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The power of close relationships and audiences: Interpersonal closeness and payment observability as determinants of voluntary payments

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Individual decision-making in Pay-What-You-Want settings is prone to social influence. Especially payment observability and the social relationship with other buyers during the payment decision are two important components of social influence. In practical applications of Pay-What-You-Want both phenomena often occur together while not being investigated yet for more than two types of social relationships. Thus, it is not clear how the presence of various types of social relationships influence voluntary payments and how they relate to payment observability. This study examines both drivers of social influence and investigates how payment observability (audience effect) and different types of social relationships (closeness effect) affect voluntary payments at the American Museum of Natural History. 1034 subjects participated in the study. I find that both, payment observability and interpersonal closeness, significantly increase payments. Voluntary payments are significantly higher if observed by other buyers and if visitors are surrounded by interpersonally close others. A high level of consistency between beliefs and behavior with increasing interpersonal closeness is discussed as potential explanation of the closeness effect. The study results are robustly confirmed in a replication study with 995 subjects.
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The power of close relationships and
audiences: Interpersonal closeness
and payment observability as determi-
nants of voluntary payments
Elisa Hofmann
JENA ECONOMIC RESEARCH PAPERS · # 2020-016
The JENA ECONOMIC RESEARCH PAPERS
is a publication of the Friedrich Schiller University Jena, Germany (www.jenecon.de).
The power of close relationships and audiences: Interpersonal
closeness and payment observability as determinants of voluntary
payments
Elisa Hofmann
September 22, 2020
Abstract
Individual decision-making in Pay-What-You-Want settings is prone to social inuence. Es-
pecially payment observability and the social relationship with other buyers during the payment
decision are two important components of social inuence. In practical applications of Pay-What-
You-Want both phenomena often occur together while not being investigated yet for more than two
types of social relationships. Thus, it is not clear how the presence of various types of social rela-
tionships inuence voluntary payments and how they relate to payment observability. This study
examines both drivers of social inuence and investigates how payment observability (audience ef-
fect) and dierent types of social relationships (closeness eect) aect voluntary payments at the
American Museum of Natural History. 1034 subjects participated in the study. I nd that both, pay-
ment observability and interpersonal closeness, signicantly increase payments. Voluntary payments
are signicantly higher if observed by other buyers and if visitors are surrounded by interpersonally
close others. A high level of consistency between beliefs and behavior with increasing interpersonal
closeness is discussed as potential explanation of the closeness eect. The study results are robustly
conrmed in a replication study with 995 subjects.
JEL classications: C90; D01; D91; L11
Keywords: social inuence; interpersonal closeness; social image concerns; experiments; Pay-What-
You-Want
elisa.hofmann@uni-jena.de, International Max Planck Research School “Adapting Behavior in a Fundamentally Uncer-
tain World”, Friedrich Schiller University Jena, Bachstraße 18k, D-07743 Jena, Germany
I would like to thank the participants of the IMPRS Uncertainty and audiences at ESA 2019 and NCBEEE 2019 for their
feedback. I am thankful to Uwe Cantner, Thomas Kessler, Susanne Büchner, and Deliah Bolesta for helpful comments.
Financial support of the International Max Planck Research School “Adapting Behavior in a Fundamentally Uncertain
World” is gratefully acknowledged.
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1 Introduction
People behave prosocially in many ways: they donate, they help others, and they pay positive amounts
in voluntary payment settings. Pay-What-You-Want (PWYW) for instance is such a voluntary payment
setting which has emerged recently (see, for recent reviews, Gerpott 2017; Grei and Egbert 2018). It
represents one practical application of prosocial behavior because the whole price determination power is
delegated to the buyer (Kim et al. 2009). As the price setting decision is made by the buyer it thus can
be assumed that higher payments reect increased prosocial behavior of the buyer towards the seller.
In many practical applications of PWYW the buyer is not alone but surrounded by other buyers
during the payment decision. The chosen price of the buyer thus is prone to social inuence processes.
Two conceptually dierent but important phenomena of social inuence in PWYW contexts are ob-
servability of behavior and the social relationship between the buyers during the payment decision. In
PWYW contexts both variables often occur together and accordingly the individual decision making can
be inuenced by both components separately or in combination. As an example, imagine a situation in
which visitors of a museum stand in a queue to pay voluntarily at the ticket counter. They either stand
close to each other such that the other visitors can observe the payment decision or the other visitors
stand far away from each other and accordingly are not able to observe the payment. In addition, the
other visitors in the queue might vary in their type of social relationship towards the buyer. In some
situations, interpersonally close other visitors such as friends or family members might stand in the
queue while in other situations the visitors in the queue might be strangers. Both characteristics of
this payment setting observability of behavior and the social relationship between the buyers might
inuence the payment decision of a buyer. It could be the case that the presence of close others already
increases prosocial behavior without being observed. Furthermore it could be that being observed in-
creases prosocial behavior irrespective of who is observing the own behavior. Finally, it might be the
case that the closeness towards the other buyers only matters if the own payments are observed.
Until now it is an open empirical question how payments are aected by varying types of social
relationships, including close others, and how the interpersonal closeness between the buyers relate to
the aspect of payment observability. And further: Does increasing interpersonal closeness amplify the
eects driven by observability or is the eect additive in total? The aim of this paper is to close this
research gap and to explore both components of social inuence, payment observability and dierent
types of social relationships, as well as their relationship to each other in a Pay-What-You-Want setting
with high external validity. The two components payment observability and interpersonal closeness
are two phenomena which can be investigated in a controlled experimental design very well. Together,
important behavioral insights for the impact of both determinants on voluntary payments are addressed,
so far lacking in the scientic literature.
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This paper builds on the broad literature on observability and prosocial behavior as one channel of
social inuence. Recent research proposes that being observed by others might change behavior. This
will be referred to as audience eect in this paper. While psychological research focuses on changes
in performance due to observation (Wolf et al. 2015; Zajonc 1965), research in economics focuses on
the impact of observability on prosocial behavior. One potential explanation for this eect are social
image concerns. Social image concerns assume that people derive a positive social image from signaling
generous behavior to others (Andreoni and Bernheim 2009; Bénabou and Tirole 2006; Ellingsen and
Johannesson 2008). Individuals can use their behavior as a mechanism to maintain such a positive social
image, for instance with high payments in a voluntary payment setting.
This paper further builds upon the literature on social relationships and more specically, on
interpersonal closeness and prosocial behavior as another channel of social inuence. Traditionally, it
has been assumed that an individual’s behavior is shaped especially by the relationship to interpersonally
close others (Cooley 1909; Forgas and Williams 2001; Gächter et al. 2017; Mashek and Aron 2004). This
will be referred to as closeness eect in this paper. It recently has been proposed that being together
with interpersonally close others can result in entering a prosocial mindset which in turn might increase
prosocial behavior (see, e.g., Brewer and Kramer 1986; Kramer and Brewer 1984; Reddish et al. 2013;
Rennung and Göritz 2016; Stel et al. 2008; Valdesolo and DeSteno 2011; van Baaren et al. 2004; Vicaria
and Dickens 2016). Furthermore, several studies exist showing that altruism and helping behavior are
raised with increasing closeness (Andersson et al. 2020; Bell et al. 1995; Cialdini et al. 1997; Korchmaros
and Kenny 2001; Maner et al. 2002; Neuberg et al. 1997). Adapted to PWYW this eect proposes that
buyers might enter a prosocial mindset if they are together with close others in the payment setting, and
thus pay more in a PWYW scheme. One potential explanation of such a closeness eect is the desire
of an individual to behave similar to the expected behavior of others. Those expectations of others are
also called normative beliefs (Ajzen and Fishbein 1970; Ajzen 1991; Fishbein and Ajzen 1975). As an
individual aims to maintain a close social relationship with another person, this view suggests that the
own behavior is more aligned with the observed or expected behavior of close others as compared to not
close others (Etcheverry and Agnew 2016). Following this line of research it thus can be proposed that
interpersonal closeness and beliefs play an important role in individual decision making, also in PWYW
contexts.
Connecting both concepts of observability and interpersonal closeness, one might expect that the
composition of the audience might inuence the strength of the audience eect. This explanation in turn
suggests that not only the observability of behavior matters but the social context in which a decision is
taking place, as well. Recent literature in economics acknowledges the relevance of social relationships
for decision making if behavior is observed (Andreoni and Bernheim 2009; Ellingsen and Johannesson
2008; Regner and Riener 2017). However, empirical studies testing these assumptions are still lacking.
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The literature on payment observability, social relationships, and their interplay in the eld of PWYW
is inconclusive. While Gneezy et al. (2010) as well as Regner and Riener (2017) have shown that ob-
servation by the seller does not signicantly increase payments, Dorn and Suessmair (2016), Dorn and
Suessmair (2017), Hilbert and Suessmair (2015), and Schlüter and Vollan (2015) report increased pay-
ments if the payment is observed by other buyers. However, these studies intertwine the concepts of
observability and interpersonal closeness and are thus not able to disentangle whether increased pay-
ments are driven by the payment observability or the presence of close others. In a laboratory experiment,
Hofmann et al. (2018) have shown that both eects are at play separately. Payment observability and
the presence of close others increase payments separately while their combined eect is additive in total.
It yet remains unclear whether this result holds true for a larger range of interpersonal closeness levels
and in a scenario with higher external validity.
The novelty of this paper is to manipulate interpersonal closeness on four dierent levels, to investigate
the interplay between such a broader range of closeness and payment observability, and to explore their
impact on voluntary payment behavior in a real practical application of PWYW. It thus contributes to
the literature on PWYW by detecting whether higher payments are due to varying levels of interpersonal
closeness between the buyers or due to payment observability. Forgas and Williams (2001) emphasize that
social inuence research should take “real social settings” (p. 6) into account in order to understand how
such processes aect behavior and thoughts. Hence, the hypothetical purchase of an entrance ticket at
the American Museum of Natural History (AMNH) in New York is used as a setting with high external
validity in this paper. The AMNH was the tenth most visited museum worldwide in 2018 (Themed
Entertainment Association 2018) and it uses Pay-What-You-Want at the admission desk in the museum
as pricing mechanism. I employed the typical payment situation in museums for the manipulation of
payment observability and interpersonal closeness between buyers in this experiment, namely standing
in a queue with other visitors around during the payment situation.
In a 4 x 2 between-subjects design I vary interpersonal closeness on four levels (very low interper-
sonal closeness IOS1, low interpersonal closeness IOS2, high interpersonal closeness IOS3, and very high
interpersonal closeness IOS4), while payment observability is varied on two levels (No Audience and
Audience). 1034 subjects participated in the online experiment. I nd an average main eect of payment
observability on voluntary payments: Payments are signicantly higher if observed by other visitors.
