Global Management Review - MAFIADOC.COM

Sona School of Management

(A Unit of Sona College of Technology - An Autonomous Institution)

Global Management Review [Publication of Sona School of Management]

Volume 6 Issue 2 February 2012

Global Management Review

Volume 6 | Issue 2 | February 2012

Expanding the Frontiers of Management Research...

This Journal is listed in EBSCO Publishing & Ulrich International Directory

01

A Study on Construction of an Optimal Portfolio with Reference to Oil & Gas Industries in Indian Capital Market P. Varadharajan

10

Store Choice Behaviour in Retail Outlets S.P. Thenmozhi Dr. D. Dhanapal

24

Work-life Imbalance among Executives: A Gender Focus B. Jayanthi Dr. T. Vanniarajan

36

Business Strategies for Sustainable Growth of Indian Steel Industry: SAIL Vs. TATA STEEL Niranjan Kumar Singh Nita Choudhary

48

Comparative Study of Indian Movies and Sport Celebrity as Brand Endorses: Analysis Based on “Q” Score Technique Satendra Thakur

57

A Study on Impact of Improved Workforce Practices on Employee Retention in PCMM certified Software Companies in Bangalore S. Deepalakshmi Dr. Lakshmi Jagannathan

66

A Study on Salaried Class Investors' Attitude towards Tax Planning in Vellore District Dr. T. Thirupathi

Dr.K.B.Akhilesh Indian Institute of Science Bangalore Dr.Ashish Sadh Indian Institute of Management Indore Dr.S.Jeyavelu Indian Institute of Management Kozhikode Dr.Jerome Joseph Indian Institute of Management Ahmedabad Dr.R.Karthikeyan Gemba Management Consulting Pvt. Ltd. Chennai Dr.S.S.S.Kumar Indian Institute of Management Kozhikode Dr.Narendra K.Rustagi Howard University Washington DC, USA Dr.P.Natarajan Coimbatore Institute of Management & Technology Coimbatore Dr.Prashanthi.N.Bharadwaj Indiana University of Pennsylvania USA Dr.Rajendra Nargundkar IFIM Business School Bangalore Dr.Satyabhusan Dash Indian Institute of Management Lucknow CA V.Sreeraman Consultant - Finance & Accounts, Chennai Mr.K.Srinivasan Phoenix Knowledge Service Chennai Dr.Sunil Shukla Entrepreneurship Development Institute of India Ahmedabad Dr.Masood Abessi University of Yazd Iran Dr.T.Vanniarajan N.M.S.S.V.N College Madurai Dr.Upinder Dhar Nirma University of Science & Technology Ahmedabad The Editor, Sona Global Management Review, Sona School of Management, Thiagarajar Polytechnic College Road, Salem - 636 005. Tamilnadu, India. Phone: +91 427 4099977(Direct), 4099999 Fax: 4099990 email: [email protected]

The Editor, Editorial Team, Editorial Advisory Board and the Publisher do not hold any responsibility for the views expressed in the Sona Global Management Review Journal or for any error or omission arising from it.

Sona School of Management

(A Unit of Sona College of Technology - An Autonomous Institution)

Global Management Review [Publication of Sona School of Management]

Volume 6 Issue 2 February 2012

Global Management Review

Volume 6 | Issue 2 | February 2012

Expanding the Frontiers of Management Research...

This Journal is listed in EBSCO Publishing & Ulrich International Directory

01

A Study on Construction of an Optimal Portfolio with Reference to Oil & Gas Industries in Indian Capital Market P. Varadharajan

10

Store Choice Behaviour in Retail Outlets S.P. Thenmozhi Dr. D. Dhanapal

24

Work-life Imbalance among Executives: A Gender Focus B. Jayanthi Dr. T. Vanniarajan

36

Business Strategies for Sustainable Growth of Indian Steel Industry: SAIL Vs. TATA STEEL Niranjan Kumar Singh Nita Choudhary

48

Comparative Study of Indian Movies and Sport Celebrity as Brand Endorses: Analysis Based on “Q” Score Technique Satendra Thakur

57

A Study on Impact of Improved Workforce Practices on Employee Retention in PCMM certified Software Companies in Bangalore S. Deepalakshmi Dr. Lakshmi Jagannathan

66

A Study on Salaried Class Investors' Attitude towards Tax Planning in Vellore District Dr. T. Thirupathi

Dr.K.B.Akhilesh Indian Institute of Science Bangalore Dr.Ashish Sadh Indian Institute of Management Indore Dr.S.Jeyavelu Indian Institute of Management Kozhikode Dr.Jerome Joseph Indian Institute of Management Ahmedabad Dr.R.Karthikeyan Gemba Management Consulting Pvt. Ltd. Chennai Dr.S.S.S.Kumar Indian Institute of Management Kozhikode Dr.Narendra K.Rustagi Howard University Washington DC, USA Dr.P.Natarajan Coimbatore Institute of Management & Technology Coimbatore Dr.Prashanthi.N.Bharadwaj Indiana University of Pennsylvania USA Dr.Rajendra Nargundkar IFIM Business School Bangalore Dr.Satyabhusan Dash Indian Institute of Management Lucknow CA V.Sreeraman Consultant - Finance & Accounts, Chennai Mr.K.Srinivasan Phoenix Knowledge Service Chennai Dr.Sunil Shukla Entrepreneurship Development Institute of India Ahmedabad Dr.Masood Abessi University of Yazd Iran Dr.T.Vanniarajan N.M.S.S.V.N College Madurai Dr.Upinder Dhar Nirma University of Science & Technology Ahmedabad The Editor, Sona Global Management Review, Sona School of Management, Thiagarajar Polytechnic College Road, Salem - 636 005. Tamilnadu, India. Phone: +91 427 4099977(Direct), 4099999 Fax: 4099990 email: [email protected]

The Editor, Editorial Team, Editorial Advisory Board and the Publisher do not hold any responsibility for the views expressed in the Sona Global Management Review Journal or for any error or omission arising from it.

GLOBAL MANAGEMENT REVIEW Volume 6

Issue2

February 2012 ISSN 0973 - 9947

Chief Patron

Patron

Prof. A. Dhirajlal

Dr. C.V. Koushik

Secretary Sona College of Technology

Principal Sona College of Technology

Editor Dr. M. Selvaraj Joint Director Sona School of Management

Editorial Team Dr. L. Shankari Prof. S.P. Thenmozhi

Quarterly Publication of Sona School of Management

Dear Readers, Greetings from Sona School of Management We acknowledge the active support and patronage extended to us by authors from various institutions for making this endeavor a success. The current issue contains articles pertaining to various topics of contemporary interest. These papers are either empirical work or concept based. We welcome your suggestions on the structure and contents of this journal. We dedicate this November issue of Sona Global Management Review to the cause of Management Research. We are happy to publish these articles and thank the authors for their contribution.

Dr. M. Selvaraj

A STUDY ON CONSTRUCTION OF AN OPTIMAL PORTFOLIO WITH REFERENCE TO OIL & GAS INDUSTRIES IN INDIAN CAPITAL MARKET P. Varadharajan Assistant Professor, PSG Institute of Management, PSG College of Technology, Coimbatore

ABSTRACT The aim of the research is to construct effective portfolio for the stocks of oil and natural gas industry. This study enables to know the performance of some selected oil and natural gas stocks. The analysis given in the project is on the basis of risk & return and Sharpe index model by then identify the stocks and proportion of stocks to be included in portfolio. The effective portfolio will give return by reducing the risk involved. This project suggests best investment decision to the investors, who has an idea for investing in oil & natural gas stocks. This analysis will suggest best stocks to invest and also helps investors to revise their portfolios. Thus portfolio management using Sharpe index model is one of the best option to create optimal portfolio. This study suggests the best oil and natural gas stocks to invest, from the selected fourteen companies.

INTRODUCTION Economic liberalization and globalization of financial markets has accelerated to the pace of Indian securities market. The role of securities market in mobilizing and channelling the private capital for the economic development of the country has increased over the years. Introduction of computerized online trading and interconnected market system have lead to further growth. However, the huge success of IPO's, public issue of many companies and disinvestments of PSU's stake has proved this. FII's have shown great interest in investing in Indian securities. Welcome change has been the active participation from retail investors. The security analysis and portfolio management has emerged as the most concerned aspect for rational investment, decision making. A portfolio is combination of securities held together as investment. A portfolio tries to trade off the risk

return preferences of an investor by not putting all eggs in single basket. A portfolio allows for sufficient diversification. Traditionally diversification means holding large number of securities scattered across industries. Many would feel that holding fifty such scattered stocks is five times more diversified than holding ten scattered stocks. However modern portfolio doesn't believe in holding many stocks. It believes in having “right kind of diversification”, “the right timing” and “the right reason”. Markowitz was the first who laid foundation for “Modern portfolio theory”. He attempted to quantity risk. He provided analytical tools for analysis and selection of optimal portfolio. This portfolio approach won him Nobel Prize in 1990. The work done by Markowitz was extended by William Sharpe. He simplified the amount and type of input data required to perform portfolio analysis. He made the numerous and complex computations easy which were essential

Global Management Review | Volume 6 | Issue 2 | February 2012

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to attain optimal portfolio. This simplification is achieved through single index model. This model proposed by Sharpe in the simplest and the most widely used one. The study focuses on finding out an optimal portfolio using single index model. According to this theory, it is not enough to look at the expected risk and return of a particular stock but diversifying into more than one security can actually lead to reduction in risk

NEED FOR THE STUDY Oil & Natural gas both are very basically used products for the survival of mankind. As India has more consumption than production. This study gives investment decisions to the investors, who want to encourage oil & Natural gas business in India. So the study construct optimal portfolio for oil & Natural gas stocks.

OBJECTIVES § To

construct an optimal portfolio for selected oil and natural gas companies using Sharpe single index model § To identify stocks and proportion of stocks to be included in portfolio. § To identify returns and risks involved in oil and natural gas stocks with market returns. § To identify the best performing companies in oil and natural gas industry.

LIMITATIONS § The project is limited to the extent of information available. § The Study is restricted to only 14 stocks from oil & natural gas industry. § The Stock prices considered are restricted to only to the previous five closing prices. § Time limited to one month 2

§ All the calculations could not be brought into the report.

THEORETICAL FRAME WORK Andrea L. (2003), given empirical evidence on the efficiency and effectiveness of hedging U.S.based international mutual funds with an AsiaPacific investment objective. The case for active currency risk management is examined for a passive and a selective hedge, which is constructed with currency futures in the major currencies. Both static and dynamic hedging models are used to estimate the risk-minimizing hedge ratio. The results show that currency hedging improves the performance of internationally diversified mutual funds. Such hedging is beneficial even when based on prior optimal hedge ratios. Further, efficiency gains from hedging, as measured by the percent change in the Sharpe Index, are greatest under a selective portfolio strategy that is implemented with an optimal constant hedge ratio. Markus Ebner and Thorsten Neumann (2008), explained the correlation instabilities in US stock returns and derive Variance – Covariance Matrices from timevarying factor model estimates. They used three different estimation approaches to overcome the problem: (1) moving window least squares, (2) flexible least squares and (3) the random walk model. The results suggest that a time-varying estimation of return correlations fits the data considerably better than time-invariant estimation and thus, increases the efficiency of risk estimation and portfolio selection. Nancy Beneda (2004), explained a simplified model for quantifiably measuring and managing various types of risk, as a portfolio of risks. An asset management firm may face a variety of risks due to the broad nature of various investments. The technique utilizes computerized simulation and optimization


modelling. The software used to administer the simulations is Crystal Ball. The use of simulation allows risk managers to combine the various categories of risk a firm faces into one risk portfolio. These techniques will enable risk managers to have the information needed to achieve the desired level of overall firm risk and the expected cost of managing risk. John A Haslem (2003), used Data envelopment analysis (DEA) to identify the largecap mutual funds in the 1999 Morningstar 500 that are efficient or inefficient. An attempt is made to identify the financial variables that differ significantly between efficient and inefficient funds, and to determine the nature of these relationships. According to study findings, there are identified input/output and profile variables that are significantly different between the 1999 Morningstar 500 large-cap mutual funds that are DEA performance-efficient and inefficient. The Sharpe index represents the DEA output variable. That is, the findings indicate the variables that are significantly different between performanceefficient and inefficient funds and the nature of their relationships. The variable values associated with efficient funds are relatively conservative in nature, not aggressive. Rachel Campbell(2001), says about optimal portfolio selection is that a portfolio selections a model which allocates financial assets by maximizing expected return subject to the constraint that the expected maximum loss should meet the Value-at-Risk limits set by the risk manager. Similar to the meanvariance approach a performance index like the Sharpe index is constructed. Furthermore when expected returns are assumed to be normally distributed, it is shown that the model provides almost identical results to the mean-variance approach. Paula A TKAC (2001) evaluated the

performance of open ended international mutual funds. It sets stage for investigating whether exploitable foreign markets in efficiencies exist by studying the large sample of open-ended funds during 1990's. The analysis sort funds into thirty two categories and the result show that large percentages of well-diversified international funds outperform their passive benchmarks in a significant manner, but regional & country funds do not. John.N.sorros (2003) given risk and return analysis in equity mutual funds operating in reek financial market, evaluated sixteen equity mutual funds from 1995-1999 and the sample mutual funds were ranked on the basis of their return, total risk, coefficient of variation, systematic risk and techniques of Treynor and Sharpe. The movements of the general index of Athens stock exchange explain more than 80 percent of the variation in return in sixteen funds and eight mutual funds ranked in the same order on either Treynor's or sharpe's technique. Prof. George P. Artikis (2003) evaluated performance of Greek balanced mutual funds. He aims to evaluate performance of ten domestic balanced mutual funds during the period of 199598. The sample mutual funds ranked based on return, total risk, and coefficient of variation, systematic risk and techniques of Treynor, Sharpe and Jensen. He found that according to Jensen seven mutual funds had superior performance, while remaining three demonstrated poor performance.

METHODOLOGY Research Design: The type of research design used was Descriptive research


3

Time Horizon: Top performing stocks in oil and gas industry are selected for the duration of 01/04/2006- 31/03/2011. Data: Secondary data – Data for portfolio construction and articles for theoretical frame work are collected from different secondary data resources like websites, data bases & journals. Sampling technique: Purposive sampling because the choice of sample is based on the availability of the necessary data. Population: All the stocks traded in NSE India. Sample size: The study used fourteen companies for portfolio construction.

Efficient Portfolio A portfolio that maximizes return for a given level of risk or minimizes risk for a given level of return is termed as an efficient portfolio. Correlation A statistical measure of the relationship between any two series of numbers representing data of any kind is known as correlation. Risk-free Rate of Return (RF) Risk-free rate of return is the required return on a risk free asset, typically a three month treasury bill. Excess Return-Beta Ratio

TOOLS USED FOR THE STUDY Beta Coefficient Beta coefficient is the relative measure of nondiversifiable risk. It is an index of the degree of movement of an asset's return in response to a change in the market's return.

Where, σ ( Y) = Standard Deviation of Individual Stock

Where, Ri= the expected return on stock Rf = the return on a riskless asset β i = the expected change in the rate of return on stock associated with one unit change in the market return. Cut-off point

σ ( X) = Standard Deviation of Market Return: The total gain or loss experienced on an investment over a given period of time, calculated by dividing the asset's cash distributions during the period, plus change in value, by its beginning-of-period investment value is termed as return.

4

2 Where, σ m= variance of the market index 2 σ ei = variance of a stock's movement that is not associated with the movement of market index i.e. stock's unsystematic risk.


Weightage to be made in each SECURITY

Where, Xi = the proportion of investment of each stock and

*

Where, C = the cut-off point. ANALYSIS & INTERPRETATION If we observe the β values and Ri values both are highest in ESSAR OIL that is like giving higher return with high risk, in the OILCOUNTUB return is higher compared to its market return β may be that is the cause for that (like “giving high returns with less risk”) to became the best security to invest in that.In these list of companies only 3 has higher risk ESSAR OIL, HINDOILEXP, RIL but only one of them got into our portfolio. Sharpe has provided a model for the selection of appropriate securities in a portfolio. The excess return of any stock is directly related to its excess return to beta ratio. It measures the additional return on a security (excess of the risk less asset return) per unit of systematic risk. The ratio provides a relationship between potential risk and reward. Ranking of the stocks are done on the basis of their excess return to beta. Based on the excess return to beta ratio the scrip's are ranked from 1 to 14. The selection of the stocks depends on Cut-off values. All stocks with higher ratios of excess return to beta are included and stocks with lower ratios will not be included in our portfolio.The highest value of Ci is taken as the cutoff point i.e. C*. Here HINDOILEXP is cut-off value above that 5 companies are there to include

in the portfolio.Always it is better to maintain less number of securities I have choosen only 5 companies to my portfolio construction. After determining the securities to be selected, we have to find out how much should be invested in each security. The percentage of funds to be invested in each security is estimated. As already mentioned all the stock with Ci greater than cut off point can be included in the portfolio. Here the top five companies according to excess return to beta ratio is taken for calculating the proportion of investment. By using Sharpe index model thus we are able to find out the proportion of investments to be made for an optimal portfolio. The maximum investment should be made in OILCOUNTUB as 40.72%.Major part of my portfolio is divided by 2 companies those are OILCOUNTUB & Indraprasta, both together almost 72%.The remaining companies are ESSAR OIL, BPCL, Shivvani. Percentage of each security has to be as shown in the below pie chat. FINDINGS § The performance of OIL &GAS sector is calculated and most of the companies are performing poorly than the market. § The stocks of higher risk yield higher return. ESSAR OIL is giving higher return with high risk. § According to Sharpe index model, oilcounttub has highest proportion in the portfolio. But if compared with Essar oil it has given less return because Essar oil has high systematic risk (beta) & residual variance. § Indraprasta has moderate return compared to Essar, but the proportion on portfolio is 31% because it has very less systematic risk and residual variance compared to Essar. § HINDOILEXP Company has more return


5

Table 1: Alignment of Companies after Ranking S.No

6

Security

Ri

σˆ2ei

β

1

OILCOUNTUB

226.0256

12.03204

0.656478

2

Indraprasta

107.2727

5.795638

0.461245

3

ESSAR OIL

247.9953

23.02941

1.461196

4

BPCL

81.16901

7.768064

0.578186

5

Shivvani

106.4555

11.84301

0.813792

6

HINDOILEXP

139.5288

18.70688

1.268587

7

HINDPETRO

60.90779

8.453773

0.620165

8

RIL

88.08296

9.195136

1.159833

9

IOC

4.54483

9.31252

0.625925

10

Gujarat gas

1.500884

11.89721

0.408097

11

ONGC

-39.2397

11.51722

0.911617

12

TIDEWATER

-898.959

97.89278

0.475914

13

SOTL

-46.6562

17.92259

0.015581

14

SANWARIA

-46.6562

17.92259

0.015581


Table 3: Portfolio Construction S.No

Security Name

Ci

β/σˆ2ei

((Ri-Rf)/β)-C*

Zi

1

OILCOUNTUB

40.06975

0.054561

240.6839

13.13191

2

Indraprasta

59.40998

0.079585

124.062

9.873455

3

ESSAR OIL

82.24112

0.063449

72.47178

4.598273

4

BPCL

86.36407

0.074431

35.20373

2.620255

5

Shivvani

90.09945

0.068715

29.43282

2.022474

Table 4: Proportion of Investment in Portfolio S.NO

Security Name

Xi

1

OILCOUNTUB

40.72%

2

Indraprasta

30.62%

3

ESSAR OIL

14.26%

4

BPCL

8.13%

5

Shivvani

6.27%

Figure 1: Proportion Investment in Portfolio

BPCL 8% ESSAR OIL 14%

Shivvani 6% OILCOUNTUB 41%

Indraprasta 31%


OILCOUNTUB Indraprasta ESSAR OIL BPCL Shivvani

7

compared to Indraprasta but it has huge risk, so it does not get placed in portfolio. § Companies like BPCL; HINDPETRO & RIL has given moderate return with moderate risk. RECOMMENDATIONS § The recommended proportion investments to the companies according to constructed portfolio are for OILCOUNTTUB 41%, Indraprasta 31%, Essar oil 14%, BPCL 8% and for Shiv-Vani 6%. § Inventors who expecting high returns with moderate returns OILCOUNTTUB recommended. § To the investors who are risk lovers ESSAR coil can be recommended. § Investors who expect with moderate returns with moderate risk, companies like Indraprasta, Shiv-Vani, HINDOILEXP, BPCL, and RIL are recommended. § Investors better not to think about Tide water, SOTL and Sanvaria for investment in Oil & gas industry. CONCLUSION As a fact Oil & Gas has their own importance to meet out requirements of human being. In the same way oil & gas industry stocks also has its impact in development of countries especially in India concern it has lot of demand. So to encourage investments in oil & gas industry we have taken up this project. According to the requirements of Sharpe index model five companies selected for the construction of portfolio out of 14 companies. It has found that oil & gas companies can meet different class of investors i.e. there were some companies which can give huge return with more risk and some other companies can give moderate returns with 8

moderate risks. So, we can conclude that profits can be possible by investing in oil & gas stocks and we also belief that this project can be help full to the investors who want to invest in oil & gas industry stocks. REFERENCES Andrea L Demaskey, Wilfred L Dellva and Jean L Heck, (2003), “Benefits from Asia pacific mutual fund investments with currency hedging”, Review of quantitative finance and accounting, Vol 21, Issue 1, pp. 49. George P. Artikis (2003), “Performance of Greek balanced mutual funds”, Managerial Finance 2003, Volume 29 No. 9. John A Haslem, Carl A Scheraga. (2003), Journal of Investing, New York: Winter, Vol. 12, Issue 4, pp. 41. John.N.sorros (2003), Risk and Return analysis in equity mutual funds operating in Greek financial market, Volume 29. Kapil R. Tuli&Sundar G. Bharadwaj, (2009), “Customer Satisfaction and Stock Returns Risk”, Journal of Marketing, Vol. 73, pp. 184-197. Lawrence Fisher (1975), “Using Modern Portfolio Theory to Maintain an Efficiently Diversified Portfolio”, Financial Analysts Journal, pp. 73-85. Marshall E. Blume, (1980), “The Relative Efficiency Of Various Portfolios: Some Further Evidence”, The Journal Of Finance, Vol. Xxxv. No. 2, pp. 269 – 281. MeirStatman,(1987) “How Many Stocks Make a Diversified Portfolio?”, Journal Of Financial And Quantitative Analysis, Vol. 22, No. 3, pp. 353-363. Nancy Beneda (2004), Managing an asset management firm's risk portfolio, Journal of Asset Management, Vol. 5, Issue 5, pp. 327.


