGov brings the government closer to the common man by the use of online platform creating an interface for healthy exchange of ideas and views involving the common citizen and experts with the ultimate goal to contribute to the social and economic transformation of India.

11. Digitize India Platform (DIP) is an initiative of the Government of India under the Digital India Programme to provide digitization services for scanned document images or physical documents for any organization. The aim is to digitize and make usable all the existing content in different formats and media, languages, digitize and create data extracts for document management, IT applications and records management.

12. DBT - was initiated with the aim to reform Government delivery system by re-engineering the existing process in welfare schemes for simpler and faster flow of information/funds and


SuGyaan 46to ensure accurate targeting of the beneficiaries, de-duplication and reduction of fraud. DBT will bring efficiency, effectiveness, transparency and accountability in the Government system and infuse confidence of citizen in the governance. Use of modern technology and IT tools will realize the dream of MAXIMUM GOVERNANCE MINIMUM GOVERNMENT.

13. E-Hospital - is an open source health information management system ( HMIS) which is configurable and easily customizable with multi-tenancy support. It is designed to deploy in cloud infrastructure to manage multiple hospitals seamlessly. The generic application addresses all major functional areas of a hospital. A workflow based HL7 complient and ISO/IEC 9126 certified end-to-end solution Software for hospital management which covers complete treatment cycle of OPD/IPD as well integrates clinical, administrative, and billing/ insurance activities.

14. E-Trade -The Department of Commerce is pursuing the project eTRADE, the purpose of which is to facilitate foreign trade in India by way of promoting effective and efficient delivery of services by various regulatory / facilitating agencies involved in foreign trade so as to enable the trade to avail services from these agencies in online environment.

15. Government e-Marketplace (GeM) - is single window solution for online procurement of common use Goods & Services required by various Government Departments / Organizations / PSUs. GeM aims to enhance transparency, efficiency and speed in public procurement. It also provides the tools for direct purchase, e-bidding and reverse e-auction to facilitate the government users to achieve the best value for the money. The portal offers online registration facilities for all stakeholders namely Government Users, Product Sellers and Service Providers.

16. AEPS - is a bank led model which allows online interoperable financial inclusion transaction at PoS (MicroATM) through the Business correspondent of any bank using the Aadhaar authentication. It is a payment service empowering a bank customer to use Aadhaar as his/her identity to access his/ her respective Aadhaar enabled bank account and perform basic banking transactions like balance enquiry, cash deposit, cash withdrawal, remittances through a Business Correspondent.

17. Aadhaar - Aadhaar identity platform is one of the key pillars of ‘Digital India’, wherein every resident of the country is provided with a unique identity or Aadhaar number. The largest biometrics based identification system in the world, Aadhaar is a strategic policy tool for social and financial inclusion, public sector delivery reforms, managing fiscal budgets, increase convenience and promote hassle-free people-centric governance. It is unique and robust enough to eliminate duplicate or fake identities and may be used as a basis/primary identifier to roll out several Government welfare schemes and programmes for effective service delivery thereby promoting transparency and good governance.


SuGyaan 47Thus the Government of India has taken major steps towards digitalizing the economy which encompasses not only government services but also private services, which will have a profound impact on transforming the way the economy functions.

Need for digitalization for Indian economyIndia is growing steadily and significantly in the last 2 decades, nevertheless in terms of economic indicators we are trailing behind the world economies such as in terms of Ease of doing business we stand in the 100th position up from 131st position in 2013 and 142 in 2015

It is estimated by NSSO that 84.7% of jobs in the Indian economy are in the informal or unorganized sector.A large informal sector impacts the government in terms of revenue foregone because the units operating in the informal sector stay out of the governments fiscal revenue.This leads to low tax GDP ratio.

It is estimated that India stands in the 75th position as far as least corrupt nations is concerned which means we have another 74 countries better than us according to world bank report 2017

Thus Digitalization helps in terms of bringing transparency of operations and thereby helps in increasing tax revenue to government.At the same time it will help in reducing corruption which is one of the biggest menace and roadblock in our progress

Benefits of Digital EconomyToday humans around the world rely on mobile communications,internet access and social media for interactions with each other and sharing information.Governments and businesses are increasingly preferring internet for disseminating information. Digital technologies have the power to transform the economy as a whole and across various sectors.

