INTERNATIONAL JOURNAL OF MANAGEMENT (IJM)

INTERNATIONAL JOURNAL OF MANAGEMENT (IJM) International Journal of Management (IJM), ISSN 0976 6502(Print), ISSN 0976 ISSN 0976 6367(Print) ISSN 0976 6375(Online) Volume 3, Issue 2, May- August (2012), pp. 291-298 IAEME: www.iaeme.com/ijm.html Journal Impact Factor (2012): 3.5420 (Calculated by GISI) www.jifactor.com IJM I A E M E MODERN RETAILING IN INDIA A CRITICAL ANALYSIS OF THE FACTORS RESPONSIBLE FOR ITS GROWTH Ayan Chattopadhyay Senior Manager Regional Trade Marketing (E), Videocon Mobiles Research Scholar & Visiting Faculty at IISWBM (Calcutta University) Postal Address: Vasundhara, Block-5, Ground Floor, 204, N. S. C. Bose Road, Kolkata- 700047. West Bengal. India. Email: ayan.c28@rediffmail.com ABSTRACT Indian Retailing, acclaimed as a sunrise industry has been on the global radar since the past decade and the market is witnessing a radical shift both in terms of investment as well as spending by consumers. Post liberalization, the shopping options and spending pattern of Indian consumers has undergone a sea change. The change is visible across most cities with ultra modern new age formats such as departmental stores, hypermarkets, supermarkets and specialty store developing by the day. Traditional retailers are either making way for the modern retailers or upgrading themselves. With every new development in this sector, industry experts have cited reasons for the same, trying to explain the effect of some outcome. With the quest for finding out the factors that are responsible for the overall growth of modern retailing in India, the researcher, in his present work has considered a host of factors that could possibly influence modern retailing and then utilize an academic framework in the form of Factor Analysis to identify the core factors. Secondary data forms the basis of this research work. The study identifies four factors that the researcher has named as Market Readiness, Living Conditions, Storage Infrastructure & Spending Habits. KEY WORDS Modern retailing, Factor Analysis, Correlation Matrix, Eigen Values, Scree Plot. 291

INTRODUCTION Indian Retail is undergoing a paradigm shift from traditional forms of retailing to an organized sector which is quite visible across the Indian landscape. Shopping in India is witnessing a revolution in terms of transformation in the consumer buying behavior along with changes in the shopping formats also. Retail Industry in India which is fast transforming towards organized or modern in nature can be seen from the emergence of multi-storied malls, huge shopping centers, and sprawling complexes which offer food, shopping, and entertainment all under the same roof. The growth of modern retailing has been attributed to a host of factors by many that includes rise in the young working population, pay packets which are hefty hence more disposable income, more nuclear families in urban areas, rise in the number of women working, increasing customer aspiration, increasing consumer base in urban areas, easy accessibility and convenience and a potentially strong rural consumer market, retailer friendly Government policies and western influences to name a few. As per Ernst & Young analysis, in the last decade the number of upper middle class and high income households has grown by a staggering 270% from 30 million households to 81 million households. A key aspect driving the sector s growth is favourable population demographics 50% of the population is less than 25 years. India s population is also urbanizing at a rapid pace with the urban Indian population projected to increase from 28% to 40% of the total population by 2020. A recent Ernst & Young study, found that the propensity to consume by the Indian population is on an acceleration path, however, the spending pattern of Indian consumers presents a great paradox to today s marketers and retailers. On one hand is the increasing discretionary income but on the other hand is the typical Indian need of value for money. In India, the five cities of Mumbai, New Delhi, Chennai, Kolkata and Bangalore have been the largest modern retail markets for most products