leading factors contributing to brand switching in apparel

Research Methodology

The research design of this study is a combination of exploratory and descriptive research; the research looks to identify factors that affect brand switching in apparel industry along with finding out relationship between various factors that affect brand switching. The study also looks at brand loyalty aspects of consumer with an objective of presenting a brief understanding of factors that lead to brand loyalty. This research looks to understand psyche that drives GenY consumers’ brand preferences while buying clothes. This research would describe various characteristics about the population in relation to their brand perception. In apparel industry, major issue faced by brands is of consumer’s loyalty towards their brand. This research will briefly provide findings which will help brands in understanding the aspect that creates loyalty among consumers and thus facilitate them in implementing strategies that will help them create a loyalty for their brand.

Sources and tools of data collection

Primary data The data is gathered through a survey based approach with the help of a questionnaire. The respondents lie within the age group of 10 to 29 years. The respondents were further classified into 4 age groups which are a) 10-14 b) 15-19 c) 20-24 d) 25-29.

Secondary data For the purpose of this study, we have collected data from the sources like internet, published data etc.

Population of the study A sample size of 250 youth (Gen Y) of Bangalore has been included in the population for this research.

Sampling technique Simple random sampling method was used to collect the data as questionnaire were administered to the people chosen randomly such that each individual among the age group has the same probability of being chosen.

Scale use in questionnaire: 4 point Likert scale Ordinal scale

Statistical Tools:

Factor Analysis Cross Tabulations Chi-Square Test

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Reliability of Constructs:

In order to perform factor analysis on the data collected, the reliability of the collected data was checked using KMO and Bartlett's Test (Table 1). Kaiser-Meyer-Olkin Measure of Sampling Adequacy is greater than 0.6, hence we can conclude that the data is reliable.

Table 1: KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .628

Bartlett's Test of Sphericity Approx. Chi-Square 670.501

Df 105

Sig. .000

Analysis and Inferences:

The sample comprised of 62% of men and 38% of women spread across the following age groups: 5.6% in 10–14, 14.4% in 15–19, 46% in 20–24 and 34% in 25-29 years of age. In order to pre-classify the respondents in one of the two categories-loyal consumers and brand-switching consumers, the consumers were asked to list the name of the brands of apparels that they purchased in the type of clothing that they buy the most. If the respondent gave the name of only one brand or they took time to think about names of brands that they bought after giving the name of one brand, they were perceived as loyal customers. The rest were termed as brand-switching consumers. Therefore, in the present research, the sample was pre-classified into loyal and non-loyal consumers and it was found that 94% of the consumers switch between apparel brands due to various reasons.

We came up with a list of 15 factors from the previous research done by various people as discussed in the literature review above. We applied factor analysis on the data derived from our questionnaires. We clubbed these factors in five major heads with eigenvalues greater than 1. Unless a factor extracts at least as much as the equivalent of one original variable, the factor was not considered (Table 2). The five factors chosen explain 59% of switching behavior between apparel brands of GenY consumers.

The five factors chosen were group of factors that were correlated the most. These were based on factor loadings (Table 3).

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Table 2: 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 2.640 17.603 17.603 2.640 17.603 17.603 1.890 12.602 12.602

2 2.047 13.644 31.247 2.047 13.644 31.247 1.796 11.975 24.578

3 1.735 11.568 42.816 1.735 11.568 42.816 1.791 11.941 36.519

4 1.298 8.653 51.469 1.298 8.653 51.469 1.703 11.355 47.874

5 1.094 7.294 58.763 1.094 7.294 58.763 1.633 10.889 58.763

6 .968 6.451 65.214

7 .869 5.791 71.005

8 .795 5.303 76.308

9 .682 4.547 80.856

10 .666 4.441 85.296

11 .547 3.649 88.945

12 .480 3.202 92.147

13 .443 2.953 95.100

14 .386 2.575 97.675

15 .349 2.325 100.000

Extraction Method: Principal Component Analysis

The first most important factor was called Quality Factor which included changes in quality, prior satisfaction with purchases and variety-seeking behavior of the customer. The second important factor was called Price Factor which included low prices and good discounts. The third factor was called Styling Factor which included design, style and fit of apparels which make people switch from one brand to another. The fourth factor was called Identity Factor which included advertisements, ‘star’ brand ambassador and peer-pressure. The fifth factor was called Retail Factor which included store-display, availability of a particular brand and customer-service.

The above five factors are the major factors in order of importance that contribute to brand switching in apparels for GenY, hence companies should focus on building upon these factors, so that brand switching can be reduced in apparels industry.

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Table 3: Rotated Component Matrix

Component

1 2 3 4 5

Low Prices .067 .795 -.077 .024 .074

Good Discounts .182 .782 -.095 .002 .021

Store Display -.140 -.026 .230 .237 .622

Design or Style .092 -.114 .788 -.042 .018

Fit .050 -.016 .785 .075 .290

Advertisements -.095 .286 .392 .491 .292

Brand Ambassador -.158 .077 .192 .762 .063

Peer-Pressure .065 -.193 -.173 .736 .076

Desire for novelty .439 .059 .420 .229 -.181

Accessibility / Availability -.040 -.011 .030 -.002 .823

To differentiate yourself .435 -.562 -.257 .184 .175

Changes in Quality .717 -.078 .059 -.224 -.269

Prior Satisfaction .630 .000 -.031 -.345 -.085

Customer Service .457 .235 .030 .074 .470

Variety-seeking behavior .529 .177 .133 .117 .140

After analyzing all the constraints and factors, we come to know that majority of the customers switch between brands in today’s scenario and that is not because they seek thrill in doing so, instead sometimes they suffer of not getting the expected quality or output from utilization of the newly adopted or switched brand.