It is estimated that the digital economy accounts for about 6% of GDP in OECD and in Sweden it reached to 8% of GDP due to significant competitive advantage in digital services.The UK’s digital economy has the largest proportion of GDP among G-20 countries (close to 10% of GDP)

Hence it can be observed that the most digital countries are the ones who are ranked high in other parameters of development such as Infrastructure, Ease of doing business,least corrupt countries and so on. Thus there is a direct proportion between digitalization and advancement in economy.Thus india which is ranked high in GDP but low in the other parameters of growth such as corruption, ease of doing business etc. will definitely benefit if it joins the club of digitalization


SuGyaan 48

Problems associated with new digital economyThere are a number of problems that the government need to think about

The problem of cyber security - Cyber security risks are growing with the increasing digitization of the economy.The government consider some aspects of cyber security to be sensitive to be handled by the private sector.However it might pursue public-private partnerships in cyber security towards creating a more secure ecosystem.Unless cyber security is addressed across the digital economy spectrum, it will be difficult to have a secure and trusted environment conducive to growing businesses and attracting investors.

Trouble in labor markets - While the digital economy is believed to create new jobs, on the other hand,the shift to automation, ability to connect are posing risks to the traditional types of employment and job security.The government needs to think through strategies to address this problem

ConclusionHence digitalization of the economy which means using information technology to conduct business transactions, governance, welfare schemes, information dissemination has the potential of changing the economy from a traditional one to that of the modern way of integrating information, thereby bringing in transparency of operations. It is the need of the hour especially in India where some of the roadblocks to growth are high level of corruption, low ease of doing business, laxity in operations and lack of accountability of operations. It has the power of converting the fat informal sector to formal sector which will enhance very fast growth.

References1. Sandeep R.Shelar,2017,”Effect of digital economy on India, published in International

journal of current research,Vol 9, Issue 2, p.no:46992-46996

2. Peter Larsen,2003,”Understanding the digital economy: Data,Tools and Research”, Journal of documentation, Vol 59, Issue 4, p.no:487-490

3. Digital Economy-Strategy Consultation, 2017, Minister of Industry, Innovation & Science, Australian Government, industry.gov.au/digital economy

4. Paula Klein, 2018, “Study on the digital economy, Performance pay, ide.mit.edu

5. Fang Zhau, Mohini Singh (2015), “ E-Government development and the digital economy: a reciprocal relationship”, Vol 25, Issue 5, p.no:734-766

MJ SSIM X (I), 5, 2018


SuGyaan 49

BUSINESS PROCESS AND RE-ENGINEERING – WITH ERP

*Srinivasa Rao. Budde **Dr.Y.V.Rao

ABSTRACTBusiness Innovation means an organization’s process for introducing new ideas, workflows, methodologies, services or products. It can be Defined as the process of translating an idea or invention into a goods or service that creates value or for which customers will pay. To be called an innovation, an idea must be replicable at an economical cost and must satisfy a specific need.

The Technology Management can be defined as the process whereby all resources related to information technology are managed according to an organization›s priorities and requirements. This includes tangible and intangible resources like networking hardware, computers, people, software and data. The main aim of IT management is to generate value through the use of latest technology. It can be achieved only with the alignment of business strategies and technology.

Technology Management has the unique functions like software development, change management, network planning and tech support including many of the basic functions of management like staffing, organizing, budgeting and control.

A business process is a collection of all the activities and tasks that are linked to accomplish an organizational goal. The management needs to be more proactive and flexible in adopting the new business strategies and technology.

Key Words: Business Process, Re-Engineering, Innovation, ERP

JEL Classification Code: M15, M150

IntroductionBusiness Process Reengineering is a studied and intensive change initiative that contains major steps to be changed or modified in current business process. It is basically rethink existing processes to deliver more value to the customer with cost effective and in more efficient way.

* Principal Software Engineer, Dell Technologies, Hyderabad & Research Scholar, Department of Management studies, Vignan’s University, Vadlamudi, Guntur, Andhra Pradesh, India

** Dean Research-Department of Management studies, Garden University Bangalore, Karnataka, India


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Business Innovations are two types1) Evolutionary Innovations – which are continuous innovation that are brought about by many incremental advances in technology or processes.