and services. However, a clear shift towards B- & C- class cities such as Pune, Chandigarh, Lucknow, Hyderabad, Ahmedabad and Jaipur, to name a few, has been observed. Mass Retailing has fueled the growth of retailing beyond metros and has added a new flavour to the consumer shopping experience. The next section makes an attempt to dig out the research works conducted on Indian modern retailing. REVIEW OF LITERATURE The growth of Organized Retailing in India has been analysed by ICRIER (Indian Council for Research on International Economic Relations ) in its study on Impact of Organized retailing on the unorganized sector, 2008. The study highlights that total retailing business in India will grow at 13% p.a. with the unorganized sector expected to grow at 10% while the organized sector is expected to grow at 40 50% p.a. thereby taking the share of organized retailing to 16% by the end of 2012. The study also highlights surprising findings that low income consumers save more than others through shopping at organized retail due to target discount shopping. Vibhuti Tripathi, Piyali Ghosh, Smit Saini & Swati Agrawal in their study on Profiling Indian Shoppers: an exploratory study on organized retail, 2008, has made an attempt to profile Indian 292

shoppers in Tier II cities and analyse their shopping orientation by a survey of consumers across two such cities in South India. Thirteen variables were identified and were treated with factor analysis; store selection, amount and time spent within store, and number of items purchased on each shopping trip have also been tracked in order to understand purchase patterns. Four shopping orientations have emerged, namely Value, Purpose, Convenience and Consultation that can be sued well for strategy purpose. In 2006, KSA Technopak also made a real time study on the Retailing Trends in India. The study reveals that organized retailing will to grow at a rate of 25% - 30% p.a. and is estimated to reach an astounding INR 1000 billion by 2012. The study highlights Fashion to drive organized retail with clothing, textiles & fashion accessories accounting for 39%, followed by food & grocery at 18%. The study also shows that 50million sq. ft of quality space is under development, 7 major cities to account for 41million sq. ft development. 300 malls, shopping centres and multiplexes under construction, 35 hypermarkets, 325 large department stores, 1500 supermarkets and over 10000 new outlets are in the pipeline, thereby adding quality space for organized retail to flourish. CRIS INFAC, 2005, conducted a research on the key sectors that will drive Indian Retailing. Food & Grocery as a segment and hypermarkets as a format are expected to be the major drivers of the organized retail industry in India, according to the research report by Cris Infac. The reasons identified for the same includes increase in disposable incomes, demographic changes, which includes more women working, rising number of nuclear families and higher incomes. Studies conducted so far reveal that the focus has primarily been on forecasting, profiling consumers, preferred retailing formats or on sectors that will drive modern retailing in India. Studies on identification of core factors that will drive modern retailing have not been found and the same has been identified as the research gap. OBJECTIVE OF STUDY To identify the core factors that will drive the growth of Indian modern retailing. METHODOLOGY In the present study where an attempt has been made to identify factors that influences or promotes modern retailing, Factor Analysis technique has been deployed. Factor Analysis (FA) is a statistical method used to describe variability among observed variables in terms of a potentially lower number of unobserved variables called factors. It is data reduction technique designed to represent a wide range of attributes on a smaller number of dimensions. Correlated variables are grouped together and separated from other variables with low or no correlation. The present study uses exploratory FA. The ensuing study uses a set of broad input parameters that the researcher considers as the most important influencers to organized or modern retailing in India. The input parameters range from those which influence private consumption demand to ones which represent spending habits & expenditure, basic living conditions, disposable income, 293

