78

Chapter 4

NATURE AND EXTENT OF DIVERSIFICATION IN

BANKS IN INDIA

Diversification strategy can be described by the extent of participation in different

businesses and the underlying pattern of relationships among various businesses of firms

(Nayyar, 1992). Banks may go for expansion via diversifying geographically or/and via

product market diversification i.e. by adding new products and business lines.

Diversification can be across financial products (services) and geographic domestic

diversification (intrastate), or international (Landskroner and Ruthenberg, 2004). Besides

diversification types and industry structure, researchers have also looked at the ways the

firms diversify (Pandya and Rao, 1998). Simmonds (1990) examined the combined

effects of breadth (related vs. unrelated) and mode (internal expansion versus mergers &

acquisitions) and found that related diversified firms are better performers than unrelated

diversified firms and R & D based product development is better than mergers and

acquisition- led diversification (Simmonds, 1990; Lamont and Anderson, 1988).

Banking industry may adopt diversification route in the following forms; -

1) Vertical or horizontal integration to create diversified financial group.

2) Alliance diversification via joint ventures, tie-ups etc.

Before discussing the nature of diversification in banks, a brief discussion is given

on the underlying relationship between the three important segments of financial markets.

i.e., commercial banks, insurance and investment business. This relationship provides a

base for diversification of banking activities and integrating these three different

businesses. Investment and insurance business generally finance a growing volume of

assets and do not have adequate capital base. In such situations commercial banks can

provide a substantial capital base. Also, wide bank-branch network can provide scope to

investment and insurance business for the distribution of their financial products like

mutual funds, debentures, policies etc. Moreover, as in the present scenario when the

proportion of bank credit in corporate debt is declining, the banks are increasingly turning

79

to new, non-traditional financial activities as a way of maintaining their position as

financial intermediaries. Integration of all the three business in the form of diversified

financial group can play a compensatory role. But inspite of this significant relationship

these three businesses require varying business skills and operational skills. There is also

a difference in attitude towards risk. Commercial banks avoid risk, insurance business

aims to cover risk while investment banks specialize in risk management. Moreover,

these three businesses have different approach towards customers. Therefore, these

factors have to be kept in consideration, while integrating and diversifying the banking

operations.

Major objective of this chapter is to study the nature and extent of diversification

in the Indian banking sector. Further, other two sub-objectives are also discussed in this

chapter i.e. to study the degree of diversification in Indian banks and the type of

relatedness in diversified banks.

The diversification of activities in the banking industry has led to the entry of

banks and other financial organizations into new segments of the financial services

industry (Canals, 1997). Diversification in financial services also enables banks to sell

services to customers who demand multiple products (financial supermarkets). In the

present study, those banks will be included in the definition of diversified banks, which

have atleast one insurance subsidiary or investment institution or both. These may be in

the form of a subsidiary, joint venture or in any other mode such as alliance distribution

etc.

4.1 Nature of Diversification in Banks

An alternative approach as prescribed by (Pitts and Hopkins, 1982) is to measure

firm's diversity is to count the number of products constituting the firm's total portfolio.

This business count method assumes that different products or services represent

different businesses, typically uses SIC codes to identify individual businesses and then

counts the number of businesses (Pitts and Hopkins, 1982).

80

Banking sector practices a wide range of organization structures to expand

their business. Nature and Extent of Diversification in Banks is analyzed through

the framework used by Ramanujam and Varadarajan (1987). This framework is

based on a two dimensional, categorical measure of firm diversity developed from the

work of Berry (1971) and Wood (1971). Berry (1971) used the SIC (Standard

Industrial Classification) data to distinguish between two distinct patterns of firm-

level diversification that is NSD (Narrow Spectrum Diversification) and BSD (Broad

Spectrum Diversification). The SIC coding system is used to differentiate between

related and unrelated business areas, related business areas being termed “Narrow

Spectrum Diversity (NSD)” and unrelated business areas “Broad Spectrum Diversity

(BSD)” (Dautwiz, 2009). A third category named as AD (Alliance Diversification) is

used to identify the nature of diversification in banks in India. For the purpose,

following definitions are used to study nature and extent of diversification in Indian

banking sector.

BSD (unrelated product diversification) - Diversification into a new four digit SIC

industry category outside the firm's present scope of activities at the two digit SIC

industry (Jacquemin and Berry, 1979). Broad Spectrum Diversification refers to

expansion other than vertical integration into a different four-digit business. i.e., into a

different non-banking business like merchant banking, factoring etc.

NSD (related product diversification) – Diversification into a new four digit SIC

industry category but within the firm's present scope of activities at the two-digit SIC

industry category level (Jacquemin and Berry, 1979). Narrow Spectrum Diversification

refers to expansion through vertical integration into similar banking business through

banking subsidiaries.

AD - Alliance Diversification refers to expansion of banking and non-banking

through tie-ups, joint ventures. Alliance Diversification may be done into related or

unrelated business.