Furthermore, I nd an average main eect of interpersonal closeness on voluntary payments: Increased
interpersonal closeness signicantly increases payments. That is, the presence of close others increases
voluntary payments regardless of payment observability. The relationship between payment observabil-
ity and interpersonal closeness is additive in total: Voluntary payments are highest if the buyers are
observed by very close others during the payment decision. However, I do not nd a signicant inter-
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action eect. Thus, the observation by very close others does not pronounce the eect of observability
more strongly than by not close others. These results are robustly conrmed in a replication study with
995 participants. The data analysis further supports the importance of beliefs as one potential driver of
the strong closeness eect. The results of this paper may help to shape a successful design of PWYW
mechanisms, taking the additive eect of interpersonal closeness and payment observability into account.
The remaining part of the paper proceeds as follows: Section 2 sets out the theoretical framework of
this paper, while Section 3 describes the experimental design and procedures. In Section 4, the behavioral
predictions are introduced. Section 5 describes the participants and Section 6 presents the main results.
The study results are discussed in Section 7, while Section 8 concludes.
2 Literature review
This paper contributes to two streams of literature: Social inuence and Pay-What-You-Want (PWYW).
I rstly review the existing literature on observability of behavior (audience eect) and secondly the
literature on varying types of social relationships (closeness eect) as two phenomena of social inuence.
Furthermore, these concepts are combined with social image concerns and normative beliefs as potential
explanations for the two behavioral eects. Finally, I review recent ndings of payment observability
and social relationships in the eld of PWYW.
Walker (2015) denes social inuence as “change in an individual’s thoughts, feelings, attitudes, or
behavior that result from interaction with another individual or a group” (p. 1). Kelman (1958) pro-
poses observability and social relationships as two important aspects of social inuence. He assumes
that being observed activates social inuence driven by the goal to gain reward from a relevant other
for a specic behavior (‘compliance’). This inuence requires to make the individual decision publicly
known to a relevant other. On the contrary, the process of ‘identication’ does not necessarily re-
quires public behavior. Kelman (1958) uses the term ‘identication’ for a process in which an individual
behaves similar to the expectations of relevant others in order to maintain the existing social relationship.
In this paper, audience eect is dened as a change in behavior due to observability by others. A
large number of studies in psychology has shown that observation aects performance (see, e.g., Triplett
1898; Wolf et al. 2015; Zajonc 1965). Similarly, research in economics provides empirical evidence that
observing the payments (via reducing anonymity) increases prosocial behavior in the form of contribu-
tions in dictator games (see, e.g., Engel 2011; Homan et al. 1996), in donations (see, e.g., Alpízar et
al. 2008; Alpízar and Martinsson 2013; Lacetera and Macis 2010; List et al. 2004; Martin and Randal
2008; Soetevent 2005), and in public goods games (see, e.g., Andreoni and Petrie 2004; Christens et al.
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2019; Rege and Telle 2004). As one possible underlying psychological mechanism social image concerns
have been introduced in the literature (Andreoni and Bernheim 2009; Ariely et al. 2009; Ellingsen and
Johannesson 2008). If behavior is observed it can be assumed that social image concerns are activated.
An audience thus can be operationalized via introducing observability of behavior. The concept of social
image concerns implicitly assumes that people strive to appear in a favorable way towards themselves
and others. Thus, cooperation and prosocial behavior might increase due to their goal of being perceived
as generous. The social image model of Ellingsen and Johannesson (2008) also takes into account that
the expectations of the audience matter. The model predicts that the behavior of the individual depends
on the values of the audience. Social reward from being observed can be expected only if the behavior
of the individual is congruent with the expectations of the audience. In line with this idea of social
reward is the idea of Kelman (1958) that an individual’s behavior is driven by the goal to gain positive
reward from relevant others if the behavior is publicly observed. In the same vein, the expectation of
being evaluated by others is proposed as underlying mechanism of the audience eect by psychologists
(Cottrell 1968; Guerin and Innes 1982). Similarly, it has been shown that even minimal social cues like
stylized faces or watching eyes can activate reputation concerns (Bateson et al. 2006; Ekström 2012; Fehr
and Schneider 2010; Haley and Fessler 2005; Krupka and Croson 2016; Rigdon et al. 2009). Together,
this literature outlines observability of behavior as important aspect of social inuence in individual deci-
sion making, possibly being driven by social image concerns and resulting in increased prosocial behavior.
As human beings do not live in social vacuums, but in social relationships, it is a well established
view that the behavior of an individual is shaped especially by the social relationship to interpersonally
close others (Cooley 1909; Forgas and Williams 2001; Gächter et al. 2017; Mashek and Aron 2004).
In line with this literature closeness eect in this paper is dened as a change in behavior due to the
presence of interpersonally close others. The concept of interpersonal closeness was rst conceptualized
as such in social psychology. Interpersonal closeness can be dened as a parameter to distinguish between
social relationship categories (Aron et al. 1992; Berscheid et al. 1989a). It thus is assumed that social
relationships dier regarding their level of interpersonal closeness between the respective individuals,
including categories such as strangers, acquaintances, friends, very close friends, partners or family
members. As noted by Agnew et al. (1998), Berscheid et al. (1989a), Gaines (2016), and Kelley and
Thibaut (1978), close relationships rely on an interdependence between two or more people and can
emerge via repeated interactions resulting in increasing amounts of seeing each other, knowing each other,
and sharing information about each other. As assumed by Arriaga et al. (2004), close relationships are
further characterized by continuation in a long-term future.
Established methods to induce interpersonal closeness in mainly laboratory experimental settings are
sharing information with each other (Aron et al. 1997; Sedikides et al. 1999), having eye-contact (Cui et
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al. 2019; Zhou et al. 2018), or acting in synchrony (Paladino et al. 2010; Rabinowitch and Knafo-Noam
2015; Tunçgenç and Cohen 2016). Furthermore, natural social relationships can be used, such as inviting
friends into the laboratory, conducting experiments in the eld or borrowing identities from real social
relationships (Aron et al. 1997; Berscheid et al. 1989b; Cialdini et al. 1997; Gächter et al. 2015). Less
explored is whether such methods can be successfully applied in an online experimental environment.
This paper closes this research gap by using borrowed identities from real social relationships as method
to induce varying degrees of interpersonal closeness in an online experiment.
The view that interpersonal closeness can be conceptualized as cognitive overlap between the self and
the other has been introduced by Aron et al. (1991, 1992, 1997). This is, the closer a social relationship
is, the more do the boundaries between the self and the other blur away. This results in the merging of
the self and the other. In this line of thought, the authors proposed to elicit the strength of interpersonal
closeness with the ‘Inclusion of Other in the Self’ (IOS) Scale (Aron et al. 1992). The IOS Scale measures
the perceived ‘interconnectedness of self and other’ (Aron et al. 1992, 1997), using a pictorial scale of
increasingly overlapping circles: The more the circles overlap, the higher the perceived interpersonal
closeness. Accordingly it is assumed that the cognitive overlap is able to dierentiate among varying
degrees of interpersonal closeness in social relationships (Aron and Fraley 1999). As compared to the
‘Relationship Closeness Inventory’ (Berscheid et al. 1989b), the IOS Scale is a more exible measure
of interpersonal closeness that is simple and fast to use at the same time. As shown by Gächter et al.
(2017), the IOS Scale is a reliable measure of interpersonal closeness. It is not only used in psychology
but also gained attention in economics recently (see, e.g., Gächter et al. 2015). The IOS Scale thus is
used in this study as it can be seen as a valid instrument to measure interpersonal closeness.
Psychological research indicates that higher levels of interpersonal closeness put people into a prosocial
mindset and thus might potentially increase prosocial behavior (Brewer and Kramer 1986; Kramer and
Brewer 1984; Reddish et al. 2013, 2016; Rennung and Göritz 2016; Stel et al. 2008; Valdesolo and DeSteno
2011; van Baaren et al. 2004; Vicaria and Dickens 2016; Wiltermuth and Heath 2009). In this vein, it
can be proposed that people enter a prosocial mindset if being together with close others. Also, Cialdini
et al. (1997), Maner et al. (2002), and Neuberg et al. (1997) propose interpersonal closeness as driver of
increased prosocial behavior. Furthermore, Korchmaros and Kenny (2001) have shown that emotional
closeness and helping behavior correlate positively with each other. Findings from neuropsychology
underline the closeness eect even in the absence of observability (van Hoorn et al. 2016). In addition,
processes that involve thinking about oneself and others are activated especially when being familar with
others (Jung et al. 2018). Following this line of research, the degree of social relationship seems to be a
relevant aspect of social inuence processes, potentially driving increased prosocial behavior.
The literature oers beliefs and the adaptation of individuals to these beliefs as one possible ex-
planation for the phenomenon of the closeness eect. The idea that individual behavior is inuenced
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by beliefs entered the economic literature with the advent of the psychological game theory (see, e.g.,
Battigalli and Dufwenberg 2007; Geanakoplos et al. 1989). Geanakoplos et al. (1989) introduced the
terms ‘rst order beliefs’ and ‘second order beliefs’. While rst order beliefs describe an individual’s
beliefs about what another individual will do, second order beliefs refer to the beliefs of an individual
about the beliefs and expectations of another individual. Thus, the behavior of others serves as either
informational source to arrive at a decision or as normative guidance of what kind of behavior is ex-
pected to be the right thing to do. These concepts are similar to ‘informative’ and ‘normative’ inuence
within the franework of conformity (Cialdini et al. 1990; Deutsch and Gerard 1955). That an individual
compares itself with similar and relevant others in order to arrive at a decision is also proposed in the
‘Social Comparison Theory’ by Festinger (1954). Some authors have further suggested that normative
beliefs might inuence individual decision making. Ajzen and Fishbein (1970) dene normative beliefs
as “what [an individual] is expected to do in that situation” (p. 467). They refer to the expectations
of relevant others and are proposed as important determinant of individual decision making. The idea
of normative beliefs has been put forward in the ‘Theory of Reasoned Action’ by Fishbein and Ajzen
(1975) and the ‘Theory of Planned Bevahior’ by Ajzen (1991). Accordingly, Ajzen (1991) argues that
individuals aim to fulll the expectations (normative beliefs) of relevant others. Similarly, Etcheverry
and Agnew (2016) emphasize the relevance of close relationships in adapting to normative beliefs. They
proposed a positive relationship between interpersonal closeness and adaption to normative beliefs such
that the expectations of close others are fullled more often than those of not close others. Also, Walker
(2015) puts forward the idea that social inuence processes occur especially between individuals who are
perceived as similar or relevant.
