Sharing Money To Make Money
Sharing Money to Make Money – Analyzing
Peer-to-Peer Sharing of Referral Rewards
Research-in-Progress
David Dose
Friedrich-Schiller-University of Jena
Carl-Zeiss-Str. 3, 07743 Jena
[email protected]
Gianfranco Walsh
Friedrich-Schiller-University of Jena
Carl-Zeiss-Str. 3, 07743 Jena
[email protected]
Abstract
Analyzing the rapidly growing sharing economy, prior research primarily focused on the
sharing of digital and underutilized goods. However, sharing such goods does not entail any
sacrifice for the giver. In contrast, this study investigates the sharing of money in terms of
referral rewards on peer-to-peer platforms. Sharing money is theoretically relevant as it is a
zero sum game. Using data from two peer-to-peer platforms with different market structures,
we investigate influential factors of peer-to-peer money sharing. The results show that an
increase in the number of recommenders (i.e., people making referrals) creates positive crossside and negative same-side network effects. Contrarily to findings from behavioral economics,
more than half of the recommenders shared more than 70% of the pie with referral receivers.
Reputation is an important competitive advantage for recommenders. The results indicate that
sharing referral rewards on peer-to-peer platforms is beneficial for referring firms,
recommenders and referral receivers.
Keywords: Sharing, referral reward, network effects, reputation, money
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Sharing Money To Make Money
Introduction
Based on peer-to-peer (P2P) platforms that enable people to disintermediate traditional commercial
channels and share resources directly with other consumers, the sharing economy is a rapidly growing
economic-technological phenomenon. In 2013, Pricewaterhouse Cooper estimated that the sharing
economy is worth around $15 billion in global revenues and could represent $335 billion in revenue
worldwide by 2025 (PWC 2014). Yet despite this astonishing growth, and whether the motivation for
sharing is sustainability, utility maximizing, or anti-consumerism, the sharing community is primarily
concerned with the following question: Can we share more? In this research in progress, we attempt to
address this research question by exploring the sharing of money in terms of referral rewards through
P2P platforms.
Prior research particularly focused on two types of resources being shared on P2P platforms. First,
sharing has been studied in the context of digital goods such as information (e.g., Nov 2007), pictures
(e.g., Nov et al. 2010), software (e.g., Ghosh et al. 2002) or music and films (e.g., Giesler 2006; HennigThurau et al. 2007; Zentner 2006). Second, research focused on sharing of underutilized physical goods
and services such as cars or accommodation (e.g., Galbreth et al. 2012; Shaheen et al. 2012). However, in
both cases people share without entailing any kind of personal material sacrifice. Digital goods are
immaterial and sharing them does not reduce anything but it adds value to whatever is being shared
(John 2012). Underutilized goods or services are resources that are, per definition, not entirely exploited
by their owners. They represent unused value and sharing these unwanted or underused goods unlocks
their hidden wealth (Botsman and Rogers 2011; Fremstad 2014). Accordingly, several P2P sharing
platforms such as Airbnb or ZipCar have established viable and profitable business models, where
consumers are empowered to capitalize on their property in order to create personal value, save economic
resources or even to become a micro-entrepreneur. In 2013, people earned an estimated $3.5 billion
worldwide in revenue through the sharing economy (Forbes 2013). The average monthly earning for a
participant of RelayRides, a car rental service in the US, is $250 per month (RelayRides 2015). And users
providing full time service on the platform TaskRabbit can earn up to $10,000 per month (Dervojeda et
al. 2013). Thus, sharing digital and underutilized goods or services is not a zero sum game in which one
person’s gain is equivalent to another’s loss and the net change in benefits is zero (John 2012). Quite the
contrary: sharing digital and underutilized goods provides economic benefits to both the recipient and the
sharer.
In addition to conventional P2P systems, various platforms, such as dealdoktor, praemien-teilen24 or
freebiestuff, allow people to share money in terms of referral rewards. Sharing money on P2P platforms is
particular interesting as it substantively differs from sharing digital or underutilized goods. Sharing
money is an act of distribution and, importantly, a zero-sum game. It requires some kind of sacrifice as it
leaves the sharer with less. Thus, the following important research question arises:
What are important influential factors of peoples’ money-sharing decisions on P2P platforms and what
are the consequent results?