Government policies & infrastructure. Fundamentally it incorporates hard, secondary data rather than the soft data such as opinion surveys, etc. to avoid bias and errors. Within this study, 23 input parameters have been mapped initially. Private Consumption Demand has been mapped for states using Urban Population, Population Density, Population growth, Towns with 1 million plus population and towns with 0.1 million plus population as parameters. Spending habits & expenditure has been considered with MPCE (Monthly Per Capita Consumption Expenditure) as its indicator. Basic living conditions, has been mapped using dwelling & occupancy status and energy used for lighting & cooking. Disposable income has been mapped using employment/ unemployment status. Basic Education that influences the taste, preference and lifestyle of an individual, has been mapped using literacy rate. Technological advancement in a state has been mapped from the No. of Engineers that graduates every year. Stamp Duty, a key Government policy that affects modern retailing has also been considered. Infrastructure parameter has been mapped and includes rail & road network, air-port facility, power facility & telecom density and cold storage & rural warehousing facility. Lastly State Domestic Product has been mapped to understand the overall health condition of a state. IDENTIFICATION OF CORE FACTORS To determine as well as to identify the common dimension of variables that would impact and also drive modern retailing in India, the Factor Analysis technique has been applied using SPSS as the software tool. Here out of 23 parameters originally selected, 15 input parameters have been finally considered for all states under study by iteration method since these set of parameters give the best output of Kaiser Meyer Olkin Measure of Sampling Adequacy. The parameters finally mapped are shown in Exhibit 1 & 2. RESULTS & DISCUSSION The correlation matrix (Exhibit 1 & 2) shows an abridged version of the R-Matrix. The top half of the table shows the Pearson correlation coefficient between all pairs of variables whereas the bottom half contains the one tailed significance of these coefficient. The correlation matrix reveals the pattern of relationship between the variables. The communalities are shown in Exhibit 3. By Kaiser's criteria one should extract 4 factors and this is what SPSS has done. This criterion is accurate when there are less than 30 variables and average communalities after extraction are greater than 0.7 or when the sample size exceeds 250 the average communality is greater than 0.6. So on both the grounds Kaiser's rule may be accurate and we should consider 4 factors. SPSS output, Exhibit 6 lists Eigen values. The Eigen values associated with each factor represent the variance explained by that particular component. SPSS has extracted all factors with Eigen values greater than 1, which leaves us with 4 factors. The first few factors explain relatively large amounts of variance, especially factors 1 & 2 whereas subsequent factors explain only small amounts of variance. The Eigen values associated with these factors are again displayed (and percentage of variance explained) in the column extraction sums of squared loadings. The table also reveals that more than 85% of the variance is explained by the 4 factors, which also means only 15% of the total variance remains 294

