Buying Apparel

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Buying apparel over the InternetRonald E. GoldsmithProfessor of Marketing, Marketing Department, College of Business,Florida State University, Tallahassee, Florida, USA

Elizabeth B. GoldsmithProfessor, Department of Textiles and Consumer Science, FloridaState University, Tallahassee, Florida, USA

Keywords Internet, Online transaction processing, Consumer behaviour,Clothing industry, Marketing

Abstract Tests ten hypotheses describing characteristics that distinguish consumers whohave purchased apparel online from those who have not. A sample of 263 men and 303women students completed a survey that measured their online and offline buyingbehavior, attitudes and predispositions. The results showed that the 99 online apparelbuyers had more online buying experience in general. Online buyers did not differ fromnon-buyers in their belief in how cheap buying online is, in their overall enjoyment ofshopping, or in how often they bought clothing by any means. The demographic variablesof age, sex and race were unrelated to online apparel buying. A further analysis showedthat the online buyers used the Internet more hours per week and were more likely to buyonline in the future than non-buyers. The findings are consistent with previous studies ofconsumer Internet behavior and with consumer theory and provide guidance for e-commerce apparel strategies.

Introduction

Electronic retailing continues to grow in size and importance as increasing

numbers of consumers buy online, and apparel purchases represent a

significant portion of online purchasing. Not only does buying apparel online

represent a new form of consumer behavior in the ``computer-mediated

shopping environment'' (Hoffman and Novak, 1996), apparel e-tailers face

intense competition. Thus, consumer researchers wish to extend current

theories of consumer behavior into this new consumption realm, and apparel

marketers and managers seek to develop effective strategies based on

knowledge of their consumers (Goldsmith and McGregor, 1999). Although

some research on consumer Internet behavior has begun to appear (e.g. Citrin

et al., 2000), little attention has been devoted specifically to buying apparelonline. Our study fills this gap by focusing on this new clothing behavior.

While the number of online buyers and value of their purchases change

constantly, growth is the dominant theme (Goldsmith and McGregor, 2000).

Americans spent $184B on total apparel in 1999 with $1.1B or 0.6 per cent

attributed to online apparel purchases (Kuntz, 2000). For 2000 the proportion

of total US apparel sales online is estimated at less than 3 per cent but still

nearly $3.5B (Vickery and Agins, 2001). Apparel spending in the UK was

30B (Wilson, 1999). According to one estimate, approximately 67 per cent

of Americans use the Internet and 52 per cent of them buy online (UCLA,

2000). Apparel is an important category of online purchases with new sites

constantly appearing (Murphy, 1999). An Internet-based research company

estimated online sales in 2000 to be $37B, up from $18.6B in 1999(eMarketer, 2000, p. 9). One estimate of total weekly online purchases in

2000 puts the number at 3.582 million, with 300,800 or 8.4 per cent of these

in the apparel category (Nelson, 2000). Two separate surveys showed

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Electronic retailing

Growth the dominant

theme

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clothing among the top six categories of holiday gifts in the USA for the

2000 Christmas season (eMarketer, 2000, p. 30). Thus, apparel is an

important consumer purchase category with a significant online component.

E-commerce is expensive, however, and many companies have found profits

hard to come by (Harvard Management Update, 2000). Selling apparel

online presents unique challenges to cybermarketers. Little is known of

consumer buyer behavior online, and e-tailers need to attract those

consumers most likely to buy in order to cover the costs of e-commerce and

make a profit to justify this new form of distribution. The first buyers of a

new product or service, however, are likely to be systematically different

from later buyers (Eastlick and Lotz, 1999; Goldsmith, 2000; Limayem et

al., 2000). Hence, the purpose of the present study was to compare

consumers who had purchased apparel online with consumers who had not

purchased apparel online with regard to demographics and attitudes toward

online purchasing. Several hypotheses about buying apparel online were

derived from consumer research and tested using data from a survey of

student consumers. Testing the hypotheses not only enhances our knowledge

of consumer behavior by extending the scope of theory into the new

shopping environment, this information may help online apparel marketers

improve their strategies designed to entice customers to buy online.

