consumer intention to utilize electronic shopping: the fishbein behavioral intention model

SOYEON SHIM MARY FRANCES DRAKE

Consumer Intention to Utilize Electronic Shopping The Fishbein Behavioral Intention Model SOYEON SHIM is currently an associate professor in the areas of consumer and merchandising in the Division of Mer- chandising. Consumer Studies and Design at the University of Arizona. Tucson. Prior to this position, she was a faculty member in merchandising at Colorado State University She received her PhD from the University of Tennessee, Knoxville. in I986 and her Master of Science from Yonsei University of Seoul, Korea, in 1983. She i s recipient of several outstanding paper awards given by various associations. Her current research interests include a variety of topics related to direct marketing such as market segmentation, satisfactions, perceived risks, and shopping orientations of electronic and catalog shoppers She has published widely in professionaljournals. MARY FRANCES DRAKE is a professor in the area of merchandising in the Department of Textiles. Merchandising. and Design at the University of Tennessee, Knoxville. She received her PhD from Pennsylvania State University Both authors are members of American Collegiate Retailing Association and Association of Consumer Research, and widely published in professionaljournals. The present study was supported by both Colorado State University and the University of Tennessee, Knoxville.

SOYEON SHIM MARY FRANCES DRAKE

ABSTRACT By utilizing the Fishbein Behavioral Intention theoretical framework, this study examined consumers' intention to use an electronic shopping mode. First, the Fishbein Behavioral Intention Model was tested in the context of electronic shopping in order to determine the importance of the model components. It was found that attitudinal component (Ab) and normative belief (NB) were similarly important in predicting electronic shopping intention (Bl) without the function of motivation to comply (MC). Second, those who had high intention level and those who had low intention level were identified. A profile of the potential users of electronic shopping w a s developed in terms of shopping habits, computer ownership and usage, mail order purchase experience, and derno- graphics.

0 1990 J o h n Wiley & Sons, Inc. a n d Direct Marketing Educational Founddt lon, Inc CCC 0892-0591/90/03022-12$04.00

22 JOURNAL OF DIRECT MARKETING VOLUME 4 NUMBER 3 SUMMER 1990

INTRODUCTION

A growing percentage of consumer buying activities takes place in the home rather than in the retail store. This takes the form of direct marketing-such as telephone shopping, mail-order purchasing, direct in-home sales, interactive video, and so forth (12) . Accordingly, these in-home buyers have developed a high level of sophistication. The News- paper Advertising Bureau predicted a continuation of growth in nonstore sales by reporting that “through this decade and beyond, consumers will be buying more through nonstore or in-home shopping” (21) . Yet, so little consumer research in direct marketing has been done (12) . Therefore, a need remains for marketers and consumer researchers to acquire definitive data on this type of shopping.

A number of consumer researchers stated that the trend in retailing will be away from more tra- ditional modes of shopping toward the use of an electronic shopping system, which is often referred to as videotex or interactive video (12,23). Videotex refers to “those information systems with two-way information flows between a user and a computer. The transmission lines can be the public telephone network, a cable T V system with two-way capabil- ities, or hybrids-such as a one-way cable into the home with a normal telephone link out of the home. Users are able to send as well as receive signals” (17:65) . The future of electronic shopping is opti- mistic for two reasons: “First, as the videotex be- comes more widespread and understood, the situation could quickly change. Second, people are al- ready buying in-home through other means, and it is likely that the greater convenience of video will catch the fancy of many once they see its potential” (12:154).

The Electronic Mall, a home shopping service for personal computer owners from CompuServe Incorporated of Columbus, Ohio, reported that since The Electronic Mall launch in February 1985, sales increased by 240 percent per year in the first two years. During 1986, shoppers made two million trips into The Electronic Mall. They also expect that, by 1990, nearly 13 million homes will have modem- equipped personal computers (7).

I n view of the rapid growth in in-home shopping, very few published empirical studies on electronic shopping exist in the marketing and retailing li t- erature (1,2,12,27). Understanding the potential in-

teractive video adopter is of critical importance for marketers and retailers in the future. Therefore, retailers considering involvement in the interactive video system should be concerned about the characteristics of the likely interactive video market and the amenability of the product lines to this type of in-home marketing (27).

