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Exploring motivations forconsumer Web use and theirimplications for e-commerceJessica L. JoinesAccount Executive, DDB San Francisco, San Francisco, USA

Clifford W. Scherer

Associate Professor, Department of Communication,Cornell University, Ithaca, New York, USA

Dietram A. ScheufeleAssistant Professor, Department of Communication,Cornell University, Ithaca, New York, USA

Keywords Internet, Electronic commerce, User studies, Privacy

Abstract This study examines the influence of demographic variables and dimensions ofmotivational factors of two types of consumer Web use: percentage of weekly Web surfing

time spent searching for product and service-related information and online shopping andtransactions. It combines data from two sources: a self-administered survey of 59

undergraduatesin an introductory communication course at Cornell University; and a mail/

Web survey of 59 New York State residents who had reported subscribing to an onlineservice in a previous mail survey. We found distinctively different patterns of relationshipsamong demographics and motivational factors for the two types of dependent variables.

Most importantly, tr ansactional privacy concerns were found to be negatively r elated topercentage of time spent on product searches and online shopping, while economic

motivations had a positive influence. In addition,online shopping was found to be predictedby information motivations, interactive control motivations, and socialization motivations.

Implications for Web-based commerce and advertising are discussed.

Introduction

The importance of the Internet as an advertising medium has generated a lot

of attention in recent years; $3.1 billion was spent on Internet advertising in

1999, a sum estimated to reach over $15 billion by 2003. More interesting,

the money spent on Internet advertising seems to be paying off for someindustries. For example, retail e-commerce revenues increased dramatically

last year, from $8 billion in 1998 to $18.6 billion in 1999, and are predicted

to reach $80 billion by 2003 (E-Land, 2000). However, few of the Internet-

based businesses are making profits, but companies like Amazon.com are

expected to produce bottom-line profits within the next few years.

The increase in numbers of Internet users also suggests we are seeing the

emergence of an important new medium for commerce. A study recently

conducted by CyberAtlas (2000a) reports that PC use in American homes has

surpassed the 50 percent mark, and that 90 percent of PC users are now

online. This translates into 45 percent of the total American population.

According to Donthu and Garcia (1999, p. 52), ``the increasing number ofcompanies that offer Internet access are providing consumers with a

convenient and inexpensive way to become members of the Internet

community . . . the increase in the quantity and quality of available

information on the Internet and the presence of well-known corporations and

brands on the Internet are also generating higher interest among consumers.

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The interest generated by consumers has provoked researchers to investigate

how the Internet fits into the marketing communication mix. Quinn (1996)

suggests that the distinct mix of communication capabilities found on the

Internet is not only changing the way businesses interact with their

customers, but also the way businesses interact with one another.

As an electronic trade show, it resembles a giant international exhibition hall

where potential buyers can enter at will and visit prospective sellers. They may do

this passively by simply wandering around, enjoying the sights and sounds,

pausing to pick up a pamphlet or brochure here, a sticker, key ring, or sample

there. Some buyers might even become vigorously interactive in their search for

information and want-satisfaction. They can talk to fellow attendees, actively seek

the booths of particular exhibitors, carefully examine products and services, solicit

richer information, and even engage in sales transactions with the exhibitor

(Berthon et al., 1996, p. 25).

The Internet presents a new paradigm for conducting business relations and

developing advertising strategies. Consumers and businesses have the ability

to shop from their homes or offices for a variety of products and services

from all over the world. They are able to view products on their computer

screens, and visualize how the products may benefit their needs. They can

also engage in online discussions with other consumers about the products

and services they seek. No other mass medium possesses all thesecharacteristics. The Internet combines the entire purchasing process, from

product exposure to product purchase, into one easily accessible medium.

Although there are many ways in which the Internet differs from other

advertising channels, three are consistently mentioned in the advertising

literature (e.g. Quinn 1996; Berthon et al., 1996). These components are

interactivity, customer intimacy, and the ability to shop online. Many argue

that these are the characteristics that are provoking interest among

consumers, and will generate success for e-companies:

Interactivity. The level of interaction possible on the Internet is one of

the prime reasons scholars suggest consumers may prefer it to other

forms of advertising. Bezjian-Avery et al. (1998, p. 23) define

interactivity as, ``the immediately interactive process by which customer

needs and desires are uncovered, met, modified, and satisfied by the

providing firm. Ghose and Dou (1998), for example, found that Web

sites that are more interactive are perceived to be of greater value by

consumers. Berthon et al. (1996) argue that the reason consumers prefer

interactive advertising is because it puts the consumer in control, since

the Web is a medium where the customer generally has to find the

marketer rather than the other way around. Giving support to this

argument, Korgaonkar and Wolin (1999) found that ``interactive

control was significantly and positively correlated with Web usage.

Customer intimacy. It has been argued that the interpersonal commu-

nication aspect of the Web allows for a more personalized advertising

experience (Bezjian-Avery et al., 1998). By way of e-mail, consumers

can request information from companies with greater ease. For example,

some companies, such as Campbells Soup, send out weekly recipes to

customers who request them.

It is the combination of mass and interpersonal communication found

on the Internet that has not only provoked advertisers to rethink their

marketing strategies but also because of its unique ability to provide a

tool for many-to-many communication scenarios. This characteristic of

the Web is what makes it such a successful advertising medium.