HypothesesConsumers differ in the extent of online buying in which they engage.

According to the standard discussions of buying frequency, relatively few

buyers in a product category account for the majority of purchases (Hallberg,

1995). Since online buying is a new consumer activity, we expect that

consumers who have previous experience in online buying will be morelikely to buy apparel online than those who lack such experience. This is

because, as consumers gain experience with online buying, perhaps with

small purchases at first, they will be likely to develop confidence and skills

that facilitate more ambitious buying (Seckler, 2000). Thus, H1 is that

consumers who have bought apparel online will have more experiencebuying online in general.

Consumers who have bought apparel online may likely be those who buy

more frequently than other consumers. In other words, consumers who buyapparel frequently are likely involved with clothing as a product category;

they not only shop frequently, they probably spend more than less involved,

less frequent shoppers. Thus, H2 is that consumers who purchase apparel

online shop for apparel by any means more frequently than those who have

not bought apparel online.

Several studies of consumer online behavior have shown that attitudes toward

the Internet and toward online buying are systematically related to online

buying behavior (Eastlick and Lotz, 1999; Goldsmith and Bridges, 2000;Karson, 2000; Katz and Aspden, 1997). Goldsmith (2000) presents Likert

scales to measure five specific attitudes toward e-commerce, describingindividual perceptions of its enjoyment, safety, speed, how economical it is,

and how much confidence consumers have in their ability to shop and buy

online. These attitudes were all related to online buying. Thus, H3 through H7are that, compared with consumers who have not bought apparel online, those

who have bought online feel that the Internet is more fun, safer, quicker,

cheaper, and they have more confidence in their ability to buy.

Similarly, how consumers feel about shopping in general should influence

whether they shop online and specifically purchase apparel online (see

Unique challenges

Previous experience

Online buying behaviour

90 JOURNAL OF PRODUCT & BRAND MANAGEMENT, VOL. 11 NO. 2 2002


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Solomon, 1999, pp. 311-13). Thus, H8 is that a positive disposition toward

shopping should be associated with buying apparel online. Finally,

consumers who are more innovative and knowledgeable with regard to the

Internet and its uses are more likely to buy online than less innovative and

knowledgeable consumers (Citrin et al., 2000; Limayem et al., 2000). H9and H10 are that online apparel buyers will describe themselves as more

innovative and knowledgeable regarding the Internet than non-buyers.

Method

Survey participantsThe data came from a survey of 566 students at a large southern university in

the USA in the spring of 2000. The students were in either marketing orhuman sciences classes. Both undergraduates and MBAs participated.

Although not representative of all consumers, these young buyers are

important, because they are heavy buyers of clothing, influence the clothing

spending of many other consumers, and represent the future of e-commerce(Hogg et al., 1998; Silverman, 2000). There were 263 (46.5 per cent) men

and 303 (53.5 per cent) women in the sample. Their ages ranged from 18 to

50, with a mean of 22.6 years (SD = 4.9). The modal age was 20 years. Most

of the participants were juniors (276, 48.8 per cent) and seniors (195, 34.5

per cent), with the rest being 17 (3 per cent) sophomores, 75 (13.3 per cent)

graduate students, and 3 (0.5 per cent) other. There were 419 (74 per cent)whites, 65 (11.5 per cent) African-Americans, 42 (7.4 per cent) Hispanics,

and 40 (7.1 per cent) others. This distribution is quite similar to the ethnic

distribution on this campus. There was no statistically significant (p < 0.05)

difference in mean age between the men and women, nor were the mean ages

of the four ethnic groups significantly different. A cross-tabulation of sex by

race showed that the proportions of men and women in each ethnic categorywere nearly identical, with the exception that the sample contained

proportionally more African-American women and proportionally fewer

white women.

