deependra pal, amul ppt , factor analysis

NAME: DEEPENDRA PAL

ROLLNO: 12BSP0343

SUBJECT:BUSINESS RESEARCH

METHODS

Objective of the study:The main objective of this study is to analyze consumer buying behaviour for

Amul Fresh Paneer in pune and nearby regions .

Scope of the study: Conducting an exploratory survey in pune and also through individual face

to face questions with shops nearby locality.

Finding out prospects by analysis of a questionnaire.

Research Question:

To analyze the consumer’s buying behavior of Fresh Paneer.

Methodology and Data collection:-

Phase1:- Exploratory Survey

Methodology:- In this phase an exploratory survey was conducted in pune city , and near locality.It was done by surveying the area and making a database that contained a list of Restaurants, Caterers, Retailers etc.Data Collection:- During this phase, the following data was collected from the various people:Current brand of Paneer they useThe current price at which they buy PaneerThe current demand of the firm

Phase2:- Finding out prospects by analysis of questionnaireMethodology:- In this phase a survey was conducted on people to find out their buying behavior while purchasing Paneer.Factor analysis was done on the data collected by means of SPSS tool.Analysis of data indicated the factors that affect the people’s buying behavior while purchasing Paneer from the market.

SAMPLING

1 Sampling Technique : Non probability sampling

2 Sample Unit : People who buy paneer available in retail outlets, superstores, etc ( Convenience sampling)

3.Sample size : 58 respondents (Age ranging between 8 yrs to 65 yrs) 4. Method : Questionnaire (including email).

5. Data analysis method : Factor analysis method

6. Area of survey : pune and nearby locality.

FACTOR ANALYSIS• Analyze the structure of the interrelationship among a large set of

decision variables to determine whether the information can be summarized into smaller set of factors.

To summarize the information contained in the number of decision variables into smaller set of factors subjected to its minimum loss of information.

OBJECTIVE

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Factor Analysis

• There are two main types of factor analysis:

1.Confirmatory Analysis, and

2.Exploratory Factor Analysis

– Here, I am only considering the second type

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Model representation– The Basic model representation of exploratory

factor analysis is:

R=FFT

where,

– R = Correlation Matrix– F = Factor Matrix– FT = Factor Matrix Transpose

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• For Example,

Correlation Matrix A B C D

A -.953 -.055 -.130

B -.953 -.091 -.036

C -.055 -.091 .990

D -.130 -.036 .990

Factor MatrixFactor I Factor II

A -.400 .900

B .251 .947

C .932 .348

D .956 .286

Eigen value 2 1.91

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R= -.400 .900 -.400 .251 .932 .956

.251 -.947 .900 -.947 .348 .286

.932 .348

.956 .286

• Because the factor matrix multiply the factor matrix transpose, it detects the one to one correspondent correlation. Then the correlation matrix is formed which is a critical element of factor anlaysis.

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Steps to perform

• Five Simple steps to follow:1. Testing assumption

2. Selecting proper sample sizes

3. Extracting factors

4. Rotating factors

5. Refining and labeling factors

,

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Testing assumption

• Two tests:a. Bartlett test of sphericity– A statistical test for the presence of correlations among

the variables. It determines if the correlation matrix has significant correlation to some of decision variables

b. K.M.O (Kaiser-Meyer-Olkin) measure of sampling adequacy

– measure calculated both for entire correlation matrix evaluating the appropriateness of applying exploratory factor analysis. This value should be greater than 0.5

• Bartlett’s measure tests the null hypothesis that the original correlation matrix is an identity matrix.A significant test tells us that R-matrix is not an identity matrix; therefore, there are some relationships between the variables we hope to include in the analysis. For this data, Bartlett’s test is highly significant (p<0.001) and therefore factor analysis is appropriate.

KMO AND BARTLETT TEST

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.669

Bartlett's Test of Sphericity Approx. Chi-Square 325.514

df 36

Sig. 0.000

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Extracting factors• Two common methods:

1. Principal component analysis• transform the original set of variables into a smaller set of linear

combinations that account for most of the variance of the original set

2. Common factor analysis• transforms the original set of variables into a smaller set of factors to

which their variances are common among the original factors

Communalities

Initial ExtractionBusiness 1.000 0.652Price 1.000 0.661Packaging 1.000 0.831Quality 1.000 0.848Brand 1.000 0.790Supply Services 1.000 0.692Shelf Life 1.000 0.765Promotion Schemes 1.000 0.757Daily Demand 1.000 0.586Extraction Method: Principal Component Analysis.