Based on the theory of two-sided networks and the importance of reputation in online transactions, we
analyze data from two P2P money-sharing platforms with different market structures. Our results reveal
that the greater the network on the recommender side 1) the greater the share of the referral reward
offered to the referral receiver and 2) the smaller the average number of acquired customers per firm.
Moreover, our results show that reputation is an important competitive advantage on P2P money-sharing
platforms: recommenders with high reputation acquire significantly more customers while offering a
smaller share of the referral reward. Accordingly, we find that some recommenders outperform others
earning more money through money sharing than their direct competitors. With these results, we take a
first step toward understanding the sharing of money on P2P platforms. However, our findings also
indicate the need for more research into P2P money-sharing.
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Sharing Money To Make Money
Research Background
Referring Firms and P2P Money-Sharing Platforms
Firms are constantly seeking to develop new marketing programs that encourage consumers’ word of
mouth behavior (WOM) (De Bruyn and Lilien 2008; Gremler et al. 2001). An increasingly popular
method of encouraging positive WOM behavior are customer referral reward programs. Customer referral
programs are “a form of stimulated WOM that provide incentives to existing customers to bring in new
customers” (Schmitt et al. 2011, p. 47). Thus, if a referral successfully leads the referral receiver to
purchase the product the recommender receives a reward from the firm in turn for the referral. Customer
referral reward programs are regarded an attractive and effective customer acquisition tool, appearing in
industries ranging from financial services (e.g., Barclaycard offers 125 Euros) to the newspaper (e.g., Der
Spiegel offers 90 Euros) and telecommunication industry (e.g., Deutsche Telekom offers 25 Euros) (Jin
and Huang 2014; Xiao et al. 2011). Naturally, recommendations within referral reward programs occur
within people´s social environment which includes friends, neighbors, family members, acquaintances or
colleagues. However, one can observe an exciting new development: on various P2P platforms such as
dealdoktor, freebiestuff, praemien-teilen24 or bonusdealer people recommend firms to strangers and
share money in terms of referral rewards with them.
The advances in information and communication technologies have enabled the development of online
platforms that promote user-generated content, sharing, and collaboration such as P2P platforms (Kaplan
and Haenlein 2010). The term P2P refers to a specific type of network structure, in which the various
members are connected to one another directly without any central hub (John 2013). Thus, P2P platforms
allow people to disintermediate traditional commercial channels and to effectively share resources with
one another. Accordingly, Sundararajan (2013) states that P2P marketplaces “transcend the simple trade
conducted on eBay, and are instead inventing an entirely new asset-light supply paradigm.” P2P business
models are applied to a variety of industries and sectors, including accommodation, car sharing, staffing
or music and video streaming. P2P money-sharing platforms, to our knowledge, are a relatively new
phenomenon.
P2P money-sharing platforms provide existing customers (recommenders) with the opportunity to
acquire new customers (referral receivers) by sharing their referral rewards. These platforms are similar
in structure and navigation (see Image 1). Before participating in any sharing behavior recommenders
need to register on the platforms providing a user name, contact information and a valid email address.
As part of the registration process, recommenders choose an online pseudonym. This, rather than the full
name is shown to referral receivers when using the platforms. Additionally, recommenders need to
indicate the industry sector and name of the referring firm as well as the total amount of the referral
reward and the share they offer to the referral receivers. When referral receivers search for recommending
customers they first click on the industry sector of interest (e.g., banking). After choosing a specific
industry sector a list of referring firms is displayed. Referral receivers can now click on the firm of interest
and a list of recommending customers is provided as well as the recommenders’ offered share of the
referral reward (see Image 2). For more details or to contact a specific recommender, referral receivers
can watch each recommender’s profile by clicking on his or her pseudonym. The individual profile pages
provide referral receivers with more information about the recommending customers such as gender or
the number of new customers the recommender already acquired for the referring firm. Some
recommending customers additionally communicate their reputation and trustworthiness by linking their
profile to one of their profiles on external websites such as eBay or amazon which contain favorable
reviews. Furthermore, each profile page provides short facts about the referring firm (including a link to
their website) and information about the referral reward program (e.g., total amount of the referral
reward, the share offered to referral receivers). Interested referral receivers can now directly contact the
recommender. Finally, in an offline process, referral receivers become customers of the referring firm and
the referring firm pays the referral reward to the recommender. The recommender now is supposed to
transfer the fixed share of the referral reward to the referral receiver.