The main constraints behind brand switching are manufacturers who are not able to keep their promise to deliver better product at competitive prices, marketers not able to put or expose their product in a better way and producers who are not delivering quality product, which makes customer look at other brands or adopt different brands to cater their needs.

Jack Trout and Al Ries have rightly said that it is all up to you what positioning and brand image your products possess and that reasonably depends on how you segment your product for varied market locations and core competency of product which only can repeat the consumer buying process. So its always better that you keep eyeing the customer prospective need sets and your competitors strategy, if you are successful in doing so, your set of potential customers will be more and brand loyalty for your product will sustain long lasting.

After deriving at the five major factors affecting brand switching in apparels, their dependence on factors like age, gender, income, profession, frequency of shopping and social

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behavior/status was analysed. A cross tabulation was made for each of the factors and their dependence was checked using chi-square test.

Age and Quality factor:

To see the dependence of quality ratings on age, cross tabs were used (Table 4). Quality ratings 1-4 are given for factor loadings derived from factor analysis. Factor loadings in the range of -4.23 – -2.62 have been given 1, factor loadings in the range of -2.62 – -1.01 have been given 2, factor loadings in the range of -1.01–0.61 have been given 3 and factor loadings in the range of 0.61–2.22 have been given 4. Lower the rating/factor loading, lesser does the quality factor affect the switching decisions of consumers in that particular age group.

Table 4: Crosstab (Age*Quality)

Quality Factor

Total1 2 3 4

Age 10-14 Count 1 8 3 2 14

% within Age 7.1% 57.1% 21.4% 14.3% 100.0%

15-19 Count 1 3 25 7 36

% within Age 2.8% 8.3% 69.4% 19.4% 100.0%

20-24 Count 1 11 67 36 115

% within Age .9% 9.6% 58.3% 31.3% 100.0%

25-29 Count 1 10 50 24 85

% within Age 1.2% 11.8% 58.8% 28.2% 100.0%

Total Count 4 32 145 69 250

% within Age 1.6% 12.8% 58.0% 27.6% 100.0%

It can be seen from the above table that Quality affects the switching behavior of consumers of apparels of different age groups differently. Greater percentage of consumers in the age group 20-29 lie in the 3-4 rating which simply shows that they are highly affected by quality while young consumers in the age group of 10-14 do not pay much attention to quality of an apparel.

To check this, a chi square test was performed at 5% level of significance.

The null hypothesis (Ho) was that the Quality Rating does not depend on age while the alternate hypothesis (H1) was that the Quality Rating depends on age.

Pearson Chi Square (χ2) = 32.961 while the Table Value (los=0.05, d.f =9) = 16.92

χ2>Table Value, therefore null hypothesis was rejected with 95% confidence. Hence, we can conclude that the Quality Rating depends on age. Not all age groups of GenY switch between brands because of quality factor. Some age groups give more importance to quality as a factor for switching between apparel brands, however some do not.

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Gender and Quality Factor:

After seeing the dependence of the quality factor on age, we checked if quality affects male and females differently. This was again done using crosstab (Table 5). Lower the rating/factor loading, lesser does the quality factor affect the switching decisions of consumers of that particular gender.

Table 5: Crosstab (Gender*Quality)

Quality

Total1 2 3 4

Gender Female Count 1 11 58 26 96

% within Gender 1.0% 11.5% 60.4% 27.1% 100.0%

Male Count 3 21 87 43 154

% within Gender 1.9% 13.6% 56.5% 27.9% 100.0%

Total Count 4 32 145 69 250

% within Gender 1.6% 12.8% 58.0% 27.6% 100.0%

It can be seen from the above table that there is not much difference between importance of

Quality affecting switching behavior between males and females.


The null hypothesis (Ho) was that the Quality Rating does not depend on gender while the alternate hypothesis (H1) was that the Quality Rating depends on gender.


χ2 < Table Value, therefore null hypothesis was accepted with 95% confidence. Hence, we can conclude that the Quality Rating does not depend on gender. Both males and females give high importance to quality as a factor for their switching behavior in apparel brands.

Profession and Quality Factor:

To check the dependence of Quality factor on profession of a consumer, crosstab was used (Table 6). Lower the rating/factor loading, lesser does the quality factor affect the switching decisions of consumers of that particular profession.

It can be seen from the below table that there is not much difference across various professions

in quality affecting switching behavior.

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Table 6: Crosstab (Profession*Quality)

Quality Factor

Total1 2 3 4

Profession Salaried Count 1 10 60 22 93

% within Profession 1.1% 10.8% 64.5% 23.7% 100.0%

Self Employed Count 0 0 13 2 15

% within Profession .0% .0% 86.7% 13.3% 100.0%

Student Count 3 22 72 45 142


Total Count 4 32 145 69 250



The null hypothesis (Ho) was that the Quality Rating does not depend on profession while the alternate hypothesis (H1) was that the Quality Rating depends on profession.


χ2 < Table Value, therefore null hypothesis was accepted with 95% confidence. Hence, we can conclude that the Quality Rating does not depend on profession. All students, salaried and self-employed people give high importance to quality as a factor for their switching behavior in apparel brands.