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Quinn (1996) supports this claim by arguing that this new breed of mass

communication has enabled companies to have a very personalized one-

on-one experience with their customers. It is believed that this

heightening of customer intimacy will enable marketers to learn more

about the individual behaviors and trends of their customers, thereby

meeting consumer needs more efficiently.

Shopping online. The ability to view products and then buy them

immediately not only distinguishes the Web from other advertising

mediums, but also seems to be a reason why many users are logging on.With the development of secure transaction systems, online shopping

continues to increase, with 44 percent of Internet users reporting that

they regularly, or at least occasionally, make purchases online

(CyberAtlas, 2000b). Moreover, the Internet shopper is becoming more

mainstream. More older Americans and people of more moderate

incomes and educational backgrounds are purchasing items online.

With a growing interest in online shopping also come many concerns about

engaging in online shopping activity, where credit card numbers and other

personal information must be given. Privacy concerns and fear of insecure

transactions have been argued to be the biggest inhibitors to shopping online

(Wang et al., 1998). According to the Harvard Business Review (1997, p. 13),the invasion of privacy is defined as, ``the unauthorized collection, disclosure,

or other use of personal information as a direct result of e-commerce

transactions. The article recommends that companies offering e-commerce

capabilities give consumers a choice in whether they want their personal

information sold to third parties. They also suggest that companies promise

security from hackers and provide access to consumers who want to view or

change online records of themselves.

As quickly as advertising researchers have realized the potential of Internet

advertising and e-commerce marketing models, research has looked to

measure the effectiveness of online advertising. The research, however, has

focused more on the content of Web advertising, rather than the Web user.For example, Berthon et al. (1996) conducted an exploratory study

investigating the role of the Web as an advertising medium and tried to offer

frameworks for measuring the effectiveness of Internet advertising and, in

particular, Web site efficiency. Briggs and Hollis (1997) also studied Web

content by analyzing banner advertisements and their ability to increase

brand awareness and persuade consumers to purchase products. Researchers

trying to test content effectiveness have also rushed to develop scales for

measuring attitude toward a Web site and other measures for evaluating the

design and effectiveness of promotional content on the Web (e.g. Chen and

Wells, 1999; Dreze and Zurfryden, 1997).

This is not to say, of course, that no research investigating the Web user hasbeen conducted. While this research offers some understanding of consumer

attitudes toward Internet advertising, however, it does not delve into the

possible underlying reasons why consumers choose to use the Internet over

other forms of mass media.

Initial attempts to empirically tap into the reasons why consumers use the

Internet has mostly applied a uses and gratifications approach (Eighmey and

McCord, 1998; Korgaonkar and Wolin, 1999). These studies have tried to gain

a better understanding of the user experience with the Web and have provided a

more in-depth understanding of Web users and their motivations for continued

use, whereas studies looking at the frequency and reach of Web sites

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(e.g. Leckenby and Hong, 1998) do not provide information about what

gratifications people receive from visiting a Web site or what their underlying

motives were for visiting the site in the first place. This information is valuable

for marketers trying to target audiences more effectively.

Uses and gratifications of Web use

The approach also suggests, however, that different uses and gratifications

may be found within one medium. Swanson (1987) argues that audience

members bring their own perspectives to different media content and that

various audience members may seek and find different uses and

gratifications within the same medium or media content. This argument is

particularly relevant when considering the diversity of content, activities and

possible reasons why people use the Internet.

Uses and gratifications is particularly useful in explaining continuing use

(McGuire, 1974). Whereas initial use may be a result of accidental exposure

or curiosity, continuing use assumes there are underlying motivations driving

repeated use of mass media. In other words, if audiences were not receiving

certain rewards or gratifications from using a mass medium, they would stop

using that medium. Therefore this study uses measures of time spent online,

rather than a dichotomous measure of having used the Web before.

Eighmey and McCord (1998) were some of the first researchers to apply the

uses and gratifications approach to the World Wide Web. They found

gratifications with viewing commercial Web sites to be similar to

gratifications found to be associated with other types of media

(i.e. entertainment motivation, information motivation, etc.), and also

revealed new dimensions called personal involvement and continuing

relationship. Personal involvement related to the degree to which users found

the Web site to be personal. Continuing relationship represented whether or

not users wanted to visit the Web site again.

Similarly, Korgaonkar and Wolin (1999) explored motivations and concerns

related to Web use and identified seven motivations and concerns regardingWeb use:

(1) social escapism motivation;

(2) transaction-based security and privacy concerns;

(3) information motivation;

(4) interactive control motivation;

(5) socialization motivation;

(6) nontransactional privacy concerns; and

(7) economic motivation.

Findings suggested that motivations and concerns play a greater role in

determining subjects actions with respect to Web usage than do

demographics.

Uses and gratifications and Web advertising

Although there has been a substantial amount of research published on

Internet advertising, professionals in the field continue to struggle with how

effectively to market and sell products online. According to Wolff (1998),

there have been numerous companies that have been less than satisfied with

Internet advertising. In fact, ``leading marketing companies, such as

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Proctor and Gamble, have been perplexed by the new medium and its

underperformance (as cited in Korgaonkar and Wolin, 1999, p. 53). This of

course raises questions as to how effective Internet advertising really is.

Companies continue, however, to pour billions of dollars into online

advertising in the race to make money on the Internet. It seems that many

companies are more fearful of not investing in an online presence rather than

gaining an ineffective Internet presence that could actually hurt business.

