03-apparel-product-teenaged-buyers.pdf

The importance of apparel productattributes for teenaged buyers

Shailesh Kumar Kaushal

AbstractThe present paper is an attempt to examine the significant factors of teenagers' apparel purchase intentions in Lucknow. The main objective of the research paper is to explore and conceptualize various factors, which influence the purchase patterns of teenagers. A questionnaire consisting of 43 items was developed to measure the construct and its dimensions. The first draft of the questionnaire was subject to a pilot testing through a focus group and an expert evaluation. Data was gathered from 187 teenagers and a structured questionnaire on a five-point rating (Likert scale) was administered by way of a

personal interview. Through this study, an attempt is made to find out the effect of fashion apparels, in-store promotions, reference group, body cathexis and its influence on purchase of apparel by teenagers. For the purpose of analysis, statistical tools like Factor analysis, GRA & RIDIT have been used. Results of the study might be useful to academicians, apparel manufacturers, and other applied researchers.

Keywords: Fashion apparels, Factor analysis, GRA & RIDIT Analysis.

The importance of apparel product attributes for teenaged buyersISSN: 0971-1023NMIMS Management ReviewVolume XXIII April - May 2013

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Introduction:The intention of this article is to present a descriptive approach to clothing brands purchasing behaviour and attitudes of teenagers in Lucknow. The purpose of the study is to analyze the attitudes of teenagers towards clothing brands from the viewpoint of fashion apparels, in-store promotions, reference group, body cathexis and purchasing choice criteria. It has been observed by some researchers that cultural values affect the purchase intentions of fashion apparel. In societies that exhibit hedonic values, fashion apparel is promoted by manufacturers and retailers to induce a sudden, compelling, socially complex buying behaviour through promotional programs to increase disposable income by facilitating credit to the consumer (Venkatesh et al, 2010). Manufacturers and retailers apply both push and pull strategies to make promotions of fashion apparel effective and advantageous to the consumers. Promotions targeted at final consumers, known as pull promotions, directly offer extra value to consumers, with the primary goals of attracting consumers to retail locations and stimulating immediate sales. Though both push and pull promotions are designed to speed up the selling process and increase sales, at least in the short term, their strategic implications as well as their impacts on fashion sales and profits are believed to be different. Such promotion led fashion retailing culture stimulates fashion oriented attitudes, debt and spending behaviour on clothing among consumers (Martin-Herran et al, 2010). Designer brands and departmental stores have redefined the strategies of retailing fashion apparel in India considering global-local buying preferences. The central and northern regions of India have witnessed an increase in specialized apparel stores, which imposes new demands on manufacturers, wholesalers, and consumers (Chavez, 2002). It has been observed that

the attributes determining overall acceptance of fashion apparel and accessories among Indian consumers are significantly influenced by product attractiveness and price sensitivity. Purchase intent was influenced by overall appearance, brand appeal, and overall liking (Rajagopal, 2006a; Herrera-Corredor et al, 2007). Fashion apparel is largely penetrating in India through cross border (American) consumer influence. Out shoppers literally go extra miles to out shop for better quality and assortment of merchandise, higher quality of personal service, a more pleasant shopping atmospherics, and more competitive prices (Guo and Wang, 2009). In-store promotion techniques are employed to increase unplanned purchases of products. The techniques include in-store settings, on-shelf positions, price-off, sampling, point-of purchase displays, coupons, demonstrations of samples to name a few. (Abratt and Goodey1990) found out that unplanned shopping was to the tune of 14 per cent in toilet soaps, 24 per cent in fizzy drinks and 9 per cent in toothpastes. The study concluded that stimulus responsible for unplanned purchase was as follows: Sign on shelf: 54%, Price 14.5%, Special display 8%, Others 5%, POP 4.5%, Friend's suggestion 4%, End of Aisle display 3.5%, Ad recall 2%, Family suggestion 2%, Size/ package 1%, Special Offer 1% and In-store advertisement 0.5%. Kessler (2004) points out that retailers worldwide are not only aware of their growing power, but flex their muscles and squeeze margins regularly. Brands respond to this in a variety of ways and one of them is in-store marketing and display. Research by point of purchase advertising institute (POPAI) has shown that 75% of purchase decisions are made in-store. Consumers have a profile of brands that they will consider purchasing and the choice of which one to buy is made at the moment of brand selection inside the store. Reference Groups - Kollat, Blackwell and

