Beyond the First Offer: Decoding Negotiation Openings and Their Impact on Economic and Subjective Outcomes

Abstract

First offers play a significant role in negotiations as they anchor negotiators’ perceptions and influence negotiation outcomes in favor of the first-offer proposer. However, negotiation is a joint decision-making process in which a first offer is typically succeeded by a counteroffer. The impact of a counteroffer has not yet been systematically researched. We propose that a counteroffer influences negotiation outcomes like a first offer. In addition, we conceptualize the “anchor zone” as the distance between the first offer and the counteroffer. We theorize that the anchor zone influences negotiation outcomes because it captures additional information compared to a single offer. To test our hypotheses, we conducted two studies: Study 1 was a vignette study (n = 190) in which participants reacted to a counteroffer that they received based on their first offer as part of a simulated negotiation. Study 2 was an online experiment (n = 212) in which participants negotiated by exchanging offers with no further communication. Our analysis suggests that the counteroffer is a significant predictor of economic outcomes. Thus, it works like a first offer, but with a lower impact. In addition, the anchor zone predicted how far the final agreement was from the first offer. Furthermore, we found that the third offer, the average concessions, and the number of offers mediated the effects of the counteroffer and anchor zone on economic outcomes. Finally, we discovered that a more aggressive counteroffer reduced the subjective value of both negotiators.

Full text

Vol.:(0123456789)
Group Decision and Negotiation (2023) 32:395–433
https://doi.org/10.1007/s10726-023-09813-5
1 3
Beyond theFirst Offer: Decoding Negotiation Openings
andTheir Impact onEconomic andSubjective Outcomes
WolframE.Lipp1 · RemigiuszSmolinski2 · PeterKesting3
Accepted: 9 January 2023 / Published online: 30 January 2023
© The Author(s) 2023
Abstract
First offers play a significant role in negotiations as they anchor negotiators’ percep-
tions and influence negotiation outcomes in favor of the first-offer proposer. How-
ever, negotiation is a joint decision-making process in which a first offer is typically
succeeded by a counteroffer. The impact of a counteroffer has not yet been system-
atically researched. We propose that a counteroffer influences negotiation outcomes
like a first offer. In addition, we conceptualize the “anchor zone” as the distance
between the first offer and the counteroffer. We theorize that the anchor zone influ-
ences negotiation outcomes because it captures additional information compared
to a single offer. To test our hypotheses, we conducted two studies: Study 1 was a
vignette study (n = 190) in which participants reacted to a counteroffer that they
received based on their first offer as part of a simulated negotiation. Study 2 was an
online experiment (n = 212) in which participants negotiated by exchanging offers
with no further communication. Our analysis suggests that the counteroffer is a sig-
nificant predictor of economic outcomes. Thus, it works like a first offer, but with a
lower impact. In addition, the anchor zone predicted how far the final agreement was
from the first offer. Furthermore, we found that the third offer, the average conces-
sions, and the number of offers mediated the effects of the counteroffer and anchor
zone on economic outcomes. Finally, we discovered that a more aggressive counter-
offer reduced the subjective value of both negotiators.
* Wolfram E. Lipp
wolfram.lipp@tum.de
Remigiusz Smolinski
remigiusz.smolinski@hhl.de
Peter Kesting
petk@mgmt.au.dk
1 TUM School ofManagement, Technical University ofMunich, Arcisstrasse 21, 80333Munich,
Germany
2 HHL Leipzig Graduate School ofManagement, Jahnallee 59, 04109Leipzig, Germany
3 Department ofManagement, Aarhus School ofBusiness andSocial Sciences, Aarhus
University, Fuglesangs Alle 4, 8210Aarhus, Denmark
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Keywords Negotiation· First offer· Counteroffer· Anchoring· Bargaining
1 Introduction
Negotiations are complex interpersonal decision-making processes, and their out-
come depends on a plurality of factors that range from individual differences to the
setting in which a negotiation takes place, be it face-to-face or through computer-
mediated communication. This complexity makes it difficult to give universal advice
to negotiators on how to improve their negotiation performance. However, one effect
has been demonstrated to be highly robust and relevant to negotiators: the first-offer
effect.
