An Empirical Study on Quality Uncertainty of Products and

Social Commerce Kyunghee Lee

KAIST Business School 85 HQEGIRO DONGDAEMOON-GU

SEOUL 130-722 KOREA +82-10-9130-9327

nickend@business.kaist.ac.kr

Byungtae Lee KAIST Business School

85 HQEGIRO DONGDAEMOON-GU SEOUL 130-722 KOREA

+82-2-958-3629

btlee@business.kaist.ac.kr

ABSTRACT

With the advance of Social Network Service (SNS) like Facebook,

Social Commerce (SC) such as Groupon now prospers, which

provides daily deals at a highly discounted price by gathering

buying power of consumers through SNS. From the perspective of

quality-uncertainty, it is unusual to sell experience and credence

goods/services on the internet as Groupon does. In traditional E-

commerce (EC) purchasing decisions rely on information

provided after the actual use of products by other consumers,

while in Groupon it heavily depends on opinion even before

purchasing. For example, traditional sites use a third-party

recommendation including feedback mechanism, while Groupon

encourages consumers to post and share their preference on

goods/services over SNS. Given this difference, focusing on the

effect of SNS, we collect and analyze changes of sales for deals

Groupon provided, using an econometric model that reflects our

understanding of consumer behavior in the presence of different

degrees of quality-uncertainty. The information from SNS is

captured by using a function called ―Facebook Like‖ that is a

recommendation system in which suggestions are brought by

one’s friends, and it is a module that can be installed in any

website. In this study, we demonstrate that the information from

SNS positively affects sales for deals, which implies that SNS

provides recommendation and encourages consumers to purchase

by reducing encountered uncertainty. In addition, we also find

that the effect of SNS is enlarged as the extent of the quality-

uncertainty increases. This result means that under the presence of

high degree of uncertainty, the information from SNS gives

consumers a stronger belief in quality than information from a

third-party. Besides, as many other studies proved, we also

confirm that the internet turns experience goods into search goods

by substituting in-store visits with virtual encounters.

Keywords

Quality uncertainty of products, Social network, Social commerce,

Groupon

1. INTRODUCTION Recently, Groupon turn down Google’s $6 Billion offer for

acquisition1. Groupon is a deal-of-the-day website that is localized

to major geographic markets worldwide. Launched in November

2008, Groupon serves more than 150 markets in North America

and 100 markets in Europe, Asia and South America and has a

cumulative 35 million registered users 2 . Groupon is now

considering an initial public offering that would value the online-

coupon company at as much as $25 billion3. Following Facebook

and Twitter, Groupon is thought to be one of the most successful

internet businesses nowadays. This is testimony to the emerging

importance of Social Commerce (SC).

Combined with a group-buying model, Groupon provides a deal-

of-the day that usually offers various kinds of local services, such

as restaurants, spas and salons, at a highly discounted price, above

50%. There is a tipping point for every daily deal so that deals

would be canceled if the point of sales is not reached. This

reduces risk for retailers, who can treat the coupons as quantity

discounts as well as sales promotion tools. Thus, they are likely to

offer attractive deals to customers and Groupon provides an

attractive marketing channel where an enormous number of

customers look for good deals.

Group-buying itself is not a new concept. In 1999, group-buying

business models on the internet had been flourishing for a time.

Bringing volume discounts to the internet, Dotcom companies like

MobShop.com combined this with a dynamic pricing mechanism.

They offered various sorts of products, ranging from dishes to

cameras, with a pricing mechanism attracting more people to get

high discounts. Of course, many researchers investigated the

group-buying model with different perspectives. Among them,

Kauffman discovered the effect of dynamic pricing mechanisms

on features and timing of consumers’ purchase decision [28, 29].

Anand investigated the difference between posted-pricing and

group-buying mechanisms and Chen developed an analytical

model to point out sellers’ optimal price curve of the group-

buying mechanism [3]. However, success was not long-lasting

and in less than 3 years most group-buying sites closed down.

Kauffman pointed out several reasons that this model shrank, such

1http://www.businessinsider.com/why-groupon-said-no-to-google-

2010-12

2 http://en.wikipedia.org/wiki/Groupon

3 http://www.businessinsider.com/why-groupon-is-worth-15-25-

billion-its-all-about-the-people-2011-3

Permission to make digital or hard copies of all or part of this work for

personal or classroom use is granted without fee provided that copies are

not made or distributed for profit or commercial advantage and that

copies bear this notice and the full citation on the first page. To copy

otherwise, or republish, to post on servers or to redistribute to lists,

requires prior specific permission and/or a fee.

Conference’10, Month 1–2, 2010, City, State, Country.

Copyright 2010 ACM 1-58113-000-0/00/0010…$10.00.

as failing to achieve the critical mass, low barriers, and price-

sensitive consumers [28, 29].

Considering the failure of the early group-buying model, we posit

that one of differences between the old versions and Groupon is

the variety of products Groupon provides. While the old version

of group-buying sites sold products whose quality can be easily

judged, Groupon sells goods and services for which quality and

performance are difficult to evaluate before purchase. For instance,

restaurants, hair-clinics, spas, and massage services are sold in

Groupon rather than the computers and audio equipment that used

to be sold in MobShop.com.

This case might be treated as questionable because in e-commerce

online retailers have always been concerned about how to reduce

consumers’ perceived uncertainty of product quality. Due to the

limitation of direct experience of goods/services, online

consumers only have a limited choice of products whose pre-

revealed quality is hardly changed and clearly captured without

using it or having direct experience. According to the search,

experience, and credence products classification [30, 38, 39],

these are distinguished as search products. If it is difficult to

discern the quality of a product before using it, the product is

classified as an experience product, whereas if you still have

difficulty in evaluating the quality even after using it, it is

classified as a credence product. In other words, search,

experience and credence products are divided by the extent to

which consumers feel difficulty in obtaining quality information

before and after use.