Talarzyk and Widing described the likely interactive video market as innovative, young (25 to 44) and having a high income, having a high-level job, and having an advanced degree (25) . Fields and Greco found that symbolic adopters of in-home video shopping as individuals who symbolically adopt the idea of an innovation: 1) were more likely to be frequent catalog/telephone shoppers than symbolic rejecters; 2 ) were younger; 3 ) were in the high income and education categories; and were more likely to be married than symbolic rejec- tors ( 1 3 ) .

According to the study done by CompuServe, the current users of The Electronic Mall were described as follows: 1) 94 percent were men; 2 ) 70 percent were married; 3 ) 38 years was their median age, 4 ) 64 percent had completed college or postgraduate degrees; 5 ) 2.9 was their median household size; 6) 75 percent were professionals, managers, exec- utives, or proprietors; 7) $56,500 was their median household income; 8) more than 70 percent had an experience with mail purchases; and 9 ) generally the buyers of the newer products, such as video cameras, compact disc players, videocassette re- corders, personal computers, and radar detectors ( 8 ) . In summary, based on the three studies of the identification of potential electronic shoppers, the demographic profile of potential electronic shoppers appeared to be young, well educated, and in a high income category.

The Study Objectives To retailers and manufacturers, the importance of understanding behavioral intentions as a predictor of consumption is as follows:

The idea that consumption can be predicted by behavioral intentions holds considerable appeal for marketers. Both manufacturers and retailers must try to anticipate the desires of the market place. If, in fact, consumers do follow through with their purchase intentions, such information can assist in fore- casting demand. Intention can also be used as ’sur- rogate’ for actual behavior. Reliance on purchase in-

JOURNAL OF DIRECT MARKETING VOLUME 4 NUMBER 3 SUMMER 1990 23

tentions provides the marketer with a far less costly alternative” (12: 133).

Keeping up with the importance of the consumer intention as a tool of demand forecast, this study was designed to examine consumers’ intentions to use electronic shopping by utilizing the Fishbein Behavioral intention Model ( 1 4 ) . More specifically, the objectives of the study were:

1. To investigate the relative importance of attitudinal and normative influences of the Fishbein Behavioral Intention Model o n the intention to utilize electronic shopping.

2. To identify individual characteristics that might distinguish those who have high intention from those who have low intention to utilize electronic shopping.

Although the predictive power of the Fishbein Intention Model has been demonstrated across a broad range of topics, evidence of predictive ac- curacy is not sufficient for evaluating a model’s ability to diagnose accurately the motivations underlying a given behavior (1 2) . Particularly, researchers were interested in determining the predictive ability of motivation to comply (MC), which is o n e of the two hypothesized subcomponents of SN (subjective norm). For instance, several previous researchers found a significant effect of normative belief ( N B ) o n subjective norm ( S N ) without motivation to comply ( M C ) (4,5,10), indicating normative belief ( N B ) is sufficient in predicting subjective norm ( S N ) . Also, past research using the Fishbein Be- havioral Intention Model focused mainly o n the brand choice behavior and models of attitude-intention in the context of retailing are few ( 3 ) . Therefore, this led to the development of the first objective of the study: to determine the predictive ability of the components of the Fishbein Behavioral Intention Model.

The second objective was developed due to a great deal of inconsistency between various studies o n in-home shopping in terms of demographics, psychographics, and other shopping behavior variables (e.g., 6,9,11,15,18,19,24). The inconsistency appeared to be d u e to: 1) different types of merchandise investigated and 2 ) different types of in- home shopping methods used. This implies that consumer’s in-home buying activities may be influenced by attitudes toward both the in-home shop-

ping method and the merchandise type. This also indicates that i t is difficult to generalize about the findings of previous studies regarding purchasers of other types ofgoods from other kinds of in-home retailers (6). Therefore, the need remains to investigate consumers’ in-home buying activities based on the specific in-home method and the specific merchandise type. Keeping this in mind, this study utilized an electronic mall shopping mode as a specific in-home method and apparel product as a specific merchandise.