There is a broad agreement among researchers that the first offer significantly pre-
dicts negotiation outcomes and that making the first offer is therefore beneficial for
the party proposing it. The importance of the first offer is attributed to the so-called
“anchoring effect,” which describes the relationship between a numeric value (some-
times randomly generated) put forward at the beginning of the decision-making pro-
cess and the subsequent decision (Tversky and Kahneman 1974). In their famous
experiment, Tversky and Kahneman (1974) asked participants to estimate different
percentages (e.g., the percentage of African countries in the United Nations). The
authors then generated a random number on a wheel of fortune and asked the par-
ticipants if the percentage was above or below that number, and then to estimate the
percentage. The random number influenced the participants’ judgment significantly:
If the random number was 10 (vs. 65), the final judgment was 25 (vs. 45).
This anchoring effect also translates to negotiations and in statistical means, the
first offer is a significant predictor of the final agreement. The currently predomi-
nant view of the mechanism behind anchoring is the selective accessibility model
(Furnham and Boo 2011). According to the selective accessibility model, seman-
tic knowledge is generated consistently with the value of the anchor (selectivity);
this information is then used to form the final judgment (accessibility) as part of a
two-step process (Chapman and Johnson 1999; Mussweiler and Strack 1999, 2001;
Strack and Mussweiler 1997). In other words, knowledge that is consistent with the
anchor is stronger and thus preferred for decision-making.
The first-offer effect has been confirmed in a meta study (Orr and Guthrie 2005)
and several other studies (Chertkoff and Conley 1967; Galinsky and Mussweiler
2001; Kristensen and Gärling 1997; Ritov 1996; Yukl 1974). In addition, the first-
offer effect has been found to be stable across culture, power, and negotiation issues
(Gunia etal. 2013). Therefore, the first offer-effect is an important determinant of
negotiation success.
A body of literature has developed around this phenomenon, improving the
understanding of the antecedents of first offers (Magee etal. 2007; Neville and
Fisk 2019), of boundary conditions and limitations of the first-offer effect (Liebert
etal. 1968; Maaravi and Levy 2017; Orr and Guthrie 2005), of subjective value
as a result of first offers (Maaravi etal. 2011), and distinguishing between differ-
ent types of first offers (Burger 1986; Leonardelli etal. 2019; Loschelder et al.
2014; Mason etal. 2013).
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Beyond theFirst Offer: Decoding Negotiation Openings and…
In most of the above-mentioned studies, the first-offer effect has been inves-
tigated singularly. Negotiation, however, is a joint decision-making process that
involves at least two parties that attempt to reach an agreement by influencing
each other’s decisions throughout the negotiation process, and starts with their
opening offers (Raiffa 2007). Raiffa emphasized this dyadic perspective in his
seminal book (Raiffa 1982) and defined the dyadic concepts of the reservation
price and the zone of agreement between the negotiator’s reservation prices as
important reference points that determine negotiation outcomes. In addition,
Raiffa asserted that there is an ongoing process of adjusting initial estimates of
reservation prices and making offers throughout the negotiation. Raiffa referred
to this process as a negotiation dance.
We contend that to improve our understanding of the dynamics and the implica-
tions of the anchoring effect in the context of negotiation, we need to consider the
interactive character of negotiation openings. This view requires us to investigate
not only the first offer, but also the counteroffer of the other party in reaction to
the first offer, and the consequences of these opening offers. We believe that, just
as a first offer undoubtedly influences the final outcome of a negotiation, a coun-
teroffer might similarly affect negotiator judgments and lead to adjustments in the
perception of reference points, as well as changes in the negotiation process. This
means that a counteroffer could correct the estimation of the counterpart’s reserva-
tion prices, which in turn leads to adjusted offers and altered outcomes. This impact
of the counteroffer, if validated, would give the responder an opportunity to act stra-
tegically, influence the counterpart’s judgment, and have an impact on the final out-
come of the negotiation. This interactive character of opening offers has largely been
ignored in the literature and is the main focus of this paper.
The negotiation opening consisting of first offer and counteroffer is comparable
to a chess opening. After the first figure is moved (first offer), the other player needs
to react to it (counteroffer). This is a very strategic process and the opening can
determine the course of the following game. In our point of view, this is a stronger
analogy compared to a negotiation dance as a dance is a coordinated movement with
the same goal while a chess game (or a negotiation) is a strategic decision-making
context with often opposing interests of the players. After the first move is made, the
move needs to be interpreted by the other player and a reaction needs to be made.