81

Table 4.1: Nature and Extent of diversification in Indian banking sector

BSD (Broad Spectrum Diversification) and UB (Unrelated Business), NSD (Narrow Spectrum

Diversification) and RB (Related Business), AD (Alliance Diversification)

DIVERSIFIED

BANKS

NON-

DIVERSIFIED

BANKS BSD NSD AD

I Public Sector Banks

1. State Bank of India and

Associates

*****

UB

****

RB

****

RB &

UB

2. Allahbad bank ****

UB

3. Andhra Bank ****

RB

4. Bank of Baroda ****

UB

****

RB

****

RB

5. Bank of India ****

UB

****

RB

&UB

6. Bank of Maharastra ****

ND

7. Canara Bank ****

UB

****

RB &

UB

8. Central Bank of India ****

UB

9. Corporation bank ****

UB

10. Dena Bank ****

ND

11. Indian Bank ****

UB

****

UB

12. Indian Overseas Bank ****

ND

13. Oriental bank of Commerce ****

ND

14. Punjab & Sind Bank ****

UB

Contd…

82

15. Punjab National bank ****

UB

16. Syndicate Bank ****

ND

17. UCO Bank ****

ND

18. Union Bank of India ****

ND

19. United Bank of India ****

ND

20. Vijaya Bank ****

ND

II Other Public Sector Bank

21. IDBI Ltd. ****

UB

III Private Sector Banks

22. Bharat Overseas Bank ****

ND

23. City Union Bank Ltd ****

ND

24. Karnataka Bank ****

ND

25. Lord Krishna Bank ****

ND

26. Nainital Bank ****

ND

27. SBI Commercial &

International Bank

****

RB

28. Tamilnad Mercantile Bank ****

ND

29. The Bank Of Rajasthan Ltd. ****

ND

30. The Catholic Syrian Bank

Ltd.

****

ND

31. The Dhanlaxmi Bank Ltd ****

ND

Contd…

83

32. The Federal Bank Ltd ****

ND

33. The Ganesh Bank Of

Kurundwad Ltd

****

ND

34. The Jammu & Kashmir Bank

Ltd.

****

ND

35. The Karur Vysya Bank Ltd ****

ND

36. The Laxmi Vilas Bank Ltd ****

ND

37. The Ratnakar Bank Ltd ****

ND

38. The Sangli Bank Ltd ****

ND

39. The South Indian Bank Ltd ****

ND

40. The United Western Bank Ltd ****

ND

41. HDFC Bank Ltd ****

UB

****

UB

42. ICICI Bank Ltd ****

UB

****

UB

43. Indusind Bank Ltd ****

UB

44. Kotak Mahindra Bank Ltd ****

UB

****

UB

45. AXIS bank (formerly

UTI)Bank Ltd

****

UB

46. Yes Bank Ltd. ****

ND

47. ING Vysya Bank Ltd. ****

UB

****

UB

48. Development Credit Bank

Ltd.

****

ND

Total No. Of Banks 13 3 13 30

84

Table 4.1, shows the nature of diversification in Indian banking sector. In respect

of public sector banks in India, out of a total of 21 banks, 12 banks were diversified and 9

banks were non-diversified as on 31st march 2008. This includes IDBI ltd (other public

sector bank), which is a well-diversified bank. State Bank of India is highly diversified.

It has adopted both BSD (Broad Spectrum Diversification) move and NSD (Narrow

Spectrum Diversification) move along with AD (Alliance Diversification). It has six

banking subsidiaries and four non-banking subsidiaries along with many foreign

subsidiaries. SBI has entered into a number of new businesses with strategic tie-ups –

Pension Funds, General Insurance, Custodial Services, Private Equity, Mobile Banking,

Point of Sale, Mergers and Acquisition, Advisory Services, Structured Products etc. SBI

Life is a joint venture with Cardiff, while SBI Asset Management a joint venture with

Societe Generale is an example of alliance diversification.

Bank of Baroda is another bank, which has adopted all the three moves of

diversification. In NSD, it has one associate bank i.e., Nanital Bank and several

subsidiaries dealing with banking and non-banking business. It has diversified into areas

of merchant banking, housing finance, credit cards and mutual funds as a move to

become a one stop financial supermarket. As part of diversification plan the bank has

ventured into the life insurance business. Bank of Baroda Asset Management Company, a

subsidiary of the Bank of Baroda in collaboration with the Italian company 'Pioneer' and

has formed a joint venture for mutual fund business. The Bank has a very large overseas

presence amongst the Indian banks and have branches and representative offices in

countries like USA, UAE, UK, Singapore, Thailand, Malaysia, Mauritius, South Africa,

Kenya, Hong Kong, China, Bahrain, Australia etc. The global business of the Bank is

over Rs.3, 71,000 crores by the end of March, 2009.

Canara Bank has adopted Broad Spectrum Diversification move and has been

scaling up its market position to emerge as a major 'Financial Conglomerate' with nine

subsidiaries/sponsored institutions/joint ventures in India and abroad. In the year 2008,

Canara bank incorporated two joint ventures namely, Canara Robeco Asset Management

Company Ltd and Canara HSBC Oriental Bank of Commerce Life Insurance Company

85

Ltd. To give a thrust on augmenting non-interest income sources and avenues, the bank

has entered into a Corporate Agency agreement with ECGC (Export Credit Guarantee

Corporation of India Ltd) for soliciting and procuring export credit insurance business.