Conceptual Framework The Fishbein Behavioral Intention Model was ut i - lized as a conceptual framework. This model pos- tulates that behavioral intention is the function of two components: an attitudinal (or personal) component (Ab) and a normative (or social) component ( S N ) ( 1 4 ) . These two components (Ab and SN) are proposed to influence behavioral intention (BI ) , which in turn influences behavior (B) . This model suggests that behavioral intention precedes and is the best predictor of actual behavior. Behavioral intention is defined as an individual’s likelihood of engaging in the behavior of interest. Behavioral intention is estimated using the formula:

B

where:

R = BI =

Ab =

S N = W, and W, =

- BI = W, (Ab) + W, ( S N )

behavior behavioral intention attitude toward performing behavior B subjective norm empirically determined weights rep- resenting the components’ relative influence.

Attitude toward performing a behavior (Ab) has an indirect relationship with behavior and is based o n the summed set of underlying salient beliefs (b,) associated with the attitude and the evaluation ( e , ) of these beliefs by consumers. Symbolically, it can

be expressed as Ah = c b,e,.

Subjective norm ( S N ) represents the influence of “important others” and is the function of the two subcomponents: the associated normative beliefs

,I

*= 1


(NR,.), and the consumer’s motivation to comply with salient referents (MC,). These determinants of

SNcan be symbolically represented as SN = 2 NBj

MC,. The normative belief (NB) reflects the person’s perception of what a specific referent thinks the person should do with respect to a certain behavior, while motivation to comply (MC) is intended to capture perceived referent influence.

n

j= 1

Null Hypotheses Null hypotheses 1 and 2 were developed to achieve the first objective, while null hypothesis 3 was developed to achieve the second objective.

H l .

H2.

H3.

Attitudinal component (Ab) and normative component ( S N ) have no influence on behavioral intention (Bf) to utilize electronic shopping. Attitudinal component ( A b ) , and two subcomponents of normative component (e.g., normative belief (NB) and motivation to comply ( M C ) separately) have no influence on behavioral intention (Bf) to utilize electronic shopping. There are no differences between those who have high behavioral intention (high BI) and those who have low behavioral intention (low B/ ) to utilize electronic shopping in terms of: a. shopping habits b. computer ownership and usage c. mail order purchase experience d. demographics

METHODOLOGY

Sampling and Data Collection A national random sample of 1,500 names from the Family Facts database was purchased from Demo- graphic System Inc. Data were collected via a six- page mail questionnaire sent to the sample of 1,500 households during September 1988. After a follow- up post card, 384 usable questionnaires were re- turned, for a return rate of 23.2 percent.

In order to address possible nonresponse biases, the demographics of the respondents were compared to the U.S. population reported by the U S . Bureau of the Census (28 ) . The respondents re-

sembled the U.S. population figures o n marital status and census region, but appeared to be younger, better educated, and have higher total household incomes than the U.S. population. Therefore, it is difficult to draw conclusions regarding the findings of the study.

About 68 percent of the respondents were married. In terms of geographic location, 26 percent were from the north central, 22 percent from the northeast, 32 percent from the south, and 20 percent from the west. The majorityof respondents (89 percent) were in the 25-to-54 age category. Approxi- mately 43 percent of the respondents reported their household incomes as $50,000 and over. The majority of the respondents (87 percent) completed some college and graduate school. The majority of the respondents (80 percent) were female. Al- though the sample including mostly female does not match the target population (i.e., 94 percent of the current users of the Electronic Mall of CompuServe were male), it should be noted that this research was designed more to study the struc- ture of behavioral intention than to make product- specific recommendation. However, the findings of the study must be interpreted with these demographics in mind.

Questionnaire Development A focus group interview was conducted with forty female and male college students at a relatively large western university. They were provided with information concerning electronic shopping and asked if they were willing to buy apparel items and non-apparel items, assuming that the system would be easily available to them in the future. Interest- ingly, those who were willing to buy apparel items through electronic shopping indicated a willingness to buy nonapparel items as well. However, those who were unwilling to purchase apparel items through electronic shopping were split into two groups: 1) those who were willing to buy non-apparel items and 2) those who were not willing to buy nonapparel items, either. This implies that if a consumer is willing to buy a high-involvemeIit product such as apparel through electronic shopping, he or she is more likely also to purchase nonapparel items than are individuals who are unwilling to buy a high-involvement product. Therefore, apparel was chosen to be used as a specific mercnan- dise type as a rather conservative approach.