To expand the scope of products sold on the internet,

recommendation systems including feedback and reviews were

introduced for the purpose of dimming distinctions between

experience and search products [25, 30] by providing information

about actual experience of use. It enables online shoppers to

gather more information on products and encourages them to

purchase as they shop in the real world. Even though these

mechanisms reveal some limitations, products sold on the internet

are still restricted to search and a few experience type products

[46].

Considering the issue about uncertainty, Jain suggested an

interesting solution, source credibility, to reduce it [27]. In his

paper, if a consumer faces uncertainty which experience goods

contain, he/she more relies on where the information about the

product comes from, not just on the information itself. As a

method of obtaining credibility, social networks could be

involved because people tend to easily believe in information that

comes from a network to which he/she belong [43].

From this point of view, we focus on a fact that Groupon is called

as SC. The reason why Groupon is regarded as SC is that

Groupon substantially uses functions of SNS, especially Facebook.

Facebook is a social networking service launched in Feb. 2004

and now has more than 600 million active users around the world4.

This online-social network that consists of known friends around

individuals allows him/her to create a personal profile, add other

users as friends, and exchange messages, including automatic

notifications when they update their profile. Through those

activities, they can easily exchange opinions with their friends

about topics that they are concerned with, and it may affect their

way of thinking, which is like an off-line social network [17].

4 http://en.wikipedia.org/wiki/Facebook

Therefore, one might gather that information generated and shared

through Facebook is more likely to be accepted by a consumer’s

friends and that this information could affect their purchasing

decisions. As quality uncertainty of products can be reduced by

getting information that comes from credible source [27], this

would further influence purchases of products when information

about the quality of the product cannot be acquired on the internet.

In other words, the fact that certain information comes from SNS

in which a network consists of his/her friends could compensate

for consumers’ uncertainty about quality of products

For those reasons, this paper explores the influence of information

from SNS on consumers’ perceived uncertainty when they buy

products whose quality information is hard to obtain. Using

Groupon sales data, the effect of information on the sales of each

deal is examined and in order to measure the degree of quality-

uncertainty good/service contains, a classical scheme for

classification along with quality uncertainty, called SEC

classification (search, experience, and credence), is brought.

The rest of this paper is organized as follows. The literature

review is given to aid understanding about the theoretical

foundation of this research. Next, an explanation of the empirical

analysis follows, including the generation of hypotheses,

regression models, method for categorization, and descriptive

statistics of data. Then, we provide results from the analysis and

present the discussion and implications afterwards.

2. Literature Review

2.1 Group-Buying In 1999, the group-buying model on the internet was introduced

by several pioneers, including Mobshop.com and Letsbuyit.com.

They sold various kinds of products including cameras and

computers with dynamic pricing mechanisms in which consumers

could decrease the price of a product as others purchased more

units. In each sale, there were several price-drop points and if the

total number of products sold exceeded the points, then price

would drop to the next level. As this model had diverse points of

interests, such as pricing mechanisms, many researchers focused

on this topic. With rigorous analysis of the pricing model, Anand

investigated the difference between the posted-pricing model and

the group-buying model. He found that the monopolists’ optimal

group-buying schedule under varying conditions of heterogeneity

in the demand regimes and compared its profits with those within

the posted-pricing model [3]. Chen compared a fixed pricing

mechanism with the group-buying auction (GBA) and found the

seller’s optimal price curve of the GBA [7]. In addition, it was

also verified that Groupon-buying is likely to be more effective in

a situation where there is larger low-valuation demand [8].

Kauffman, on the other hand, focused on consumers’ purchase

behavior in dynamic pricing setting, or a group-buying model. He

indicated a price-drop in a group-buying setting as uncertainty of

future benefits, which results in consumers buying more at points

before the price-drop occurs [28].

Even though this pricing model was considerably innovative on

the internet and acceptable by both consumers and sellers, it also

had some weaknesses mentioned by Kauffman [29], and it could

not persist due to consumers’ indifference and so its business

declined.

2.2 Search, Experience, Credence Products In the discipline of marketing, the quality of services has been

well researched from various perspectives. Among them,

Parasuraman suggested one quite-well accepted conceptual model

of service quality from intensive interviews: quality that a

consumer perceives in a service is a function of the magnitude and

direction of the gap between expected service and perceived

service [42].

Along with other factors that cause changes in quality, quality

varies to the extent to which the consumer has difficulty with

judging goods/services’ performance, or expected quality, before

or after purchase [38, 39]. By the Nelson’s classification of search

and experience products, each good/service can be classified as a

search and experience good/service. Search attributes are a

property which consumer can evaluate before use or purchase and

experience attributes can only be judged after use or purchase. In

addition, further research added to Nelson’s classification a third

category, credence properties, in which quality could not be

determined even after use [11, 30]. For instance, only a few

consumers have medical information sufficient to evaluate

whether surgery, a credence-dominant service, is performed

properly.

Considering the lack of proper information about quality, as

uncertainty is exposed to consumers, consumers may reduce the

intention to buy as well as lessen actual purchases [19]. As

consumers have more uncertainty about quality, they tend to rely

on other information sources available, such as brand equity [32],

in order to reduce the uncertainty. For example the demand for art

performances is influenced by other sources of information such

as reviews, reputation of the author, producer and cast which are

the essential parameters by which to judge the quality of service

[1]. Jain and Posavac also found that a source high in credibility

can be employed to make experience claims more persuasive [27].

This result indicated that when consumers face a high level of

uncertainty about quality, they heavily rely on the source of

information, not just information itself.

2.3 Trust in E-commerce and

Recommendation Systems Before examining how the quality uncertainty affects consumers

behavior in EC, it is worth checking the fundamental theory of

what trust is and how it is constructed, which is different across

diverse disciplines including psychology, sociology, political

science, and economics [34]. Trust is defined as ―the willingness

of a party to be vulnerable to the actions of another party based on

the expectation that the other will perform a particular action

important to the trustor, irrespective of the agility to monitor or

control that other party‖ [36]. Simply, trust is the willingness of

an individual to behave in a manner that assumes another party

will behave in accordance with expectations in a risky situation

[13]. In this point of view, as trust declines, people are

increasingly unwilling to take risks and demand greater

protections against the probability of betrayal [44]. This situation

also increases transaction costs because individuals have to

engage in self-protective actions and prepare for the possibility of

others’ opportunistic behavior [31].

