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Factor Analysis

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Assemblage of factors affecting Success of Fast Moving Consumer Goods

Parmindar Singh1

1 Research Scholar, Department of Commerce, Chaudhary Devi Lal University, Sirsa.

Abstract

This paper has mainly focused to the study about the factors which affects on the success of Fast Moving Consumer Goods. Factor analysis is employed on data of 24 items that have main effect on the individual consumer. The major objective of this study is to determine the factors which affects the success of FMCG. The response of the 100 respondents has been selected for the purpose of the study. The findings indicate that factor 4 (v4) is at the top by which consumers make their opinion for use of buying in products with mean value (3.12) which is more effective in the comparison of the low mean value (1.31) of the factor 2 (v6) that is less effective in making the opinion for use of buying the products . Overall the analysis provides an understanding the consumer differ significantly by age, education and income level as consumer wise on intrinsic motivation. The results are important for the group of the consumers in making their purchase decision, companies selling their products and the various parties involve advertiser, investors etc.

1. Introduction

Fast moving consumer goods is also known as consumer packaged goods. The Fast Moving Consumer Goods (FMCG) industry in India is one of the largest sectors in the country and it is at present the fourth largest sector with a total market size in excess of USD 13 billion as of 2012. Fast moving consumer goods (FMCGs) are products that are sold quickly at a relatively low cost. FMCG companies have immense possibilities for growth. And if the companies are able to change the mindset of the consumers, i.e. if they are able to take the consumers to branded products and offer new generation products, they would be able to generate higher growth in the near future. The most common things in FMCG are toilet soaps, detergents, shampoos, toothpastes, oils etc. This sector has seen the emergence of new product categories and products that seek to fulfill the increasing aspirations of a new generation of Indians, who are turning out to be very demanding consumers. The volume of money circulated in the economy due to FMCG products is very high, As the number of consumers for such products is very high. Consumers generally put less thought into the purchase of FMCG than they do for other products. Though the absolute profit made on FMCG products is relatively small, they generally sell in large numbers and so the cumulative profit on such products can be large. The fast moving consumer sector with annual revenues of nearly $18 billion, has emerged as a major component of the Indian economy. It has been recording double-digit growth rates over the past few years and continues to expand phenomenally major component. The consumption demand for FMCG products continues to be strong in both local and international markets. Also, the domestic consumption is growing irrespective e of the interest rate cycle and the domestic economic scenario. It has a strong MNC presence and is characterized by a well-established distribution network, intense competition between the organized and unorganized segments and low operational cost. FMCG sector is expected to grow by over 60% by 2010. That will translate into an annual growth of 10% over a 5-year period. It has been estimated that FMCG sector will rise from around Rs. 56,500 crores in 2005 to 92,100 in 2010. As a result, the FMCG sector is expected to do well in the future. Fast moving consumer goods will become a Rs 400,000-crore industry by 2020. Consumption patterns have evolved rapidly in the last five to ten years. The consumer is trading up to experience the new or what he hasn’t. He is looking for products with better functionality, quality, value, and so on. What he ‘needs’ is fast getting replaced with what he ‘wants’. A new report by Booz & Company for the Confederation of Indian industry (CII), called FMCG Roadmap to 2020: The Game Changers, speels out the key growth drivers for the Indian fast moving goods (FMCG) industry in the past ten years and identifies the big trends and factors that will impact its future. Various factors affects the success of FMCG which involves pricing that gives the option to the consumer. Different types of packings are available in the market for the consumer, it is the option for the consumer that which packing he wants big or small that is depend on his purpose of utilization. Online sale of the products of FMCG play a crucial role in the success of it. Any literate consumer purchase the product from online shopping in the domestic as well as international market. Place is the another factor for the success, now we can see that there are huge of traditional shops available here and there for buying the products. Malls, super market are available at the different location in the market. Side-effects and Quality is also the important factor for the success of FMCG. These two factors put a negative and the positive effects on the consumer. Goodwill, Authenticity, Advertisement, Brand are also important factors for the success of FMCG.

Review of literature:-

Dr M. Selvakumar, M. Maria Jansi Rani and K. Jegatheesan (2013), study on the base of market survey, tells about the bright future of FMCG in India. On the basis of market survey they tells about the SWOT analysis of Indian FMCG sector. Vihag Mishra, Atishay Jain, Prashant Sahni (2013) says about the growth drivers and key players in the success of the FMCG. They study on various companies like HUL, ITC, and GODREJ consumer limited. AMBER and Styles (2006) argue that a brand is more than just a product and that is combination of all elements of the marketing mix. Rajeshwari Adappa Thakur says “The sector’s sustained growth has been possible due to continuous and steady improvement in consumer incomes” and also says “As income levels continue to grow more people are likely to shift to consumption of branded products” Leslie de Chernatony, Dr Leif E. Hem and Nina M. Iversen (2001) says the factors influencing the brand extention. Francis Holly Adzah (2011) says about the FMCG and its key factors in the success of the FMCG.

