ANALYSIS OF FACTORS AFFECTING THE CONSUMER BEHAVIOUR REGARDING MEN’S

FORMAL WEAR

FINAL REPORT

SUBMITTED TO: Ms.SUSHAMA MARATHE

DATE OF SUBMISSION: JANUARY 15,2010

SECTION KPrarabdha Chandrakara – Seat No. 61

Anmol Kumar - Seat No.63

Khyati Jagani - Seat No. 65

Kavish Jagwani - Seat No. 67

Neha Goyal - Seat No. 69

Ritika Gupta - Seat No. 71

ABSTRACT

This project explores young Indian consumers’ buying behavior toward formal men’s wear in Hyderabad, India. Specifically, it offers empirical results on the relationship between consumers’ decision-making and clothing choice criteria towards buying formalwear. A questionnaire is also designed for further investigation to collect information of youth buying behavior and their attitude towards formal wear. Based on questionnaire and SPSS software, attributes influencing buying behavior of men’s (youth) in Hyderabad was analyzed.

ACKNOWLEDGEMENT

We would like to express our sincere gratitude towards Prof. SHUSHAMA MARATHE, who gave us an opportunity to work on this project. We thank her for all the suggestions and valuable inputs she gave us without which we could not have been successful in accomplishing this task. This report is on ANALYSIS OF FACTORS AFFECTING THE CONSUMER BEHAVIOUR REGARDING THE MEN’S FORMAL WEAR would not have been what it is without her constant encouragement and support.

Last but not the least we would also sincerely thank all the people who contributed their valuable insights and time to help us carry out this report.

TABLE OF CONTENTS

1. Introduction2. Objective of the final report

3. Research design4. Factor analysis5. Analysis of factor analysis6. Cluster analysis

7. Analysis of Cluster analysis

8. Conclusion

INTRODUCTION:

As a nation Indians were noted to wear garments that are unstitched, for example the lungi, dhoti, sari etc. But with the advent of modernization certain changes in the dressing habits of men have undergone innovation. Within a span of just twenty years the readymade garment industry has grown thousand fold. The export figures are estimated to touch $ 11.6bn in 2010 but unfortunately the figures for the local market are not available due to the disorganized nature of the business. But a rough estimate points to $ 11.6bn with a fifty-fifty breakup in men’s and women’s wear.

According to census.gov 40% of the complete population of India falls in between 21-35yrs which makes youth market segment (school students, college students and young executives) very attractive and is believed to be one with good potential and a profitable market in formal garments. At the same time, youth now days in India are more willing to spend on apparels because the income level has increased and living standard has upgraded. The apparel retailing market is expected to become very competitive when no restriction on market access in equity, geographic area, number and form of establishment is imposed on foreign investors. In view of the keen competition in the future apparel market in India, having a better understanding of fashion consumer buying behavior, especially factors affecting decision-making behaviors and the critical evaluative criteria in apparel buying, will assist marketers to compete in this market.So our project focuses more on the analysis of factors affecting the consumer behavior regarding men’s formal wears in India.

OBJECTIVE OF THE FINAL REPORT:

To know the buying behavior for men’s formal wear in the age group of 20-35 on the basis of various attributes that a consumer takes into consideration while making a purchase decision. The purpose is to scale down the no. of factors and to see the effect of various independent variables (i.e.: factors) on the consumer preference and final purchase.

RESEARCH DESIGN

Steps of research design:

DEFINING THE POPULATION

The population chosen for the survey comprises post graduates students, who are currently pursuing professional courses (students from IBS Hyderabad). This covered age range of 20-35. They are the apt population because the product under consideration is to be designed for the B-school students only.

SAMPLING

200 respondents were randomly selected from the above defined population for the survey we conducted. We think it is apt to represent the complete population. This sample size has helped us in arriving to the various attributes that the B-school students seek from any formal apparel, as the complete population is a homogeneous group of respondents who may differ on geographical front and income levels, but the demands of the population will not vary to a great extent amongst each other.