Because the parties to a transaction are not in the same place, trust

may be even more important in the virtual world, or online, than it

is in the real world [23, 40, 44]. As Nohria and Eccles [41]

pointed out, there is the lack of the entire human bandwidth which

includes sight, hearing, smell, taste and touch, thus development

of trust is far more difficult on the internet than the real world,

although it is more important online. Combining the quality-

uncertainty of different product-categories here, these previous

works indicate that the uncertainty is an essential factor affecting

consumers’ behavior on the internet. Particularly, Levin examined

consumers’ preference of online/offline shopping along with

search and experience goods/services [33]. He concluded that

goods including search-dominant attributes are more appropriate

when sold online while goods that have experience-dominant

attributes are better sold offline. In addition, Hsieh investigated

the effects of various relational bonds on consumers’ commitment

across search-experience-credence goods/services on internet [24].

From the seller-side of view, Animesh indicated that bidding

strategies of online advertisers differ in search, experience, and

credence goods with empirical evidence [4]. Due to fact that

experience or credence goods have a high degree of quality-

uncertainty, consumers find buying these kinds of products

inappropriate on the internet.

To counter this problem of uncertainty, online retailers who want

to sell various kinds of goods/services through online channels

have tried to provide additional information related with

reviews/experiences about products sold, which are called

recommendation systems. In recommendation systems, online

user reviews are an important source of information to consumers,

substituting other forms of offline word-of-mouth communication

about product quality [9]. Since information asymmetry definitely

exists in the online environment, consumers need that kind of

information to mitigate this problem. One point of thought is that

a recommendation system is a tool for building the reputation by

the seller in order to reduce consumers’ perceived uncertainty

about products [16, 22]. As uncertainty increases, consumers tend

to look for information to verify. In that sense, recommendation

systems including feedback, rating, and WOM affect consumers

intention to buy and actual sales [9, 12, 15], although there is still

a conflict over whether the feedback system influences product

sales positively or negatively [47]. It was also found that the

presence of product reviews from other consumers and

multimedia that enable consumers to interact with products before

purchase have a greater effect on consumer search and purchase

behavior for experience goods than for search goods [25]. These

outcomes were predicted by Mcknight [37] in his paper indicating

that the interaction provides the customer with evidence that the

vendor has various positive attributes, thereby enhancing trusting

beliefs.

The systems enable online shoppers to gather more information

on products and encourage them to purchase as they shop in the

real world. Despite this, these mechanisms reveal some limitations

on dimming the boundary of products sold on the internet, thus

the types of products sold online are still restricted to search and a

few experience type products [46]. It reveals that this additional

information would be not enough to compensate for consumers’

uncertainty on the internet.

2.4 Social Network and SNS A social networking service is an online service, platform, or site

that focuses on building and reflecting of social networks or social

relations among people5. Imitating off-line social network, SNS

provides cheap and efficient ways to manage these relationships

[14]. Facebook, especially, overlaps both off-line and on-line

social networks [14] so it mitigates the distinction of the two

networks. For this reason, it is assumable that Facebook reflects

5 http://en.wikipedia.org/wiki/Social_networking_service

characteristics of real world relations among people and obtains

several virtues of them.

When discussing how people obtaining benefits from networks,

social ―capital‖ is often used to refer to social networking. Social

capital is regarded as the cumulative resources through the social

network that consists of relationship of people [10]. Bourdieu and

Wacquant [6] define social capital as ―the sum of the resources,

actual or virtual, that accrue to an individual or a group by virtue

of possessing a durable network of more or less institutionalized

relationships of mutual acquaintance and recognition‖. While

many studies focus on the link between social capital and positive

social outcomes, Paxton investigated that social capital leads a

person to rely on resources from other members of the networks to

which one belongs [43]. These ―resources‖ include useful

information or personal relationships.

If we expect that Facebook also has the above characteristics like

most social networks, people would tend to rely on resources from

networks to which they belong in Facebook. There is some

evidence of a relationship between the use of Facebook and

individuals’ social capital [17]. The fact that a group in Facebook

consists of known people also supports that events happen in

Facebook as it would happen in off-line social network. Based on

this prior research and our guesses, it is assumed that in a certain

situation the network in SNS such as Facebook would play an

equivalent role as offline social networks. Then it could be

expected that users of Facebook also draw on resources from the

networks to which they belong. This feature may apply to a

situation in which consumers face quality-uncertainty of products,

and as additional information it may allow consumers to reduce

uncertainty. We focus on this and assume that information from

social network and SNS could generate a credible signal which

can be used by consumers to compensate the exposed uncertainty.

2.5 Facebook Like In this paper, we use an installed-function called ―Facebook Like‖

in order to capture the impact of information in SNS. Facebook

Like is well defined in the Facebook developers’ page 6 :

―Facebook Like is currently designed for Web pages representing

profiles of real-world things — things like movies, sports teams,

celebrities, and restaurants. By including Open Graph tags on

one’s web page, one can make one’s webpage similar to a

Facebook Page.

Figure1. Fackbook Like in Groupon page.

Specifically, Facebook Like is a number that is constructed by the

participation of users who are interested in topics or items to

6 http://developers.facebook.com/docs/reference/plugins/like/

which the Like button is attached. In other words, Facebook Like

is an aggregated preference of users collected through SNS on

topics or items. It is used not only within Facebook, but also

distributed and posited globally no matter which platform or

website it is installed. If a user likes items offered on a website,

he/she just clicks the Like button as in Figure 1. Then, through the

linked Facebook account, information of this item is posted in the

user Wall, or board, with a remark of ―00 like a link‖ as long as it

increases the total number of the Like as one unit.

This action causes two different consequences, increasing the

number of Likes, and posting it in the SNS page, so the results

also should be interpreted differently. First, the increasing number

of Likes are similar to a rating such as in recommendation system.

