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EM – Electronic Markets
the International Journal of Electronic Commerce & Business Media
Appeared in EM-Electronic Markets 15(2) 158-175
[Preprint]
Journal Volume & Issue
15/2
Article Identification Number:
15242
Name of Author(s):
Sheizaf Rafaeli and Avi Noy
Article Title:
Social Presence: Influence on Bidders in Internet Auctions
Main Author’s Mailing Address
(Street):
Graduate School of Business Administration, University of
Haifa
Postal Code, City:
31905 , Haifa
Country:
Israel
Telephone: (including country code)
972-4-8249578
Fax:
972-4-8249194
Email:
sheizaf@rafaeli.net
Publish the above email address
along with the article's abstract:
Yes, publish both electronically and in paper
Publish only in paper
Publish neither in paper nor electronically
Date of Submission
06 March 2016
Electronic links to your institution,
c.v., project description, etc:
Auction research site
(http://research.haifa.ac.il/~avinoy/auction/)
The Center for the Study of the Information Society
(http://infosoc.haifa.ac.il/index_eng.htm)
Prof. Sheizaf Rafaeli (http://sheizaf.rafaeli.net)
Avi Noy (http://research.haifa.ac.il/~avinoy/)
Brief Biography (including eMail address and not exceeding 50 words) of author(s).
Sheizaf Rafaeli (sheizaf@rafaeli.net) is a professor of Information Systems and is the Director of the
Center for the Study of the Information Society at the Graduate School of Business Administration,
University of Haifa, Israel. He has published extensively about computer-mediated communication,
virtual communities and the value of information. Sheizaf has been involved in studying and building
Internet-based activities, and is interested in their social and organizational impacts and potentials.
Avi Noy (avinoy@gsb.haifa.ac.il) is a doctoral student at the Graduate School of Business
Administration, University of Haifa, Israel. His study focuses on internet auctions and CMC (Computer
Mediated Communication). He is also a teaching fellow at the Business School, University of Haifa,
Israel.
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Abstract
We study effects of social processes' representation in internet auctions using laboratory experiments.
The inability of participating parties to learn about each others’ bidding behavior and to extend their
comprehension beyond what is presented in auction sites today decreases the social influence compared
to traditional face-to-face auctions. This reality is about to change since new interaction and
collaboration technologies are emerging. A conceptual model used here associates social factors
influencing bidders and the resulting bidding behavior. We utilized an online auction simulation
framework that facilitates transmission of social cues during an auction and conducted empirical study
of behavior in both English and Dutch auctions. We found that social presence, expressed by virtual
presence and interpersonal information, significantly affects both bidding behavior and market
outcomes.
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Social Presence: Influence on Bidders in Internet
Auctions
Sheizaf Rafaeli and Avi Noy
1
Introduction
Internet auctions are software-based implementations of a traditional auction format. Online
auctions may have some inherent advantages. They allow access to a larger pool of potential buyers;
remove some time and space constraints; decrease transaction costs and increase information
availability. At the same time, in a computer-mediated environment, as opposed to face-to-face
interactions, buyers cannot manually touch or handle the item that is on sale, and both sellers and
buyers have more difficulties sizing up the other. This limitation raises issues of perceived risk and
imposes behavioral limitations. Since direct and indirect interaction among auction bidders is limited,
the influence of one on the others is ostensibly precluded.
Many scholars have noted the influence of technology on the social context in shaping the ways in
which the medium is used. There is even a popular slogan about the medium being the message. In
this study the focus is on the reverse influence – from social context and layout to technology. Social
factors which affect bidding strategies in brick and mortar auctions may have different effects when
using computerized interaction mechanisms that enable transmission of social cues.
The internet is perceived to be "cold" and impersonal. Many e-commerce web sites enhance
usability and sociability by providing visual display of sales representatives, sales agents and virtual 3D
environments. Social presence in traditional face-to-face commerce is replicated in online
environments. However, though online auctioning is tremendously popular and social interaction is an
essential part of traditional auctions, internet auction sites were not affected by this trend.
This study tests how social presence of bidders in online auctions affects bidding behavior. The
conceptual model emphasizes the relationship between social factors affecting the individual bidder,
the influence that is generated by others, and the resulting bidding behavior. We utilize an innovative
auction framework that embodies communication technology to extend and express social context
during the auction. We report on the results of a study conducted with this framework.
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Two auctions models: English and Dutch auctions were used in this study. An English auction is an
ascending price auction and is the most popular auction mechanism in traditional and online
environments. A Dutch auction differs from the English auction mechanism in its descending price
mechanism. This type of auction became famous as the mechanism used in flowers market in
Netherlands.
The rest of this paper is organized as follows. First we review previous literature on auctions, social
processes in groups and emerging internet communication and presentation technologies. Next we
present our research questions and describe the conceptual and research models. Hypotheses regarding
the variables in the model are presented and the research method is described. Following a report of the
results of our online simulations we conclude by identifying the experiments' limitations. Finally, we
present the contribution and propose future research directions.
2
2.1
Theoretical Background
Research on auctions
Auctions are of research interest in the fields of economics, marketing and consumer behavior.
Various laboratory experiments and empirical field studies have been conducted in traditional markets
(see Kagel 1995; Kagel and Levin 2002 for a thorough review). Internet auctions have been studied
following the increase in trade values. Many empirical field studies collect data available in auction
sites to analyze bidding behavior: In what auctions do bidders participate? How do various auction
rules affect the bidding? (for example: Bapna et a. 2001; Bapna 2003; Bapna et al. 2004; Dholakia and
Soltysinski 2001; Dholakia et al. 2002; Lucking-Reiley et al. 2000; Wilcox 2000). Most of these data
were collected at eBay, the largest internet auction site. These studies relate to various aspects of online
auctioning. The next paragraph relates to studies of various factors affecting the individual bidder.
2.2
Determinants of bidding behavior
A range of studies identify the determinants of bidding in internet auctions: price, time of bidding
and number of bids. These determinants can be classified into three groups according to their source:
(1) Personal factors - these factors include perceived risk (Ha 2002; Resnick et al. 2003), internal
independent estimates (Chakravarti et al. 2002; Dholakia and Soltysinski 2001), pre-purchase
information search (Ha 2002; Hoyer and Brown 1990), level of experience in previous auctions
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(Dholakia et al. 2002; Hayes et al. 1995; Shogren et al. 2000; Wilcox 2000), information and learning
(Hoffman et al. 1995; Jeitschko 1998) and enjoyment and thrill derived from the participation
(Herschlag and Zwick 2000). These factors affect the price a bidder is willing to pay.