Questionnaire

An initial version of the questionnaire was pilot-tested with 39 students in a

marketing research class for readability, ease of use, and clarity. After

correcting obvious errors and making their suggested changes in wording

and organization, the revised questionnaire was fielded by requesting student

volunteers to complete it.

The questionnaire contained demographic questions asking for the

participants' sex, age, race, and class standing. Other questions asked

whether the respondents had access to the Internet, how many hours they

used it per week, and whether they had ever purchased any apparel online. It

also contained rating scales to measure their online purchasing behavior,

likelihood of future online purchases, and apparel purchase. Table I shows

these questions and the responses. For the chief variable of interest to this

study, whether a respondent had ever purchased apparel online (termedEVER), 99 or 17.5 per cent of the respondents affirmed that they had so

purchased, and 467 (82.5 per cent) said that they had not. This is similar to

one report that 16 per cent of Internet users purchased apparel in cyberspace

during the previous month (Seckler, 2000).

The next section of the questionnaire contained 25 Likert-type statements

reflecting attitudes toward shopping over the Internet and enjoyment ofshopping in general. A portion of these items appears in Table II. These

Internet shopping items were adapted from a set of online buying attitude

Ethnic distribution

Questions and responses

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items developed by Goldsmith (2000). Three of the shopping enjoyment

items were adapted from O'Guinn and Faber (1989), and one original

shopping item was added for this study.

Finally came a section containing the Domain-Specific Innovativeness Scaleor DSI (Goldsmith and Hofacker, 1991). This scale was included to measure

Internet innovativeness. A factor analysis of the six items revealed a two-

factor solution, with the three positive items forming one factor and the three

negative items a second factor. We decided to use only the three negative

items as a summed scale, because this subscale (termed DSI) had the higher

internal consistency (coefficient alpha = 0.79). The items appear in Table III

along with a five-item subjective knowledge scale (Flynn et al., 2000) used

to measure knowledge of the Internet. Factor analysis showed that these

Variable Questionnaire item Response N %

ACCESS Do you have access to the

Internet?

Yes

No

562

4

99.3

0.7

EVER Have you ever purchased any

clothing online?

Yes

No

99

467

17.5

82.5

OFTEN How often would you say that

you purchase online?

Very often

Often

Sometimes

Rarely

Never

4

18

119

201

224

0.7

3.2

21.0

35.5

39.6

BUY Asked another way, how often

do you purchase online?

More than once a week

About once a week

Only about once every two

weeks

Less than once every two

weeks, but more than once

a month

Less than once a month

I never do

3

6

12

30

283

232

0.5

1.1

2.1

5.3

50.0

41.0

TIMES How many times have you

bought something online since

January 1, 2000?

times

MEANS How often do you purchase

clothing by any means?

Very often

Often

Sometimes

Rarely

Never

Missing

83

179

226

55

21

2

14.7

31.6

39.9

9.7

3.7

0.4

HOURS On average, about how many

hours a week do you spend

using the Internet?

None

Less than one

One to five

Five to ten

Ten to 20

More than 20

Missing

5

68

244

164

63

21

1

0.9

12.0

43.1

29.0

11.1

3.7

0.2

LIKELY Regardless of how much you

buy online now, how likely are

you to buy online in thecoming year?

Definitely will buy

Probably will buy

Might buyProbably will not buy

Definitely will not buy

Missing

82

107

208141

23

5

14.5

18.9

36.724.9

4.1

0.9

SPEND How much do you spend on

clothing purchases in an

average month?

Table I. Internet and buying questions

Internet innovativeness



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items formed a unidimensional scale (termed KNOW) with acceptably high

internal consistency (coefficient alpha = 0.90).