Banks like Central Bank of India, Corporation Bank, Allahbad bank have

diversified through BSD move. As a part of plan to diversify into gold trade,

Corporation Bank has opened a gold desk at Mumbai. It has already started marketing

gold and launched dealings at Ahmedabad, Bangalore, Delhi, Mangalore and Mumbai.

While Allahbad bank Instituted AllBank Finance Ltd., a wholly owned subsidiary for

Merchant Banking. In a bid to diversify its revenue stream, Allahabad Bank set up its

syndication desk to generate more fee based income.

Other State-owned banks such as Union Bank of India, Bank of India,

Corporation Bank, UCO, United Bank of India are gradually making inroads to

become diversified through BSD move to enter into the domain of loan syndication and

giving the traditional leaders a tough competition in the field of loan syndication market

such as SBI Capital Markets, Axis Bank, IDBI Bank. Also, Life Insurance Corporation of

India has entered into an agreement with the Corporation bank in a bid to market its

credit card business across the country.

Indian Bank has adopted both BSD move and AD move for diversifying.

Indian Bank has a separate subsidiary dealing in Merchant Banking, as Indbank Merchant

Banking Services Limited (Indbank) incorporated in the year 1989.

Andhra Bank and Punjab and Sind Bank have started diversification move

through AD. i.e., Alliance Diversification. Andhra Bank entered MOU with Bank of

Baroda and Legal & General Group of UK to form a joint venture life insurance

company. To provide all the insurance related services under one roof, Punjab & Sind

Bank has tie-up arrangements for Non Life insurance business with M/s Bajaj Allianz

General Insurance Company and Life Insurance business arrangements with M/s Aviva

Life Insurance Company India Pvt. Ltd.

86

In case of Private sector banks, old private sector banks are not diversified at

all, while most of the new generation private sector banks are highly diversified.

HDFC Bank, ICICI Bank, Indusind Bank, Kotak Mahindra Bank and ING Vysya

Bank are categorized as banks that have followed BSD (Broad spectrum

Diversification) and AD (Alliance Diversification) to expand their banking and non-

banking products and services.

HDFC Bank’s target market ranges from large, blue chip manufacturing

companies in the Indian corporate to small & mid-sized corporate and agri-based

businesses. HDFC India commenced operations as a Scheduled Commercial Bank in

January 1995. The bank provides a wide range of commercial and transactional banking

services, including working capital finance, trade services, transactional services, cash

management, etc. HDFC bank has entered into a strategic merger with Standard Life

(largest insurer in the UK to form HDFC Standard Life Insurance Company. HDFC

Bank, offer exposure to the real estate asset class through portfolio management schemes.

ICICI Bank is India's second-largest bank. ICICI Bank offers a wide range of

banking products and financial services to corporate and retail customers through a

variety of delivery channels and through its specialized subsidiaries and affiliates in the

areas of investment banking, life and non-life insurance, venture capital and asset

management. ICICI bank is expanding and diversifying in fund-generation profiles and

revenue streaming activities like insurance intermediaries, reinsurance brokers,

consultants, risk managers, loss assessors and to buy sell, market, distribute deal in or

dispose of insurance products and related instruments. In the fiscal year 1985, bank had

a networth of Rs 1.75 billion, assets of about Rs 21 billion and profits of Rs 0.36

billion. In the fiscal year 2009, it had a networth of about Rs 500 billion, assets of about

Rs 3,800 billion and profits of Rs 37.58 billion. This represents over 20 per cent

compounded annual growth over a 24-year period. Most of the new generation private

sector banks are diversifying through Broad Spectrum diversification, either through

their own non-banking subsidiaries such as securities houses or insurance companies or

have become heavily involved in product sales such as bancassurance or mutual funds

etc.

87

Yes bank is dealing in single banking business only. Yes bank was established

in the year 2004. Yes bank is the only bank which has been bestowed with the

Greenfield3 License by the RBI. The main activities of the bank include offering

banking and financial solutions, corporate and institutional banking services, retail

banking services, and wealth management. However, Yes bank is associated with

several global leadership forums like initiatives like Clinton Global Initiative (CGI),

Triple Bottom Line Investing (TBLI) and Tallberg Forum.

ING Vysya Bank was formed in October 2002 when the former Vysya Bank

Ltd, entered into collaboration with ING, a global financial powerhouse of Dutch

origin. ING Vysya Bank has diversified through BSD move as it has four subsidiaries

dealing in banking, insurance and asset management.

Kotak Mahindra Bank another diversified bank offers a variety of products

like commercial banking, stock broking, mutual funds, life insurance and investment

banking through its subsidiaries namely Kotak Mahindra Primus Ltd (KMPL), Kotak

Securities, Mahindra Invests, Kotak Forex Brokerage, Kotak Mahindra Capital Co.,

Kotak Mahindra Trustee Co. and Kotak Mahindra Asset Management. The net worth of

the Kotak Mahindra Group, to which the Kotak Mahindra Bank belongs, is over

Rs.7,100 crore.