unexplained by them. The result of Factor Analysis along with factor loadings is shown in Exhibit 4. The factor loadings indicate a close relationship of 15 variables within 4 underlying factors. Now to determine which variable is worth considering under a factor, we have used index numbers based on the total Eigen value divided by the total number of variables used in the analysis. We have selected variables with either factor loadings more than or in the vicinity of the index number and then loaded them on extracted factors. The variables with higher loadings within the extracted factors (highlighted) strongly influence the name or the label. The Eigen values indicate the relative importance of each factor in accounting for a particular set of variables. The cumulative total of Eigen values is 12.8. Hence the index is 0.85. Variables explaining a factor with considerable loading and significant correlation have been considered even if its value is less than the index. However in such cases the loadings are in the vicinity of the index. A low value of loadings indicates that the variables are not related and hence not considered. The chart of Scree Plot is shown in Exhibit 7 with arrows showing the points of inflexion on the curve. Clearly 4 such points have been identified which further justifies consideration of 4 factors. KMO & Bartlett s test was performed. The Kaiser Meyer Olkin measure of Sampling Adequacy, shown in Exhibit 5, has been found to be 0.6898 which falls in the range of acceptable value. Hence the SPSS output has been further considered to be justified. Correlation Matrix mpce cooking energy light energy dwelling (pucca) road network rail network power telecom density rural godown cap Correlation mpce 1 0.127841439 0.2109376 0.197452506 0.20826929 0.11105191 0.377057 0.266136555-0.029820386 cooking energy 0.127841 1 0.65148904 0.573782039-0.0653818-0.19522445 0.043105 0.754198669 0.236453626 light energy 0.210938 0.65148904 1 0.44301014 0.06102984-0.19502182 0.187165 0.59113365 0.292829555 dwelling (pucca) 0.197453 0.573782039 0.44301014 1 0.21007248 0.36346155 0.493665 0.657867592 0.457403131 road network 0.208269-0.065381826 0.06102984 0.210072482 1 0.48723947 0.732637 0.147507749-0.022353113 rail network 0.111052-0.195224445-0.19502182 0.363461554 0.48723947 1 0.793452-0.058205466 0.222812234 power 0.377057 0.043104615 0.187164867 0.493665082 0.73263698 0.79345247 1 0.288978852 0.318495286 telecom density 0.266137 0.754198669 0.59113365 0.657867592 0.14750775-0.05820547 0.288979 1 0.146613034 rural godown capacity -0.02982 0.236453626 0.292829555 0.457403131-0.0223531 0.22281223 0.318495 0.146613034 1 state domestic product 0.207615 0.005958231 0.047252415 0.475189536 0.76897405 0.82264587 0.941553 0.257008375 0.213996172 urban population 0.305044 0.168101573 0.052784596 0.479983677 0.67851428 0.79168625 0.883497 0.403671261 0.121741446 population density 0.069551 0.712488516 0.234151225 0.413887999-0.0850782-0.09928715 0.019775 0.813293143-0.056239539 no. of towns 0.58637-0.146487036-0.11596976 0.291985852 0.4704861 0.79206057 0.746066 0.043450826 0.085192973 one lac plus pop 0.204374-0.156819612-0.10995702 0.392040992 0.49255696 0.92192559 0.843434 0.03309913 0.292350459 one mn plus pop 0.000957 0.005814491-0.06543376 0.311272342 0.73854147 0.77208456 0.759505 0.141254147 0.097695488 Sig. (1-tailed) mpce 0.254343228 0.136018499 0.152282189 0.13914393 0.28315117 0.021884 0.081438114 0.438979231 cooking energy 0.254343 6.45773E-05 0.000568333 0.36807158 0.15508174 0.41215 1.15064E-06 0.108427456 light energy 0.136018 6.45773E-05 0.008046636 0.37657356 0.15533792 0.165474 0.000366709 0.061587019 dwelling (pucca) 0.152282 0.000568333 0.008046636 0.13702682 0.02630723 0.003249 5.25675E-05 0.006302183 road network 0.139144 0.368071577 0.376573562 0.137026815 0.00367274 3.11E-06 0.222551734 0.45418517 rail network 0.283151 0.155081744 0.155337917 0.026307233 0.00367274 1.42E-07 0.382121879 0.122662872 power 0.021884 0.412150002 0.16547411 0.003249163 3.1089E-06 1.4153E-07 0.064205844 0.046102605 telecom density 0.081438 1.15064E-06 0.000366709 5.25675E-05 0.22255173 0.38212188 0.064206 0.223949915 rural godown capacity 0.438979 0.108427456 0.061587019 0.006302183 0.45418517 0.12266287 0.046103 0.223949915 state domestic product 0.139917 0.487764416 0.403845948 0.004593472 5.4843E-07 2.1774E-08 1.4E-14 0.089169097 0.132492156 urban population 0.053801 0.191696489 0.392834203 0.004206258 2.6102E-05 1.5698E-07 1.09E-10 0.014943638 0.264639608 population density 0.359984 7.2562E-06 0.11074774 0.012808163 0.3304018 0.30417581 0.45945 4.10741E-08 0.38599742 no. of towns 0.000415 0.224147191 0.274562268 0.062153969 0.00500207 1.5359E-07 1.69E-06 0.411455402 0.330186536 one lac plus pop 0.143789 0.20828195 0.285079777 0.017716558 0.0033191 6.2123E-13 4.61E-09 0.432328049 0.061908492 one mn plus pop 0.498035 0.48805951 0.36797048 0.050124874 2.3907E-06 4.6599E-07 8.87E-07 0.232422048 0.307069839 a Determinant = 2.837E-11 Exhibit 1. Correlation Matrix. 295