Paula A TKAC (2001), “Evaluated the performance of open ended international mutual funds”, Economic Review, Federal Reserve Bank of Atlanta, Vol. 86, No. 3. Rachel Campbell, Ronald Huisman, Kees Koedijk (2001), Journal of Banking & Finance Amsterdam: Vol. 25, Issue 9, pp. 1789. Wai Cheong Shum and Gordon Y.N. Tang, (2010), “Risk-Return Characteristics Comparison of China's Stock Market and Three Other Emerging Markets”, The Chinese Economy, vol. 43, no. 5, pp. 15–31.

William P. Lloyd, John H. Hand and Naval K. Modani (1981), “The effect of portfolio construction rules on the relationship between portfolio size and effective diversification”, The journal of Financial Research, Vol IV, No. 3, pp. 183-193. www.nseindia.com www.rbi.org.in www.google.com

Author can be reached at: [email protected]


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STORE CHOICE BEHAVIOUR IN RETAIL OUTLETS S.P. Thenmozhi Associate Professor, Sona School of Management, Sona College of Technology, Salem

Dr. D. Dhanapal Professor, MBA Department, Coimbatore Institute of Engineering and Technology, Coimbatore

ABSTRACT The study aims to identify the profile variables and to analyse the association between the profile variables and factors leading to choose the store in the Indian retailing environment. The research design is descriptive in nature. A structured questionnaire is designed to collect data from selected retail outlets from six cities in Tamilnadu. The sample size is 463 customers and purposive sampling method has been applied. The important factors considered for the selection of store are Value added services, Price, Personal Interaction and Physical Aspects. The most important factors identified by present research is Value added services. The study reveals that there is a close association between the profile variables and store choice factors. INTRODUCTION Store choice of customers depends on their socio-economic background, personality and past purchase experience. Nowadays, people consider shopping as a recreational activity and choose a store which provides entertainment. Customers choose a store which offers the lowest prices and which is the most convenient in order to reduce the cognitive dimension in the decision problem. It is found that shoppers use a combination of the quality of staff, low prices and frequency of promotions in choosing a store. The role of ambience in store choice has also been found significant. There has been a change in store choice behavior in urban India over the past few years with consumers looking for convenience. They want everything under one roof and a bigger choice of products. With an increase in doubleincome households, people do not have much leisure time and seek the convenience of one-stop 10

shopping in order to make the best use of their time. They also look for speed and efficiency. Increased awareness has also meant that consumers now seek more information, variety, product availability, better quality and hygiene as well as increased customer service. The concept of “Value for Money” is picking up. REVIEW OF LITERATURE Different retail formats have emerged as choices of people in terms of store differs, especially in case of grocery as it is one of the necessary purchases. Sinha and Banerjee (2004) in their research propose that store choice is recognized as a cognitive process. It is as much an information processing behavior as any other purchase decision. Store choice (Mahua Datta & Debraj Datta, 2009) depends on socio-economic background of consumers, their personality and past purchase experience. Lumpkin (1985) has found that as


compared to young shoppers, elderly shoppers are less price-conscious and proximity of residence to store is not an important factor for them. They consider shopping as recreational activity and therefore choose a store that is high on 'entertainment' value. The concept of positioning of stores has been captured in marketing literature in the last decade (Woodside, 1992). This study has found that shoppers look for “hot buttons” which they have developed themselves to help them in choosing among stores. The shoppers can quickly name the store that provide them with these buttons, such as most convenient or lower prices, hence reducing the cognitive dimension in the decision problem. Hutcheson and Mutinho (1998) have found that shoppers use a combination of the quality of staff and the occurrence of low prices and the frequency of promotions in choosing a store. The role of ambience in store choice has also been found significant. Kotler (1973) has proposed atmospherics as an important part of retail marketing strategy. It is also found that the shoppers determine the value of the merchandise based on monetary as well as non-monetary costs (Zeithaml, 1988). It is found that recreation (a non-monetary value) is the major driver for visiting a regional shopping centre (Treblanche, 1999). The shopping experience, as created by the store environment, has been found to play an important role in building store patronage. Along with the merchandise, it has triggered affective reaction among shoppers (Baker, 1992). It also contributes to creating store patronage intentions (Baker, 2002). In recent times, Leszczyc and Sinha (2000) have indicated that store choice is a dynamic decision and can be conceptualized as a problem relating to the timing of shopping trips.

The two decision processes are correlated. Location helps retailers gain a competitive advantage that competitors may not be able to copy easily. The selection of store location apart from retailer, merchandise, price etc. also depends on the customer being targeted, the expectations of customers in terms of price, service and convenience and the financial strength of the retailer. STORE CHOICE BEHAVIOR Following are the variables included in the study to measure the store choice behavior of the respondents namely price, availability of products, proximity, variety of products, value added services, personal interaction, promotional activities, reliability, ambience and physical appearance. The respondents are asked to rate the above factors on a five point scale according to the level of importance they attach for choosing the store. ? Price: Price is an important factor for shopping in a retail outlet as customers are 'value for money' conscious today. ? Availability of Products: This factor refers to the availability of merchandise in the store convenient for shoppers. ? Proximity: This refers to the nearness of the retail outlet for the retail customer. Generally, people prefer retail outlets to be nearby their house for convenience and making immediate purchases. In the case of shopping in organized outlets for the purpose of entertainment or family shopping, people do not mind travelling to the outlet. ? Variety of Products: Variety is the number of different items in a merchandise category ie., the depth of merchandise


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category. This factor refers to the variety of items available for a given product category in terms of price ranges, size, design, colors etc. ? Value Added Services: It includes all the extra services, which the store extends to satisfy its customers. The supporting services include: acceptance of all credit / debit cards, facilities of return / exchange of merchandize, alteration facilities, home delivery, child care and children entertainment services. ? Personal Interaction: Through their personal interaction with customers, the employees in retail outlets make them feel well treated and assured. ? Promotional activities: This dimension tries to capture the effect of different promotional initiatives taken by the store (like discounts and special offers, sale items, rewarding loyal customers from time to time) on building loyalty with its customers. The sales promotion here refers to only in-store promotional activities offered by retailers from time to time. It does not include the advertising campaigns undertaken by the retail store. ? Reliability: Reliability means performing the services at promised time and solving customer problems. ? Ambience: It includes design of the retail environment via visual communication, lighting, colors, air conditioning and music to stimulate customers perceptual and emotional responses and ultimately to affect their purchasing behavior. ? Physical appearance: Physical appearance includes modern outlook of the store. High level of these factors signals 12

that the retailer offers good quality goods and services and also influences customer's evaluation of other intangible factors determining perceived service quality viz. reliability, responsiveness, assurance and empathy. RESEARCH METHODOLOGY The study is confined to six major cities in Tamilnadu namely Chennai, Trichy, Madurai, Coimbatore, Erode and Salem. The study is focused on retail outlets in Tamilnadu. From each city, one retail outlet has been selected for the study. For each outlet, 150 customers have been selected purposively for the present study. Out of 900 customers (150x6), only 463 customers have responded the Interview schedule at the usable level. The 463 respondents have been taken as the sample for the present study. Hence, the applied sampling procedure is purposive sampling. The sample consists of 249 female and 214 male customers. Since the outlets at the selected 6 cities do not provide any records on the customer database, the researcher is forced to apply purposive sampling to determine the sample size and selection of sample respondents. In order to have an equal representation of all the cities namely Chennai, Trichy, Madurai, Coimbatore, Erode and Salem, 150 customers from each city have been taken as a sample for the present study. Out of the 900 customers, only 463 customers have responded the questionnaire at the usable level. The statements in Interview schedule are related to Store Choice Behaviour and it is rated on five point scale. RESEARCH OBJECTIVES The objectives of the present study are confined to 1. To Exhibit the profile of the customers;


2. To analyze the association between Profile of the Customer and the factors leading to choose the store. PROFILE OF CUSTOMERS AND THEIR STORE CHOICE BEHAVIOR IN RETAIL OUTLETS The profile of customers in the present study includes Gender, Age, Level of Education, Occupation, Marital Status, Family Size, Personal Income, Family Income, Average Monthly Expenditure, Average Monthly Expenditure at the store and Shopping Frequency. Factors leading to choose the store among the different Gender of respondents In order to find the significant difference between male and female gender groups of customers for the factors leading to choose the store, 't' statistics has been administered. The mean scores of male and female gender groups among the customers and the respective 't' statistics are presented in Table 1. The significant difference between male and female respondents is found in the perception on the factors namely Price, Availability of Products, Proximity, Variety of Products, Personal interaction, Promotional activities, Reliability, Ambience and Physical appearance since their respective 't' statistics are significant at 5 per cent level. The analysis reveals that the important factor leading to the store choice among male is Personal Interaction and among female respondents it is Variety of Products. Factors leading to choose the store among the Different Age Group of respondents In order to find the significant difference among different age group of respondents

regarding the importance given to the factors leading to choose the store, the one way ANOVA has been tested. The mean score of the various factors among the various customer groups has been computed and shown in Table 2 The significant difference among the six age group of respondents is found in the factors namely Price, Availability of Products, Proximity, Promotional activities, Ambience and Physical appearance since their respective F-statistics are significant at five per cent level. The analysis reveals that Value Added Services is the most important factor among the respondents belonging to lower age group, whereas it is Reliability in case of medium age group and Proximity in case of higher age group of respondents. Factors leading to choose the store among the Different Educated Groups of respondents In order to find the significant difference among different educated group of customers regarding the importance given to the factors leading to choose the store, the one way ANOVA has been tested. The mean score of the various factors among the various customer groups has been computed and shown in Table 3 The significant difference among the six educated groups of respondents is found in the factors namely Price, Availability of products, Variety of Products, Value Added Services, Promotional Activities, Ambience and Physical Appearance since their respective F-Statistics are significant at 5 per cent level. The analysis reveals that the Price is the most important factor among the respondents with lower level of education whereas it is Value added services among respondents with higher level of education.


13

Table 1: Factors leading to choose the Store among the different Gender of respondents Mean score among S.No Factors t-statistics Male Female 1

Price

2.8186

3.6542

-2.7574*

2

Availability of products

3.2765

3.7886

-2.0651*

3

Proximity

3.0441

3.8091

-3.1442*

4

Variety of products

3.4542

3.9908

-2.1143*

5

Value added services

3.6563

3.9661

-0.9962

6

Personal interaction

3.9092

3.2562

2.5086*

7

Promotional activities

3.0446

3.8145

-3.1442*

8

Reliability

3.4554

3.9086

-2.1651*

9

Ambience

3.3309

3.8582

-2.0962*

10

Physical appearance

3.0881

3.7664

-2.7664*

* Significant at five per cent level

Table 2: Factors leading to choose the store among the Different Age Group of respondents Mean score among customers in the age group of (in Years)

S.No

Factors

21-25

26-30

31-35

36-40

Above 40

2.6562

2.8245

3.3089

3.6141

3.8042

4.4133*

1

Price

2


3.9033

3.9165

3.1415

3.8608

3.7814

3.2145

3.0898*

3

Proximity

2.7334

2.6808

2.9192

3.5032

3.8445

3.9694

3.4542*

4

Variety of products

3.9461

4.1231

3.7086

3.8446

3.5086

3.4102

2.0641

5


3.9908

3.8142

3.3414

3.2616

3.0969

3.9142

2.1886

6


3.0415

3.9694

3.4516

3.7616

3.3141

3.8208

1.9617

7


3.9334

3.8662

3.0465

2.8969

2.9108

3.2456

2.3969*

8

Reliability

3.6955

3.7033

3.8189

3.9865

3.2456

3.3041

1.4508

9

Ambience

3.9718

3.6508

3.8442

3.3133

3.0841

2.8445

2.6562*

10 Physical appearance 2.5085

2.6556

2.9192

3.2146

3.0145

2.9962

3.4514*

*Significant at five per cent level 14

FStatistics

Upto 20 2.6091


Table 3: Factors leading to choose the store among the Different Educated Groups of respondents Mean score among customers with the education of

S.No

Factors

Upto 10th Std

F-

Higher Certificate/ Under Post Statistics Secondary Diploma graduation graduation

1

Price

3.9035

3.8546

3.8011

2.8509

2.5607

2.7891*

2


2.8184

3.0451

3.5664

3.9145

3.8684

3.2109*

3

Proximity

3.2446

3.6869

3.8184

3.7664

3.9149

3.8914*

4

Variety of products

3.2908

3.5644

3.0445

3.9168

3.8364

2.4547*

5


2.9161

3.0868

3.7882

3.9906

4.1448

3.4108*

6


3.8142

3.1445

3.9691

3.8108

3.7161

1.8904

7


2.8552

3.3445

3.9108

3.9646

3.8246

3.1408*

8

Reliability

3.4089

3.8281

3.7397

3.6584

3.9983

1.5082

9

Ambience

3.2141

3.3083

3.4514

3.6082

3.9192

1.3581*


3.7711

3.3086

3.6566

3.8996

1.0442*

*Significant at five per cent level

Factors leading to choose the store among the respondents with different Occupation In order to find the significant difference among different occupation of customers regarding their importance given to the factors leading to choose the store, the one way ANOVA has been tested. The mean score of the various factors among the various customer groups has been computed and shown in Table 4 The significant difference among the different occupation group of respondents is found in the factors like Proximity, Variety of Products, Personal Interaction and Physical Appearance since their respective F-Statistics are significant at five per cent level.

The analysis reveals that the Physical Appearance is an important factor to choose the store among students, private employees and house wives and others categories whereas it is Reliability and Proximity among the Government Employed and Business people respectively. Factors leading to choose the store among the customers with different Marital Status In order to find the significant difference among different marital status of customers regarding their importance given to the factors leading to choose the store, the one way ANOVA has been tested. The mean score of the various factors among the various customer groups has


15

Table 4: Factors leading to choose the store among the respondents with different Occupation Mean score among customers in the occupation of

S.No

Factors

Private Govt. Student Business Employment Employment

FHouse wife Statistics & Others 2.6616 1.3449

1

Price

2.7326

3.7317

2.9316

2.8081

2


3.8184

3.8082

3.1085

3.9184

3.3445

1.9962

3

Proximity

2.8096

2.7664

3.9196

3.9792

3.5056

2.5848*

4

Variety of products

3.9969

3.8644

3.5845

3.2451

3.0861

2.3891*

5


3.8184

4.0144

3.9691

3.4516

3.5035

1.3308

6


3.6845

3.9045

3.7416

2.9451

3.0411

2.4542*

7


3.9969

3.0123

3.6566

2.8584

3.5856

1.4481

8

Reliability

3.7441

3.8565

3.9899

3.3345

3.2616

0.9692

9

Ambience

3.9142

3.6502

3.2445

3.6866

3.1773

1.5081


3.8969

3.0819

2.8544

3.6641

2.4508*


been computed and shown in Table 5 The significant difference among the customers of different marital status is found in the factors namely Price, Availability of products, Proximity, Promotional activities, Ambience and Physical appearance. The analysis reveals that Variety of Products is an important factor to choose the store among the unmarried customers whereas it is Personal Interaction among the married customers. Factors leading to choose the store among the customers with different Family Size In order to find the significant difference among different family size of customers 16

regarding their importance given to the factors leading to choose the store, the one way ANOVA has been tested. The mean score of the various factors among the various customer groups has been computed and shown in Table 6 The significant difference among the customers of different family size is found in the factors like Price, Variety of Products, Personal Interaction, Promotional activities, Ambience and Physical appearance. The analysis reveals that Personal interaction is an important factor to choose the store among the respondents with lower family member size, whereas it is Price among respondents with higher family member size.


Table 5: Factors leading to choose the store among the customers with different Marital Status S.No

Factors

Mean score among customers Unmarried Married

t-statistics

1

Price

2.3408

3.5194

-3.6845*

2


3.0411

3.9336

-2.8029*

3

Proximity

3.1447

3.8696

-2.7311*

4

Variety of products

3.9884

3.4016

1.8082

5


3.8774

3.7464

0.3916

6


3.7894

3.9986

-0.4508

7


3.0145

3.9208

-2.9919*

8

Reliability

3.6889

3.8551

0.9965

9

Ambience

3.9091

3.2242

2.7331*

10

Physical appearance

3.1084

2.0211

2.9968*


Table 6: Factors leading to choose the store among the customers with different Family Size Mean score among customers with the Family Size

S.No

4

5

Above 5

FStatistics

Factors Upto 2

3

1

Price

2.6562

2.7178

3.0845

3.6266

3.7182

4.1788*

2


3.8186

3.6024

3.3408

3.2145

3.0866

2.0885

3

Proximity

3.6611

3.7244

3.2556

3.3081

2.9192

1.7374

4

Variety of products

3.9989

3.9796

3.6086

3.2641

3.0451

2.4544*

5


4.0144

3.9233

3.8442

3.5059

3.3455

1.9969

6


4.1785

3.7962

3.7085

3.2165

3.0451

3.6318*

7


3.9092

3.9104

3.5405

3.0845

2.9797

2.5216*

8

Reliability

3.6641

3.7122

3.5085

3.4041

3.2565

0.9775

9

Ambience

3.9192

3.6566

3.4144

3.3038

3.1718

2.5084*


3.9092

3.0565

2.8584

2.7334

3.9089*

*Significant at five per cent level Global Management Review | Volume 6 | Issue 2 | February 2012

17

Table 7: Factors leading to choose the store among the customers with different Personal Income per month Mean score among customers with the personal income of

S.No

Factors

Less Rs. 5001 Rs. 10001 Rs. 15001 Rs. 20001 Above than Rs. 25001 Rs. 5000 Rs. 10000 Rs. 15000 Rs. 20000 Rs. 25000

FStatistics

1

Price

3.6414

3.6568

3.4145

3.2457

2.7366

2.5156

2.7378*

2


2.9089

3.1763

3.2081

3.5103

3.6536

3.8994

2.6509*

3

Proximity

2.9817

2.9622

3.3088

3.4021

3.7343

3.9116

2.8182*

4

Variety of products

3.1446

3.2086

3.5144

3.7673

3.8082

3.9899

2.5314*

5


3.4245

3.3084

3.6504

3.8208

3.9317

3.9904

1.2146

6


3.1872

3.2566

3.4017

3.5752

3.6818

3.8144

2.3341*

7


2.9667

3.1714

3.3661

3.4251

3.5969

3.8506

2.6086*

8

Reliability

3.4778

3.6086

3.4567

3.6608

3.7331

3.8114

1.2762

9

Ambience

3.1706

3.2162

3.384

3.5946

3.8546

3.8662

2.4481*


2.8088

3.5142

3.4279

3.6568

3.9196

2.6696*


Factors leading to choose the store among the customers with different Personal Income per month In order to find the significant difference among different personal income group of customers regarding their importance given to the factors leading to choose the store, the one way ANOVA has been tested. The mean score of the various factors among the various customer groups has been computed and shown in Table 7 The significant difference among the customers with different Personal income per month is found in the factors like Price, Availability of Products, Proximity, Variety of 18

Products, Personal Interaction, Promotional Activities, Ambience and Physical Appearance. The analysis reveals that Price is an important factor to choose the store among the lesser income respondents, whereas it is Value Added Services among the respondents of higher Personal income. Factors leading to choose the store among the customers with different Family Income per month In order to find the significant difference among different family income of customers regarding their importance given to the factors


leading to choose the store, the one way ANOVA has been tested. The mean score of the various factors among the various customer groups has been computed and shown in Table 8 The significant difference among the customers with different Family income per month is found in the factors such as Price, Availability of products, Variety of Products, Value Added Services, Personal Interaction, Promotional Activities and Physical Appearance. The analysis reveals that Value Added Services is perceived as an important factor leading to choose the store among the respondents with higher family income, whereas it is Price among respondents with lower family income.