Income and Quality Factor:

Changing income plays an important in switching behaviour of consumers. If consumers are willing to pay more, they definitely ask for better quality of apparels. To check this tendency of consumers, a cross tab was used (Table 7). Lower the rating/factor loading, lesser does the quality factor affect the switching decisions of consumers of that particular profession.

It can be seen from the below table that there is not much difference across various income groups in quality affecting switching behavior. However, in higher income groups, 50% of the people have a rating of 4 implying that quality is an important factor affecting their switching behavior. In income groups more than Rs. 45K, there are 0% people for whom quality is not an important factor for brand switching. Therefore, little variation is there between income groups, but not to an extent like seen in various age groups.

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Table 7: Crosstab (Income*Quality)

Quality Factor

Total1 2 3 4

Monthly Income 0-15k Count 1 1 21 2 25

% within Monthly Income 4.0% 4.0% 84.0% 8.0% 100.0%

15001-30k Count 0 6 24 9 39

% within Monthly Income .0% 15.4% 61.5% 23.1% 100.0%

30001-45k Count 0 3 15 9 27


45001-60k Count 0 0 10 1 11

% within Monthly Income .0% .0% 90.9% 9.1% 100.0%

More than 60k Count 0 0 3 3 6


Total Count 1 10 73 24 108



The null hypothesis (Ho) was that the Quality Rating does not depend on income while the alternate hypothesis (H1) was that the Quality Rating depends on income.


χ2 < Table Value, therefore null hypothesis was accepted with 95% confidence. Hence, we can conclude that the Quality Rating does not depend on income.

Social Behaviour/Status and Quality Factor:

Social status plays an important role in switching behavior of apparel consumer bahaviour. Growing social status makes them more conscious of quality of clothes. To check this, Crosstab was used (Table 8).

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Table 8: Crosstab (Socially Engaged*Quality)

Quality Factor

Total1 2 3 4

Socially Engaged No Count 1 9 21 9 40

% within Socially Engaged 2.5% 22.5% 52.5% 22.5% 100.0%

Yes Count 3 23 124 60 210


Total Count 4 32 145 69 250


It can be seen from the above table that socially engaged people have slightly more percentage of

people in quality rating 3 and 4 than non-socially engaged people. However, both kinds of

people react in a similar way to quality factor.


The null hypothesis (Ho) was that the Quality Rating does not depend on social behavior/status while the alternate hypothesis (H1) was that the Quality Rating depends on social behavior/status.


χ2 < Table Value, therefore null hypothesis was accepted with 95% confidence. Hence, we can conclude that the Quality Rating does not depend on social behavior/status.

Age and Price Factor:

Another important factor is the price factor which is majorly responsible for the brand switching of consumers. To see the dependence of price ratings on age, cross tabs were used (Table 9). Price ratings 1-4 are given for factor loadings derived from factor analysis. Factor loadings in the range of -2.91 – -1.69 have been given 1, factor loadings in the range of -1.69 – -0.46 have been given 2, factor loadings in the range of -0.46–0.77 have been given 3 and factor loadings in the range of 0.77–1.99 have been given 4. Lower the rating/factor loading, lesser does the price factor affect the switching decisions of consumers in that particular age group.

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Table 9: Crosstab (Age*Price)

Price Factor

Total1 2 3 4

Age 10-14 Count 3 6 5 0 14

% within Age 21.4% 42.9% 35.7% .0% 100.0%

15-19 Count 1 7 20 8 36

% within Age 2.8% 19.4% 55.6% 22.2% 100.0%

20-24 Count 5 27 59 24 115

% within Age 4.3% 23.5% 51.3% 20.9% 100.0%

25-29 Count 6 17 36 26 85

% within Age 7.1% 20.0% 42.4% 30.6% 100.0%

Total Count 15 57 120 58 250

% within Age 6.0% 22.8% 48.0% 23.2% 100.0%

It can be seen from the above table that Price affects the switching behavior of consumers of apparels of different age groups differently. Greater percentage of consumers in the age group 15-29 lie in the 3-4 rating which simply shows that they are highly affected by price while young consumers in the age group of 10-14 do not pay much attention to price of an apparel.


The null hypothesis (Ho) was that the Price Rating does not depend on age while the alternate hypothesis (H1) was that the Price Rating depends on age.


χ2>Table Value, therefore null hypothesis was rejected with 95% confidence. Hence, we can conclude that the Price Rating depends on age. Not all age groups of GenY switch between brands because of price factor. Some age groups give more importance to price as a factor for switching between apparel brands, however some do not.

Gender and Price Factor:

Females are said to be more calculative as they are the epitomes in shopping of apparels. Therefore, price and discounts form are important factors for switching between apparel brands for them. A crosstab was used to analyse this (Table 10). Lower the rating/factor loading, lesser does the price factor affect the switching decisions of consumers in that particular gender.

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Table 10: Crosstab (Gender*Price)

Price Factor

Total1 2 3 4


% within Gender 4.2% 10.4% 52.1% 33.3% 100.0%

Male Count 11 47 70 26 154

% within Gender 7.1% 30.5% 45.5% 16.9% 100.0%

Total Count 15 57 120 58 250

% within Gender 6.0% 22.8% 48.0% 23.2% 100.0%

In the above table, Females have more percentage in rating 3 and 4 than males implying that

price is dearer to them when it comes to shopping for apparels.