One way to gaining a clearer understanding of Internet advertising is to

investigate the reasons consumers use the Internet to get information about

products and services. While there has been some research investigating the

user experience with the Web, only one study has attempted to gain a clearer

understanding of the underlying motivations and possible concerns related to

Web usage (Korgaonkar and Wolin, 1999). This study, however, examined

the ``typical Web user. Research has yet to examine underlying motivations

for using the Web for consumer-related activities, such as searching for

information on products and services and shopping online.

Moreover, it appears that, there has yet to be any published research

identifying how motivations may vary across different Web activities. The

Internet is unlike any other medium, in that the reasons for using the Internet

are varied. This is in part due to the nature of the communication activities

available on the Web. Users can use the Internet to do an array of things such

as communicate with friends, find out news information, or go shopping

three very distinct activities. Therefore, it seems that a study investigating

user motivations for using the Internet would want to study motivations in

the context of the distinct activities offered online.

Using the Web for consumer purposes is increasingly becoming one of the

primary reasons people are using the Internet. Not only is online shopping on

the increase, as reported earlier, but consumers are also reporting spending

more time online searching for information on products and services.

CyberAtlas (2000a, b) reports that 58 percent of households who use the

Internet spend time online searching for information on products and services.

Another study conducted by E-Marketer (2000a), found that 23.7 percent of

users total online activities was spent learning about products and services.

Therefore, investigating the underlying motivations and concerns of users

who report using the Web to learn about products and services and/or to shop

online seems worthy of investigation. Understanding the possible underlying

reasons for using the Web as a way to gain product knowledge or to shop for

products and services will enable advertisers to target audiences and tailor

Web content more effectively.

Addressing the shortcomings discussed above, this study investigates the

underlying motivations of the consumer Web user. Consumer Web usage isdefined by two distinct activities:

(1) Level 1. The percentage of total Web surfing time spent online each

week getting information about products and services.

(2) Level 2. The frequency of purchases made on the Web across different

types of products and services.

These two levels of Web usage will be investigated using the seven factors/

motivations that were developed by Korgaonkar and Wolin (1999).

Moreover, this study expands on the research of Korgaonkar and Wolin

(1999) by investigating whether and how these seven factors are related to

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time spent online getting information about products and services and

various purchasing activities.

We also investigate the changing demographic make-up of Web users. While

it has been argued that the Web user and the Web shopper are becoming

more mainstream (CyberAtlas, 2000a, b), we believed that demographics

still play a significant role in predicting consumer Web usage.

Research hypotheses

This study applies a uses and gratifications approach to study two veryspecific types of Web usage: percentage of weekly Web surfing time spent

searching for product/service information, and shopping online. It will

investigate whether the motivations and concerns validated in Korgaonkar

and Wolins (1999) study serve as predictors of consumer Web usage.

Further, this study examines whether the demographics, age, gender, income,

and education significantly correlate with the two consumer activities under

investigation.

In Korgaonkar and Wolins (1999) study, age, income, and education were

the only demographic variables that correlated significantly with Web usage.

In 1998, it was shown for the first time that gender was becoming

increasingly less important in predicting Web usage (GVU survey). Sincethen, reports continue to document this fact (CyberAtlas, 2000a, b).

However, age, income, and education seem to still be important predictors,

although these demographics continue to move toward the general

population as well. For example, a report recently published by CyberAtlas

(2000b) found that age, income, and, education were significantly related to

Web usage, although they argue that the gap between the Web user and the

general population appears to be lessening:

H1a. Younger people are expected to spend a larger percentage of Web time

per week searching for information about products and services.

H1b. The percentage of weekly Web time spent searching for information on

products and services will be significantly and positively related toincome.

H1c. The percentage of weekly Web time spent searching for information on

products and services will be significantly and positively related to

education.

Shopping online is still a relatively new activity for Internet users. Studies show

support for many demographic variables to be strong predictors of online

shopping. Donthu and Garcia (1999) found age and income to be significantly

correlated to online shopping, while Korgaonkar and Wolin (1999) found

gender and age to be significantly correlated to online shopping. However, a

study recently conducted by CyberAtlas (2000b) reports that all four variablesare still significantly related to online shopping, although the online shopper is

becoming more similar to the Web user. In this study it was found that online

shoppers are more often male and of a younger age than the general population.

Shoppers are also more educated and have higher incomes:

H2a. Males are expected to be more likely to shop online than females.

H2b. Younger people are expected to be more likely to shop online.

H2c. Shopping online will be significantly and positively related to income.

H2d. Shopping online will be significantly and positively related to

education.

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Using the Web to search for information on products and services directly

implies use of the Web to obtain information. In Korgaonkar and Wolins

(1999) study, it was found that information motivation was significantly

correlated with the number of hours spent online per day and Web purchases

made online:

H3a. The percentage of weekly Web time spent searching for information

about products and services will be positively and significantly related

to information motivation.

Researchers have argued that one of the primary reasons Web users prefer

Internet advertising is that they can control what content they want to view,

and what content they choose to skip over (Berthon et al., 1996). Further,

they can control when they want to view the information. Korgaonkar and

Wolins (1999) findings show interactive control motivation to be

significantly correlated with hours spent online per day, percentage of

personal Web use, and purchases made online:

H3b. The percentage of weekly Web time spent searching for information on

products and services will be positively and significantly related to

interactive control motivation.