IndusInd Bank is also diversifying through AD into distribution of life and

non-life insurance products. The Bank became one of the first bank to implement the

RBI - Electronic Funds Transfer Scheme. It has its representative Office in Dubai.

4.2 Measurement of Degree of Diversification

Different methods have been devised for representing the relative importance of

the individual business areas more precisely and allow the weighting of the individual

business units when measuring their relatedness (Dautwiz, 2009). In the Table 4.2

3 Greenfield investment is defined as an investment in a start-up project, usually for a major capital

investment and the investment starts with a bare site in a greenfield.

88

below, different approaches to measure the degree of diversification is presented as cited

in (Dautwiz, 2009).

Table 4.2: The development of continuous approaches to measuring the

diversification of companies

Index Formula Definition

Weighted Index

Weighting of the individual business

units P for assessing their relative

importance. Disadvantages: arbitrary

choice of the weighting factor W

Herfindhal -

Index

Weighting of the relative proportion

of overall turnover for the business

unit Pi. The value for H measures

the degree of concentration and

becomes smaller as diversification

increases.

Berry-Index

A transferal of the Herfindhal-Index

from a measured of concentration to

one of diversification by subtraction

it from the value 1.

Jacquemin-Berry

Index/Entropy

Measure

Determination of diversification

growth taken a refined matrix for

structuring economic activity as its

because Pi as a business unit’s

proportion of the overall turnover

after predefinition of a level of

structural depth in the classification

matrix (e.g. a 4 stage SIC matrix).

Source: Dautwiz, 2009

89

To measure the degree of diversification numerous methods like Entropy measure

(Jacquemin & Berry, 1979), Concentric Index and Herfindhal Index have been used in

the literature of strategic and financial management. However, entropy measure

“quantified as a continuous one” has remained widely used as a measure for

diversification (Palepu, 1985). Using the Berry-Index and the entropy measure from

Jacquemin and Berry it is possible to assess the relative importance (in terms of size,

generally measured as size of turnover) of individual business units (Dautwiz, 2009). The

entropy measure is calculated using the following formula:

Entropy Measure of Diversification

( )∑=

=n

i

ii PPmeasureEntropy1

/1ln

where,

(Pi) is the proportion of total operations within each segment I, i.e.,

proportion of ‘income from diversified operation and services’.

n is number of segments in which bank operates.

1n is natural logarithm of the inverse of the proportion of total operation in

each segment.

(1/Pi) is the relative weight of each segment i.

In the Table 4.3 below, diversification index for banking groups namely SBI

Group (State Bank of India and associates), NB (Nationalized banks) and PB banks

(New Generation Private Bank) operating in India is computed for the time period

between 1994 to 2008.This time period is selected because most banks have started

diversification moves after the implementation of economic and financial sector

reforms of 1991. From these respective banking groups, income of those banks which

are not diversified (as shown in Table 4.1) is excluded as they operate in single

banking business. For computing Pi i.e., proportion of total operations within each

segment I i.e., proportion of ‘income from diversified operation and services’, which

is income from other sources, is taken. Income from other sources consist of

commission, exchange and brokerage, net profit (loss) on sale of land, building &

other assets, net profit (loss) on exchange transaction, miscellaneous income, net

profit (loss) on sale of investments, net profit (loss) on revaluation of investments, is

taken. By using the entropy index of Jacqueimin and Berry (1979), diversification

index of banking sector operating in India is computed.

90

Table 4.3: Measure of Diversification in Indian Banks (Entropy measure)

YEAR SBI Group NB PVTBS

1994 0.529644 0.4099 0.22

1995 0.526056 0.4151 0.2810

1996 0.53017 0.3286 0.4082

1997 0.529484 0.3274 0.4360

1998 0.521502 0.3275 0.3833

1999 0.528029 0.3280 0.3593

2000 0.522042 0.3294 0.4031

2001 0.524283 0.3296 0.3722

2002 0.499872 0.3221 0.4431

2003 0.501679 0.4256 0.4470

2004 0.512042 0.3231 0.4571

2005 0.512246 0.3542 0.4582

2006 0.509631 0.4301 0.4872

2007 0.464591 0.3286 0.5163

2008 0.472091 0.3376 0.5191

91

Diversification Index in Indian Banks

0

0.1

0.2

0.3

0.4

0.5

0.6

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Div

ersif

ica

tio

n I

nd

ex

SBI GROUP NB PVTBS

Fig. 4.1: Diversification index in Indian Banks

As shown in the Table 4.3 and Fig. 4.1, in the year 1994, diversification level is

the highest in SBI group (0.529) followed by nationalized banks (0.4099). It is least i.e.

