1. INTRODUCTION:

India is one of the fastest growing economies with a projected CAGR of 10 per cent making it a lucrative market. In comparison to India, the developed markets of the US, Europe and Japan are expected to grow at a meagre rate of 2-3 per cent. According to an Investindia publication, the domestic textiles and apparel industry stood at $108.5 bn in 2019-20 of which $75 bn was domestically consumed while the remaining portion worth $28.4 bn was exported to the world market. Textiles and garments industry is expected to reach $190 bn by 2025-26 from $103.4 bn in 2020-21. Currently menswear is the major chunk of the market at 41% (Rs 72,000 crore) and is growing at a compounded annual growth rate (CAGR) of 9%. The gender wise apparel market segment is shown in Fig.1.

Figure 1: Gender Wise Market Segment; Source: India Retailing

Men segment (41%)

· Found to be the major segment of the Indian apparel market.

· Shirts and trousers form the major sub segments.

· Denim and fitness wear are the fastest growing sub-segment

Women segment (38%)

· Traditional/ethnic wear is the major sub-segment.

· The robust growth in this segment is mainly attributed to the rising income levels, increase in the number of working women and more college going females.

· Craving for Western styles are also steadily increasing.

Kids segment (21%)

· Forms the smallest yet the fastest growing segment

· School uniforms form the largest sub-segment

One of the most critical factors determining the success of fashion retailers in India is the ability to gauge trends in consumer purchase decisions. The Indian fashion consumer is undergoing an evolution and is rapidly adapting to international fashion statements. Increasing disposable incomes, exposure to international events and fashion icons, and rising confidence levels are driving the changes in the consumer purchase behaviour. Some distinct consumer trends expected to impact the Indian fashion market are:

There was a time when fashion items were being purchased as and when required. Now-a-days, fashion clothing is more than a basic need; it is a reflection of aspiration, personality, and one of the biggest status symbols. The Indian fashion consumers can tell the difference between unbranded and branded apparel. They are able to decode the messages communicated by different brands on different occasions. The aspirational youth is also influenced by peer groups working with multinational companies and having international exposure. Though basic textiles and footwear continue to be a part of the consumer’s basket, the demand for aspirational fashion clothing and fashion accessories has increased substantially in recent years.

Although the fashion consumer is willing to spend more on clothing and accessories of choice, the consciousness of value received for the money spent has increased manifold. The weak economic outlook and higher inflation rates have also contributed to this heightened value consciousness. The consumer is inclined towards value and affordability, but, at the same time, there is an inherent need for a fashionable look.

Gagliano and Hathkote (1994) suggested that the perception of the quality of service strongly influences customer preferences. Jackson Donald (1999) identified factors that improve customer satisfaction. Johnson Kurt (1999) performed an elaborative study on making loyalty programs more rewarding in order to increase customer retention. Mattila and Wirtz (2001) showed the importance of enhancing store environment through music and scent to boost impulse buying behaviour and satisfaction. Summers and Hebert (2001) studied the influence of lighting on customer behaviour. The findings of the research showed that lighting could help attract and retain customers. Baker, Parasuraman, Grewal and Voss (2002) proposed that factors such as music, layout, crowd and convenience affected sales. Solgaard and Hansen (2003) proved that location, quality, clean surroundings, variety, layout and sales assistants were the most important factors. Fox, Montgomery and Lodish (2004) proposed customers are more influenced by variety and advertisement than to prices. Radha Krishna and Shylajan (2007) studied the influences of marketing and demographic factors on consumers’ buying behaviour towards branded articles. However, factors that attract customers to one exclusive brand outlet more than another remain uncovered. Aamir Hasan and Subhash Mishra (2014) state that shopping experience, store image and value for money are the prominent factors influencing customer shopping behaviour.

Factor analysis is a useful tool for investigating complex variable relationships. It allows researchers to investigate concepts that are not easily measured directly by collapsing a large number of variables into a few interpretable underlying factors. There are six main steps in a factor analysis:

1. Selecting and measuring set of variables in a given domain

2. Data screening in order to prepare the correlation matrix

3. Factor extraction

4. Factor rotation to increase interpretability

5. Interpretation

6. Reliability and validation

2. METHODOLOGY:

The objective of this study was to find factors that motivate customer to purchase from one brand more than another through a survey. Factor analysis was used to condense the copious variables into 5 prominent factors by detecting a structure in the relation between the variables.