4. Research Methodology -:
Hypothesis-There is no significant effect of demographic variables of consumers on items in the factors related to his opinion making for success of FMCG. The response on 24 items chosen of consumers used generally for opinion making before success of FMCG were collected on 5-point Likert scale from 1 for strongly agree, 2 for agree, 3 for neutral, 4 for disagree and 5 for strongly disagree. The different items in the questions contain in the questionnaire cover the contents of the research significantly. The study is mainly based on primary data collected from the questionnaires The sample size or the response of 100 peoples are collected from the different consumer from the district of Sirsa and Fatehabad. To analysis and interpret mean, standard deviation, factor analysis has been applied. For confirmation of descriptive statistics F-test Statistic is used. The correlation matrix of 24 reaction items which were developed to know the overall opinion making for factors of the success of FMCG. To test the appropriateness of factor analysis technique the correlation between the variables are checked and Keiser-Meyer-Olkin (KMO) measure of sample adequacy is also used for the same. The population correlation matrix is an identity matrix, is rejected by Barlett’s Test of Sphericity. The approximate Chi-square value is 752.811 with 276 degree of freedom, which is significant at 0.05 levels. The value of KMO statistic, .703 is large than 0.6. The extraction communalities for each variable is also calculated which give the average of each variable has been found 0.621 which is the amount of variance , a variable share with all the variables being considered. It is also the proportion of variance explained by the common factors. Theoretically, sample size is enough to calculate factor analysis. The reproduced correlation matrix of overall items in opinion making of factors affecting the success of FMCG has shown that are 120 (43.0%) non redundant residuals with absolute values greater than 0.05, indicating an acceptable model fit.

Table 1: Descriptive Statistics | | Mean | Std. Deviation | Analysis N | According to you,which is the place which you like to go for shopping of household consumables(Super market)-V1 | 2.40 | 1.137 | 100 | According to you,which is the place which you like to go for shopping of household consumables(Mall)-V2 | 2.30 | .980 | 100 | According to you,which is the place which you like to go for shopping of household consumables(Traditional shop)-V3 | 1.79 | .808 | 100 | According to you,which is the place which you like to go for shopping of household consumables(E- shopping)-V4 | 3.12 | 1.373 | 100 | To what extent the underwritten parameters affect your buying decisions in FMCG (Price)-V5 | 1.50 | .718 | 100 | To what extent the underwritten parameters affect your buying decisions in FMCG (Quality)-V6 | 1.31 | .662 | 100 | To what extent the underwritten parameters affect your buying decisions in FMCG (Easy availability )-V7 | 1.63 | .691 | 100 | To what extent the underwritten parameters affect your buying decisions in FMCG (Reliability)-V8 | 1.79 | .756 | 100 | To what extent the underwritten parameters affect your buying decisions in FMCG (Quantity)-V9 | 2.15 | .869 | 100 | To what extent the underwritten parameters affect your buying decisions in FMCG (Taste)-V10 | 1.42 | .638 | 100 | To what extent the underwritten parameters affect your buying decisions in FMCG (Internet)-V11 | 2.87 | 1.220 | 100 | To what extent the underwritten parameters affect your buying decisions in FMCG (Environment)-V12 | 2.10 | .732 | 100 | To what extent the underwritten parameters affect your buying decisions in FMCG (Side-effects)-V13 | 1.65 | 1.086 | 100 | To what extent the underwritten parameters affect your buying decisions in FMCG (Place)-V14 | 2.02 | .791 | 100 | To what extent the underwritten parameters affect your buying decisions in FMCG (After sale service)-V15 | 1.84 | 1.042 | 100 | To what extent the underwritten parameters affect your buying decisions in FMCG (discounts)-V16 | 1.85 | .903 | 100 | To what extent the underwritten parameters affect your buying decisions in FMCG (Guarantee/Warranty)-V17 | 1.43 | .756 | 100 | To what extent the underwritten parameters affect your buying decisions in FMCG (Life-span)-V18 | 1.83 | .865 | 100 | To what extent the underwritten parameters affect your buying decisions in FMCG (Goodwill)-V19 | 1.60 | .696 | 100 | To what extent the underwritten parameters affect your buying decisions in FMCG (Brand)-V20 | 1.72 | .944 | 100 | To what extent the underwritten parameters affect your buying decisions in FMCG (Weather)-V21 | 2.15 | .757 | 100 | To what extent the underwritten parameters affect your buying decisions in FMCG (Authenticity)-V22 | 2.23 | .941 | 100 | To what extent the underwritten parameters affect your buying decisions in FMCG (Publicity)-V23 | 2.42 | .806 | 100 | To what extent the underwritten parameters affect your buying decisions in FMCG (Advertisement)-V24 | 2.18 | .833 | 100 |
Source: Primary (Data processed through SPSS 16.0 Version)

Table 1 shows descriptive statistics which gives the value of Mean and Standard deviation make analysis on total sample size (n) is 100. The response on 24 items chosen of consumers used generally for opinion making before success of FMCG were collected on 5-point Likert scale from 1 for strongly agree, 2 for agree, 3 for neutral, 4 for disagree and 5 for strongly disagree. If the value is between (1-1.99) then it comes under the 1st option that is strongly agree, if value is between (2-2.99) then it comes under the 2nd option, value between (3-3.99) then it comes under the 4th option and for the 5th option value is between (4-4.99). There is important thing that which variable has high mean value then it mostly affect the opinion. In the above table variable 4 (E-shopping) is more effective and the variable 6 (Quality) has low mean value is less effective.