(2) Environmental factors – these include the auction mechanism and rules (Pinker et al. 2003) such
as: initial price, reserved price, bid increment and auction ending rule. These factors affect the price,
time of bidding and the number of bids.
(3) Social factors – these factors include people who are physically or virtually present. These factors
also affect the price, the time of bidding and the number of bids. Social factors are composed of three
sub-groups:
(3.1) Other bidders – the history of other bidders' behavior may provide valuable
information. The behavior may also be perceived as having greater credibility than selleroriginating content. For example: it was found that herding bias is negatively related to
the bid price (Dholakia and Soltysinski 2001). The number of bidders at the auction may
also affect bidding behavior and have a negative effect. The 'winner's curse', a term
which was coined while analyzing loss of profits of winners at auctions is explained by
the rules of an auction - the prize goes to whoever has the most optimistic view of the
value of the object being bid upon. In some cases, this implies that the winner is the
person who has overestimated the most. A larger number of bidders implies higher
winner's curse (Andreoni and Miller 1995; Kagel and Levin 1986).
(3.2) The seller – a large body of empirical studies on effects of seller's reputation have
been undertaken in the last few years (for overviews see: Dellarocas 2003; Resnick et al.
2003). It was found that seller’s past history and feedback rating affect the probability of
the sale as well as the price paid. History and feedback ratings have a measurable effect
on the auction price (Lucking-Reiley et al. 2000).
(3.3) Other people – these are people who are not involved directly in the auction but
may be the cause for participating in the auction, such as family members and other
consumers.
While this classification identifies the influencing factors, a range of variables measures bidding
behavior:
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(1) The decision to participate in a specific auction (Dholakia and Soltysinski 2001;
Dholakia et al. 2002; Lucking-Reiley et al. 2000) and the number of bidders in a
specific auction (Wilcox 2000).
(2) Times of entry and exit (Bapna et al. 2004; Bapna 2003; Dholakia et al. 2002).
(3) The values of last and winning bids (Bapna 2003; Bapna et al. 2001, 2004; Dholakia et
al. 2002; Lucking-Reiley et al. 2000; Resnick et al. 2003; Wilcox 2000).
(4) Number of bids (Bapna et al. 2004; Dholakia et al. 2002; Resnick et al. 2003; LuckingReiley et al. 2000).
Social factors presented earlier are generated by other people who are physically or virtually present
at the auction site.
Contemporary consumers, including auction participants, are exposed to a great amount of
information through both offline and online advertising. Interpersonal information in the form of WOM
(Word-Of-Mouth) is one of the oldest mechanisms in the history of human society. WOM has been
identified as an influencing factor on consumers both in traditional and online environments
(Dellarocas 2003; Sylvain and Nantel 2001). WOM is used to eliminate risk involved in e-commerce
transactions (Ha 2002) and it forms a basis for various online reputation mechanisms.
In the next paragraph we explore the type of influence that is induced by other bidders and discuss
differences in the social influence between traditional (brick and mortar) and CMC (ComputerMediated Communication) environments.
2.3
Social influence in traditional and CMC groups
To classify the type of influence imposed by other bidders we need to explore the context in which
this influence arises and examine the conditions favoring each type. We begin with a discussion of
social influence theory in traditional groups followed by social influence in CMC groups. Then, we
examine relevant studies of social influence in traditional and online auctions.
Groups are of interest in the fields of marketing, consumer studies, and other applied research
because an individual consumer belongs to several groups and is influenced by others. Behavior in
groups is often more readily predictable than that of individuals (Foxall et al. 1998). The
interdependence of group members is made enduring by the evolution of group identity that integrates
beliefs, values and norms of the group. A reference group, in its social context, is any individual or
collection of people whom an individual uses as a source of attitudes, beliefs, values or behavior
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(Wilkie 1994). The group is used by the individual as a source of comparison for behavior, personal
attitudes and performance. There are extensive implications of reference groups for consumer behavior
since a reference group may influence the individual. One example is of purchases that are subject to
group pressure.
Membership in a group involves accepting a degree of conformity. Conformity also occurs through
the referent power of groups. An individual who conforms to a group gives the group the power to
influence her actions. The group acts as a standard of social comparison or as a point of reference. The
individual compares her behavior to that of the group and adjusts to match what is observed.
Sources and process of social influence are explained by two general theories: normative and
informational influence (Bearden and Etzel 1982; Kaplan 1987; Latane 1981). Normative and
informational influence models differ in the underlying mechanism of persuasion that they propose and
in assumptions made about human nature. Normative influence presupposes an emphasis on the group
and on individual’s position in it. The motivation for normative influence is based on social rewards
and is centered on interpersonal relations. Informational influence emphasizes the task concerns and is
driven by individual’s desire to arrive at accurate or correct decisions. If a change or a shift in the
decision occurs, it is caused by the incorporation of new data that was provided to the individual. When
both models are introduced, studies show that informational influence is more common than normative
influence and causes stronger shifts (Kaplan and Miller 1983).
Social Impact Theory (Latane 1981) proposes that social influence is a function of the strength (S),
the immediacy (I), and number (N) of the people (or sources) present. Despite the lack of direct
physical contact or immediacy, even mediated groups can be very real to their members (Postmes et al.
1998, 1999; Sudweeks et al. 1998). Social influence in CMC groups is at the focus of many studies.
These investigate how limitations of social interaction, when using CMC, affects group communication
and decisions (Kiesler and Sproull 1992; Rafaeli 1988; Spears and Lea 1992; Sproull and Kiesler
1991). It was found that content and form of communication within a CMC group is normative and that
conformity to group norms increases over time (Postmes et al. 2000). While early theories suggested
that face-to-face interaction strengthen the interpersonal bonds that transmit social influence, whereas
isolation and anonymity weaken them, more recent theories including the Social Identity model of
Deindividuation Effects (SIDE) (Reicher et al. 1995) predicts that relative anonymity in CMC group
can enhance the influence of group norms.