A large number predicts that many users are interested in this

topic or item, thus one can assume that if the number of Facebook

Like is high, then he/she regards it as a sign of quality, or

preference as a rating system does.

Second, when you click the Like button, all friends who have

connection with you in Facebook can see your posting about the

item as in Figure 2. Then your friends can make their own

opinions about the posting, or further spread this information by

just clicking the Like button again, which means that this

information you generate is distributed through your social

network consisting of friends who have a relationship with you.

Figure2. Facebook page after clicking “Like” button.

3. Empirical Analysis

3.1 Hypotheses Building trust has been one of the most important features in E-

commerce because there is significant information asymmetry

about product quality between sellers and buyers online [18, 23,

33, 37, 44]. Although there is some research indicating that

various channels and information sources which the internet

provides makes searching for information easier[11, 25, 30],

quality-uncertainty still exists and it prevents consumers from

purchasing products whose information about the quality is

difficultly obtained in an online-shopping environment [33]. Thus,

practically and academically, researchers have been working on

resolving the unbalanced conditions using recommendation

systems, such as feedback and rating systems [9, 12, 45]. As

mentioned in the literature reviews, these systems influence

consumers’ perceived usefulness, intention to buy and also the

actual sales of online retailers [12, 15]. Since Facebook Like also

has a function of rating products, it is reasonable to expect that

Facebook Like, which is pre-purchased and aggregates preference

of users collected through SNS on products, will also positively

influence product sales of daily deals in Groupon as through other

recommendation systems.

H1: Users’ preference for products collected through

SNS has a positive effect on sales

Another perspective of understanding the influence of

recommendation systems is that the impact can differ across

various product categories. Especially since feedback systems

exist to reducing quality-uncertainty of products, classification for

search, experience, and credence goods/services are interesting

topics in this area [2, 25, 46]. For example, consumers have more

difficulty obtaining information about the quality of experience-

products before use than search-products, and are more affected

by the existence of reviews from other consumers/multimedia that

enable consumers to interact with products [25]. Even though

there have been many studies considering the effect on search and

experience goods, however, just a small amount of research on the

influence on credence goods and services was discussed on the

internet. It might be caused by the fact that many sellers online do

not attempt to sell credence goods or services because they

contain too much uncertainty about the quality for consumers to

purchase.

On the other hand, creating and sharing information online now

involves a huge variation of emerging SNS. A social network

itself is not a new concept. Instead, it has always existed around

people, and there are numerous studies about the importance of

social networks in terms of trust and credibility. For instance, a

social network builds trust within the network, and people tend to

easily believe in information that comes from network he/she

belongs to [43]. In this point of view, it is accepted that SNS,

especially Facebook, works like an offline social network.

Facebook is now regarded as a place for creating and sharing

information, which leads users to get closer as they do in offline

activities. Since individuals’ networks in SNS consist of friends

they already know, information which is generated and shared

through Facebook is likely to be thought of as more friendly,

familiar, and credible by users, compared with information from

other sources such as an internet portal or random online boards

[17]. Specifically, Liu investigated whether social network

information can be used to increase recommendation effectiveness

by incorporating collaborative filtering [35].

As Grewal found, the influence of price on consumers’

perceptions of performance risk is greater when the credibility of

the source is low [21]. Jain and Posavac also support the

prediction that source credibility is more important for evaluating

experience attributes than search attributes [27]. In same manner,

we assume that information from SNS regarded as relatively

credible might be a method for reducing consumers’ perceived

uncertainty about products so that it could lead consumers more

likely to buy. Moreover, since uncertainty varies across search,

experience, and credence products, it is hypothesized that

Facebook Like will influence sales of products differently across

the SEC classification and the effect will become larger as quality-

uncertainty increases, as previous research suggests. Due to a fact

that credence attributes are also characterized in the same way that

search and experience products are characterized[11, 30], it is

acceptable to expand previous predictions to the credence

products.

H2: The effect of users’ preference for products

collected through SNS on sales is higher for

experience goods than search goods

H3: The effect of users’ preference for products

collected through SNS on sales is higher for

credence goods than experience goods

H4: The effect of users’ preference for products

collected through SNS on sales is higher for

credence goods than search goods

3.2 Regression Models These hypotheses are tested with the econometric model. First of

all, sales of daily deal are used as a dependent variable in this

model. In order to explain the effect of information from SNS, the

number of Facebook Like in each deal and dummy variables, S

and E, for distinguishing search, experience, and credence

products respectively are used as independent variables. Moreover,

interaction terms between Facebook Like and categories are given

for verifying the difference of the effect across search, experience,

and credence goods/services. There are also a number of control

variables such as Price, Saved, Tipp, Pop, and Global. Price is

product price of each deal and Saved indicates the amount of

money obtained by the original price minus the offered price, or

Price. Tipp is the minimum amount of sales that consumers can

buy a product if total sales are above. Pop is the population of the

city where the deal is provided. Finally, using Global, we

distinguish companies that are selling products in multiple cities

so that we can reduce the asymmetry of selling power between

firms in this model. For example, Kodak was selling their

products across more than 30 cities, which means that this

company is relatively well known to customers and this fact may

cause a difference in sales among given deals. In this case we use

Table 1. Collinearity Diagnostics (intercept adjusted)

N

Eigen Cond Proportion of Variation

Value Index price2 saved2 Tipp Pop Global FB inter_s inter_e

1 2.302 1.000 4.7E-06 7.9E-05 0.053 0.043 5.3E-

04

8.2E-04 7.9E-04 7.1E-04

2 1.669 1.174 0.173 0.167 0.002 0.019 0.036 2.4E-05 2.0E-04 1.1E-05

3 1.312 1.325 0.007 0.013 0.078 0.059 0.101 2.7E-04 0.006 1.1E-03

4 0.969 1.541 1.4E-06 0.004 0.091 0.194 0.370 3.8E-04 0.005 7.1E-05

5 0.827 1.668 0.012 0.062 0.016 0.013 0.475 6.7E-05 0.015 2.2E-04

6 0.510 2.125 0.030 0.100 0.705 0.550 0.004 2.8E-05 5.1E-04 2.5E-06

7 0.409 2.371 0.772 0.652 0.053 0.122 0.012 1.5E-06 1.8E-04 2.0E-06

8 0.003 29.920 0.005 0.003 5.3E-

04

4.3E-

04

0.001 0.998 0.972 0.998

the Global variable to explain the relative large amount of sales.