Table 2: Correlation matrix

| | V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | V10 | V11 | V12 | V13 | V14 | V15 | V16 | V17 | V18 | V19 | V20 | V21 | V22 | V23 | V24 | l | v1 | 1.000 | .163 | -.139 | .325 | -.087 | .102 | -.080 | -.054 | .194 | -.011 | .184 | .061 | -.147 | .182 | -.056 | -.108 | .068 | .018 | -.038 | .247 | -.094 | -.011 | .145 | .222 | | v2 | .163 | 1.000 | -.060 | .439 | -.029 | .058 | -.058 | -.050 | .077 | .039 | .244 | .056 | -.081 | -.099 | .067 | -.006 | .151 | -.082 | -.044 | .059 | .061 | -.010 | -.020 | -.005 | | v3 | -.139 | -.060 | 1.000 | -.132 | .200 | .085 | .149 | .076 | -.055 | .232 | -.172 | -.135 | .065 | .086 | .044 | .136 | .100 | .093 | -.043 | -.025 | -.064 | -.122 | -.220 | -.183 | | v4 | .325 | .439 | -.132 | 1.000 | -.062 | -.086 | .047 | .015 | -.015 | -.185 | .504 | .028 | -.154 | -.142 | -.050 | -.132 | -.031 | -.085 | -.108 | .011 | -.066 | -.108 | .255 | .272 | | v5 | -.087 | -.029 | .200 | -.062 | 1.000 | .244 | .254 | .177 | .219 | .309 | -.144 | .038 | .305 | -.053 | .324 | .382 | .456 | .041 | .141 | .060 | .084 | -.052 | -.192 | -.236 | | v6 | .102 | .058 | .085 | -.086 | .244 | 1.000 | .010 | .192 | .252 | .406 | -.062 | .269 | .237 | .219 | .351 | .180 | .397 | .305 | .294 | .173 | .229 | .079 | -.114 | .026 | | v7 | -.080 | -.058 | .149 | .047 | .254 | .010 | 1.000 | .294 | .245 | .127 | .038 | .074 | .149 | .198 | .212 | .250 | .056 | .012 | .130 | .010 | .126 | .117 | -.117 | -.129 | | v8 | -.054 | -.050 | .076 | .015 | .177 | .192 | .294 | 1.000 | .464 | .268 | -.085 | .203 | .242 | .125 | .329 | .013 | .071 | .300 | .376 | .242 | .179 | .239 | -.102 | -.148 | | v9 | .194 | .077 | -.055 | -.015 | .219 | .252 | .245 | .464 | 1.000 | .431 | .076 | .437 | .281 | .436 | .317 | .132 | .239 | .370 | .384 | .434 | .273 | .143 | -.048 | -.177 | | v10 | -.011 | .039 | .232 | -.185 | .309 | .406 | .127 | .268 | .431 | 1.000 | -.085 | .234 | .214 | .183 | .269 | .198 | .292 | .368 | .382 | .365 | .286 | .291 | -.130 | -.049 | | v11 | .184 | .244 | -.172 | .504 | -.144 | -.062 | .038 | -.085 | .076 | -.085 | 1.000 | .230 | -.103 | -.008 | -.009 | -.165 | .072 | -.069 | .021 | .091 | .076 | -.018 | .169 | .222 | | v12 | .061 | .056 | -.135 | .028 | .038 | .269 | .074 | .203 | .437 | .234 | .230 | 1.000 | .210 | .119 | .366 | .191 | .196 | .394 | .238 | .304 | .374 | .201 | -.089 | -.179 | | v13 | -.147 | -.081 | .065 | -.154 | .305 | .237 | .149 | .242 | .281 | .214 | -.103 | .210 | 1.000 | -.062 | .173 | .100 | .173 | .269 | .120 | .120 | .212 | .178 | -.373 | -.276 | | v14 | .182 | -.099 | .086 | -.142 | -.053 | .219 | .198 | .125 | .436 | .183 | -.008 | .119 | -.062 | 1.000 | .237 | .131 | .070 | .271 | .345 | .305 | .214 | .102 | .145 | .040 | | v15 | -.056 | .067 | .044 | -.050 | .324 | .351 | .212 | .329 | .317 | .269 | -.009 | .366 | .173 | .237 | 1.000 | .511 | .512 | .362 | .426 | .211 | .274 | .265 | .045 | -.141 | | v16 | -.108 | -.006 | .136 | -.132 | .382 | .180 | .250 | .013 | .132 | .198 | -.165 | .191 | .100 | .131 | .511 | 1.000 | .436 | .213 | .128 | .021 | .225 | .077 | .060 | -.058 | | v17 | .068 | .151 | .100 | -.031 | .456 | .397 | .056 | .071 | .239 | .292 | .072 | .196 | .173 | .070 | .512 | .436 | 1.000 | .252 | .196 | .142 | .186 | -.069 | -.117 | -.156 | | v18 | .018 | -.082 | .093 | -.085 | .041 | .305 | .012 | .300 | .370 | .368 | -.069 | .394 | .269 | .271 | .362 | .213 | .252 | 1.000 | .305 | .374 | .425 | .272 | -.128 | -.223 | | v19 | -.038 | -.044 | -.043 | -.108 | .141 | .294 | .130 | .376 | .384 | .382 | .021 | .238 | .120 | .345 | .426 | .128 | .196 | .305 | 1.000 | .520 | .307 | .265 | .068 | .021 | | v20 | .247 | .059 | -.025 | .011 | .060 | .173 | .010 | .242 | .434 | .365 | .091 | .304 | .120 | .305 | .211 | .021 | .142 | .374 | .520 | 1.000 | .201 | .153 | .010 | .001 | | v21 | -.094 | .061 | -.064 | -.066 | .084 | .229 | .126 | .179 | .273 | .286 | .076 | .374 | .212 | .214 | .274 | .225 | .186 | .425 | .307 | .201 | 1.000 | .305 | -.137 | -.091 | | v22 | -.011 | -.010 | -.122 | -.108 | -.052 | .079 | .117 | .239 | .143 | .291 | -.018 | .201 | .178 | .102 | .265 | .077 | -.069 | .272 | .265 | .153 | .305 | 1.000 | -.075 | .037 | | v23 | .145 | -.020 | -.220 | .255 | -.192 | -.114 | -.117 | -.102 | -.048 | -.130 | .169 | -.089 | -.373 | .145 | .045 | .060 | -.117 | -.128 | .068 | .010 | -.137 | -.075 | 1.000 | .623 | | v24 | .222 | -.005 | -.183 | .272 | -.236 | .026 | -.129 | -.148 | -.177 | -.049 | .222 | -.179 | -.276 | .040 | -.141 | -.058 | -.156 | -.223 | .021 | .001 | -.091 | .037 | .623 | 1.000 |