ISSN: 0971-1023NMIMS Management ReviewVolume XXIII April - May 2013

The importance of apparel product attributes for teenaged buyers46

Engel (1970) define reference groups as follows: When an interacting group of persons or even a single person influences the attitudes or behaviour of another individual, that group is said to be a reference group for the influenced individual. The group serves as an evaluation or normative point for the individual. Body cathexis, as defined by LaBat and DeLong, is the evaluative dimension of body image and is defined as positive and negative feelings toward one's body and is an integral part of body image and self concept. (LaBat's and DeLong's, 1990) study regarding the relationship between body cathexis and satisfaction with apparel fit revealed that, indeed, there is a positive relationship between satisfaction with the body and satisfaction with the fit of clothing. The current study attempts to test the relationship between satisfaction with fit of clothing and fashion interest for teenagers. The results of the study by (Shim, Kotsiopulos and Knoll, 1991) in which the men illustrated relationships between the body cathexis and clothing attitude is a significant finding for the current research from which a prediction between the satisfaction with fit and fashion interest variables is made. The findings from the (Shim, et al. 1991) study may also prove significant in the relationship between store patronage and satisfaction with fit variables in the current study as those that have higher levels of satisfaction with fit are more likely to be more involved in fashion and will have more discriminatory tastes in where to shop for clothing.

This paper is an attempt to gain an insight into the purchase intention and the vital factors that influence teenage behaviour. The paper is an honest endeavour which will throw light on the significant aspects that the marketer can afford to ignore only at his own risk. Some of these issues discussed are: fashion apparels, in-store promotions, reference group, and body

cathexis. The paper would also try to analyze the purchasing and spending patterns at the retail level and will make an effort to provide an insight into the media that usually appeal to their psyche.

Literature review:The literature review revealed many aspects of teenagers' purchasing habits towards apparels. Clothing has frequently been recognized as a product category likely to induce high involvement. In general terms, involvement is a state of motivation, arousal, or interest. Personal relevance is a key concept in explaining, defining, and operationalizing involvement (Kim, Damhorst and Lee 2002). Seo, Hatchote and Sweaney (2001, p. 210) define clothing involvement as the amount of time and effort a consumer spends in the selection of clothing. Some researchers have examined fashion involvement as a multidimensional construct that involves fashion innovativeness and early adoption, interpersonal communication about fashion, fashion knowledge ability, and fashion awareness (Kim, Damhorst and Lee 2002). Consumers vary greatly in their knowledge about a product and their degree of familiarity with it. Knowledge can come from product experiences, such as ad exposure, interactions with salespeople, information from friends or the media, previous consumption and usage experiences (O'Cass 2001b). When consumers form an attitude toward the product, they make evaluative associations between the product and its attributes. Some of those attributes may be utilitarian - such as durability or comfort - or hedonic - such as colour, fashion ability, or styling (Kim, Damhorst and Lee 2002). Beaudion, Moore, and Goldsmith (1998) analyzed the attitudes toward buying domestic and imported apparel products using a selection of attributes: good fit, durability, ease of care, good price, comfort, quality, choice of colour, attractiveness,