Correlation Matrix sdp urban population population density no. of towns one lac plus pop one mn plus pop Correlation mpce 0.207615272 0.305043692 0.069550589 0.586369706 0.204373881 0.000956868 cooking energy 0.005958231 0.168101573 0.712488516-0.146487036-0.156819612 0.005814491 light energy 0.047252415 0.052784596 0.234151225-0.11596976-0.109957018-0.065433759 dwelling (pucca) 0.475189536 0.479983677 0.413887999 0.291985852 0.392040992 0.311272342 road network 0.768974053 0.678514281-0.085078189 0.470486098 0.492556959 0.738541468 rail network 0.822645874 0.791686247-0.099287151 0.792060573 0.921925587 0.772084555 power 0.941552639 0.883497009 0.019775269 0.746066139 0.843433964 0.75950479 telecom density 0.257008375 0.403671261 0.813293143 0.043450826 0.03309913 0.141254147 rural godown capacity 0.213996172 0.121741446-0.056239539 0.085192973 0.292350459 0.097695488 state domestic product 1 0.931987849 0.070167259 0.723689101 0.868188773 0.863601029 urban population 0.931987849 1 0.331777151 0.785871816 0.825598348 0.850541901 population density 0.070167259 0.331777151 1-0.039311023-0.077905462 0.052854848 no. of towns 0.723689101 0.785871816-0.039311023 1 0.817276527 0.633800878 one lac plus pop 0.868188773 0.825598348-0.077905462 0.817276527 1 0.756321401 one mn plus pop 0.863601029 0.850541901 0.052854848 0.633800878 0.756321401 1 Sig. (1-tailed) mpce 0.139916957 0.053800505 0.359984237 0.000414596 0.143788762 0.498034731 cooking energy 0.487764416 0.191696489 7.2562E-06 0.224147191 0.20828195 0.48805951 light energy 0.403845948 0.392834203 0.11074774 0.274562268 0.285079777 0.36797048 dwelling (pucca) 0.004593472 0.004206258 0.012808163 0.062153969 0.017716558 0.050124874 road network 5.48433E-07 2.61019E-05 0.330401795 0.005002073 0.003319103 2.39072E-06 rail network 2.17738E-08 1.56984E-07 0.304175806 1.53585E-07 6.21225E-13 4.65994E-07 power 1.39888E-14 1.0926E-10 0.459450384 1.69328E-06 4.60952E-09 8.87115E-07 telecom density 0.089169097 0.014943638 4.10741E-08 0.411455402 0.432328049 0.232422048 rural godown capacity 0.132492156 0.264639608 0.38599742 0.330186536 0.061908492 0.307069839 state domestic product 1.02474E-13 0.358792814 4.57066E-06 5.26454E-10 8.12151E-10 urban population 1.02474E-13 0.039350455 2.1932E-07 1.76844E-08 2.57335E-09 population density 0.358792814 0.039350455 0.419777636 0.3439557 0.392694866 no. of towns 4.57066E-06 2.1932E-07 0.419777636 3.1483E-08 0.000111561 one lac plus pop 5.26454E-10 1.76844E-08 0.3439557 3.1483E-08 1.03774E-06 one mn plus pop 8.12151E-10 2.57335E-09 0.392694866 0.000111561 1.03774E-06 a Determinant = 2.837E-11 Exhibit 2. Correlation Matrix. Communalities Initial Extraction mpce 1 0.967 cooking energy 1 0.857 light energy 1 0.659 dwelling (pucca) 1 0.755 road network 1 0.577 rail network 1 0.875 power 1 0.928 telecom density 1 0.919 rural godown capacity 1 0.856 state domestic product 1 0.951 urban population 1 0.977 population density 1 0.845 no. of towns 1 0.883 one lac plus pop 1 0.894 one mn plus pop 1 0.881 Extraction Method: Principal Component Analysis. Total 12.822 Average 0.855 Exhibit 3. Communalities. Component Matrix Component 1 2 3 4 state domestic product 0.965-0.082 0.010-0.117 urban population 0.960 0.065-0.187-0.130 power 0.954-0.018 0.072 0.114 one lac plus pop 0.894-0.265 0.155 0.009 rail network 0.858-0.334 0.142-0.084 one mn plus pop 0.855-0.168-0.041-0.346 no. of towns 0.823-0.242-0.180 0.338 road network 0.715-0.145-0.189-0.093 cooking energy 0.097 0.917 0.010-0.080 telecom density 0.323 0.880-0.188-0.073 population density 0.136 0.758-0.373-0.336 light energy 0.100 0.725 0.217 0.275 dwelling (pucca) 0.558 0.603 0.283 0.014 rural godown capacity 0.265 0.238 0.840 0.152 mpce 0.341 0.163-0.392 0.819 Extraction Method: Principal Component Analysis. a 4 components extracted. Exhibit 4. Component Matrix. KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.689807 Bartlett's Test of Sphericity Approx. Chi-Square 538.3343 df 105 Sig. 1.96E-59 Exhibit 5. KMO & Bartlett s Test. 296