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Sharing Money To Make Money
Image 1. Structure of P2P Money-Sharing Platforms exemplified by praemien-teilen24
Image 2. Offers from Recommending Customers on dealdoktor
Recommender-Referral Receiver Interaction – Brief Insights from Behavioral
Economics
As mentioned before, sharing money on P2P platforms is a relatively new phenomenon. However, sharing
money has extensively been studied in behavioral economics, particularly in ultimatum games. In simple
ultimatum games (Güth et al. 1982), a first player (proposer) has an amount of money, from which he
must propose a division between himself and an anonymous second player (the responder). If the
responder accepts the proposed split, the players split the money in the way the proposer suggested. If the
responder rejects, neither player gets any money. Under a strictly utilitarian view of economics, the
proposer would offer the lowest possible amount and the responder would accept. However, various
experiments conducted with different incentives in different countries show that the vast majority of
proposers offer on average 30% to 50% of the total sum; offers below 10% and above 60% of the total
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Sharing Money To Make Money
amount are rarely observed. Respondents usually decline offers below 30% (e.g., Bolton and Zwick 1995;
Camerer 2003; Güth and Tietze 1990; Thaler 1988). In ultimatum games with more than one responder,
so called responder competition, the results differ significantly. With two responders the responders’
share is on average reduced by 16.5% relative to the bilateral case. Thus, the addition of just one more
responder has a dramatic impact on the share of the responders. If three additional responders are added
the share goes down even further and is below 20%. The reason why the responders’ share decreases
when competition increases is that the rejection probability declines with increasing competing
responders (Fischbacher et al. 2005). In particular, if one of the other responders accepts a given low
offer, it is impossible for a reciprocal responder to punish the proposer. Therefore, reciprocal responders
will reject offers less frequently the larger the number of competing responders is. However, we expect a
noticeable change in responder-receiver behavior on P2P platforms. Based on the theory on two-sided
markets as well as the importance of reputation in online transactions, we next develop four hypotheses.
Theoretical Background and Hypotheses Development
Network Effects on P2P Money-Sharing Platforms
In two-sided markets, an intermediary provides a platform enabling two different user groups (i.e.,
recommenders and referral receivers) to interact (Rochet and Tirole 2003). However, previous research
on two-sided markets (e.g., Armstrong 2006; Voigt and Hinz 2015) indicates that the two user groups
exhibit two different kinds of network effects: cross-side network effects and same-side network effects.
Cross-side network exist if an increase in usage by one side of users (e.g., recommenders) influences the
utility of users on the other market side (e.g., referral receivers). Same-side network effects arise if a user’s
utility is affected by the installed user base of his or her own user group (Eisenmann et al. 2006; Lin et al.
2015). On P2P money-sharing platforms, both types of network effects can emerge out of an increasing
number of recommenders or an increasing number of referral receivers. However, we concentrate on
cross-side and same-side network effects arising from an increasing number of recommenders.
Cross-Side Network Effects
On P2P money-sharing platforms recommending customers share parts of their referral reward with
referral receivers. We propose that the economic benefit of referral receivers (i.e., the part of the referral
reward they receive from the recommender) depends on the degree of competition between
recommenders, that is the number of recommenders referring the same firm. Competition, in general,
leads to an increased rivalry and, thus, forces suppliers to increase customers’ utility by lowering prices or
increase product variation (McNulty 1968). If a single supplier in a competitive market situation decides
to increase its selling price of a good, the consumers can just turn to the nearest competitor for a better
price, causing any firm that increases its prices to lose market share and profits. The described market
mechanism is particularly true for two-sided market platforms where buyers can easily switch from one
seller to another in order to increase utility (Li et al. 2010; Rysman 2009). Peters and Severinov (1997)
show that sellers on auction sites compete for announced reserve prices (i.e., the minimum bid for the
auction), particularly with an increasing number of sellers. If a seller increases the announced reserve
price, the number of buyers participating in the auction falls. Accordingly, Anwar et al. (2006) find that
eBay buyers tend to bid on auctions where the standing bid is the lowest and switch to lower priced
auctions to increase their economic benefits by paying less. Thus, as consumers on competitive two-sided
markets can choose between different suppliers, suppliers strive for making better offers than their rivals.