2) Revolutionary innovations – also called discontinuous innovations.

Enterprise Resource Planning (ERP) Implementations is a solution allows each department or business domain to be managed centrally while operating independently.

Companies are increasingly implementing Enterprise Resource Planning (ERP) software solutions to improve operations and provide faster customer response. Advantages include interoperability of data, increased communication and increased data reliability through the use of a single database. The goals are long-term customer satisfaction and increased profitability. Core idea of ERP implementation is complete integration with various modules of each segment of the business process to take care the overall smooth flow of business process with more effective and less cost.

Benefits of an effective ERP implementations, by reengineering of business process.

• User-friendly and take care of all the stakeholders in the organization

• Enterprise Resource Planning is customizable and adaptable software for any kind of business process and operations.

• ERP takes advantage of the cloud and mobility with more integrity among the departments of business.

• All stake holders feel the difference with ERP wherein they can have access to the readily available data.

• Integration with Customer Relationship Management there by retaining more customers.

• ERP Improves efficiency across departments

• It benefits both start-ups and traditional businesses

• ERP is known to give positive ROI in a year

• ERP Software Improves Visibility for the business process and operations.

• ERP Software Streamlines Operations of business

• Provide a Holistic View of Customer Interactions with better customer satisfaction.

• Improve Customer Service Response with increased customer loyalty.

• Help with Upsell Opportunities from the competitive market.

• Streamline Customer Service Processes


SuGyaan 51There are various ERP packages available in the market, below are few of the popular ERP packages in table 1. Organizations can select one or combination of more than one ERP packages to re-engineer their existing Business Process based on their industry specifics like Epicor, Infor, Microsoft, Oracle, SAP etc.,

Table 1 –ERP Packages & Table 2 Customer Strength

Epicor at a glance Infor at a glance Microsoft at a glance Oracle at a glance SAP at a glance

Long history of reputable products

3rd largest global ERP maker

Over 83,000 ERP customers

Over 37,000 application customers

More than 35,000 customers, 120 countries

Over 20,000 customers, 140 countries, 30 languages

Over 70,000 customers

Strong SMB/mid-market solution

Claim #1 CRM market share leader

Claim #1 CRM market share leader

In major growth mode Several different ERP systems

Very strong partner channel

#2 ERP market share leader

Built the client/server ERP market

Reasonable VAR channel Vertically focused ERP solutions

Only sold through VAR channel

30 year proven credibility

Definite #1 ERP market share leader

Several strong industry solutions

Lean manufacturing capabilities

Multiple ERP products

New SOA architecture

Very impressive distribution/SCM

ERP consultant strengthComplex and discrete manufacturing

ERP road map questionable

Deep software functionality

Several industry solutions

MS/SQL/SOA technology

Process manufacturing

Solutions often vary by global region

Outrageous flexibility

Netweaver, SQL and a chasm of technologies

Graph-1 –Percentage Wise ERP Packages Customer Strength

Table 2 - Customer Base of various ERP Packages

ERPCustomer strength

Epicor 20,000

Infor 70,000

Microsoft 83,000

Oracle 37,000

SAP 35,000

Total 245,000

Source - http://www.erpsoftware360.com/erp-software.htm Table 2 indicates that though the SAP and Oracle have less number of customer share, they take the advantages of serving big global clients like Walmart, Dell, Shell, PayPal, Asian Paints, Reliance Retail, Mitsubishi UFJ Financial Group and ICS Consultancy Services etc., Infor and Microsoft serve small customers.


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ERP ModulesMore or less each ERP package includes the following modules with a capability to integrate with each other. Below are the popularly used ERP Modules throughout the business world.

Table 3 – Various ERP Modules

SD Sales & Distribution

MM Materials Management

PP Production Planning

QM Quality Management

PM Plant Maintenance

HR Human Resources

FI Financial Accounting

CO Controlling

AM Asset Management

PS Project System

WF Workflow: prompt actions

IS Industry solutions: best practices

Table 4 - Comparative Modules – In Various ERP Packages.