The importance of apparel product attributes for teenaged buyers 47

fashionableness, brand name, appropriateness for occasion, and choice of styles. Brand image of the clothing store is particularly critical. Previous studies found that some attributes like fashion and style of clothing, store design and layout, price, quality of the clothes, refund and exchange policy, company reputation, selection of clothes, knowledge and friendliness of staff were particularly important to consumers (Birtwistle and Freathy 1998; Partolese and Dias 2003). When we are talking about fashion clothing consumption, we should take some dimensions into consideration: fashion consciousness, individual orientation, status orientation, style opinion leadership, price orientation and shopping habits (Seo, Hathcote and Sweaney 2001, p.210). Therefore, shopping involves many dimensions, and it is an important function of the consumers' choice of clothes (Seo, Hathcote and Sweaney 2001). Informational sources such as print media, commercial broadcast, word of mouth, and store displays are generally used to ascertain information about clothing. Mass media has been found to be the dominant information source for the younger age group (Lumpkin 1985; Shim and Kotsiopoulos1992).

Respondents' Profile:The profiles of respondents are as follows in Table-1.

Table-1: Respondents' profileObjective of the Study: To know teenagers' purchasing intentions towards purchase of apparel.

Material and Methods: In order to measure the purchase intentions of teenagers for fashion apparel, respondents including both males and females in the age group 14-18 years were selected. The method of purposive sampling was employed whereby the respondents had to fulfil the criteria of having visited

shopping malls in the past even if they had not made any purchases. A total of 200 responses were collected from the field and 187 responses were used for the final analysis after data filtration. Respondents were selected from two shopping malls - Saharaganj and Fun Republic at Lucknow. The questionnaires were personally hand-delivered to teenagers at shopping malls. The questionnaire was developed with the help of literature, consultation with academicians and teenagers. Respondents were asked to rate 43 statements relating to apparels. Responses to all the statements in the questionnaire were measured on a five-point Likert scale, ranging from 1= strongly disagree to 5= strongly agree. Demographic information such as gender, age, and income was also collected. The validation of the survey instrument was checked through pilot testing of 50 respondents and variables were finalized after ensuring a balanced approach and objectivity of the survey. A proposed hypothetical model was developed for the purpose of applying exploratory factor analysis. After factor analysis, we have used two techniques i.e., RIDIT and Grey Relational Analysis (GRA) in the paper. The study was carried out during summer 2011. Collected data was processed in the statistical software package of SPSS-17.

Analysis and Discussions: To get an idea of prominent factors for purchasing apparel, the following 43 pre-decided statements have been used:

Table-2: Statements on fashion apparelsExploratory factor analysis: An exploratory factor analysis was carried out to determine the various motivational factors of visiting malls in Lucknow. Principal Component analysis was employed for extracting factors and orthogonal rotation with Varimax was applied. As latent root criterion was used



for extraction of factors, only the factors having latent roots or Eigen values greater than one were considered significant; all other factors with latent roots less than one were considered insignificant and disregarded. The extracted factors along with their Eigen values are shown in Table-2.

Table-3: Exploratory factor analysis resultsThe factors have been given appropriate names on the basis of variables represented in each case. The names of the factors, the statements, the labels and factor loading have been summarized in Table-3. There are four factors each having Eigen value exceeding one for shopping motivational factors. Eigen values for four factors are 4.936, 2.735, 1.932 and 1.532 respectively. The remaining 43 items were again subjected to EFA and a final four-factor model was estimated, while none of the items exhibited low factor loadings (0.40). The four-factor solution accounted for 65.017% of the total variance, and exhibited a KMO measure of sampling adequacy of 0.652. It is a pretty good extraction because we are able to economize on the number of choice factors (from 43 to 4 underlying factors) while we lost 34.893% of information content for choice of variables. The percentages of variance explained by factors one to four are 23.424, 19.163, 14.265 and 8.255 respectively. Large communalities indicate that a large number of variances have been accounted for by the factor solutions. The first factor, fashion apparel buyer, accounted for the largest proportion, that is, 23.424% of the total explained variance. This factor was defined by four scale items and was primarily related to the fashion apparel buyer. The second factor, promotion conscious buyer, explained 19.163% of the variance and was constructed by three scale items, which were primarily associated with the concept of providing promotion to customers, namely, promotion

conscious buyer. The third factor, reference group buyer, explained 14.265% of the variance and was constructed by two scale items, which were primarily associated with friends and parents. Finally, the fourth factor, body cathexis buyer, explained 8.255 % of the variance, and encompassed two items. Varimax rotated factor analysis results for teenagers' apparel purchase intentions are shown in Table-2 which indicates that after 4 factors are extracted and retained, the communality is 0.863 for variable1, 0.784 for variable 2 and so on. It means that approximately 65.017% of the variance of variable1 is being captured by 4 extracted factors together. The proportion of the variance in any one of the original variables which is being captured by the extracted factors is known as communality (Nargundkar, 2002).