Equation (1) is for testing H1 only and (2) is brought to verify the

remained hypothesis from H2 to H4.

1 1 2 3 4 5

1

(1) Pr

i i i i i i

i

Sales ice Saved Tipp Pop Global

Facebook

1 1 2 3 4 5

1 2 3

(2) Pr

( ) ( )

i i i i i i

i i i i i

Sales ice Saved Tipp Pop Global

Facebook S Facebook E Facebook

One might be interested in the fact that there are only interaction

terms between dummy variables and Facebook Like, and no

intercept for the dummy. This is because we believe that

categories themselves do not influence sales independently for

several reasons. First, the firm’s familiarity with consumers that

can cause a different level of sales across deals is well controlled

by the Global variable. Also, firms working with Groupon are

usually local and not famous so no firm has distinct sales power,

or popularity, compared with other deals on Groupon. Finally,

when it is run including the dummy for intercepts, the results also

do not come out with any statistically significance for those

variables (p=0.1842, p=0.8145 for Search and Experience,

respectively).

A diagnostic check for multi-collinearity among independent

variables is also conducted. According to the result in Table 1,

there is no significant collinearity between the variables, which is

determined by observing the condition indices in third column

having low scores less than 30. From the results of tests, therefore,

this model is free from a multi-collinearity problem.

In addition, there is an econometric issue about heteroscedascity,

so White test is adopted for verifying it. Null hypothesis of the

test implies homoscedascity, thus the high F value leads us to

reject the null hypothesis and concludes that the model has a

heteroscedascity problem. The result of the test comes out with F-

statistic (25.696) which is far exceeding the value at 5%

significance level. Therefore, in this case the result of simple OLS

estimate is going to be biased and instead GLS should be

employed for handling this problem.

3.3 Categorization by Search, Experience,

and Credence Goods/Services Iacobucci [26] empirically explored goods/services according to

search-experience-credence ratings. However, sample of

goods/services used in his work are quite different than what is

concerned in this research. Therefore, a pretest was conducted to

determine whether consumers perceive differences in the search,

experience, and credence characteristics of services, as suggested

in the literatures [11, 24, 25, 32].

43 KAIST business school students are asked to participate in this

survey. First, participants are supplied with a short explanation

about purchase decisions that described how some services can be

easily evaluated before purchase, while others cannot even after

use. Then participants are asked to evaluate their ability to judge

the performance of each service before purchase using a seven-

point scale ranging from ―Not at all‖ to ―Very well.‖ The total

number of services is 33. Participants were then asked again to

evaluate their ability to judge the performance of each service

after using it on the same seven-point scale.

Services that have high scores on both scales are regarded as

search dominated products because their performance can be

evaluated before purchase. In addition, services that have low

scores at first, but high on the second scales are classified as

experience dominated products while services having low score

on both scales are distinguished as credence dominated products.

At the 5% significance level, the midpoint 4 is used to determine

whether the score is low or high (i.e. 0: 4, i={before, after}iH M ).

Table 2 shows the results of the classification. The results show

that among 33 goods/services, 11 are determined as search, 20 as

experience, and 2 as credence goods/services. While most

goods/services Groupon provides are experience, which is in line

with our presumption that Groupon sells products which have

high quality-uncertainty, only two services, dental care and facial,

are regarded as credence services. The validity of the pretest was

established based on the fact that the respondents did not identify

any service as being easy to evaluate prior to purchase, but

difficult to evaluate after consumption [32].

Table 2. Categorization of products

Category Products Before use After use

Mean Std. Mean Std.

Search Sports game 4.51 1.61 5.81 1.28

Performance 4.88 1.55 6.09 0.95

Admission 4.37 1.45 5.79 0.97

Restaurant 4.37 1.29 6.07 0.86

Food 4.42 1.30 6.23 0.75

Dessert 4.37 1.23 6.12 0.82

Café 4.49 1.49 5.84 1.02

Hotel 4.74 1.18 5.86 0.97

Books 5.23 1.34 6.05 0.92

Photo 4.30 1.06 5.58 1.05

Subscription 4.60 1.29 5.37 1.22

Experience Fitness 3.35 1.41 5.28 1.08

Yoga 2.81 1.37 4.88 1.29

Sports lessons 2.77 1.17 5.09 1.17

Education 2.77 1.29 5.14 1.06

Activity 3.35 1.43 5.56 1.08

Tour 4.07 1.42 5.65 1.11

Occasion 3.56 1.42 5.60 1.06

Salon 2.79 1.25 5.79 1.12

Beauty clinics 2.79 1.12 4.98 1.34

Spa and

Massage 3.16 1.09 4.93 1.33

Pub 4.12 1.33 5.60 1.16

Wine 3.47 1.26 5.07 1.47

Car services 3.58 1.35 4.93 1.16

House services 2.84 1.09 5.09 1.19

Dry cleaning 3.31 1.30 4.98 1.42

Apparel 4.14 1.44 5.88 0.82

Furniture 3.81 1.20 5.23 1.13

Household

items 4.12 1.18 5.35 0.97

Other goods 4.17 1.10 5.52 0.92

Credence Facial 2.79 1.06 4.24 1.50

Dentalcare 3.00 1.29 4.28 1.44

3.4 Descriptive Statistics of Data To analyze the hypotheses, data was obtained by crawling the

Groupon site (www.groupon.com) with an automated crawler

specifically designed for Groupon, and includes sales of daily

deals across 124 cities in United States and Canada from Nov 2 to

Dec 9, 2010. Data also contains various attributes about each

daily deal. Details are presented in Table 3 and Table 4.