The null hypothesis (Ho) was that the Price Rating does not depend on gender while the alternate hypothesis (H1) was that the Price Rating depends on gender.

Pearson Chi Square (χ2) = 18.794 while the Table Value (los=0.05, d.f =3) = 7.82χ2>Table Value, therefore null hypothesis was rejected with 95% confidence. Hence, we can conclude that the Price Rating depends on gender. Females switch between brands because of price while males don’t.

Profession and Price Factor:To check if there is any dependence of price rating on one’s profession, a crosstab was used (Table 11). Lower the rating/factor loading, lesser does the price factor affect the switching decisions of consumers in that particular gender.

Table 11: Crosstab (Profession*Price)

Price Factor

Total1 2 3 4




% within Profession 13.3% 33.3% 53.3% .0% 100.0%



Total Count 15 57 120 58 250


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It can be seen from the above table that there is not much difference across various professions in price affecting switching behavior. However, students and salaried people seem to be a little more affected by price than self employed people. Therefore, little variation is there between various professions, but not to an extent like seen in various age groups.


The null hypothesis (Ho) was that the Price Rating does not depend on profession while the alternate hypothesis (H1) was that the Price Rating depends on profession.


χ2<Table Value, therefore null hypothesis was accepted with 95% confidence. Hence, we can conclude that the Price Rating does not depend on profession.

Income and Price Factor:

Income is an important factor because of which consumers change their buying decisions. To check the dependence of price as a factor affecting brand switching behaviour on income, a crosstab was used (Table 12)

Table 12: Crosstab (Income*Price)

Price Factor

Total1 2 3 4



15001-30k Count 2 12 16 9 39


30001-45k Count 4 7 12 4 27


45001-60k Count 2 2 7 0 11

% within Monthly Income 18.2% 18.2% 63.6% .0% 100.0%


% within Monthly Income .0% 66.7% 33.3% .0% 100.0%

Total Count 8 26 48 26 108


In the above table, low income groups have more percentage of people in higher price ratings,

while people with more than Rs. 60K per month, price is not an important factor affecting brand

switching in apparels. Therefore, price as a factor affecting brand switching behavior, varies

across income groups.


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The null hypothesis (Ho) was that the Price Rating does not depend on income while the alternate hypothesis (H1) was that the Price Rating depends on income.


χ2>Table Value, therefore null hypothesis was rejected with 95% confidence. Hence, we can conclude that the Price Rating depends on income. Consumers with lower income switch between brands because of price while high income consumers don’t.

Frequency of shopping and Price Factor:

To check if there is any dependence of price rating on one’s profession, a crosstab was used (Table 13). Lower the rating/factor loading, lesser does the price factor affect the switching decisions of consumers in a level of frequency of shopping.

Table 13: Crosstab (Frequency of shopping*Price)

Price Factor

Total1 2 3 4

Frequency of Shopping Very Low Count 0 1 5 4 10

% within Frequency of

Shopping

.0% 10.0% 50.0% 40.0% 100.0%

Low Count 5 20 59 35 119


Shopping

4.2% 16.8% 49.6% 29.4% 100.0%

Medium Count 7 22 30 14 73


Shopping

9.6% 30.1% 41.1% 19.2% 100.0%

High Count 1 5 11 1 18


Shopping

5.6% 27.8% 61.1% 5.6% 100.0%

Very High Count 2 9 15 4 30


Shopping

6.7% 30.0% 50.0% 13.3% 100.0%

Total Count 15 57 120 58 250


Shopping

6.0% 22.8% 48.0% 23.2% 100.0%

It can be seen from the above table that there is not much difference across various frequencies of shopping in price affecting switching behavior. However, consumers who go less frequently for shopping seem to be a little more affected by price than frequent shoppers. Therefore, little

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variation is there between various levels of frequency of shopping, but not to an extent like seen in various age groups.


The null hypothesis (Ho) was that the Price Rating does not depend on frequency of shopping while the alternate hypothesis (H1) was that the Price Rating depends on frequency of shopping.


χ2<Table Value, therefore null hypothesis was accepted with 95% confidence. Hence, we can conclude that the Price Rating does not depend on frequency of shopping.

Social Behaviour/Status and Price Factor:

To check if there is any dependence of price rating on one’s social status, a crosstab was used (Table 14).

Table 14: Crosstab (Socially Engaged*Price)

Price Factor

Total1 2 3 4

Socially Engaged No Count 2 14 8 40


Yes Count 13 43 104 50 210


Total Count 15 57 120 58 250


It can be seen from the above table that there is not much difference between socially engaged and non-socially engaged people in price affecting switching behavior. However, consumers who are socially engaged seem to be a little more affected by price than non-socially engaged consumers.


The null hypothesis (Ho) was that the Price Rating does not depend on social behavior/status while the alternate hypothesis (H1) was that the Price Rating depends on social behavior/status.


χ2<Table Value, therefore null hypothesis was accepted with 95% confidence. Hence, we can conclude that the Price Rating does not depend on social behavior/status.

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Age and Styling Factor:

Another important factor is the styling factor which is majorly responsible for the brand switching of consumers especially in young consumers. To see the dependence of style ratings on age, cross tabs were used (Table 15). Styling ratings 1-4 are given for factor loadings derived from factor analysis. Factor loadings in the range of -3.64 – -2.17 have been given 1, factor loadings in the range of -2.17 – -0.69 have been given 2, factor loadings in the range of -0.69–0.79 have been given 3 and factor loadings in the range of 0.79–2.26 have been given 4. Lower the rating/factor loading, lesser does the styling factor affect the switching decisions of consumers in that particular age group.