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Bidders aggregated or congregated with other bidders present at an auction share some
characteristics of a group, though of course this group is not a community. A collection of individuals
who congregate at an auction has a limited life span. The individuals are present at the same place,
have a similar goal, and are influenced by one another. Earliest research insights in the literature about
auctions claimed that a bidder should study the past behavior of competitors (Friedman 1956). Other
auction literature emphasized the importance of learning and being aware of the influence of other
bidders (Kagel 1995; McAfee and McMillan 1987; Milgrom 1989; Smith 1989; Wilson 1992). The
influencing process in auctions is strengthened as the vast majority of auctions contain a CV (Common
Value) component. Items sold in CV auctions do not have a definite market value. It is a situation
where the individual may need to refer to other bidders' estimations. The individual may also change
valuation due to the influencing power of others.
Research on auctions in the digital market reported similar observations. Despite the anonymity,
and only weak sense of a group, bidders are influenced by one another (Chakravarti et al. 2002;
Herschlag and Zwick 1999; Dholakia and Soltysinski 2001; Dholakia et al. 2002; Jeitschko 1998;
Sylvain and Nantel 2001; Varian 2000; Wilcox 2000). This influence occurs despite the limited ability
to receive social cues in online auctions.
Social cues which consist of verbal and non-verbal signs provide signals to behavior in face-to-face
environments and are replicated in some online environments (as will be discussed later).
Though online auctioning is tremendously popular and both direct and indirect social interactions
are part of traditional auctions, internet auction sites do not provide virtual presence and real-time
interaction capabilities. One reason for limiting communication and interaction among bidders during
the auction is a concern about an increase in fraud.
Absence of social cues in online auctions may limit bidder's ability to analyze behavior of other
bidders and make improved decisions. In the next paragraph we discuss theories about social cues in
different media and describe several online mechanisms used today to enhance social cues in CMC
environment. Later we examine effects of incorporating similar mechanisms in an online auction
framework.
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2.4
Enhancing social cues in CMC
Social cues can be enigmatic in CMC, especially in the absence of supporting technologies that
facilitate interaction. Two theories should be addressed in this context: (1) Social Presence theory and
(2) Media Richness theory.
Social presence is a variable in mediated communication (Short et al. 1976). It is defined as the
sense of intimacy and immediacy, leading to increased satisfaction, involvement, task performance and
social interaction (Lombard and Ditton 1997). It affects the way individuals perceive medium and
people whom they receive messages and communication from. The amount of social presence varies
among each type of medium. While face-to-face yields a high level of social presence, computers have
been found to have less social presence than other mediums due to an absence of nonverbal cues
(Papacharissi and Rubin 2000). Social presence has two dimensions related to intimacy (interpersonal
versus mediated) and immediacy (asynchronous versus synchronous). Social presence theory predicts
that several types of CMC can create in users a sense of intimacy and immediacy.
A medium’s richness is measured by its capacity for immediate feedback, multiple cues, language
variety and personalization (Daft and Lengel 1986). Media affect the process of communication and
collaboration between people in different ways due to which modalities it supports. It has been argued
that media differ in their capacity to carry data that is rich in information (Daft and Lengel 1986; Rice
1993; Short et al. 1976). Social presence ranking depends on an interaction between the medium and
the task at hand, and is based on the subjective judgment of the user (Lombard and Ditton 1997).
In the years since the formulation of the medium richness approach, technology’s evolution has
contended with the challenge of limited social cues of computers. In contrast, recent were marked by
the emergence of numerous communication and visualization technologies that enhance person-toperson interaction. These new arrangements include but are not limited to Instant Messaging (IM),
interpersonal interfaces, peer-to-peer, streaming audio, Voice over IP, video and much more.
With the incorporation of human or ‘human like’ faces into mediated environments the participants
gain a stronger sense of their community (Donath 2001). People react differently to a human-like
interface. They are more aroused and present themselves in a more positive light (Sproull et al. 1996).
Mediated entities, as social actors, have been introduced in computers just after television (Lombard
and Ditton 1997). Microsoft, for example, presented ’Bob’ which featured several characters and other
products, tools and components with social interfaces and human like appearance such as ‘Office
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Assistant’ and ‘Microsoft Agent’. However, we should not overlook the distinction between humanlike impersonations of real people, and those of imaginary entities.
Numerous social and personalizing technologies were adopted by educational and e-commerce
sites. To name a few: BuddySpace1 provides enhanced presence management in IM and other contexts
by presenting location maps and visual representations of the parties in conversation. MSN messenger 2,
a popular IM tool enables users to add their picture and even conduct video chats. LivePerson3 offers
online salesperson assistant tools each with intuitive ability to chat with a customer. OddCast 4 creates
broadband interfaces. Included are realistic, computer-generated ’spokespeople’ who guide users
through a variety of tasks (Berman 2003). OddCast found that talking characters enhance users'
experience to positively improve most key business metrics, such as: ‘click through rate’, ‘visitors
converted into registrants’, ‘conversions’ and ‘traffic’. Streaming animation of a face and other
characters is also used in mobile technologies.
Although high bandwidth connections have eliminated many technical barriers to making CMC
features real world appearance, a large body of research (including several research groups5) has
focused on the presentation of participants and social interaction in a minimalist way. Interaction maps
(Baker 2002), visualization of crowds (Erickson et al. 2002; Minar and Donath 1999), conversations
(Donath et al. 1999; Erickson et al. 1999) and other online activities (Donath 1995; Erickson et al.
2002) are a few examples.
3
Research Questions, Conceptual Model and Hypotheses
Earlier, we described evidence of social influence in traditional and in internet auctions. Of course,
traditional and internet auctions are very different. In internet auctions, due to the lack of interaction
among bidders, a participant is limited to relevant information that is transmitted by the explicit
behavior of other bidders. While interaction technologies have been adopted in various e-commerce
domains, online auctions have been left out. Our research turns attention to the following question:
How does social presence of bidders in online auctions affects strategic bidding behavior? The term
'strategic bidding behavior' refers to decisions and activities made during an auction, such as the
decision to participate in a specific auction, time of entry and time of exit from the auction, the highest
bid and number of bids a bidder submit
Studying this question will provide better understanding of the impact of social influence in CMC
environment and will test the SIDE model in a competitive environment as online auctioning.
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Additionally, by analyzing the influence of the underlying components of social presence and by
utilizing the 'Media Richness theory' and 'Social Presence theory', it will provide recommendation to
constructing richer online environments.