A questionnaire consisting of 16 questions was prepared after a comprehensive review of previous research in the field. Each variable was analysed based on a 7-point Likert scale ranging from strongly agree to strongly disagree. The questionnaire consisted of two parts; part 1 included questions regarding the demographic characteristics of the customers and the part 2 (Table 1) consisted of variable related to shopping behaviour of customers at apparel retail outlets. Totally 480 customers from various parts of the country were surveyed. The participation in the survey was voluntary in order to ensure reliability.

The accuracy and quality of the data was increased by eliminating the data rows with missing values. Further, the rows with a standard deviation of 0 were identified and removed in order to eliminate outliers. The data was entered in SPSS and factor analysis was carried out. Further, the rows with values greater than 60% on the diagonal of anti-image correlation matrix were removed. Next, inverse relationships were checked through the identification of negative values in the rotated component matrix. The 16 variables were grouped under 5 factors through factor analysis. The reliability of the factor analysis was checked by analysing whether Cronbach’s alpha > 0.7, AVE > 0.5 and Cronbach’s alpha > AVE.

Table 1: Questionnaire – Part - 2

1	I expect fair price in comparison to similar products in market
2	I am willing to pay a premium for novel products
3	I prefer purchasing garments from reputed brands
4	I buy garments that have a good stitch and fabric quality
5	I purchase garments with easy wash care
6	Physical fit (such as tightness, length) carries importance
7	Aesthetic fit (overall appearance) carries importance
8	Functional fit (ease of movement) carries importance
9	Social fit (feedback & fitting in) carries importance
10	I pay attention to the material used in the garment
11	I try multiple garments until I can find the right size
12	I do not buy clothes that would make me stand out from others
13	I purchase from stores that provide variety
14	I prefer brands where collection is in corroboration with latest Fashion
15	I like trying new styles
16	I buy from stores where garments are available in a variety of colors

Table 2: Demographics

Variable	Category	N	n	%
Gender	Male	193	400	48.2
	Female	207	400	51.8
Age Group	20-24	104	400	26.0
	25-29	108	400	27.0
	30-34	98	400	24.5
	Above 35	90	400	22.5
Monthly Income	Less than 1,00,000	64	400	16.0
	1,00,000 - 3,00,000	95	400	23.8
	3,00,001 – 5,00,000	79	400	19.8
	5,00,001 - 7,00,000	91	400	22.8
	Above 7,00,000	71	400	17.8

Each question was answered based on a 7-point Likert scale as mentioned below:

1. Strongly agree; 2. Agree; 3. Somewhat agree;

4. Neutral; 5. Somewhat disagree; 6. Disagree;

7. Strongly disagree

3. RESULTS AND DISCUSSION:

The consolidated demographic details of the 480 respondents are given in the Table 2 and it shows that there were approximately equal number of male and female respondents. Moreover, there were almost equal number of respondents from every age group, that is, 20-24, 25-29, 30-34 and above 35. Lastly, most respondents were from the income category 1,00,000 – 3,00,000 and 5,00,001 – 7,00,000, followed by 3,00,001 – 5,00,000, above 7,00,000 and less than 1,00,000.

First, the survey responses with missing data were removed. Second, the standard deviation between the responses of each person surveyed were checked. If the responses has a standard deviation of 0, the response was eliminated from the dataset.

To ascertain the appropriateness of factor analysis Kaiser-Mayer-Oklin (KMO) measure of sampling adequacy (MSA) was performed. In this study the KMO was found to be 0.815, which indicates that the proportion of variance in the variables might be caused by underlying factors, i.e. the KMO value above 80% proves that the survey was valid.

As given in the Anti-Image Correlation Matrix shown in the Table 3, the values on the diagonal for all questions were above 0.600, these questions were eliminated from the dataset.

According to the rotated component matrix (Table 4), the variables were distributed into five factors.

The first factor, fit, consisted of variables related to the physical fit, aesthetic fit, functional fit and social fit. The second factor, assortment, includes influences such as variety, latest fashion, style and colour. The third factor, perceived value, consists of fair price, novel product and value for money. The fourth factor, comfort, consist of material, size and distinction. The last factor, prolongation is influenced by quality and wash care. The variables and factors has been summarized in Table 5.