Social cohesion might be one underlying motive for the aim to fulll the normative beliefs of close
others. Social cohesion captures the desire to be included in a social relationship with one or more
others and to maintain this relationship (Carless and De Paola 2000). Connecting social cohesion and
interpersonal closeness it can be assumed that the desire to maintain a social relationship with a person
increases, the closer the social relationship is with this other person. Mapping this onto PWYW settings
it thus can be expected that the closer an individual feels to another buyer the more does the desire
to behave similar in order to maintain the social relationship increases. Accordingly, it can be assumed
that with higher levels of interpersonal closeness people adjust their own behavior more towards the
behavior and expectations of others as the maintenance of these relationships is increasingly important
to an individual. More specically, this implies for PWYW settings that payments may increase up to
the amount individuals expect others to pay.
Overall, the presented literature provides important insights on the role of beliefs in individual decision
making. In view of what has been mentioned so far one may suppose that the closeness eect is driven
by the aim to be consistent with rst order and normative beliefs of interpersonally close others.
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In the eld of PWYW, audience and closeness eects by themselves and in combination are under-
investigated. Although payment observability and the presence of close others are already quite well
investigated in public goods games or charitable giving settings, only a limited number of studies in the
eld of PWYW exist focusing on these topics. Furthermore, existing studies reveal mixed evidence of
audience and closeness eects on voluntary payments. Payment observability towards the seller does not
signicantly aect payments in an online music store (Regner and Riener 2017) and a restaurant setting
(Gneezy et al. 2012).
If payments are observed by other buyers they increase in general (Dorn and Suessmair 2016, 2017;
Hilbert and Suessmair 2015; Schlüter and Vollan 2015). Hilbert and Suessmair (2015) nd higher pay-
ments if buyers are observed by another subject in a laboratory experiment. Further, Dorn and Suessmair
(2016, 2017) nd that payments increased, when participants were observed by friends. A eld experi-
ment of Schlüter and Vollan (2015) explored payment observability by close others in a eld experiment.
They found that payments were higher if buyers were together with known others. Summarizing, these
four studies have shown that, on average, voluntary payments were higher in settings where buyers were
observed by other buyers compared to settings where buyers could pay anonymously. This was found
to hold true for dierent products, namely the hypothetical purchase of a Big Mac (Dorn and Suess-
mair 2016, 2017), for the purchase of owers via an honour box (Schlüter and Vollan 2015) and for the
purchase of mugs in a laboratory experiment (Hilbert and Suessmair 2015). However, these studies can
not disentangle the eect of payment observability and the eect of being together with close others.
The interpretation of the results thus is limited as the studies were not able to detect whether higher
payments arose due to payment observability or due to the presence of close others in the payment
situation. Increased payments might either have been driven by the observability of payments or by the
presence of close other buyers during the payment decision.
A recent study by Hofmann et al. (2018) provides for the rst time empirical evidence from a labora-
tory experiment that both, audience and closeness eects, are at play separately. The results indicate an
additive eect of payment observability and interpersonal closeness. In a laboratory experiment the au-
thors varied payment observability on two levels (No Audience vs. Audience) and interpersonal closeness
on two levels (Strangers vs. Acquaintances). The results showed that payments signicantly increased
if subjects were observed or if participants were together with close others but that the two eects did
not interact signicantly. Thus, an interactive relationship, assuming that the signaling of the payment
in an observed setting is aected by the interpersonal closeness between an individual and the audience,
can not be supported by empirical data. As a consequence, it is more plausible to assume an additive
relationship between observability and interpersonal closeness.
A limitation of the study by Hofmann et al. (2018) is that it manipulated interpersonal closeness on
two levels only whereas in reality there is a broad range of varying intensities in social relationships. The
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study of Hofmann et al. (2018) might not have found a signicant interation eect due to the reason
that only two levels of interpersonal closeness were investigated which were strong enough to result in a
separate closeness eect but not in an interaction eect between observability and closeness. Possibly,
suciently high levels of interpersonal closeness might amplify the audience eect more strongly. In ad-
dition, a non-linear eect might explain why Hofmann et al. (2018) did not nd an interaction between
payment observability and interpersonal closeness. Thus it still remains an open question how more than
two levels of interpersonal closeness towards other buyers (strangers vs. dierent kinds of close others)
inuence voluntary payments. Accordingly, there is still uncertainty on how audience eects and varying
levels of interpersonal closeness relate to each other. Further, the generalizability of the results is limited
as the study was conducted as laboratory experiment. Although internal validity can be assumed to be
high, external validity might be an issue.
The contribution of this paper to the literature is threefold. It rstly aims to investigate how payment
observability and varying intensities of interpersonal closeness as two components of social inuence aect
voluntary payments. It further tests how varying levels of interpersonal closeness relate to payment
observability: either additively or interactively. In this context it aims to explore the relevance of beliefs
as possible underlying mechanism for the closeness eect. Secondly, it contributes methodologically to
the operationalization of interpersonal closeness by using borrowed identities of real social relationships.
It thus transfers this complex phenomenon into a controlled online experimental setting to gain insights
for understanding the processes at play. Finally, it adds external validity to previous experiments as the
experimental setting is embedded in a real-world application of PWYW, namely the American Museum
of Natural History (AMNH) in New York.
3 Experimental design and procedures
This experiment applies a 4 x 2 between-subjects design, resulting in eight treatments. The hypothetical
purchase of a ticket for the American Museum of Natural History (AMNH) in New York is used as the
product. I vary the degree of interpersonal closeness between the buyers on four levels (IOS1, IOS2,
IOS3 and IOS4) and payment observability on two levels (No Audience and Audience). The interpersonal
closeness condition IOS1 serves as baseline condition as here a stranger is described as the other visitor
being present during the purchase of the ticket. The three interpersonal closeness conditions IOS2,
IOS3, and IOS4 represent peer conditions and use borrowed identities from real social relationships
of the participants. A low interpersonal closeness is manipulated in the IOS2 condition, while a high
interpersonal closeness is manipulated in the IOS3 condition. Finally, in the IOS4 condition, a very
high interpersonal closeness is manipulated. The payment observability condition No Audience (NA)
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represents a scenario in which payments are not observed by a specic other visitor (standing far away
from the participant in the queue), whereas in the Audience (A) condition own payments are observed
by a specic other visitor (standing in the queue direcly behind the participant). Table 1 summarizes
the number of participants, almost equally distributed among the eight treatments.
Interpersonal Closeness No Audience (NA) Audience (A) Total
Very low interpersonal closeness (IOS1) 129 131 260
Low interpersonal closeness (IOS2) 130 130 260
High interpersonal closeness (IOS3) 126 123 249
Very high interpersonal closeness (IOS4) 135 130 265
Total 520 514 1034
Table 1: Treatments and observations
The experiment consisted of six steps which are described hereinafter. The timeline of the experiment
is shown in Table 2.
Step 1 Specifying IOS2 - IOS4 persons
Step 2 One interpersonal closeness level randomly chosen: Strengthen relation-
ship
Step 3 AMNH scenario description; Treatment manipulation
Step 4 Decision: Willingness to pay
Step 5 Manipulation check
Step 6 Measure of controls, beliefs, preference for xed price vs. Pay-What-
You-Want, and demographics
Table 2: Timeline of the experiment
In Step 1, three real social relationships were elicited to be used in the further procedure of the
experiment. The participants were shown the picture of a four-level-adapted version of the original
seven-level IOS Scale (Aron et al. 1992), familiarizing them with the concept of interpersonal closeness
(see Figure 1).
Figure 1: Adapted IOS Scale with four levels
The subjects were asked to think about three same sex people representing dierent levels of perceived
interpersonal closeness to them, namely picture 2, picture 3, and picture 4 of Figure 1. Subjects provided
the rst name, age, time since they had known the person for, and type of relationship for each of the three
persons. As an example, participants were told that a social relationship towards a stranger, about whom
they do not know anything, would represent picture 1 of Figure 1. Participants were asked to name same
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sex persons and not to name persons that they share a common income for livelihood with. The same sex
criteria has been checked before participants were able to continue with the experiment. This method to
induce various degrees of closeness relying on the subject’s assessment captures possible interindividual
variance regarding the meaning of closeness while at the same time structuring the assessment by means of
the IOS Scale. It thus allows the subjects to dene for themselves how to classify their social relationships
regarding dierent degrees of closeness. This approach represents an advantage compared to external
classications by the experimenter to dene what manifests a close relationship (Berscheid et al. 1989a).
In Step 2, the level of interpersonal closeness was manipulated. One out of the four levels of
interpersonal closeness was randomly chosen and each subject was assigned to one interpersonal closeness
condition (IOS1, IOS2, IOS3, or IOS4) only. Subjects were asked to write a short text about their daily
routine on a weekday (baseline condition IOS1) or to write a short text about their relationship with the
assigned person (conditions IOS2, IOS3, and IOS4) as for instance how they met each other and which
kind of activities they usually do together. The aim of this procedure was to strengthen the randomly
assigned level of interpersonal closeness.
In Step 3, subjects read the scenario description. Participants were asked to imagine visiting the
American Museum of Natural History (AMNH) in New York and standing in the queue at the ticket
counter. A picture of the AMNH and information about the museum were provided to activate possible
knowledge about the museum and to make the scenario more salient. The subjects were told that the
AMNH allowed visitors to pay what they want for the entrance ticket. They further read that they could
expect the visit to fulll their expectations to keep expected satisfaction homogeneous in all treatments.
In the scenario description, the manipulation of the audience has been embedded. Subjects were told
that they recognized the randomly assigned person from Step 2 also standing in the queue. In the No
Audience condition they read that the person was standing far away behind them with other stranger
visitors standing between them. Thus, while paying at the ticket counter, this person would still be far
away behind them and could not observe how much they paid for their visit. In the Audience condition
they read that the person was standing right behind them. Thus, while paying at the ticket counter,
this person was still right behind them and could observe how much they paid for their visit. It was
kept constant across treatments that the sta person at the ticket counter would learn about the price
the subjects were willing to pay. Additionally, the subjects were given the information that the museum
suggests a price of $23 for a visit, thus providing an external reference price.
Step 4 contained the measure of willingness to pay, operationalized as the price the subjects would
be willing to pay in $ for a ticket to visit the AMNH.