Table3. Definitions of Model Variables

Variable Description

Sales

Price

Saved

Tipp

Population

Facebook

Search

Experience

Inter_s

Inter_e

Amount of sales for each deal

Price of goods/services

Original price minus offered price

Sales point for a deal to be on

Population of a city

Number of Facebook Like for each deal

Dummy variable for search goods/services

Dummy variable for experience goods/services

Interaction term between Facebook and Search

Interaction term between Facebook and Exp.

Nofb Number of Facebook users in a city (Age:25~35)

At first, Facebook Like data was not collected because of some

technical problems, thus data collecting was conducted except for

Facebook Like. Later, the numbers were captured manually by

researchers, and through this work the validity of the data was

established by checking the exact number on the web site with

crawled results.

Among 2673 samples except for data that have some missing

observations or errors caused by crawler, 1378 samples are

obtained excluding unattained information of Facebook Like

associated with each deal. There were many samples that did not

have Facebook Like information so we dropped out a large

amount of data. For this reason, the number of cities that were

covered in the data was also reduced from 124 to 53. Even though

the number of samples decreases substantially, the remaining data

is still enough to reflect the generality of the nature in the

Groupon site.

Table4. Descriptive Statistics

Variable N Mean Median Std Min Max

Sales

Price

Saved

Tipp

Population

Facebook

Nofb

1378

1378

1378

1378

1378

1378

1378

599

29

55

43

510570

29

290472

218

20

24

25

375744

11

138740

1270.03

29.56

141.02

69.27

514760.77

81.94

316206.2

0

3

3

1

41058

0

11120

17473

200

3150

1000

2833321

2000

1718500

4. Results

4.1 Results of the GLS estimation According to Table 5, the result of the GLS estimate shows that

the number of Facebook Like shows a significant and positive

relationship with sales in goods/services (t=5.298, p<.0001). With

this, H1 can be supported. Moreover, all the other control

variables show a significant relationship with the dependent

variable, sales, except for Pop and Global whose p-value is

above .10. Verified by the fundamental theory of the economy, it

is predictable that sales (demand) go down as the price increases.

It is also confirmed that consumer tends to buy more when they

are offered goods/services at a high-discount rate, which is

justified as being the same as a lower price.

The results, shown in Table 6, show that number of Facebook

Like positively affects sales of goods/services across all pre-

defined categories including search, experience, and credence

(p<0.1, p<0.0001, p<0.05, respectively), and as suggested in the

hypothesis, the influence of Facebook Like varies across different

categories. The Coefficient of Facebook (18.45) indicates the

impact of Facebook Like when a credence good is offered, that of

Inter_s plus the coefficient of Facebook (18.45 - 7.82 = 10.63) is

for a search good, and that of Inter_e plus the Facebook

coefficient (18.45 – 7.68 = 10.77) for an experience good.

Because all the concerned coefficients are significantly differ from

zero, supported by t-values (11.95, -3.74, and -3.81, respectively),

it is shown that goods/services that belong to the credence

category, are more increasingly affected by Facebook Like than

search, and experience goods/services, which means that H3 and

H4 are supported.

Table5. Results of the GLS for Model (1)

Variable Coefficient Std. Pr > |t|

Intercept 124.46 41.418 0.0027

Price -4.33 0.689 <.0001

Saved 0.39 0.100 0.0001

Tipp 4.57 0.736 <.0001

Pop 9.14E-05 6.26E-05 0.1442

Global 71.53 57.186 0.2112

Facebook 10.77 2.033 <.0001

R-Square 0.6792 Adj R-Sq 0.6778

However, H2 (that Facebook Like shows a stronger effect on

experience than search goods/services) is not supported with the

results. The related Wald test for deciding whether the coefficient

of Inter_s and Inter_e are equal or not, indicates that these two

coefficient are not significantly different in a statistical manner

(F=0.0028). This outcome, however, can be predicted by the

presence of an online environment that forces products which

have the aspect of experience goods/services turning into search

goods/services [11, 25, 30]. As previous research indicates, it

might be caused by the fact that online, consumers can easily

create and share product information that could not be obtained

unless they actually use it. For example, they can share the

experience that they obtain when they use products, and users of

the internet easily access these reviews by searching. Through this

procedure, one can reduce quality-uncertainty by consuming the

information. Thus, our results can be explained in the same way.

As the online environment blurs the distinction between search

and experience products sold online, the effect of the information

on sales of both search and experience products also should not

vary.

When the information cannot be obtained prior to use or even

after use due to inherent features the product bears, consumer may

look to a source as a cue. Consumers may verify experience and

credence related information only when the source is credible [17,

43]. From the results, we confirm that under the presence of

quality-uncertainty, the consumer also looks for additional

information whose source is credible rather than relying only on

information contained in the product message. Moreover, if the

uncertainty increases, reliance on the source is also enlarged.

5. Conclusion This research investigates the influence of information from SNS,

such as Facebook, on consumers’ perceived quality-uncertainty of

products in E-commerce. To verify this, we use 1378 samples of

Groupon sales data obtained by crawling the Groupon website.

An additional pretest was conducted to classify products

according to the degree of quality uncertainty.

Using classified product categories of search, experience, and

credence products we conclude that information from SNS can

compensate for the quality-uncertainty that consumer faces and it

can increase the likelihood of making a purchase. In addition, it is

also supported that this influence varies across the degree of the

quality-uncertainty which changes along with various products. In

other words, as the difficulty of judging the quality of the product

increases, the influence significantly get stronger. Moreover, our

finding that the effects on search and experience are not

significantly different is well supported by the fact that the

internet blurs distinctions between experience and search goods

by providing mechanisms that enable online shoppers to gather

information on experience and search attributes [11, 25, 30].