Results

The first preliminary analysis reduced the three online purchasing questions(OFTEN, BUY, and TIMES from Table I) into a composite measure of the

self-reported amount of online buying of each respondent. This was done

using a principal components analysis of the three items (Hair et al., 1998,

Ch. 3) and computing factor scores using the SPSS regression method. The

analysis extracted a single component with an eigenvalue of 2.37 that

explained 79 per cent of the variance in the correlation matrix of the three

variables. The resulting variable was labeled PURCH. Summary descriptive

statistics appear in Table IV.

Attitude itema Fun Shop Safe Conf. Cheap Quick

Buying over the Internet is more fun than

buying in a store 0.79

I enjoy buying over the Internet 0.56

I find shopping on the Internet less pleasant

than shopping in storesb 0.49

I sometimes shop for goods, but then buy

them on the Internet 0.41

I get a real ` high'' from shopping 0.86

Shopping is fun 0.83

I shop because buying things makes me

happy 0.80

I do not mind spending a lot of time

shoppingb 0.69

Buying over the Internet is no riskier than


It is risky to buy over the Internetb 0.73

Buying over the Internet is safer than buying

in a store 0.33 0.48

I lack the confidence to buy correctly on the

Internetb 0.64

I am confident in my ability to buy

successfully over the Internet 0.55

There are so many dot.com companies out

there it's confusingb 0.49

I cannot get the buying information I want

over the Internetb 0.40

I cannot save much money buying over the

Internetb 0.89

Buying over the Internet is cheaper than


Buying over the Internet is quicker than


Buying over the Internet is more efficient

than buying in a store 0.39

It takes a lot of time and trouble to buy on

the Internetb 0.32 0.37

Eigenvalue 5.5 2.8 1.5 1.4 1.1 1.0Percent of variance 27.4 13.9 7.6 6.8 5.4 5.1

Kaiser-Meyer-Olkin measure of sampling

adequacy = 0.840

Notes: Only loadings > 0.30 are shown; a using a five-point agree-disagree responseformat; b reverse-coded items

Table II. Factor analysis of attitude items

Composite measure

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The second preliminary analysis examined the structure of the 25 attitudeitems by submitting them to a common factor analysis followed by an

oblique rotation on the assumption that the attitude dimensions would be

correlated with one another (Hair et al., 1998, Ch. 3). The analysis was

conducted four times, each time identifying items that did not load on a

factor with other items or which had small loadings (< 0.03) or sizeable

(> 0.30) cross-loadings on more than one factor. Items were retained for

factors if they had sizeable loadings (> 0.30) on factors made of items with

similar content. These analyses reduced the initial pool of attitude items to

20 items that combined into six easily interpretable subscales that were

similar to those reported by Goldsmith (2000). The final analysis results

appear in Table II, where the six factors represent the attitudes that shopping

on the Internet is fun, safe, cheap and quick, and that the respondent had

confidence in his/her ability to shop online, as well as the general

``enjoyment in shopping'' scale. The scales are labeled: FUN, SAFE,

CHEAP, QUICK, CONFIDENCE, and SHOP. The individual items were

summed to form short scales (see Table IV).

Next, the Internet innovativeness and knowledge items were factor-analyzed

via common factor analysis, which revealed that the items loaded on two

distinct factors, indicating discriminant validity for these items (see Table

III). The individual items were summed to form two scales, DSI and KNOW.

Thus, the focal variables in the study were amount of online buying

(PURCH), how often clothing was purchased by any means (MEANS), the

attitudes toward online buying (FUN, SAFE, CHEAP, QUICK, and

CONFIDENCE), attitude toward shopping (SHOP), Internet innovativeness(DSI), and knowledge of the Internet (KNOW).

