its about consumer

ORI GIN AL ARTICLE

The impact of age and shopping experienceson the classification of search, experience,and credence goods in online shopping

Yun Wan • Makoto Nakayama • Norma Sutcliffe

Received: 21 December 2009 / Revised: 19 April 2010 / Accepted: 14 May 2010 /

Published online: 23 November 2010

� Springer-Verlag 2010

Abstract This study explores how age and consumers’ Web shopping experience

influence the search, experience, and credence (SEC) ratings of products and ser-

vices in online shopping. Using the survey data collected from 549 consumers, we

investigated how they perceived the uncertainty of product quality on six search,

experience, and credence goods. The ANOVA results show that age and the Web

consumers’ shopping experience are significant factors. A generation gap is iden-

tified for all but one experience good. Web shopping experience is not a significant

factor for search goods but is for experience and credence goods. There is an

interaction effect between age and Web shopping experience for one credence good.

Implications of these results are discussed.

Keywords Online shopping � Generation gap � Quality perceptions �Search goods � Experience goods � Credence goods

1 Introduction

Since the popularity of World Wide Web was facilitated by the first multimedia Web

browser, Mosaic, in 1994, online shopping has transformed from a trendy emerging

shopping channel into the shopping mainstream. Most consumers have overcome their

initial concerns about risk in online transactions. The general public have developed

trust with major shopping portals though there are still issues to be investigated

(Holsapple and Sasidharan 2005; Yan et al. 2008). In addition, mobile commerce

Y. Wan (&)

University of Houston, Victoria, 14000 University Blvd, Sugar Land, TX 77479, USA

e-mail: [email protected]

M. Nakayama � N. Sutcliffe

DePaul University, 243 South Wabash Avenue, Chicago, IL 60604, USA

e-mail: [email protected]

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Inf Syst E-Bus Manage (2012) 10:135–148

DOI 10.1007/s10257-010-0156-y

(Gebauer et al. 2008) and collaborative commerce (Hartono and Holsapple 2004) are

building upon the existing B2C infrastructure and adding new dynamics to online

shopping. However, among all these B2C opportunities and challenges, there is a

fundamental issue that has not been thoroughly addressed: the impact of age and

shopping experience on consumers’ perception of online products and services.

Conventional wisdom is that teens and young adults have advantages in online

shopping because they are quick to learn and adapt to the new online shopping

environment. The older generation probably does not shop online as much because

they are less familiar with and slower to adapt to the new environment. Thus, the older

generation shies away from online shopping more than the younger generation.

But, several recent surveys indicate that this may not be the case. For example,

according to a survey by Pew Research (Jones 2009), although older generations use

the Internet less for socializing and entertainment, they do use it more as a tool for

searching for information, emailing, and buying products. In addition, now both young

and old equally pursue video downloads, online travel reservations, and work-related

research. Another survey conducted by the University of Southern California found

that older Americans have equal or even more enthusiasm to Web 2.0 than their

younger, more tech-savvy counterparts (USC 2008). The same survey indicates that

while instant messaging and video downloading still remain more popular with the

younger generation, older Americans check the Internet more frequently for news. The

older generations are logging onto online communities, researching purchases,

becoming socially active and playing games in increasing numbers (USC 2008). A

survey by a UK-based media company in December 2008 found that there were no

significant differences between younger and older generations in terms of their general

shopping behavior and concerns about online fraud (NWA 2009).

The older generation might be slow in learning new technologies and find

themselves in a disadvantageous position in terms of keeping up with the trends on

the Web when being compared to their younger counterparts, but older generation

do have more shopping experience, even though most of such experiences are

rooted in a traditional environment. Such experiences may give them an edge in

evaluating and purchasing certain types of products or services on the Web.