In Step 5, a manipulation check was conducted in which participants were asked to rate their
perceived level of closeness towards the person randomly chosen in Step 2 on the same adapted IOS
Scale from one to four (Figure 1).
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In Step 6, a couple of control variables were measured. First order beliefs and normative beliefs of
the participants were measured as well as their general interest in and usage of various cultural activities.
Additionally, their aliation to the AMNH in general was collected. Further, subjects were asked to
indicate their preferences for a xed price or a Pay-What-You-Want pricing mechanism. At the very end
of the experiment, demographics were measured.
4 Behavioral predictions
This paper aims to investigate audience eects and closeness eects on voluntary payments for two levels
of payment observability and four levels of interpersonal closeness. The behavioral predictions are derived
in accordance with the related literature outlined earlier.
If behavior is observed, social image concerns are activated. In the Audience condition, these social
image concerns are activated via payment observability. Related literature on payment observability
showed increased contributions if payments were observed. Accordingly, I expect that, on average,
payments are higher if observed by other buyers. I thus predict an average audience eect:
Hypothesis 1 Payments are, on average, higher in the Audience condition than in the No Audience
condition.
Recent literature suggests that being together with interpersonally close others might lead to entering
a prosocial mindset which in turn results in increased prosocial behavior. As in this study four levels of
interpersonal closeness are induced, I expect that, on average, payments increase with increasing levels
of interpersonal closeness. Ceteris paribus, I predict an average closeness eect:
Hypothesis 2 Payments increase with increasing levels of interpersonal closeness.
Regarding the relationship between an audience and interpersonal closeness, two competing hypothe-
ses are tested. Based on recent empirical ndings, I rstly test the prediction that the eects of an
audience and interpersonal closeness are additive. Both constructs may increase payments separately,
without amplifying each other. This would not lead to a signicant interaction eect but nevertheless to
highest payments in the treatment in which the behavior is observed by a very close other. Consistently
with the more plausible additive relationship, I predict:
Hypothesis 3 The relationship between observability and interpersonal closeness is additive in total.
Hence, payments are highest when observed by a very close other visitor.
Secondly, taking into account that the relevance of the audience matters regarding social image
concerns, it can alternatively be assumed that the motivation to maintain a positive social image increases
with increasing levels of interpersonal closeness if behavior is observed. Thus, it might be expected that
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interpersonal closeness amplies dierences in the audience eect regarding payment. Close others might
induce a stronger activation of social image concerns because close others might be more important than
less close others. Currently, this assumption is theoretically possible in economic models of social image
concerns but not yet supported by empirical analysis in PWYW settings. Accordingly, I predict the
following interactive relationship as alternative to the more plausible additive relationship:
Hypothesis 4 Audience eects are more pronounced if interpersonal closeness is very high.
5 Participants and manipulation check
The subjects were recruited via Amazon Mechanical Turk (MTurk) and the experiment was programmed
with Qualtrics. 1034 subjects participated in the exeriment, randomly assigned to one of the eight
treatments. Data collection took place in December 2018. Participants took on average 11.6 minutes
(SD = 7.12) to complete the survey and they earned $1.20 for their participation. The participants were
on average 38.4 (SD = 11.87) years old. 55.1% of the subjects were female. On average, subjects had
0.84 children (SD = 1.14). The sample is balanced between liberals and conservatives (M= 3.53, SD
= 1.82, 7-point Likert scale ranging from “Strongly liberal” (1) to “Strongly conservative” (7)).
To ensure that the manipulation of interpersonal closeness was successful, I ran a manipulation check.
Using a four-level IOS Scale at the end of the experiment, subjects were asked which of the four presented
IOS pictures best described their relationship to the person mentioned in the experimental Steps 2, 3,
and 4. The mean IOS score in condition IOS1 is M= 1.30 while it is M= 2.10 in condition IOS2. In
condition IOS3, the mean IOS score is M= 2.92 and in condition IOS4 it is M= 3.85. The results
indicate signicant dierences: The higher the interpersonal closeness level of the condition, the closer
the subjects felt to the named person. A nonparametric comparison of the four conditions (Kruskal-
Wallis test, H(3) = 815.52, p< .001) and a one-way analysis of variance (F(3, 1030) = 1296, p< .001)
support this result. Also, post hoc tests indicate that the manipulation of interpersonal closeness was
successful between all four levels on the 0.1% signicance level. Thus, I conclude that the manipulation
of interpersonal closeness via borrowed identities from real-world social relationships succesfully induced
dierent levels of interpersonal closeness.
6 Results
On average, participants were willing to pay $19.66 (SD = 6.90) for a ticket at the AMNH. The average
price paid is signicantly dierent from zero (one-sample t-test against zero, p< .001). The amount
participants were willing to pay varied in a broad range between $0 and $50.
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Table 3 provides summary statistics of the willingness to pay for all eight treatments and means
averaged across the conditions. The results show that payments are higher in the Audience (A) treat-
ments than in the No Audience (NA) treatments. Further, the results suggest increasing payments with
increasing levels of interpersonal closeness.
Interpersonal Closeness No Audience
(NA)
Audience
(A)
Average
Very low interpersonal closeness (IOS1) 17.74 (7.18) 18.48 (8.04) 18.12 (7.62)
Low interpersonal closeness (IOS2) 19.47 (6.79) 20.23 (6.24) 19.85 (6.52)
High interpersonal closeness (IOS3) 20.12 (6.24) 20.33 (7.02) 20.22 (6.62)
Very high interpersonal closeness (IOS4) 19.39 (7.08) 21.55 (5.77) 20.45 (6.55)
Average 19.18 (6.88) 20.14 (6.89) 19.66 (6.90)
Note: Standard deviations are provided in parentheses.
Table 3: Mean payments in $ for all eight treatments and averaged across conditions
In the Audience condition (M= $20.14, SD = 6.89), the willingness to pay is signicantly higher
than in the No Audience condition (M= $19.18, SD = 6.88, one-sided two-sample t-test, t(1032) =
-2.24, p= .013)). Observed participants thus tended to pay on average 5 % more than subjects in the
not observed condition. This result is further conrmed by a one-way analysis of variance, exploring the
eect of payment observability on payments. Subjects pay signicantly more if payments are observed
(F(1, 1032) = 5.03, p= .025).
Result 1 Payments are, on average, signicantly higher in the Audience condition than in the No
Audience condition.
The higher the level of interpersonal closeness, the more do subjects tend to pay. On average, subjects
paid $18.12 (SD = 7.62) in closeness condition IOS1, $19.85 (S D = 6.52) in closeness condition IOS2,
$20.22 (SD = 6.62) in closeness condition IOS3, and $20.45 (S D = 6.55) in closeness condition IOS4.
A Kruskal-Wallis test indicates that the dierences between the four interpersonal closeness conditions
is statistically signicant on the 0.1% level (H(3) = 17.46, p< .001).
Result 2 Payments increase signicantly with increasing levels of interpersonal closeness.
This result is also supported by a one-way analysis of variance, examining the eect of interpersonal
closeness (factor variable with four levels) on payments. Subjects pay signicantly more if interpersonal
closeness increases (F(3, 1030) = 6.21, p< .001). Comparing the four levels of interpersonal closeness
via post hoc tests, I nd that only the stranger condition (IOS1) is signicantly dierent from each
of the three peer conditions (IOS1-IOS2:p= .020; IOS1-IOS3:p= .003; IOS1-IOS4:p< .001) but
that the three peer conditions do not dier signicantly from each other. Thus, payments seem to only
marginally dier between the peer conditions IOS2, IOS3, and IOS4.
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Results 1 and 2 are further supported by a linear regression (Table 4). In each of the four speci-
cations, willingness to pay is the dependent variable. I included an audience dummy (Audience = 1,
zero otherwise) in Model 1 as explanatory variable. In Model 2, I entered interpersonal closeness as
independent variable (continuous variable). In Model 3, I added both, audience and interpersonal close-
ness, into the regression model. Finally, in Model 4 I added an interaction term between audience and
closeness. In all four Models, I included various control variables in the regressions, such as rst order
and normative beliefs, preference for xed price, sociodemographic variables, control variables regarding
the American Museum of Natural History as well as control variables for rated importance of cultural
activities in general and frequency of visits of cultural activities.
(1) (2) (3) (4)
Audience 0.85(0.34) 0.86(0.34) 0.38 (0.83)
Closeness 0.64∗∗∗ (0.15) 0.64∗∗∗ (0.15) 0.54(0.21)
Audience x Closeness 0.19 (0.30)
First Order Beliefs 0.31∗∗∗ (0.03) 0.29∗∗∗ (0.03) 0.29∗∗∗ (0.03) 0.29∗∗∗ (0.03)
Normative Beliefs 0.34∗∗∗ (0.04) 0.35∗∗∗ (0.04) 0.35∗∗∗ (0.04) 0.35∗∗∗ (0.04)
Preference for Fixed Price 2.16∗∗∗ (0.35) 2.14∗∗∗ (0.35) 2.12∗∗∗ (0.35) 2.12∗∗∗ (0.35)
Age 0.04(0.02) 0.04(0.02) 0.04(0.02) 0.04(0.02)
Gender 0.33 (0.36) 0.16 (0.36) 0.21 (0.36) 0.21 (0.36)
Political Orientation 0.19(0.10) 0.14 (0.10) 0.15 (0.10) 0.15 (0.10)
Children 0.03 (0.16) 0.03 (0.16) 0.01 (0.16) 0.01 (0.16)
Degree 0.31(0.13) 0.35∗∗ (0.13) 0.34∗∗ (0.13) 0.34∗∗ (0.13)
Marital Status 0.03 (0.21) 0.07 (0.21) 0.06 (0.21) 0.06 (0.21)
Religion 0.01 (0.05) 0.02 (0.05) 0.02 (0.05) 0.02 (0.05)
AMNH Known 0.19 (0.30) 0.22 (0.30) 0.23 (0.30) 0.22 (0.30)
AMNH Already Visited 0.53 (0.43) 0.53 (0.43) 0.47 (0.43) 0.47 (0.43)
AMNH Intention to Visit 0.30 (0.37) 0.33 (0.37) 0.35 (0.37) 0.35 (0.37)
Importance: Cultural Activities 0.26(0.15) 0.25(0.14) 0.25(0.14) 0.25(0.14)
Visits: Cultural Activities 0.76(0.31) 0.66(0.31) 0.68(0.30) 0.69(0.30)
Constant 0.15 (1.79) 0.24 (1.79) 0.78 (1.79) 0.56 (1.83)
Observations 1020 1020 1020 1020
R20.38 0.39 0.39 0.39
Adjusted R20.37 0.38 0.38 0.38
Notes: Results from linear regressions. Price paid is the dependent variable in all four Models. Standard errors are provided in
parentheses. p< .1; p< .05; ∗∗p< .01; ∗∗∗ p< .001
Table 4: Determinants of the paid price
I nd a signicant main eect of an audience on voluntary payments across interpersonal closeness
conditions (Model 1, coecient = 0.85, p= .02). Payments signicantly increase if observed by others.
