A Study of the Network Externality Effects on the Online Group Buying
Zhenli Bi1, Bin Shi2
1
Guangdong University of Education, Guangzhou, P.R.China
School of Management, Shenzhen Polytechnic, Shenzhen, P.R.China
(1zhenlibi@126.com, 2shib1972@126.com)
2
Abstract - The main features of group-buying are:
businesses exchange for consumer flow and exposure with
low prices, i.e. Group-Buy websites attracts consumers with
low prices, and so consumers participate in online shopping
due to the low prices. In this pattern, the network externality
effect is magnified and becomes the main factor to draw
consumers to participate in the online purchase. This
empirical study reveals the mechanism of the network
externality effects on the willingness to participate in the
online group buying. The results of the study indicate that
network externalities have no direct effect on the willingness
to participate in online Group-Buy, but the indirect effect
has been revealed. Furthermore, the indirect effect performs
its functions by affecting the participants’ trust in GroupBuy websites and affecting the consumers’ cognition of
discounts.
online group buying not only has competitive advantages
of quantity system price and delivery service, but also
cooperates and shares shopping experiences with netizens.
The web-based group-buying mechanisms, a refinement
of quantity discounting, are being used for both B2B and
B2C transactions [1]. If the buyers with similar product or
service requirements can form a virtual coalition to
bargain collectively with the sellers, it can reduce the
transaction cost of each side. Therefore, the first
hypothesis is proposed [2].
H1：The cognition of discount has positive effects
on the willingness to participate in online group buying.
Keywords – Discount, externality, online group buying,
price trust, willingness to buy
Different researches gave different descriptions for
the online trust. Kim and Benbasat[3] identified four
categories of trust related issues: “personal information,
product quality and price, customer service, and store
presence”. In the case of e-commerce shopping, Ang,
Dubelaar, and Lee[4] built three dimensions of trust
which improve the perception of trust on the internet. The
three dimensions include the ability of the online
merchant to deliver a product or service that performs as
promised, the willingness of the online merchant to rectify
should the purchase not meet the customer’s satisfaction,
and the presence of a privacy policy or statement on the
web site. The researchers built the theoretical framework
of online trust, covering the different stages that a
consumer went through to complete an online transaction.
Lee, J. N., and Kim[5] proposed that consumer could
perceive trust before, during, or after the online
transaction, furthermore, the researchers emphasized that
different determinants of trust were associated with
different stages of the transaction. According to the
statement above, we can see that the different divisions of
online trust are similar and overlapped in some extent.
Lee and Turban[6] point out that trust is an important
factor in the uncertain and risky environment, and
consumer’s purchase decision is based on the trust on
vendor, which also explains the importance of trust for
online transactions. Grazioli and Wang[7] found that
perceived risk and trust determine the consumer’s attitude
toward online purchase, which subsequently affected
willingness to purchase and actual purchase behavior.
Based on the discussed above, this study supposes that the
better initiator’s reputation is perceived by customers, the
higher trust on the initiator will be created. Thus the
second hypothesis is proposed.
I. INTRODUCTION
A distinguishing feature of the Group-Buy business
model is that the traditional quantity discount model is
overlaid with a coordination problem among consumers.
The pure coordination problem occurs when all players
optimize a common objective function; in this case, the
players are said to be a team. Clearly, the customers under
Group-Buy are not operating as a team; they maximize
their expected individual surpluses from their purchases.
However, each consumer’s behavior (whether bidding or
not bidding, and the timing of the bid) affects other
consumers’ surplus. Thus, the problem of the Group-Buy
is relevant to externalities. The network externalities will
enable us to gain insights into the performance and
mechanics of group-buying. The aim of this study is to
prove the externality effects on the consumer’s purchase
decision and how the externality effects work.
II. LITERATURE REVIEW AND HYPOTHESES
A. Discount
The key to the online group-buying is the
aggregation of geographically dispersed consumer
purchasing power from all over the countries that have a
common interest in a certain product and come together
on the group-buying-sites to get discounts[1]. In attending
This article were funded by the Research Fund of Shenzhen Polytechnic,
the Item Number is 212s3210003.
B. Trust in Online Group Buying Context
H2：The perception of trust in Group-Buy websites
has positive effects on the willingness to participate in
online Group-Buy.
C. Demand Externalities
consumer’s willingness to participate in online GroupBuy or to purchase, are demand externalities, the
magnitude of the discounts and the consumer’s trust in the
Group-Buy websites. Model 1 (see Figure 1) unfolds the
relations of the variables (the causes).
H5
Network Externalities are a special kind of
externalities in which people’s utility for a goods depends
on the number of other people who consume this goods.