Table 15: Crosstab (Age*Styling)

Styling Factor

Total1 2 3 4

Age 10-14 Count 0 3 8 3 14

% within Age .0% 21.4% 57.1% 21.4% 100.0%

15-19 Count 1 5 23 7 36

% within Age 2.8% 13.9% 63.9% 19.4% 100.0%

20-24 Count 4 18 73 20 115

% within Age 3.5% 15.7% 63.5% 17.4% 100.0%

25-29 Count 3 12 49 21 85

% within Age 3.5% 14.1% 57.6% 24.7% 100.0%

Total Count 8 38 153 51 250

% within Age 3.2% 15.2% 61.2% 20.4% 100.0%

It can be seen from the above table that there is not much difference across various age groups of GenY in styling affecting switching behavior.


The null hypothesis (Ho) was that the Styling Rating does not depend on age while the alternate hypothesis (H1) was that the Styling Rating depends on age.


χ2<Table Value, therefore null hypothesis was accepted with 95% confidence. Hence, we can conclude that the Styling Rating does not depend on age. Therefore, design, style and fit affect switching behavior of consumers but similarly across all age groups.

Gender and Styling Factor:It has been generally noticed that females are more style conscious than men. However, to check this, a cross tab was used (Table 16)

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Table 16: Crosstab (Gender*Styling)

Styling Factor

Total1 2 3 4


% within Gender 3.1% 11.5% 62.5% 22.9% 100.0%

Male Count 5 27 93 29 154

% within Gender 3.2% 17.5% 60.4% 18.8% 100.0%

Total Count 8 38 153 51 250

% within Gender 3.2% 15.2% 61.2% 20.4% 100.0%

It can be seen from the above table that there is not much difference between males and females of GenY in styling affecting switching behavior. However, females seem to be slightly more affected by styling factor.


The null hypothesis (Ho) was that the Styling Rating does not depend on gender while the alternate hypothesis (H1) was that the Styling Rating depends on gender.


χ2<Table Value, therefore null hypothesis was accepted with 95% confidence. Hence, we can conclude that the Styling Rating does not depend on gender. Therefore, design, style and fit affect switching behavior of consumers but similarly across both males and females.

Profession and Styling Factor:

To check if there is any dependence of styling rating on profession, a crosstab was used (Table 17).

Table 17: Crosstab (Profession*Styling)

Factor 3

Total1 2 3 4




% within Profession .0% 6.7% 73.3% 20.0% 100.0%



Total Count 8 38 153 51 250


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It can be seen from the above table that there is not much difference between various professions in styling affecting switching behavior.


The null hypothesis (Ho) was that the Styling Rating does not depend on profession while the alternate hypothesis (H1) was that the Styling Rating depends on profession.


χ2<Table Value, therefore null hypothesis was accepted with 95% confidence. Hence, we can conclude that the Styling Rating does not depend on profession. Therefore, design, style and fit affect switching behavior of consumers but similarly across all professions.

Income and Styling Factor:

To check if there is any dependence of styling rating on income, a crosstab was used (Table 18)

Table 18: Crosstab (Income*Styling)

Styling Factor

Total1 2 3 4



15001-30k Count 0 8 21 10 39


30001-45k Count 2 8 12 5 27


45001-60k Count 0 0 9 2 11




Total Count 4 19 59 26 108


It can be seen from the above table that there is not much difference between various income groups in styling affecting switching behavior. However, higher income people seem to be slightly more affected by styling as a factor affecting brand switching behaviour.


The null hypothesis (Ho) was that the Styling Rating does not depend on income while the alternate hypothesis (H1) was that the Styling Rating depends on income.

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χ2<Table Value, therefore null hypothesis was accepted with 95% confidence. Hence, we can conclude that the Styling Rating does not depend on income.

Frequency of shopping and Styling Factor:To check if there is any dependence of styling rating on frequency of shopping, a crosstab was used (Table 19)

Table 19: Crosstab (Styling*Frequency of shopping)

Styling Factor

Total1 2 3 4

Frequency of

Shopping

Very Low Count 1 2 7 0 10


Shopping

10.0% 20.0% 70.0% .0% 100.0%

Low Count 3 12 71 33 119


Shopping

2.5% 10.1% 59.7% 27.7% 100.0%



Shopping

2.7% 20.5% 63.0% 13.7% 100.0%



Shopping

11.1% 22.2% 50.0% 16.7% 100.0%



Shopping

.0% 16.7% 66.7% 16.7% 100.0%

Total Count 8 38 153 51 250


Shopping

3.2% 15.2% 61.2% 20.4% 100.0%

It can be seen from the above table that there is not much difference between more frequent and less frequent shoppers in styling affecting switching behavior.


The null hypothesis (Ho) was that the Styling Rating does not depend on frequency of shopping while the alternate hypothesis (H1) was that the Styling Rating depends on frequency of shopping


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χ2<Table Value, therefore null hypothesis was accepted with 95% confidence. Hence, we can conclude that the Styling Rating does not depend on frequency of shopping.