Grey Relational Analysis: A system having incomplete information is called Grey system. Grey relation is the relation with incomplete information (Chih-Hung Tsai, 2003). Grey relational analysis is a highly effective method for determining how a discrete data sequence is related to other data sequence. Data for grey relational analysis must meet the following requirements: non-dimension, scaling, and polarization. Grey relational analysis is a method to analyze the relational grade for discrete sequences. Grey relational analysis is unlike the traditional statistics analysis handling the relation between variables. Some of the drawbacks of the latter are: (i) it must have plenty of data; (ii) data distribution must be typical; (iii) a few factors are allowed and can be expressed functionally. But Grey relational analysis requires less data and can analyze many factors that can overcome the disadvantages of statistics method (Chih-Hung Tsai, 2003). Grey Relational Analysis (GRA) is used in order to build a ranking and suggest a best choice on a set of alternatives. Through GRA, a Grade



Relation Grade (GRG) is obtained to evaluate the multiple performance characteristics (Kuang, 2008). The validity of traditional statistical analysis techniques is based on assumptions such as the distribution of population and variances of samples. Nevertheless sample size will also affect the reliability and precision of the results produced by traditional statistical analysis techniques. J. Deng argued that many decision situations in real life do not conform to those assumptions, and may not be financially or pragmatically justified for the required sample size. Making decisions under uncertainty and with insufficient or limited data available for analysis is actually a norm for managers in either public or private sectors. To address this problem, J. Deng developed the grey system theory, which has been widely adopted for data analysis in various fields.

The grey relational analysis introduced in the following section is a method in grey system theory for analyzing discrete data series. A procedure for the grey relational analysis, which is appropriate for Likert scale data analysis, consists of the following steps.

1. Generate reference data series

Where m is the number of respondents, the reference data series consists of m values representing the most favoured responses.

xo

2. Generate comparison data series

Where i = 1........ k. k is the number of scale items. So there will be k comparison data series and each comparison data series contains m values.

3. Compute the difference data series.

4. Find the global maximum value and minimum maxvalue in the difference data series.min

5. Transform each data point in each difference data series to grey relational coefficient. Let y (j) i

threpresent the grey relational coefficient of the j thdata point in the i difference data series, then

Where is the value in difference data series. is a value between 0 and 1. The coefficient is used to compensate the effect of should be an max maxextreme value in the data series. In general, the value of can be set to 0.5.

6. Compute grey relational grade for each difference data series. Let represent the grey relational

thgrade for the i scale item and assume that data points in the series are of the same weights, then

The magnitude of reflects the overall degree of thstandardized deviance of the i original data series

from the reference data series. In general, a scale item with a high value of indicates that the respondents, as a whole, have a high degree of favoured consensus on the particular item.