Total Variance Explained Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Component Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative % 1 6.85625 45.70834 45.70834 6.8563 45.7083 45.7083 6.4898 43.2650 43.2650 2 3.46372 23.09148 68.79982 3.4637 23.0915 68.7998 3.3982 22.6545 65.9196 3 1.31495 8.76632 77.56614 1.3149 8.7663 77.5661 1.5470 10.3132 76.2327 4 1.18687 7.91247 85.47861 1.1869 7.9125 85.4786 1.3869 9.2459 85.4786 5 0.89650 5.97665 91.45526 6 0.36301 2.42009 93.87535 7 0.32719 2.18124 96.05659 8 0.25220 1.68134 97.73792 9 0.11818 0.78789 98.52581 10 0.07905 0.52702 99.05283 11 0.05929 0.39523 99.44807 12 0.04095 0.27298 99.72105 13 0.02186 0.14570 99.86675 14 0.01650 0.11002 99.97677 15 0.00348 0.02323 100.00000 d: Principal Component Analysis. Exhibit 6. SPSS Output Eigen Values. 8 Scree Plot 6 4 Eigenvalue 2 0 1 Exhibit 10. Scree Plot. 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Component Number Exhibit 7. Scree Plot The four factors that have been named as per appropriateness by the researcher are shown in Exhibit 8. The parameters within each factor are also shown. Factor 1: Market Readiness 1. State Domestic Product 2. Urban Population 3. Power 4. One lac plus pop 5. Rail network 6. One mn plus pop 7. No. of towns 8. Road network Factor 2: Living Conditions 1. Cooking energy 2. Telecom density 3. Population Density 4. Light energy 5. Dwelling (pucca) Exhibit 8. Factor Names. Factor 3: Storage Infrastructure 1. Rural Godown Capacity Factor 4: Spending Habits 1. Monthly Per Capita Consumption Expenditure 297

CONCLUSION Out of the four factors; Market Readiness & Living Conditions impacts growth of modern retailing in India by 45% & 23% respectively. Storage Infrastructure & Spending Habits influence by almost 9% & 8% respectively. Of all the four factors the most important factor is Market Readiness followed by Living Conditions, Storage Infrastructure and Spending Habits in order of decreasing importance. REFERENCES 1. Annual Report. (2007). Ministry of Power, Government of India. 2. Annual Report. (2008). New Delhi: Central Road Research Institute. 3. Annual Report. (2008). New Delhi: National Council of State Agricultural Marketing Board. 4. Annual Report. (2009). New Delhi: Airports Authority of India. Ministry of Civil Aviation, Government of India. 5. Census of India. (2001). New Delhi: Ministry of Home Affairs, Government of India. 6. Economic Profile of Bihar. (2007). Directorate of Economics & Statistics, Government of Bihar. 7. Economic Profile of Orissa. (2007). Department of Economics & Statistics, Government of Orissa. 8. Economic Statistics Database of Jharkhand. (2007). Directorate of Economics & Statistics, Government of Jharkhand. 9. Economic Survey of West Bengal. (2007). Directorate of Economics & Statistics, Government of West Bengal. 10. Household Consumer Expenditure & Employment - Unemployment Situation in India. (2003). National Sample Survey Report No. 490. New Delhi: NSSO. 11. India Retail Report. (2006). New Delhi: Images KSA Technopak. 12. Joseph, M., Soundararajan, N., Gupta, M., & Sahu, S. (2008). Impact of Organized Retail on the unorganized sector. ICRIER Working Paper, 222, 2. 13. Key Sectors of Indian Retail. (2005). CRIS INFAC. 14. Majumdar, R. (1996). Marketing Research Text, Application and Cases, 2 nd ed. New Delhi: New Age International Publishers. 15. State Routes. (2005). New Delhi: Indian Railways Publication, Ministry of Railways. 16. State wise Engineering Graduates. (2008). Centre for Monitoring Indian Economy. 17. Tele density per 100 person (based on direct exchange lines), Lok Sabha Starred Question No. 517. (2001). Department of Telecommunication, Government of India. 18. Tripathi, V., Ghosh, P., Saini, S., & Agarwal, S. (2008). Profiling Indian Shoppers: An Exploratory Study on Organized retail International. Journal of Indian Culture and Business Management, 3 (6), 669-683. 298