The conceptual model presented in Figure 1 correlates between three groups of influencing factors
that were identified earlier: (1) Personal factors that characterize the individual bidder as: perceived
risk, internal independent estimate and level of enjoyment from the auction; (2) Environmental factors
which include auction's mechanism and rules; and (3) Social factors: relating to other people who are
physically or virtually present during the auction such as other bidders, the seller and other influencing
people. These factors, and especially social factors, generate social influence on the individual bidder
which may have both normative and informational constructs and some other types of influence. As a
result of the influence, strategic behavior of the bidder, which is demonstrated by: selection of the
auction site and the specific auction, time of entry and exit from the auction, her bids and number of
bids, may change.
-- INSERT [Figure 1] Here --
Social cues can be generated by multiple sources. In this study we focus on Virtual Presence
because of its role in the 'Social Presence theory', the 'Media Richness theory' and the SIDE model. A
second source of social signals is Interpersonal Information which may be generated by consumer's
Word of Mouth (WOM).
In this study we use these two sources as independent variables that may generate normative and
informational influence. We are interested in finding the effects on three dependent variables that
comprise the ‘strategic bidding behavior’ construct: (1) Purchasing decision – the purchasing decision
variable gauges bidders’ eagerness to succeed in the auction; (2) High bid – the price a bidder is willing
to pay for the item; (3) Number of bids – submitted by a bidder throughout the auction. This variable
reflects the level of involvement in the auction (Bapna et al. 2004).
These three variables have been identified as measures of bidding behavior in an earlier section. We
intentionally held constant two other (real life) variables: (1) Time of entry; and (2) Time of exit from
the auction, as we wanted to control the exposure time to the manipulation of the independent
variables.
The research model presented in Figure 2 utilizes Virtual Presence and Interpersonal Information
as the independent variables and Purchasing Decision, High Bid, and Number of Bids as the
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dependent variables. Social Influence Theory forms a relationship between the independent and
dependent variables, while 'Social Presence theory', 'Media Richness theory' and the SIDE model are
used as part of the explanations for the hypotheses.
-- INSERT [Figure 2] Here --
We assume that in settings with higher levels of Virtual Presence or Interpersonal Information a
bidder may demonstrate:
(a) A stronger willingness to make a purchase. The desire to win the auction and make a purchase
may increase due to the following reasons. First, in setups with higher levels of Virtual
Presence or Interpersonal Information additional data becomes available and some
uncertainties about the good and its qualities, about the price and the trust of other bidders and
the seller, are removed. Second, a higher level of information is related to a lower perceived
risk (Ha 2002; Resnick et al. 2003). Third, higher level of Virtual Presence of other bidders
may be perceived by the individual as having a bigger crowd at the auction and may drive
competition, which sometimes leads to a winner's curse (Andreoni and Miller 1995; Kagel and
Levin 1986). And finally higher level of Social Presence increases enjoyment, involvement
and thrill from the auction which may increase the willingness to win. Hence we hypothesize:
H1(a):
Purchasing Decision is more likely to occur when participating in an
auction with a higher level of Virtual Presence of other bidders.
H2(a):
Purchasing Decision is more likely to occur when participating in an
auction with higher level of Interpersonal Information of and from bidders.
(b) Paying lower prices. Two explanations lead to this hypothesis. First, the ability to analyze
social cues transmitted by other bidder and the addition of information provided by
interpersonal sources can improve decision making process during the bidding phase. In this
situation a bidder may examine other's strategic behavior from a different point of view and
can arrive at an improved strategic behavior in order to win the auction but avoid a winner's
curse. A second explanation which is derived from the SIDE model is that anonymity may
obscure individual inputs and thereby enhance the salience of the group and of its norms.
SIDE model predicts that in CMC settings with high level of social presence, the social
influence will be weaker compared to setups with relatively low level of social presence,
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When the level of social presence is higher a bidder will feel confidence in her evaluation and
be less dependent in others’ valuation. In this situation a bidder will ignore other's valuation
when it is higher then her own valuation. The formal hypotheses are:
H1(b):
Winning at lower prices is more likely to occur when participating in an
auction with higher level of Virtual Presence of other bidders.
H2(b):
Winning at lower prices is more likely to occur when participating in an
auction with higher level of Interpersonal Information of and from bidders.
(c) A tendency to bid less often. This outcome is explained by two main justifications. First, even
if other participants submit a high number of bids, the bidder may ignore it and rely on her
own considerations. When the level of social presence is high, the bidder has a lower need to
follow other's strategic behavior due to a removal of uncertainties and the integration of
additional information provided by interpersonal sources,. In this situation the bidder will
submit fewer bids compared to a situation where she has a higher need to follow other's
behavior. Second, at setups with a high level of social presence a bidder will try to hide her
valuation of the item by submitting less bids, as much as possible, since in this setup she may
perceive other bidders more realistically. The hypotheses derived from these arguments are:
H1(c):
When participating in an auction with higher level of Virtual Presence of
other bidders a participant will likely post fewer Number of Bids.
H2(c):
When participating in an auction with higher level of Interpersonal
Information of and from bidders a participant will likely post fewer
Number of Bids.
Auction theorists identify four basic auction models: First Price Sealed Bid, English, Second Price
and Dutch (McAfee and McMillan 1987; Milgrom 1989). Recent internet auction research has focused
on eBay participants. eBay uses a hybrid of the English and Second Price sealed bid formats. Other
auction formats are often overlooked in recent studies but are common in traditional auction research.
We chose to test our hypotheses in the two contexts of English and Dutch formats.
English and Dutch auctions differ in their underlying bidding rules. An English auction is defined
by an ascending price mechanism and an ability to adjust bids during the auction. A Dutch auction has
a descending price mechanism with only one bid allowed. However both are characterized by their
dynamic format: the ability of participants to react during the auction by posting a bid. We assume that
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bidding behavior in these two auction formats will slightly vary due to higher levels of involvement
and ability to adjust bids in the English auction. The formal hypotheses are:
H3(a):
Virtual Presence will have a higher effect on bidders in an English auction
compared to the effect on bidders of a Dutch auction.
H3(b):
Interpersonal Information will have a higher effect on bidders in an English
auction compared to the effect on bidders of a Dutch auction.
During the auction several output variables can be measured. Table 1 correlates between these
variables and the dependent variables in each of the two auction models.
-- INSERT [Table 1] Here --
4
Method
We test the hypotheses by conducting two empirical experiments: one for each format. Each
experiment included several sessions where human participants competed with simulated bidders in a
simulated online auction. Data pertaining to the hypotheses were collected during a series of eight
meetings. To improve reliability of the measures, a pre-test was carried out to detect any necessary
changes in the wording of the instruction pages, in the display and in the operation of the auction
software.