QUESTIONNAIRE DESIGN

Most of the questions in the survey will be prepared based on the Likert scale (1 to 5). In this “1” will represent strongly agree and “5” will represent strongly disagree. This will help to find the general attitudinal characteristic of the respondents. Our questionnaire consist 12 questions where one of them has 18 attributes which are to be answered on Likert scale describing the extent to which they affect the choice of apparel.

QUESTIONNAIREPLEASE NOTE

Following questionnaire aims at identifying the factors that affect the choice/ preference of the consumers on formal apparels

Apart from taking up some of your time, answering this questionnaire presents no risk whatsoever.

Feel free to seek any clarification and ask any question regarding this project. All responses will be treated in strict confidentiality and will be used for academic

research purposes only. Your individual opinion is highly valued; therefore if possible, do not confer with others

during the completion of the questionnaire. There is no right or wrong answers; a quick response is generally the most useful.

Name:Age:

1. What is your annual family income?Below Rs 2, 50,000Rs 2, 50,000 – 4, 99,999Rs. 5, 00,000 – 7, 49,999Rs. 7, 50,000 and above

2. On what occasions do you prefer wearing formals?At work/educational institutesFor meetingsSocial gatheringsPersonal satisfaction

3. Purpose of wearing formalsComfortAppearanceObligation at workNo particular purpose

4. Which brand do you prefer?

Rate the following as 1st to the most preferred and there after 5th to the least preferred

Particulars Most preferred

Preferred Neutral Not Preferred

Not at all preferred

KoutonsOxembergCharlie OutlawCotton CountyLocal Vendors

5. How frequently do you purchase formals? Less than a month 1-3 month4- 6 month More than a year

6. On an average how much money do you spend to buy a formal wear at a time?Less than 1000 1000-19992000-2999 Greater than 3000

7. Who influence your purchase decision the most?Self Family FriendsPeer group Spouse Others______

8. Do the following attributes affect your purchase decision? Please tick the suitable column.Attributes Strongly

AgreeAgree Neutral Disagree Strongly

DisagreePriceBrand NameStore imageDisplayDesignColorFabricMaintenanceFittingCountry of OriginOffersCelebrity influenceProduct placementCurrent trendsPreference for cuff lingsPreference for full sleeves

Preference for half sleevesPrevious Experience

9. Do you prefer buying from shopping store?Yes No

10. If yes, then which store do you prefer to shop?Please select all that apply

Westside Pantaloons MaxGlobus Big Bazaar Reliance Trend

11. Which type of fabric do you preferLenin Pure Cotton Polyester Blend Others_______

12. How do you gather information about the formal brands?Television Print Media Word of MouthInternet Others______

Thank You

FACTOR ANALYSIS:

Factor analysis is an interdependence technique in that an entire set of interdependent relationships is examined. Factor analysis is used in the following circumstances:-

To identify underlying dimensions, or factors, that explains the correlation among a set of variables.

To identify a new, smaller set of uncorrelated variables to replace the original set of correlated variables in subsequent multivariate analysis (regression or discriminant analysis).

To identify a smaller set of salient variables from a set for use in subsequent multivariate analysis.

DESCRIPTIVE STATISTICS

Correlation Matrix

A Correlation matrix shows the simple correlations between all possible pairs of variables included in the analysis. The most commonly used correlation in the correlation matrix is the Pearson Product Moment Correlation, which is also used here.

Two tests, KMO statistic for sampling adequacy and Bartlett’s test of sphericity, were undertaken to test whether factor analysis would be appropriate for this study. The KMO statistic is .833 (Table 1), and Bartlett’s test of sphericity is significant, hence, the correlation matrix can be used for factor analysis.

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .833

Bartlett's Test of Sphericity

Approx. Chi-Square 1407.492df 171Sig. .000

EXTRACTING THE FACTORS

The process of factor analysis involves the following steps: Defining the unrotated factor matrix; and Rotating the factor matrix to obtain better interpretation.