Table 2 is correlation matrix which tells about the correlation with each variable. The interior portion of the table contains correlation coefficients for all pairs of variables. Values along the diagonal, which represent associations between each variable and itself, equal +1.00. This diagonal also serves as a line of symmetry. Correlation matrix contains correlation coefficients as well as significance values and sample sizes for the data used to analyze each pair of variables. This table gives the correlations between the original variables (which are specified on the /variables subcommand). Before conducting a principal components analysis, we want to check the correlations between the variables. There should be at least 3 items which show the value above 0.3. There are 24 variables in this table and there are 22 items that has value above the 0.3. So, we can say that this table fulfill the condition.

Table 3: KMO and Bartlett's Test | Kaiser-Meyer-Olkin Measure of Sampling Adequacy. | .703 | Bartlett's Test of Sphericity | Approx. Chi-Square | 752.811 | | Df | 276 | | Sig. | .000 | Source: Primary (Data processed through SPSS 16.0 Version)Table 3 shows the value of KMO (Keiser-Meyer-Olkin) and Barlett’s test. This table shows two tests that indicate the suitability of the data in making the structure detection. This table measure the sample adequacy of the data that the value is according to the sample or not.. In this table we find the value of Chi- Square is approx. 752.811 at the degree of freedom of 276 and the significant value is .000. The KMO value should be greater than 0.6 if sample is adequate, in this table KMO value is .703 which is greater than 0.6. so we can say that the sample is adequate. These tests provide a minimum standard which should be passed before a principal components analysis (or a factor analysis) should be conducted. Table 4: Communalities | | Extraction | According to you,which is the place which you like to go for shopping of household consumables(Super market)-V1 | .542 | According to you,which is the place which you like to go for shopping of household consumables(Mall)-V2 | .496 | According to you,which is the place which you like to go for shopping of household consumables(Traditional shop)-V3 | .426 | According to you,which is the place which you like to go for shopping of household consumables(E- shopping)-V4 | .746 | To what extent the underwritten parameters affect your buying decisions in FMCG (Price)-V5 | .650 | To what extent the underwritten parameters affect your buying decisions in FMCG (Quality)-V6 | .544 | To what extent the underwritten parameters affect your buying decisions in FMCG (Easy availability )-V7 | .735 | To what extent the underwritten parameters affect your buying decisions in FMCG (Reliability)-V8 | .610 | To what extent the underwritten parameters affect your buying decisions in FMCG (Quantity)-V9 | .681 | To what extent the underwritten parameters affect your buying decisions in FMCG (Taste)-V10 | .618 | To what extent the underwritten parameters affect your buying decisions in FMCG (Internet)-V11 | .574 | To what extent the underwritten parameters affect your buying decisions in FMCG (Environment)-V12 | .621 | To what extent the underwritten parameters affect your buying decisions in FMCG (Side-effects)-V13 | .489 | To what extent the underwritten parameters affect your buying decisions in FMCG (Place)-V14 | .721 | To what extent the underwritten parameters affect your buying decisions in FMCG (After sale service)-V15 | .678 | To what extent the underwritten parameters affect your buying decisions in FMCG (discounts)-V16 | .733 | To what extent the underwritten parameters affect your buying decisions in FMCG (Guarantee/Warranty)-V17 | .723 | To what extent the underwritten parameters affect your buying decisions in FMCG (Life-span)-V18 | .565 | To what extent the underwritten parameters affect your buying decisions in FMCG (Goodwill)-V19 | .575 | To what extent the underwritten parameters affect your buying decisions in FMCG (Brand)-V20 | .579 | To what extent the underwritten parameters affect your buying decisions in FMCG (Weather)-V21 | .515 | To what extent the underwritten parameters affect your buying decisions in FMCG (Authenticity)-V22 | .583 | To what extent the underwritten parameters affect your buying decisions in FMCG (Publicity)-V23 | .749 | To what extent the underwritten parameters affect your buying decisions in FMCG (Advertisement)-V24 | .774 | Extraction Method: Principal Component Analysis.Source: Primary (Data processed through SPSS 16.0 Version). |
Table 4 – communalities shows that the each variable divide the variation with each other. Communalities are for correlation analyses, the proportion of variance accounted for in each variable by the rest of the variables. This is the proportion of each variable's variance that can be explained by the principal components The extraction communalities for each variable is also calculated which give the average of each variable has been found 0.621 which is the amount of variance , a variable share with all the variables being considered. Variables with high values are well represented in the common factor space, while variables with low values are not well represented. They are the reproduced variances from the number of components that we have saved. we can find these values on the diagonal of the reproduced correlation matrix