Factors leading to choose the store among the customers with different Average Monthly Expenditure In order to find the significant difference among the respondents with different monthly expenditure regarding their importance given to the factors leading to choose the store the one way ANOVA has been tested. The mean score of the various factors among the various customer groups has been computed and shown in Table 9 The significant difference among the respondents with different monthly expenditure is found in the factors namely Availability of Products, Proximity, Variety of Products, Value Added Services, Personal Interaction,

Table 8: Factors leading to choose the store among the customers with different Family Income per month Mean score among customers with the Family income of

S.No

Factors

Less Rs. 5001 Rs. 10001 Rs. 15001 Rs. 20001 Above than Rs. 25001 Rs. 5000 Rs. 10000 Rs. 15000 Rs. 20000 Rs. 25000

FStatistics

1

Price

3.9798

3.8645

3.3145

3.4108

3.7344

3.3568

2.2786*

2


3.0965

3.1708

3.4085

3.5312

3.8103

3.6778

2.3034*

3

Proximity

3.9144

3.8082

3.7123

3.4211

3.6861

3.5616

1.7374

4

Variety of products

2.9265

3.1145

3.5144

3.7034

3.6336

3.8785

2.2904*

5


2.6556

3.0841

3.4563

3.8508

3.8641

3.9192

3.1443*

6


2.9933

3.0641

3.3919

3.5744

3.5644

3.8982

2.9664*

7


2.8646

3.0147

3.2144

3.4256

3.5132

3.6086

3.2144*

8

Reliability

3.6703

3.6079

3.5344

3.6711

3.8212

3.7086

0.8586

9

Ambience

3.4646

3.3821

3.4411

3.5909

3.6044

3.6781

0.5146


3.0845

3.2091

3.4568

3.5084

3.8182

2.8082*

*Significant at five per cent level Global Management Review | Volume 6 | Issue 2 | February 2012

19

Table 9: Factors leading to choose the store among the customers with different Average Monthly Expenditure S.No

Mean score among customers with the Monthly Expenditure of (Rs.)

Factors

1

Price

3.8587

2


2.5643

3.1086

3.9197

3.8582

3.5779

3.3383*

3

Proximity

2.4146

2.7673

3.0244

3.1644

3.6497

3.2069*

4

Variety of products

2.9798

3.2171

3.4096

3.7271

3.8586

2.9343*

5


3.0845

3.5762

3.6546

3.7078

3.9445

2.8091*

6


3.0241

3.1907

3.2772

3.5884

3.8442

2.7379*

7


2.6264

2.8545

2.9098

3.1773

3.8646

3.1462*

8

Reliability

3.3382

3.4452

3.5147

3.6561

3.9297

2.2161

9

Ambience

2.9334

3.0891

3.2149

3.4082

3.6618

2.4818*


2.7142

3.0565

3.3132

3.8028

4.5145*

> 1,000

5,00110,000 3.7671

10,00120,000 3.2511

Above 20,000 3.4541

FStatistics

1,0015,000 3.2562

1.4142


Promotional Activities, Ambience and Physical Appearance. The analysis reveals that Price is an important factor to select the store among the respondents of lower average monthly expenditure, whereas it is Value Added Services among respondents of higher average monthly expenditure. Factors leading to choose the store among the customers of different Average Monthly Expenditure at the Store In order to find the significant difference among customers with different levels of average monthly expenditure at this store regarding their 20

importance given to the factors leading to choose the store, the one way ANOVA has been tested. The mean score of the various factors among the various customer groups has been computed and shown in Table 10 The significant difference among the customers with different Average monthly expenditure at the store is found in the factors namely Price, Availability of Products, Proximity, Variety of Products, Value Added Services , Personal Interaction and Promotional Activities. The analysis reveals that Value Added Services is an important factor leading to choose the store among the respondents of higher average monthly expenditure at the store, whereas it is


Table 10: Factors leading to choose the store among the customers with different Average Monthly Expenditure at the Store Mean score among customers with the Monthly Expenditure at the Store (Rs.)

S.No

Factors

1

Price

3.9866

2


3.0411

3.2676

3.5038

3.5969

3.8208

2.6686*

3

Proximity

3.1144

3.2646

3.4143

3.5662

3.7124

2.3314*

4

Variety of products

2.7093

3.3081

3.6899

3.9192

3.9969

2.8606*

5


3.2446

3.6562

3.8082

3.9293

3.9787

2.5108*

6


3.0671

3.1441

3.5909

3.6086

3.8185

2.6266*

7


3.1556

3.3141

3.4082

3.5657

3.7331

2.2591*

8

Reliability

3.2665

3.4587

3.6817

3.6992

3.7033

2.0841

9

Ambience

3.4616

3.4245

3.5881

3.6289

3.7144

1.9717


3.2144

3.4509

3.3961

3.5786

1.3345

> 1,000

5,00110,000 3.7172

10,00120,000 3.7562

Above 20,000 3.2141

FStatistics

1,0015,000 3.8586

2.4501*


Price among respondents of lower average monthly expenditure at the store. Factors leading to choose the store among the customers with different Shopping Frequency In order to find the significant difference among different shopping frequency of customers regarding their importance given to the factors leading to choose the store, the one way ANOVA has been tested. The mean score of the various factors among the various customer groups has been computed and shown in Table 11 The significant difference among the customers with different Shopping frequency is found in the factor namely Personal Interaction.

The analysis reveals that Value Added Services is the most influencing factor leading to choose the store among the respondents of different shopping frequency. RESULTS AND DISCUSSION Value added services is the most important factor for store choice among the respondents belonging to the profile of lower age group, higher personal income, higher family income, higher average monthly expenditure, higher average monthly expenditure at the store and different shopping frequency Price is the most important factor for store choice among the customers with lower level of


21

Table 11: Factors leading to choose the store among the customers with different Shopping Frequency

S.No

Factors

Mean score among customers with a frequency of Daily

Weekly

Once in Monthly fortnight 3.4085 3.2142

t-statistics

1

Price

3.8862

3.7031

2.5608

2


3.7074

3.5646

3.3939

3.1996

2.4145

3

Proximity

3.6816

3.3889

3.2142

3.0969

1.9969

4

Variety of products

3.8089

3.7039

3.6562

3.5441

0.8434

5


3.9196

3.9394

3.7865

3.8025

0.3089

6


3.8304

3.5811

3.4546

3.0845

2.7345*

7


3.5445

3.6081

3.0842

2.9146

2.4508

8

Reliability

3.7378

3.6802

3.6509

3.5114

0.5193

9

Ambience

3.6109

3.5442

3.3942

3.4642

0.4565

10

Physical appearance

3.5108

3.4286

3.3216

3.2441

0.6646


education, higher family member size, lesser personal income, lower family income, lower average monthly expenditure, lower average monthly expenditure at the store and private employed. Personal interaction is an important factor to choose the store among the respondents with lower family member size, higher average monthly expenditure at the store and married customers. Reliability is the most important factor for store choice among medium age group and Government employed customers. Physical appearance is an important factor to choose the store among students and house wives and others category. Proximity is an important factor to choose the store among business people. The important factors considered for the

22

selection of store are Value added services, Price, Personal Interaction and Physical Aspects. The most important factor identified by present research is Value added services. MANAGERIAL IMPLICATIONS ? The retailers can form profile clusters like frequent female shoppers with higher family income, male shoppers with low income etc. and plan for merchandize accordingly. ? Retail managers can develop value propositions that emphasize value added services like home delivery, free parking facilities, zone for children to play, acceptance of credit/debit card, facilities to return / exchange, discounts and offers provided by the store etc.


? Training programs can be provided to staff

members in interpersonal communication skills, product knowledge and customer relationship management to improve their customer service. ? The retailers can track the customer's purchase behavior and match them with the demographic profiles of the customer. This will help them in understanding the market segmentation better, identifying the target segment and attacking them with suitable positioning strategies. CONCLUSION Retail is the fastest growing sector in Indian economy with a compounded annual growth rate of 46.4 per cent for the past three years. Traditional retail outlets are paving way to newer formats like Supermarkets, Specialty store and Hypermarkets. With the mushrooming western style malls found in metros and second rung cities, the Indian consumer is introduced to an unparalleled shopping experience. This study has revealed the factors leading to choose the store in the Indian retailing environment. REFERENCES Baker, J., Grewal, D. and Levy, (1992), “An experimental approach to making retail store environmental decision”, Journal of Retailing, 68, pp. 445-60. Baker, J., Grewal, D., Levy, Parasuraman, A. and Glenn, B. (2002), “The influence of multistore environmental clues on perceived merchandise value and patronage intentions”, Journal of Marketing, 66, pp. 120-41. Hutcheson, G.D. and Mutinho, L. (1998), “Measuring preferred store satisfaction using consumer choice criteria as a mediating factor”, Journal of Marketing Management.

Kotler, P., (1973), “Atmospherics as a Marketing tool”, Journal of Retailing, 49, pp. 48-64. Leszczyc, P.T.L.P., Sinha, A. and Timmermans, H.J.P. (2000), “Consumer store choice dynamics: an analysis of the competitive market structure for grocery stores”, Journal of Retailing, 76, 3, pp. 323-45. Lumpkin, J.R., Greenberg, B.A. and Goldstucker, J.L. (1985), “Marketplace needs of the elderly: Determinant Attributes and Store Choice”, Journal of Retailing, 61, 2, pp. 75-105. Mahua Datta and Debraj Datta, (2009), “A study of Local Derivatives in Retail Purchase Behavior of Grocery to formulate retail strategy for the global players proposing the use of data envelopment analysis”, Focus, April – October 2009, pp. 11. Sinha, P.K. and Banerjee Arindam, (2004), “Store Choice Behaviour in an Evolving Market”, International Journal of Retail and Distribution Management, Vol. 32, No.10. Treblanche, N.S. (1999), “The perceived benefit derived from visits to a super regional shopping centre”, South African Journal of Business, 30, 4, pp. 141-6. Woodside, A.G., Trappey, R.J. III and Randolph, J. (1992), “Finding out why customers shop your store and buy your brand: Automatic Cognitive Processing Models of Primary Choice”, Journal of Advertising Research, pp. 59-78. Zeithaml, V.A. (1988), “Consumer perception of price, quality and value: a means – end model and synthesis of evidence”, Journal of Marketing, 52, pp. 2-22.

Authors can be reached at: [email protected] [email protected]


23

WORK-LIFE IMBALANCE AMONG EXECUTIVES: A GENDER FOCUS B. Jayanthi Assistant Professor, Department of Management Studies, N.P.R. Engineering College, Nathanam.

Dr. T. Vanniarajan Reader in Business Administration, NMSSVN College, Madurai

ABSTRACT Working environment is growing a high pressured environment especially after the globalization. The executives are highly affected by their work pressures and also by their work-life imbalance. The work-life imbalance among them affects the performance of the executives not only in organization but also in their family. It results in the poor performance of executives in both these two places. The present study has made an attempt to identify the important factors leading to work-life imbalances. It identified that the lack of role autonomy, role ambiguity, role conflict and role overload are the important factors leading to work-life imbalance. The level of above said stressors are identified as higher in private sector than in public sector organization.. INTRODUCTION Integration of work and social life is a critical task of early and middle adulthood (Lackman and Boone James, 1997). As we jump into job life, success on balancing work and social roles becomes a stronger contributor to how we feel. The restructuring of work time is one of the key points at which the managerial drive for flexibility in human resource utilization and employee demands for work-family coverage. While employment literature indicates that employees have driven most bargaining on flexible work time (Arrowsmith and Sisson, 2002), there is also going evidence that many employees want more autonomy in working hours arrangements to minimize work-family conflicts (Bielenski et al., 2002; Webster, 2001). Research on work and personal life issues has been conceived of in terms of work-family balance (Hills et al., 2001) and work-family 24

conflict. Work-family balance reflects integration of work demands with family roles, whereas workfamily represents incompatibilities between work and family responsibilities because of limited resources, such as time and energy (Kahn et al., 1964). Integration of work and family roles is relevant to married individuals but whereas it does not concern people without a family. Addressing the proposition of WLB that the problems of balancing life demands depends on time allocation across various life roles (Senecal, Vallerand, and Guay 2001). SUPPORTS FOR WORK-LIFE BALANCE The impact of work-life supports on the performance of projects have been linked to higher levels of organizational effectiveness (Stavrou, 2005). Sahilzada et al., 2005 and Breaugh and Frye, 2007 identified that there is a link between non standard work patterns (e.g. flextime, job


sharing, part-time work and annual hours) and lower turnover. The home-base work was linked to higher levels of performance and lower absenteeism (Kopelman et al., 2006). Arthur (2003) reports a positive relationship between the announcement of organizational work-life benefits and shareholders returns. BARRIERS TO WORK-LIFE BALANCE Organisational and intra-group culture and norms define how individuals are expected to respond in different circumstances (Perlow, 1998). Working long hours can serve as an indicator of commitment and productivity in some organizations (Thompson et al., 1999). The same thing is acting as a barrier to work-life balance. (Glass and Fujimoto, 1995). Lack of support of management is an another barrier to work-life balance (Kirby and Krone, 2000). The long working hours; long working (White et al., 2003), hours culture (Lewis, 2000), time pressures (Mauno and Kinnunen, 1999), Lack of flexibility (White et al., 2003), hours culture (Lewis, 2000), time pressures (Manuno and Kimunner, 1999), Lack of flexibility (White et al., 2003), financial pressure (Warren et al., 2004), employer practices (De Cieri et al., 2003), and lack of communication with staffs acted as the barriers to work life balance. WORK-LIFE BALANCE AMONG EXECUTIVES Organization as a work environment seemed reasonably pressure – free, with few averages over the mid-point of the male: “deadlines and time pressures” before globalization (Linchan and Walsh, 2000). The executives consistently rated work environment pressures more (Liff, 1999). The executives are

associating more pressures regarding their managerial roles. They are facing lack of important from superiors and inadequate supervision (Kramer and Lanabert, 2007). The executives are facing more work–life balance issues at work (Granleese, 2004) like conflicting responsibilities of home and career, excluded from social and business events. This work-life balance result in poor performance in both personal and corporate life. In this juncture, the present study focuses on the objective namely identification of the important factors leading to work-life imbalances among the executives in service industry. CONCEPTUAL FRAMEWORK OF THE STUDY The work life imbalance may be caused by some work related variables. Work-specific variables as a source of work-family conflict, because individuals have relatively less control over their work lives than their family lives (Higgins and Duxbury, 1992). The following four role stressors namely lack of autonomy, ambiguity, conflict and overload are conceptualized in the present study. 1. Role Autonomy According to Hockman (1977), autonomy is the degree to which the job provides substantial freedom, independence and discretion to the individual in scheduling the work and in determining the procedures to be used in carrying it out. Individuals who control over their work activities have more flexibility in allocating their limited resources at work and at home. As such, the degree of interference from work to family is minimized (Voydanoff, 1988). The degree of role autonomy among the employees have


25

been measured with the help of variables drawn from the previous studies (Pareek and Mehta, 1997; Mathur, 1997). 2. Role Ambiguity It occurs when an individual does not have clear information about what is expected on the job or how the reward system works (Kahm, et al., 1964). Those who suffer from role ambiguity experience lower levels of job satisfaction, high job-related tension, greater fertility and lower self-confidence (Greenhaus and Beutell, 1985). The role ambiguity among the staffs in the present study is measured with the help of six related statement drawn from the previous studies (Srivastav 2006; Pareek, 1983; and Avinash, 2007). 3. Role Conflict It is the simultaneous occurrence of two or more sets of pressures, such that compliance with one makes compliance with the other more difficult (Kahn et al., 1964). Beehr and McGrath (1992) state that role conflict occurs when an employee is expected, as part of the job, to do something that would conflict with other job or non-job demands or with his or her personal values. Thus conflict at work may draw resources away from the family conflict (Greenhaus and Bentell, 1985). The role conflict of the employees in the present study have been measured with the help of some statements drawn from the previous studies (Gil-Monte et al., 1993; Madhuri and Rachana, 2008; and Tharakan, 1992). 4. Role Overload Role overload occurs when the total demands on time and energy are too great for an individual to perform the role adequately 26

or comfortably (Cooper and Hensman, 1985). Individuals who perceive their workload to be more than they can handle experience negative emotions, fatigue, tension and other mental health symptoms (Gutek et al., 1991). It is also likely that they will experience higher levels of work interference with family (WIF) conflict, because time and energy are limited resources (Grant et al., 1990). The role overload among the employees in the present study have been measured with the help of six statements which drawn from previous studies (Berardo, et al., 1987; Lent et al., 1987; Thoits, 1991 and Williams et al., 1992). FACTORS LEADING TO WORK LIFE BALANCE Work and family conflicts have emerged as an increasingly important research topic in the last few decades. According to Zedeck (1992), this phenomenon is in part due to the increase in number of women in the work place, the changing attitudes towards work and the changing roles of family members. Further more, to day's work place is increasingly populated with working ponents, single parents, and dual-career couples (Thomas and hauster, 1995). The potential for work-family conflict increase as these working parents or dual couples struggle with the everyday work and home responsibilities. More than 50 per cent of work force is married with children, which suggests that information about this group should be extremely relevant for strategic human resource management and exployees (Duxlury and Higgins, 1991). Studies have investigated the antecedents of work-family conflict. (Frone et al., 1997; Frone and Yardley, 1996), According to them, the more hours an individual spend on roles


associated with work and / or family domains, role stress, role overload, social support, job complexity., career development issues and job security are the important antecedents of workfamily interface.

The work family interfaces results from incompatible work and family demands (Kopelam et al., 1983). These are two forms of work and family conflicts namely work interference with family (WIF) and Family interference with work

Table 1: Variables related to Role Stressors S.No

Variables

S.No

Variables

I

Lack of Role Autonomous

I

Lack of Role Conflict

1.

I have freedom to design my work schedule

1.

I work against my expected role

2.

I have independence and responsibility in my work

2.

Incompatiable instructions from several people

3.

Higher personal responsibility in my work

3.

My values conflict with organization values

4.

I create procedures to be used in my work

4.

The expectation of seniors conflict with those of mine

5.

I have authority to allocate resources

5.

I am unable to satisfy the conflicting demands

6.

Flexibility in my job is higher

6.

I do things acceptable by a few but not others

II

Lack of Role Ambiguity

II

Role overload

1.

Unclarity on scope and responsibility on the job

1.

My work load is heavy

2.

No established procedure in my job

2.

I have no sufficient assistance to complete my assignment

3.