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• Criteria for extraction – there are several criteria to extract factors: Latent Root

Criterion, A Prior Criterion, Percentage of Variance Criterion, Scree Test Criterion.

– The most commonly used technique is the latent root criterion. The rationale for the latent root criterion is that any individual factor should account for the variance of at least a single variable if it is to be retained for interpretation.

– Each variable contributes a value of one to the total Eigenvalue. Thus, only the factor having Eigenvalue greater than one is considered significant

Total Variance Explained

Component

Initial EigenvaluesExtraction Sums of Squared

LoadingsRotation Sums of Squared

Loadings

Total% of

VarianceCumulati

ve % Total% of

VarianceCumulati

ve % Total% of

VarianceCumulati

ve %1 3.272 36.353 36.353 3.272 36.353 36.353 2.870 31.885 31.8852 2.047 22.747 59.100 2.047 22.747 59.100 1.929 21.434 53.3183 1.263 14.035 73.135 1.263 14.035 73.135 1.784 19.817 73.1354 0.710 7.891 81.026            5 0.653 7.260 88.286            6 0.381 4.235 92.521            7 0.302 3.354 95.875            8 0.193 2.145 98.020            9 0.178 1.980 100.000            

Here we are selecting only those variables which have eigen values greater than 1

Extraction Method: Principal Component Analysis.

Component Matrix(a)

Component

1 2 3Business -0.151 0.686 0.397Price 0.312 0.546 -0.516

Packaging 0.796 0.421 0.141Quality 0.708 -0.411 0.421Brand 0.596 -0.453 0.479

Supply Services 0.519 -0.330 -0.560

Shelf Life 0.848 0.191 -0.092

Promotion Schemes 0.814 0.291 -0.096

Daily Demand -0.097 0.704 0.285Extraction Method: Principal Component Analysis.a. 3 components extracted.

Rotated Component Matrix(a)

Component

1 2 3Business 0.090 -0.118 0.794

Price 0.652 -0.485 0.008

Packaging 0.833 0.288 0.232

Quality 0.297 0.851 -0.187

Brand 0.168 0.859 -0.156

Supply Services 0.430 0.008 -0.712

Shelf Life 0.830 0.256 -0.098

Promotion Schemes 0.850 0.188 -0.019

Daily Demand 0.173 -0.182 0.723

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 6 iterations.

There are 3 factors and the variables load highly onto only one factor. The result of our output regarding the same can be interpreted as follows:The first factor consists of Price, Packaging, Shelf Life and Promotion Schemes.The second factor consists of Quality and Brand.The third factor consists of Business/occupation and Supply Services

Component Transformation Matrix

Component 1 2 31

0.845 0.490 -0.213

20.467 -0.484 0.740

3-0.259 0.725 0.638

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

From our output it is evident that Factors have changed considerably as correlation between component 1 & 3 is -0.259, between 1 & 2 is 0.467. But for some components the correlation is >0.5 so it remains the same.

FINDINGS AND CONCLUSION OF FACTOR ANALYSIS

From the above analytical figures we have come to reduce our data comprising of 9 variables into broadly 3 factors.With this outcome we would be able to group all the variables under consideration into discrete factors.

The first factor consists of Price, Packaging, Shelf Life and Promotion Schemes.

The second factor consists of Quality and Brand.

The third factor consists of Business/occupation, Daily Demand and Supply Services.

So now we can concentrate on the broader terms rather than individually focusing on the variables.

RECCOMENDATIONS ON THE BASIS OF PHASE I & II

The company should only focus on selling the Fresh Paneer to Restaurants and Caterers i.e. the institutional buyers.

Retailers should not be considered for selling Fresh Paneer because the demand is very uncertain for the retailers. Also the shelf life of the product is less (about 6 to 8 days), and we don’t provide facility of replacement, it will be a burden on the retailers who will not be able to sell the product on time.

Also our findings give us the following 3 factors affecting the consumer buying behavior of paneer in India.•Basic Expectation: Price, Packaging, Shelf Life and Promotion Schemes.•Brand Image: Quality and Brand.•Operational Factor: Business/occupation and Supply Services.