Thus the reality of online shopping by different age groups may be more complex

than a simple dyad of young and fast versus old and slow. It is possible that both

groups have their advantages and disadvantages when shopping online, thus,

different needs and expectations of online vendors. Their behavior in online

shopping and reactions to shopping technologies might also be different because of

their accumulated shopping experience on Main Street as well as online. The same

survey by Pew Research found that, in terms of preference for online shopping,

instead of a downward linear trend with age, interest in online shopping is

significantly lower among both the youngest and oldest groups—‘‘38% of online

teens buy products online, as do 56% of Internet users ages 64–72 and 47% of

Internet users age 73 and older’’—and significantly higher among those in the

medium range, with 80% for age 33–44 and 71% for age 18–32 (Jones 2009).

Therefore, the conventional wisdom may underestimate the online shopping

tendency and ability of older generation online shoppers. They may not only be

more active in online shopping than expected but also have more experience

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because of their accumulated shopping experienced in a traditional environment. If

we stick with the conventional wisdom, it may lead to bad strategies for online

vendors and ignore potential business opportunities that exist in older generation

online shoppers. Thus it is critical for us to explore this research question in a more

rigorous way.

In this study, we examined how the age and shopping experience influence

consumer’s classification of search, experience, and credence goods. Through the

lens of SEC, we found that age, Web shopping and search knowledge, as well as

prior purchase experiences all have a significant influence on a consumer’s quality

evaluation as reflected in an SEC rating, which leads to different SEC ratings for the

same product.

The remainder of this paper is arranged as follows. First, we review previous

studies of factors that have impact on online shopping as well as the SEC

framework. Then we explain our survey-based empirical experiment design as well

as the outcomes. Finally, we analyze the results and conclude the study.

2 Previous studies

2.1 Search, experience, and credence goods perception

Consumers have been used to conducting shopping in an environment where they

can inspect the goods directly and converse with the sellers or service providers in a

face-to-face environment to assess the quality of the goods to be purchased. We can

classify consumer goods and service into three categories—search goods, experi-

ence goods, and credence goods—based on the point in time consumers evaluate the

quality of the goods they have purchased, hence the SEC framework (Nelson 1970;

Darby and Kami 1973; Nelson 1974). Specifically, search goods are those that

consumers can confidently evaluate the quality of before the purchase. Experience

goods are those that consumers can evaluate the quality of once they are consumed

or serviced. Credence goods are those that consumers cannot evaluate the quality of

even a long time after the purchase.

Because of the differences in times of which consumers can evaluate a product

confidently, they have to use different product evaluation strategies when making

shopping decisions. In a traditional brick-and-mortar market, consumers may use a

direct inspection method to evaluate search goods and a sampling strategy for

experience goods. However, for credence goods, they largely depend on the brand

name and recommendations to make decisions because they have difficulty

evaluating the quality directly. Different shopping and product evaluation behaviors

by consumers lead to different advertising and promotion strategies by vendors for

different SEC categories. The SEC framework has been widely adopted in the

advertising industry and used in consumer behavior research (Ekelund et al. 1995).

The SEC framework provides a relatively objective classification schema for

commodities. The fact that it is being widely adopted in the advertising industry

indicates the classification method applies to most populations. However, we also

realized that for a specific product or service, depending on the consumers’

The impact of age and shopping experiences 137

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familiarity with it, one consumer may rate it in a different SEC category from

another consumer. For example, a Dell laptop is a search product for computer

geeks, but it could be an experience product for computer rookies.

Such differences in individual perception originate from two sources.

Firstly, any product or service has multiple attribute dimensions and these

attributes can range from search and experience to credence categories—that is,

some attributes’ quality can be evaluated prior to purchase, others after purchase,

while still others cannot be evaluated even after long-term use. In other words, all

goods can have search, experience, and credence attributes simultaneously. We

classify a product or service into a certain SEC category because the attributes in

that category are those we are most concerned with. Though the generally accepted

SEC category for a particular product indicates the most important attributes for the

majority of consumers, it is possible that for a specific consumer, such attributes are

less important than a different set of attributes in other SEC categories. So the SEC

rating for this product is different for this consumer compared with others.