Users who are using the Web to learn about a companys products andservices are most likely in the market to purchase something. One argument

that is also made about why people use the Web to get information about

products and services is that they are searching for bargain prices available

only online. Korgaonkar and Wolin (1999) found economic motivation to be

significantly correlated to hours spent online per day, percentage of business

use, percentage of personal use, and shopping online. In other words, they

found that economic motivation was significantly correlated with all levels

of Web use as defined in their study:

H3c. The percentage of weekly Web time spent searching for information on

products and services will be positively and significantly related to

economic motivation.

Countless articles and reports have argued that one of the biggest inhibitors

to shopping online is transaction-based security and privacy concerns (Wang

et al., 1998). It has been argued that companies need to promise security

from online hackers or disgruntled employees if they want to really see the

e-commerce business structure take off (Kiely, 1997). Korgaonkar and

Wolin (1999) found transaction-based security and privacy concerns to be

significantly correlated to shopping online:

H4a. Shopping online will be negatively and significantly related to

transaction-based security and privacy concerns.

Information about a product or service is almost always obtained before apurchase. Many online users may log on to learn about a product or service,

and end up buying that product or service from the company home page.

Many companies are integrating advertising and point of purchase into their

company home page. According to Quinn (1996), it is this characteristic of

the Internet that makes the Web a superior advertising medium. Users can

view advertisements and then immediately purchase the items they desire.

Korgaonkar and Wolin (1999) found information motivation to be

significantly correlated to shopping online:

H4b. Shopping online will be positively and significantly related to

information motivation.

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Interactive control gives shoppers the ability to shop for items at their

convenience. Moreover, shopping sites like eBay and Priceline, give

consumers bargaining power. For this item, Korgaonkar and Wolin (1999)

found a positive correlation:

H4c. Shopping online will be positively and significantly related to

interactive control motivation.

In Korgaonkar and Wolins study, they found online shopping to be

significantly correlated with social motivation. They argue that people who

enjoy shopping are also those who enjoy a sense of community. One further

argument can also be made social motivations may be directly related to

online shopping sites such as eBay, where interaction is part of the

purchasing process. Thus, the following hypothesis is put forth:

H4d. Shopping online will be significantly and positively related to social

motivation.

Statements listed on the scale for economic motivation describe online

shopping as convenient and a way to save time and money. For example, one

of the main reasons for using a site like eBay is to save both time and money.

Korgaonkar and Wolin (1999) found this item to be significantly correlated

to online shopping:

H4e. Online shopping will be positively and significantly related to

economic motivation.

In sum, the two dependent variables are expected to be related to distinctly

different sets of antecedent variables.

Methods

The study is based on a sample of 59 Web users from the state of New York,

excluding the larger New York City area. The sample came from a study

examining how New York residents get and use information essential to their

daily lives. A probability sample of 1,600 New York residents was mailed a

questionnaire. A total of 583 subjects volunteered to participate for a

response rate of 37.1 percent. One question on the questionnaire asked

subjects if they subscribe to an online service such as AOL. Of the 583

subjects, 300 indicated they subscribed to an online service.

These 300 subjects were mailed a letter asking them to participate in the second

phase of the study by filling out another questionnaire related to Web usage.

The subjects had the option of completing the questionnaire on the Web,

having a paper copy of the questionnaire mailed to them, or to not participate at

all. After this first mailing wave, 30 subjects agreed to participate in the study.

Approximately ten days after the first wave, a postcard was sent to the subjects

asking them once again to participate in the study. After this second mailing

wave, 29 more subjects completed the survey, for a total sample size of 59

online subscribers, and a total response rate of about 20 percent for the second

study. The sample consisted of more males (73 percent) than females. The

mean age was 49.6 years. Education was measured on a five-point scale. A

total of 61.8 percent of all respondents were college graduates. The average

income reported was between $40,000 and $49,999.

Measures

Two categories of independent variables were measured in this study:

motivations/concerns and demographics. To test the different underlying

motivation variables, Korgaonakar and Wolins (1999) scale was used. This

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scale measures seven underlying dimensions: social escapism motivation,

transaction-based security and privacy concerns, information motivation,

interactive control motivation, socialization motivation, nontransactional

privacy concerns, and economic motivation. Some minor adjustments were

made to Korgaonkar and Wolins (1999) original scale to increase reliability

ratings after the scale was pretested on 59 college students from Cornell

University. The original scale comprised 41 items capturing seven

underlying constructs: companionship, entertainment, interactivity, learning

about things, privacy concerns and security concerns, shopping, and passtime. After the modifications, the motivation scale was reduced to 40 items

measuring the same underlying constructs listed above. Each item was

measured along a four-point scale asking respondents to indicate the degree

to which they agreed or disagreed.

The dependent variable, consumer Web use, was measured by two distinct

activities:

(1) percentage of time spent learning about products and services; and

(2) number of items purchased online.

The first activity was measured by asking respondents to indicate the

percentage of time they spent engaging in various online activities each

week. Activities included: entertainment activities, getting information about

hobbies, getting information about jobs, getting news information, getting

weather information, getting travel information, getting health or family care

information, and getting information about local communities. A final

activity listed was getting information about products and services. For each

activity, respondents were asked to indicate the percentage of their total

weekly time spent online that they engaged in any one of these activities. For

the purposes of this study, the percentage respondents indicated they spent

online searching for information about products and services was the only

item of importance.