My role in the work is vague

3.

I feel over burdened in my role

4.

Lack of facts and information given to me about my work

4.

Too much expectation rest on me

5.

Not knowing the level of expectation of my authorities

5.

My job assignments are very taxing

6.

My role has been reduced to nothing

6.

Too many suffering hours are imposed on me


27

(FIW) (Gutek et al., 1991). Work can interfere with family when work demands prevent the fulfillment of family demands. Family can interfere with work when family demands prevent the fulfillment work demands. In the present study, the antecedents of work life balance have been examined with the help of organizational role stressors alone. R O L E S T R E S S I N T H E S E RV I C E INDUSTRY Human behaviour in an organization is influenced by various physical, social and psychological factors. An important aspect of organization that integrates an individual with the organization is the role assigned to her within overall structure of the organization. It is through the role that an individual interacts and becomes integrated with the system. In fact, an organization can be defined as the system of roles. Kahn et al., (1964) in their comprehensive and integrated model of stress postulated that the quest for identity is a central concern for many individuals. They considered a specific type of stress in the form of role stress. Constructs like the conflict, role ambiguity, and role overload were put under the rulic of role stress. Even though, the organizational role stress sale (ORS) developed by Pareek (1983) consists of to role stressors, the present study confine it to lack of role autonomy, role ambiguity, role conflict and role overload since these are most appropriate to estimate the factors leading to work life imbalance among the executives in service industry (Pareek, 1997). The variables related to the four important factors leading to work-life imbalances in the present study is given in Table 1. 28

The executives are asked to rate the above said variables (statements) at five point scale according to their order of acceptance. METHODOLOGY The executives working in service industry at Madurai district of Tamilnadu have been included for the study. The included service industries are banks, insurance companies, hospitals and transport. From each industry, 20 each male and female executives and purposively selected for the study. Hence, the sample size came to 160 executives which consists of 80 male and 80 female executives. COLLECTION OF DATA Since the present study is based on the primary data, the primary data is proposed to collect through the pre-structured questionnaire. The questionnaire will be designed on the basis of the requirements of the details for the fulfillment of the objectives of the study. The pilot study will be conducted among 20 male and 20 female executives to enrich the quality of the questionnaire. ANALYSIS OF DATA The collected data is planned to process with the help of appropriate statistical tools like confirmatory factor analysis, discriminant analysis and 't' test. RESULTS AND DISCUSSION Factors Leading to Work-Life imbalance among the Executives The four important factors leading to work life imbalance among the executives have been measured with the help of 6 variables in each factor. The executives are asked to rate the


variables in each factor at five point scale. The score of the variables included for the reliability and validity analysis with the help of confirmatory factor analysis. The results are given in Table 2. The standardized factor loading of the variables in each factor are greater than 0.60 which shows the content validity (Fornell and Larcher, 1980). The significance of 't' statistics of the standardized factor loading of the variables in each factor reveals the convergent validity (Meyer and Collier, 2001). It is also proved by the composite reliability since these are greater than its minimum

threshold of 0.50 and 50.00 per cent respectively. The cronbach alpha of all four antecedents are also greater than its standard minimum of 0.60 (Nunnally, 1967). The results indicates the reliability of and validity of variables in each factor. Comparative View on the Role Stressors among the Executives The role stressors in the present study is classified into lack of role autonomy, role ambiguity, role conflict and role overload. The

Table 2: Antecedents of Work Life Imbalance among the Executives

Antecedents

S.No

No. of Variables

Range of Standardized factor loading

Average Range of Cronbach Composite variance alpha reliability 't' statistics extracted

1

Lack of role autonomy

6 6

0.9145 –0.6886

42535* –2.6586*

0.8183

0.7884

56.17

2

Lack of role ambiguity

6 6

0.8904 –0.6458

3.9696* –2.0886*

0.7804

0.7511

54.08

3

Role conflict

0.9331 –0.6917

4.3885* –2.8589*

0.8248

0.7917

58.04

4

Role overload

0.9085 –0.7244

4.0144* –2.1172

0.8089

0.7616

55.87

*Significant at five per cent level.

Table 3: Comparative Analysis on Role Stress among the Male and Female Executives S.No

Role Stressor

Mean score among the executives Male Female

't' statistics

1


2.9120

3.8455

–2.9041*

2

Lack of role ambiguity

2.9983

3.6710

–2.5149*

3

Lack of role conflict

2.8798

3.6940

–2.8647*

4

Lack of role overload

3.1392

3.9639

–2.6603*

* Significant at five per cent level.


29

score on four important role stressors have been computed by the mean score of variables in each role stress. The mean score of each important role stress has been computed to exhibit the level of role stress among the male and female executives separately. Regarding the perception on the above said four role stressors, the significant difference among the male and female executives have been computed with the help of 't' test. The mean scores of four role stressors among the executives and the in respective 't' statistics are given in Table 3. The highly viewed role stressors among the male executives is role overload and role ambiguity since their respective mean scores are 3.1392 and 2.9983. Among the female executives, these two are role overload and lack of role autonomy since their respective mean scores are 3.9639 and 3.8455. Regarding the role stressors, the significant difference among the male and female executives executives have been noticed in all four role stressors since their respective 't' statistics are significant at five per cent level. The level of role stressors among the female executives

in PRSBs than among the male executives. Discriminant Role Stressors among the Male and Female Executives The role stressors among the male and female executives may be different in different degree. In order to reach some policy implications, it is imperative to identify the important discriminant role stressors among the two group of executives. Initially, the mean difference in all four role stressors and the discriminant power of the role stressors have been computed and presented in Table 4. The significant mean difference is identified in all four role stressors since their respective 't' statistics are significant at five per cent level. The higher mean difference is identified in the case of lack of role autonomy and role overload since their respective mean differences are –0.9335 and –0.8247. The higher discriminant power of the role stressors is identified in the case of role overload and role ambiguity since the respective Wilk's Lambda are

Table 4: Mean Difference and Discriminant Power of Stressors among Executives in PUSBs and PRSBs

30

Mean scores among Mean executives Difference Male Female

‘t’ Statistics

Wilk’s Lambda

–0.9335

–3.3996*

0.2583

3.6710

–0.6727

–2.4403*

0.1209

2.8798

3.6940

–0.8142

–2.6676*

0.3091

3.1392

3.9639

–0.8247

–2.7391*

0.1193

S.No

Role Stressors

1

Lack of role autonomy (X1)

2.9120

3.8455

2

Role ambiguity (X2)

2.9983

3

Role conflict (X3)

4

Role overload (X4)


0.1193 and 0.1209. The significant role stressors are included to estimate the two group discriminant function. the unstandardised procedure has been followed to estimate the function. The estimated function is: Z = –1.2344 – 0.0473X1 – 0.2453X2 – 0.2865X3 – 0.3861X4 The relative contribution of discriminant role stressors in total discriminant score (TDS) have been calculated by the product of the discriminant co-efficient and the respective mean difference of the role stressors. The results are given in Table 5. The higher discriminant co-efficient is identified in the case of role overload and role conflict since its discriminant co-efficients are –0.2788 and –0.2099 respectively. It represents the higher degree of influence of above two role stressors in the discriminant function. The higher relative contribution in TDS is noticed in the case of role overload and role conflict since its contributions are 65.95 and 14.28 per cent respectively. The estimated function correctly classifies the cases to the extent of 71.09 per cent.

The analysis reveals that the important discriminant role stressors among the male and female executives in service industries is role overload and role conflict whereas these two are very high among the female executives their among the male executives. CONCLUDING REMARKS The present study concluded that the important antecedents of work-life imbalance among the executives in the service industries are lack of role autonomy, role ambiguity, role conflict and the overload. The above said role stressors are identified as higher among female executives their among the male executives. The important discriminant (role stressors) antecedents of worklife imbalance among the male and female executives in service industries is role overload and role conflict which are higher among female than among male executives. The human resource manager should concentrate on the above said stressors in order to reduce the over workload of executives especially female executives. It is also advisable to overcome the problem of role conflict

Table 5: Relative Contribution of Discriminant Role Stressors in Total Discriminant Score (TDS) S.No

Role Stressor

Discriminant co-efficient

Mean Differences

Product

Relative Contribution in TDS

1


–0.0392

–0.9335

0.0366

10.69

2

Role ambiguity

–0.1886

–0.6727

0.0311

9.08

3

Role conflict

–0.2099

–0.8142

0.0489

14.88

4

Role overload

–0.2738

–0.8247

0.2258

65.95

0.3424

100.00

Total

Per cent of cases correctly classified: 78.65


31

through the placement of right person at right job. The human resource managers have to accept the truth of the linkage between the work life balance and productivity of the employees and finally with the performance of the organization. SCOPE FOR FUTURE RESEARCH The present study has some limitations namely limited scope and focus only on role stressors. If the scope of the study extended to the measurement of work-life imbalance and its causes and consequences may provide better result in future. The sectoral comparison may be also done in future research work. The impact of work life imbalance on the various outcomes may be discussed in near future. The gender focus may be extended to all industries in the service factor at the future research work. REFERENCES Arrowramth, J. and Sisson, K. (2002), “Working Time Developments and the Quality of work”, European Industrial Relations Observatory on-line, European Foundation for the Improvement of Living and Working conditions, Dublin. Arthur, M.M., (2003), “Share Price Reactions to work-family initiatives: an institutional perspective”, Academy of Management Journal, 46 (4), pp.497-505. Avinash Kumar Srivastav, (2007), “Stress in Organizational Roles: Individual and Organizational Implications”, The Icfaian Journal of Management Research, 6 (12), pp.65-74.. Beehr, T.A., and Mc Grath, J.E., (1992), “Social support, occupational stress and anxiety”, Anxiety, Stress and Copying, Vol.5, pp.719. Bernardo, P.H., Shehar, C.L. and Leslie, G.R., 32

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Mauno, S., and Kinnunen, U., (1999), “The Effects of Job Stressors on Marital Satisfaction in Finish Dual-Earner Couples”, Journal of Organizational Behaviour, 20 (1), pp.879-895. Meyer, S.M. and Collier, D.A., (2001), “An empirical test of the causal relationships in the baldrige health care pilot criteria”, Journal of Operations Management, 19 (4), pp.403-425. Nunnally, J.C., (1967), Psychometric Methods, Mc Graw Hill Book Company, New York, NY. Paree, A., and Mehta, M., (1997), “Role stress among working women” in Pestonjee, D.M. and Pareek, V., (Eds.), Studies in Organizational Role Stress and copying, Rawat Publications, Jaipur, pp.173-181. Pareek, U. (1983), “Organizational Role Stress”, in Goodstein, L.D. and J.W. Pfeiffer (Eds.), The 1983 Annual, University Associates, San Diago, California, pp.115-123. Pareek, U., (1997), Training Instruments for Human Resource Development, Tata McGraw Hill, New Delhi. Pareek, V., (1983), Role Stress Scale: ORS Scale Booklet, Answer Sheet and Manual, Navin Publications, Ahmedabad. Perlow, L.A., (1998), “Bundary Control: The Social Ordering of Work and Family time in a high tech corporation”, Administrative Science Quarterly, Vol.43, pp.328-357. Senecal, C., Vallerand, R.J. and Guay, F., (2001), “Antecedents and Outcomes of Workfamily Conflict: Toward a Motivation Model”, Personality and Social Psychology Bulletin, 27 (2), pp.176-186. Simon, R.W., (1995), “Gender, Multiple roles, role meaning, and mental health”, Journal of


Health Social behaviour, Vol.36, pp.182194. Srivastav, (2006), “Role Stress and Ageing in Organization–An Empirical Study across function”, Gitan Journal of Management, 4(1), January-June, pp.26-39. Starrou, E.T., (2005), “Flexible-work bundles and organizational competitiveness: across national study of European work context”, Journal of Organizational Behaviour, Vol.26, pp.923-947. Tharakan, P.N., (1992), “Occupational Stress and Job Satisfaction among working women”, Journal of the Indian Academy of Applied Psychology, 18 (1 & 2), pp.3740. Thoits, P.A., (1991), “On merging identity theory and stress research”, Social Psychology Quarterly, Vol.54, pp.101-112. Thomas, L.T., and Ganster, D.C., (1995), “Impact of Family-support work variables on work-family conflict and strain: A control perspective”, Journal of Applied Psychology, 80(4), pp.6-15. Voydanoff, P. (1988), “Work role characteristics, family structure demands, and workfamily conflict”, Journal of Marriage and the Family, 50 (2), pp.749-761.

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BUSINESS STRATEGIES FOR SUSTAINABLE GROWTH OF INDIAN STEEL INDUSTRY: SAIL VS. TATA STEEL Niranjan Kumar Singh Research Scholar, Dept. of Management Studies, Jain University, Bangalore

Nita Choudhary Research Scholar, Dept. of Management Studies, Jain University, Bangalore

ABSTRACT The effect of globalization on steel industries in different regions has not been uniform. Each region is unique in its own way in terms of raw materials availability, technology adopted, market conditions, trading policies, etc. Consequently, iron and steel industries have structured their business in such a way that best suits the needs and situations of the region. Two top Indian steel tycoons SAIL and Tata Steel had shown remarkable achievements in international arena in varieties of economic conditions. The secret of sustainable growth lies in how SAIL & Tata Steel faces the challenges and develops a business strategy for future growth and survival. Business strategy of both the companies differs as SAIL is under government control and Tata Steel belongs to most renowned business family of India i.e. Tata Group with a common goal towards business excellence. This paper will focus on factors responsible for strategic decision making, SWOT analysis, comparison of strategic decisions taken , financial performance comparison of SAIL & Tata Steel in the financial year 2005 – 2010.

INTRODUCTION The Iron and Steel Industry is a major industry in India. This industry drives the industrial progress of the country. It is one of the key industries in India, and several small- and mediumscale industries depend on it. The Indian steel manufacturing industry has played a key role in putting India on the global map. Steel Authority of India Limited (SAIL) is one of the Maharatna public sector undertaking (PSU) under Ministry of Steel. The Government of India owns about 86% of SAIL's equity and retains voting control of the Company. However, SAIL, by virtue of its 36

'Maharatna' status, enjoys significant operational and financial autonomy. Tata Steel is one of the leading global steel producers having a vision of producing 50 MTPA by 2015 (Figure 1). The Government of India has accorded the status of 'Navratna' and 'Maharatna' to Steel Authority of India (SAIL) through a memorandum DPE O.M. No. DPE/11(2)/97-Fin. dated 22nd July, 1997 and No. 22 (1) / 2009-GM-GL-101 dated 19th May 2010 respectively. A Navaratna company can invest up to Rs 1,000 crore without government approvals whereas, a maharatna company can invest upto


Figure 1: TATA STEEL - Vision Strong Base in India Primary Steel Making in countries rich in iron ores and/or Coal/Gas ? Single Location

? World Class ? Small in Size

Overseas acquisitions in growing & mature markets

• Multi Location • Strong regional

presence

• Auto & Packaging &

Construction Led Ownership of Strategic Raw Materials

2005 ? 5 mtpa ? Dominant in selected domestic markets ? EVA +

More from Steel and Branding Control over Logistics

2015 • 50 mtpa capacity • De-integrated

production

• Strong Brands • EVA +

Participation in Alternate Technology World Class Organisation / Structure Source: Tata Steel Annual Report 2005-2006

Rs. 5000 crore (DPE, 1997). After the melt down of global economic crises (1998 – 2003), SAIL & Tata Steel made a massive plan for business expansion. The only limitation of SAIL was an investment upto to maximum Rs.1000. crores. Government of India announced National Steel Policy 2005 where domestic production and consumption of 100 MTPA of steel was targeted by 2019-2020. This was also one of the motivation behind the growth strategic decision taken by SAIL & Tata Steel. Based on the performance of the SAIL & Tata Steel from financial year 2005 to 2010, strategies adopted by them can be modeled and is shown in figure 2. LITERATURE REVIEW In 2001 and 2005, Tata Steel was conferred

as lower cost best steel producer by World Steel Dynamics (Business Standard, 2001) and (Tata, 2005). World Steel Dynamics has ranked SAIL second in its list of world-class steelmakers, giving the company the right exposure ahead of its globalization drive, its forthcoming public issue, size, expansion plan, adaptation of new technology and products, pricing power, raw material security, and labour and energy cost (Subhash,2010). The WSD ranking is based on a scores of 23 parameters that include size, expansion plan, adaptation of new technology and products, pricing power, raw material security, and labour and energy cost. Tata Steel made its debut in 2008 at 315th position in Fortune 500 Global list. It had also been named as the company with highest revenue growth of over 353 per cent over the past year. Tata Steel recorded 17th fastest growth in


37

Figure 2: Strategic Decision Model

Corporate Strategy

Growth Strategy

Business Strategy

Growth & Survival Strategy

Complimentary Strategic Options

Operational Strategy

profit among all the companies globally (Expressindia, 2008). Cost cutting strategy did the magic in turnaround of SAIL in 1997-2002 (Sinha, 2002). Ultimately strategies will viable if the business is managed to achieve that viability. Today steel industry operates at high levels business complexity. Today there is need that they eliminate all unnecessary complexity and focus on selected products and markets to achieve as in many other industries (Mazumdar, 2003). Production of one ton of hot metal requires around 1.65 t of iron ore, 0.45t of fluxes and 1.0 t of coking coal. Cost of hot metal is strongly influenced by the consumption of iron ore and coking coal per ton of hot metal produced. While cost of iron ore and flux account for 20% of the raw material cost, coke accounts for about 50% of the total cost of production of hot metal (Srivastava, 2004). OBJECTIVES OF THE STUDY 1) Factors responsible for strategic decision making 2) SWOT Analysis of SAIL & Tata Steel 3) A comparison of strategic decisions taken by SAIL & Tata Steel in the period 2005 – 2010. 38

4) Financial performance comparison of SAIL & Tata Steel RESEARCH METHODOLOGY USED The entire data used for the present study have been obtained from the secondary sources. The secondary data sources are :--1) SAIL and Tata Steel Annual Reports 2) Reports of department of public enterprise, Ministry of Steel, Planning Commission, government of India 3) National Steel Policy 2005 4) Data published by the Steel Exporters Forum (SEF), World Steel Association 5) IE (I) Journal - MM 6) Reports by Joint Plant Committee empowered by the Ministry of Steel / Government of India to collect data on the Indian iron and steel industry. Based on the secondary data, the SWOT analysis has been done. RESULTS 1) Factors responsible for strategic decision making a) National Steel Policy 2005


Figure 3: Production and Apparent Consumption of Finished Carbon Steel in India

Quantity of Steel (in MT)

70 60 50

65.465

63.445 58.233

55.146 42.636

56.475

52.351

52.125

49.777

40 38.151 30 20 10 0

2005-06

2006-07

2007-08

Financial Year Production

2008-09

2009-10

Apparent Consumption

Source: Data from www.steel.nic.in/development.htm

b) Gov. of India plan to invest $350 billion on infrastructure and construction projects. c) Production and apparent consumption of Finished Carbon Steel in India(Figure 3)

d) G D P g r o w t h r a t e o f I n d i a a n d China(Figure 4) e) Trend of Metallurgical Coal prices: Metallurgical coking coal is very

Figure 4: GDP Comparison 14

GDP - Real Growth Rate

12

11.9 10.7

10.2

9

10 8 6

9.5 4.7

4 2 0

9.7

9.2

5.3

5.2

3.1 1.7

3

9.1

7.4

6.7 3.1 0.8

2005-06

2006-07

-2

2007-08

-0.7

2008-09

Financial Year

2009-10 -4

-4 -6 World

India

Europe


China

39

Figure 5: Trends of Metallurgical Coal Prices 160 142.4

Cost (in $ Ton)

140 114.29

120 100

86.4

90.81

90.89

2006-07

2007-08

80 60 40 20 0

2005-06

2008-09

2009-10

Financial Year

important for good quality of steel. India does not have good reserve of high quality of coking coal except BCCL, Dhanbad. Either it is imported from Australia or

African countries (Figure 5). 2. a) SWOT Analysis of Steel Authority of India Limited (Table 1). b) SWOT Analysis of TATA Steel (Table 2)

Table 1: SWOT Analysis of Steel Authority of India Limited

40

Strengths 1. Efficient use of resources. 2. Strong liquidity position. 3. Leading steel company in India. 4. Broad product mix 5. Captive sources of raw materials.