Table 5: Total Variance Explained | Component | Initial Eigenvalues | Extraction Sums of Squared Loadings | Rotation Sums of Squared Loadings | | Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | 1 | 5.011 | 20.878 | 20.878 | 5.011 | 20.878 | 20.878 | 2.540 | 10.584 | 10.584 | 2 | 2.669 | 11.120 | 31.998 | 2.669 | 11.120 | 31.998 | 2.374 | 9.891 | 20.475 | 3 | 1.848 | 7.699 | 39.697 | 1.848 | 7.699 | 39.697 | 2.214 | 9.225 | 29.700 | 4 | 1.662 | 6.926 | 46.623 | 1.662 | 6.926 | 46.623 | 2.121 | 8.838 | 38.538 | 5 | 1.346 | 5.607 | 52.230 | 1.346 | 5.607 | 52.230 | 2.069 | 8.621 | 47.160 | 6 | 1.317 | 5.489 | 57.719 | 1.317 | 5.489 | 57.719 | 2.041 | 8.505 | 55.665 | 7 | 1.073 | 4.469 | 62.188 | 1.073 | 4.469 | 62.188 | 1.566 | 6.523 | 62.188 | 8 | .988 | 4.116 | 66.304 | | | | | | | 9 | .875 | 3.645 | 69.949 | | | | | | | 10 | .843 | 3.514 | 73.464 | | | | | | | 11 | .787 | 3.281 | 76.744 | | | | | | | 12 | .746 | 3.107 | 79.852 | | | | | | | 13 | .660 | 2.750 | 82.601 | | | | | | | 14 | .642 | 2.674 | 85.275 | | | | | | | 15 | .573 | 2.389 | 87.664 | | | | | | | 16 | .533 | 2.220 | 89.885 | | | | | | | 17 | .442 | 1.843 | 91.727 | | | | | | | 18 | .414 | 1.724 | 93.452 | | | | | | | 19 | .322 | 1.342 | 94.794 | | | | | | | 20 | .311 | 1.297 | 96.091 | | | | | | | 21 | .262 | 1.090 | 97.180 | | | | | | | 22 | .247 | 1.028 | 98.209 | | | | | | | 23 | .219 | .912 | 99.120 | | | | | | | 24 | .211 | .880 | 100.000 | | | | | | | Extraction Method: Principal Component Analysis.Source: Primary (Data processed through SPSS 16.0 Version) | | | | | | |
Table 5 is the total variance explained table, there are as many components extracted during a principal components analysis as there are variables that are put into it. Eigen values are the variances of the principal components. Because we conducted our principal components analysis on the correlation matrix, the variables are standardized. This column contains the eigenvalues. The first component will always account for the most variance (and hence have the highest eigenvalue), and the next component will account for as much of the left over variance as it can, and so on. Hence, each successive component will account for less and less variance. The column of % Variance contains the percent of variance accounted for by each principal component. .The column of Cumulative % - contains the cumulative percentage of variance accounted for by the current and all preceding principal components. For example, the eight row shows a value of 66.304. This means that the first eight components together account for 66.304% of the total variance. (Remember that because this is principal components analysis, all variance is considered to be true and common variance. In other words, the variables are assumed to be measured without error, so there is no error variance.) . In the Extraction Sums of Squared Loadings, three columns of this half of the table exactly reproduce the values given on the same row on the left side of the table.

The scree plot graphs the eigenvalue against the component number. You can see these values in the first six columns of the table immediately above. From the seventh component on, we can see that the line is almost flat, meaning the each successive component is accounting for smaller and smaller amounts of the total variance. In general, we are interested in keeping only those principal components whose eigenvalues are greater than 1. Components with an eigenvalue of less than 1 account for less variance than did the original variable (which had a variance of 1), and so are of little use. Hence, we can see that the point of principal components analysis is to redistribute the variance in the correlation matrix (using the method of eigenvalue decomposition) to redistribute the variance to first components extracted

Table 6 : Component Pattern | Component | Rotated Component Matrix | Reaction items | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | v1 | -.014 | .550 | .113 | -.050 | .392 | -.242 | -.108 | -.094 | .093 | .156 | .445 | -.182 | .472 | -.214 | v2 | .000 | .340 | .475 | -.363 | .020 | -.073 | .128 | .105 | .071 | -.075 | .675 | -.004 | -.082 | -.107 | v3 | .138 | -.418 | .139 | .164 | .431 | .007 | .015 | .192 | .147 | -.248 | -.231 | -.478 | .065 | .142 | v4 | -.196 | .594 | .437 | -.292 | .085 | .242 | .111 | -.063 | -.075 | .210 | .818 | -.053 | -.044 | .135 | v5 | .437 | -.347 | .496 | .121 | .158 | .118 | .199 | .581 | .314 | -.242 | .012 | -.258 | -.144 | .260 | v6 | .543 | .050 | .163 | .122 | .058 | -.381 | .237 | .392 | .560 | -.060 | .020 | .051 | .148 | -.218 | v7 | .316 | -.111 | .113 | .032 | .162 | .751 | -.138 | .196 | -.066 | -.087 | .012 | -.030 | .096 | .821 | v8 | .526 | .017 | -.197 | -.137 | .207 | .416 | .244 | -.057 | .468 | -.138 | .003 | .146 | .130 | .575 | v9 | .677 | .250 | -.092 | -.142 | .301 | .113 | -.165 | .119 | .343 | -.203 | .149 | .192 | .593 | .312 | v10 | .645 | -.012 | -.052 | .083 | .173 | -.162 | .369 | .223 | .722 | -.096 | -.089 | .065 | .154 | .050 | v11 | -.069 | .596 | .267 | -.322 | -.085 | .159 | -.081 | -.053 | -.149 | .144 | .687 | .208 | .091 | .062 | v12 | .544 | .246 | -.007 | -.318 | -.285 | -.074 | -.278 | .212 | .059 | -.248 | .211 | .607 | .313 | -.004 | v13 | .451 | -.329 | -.014 | -.356 | -.040 | .020 | .218 | .088 | .370 | -.505 | -.047 | .197 | -.152 | .157 | v14 | .403 | .272 | -.285 | .359 | .280 | .030 | -.441 | .111 | .034 | .179 | -.217 | .063 | .774 | .160 | v15 | .679 | .060 | .264 | .234 | -.270 | .115 | -.059 | .667 | .217 | .042 | -.010 | .344 | .135 | .219 | v16 | .444 | -.162 | .407 | .439 | -.316 | .095 | -.205 | .807 | -.065 | .082 | -.175 | .141 | .013 | .141 | v17 | .521 | -.045 | .599 | .133 | -.043 | -.260 | -.051 | .768 | .215 | -.162 | .174 | -.020 | .098 | -.142 | v18 | .645 | .054 | -.212 | -.099 | -.115 | -.231 | -.156 | .189 | .299 | -.246 | -.127 | .457 | .385 | -.074 | v19 | .612 | .247 | -.249 | .193 | .028 | .066 | .187 | .124 | .534 | .174 | -.107 | .297 | .313 | .218 | v20 | .512 | .380 | -.233 | -.050 | .286 | -.181 | .023 | -.050 | .480 | -.007 | .110 | .164 | .554 | -.011 | V21 | .541 | .100 | -.116 | -.152 | -.408 | -.014 | -.096 | .220 | .153 | -.140 | -.018 | .637 | .123 | .050 | v22 | .366 | .110 | -.377 | -.091 | -.386 | .171 | .330 | -.114 | .374 | .090 | -.121 | .589 | -.140 | .203 | v23 | -.232 | .579 | .002 | .568 | -.131 | .129 | .049 | .042 | -.099 | .848 | .089 | .010 | .102 | -.002 | v24 | -.293 | .574 | -.029 | .457 | -.096 | .025 | .374 | -.111 | .140 | .835 | .155 | -.040 | -.091 | -.103 | Source: Primary (Data processed through SPSS 16.0 Version) Extraction Method: Principal Component Analysis. Table 6 component pattern shows that the various values of the component matrix and the rotation component matrix. component matrix contains component loadings, which are the correlations between the variable and the component. Because these are correlations, possible values range from -1 to +1.Component matrix contains the loading of each variable on to each other. The component matrix indicates how each item in the analysis correlate with each of the 7 retained factors. This matrix is not particularly used for interpretation. The interpretability of the factors can be improved trough rotation. The rotation matrix gives us a clear indication how much items correlates with each other. Rotation matrix maximizing the loading of each variable on one of the extracted factors at the same time as minimizing the loading on all other factors. The purpose of rotation is to simplify the structure of the analysis, so that the each factor will have nonzero loadings for only some of the variables without affecting the communalities and the percent of variance explained.