Social Behaviour/Status and Styling Factor:

To check if there is any dependence of styling rating on income, a crosstab was used (Table 20). In the table below, there is not much difference between socially engaged and non-socially engaged people in styling affecting switching behavior. However, socially engaged people seem to be slightly more affected by styling as a factor affecting brand switching behaviour.

Table 20: Crosstab (Socially Engaged*Styling)

Styling Factor

Total1 2 3 4



Yes Count 5 29 130 46 210


Total Count 8 38 153 51 250



The null hypothesis (Ho) was that the Styling Rating does not depend on social behavior/status while the alternate hypothesis (H1) was that the Styling Rating depends on social behavior/status.

Pearson Chi Square (χ2) = 5.984while the Table Value (los=0.05, d.f =3) = 7.82

χ2<Table Value, therefore null hypothesis was accepted with 95% confidence. Hence, we can conclude that the Styling Rating does not depend on social behavior/status.

Age and Identity Factor:

Another important factor is the identity factor which is majorly responsible for the brand switching of consumers especially in young consumers. To see the dependence of identity ratings on age, cross tabs were used (Table 21). Identity ratings 1-4 are given for factor loadings derived from factor analysis. Factor loadings in the range of -2.23 – -0.91 have been given 1, factor loadings in the range of -0.91 –0.41 have been given 2, factor loadings in the range of 0.41–1.72 have been given 3 and factor loadings in the range of 1.72–3.04 have been given 4. Lower the rating/factor loading, lesser does the identity factor affect the switching decisions of consumers in that particular age group.

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It can be seen in the table below that young consumers have more percentage of people in higher

identity ratings, while for consumers in the age group of 20-29, identity is not an important factor

affecting brand switching in apparels. Therefore, identity as a factor affecting brand switching

behavior varies across age groups. It is generally seen that children get influenced by peers and

brand ambassadors. Therefore, it affects their purchasing of brands.

Table 21: Crosstab (Age*Identity)

Identity Factor

Total1 2 3 4

Age 10-14 Count 1 1 6 6 14

% within Age 7.1% 7.1% 42.9% 42.9% 100.0%

15-19 Count 2 14 18 2 36

% within Age 5.6% 38.9% 50.0% 5.6% 100.0%

20-24 Count 22 59 30 4 115

% within Age 19.1% 51.3% 26.1% 3.5% 100.0%

25-29 Count 17 52 16 0 85

% within Age 20.0% 61.2% 18.8% .0% 100.0%

Total Count 42 126 70 12 250

% within Age 16.8% 50.4% 28.0% 4.8% 100.0%


The null hypothesis (Ho) was that the Identity Rating does not depend on age while the alternate hypothesis (H1) was that the Identity Rating depends on age.


χ2>Table Value, therefore null hypothesis was rejected with 95% confidence. Hence, we can conclude that the Identity Rating depends on age. Children are affected more by brand ambassadors and peers while adults are not.

Gender and Identity Factor:

After seeing the dependence of identity factor on age, it was analysed if identity factor is dependent on gender. To check this, a cross tab was used (Table 22).

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Table 22: Crosstab (Gender*Identity)

Identity Factor

Total1 2 3 4


% within Gender 17.7% 42.7% 36.5% 3.1% 100.0%

Male Count 25 85 35 9 154

% within Gender 16.2% 55.2% 22.7% 5.8% 100.0%

Total Count 42 126 70 12 250

% within Gender 16.8% 50.4% 28.0% 4.8% 100.0%

It can be seen from the above table that there is not much difference between males and females of GenY in identity affecting switching behavior. However, females seem to be slightly more affected by identity factor.


The null hypothesis (Ho) was that the Identity Rating does not depend on gender while the alternate hypothesis (H1) was that the Identity Rating depends on gender.


χ2<Table Value, therefore null hypothesis was accepted with 95% confidence. Hence, we can conclude that the Identity Rating does not depend on gender. Therefore, peer pressure, advertisements and star brand ambassadors affect switching behavior of consumers but similarly across both males and females.

Profession and Identity Factor:

To check the dependence of Identity as a factor affecting brand switching on Profession, a cross tab was used (Table 23).

It can be seen from the above table that more percentage of people fall in the rating 2. Therefore,

identity as a factor does not affect brand switching behaviour to the extent quality and price did.

However, the trend seems to be same across all professions.


The null hypothesis (Ho) was that the Identity Rating does not depend on profession while the alternate hypothesis (H1) was that the Identity Rating depends on profession.


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χ2<Table Value, therefore null hypothesis was accepted with 95% confidence. Hence, we can conclude that the Identity Rating does not depend on profession. Therefore, peer pressure, advertisements and star brand ambassadors affect switching behavior of consumers but similarly across all professions.

Table 23: Crosstab (Profession*Identity)

Identity Factor

Total1 2 3 4




% within Profession 26.7% 60.0% 13.3% .0% 100.0%



Total Count 42 126 70 12 250


Income and Identity Factor:

To check the dependence of Identity as a factor affecting brand switching on Profession, a cross tab was used (Table 24).

Table 24: Crosstab (Income*Identity)

Identity Factor

Total1 2 3 4



15001-30k Count 10 21 8 0 39


30001-45k Count 8 16 3 0 27


45001-60k Count 3 7 1 0 11



% within Monthly Income .0% 100.0% .0% .0% 100.0%

Total Count 23 59 25 1 108


It can be seen from the above table that more percentage of people fall in the rating 2. However,

consumers with a low income have high dependency on Identity factor.