SAP Oracle Epicor Infor Microsoft SD - Sales and Distribution

Marketing, Sales Supply Chain Management

Sales & Order Management

Supply Chain Management

MM - Material Management

Procurement Production Management

Material Management

Manufacturing Solutions

PP - Production Planning

Manufacturing Product Data Management

Production Management

Master Production Scheduling

QM - Quality Management

Planning and Scheduling

Planning and Scheduling

Enterprise Planning Materials Requirements Planning

PM - Plant Maintenance

Service Service Management

Service Management

Service Call Management

HR - Human Resources

Human Resources Human Capital Management

Human Capital Management

Human Resource Management


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FI - Financial Accounting

Financials Financial Management

Financial Management

Financial Management

AM - Asset Management

Asset Management Asset Management Master Data Management

Fixed Asset Management

PS - Project System Projects Project Management

Project Management

Project Management

Make to Order (CR)

Order Management Order Management Sales & Order Management

Order Management

Depending on the need of the business and complexity of business process, the implementation can be done for all the modules or few of the modules. The modules which are implemented will be integrated with each other for smooth and efficient new way of operations.

There are Various Phases of Business Process and Re-Engineering in ERP implementation. There will be a series of project phases and threads. Project phases are key groupings of activities that follow a progression through the project lifecycle. Project threads relate to common themes.• Vision• Plan• Design• Build• Go-live• Operate.

Vision – In this phase the value of the project is documented to have a clear knowledge among all the stakeholders of the project. The management has to think very futuristic and convince all the stakeholders of the projects, especially the top level management of the firm.

Strategy – A well designed implementation plan/Strategy is the key to success. In this phase the project is initiated, the team is identified, evaluation of the current business process, Setting up objectives. The management has to be very careful in identifying the project team should consist of key member from each department like sales, customer service, accounting, purchasing, operations and senior management. Each team member should be committed to the success of the project and accountable for specific tasks assigned. At this stage a detailed comparative study of available ERP packages is done and a most suitable ERP package is identified for implementation. A decision needs to be made whether it is implemented internally or externally. Setting of objectives is done at this stage with clear time lines.


SuGyaan 54Design – In this phase a detailed design is developed by using the outputs from the requirements collection, design confirmation, workshops. A FIT and GAP analysis is done with detailed discussion between the business core members and ERP implementers. Understanding the business requirements and conveying the same to ERP system. At this phase the customization for the specific needs of the business process is identified and assigned to a particular team of the implementation.

Build – In this phase full configuration of the system is done for all the requirements of the business. A complete testing is done for each configuration, if any modifications to the requirements and their related configuration is done based on the testing results. A signoff from the business user is taken against each testing result.

Go-live – In this phase a final system testing is done with proper cut-over activities. A detailed user level training will be done. Go-live is very crucial in the ERP implementation. The success or failure of the entire implementation team depends on the smooth flow of Go-live and good response from business user in using the newly implemented system.

Production Operations – After a successful Go-live the next stage is day-to-day business operation with new ERP environment. In this phase, if the user faces any issues and if any of the business functionality is not taken care with a proper change request process.

Each of the above phase would have a project thread relate to common themes of implementers/consultants expertise that “cut across”. Like the People, Value, Change and Learning, Process and Package, Business Intelligence, Information Technology, Security and Controls, Support, Tax, and Project Management.

Diagram 1: Indicating the life cycle of Phases of Business Process in Reengineering with ERP implementations.


SuGyaan 55The role of key stakeholders in Business Process and Re-Engineering – With ERP.

• Project Sponsor

• Steering Committee

• Project Manager

• Business Analyst

• Team Leader

• Project Administrator

• Project Control Officer

• Project Director

Project Sponsor – A member of the Senior Management Team (may be the CEO or CFO level person). Provides agreement for funding the project and executive involvement to overcome organizational roadblocks. Provides the resources, direction, and support needed to implement the project Provides input and makes decisions as needed. Communicates support for the project to external and internal audiences. Project Sponsor is the person who vision about the business process and re-engineering, guides and supports others in process of ERP implementations.

Steering Committee – It is a combination of top level business executives who guides project progress and provides senior level advice for aspects outside the dominion of influence of the Project Manager. The executive steering committee ensures that a project stays on course and achieves the desired benefits through supporting the project within the organization. ERP implementation is a large project sometimes it spreads more than 1 year and in the course of implementation there would be several variances from the actual planning, with proper process steering committee approve project changes and decisions. A successful ERP implementation relies on the engagement of many stakeholders, but the involvement of executive-level leadership is especially critical. Considering the vast number of variables that affect an ERP implementation, effective governance from the very top of an organization is an absolute must.