7. Sort values into either descending or ascending order to facilitate the managerial interpretation of the results.

RIDIT Analysis: The RIDIT analysis is an acronym ('Relative to an Identified Distribution') plus the productive suffix '-it' denotes a transformation" (Bross, 1981). We may quote the inventor of this analysis to understand its meaning and relevance: - "In 1950s studies of crash-injuries in highway accidents, the response variable used a graded scale (e.g., none, minor, moderate, severe, fatal). The common practice in analysis of contingency table data then (and sometimes now) was to avoid empty cells by collapsing to a dichotomous scale (e.g., nonfatal, fatal). In an effort to avoid losing information in this way, Ridit analysis is used, which involves a simple empirical cumulative probability transformation of the entire scale. Fleiss et al. (1979) have reported that Ridit analysis begins with the identification of a population to serve as a standard or reference group. Virtually the only assumption made in Ridit analysis is that the discrete categories represent intervals of an underlying, but unobservable, continuous distribution. Given the distribution of any other group over the same categories, the mean Ridit for that group may be calculated. The resulting mean value is interpretable as a probability. In summary, Ridit analysis provides a simple alternative or adjunct to rank order statistical analysis, and may be viewed as adding an intuitively appealing, descriptive element to it. RIDIT analysis was first proposed by I. Bross and has been applied to business management and behaviour studies. RIDIT analysis is "distribution free" in the sense that it makes no assumption about the distribution of the population under study. Algorithm for RIDIT Analysis (Chien-Ho Wo, 200 7): Suppose there are m items and n ordered categories listed from the

'strongly disagreed' to the 'strongly agreed' in the scale, and then RIDIT analysis goes as follows:

1. Compute Ridits for the reference data set.a) Select a population to serve as a reference data set.

For a Likert scale survey, the reference data set can be the total responses of the survey, if the population cannot be easily identified.

b) Compute frequency? fj for each category of responses, where j = 1, 2, n.

c) Compute mid-point accumulated frequency Fj for each category of responses.

Where j =2, 3, ..n.

(d) Compute Ridit value R for each category of jresponses in the reference data set.

Where j =1, 2, 3, ..n.

N is the total number of responses from the Likert scale survey of interest. By definition, the expected value of R for the reference data set is always 0.5.

2. Compute Ridits and mean Ridits for comparison data sets. Note that a comparison data set is comprised of the frequencies of responses for each category of a Likert scale item. Since there are m Likert scale items in this illustration, there will be m comparison data sets.

a) Compute Ridit value for each category of scale items.



Where i =1, 2, 3, ..m.

is the frequency of category j for the scale item, and is a short form for the summations of frequencies for scale item i across all categories, i.e.

b) Compute mean Ridit for each Likert scale item.

c) Compute confidence interval for When the size of the reference data set is very large relative to that of any comparison data set, the 95% confidence interval of any is:

d) Test the following hypothesis using Kruskal-Wallis statistics W.

W follows a distribution with (m1) degree of freedom. If cannot be accepted, examine the relationships among confidence intervals of The general rules for interpreting the values of are shown below.

1. A scale item with its value statistically deviate from 0.5 implies a significant difference in the response patterns between the reference data set and the comparison data set for the particular scale item. If the confidence interval of a contains 0.5, then it is accepted that the value does not significantly deviate from 0.5.

2. A low value of is preferred over a high value of because a low value of indicates a low probability of being in a negative propensity.

3. The response patterns of scale items with overlapped confidence intervals of are considered, among the respondents, to be statistically indifferent from each other.

Grey Relational Analysis1. Generate reference data series x .0

2. Generate comparison data series x .i

Table-4: Apparels data set (187cases)For ease of explanation of the computation procedure for grey relational analysis, 187 cases in the data table were used. In Table 4, xo is the reference data series. Because the apparel scale is a five-point Likert scale, xo is set to contain values of 5, x1-x19 is the original comparison data series which contains responses of the respondents.

Table-5: Difference data seriesFrom Table - 5, it can be seen that max = 4 and min = 0 across 187 cases.

Table-6: Grey Relational GradeTable-5 can then be transformed to grey relational coefficients shown in Table-6. values represent the degrees of agreement to scale items. A large value represents a high degree of agreement. According to the magnitude of the values of scale items shown in Table-5, the scale items can be arranged in the following order. From this order, it can be said that, in



general, the respondents as a whole expect more good things to happen. However, in reality the respondents are more optimistic than pessimistic. The values are calculated from the entire apparel data set.

RIDIT Analysis: The first step in doing RIDIT analysis is to identify a reference data set to calculate the Ridits.