Table 7: Reproduced Correlation Reaction Items | V1 | V2 | V3 | V4 | V5 | V6 | V8 | V9 | V10 | V11 | V12 | V13 | V14 | V15 | V16 | V17 | V18 | V19 | V20 | V21 | V22 | V23 | V24 | v1 | .542a | .271 | -.059 | .356 | -.135 | .122 | -.059 | .233 | .042 | .312 | .079 | -.215 | .243 | -.086 | -.196 | .081 | .029 | .065 | .331 | -.105 | -.211 | .206 | .210 | v2 | .271 | .496a | -.125 | .514 | .094 | .109 | -.033 | .069 | .003 | .422 | .160 | .036 | -.227 | .039 | -.061 | .232 | -.052 | -.085 | .059 | .014 | -.087 | -.015 | .059 | v3 | -.059 | -.125 | .426a | -.222 | .366 | .122 | .112 | .080 | .179 | -.311 | -.208 | .125 | .075 | .027 | .119 | .175 | -.033 | -.007 | -.007 | -.186 | -.223 | -.235 | -.245 | v4 | .356 | .514 | -.222 | .746a | -.047 | -.102 | .006 | .051 | -.165 | .600 | .056 | -.161 | -.165 | -.052 | -.161 | .021 | -.241 | -.099 | .021 | -.102 | -.099 | .250 | .291 | v5 | -.135 | .094 | .366 | -.047 | .650a | .327 | .240 | .174 | .352 | -.155 | .001 | .301 | -.056 | .394 | .425 | .509 | .069 | .131 | .000 | .040 | -.051 | -.228 | -.224 | v6 | .122 | .109 | .122 | -.102 | .327 | .544a | .150 | .283 | .511 | -.089 | .213 | .224 | .130 | .370 | .250 | .479 | .351 | .349 | .344 | .220 | .122 | -.072 | -.005 | v7 | -.158 | -.065 | .184 | .092 | .323 | -.121 | .450 | .328 | .058 | .049 | .070 | .145 | .206 | .296 | .266 | .046 | .000 | .173 | .000 | .078 | .079 | -.050 | -.194 | v8 | -.059 | -.033 | .112 | .006 | .240 | .150 | .610a | .467 | .396 | -.006 | .177 | .337 | .186 | .252 | .015 | .008 | .237 | .428 | .318 | .216 | .353 | -.152 | -.120 | v9 | .233 | .069 | .080 | .051 | .174 | .283 | .467 | .681a | .400 | .129 | .427 | .229 | .477 | .358 | .109 | .234 | .449 | .457 | .533 | .315 | .172 | -.126 | -.205 | v10 | .042 | .003 | .179 | -.165 | .352 | .511 | .396 | .400 | .618a | -.163 | .182 | .336 | .182 | .356 | .158 | .333 | .378 | .484 | .421 | .237 | .274 | -.135 | -.039 | v11 | .312 | .422 | -.311 | .600 | -.155 | -.089 | -.006 | .129 | -.163 | .574a | .244 | -.128 | -.041 | .030 | -.102 | .020 | -.052 | -.030 | .090 | .080 | .002 | .207 | .189 | v12 | .079 | .160 | -.208 | .056 | .001 | .213 | .177 | .427 | .182 | .244 | .621a | .227 | .215 | .392 | .199 | .272 | .490 | .269 | .315 | .511 | .263 | -.150 | -.242 | v13 | -.215 | .036 | .125 | -.161 | .301 | .224 | .337 | .229 | .336 | -.128 | .227 | .489a | -.138 | .200 | .061 | .179 | .278 | .171 | .117 | .262 | .258 | -.479 | -.398 | v14 | .243 | -.227 | .075 | -.165 | -.056 | .130 | .186 | .477 | .182 | -.041 | .215 | -.138 | .721a | .253 | .181 | .078 | .329 | .382 | .423 | .151 | .004 | .213 | .019 | v15 | -.086 | .039 | .027 | -.052 | .394 | .370 | .252 | .358 | .356 | .030 | .392 | .200 | .253 | .678a | .610 | .525 | .375 | .399 | .198 | .421 | .239 | .058 | -.059 | v16 | -.196 | -.061 | .119 | -.161 | .425 | .250 | .015 | .109 | .158 | -.102 | .199 | .061 | .181 | .610 | .733a | .541 | .194 | .174 | -.063 | .258 | .022 | .097 | -.078 | v17 | .081 | .232 | .175 | .021 | .509 | .479 | .008 | .234 | .333 | .020 | .272 | .179 | .078 | .525 | .541 | .723a | .267 | .157 | .137 | .214 | -.097 | -.101 | -.157 | v18 | .029 | -.052 | -.033 | -.241 | .069 | .351 | .237 | .449 | .378 | -.052 | .490 | .278 | .329 | .375 | .194 | .267 | .565a | .394 | .411 | .459 | .284 | -.198 | -.250 | v19 | .065 | -.085 | -.007 | -.099 | .131 | .349 | .428 | .457 | .484 | -.030 | .269 | .171 | .382 | .399 | .174 | .157 | .394 | .575a | .456 | .325 | .390 | .125 | .127 | v20 | .331 | .059 | -.007 | .021 | .000 | .344 | .318 | .533 | .421 | .090 | .315 | .117 | .423 | .198 | -.063 | .137 | .411 | .456 | .579a | .233 | .188 | .013 | .028 | V21 | -.105 | .014 | -.186 | -.102 | .040 | .220 | .216 | .315 | .237 | .080 | .511 | .262 | .151 | .421 | .258 | .214 | .459 | .325 | .233 | .515a | .390 | -.107 | -.164 | v22 | -.211 | -.087 | -.223 | -.099 | -.051 | .122 | .353 | .172 | .274 | .002 | .263 | .258 | .004 | .239 | .022 | -.097 | .284 | .390 | .188 | .390 | .583a | .015 | .090 | v23 | .206 | -.015 | -.235 | .250 | -.228 | -.072 | -.152 | -.126 | -.135 | .207 | -.150 | -.479 | .213 | .058 | .097 | -.101 | -.198 | .125 | .013 | -.107 | .015 | .749a | .694 | v24 | .210 | .059 | -.245 | .291 | -.224 | -.005 | -.120 | -.205 | -.039 | .189 | -.242 | -.398 | .019 | -.059 | -.078 | -.157 | -.250 | .127 | .028 | -.164 | .090 | .694 | .774a |