Weaknesses 1. Majority stake controlled by GOI. 2. Increasing total debt of the company. 3. Weak presence in international market

Opportunities 1. Joint ventures and MOU. 2. Booming automobiles and construction industry. 3. Expansion and acquisition plan. 4. Development of SEZ in Salem.

Threats 1. Slump in steel demand. 2. Operational risk


Table 2: SWOT Analysis of TATA Steel Strengths 1. Captive sources of raw materials. 2. Highly credible Management team. 3. Adaptability 4. Brand value 5. Banking upon opportunities. 6. Strong market position. Opportunities 1. Expansion plan in India and foreign. 2. Introduction to newer technology 3. Opportunity in infrastructure 4. Acquisition opportunity. 5. Movement in value chain front.

3. A comparison of strategic decisions taken by SAIL & TATA Steel in the period 2005-10 (Figure 6) 4. Financial Performance Comparison (Figure 7 & Figure 8). ANALYSIS & DISCUSSION 1. Factors responsible for strategic decision making: Coking coal and iron ore are two basic raw materials for steel making. SAIL and Tata Steel are having good reserves of iron ore mines. The basic problem is with the Coking coal reserves in India. In India, only BCCL is manufacturing coking coal which is not sufficient for Indian steel industries. Coking coal constitutes nearly 30% of cost of steel production in India (Prakash, 2003). Coking coal is being imported from Australia and African countries. Fig:1.5 shows that coking coal

Weaknesses 1. Huge debt burden of 10.2 bn USD 2. High attrition rate 3. Downgrading in rating from stable to negative 4. Financial crisis. Threats 1. Competition from Indian & foreign companies. 2. Environmental regulations.

prices increased from $ 86.4/ton in 200506 to $ 142.4/ton in 2008-09 and then it decreased to $ 114.29/ton. Fig. 1.3 shows that steel production and consumption in India & China is increasing due major infrastructure and construction projects whereas overall Europe and world GDP is decreasing. 2008-2009 financial year was a economic crisis year. As per the National Steel Policy, 100 MTPA steel consumption had been targeted by 2019-2020. As a Navratna company, SAIL can take an investment decision upto maximum Rs.1000 crore without government approval whereas Tata Steel is having a long vision of growth which is indicated by its acquisition of Corus. 2. SWOT Analysis of SAIL and Tata Steel: From Table No. 1 & 2, we can see the strong liquidity position of SAIL and huge debt burden of Tata Steel. SAIL is having


41

Figure 6: Comparison of Business Strategic Model of SAIL & TATA Steel SAIL

TATA Steel

Basic Competitive Strategic Options (A Company's First Strategic Option)

Broad Differentiation

Low Cost Provider

Complementary Strategic Options (A Company's Second set of Strategic Choices Alliances, Collaborative partnership, Joint Ventures

Joint Ventures Use of Internet as a Distribution Channel

Mergers, Expansion, Modernization

Merger, Acquisition Outsourcing of Value Chain Activities

Functional Area Strategies to support the above strategic choices (A Company’s third set of strategic choices) Production, Marketing

R & D, Production, Human Resource

Timing a company Strategic Moves in the Marketplaces (A Company’s fourth set of strategic choices) Late Mover

limitation of investment because of the government control whereas Tata Steel does have this limitation having strong position in the international market. Tata Steel is a brand whereas SAIL enjoys the privilege of broad product mix. 42

Fast Follower

3. Strategic Options: 3.1 Basic Competitive Strategic Options: Tata Steel is known as the low cost steel producer in the world whereas SAIL is known for broad variety of product like stainless steel and special steels used for


Figure 7: Turnover Comparison 160000

147329 131534

Turnover (in Crores)

140000 120000

102393

100000 TATA Steel SAIL

80000 60000

17144

43935

39189

32280

40000 20000

48681

45555 25650

0

2005-06

2006-07

2007-08 2008-09 Financial Year Source: Data taken from Annual reports of Tata Steel & Sail

2009-10

Figure 8: Profit After Tax Comparison 14000 12350 12000

Profit after tax(in crores)

10000 7537

8000 6202

6175 4951

6000 4000

3506

4013

6754

TATA Steel SAIL

4177

2000 0

2005-06

2006-07

2007-08

2008-09

-2000 -4000

2009-10 -2009

Financial Year

Source: Data taken from Annual reports of Tata Steel & Sail


43

aerospace and defense. 3.2 Complementary Strategic Options:Table 3

3.3 Functional Strategy to support the above strategies: Table 4

Table 3: Complementary Strategic Options SAIL TATA Steel a. JOINT VENTURES § SAIL-Bansal Service Center Ltd. in July'2006 on 40:60 basis § Bhilai JP Cement Ltd. in July'2007 on 26:74 basis § Bokaro JP Cement Ltd in Feb' 2008 on 26:74 basis § SAIL& MOIL Ferro Alloys (Pvt.) Limited in July, 2008 on 50:50 basis § S&T Mining Company Pvt. Ltd in Sept' 2008 on 50:50 basis § International Coal Ventures Private Limited in May,2009 on 28:72 basis § SAIL to form shipping JV company with SCI in March' 2010 on 50:50 basis § SAIL to form JV company with SCL, Kerala in Dec' 2000 on 50:50 basis

§ Tata BlueScope Steel in Nov'2005 on 50:50 basis § Tata NYK Shipping Pvt. Ltd. in Dec' 2006 on 50:50 basis § S&T Mining Company Pvt. Ltd in Sept' 2008 on 50:50 basis § With Riversdale, Mozambique in November 2007 on 40:60 basis § Tata Steel Cote d'Ivoire in Dec'2007 on 85:15 basis § With Oman Limestone Project in Jan'2008 on 70:30 basis

b. ALLIANCES/PARTNERSHIP § With POSCO in August'2007 for manufacturing and technology § With RINL in Jan'2008 for for Lime Stone acquisition in Oman § With SCIL in July'2008 for logistics § With L&T in Dec'2008 for power plant § With MECL to explore iron ore & flux mines § With HEC for equipment/spares required for modernization/expansion § With BSLC for dolomite § With Indian Railways for procurement of high power locomotives § With BEML for supply of crucial equipment § With RSMML for long-term supply of low-silica limestone § With IIM, Ahmadabad, BITS, Pilani and MDI, Gurgaon for knowledge sharing § With Siemens Ltd for manpower training § With Military Engg. Services (MES) and Married Accommodation Project (MAP)of Ministry of Defence, Govt. of India for long-term supply of construction steel § With NMDC to jointly develop limestone mine at Arki § Kobe Steel Limited (KSL), Japan to explore the technical & economic feasibility of ITmk3 technology

44

§ With

Carborough Downs Coal Project, Australia for an underground coking coal project.


Contd.. Table 3: Complementary Strategic Options SAIL

TATA Steel

c. MERGER/ACQUISITION § IISCO merger in Feb'2006 § Bharat Refractory merger with SAIL in April'2007 § Acquisition of Malvika Steel, Jagdishpur in

Feb,2009 for Rs.209 crores

§ Acquisition of Millennium Steel, Thailand in Rs. 780 crores to acquire 67.11% stake in Dec'2005 § Acquisition of Corus in Rs. 56,150 crores in April'2007 § Merger with Hooghly Met Coke and Power Company Ltd. (HMPCL) from 1st April, 2009

d. USE OF INTERNET AS A DISTRIBUTION CHANNEL Yes

Yes

e. EXPANSION/MODERNIZATION § SAIL targeted for raising production capacity to

20.2 MTPA by 2012-2013. § SAIL has already spent Rs.18020 crores in expansion and modernization in three financial years i.e. 2007-2010 § SAIL will be spending Rs 12,254 crore in 20102011 financial year for expansion § Sail planned to set up Steel Processing Unit in four states of India in 2008-09.

§ In 2005, Tata Steel proposed to establish three Greenfield facilities in Orissa, Chhattisgarh and Jharkhand with an aggregate capacity of 23 million tons by 2015 total estimated investment of Rs. 70,000 crores. § Brownfield project at existing Jamshedpur plant from 5MTPA to 10 MTPA by Dec'2011

f. OUTSOURCING OF VALUE CHAIN ACTIVITIES § Outsourcing of value chain activities was being done to subsidiary companies

3.4 C o m p a n y ' s S t r a t e g i c m o v e s i n Marketplace: Table 5 4. Financial performance comparison of SAIL & Tata Steel: Fig: 7 & 8 shows the consistence performance of SAIL except from Oct' 2008 to Sept' 2009 due to economic slow down whereas Tata Steel profit declined in 2008-2009, incurred losses of Rs.2009 crores.

CONCLUDING REMARKS WITH SUGGESTIONS 1) SAIL's strategic decision is based on its limitation to invest upto Rs.1000 crores without government permission in the form of small joint ventures and acquisition in the view of National Steel Policy 2005 and expected global steel demand whereas Tata Steel strategic


45

Table 4: Functional Strategy to support the above strategies SAIL § SAIL's Central Marketing Organization transacts business through its network of 37 Branch Sales Offices, 25 Dept. Warehouses, 42 Consignment Agents and 27 Customer Contact Offices & dealers above 2000. § Increase in productivity by optimal utilization of resources and several improvement initiatives

TATA Steel § Tata Steel Group has five research centers with over 1000 people. 133 patents have been granted, 360 have been filed and are at different stages § Increase in productivity by optimal utilization of resources and several improvement initiatives like ASPIRE T3 programme § Tata Steel is an equal opportunity employer. It recognizes that its people are the primary source of its competitiveness

Table 5: Company's Strategic moves in Marketplace SAIL Late mover due to government restrictions

decision is without any constraint to investment expenditure based on its vision to become global player. SAIL got Maharatna status in July'2010 which enabled SAIL to invest upto Rs.5000 crore without any government approval. 2) SAIL strength is its broad product mix, efficient use of resources and captive source of raw material whereas Tata Steel strength is it Brand value and captive source of raw material. Both the companies are having opportunities to show its global presence. The biggest weakness with SAIL is government control whereas attrition rate with Tata Steel. 3) Basic competitive strategy of SAIL & Tata Steel is broad differentiation and low cost provider. SAIL's complementary business 46

TATA Steel Fast mover as the company is having long term vision

strategies are in the form of small joint ventures, Brownfield projects, alliances, mergers of the subsidiary companies and acquisitions whereas Tata Steel opted for big acquisition like Corus, Millenium Steel and Greenfield projects. In functional area strategy, SAIL is expertise over production and marketing whereas Tata Steel for production, human resource and its strong R & D base. To make its European operations more competitive, Tata Steel is hastening the speed of its functional strategy programme “Weathering the Storm” and “Fit for the Future”. SAIL had lost its market-leadership as it had failed to modernize and add capacity at a time when others were doing so 4) Tata Steel incurred losses in 2009-2010 but


it has a stronger global presence and customer base than SAIL. REFERENCES Business Standard (2001) “WSD accolade Irani's parting gift to Tata” accessed on 06.02.2011 in the Business Standard website at http://www.businessstandard.com/india/news/wsd-accoladeiranis-parting-gift-to-tata/93146/ DPE (1997) “Chapter IX : Maharatna/ Navratna/Miniratna Status of CPSEs” accessed on 06.02.2011 at http://dpe.nic.in/newgl/glch9index.htm Expressindia (2008) “RIL tops, Tata steel debuts on Fortune 500” accessed on 06.02.2011 at http://www.expressindia.com/latestnews/RIL-tops-Tata-steel-debuts-onFortune-500/333467/ Mazumdar, S Mitra and Ghosal (2003), T. Strategies for Sustainable Turnaround of Indian Steel Industry, IE (I) Journal-MM, Oct' 2003, pp. 64-78. Prakash, C (2003). Consumers' views on Coal Supply: India-IEA Joint Conference on Coal and Electricity in India, 22-23 Sept.,

New Delhi. Sinha, Rabindra Nath (2002). SAIL: Cost control measures do the trick, Financial Daily from THE HINDU group of publications, Aug 06. Srivastava, M P et al. (2004), T. Quality of Raw Materials - a Key to Cost Reduction, IE (I) Journal-MM, Oct' 2004, pp. 71-81. Subhash Narayan (2010) “Class Act: SAIL moves up to 2nd spot on the World Steel Dynamics list” accessed on 07.02.2011 at http://economictimes.indiatimes.com/ne ws/news-by-industry/indl-goods-/svs/steel/Class-Act-SAIL-moves-up-to2nd-spot-on-the-World-Steel-Dynamicslist/articleshow/5900652.cms Tata (2005) “Tata Steel ranked world's best steel maker by World Steel Dynamics” accessed on 06.02.2011 in the Tata group w e b s i t e a t “http://www.tata.com/company/releases/ inside.aspx?artid=nAIH2iibp8Q=

Authors can be reached at: [email protected] [email protected]


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COMPARATIVE STUDY OF INDIAN MOVIES AND SPORT CELEBRITY AS BRAND ENDORSES: ANALYSIS BASED ON “Q” SCORE TECHNIQUE Satendra Thakur Assistant Professor, Department of Management Studies, RKDF Group of Institute, Bhopal.

ABSTRACT As we may say that celebrity endorsement has become significantly across the word. In this research article we have discuss about comparative study between Indian sport celebrity and movies star celebrity as familiar, and popular in the view of Indian people. This paper rifles through the concept of celebrity endorsement and provides insights on what it is and how the increasing number of endorsements throws a valid question to the consumers. First let we have discuss about the level of awareness about 20 Indian sport celebrity and movies star celebrity among the respondents. Secondly the study focuses to determine the potential of the Indian sport and movies star celebrity as probable brand endorsers. Finally we have give guideline to selecting the right celebrity for advertising. The finding of the study clear indicate that Indian movies star celebrity enjoying 100% awareness and only cricket celebrity also enjoying 100% awareness then other sport celebrity. In the popularity test this is clear that only movies star celebrity most popular then Indian sport celebrity. In this research article we also have discuss about the selection criteria of the celebrity.

INTRODUCTION Businesses have long sought to distract and attract the attention of potential customers that live in a world of ever-increasing commercial bombardment. Everyday consumers are exposed to thousands of voices and images in magazines, newspapers, and on billboards, websites, radio and television. Every brand attempts to steal at least a fraction of an unsuspecting person's time to inform him or her of the amazing and different attributes of the product at hand. Because of the constant media saturation that most people experience daily, they eventually become numb to the standard marketing techniques. The challenge of the marketer is to find a hook that will hold the subject's attention. Also 48

from a marketing communications (marcoms) perspective, it is vital that firms design strategies that help to underpin competitive differential advantage for the firm's product or services. Accordingly, marcom activities back-up other elements in the marketing mix such as designing, branding, packaging, pricing, and place decisions in order to attempt to create positive effects in the minds of the consumers. In helping to achieve this, use of celebrity endorsers is a widely used marcom strategy. Companies invest large sums of money to align their brands and themselves with endorsers. Such endorsers are seen as dynamic with both attractive and likeable qualities (Atkin and Block, 1983), and companies


plan that these qualities are transferred to products via marcom activities (Langmeyer & Walker, 1991a, McCracken, 1989). Furthermore, because of their fame, celebrities serve not only to create and maintain attention but also to achieve high recall rates for marcom messages in today's highly cluttered environments (Croft et al, 1996, Friedman and Friedman, 1979).

Woods was one of the parts of an entire branding process that Nike has been practicing consistently. Contrary to this, most of the brands in Asia that have used celebrity endorsements have used it as the main brand building tool. Before any brand signs on a celebrity, they should consider three main aspects. § Attractiveness of the celebrity:

CELEBRITY ENDORSEMENTS – A BRIEF INTRODUCTION Endorsement is a channel of brand communication in which a celebrity acts as the brand's spokesperson and certifies the brand's claim and position by extending his/her personality, popularity, stature in the society or expertise in the field to the brand. In a market with a very high proliferation of local, regional and international brands, celebrity endorsement was thought to provide a distinct differentiation. But over the years, many aspiring brands in Asia have jumped on to this celebrity endorsement bandwagon. Even though endorsements have taken on a quasi-industry stature, there is hardly any hugely successful collaboration as those of Nike's. There are many reasons for such a happening. The next section addresses this issue. ESSENTIALS OF CELEBRITY ENDORSEMENTS Even though to an observer it may seem that Nike's success is totally based on Tiger Wood's association with the brand, nothing can be far from the truth. As a brand, Nike has established a very strong brand identity and a brand personality over the years. What Nike did was to use celebrity endorsement as one of the main channels of communicating its brand to a highly focused set of customers. So, Nike's association with Tiger

This principle states that an attractive endorser will have a positive impact on the endorsement. The endorser should be attractive to the target audience in certain aspects like physical appearance, intellectual capabilities, athletic competence, and lifestyle. It has been proved that an endorser that appears attractive as defined above has a grater chance of enhancing the memory of the brand that he/she endorses. § Credibility of the celebrity: This principle states that for any brandcelebrity collaboration to be successful, the personal credibility of the celebrity is crucial. Credibility is defined here as the celebrities' perceived expertise and trustworthiness. As celebrity endorsements act as an external cue that enable consumers to sift through the tremendous brand clutter in the market, the credibility factor of the celebrity greatly influences the acceptance with consumers. § Meaning transfer between the celebrity and the brand: This principle states that the success of the brand-celebrity collaboration heavily depends on the compatibility between the brand and the celebrity in terms of identity, personality, positioning in the market vis-à-vis competitors, and lifestyle. When a brand signs on a celebrity, these are some of the compatibility factors that


49

have to exist for the brand to leverage the maximum from that collaboration.

Brand Ambassador for INX News and will also be seen hosting a show on News,

S P O RT C E L E B R I T I E S A S B R A N D ENDORSERS Celebrities in India starts since 1980s, India sport celebrity's gets success after the Asian games in 1982 and cricket world cup victory in 1983.now a days the demand for sport celebrities has increased for success and effect full advertising. If we just talk about the cricket so we can say that cricket is most popular games in India and people crazy about cricket. Some of the famous cricketers such as Sachin Tendulkar, Saurabh Ganguly, M S Dhoni, Yuvraj Singh, Kapil Dev, and Sunil Gawaskar are brand endorsing with multiple brands. And other game like tennis, football, chess, hockey etc. are also popular for brand endorsing and marketer also signing other Indian sport for brand endorsing their brands. Vishwanathan anand has been tied up with AMP Range of product as a Brand ambassador, Abhinav Bindra is brand ambassador for its consumer electronic business, Mahesh Bhupati and Leander peas are brand endorse for J Hampsted, similarly Bhaichung Bhutia is seen endorsers anmol Marie and he is also the brand ambassador for Nike India if we look back at 1982 se we can recall brands like Vimal, Thums Up, Gwalior and Dinesh using star appeal in the early days of mass advertising. There was a burst advertising, feature stars like Kapil Dev ( Palmolive shaving cream), Sunil Gawaskar( Dinesh Suiting, Thums Up), and Ravi Shastri (Vimal) if we talk abiut present , Ms Dhoni is brand ambassador for Aircel, Sachin Tendulkar, brand ambassador for Canon, Professional Management Group, has signed cricketer Virender Sehwag for a period of five years as a brand ambassador, Sourav Ganguly is chosen as the

MOVIES STAR CELEBRITIES AS BRAND ENDORSERS Now a day all the people have been surrounded by the magic of entertainment, they are crazy about it. In India most of movies star are famous, they are engaged with multiple brand of product as a brand endorse .at present the tradition of brand endorse has been reached on the top all the domestic and foreign company signing famous celebrity for their brand image . There are many movie stars whose are engaged with multiple brand of product as a brand ambassador, they attract the people for their own product. If we talk about some famous star such as Amir Khan, Sharukh Khan, Katrine Kaif, Kareena Kapoor, John Abhram, Amitabh Bachchan, Abhishek Bachchan, Heretic Roshan, Salman Khan, Aishwarya Rai, and so on are engaged with many company as a brand ambassador. Shahrukh Khan is brand ambassador for head & solders shampoo, Katrina Kaif is engaged with Dyna soap and Veet Hair remover cream, Kareena Kappor is brand ambassador for Boro Plus cream, John Abhram is brand ambassador for Wild Stone Body Spray, and so on. To maintain there brand image all the domestic and multinational company has been signed famous star celebrity as a brand endorse for there particular brand.