Table-7: RIDITS for the reference data setThe whole survey data on apparel is chosen as the reference data set. The frequencies of the responses are shown in Table - 7. The last row of Table - 7 shows the Ridits of the reference data set for each ordered category. The Ridit value is 0.93 for the category "strongly agrees".

Table-8: RIDIT for the comparison data sets

Since the Kruskal-Wallis (W) = 206.448 is significantly greater than it can be inferred that the opinions about the scale items among the respondents are statistically different somehow.

Table-9: GRA Grades and RIDIT ValuesTable -8 along with 19 parameters are used. The findings have been sorted as warranted by the respective analysis (Chien-Ho, 2007), so as to compare the rankings of the scale items for their degree of importance or agreement. It is observed that there is positive correlation between the two methods used for prioritizing the factors.

Table-10: GRA and RIDIT Comparative rankingIt is interesting to observe from Table -10 that 4 out of 19 ranks as assigned by two techniques are matching. Table -10 shows that the top most reason for selecting apparels based on the findings is salesperson behaviour. It is followed closely by the fact that teenagers are influenced by celebrities. The third important reason is the shopping mall where teenagers can get a variety of colours, designs and discounted apparels.

Discussion and managerial implications: Prior research has called for identifying and investigating the teenagers' apparel purchasing intentions, which are likely to vary across retail shopping formats and occasions. Our findings reveal fashion apparel buyer, promotion conscious buyer, reference group buyer and body cathexis buyer to be important factors that drive teenagers to shop in India.

The first dimension of our study is fashion apparel buyers: In view of increasing competition among fashion apparel providers, managers may choose to assist teenagers in making dynamic shopping decisions by establishing price-value relationship to affirm their purchase intentions. Fashion apparel providers may also attract teenagers on multiple retail channels like catalogues, web sites and e-bay. Multi-channel retailing outlets including catalogue and virtual outlets on the Internet offer quick product search, comparative data of products, price, promotion, availability and additional services to shoppers, and build shopping motivation. Managers of fashion stores must understand that shopping behaviour among teenagers is governed by various platforms such as credit incentives, referrals, and shopping motivations. Fashion brands should be able to develop platforms that successfully connect various groups of teenagers



with shopping interests. Arousal among teenagers plays a key role in buying decisions.

Similarly, the second dimension is promotion conscious buyer: A company may also need to consider emphasizing an integrated promotion strategy for new brands with reference to attributes, awareness, trial, availability and repeat principle. One of the challenges for the manager of a retail store is to enhance the in-store ambience to influence the teenagers for prolonged stay in the store for apparel shopping and explore the zone of experience of new products. Systematically explored preferences of teenagers and arousal driven retailing approach towards apparel would be beneficial for a company to derive a long term profit optimization strategy over the period. This needs careful attention and the application of managerial judgment and experience to generate teenager arousal and develop appropriate point of sales strategies for stimulating the buying decision. Appropriate promotional strategies considering the economic and relational variables discussed in the study may be developed by the managers upon measuring the intensity of leisure shopping and the scope of expanding the tenure of leisure shopping in view to maximize consumer satisfaction and increase the volume of sales. The promotional effects generated from various promotional programs may be monitored for longer periods of time and measured with reference to achieving the long-term goals of fashion apparel manufacturing and marketing firms. At times, fashion stores also need to alter their promotion, advertising and merchandising strategies to better respond to the preferences of potential teenagers. Thus, sales promotion programs should be strategically conceived considering long term effects on volume of sales and building loyalty among teenagers.

Furthermore, the third dimension is reference group buyer: Although teenagers seem to be more independent in apparel purchasing, parents are found to be significant influencers of apparel purchase decisions. Not only were parents the reference group with which the teenagers most often shopped, but parents most often gave advice about apparel purchases, helped make the final decision on purchases, and were seen by the teenagers as knowing the best apparel purchases appropriate. Some teenagers indicated that friends most often gave them advice about apparel purchases and teenagers think that their friends' opinions matter most when shopping for apparels. In this instance, although friends' opinions appear to be more important, they are however, not as important as the respondent's personal opinion. (Lewis, et al. 1995) also found the peer group to be a significant influencer, even more significant than parents, of how the respondents chose to dress. Due to the findings of previous research, it is surprising that friends' opinions were not as significant as expected.