The questionnaire provided respondents with a description concerning the availability of electronic shopping service. The respondents were then presented with the questions regarding apparel shopping through an electronic system. The format of the behavioral intention components scales (i.e., Ki, A,, and SN) followed that of the Fishbein Be- havioral Intention Model (14) .

Criterion Variable Behavioral intention to utilize electronic shopping (Bi) was measured by asking the question, “As- suming electronic shopping is easily available to consumers, indicate the probability that you will buy clothing by electronic shopping in the future.” This was measured on a semantic differential scale ranging from “very probable” ( + 3 ) to “very im- probable” (-3).

Predictor Variables Attitude toward purchasing apparel through elec-

tronic shopping (Ab = C biei) was derived by mul-

tiplying the score of 6, and the score of ei and then summing up the nine patronage attributes. In order to measure ei (evaluation of consequence), the nine attributes for the selection of an apparel store were adopted from Seitz (22). These attributes included variety of brands, assortment, quality, value for the price, variety of services, ease of credit for guaran- teed or defective merchandise, adequate sales information, convenience, and up-to-date, fashionable items. The respondents were asked to indicate how important each of these nine attributes was when they decided “where to purchase” clothing. This was measured on a seven-point scale from “very important” (6) to “very unimportant” (0). This coding system was employed because an attribute that is unimportant should contribute zero toward attitude regardless of how likely that attribute would be provided by electronic shopping.

Beliefs (6 , ) that performing the behavior would lead to the desired outcome were measured by asking the subjects to evaluate how likely it was that electronic shopping for apparel would provide each ofthe nine patronage attributes. This was measured on a seven-point scale from “very likely” ( + 3 ) to “not at all” ( - 3 ) .

9

l= l

1

The subjectizw norm ( S N = c NB MC) was de- j = 1

rived by multiplying the score of NB and MC. In order to measure normative belief (NB) , subjects were asked “how likely it would be that other people who are important to them would recommend that they purchase apparel by an electronic shop^ ping system.” This question was measured on a seven point scale from “very likely” ( + 3 ) to “very unlikely” ( - 3 ) . Motivation. to comply ( M C ) was scaled to measure “how much the subjects wished to do as others who were important to them thought they should” on a seven point scale from “very much” (6) to “not at all” (0). Responses were coded 6 to 0 instead of +3 to -3 as with other measures, because this coding results in the NB. MC mean (close to 0 ) occurring when MC = 0.

Shopping habit questions (28 items) were adopted from previous studies (20,26) and measured by using Likert-type, ranging from “strongly agree” (5) to “strongly disagree” (1). In order to reduce 28 items to a few underlying factors of shopping habits, a principal component factor analysis with varimax rotation was performed (See step 4 in Figure 1). Factor loadings .45 was used as a criterion to include an item for each factor. I f an item loaded o n more than a factor, it was excluded and a second factor analysis was performed. As a result, six factors were developed and they were labeled: 1) fashion interests, 2) self-confidence/venturesome, 3 ) shopping center interests, 4 ) time consciousness, 5 ) satisfaction with local shopping, and 6) impulse buying. Table 1 presents the items for each factor and pertinent statistical information.

Computer ownership and usage included the ownership at home and the number of hours of the computer usage per week either at home or at work. Mail order purchase experience was measured by asking the respondents to indicate if they had ever purchased clothing by mail. Demographics included sex, age, marital status, education, occupation, the number of preschool children, and the total household income.

Statistical Procedure Figure 1 presents the statistical procedure. Two multiple regression analyses were utilized to test null hypotheses 1 and 2, while stepwise discriminant analysis and Chi-square were utilized to test null hypothesis 3 .


Behav id Intention @I)

& 4l B. M SN

- - (Null Hypothesis 3)

Step 4 Factor

Analysirof c o m p ~ ~

FIGURE 1 Statistical Procedure

Shopping Hatnu

(28 items)

RESULTS Testing Null Hypotheses 1 and 2 THE IMPORTANCE OF ATTITUDINAL AND NORMATIVE INFLUENCES ON ELECTRONIC SHOPPING BEHAVIORAL INTENTION. The relative importance, or weights ( w , , w2) of attitudinal ( A b ) and normative ( S N ) components in predicting behavioral intention to utilize electronic shopping ( B I ) was examined via standardized regression coefficients (beta weights) as recommended by Fishbein and Ajzen ( 1 4 ) .