Principal Component Analysis (PCA) takes all the three sources of variance, viz., common, specific and error into consideration in the identification of factors or components. Each of the factors extracted are independent of each other. The variables being used need to be standardized, i.e., they should be based on similar units of variance. This is accomplished by the factor analysis as it uses matrix correlation, which is a ratio and hence, independent of units. The initial factors were extracted using the Eigen values >1.

COMMUNALITIES

Communality explains the proportion of variance of the variable captured in the extracted factor. Initial communalities are estimates of the variance in each variable accounted for by all components or factors. For principal components analysis, this is always equal to 1.0 (for correlation analyses) or the variance of the variable (for covariance analyses). Extraction communalities are estimates of the variance in each variable accounted for by the factors (or components) in the factor solution. The table below shows the communalities values and the percentage variance explained by each variable for the prerecession condition.

Communalities

Initial ExtractionSource of information 1.000 .816Store image 1.000 .677Display 1.000 .684Previous Experience 1.000 .484Maintenance 1.000 .668Celebrity influence 1.000 .761Product Placement 1.000 .348current Trends 1.000 .760Price 1.000 .602Offers 1.000 .698Design 1.000 .752Color 1.000 .698Fabric 1.000 .720Fitting 1.000 .594Full Sleeves 1.000 .651Cufflings 1.000 .526Half sleeves 1.000 .541Brand name 1.000 .823Country of origin 1.000 .828

Extraction Method: Principal Component Analysis.

VARIANCE EXPLAINED

In below table (explaining the total variance) we can see the Eigen values of all the given variables. The first column shows the components also called factors. The "Total" column gives the amount of variance in the observed variables accounted for by each component or factor. In the next column we have the "% of Variance" column that gives the percent of variance accounted for by each specific factor or component, relative to the total variance in all the variables, and then is the cumulative variance that sums up to 100

Earlier while running factor analysis we had defined that we will take those factors whose Eigen value is more than 1. So in this case we are getting 6 components that have the Eigen value more than 1. Among these six factors the first factor would be contributing the maximum information. The second factor will contribute second most important information and so on.

Total Variance Explained

Component Initial Eigenvalues

Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings

Total% of

VarianceCumulative

% Total% of

VarianceCumulative

% Total % of Variance Cumulative %1 5.586 29.399 29.399 5.586 29.399 29.399 5.519 29.045 29.0452 1.853 9.754 39.153 1.853 9.754 39.153 1.819 9.575 38.6203 1.692 8.904 48.057 1.692 8.904 48.057 1.688 8.882 47.5024 1.300 6.844 54.901 1.300 6.844 54.901 1.299 6.836 54.3385 1.185 6.239 61.140 1.185 6.239 61.140 1.187 6.248 60.5866 1.013 5.334 66.474 1.013 5.334 66.474 1.119 5.888 66.4747 .942 4.959 71.4338 .828 4.357 75.7909 .714 3.757 79.54710 .656 3.451 82.99811 .553 2.912 85.91012 .462 2.434 88.34413 .447 2.354 90.69814 .399 2.099 92.79715 .337 1.774 94.57116 .302 1.591 96.16217 .285 1.499 97.66118 .235 1.237 98.89819 .209 1.102 100.000Extraction Method: Principal Component Analysis.

ROTATED COMPONENT MATRIX

As unrotated solutions are hard to interpret, we use varimax rotation. Varimax rotation is an orthogonal rotation of the factor axes to maximize the variance of the squared loadings of a factor (column) on all the variables (rows) in a factor. A varimax solution yields results which make it as easy as possible to identify each variable with a single factor. The rotated matrix, using PCA analysis, is shown in Table below.

Rotated Component Matrix(a)

Component

1 2 3 4 5 6Source of information .897Store image .741 Display .762 Previous Experience .637 Maintenance .775 Celebrity influence .856 Product Placement .562 current Trends .859 Price .763 Offers .828 Design .845 Color .816 Fabric .834 Fitting .746 Full Sleeves .737 Cufflings .713 Half sleeves .722 Brand name .905 Country of origin .903

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.a Rotation converged in 6 iterations.