Secondly, consumers may have the capability to infer the attributes information

in one category, say credence attributes, by evaluating related attributes in other

categories, say search attributes. Since such capability varies among consumer

groups, their perception of SEC rating for a product or service also varies.

For example, a computer geek may rate a Dell laptop as a search product because

based on his prior experience of using laptops, he can infer the quality of experience

and credence attributes of the laptop by observing its search attributes. He can check

the smoothness of program launch, listen to the noise of the hard disk and CPU fan,

observe the screen display quality, and touch the keyboard to infer those experience

or credence attributes like the overall integration soundness and efficiency of heat

dissipation. The latter may greatly influence the life span of a laptop, which is

largely a credence attribute. Since a rookie user of laptops does not have such

experience, he or she may either regard it as an experience product or even a

credence product.

Thus, though the SEC rating for the same product and service could be the same

in the traditional environment for the majority of consumers, it could be different

when the evaluation environment becomes the Web, as well as when the product or

service being evaluated by consumers with different sets of characteristics.

2.2 Goods evaluation in Web shopping environment

With the growing popularity of World Wide Web since 1994, online shopping has

become part of our daily life. In this new online environment, no goods can be

inspected directly, and only limited interactions with service providers are possible

(Alba et al. 1997). Thus, by default it seems, goods in online shopping automatically

become either experience or credence goods. However, because online retail site

designers have used various methods to help consumers evaluate physical products

(Smith et al. 2005), goods in online shopping could also be search goods—as our

experiments indicated.

Regarding how to evaluate goods in an online environment, the behaviors and

strategies employed by shoppers are influenced by several key factors. Bhatnagar

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et al. (2000) found that age, years of using the Web, and gender affect purchase risk

perceptions differently, thus leading to different behaviors and strategies. A more

recent study that examines the impacts of demographic factors on online shopping

found that age, gender, income, and location influence online purchase frequency

and expenditures (Chang and Samuel 2004).

Age is essentially a rough indicator of Main Street shopping experiences

accumulated through time. The younger generation has limited shopping experience

but could cleverly use those shopping tools to leverage their existing shopping

experience. For older generation, we speculate that, while the adaptation process

may take longer, their rich Main Street shopping experiences give them advantages.

For example, on the correlation between the online search and the actual purchase of

products, it was found that although young people are likelier to purchase online the

longer they searched for the product, older generations are comparatively more

likely to purchase because they spend less time searching (Sorce et al. 2005).

Like age, gender is another important factor in explaining many differences in

consumers’ shopping behaviors and perception of goods. However, it seems gender

is not as significant factor as age in predicting online shopping behavior. A recent

study for online shopping behaviors from international and cross-cultural perspec-

tives found gender has no significant influence on shopping behavior (Stafford et al.

2004). But it found that the 25–34 age group was the most active online shopping

group. Another e-commerce study found gender and social class were not significant

factors for mobile commerce adoption though it found that younger consumers are

more predisposed to use mobile equipment as a shopping channel (Bigne et al.

2005).

Web shopping experience, including using various Web-based decision support

tools for searching, comparing, and analyzing products and services in the online

environment, has positive influence on the perception and evaluation of goods on

the Web. Dennis et al. (2002) found that younger people are ‘‘more Web-literate

than older age groups’’ and those young consumers with more Web shopping

experience have a more positive attitude towards Web shopping than those without

it (Dillon and Reif 2004). It was also found that Web shopping experience has a

positive influence on m-commerce adoption (Bigne et al. 2005).

In summary, in the online environment, we identify the following factors that

may influence an online shopper’s SEC classifications. They are age, gender, Webshopping experiences, Web search experience, and prior purchase experience forthe same goods.

3 Hypothesis

As mentioned previously, goods are classified as search, experience, and credence

goods depending on when a consumer can confidently evaluate their quality.