Consumers, however, accrue additional benefits from every additional seller on a platform (Li et al. 2010).
On P2P money-sharing platforms recommenders compete for transactions with referral receivers. With
an increasing number of recommenders competition increases and recommenders are forced to
strengthen their competitive situation by increasing referral receiver’s utility, that is, their share of the
referral reward. Thus, we assume that with increasing competition on the recommender side, referral
receivers enjoy positive cross-side network effects in terms of increasing shares of the referral reward.
H1: The number of recommenders referring one firm positively predicts the share of money given to the
referral receiver.
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Same-Side Network Effects
Network effects can also emerge within one user group, known as same-side network effects. We predict
that an increasing number of recommenders also affects their own user base. However, same-side
network effects on supplier sides are often negative as an increasing number of supplier’s increases
competition and, by trend, reduces individual market shares and profits (Eisenmann et al. 2006; Yoo et
al. 2007). Prior research argued that increasing supplier competition on platforms exacerbates the
competition for buyers and even may deter future sellers from joining the platform (e.g., Kraemer et al.
2012; Li et al. 2010). Accordingly, Wang and Seidmann (1995) show that the participation of more
suppliers generates negative externalities for other suppliers in an electronic data interchange network.
Relatedly, Riggins et al.’s (1994) two-stage economic model reveals that in networks the benefit of
participating suppliers’ decreases as more suppliers join in. We thus propose that recommenders
competing for a given number of potential referral receivers reduce each other’s chances of transacting
with a referral receiver. In other words, every new recommender per firm increases competition and may
snatch away potential referral receivers.
H2: The number of recommenders per firm negatively predicts the on average acquired customers per
recommender (referring the same firm).
Reputation as Competitive Advantage
As people engage in online markets in transactions with counterparts with whom they have had little or
no previous interaction, they typically face transaction risks rooted in information asymmetries and
uncertainty about transaction fulfillment (Bajari and Hortaçsu 2004). As trust mitigates information
asymmetries and reduces transaction specific risks, it is a critical and enabling factor in online
transactions in general and P2P sharing in particular (e.g., Dambrine et al. 2015; Pavlo and Geven 2004).
Analyzing various trust-building mechanisms in online environments, prior research has recognized
reputation as an effective means of signaling trust between strangers and, thus, enforcing cooperation on
online platforms (e.g., Jarvenpaa et al. 2000; Resnick and Zeckhauser 2002). Gefen (2002) as well as
Dellarocas (2003) argue that reputation strengthens the belief that sellers will behave in accordance with
the confident expectations of buyers. Resnick and Zeckhauser (2002) show empirically that a good
reputation on eBay encourages transactions and is predictive of future selling performance. Taken
together, reputation is essential for online transactions, where the parties have little or no prior
experience with another. Applying these insights to P2P money-sharing platforms, we assume that
recommenders’ positive reputation reduces the perceived risk for potential referral receivers. This
reduced risk arises from the increase in assurance that the recommender will complete the transaction as
contracted and transfer the money to the referral receiver. This gives the reputable recommender a
strategic advantage over other recommenders with less reputation.
H3: Recommenders with high reputation acquire significantly more new customers than recommenders
with low reputation.