Source: Primary (Data processed through SPSS 16.0 Version)
Extraction Method: Principal Component Analysis
a) Reproduced communalities.
b) Residuals are computed between observed and reproduced correlations. There are 120 (43.0%) nonredundant residuals with absolute values greater than 0.05
Table 7 is the reproduced correlation which contains two tables, the reproduced correlations in the top part of the table, and the residuals in the bottom part of the table. The difference between the reproduced correlation matrix and the original correlation matrix is the residual matrix.The reproduced correlation matrix is the correlation matrix based on the extracted components. The reproduced correlation matrix of overall items in opinion making of factors affecting the success of FMCG has shown that are 120 (43.0%) non redundant residuals with absolute values greater than 0.05, indicating an acceptable model fit

Table 8: Component Transformation Matrix | Component | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 1 | .489 | .563 | -.274 | -.076 | .389 | .384 | .254 | 2 | -.159 | .081 | .584 | .623 | .265 | .404 | -.075 | 3 | .692 | -.139 | -.073 | .565 | -.337 | -.251 | -.039 | 4 | .409 | .017 | .694 | -.513 | -.269 | .122 | .000 | 5 | -.236 | .257 | -.172 | .101 | -.762 | .479 | .171 | 6 | -.069 | -.212 | .144 | .072 | .042 | -.155 | .948 | 7 | -.172 | .739 | .216 | .092 | -.105 | -.598 | .020 | Source: Primary (Data processed through SPSS 16.0 Version) Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. | | |

Table 9: Overall Items Variation Explained in Factor-Wise SERIAL NO. | FACTOR | COFF. VALUE | NAME OF ITEM | 1 | PRICE AND PROMOTION(20.878) | .581 | PRICE (V5) | 2 | | .667 | AFTER SALE SERVICE(V15) | 3 | | .807 | DISCOUNT(V16) | 4 | | .768 | GUARANTEE /WARANTEE(V17) | 5 | PRODUCT ACKNOWLEDGE(11.120) | .560 | QUALITY(V6) | 6 | | .722 | TASTE(V10) | 7 | | .534 | GOODWILL(V19) | 8 | KNOWLEDGE THROUGH PROMOTION(7.699) | .505 | SIDE EFFECTS(V13) | 9 | | .848 | PUBLICITY(V23) | 10 | | .835 | ADVERTISEMENT(V24) | 11 | E COMMERCE FACILITY(6.926) | .675 | MALL (V2) | 12 | | .818 | E SHOPPING(V4) | 13 | | .687 | INTERNET(V11) | 14 | ENVIRONMENT FACILITY(5.607) | .478 | TRADITIONAL SHOP(V3) | 15 | | .607 | ENVIRONMENT(V12) | 16 | | .457 | LIFE SPAN(V18) | 17 | | .637 | WEATHER(V21) | 18 | | .589 | AUTHENTICITY(V22) | 19 | MARKETING MIX FACILITY(5.489) | .472 | SUPER MARKET(V1) | 20 | | .593 | QUANTITY(V9) | 21 | | .774 | PLACE(V14) | 22 | | .554 | BRAND(V20) | 23 | POSITIVE DISTRIBUTION(4.469) | .821 | EASY AVALABILITY(V7) | 24 | | .575 | RELIABILITY(V8) |
Source: Primary (Data processed through SPSS 16.0 Version)