The questionnaire also included questions investigating subjects online

shopping behavior. The survey contained questions inquiring how often

respondents purchase certain types of products and services online. Choices

included: investments, small goods and services, lodging services,

transportation services, online subscriptions, computer-related products, and

online auctions. Subjects were asked to indicate how often they had

purchased each item/service category along a four-point scale (never, at least

once, two to five times, and more than six times).

Results

In order to test H1a to H4e, we tested a total of four separate ordinary-least-squares hierarchical regression models. In this type of analysis, variables are

entered according to their assumed causal order beginning with demographic

variables. Each block therefore accounts only for the proportion of variance

that is not accounted for by previous blocks in the model.

The first two models were based on data from the sample gathered from the

New York State population using either the online survey or a paper-and-

pencil questionnaire. In order to control for a potential influence of the mode

of data collection on the dependent variables, a dummy code for people who

had filled out the questionnaire by hand was entered as a first block in the

model (see Table I).

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As a second block, demographic variables were entered in order to test H1

and H2. These included age, gender, education and income. The final block

were motivations and concerns about Web use.

New York State sample

Online shopping. People who chose to fill out the survey online rather than

on paper were significantly more likely (-= 0.35) to shop online. Overall,

the mode of data collection accounted for 12.4 percent of the variance inonline shopping.

In H2a to H2d expectations about relationships between all demographic

controls and online shopping were formulated. Even though the model

produced only one marginally significant beta for age (- = 0.21) due to

the relatively small sample size three out of four coefficients were larger

than 0.10 and might have been significant in a larger sample. With the

significance levels based on this sample, only younger people were

significantly more likely to shop online. In addition to the previous block,

i.e. the paper dummy variable, demographics added about 10 percent of the

variance accounted for in online shopping. Due to the small sample size, this

number was not significant.

The main focus, of course, were the motivations for shopping online.

Consistent with previous research, this model found support for all

hypotheses. Respondents who reported higher levels of transaction-based

security concerns were significantly less likely to shop online (- = 0.48).

Information motivation (-= 0.32), interactive control motivation (-= 0.31),

socialization motivation (- = 0.21), and economic motivations (- = 0.63)

were all significantly and positively related to online shopping. As a block,

motivations and concerns accounted for a significant increase of about 42

percent in the variance accounted for in online shopping.

Shopping

(beta weights)

Paper survey (1) 0.35**

Incremental R-square (%) 12.4**

Demographic variabl es (4)

Age 0.21***

Gender 0.09

Education 0.15

Income 0.10

Incremental R-square (%) 9.9

Motivati ons/Concerns (7)

Social escapism motivation 0.05

Information motivation 0.32**

Interactive control motivation 0.31*

Socialization motivation 0.21***

Transaction-based security concerns 0.48**

Nontransactional privacy concerns 0.04

Economic motivation 0.63**


Total R-square (%) 63.7

Notes:* 0.05; **p 0.01; ***p 0.10

Table I. The influence of demographics and motivations on online shopping

(n = 59)

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Product/service searching. There were no significant findings with respect to

the mode of data collection and searching for information on products and

services. A moderate beta of 0.19 was found for this item, suggesting that

respondents who filled out the survey online were more likely to spend time

searching for information on products and services, but the sample size was

not large enough to produce significant findings. As a result, the mode of

data collection accounted for only 3.6 percent of the variance in product/

service information searches (see Table II).

H1a to H1c predicted relationships between percentage of time spentsearching for product/service information and the demographics: age,

income, and education. None of these hypotheses were supported by the

data. Age and income produced betas of 0.12 and 0.15, respectively, while

gender and income both showed betas of 0.08. None of these coefficients

was significant. The additional variance accounted for by demographics in

this model was a 6.5 percent, which was not significant.

Motivations and concerns for using the Web to search for information on

products and services produced two significant findings. Supporting H3c,

respondents who spend more time searching for information on products and

services report greater economic motivations (- = 0.28). Respondents who

report having more transaction-based security concerns spend less timesearching for information on products and services (- = 0.27). However,

this second finding was not hypothesized in the current study.

Interactive control motivation (H3b) showed a moderate positive relationship

with searching for information on products and services (- = 0.13), which

was not significant. The other hypothesis for this dependent variable, H3a,

predicted that information motivation would be positively related to

searching for product/service information. However, the data did not support

this hypothesis (-= 0.06). The other motivations, although not hypothesized

to be related to product/service information searches, all produced betas at

Product/service searching

(beta weights)

Paper survey (1) 0.19


Demographic variables (4)

Age 0.12

Gender 0.08

Education 0.08

Income 0.15




Information motivation 0.06

Interactive control motivation 0.13

Socialization motivation 0.12

Transaction-based security concerns 0.27*


Economic motivation 0.28*



Note:* p 0.10

Table II. The influence of demographics and motivations on getting information

on products and services (n = 59)

T w o s ig n ifi c a n t fi n d in g s

1 0 0 J O U R N A L O F C O N S U M E R M A R K E T I N G , V O L . 2 0 N O . 2 2 0 0 3


12/19

0.09 or higher; however, none of these betas were significant. A larger

sample size, however, may have produced significant results.

Overall, motivations and concerns accounted for a 13.9 percent increase in

the total variance for searching for product and service information, which

was again not significant.

Combined sample

Since the two models discussed so far did not produce many significant

findings probably in part due to the size of our sample the pretest group of59 college students was added to the original sample, for a combined sample

size of 118. However, demographic information was not collected from the

pretest sample, so this variable was dropped from the new combined sample

regression models. Therefore, the next two models discussed, only predict the

influence of motivations and concerns on the two dependent variables. In order

to control for a potential influence of the differences between the student

sample and the original sample, a dummy code for the student sample was

entered as a second block in the model (see Table III). The dummy code for

mode of data collection was left as a first block on the model.