Kauffman and Wang [8] posited that the presence of
network effects in group-buying auctions will have
beneficial consequences. Consumers will express a
greater willingness-to-bid in a group-buying auction as
more bids arrive and greater consumer participation
ensues. In all of these network settings, positive network
effects arise due to the fact that network participants’
utility increases with the number of other users. It
typically will influence consumer decision-making as well.
So in the online group-buying auction context, the more
people that are willing to participate by making a bid on a
given product, the more value the auction will be
perceived to have by the seller and the buyers. The
participants’ willingness-to-bid will increase due to their
perception that the auction will succeed. Hence, the
following three hypotheses are proposed.
H3：Demand externalities have positive effects on
the cognition of the group buying discounts.
H4：Externalities have directly positive effects on
the willingness to participate in online Group-Buy.
H5：Demand externalities, through the cognition of
discounts, have indirectly positive effects on the
willingness to participate in online Group-Buy.
Tzyy-Ching Yang, Hsiangchu Lai[9] probed the
factors affecting participants’ trust in the online GroupBuy organizers and their willingness to participate in
online Group-Buy, taking the number of Group-Buy
participants as the independent variable. Their research
suggests that the more people who participate in the
Group-Buy, the more the consumers trust the online
Group-Buy organizers, further, the more actively the
consumers participate in Group-Buy. Thus, the conclusion
can be drawn that the demand externalities have positive
effects on the consumer’s purchase decision. Dybvig and
Spatt[10] proved in their study that Network externalities
in the form of current installed base and expected installed
base are important factors influencing a potential
adopter’s decision to select a product. Based on the above
review, another two hypotheses are proposed.
H6：Demand externalities have positive effects on
the perception of trust in Group-Buy websites.
H7：Externalities, through the perception of trust in
Group-Buy websites, have indirectly positive effects on
the willingness to participate in online Group-Buy.
D. Conceptual Model
In the light of the above analysis, the hypothesis of
the study is proposed that the causes or premises for
H3
Cognition of
Discount
H1
H4a
Willingness
to Purchase
Externalities
H4b
H6
Trust in Websites
H2
H7
Fig. 1. Conceptual Model
III. RESEARCH DESIGN AND DATA COLLECTION
A. Questionnaire Design
The questionnaire consists of four sections. Section 1
involves only one filterable question by which the
respondents are to be identified. The question is whether
the respondents have done online shopping. Section 2 is
about the resources that the respondents use for online
shopping, including websites and the categories of the
items they bought. Section 3 is the scale designed on the
basis of this research constructs. Section 4 is about the
classified information including age, gender and
occupation of the respondents. Four constructs are
involved in the concept model, and there are four
corresponding scales with four constructs respectively.
What’s more, four measurements for the four constructs
are designed according to the scales in the literature
reviewed above.
Q1: A lot of discount is supplied on group buying
websites.(Voss,1998[11])
Q2: People often buy things above the price in value
on group buying websites.
Q3: Buyers can be benefited from group buying
which is a means of promotion.
Q4: I will decide to do group buying if a lot of
people are doing group buying.( Shu-Mei Tseng[12])
Q5: I believe I’ll not be cheated if a lot of people are
doing group buying.( I-Shan Lee[13])
Q6: I believe the prices are low if a lot of people are
doing group buying. (Lee and Park[14])
Q7: I’ll join the buying group if they need me.
Q8: Fraud on group buying websites is rare.
Q9: Group buying websites attach importance to
their reputations.
Q10: Group buying websites take complaints
seriously.
Q11: I will do group buying if possible.
Q12: I’m going to take group buying as a way of
purchase.
Q13: I will try group buying if this way of purchase
is provided. (Taylor and Todd[15])
B. Data Collection
The data employed in this study were collected via a
survey website called Questionnaire Star on which a
questionnaire was posted first. And through the
questionnaire invitation provided by the survey platform,
the questionnaire links were sent to the known e-mails
which belonged to the researcher’s friends, colleagues, and
the teachers and managers who were pursuing studies or
doing research in the same college which the researcher of
this study is working for.
C. Samples Constitutents
Totally, 420 invitation e-mails were sent, and 232
valid replies were collected. And the invalid replies were
from those respondents who hadn’t done online shopping,
or the incomplete replies. Table I is Sex and Age of
Respondents which shows the number and the percentage
of the people grouped in terms of gender and age. From
the distribution of the ages, the proportion of the sample
group concords with that of the netizens.
TABLE I
SEX AND AGE OF RESPONDENTS
Age
26-35
36-45
Over 46
Total
Male
45(19.4%)
28(12.1%)
1(0.4%)
93(40.1%)
Female
62(26.7%)
31(13.4%)
6(2.6%)
139(59.9%)
Total
107(46.1%)
59(25.4%)
7(3.0%)
232(100.0%)
Among the listed major Group-Buy websites which
the respondents employed to purchase, Lashou was the
most frequently dused. 171 respondents did online
shopping via Lashou, which accounted for 73.7% of the
respondents. If all the listed websites are taken as an
entire market, Lashou occupies 17.3% of the whole
market. Further calculation shows that each one surveyed
used 4.26 websites on average.