It is important to note that these opposing interests do typically persist in distribu-
tive negotiation situations. Contrary, in integrative or mixed-motive negotiations, an
integrative negotiations approach (like a dance) could lead to better results.
In chess, there are many books that describe common openings and the best
responses to them, but in negotiations, there are only few insights into this topic.
We propose that both first offers and counteroffers function as an anchor in simi-
lar ways, and that they both predict the process and outcomes. This would mean that
if a first offer predicts the outcome, a counteroffer would equally do so. Further, we
propose that first offers and counteroffers together form the “anchor zone” (the dis-
tance between the first offer and counteroffer) and that this anchor zone influences
the subsequent negotiation process, as well as economic and subjective outcomes.
Based on a review of the literature, we developed hypotheses to (1) explore
the role of opening offers in predicting negotiation outcomes; (2) test if the
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aggressiveness of the counteroffer—measured as the anchor zone—impacts the
negotiation in the counteroffer proposer’s favor; and (3) uncover the mechanics
(mediators) of this process. To test our hypotheses, we carried out two studies. In
Study 1, we conducted a vignette study in which participants formulated first offers,
received programmed counteroffers, and formulated a reply to the counteroffer. The
goal of Study 1 was to understand if a counteroffer influences subsequent behav-
iors, and also to test our manipulation magnitude (the size of the counteroffer). In
Study 2, we extended the setting to a full negotiation between two individuals in an
online experiment. The study was highly controlled and allowed for an alternating
exchange of offers without any additional communication between the parties.
The key contribution of this paper is to shift the fundamental understanding of
the opening of negotiations away from a unipolar view of first offers to a more com-
prehensive understanding of the anchor zone and its role in shaping negotiators’ fur-
ther judgment and behavior. We point to the relevance of counteroffers and provide
empirical support for the impact of these counteroffers. In addition, we introduce a
new concept—the anchor zone—, which extends our grasp of negotiation openings.
We conclude that negotiation openings seem to be more complex than suggested by
current research.
2 The Role ofReference Points inNegotiation Openings
Negotiators are required to gather and process multiple pieces of information to be
able to negotiate efficiently (White etal. 1994). A discussed above, the first offer is
an important reference point for judgment. However, there are additional reference
points that play a critical role in negotiations and that could potentially influence
each other. VanPoucke and Buelens (2002) mentioned three important reference
points: (a) the reservation price, (b) the aspiration price, and (c) opening offers. In
this study, we focused on the reference points, which need to be estimated to effi-
ciently negotiate or which are openly communicated at the negotiation opening: the
reservation price and opening offers.
2.1 The Troublesome Search foraReservation Price
A reservation price is an “indifference point, the point where the negotiator princi-
pally should be indifferent between accepting the offer or ending the negotiation (the
walk away price)” (VanPoucke and Buelens 2002,p. 68). Knowledge about the res-
ervation price is highly relevant to negotiators but typically, only one’s own reserva-
tion price is known. In addition, one’s own reservation price might not be absolutely
firm, and negotiators might only have a rough idea of their reservation price. In
order to form an understanding of the counterpart’s reservation price, Raiffa (1982)
recommended probabilistically assessing the reservation price and reassessing it
informally. However, Raiffa also warned that the counterpart might wish to deceive
the other party regarding the real reservation price.
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Beyond theFirst Offer: Decoding Negotiation Openings and…
The reservation price of the buyer and seller form the so-called “zone of agree-
ment” in which an agreement is possible (Raiffa 1982). It is important to note that
this concept is only suitable for distributive bargaining situations.
White etal. (1994) demonstrated that the reservation price was the most relevant
predictor of negotiation success. However, the reservation price is also influenced
by the first offers of the other party. Kristensen and Gärling (2000a) indicated that
the first offer of a seller influenced the reservation price of the buyer. This could
also explain the analysis of VanPoucke and Buelens (2002), who showed that res-
ervation prices did not influence negotiation outcomes. However, VanPoucke and
Buelens (2002) added reservation prices after first offers in a step-wise regression
model, and the addition of the first offer in the first step had likely assumed most of
the effect already.