50

SELECTION OF BRAND ENDORSERS Selection of right and appropriate celebrity for brand endorse is very challenging work. The various challenge faced by the advertisers and also for there agency. Regarding the selection of advertising there should be following accepts §Celebrity should be fit with brand image


§Celebrity

should familiar among the target people §Celebrity should popular among the target people §Celebrity should physical attractive §Celebrity should match with product §Celebrity popularity. §Celebrity availability. §Whether celebrity is a brand user. §Celebrity profession. §Celebrity controversy risk. OBJECTIVE OF THE STUDY The objective of the study is as under § To determine the concept of sport and movie star celebrity in India § To determine the level of awareness about the sport and movie star celebrity as a brand endorse § To determine the popularity among all the celebrity such as sport and movie star § To determine the comprising between sport and movie star celebrity with the help of respondents §To provide guidelines for select best celebrity as a brand endorse “Q” SCORE TECHNIQUE The Q Score is a metric developed by Marketing Evaluations, Inc. that determines a "quotient" ("Q") factor or score through mail and online panelists who make up representative samples of the United States. The Q score identifies the familiarity of an athlete, celebrity, licensed property, TV show, or brand and measures the appeal of each among those persons familiar with each. Other popular synonyms include Q

rating, Q factor, or simply Q.Since 1963, Marketing Evaluations' Q Scores have provided clients with data to aid in their marketing, advertising, licensing, and media efforts. Q Scores are the industry standard for measuring familiarity and appeal of performers, broadcast and cable programs, sports and sports personalities, company and brand names, characters, as well as deceased performers. Based on its “one of my favorites” concept, Q Scores actually summarize the various perceptions and feelings that consumers have, into a single, but revealing, “likeability” measurement. Currently, there are eight Q Score services, including Performer Q, TVQ, Cable Q, Cartoon Q, Sports Q, Brand Attachment Q, Kids Product Q, and Dead Q. Marketing Evaluations claims that the Q Score is more valuable to marketers than other popularity measurements, such as the Nielsen ratings because Q Scores indicate not only how many people are aware of or watch a TV show but also how those people feel about the TV show. A well-liked television show, for example, may be worth more as a commercial venue to an advertiser than a higher-rated show that people don't like as much. High emotional bonding with a show means strong viewer involvement and audience attention, which are important indicators for the quality of the show's advertising environment. Viewers who regard the show as a "favorite" have higher awareness of the show's commercial content. Percentage of Familiarity Q Score = ---------------------------------- X 100 Percentage of Popularity METHODOLOGY Due to the constraint in obtaining


51

Table 1: Show Comparative Level of Awareness about the Sport and Movie Star Celebrity S.No

Sport celebrities

No of Respondents

%

Movies

No of Respondents

%

1

M S Dhoni

100

100

Amir Khan

100

100

2

Sachin Tendulkar

100

100

Sharukh Khan

100

100

3

Saurabh Ganguly

100

100

John Abrahim

100

100

4

Yuvraj Singh

100

100

Katrina Kiaf

100

100

5

Virendra Sehwag

100

100

Kareena Kapoor

100

100

6

Sania Mirza

100

100

Hritick Roshan

100

100

7

Sunil Chettri

100

100

Abhishek Bacchan

100

100

8

Vijendra Singh

100

100

Amitabh Bacchan

100

100

9

Leander Paes

100

100

Salman Khan

100

100

10

Viswanathan Anand

100

100

Yeswarya Rai

100

100

Table 2: Show Comparison of Popularity between Sport and Movie Star Celebrity S.No

Sport celebrities

No of Respondents

%

No of Respondents

%

1

M S Dhoni

21

21

Amir Khan

50

50

2

Sachin Tendulkar

52

52

Sharukh Khan

82

82

3

Saurabh Ganguly

55

55

John Abrahim

39

39

4

Yuvraj Singh

29

19

Katrina Kiaf

89

89

5

Virendra Sehwag

14

14

Kareena Kapoor

57

57

6

Sania Mirza

37

41

Hritick Roshan

41

41

7

Sunil Chettri

05

11.111 Abhishek Bacchan

49

49

8

Vijendra Singh

04

7.40 Amitabh Bacchan

78

78

9

Leander Paes

17

20.73 Salman Khan

72

72

10

Viswanathan Anand

25

28.084 Yeswarya Rai

65

65

necessary information about the population element this study utilized sampling technique according to the information gathering from the 52

Movies

people.simple size of the study 100 individual both male and female, data is to be collected with the help of personal interview


Table3: Show Comparative Familiarity, Popularity and Q Score of all Celebrities S.No

Familiarity score Familiarity Popularity score % /awareness

Celebrities

Popularity %

Q Score

1

M S Dhoni

100

100

21

21

21

2

Sachin Tendulkar

100

100

52

52

52

3

Saurabh Ganguly

100

100

55

55

55

4

Yuvraj Singh

100

100

29

29

29

5

Virendra Sehwag

100

100

14

14

14

6

Sania Mirza

90

90

37

37

45.55

7

Sunil Chettri

45

45

05

05

24.69

8

Vijendra Singh

54

54

04

04

13.70

9

Leander Paes

82

82

17

17

25.280

10 Viswanathan Anand

89

89

25

25

31.555

11 Amir Khan

100

100

50

50

50

12 Shahrukh Khan

100

100

82

82

82

13 John Abraham

100

100

39

39

39

14 Katrina Kiaf

100

100

89

89

89

15 Kareena Kapoor

100

100

57

57

57

16 Hritick Roshan

100

100

41

41

41

17 Abhishek Bachchan

100

100

49

49

49

18 Amitabh Bachchan

100

100

78

78

78

19 Salman Khan

100

100

72

72

72

20 Aishwarya Rai

100

100

65

65

65

DISCUSSION AND FINDINGS From table 1 it is clear that all the respondents are 100% aware of 10 movies star celebrities and 1 sport celebrity selected for the study. Table 2 shows that the entire 10 respondent is aware for the movies star celebrities than the sport celebrity. Finding regarding table 2 popularity of the movies star celebrity shows that among sport celebrity Katrina Kiaf is rated as the

most popular by 89% of respondent followed by Shahrukh Khan 82%, Amitabh Bacchan 82% and Salman Khan78%. Research finding show that among sport celebrity Saurabh Ganguly gets maximum popularity with 54% of respondent followed by Sachin Tendulkar by 52% and Sania Mirza 39% from the table 3 and 4 this is clear that Katrina kaif gets highest Q score and shahrukh is next . if we talk about sport celebrity so this is also


53

Table4: Show Ranking between Celebrities S.No

Sport Celebrities

Rank

Rank

1

Saurabh Ganguly

1

Katrina Kaif

1

2

Sachin Tendulkar

2

Shahrukh Khan

2

3

Sania Mirza

3

Amitabh Bachchan

3

4

Viswanathan Anand

4

Salman Khan

4

5

Yuvraj Singh

5

Aishwarya Rai

5

6

Leander Paes

6

Kareena Kapoor

6

7

Sunil Chettri

7

Amir Khan

7

8

M.S Dhoni

8

Abhishek Bachchan

8

9

Virendre Sehwag

9

Hrithik Roshan

9

10

Vijendra Singh

10

John Abraham

10

clear that saurabh ganguly and sachin tendulkar is most popular then other and sania mirza is popular in other sport. OUTCOME OF Q SCORE The familiarity score indicate the percentage of people who have heard the sport and movie star celebrity “for there favorite” score is an absolute measure of the appeal or popularity of the sport and movies star celebrity with a highest Q score gets the top rank by other (show in table 5) CONCLUSION The finding of the study show that awareness among all the respondent all movies star celebrity gets 100% awareness and only cricket celebrity also gets 100% awareness then other sport celebrity. When we talk about popularity criteria so we find that only Indian movies star celebrity are maximum popular then Indian sport celebrity although Katrina Kaif is rated as the most 54

Movies

popular and sharukh khan is on second position. Similarly if we just discus about Indian sport celebrity so this is clear that only Indian cricket celebrity is popular then other sport celebrity although Saurabh Ganguly gets maximum popularity and Sachin Tendulkar is followed him .the practical implication of the research is that marketer can identify the way to select best popular celebrity for there brand. In terms of popularity measure among the respondents not just movies star celebrity but Indian sport celebrity also have the potential to be brand endorsers. In today's competitive market all the home and multinational companies follow innovative concept and technique for selection the appropriate celebrity for their brand endorsement. REFERENCES Agrawal, J., & Kamakura, W.A. (1995), “The economic worth of celebrity endorsers: An event of study analysis”, Journal of Marketing, 59(3), pp. 56-62.


Table 5: Show Overall Ranking between 20 Celebrities based on their Q Score S.No 1

Celebrities Katrina Kaif

Q Score 89

Rank 1

2

Shahrukh Khan

82

2

3

Amitabh Bachchan

78

3

4

Salman Khan

72

4

5

Aishwarya Rai

65

5

6

Kareena Kapoor

57

6

7

Saurabh Ganguly

55

7

8

Sachain Tendulkar

52

8

9

Amir Khan

50

9

10

Abhishek Bachchan

49

10

11

Sania Mirza

45.55

11

12

Hrithik Roshan

41

12

13

John Abraham

39

13

14

Viswanathan Anand

31.55

14

15

Yuvraj Singh

29

15

16

Leander paes

25.20

16

17

Sunil Chettri

24.69

17

18

M.S Dhoni

21

18

19

Virendra Sehwag

14

19

20

Vijendra Singh

13.75

20

Barak, B. (1998), “Inner-ages of middle-aged prime-lifers”, International Journal of Ageing and Human Development, 46(3), pp. 189-228. Chan, K., & Zhang, C. (2007), “Living in a celebrity-mediated social world: The Chinese experience”, Young Consumers, 8(2), pp. 139-152. Cheng, H. (1994), “Reflections of cultural values: A content analysis of Chinese magazine

advertisements from 1982 and 1992”, International Journal of Advertising, 13(2), pp. 167-183. Cheng, H., & Schweitzer, J.C. (1996), “Cultural values reflected in Chinese and U.S. television commercials”, Journal of Advertising Research, 36(3), pp. 27-45. Erdogan, B.Z. (1999), “Celebrity endorsement: A literature review”, Journal of Marketing Management, 15, pp. 291-314.


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Erdogan, B.Z., & Baker, M.J. (2000), “Towards a practitioner-based model of selecting celebrity endorsers”, International Journal of Advertising, 19(1), pp. 25-43. Freiden, J.B. (1984), “Advertising spokesperson effects: An examination of endorser type and gender on two audiences”, Journal of Advertising Research, 24(5), pp. 33-41. Friedman et. al., (1978), "Correlates Trustworthiness for Celebrities" Journal of Academy of Marketing Science; 6, pp. 291-299. Friedman, H., & Friedman, L. (1979), “Endorser effectiveness by product type”, Journal of Advertising Research, 19(5), pp. 63-71. Friedman, H., Termini, S., & Washington, R. (1976), “The effectiveness of advertisements utilizing four types of endorsers”, Journal of Advertising, 5(3), pp. 22-24. Furnham, A., Babitzkow, M., & Uguccioni, S. (2000), “Gender stereotyping in television advertisements: A study of French and Danish television”, Genetic, Social, and General Psychology Monographs, 126, pp. 79-104. Gilly, M.C. (1988), “Sex roles in advertising: A comparison of television advertisements in Australia, Mexico, and the United States”, Journal of Marketing, 52(2), pp. 75-85. Goldsmith, R.E., Lafferty, B.A., & Newell, S.J. (2000), “The impact of corporate credibility and celebrity credibility on consumer reaction to advertisements and brands”, Journal of Advertising, 24(3), pp. 43-54.

56

Houston, M.J., & Rothschild, M.L. (1977), “A paradigm for research on consumer involvement”, Working Paper 11-77-46. Kahle & Homer (1985), "Physical Attractiveness of the Celebrity Endorser: A Social Adoption Perspective" Journal of Consumer Research Vol. 11, No. 4, pp. 954-961. Michel J. Baker Gilbert A.Churchill (1977), "The Impact of physically Attractive Models on Advertising Evaluations" Journal of Marketing Research, Vol. XIV, pp. 53855. Neto, F., & Pinto, I. (1998), “Gender stereotypes in Portuguese television advertisements”, Sex Roles, 39(1/2), pp. 153-164 Ohanian, R. (1990), “Construction and validation of a scale to measure celebrity endorsers' perceived expertise, trustworthiness and attractiveness”, Journal of Advertising, 19(3), pp. 39-52. Petroshius and Crocker (1989), "An Empirical Analysis of Spokesperson Characteristics on Advertisement and Product Evaluations" Journal of the Academy of Marketing Science, Vol. 17, No. 3, pp. 217-225. SubhadipRoy "An Exploratory Study in Celebrity Endorsements" Journal of Creative Communications, Vol.1, pp 139-153.



A STUDY ON IMPACT OF IMPROVED WORKFORCE PRACTICES ON EMPLOYEE RETENTION IN PCMM C E R T I F I E D S O F T WA R E C O M PA N I E S I N BANGALORE S. Deepalakshmi Faculty, Department of Management Studies, Dayanandasagar College of Engineering, Bangalore

Dr. Lakshmi Jagannathan Professor & Head, Department of Management Studies, Dayanandasagar College of Engineering, Bangalore

ABSTRACT In this research work, the impact of Workforce practices on employee retention was analyzed in PCMM certified software companies in Bangalore. . It is a descriptive research process. The method of data collection was communication study. In this method, collected data resulted from self administered questionnaires sent through e-mail. Questions in the questionnaire are structured, constructed using 5 point measurement (Likert) scale. Employees of Software companies (which are PCMM certified) were chosen for data collection as the target population. Convenience sampling method was used. 120 questionnaires were sent. In that only 102 questionnaires were returned. Response rate is 85%. From this research nine factors which are the main contributors of Employee retention were identified. Using the regression analysis, it was revealed that among 9 factors, 6 factors were identified as major contributors. Research concluded that the software companies can reduce the turnover by improving their workforce practices by implementing PCMM framework in their organizations. INTRODUCTION Management makes work productive and the worker achieving. One true and most important resource in the organization is the Human resource. Managers must work effectively with their subordinates to achieve maximum performance” – Peter. F. Drucker (Father of Modern Management). In today's business world, every organization is competing in the global market where quality and timely delivery of the product/ service are the deciding factors of its future. Especially in Knowledge based Industry, talented workforce is the critical role player of quality. By understanding this, nowadays, organizations are concentrating

more on people management. Even though supply is more, demand for the Software employees are also more due to the heavy attrition rate prevailing in Software industry. Organizations are spending much on recruiting, selecting, training, retaining the talented workforce in order to compete in the quality and innovative market where customers' expectations are ever changing. Total Quality Management practices are used not only to improve the quality of the product/service, but also to improve the employee involvement, empowerment so that the organization can retain the talent pool.


57

LITERATURE REVIEW TQM is a buzz word to guarantee an organization's survival in a competitive world. TQM began to be introduced in the US around 1980, primarily in response to severe competitive challenges from Japanese companies. The recognition of TQM as a competitive advantage is widespread around the world, especially in Western countries, and today very few companies can afford to ignore the term TQM (Dean and Bowen, 1994). TQM is defined as both a philosophy and a set of guiding principles that represent the basis of a continuously improving organization. It is the application of quantitative methods and human resources to improve all the processes within an organization and exceed customer needs now and in the future. Besterfield enumerated in his book ” Total Quality management”that “TQM integrates fundamental management techniques, existing improvement efforts, and technical tools under a disciplined approach". Software plays an increasingly important role in today's society. 50 years ago, software was used only in exclusive calculation machines, but now most consumer electronics as well as safety critical equipment contains software (Runeson, Peter Isacsson, 1998).The ultimate goal of Software engineering is to develop a high quality product in time and at reasonable costs. But since the time software is developed a phenomenon called “software crisis” exists subsuming wrong schedules and cost estimates , low productivity of people as well as low product quality(Dieter,1996) . TQM is an enhancement to the traditional way of doing business. It is the art of managing the whole to achieve excellence The terminology of TQM includes expressions such as “continuous improvement”, "customer focus“, “empowerment 58

of the worker” and “supportive business culture” (Dellana and Wiebe, 1992). Many of the Software companies (which are already certified CMM companies) have found that their continued improvement requires revolution in the way they manage, develop, and use their people for developing and maintaining software and information systems. Because, quality of workforce practices has direct impact on the retention of employees and turnover rate (Ahmed, 2008). PROBLEM OF THE STUDY The aim of the study was to investigate the impact of improved workforce practices on employee retention in PCMM Certified Software companies in Bangalore. The study on Software professionals was particularly selected because a high - attrition rate prevails in the software market worldwide in general (Rathi, 2005). RESEARCH METHODOLOGY Descriptive Research design was used for the study on impact of workforce practices on employee retention in PCCM Certified software companies in Bangalore. Descriptive research design is a scientific method which involves observing and describing the behavior of a subject without influencing it in any way. In this research, mailed Questionnaire method is used to collect the primary data. As per this method a structured Questionnaire (a list of questions relating to workforce practices, Employee Satisfaction in terms of Job, Treatment, appreciation and overall, Willingness to leave, Demographic details) was prepared and space for the answers to be filled by the software professionals working in PCMM certified Companies in Bangalore was given. The


Figure :1 Willing to Leave 35.29 64.71

Yes No

Table :1Willingness to leave Vs. Gender Cross tabulation Gender Male Female

Count

Willingness to leave

Total

Yes

27

9

36

No

46

20

66

Total

73

29

102

Table :2 Correlation Analysis (Gender Vs. Willingness to leave) Value

Asymp. Std. a Error

b

Approx. T

Approx. Sig. c

Interval by Interval Pearson's R

.056

.097

.563

.575

Ordinal by Ordinal Spearman Correlation

.056

.097

.563

.575

N of Valid Cases

102

questionnaire was mailed to the respondents with a request for quick response within the specified time. Secondary data was collected from research papers published in journals, books (references added in the Bibliography). Convenience sampling plan is selected for this research. Convenience sampling is a non probability sampling method in which researcher will decide the choice of sampling units based on their

c

convenience. In this study, Employees working in PCMM Certified Software Companies in Bangalore is the population. 120 questionnaires were sent. In that 102 filled in questionnaires were returned back. The response rate was 85%. DATA ANALYSIS AND INTERPRETATION From the analysis (Refer Figure 1), it was understood that 64.7% of the employees do not


59

Table : 3 Total Variance Explained

Component

% of Variance

Total

Rotation Sums of Squared Loadings

Extraction Sums of Squared Loadings

Initial Eigenvalues Cumulative %

% of Cumulative Total Variance Total %

1

6.724

20.375

20.375 6.724

20.375

20.375

4.625

14.015

14.015

2

4.810

14.577

34.952 4.810

14.577

34.952

3.872

11.732

25.747

3

4.083

12.372

47.324 4.083

12.372

47.324

3.824

11.587

37.335

4

3.691

11.186

58.510 3.691

11.186

58.510

3.454

10.466

47.801

5

3.329

10.087

68.597 3.329

10.087

68.597

2.970

9.001

56.802

6

2.247

6.810

75.407 2.247

6.810

75.407

2.774

8.407

65.209

7

1.715

5.198

80.605 1.715

5.198

80.605

2.757

8.354

73.563

8

1.247

3.778

84.383 1.247

3.778

84.383

2.411

7.306

80.869

9

1.158

3.509

87.892 1.158

3.509

87.892

2.318

7.023

87.892

10

.769

2.330

90.221

11

.637

1.930

92.151

12

.499

1.512

93.663

13

.422

1.278

94.941

14

.400

1.213

96.154

15

.298

.903

97.058

16

.272

.823

97.881

17

.233

.706

98.587

18

.156

.472

99.059

19

.111

.337

99.396

20

.073

.222

99.617

21

.055

.167

99.785

22

.036

.109

99.893

23

.025

.075

99.969

24

.008

.025

99.994

25

.002

.006

100.000

26

1.334E-15

4.044E-15

100.000

27

9.658E-16

2.927E-15

100.000

28

4.595E-16

1.392E-15

100.000

29

3.081E-16

9.335E-16

100.000

30

5.803E-17

1.758E-16

100.000

31

-2.281E-16 -6.912E-16

100.000

32

-9.903E-16 -3.001E-15

100.000

33

-3.912E-15 -1.185E-14

100.000

Extraction Method: Principal Component Analysis.