The regression results (Model 2) further show a signicant main eect of interpersonal closeness on the
paid price (coecient = 0.64, p< .001). The main eects of audience and interpersonal closeness remain
to a similar extent if added simultaneously in the regression (Model 3). Summarized, these results lend
support for Hypothesis 1 and Hypothesis 2.
I further do not nd a signicant interaction eect between audience and interpersonal closeness
(Model 4, coecient = 0.19, p= .524). Although the payments are highest when very close people
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observe the payment, the audience eect does not seem to be more pronounced if the audience consists
of very close others. The data analysis thus lends support for the prediction that the audience eect is
not amplied when interpersonal closeness is increasing. This nding does not support the interaction
Hypothesis 4 but rather provides further empirical evidence for the additive Hypothesis 3.
Result 3 Audience eects are not more pronounced if interpersonal closeness increases. An additive
relationship between audience and interpersonal closeness can be assumed.
To test this result on robustness, I ran an analysis of variance including an interaction between pay-
ment observability and interpersonal closeness. The result is similar to the linear regression summarized
in Table 4. While the main eects of an audience (F(1, 1026) = 5.11, p= .024) and of interpersonal
closeness (F(3, 1026) = 6.30, p< .001) are signicant, the interaction is not signicant (F(3, 1026) =
0.97, p= .407).
Looking at the inuence of the control variables on the paid price, the regression results indicate
a positive and signicant eect of rst order and normative beliefs on voluntary payments in all four
Models. If visitors expect the other person to pay much for the ticket (rst order beliefs), they adapt
their behavior and also pay more. Similarly, if visitors expect the other person to expect higher payments
from themselves (normative beliefs), they pay more. Additionally, those participants who prefer xed
prices (FP) over Pay-What-You-Want (PWYW) pay signicantly more than subjects preferring PWYW
over FP. This result holds true for all four Models. Regarding the sociodemographic variables, age and
degree have a signicant inuence on price paid in all four Models. Older participants pay signicantly
more. Further, the higher the education of the participants, the less they pay for their ticket voluntarily.
Finally, subjects who visit cultural activities such as museums of concerts more often, pay signicantly
higher prices. This inuence is similar in all four tested regression Models.
How can it be explained that the audience eect is not more pronounced if interpersonal closeness
increases but that there is rather an additive relationship between the two constructs? Figure 2 visualizes
the distribution of payments for all eight treatments.
0.0
0.1
0.2
0.3
0 10 20 30 40 50
Payment in $
Density
IOS1 Audience
IOS1 NoAudience
IOS2 Audience
IOS2 NoAudience
IOS3 Audience
IOS3 NoAudience
IOS4 Audience
IOS4 NoAudience
Figure 2: Kernel density functions of willingness to pay split by treatment
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The graph indicates that there is sucient variation in willingness to pay in each treatment. Thus,
missing variation in payments is not an issue in nding a signicant interaction eect between interper-
sonal closeness and payment observability.
As already shown in Table 3, in both payment observability conditions payments increase with in-
creasing levels of interpersonal closeness. This result provides empirical evidence that the closeness eect
is at work to similar degrees in the No Audience and the Audience treatments. I ran linear regressions
for the No Audience and the Audience condition separately (see Table 5) to test whether the main eect
of interpersonal closeness holds true for both payment observability conditions. In Model 1 and Model
2, only data from the No Audience condition are used, while in Model 3 and in Model 4, data from
the Audience condition are used. In Model 1 and Model 3, interpersonal closeness is included as only
predictor on prices paid, while I added various control variables in Model 2 and Model 4, respectively.
The paid price is the dependent variable in all four Models.
(1) (2) (3) (4)
Interpersonal Closeness 0.55(0.27) 0.57∗∗ (0.22) 0.93∗∗∗ (0.27) 0.76∗∗∗ (0.22)
First Order Beliefs 0.28∗∗∗ (0.05) 0.31∗∗∗ (0.04)
Normative Beliefs 0.39∗∗∗ (0.06) 0.33∗∗∗ (0.05)
Preference for Fixed Price 2.27∗∗∗ (0.49) 2.12∗∗∗ (0.50)
Age 0.07∗∗ (0.02) 0.01 (0.02)
Gender 0.01 (0.51) 0.37 (0.51)
Political Orientation 0.19 (0.15) 0.07 (0.14)
Children 0.21 (0.21) 0.22 (0.24)
Degree 0.39(0.19) 0.27 (0.18)
Marital Status 0.05 (0.30) 0.10 (0.31)
Religion 0.07 (0.08) 0.02 (0.08)
AMNH Known 0.52 (0.44) 0.01 (0.42)
AMNH Already Visited 0.81 (0.60) 0.24 (0.62)
AMNH Intention to Visit 0.01 (0.50) 0.76 (0.55)
Importance: Cultural Activities 0.17 (0.20) 0.36(0.21)
Visits: Cultural Activities 1.08∗∗ (0.41) 0.16 (0.47)
Constant 17.80∗∗∗ (0.74) 2.01 (2.58) 17.82∗∗∗ (0.73) 0.86 (2.51)
Observations 520 513 514 507
R20.01 0.40 0.02 0.40
Adjusted R20.01 0.38 0.02 0.38
Notes: Results from linear regressions. The paid price is the dependent variable in all four Models. Standard errors are provided in
parentheses. p< .1; p< .05; ∗∗p< .01; ∗∗∗ p< .001
Table 5: Determinants of the paid price split by No Audience condition (Model 1 and Model 2) and
Audience condition (Model 3 and Model 4)
The coecients of interpersonal closeness range between 0.55 in Model 1 and 0.93 in Model 3, sig-
nicantly increasing payments in all four Models. The results thus suggest that the strong closeness
eect is at work irrespective of payment observability. However, the eect of interpersonal closeness
is stronger in the Audience condition (Models 3 and 4) than in the No Audience condition (Models 1
and 2), indicating steeper slopes of interpersonal closeness in the Audience condition as compared to the
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No Audience condition. Thus, the closeness eect irrespective of payment observability might explain
the non signicant interaction between audience and interpersonal closeness. The regression analysis in
Table 4 revealed beliefs as relevant predictor of payments. It thus seems likely that individuals tend to
behave similar to the expected behavior and the normative expectations of others which might lead to
increased payments with increasing levels of interpersonal closeness.
6.1 Inuence of beliefs
Homogeneity in beliefs and behaving consistently with these beliefs with increasing degrees of closeness
are considered as potential explanations for the strong closeness eect, potentially explaing the missing
interaction eect between an audience and interpersonal closeness. First order beliefs and normativebe-
liefs are relevant when it comes to the question whether people behave in alignment with the behavior of
others and their expectations, respectively. Table 6 summarizes rst order beliefs and normative beliefs
for all eight treatments. Regarding rst order beliefs, participants expected the other visitor to pay
on average $19.91 (SD = 6.67). Regarding normative beliefs, participants believed the other person
expected them to pay on average $20.34 (SD = 6.33). In all eight treatments, rst order beliefs and
normative beliefs are quite similar. Subjects believe in all eight treatments that others pay on average
around $20 and that this amount is expected by others as well.
Interpersonal Closeness No Audience
(NA)
Audience
(A)
Average
First Order Beliefs
Very low interpersonal closeness (IOS1) 19.58 (5.89) 19.58 (5.93) 19.58 (5.90)
Low interpersonal closeness (IOS2) 19.79 (6.43) 19.02 (7.70) 19.41 (7.09)
High interpersonal closeness (IOS3) 20.10 (6.63) 20.10 (7.67) 20.10 (7.14)
Very high interpersonal closeness (IOS4) 19.92 (6.71) 21.22 (6.14) 20.56 (6.46)
Average 19.85 (6.41) 19.98 (6.92) 19.91 (6.67)
Normative Beliefs
Very low interpersonal closeness (IOS1) 20.88 (4.98) 20.53 (4.88) 20.71 (4.92)
Low interpersonal closeness (IOS2) 20.15 (6.07) 20.63 (7.25) 20.39 (6.68)
High interpersonal closeness (IOS3) 20.25 (6.32) 19.49 (7.42) 19.88 (6.89)
Very high interpersonal closeness (IOS4) 20.04 (5.83) 20.67 (7.41) 20.35 (6.65)
Average 20.33 (5.82) 20.34 (6.81) 20.34 (6.33)
Note: Standard deviations are provided in parentheses.
Table 6: Mean rst order beliefs and normative beliefs in $ for all eight treatments
Result 4 First order beliefs and normative beliefs resemble to one another between the eight treatments.
This result is supported by nonparametric Kruskal-Wallis tests. First order beliefs are not statistically
signicantly dierent between the eight treatments (H(7) = 12.46, p= .086). A similar result occurs
for normative beliefs (H(7) = 5.18, p= .638). Further, rst order beliefs and willingness to pay are
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positively correlated (correlation coecient = 0.53, Pearson’s p< .001). Similarly, normative beliefs and
willingness to pay are positively correlated (correlation coecient = 0.53, Pearson’s p< .001). These
similarities between own payments, rst order beliefs, and normative beliefs are illustrated in Figure 3.
0.0
0.1
0.2
0.3
0.4
0 10 20 30 40 50
Amount in $
Density
Own Payment
First Order Beliefs
Normative Beliefs
Figure 3: Kernel density functions of own payments, rst order beliefs, and normative beliefs aggregated
over all treatments
However, the degree of alignment between own behavior and the beliefs diers systematically between
the four interpersonal closeness conditions. A high level of consistency is supposed if the dierence be-
tween own payment and beliefs is small while a low level of consistency is supposed if this dierence is
large. To test this relationship, I calculated the dierence between own payment and beliefs for each
subject, measuring the degree of consistency. If the dierence is zero, then subjects completely align the
own behavior to the beliefs. If the dierence is negative, beliefs were lower than own payments, indicating
that subjects paid more than they expected the other to pay and more than the other expected them to
pay. On the other hand, if the dierence is positive, beliefs were higher than own payments, indicating
that subjects paid less than they expected the other to pay and less than the other expected them to pay.