However, such classification is mostly dependent on an individual’s previous

purchase and usage experience, especially for experience and credence goods. A

consumer from the older generation, because of greater shopping experience, may

be less likely to categorize a specific product or good as an experience or credence


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good than a younger consumer would. We expect such differences to also exist in

the online shopping environment. Thus, we have our first hypothesis:

H1 Online shoppers assess the SEC ratings of the same goods differently

depending on their age group, with the older generation rating the same goods as

less credence than the younger generation.

Based on existing research of the gender impact on goods perception in online

shopping, we have the following hypothesis:

H2 Gender has no significant influence on online shoppers’ SEC classification of

the same goods.

Since online shopping is a relatively new shopping mode and is still less than

15% of the US retail market, we expect that the extent to which an online shopper

evaluates goods online is highly influenced by that shopper’s Web purchasing

experience and online search skills. A Web-savvy shopper may rate credence goods

more like experience goods and experience goods more like search goods in the

online environment. Thus, we have hypothesis 3:

H3 Online Shoppers assess the SEC classification of the same goods differently

based on their level of Web shopping experience, and those shoppers who have

more Web shopping experience tend to rate the same goods as less credence than

those with less Web shopping experience.

Since evaluating a product or service in an online environment also depends on

searching and processing information from the Web, an online shopper’s search

engine experience may influence his or her SEC rating for the product or service. If

an online shopper is familiar with using a search engine to find product or service

quality information, he or she may tend to evaluate such a product or service more

as a search good compared with those consumers who are less familiar with using a

search engine, with all other conditions being the same. Thus, we have hypothesis 4:

H4 Online shoppers assess the SEC classification of the same goods differently

based on their level of online search engine experience, and those shoppers who

have more search engine experience tend to rate the same goods as less credence

than those with less search engine experience.

In addition to general shopping experience accumulated with age, when someone

has prior purchase experience of a particular product or service, he or she may feel

less uncertain about this product or service in later purchases, and such confidence

may be accumulated or enhanced with each additional purchase of the same product

or service. Thus, consumers may change their perception of this product or service

from credence to experience or from experience to search category. Such experience

accumulation can come from either online or Main Street shopping. Thus we have

hypothesis 5:

H5 Online shoppers assess the SEC classification of the same goods differently

based on whether or not they have prior purchasing experience of the goods, and

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those shoppers who have prior purchase experience of the goods tend to rate the

same goods as less credence than those who do not have such experience.

Now we have explained all our hypotheses. In the next section, we explain the

design of our experiment to verify these hypotheses.

4 Research design

Since the most direct method of obtaining a product or service’s SEC rating is

asking the shopper to rate it (Iacobucci 1992; Ekelund et al. 1995; Girard et al.

2002), we use similar survey-based questionnaires by asking subjects for their SEC

ratings of a pre-selected set of goods.

The selection of the candidate goods was a critical step in this research. To avoid

reinventing the wheel, we conducted a comprehensive review of previous literature

to identify candidate goods that had been repeatedly identified in the same SEC

category by at least two prior studies, though all the contexts of such ratings were in

the traditional shopping environment. As a result, we got a relatively long list of

more than ten products and services. Then we reduced the list by using only those

common goods whose purchases are relatively neutral to age, gender, income and

ethnic groups. This process led to six goods as representative SEC goods, two in

each SEC category:

Search goods include PCs and bestselling books (Ekelund et al. 1995; Girard

et al. 2002, 2003; Hoskins et al. 2004).

Experience goods include cell phones and cars (Nelson 1970; Iacobucci 1992;

Girard et al. 2002).

Credence goods include vitamins and auto insurance (Girard et al. 2002; von

Ungern-Sternberg 2004; Chiu et al. 2005).

We created three scenarios to examine the effect of shopping contexts. In the first

scenario, shoppers can shop only online for the above six items (‘‘Web Only’’). In

the second scenario, they can only evaluate the six items in a traditional shopping

environment and cannot use the Web at all (‘‘No Web’’). In the third scenario,

consumers can shop for these six items by using any means—whether using the

Web or not (‘‘No Restriction’’).