Table6. Results of the GLS for Model (2)

Variable Coefficient Std. Pr > |t|

Intercept 127.53 36.487 0.0005

Price -4.49 0.753 <.0001

Saved 0.37 0.084 <.0001

Tipp 4.57 0.684 <.0001

Pop 9.01E-05 6.35E-05 0.1561

Global 74.03 63.868 0.2466

Facebook 18.45 1.544 <.0001

Inter_s -7.82 2.089 0.0002

Inter_e -7.68 2.016 0.0001

R-Square 0.6805 Adj R-Sq 0.6787

2 30 :H F Value Pr > F

2

3

:coefficient of Inter_s

:coefficient of Inter_e

0.002815 0.9577

From the previous studies, it is predicted that when it is difficult

to obtain quality information before use or even after use,

consumer may look to a source of information as a cue and verify

experience information only when the source is credible [27].

This is because if a consumer could not distinguish the exact

quality among different messages given, these are treated as equal

and then consumers are likely to find other information that they

can use to verify the quality of products, which is the source of

information in this case. For instance, if he/she could not find

information about the quality of products even after use, reviews

do not contain any of useful information about products and are

not different from information given before use. Then, he/she has

to discover other cues to rely on. Because SNS consists of friends

who have connections with users and who are relatively credible

compared with unknowns, information that comes from SNS can

be used by consumers to verify uncertain quality information.

According to the results in this paper, we suggest that SNS is

treated as a credible source of information and provides additional

messages which are not contained in traditional information

shared over the internet. Those additional messages can give

consumers clues to help verify information when they are exposed

to uncertainty.

This study has several limitations. First, data does not cover all

deals in Groupon during the period. Since there were some

technical and observational errors, we eliminate a large amount of

data and this affects the generality of our results. In addition, since

we use aggregated preference, not individual, there is a little

doubt about revealing the low-level behavior concluded in this

paper. Although aggregated data is enough to represent the norm

of consumer behavior conducted in Groupon, it would be better if

a survey is conducted to capture individual conducts. The third

limitation is the problem of convenience sampling when

categorization is carried out. Future study should consider

limitations discussed above. Furthermore, we have initiated

additional research that will add more value to our findings in this

research, by using time-series data of sales. Deals in Groupon

have a special feature in that they sell products only for one day,

thus actual bidding or purchasing timing can be observed. Using

this, we would expect that according to the degree of quality-

uncertainty, consumers’ actual bid-timing could be changed and

this change also could be influenced by the source of information

which is, in this case, SNS. Usually, sale periods are all different,

so bid-timing is not observable in the real world case. With this

data, however, we could derive the effect of information from

SNS on actual bid-timing, and thus this research would provide a

better understanding about consumers’ behavior under the

presence of quality-uncertainty.

This paper contributes to a better understanding of consumer

behavior under the existence of quality-uncertainty. It also

certifies that the more uncertainty there is, the more the consumer

relies on the source, not the information itself. Using Groupon

sales data, this research also analyzes the dynamics of Social

Commerce empirically, and points out fundamental factors that

impact sales. For managerial implications, the use of SNS for the

purpose of marketing should be concerned with what

characteristic of products the company sells. According to the

results, SNS might be more efficient in a situation in which

consumers face a high degree of uncertainty to verify the quality

of products. Like the brand name, information from their friends

also compensates the ambiguity by generating credible cues. Thus,

a company that deals for products having inherent uncertainty

should consider and manage its marketing strategy using SNS.

SNS is one the most interesting issue on the internet nowadays;

most people, however, have insufficient understanding of the

implications on how and when it should be used. This paper shed

lights on the effective use of SNS and on Social Commerce.

6. REFERENCES [1] Abbe-Decarroux, F. 1994. "The perception of quality and the

demand for services:: Empirical application to the

performing arts," Journal of Economic Behavior &

Organization (23:1), pp. 99-107.

[2] Aggarwal, P. and R. Vaidyanathan. 2005. "Perceived

effectiveness of recommendation agent routines: search vs.

experience goods," International Journal of Internet

Marketing and Advertising (2:1), pp. 38-55.

[3] Anand, K.S. and R. Aron. 2003. "Group buying on the web:

A comparison of price-discovery mechanisms," Management

Science), pp. 1546-1562.

[4] Animesh, A., V. Ramachandran, and S. Viswanathan. 2005.

"Online Advertisers Bidding Strategies for Search,

Experience, and Credence Goods: An Empirical

Investigation," Citeseer.

[5] Bennedsen, M., et al. 2007. "Inside the Family Firm: The

Role of Families in Succession Decisions and Performance,"

Quarterly Journal of Economics (122:2), pp. 647-691.

[6] Bourdieu, P. and L.J.D. Wacquant. 1992. An invitation to

reflexive sociology. University of Chicago Press.

[7] Chen, J., X. Chen, and X. Song. 2007. "Comparison of the

group-buying auction and the fixed pricing mechanism,"

Decision Support Systems (43:2), pp. 445-459.

[8] Chen, J., et al. 2010. "Segmenting uncertain demand in

group-buying auctions," Electronic Commerce Research and

Applications (9:2), pp. 126-147.

[9] Chevalier, J.A. and D. Mayzlin. 2006. "The Effect of Word

of Mouth on Sales: Online Book Reviews," Journal of

Marketing Research (JMR) (43:3), pp. 345-354.

[10] Coleman, J.S. 1988. "Social capital in the creation of human

capital," American Journal of Sociology), pp. 95-120.

[11] Darby, M.R. and E. Karni. 1973. "Free competition and the

optimal amount of fraud," Journal of Law and Economics

(16:1), pp. 67-88.

[12] Davis, A. and D. Khazanchi. 2008. "An empirical study of

online word of mouth as a predictor for multi-product

category e-Commerce sales," Electronic Markets (18:2), p.

130.

[13] Deutsch, M. 1960. "The effect of motivational orientation

upon trust and suspicion," Human Relations (13:2), pp. 123-

139.

[14] Donath, J. and D. Boyd. 2004. "Public displays of

connection," BT Technology Journal (22:4), pp. 71-82.

[15] Duan, W., B. Gu, and A.B. Whinston. 2008. "The dynamics

of online word-of-mouth and product sales - An empirical

investigation of the movie industry," Journal of Retailing

(84:2), pp. 233-242.