Figure 4 shows that the consistency with the payment of the other (rst order beliefs) and the
expectations of the other (normative beliefs) diers between the four interpersonal closeness conditions.
Let us rst look at the consistency between own payments and rst order beliefs. While the mean
dierence between rst order beliefs and own payment in condition IOS1 is M= $1.43 (SD = 7.13), it
is M= $-0.47 (SD = 6.71) in IOS2. In condition IOS3, the mean dierence is M= $-0.15 (SD = 6.18),
while it is almost zero in condition IOS4 (M= $0.05, SD = 6.17). Thus, the highest level of consistency
occurs in very close relationships. Individuals align their own behavior with the behavior of close others
and thus adapt their payments. A nonparametric Kruskal-Wallis test supports the result that the degree
of consistency signicantly diers between the four interpersonal closeness conditions (H(3) = 13.32, p
= .004). The result is further supported by a one-way analysis of variance (F(3, 1023) = 4.22, p= .006).
Post hoc tests reveal that the degree of consistency between own payment and rst order beliefs does
not statistically dier between the three peer conditions (IOS2, IOS3, and IOS4).
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−40
−20
0
20
40
IOS1 IOS2 IOS3 IOS4
Interpersonal Closeness
Deviation: First Order Belief − WTP in $
(a) Deviation between rst order beliefs and
own payment
−40
−20
0
20
40
IOS1 IOS2 IOS3 IOS4
Interpersonal Closeness
Deviation: Normative Belief − WTP in $
(b) Deviation between normative beliefs and
own payment
Figure 4: Deviations between rst order beliefs, normative beliefs, and own payments split by interper-
sonal closeness condition
I nd a similar result when looking at normative beliefs. While the mean dierence between normative
beliefs and own payment in condition IOS1 is rather large with M= $2.60 (SD = 7.52), it rapidly
decreases in IOS2 (M= $0.54, SD = 6.39) and IOS3 (M= $-0.33, SD = 5.89), and reaches its
respective minimum at IOS4 (M= $-0.10, SD = 5.39). A nonparametric Kruskal-Wallis test supports
the result that consistency diers signicantly between the four interpersonal closeness conditions (H(3)
= 42.68, p< .001). Furthermore, this result is supported by a one-way analysis of variance (F(3, 1027)
= 11.39, p< .001) and post hoc tests, revealing that the degree of consistency between own payment
and normative beliefs does not statistically dier between the three peer conditions (IOS2, IOS3, and
IOS4).
Result 5 The degree of consistency between own behavior and beliefs diers signicantly between the
four levels of interpersonal closeness, potentially driving the strong closeness eect.
The similarity in consistency in the three peer conditions might be a reason why also in the No
Audience treatments payments in the three peer conditions (IOS2, IOS3, IOS4) are similar and higher
than payments in the stranger condition (IOS1). Subjects might be driven by the aim to behave con-
sistently with the expectations of others. This is, they want to behave similarly to their close others.
Subjects seem to anticipate that social pressure to fulll the expectations of the other buyer increases
with increasing levels of closeness. As the probability to meet a close other is higher than for a stranger,
subjects fulll this pressure to behave consistently and accordingly increase their payments up to the
expected level, also in the No Audience treatments. I thus conclude that the increasing level of consis-
tency between own payment and beliefs with increasing levels of interpersonal closeness might drive the
strong closeness eect found in this study.
6.2 Robustness of results: Replication study
In order to test the results for robustness, I ran a replication study in December 2019. I used the
identical experimental design as conducted in the original study but added various control questions
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after the replication measures in order to gain deeper insights into the potential underlying mechanisms
inuencing the willingness to pay. I further added an additional baseline treatment in which the museum
visitor was alone at the ticket counter with no other visitor being present during the payment decision.
Firstly, to rule out possible confounds between the treatments due to dierent arousal levels, I col-
lected data on pleasure, arousal, and dominance via the Self-Assessment Manikin (SAM) scales (Bradley
and Lang 1994). I further added a manipulation check of the payment observability by the other visitor,
asking “How much did you feel observed by the other visitor while making your payment decision?”
(7-point Likert Scale from “Not at all” (1) to “Very much” (7)). To gain more insights into the aspect
of consistency with beliefs, social cohesion with the other visitor was elicited, using adopted items from
Carless and De Paola (2000) and Delfgaauw et al. (2020). Besides that, I collected data on feelings
of guilt (adopted from Cohen et al. (2011)) when participants paid less than they believed the other
visitor would pay, less than expected by the other visitor, and less than expected by the AMNH. I also
measured the perception to feel uncomfortable when paying more than the other visitor, more than the
other visitor expected the subjects to pay, and more than the AMNH expected. Finally, I elicited the
participants social preferences using the six-item version of the social value orientation (SVO) slider
measure (Murphy et al. 2011).
995 subjects participated in the replication experiment which took on average 16.2 minutes to be
completed. Subjects earned $1.50 as participation fee. As shown in Table 9 in the Appendix, participants
in the replication study diered only regarding age from subjects in the original study. The manipulation
check of the replication study reveals that participants show similar IOS scores as in the original study.
Thus, the manipulation of interpersonal closeness was successful. The mean IOS score in condition IOS1
is M= 1.21, while it is M= 2.13 in condition IOS2,M= 2.99 in condition IOS3, and M= 3.94 in
condition IOS4. These values dier statistically signicantly from each other (nonparametric Kruskal-
Wallis test, H(3) = 877.71, p< .001) as it was the case in the original study. An additional manipulation
check regarding payment observability shows that subjects in the Audience condition felt signicantly
more observed (M= 4.57, SD = 1.89) than participants in the No Audience condition (M= 2.24, SD =
1.68, one-sided two-sample t-test, t(986) = -20.62, p< .001). The similarities between the original study
and the replication regarding paticipants allow for the conclusion that the two samples were not dierent
from each other. The descriptive data analysis of the willingness to pay in the replication study conrms
this assumption and reveals a successful replication of the original study results (see Table 7). None of
the mean payments in the replication experiment is signicantly dierent from the mean payments in
the original study as indicated by two-sided two-sample t-tests.
Result 6 The results of the original study (N = 1034) are robustly conrmed in a replication study (N
= 995).
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Interpersonal Closeness No Audience
(NA)
Audience
(A)
Average
Replication Study
Very low interpersonal closeness (IOS1) 17.91 (8.03) 18.48 (6.59) 18.20 (7.32)
Low interpersonal closeness (IOS2) 19.96 (6.27) 21.28 (5.26) 20.63 (5.81)
High interpersonal closeness (IOS3) 18.90 (7.44) 19.57 (7.02) 19.25 (7.22)
Very high interpersonal closeness (IOS4) 19.83 (6.90) 20.91 (6.68) 20.37 (6.80)
Average 19.13 (7.24) 20.02 (6.53) 19.58 (6.90)
Additional Baseline (being alone) 17.64 (7.51) - -
Note: Standard deviations are provided in parentheses.
Table 7: Mean payments in $ for all eight treatments in the replication study as well as for the additional
baseline (being alone)
The full replication is further supported by a regression analysis. I ran a linear regression with the
replication data, identical to the regression run with the original study data (see Table 4). The data
analysis leads to similar results as the regression with the original study data (see Table 8).
(1) (2) (3) (4)
Audience 0.59(0.34) 0.60(0.34) 0.35 (0.83)
Closeness 0.50∗∗∗ (0.15) 0.50∗∗∗ (0.15) 0.31 (0.21)
Audience x Closeness 0.38 (0.30)
First Order Beliefs 0.31∗∗∗ (0.03) 0.30∗∗∗ (0.03) 0.30∗∗∗ (0.03) 0.30∗∗∗ (0.03)
Normative Beliefs 0.37∗∗∗ (0.03) 0.37∗∗∗ (0.03) 0.37∗∗∗ (0.03) 0.38∗∗∗ (0.03)
Preference for Fixed Price 2.00∗∗∗ (0.35) 2.00∗∗∗ (0.35) 1.98∗∗∗ (0.35) 1.99∗∗∗ (0.35)
Age 0.02 (0.02) 0.02 (0.02) 0.02 (0.02) 0.02 (0.02)
Gender 0.18 (0.36) 0.09 (0.35) 0.11 (0.35) 0.11 (0.35)
Political Orientation 0.15 (0.11) 0.17 (0.11) 0.17 (0.11) 0.16 (0.11)
Children 0.18 (0.16) 0.20 (0.16) 0.19 (0.16) 0.19 (0.16)
Degree 0.37∗∗ (0.13) 0.38∗∗ (0.13) 0.38∗∗ (0.13) 0.38∗∗ (0.13)
Marital Status 0.30 (0.21) 0.30 (0.21) 0.29 (0.21) 0.27 (0.21)
Religion 0.04 (0.05) 0.03 (0.05) 0.03 (0.05) 0.03 (0.05)
AMNH Known 0.07 (0.29) 0.01 (0.29) 0.01 (0.29) 0.02 (0.29)
AMNH Already Visited 0.18 (0.42) 0.08 (0.42) 0.08 (0.42) 0.05 (0.42)
AMNH Intention to Visit 0.13 (0.36) 0.12 (0.36) 0.11 (0.36) 0.08 (0.36)
Importance: Cultural Activities 0.39∗∗ (0.15) 0.35(0.15) 0.35(0.15) 0.34(0.15)
Visits: Cultural Activities 0.45 (0.35) 0.54 (0.35) 0.54 (0.35) 0.55 (0.35)
Constant 3.26(1.74) 2.33 (1.76) 2.01 (1.77) 2.45 (1.80)
Observations 995 995 995 995
R20.41 0.42 0.42 0.42
Adjusted R20.40 0.41 0.41 0.41
Notes: Results from linear regressions. The paid price is the dependent variable in all four Models. Standard errors are provided in
parentheses. p< .1; p< .05; ∗∗p< .01; ∗∗∗ p< .001
Table 8: Determinants of the paid price with replication data
In Model 1, audience positively and marginally signicantly predicts payment behavior (coecient =
0.59, p= .076). A similar result occurs in Model 3 (coecient = 0.60, p= .071), when audience and in-
terpersonal closeness were both added to the regression. Furthermore, interpersonal closeness positively
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and signicantly predicts payment in Model 2 (coecient = 0.50, p< .001) and Model 3 (coecient
= 0.50, p< .001). This result is also in line with the regression analysis of the original data. Finally,
also being similar to the original study, I do not nd a signicant interaction eect between audience
and interpersonal closeness in Model 4 (coecient = 0.38, p= .228). Regarding controls included in
the regression, I nd rst order beliefs and normative beliefs, preference for xed price, degree, and
importance of cultural activities as signicant determinants of the paid price, being in line with the
regression analysis of the original study. Age is not a signicant predictor anymore which might be due
do the signicant dierence between the original and the replication data regarding age. Summarizing
the results from the replication data analysis, I conclude that the eects found in the original study are
robustly conrmed in a replication study.