Project Manager – Project Manager is point of contact to resolve all issues from project team members and vendors. Plays key role in gathering the requirements and documenting the business process…. Project Manager manages scope, budget and timing of the projects, Provides time to time reports and progress of the project to steering committee. One of the key responsibility of the Project Manager is direct and motivate the project team members, help the team members to do cross functional and module activities. The major activities of Business process re-engineering is done from the directions of Project Manager to his team members.


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Business AnalystThe primary responsibilities are performing, analyzing and providing project analysis and support to the entire project team. A business analyst is generally a junior/mid-level position that works with or directly reports to the project manager. The project analyst level includes few tasks requiring critical decision making skills. Few of the responsibilities of business analyst includes Creating, managing and disbursing reports related to the project, Maintaining project assets, communications and related database(s),Evaluating and monitoring the overall project, Reviewing and reporting the project’s budget and finances, Routinely performing complete or component analysis, Notifying the entire project team about abnormalities or variances. Business analyst need to understand the entire current business process in detail, help and support the ERP implementation by timely updating the project manager with any kind of project issues. Business Analyst need to think innovative and update the knowledge with the latest technology.

Team Leader – While doing the re-engineering of business process with ERP implementation the overall activities are broadly divided in to Functional activities and Technical activities. A person with more subject knowledge and managerial skills will be appointed as lead. There will be a separate lead for each of Functional activities and Technical activities.

Project Administrator - Project administrator requires strong executive administrative skills, as well as experience in finance budgeting and reporting. The project administrator is like a super secretary who does most of the work for the project manager on the staff level, allowing the project manager to spend his time communicating with higher level managers and other stakeholders regarding the project. Most of the time the Project Administrator do the Planning the financial budget necessary for the project, Coordinating with his/her team members frequently for updates regarding the work in progress, Monitoring the progress of the project and acknowledging team suggestions, Supervising the team members and ensuring that guidelines are met, Initiating the project or contract and working until the project is completed, Discussing updates with senior officials and the client.

Project Control Officer - A project control officer will typically provide a management layer for making sure that IT professionals have clear roles in individual projects. The duties of a project control officer may also include budgetary and risk management components.

Since one of the major roles of a project control officer is working directly with project managers, some companies ask that these professionals use a diplomatic approach to work as an effective liaison between different levels of management. Project control officers may execute deliverables, handle issues of confidentiality, prioritize workloads, and work on change orders or present documentation. Through all of these tasks and others, a project control officer coordinates multiple elements of projects to make sure they’re done effectively and efficiently.


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Project DirectorA project director leads a team of one or more project managers (PM) and various software developers, Web developers, graphic designers, testers, network engineers and other staff essential to a project. A key job responsibility is completing an IT project on schedule and within budget while working within the project’s scope of work (SOW) or function specification document (FSD).A project director has the authority to make key decisions, adjust budgets and add resources and related project governance processes. A project director receives regular reports from the project manager and/or team leads. Also, he or she reports directly to senior management, such as a vice president (VP), chief technology officer (CTO) and/or company president.In addition, a project director often manages multiple projects simultaneously.

Diagram 2: key stakeholders in Business Process and Re-Engineering.

ConclusionThere is no doubt that ERP can help in complete reengineering the business process with more efficiency and less efforts. However, proper care has to be taken at each and every stage of implementation and maintenance of systems with complete ownership of all the stake holders of the firm. If this is taken care, the firm can reap all the benefits of the successful ERP implementation in business process reengineering.

References1. ERP Documents - various IT companies like Deloitte Technologies

MJ SSIM X, (I), 6, 2018


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SIVA SIVANI INSTITUTE OF MANAGEMENTS.P Sampathy’s Siva Sivani Institute of Management is promoted by the Siva Sivani Group of Educational Institutions, which has been running the prestigious and internationally renowned Siva Sivani Public Schools for more than four decades. Approved by the All India Council for Technical Education, Ministry of Human Resource Development, Government of India, New Delhi, Siva Sivani Institute of Management started functioning as an autonomous institute in 1992.

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