4.1
Participants
Participants were undergraduate business and MBA students in Israeli universities. Participation
was voluntary, but class credit points were offered as inducement to the best performers in each
experiment. Some of the participants performed the experiments online via a home connection, while
others performed the experiment in a classroom computing laboratory. No differences were found
between location groups. A total of 188 students participated in the English auction experiment and
123 students participated in the Dutch auction experiment.
4.2
Experimental procedure
A 2x2 design was used in each experiment. Each independent variable: Virtual Presence and
Interpersonal Information, was tested in two levels: high and low. While at the highest level presence
or information components were included in the setup, at the low level they were absent. This research
design generated four conditions (Condition 1-4) as presented in Table 2
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-- INSERT [Table 2] Here -Figure 3 and Figure 4 present a screen shot of part of the conditions.
-- INSERT [Figure 3] Here --- INSERT [Figure 4] Here -The two experiments were identical in their design. Participants were randomly assigned to one of
four Presence and Information conditions and each trial was composed of four or more phases: (1)
Instructions; (2) Trial session; (3) First auction session; (4) Second auction session; and (optional) (5)
Additional auction sessions. The total number of auction sessions a participant would experience was
not limited and varied between participants. Every session included participation in an online auction
taking 2 - 3 minutes and followed by a period that allowed viewing results. The only difference
between the two experiments was in the underlying auction mechanism.
Experiments were constructed entirely online to reduce experimenter availability or interference. In
order to eliminate external distraction participants were instructed to surf only to the auction site and to
avoid any other internet related activities throughout their participation. During the experiment
participants received online feedback about their results after each session. Results included a list of all
bids placed during the session in descending order. However participants were not informed about the
value of the item and did not know whether the winning bid is relatively high or low. Final results
comparing the winning bids to a market value (which was the mean of all winning bids) included
names of the best performers. These were presented following the completion of the assignment.
4.2.1
Auction rules
No real or artificial money was used, no real payment was made and no real item was awarded to
the winner. Winning determination was based on buying at lower prices relative to an unknown market
value. In every session a single item (an antique watch) was ‘sold’. This item has a high CV component
as its market price was unclear and participants bought it for trading purposes. In the English auction
experiment participants joined a seemingly already ongoing auction. The auction ending rule was
‘hard’ (meaning the that the ending time was pre-defined) and a bidder with a highest bid at the end
‘won’ the item and ‘paid’ according to this bid. As the auction progressed, five simulated bidders
joined the arena and actively participated in the auction. Each of the joining simulated bidders was
endowed with an independent predefined bidding strategy and a price limit higher than the initial price.
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These characteristics of the simulated bidders were interpreted by the simulation software into a
sequence of bids (in the English auction) until they reached their price limit.
In each session of the Dutch auction participants joined an auction just before it started and initiated
it by pressing a button. Five additional simulated bidders also participated in the auction, each having
an independent price limit. Upon reaching this limit, the simulated bidders were programmed to post a
bid.
4.2.2
Auction simulation framework
The experiment tool was an auction simulation framework program developed for research and
teaching purposes (Rafaeli et al. 2003). This framework is composed of software components operating
on both client and server computers and simulating an internet auction site, auction mechanism and
some of the auction bidders. The client side of the framework was implemented in Java, HTML pages
and JavaScripts, operating in any Java enabled internet browser. The server’s components include
CGI/Perl scripts and operated on an Apache Web sever.
4.2.3
Simulation of other bidders
All simulations are based on a simplified but accurate representation of some aspects of the real
world (Maidment and Bronstein 1973). A use of a simulation program in our context enabled us to
control the underlying behavioral characteristics of the simulated bidders. Nevertheless, these were not
independent variables and were not manipulated.
Developing a social simulation that models behavior in a social context is harder than a
development of a physical simulation. There is no accurate description of the behavior and behavior of
people and groups can’t be predicted because of the numerous variables with which to contend. For the
purpose of this study we simplified the characterizing variables to a minimal set which includes two
major groups. The first group of variables controls bidding strategy, while the second group controls
the interaction of the simulated bidders via a chat mechanism. The simulated bidders were programmed
to have a similar bidding strategy in all experimental conditions; however their visual appearance and
interaction abilities were dissimilar. The bidding strategy was based on a maximal price limit which
was assigned randomly to each of them and on predefined parameters which controlled their response
time and willingness to react to a previous bid and post a higher bid during different phases of the
English auction.
Page 17
4.2.4
Social and informational cues
Four conditions were created as two levels of Virtual Presence and two levels of Interpersonal
Informational. Cues were implemented by inducing different components to the auction site. Virtual
Presence components included:
(1) Pictures of other bidders’ faces - As other bidders joined the arena, their pictures were
shown on the screen.
(2) Instant messaging – simulation of an online chat room. Since other bidders were simulated,
this chat process was simulated too. Messages ostensibly posted by simulated bidders were
displayed and the participant could send messages of their own. A very simple feedback
mechanism was implemented to respond to these sentences (Rafaeli and Noy 2002),
preserving the illusion of a chat room.
(3) Auction proxy – An auction proxy was constructed based on Erickson (2001) who visualized
a socially translucent traditional bidding room by minimal graphical representation. A
bounding circle represents the bidding room and bidders are represented by color dots which
changed according to their position in the auction and according to others' bids.
(4) Additional presence cues - displaying the number of the previous interested bidders,
displaying names of the bidders near their bids (instead of presenting a general data such as:
‘Bidder #4’).
The Interpersonal Information components were:
(1) Names of other bidders – these are rarely presented in real auction sites.
(2) Bidders’ information – Auction sites provide data about the reputation of the seller and
sometimes of the buyer. The simulation framework used here provides additional data about
the bidders: (1) Type of participation which was classified into: business or private
participation, indicating the intentions and the eagerness to win; (2) Past winning history; (3)
Experience level, which was classified to either professional or amateur levels; (3) Bidding
interest – level of interest in the current auction, which was classified to: high, medium and
low levels.
Page 18
(3) Recommendations – During auction sessions, interpersonal recommendation about the
auction and the item on sale were presented to the participants.
5
Results
Data analysis consisted of classifying auction sessions into groups and comparing the independent
results of the first auction session of each participant. The additional auction sessions contained a
reduced amount of data and were not independent. Auction sessions were classified into one of the
following groups according to the condition.