(0.223) in private sector banks. This was the period when new generation private sector

banks were being setup. By 1995-96, a total of nine new generation private sector banks

were in operation. From the year 1994 to 1999, a mixed trend is seen but after 2002 year

onwards, index of private banks has increased continuously indicating that these banks

emphasize more on diversified source of income that is on “other income”. In the year

2008, the diversification index of private banks rose to 0.519, which is even higher then

SBI group followed by nationalized banks

Diversification index of state bank of India, over the time period of 1994 to 2006

remained the highest with six banking subsidiaries and a number of non-banking

subsidiaries and joint ventures. But after year 2002, a declining trend is seen because of

92

the competition given by new generation private banks along with foreign sector banks in

the field of retail banking and micro banking segment. Another, reason is that in the year

2002-03, trading income of SBI group has increased in larger proportion as compared to

its non-interest income

In respect of nationalized banks, a mixed trend is seen in their diversification

index, which indicates that banks have started realizing the significance of diversification

in the present scenario and are becoming diversified in their approach.

4.3 Diversification Status and Type of Relatedness

The diversity status and mode is an indication of how diversified the firm is in

terms of product/market combinations (Verweire, 1999). Relatedness and Unrelatedness

concepts are of relevance to highlight the nature of business combinations. Some

business combinations may result in more synergetic potential. Stimpert and Duhaime

(1997) interviewed top managers of diversified companies regarding their perception and

understanding of the relatedness in their portfolio of business and came to the conclusion

that executives should try to capture synergies by coordinating activities across their

firms’ businesses. Stimpert and Duhaime (1997) and Nayyar (1992), emphasized on

managerial conceptualism with regards to diversification strategy.

Rumelt (1982) defined relatedness as “drawing on the same common core skill,

strength, or resource”.

As per Nayyar, (1992), “relatedness either encompasses (1) sharing of markets,

distribution systems, product and process technologies, or manufacturing facilities, (2)

relying on common technologies, managerial capabilities and routines and repertoires”.

While Prahalad and Hamel (1990) described, relatedness as based on the idea of

leveraging core competences across multiple businesses. Verweire (1999) in his study

found four types of relatedness namely; -

• Differentiation relatedness-all items are included which are associated with

porter’s generic model of differentiation (a product's difference from other

products of a similar nature) or the functional skills of marketing.

93

• Strategic relatedness encompass of all items, which are considered in strategic

decisions.

• Size relatedness encompasses variables, which managers perceive as related in

terms of equality of size of the different parts of the group.

• The product-market relatedness-all the items that load on this factor reflect

similarities in businesses' product characteristics or market segments.

In the present study, to analyse the nature of relatedness in diversified banks, a

standardized questionnaire was drafted in which managers of different diversified public

and private banks were asked as to how they perceived the relatedness of their banking

business with other lines of business like insurance, investment and other non-banking

business. To test the validity of data collected, Bartlett’s test of sphericity and Kaiser-

Meyer-Olkin (KMO) Measure of sampling adequacy value is calculated.

Measure of Sample Adequacy -KMO and Bartlett’s Test

Measure of sample adequacy such as Bartlett’s test of spherecity and KMO Value

(Table 4.4) showed that data is fit for factor analysis. Generally, KMO value greater than

0.5 is desirable. As the Table 4.4, shows that KMO value is 0.685, it makes the data fit

for factor analysis and Bartlett’s test also satisfies the condition of significance of chi

square value.

Table 4.4: KMO and Bartlett’s test

KMO and Bartlett’s test

Kaiser-Meyer- Olkin Measure of Sampling Adequacy 0.685

Bartlett’s Test of Sphericity Approx. Chi-Square 485.94

Df 105

Sig. 0.000

94

In the questionnaire twenty-two variables (as shown in Table 4.5(I)) with respect to the

underlying nature of relatedness in diversified banks were measured on a five point scale

(Likert scale) ranging from 5 to 1 depending on the importance attached to each variable.

For example, “Strongly agree” was ranked 5 followed by “Agree” with value 4, “Neither

Agree nor Disagree” with 3, “Disagree” with 2 and “Strongly Disagree” with 1.

Table 4.5 (I): Nature of Relatedness (List of Variables)

S.No. List of Variables

V01 Businesses are required to meet financial targets

V02 Businesses share customers

V03 Businesses share distribution network

V04 Businesses emphasize on low costs/cost leadership strategy

V05 Businesses are similarly impacted by economy

V06 Businesses emphasize on product development

V07 Businesses produce high value-added services

V08 Businesses share back office

V09 Businesses emphasize on R&D

V10 Businesses emphasize on marketing

V11 Businesses share management information system (M.I.S.)

V12 Businesses require the same management skills

V13 Businesses have strong brand names

V14 Businesses share technology

V15 Businesses share corporate culture

V16 Businesses aim at the same corporate goals

V17 Businesses use the same strategic concepts to run the business

V18 Businesses share common core business

V19 Businesses are about the same size

V20 Businesses have about the same market share

V21 Businesses serve niche markets

V22 Businesses pay attention to service

95

Table 4.5 (II): The correlation matrix (Nature of Relatedness)