In each scenario, there are two sections. The first section collected subjects’ age,

gender, Web shopping experience, and Web information search experience.

In the second section, subjects were asked to identify the SEC category for the six

items we selected. We used the same survey instrument as Iacobucci (1992) and

asked respondents to rate items in their respective SEC category by using a 7-point

Likert scale on a single item construct. That is, we asked the respondent to evaluate

if the quality of an item ‘‘could be assessed prior to purchase’’ (search), ‘‘could be

evaluated only after purchase’’ (experience), or ‘‘would be difficult evaluate even

after trial’’ (credence). And similar ratings were conducted in all three scenarios.

For each of the six items, we asked if the subject had previously purchased it

from the Web or Main Street, the frequency of purchases, and the ratio of purchases

frequency between online and Main Street.


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5 Pilot study and data collection

To examine the effectiveness of the experiment, we conducted a pilot study with

students from two Midwest and Southwest universities. Based on the feedback of

the students, we made improvements on the readability of several statements. The

overall structure of the experiment proved effective.

We then recruited subjects from the public by using online forums and sites like

Craigslist. A modest $5 Amazon.com gift certificate was used as an incentive for

participation. We removed those responses that were incomplete or invalid (e.g.,

entering the first choice for all the questions).

Altogether this study received 549 valid completed responses, 52.4% were male,

and 47.6% were female. This indicates a largely balanced sample of the population.

6 Data analysis and findings

We first calculated the mean SEC ratings for each item and the summary is listed in

Table 1. The overall mean was calculated based on all cases. The subsequent three sets

of means were calculated based on three scenarios as indicated in the research design.

As indicated in the pattern, the overall order of SEC ratings of these six items is

in accordance with the ratings collected from the previous literatures in the

traditional shopping environment. That is, PC and Book, as search goods, have

lower SEC ratings than experience goods, such as cell phones and cars. Cell phones

and cars have lower SEC ratings than vitamins, the credence goods. The only

exception is auto insurance. Although auto insurance was considered a credence

good and was much more difficult to evaluate than search and experience goods in

previous literature, in this study, its SEC rating was between cell phones and cars,

the two experience goods (Fig. 1).

In addition, through t-test, we found that there were no statistically significant

differences between the mean ratings of the same item among three scenarios. This

indicated that those selected items are neutral to shopping environmental factors.

Further analysis indicates the skewness and kurtosis statistics have a range of

0.185–0.55 and -0.212 to -0.722 respectively. This indicates a cluster to the low

Table 1 Summary statistics for SEC rating means

Overall Web only No web No restriction

Mean SD Mean SD Mean SD Mean SD

PC 1.90 1.26 1.98 1.18 1.77 1.27 1.93 1.34

Book 2.08 1.30 2.04 1.28 2.12 1.33 2.10 1.29

Cell phone 2.21 1.30 2.19 1.27 2.20 1.30 2.25 1.34

Car 2.37 1.46 2.25 1.38 2.52 1.53 2.37 1.46

Vitamin 2.69 1.60 2.74 1.64 2.68 1.61 2.64 1.57

Auto insurance 2.28 1.51 2.20 1.48 2.30 1.49 2.36 1.56

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value range of SEC ratings as well as flatness in the distribution with most cases in

the border ranges.

We used hierarchical multiple regression analysis to examine the impact of

independent variables like age and Web shopping experience. Table 2 is the

summary of statistics.

The outcome indicates that H1 is generally supported. H2 is not supported. H3 is

partially supported by experience goods. H4 is not supported. H5 is partially

supported by search products.

6.1 The impact of age on SEC ratings

As indicated in Table 2, we found that H1 is supported for all goods except cell

phones and vitamins. Specifically, a PC’s SEC rating is higher for age 30–39 than

for age 50–59. A bestselling book’s SEC rating is higher for age 20–29 than for age

40–49. A car’s SEC rating is higher for age groups 18–19, 20–29 and 30–39 than for

age 40–49. Auto insurance’s SEC rating is higher for age 20–29 than for age 30–39

and 40–49.