IV. RELIABILITY AND VALIDITY
A. Reliability Analysis
To ensure reliability in qualitative research,
examination of trustworthiness is crucial. Cronbach's
alpha is an index of reliability associated with the
variation accounted for by the true score of the
"underlying construct." Construct is the hypothetical
variable that is being measured. Alpha coefficient ranges
in value from 0 to 1 can be used to describe the reliability
of factors extracted. The higher the score, the more
reliable the generated scale is. Nunnaly (1978) has
indicated 0.7 to be an acceptable reliability coefficient but
lower thresholds are sometimes used in the previous
studies. The statistics by means of SPSS show that
Cronbach's Alpha is .858, which indicates the
questionnaire has a high level of reliability.
B. Discriminant validity
Discriminant validity tests whether concepts or
measurements that are supposed to be unrelated are, in
fact, unrelated. Campbell and Fiske (1959) introduced the
concept of discriminant validity within their discussion on
evaluating test validity. They stressed the importance of
using both discriminant and convergent validation
techniques when assessing new tests. A successful
evaluation of discriminant validity shows that a test of a
concept is not highly correlated with other tests designed
to measure the theoretically different concepts.
Component (Factor) analysis is to test whether crossfactor phenomena take place in the measurements of each
construct, through exploratory factor analysis, namely, not
only each measurement item should significantly reflect a
single component, and hopefully the factor loading value
is over 0.6, but also, for the other components, the factor
loading value of each measurement item should be under
0.3. Table II shows the rotated component matrix. It is set
to extract four components in the component (factor)
analysis. The factor loading values shown are only those
over 0.3. Table II indicates the first component consists of
the measurement items of the network externalities, and
the second, third and fourth components consist of the
measurement items of the willingness to purchase, the
perception of trust in the Group-Buy websites, and the
cognition of discounts. The values suggest the good
structure of the scale. Q7, Q11 and Q2 respectively have
a high factor loading value in one component, but have a
high value in another component, that is over 0.3, which
demonstrates
that
the discriminant
validity of
these questions are a little poor.
TABLE II
ROTATED COMPONENT MATRIX
construct
externality
willingness
trust
discounts
Items
Factor1
Q 4→Ex1
Q 5→Ex2
Q 6→Ex3
Q 7→Ex4
Q13→Bu1
Q12→Bu2
Q11→Bu3
Q10→Tr1
Q 9→Tr2
Q 8→Tr3
Q 1→Pr1
Q 3→Pr2
Q 2→Pr3
0.80
0.74
0.74
0.71
Factor 2
Factor3
Factor 4
0.32
0.87
0.83
0.74
0.38
0.81
0.80
0.78
0.40
0.79
0.77
0.71
C. Convergent Validity
Convergent validity is the degree to which an
operation is similar to other operations that it theoretically
should also be similar to. Composite reliability, c.r, as a
common research approach in PLS, and another approach
average variance extracted, AVE, are utilized in this study
to test the internal consistency of the construct
measurements. Chin (1991) and Fornell (1982) suggested
that the value of CR should be over 0.7, while that of
AVE should be under 0.5.An AMOS is set up based on
the four constructs. Thus the measurement model of the
four constructs is set up. In the model,
CMIN,
CMIN/DF suggests the model fits well. Table III indicates
that the reliability coefficients are all over 0.7, and the
AVE demand externality constructs is near 0.5, which
suggests a good convergent validity of the research
constructs.
TABLE III
externality effects on the willingness to purchase. Model 1
takes discounts as an intermediary variable, and Model 2
takes the trust in Group-Buy websites as an intermediary
variable. Table IV is about Model Fit.
TABLE IV
MODEL FIT
χ2
Fit indices
Model1
Model2
29.45
26.87
df
P
χ2/d f
28
29
>0.05
0.39
0.58
<2
1.05
0.93
RMSEA
<0.05
0.02
0.00
N FI
>0.90
0.97
0.97
CFI
>0.90
0.99
1.00
STANDARDIZED LOADINGS AND RELIABILITIES
constructs
externality
willingness to buy
trust
discounts
items
4
3
3
3
c.r
0.79
0.84
0.79
0.80
ave
0.49
0.64
0.56
0.58
loading
0.69, 0.71, 0.79,0.60
0.78, 0.86, 0.76
0.74, 0.76, 0.75
0.62, 0.88, 0.75
V. FINDINGS AND CONCLUSION
A. Testing the Model
Based the conceptual model, the two path analysis
structural equation models are established. The
standardized estimates are shown in Figure 2 and Figure 3.