Online shopping. In the new combined sample, people who filled out the

questionnaire online were still significantly more likely to report shoppingonline (- = 0.37). Overall, the mode of data collection for this sample

accounted for a significant 13.9 percent of the overall variance in online

shopping.

There appears to be no difference between the student sample and the

original sample with respect to online shopping. While the model produced a

moderate beta for this item, it was not significant (- = 0.11). Therefore, the

differences between the two samples only accounted for an additional 0.6

percent increase in the variance associated with online shopping, which was

not significant.

Consistent with Korgaonkar and Wolins (1999) research, all hypotheses formotivations and concerns related to shopping online were supported by this

model. Respondents who reported greater transaction-based security and

privacy concerns were significantly less likely to shop online (- = 0.33).

Shopping

(beta weights)

Paper survey (1) 0.37**


Student (1) 0.11




Information motivation 0.21*

Interactive control motivation 0.19*

Socialization motivation 0.17*

Transaction-based security concerns 0.33**


Economic motivation 0.41**



Notes:*

p 0.05; ** p 0.01

Table III. The influence of motivations on online shopping (n = 118)

D u m m y c o d e

J O U R N A L O F C O N S U M E R M A R K E T I N G , V O L . 2 0 N O . 2 2 0 0 3 1 0 1


13/19

Information motivation (-= 0.21), interactive control motivation (-= 0.19),

socialization motivation (- = 0.17), and economic motivation (- = 0.41)

were also all significantly and positively related to shopping online. Overall,

motivations and concerns accounted for a significant 21.6 percent increase in

the total variance for shopping online.

Product/service searching. The mode of data collection did not produce any

significant relationship for using the Web to search for information on

products and services. The beta for this item was 0.09, and accounted for

0.9 percent of the total variance (see Table IV).

Students were significantly less likely (- = 0.48) to spend a large

percentage of their time searching for information on products and services

than the original sample. Overall, the differences between students and

non-student respondents accounted for a significant 12 percent increase in

the total variance.

Motivations and concerns related to using the Web to search for information

on products and services produced few significant findings. In this model,

only one of the hypotheses for the product/service searching variable was

supported. People who reported greater economic motivations were more

likely (- = 0.21) to use the Web to search for information on products and

services. With the original sample, transaction-based security concerns were

negatively and significantly related to percentage of time spent searching for

product/service information. However, this model only produced a beta of

0.15, which was not significant.

The other two hypotheses, H3a and H3b predicted that information

motivation and interactive control motivation would be positively and

significantly related to product/service information searches. However, this

model did not produce significant coefficients for the two motivations.

The other motivational factors, social escapism, socialization motivation, and

nontransactional privacy concerns all produced betas of 0.03 or less. As a

block, motivations and concerns accounted for an additional increase of 6.1percent of the variance, which was not significant. The total model accounted

for about 19 percent of the variance in product/services information searches.

Product/service searching

(beta weights)

Paper survey (1) 0.09


Student (1) 0.48**


Motivati ons/concerns (7)


Information motivation 0.03Interactive control motivation 0.04

Socialization motivation 0.00

Transaction-based security concerns 0.15


Economic motivation 0.21*



Notes:*

p 0.05; **p 0.01

Table IV. The influence of motivations on getting information about products

and services (n = 118)

T r a n s a c ti o n -b a s e d s e c u ri t y

c o n c e rn s



14/19

Conclusions

Before some of the key results are discussed, it is necessary to outline some

of the limitations of this study. The first and most obvious limitation of this

study is the nature of the sample. Two characteristics are especially

noteworthy, the size of the sample and the distribution of key demographics.

First, the size of the sample matters. As the sample size decreases, the

sampling error increases. In other words, it is a lot easier to identify social

regularities in larger samples than in small samples that are more susceptible

to random differences between subjects. This seems to be less of a concern inthis study given that the direction of most relationships found in the models

seems to be consistent with the hypotheses. Another problem, however,

remains: the likelihood of making a Type II error, i.e. of missing a

relationship that exists in the population. In order to minimize the chance of

this type of error, significance levels of up to p 0.10 were reported.

Second, as far as the nature of the non-student sample is concerned, it is clear

that males, more educated respondents, and individuals with higher levels of

income are over-represented, compared to the overall population. This

criticism, of course, misses the point. As outlined earlier, previous research

(CyberAtlas, 2000a, b) suggests that the digital divide is closing but still

exists. As a result, sampling biases, in terms of over-representation ofgroups, can still be expected for any type of Web research.

Key findings

In spite of these limitations, there are several important findings to discuss.

As outlined earlier, younger people were significantly more likely to shop

online. Even though none of the other coefficients in the demographic block

reached statistical significance, it is reasonable to assume that this was

largely due to the small sample size. Given the size of the incremental

R-squares in both models, demographics very likely continue to play an

important role in predicting shopping and informational searches for

products and services.More importantly, however, motivational factors play a key role in both

models. Their roles differ, however, depending on the dependent variable.

For both dependent variables, economic motivations and transactional-

privacy concerns had significant influences. However, when the student

sample was added, transactional privacy concerns dropped below

conventional levels of significance for product/service information searches.