Beyond the replication of the original study results, I retrieved additional variables in the replication
to gain further insights, especially about the closeness eect. These additional variables are explored
in the following. There are no signicant dierences between the eight treatments regarding pleasure
(Kruskal-Wallis test, H(7) = 10.04, p= .186), arousal (Kruskal-Wallis test, H(7) = 6.60, p= .472) or
dominance (Kruskal-Wallis test, H(7) = 7.75, p= .355). Furthermore, the eight treatments do not dier
regarding SVO types (χ2test, χ2(21, N= 995) = 19.52, p= .552). Thus, neither dierences in arousal
levels nor dierences in social preferences might explain the closeness eect.
Social cohesion values signicantly dier between the four interpersonal closeness conditions (Kruskal-
Wallis test, H(3) = 636.48, p< .001). The higher the interpersonal closeness, the more subjects care
about their connectedness with the other visitor. This result supports the suggested proposition that
with increasing interpersonal closeness individuals tend to align own behavior and expectations of others
in order to maintain the social relationship. Feelings of guilt do not signicantly dier between the four
closeness conditions when paid less than the AMNH expected (Kruskal-Wallis test, H(3) = 4.42, p=
.220) but they signicantly dier when paid less than the other visitor (Kruskal-Wallis test, H(3) = 8.49,
p= .037), and when paid less than the other visitor expected the subjects to pay (Kruskal-Wallis test,
H(3) = 35.02, p< .001). With increasing levels of closeness feelings of guilt increased, being another
indicator for the assumption that consistency with the expectations of close others plays an important
role in explaining the closeness eect. This holds true not only for feelings of guilt when paid less but in
a reversed relationship also for the case when subjects would pay more than the other visitor expected
(Kruskal-Wallis test, H(3) = 13.82, p= .003).
I further ran an additional baseline treatment, in which the subjects were alone during the payment
situation. 105 subjects participated in this treatment. The average payment in this additional baseline
treatment is $17.64 (SD = 7.51), being at a similar level as the average payment in the baseline treatment
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of the original study, IOS1 No Audience (M= 17.74, SD = 7.18). The dierence between the additional
baseline and the IOS1 No Audience treatment in the original study is not signicant (two-sample t-test,
t(232) = -0.11, p= .912). This result suggests that a payment situation, in which a stranger visitor is
present, is identical to a scenario, in which a visitor is alone.
7 Discussion
How do payment observability and various levels of interpersonal closeness between buyers aect vol-
untary payments for a ticket at the American Museum of Natural History in New York? This paper
aims to explore both aspects of social inuence in a setting with high external validity. The results lend
strong support to the assumption that the eects of an audience and interpersonal closeness are at work
separately.
Result 1 conrms the presence of an audience eect, suggesting that payments signicantly increase
if observed by others. This result is in line with the literature on increased prosocial behavior due to
observability (see, e.g., Alpízar et al. 2008; Andreoni and Petrie 2004; Engel 2011) and with social image
concerns being acivated if behavior is observed (Andreoni and Bernheim 2009; Bénabou and Tirole 2006;
Ellingsen and Johannesson 2008). It further is in line with ndings from Pay-What-You-Want settings,
suggesting that observation by other buyers increases payments (Dorn and Suessmair 2016, 2017; Hilbert
and Suessmair 2015; Hofmann et al. 2018; Schlüter and Vollan 2015).
Result 2 further conrms the presence of a closeness eect, indicating that voluntary payments
signicantly increase if individuals are together with interpersonally close other buyers. Thus, the social
relationship between buyers in a voluntary payment setting seems to be an important aspect in explaining
the individual payment decision. The result is consistent with previous studies showing that increased
interpersonal closeness leads to increased prosocial behavior (see, e.g., Cialdini et al. 1997; Korchmaros
and Kenny 2001; Kramer and Brewer 1984; Maner et al. 2002; Reddish et al. 2013; Rennung and Göritz
2016; Stel et al. 2008; Valdesolo and DeSteno 2011; van Baaren et al. 2004). The result also is in line
with the nding of Hofmann et al. (2018), namely that the interpersonal closeness between buyers itself
aects the payment decision irrespective of payment observability. This result is interesting in particular
as this study provides empirical evidence for the existence of such a strong closeness eect in voluntary
payment settings for varying degrees of interpersonal closeness between buyers for the rst time.
Furthermore, the data analysis reveals an additive relationship between payment observability and
interpersonal closeness. Payments are highest if being observed by a very close other. However, Result
3 suggests that the eect is not interactive but rather additive in total. This paper thus adds empir-
ical evidence to the proposition that the audience eect is not amplied when interpersonal closeness
increases. The data analysis is in line with the ndings of Hofmann et al. (2018), also for more than two
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Jena Economic Research Papers # 2020 - 016
levels of interpersonal closeness and in a setting with high external validity. It thus does not seem to
be the case that high interpersonal closeness pronounces social image concerns more when activated via
observability. The results regarding audience and interpersonal closeness were robustly conrmed in a
replication study with 995 subjects as indicated by Result 6. Future theories modeling the relationship
between observability and interpersonal closeness might take these ndings into account and emphasize
more on an additive relationship instead of an interaction.
As proposed in this paper, a high level of consistency between own behavior and beliefs might drive
the closeness eect which leads to a non-signicant interaction eect. Increasing consistency between
own behavior and expectations of others due to increasing social pressure with increasing interpersonal
closeness has been explored, summarized in Result 5. The results of this study thus add the aspect of
consistency of own behavior with beliefs to the theoretical literature on the inuence of interpersonal
closeness on prosocial behavior.
However, the data analysis also indicates that payments are lower if they are not observed which holds
true for all levels of interpersonal closeness. Dreber et al. (2013) and Jung et al. (2018) outline a conict
between conformity and own economic utility maximization in not observed prosocial consumer settings
as a possible explanation for this result, focusing on decision making in groups. Their results show that
in not observed decisions, brain regions are activated which are related to internalized prosocial behavior
(e.g. conformity due to social pressure) and that prosocial behavior is shown especially for appropriate
price levels. Further, the results indicate that in observed decisions brain regions are activated which are
related to strategic behavior (e.g. social image concerns). Applying this to the experiment, in unobserved
settings the social pressure might be at work only up to an assessed appropriate price which is a similar
concept to rst order beliefs and normative beliefs. It thus might be the case that the closeness of others
only facilitates higher payments up to this point, but not beyond, as no further rewards are expected from
paying more than the appropriate price in unobserved settings. Thus, economic utility maximization
kicks in as stronger inuence beyond the expected appropriate price and becomes more relevant than
social pressure. In observed settings on the contrary, social image concerns might explain why individuals
pay prices beyond the appropriate price level as they expect additional social reward from this behavior.
This line of argumentation is partly supported by the data. Although expectations about an appro-
priate price range around $20 in all eight treatments, payments go beyond this level only in the IOS4
Audience treatment which represents a scenario in which the buyers are observed by a very close other
visitor. Payments in the other treatments increase with increasing levels of interpersonal closeness only
up to the appropriate price level of around $20 without going beyond this point. This result lends support
to the assumption that amplied social image concerns are at play only for social relationships with very
high interpersonal closeness. Thus, the data suggests that payments increase only beyond appropriate
price levels if social pressure is high enough. Further research thus is necessary to better understand
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the relationship between beliefs, fullling these beliefs, observability, and interpersonal closeness. As
suggested by Dreber et al. (2013) it might be of interest for instance whether individuals dier regarding
their “norm-compliance parameter” (p. 353), leading to hetereogeneous levels of compliance.
Further, the ndings regarding beliefs and own behavior are limited by the correlational structure of
the data. Thus, it is not possible to causally elicit whether the expected behavior of the other buyer
inuences the own payment or vice versa. Literature on the false-consensus eect (see, e.g., Ross et
al. 1977) for instance raises the possibility that individuals tend to think that others behave similar
to themself. This is, individuals overestimate their own behavior as being the right thing to do and
accordingly as the behavior that others would show as well. Thus, the increasing level of consistency
between own behavior and beliefs with increasing levels of interpersonal closeness might be due to other
inuences and this paper does not claim exhaustiveness in this respect. Future studies on the relation
between beliefs and own willingness to pay should follow an experimental approach to better disentangle
the direction of the eect. Another possible approach of future research would be to elicit beliefs in a
randomized order before and after the own payment decision.
In addition, the current paper can not rule out that the relationship between interpersonal closeness,
beliefs, and own behavior is not necessarily linear. The own payment behavior of an individual for
instance might be positively aected if he believes that a very close other buyer would pay a high
amount while it would be negatively aected if he believes that a very close other buyer would pay a
very low amount. Such a possible net closeness eect of zero is suggested by Bicchieri et al. (2020) and
Dimant (2019). They show that with close others being present in a decision making setting, compliance
to pay less and compliance to pay more increases depending on the beliefs. It thus is relevant to explore
in future studies whether low or high levels of expectations might change the amplitude of the audience
and closeness eects. Summarized, a complete motivational explanation and a test for a non-linearity of
the behavioral eects lie beyond the scope of this paper but remains an open task for future studies.
What are the practical implications of the results? This study provides empirical evidence that not
only observability of behavior matters in a Pay-What-You-Want setting but also the buyer structure.
It has been shown that both, payment observability and the presence of close others in the buying
setting, are sucient to increase payments, not necessarily a combination of both. It thus is crucial for
the practical implementation of PWYW settings to take both eects into account. Making payments
observable is one successful strategy to increase payments. Further, designing settings in which buyers are
together with close others seems to be another successful strategy to increase payments. Accordingly,
voluntary payment mechanisms might also work successfully under anonymity (e.g. online products,
anonymous cashboxes) when buyers know that close others are also consuming. The results of this study
provide empirical evidence towards the assumption that it might be sucient that buyers are aware that
close others are consuming the product as well.