Table 10: Overall confirmation of factors affecting success of FMCG by demographic variable

Factors | | Gender | Age | Edu. Qual. | Income | | MEAN | S.D. | F-value | Sigvalue | F-value | Sigvalue | F-value | Sigvalue | F-value | Sig value | PRICE AND PROMOTIONPrice(V5) After sale service(V15)Discount(V16) Guarantee /Warrantee(V17) | (1.655) | .854 | 15.652 | .000* | 2.650 | .053 | 1.234 | .302 | 1.872 | .140 | | 1.50 | .718 | 10.349 | .002* | 2.202 | .093 | .642 | .634 | .617 | .606 | | 1.84 | 1.042 | 6.240 | .014* | 1.921 | .131 | 1.184 | .323 | 1.710 | .170 | | 1.85 | .903 | 9.169 | .003* | 1.781 | .156 | 1.029 | .397 | .596 | .619 | | 1.43 | .756 | 8.845 | .004* | .770 | .513 | .495 | .740 | 2.893 | .039 | PRODUCT ACKNOWLEDGE Quality(V6) Taste(V10) Goodwill(19) | (1.443) | .499 | .582 | .447 | .780 | .508 | .843 | .501 | 2.378 | .075 | | 1.31 | .662 | .976 | .326 | .492 | .689 | .290 | .884 | .758 | .521 | | 1.42 | .638 | .045* | .832 | .317 | .813 | 1.590 | .183 | 2.380 | .074 | | 1.60 | .696 | 1.077 | .302 | .960 | .415 | .811 | .521 | 1.212 | .310 | KNOWLEDGE THROUGH PROMOTION Side effects(V13)Publicity(23)Advertisement(24) | (2.083) | .681 | 2.359 | .128 | 3.508 | .018* | 3.261 | .015* | 2.087 | .107 | | 1.65 | 1.086 | 2.155 | .145 | .874 | .458 | 2.507 | .047* | .229 | .876 | | 2.42 | .806 | .173 | .678 | 2.489 | .065 | 1.801 | .135 | 1.170 | .325 | | 2.18 | .833 | 3.543 | .063 | 3.732 | .014* | 2.133 | .083 | 3.518 | .018* | E COMMERCE FACILITYMall(V2) E Shopping(V4) Internet(V11) | (2.763) | .893 | 4.625 | .034* | 2.744 | .047* | 2.357 | .059 | 1.392 | .250 | | 2.30 | .980 | .427 | .515 | .650 | .585 | .785 | .538 | .975 | .408 | | 3.12 | 1.373 | .263 | .609 | 2.248 | .088 | 3.958 | .005* | 1.847 | .144 | | 2.87 | 1.220 | 2.208 | .140 | 4.049 | .009* | 3.587 | .009* | .139 | .936 | ENVIRONMENT FACILITYTraditional shop(V3)Environment(V12) Life span(V18) Weather(V21) Authenticity(V22) | (2.02) | .102 | .602 | .440 | 1.132 | .340 | 6.537 | .000* | .665 | .575 | | 1.79 | .808 | 5.635 | .020* | .230 | .875 | .842 | .502 | 3.293 | .024* | | 2.10 | .732 | 3.354 | .070 | 2.775 | .046 | 1.697 | .157 | .191 | .902 | | 1.83 | .865 | 2.076 | .153 | .210 | .889 | 3.953 | .005* | 1.759 | .160 | | 2.15 | .757 | .001* | .979 | 1.387 | .252 | 6.917 | .000* | 1.461 | .230 | | 2.23 | .941 | .529 | .469 | 2.814 | .043* | 2.755 | .032 | 1.552 | .206 | MARKETING MIX FACILITYSuper market(V1) Quantity(V9) Place(V14)Brand(20) | (2.763) | .935 | .829 | .365 | .492 | .689 | 2.290 | .065 | .203 | .894 | | 2.40 | 1.137 | .976 | .326 | .529 | .664 | 1.670 | .163 | 1.132 | .340 | | 2.15 | .869 | 5.461 | .021* | .941 | .424 | 3.418 | .012* | .552 | .648 | | 2.02 | .791 | .074 | .786 | .382 | .766 | 1.469 | .218 | .328 | .805 | | 1.72 | .944 | .766 | .384 | 1.075 | .363 | 1.458 | .221 | .117 | .950 | POSITIVE DISTRIBUTIONEasy availability(V7)Reliability(V8) | (1.71) | .361 | 14.315 | .000* | 3.042 | .033* | 2.427 | .053 | .407 | .748 | | 1.63 | .691 | 16.401 | .000* | 1.159 | .329 | 1.267 | .288 | 1.064 | .368 | | 1.79 | .756 | 3.906 | .051 | 3.208 | .027* | 1.807 | .134 | .266 | .850 |
Source: Primary (Data processed through SPSS 16.0 Version)
The findings indicate that factor 4 (v4) is at the top by which consumers make their opinion for use of buying in products with mean value (3.12) which is more effective in the comparison of the low mean value (1.31) of the factor 2 (v6) that is less effective in making the opinion for use of buying the products

5. Disscussions and Suggestions-: It is found that
Further area of research
References…...

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