Table 3: Anti-Image Correlation Matrix

	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11	Q12	Q13	Q14	Q15	Q16
Q1	.869a	-0.443	0.038	-0.211	0.088	-0.140	0.005	-0.059	0.163	-0.094	-0.073	0.114	0.078	-0.198	-0.046	0.084
Q2	-0.443	.852a	-0.423	-0.128	0.116	-0.020	0.191	-0.057	-0.080	0.036	-0.114	0.080	-0.005	-0.089	-0.189	0.127
Q3	0.038	-0.423	.881a	-0.380	-0.172	0.007	-0.215	0.042	0.048	-0.020	-0.006	-0.084	-0.007	0.093	0.037	-0.031
Q4	-0.211	-0.128	-0.380	.897a	-0.193	0.058	-0.272	0.139	-0.066	-0.003	-0.042	-0.076	0.035	0.076	-0.039	-0.009
Q5	0.088	0.116	-0.172	-0.193	.840a	-0.231	0.130	-0.250	-0.028	0.055	-0.110	0.092	-0.064	-0.051	-0.220	0.242
Q6	-0.140	-0.020	0.007	0.058	-0.231	.832a	-0.241	-0.549	0.059	0.028	-0.016	0.098	-0.084	-0.077	0.111	-0.058
Q7	0.005	0.191	-0.215	-0.272	0.130	-0.241	.881a	-0.259	0.056	-0.041	-0.044	0.053	-0.002	-0.047	-0.051	0.049
Q8	-0.059	-0.057	0.042	0.139	-0.250	-0.549	-0.259	.836a	-0.081	-0.022	-0.014	-0.152	0.045	0.164	-0.091	0.041
Q9	0.163	-0.080	0.048	-0.066	-0.028	0.059	0.056	-0.081	.749a	-0.739	-0.313	-0.154	0.113	0.020	0.054	0.012
Q10	-0.094	0.036	-0.020	-0.003	0.055	0.028	-0.041	-0.022	-0.739	.758a	-0.168	0.234	-0.281	0.054	-0.051	0.050
Q11	-0.073	-0.114	-0.006	-0.042	-0.110	-0.016	-0.044	-0.014	-0.313	-0.168	.898a	-0.090	0.267	-0.141	0.105	-0.152
Q12	0.114	0.080	-0.084	-0.076	0.092	0.098	0.053	-0.152	-0.154	0.234	-0.090	.706a	-0.613	-0.425	-0.038	0.099
Q13	0.078	-0.005	-0.007	0.035	-0.064	-0.084	-0.002	0.045	0.113	-0.281	0.267	-0.613	.725a	-0.387	-0.074	-0.016
Q14	-0.198	-0.089	0.093	0.076	-0.051	-0.077	-0.047	0.164	0.020	0.054	-0.141	-0.425	-0.387	.779a	0.068	-0.080
Q15	-0.046	-0.189	0.037	-0.039	-0.220	0.111	-0.051	-0.091	0.054	-0.051	0.105	-0.038	-0.074	0.068	.627a	-0.857
Q16	0.084	0.127	-0.031	-0.009	0.242	-0.058	0.049	0.041	0.012	0.050	-0.152	0.099	-0.016	-0.080	-0.857	.758a

Table 4: Rotated Component Matrix

		Component
		1	2	3	4	5
Q1	Physical fit (such as tightness, length) carries importance	0.854
Q2	Aesthetic fit (overall appearance) carries importance	0.801
Q3	Functional fit (ease of movement) carries importance	0.800
Q4	Social fit (feedback & fitting in) carries importance	0.791
Q5	I purchase from stores that provide variety		0.885
Q6	I prefer brands where collection is in corroboration with latest fashion		0.881
Q7	I like trying new styles		0.727
Q8	I buy from stores where garments are available in a variety of colors		0.705
Q9	I pay attention to the material used in the garment			0.969
Q10	I give emphasis to the finish of the garment			0.963
Q11	I do not buy clothes that would make me stand out from others			0.959
Q12	I expect fair price in comparison to similar products in market				0.936
Q13	I am willing to pay a premium for novel products				0.910
Q14	I prefer purchasing garments from reputed brands				0.757
Q15	I purchase garments with easy wash care					0.959
Q16	I buy garments that have a good stitch and fabric quality					0.929