B. Direct Effect
Table V presents parts of the statistics of AMOS.
The estimates of regression weights are non-standardized
regression coefficients, and S.E. stands for the standard
errors of the estimates.
C.R. is the value of the regression coefficient divided
by the corresponding standard error of the estimate. The
calculation shows that the absolute value of C.R. is over
1.96, which indicates that the estimates are significant. If
the significant probability value P is under 0.001, the
symbol“***”will appear, instead, if the value P is over
0.001, the value of P will appear. Table V shows the
estimates of the two models respectively. Thus, the test
results can be obtained directly according to the
probability value. The research findings support the
following hypotheses, H1, H2, H3 and H6, but not H4, as
the two models obviously don’t support it. Namely, the
findings suggest that demand externalities have directly
positive impact on the online buyers’ cognition of
discounts and their perception of trust in the Group-Buy
websites. Besides, the online buyers’ cognition of
discounts and their perception of trust in the Group-Buy
websites have positive effects on the willingness to
participate in online group buying. The direct impact of
network externalities on the willingness to participate in
online group buying is not significant.
TABLE V
Fig. 2. Standardized Estimates of Model 1
REGRESSION WEIGHTS OF MODEL
Relationship
discount <-- externality
1 willingness<-- externality
Estimate
S.E.
C.R.
P
H
0.49
0.10
4.80
***
H3
0.00
0.09
0.01
.994
H4
willingness<-- discounts
0.86
0.12
7.15
***
H1
trust<-- externality
0.65
0.11
5.79
***
H6
0.20
0.12
1.72
0.09
H4
0.33
0.10
3.32
***
H2
2 willingness<-- externality
willingness<-- trust
C. Mediating Effects
Fig. 3. Standardized Estimates of Model 2
Both models comprise the path of the network
When a variable can explain the relationship between
a self-dependent variable and an independent variable, it
is believed that the variable plays its medium role. The
purpose of researching into medium is to explore the
internal mechanism of the known relationships. Mediating
variables, in theory, have great significance in two aspects:
(1) mediating variables can integrate the existing theories;
(2) the mediating variables can explain the deep-seated
mechanism behind their relations. This study uses
Bootstrap of AMOS to test the direct effect and the
indirect effect. Statistics show the value P and the
confidence interval as well. Table VI presents the
summary statistics of the two models.
TABLE VI
EFFECTS OF “EXTERNALITY” ON “TENDENCY”
Model
1
2
Type
Direct
Indirect
Total
Direct
Indirect
Total
s
0.001
0.375
0.376
0.181
0.186
0.367
s.g
0.939
0.001
0.002
0.122
0.003
0.002
Hypothesis
H5
H7
Table VI suggests that, in the two models, the
cognition of network externality of Group-Buy has no
direct effect on the willingness to participate in online
group buying. And this result is consistent with the
previous one in the same study. In Model 1, the cognition
of network externality has a significantly indirect effect
on the willingness to participate in online group buying
through the cognition of discounts. In Model 2, the
cognition of network externality has a significantly
indirect effect on the willingness to participate in online
group buying through the perception of trust in GroupBuy. In addition, the two indirect effects of the cognition
of network externality on the participant willingness are
0.376 and 0.186, in the two Models respectively, which
suggests a greater medium function of the cognition of
discounts.
The empirical results demonstrate the significant
effect of network externality on the consumer’s
willingness to participate in online group buying, which
on the one hand, provides some reliable underlying causes
for the main features of Group-Buy websites, on the other
hand, forcefully explains why those websites can expand
so rapidly and successfully. The network externality most
forcefully explains the success of the typical Group-Buy
websites.
D. Marketing of Group-Buy Websites
Group-Buy websites need to continuously collect
and analyze the consumers’ information, learn about their
psychological, cultural and social properties, and know
about their shopping patterns, so as to make sure what
factors will affect their purchase decision. Since network
externalities are the important factors that influence
consumers’ willingness to purchase, it is essential for
Group-Buy websites to strengthen and highlight network
externalities whose features involve the display of the
number of the submitted order, setting the minimum
orders in effect, setting the termination time, and setting
the discount rate. Superficially, the mentioned features are
not favorable to online transactions, or they are even
unnecessary, for instance, some websites do not set the
minimum orders in effect. However, such a feature can
highlight the network externality effects , thereby enhance
the consumer’s willingness to participate in online group
buying. Furthermore, it is a significant way for GroupBuy websites to establish network community in a bid to
attract consumers. From the academic view and in
practice, network community makes an effective means to
increase consumer viscosity. In reality, if the network
externalities can be reinforced by the online community, a
striking promotional effect will emerge.
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