Furthermore, reputation affects the monetary outcomes of sellers, mainly because a seller’s favorable
reputation increases buyers’ willingness to pay (Ba and Pavlou 2002; Livingston 2005). Houser and
Wooders (2006) show that sellers with better reputations obtain higher prices on eBay. Similarly, Resnick
et al. (2006) report that eBay buyers are willing to pay 8.1% more for goods sold by suppliers with high
reputation. With riskier online transactions, like buying expensive goods where sellers have a higher
incentive to cheat, reputable suppliers are likely to generate an even higher price premium (i.e., the
monetary amount above the average price received by multiple sellers for a certain matching product)
whereas a negative reputation lowers the closing price (Ba and Pavlou 2002). An important reason for the
existence of reputation-based price premiums is the buyer’s willingness to compensate the seller for
reducing transaction risks (Rao and Monroe 1996). Given this research, we assume that referral receivers’
trust in a recommender’s reputation reduces perceived transaction-specific risks, allowing the
recommender to obtain a price premium. More specifically, we propose that referral receivers are willing
to compensate the reduction of transaction risk by accepting a smaller share of the referral reward.
H4: Recommenders with high reputation share a smaller part of the referral reward with referral receivers
than recommenders with low reputation.
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Data Collection and Variables
To test our hypotheses, we collected primary data from two German P2P platforms, namely praemienteilen24 and dealdoktor, in March and April 2014. We choose these two platforms mainly because they
differ in their “market structure”. Dealdoktor is a polypoly with many-to-many recommender-receiver
relations. In contrast, the platform praemien-teilen24 is a local monopoly with only one recommending
customer per firm and many responders. In total, we collected data from 701 recommending customers
from both platforms (praemien-teilen24: n = 137; dealdoktor: n= 564). Our main independent variables
are market type, number of competing recommenders and reputation. Market type is a binary variable
referring to the two different platforms we collected data from. The platform praemien-teilen24 is a
monopoly with only one recommender per firm and many referral receivers whereas dealdoktor is a
polypoly with many competing recommenders per firm and many referral receivers (coding: monopoly =
0; polypoly = 1). The number of competing recommenders includes all recommenders referring the same
firm within the same referral reward program. If the market type is a monopoly, the number of competing
recommenders is 1. Reputation in our dataset is also a binary variable (coding: 0 = no reputation; 1 =
reputation). Some of the recommenders linked their profile on praemien-teien24 or dealdoktor to
external websites (e.g., eBay, amazon) to indicate they received good reviews and project their reputation.
With new online transaction services like P2P money-sharing platforms, where buyers routinely engage
with sellers with whom they have little or no prior interaction, links to websites providing reviews of the
seller are a possibility to increase reputation (Jarvenpaa et al. 2000). With this “indirect reputation” (Miu
et al. 2002, p. 283) or “witnessed reputation” (Sabater and Sierra 2002, p. 478) experiences garnered
from others in the online environment serve as a proxy for the sellers’ reputation. Accordingly,
recommenders indicating their good reviews on eBay or amazon were considered to be more trustworthy
than recommenders without such links. The dependent variables are: percentage of the referral reward
shared with the referral receiver, number of acquired new customers, and average number of acquired
customers per recommender per firm. The percentage shared with the referral receiver is the share of the
total referral reward the recommender offered to the referral receiver. The number of acquired new
customers refers to the total number of new customers a single recommender acquires for a given firm.
We calculated the average number of acquired customers per recommender per firm by dividing the
number of acquired new customers per firm by the total number of recommenders referring the same
firm. Accordingly, when ten recommenders acquire a total of ten new customers for a referring firm, than
on average every recommender acquired one customer. Additionally, we included the recommenders’
gender (female = 0; male = 1), reward size, that is, the total amount of the referral reward the referring
firm is offering, industry type (five dummy variables) and brand strength (strong brand = 0; weak brand =
1) as covariates in our model. In accordance with the platforms structure, we differentiated between the
following six industry types: internet and communication, banking, insurance, news and media, energy,
and others. We transformed the six industry types into five dichotomous dummy variables using the
category “others” as baseline category. Regarding brand strength we differentiate between strong and
weak brands (e.g., Keller 1993; Heath et al. 2000). Following prior research (e.g., Ryu and Feick 2007),
we classified the leading brands in each industry sector being recognized for their high quality as strong
brands (e.g., in the internet and telecommunication sector we classified German Telekom or Vodafone as
strong brands) whereas relatively less-known brands with a moderate quality were regarded to be weaker
brands (e.g., in the internet and telecommunication sector we classified UnityMedia or Fonic as weaker
brands).