Six factors, such as Product Features, Brand Association, Brand identification, Brand Location, Brand Usage, and Source of information were identified from the above table.

Factor 1 (Product Features)

Factor 2(Brand Association)

Factor 3 (Brand Identification)

Factor 4 (Brand Location)

Factor 5 (Brand Usage)

Factor 6 (Source of information)

Price Celebrity Influence

Brand Name Store image

Previous Experience

Source of information

Offers Product Placement

Country of Origin

Display Maintenance

Design Current TrendsColorFabricFittingFull SleevesCufflingsHalf Sleeves

CLUSTER ANALYSIS

The main aim of cluster analysis is to identify clusters. These clusters help us to identify those groups of cases which are closely related to each other and those which are not closely related. The cases within a cluster are homogeneous to each other but are heterogeneous to cases in other clusters.

It is basically used when the researcher does not have a very clear idea about the groups in advance but wishes to establish groups and analyze them to get better insights about the population. There are two ways of cluster analysis:

Hierarchical Cluster Analysis

K-means clustering

As we have 200 cases, we did K-Means clustering, as hierarchical clustering will not give the clear picture of clusters for such long data sets. we wanted to run the cluster analysis to identify those cases which fall in a cluster and depict a common perception towards purchase of formal wear.

In this case we identified 4 groups to be an appropriate number. This table shows the value for the initial cluster centers. This value is the mean of each variable within each cluster. So for instance we can say that the mean of income in the first cluster is 4, that is to say that maximum number of people in cluster 1 have income in between 750000 and above. For them the source of information is of not much importance as they mostly rated 2 for it.These initial clusters are those dissimilar cases which the program chooses and then these values are used to define the initial clusters.

Initial Cluster Centers

Cluster

1 2 3 4

Income 4 3 2 2Occasion 2 3 3 2Purpose 2 2 2 4Koutons 2 5 2 4Oxemberg 4 4 5 1Charlie Outlaw 2 3 3 4Cotton County 2 2 5 2Local Vendors 4 1 5 4Purchase Frequency 3 2 2 3

Money Spend 3 3 2 3Influencer 1 5 1 3Source of information 2 5 5 2

Store image 4 1 1 5Display 4 1 1 4Previous Experience 3 3 2 5

Maintenance 5 3 1 4Celebrity influence 5 5 2 1Product Placement 3 5 2 1current Trends 5 5 1 1Price 3 5 1 5Offers 2 5 1 5Design 1 5 2 5Color 2 5 1 5Fabric 3 5 2 5Fitting 3 5 2 5Full Sleeves 2 5 2 5Cufflings 1 3 1 5Half sleeves 1 1 2 5Brand name 1 5 4 4Country of origin 1 5 5 4Shopping Store 2 1 1 1Selected Store 0 1 6 5Type of Fabric 3 2 2 2Information Gathering 5 2 1 5

As against the initial cluster centers this table shows the value of the final cluster centers. So now we can say that the mean of source of information in the first cluster is 3, that is to say that maximum number of people in cluster 1 rated source of information as neutral in case of influencing their purchase of formals which if we compare from the initial cluster was not so important(2). Now the income group is also 3 i.e. 500000-749999. Final Cluster Centers

Cluster

1 2 3 4Income 3 3 3 3Occasion 2 2 2 2Purpose 2 2 2 2Koutons 3 4 3 4Oxemberg 3 4 3 3Charlie Outlaw 3 3 3 3Cotton County 3 3 3 2Local Vendors 4 2 4 4Purchase Frequency 3 3 3 3

Money Spend 3 3 2 2Influencer 2 4 2 2Source of information 3 5 3 3

Store image 4 1 3 3Display 4 2 4 4Previous Experience 2 2 2 3

Maintenance 2 2 2 3Celebrity influence 3 2 2 1Product Placement 2 3 2 2current Trends 3 3 2 2Price 2 5 2 5Offers 2 5 2 4Design 2 5 2 4Color 2 5 2 5Fabric 2 5 2 5Fitting 2 5 2 5Full Sleeves 2 5 2 4Cufflings 2 4 2 4Half sleeves 2 4 2 5Brand name 3 4 5 4Country of origin 3 4 4 4Shopping Store 1 1 1 1Selected Store 3 1 3 3Type of Fabric 2 2 2 3Information 2 2 2 3

In the output we have cluster membership which gives the membership of each case in particular cluster and its distance from the cluster’s centroid. The table shown below shows the distances of various clusters with each other. All the pairs of cluster centers are well separated.