In short, distinctive differences between the predictors for the two dependent

variables were found. For product/service informational searches economic

motivations and transactional privacy concerns mattered most. However,

actually engaging in online shopping activity is predicted by a wider range of

motivations: information motivations, interactive control motivations, and

socialization motivations.

Practical implications for advertisers

Motivations matter. The finding that motivations for using the Web and

Internet use might seem trivial. But, the relationship between different

motivations and Web use is in fact relevant given the fact that the Web is

constantly changing and that vendors constantly develop new ways of

providing product information and selling products online. Ideally, these new

ways of shopping will increasingly satisfy a wide range of motivations

among a growing number of customers.

T h e s iz e o f th e s a m p le

m a t t e r s

M o ti v a t io n a l f a c t o rs p la y a

k e y ro le in b o th m o d e ls



15/19

Therefore, motivations for searching for information on products and

services and shopping online have important practical implications for online

vendors and Internet businesses.

For product information searches one of the two predictors was transactional

privacy concerns. This has important implications for how online vendors

provide product-related information to potential customers. One reason for

the negative influence of transactional privacy concerns on percentage of

time spent on product information searches may be the fact that many

vendors require customers to fill out elaborate user profiles before they grantthem access to product information databases. One way of addressing

consumers concerns, therefore, may be to do one of two things: first to

increasingly provide customers with anonymous guest access to information

databases and, second, to display confidentiality statements more

prominently on the Web site.

Economic motivations were found to be positively related to the percentage

of time spent conducting product/service information searches. This is not

too surprising, given the fact that economic motivations are also positively

related to the amount of online shopping consumers engage in on the Web. It

also suggests that the strategy of companies like Amazon.com is the correct

one. These companies emphasize the savings they offer over other, moretraditional vendors. Given my findings, Internet companies, should focus a

large part of their advertising on this aspect of their sales strategy.

Not surprisingly, economic motivations are also strongly and positively related

to shopping itself. The conclusion is trivial: people shop online to save money.

Similar to information searchers, transactional privacy concerns also have a

detrimental affect on online shopping. In the case of shopping, these concerns

are far more likely to be related to credit card transactions and sharing personal

information, than is the case to product information searches. This is a concern

that the industry needs to address as a whole, since it requires establishing

commonly shared security protocols for online transactions.

In addition, however, there are a number of other motivations for purchasingproducts online. First, not surprisingly, informational motivations are positively

related to shopping. In other words, online customers feel that they can acquire

information more efficiently and inexpensively on the Web, which, in turn,

helps them to make better and more efficient purchasing decisions.

Second, interactive control motivations were found to be positively related to

online shopping behaviors. In other words, people who use the Web because

it gives them greater control over what to look at or which advertisements to

be exposed to are also more likely to use the Internet for shopping. This

suggests that Web pages should not follow a typical paper catalog format,

but rather incorporate more interactive and reactive components such as

individualized recommendations for customers based on their previouspurchases, or the opportunity for customers to review products and make

recommendations to other customers visiting the site.

Finally, the findings show that social motivations for using the Web are

positively related to shopping online. This might seem to be surprising at

first glance. However, it is not. Traditional stores, rely on similar strategies

of building social relationships with their customers. For example, loyalty

bonuses and customer feedback forms are very basic attempts of creating a

sense of community between companies and customers. The Web, of course,

offers far more elaborate ways of creating a sense of pseudo-community

among its customers, i.e., the idea of providing input and exchanging ideas

P r o d u c t - r e la t e d

in fo r m a t io n

W e b p a g e s s h o u ld n o t

fo llo w a t y p ic a l p a p e r

c a ta lo g fo rm a t



16/19

with other customers. One example are large online music stores such as

CDNow or CD Universe, who link product information directly to related

Web sites or, more importantly, chatrooms that allow customers to directly

interact with other Web users with similar interests.

Overall, it might seem somewhat intuitive to find links between various

motivations for using the Web and different types of Internet use. As this

study has shown, however, what matters far more than the statistical

relationship between the two variables are the implications that these

findings have for commercial strategies in an emerging and fast-growingonline marketplace.

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&

http://mustafa.emeraldinsight.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0092-0703^28^2925:4L.329[aid=949456]http://mustafa.emeraldinsight.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0021-9916^28^2946:1L.39[aid=356032]http://mustafa.emeraldinsight.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0021-8499^28^2937:3L.59[aid=2299524]http://mustafa.emeraldinsight.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0021-8499^28^2936:5L.21[aid=348655]http://mustafa.emeraldinsight.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0021-8499^28^2939:5L.27[aid=2860398]http://mustafa.emeraldinsight.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0021-8499^28^2936:1L.43[aid=220293]http://mustafa.emeraldinsight.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/1094-9968^28^2913:3L.34[aid=348678]http://mustafa.emeraldinsight.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0021-8499^28^2931:5L.11[aid=347822]http://mustafa.emeraldinsight.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0092-0703^28^2925:4L.329[aid=949456]http://mustafa.emeraldinsight.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0021-9916^28^2946:1L.39[aid=356032]http://mustafa.emeraldinsight.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0021-8499^28^2937:3L.59[aid=2299524]http://mustafa.emeraldinsight.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0021-8499^28^2936:5L.21[aid=348655]http://mustafa.emeraldinsight.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0021-8499^28^2939:5L.27[aid=2860398]http://mustafa.emeraldinsight.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0047-5394^28^293L.45[aid=4814917]http://mustafa.emeraldinsight.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0021-8499^28^2937L.33[aid=188176]http://mustafa.emeraldinsight.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0021-8499^28^2936:1L.43[aid=220293]http://www.rkinc.com/TopRankings/TopRankings.htmlhttp://www.rkinc.com/TopRankings/TopRankings.htmlhttp://voicendata.com/sep98/gwatch.html