I n order to examine the effect of attitudinal ( A b ) and normative ( S N ) components, two regression tests were conducted. The first regression model (step 1 in Figure 1 ) included attitudinal component ( A b ) and subjective norm ( S N ) , while the second regression model (step 2 in Figure 1) included attitudinal component ( A b ) and two subcomponents of subjective norm ( S N ) separately: normative belief (NB) and motivation to comply ( M C ) (Table 2 ) .

The results of the first model indicated significant effects of attitudinal component, Ab (beta = . 3 4 , p < . O O l ) , and subjective norm, SN (beta = .31, p < . O O l > on behavioral intention. Overall, f? was .30 at the level of p < ,001.

The second model included normative beliefs ( N B ) and motivation to comply ( M C ) separately, in

M P & usage

conjunction with attitudinal component (A, , ) . As a result, t h e predictive power of normative belief, N B (beta = .35, p < 001) and attitudinal component, Ab (beta = .33, p < ,001) were highly significant in influencing behavioral intention to utilize electronic shopping mode (I? = 3 2 ) . However, motivation to comply (MC) was not significant. Therefore, null hypotheses 1 and 2 were partially rejected.

These results reveal two important points: First, when SN as a function of N B and M C was entered in the model, SN was significant. Nowever, when the subcomponents of SN (i.e., NB and M C ) were entered separately, NB was highly significant in i n fluencing behavioral intention to utilize electronic shopping. This may indicate that the effects of normative belief (NR) may be sufficient in the Fishbein Behavioral Intention Model as social influence without the function of motivation to comply (MC). However, it should be noted that the lack of sig- nificance of MCdoes not necessarily mean “110 mc - tivation to comply.” Second, normative belief ( N R ) had slightly higher beta coefficient than attitudinal component ( A b ) . This indicates that normative belief ( N B ) as social influence was more powerful than attitudinal component in predicting behavioral intention.

*

? step J ’

JOURNAL OF DIRECT MARKETING

Mailadm

expcncna P+= -

VOLUME 4 NUM5ER 3 5UMMCR I990 27

Low BI Step 1

Multiple c - Step 2 Demo Regression

(Null Hypothesis 1) MddPle Regression

graphics I A

stepwsc Multiple Chi-square Discriminan1 Analysis

step 5 step 6

TABLE 1 Factors of Shopping Habits

Percent of Factor Eigen- Variance

Factor Item Loadings Value Explained

Factor 1 : Fashion Interests . Compared with most people, I am more likely to be asked for advice about new clothing fashions 84

. I am more interested in clothing fashions than most other people 84

- I read the fashion news regularly and fry to keep my wardrobe up.to-date with the fashion trend 83

.79

- 63

I buy new clothing fashions earlier in the season than most other people

. I give very little fashion information to my friends

Factor 2. Self-Confrdence/Venturesome I like to be considered a leader .79

. I am more independent than most people .76

- I think I have more self-confidence than most people . 7 7

. I enjoy doing new things 68

I like to experiment

. I like to try new and different things

.67

.49

I don't take chances i f I don't have to -.46

Factor 3. Shopping Center lnteresrs . I enjoy going to big shopping centers

- Shopping centers are the best places to shop

. I enjoy shopping and walking through malls

Factor 4. Time Consciousness . I usually buy at the most convenient store

71

.72

66

. 72

. I shop where i t saves me time .77

- It takes too much time to shop 64

Factor 5. Satisfaction with Local Shopping . Local stores offer me good quality for the price .69

. Local stores are attractive places to shop .68

. - 67

. Local salesclerks are poorly informed -.48

Local prices are out of line with other towns

Factor 6: Impulse Buying . When I find what I like, I usually buy without much thinking

. I don't like to spend too much time planning my shopping

. I usually watch the advertisements for announcements of sales -

.67

.66

56

. I shop a lot for specials -.55

5.5

3 26

2 24

I .8

1.67

19.6

1 1 7

8.0

6.4

6.0

1.35 4.8

Testing Null Hypothesis 3 GROUPING. In order to test nul l hypothesis 3 , it was necessary to classify the subjects into two groups: high electronic shopping intention and low electronic shopping intention. The criterion variable,

behavioral intention (RI ) , was used to identify two groups (step 3 in Figure I ) . Those who indicated (+I ) through (+3) on a seven-point scale were classified as high electronic shopping intention ( n = 1 2 4 ) , while those who indicated (-1) through