Distances between Final Cluster Centers

Cluster 1 2 3 41 10.431 3.136 8.3992 10.431 10.079 5.4353 3.136 10.079 7.9334 8.399 5.435 7.933

ANOVA

Cluster Error

F Sig.Mean Square df

Mean Square df

Income .444 3 .605 196 .734 .533Occasion 1.248 3 .753 196 1.659 .177Purpose 1.244 3 .587 196 2.119 .099Koutons .828 3 1.228 196 .674 .569Oxemberg .473 3 1.084 196 .437 .727Charlie Outlaw .835 3 1.060 196 .787 .502Cotton County 1.737 3 .981 196 1.770 .154Local Vendors 4.381 3 .974 196 4.499 .004Purchase Frequency .957 3 .638 196 1.500 .216

Money Spend .445 3 .628 196 .709 .548Influencer 10.744 3 1.287 196 8.348 .000Source of information 6.353 3 1.948 196 3.261 .023

Store image 12.279 3 2.040 196 6.018 .001Display 6.401 3 1.711 196 3.741 .012Previous Experience .941 3 1.566 196 .601 .615

Maintenance 2.572 3 1.409 196 1.825 .144Celebrity influence 34.856 3 1.073 196 32.473 .000Product Placement 2.697 3 .785 196 3.435 .018current Trends 21.027 3 1.131 196 18.590 .000Price 46.254 3 .672 196 68.788 .000Offers 54.171 3 .527 196 102.722 .000Design 57.127 3 .445 196 128.383 .000Color 50.508 3 .655 196 77.080 .000Fabric 57.357 3 .552 196 103.949 .000Fitting 52.016 3 .797 196 65.240 .000Full Sleeves 49.612 3 .755 196 65.684 .000Cufflings 42.211 3 .844 196 49.984 .000Half sleeves 40.968 3 .753 196 54.410 .000

Brand name 24.363 3 1.218 196 19.998 .000Country of origin 26.329 3 1.107 196 23.789 .000Shopping Store .071 3 .091 196 .785 .504Selected Store 3.048 3 2.148 196 1.419 .238Type of Fabric .412 3 .502 196 .820 .484Information Gathering 3.110 3 .914 196 3.405 .019

The F tests should be used only for descriptive purposes because the clusters have been chosen to maximize the differences among cases in different clusters. The observed significance levels are not corrected for this and thus cannot be interpreted as tests of the hypothesis that the cluster means are equal.

Now when we look at the above table we see the F Ratio. The F Ratio associated with Design is the largest at 128.383 meaning that this variable is most helpful in forming and differentiating the clusters. Though this is the highest but there are certain other variables as well which play a very critical role while forming the clusters. These are basically:

Offers Fabric Color Fitting Full sleeves Price Half sleeves

At the same time the ones that are least useful to form these clusters are: Income Previous experience Shopping store Money spent Maintenance Other brands

The table below defines the number of cases that have been allotted to each cluster.

Number of Cases in each Cluster

Cluster 1 55.000

2 4.0003 121.0004 20.000

Valid 200.000Missing .000

Basically by this test we conclude that we have four different clusters to focus on. Also among them the most important segment is the 3rd cluster which consists of 121 people out of 200 in a sample. These people are mostly influenced by the display and like to shop from local vendors. so accordingly we will have to target the market and fulfill their needs.

REFRENCES

1. Marketing research – by Naresh Malohtra2. http://faculty.chass.nscu.edu/garson/PA756/factor.htm 3. http://faculty.chass.nscu.edu/garson/PA756/cluster.htm 4.