LP (Low Presence – low level of Virtual Presence) – Conditions 1 and 3

HP (High Presence – high level of Virtual Presence) – Conditions 2 and 4

LI (Low Information – low level of Interpersonal Information) – Conditions 1 and 2

HI (High Information – high level of Interpersonal Information) – Conditions 3 and
4
Hypotheses were tested by comparing results in LP and HP groups, and in LI and HI groups in both
English and Dutch setups. To test differences in the ‘High Bid’, ‘Win Bid’, ‘Num of bids’ and ‘Num of
bids if won’, four separate one-way ANOVA (Analyses Of Variance) were performed. Difference in
the ‘Wins’, ‘Bids Zero’ and ‘Continue’ variables were measured by performing four separate ChiSquare tests.
Hypotheses H1(a) and H2(a) were evaluated through the results of the ‘Wins’, ‘Bids Zero’,
‘Continue’ and ‘High Bid’ variables. The result of each is attributed to a purchasing decision's
component. Participants who offer higher bids and win are more likely to desire the item. In contrast, if
a participant is involved in an auction without placing a bid, she may exhibit lower buying intentions.
The ‘Continue’ variable provides supplementary indication about a willingness to continue to an
additional auction session, and hence, is also attributed to a purchasing decision's components.
Hypotheses H1(b) and H2(b) were evaluated through the ‘Win Bid’ variable. Hypotheses H1(c) and
H2(c) were evaluated through the ‘Num of bids’ and ‘Num of bids if won’ variables.
5.1
English auction results
188 different bidders participated in 734 different auction sessions. Four additional sessions were
disqualified and dropped from the data set due to extreme outlier, inconsistent and erroneous data.
Some of the participants quit after a single session and some played up to 26 auction sessions. Overall
Page 19
there were 3.9 sessions on average per participant. Participants won in 473 sessions (64.44%) and lost
in 230 (31.33%). In 31 auctions (4.22%) no bid was submitted. The range of winning bid prices was
290$-1000$ and market value (mean winning bid) was 517.73$. Up to 18 bids were posted in each
session with a mean number of 3.31 bids. Winning bidders posted up to 16 bids with a mean of 3.22
bids.
In their first auction session 109 (57.97%) participants won and 79 (42.02%) lost. Mean winning
bid was 504.30$ and mean number of bids of a participant was 4.71 bids per session.
5.1.1
Effects of Virtual Presence and Interpersonal Information
Results strongly support H1(b) and partially support H1(a). Virtual Presence created significant
difference in ‘High Bid’ (see Figure 5). HP (High Presence) participants produced a ‘High Bid’ lower
by 12.34% than LP (Low Presence) participants (F(1, 186)=7.35 p<0.01). A significant difference of
20.86% was found in the ’Win Bid‘ variable (F(1,107)=21.01 p<0.0001).
Results strongly support H2(b) and partially support H2(a). Interpersonal Information did not
produce any significant difference in the ‘High Bid’ parameter. However, a significant difference was
found in the ’Win Bid‘ parameter. HI (High Information) participants won at 10.19% lower prices than
LI (Low Information) participants (F(1,107)=6.41 p<0.01). These results are presented in Figure 5.
- INSERT [Figure 5] Here HP participants posted 30.58% fewer bids (‘Num of bids’) than LP participants (F(1,186)=9.78
p<0.005). Winning participants showed the same tendencies. ‘Num of Bids if won’ in HP was 25.96%
lower than in LP (F(1,107)=3.95 p<0.05). HI participants posted 8.10% fewer bids than LI participants
(insignificant). Winning bidders at both HI and LI placed almost the same number of bids (see Figure
6).
-- INSERT [Figure 6] Here -HP participants won 26.80% more times than LP participants (t=5.37 p<0.05). LP participants
’Continue‘ 8.02% more times to the next session than HP participants (insignificant). The number of
participants that ’Bid Zero’ in the two conditions was almost identical (five in HP, four in LP). HI
participants won 9.63% more times than LI participant (see Figure 7). LI participants ’Continue‘ to the
subsequent auction session 7% more times than HI participants and the number of participants who did
not bid at all was almost identical. None of these differences are significant.
-- INSERT [Figure 7] Here --
Page 20
Regarding the Virtual Presence effect; results strongly support H1(b) and H1(c), namely that with
higher level of Virtual Presence of other bidders, winning is expected at lower prices and with less
bids. Results partially support H1(a) (’High Bid‘ and ’Wins‘ percentage were significantly higher in
HP but ’Continue‘ and ’Bid Zero‘ results - were not), namely that with higher level of Virtual
Presence winning may, but will not necessarily be achieved.
Regarding Interpersonal Information effect; results strongly support H2(b), namely that with
higher level of Interpersonal Information of and from bidders, winning is expected to occur. On the
other hand results provide less support to H2(a), namely that with higher level of Interpersonal
Information winning bids may occur in lower price. H2(c) was rejected, namely that with higher
levels of Interpersonal Information a bidder does not necessarily post fewer bids.
5.2
Dutch auction results
123 different bidders participated in 426 auction sessions. Some of the bidders participated in a
single session and some continued playing up to 37 sessions for a single participant. The mean number
of auction sessions per participant was 3.46. Participants won 268 (62.91%) sessions and lost 158
(37.08%) sessions. In their first auction session 78 (63.41%) participants won and 45 (36.59%) lost.
The range of winning bids was 268$-997$ and market value was 509.75$.
5.2.1
Effects of virtual presence and interpersonal information
Results strongly support H1(b) and partially support H1(a). Virtual Presence produced significant
difference in the ’Win Bid‘ (see Figure 8). HP participants won at 19.40% lower bids than LP
participants (F(1, 76)=7.1 p<0.01). Interpersonal Information registered an opposite effect. Results
partially support H2(a) but did not support H2(b). HI participants won at 4.24% higher prices than LI
participants (this difference is not statistically significant).
-- INSERT [Figure 8] Here --
HP participants won significantly more times (t=4.15, p<0.05) than LP participants (32.07%). They
’Continue‘ 9.32% fewer times to the next auction session (insignificant). HI participants won more
times (57.42%) than LI participants (t=10.56, p=0.0012). They also exhibited a higher tendency
(7.49%) to participate in an additional auction session, than LI participants (see Figure 9).