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The null hypothesis (Ho) was that the Identity Rating does not depend on income while the alternate hypothesis (H1) was that the Identity Rating depends on income.


χ2>Table Value, therefore null hypothesis was rejected with 95% confidence. Hence, we can conclude that the Identity Rating depends on income. Therefore, peer pressure, advertisements and star brand ambassadors affect switching behavior of low income consumers which is a strange phenomenon. We can attribute this probably to their fan following behavior etc.

Frequency of shopping and Identity Factor:

To check the dependence of Identity as a factor affecting brand switching on frequency of shopping, a cross tab was used (Table 25).

Table 25: Crosstab (Frequency of shopping*Identity)

Identity Factor

Total1 2 3 4

Frequency of Shopping Very low Count 0 3 6 1 10


Shopping

.0% 30.0% 60.0% 10.0% 100.0%

Low Count 17 65 33 4 119


Shopping

14.3% 54.6% 27.7% 3.4% 100.0%



Shopping

20.5% 53.4% 21.9% 4.1% 100.0%



Shopping

22.2% 38.9% 22.2% 16.7% 100.0%



Shopping

20.0% 40.0% 36.7% 3.3% 100.0%

Total Count 42 126 70 12 250


Shopping

16.8% 50.4% 28.0% 4.8% 100.0%

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It can be seen from the above table that people who hardly go for shopping are affected by

identity factors for switching between apparel brands. However, consumers who are quite

frequent shoppers are slightly affected by identity factor.


The null hypothesis (Ho) was that the Identity Rating does not depend on frequency of shopping while the alternate hypothesis (H1) was that the Identity Rating depends on frequency of shopping.


χ2<Table Value, therefore null hypothesis was accepted with 95% confidence. Hence, we can conclude that the Identity Rating does not depend on frequency of shopping. Therefore, peer pressure, advertisements and star brand ambassadors affect switching behavior of consumers but similarly across all frequencies of shopping.

Social Behaviour/Status and Identity Factor:

To check the dependence of Identity as a factor affecting brand switching on Social Behaviour/Status, a cross tab was used (Table 26).

Table 26: Crosstab (Social Behaviour/Status*Identity)

Identity

Total1 2 3 4



Yes Count 38 108 56 8 210


Total Count 42 126 70 12 250


It can be seen from the above table that identity is not a very important factor affecting brand

switching behavior of both socially engaged and non-socially engaged people.


The null hypothesis (Ho) was that the Identity Rating does not depend on Social Behaviour/Status while the alternate hypothesis (H1) was that the Identity Rating depends on Social Behaviour/Status.


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χ2<Table Value, therefore null hypothesis was accepted with 95% confidence. Hence, we can conclude that the Identity Rating does not depend on Social Behaviour/Status.

Age and Retail Factor:

Another important factor is the retail factor which is majorly responsible for the brand switching of consumers. A store’s appeal and providing good services are very important in today’s world and attracts consumers in a big way. To see the dependence of retail ratings on age, cross tabs were used (Table 27). Retail ratings 1-4 are given for factor loadings derived from factor analysis. Factor loadings in the range of -3.05 – -1.77 have been given 1, factor loadings in the range of -1.77 –0.49 have been given 2, factor loadings in the range of -0.49–0.80 have been given 3 and factor loadings in the range of 0.80–2.08 have been given 4. Lower the rating/factor loading, lesser does the retail factor affect the switching decisions of consumers in that particular age group.

The below table shows that retail factor is important to all age groups in a similar way. All age groups react in a similar fashion when it comes to service, Store display etc.

Table 27: Crosstab (Age*Retail)

Retail Factor

Total1 2 3 4

Age 10-14 Count 1 4 8 1 14

% within Age 7.1% 28.6% 57.1% 7.1% 100.0%

15-19 Count 2 3 29 2 36

% within Age 5.6% 8.3% 80.6% 5.6% 100.0%

20-24 Count 13 17 64 21 115

% within Age 11.3% 14.8% 55.7% 18.3% 100.0%

25-29 Count 6 14 44 21 85

% within Age 7.1% 16.5% 51.8% 24.7% 100.0%

Total Count 22 38 145 45 250

% within Age 8.8% 15.2% 58.0% 18.0% 100.0%


The null hypothesis (Ho) was that the Retail Rating does not depend on age while the alternate hypothesis (H1) was that the Retail Rating depends on age.


χ2<Table Value, therefore null hypothesis was accepted with 95% confidence. Hence, we can conclude that the Retail Rating does not depend on age of the consumer.

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Gender and Retail Factor:

After seeing that retail factor as a factor affecting brand switching behavior has no dependence on age, we checked the dependence of retail factor on gender.

Table 28: Crosstab (Gender*Retail)

Retail Factor

Total1 2 3 4


% within Gender 8.3% 14.6% 60.4% 16.7% 100.0%

Male Count 14 24 87 29 154

% within Gender 9.1% 15.6% 56.5% 18.8% 100.0%

Total Count 22 38 145 45 250

% within Gender 8.8% 15.2% 58.0% 18.0% 100.0%

After analyzing the table above, we see that both males and females react in a similar way to

retail factor. Therefore, there is no dependence either way.


The null hypothesis (Ho) was that the Retail Rating does not depend on gender while the alternate hypothesis (H1) was that the Retail Rating depends on gender.