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8 Conclusion
This paper contributes to the literature by examining two important channels of social inuence on
buyer’s willingness to pay: the eect of payment observability (audience eect) and the eect of varying
degrees of interpersonal closeness (closeness eect). It explores both factors in the context of voluntary
payments for a visit at the American Museum of Natural History (AMNH) in New York. In addition, it
applies the method of borrowed identities of real social relationships in order to manipulate interpersonal
closeness in an online experiment with a high external validity. Furthermore, it investigates the impact
of interpersonal closeness on prosocial behavior for more than two levels. By means of a 4 x 2 online
experiment, I explore four levels of interpersonal closeness (very low interpersonal closeness IOS1, low
interpersonal closeness IOS2, high interpersonal closeness IOS3, and very high interpersonal closeness
IOS4) and two levels of payment observability (No Audience and Audience). This controlled setting
allows me to separately investigate the eect of payment observability (audience eect) and the eect of
interpersonal closeness (closeness eect), as well as their relation to each other, on voluntary payments.
I nd a signicant eect of an audience on payments: If subjects are observed by other visitors, the
mean payment is signicantly higher than when the visitors are not observed. I further nd a signicant
eect of interpersonal closeness on payments: If interpersonally close others are present, payments are
signicantly higher compared to a setting where strangers are present. The data analysis further reveals
an additive relationship between payment observability and interpersonal closeness. One possible expla-
nation for the additive relationship is the strong closeness eect driven by increasing consistency of own
paymet behavior with beliefs if degrees of interpersonal closeness increase.
On the basis of the results I conclude that interpersonal closeness and observability can be seen as two
separate drivers of voluntary payments. Clearly, further research will be required to validate the impact
of both drivers in other domains of prosocial behavior (such as donating and helping) or other types of
economic games (such as dictator games or public goods games). It further is of relevance for future
research whether the results of this study hold true for dierent products oered under PWYW conditions
and whether beliefs moderate the closeness eect on own behavior in a negative of positive direction. This
information can be used to develop a better understanding of the eects that observability of behavior
and the social relationships between the buyers have on individual payment decisions in PWYW settings.
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Appendix
A. Additional Material and further analyses
Variable Mean
Study
Freq.
Study
Mean
Repli-
cation
Freq.
Repli-
cation
p value
Age 38.37 39.87 .006
Female 0.551 0.537 .510
Political Orientation 3.53 3.41 .135
Children 0.84 0.75 .082
Degree .619
Less than high school graduate 0.3 0.5
High school graduate 9.7 9.3
Some college / associate’s degree 34.6 36.1
Bachelor’s degree 39.9 36.7
Advanced degree 15.6 17.4
Marital Status .562
Married / Partnership 52.1 49.0
Widowed 1.5 1.1
Divorced 8.4 8.7
Separated 1.1 1.4
Single / Never married 36.9 39.7
Employment Status .657
Employed 82.6 84.2
Unemployed 17.4 15.8
Net Income .195
Less than $ 20.000 11.4 11.1
$ 20.000 to $ 34.999 15.8 17.8
$ 35.000 to $ 49.999 19.9 19.2
$ 50.000 to $ 74.999 27.3 23.3
$ 75.000 to $ 99.999 13.4 14.5
Over $ 100.000 12.3 14.5
Religion .127
Protestant 24.4 26.3
Catholic 21.1 18.9
Other Christian 8.2 5.2
Jewish 1.9 1.5
Muslim 1.4 1.1
Buddhist 1.6 1.6
Hindu 0.5 0.6
Other 3.7 5.0
None 37.3 39.7
Note: P values for variables age, political orientation, and children stem from two-sided two-sample t-tests and
from χ2tests for variables gender, degree, marital status, employment status, net income, and religion.
Table 9: Characteristics of study participants and replication participants
B. Experimental instructions
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Instructions
The text in curly brackets {. . .} indicates treatment differences.
The text in brackets [. . .] indicates comments of the experimenter.
Welcome Dear participant,
thank you for participating in this survey!
This survey investigates decision-making. The focus is on your personal view, therefore, there are
no right or wrong answers! Please just answer according to your individual point of view. All the
data you provide will be processed anonymously and will only be used for academic research
purposes. On the following pages we will provide you with the instructions. Please read them
carefully.
Please note that we check every submission for plausibility! Whenever arbitrary answering
patterns are detected we will not pay you for the HIT submitted. So if you don't plan on
filling in this survey sincerely - save your time.
Please solve the following equation: 98 + 2 =
We all have social relationships with each other. They differ with regard to the perceived
closeness between you and other people. Some of these relationships are more close, others are
less close.
The picture below illustrates the different levels of closeness between you and another person
X.
Please think about your social relationships now and name relationships with regard to the
perceived closeness towards this people (see picture below).
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Jena Economic Research Papers # 2020 - 016
Please take into account:
Do only name people of the same gender as you.
Do not name people with whom you share a common income for livelihood or for whom you
are responsible financially (for instance children).
For some of you, these persons may be neighbors, personal friends or family members. For others
of you, these persons might be acquaintances or colleagues. Please select these people carefully
since these decisions will affect the rest of this survey.
Example: A social relationship towards a stranger, about whom you do not know anything, would
represent Picture 1.
Which gender do you identify with?
Please provide the first name of the person representing the
picture below.
Please remember: Name a person of the same gender as you.
Please remember: Do not name a person with whom you share a
common income for livelihood or for whom you are responsible
financially (for instance children).
Please enter the first name: [IOS2 Name]
With this person in mind, please respond to the following questions.
Age of person representing the picture above:
Gender of this person (please remember: only name a person of the same gender as you)
How long have you known this person? Please indicate the number of years.
Which of the following best describes your relationship with this person? (Check only one)
Please provide the first name of the person representing the
picture below.
Please remember: Name a person of the same gender as you.
Please remember: Do not name a person with whom you share a
common income for livelihood or for whom you are responsible
financially (for instance children).
Please enter the first name: [IOS3 Name]
With this person in mind, please respond to the following questions.
Age of person representing the picture above:
Gender of this person
How long have you known this person? Please indicate the number of years.
Which of the following best describes your relationship with this person? (Check only one)
39
Jena Economic Research Papers # 2020 - 016
Please provide the first name of the person representing the picture
below.
Please remember: Name a person of the same gender as you.
Please remember: Do not name a person with whom you share a
common income for livelihood or for whom you are responsible
financially (for instance children).
Please enter the first name: [IOS4 Name]:
With this person in mind, please respond to the following questions.
Age of person representing the picture above:
Gender of this person
How long have you known this person? Please indicate the number of years.
Which of the following best describes your relationship with this person? (Check only one)
{[only condition IOS1:]
Please describe your daily routine on a weekday in a couple of sentences, for instance what kind
of activities you do.
}
{[only condition IOS2:]
Please focus now on only one social relationship, namely the one you
indicated as representing the picture below.
Think about this specific person ([IOS2 Name]) while answering the
following question.
Please describe your relationship with [IOS2 Name] in a couple of
sentences, for instance what kind of activities you do together, where you met the first time or
which things you have in common.
}
{[only condition IOS3:]
Please focus now on only one social relationship, namely the one you
indicated as representing the picture below.
Think about this specific person ([IOS3 Name]) while answering the
following question.
Please describe your relationship with [IOS3 Name] in a couple of
sentences, for instance what kind of activities you do together, where you met the first time or
which things you have in common.
}
40
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{[only condition IOS4:]
Please focus now on only one social relationship, namely the one you
indicated as representing the picture below.
Think about this specific person ([IOS4 Name]) while answering the
following question.
Please describe your relationship with [IOS4 Name] in a couple of
sentences, for instance what kind of activities you do together, where you met the first time or
which things you have in common.
}
Please put yourself into a specific situation now and answer the next
question while keeping in mind the following circumstances.
Imagine you are visiting the American Museum of Natural
History (AMNH) in New York, where you can pay what you wish
for your entrance ticket. That means that the amount you pay is up to
you.
The American Museum of Natural History is one of the biggest
natural history museums in the world and contains more than 30
million objects. It is not only famous for its exhibitions, but is also
known from movies, as for instance the movie "Night at the Museum"
has been shot in the American Museum of Natural History.
You expect the visit to fulfill your expectations.
{[only condition Audience:]
You are standing in the queue at the ticket counter together with other visitors.
You recognise {another stranger (same gender as you) / IOS2 Name / IOS3 Name / IOS4
Name} also standing in the queue, right behind you.
As you reach the ticket counter and tell the staff how much to pay for your ticket, {this stranger
/ IOS2 Name / IOS3 Name / IOS4 Name} is still right behind you in the queue and observes
what you choose to pay.
That means, you, the staff person at the ticket counter and {this other stranger / IOS2 Name /
IOS3 Name / IOS4 Name} get to know how much you pay for your visit.
Which price would you be willing to pay for your ticket considering these given circumstances?
You, the staff person at the ticket counter and {the stranger / IOS2 Name / IOS3 Name / IOS4
Name}, standing right behind you, get to know how much you pay for your visit.
Please remember: At the American Museum of Natural History in New York you can pay what
you wish for your entrance ticket.
American Museum of
Natural History (Source:
https://pixabay.com/phot
o-719989/)
41
Jena Economic Research Papers # 2020 - 016
The American Museum of Natural History suggests to pay the following price: $23
You expect to be satisfied with your visit.
Please indicate the exact price in $:
}
{[only condition No Audience:]
You are standing in the queue at the ticket counter together with other visitors.
You recognise {another stranger (same gender as you) / IOS2 Name / IOS3 Name / IOS4
Name} also standing in the queue, far away behind you.
As you reach the ticket counter and tell the staff how much to pay for your ticket, {this stranger
/ IOS2 Name / IOS3 Name / IOS4 Name} is still far away behind you in the queue and does not
observe what you choose to pay.
That means, only you and the staff person at the ticket counter - but not {this other stranger /
IOS2 Name / IOS3 Name / IOS4 Name} - get to know how much you pay for your visit.
Which price would you be willing to pay for your ticket considering these given circumstances?
You and the staff person at the ticket counter, but not {the stranger / IOS2 Name / IOS3 Name
/ IOS4 Name}, standing far away behind you, get to know how much you pay for your visit.
Please remember: At the American Museum of Natural History in New York you can pay what
you wish for your entrance ticket.
The American Museum of Natural History suggests to pay the following price: $23
You expect to be satisfied with your visit.
Please indicate the exact price in $:
}
42
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IMPRESSUM
Jena Economic Research Papers
ISSN 1864-7057
Friedrich Schiller University Jena
Faculty of Economics and Business Administration
Carl-Zeiss-Str. 3
D-07743 Jena, Germany
Email: oce.jerp@uni-jena.de
Editor: Silke Übelmesser
Website: www.wiwi.uni-jena.de/en/jerp
© by the author
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