TABLE 2 Multiple Regression Analyses of A*, SN, NB, and MC on Behavioral Intention to Utilize an Electronic Mall

Model 1 Model 2

Fishbein Fishbein Behavioral Standardized Behavioral Standardized Intention Regression Intention Regression Components Coefficient Components Coefficient

A, .34*** NB .36***

SN .3 1 *** A0 .29***

F = 70.3*** F = 76. I4***

R‘ = .30 R’= 32

**- p < 001 A. Altitude roward utilizing a n electronic mall 1, Subjective Norm (the function of NE and MC) NB Norrnarive belief (what other people think) MC Motivation to comply

( -3 ) were classified as low electronic shopping intention ( n = 164). Those who indicated the middle (0) were not included in the analysis ( n = 50).

For the reliability of the classification ability of the criterion variable, an additional question after the description of electronic mall shopping was asked as follows: “If this system is easily available to you in the future, would you be interested in buying apparel through an electronic mall?” One hundred twenty-three answered “yes,” while 162 answered “no.” The numbers based on “yes” and “no” were very similar to the numbers based on t h e seven-point scale measure. Approximately 90 percent of the respondents showed the same responses (Chi-square = 154.3, p < . O O l > , indicating the relatively high reliability of this scale.

Shopping habirs, computer ownership and usage, and mail order purchase experience. Stepwise multiple discriminant analysis was performed to compare high to low level of electronic shopping intention on six factors of shopping habits, computer ownership and usage, and mail order purchase experience (step 5 in Figure 1). Table 3 presents the results of the stepwise multiple discriminant analysis, including significant variables, standardized coefficients, mean scores of both groups, and pertinent statistical information.

Seven out of nine variables appeared to have discriminating powers between high electronic shopping intention and low electronic shopping inten-

tion. Mail order purchase experience, time consciousness, fashion interests, and computer usage had positive standardized canonical discriminant function coefficients; while satisfaction with local shopping, impulse buying, and shopping center interests had negative coefficients. However, self- confidence/venturesome and personal computer ownership were not significant. As a result of testing the ability of the discriminant function to predict group membership of the original sample, it correctly classified 71 percent of the two groups, indicating that it was relatively successful in predicting group membership.

Based on the results, those who had high electronic shopping intention were more likely: 1) to have experience with mail order purchase; 2 ) to be dissatisfied with local shopping; 3 ) to feel time pressure for shopping; 4 ) to be planned buyers; 5 ) to be interested in fashion; 6 ) to be personal computer users either at home or office; and 7) not to enjoy shopping at a shopping center.

DEMOGRAPHICS. Chi-square analyses were used to compare high level of electronic shopping intention and low level of electronic shopping intention based on sex, marital status, age, income, education, occupation, and the number of preschool children in the household. The results are presented in Table 4 . Significant difference between high level of electronic shopping intention and low level of electronic shopping intention was obtained for age (Chi- square = 8.89,p < .05) and the number of children (Chi-square = 10.2, p < ,051; however, sex, marital status, income, education, and occupation were not significant.

Young people appeared to adopt an electronic shopping system more than older people. Only 16 percent of those in high electronic shopping intention were in the category of 45 years and over, while 40 percent of those in low electronic shopping intention were in the category of 45 years and over. Those who had a high level of electronic shopping intention tended to have more preschool children than those who had a low level of electronic shopping intention. Fifty percent of the high intention group reported having preschool children, while only 20 percent of the low intention group indicated having preschool children. Although income and education were not significant, it should be noted


TABLE 3 Discriminant Coefficients, Means and Standard Deviations of Discriminating Variables, and Classification Results

Means (S.D.) Standardized Canonical

Step Discriminant Funcuon Group I Group 2 No. Variables Coefficients (High Intention) (Low Intention)

1 Mail order purchase (cl .58*** 87 .58 experience 1.391 I491

2 Satisfaction with (4 -.46*** 3.25 3 53 local shopping f.64 1.591

3 Time consciousness la1 .49*** 3.56 3.16 1.851 I 871

4 Impulse buying (4 -.35*** 2 93 3.07 (1.16) (1 .17)

I 891 1.961

( 1 131 (1.10)