Results
We first analyzed a pooled sample using the data from both platforms. The average reward size in this
sample was 36.93 Euros. In total, 14 recommenders were not willing to share their referral reward. More
than half of the recommenders shared more than 70% of their referral reward with the referral receivers.
The most common sharing behavior (n = 136) was between 80% and 90% of the total reward. Eightyseven people offered shares greater than 90% of the total sum. Seven participants were even willing to
give the total referral reward to the referral receivers. These first results indicate that people on P2P
platforms are more willing to share their money than in ultimatum games. People on P2P platforms share
on average more than 20% more than people in simple ultimatum games and nearly 40% more than in
ultimatum games with responder competition. Maybe the lower transaction costs for both parties in
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splitting the reward (on P2P platforms compared to ultimatum games) suppress overly greedy behavior
on part of the recommender.
We then examined the influence of market structure on the sharing behavior of recommenders by
conducting an independent t-Test determining the mean difference between P2P platforms structured as
monopoly and P2P platforms structured as polypoly. In support of H1, the results showed that
recommenders on competitive platforms (ME = 73.56, SE = 17.99) share more than recommenders in
monopolies (ME = 52.10, SE = 18.56), t(699) = -12.44, p < .001. To get further insights into the influence
of recommender competition on sharing behavior we performed additional analyses by using only the
data from the P2P platform structured as polypoly. We conducted a linear regression using the number of
recommenders referring the same firm as independent variable, the percentage of the referral reward
given to the referral receiver as dependent variable and gender of the recommender, reward size, industry
type and brand strength as covariates. The regression showed that competition on the recommender side
(b = .362, t = 5.78, p < .001) influences sharing behavior and explained 20.1% of the variation in sharing
behavior. However, in support of H2 and indicating a negative same-side network effect, the number of
recommenders referring the same firm (b = -.110, t = -2.05, p < .05) negatively influences the average
number of acquired customers per firm (gender of the recommender, reward size, industry type and
brand strength were used as covariates, again). Next, and by using a pooled data sample (data from both
platforms) again, we examined the influence of reputation on the number of acquired new customers and
on recommenders’ sharing behavior. In support of H3, recommender with high reputation (ME = 10.88,
SE = 12.72) acquired more new customers than participants with no reputation (ME = .83, SE = 4.05),
t(699) = -10.68, p < .001. Finally, an independent t-Test determined the difference between
recommenders with and without reputation regarding their sharing behavior. Supporting H4, the results
revealed that recommender with no reputation (ME = 69.82, SE = 19.86) shared more than participants
with reputation (ME = 56.97, SE = 20.11), t(699) = 3.18, p < .01.
In total, on the two analyzed platforms 259 recommenders acquired 612 new customers for 149 firms and
earned 11,992 Euros by simply sharing their referral rewards. However, Table 1 indicates that the gains on
the recommender-side are not equally distributed. In fact, the five depicted recommenders (1.9% of all
recommenders) acquired 75.9% of all new customers and gained 75.8% of all earnings. However, the
relationship between sharing behavior and acquired new customers is not linear. The five most successful
recommenders shared on average 58.3% of their referral reward with their responders, less than the
average shared amount on both platforms. Accordingly, a linear regression between sharing and acquired
new customers was not significant. However, further investigation of the relationship between sharing
behavior and number of acquired new customers is warranted.
Table 1. Most Successful Recommenders
Recommender
Average
Percentage
Shared
Reputation
Recommended
New
Customers
Earnings in €
Christine
63.7
High
119
2,475
Steve
56.7
High
62
2,452
Marc
71.3
Low
131
1,748
Nick
55
High
69
1,230
Christian
45
High
84
1,180
Implications and Next Steps
Based on the theory of two-sided markets and the importance of reputation in online transactions this
study investigates money-sharing on P2P platforms in terms of referral rewards. Our preliminary results
provide first evidence for the hypothesized cross-side and same-side network effects. Furthermore, our
study shows that a good reputation is an important competitive advantage for recommenders competing
for referral receivers. Specifically, we find that some recommenders outperform others, earning more
money through money-sharing than their direct competitors. Surprisingly, when compared with finding
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from behavioral economics, more than half of the recommenders shared more than 70% of their referral
reward with the referral receivers.