Correlation

Matrix

VAR00001 VAR00002 VAR00003 VAR00004 VAR00005 VAR00006 VAR00007 VAR00008 VAR00009 VAR00010 VAR00011

VAR00001 1

VAR00002 -0.2496 1

VAR00003 -0.26342 0.236221 1

VAR00004 -0.06104 0.393702 0.349488 1

VAR00005 -0.0428 0.222681 -0.26149 0.10991 1

VAR00006 -0.0706 0.455334 0.244125 0.262014 0.108253 1

VAR00007 0.178516 0.166715 -0.09997 0.204389 0.699753 0.024559 1

VAR00008 -0.26264 -0.41077 -0.14633 -0.32636 0.064754 -0.32785 -0.12306 1

VAR00009 0.402422 0.038169 -0.05798 -0.15169 0.028048 0.2591 -0.13999 -0.11065 1

VAR00010 -0.1416 -0.01343 0.069796 -0.27713 0.065795 0.170941 0.190879 0.237934 -0.0406 1

VAR00011 0.034157 -0.0216 -0.08634 -0.21788 -0.32007 0.345154 -0.23963 -0.25741 0.335163 0.022974 1

VAR00012 0.097823 0.222681 0.037091 0.354548 0.215909 0.183702 0.161645 -0.04981 -0.18699 0.065795 0.10581

VAR00013 0.28995 -0.27212 -0.02314 0.287611 -0.05318 0.073691 0.021235 -0.25487 -0.15169 -0.04106 0.313622

VAR00014 0.203785 -0.27704 -0.4855 -0.11079 0.359037 -0.3508 0.336739 0.254228 -0.24021 0.268418 -0.2443

VAR00015 0.051879 0.118094 -0.01049 0.476336 0.345518 -0.22268 0.397043 -0.06692 -0.40328 -0.23262 -0.37409

VAR00016 -0.5035 0.40452 0.170694 0.051525 0.075691 -0.03178 -0.18031 0.048259 -0.33968 0.119523 -0.39724

VAR00017 0.141598 -0.29547 -0.0698 0.041056 0.312527 0.047484 0.3756 -0.23793 -0.27067 0.095238 -0.02297

VAR00018 0.226294 -0.4722 -0.24311 -0.18591 -0.20592 -0.27319 -0.15417 -0.02689 -0.10093 0.152204 -0.03672

VAR00019 0.009429 0.186018 0.085803 0.442894 -0.10515 0.308601 -0.07872 -0.29191 -0.26675 0.152204 0.150941

VAR00020 0.291492 -0.1614 -0.20551 0.03198 0.453923 0.207122 0.655013 -0.16688 -0.07228 0.317928 0.040903

VAR00021 0.400332 -0.16357 -0.00934 0.321441 0.143091 -0.29328 0.107128 0.012544 -0.0883 -0.44531 -0.17987

VAR00022 0.004336 -0.14146 0.220938 0.203649 -0.5117 -0.01861 -0.48716 -0.01236 0.258567 -0.19829 0.155692

Contd…

96

Correlation

Matrix

VAR00012 VAR00013 VAR00014 VAR00015 VAR00016 VAR00017 VAR00018 VAR00019 VAR00020 VAR00021 VAR00022

VAR00001

VAR00002

VAR00003

VAR00004

VAR00005

VAR00006

VAR00007

VAR00008

VAR00009

VAR00010

VAR00011

VAR00012 1

VAR00013 0.680731 1

VAR00014 0.0868 0.002462 1

VAR00015 0.53033 0.361012 0.142283 1

VAR00016 -0.08257 -0.3435 -0.06212 0.068119 1

VAR00017 -0.0658 0.277131 0.38835 -0.03489 -0.11952 1

VAR00018 -0.10515 0.191374 0.410716 0.086744 -0.19101 0.13952 1

VAR00019 0.701 0.694414 -0.14907 0.229253 0.05306 0.13952 -0.08784 1

VAR00020 0.117141 0.242133 0.536356 0.062124 -0.399 0.657051 0.141137 0.011291 1

VAR00021 0.4722 0.526807 0.01242 0.657674 -0.33792 -0.03107 0.204124 0.204124 -0.0507 1

VAR00022 -0.04835 0.145823 -0.35812 -0.02564 -0.08783 -0.60654 0.173984 0.031068 -0.53215 0.152204 1

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The inter correlation among the variables were calculated as shown in Table

4.5 (II). The Table 4.5 (II) revealed the following variables, which showed greater

correlation:

1. Businesses produce high value-added services with Businesses are similarly

impacted by economy.

2. Businesses have about the same market share with Businesses produce high

value-added services

3. Businesses are about the same size with Businesses have strong brand names

4. Businesses serve niche markets with Businesses share corporate culture

5. Businesses have about the same market share with Businesses use the same

strategic concepts to run the business.

To analyze the perceived significance of variables showing the nature of

relatedness among diversified banks, twenty-two variables (as shown in Table

4.5(I)) have been used for factor analysis. These variables were coded using a five-

point scale. The results of factor analysis have been shown in Table 4.5 (III) and 4.5

(IV). Principal Component Analysis was used for extracting factors from Table 4.5

(III) and six (6) factors were retained depending on Eigen values and variance

explained by each factor. Components with Eigen values over 1 are usually said to

be the factors are considered. Eigen values are the variances of the factors.