7 Shopping center la) -. 17*** 3.15 3.59 interests (I ,041 ( 1 051

5 Fashion interests (4 .28*** 2.73 2.63

6 Computer usage PI .19**" 2.04 I81

Centroid of the groups

Group I (High Intention) Group 2 (Low Intention]

56 -.43

Canonical Correlation

Wilks' Lambda

Chi-Square

.44

.80

58.1 ***

Predicted Group Membership

Discriminant Analysis Classification Results Actual Group

N o of Cases

Group 1 %

Group 2 %

Group 1 (High) 124 71 7

Group 2 (Low) 164 32 9

Percent of Grouped Cases Correctly Classified

28 3

67 I

71

* * * p < ,001 Nore (a1 Shopping habit factors, (bl Coded as I = None, 2 = 1-2 hours; 3 = 3-5 hOUfS; (c) Coded as. 0 = N O ; I = Yes

that the sample was better educated and had a higher income than the U S . population. I f the sample included a whole spectrum of education and income level, the results might be somewhat different. The fact that sex WAS not significant indicates that males and females have the same behavioral intention to utilize electronic shopping, although most of the current users of the Electronic Mall were male.

I n summary, those who had high intention to utilize electronic shopping were differentiated from

those who had low intention based on five factors of shopping habits, computer usage, mail order purchase experience, and some demographics, re- sulting in null hypothesis 3 being mostly rejected.

DISCUSSION AND IMPLICATIONS

This study was designed to examine the consumer's intentions to shop for apparel items through electronic shopping in the context of the Fishbein Be-

30 JOURNAL OF DIRECT MARKFTING VOLUME 4 NUMBER 3 SUMMER 1990

TABLE 4 Chi-square Analyses of Demographic Characteristics by Level of Electronic Shopping Intention

Group 1 Group 2 [n = 124) (n = 164)

High Elecrronic Low Electronic Shopping Shopping Chi-

Variable Intention Intention . Square

Sex female

Male

Marital Status Married

Single

Age 24 and under

25-34

35-44

45-54

55 and over

Total Household Income under 5 19,999

520.000-29.999

5 30,000-49.999

5 50.000-69.999

57O.ooO-89.999

590.000 and over

Education High school

Some college

College degree

Graduate degree

Occupation Lawyer/Accountanr

Physician/Dentist

Professional/Technrcal/

Craftsman/Tradesman

Clerical/White Collar

Upper Management/

Sales/Service/Middle Management

Full time student

Number of Preschool

Educator

Adminiztration

Children None

One

Two

Three and above

82%

18

b9%

31

8%

37

39

15

1

5%

13

28

26

14

1 4

14%

31

35

20

2%

2

34

3

16

18

23

2

50%

25

18

-7

79%

21

65%

35

9%

27

24

35

5

8%

19

30

21

I I

1 1

12%

34

32

23

5%

1

42

5

18

8

19

3

80%

I I

8

1

.34

.37

8.89'

3.66

90

6.65

10.2'

p < .05

havioral Intention Model. First, the importance of the attitudinal component (Ab) and the normative component ( S N ) in influencing the behavioral intention were tested. When attitudinal component ( A b ) and subjective norm ( S N ) were entered in the regression model, Ab and SNwere significant in influencing behavioral intention (BI). However, when the two subcomponents of SN, normative belief (NB) and motivation to comply ( M C ) , were entered in the model separately, attitudinal component (Ab) and normative belief (NB) were highly significant without motivation to comply ( M C ) , on the behavioral intention to utilize electronic shopping.

This result is consistent with previous research in that normative belief (NB) was significant as social influence without MC (e.g., 4,5,10). This result has two important implications. First, the fact that the effect of normative belief ( the person's perception of what other important people think) may be sufficient in the context of the Fishbein Model as social influence in predicting behavioral intention without motivation to comply (the person's motivation to comply with other important people). This indicates that perhaps the components of the Fish- bein Behavioral Intention Model need to be reex- amined.

Second, both attitudinal component (Ab) and normative belief (NB) were important in intluenc- ing behavioral intention to utilize electronic shopping, with normative belief coefficient being slightly higher. Perhaps one of the reasons why normative belief as social influence is important on purchasing apparel items through an electronic system may be due to the nature of the new shopping mode. The decision concerning where to buy (e.g., at home) and how to buy (e.g., through interactive video), can be one of high involvement, especially if high- involvement products such as apparel are being purchased (12). The highly involved consumers are strongly influenced by the reference-group (16).