Cross-tabulation was used to assess the relationship between EVER (those

who had purchased apparel online versus those who had not) and sex and

race. These analyses showed no statistically significant relationships

between these variables. A t-test showed no statistically significant

difference in the mean age of those who had purchased apparel online versus

those who had not. The correlations in Table IV provide internal evidence for

the validity of the measures. The significant correlations of the DSI with

Scale itema Factor 1 Factor 2

Internet knowledge (KNOW)

When it comes to the Internet, I really do not know a lot b 0.88

I know pretty much about the Internet 0.83

Compared with most other people, I know less about the Internet b 0.82

I do not feel very knowledgeable about the Internet b 0.81

Among my circle of friends, I am one of the ``experts'' on the

Internet 0.65

Internet innovativeness (DSI)

In general, I am among the last in my circle of friends to purchasesomething over the Internetb 0.81

Compared with my friends, I do little shopping over the Internet b 0.79

In general, I am the last in my circle of friends to know the names

of the latest places to shop on the Internetb 0.62

Eigenvalue 4.15 1.55

Percent of variance 51.8 19.4

Kaiser-Meyer-Olkin measure of sampling adequacy = 0.865

Notes: Only loadings > 0.30 are shown; a using a five-point agree-disagree responseformat; b reverse-coded items

Table III. Factor analysis of Internet knowledge and innovativeness items

Common factor analysis

Focal variables



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Variables

R

ange

Mean

SD

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

1.

Age

1

8-50

22.6

4.9

2.

Sex

0-1

a

0.06

3.

Ever

0-1

b

0.02

0.0

5

4.

Purch

0.88-8.6

8

0

1.0

0.08

0.1

8

0.4

2

5.

Means

1-5

3.4

4

0.9

8

0.11

0.3

1

0.0

1

0.0

2

6.

Fun

4

-20

10.5

2.7

0.08

0.1

6

0.3

5

0.5

6

0.0

2

(0.7

4)c

7.

Safe

3

-15

7.2

2.2

0.03

0.1

0

0.1

5

0.3

7

0.0

6

0.4

4(0.7

6)

8.

Cheap

2

-10

6.1

1.6

0.08

0.2

4

0.0

2

0.3

8

0.0

8

0.4

4

0.2

9

(0.7

5)

9.

Quick

3

-15

9.3

2.7

0.10

0.0

9

0.1

7

0.2

9

0.0

4

0.4

7

0.2

9

0.4

0

(0.5

8)

10.

Conf

4

-20

13.6

3.0

0.06

0.0

8

0.2

3

0.4

3

0.0

0

0.4

6

0.3

6

0.4

3

0.3

9

(0.7

0)

11.

Shop

4

-20

13.2

3.9

0.26

0.4

5

0.0

5

0.1

1

0.4

00.1

60.0

4

0.2

1

0.1

3

0.1

6

(0.8

6)

12.

DSI

3

-15

9.4

2.7

0.04

0.0

4

0.2

8

0.4

8

0.1

5

0.4

2

0.2

9

0.2

5

0.1

9

0.4

7

0.0

4

(0.79

)

13.

Know

5

-25

18.4

4.0

0.00

0.0

8

0.1

1

0.2

8

0.0

6

0.2

6

0.1

2

0.1

6

0.1

7

0.4

9

0.0

5

0.40

(0.9

0)

14.

Spend

0

-500

89.3

74.8

0.09

0.2

2

0.0

3

0.0

6

0.4

30.0

1

0.0

3

0.0

8

0.0

1

0.0

1

0.3

0

0.13

0.0

1

15.

Hours

1-6

3.5

1.0

0.11

0.1

1

0.1

8

0.3

6

0.0

4

0.3

1

0.1

6

0.2

0

0.1

6

0.3

4

0.0

8

0.28

0.4

7

0.0

4

16.

Likely

1-5

3.2

1.1

0.08

0.1

2

0.3

3

0.6

7

0.0

4

0.5

9

0.4

0

0.3

5

0.3

7

0.4

6

0.0

7

0.47

0.2

8

0.0

5

0.3

4

Notes:Correlationsof0.0

9andlargerarestatisticallysignificantatp

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