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Table 4.5 (III): Eigen Values and Percentage of Variance

Component

Total Variance Explained

Cumulative % Initial Eigen values

Total % of Variance

1 3.770239 17.13745 17.13745

2 3.464241 15.74655 32.884

3 3.04332 13.83327 46.71727

4 2.436326 11.07421 57.79148

5 1.950882 8.867645 66.65912

6 1.473923 6.699652 73.35877

7 0.88456 5.646464 79.00523

8 0.848619 3.857359 82.86259

9 0.75361 3.425534 86.28809

10 0.639006 2.904571 89.19266

11 0.602656 2.739344 91.93201

12 0.414715 1.885067 93.81708

13 0.383126 1.741484 95.55856

14 0.302703 1.375925 96.93448

15 0.233058 1.059354 97.99384

16 0.190662 0.866645 98.86048

17 0.084161 0.38255 99.24303

18 0.061378 0.278992 99.52202

19 0.049997 0.227258 99.74928

20 0.040141 0.182461 99.93174

21 0.008467 0.038488 99.97023

22 0.006549 0.029768 100

Extraction Method: Principal Component Analysis.

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Total variance explained by extracted six factors was 73.35%. Eigen Values and

Percentage of Variance are shown in Table 4.5 (III).

Rotated Component Matrix

The results were obtained through orthogonal rotations with variance and all

factors loadings greater than 0.50 (ignoring the sign) were retained. The twenty-two

variables from the Tables were then loaded on the six factors respectively (Table 4.5

(IV)).

Table 4.5 (IV): The Rotated Factor Matrix, The Final Statistics.

Component Matrix

Component

1 2 3 4 5 6

V01 0.252686 0.825631 0.211675 -0.37081 0.267838 -0.13189

V02 -0.09359 0.236153 -0.01774 0.464786 0.361721 0.519236

V03 -0.23426 0.309869 0.075731 0.510553 -0.35076 -0.19413

V04 0.351909 0.681094 0.005925 0.112274 0.002321 -0.14525

V05 0.411093 -0.12006 -0.50146 0.307723 0.309997 0.187732

V06 -0.06133 0.404418 0.653679 0.312785 -0.05436 -0.0139

V07 0.242324 -0.09039 0.660253 0.269379 0.130901 -0.05319

V08 -0.13105 -0.4206 0.601852 0.301279 -0.32648 0.277911

V09 -0.40074 -0.22282 0.401137 0.226926 0.826025 0.179991

V10 -0.44014 -0.19815 0.406056 0.801967 0.395461 0.174626

V11 0.735754 -0.43766 0.225508 0.287329 -0.38587 0.470359

V12 0.755009 0.078384 -0.050321 -0.22572 0.204708 0.044801

V13 -0.17023 -0.00912 0.350826 -0.34676 0.501134 0.128065

V14 0.4559174 0.498607 0.101309 -0.21799 0.852236 0.242434

V15 0.576454 0.290902 0.328465 -0.38929 -0.07577 0.114961

V16 0.572964 -0.45394 -0.29576 0.016793 0.146009 0.065894

V17 0.567547 0.352623 -0.33741 0.006328 0.114622 0.19807

V18 -0.22976 0.354459 -0.20636 0.268891 0.73382 -0.06543

V19 0.279613 -0.24718 0.21267 0.101136 -0.1882 0.52547

V20 0.223712 -0.43002 -0.15046 0.509616 -0.03787 -0.15895

V21 0.407813 0.261831 0.211345 0.635056 -0.13529 0.154998

V22 0.292196 -0.34306 0.880227 0.270291 -0.04414 -0.19888

Extraction Method: Principal Component Analysis.

Naming of the factors has been done on the basis of the size of factor loadings of

the variables. Factors are being named according to the factor loading values, larger the

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value, greater is the chance of the factor being named after these variables.

Table 4.5 (V): Types of Relatedness in Diversified Banks

Factor

No

Factor

Name

Statements Score

1 Managerial

Relatedness

V11

Businesses share management information

system (M.I.S.)

0.734

V12

Businesses require the same management skills

0.755

V15

Businesses share corporate culture

0.576

V16

Businesses aim at the same corporate goals

0.572

V17 Businesses use the same strategic concepts

to run the business

0.567

2 Financial

Relatedness

V01

Businesses are required to meet financial targets

0.825

V04

Businesses emphasize on low costs/cost

leadership strategy

0.681

3 Service

Relatedness

V06

Businesses emphasize on product development

0.653

V07

Businesses produce high value-added services

0.660

V22

Businesses pay attention to service

0.880

V05

Business are similarly impacted by economy

0.501

V08

Business share back office

0.601

Contd…

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4 Marketing

Relatedness

V10

Businesses emphasize on marketing

0.801

V20

Businesses have about the same market share

0.509

V21

Businesses serve niche markets

0.635

V03

Business share distribution network.

0.510

5

Technology

Relatedness

V09

Businesses emphasize R&D

0.826

V13

Businesses have strong brand names

0.501

V14

Businesses share technology

0.852

V18

Businesses share common core business

0.733

6 Size

Relatedness

V19

Businesses are about the same size

0.524

V02

Businesses share customers

0.519

Further, in order to find out significance level of factors, the factor wise average

scores (from the five point Likert scales) are calculated and accordingly ranking is done.