-- INSERT [Figure 9] Here --
Page 21
Results regarding Virtual Presence effect provides strong support for H1(b) and partial support for
H1(a) (the ’Wins‘ percentage was significantly higher in HP, but HP participants ’Continue‘ fewer
times than LP participants). Results regarding Interpersonal Information effect provides some support
to H2(a) (‘Wins‘ was significantly higher at HI and ’Continue‘ was only higher) but H2(b) was
rejected.
5.3
English and Dutch auction comparison
Hypotheses H3(a) and H3(b) received partial support. The change in the winning percentages (%
Wins) due to Virtual Presence is higher in the English auction compared to the Dutch auction. The
change in Win Bid due to Virtual Presence is higher in the Dutch auction compared to the English
auction. The change in the winning percentages (% Wins) due to Interpersonal Information is higher
in the Dutch auction compared to the English auction. The change in Win Bid due to Interpersonal
Information in the Dutch auction is in a reverse direction compared to the English auction.
Table 3 summarizes the hypotheses and whether they received any support (significant or partial) or
were rejected.
-- INSERT [Table 3] Here --
6
Discussion
Though online auctions are popular and have operated successfully without enabling social
interaction among bidders, it has been observed that both direct and indirect communication is part of
traditional auctions and may influence bidding behavior.
In this study we found evidence that the introduction of direct and indirect communication among
bidders in internet auctions as expressed in Virtual Presence and Interpersonal Information produced
social influence that affected bidding behavior in both English and Dutch auctions.
Results strongly support hypotheses H1(b) and H1(c) and provide strong support to some of the
constructs of H1(a) in the English auction. ’High bid‘ and ’number of bids‘ of both participants and
winners were significantly reduced due to Virtual Presence effect.
Participants’ eagerness to succeed (as measured by ‘Wins’, ‘Bids Zero’ and ‘Continue’) was
strengthened due to an increase in the winning percentage, but the number of participants who did not
place a bid was not reduced, and ’Continue‘ percentage was not increased. Taking into account that
Page 22
only a negligible number of bidders did not place a bid (4-5 participants), and that continuation is not
an implicit determinant of purchasing decision, since satisfaction with the current settings may lead to a
decision not to leave the state; we can conclude that Purchasing Decision was also affected by the
virtual presence. The results of the Dutch auction experiment provide full support for H1(b) and partial
support for H1(a).
When introducing Interpersonal Information cues, winning bid was reduced in the English setting,
thus H2(b) received strong support. Bidders did not submit fewer bids, thus H2(c) was not supported.
H2(a) was partially supported. Percentage of ’Win‘ was increased and the percentage ’Bid Zero‘ and
’Continue‘ were decreased. These findings allude to a presence effect, but were not significant. In the
Dutch auction settings ’Win‘ percentage was significantly affected by Interpersonal Information
providing some support to H2(a), but H2(b) was not supported, namely ‘Win Bid’ was not reduced.
Results provide some support for H3(a) and H3(b). The difference in the percentage of ‘Win’ due to
Virtual Presence was higher in the English auction than in the Dutch auction. However, difference in
‘Win Bid’ was higher in the Dutch auction. The difference in the percentage of ‘Win’ due to
Interpersonal Information was higher in the Dutch auction but the difference in ‘Win Bid’ was higher
in the English auction.
The effect of Interpersonal Information was not as pronounced as the effect of Virtual Presence.
One possible explanation is that most of the participants were inexperienced bidders. Studies of
experience in internet auctions realized differences between experienced and novice bidders (Andreoni
and Miller 1995; Dholakia et al. 2002; Wilcox 2000). The firsts are more likely to appreciate
information and informational cues than inexperienced bidders who (in general) are more emotional.
This explanation is supported by the observation in the literature that informational influence is more
cognitive than normative influence and less emotional (Kaplan and Miller 1983). Another explanation
lies in the enthusiasm with facial cues and perception of others' presence signals. The addition of these
may have been deemed more attractive at earliest stages of the experiment (first auction session) than
Interpersonal Information which mostly was displayed in a textual manner. To verify this explanation
we analyzed all auction sessions in each experiment. We found that Virtual Presence effect, which
caused lower winning bids and higher tendencies to win, declines as participants became familiar with
the setting and results of the effect becomes comparable to results of the effect of Interpersonal
Information. However there is another explanation for a change in bidders’ outcome overtime.
Page 23
Because participants experienced the auction in consecutive trials they might have learned the bidding
rules of the agents. Once these rules were extracted it becomes easier to play against the agents and
exploit the rules.
Internet auction sites today do not provide means for communication among bidders, probably
because of concerns about fraud. Bidders and sellers may harness technology and use it to win
unethically or unlawfully. Another explanation is that some bidders and sellers may prefer the
anonymity enabled by the internet. However, the inability to interact may damage bidders' decision
processes by limiting information provided by the social environment. Previous research shows that
bidders rely on past behavior of sellers and other bidders. In this study we look one step ahead. We
found that, when provided, Virtual Presence and Interpersonal Information, generate social influence
which affects bidding behavior.
7
7.1
Limitations, Contribution and Further Research
Research limitations
The implications of our findings are of theoretical interest, and they have importance for the design
of online auction systems. These findings are of interest to the understanding of the dynamics of
auctions. They might contribute to the broader study of social networks and the influence flows in
them. Another field that may benefit from this study is the impact of social influence on economic
behavior .
The study suffers from several limitations. First, we use a simulation as the primary research
method. Although participants were not aware of the fact that they competed with simulated bidders,
real-life bidders may act slightly different under human or mechanized contexts. The major reason for
using a simulation was to control for social presence cues. However, a growth in the use of
autonomous bidding agents (i.e. Proxy bidding in eBay) in the past few years (Bapna et al. 2004) may
mitigate this problem. We suggest that field studies will supplement our findings when such cues
become more prevalent in real implementations of online auctions.
A second limitation is that auctions were not about real money or real items. While winning was
encouraged by having credit point incentives, participants had small stakes compared to real-life
auctions. Wilson (1992) and Kagel (1995) addressed the absence of economic risk in experimental
Page 24
settings. Despite the differences between field studies and experiments, results are complementary
(Wilson 1992) and enhance understanding.
Another limitation of the method is not controlling for the experience level of participants. This
limitation was moderated by randomly and evenly assigning participants to each setting and by
exposing them to a new auction setting and rules with which no participant had any prior experience.