Table 5: Summary of Variables & Factors

Factors	Factor Interpretation (% variance explained)	Variables Included in the Factor
Fit	Eigenvalue (37.704)	Physical Fit (0.801)
		Aesthetic Fit (0.854)
		Functional Fit (0.800)
		Social Fit (0.791)
Assortment	Eigenvalue (19.054)	Variety (0.885)
		Latest Fashion (0.881)
		Style (0.727)
		Color (0.705)
Perceived Value	Eigenvalue (10.901)	Fair price (0.969)
		Novel Product (0.963)
		Brand Name (0.959)
Characteristics	Eigenvalue (9.955)	Material (0.936)
		Finish (0.910)
		Distinction (0.757)
Prolongation	Eigenvalue (6.535)	Quality (0.959)
		Wash care (0.929)

Table 6: Ranking of Factors

Rank	Factor	Mean Values
1	Fit	4.79
2	Assortment	4.70
3	Perceived Value	4.69
4	Prolongation	4.31
5	Characteristics	4.14

From the Table 6, it is clear that fit is the most influential factor while purchasing a garment. This includes:

· Physical fit: features of fit that are physically perceived when evaluating fit in terms of the relationship between clothing and body, such as tightness and length.

· Aesthetic fit: features of fit that are visually perceived and assessed when looking at an individual’s dressed body, such as overall appearance related to the body and attractiveness.

· Functional fit: features of fit that are perceived when the dressed body is moving for activities, related to restriction or lack of restriction of movement.

· Social fit: feeling of well-being resulting from satisfaction with fit attained through feedback from others.

The second most important factor is assortment which includes:

· Variety: number of options the customer can see before making a buying decision

· Latest fashion: garments that corroborate with the current trends in the market

· Style: innovative styles that provide customers with diversity for their wardrobe

· Colour: a large number of colours in the collection

The third most influential factor is perceived value which includes:

· Fair price: similar price as compared to the same product of another brand

· Novel product: acceptable premium pricing for a new and original product

· Brand name: perceived value of a brand

The fourth most dominant factor is prolongation which includes:

· Quality: the stitch and fabric quality of the garment

· Wash care: ease of maintenance through machine wash.

The least important factor is characteristics of the product such as:

· Material: fibre composition and other properties of the fabric used in the garment

· Finish: finishing process that the fabric or garment was exposed to

· Distinction: garment that makes you stand out from others in a crowd.

The reliability of the factor analysis was checked by analysing whether Cronbach’s alpha > 0.7, AVE > 0.5 and Cronbach’s alpha > AVE and the conditions for reliability were satisfied as shown in Table 7.

Table 7: Reliability Test

Factor	Question	R	R2	α	AVE
Fit	Physical Fit	0.854	0.729
	Aesthetic Fit	0.801	0.641
	Functional Fit	0.800	0.640
	Social Fit	0.791	0.625	0.910	0.659
Assortment	Variety	0.885	0.783
	Latest Fashion	0.881	0.776
	Style	0.727	0.529
	Color	0.705	0.497	0.877	0.646
Perceived Value	Fair Price	0.969	0.939
	Novel Product	0.963	0.928
	Brand Name	0.959	0.919	0.969	0.929
Characteristics	Material	0.936	0.876
	Finish	0.910	0.828
	Distinction	0.757	0.574	0.911	0.759
Prolongation	Quality	0.959	0.919
	Wash care	0.929	0.863	0.902	0.891

4. CONCLUSION:

The consumer survey conducted in order to understand the factors affecting the apparel sales were able to be identified using the factor analysis statistical tool. The factor analysis performed showed that the five factors, namely Fit, Assortment, Perceived Value, Characteristics and Prolongation made the garments more likable and gave them a higher probability of being purchased. Further, the reliability test done has proved that the identified five factors are valid in determining the apparel purchase decision making.

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Received on 06.07.2022 Modified on 08.08.2022

Asian Journal of Management. 2022;13(4):330-334.

DOI: 10.52711/2321-5763.2022.00054