60

% of Cumulative Variance %


Table: 4 Rotated Component Matrix

1 Proper Reaction to customer demand

.839

Part time work

-.831

Knowledge exchange within orgn/.

.791

Availability of Dir. Supervisor

.757

Orgn. Informs People

.686

Good salary compared to other orgns/.

-.575

2

3

7

8

.834

Direct work envt. obj

.754

feedback on customer satisfaction

.711

Supervisor's stimuli in people's Skill development

-.601

Importance to customer service

.597

.550

Office/group meetings info/.

.826

Appreciation of Knowledge exchange within orgn/.

.802

Cooperation b/w offices/groups/units

.753

work from home/teleworking

.632 .585

-.541

.619

E-mail Information

.863

Understanding customer demands/expectations

-.658

Dept. Objectives

.637

life balance developments info/.

.626

organization objectives

.886

info/.abt plans of var. depts

.694

info/. abt results of var. depts

.662

Cooperation b/w various business units

.874

Cooperation b/w various country orgns/.

.865

Other sources of Info/.

.627

child care facilities

.778

Dir. Supervisor Effective Comm/.

-.740

Stimulation of Cooperation within depts

.818

Better secondary work conditions

.663 .587

Parental leave

9

.507

Appreciation of Cooperation within depts

cooperation b/w operations and support groups

Component 4 5 6

.623

Not getting underpaid for the work done

.880

not underpaid compared to colleagues

.778

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 14 iterations. Global Management Review | Volume 6 | Issue 2 | February 2012

61

Figure:2 Workforce Practices that Contributing employee Retention

Communication Salary

Customer Focus

Secondary working conditions

Employee Satisfaction

coordination

Childcare

Information System

Cooperation

Organisation performance

Table :5 Model Summary Std. Adjusted R R Error of the R Square Square Estimate Square Change

Model

R

1

.945

a

.894

.884

.164

.894

Change Statistics F Change

df1

df2

Sig. F Change

86.144

9

92

.000

a. Predictors: (Constant), F9, F8, F7, F6, F5, F4, F3, F2, F1

62


Table :6 ANOVA Model 1

Regression Residual Total

Sum of Squares

df

20.823 2.471 23.294

9 92 101

Mean Square 2.314 .027

F

Sig.

86.144

.000a

a. Predictors: (Constant), F9, F8, F7, F6, F5, F4, F3, F2, F1

Table :7 Regression Coefficients Unstandardized Coefficients

t

Sig.

B

Std. Error

(Constant)

1.647

.016

-.034

101.501

.000

F1

-.016

.016

.117

-.992

.324

F2

.056

.016

.471

3.443

.001

F3

.226

.016

-.334

13.871

.000

F4

-.161

.016

-.054

-9.848

.000

F5

-.026

.016

.212

-1.604

.112

F6

.102

.016

.688

6.230

.000

F7

.330

.016

.030

20.248

.000

F8

.014

.016

.155

.877

.383

F9

.075

.016

4.577

.000

Model 1

Standardized Coefficients Beta

a. Dependent Variable: Not willing to leave (Employee Retention)

have serious intention to leave. But 35.3% of the employees answered that they are willing to leave immediately. From the research it was tried to find whether any relationship between employees' gender and their willingness to leave. For this, Hypothesis 1 was set as H1: There is no relationship between Gender and Employee's willingness to leave the organization

From the above table 2, from the last column, the p value is = .575 which is very high compared to significance level of 0.05 .So, this relationship is not statistically significant at .05 level. Therefore there is no sufficient evidence to reject null hypothesis. Therefore H0 was accepted. From the cross tabulation (Table no.1), it was understood that 66 out of 102 employees answered that they are not willing to leave the organization.


63

Out of 29 female employees 20 employees are not willing to leave the organization. Out of 73 male employees 46 employees are not willing to leave the organization. From the correlation analysis (Table no. 2), it was understood that there is no significant relationship between gender of the employee and their willingness to leave the organization. On the collected data, factor analysis was done to identify the workforce practices which have impact on employee retention. According to literature review, Organisations that use PCMM have experienced decreases in turnover to 5%to 10% below the industry average. (Curtis,et.al 2000).And Huslid (1995) identified that one standard deviation increase in use of such practices is associated with… a 7.05 percent decrease in turnover ( i.e. employee departure rate). From the factor analysis, 9 factors (Workforce practices) which have impact on Employee retention were derived. The output of the factor extraction using SPSS software is shown in Table no. 3 The output of the rotated factor matrix using SPSS software is shown in Table no.4 The first step in interpreting the output was to look at all factors extracted and their Eigen values and the cumulative percentage of variance. In Table No.3, it can be seen from the cumulative % column that 9 factors are extracted together to form 87.892% of Total variance Explained. This is good as it is able to economize in the number of variables from 33 to 9 factors, but we lost 12% of information content. This interpretation is based on the Eigen values. All those factors whose Eigen values are close to 1 are considered. From the Table no.4, Derived factors are as follows: Communication, customer focus, Coordination, Information system, Organization performance, Cooperation, 64

Childcare facilities, Secondary working conditions, salary. Then regression analysis was done to identify the workforce practice(s) which has(have) more impact on employee retention. Through the regression analysis (from Table no: 7) five workforce practices are identified as major causes for employee retention. Childcare facilities, Coordination, Cooperation, customer focus and salary have more impact on retention of the employees compared to other workforce practices. From the regression Coefficients (b values) values, ranking was given to the workforce practices which have more impact on employee retention. in that childcare facilities (F7) got the rank 1, since b value is 0.688. Similarly ranking 2,3,4,5 were given to Coordination (F3), Cooperation (F6), Salary (F9), and Customer Focus (F2) respectively. CONCLUSION From this research, it was concluded that, in PCMM Certified companies TQM workforce practices such as Coordination, Cooperation, Compensation (in terms of Salary), and customer focus are identified as major contributors in employee retention. Organisations can communicate better about the plans and results achieved by various departments which will increase coordination and cooperation by that employee's perception about the teamwork can be improved. From the research it came to know that nearly half the sample size only satisfied about the cooperation between business units, Cooperation between country organizations. Therefore Organisation can put effort to improve their information system in such a way, by that coordination and cooperation among employees will increase which will lead to reap the benefits of


team work which is the core activity of TQM. Finally, as said in the literature (Curtis,et.al;2009), from this research also it was understood that the implementation of the People Capability Maturity Model framework, definitely will improve the workforce practices which in turn has a significant impact on Employee retention. REFERENCES Ahmed M. Gamal,2008, “A Blueprint for Building IT Workforce Empowerment Program Based PCMM Level 3”, 6th International conference on Information and C o m m u n i c a t i o n s Te c h n o l o g y (ICICT,2008), IEEE CCC Code: 978-14244-2929-5/08 Bill Curtis, William E. Hefley, Sally A. Miller “The People Capability Maturity Model: Guidelines for improving the workforce”, Pearson Education, 2009 edition Curtis, B., Hefley, W.E., and Miller, S. (2007), “The People Capability Maturity Model: Guidelines for Improving the Workforce”. ISBN 81-317-0798-9). Delhi, India: Dorling Kindersley (India) Pvt. Ltd. Dale H. Besterfield,et.al, “Total Quality Management”, Pearson Education, Revised 3rd Edition

Dean, J.W., Bowen, D.E., 1994. “Management theory and total quality: improving research and practice through theory development.” Academy of Management Review 19 (3), 392–418. Dellana and Wiebe, 1992,. 'Managing in a Global Environment' Engineering Management Conference, 1992,IEEE International, Oct 1992, Pg no 327-331, ISBN: 0-78030854-9 Huselid,M.A. “The Impact of Human Resource Management Practices on Turnover, Productivity, and corporate Financial Performance”, Academy of Management Journal 38 (1995):635-672 Per Runeson' and Peter Isacsson,1998 , “Software Quality Assurance - Concepts and Misconceptions”, Euro micro Conference Proceedings.24th, Vol.2, 853-859,1089-6503/98, IEEE Rathi, D (2005, May 15) “The dragon is rising” Retrieved 22nd September, 2005, from http://www.financialexpress.com

Author can be reached at: [email protected] [email protected]


65

A STUDY ON SALARIED CLASS INVESTORS’ ATTITUDE TOWARDS TAX PLANNING IN VELLORE DISTRICT Dr. T. Thirupathi Assistant Professor of Commerce, Government Arts College for Men (Autonomous), Salem.

ABSTRACT Tax planning is the arrangement of one's tax affairs so that without violating any legal provision, full advantage is taken of all reliefs and benefits under the income tax act. Attitude is the state of mind. One's attitude certainly has a bearing on all the decisions taken by an individual. This study focuses on the attitude of the salaried class invetors on tax planning. The objective of the study is to analyze the attitude of the investors towards tax planning. Both primary and secondary data were used for the study. The data collected in the consumer survey through a structured questionnaire constitute primary data. The sample size of the tax payers was 750. Convenience sampling method was followed in selecting these salaried class investors. The data collected from these 750 individual salaried class investors were tabulated and analysed using percentage analysis, mean and standard deviation analysis, Chi-square test, T-test, Analysis of Variance (ANOVA - F Test). This study analyses how different factors like sex, age, marital status, educational qualification, nature of work, type of employment, number of earning members in the family, number of dependents, monthly income, and monthly investment affect their attitude towards tax planning. Gender, and level of education have an effect on the attitude of the investors towards tax planning whereas age, marital status, nature of work, type of employment, number of earning members in the family, number of dependents, monthly income, and monthly investment do not have any impact on the attitude of the investors towards tax planning. This analysis would help the government to work out various schemes, based on the findings of this study, to mobilize finance from the salaried class investors. The study assumes significance in the light of the fact that while majority of the studies concentrate on behaviour towards equity share process, studies on the investors' attitude towards tax planning are small in number. INTRODUCTION Tax planning is the arrangement of one's tax affairs in such a way that without violating any legal provision, full advantage is taken of all exemptions, deductions, concessions, rebates, allowances and other reliefs or benefits permitted under the income tax act so that the burden of taxation on assessee is eliminated or reduced. 66

Attitude means a state of mind or feeling with regard to some matter. Investment is a strategy for earning an additional income and this strategy is adapted by the people who have savings. Investment is the employment of funds with the aim of achieving additional income or growth in value. Investment is the allocation of monetary resources to assets that are expected to yield some


gain or positive return over a given period of time. These assets may range from safe investments to risky investments. Savings are excess of income over expenditure for any economic unit. Thus, S=Y-E where S is savings, Y is income and E is expenditure. Excess funds or surplus in profits or capital gains are also available for investment. Thus, S=W2-W1 where W1 is wealth in period 1 and W2 is wealth in period 2 and the difference between them is capital gains or losses. Investment is also made by many companies and individuals by borrowing from others. Thus the corporate sectors and government sectors are always net borrowers, as they invest more than their savings. Thus, S=B-L where B is borrowings and L is lending. Investments have become a basic necessity for everyone. Each investor has different objectives that need to be met depending on age, income, and attitude towards risk. Investors have to work out their investment profile to determine which investments are right for them and should consider important factors such as personal status, plans, and constraints. To achieve progress and maintain the status quo in the ever-changing scenario of the present day world, it is important to raise the income level adapting some mode of saving or the other. But all savings are not invested. It is true that saving and investment of money has direct bearing on one's behaviour and attitude. Proper tax planning will help improve the efficiency of investments. While selecting an investment option, care has to be taken that investment should not result in increase in taxable income.

This study is concerned with the evaluation of individual finance that is savings and investment practices of salaried class investors. The study will help the individuals concerned, i.e. salaried class, to plan savings and investments towards maximizing the returns. The in-depth analysis of the behavioural pattern of the investors would help the government to work out various schemes to mobilize finance from the salaried class investors by bringing out tax saving schemes, retirement benefit schemes, etc. The study assumes significance in the light of the fact that very few research works are available on investment decision process of investors in India. While majority of the studies concentrate on behaviour towards equity share process, studies on the investors' attributes are small in number. REVIEW OF LITERATURE Ledereich and Siegel (1988) emphasized the role of factors like age, health, martial status, family status; objectives risk tolerance, investment preferences, liquidity, employment stability, and tax rate in personal financial planning. Radha V in her study titled 'A study of Investment behaviour of Investors of Corporate Securities' (1995) has examined the investment plan of corporate security investors in TamilNadu. An All-India Survey, titled “Household Investors' Problems, needs and Attitudes”, conducted by the Society for Capital Market Research and Development revealed the fact that majority of the retail investors lost confidence in various agencies like SEBI, credit rating agencies etc. A survey was conducted by the Ananda


67

Vikatan (a Tamil weekly magazine) during January 1999. Public were interviewed on the aspect of savings and their saving habits. Gnana Desigan C in his study titled “Investors' Perception Towards Equity Share Investment – An Empirical Study” (2003) has examined the investment pattern of the equity investors and the problems of equity share investors in primary and secondary market. The analysis revealed the attitude and perception of the investors towards equity share investment. The attributes and attitudes of individual investors have been studied by Ronald Lease et. al. (1974). Krishnamoorthi.C in his study titled, “Investment Patterns and Tax Planning in the Nilgiri District”, (2008) has examined the attitude of the investors in Nilgiri District. The Analysis reveals that there is no significant relationship between Sex, Age, Maritial Status, Educational Qualification, Monthly Income and Monthly Investment with Attitude on Tax Planning. OBJECTIVES OF THE STUDY The study is undertaken with the following objectives: §To outline the conceptual background with focus on investment and tax planning. §To analyze the attitude of the salaried class investors towards tax planning §To offer suggestions for educating individual investors on balanced investment patterns. HYPOTHESIS In line with the objectives of the study, 68

following hypothesis has been formulated and tested. There is no significant relationship between demographic factors and the attitude of the salaried class investors towards tax planning. METHODOLOGY The sources of data were primary as well as secondary. The data collected in the consumer survey constitute primary data. A structured questionnaire was tested with 50 individual tax payers and the exercise ensured the adequacy of the questions in the questionnaire. The information gathered from books, journals, magazines, reports, and dailies was the secondary data. The data collected from both these sources were scrutinized, edited, and tabulated. The sample size of tax payers was 750. Period of the Study The study covers a period of 6 Months from 1st July 2010 to 31st December 2010. The data collected, and opinions and expectations revealed pertain to the same period. Area of the Study Vellore District is one of the 32 districts in the Tamilnadu state of India. Vellore City is the headquarters of this district. This district has a population of 34,77,317 as on 2001 consisting 5,72,481 individual tax payers. Tools of Analysis The data collected were analyzed by preparing suitable tables. The information collected with the help of questionnaire were tabulated and analyzed by using various statistical measures like percentage analysis, mean and standard deviation analysis, Chi-square test, Ttest, Analysis of Variance (ANOVA - F Test).


ANALYSIS AND INTERPRETATION OF DATA An attempt is made to analyse the attitude of the salaried class investors towards tax planning. The data obtained were analysed and interpreted as presented below

Attitude of Investors towards Tax Planning To measure the attitude of the investors towards tax planning, Rensis Likert's summated a scaling technique. Based on this technique, the various responses are assigned scale values. In this study 5,4,3,2, and 1 scale values were used to

Neutral

Disagreed

Strongly Disagreed

Total

Tax saving can add to your income

No. %

701 93.5

20 2.7

17 2.3

2 0.2

10 1.3

750 100

Money saved is money lost unless paid tax

No. %

461 61.5

135 18.0

75 10.0

40 5.3

39 5.2

750 100

Your tax rate affects your investment return

No. %

728 97.0

5 0.7

6 0.8

6 0.8

5 0.7

750 100

Tax planning is an important part of investment planning

No. %

750 100.0

– --

– --

– --

– --

750 100

Save tax and earn more; be a prudent investor

No. %

729 97.2

6 0.8

10 1.3

4 0.5

1 0.2

750 100

Save income tax and cover your medical expenses with a smile

No. %

139 18.5

235 31.3

190 25.3

110 14.7

77 10.2

750 100

Looking for protection and Growth

No. %

77 10.2

201 26.8

235 31.3

148 19.7

90 12.0

750 100

Tax savings is a key part of financial planning

No. %

548 73.0

47 6.2

64 8.5

49 6.5

43 5.8

750 100

Pay less tax; earn more.

No. %

617 82.2

28 3.8

51 6.8

32 4.2

17 2.3

750 100

Tax should be simple and easy to understand

No. %

750 100.0

– --

– --

– --

– --

750 100

Tax system affects the production No. & distribution of wealth seriously %

594 79.2

36 4.8

55 7.3

45 6.0

20 2.7

750 100

The hardest thing in the world to understand is income tax

750 100.0

– –

– –

– –

– –

750 100

Strongly Agreed

Agreed

Table 1: Attitude towards Tax Planning

No. %


69

measure investors' attitude on tax planning. The rating 5 or 4o r 3 or 2 or 1 indicates whether the statement is strongly agreed or agreed or neutral or disagreed or strongly disagreed respectively. A total score for each respondent from all the 12 statements was calculated using the above scoring procedure. The twelve statements and their scores are given in Table 1. The scores of 750 sample investors were calculated. An individual's is the mere summation of the scores secured from the 12 statements. The scores of the respondents range from 12 to 60. The average score is 36. The respondents were grouped into two on the basis of average score, in the first group, those who have scored above 36 and in the second group, and those who have score below 36. Further, for the purpose of in depth analysis, the respondents are grouped into three groups, viz., Low level of attitude towards tax planning, Moderate level of attitude towards tax planning, and High level of attitude towards tax planning. Those who score between 112 and 33 are low, those who score between 34 and 36 are moderate, and those who score above 36 are high. The data collected are presented in Table 2.

70

The above table reveals that out of the 750 sample investors, 21.8 percent have low level of attitude, 45.2 percent have moderate level of attitude, and 33 percent have high level of attitude towards tax planning. There are several factors that determine the attitude of the sample investors and these factors are analyzed here. The mean and standard deviation of the sample investors' attitude towards tax planning of the investors are given in the following table. Sex of the Investors and Investors' Attitude towards Tax Planning It is observed that among the male sample investors, 23.3 percent have low level of attitude towards tax planning, 42.9 percent have moderate level of attitude towards tax planning, 33.8 percent have high level of attitude towards tax planning, whereas 18.4 percent of the female sample investors have low level, 51 percent have moderate level, 30.6 percent have high level of attitude towards tax planning. Hypothesis: There is no significant association between sex and investors' attitude towards tax planning.