7.2
Research contribution
There are numerous theoretical and practical implications of this study. The key theoretical
implication is in demonstrating the social influence determinants in internet auctions. We found that
normative and information influence affects bidding behavior. Our findings support conclusions of the
SIDE model. Relative anonymity of auction bidders enhances the influence of the group valuation. The
study also contributes to an ongoing research in both: marketing, consumer behavior and social
psychology. From a practical perspective the research demonstrates the possibility to construct social
influence even with relatively minimal technological cues. It suggests further directions to commercial
auction sites and other e-commerce initiatives.
7.3
Further research
Social software and the exploitation of communication avenues between anonymous and semi-
anonymous players on the net are growing in popularity and use. This phenomenon is, therefore, of
further concern. We propose further research in several directions. The first is to complement the
experimental paradigm described here with field studies. Can better data regarding informational and
normative influence be collected in real settings? It should also be interesting to extend scope of this
study to other forms of online interaction where a group has a power to influence the individual.
Examples are online games and online gambling settings. We believe that in these settings, because of
the similar influence conditions, influence results will replicate those found here. A third direction is an
extension of the research to other types of e-commerce where the influencing power of the group is
lower, such as electronic stores. In this type of setting social presence of the seller may act as a source
of influence.
Notes
[1] http://kmi.open.ac.uk/projects/buddyspace/
Page 25
[2] http://messenger.msn.com/
[3] http://www.liveperson.com/
[4] http://www.oddcast.com
[5] For example, Social media group at MIT Media Lab: http://smg.media.mit.edu/; Social
computing group at IBM: http://www.research.ibm.com/SocialComputing/; Social Computing
Group at Microsoft: http://research.microsoft.com/scg/
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Page 31
Table 1 – Relationship between variables
Variable
Meaning
Measure
English
Dutch
dependent
auction
auction
+
-
variable
High Bid
Highest bid posted by a participant
Purchasing
decision
Win Bid
Winning bid of a participant
Bid
+
+
Num of bids
Number of bids submitted
Number of Bids
+
-
Num of bids if won
Number of bids submitted by a
Number of Bids
+
-
Purchasing
+
+
+
-
+
+
winner
Wins
Percentage of winning participants
decision
Bids Zero
Continue
Percentage of participants that
Purchasing
didn’t submit a bid
decision
Percentage of participants that
Purchasing
continued to additional session
decision
(+) Was measured
(- ) Was not measured
Table 2 – Conditions of the experiment
Independent variable
Condition 1
Condition 2
Condition 3
Condition 4
Virtual Presence
Low
High
Low
High
Interpersonal Information
Low
Low
High
High
Table 3 – Summary of results
Hypothesis
H1(a) – Stronger purchasing
English Auction
Dutch Auction
Partial support
% Wins: (t=5.37 p=0.02)
Partial support
% Wins: (t=4.15 p=0.041)
% Continue: (t=2.36 p=0.12)
% Continue: (t=3.21 p=0.073)
decision as a result of virtual
presence
% Bid Zero: (t=0.21 p=0.64)
H1(b) – Lower bids as a
Strong support
High Bid: (F(1, 186)=7.35 p<0.01)
result of virtual presence
Strong support
Win Bid: (F(1,76)=7.1
p=0.0094)
Win Bid: (F(1,107)=21.01
p<0.0001)
H1(c) – Fewer bids as a result
of virtual presence
Strong support
Num of Bids:(F(1,186)=9.78
N.A.
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p=0.002)
Num of Bids if won
(F(1,107)=3.95 p=0.049)
H2(a) – Stronger purchasing
Partial support
% Wins: (t=0.63 p=0.43)
Partial support
% Wins: (t=10.56 p=0.0012)
% Continue: (t=1.87 p=0.17)
% Continue: (t=1.81 p=0.1781)
decision as a result of
interpersonal information
% Bid Zero: (t=0.01 p=0.92)
H2(b) – Lower bids as a
Strong support
High Bid: (F(1,186)=0.7 p=0.40)
Rejected
Win Bid: (F(1,76)=0.22 p=0.64)
result of interpersonal
Win Bid: (F(1,107)=6.41 p=0.01)
information
H2(c) – Fewer bids as a result
Rejected
Num of Bids: (F(1,186)=0.49
N.A
of interpersonal information
p=0.48)
Num of Bids is won:
(F(1,107)=0 p=1)
H3(a) – Higher effect of
virtual presence in an English
auction
H3(b) – Higher effect of
interpersonal information in
an English auction
Partial support
The change in the winning percentages (% Wins) due to Virtual
Presence is higher in the English auction compared to the Dutch
auction.
The change in Win Bid due to Virtual Presence is higher in the Dutch
auction compared to the English auction.
Partial support
The change in the winning percentages (% Wins) due to Interpersonal
Information is higher in the Dutch auction compared to the English
auction.
The change in Win Bid due to Interpersonal Information is in a reverse
direction in the Dutch auction compared to the English auction.
Page 33
Figure 1 – Conceptual model
Influencing
factors
Personal
factors
Type of influence
Social influence
Strategic behavior of
auction bidders
Enviromental
factors
Other influence
Social
factors
Figure 2 – Research model
Strategic bidding behavior
Purchasing decision
H1(a)
Social influence sources
H2(a)
Virtual Presence
H1(b)
High bid
H2(b)
Interpersonal
Information
H1(c)
H2(c)
Number of bids
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Figure 3 – Conditions 1 (left) and 4 of the English auction
Figure 4 – Conditions 1 (left) and 4 of the Dutch auction
Figure 5 - High and Win bid in the English auction
Win Bid
580
560
Win Bid
Bids
540
520
High Bid
500
High Bid
480
460
440
HP Participants
LP Participants
HI Participants
LI Participants
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Figure 6 - Number of bids intheEnglish auction
6
Number of bids
Number of bids
Of winner
Number of bids
Number of bids
5
Number of bids
Of winner
4
3
2
1
0
HP Participants
LP Participants
HI Participants
LI Participants
Figure 7 - Wins and continuation percentages in the English auction
100%
% Continue
% Continue
90%
80%
70%
% Wins
% Wins
60%
50%
40%
30%
20%
HP Participants
LP Participants
HI Participants
LI Participants
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Figure 8 - Win Bid intheDutch auction
620
Win Bid
600
Bids
580
Win Bid
560
540
520
500
480
HP Participants
LP Participants
HI Participants
LI Participants
Figure 9 - Wins and continuation percentages in theDutch auction
100%
% Continue
% Continue
90%
80%
% Wins
% Wins
70%
60%
50%
40%
30%
20%
HP Participants
LP Participants
HI Participants
LI Participants
Page 37