The charts on SEC ratings versus age groups are shown in Fig. 2. For cell phones,

the SEC ratings of the Web-only group of age 40–49 is more than a 0.5 point lower

Fig. 1 Overall SEC ratings

Table 2 Significant impact factors on SEC ratings

Product Significant factors for SEC rating

PC Age* (Beta = -0.103; Sig. = 0.034)

Prior PC Purchase** (Beta = 0.189; Sig. = 0.006)

Time spend to collect information about PC before purchase*

(Beta = -0.089; Sig. = 0.037)

Bestselling book Age** (Beta = -0.132; Sig. = 0.009)

Cell phone Web shopping experience** (Beta = -0.147; Sig. = 0.004)

Car Age* (Beta = -0.116; Sig. = 0.045)

Web shopping experience** (Beta = -0.178; Sig. = 0.002)

Vitamins Time spend to collect information about Vitamin before purchase*

(Beta = -0.113; Sig. = 0.028)

Auto insurance Age** (Beta = -0.207; Sig. = 0.001)

* a = 0.05; ** a = 0.01


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than those of the no-Web and no-restriction groups of the same age group. For cars,

the SEC rating of the Web-only group of age 18–19 is 0.75 point lower than those of

the no-Web and no-restriction groups of the same age. In the credence goods

category, for vitamins, the SEC ratings of the Web-only group are much lower (by

0.8–1.3) than those of the no-Web group among age groups 18–19 and 40–49. For

web only No restrictionNo web

Fig. 2 Age group and SEC ratings

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auto insurance, the SEC ratings of the Web-only group are lower by 0.5–1.0 point

than those of the no-Web group among age groups 40 or above.

6.2 The impact of Web shopping experience on SEC ratings

Upon further examination of the Web shopping experience variable, we found that

H3 is supported for experience goods. In the experience goods category, shoppers

with less Web shopping experience generally gave higher SEC ratings for cell

phones. There is a statistically significant difference between the shoppers with the

least Web shopping experience and those with the most Web shopping experience.

For cars, similar results are observed. The shoppers with the most Web shopping

experience have statistically significant lower SEC ratings than the shoppers with

the modest Web shopping experience.

6.3 The interaction effect of age and Web shopping experience

There is a concave relationship between age and Web shopping experience (Fig. 3).

Web shopping experience increases steadily from age group 18–19 to 40–49 and

then declines. This is probably due to patterns in income levels and family/life style.

This pattern parallels that of consumer spending figures by age in the Consumer

Expenditure Survey of the US Bureau of Labor Statistics.1

Since both age and Web shopping experience have significant impacts on SEC

ratings, their interaction effect may also have influence. It is possible that the SEC ratings

for the same goods are rated differently in their SEC category due to the interaction effect

of age and Web shopping experience. Older generations with more Web shopping

experience may rate credence, experience, and search goods more towards experience

and search goods compared with other combinations. Through our analysis, we found

this is supported for search and credence goods, but the outcome is mixed for experience

goods. Specifically, age only influences the SEC ratings of search goods. For experience

goods, car’s SEC ratings are affected by age and Web shopping experience. However,

cell phone’s SEC ratings are affected only by Web shopping experience. Both age and

Web shopping experience impact the SEC ratings of credence goods. The summary of

ANOVA with post-hoc tests are as follows (Table 3).

Fig. 3 Web shopping experience and age group

1 Details could be found via http://www.bls.gov/cex/2007/Standard/age.pdf.


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http://www.bls.gov/cex/2007/Standard/age.pdf

6.4 The influence of other factors on SEC rating

In addition to age and Web shopping experience, we found that prior purchase

experience for the product had an impact on SEC rating for search product like PC.