In terms of managerial implications, first, efforts focusing on social influences (what other people think) appear to be important as an attempt to influence electronic shopping intention. For example, a social influence situation may be portrayed in advertising by stimulating communication and favorable word-of mouth among people.

Second, since the attitudinal component was also significantly predictive of an electronic shopping intention, perhaps the marketing efforts for creating


favorable attitudes would be desirable also. Keeping in mind that attitudes are a function of evaluation (e , ) and belief (6,) advertising strategies can be designed to influence the target market to perceive an electronic shopping mode as possessing posi- tively evaluated attributes. For instance, the attributes that were highly evaluated in deciding “where to purchase” clothing were quality, value for the price, and ease of credit arrangements for guaran- teed or defective merchandise. Therefore, direct marketers may do well to focus on these attributes.

The second objective of the study was to develop a profile of the potential users of electronic shopping. Based on the shopping habits, computer usage, mail order purchase experience, and demographics, the profile of those who had high electronic shopping intention were developed.

The potential users of electronic shopping were more likely to be previous mail order purchasers and appeared to be relatively young. This was consistent with Fields’ and Greco’s study in that symbolic adopters of in-home video shopping were younger and frequent in-home catalog shoppers (13). This is true of the current users of The Elec- tronic Mall, CompuServe, who were described as relatively young and previous mail order purchasers (8). The potential users of electronic shopping appeared to have preschool children. This is consistent with the majority of in-home shopping studies (1 1,18,19,20). The potential users of electronic shopping were more likely to feel time pressure for shopping. This is supportive of Korgaonkar and Moschis’s result that time consciousness was posi- tively related to favorable attitudes toward videotex (17). However, this is not consistent with Reynolds who found that time was not significant in catalog buying of general goods (20).

This study also found that potential users of electronic shoppers of apparel were fashion conscious, which supports the finding of Smallwood and Wie- ner in that heavy catalog users were fashion opinion leaders ( 2 4 ) . The potential users of electronic shopping appeared to be less satisfied with local shopping, which is consistent with Reynolds (20). The potential users were less likely to enjoy the shopping center, which is inconsistent with Reyn- olds (20). Finally, it is not surprising to find that the potential users of electronic shopping were likely to be regular computer users. As discussed earlier, inconsistencies exist across studies; there-

fore, much more research is encouraged in the context of electronic shopping.

Importance of Previous In-home Shopping In terms of managerial implications, the profile of those who had high intention to utilize an electronic mall indicates where to locate potential adopters and what to communicate to influence them. For instance, the most powerful discriminant variable between high intention group and low intention group was the experience with mail order purchase. This indicates that electronic marketers need to identify previous in-home shoppers. In addition, a nationwide study can be conducted to investigate the level of satisfaction with local shopping facilities in order to identify areas with less satisfied cus- tomers.

Personal computer users can be easily reached not only through their homes but also through their offices. In promoting this shopping mode, themes such as time saving, planned buying, fashion, and perhaps negative aspects of outshopping mode such as inadequate parking facilities and inconvenience can be utilized. Demographically, these potential adopters appeared to be young and have preschool children. Therefore, marketers may do well by focusing on people with these characteristics.

CONCLUSIONS

In conclusion, this study found the importance of both attitudinal component and normative belief in predicting the behavioral intention to purchase an apparel item by using electronic shopping and the insignificant effect of motivation to comply. How- ever, further research is needed to investigate the role of motivation to comply ( M C ) in the context of electronic shopping by utilizing the Fishbein Be- havioral Intention Model. In addition, some of the Fishbein Model variables such as Bf, NB, and M C were measured by a single item. Therefore, it is recommended to include multiple items for each variable to improve the reliability of the scale.

This study found that there are distinct differences in terms of shopping habits, computer usage, mail order purchase experience, and some demographics between high level of electronic shopping intention group and low level of intention group. Further research is recommended: 1) including


other variables such as involvement, perceived risk, innovativeness and 2) perhaps utilizing other merchandise types such as non-apparel items.

REFERENCES

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consumer intention to utilize electronic shopping: the fishbein behavioral intention model

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