χ2<Table Value, therefore null hypothesis was accepted with 95% confidence. Hence, we can conclude that the Retail Rating does not depend on gender of the consumer.

Profession and Retail Factor:

The dependence of Retail factor on profession was checked using cross tab (Table 29). The table clearly shows that there is no dependence of retail factor on profession like age and gender.

Table 29: Crosstab (Profession*Retail)

Retail Factor

Total1 2 3 4







Total Count 22 38 145 45 250


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The null hypothesis (Ho) was that the Retail Rating does not depend on profession while the alternate hypothesis (H1) was that the Retail Rating depends on profession.


χ2<Table Value, therefore null hypothesis was accepted with 95% confidence. Hence, we can conclude that the Retail Rating does not depend on the profession of the consumer.

Income and Retail Factor:

The dependence of Retail factor on income was checked using cross tab (Table 30). The table clearly shows that there is no dependence of retail factor on income like age, profession and gender.

Table 30: Crosstab (Income*Retail)

Retail Factor

Total1 2 3 4



15001-30k Count 5 8 20 6 39


30001-45k Count 1 4 12 10 27


45001-60k Count 2 1 7 1 11




Total Count 11 17 59 21 108



The null hypothesis (Ho) was that the Retail Rating does not depend on income while the alternate hypothesis (H1) was that the Retail Rating depends on income.


χ2<Table Value, therefore null hypothesis was accepted with 95% confidence. Hence, we can conclude that the Retail Rating does not depend on the income of the consumer.

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Frequency of shopping and Retail Factor:

The dependence of Retail factor on frequency of shopping was checked using cross tab (Table 31). The table clearly shows that there is no dependence of retail factor on frequency of shopping like age, profession and gender.

Table 31: Crosstab (Frequency*Retail)

Retail Factor

Total1 2 3 4

Frequency of Shopping Very Low Count 1 2 5 2 10


Shopping

10.0% 20.0% 50.0% 20.0% 100.0%

Low Count 15 16 71 17 119


Shopping

12.6% 13.4% 59.7% 14.3% 100.0%



Shopping

8.2% 16.4% 53.4% 21.9% 100.0%



Shopping

.0% 22.2% 55.6% 22.2% 100.0%



Shopping

.0% 13.3% 66.7% 20.0% 100.0%

Total Count 22 38 145 45 250


Shopping

8.8% 15.2% 58.0% 18.0% 100.0%


The null hypothesis (Ho) was that the Retail Rating does not depend on frequency of shopping while the alternate hypothesis (H1) was that the Retail Rating depends on frequency of shopping.


χ2<Table Value, therefore null hypothesis was accepted with 95% confidence. Hence, we can conclude that the Retail Rating does not depend on the frequency of shopping of the consumer.

Social Behaviour/Status and Retail Factor:

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The dependence of Retail factor on Social Behaviour/Status was checked using cross tab (Table 32). The table clearly shows that there is no dependence of retail factor on Social Behaviour/Status like frequency of shopping, age, profession and gender.

Table 32: Crosstab (Social Behaviour*Retail)

Retail Factor

Total1 2 3 4



Yes Count 20 34 118 38 210


Total Count 22 38 145 45 250



The null hypothesis (Ho) was that the Retail Rating does not depend on Social Behaviour/Status while the alternate hypothesis (H1) was that the Retail Rating depends on Social Behaviour/Status.


χ2<Table Value, therefore null hypothesis was accepted with 95% confidence. Hence, we can conclude that the Retail Rating does not depend on the Social Behaviour/Status of the consumer.

Conclusions and Implications

Our analysis throws up five broad factors leading to brand switching in apparels of GenY consumers which are as follows in order of importance: quality factor, price factor, styling factor, identity factor and retail factor. This is a broad classification of factors that we derived from a set of 15 factors. Previous research by Ryan et al. (1999) has shown that price is probably the most important consideration for the average consumers and serves as the strongest loyalty driver. However, in our analysis price comes second in order of importance for GenY consumers.

We also analysed how demographics like age, gender, profession, income etc. behave with respect to these five brad classification of factors the result of which is summarised in the table below (Table 33):

Table 33: Summary- Factors and Demographics

Factors Age Gender Profession Income Frequency of shoppingSocial

behaviour

Quality Dependent Independent Independent Independent Independent Independent

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Price Dependent Dependent Independent Dependent Independent Independent

Styling Independent Independent Independent Independent Independent Independent

Identity Dependent Independent Independent Dependent Independent Independent

Retail Independent Independent Independent Independent Independent Independent

The analysis done can be used by different apparel brands in forming their overall brand strategy like children apparel brands can give more importance to identity factor and less importance to quality and price factor, i.e., they can have more attractive advertisements, have better brand ambassadors to attract children to their brand. However, brands catering to adults should focus on price and quality.

Also, brands catering to females should focus more on price and discounts as compared to brands catering to males. Smaller brands which cater to the low income or middle income group should also focus on price and discounts and identity factors like brand ambassadors and attractive advertisements much more than high-end brands.

Further from our market research, we found that 50.4 percent people hold a loyalty card of a particular brand and usually redeem points through it. However we also found that among those 50.4% people, only 16.7 percent people go back to the same brand to shop because they hold its loyalty card. Therefore, the analysis can be extended to design loyalty programs like giving more discounts, good customer services etc. differently to various demographics which will help them retain their customers.

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leading factors contributing to brand switching in apparel

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