However, we didn’t detect its impacts on the other search product or other

categories. Thus, it seems the impact of direct purchase experience for a product or

service may be effectively translated into online experience, but this only applies to

certain product categories, like PC.

The time a consumer spent on collecting information about a product or service

also helped lower the SEC rating for the product as indicated in our survey for PC

and vitamins. The time spent by a consumer on collecting product information is

related to search engine experience but they are two different concepts. The former

is in proportion to the information collected about the product or service. We expect

the more time spent on collecting information about a product or service, the more

the consumer would be familiar with it. We suspect that the rich information about

PC on the Web makes access to such information very easy, thus consumers tend to

lower the SEC rating of PC because they feel it is easier to evaluate PCs based on

such information. Vitamin belongs to a different SEC category but its consumers

have the same advantage of obtaining a large amount of information about usage

online, such as from health forums.

7 Implications

There are several important implications from this research.

First, we find that the more Web shopping experience individuals have, the less

they feel uncertain about product quality regardless of age. This indicates that the

traditional SEC classification for goods and its directive function on advertising may

be limited by an individual’s Web shopping experience. For younger generations,

Table 3 SEC ratings by age group, Web shopping experience and their interactions

Product Significant factors for SEC rating

PC Age** (30–39 vs. 50–59*)

Bestselling book Age*** (20–29 vs. 40–49***)

Cell phone Web shop experience** (slightly above novice vs. expert Web shoppers*)

Car Age*** (18–19 vs. 40–49**, 20–29 vs. 40–49***, 30–39 vs. 40–49***)

and Web shopping experience**

Web shop experience*** (moderate vs. expert Web shoppers***)

Vitamins Age* (no between-age group significance)

Web shop experience** (occasional vs. moderate Web shoppers***,

moderate vs. expert Web shoppers***)

Auto insurance Age*** (20–29 vs. 40–49***, 30–39 vs. 40–49*)

Web shop experience** (moderate vs. expert Web shoppers**)

Interaction between age and Web shop experience*

* a = 0.10; ** a = 0.05; *** a = 0.01

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though they have less shopping experience that can be used to evaluate products and

services, their relatively rich Web shopping experience may compensate for this

limitation.

Second, even controlling for Web shopping experience, the age gap exists regarding

how uncertain consumers feel about product quality. As indicated previously, age group

40–49 seems to benefit most from their past Web shopping experience because they have

an optimal combination of Main Street and Web shopping experience. They are the first

generation that has both the income and opportunity to be familiar with the Internet and

the Web as well as to conduct online shopping. Thus, they have the best combined

advantage. Their perception of goods, which is reflected in SEC ratings, is also

significantly lower for most item categories in the experiment.

Third, the impact of online shoppers’ age and Web shopping experience are

different on search, experience, and credence goods. The evaluation of credence

goods probably requires both cumulative (long-term) Web shopping experience and

Main Street experience (age) to lower uncertainty about product quality. This

indicates age and Web-shopping experience are both very important to reduce the

challenge of evaluating product and service online. The SEC ratings of search

goods, on the other hand, are more sensitive to age.

It is a bit surprising to know that for most of the items we selected, their SEC

ratings are not affected by prior purchase experience. This could be the benefit of

easy access to product or service review information on the Web—since an

individual could always utilize others’ evaluation experience through electronic

decision aids like comparison-shopping agents.

8 Conclusion

The generation gap exists in many shopping scenarios. This research explored the age

gap in the perception of goods in search, experience, and credence goods, or the SEC

framework, specifically for the online shopping environment. We found that age and

Web shopping experience, and in some cases, their interaction, have significant

influence on online shoppers’ perception of search, experience, and credence goods.

Even controlling Web shopping experience, we found the effect of a generation gap on

how consumers feel about product quality. Web shopping experience and senior age

can reduce the uncertainty towards credence goods while the perception of search

goods is only sensitive to age. We believe these findings will have important

implications for future research on the SEC framework in the online environment.

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