Result: Chi-square test was applied to find out whether there is any significant association between sex and investors' attitude towards tax planning. The calculated value of chi-square is 2.885 which is less than the table value of 5.99 at 5% level of significance. Since the calculated value is less than the table value, it is inferred that there is no significant association between sex and investors' attitude towards tax planning. Hence, the hypothesis is accepted. Hypothesis: There is no significant difference between the male and female investors in their average level of attitude towards tax planning. Result: The t-test was applied to find out whether there is significant difference between the male and female in their average level of attitude towards tax planning. The calculated t-test value is 0.896 which is less than the table value of 1.964 at 5% level of significance. Since the calculated value is less than the table value, it is inferred that there is no significant difference between the male and female investors in their average attitude scores towards tax planning. Hence, the hypothesis is accepted. Age and Investors' Attitude towards Tax Planning To know the relationship between the age

of the investors and their levels of attitude on tax planning an analysis was made and the results are presented in the following table. From the data collected, it is observed that among the sample investors in the age group 21-30 years, 17.6 percent have low, 51.5 percent have moderate, and 30.9 percent have high levels of attitude towards tax planning. Among the sample investors in the age group of 31-40 years, 17.8 percent have low, 48 percent have moderate, and 34.2 percent have high levels of attitude towards tax planning. Among the sample in the age group of 41-50 years, 25 percent have low, 43.5 percent have moderate, and 31.5 percent have high levels of attitude. Among the sample investors in the age group of 51-60 years, 24.1 percent have low, 40.2 percent have moderate, and 35.7 percent have high levels of attitude towards tax planning. Hypothesis: There is no significant association between the age of the investors and investors' attitude towards tax planning. Result: Chi-square test was applied to find out whether there is any significant association between the age of the investors and investors' attitude towards tax planning. The calculated value of chi-square is 4.794 which is less than the table value of 12.6 at 5% level of significance. Since the calculated value is less than the table value, it is

Table 3: Association between Age and Investors' Attitude towards Tax Planning Sum of Square Between Groups

12.936

d.f

Mean Squre

3

4.312 10.596

Within Groups

7915.212

747

Total

7928.148

750


F-Value

0.407

71

inferred that there is no significant association between the age of the investors and investors' attitude towards tax planning. Hence, the hypothesis is accepted. Hypothesis: There is no significant difference among the groups of investors based on their age in their average attitude score towards tax planning. Result: The ANOVA test was applied to find out whether there is any significant difference among the groups of the investors in their average attitude score towards tax planning. The ANOVA result (Table 3) shows that the calculated F-ratio value is 0.407 which is less than the table value of 2.6049 at 5% level of significance. Since the calculated value is less than the table value, it is inferred that there is no significant difference among the age groups in their average attitude scores towards tax planning. Hence, the hypothesis is accepted. Marital Status and Investors' Attitude towards Tax Planning In order to know whether there is any significant association between marital status and the investors' attitude towards tax planning an analysis was made and the details are presented in the following paragraphs. Among the married sample investors, 23 percent have low, 44 percent have moderate, and 33 percent have high levels of attitude towards tax planning. Among the unmarried sample investors, 17.5 percent have low, 50.0 percent have moderate, and 32.5 percent have high levels of attitude towards tax planning. Hypothesis: There is no significant association between the marital status of the investors and investors' attitude towards tax planning. Result: Chi-square test was applied to find out 72

whether there is any significant association between the marital status of the investors and investors' attitude towards tax planning. The calculated value of chi-square is 1.278 which is less than the table value of 5.99 at 5% level of significance. Since the calculated value is less than the table value, it is inferred that there is no significant association between the marital status of the investors and investors' attitude towards tax planning. Hence, the hypothesis is accepted. Hypothesis: There is no significant difference between the married and unmarried investors in their average attitude towards tax planning. Result: The t-test was applied to find out whether there is significant difference between the married and unmarried investors in their average investors' attitude scores towards tax planning. The calculated t-test value is 0.448 which is less than the table value of 1.964 at 5% level of significance. Since the calculated value is less than the table value, it is inferred that there is no significant difference between the married and unmarried investors in their average attitude scores towards tax planning. Hence, the hypothesis is accepted. Educational Qualification and Investors' Attitude towards Tax Planning From the data obtained, it is ascertained that among the sample investors with Higher Secondary level of education, 22.9 percent of investors have low, 46.7 percent have moderate, and 30.5 percent have high levels of attitude towards tax planning. Among the sample investors who are degree holders, 26.2 percent have low, 46.1 percent have moderate, and 27.7 percent have high levels of attitude towards tax planning.


Among the sample investors who are postgraduates, 18.3 percent have low, 43.5 percent have moderate, and 38.2 percent have high levels of attitude. Among the sample investors with professional degree, 23.8 percent have low, 48.6 percent have moderate, and 43.2 percent have high levels of attitude. Hypothesis: There is no significant association between the educational qualification and investors' attitude towards tax planning. Result: Chi-square test was applied to find out whether there is any significant association between educational qualification and investors' attitude towards tax planning. The calculated value of chi-square is 10.260 which is less than the table value of 15.5 at 5% level of significance. Since the calculated value is less than the table value, it is inferred that there is no significant association between educational qualification and investors' awareness towards tax planning. Hence, the hypothesis is accepted. Hypothesis: There is no significant difference among the groups of investors with different educational qualifications in their average attitude score towards tax planning. Result: The ANOVA test was applied to find

whether there is any significant difference among the different levels of educational qualification groups of the investors in their average attitude score towards tax planning. The ANOVA result (Table 4) shows that the calculated F-ratio value is 1.799 which is less than the table value of 2.3719 at 5% level of significance. Since the calculated value is less than the table value, it is inferred that there is no significant difference among the groups of the investors with different educational qualifications in their average attitude scores towards tax planning. Hence, the hypothesis is accepted. Nature of Work and Investors' Attitude towards Tax Planning The analysis of the data regarding the nature of work of the sample investors and their level of attitude towards tax planning reveals that among the clerical cadre sample investors, 17.3 percent have low, 47 percent have moderate, and 35.7 percent have high levels of attitude towards tax planning. Among the managerial cadre sample investors, 33.3 percent have low, 31.3 percent have moderate, and 35.4 percent have high levels of attitude towards tax planning. Among the sample

Table 4: Association between Educational Qualification and Investors' Attitude towards Tax Planning Sum of Square

d.f

Mean Squre

75.599

4

18.900

Within Groups

7838.968

746

10.508

Total

7914.567

750

Between Groups


F-Value

1.799

73

Table 5: Association between Nature of Work and Investors' Attitude towards Tax Planning Sum of Square Between Groups

Mean Squre

79.636

4

19.909

Within Groups

7834.492

746

10.502

Total

7914.128

750

investors in professional cadre, 23.4 percent have low, 48.9 percent have moderate, and 27.7 percent have high levels of attitude towards tax planning. Among the teaching class sample investors, 19.2 percent have low, 44.6 percent have medium, and 36.3 percent have high levels of attitude towards tax planning. Among the other category sample investors, 28.3 percent have low, 47.0 percent have medium, and 24.7 percent have high levels of attitude towards tax planning. Hypothesis: There is no significant association between the nature of work and investors' attitude towards tax planning. Result: Chi-square test was applied to find out whether there is any significant association between the nature of work of the investors and investors' attitude towards tax planning. The calculated value of chi-square is 12.447 which is less than the table value of 15.5 at 5% level of significance. Since the calculated value is less than the table value, it is inferred that there is no significant association between the nature of work of the investors and investors' awareness towards tax planning. Hence, the hypothesis is accepted. Hypothesis: There is no significant difference

74

d.f

F-Value

1.896

between the investors based on the nature of work in their average attitude score towards tax planning. Result: The ANOVA test was applied to find out whether there is any significant difference between the investors based on their nature of work in their average attitude score towards tax planning. The ANOVA result shows that the calculated F-ratio value is 1.896 which is less than the table value of 2.3719 at 5% level of significance. Since the calculated value is less than the table value, it is inferred that there is no significant difference among the groups of the investors based on their nature of work in their average attitude scores towards tax planning. Hence, the hypothesis is accepted. Type of Employment and Investors' Attitude towards Tax Planning To find out the influence of the type of employment, - Government or private – on the investors' attitude on tax planning, an analysis was made and the results are presented below. 21.8 percent of Government and 21.9 percent of private sectors sample investors have


low attitude level towards tax planning, 44.5 percent of Government and 47.3 percent of private sectors sample investors have moderate level of attitude towards tax planning, and 33.7 percent of Government and 30.8 percent of private sectors sample investors have high level of attitude towards tax planning. Hypothesis: There is no significant association between the type of employment of the investors and investors' attitude towards tax planning. Result: Chi-square test was applied to find out whether there is any significant association between the type of employment of the investors and investors' attitude towards tax planning. The calculated value of chi-square is 0.465 which is less than the table value of 5.99 at 5% level of significance. Since the calculated value is less than the table value, it is inferred that there is no significant association between the type of employment of the investors and investors' awareness towards tax planning. Hence, the hypothesis is accepted. Hypothesis: There is no significant difference between the investors employed in Government and those employed in private sectors in their average attitude scores towards tax planning. Result: The t-test was applied to find out whether there is significant difference between the investors employed in Government sectors and those employed in private sectors in their average investors' attitude scores towards tax planning. The calculated t-test value is 0.158 which is less than the table value of 1.964 at 5% level of significance. Since the calculated value is less than the table value, it is inferred that there is no significant difference between the investors employed in

Government sectors and those employed in private sectors in their average attitude scores towards tax planning. Hence, the hypothesis is accepted. Number of Earning Members and Investors' Attitude towards Tax Planning In order to understand the extent of the influence of the number of earning members in the investors' families on their attitude towards tax planning, an analysis was made and the details are presented as follows. Among the sample investors with one earning member, 24.3 percent have low, 42.3 percent have moderate, and 33.4 percent have high levels of attitude towards tax planning. Among the sample investors with two earning members, 18.6 percent have low, 50.5 percent have moderate, and 30.9 percent have high levels of attitude towards tax planning. Among the sample investors with three or more earning members, 19.1 percent have low, 44.1 percent have moderate, and 36.8 percent have high levels of attitude towards tax planning. Hypothesis: There is no significant association between the number of earning members in the investors' families and investors' attitude towards tax planning. Result: Chi-square test was applied to find out whether there is any significant association between the number of earning members in the investors' families and investors' attitude towards tax planning. The calculated value of chi-square is 4.516 which is less than the table value of 9.49 at 5% level of significance. Since the calculated value is less than the table value, it is inferred that there is no significant association between the number of earning members in the investors'


75

Table 6: Association between Number of Earning Members and Investors' Attitude towards Tax Planning Sum of Square Between Groups

22.820

Mean Squre

F-Value

2

11.410

0.080

10.562

Within Groups

7900.376

748

Total

7923.196

750

families and investors' awareness towards tax planning. Hence, the hypothesis is accepted. Hypothesis: There is no significant difference among the groups of investors based on the number of earning members in their families in their average attitude score towards tax planning. Result: The ANOVA test was applied to find out whether there is any significant difference among the groups of the investors based on the number of earning members in their families in their average attitude score towards tax planning. The ANOVA result (Table 6) shows that the calculated F-ratio value is 1.080 which is less than the table value of 2.9957 at 5% level of significance. Since the calculated value is less than the table value, it is inferred that there is no significant difference among the groups of the investors based on the number of earning members in their families in their average attitude scores towards tax planning. Hence, the hypothesis is accepted. Number of Dependents and Investors' Attitude towards Tax Planning The Table 7 gives clear information regarding the number of dependents of the sample

76

d.f

investors and investors' attitude. Hypothesis: There is no significant association between the number of dependents of the investors and investors' attitude towards tax planning. Result: Chi-square test was applied to find out whether there is any significant association between the number of dependents and investors' attitude towards tax planning. The calculated value of chi-square is 2.710 which is less than the table value of 12.6 at 5% level of significance. Since the calculated value is less than the table value, it is inferred that there is no significant association between the number of dependents of the investors' and investors' attitude towards tax planning. Hence, the hypothesis is accepted. Hypothesis: There is no significant difference among the groups of the investors based on the number of dependents in their average attitude score towards tax planning. Result: The ANOVA test was applied to find out whether there is any significant difference among the groups of investors based on the number of dependents in their average attitude score towards tax planning. The ANOVA result (Table 8) shows


Table 7: Number of Dependents and Investors' Attitude towards Tax Planning Number of Dependents

S.No

Attitude on Tax Planning

Number of Investors

1

None

Low 7 (22.7)

Moderate 15 (45.4)

High 10 (31.8)

2

1-2

56 (23.7)

110 (46.5)

70 (29.8)

236 (100)

3

3-4

75 (19.9)

172 (45.4)

130 (34.6)

377 (100)

4

5 & above

26 (25.0)

42 (39.7)

37 (35.3)

105 (100)

164

339

247

750

Total

32 (100)

Note: Figures within brackets indicate percentage.

Table 8: Association between Number of Dependents and Investors' Attitude towards Tax Planning

Between Groups

Sum of Square

d.f

5.128

3

1.709 10.609

Within Groups

7924.923

747

Total

7930.051

750

that the calculated F-ratio value is 0.161 which is less than the table value of 2.6049 at 5% level of significance. Since the calculated value is less than the table value, it is inferred that there is no significant difference among the groups of the investors based on the number of earning members in their average attitude scores towards tax planning. Hence, the hypothesis is accepted. Monthly Income and Investors' Attitude towards Tax Planning Every family depends on its income for its survival, development and its growth. In order to understand the attitude of the sample investors towards tax planning with different income classification, an attempt was made and the data

Mean Squre

F-Value

0.161

are presented in the Table 9. Hypothesis: There is no significant association between the monthly income of the investors and investors' attitude towards tax planning. Result: Chi-square test was applied to find out whether there is any significant association between the monthly income of the investors and investors' attitude towards tax planning. The calculated value of chi-square is 2.943 which is less than the table value of 15.5 at 5% level of significance. Since the calculated value is less than the table value, it is inferred that there is no significant association between the monthly income of the investors and investors' attitude towards tax planning. Hence, the hypothesis is accepted.


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Table 9: Monthly Income and Investors' Attitude towards Tax Planning Monthly Income (Rs.)

S.No

Attitude on Tax Planning Low 9 (16.9)

Moderate 27 (49.3)

High 18 (33.8)

Number of Investors

1

0-8000

2

8000-12000

38 (24.2)

70 (44.5)

50 (31.3)

158 (100)

3

12000-16000

37 (20.5)

80 (44.7)

63 (34.8)

180 (100)

4

16000-20000

58 (25.4)

103 (45.2)

67 (29.4)

228 (100)

5

20000 & above

22 (17.1)

59 (45.7)

49 (37.1)

130 (100)

164

339

247

750

Total

54 (100)

Note: Figures within brackets indicate percentage

Table 10: Association between Monthly Income and Investors' Attitude towards Tax Planning Sum of Square Between Groups

26.570

Mean Squre

4

6.643 10.591

Within Groups

7900.886

746

Total

7927.456

750

Hypothesis: There is no significant difference among the groups of the investors based on monthly income in their average attitude score towards tax planning. Result: The ANOVA test was applied to find out whether there is any significant difference among the groups of investors based on monthly income in their average attitude score towards tax planning. The ANOVA result (Table 10) shows that the calculated F-ratio value is 0.627 which is less than the table value of 2.3719 at 5% level of significance. Since the calculated value is less than the table value, it is inferred that there is no significant difference among the groups of the investors based on monthly income in their 78

d.f

F-Value

0.627

average attitude scores towards tax planning. Hence, the hypothesis is accepted. Monthly Investments and Investors' Attitude towards Tax Planning To know whether the monthly investment is an execution of the tax planning method of the investors, an analysis was made based on the data obtained from the investors. The break-up details are given in Table 11. Hypothesis: There is no significant association between monthly investments of the investors and investors' attitude towards tax planning. Result: Chi-square test was applied to find out whether there is any significant association


Table 11: Monthly Investments and Investors' Attitude towards Tax Planning S.No

Monthly Investment (Rs.)

Attitude on Tax Planning

Number of Investors

1

0-2500

Low 69 (19.9)

Moderate 168 (48.6)

High 108 (31.5)

2

2500-5000

68 (23.5)

119 (40.6)

105 (35.9)

292 (100)

3

5000-7500

23 (24.0)

41 (44.0)

30 (32.0)

94 (100)

4

7500 & above

4 (20.0)

11 (20.0)

4 (20.0)

19 (100)

164

339

247

750

Total

345 (100)

Note: Figures within brackets indicate percentage.

Table 12: Association between Monthly Investments and Investors' Attitude towards Tax Planning

Between Groups

Sum of Square

d.f

1.597

3

0.532 10.615

Within Groups

7929.405

747

Total

7931.002

750

between the monthly investments of the investors and investors' attitude towards tax planning. The calculated value of chi-square is 5.046 which is less than the table value of 12.6 at 5% level of significance. Since the calculated value is less than the table value, it is inferred that there is no significant association between the monthly investments of the investors and investors' attitude towards tax planning. Hence, the hypothesis is accepted. Hypothesis: There is no significant difference among the groups of investors based on monthly investments in their average attitude scores towards tax planning. Result: The ANOVA test was applied to find out whether there is any significant difference among

Mean Squre

F-Value

0.050

the groups of investors based on monthly family investments in their average attitude scores towards tax planning. The ANOVA result (Table 12) shows that the calculated F-ratio value is 0.050 which is less than the table value of 2.6049 at 5% level of significance. Since the calculated value is less than the table value, it is inferred that there is no significant difference among the groups of the investors based on monthly investments in their average attitude scores towards tax planning. Hence, the hypothesis is accepted. RESULTS AND FINDINGS There is much difference in the attitude of the male and female sample investors towards tax planning. People are really interested in planning


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their investments and tax irrespective of their age. Whether they are married, or not, the sample investors bother to plan their investments in view of tax. The sample investors with “Other” levels of education have a high attitude towards tax planning. There is not much difference in the percentages of the sample investors with different natures of work in their levels of attitude. Whether they are employed in government sectors or in private sectors, many sample investors have either moderate level or high level of attitude towards tax planning. How many members be there in the family to earn, the attitude towards tax planning does not change. Irrespective of the number of dependents, the percentages are almost equal in all the three levels of attitude towards tax planning. Most of the sample investors have either moderate or high level of attitude towards tax planning, how much ever be the monthly income. Irrespective of the monthly investments, most of the sample investors have either moderate level or high level of attitude towards tax planning. SCOPE FOR FURTHER RESEARCH The current study has covered the aspects like investment climate of salaried class investors, attitude on tax planning of the investors. Therefore, a study based on awareness, attitude, satisfaction of salaried class investors and also other sections of the community namely, business men, self employed, pensioners and senior citizens may be carried out. CONCLUSION This study throws light on the investors' attitude towards tax planning. As the salaried class

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investors provide a considerable part of the financial resources for the government, it has to be the concern of the government to provide them with adequate plans that will help the salaried class individuals to save their money investing it in some mode that will give them the returns they expect and save them from paying tax without eluding law. REFERENCES Ambirajan, S., (1964), “Taxation of Corporate Income in India”, Asia Publishing House, Bombay. Ando. A., and F. Modigiani, (1972), “The Life Cycle Hypothesis of Saving: Aggregate Implications and Tests”, American Economic Review. Vol. 53, pp. 111-13. Avadhani.V.A., (2003), “Investment and Securities Markets in India”, Himalaya Publishing House, New Delhi. Barua, Samir K and G Srinivasan, (1987), “Investment of Decision Criteria for Investments in Risky Assets”, OMEGA, May, Vol 15 No.3,. Central Statistical Organization, (1963), “Estimates of National Income 1948-49 to 1961-62”, Government of India. Chawla, O.P., (1972), “Personal Taxation in India”, Somaiya Publication Private Ltd., Bombay. Chopra, Ranjana, (1972), “Sectoral Saving Functions for India”, Margin, April, pp. 198-206. Deaton, Angus, (1989), “Saving in Developing Countries: Theory and Review, World Bank Economic Review”, (Supplement, Proceedings of World Bank Annual Conference on Development Economics). pp. 61-96.


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Gupta.S.P., (2004),” Statistical Methods”, Sultan Chand & Sons Publisher, New Delhi,. Gurley, J. G. and E. S. Shaw, (1956), “Financial Intermediation and the saving Investment Process”, The Journal of Finance. Vol. 11, pp. 257-76. Hardy, C.Colburn, (1978), “Investor's Guide to Technical Analysis”, New York, McGraw-Hill. Jindal, K. B., (1962), “Income Tax – Past and Present”, Kitab Mahal Private Ltd., Allahabad. Juster, F. T. and L. D. Taylor, (1975), “Towards a Theory of Saving Behaviour”, American Economic Review, Vol. 65, pp. 203-9. Kelley, A. C. and J. G. Williamson, (1968), “Household Saving Behaviour in Developing Economies – The Indonesian Case”, Economic Development and Cultural Change, April, pp. 385-404. Kothari.C.R., (1990), “Research Methodology Methods and Techniques”, Wishwa Prakashan, New Delhi,.

Pillai.R.S.N. and Bagavathi, (1996), “Statistics, 2nd Edition”, Sultan Chand & Company Ltd.,. Shobhana.V.K. and J.Jayalakshmi, (2006), “Investors' Awareness and Preferences-A Study”, in Journal of Organizational Management, VolXXII, No3 Oct-Dec., pp. 16-18. SomasundaramV.K., (1997), “A Study on the Savings and Investment Pattern of Salaried Class in Coimbatore District”, Unpublished M.Phil Dissertation, submitted to Bharthiyar University, Unpublished M.Phil Dissertation, submitted to Venkateswara University, Tirupathi, pp. 31-39. Vinoth K. Singhania. (2009), “Students' Guide to Income Tax”, 40th Edition, Taxman Publication (P) Ltd., New Delhi.


Krishnamurty, K. and P. Saibaba, (1981), “Determinants of Saving Rate in India”, Indian Economic Review, Vol. XVI, No. 4, pp. 225-50. Global Management Review | Volume 6 | Issue 2 | February 2012

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