The level of the importance of factors on the basis of factor wise average scores has been

categorized as follows in table 4.5 (VI).

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Table 4.5 (VI): Ranking and Average score of factors

Factors Average scores Ranking

Managerial Relatedness 1.53 6

Financial Relatedness 4.34 1

Service Relatedness 3.40 3

Marketing Relatedness 3.44 2

Technology Relatedness 2.03 5

Size Relatedness 2.86 4

Explanation of Table 4.5 (IV), Table 4.5 (V) and Table 4.5 (VI)

Financial Relatedness

The Financial Relatedness has been perceived as the first important factor in

banks decision to adopt diversification move. “Financial relatedness” factor includes

variables V01 “Businesses are required to meet financial targets” and V04

“Businesses emphasize on low costs strategy” which indicate that diversification will

result in a greater financial synergies by meeting financial targets.

Marketing Relatedness

Marketing Relatedness factor ranked second important factor of relatedness in

diversified banks. Diversification strategy mainly aimed at expanding scale of

business. Variables V10 “Businesses emphasize on marketing”, V20 “Businesses

have about the same market share”, V03 “Business share distribution network” and

V21 “Businesses serve niche markets” reflects relatedness in term of marketing

characteristics. Diversified banks can use the marketing strategy in offering one

service to recommend their other services in order to expand scale of business.

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Service Relatedness

Service relatedness is ranked as third important factor of relatedness in

diversified banks. All variables loaded under “service relatedness” factor reflect

similarities in businesses' product characteristics or market segments like V06

“Businesses emphasize product development” and V07 “Businesses produce high

value-added services” which help in developing core competencies for the business as

a whole. Though nature of business is different in banking and non-banking business

but the product in which they deal is mainly “money and finance” which is same. It

includes variables namely, V08 “Businesses share back office”, V05 “Banks are

similarly impacted by economy” and V22 “Businesses pay attention to service”.

Size Relatedness

“Size relatedness” ranked as fourth important type of relatedness, which

diversified banks aimed to achieve from diversification. It consisted of variable V19

“Businesses are about the same size” and V02 “Businesses share customers”. As

market share is often used as a proxy for size, so it depicts that size relatedness is

another variable, which shows the relatedness of business.

Technology Relatedness

As diversified banks operate in different businesses like retail banking,

investment banking and other specialized financial and non-banking activities (such

as leasing, factoring, etc.). Some business combinations depending upon their

relatedness may result into more synergetic potential. Technology relatedness factor

encompasses variables V09 “Businesses emphasize R&D”, V13 “Businesses have

strong brand names”, V14 “Businesses share technology” and V018 “Businesses

share common core business”. It is ranked as fifth important factor. Technology helps

in creating synergy, which occurs when the sum of all businesses together equals,

more than the sum separately (Hitt et al., 2001). Amit and Livnat (1988) argue that

diversification into related businesses may augment the market power of the

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diversified company which in turn may help the company enhance its long-term

strategic position. Sharing technology in turn leads to cross-promotion.

Managerial Relatedness

“Managerial relatedness” factor encompasses five variables namely V11

“Businesses share management information system (M.I.S.)”, V12 “Businesses

require the same management skills”, V15 “Businesses share corporate culture”, V16

“Businesses aim at the same corporate goals” and V17 “Businesses use the same

strategic concepts” to run the business. This factor encompassed of those items, which

are important in strategic decisions. “Managerial Relatedness” factor is ranked as

sixth important factor.

So, it was found through the questionnaire that managers perceive financial

relatedness as highest important followed by market relatedness and service

relatedness, which are ranked as second and third respectively. Size relatedness and

Technology relatedness perceived as fourth and fifth important factors respectively,

according to the ranking. Managerial relatedness ranked as sixth important type of

relatedness aimed to be perceived from diversification.

4.5 Conclusion

This chapter has outlined the nature and extent of diversification in banks in

India. In respect of the nature of diversification in banks in India, mostly banks have

started adopting diversified measures after the enactment of financial and economic

sector reform of 1991.Out of the total 48 banks, consisting of public and private

sector banks, 19 banks are diversified while majority of banks i.e, 29 banks are not

diversified. But here it is important to mention that those of the non-diversified banks

hold very less proportion in term of market and asset size. The trend towards adopting

diversification in banks was found highest in respect of SBI and nationalized banks

followed by private sector banks as only new generation banks are diversified. SBI

group has emerged as one of the well-diversified bank by venturing into number of

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banking and non-banking business through subsidiaries, joint ventures and tie-ups.

In second part, diversification index of banking sector in India is measured in

which State Bank of India, over the time period of 1994 to 2006 remained highly

diversified as per entropy measure. A mixed trend is seen in the diversification index

of private banks and nationalized banks. However from year 2002 onwards,

diversification index of private banks has increased continuously. In the third part,

manager’s perception regarding type of relatedness, across different business lines of

banks. It was found that managers perceive broadly six types of relatedness from

diversification in the banking sector namely, Financial Relatedness, Managerial

Relatedness, Service relatedness, Size Relatedness, Technology Relatedness and

Marketing relatedness.