Our preliminary results contribute to the theoretical discussion in two important literature streams. First,
this research expands the theoretical discussion about the sharing economy by analyzing a new form of
cooperation and sharing. In particular, we refer to cross-side and same-side network effects and the role
of reputation on P2P money-sharing platforms. To the best of our knowledge, this study represents the
first attempt to theorize and empirically test the sharing of money on P2P platforms. In contrast to the
prevailing assumption in the sharing literature that people only share immaterial goods and goods that
are not totally exploited by their owners, money-sharing is a zero sum game in which one person’s gain is
equivalent to another’s loss. Thus, this study particularly enhances the current understanding of the
sharing economy and opens up a new avenue for research into the sharing economy. Second, this research
contributes to research in behavioral economics analyzing money-sharing in game theoretical settings.
Particularly, our results extend existing game theoretical results by indicating that sharing money on P2P
platforms (i.e., two-sided markets) with cross-side and same-side network effects substantially differs
from “offline” sharing. More than half of the recommenders shared more than 70% of their referral
reward with the referral receivers. The most common sharing behavior (n = 136) in our study was between
80% and 90% of the total reward, whereas in ultimatum games offers rarely exceed 60% of the total
amount. These somewhat inconsistent results stimulate the vital debate on peoples’ desire for an
equitable distribution of material payoffs and aversion to advantageous and disadvantageous payoff
differences (Fehr and Fischbacher 2002; Segal and Sobel 2007).
Moreover, our research has important practical implications, particularly for P2P money-sharing platform
managers regarding the design of a competitive and successful business model. First, our results
specifically offer suggestions for pricing strategies on P2P money-sharing platforms. Both analyzed
platforms do not charge any fees from neither the recommender nor the referral receiver. However, our
results show that referral receivers exert strong positive cross-side network effects on money-sharing
platforms by receiving a large share of the referral reward while recommenders face same-side network
effects reducing their utility. Platform managers could quantify the observed network effects and charge a
transaction fee from referral receivers and subsidize recommenders who are indeed affected by stronger
competition. This way, money-sharing platforms capitalize on positive network effects while mitigating
negative ones. Second, money-sharing platforms offer “outside” monetization opportunities as these
platforms are highly valuable for referring firms. It is important for managers of customer referral reward
programs to increase the awareness for their referral programs and to facilitate the referral process
(Schmitt et. al 2011). P2P sharing platforms do both: they increase the reach of customer referral reward
programs and make it easier for both recommender and referral receiver to participate in a referral
program. Accordingly, platform managers should cooperate with referring firms and 1) charge them a fee
for every referred customer when the relationship of the new customer with the referring firm starts on
their platform and 2) offer referring firms advertisement space on their platform. Third, money-sharing
platforms should spend resources to increasingly differentiate themselves from relevant rival platforms
and strengthen their competitive position. As our study suggests, reputation is an important element of
P2P money-sharing platforms fostering both successful referral process and users’ monetary outcomes.
Implementing a professional reputation mechanism, thus, should be an effective differentiator that
increases users’ value perceptions (Eisenmann et al. 2006).
Although this research in progress makes important contributions, it also has several limitations. First,
our analyses are based on a limited set of variables. Thus, an important next step is to include further
important variables in our analyses. For example, we included reward size as control variable in our
preliminary analyses. However, results from behavioral economics indicate that sharing behavior changes
with increasing stakes (Andersen et al. 2011). In particular, sharing a smaller percentage from a large
stake can be a higher amount of money in absolute terms than sharing a larger percentage from a small
stake. Thus, a next research step is to amend our percentage-based results with calculations including the
total amount of the referral reward. Second, our study concentrates on important variables on the
recommender-side and on cross- and same-side network effects resulting from an increasing number of
recommenders. We acknowledge that variables on the referral receiver-side are important, too and that
both types of network effects can also emerge from an increasing number of referral receivers. Identifying
the magnitude of both network effects for both user groups will yield a more comprehensive
understanding of the processes and results of money-sharing on P2P platforms.
Thirty Sixth International Conference on Information Systems, Fort Worth 2015
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Sharing Money To Make Money
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