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Executive summary and implications for managers andexecutives

E-commerce it is all about time and money

The Internet has begun to change the dynamics of retailing. Traditional

retailing remains the dominant force and the mail order businesses

whether paper-based or delivered via TV are still significant but online

retailing has now begun to take a more and more significant role in the

picture. As marketers we need to understand the reasons why this change is

occurring (other than as a result of the Internets existence) and the key tothis understanding lies in an appreciation of the motivations that lie behind

consumer Web use.

Joines, Scherer and Scheufele present a study examining these motivations

and the barriers that still constrain the whole-hearted embracing of the

Internet by consumers. Unlike other studies their examination is general

rather than focused on a specific situation and it also differs in focusing on

what consumers do rather than on what business do to respond to what

consumers do. There has been considerable assessment of Web site content,

analyses of click through rates and assessment of Web site performance but

the motives of the consumer are more often assumed rather than

investigated.

It is about saving money, stupid!

As Joines et al. put it: ``people shop online to save money. Us marketers

tend to shy away from the baser motivations shown by consumers we try to

get underneath what seems to be a straightforward desire for a bargain. Yet,

when we examine successful e-commerce operations such as Amazon.com

and eBay, we see an unequivocal concentration on offering the consumer a

deal cheaper books, the chance to get something in an auction at less than

the normal retail price.

Joines et al. identify this desire for a bargain as one of the primary

motivations behind online shopping. It is true that consumers like theconvenience of the Internet no crowds, no queuing, no grumpy shop

assistants and appreciate the availability of information but both of these

undoubted benefits support the search for that elusive bargain. The

convenience means we get the special offer without fighting our way through

sale day crowds and the information allows us to search out the best offers

more easily.

There are, in addition to the simple search for a bargain, other economic

motivations for online shopping and we need to add these into the equation

in order to get to the character of our typical Web shopper.

Time is money or ``I would rather be enjoying myselfThe Internet is convenient, comfortable and private. My product search is

conducted in a warm room, sat on a comfortable chair with a cup of tea sat

by my hand. Beats going shopping any day! I spend less time (certainly I

save the time ``wasted getting to and from the shops) and am able to deal

with more information than would be the case with traditional shopping. My

search can be stopped or started as and when I like if Joe calls and asks if I

want to take in the game, I can stop, take in the game and go back to my

shopping.

Joines et al. point out that this control represents an important factor in

consumer motivation by being a more effective and efficient use of my time,


T h is s u m m a ry h a s b e e n

p r o v id e d t o a l l o w m a n a g e r s

a n d e x e c u t i v e s a r a p id

a p p re c i a t i o n o f t h e c o n t e n t

o f t h is a rt ic le . T h o s e w it h a

p a r t i c u la r i n t e re s t i n t h e

t o p ic c o v e re d m a y t h e n r e a d

t h e a rt ic le i n t o t o t o t a k e

a d v a n t a g e o f t h e m o r e

c o m p re h e n s iv e d e s c ri p ti o no f t h e r e s e a r c h u n d e rt a k e n

a n d i t s r e s u lt s t o g e t t h e f u l l

b e n e f i t o f t h e m a t e ri a l

p r e s e n t


19/19

the Internet represents a second economic benefit. Not only can I get a

bargain online but I am spending less time getting that bargain. I do not have

to put myself out to get the gain.

But and there is always a but we are still a little wary about the Internet.

We remain a little unsure about whether were really making a saving and

we are concerned about the risks attendant with online activity.

Privacy and security the big barriers to e-commerce

We are quite happy to give out our credit card details willy-nilly in the realworld and take it as given that the stores in which we buy and the restaurants

in which we eat will not steal our details. Why are we so worried about the

same problem online?

Joines et al. identify security as a big consumer concern (indeed, just about

every piece of research into e-commerce identifies this barrier). Crossing

this barrier requires two factors trust from the consumer (this company is

not going to rip me off) and protection of the captured information. Trust can

be achieved through branding and through experience and is perhaps too big

an issue to consider in detail here. Protection is more prosaic and can be

addressed by putting the necessary guards in place and telling the potential

buyer exactly what those protections are.Privacy is another huge issue. Consumers are genuinely concerned about the

way in which personal information is used by those who collect and hold

such information. Time and time again privacy is cited as a significant

concern and businesses need to act to protect their customers before the

Government starts poking its oar in. Set out a clear policy (ideally one that

says we do not sell private information to third parties) and make sure that

the consumer knows what that policy means.

Motivations matter

Joines et al. make clear that understanding the motivations and concerns of

the on-line consumer really do matter to the marketer. We need to appreciatewhy people shop on-line (to save time and money) and why some consumers,

despite being comfortable with using the Internet remain unwilling to shop

on-line. This research despite its admitted limitations takes us an

important step further in understanding by concentrating on the general

issues surrounding consumer motivations and linking these to specific

actions for marketers and advertisers.

(A precis of the article ``Exploring motivations for consumer Web use and

their implications for e-commerce. Supplied by Marketing Consultants for

Emerald.)

Implications on E-commerce

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