The fourth dimension is body cathexis buyers: Teenagers were analyzed for satisfaction with fit. Apparels can be used to bring us closer to our ideal self; however, when it does not, a lack of confidence can result as deficiencies are attributed to the body rather than to the fit of clothing (Storm, 1987). In this study, teenagers covered in the study were least satisfied with the fit of clothing to lower body parts including pant length, crotch, thigh, buttocks, and hips. An explanation for the inconsistency may be due to the fact that teenagers have not quite physically matured and therefore, their bodies may not better fit the apparel industry's standard measurements. Under the present study, teenagers were satisfied with the fit of upper body garments. It should be noted that t-shirts



were the apparel item with which majority of the teenagers were satisfied. It appears that majority of the teenagers are most likely to be comfortable in a t-shirt and jeans ensemble.

Conclusion: Trends in the apparel industry are changing rapidly and marketers are not always certain about how the market will accept the latest trend. This study discusses the teenagers' purchasing intention towards apparel with reference to fashion apparels, in-store promotions, reference group and body cathexis that mediate the teenagers' behaviour. An understanding of teenagers' buying behaviour, and specifically of what teenagers value when apparel purchase decisions are made, can be of great value to marketers. The results of this study indicate that fashion is the most important attribute of apparel to teenagers when making purchasing decisions, with designer brands being the second most important attribute. In-store promotion, reference group and body cathexis also play very important roles to buy the

apparels. The insight gained from this study could help apparel manufacturers to: Understand teenagers' selection criteria when purchasing apparel, plan their merchandise mix more efficiently, and plan their in-store promotional messages and strategies better. This information could also be used for the development of a theoretical model towards understanding teenagers' apparel purchasing decisions. There is a definite lack in theoretical knowledge in the models that explain what is important to teenagers concerning apparel purchase decisions, and specifically how they make trade-offs between various product attributes when purchasing decisions for apparel are made. Marketers may find it useful to investigate the possibility of using the attribute importance construct as a means of segmenting future markets. Studies in this field could investigate the differences in attribute importance for apparel among teenagers in various cultural groups. Studies could also be done to determine how teenagers value various attributes in clothing items before purchases are made.



Appendix:Table-1: Respondents' profile

Table-2: Statements on fashion apparels



Table-3: Exploratory factor analysis results

Note: Extraction Method Principal Component Analysis, Rotation Method Varimax with Kaiser Normalization 2 KMO (Kaiser-Meyer-Olkin Measure of Sampling Adequacy) = 0.633, Bartlett's Test of Sphericity: p = 0.000 (x =

1960.958, d.f = 903)

Abbreviations: F.A.B- Fashion Apparel Buyer, P.C.B Promotion Conscious Buyer, R.G.B- Reference Group Buyer, B.C.B Body Cathexis Buyer.

Table-4: Apparels data set (187cases)



Table-5: Difference data series

Table-6: Grey Relational Grade



Table-7: RIDITS for the reference data set

Table-8: RIDIT for the comparison data sets



Abbreviations: S.D-Strongly agree, D-Disagree, N-Neutral, A-Agree, S.A- Strongly agree

Table-9: GRA Grades and RIDIT Values



Table-10: GRA and RIDIT Comparative ranking



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Dr. S. K. Kaushal is an Assistant Professor in Department of Business Administration at the University of Lucknow. He has more than 10 years of experience in academe. He has published more than 30 research papers in various national and international journals. His area of specialisation is Statistics, Research Methodology and Operations Research. He conducts workshop on Multivariate Data Analysis and Structural Equation Modelling (AMOS). Dr. Kaushal can be reached at [email protected]