[16] Eaton, D.H. 2005. "Valuing information: Evidence from

guitar auctions on eBay," Journal of Applied Economics and

Policy (24:1), pp. 1-19.

[17] Ellison, N.B., C. Steinfield, and C. Lampe. 2007. "The

benefits of Facebook "friends:" Social capital and college

students' use of online social network sites," Journal of

Computer Mediated Communication (12:4), pp. 1143-1168.

[18] Gefen, D. 2000. "E-commerce: the role of familiarity and

trust," Omega (28:6), pp. 725-737.

[19] Girard, T., P. Korgaonkar, and R. Silverblatt. 2003.

"Relationship of type of product, shopping orientations, and

demographics with preference for shopping on the Internet,"

Journal of Business and Psychology (18:1), pp. 101-120.

[20] Greene, H. 2008. "Econometric Analysis (6-th ed.)."

Prentice-Hall, New Jersey.

[21] Grewal, D., J. Gotlieb, and H. Marmorstein. 1994. "The

Moderating Effects of Message Framing and Source

Credibility on the Price-Perceived Risk Relationship," The

Journal of Consumer Research (21:1), pp. 145-153.

[22] Grund, C. and O. Guertler. 2008. "The effect of reputation on

selling prices in auctions," Journal of Economics and

Statistics (Jahrbuecher fuer Nationaloekonomie und

Statistik) (228:4), pp. 345-356.

[23] Hoffman, D.L., T.P. Novak, and M. Peralta. 1999. "Building

consumer trust online," Communications of the ACM (42:4),

pp. 80-85.

[24] Hsieh, Y.C., H.C. Chiu, and M.Y. Chiang. 2005.

"Maintaining a committed online customer: A study across

search-experience-credence products," Journal of Retailing

(81:1), pp. 75-82.

[25] Huang, P., N.H. Lurie, and S. Mitra. 2009. "Searching for

experience on the web: An empirical examination of

consumer behavior for search and experience goods,"

Journal of Marketing (73:2), pp. 55-69.

[26] Iacobucci, D. 1992. "An empirical examination of some

basic tenets in services: goods-services continua," Advances

in services marketing and management (1), pp. 23-52.

[27] Jain, S.P. and S.S. Posavac. 2001. "Prepurchase Attribute

Verifiability, Source Credibility, and Persuasion," Journal of

Consumer Psychology (Lawrence Erlbaum Associates)

(11:3), pp. 169-180.

[28] Kauffman, R. and B. Wang. 2001. "New buyers' arrival

under dynamic pricing market microstructure: The case of

group-buying discounts on the Internet," Published by the

IEEE Computer Society, p. 7034.

[29] Kauffman, R.J. and B. Wang. 2002. "Bid together, buy

together: On the efficacy of group-buying business models in

Internet-based selling." CRC Press, pp. 99-137.

[30] Klein, L.R. 1998. "Evaluating the potential of interactive

media through a new lens: Search versus experience goods,"

Journal of Business Research (41:3), pp. 195-203.

[31] Kramer, R. and T. Tyler. 1996. "Whither trust," Trust in

Organizations. Thousand Oaks: Sage Publications).

[32] Krishnan, B. and M. Hartline. 2001. "Brand equity: is it

more important in services?," Journal of Services Marketing

(15:5), pp. 328-342.

[33] Levin, A.M., I.P. Levin, and J.A. Weller. 2005. "A multi-

attribute analysis of preferences for online and offline

shopping: Differences across products, consumers, and

shopping stages," Journal of Electronic Commerce Research

(6:4), pp. 281-290.

[34] Lewicki, R. and B.B. Bunker. 1996. "Developing and

maintaining trust in work relationships," Trust in

organizations: Frontiers of theory and research), p. 114.

[35] Liu, F. and H.J. Lee. 2010. "Use of social network

information to enhance collaborative filtering performance,"

Expert Systems with Applications (37:7), pp. 4772-4778.

[36] Mayer, R.C., J.H. Davis, and F.D. Schoorman. 1995. "An

integrative model of organizational trust," The Academy of

Management Review (20:3), pp. 709-734.

[37] McKnight, D.H. and N.L. Chervany. 2001. "What trust

means in e-commerce customer relationships: an

interdisciplinary conceptual typology," International Journal

of Electronic Commerce (6:2), pp. 35-59.

[38] Nelson, P. 1970. "Information and consumer behavior," The

Journal of Political Economy), pp. 311-329.

[39] Nelson, P. 1974. "Advertising as information," The Journal

of Political Economy (82:4), pp. 729-754.

[40] Ngai, E. and F. Wat. 2002. "A literature review and

classification of electronic commerce research," Information

& Management (39:5), pp. 415-429.

[41] Nohria, N. and R. Eccles. 2000. "Face-to-face: Making

network organizations work," Technology, Organizations

and Innovation: Towards' real virtuality'?), p. 1659.

[42] Parasuraman, A., V.A. Zeithaml, and L.L. Berry. 1985. "A

conceptual model of service quality and its implications for

future research," The Journal of Marketing (49:4), pp. 41-50.

[43] Paxton, P. 1999. "Is social capital declining in the United

States? A multiple indicator assessment," American Journal

of Sociology), pp. 88-127.

[44] Ratnasingham, P. 1998. "The importance of trust in

electronic commerce," Internet research (8:4), pp. 313-321.

[45] Reinstein, D.A. and C.M. Snyder. 2005. "The influence of

expert reviews on consumer demand for experience goods: A

case study of movie critics," Journal of Industrial Economics

(53:1), Mar, pp. 27-51.

[46] Weathers, D., S. Sharma, and S.L. Wood. 2007. "Effects of

online communication practices on consumer perceptions of

performance uncertainty for search and experience goods,"

Journal of Retailing (83:4), pp. 393-401.

[47] Yoo, B.-J., Y.B.J. and G.-W. Lee, L.G.W. 2007. "An

Alternative Approach in Analyzing the Impacts of Online

Feedback System; A Bayesian Inference Model," Korea

Society of Management Information Systems), pp. 395-400.