As stated above, the reservation price of the counterpart is typically not avail-
able to negotiators and thus, the reservation price needs to be estimated. This esti-
mation is an iterative process and the estimation gets better over time (Bottom and
Paese 1999). Bottom and Paese (1999) also reported that the costs of an erroneous
judgment are asymmetric: In the case of an overestimation of the concession ability
of the counterpart (optimistic bias), negotiators yielded better outcomes than when
pessimistically biased.
Another asymmetry in the estimation of reservation prices is the asymmetric dis-
confirmation (Larrick and Wu 2007). Larrick and Wu (2007) found that negotiators
differently adjust their initial estimates of the counterpart’s reservation price. If the
estimate lies outside the bargaining zone, disconfirming evidence leads to an adjust-
ment of the estimate. If the estimate lies inside the bargaining zone, the negotiators
behave in line with the estimation, and the estimate becomes a “self-fulfilling proph-
ecy.” Thus, economic results are better if the estimate is outside the bargaining zone
as the negotiators approach the bargaining zone from the ambitious “outside point.”
As noted above, the reservation price is typically not known by the counterpart
and needs to be estimated. However, there is another reference point that is known
to both negotiators: the first offer made. In the following, we introduce the impact of
the first offer in negotiations.
2.2 The Importance oftheFirst Offer
In the introduction, we discussed the strong support for the first-offer effect on nego-
tiation outcomes. Research on first offers in negotiation, however, goes beyond the
mere effects on negotiation outcomes. In the following, we briefly introduce the
most relevant findings on top of the first-offer effect on economic outcomes, and
also relate them to the issue of negotiator judgment.
First, previous studies have shown that the structure of information availability
among the negotiators influences the first-offer effect. A high level of information
asymmetry leads to more effective first offers (Liebert et al. 1968). It could even
be beneficial for a negotiator not to make the first offer if information happens to
be distributed asymmetrically (Maaravi and Levy 2017). This relates well to the
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estimation of reference points since if information is absent, the first offer is one of
the few data points to draw upon.
Second, different types of first offers impact negotiation processes and outcomes.
Several authors have investigated the issue of precise offers versus round offers
and found that precise anchors (e.g., 1437 EUR) work better than round anchors
(e.g., 1400 EUR) (Loschelder etal. 2014; Mason etal. 2013). This precision effect
is related to an attribution of higher competence for the party proposing the pre-
cise anchor; thus, the value is deemed to be of higher value as a reference point. In
addition, an anchoring value could be unrelated to the negotiation at hand but still
influence outcomes significantly (Kristensen and Gärling 2000b; Whyte and Sebe-
nius 1997). In their research, the authors provided unrelated anchors (either an arbi-
trary price example or an error from an employee stating the wrong value) that also
worked as powerful anchors.
Moreover, it seems that negotiators use non-numeric variables to judge refer-
ence values. A strategic flinch in response to a first offer could improve results at
the expense of a worse relationship (Fassina and Whyte 2014). But also non-verbal
cues, like displayed wealth, influence the first offers: Maaravi and Hameiri (2019)
found that if wealth cues are present, first offers were higher than without wealth
cues. This impact of non-verbal cues indicates that negotiators use a multitude of
variables to evaluate the negotiation situation and to infer reservation prices or the
ability to concede.
Another body of literature has provided some evidence that the first offer is also
expected to influence subsequent negotiation behaviors. Initially, the first offer may
have already affected whether a negotiation takes place at all. The initial offer could
lead to a barrier to entry when the counterpart is perceived to be too aggressive (Lee
et al. 2018). When a negotiation takes place, the negotiators could use the initial
offer to draw conclusions about the appropriateness of their own aspirations (Liebert
etal. 1968). As such, negotiators might change their assessment of the situation. In
terms of negotiation behaviors, extreme first offers lead to higher concession-mak-
ing (Bateman 1980), more favorable offers, lower aspirations, and a higher perceived
toughness of the counterpart (Yukl 1974). Moreover, Jeong etal. (2020) found that
if buyers made higher first bids on a classified ad, sellers more often shared unfa-
vorable information like defects even though this information-sharing behavior
weakened their negotiation position.
Fourth, in addition to the economic outcomes and the negotiation behaviors, first
offers impact subjective value. Curhan etal. (2006) developed a commonly used
scale of subjective value: the subjective value inventory (SVI). This inventory cap-
tures four outcome categories: instrumental, self, process, and relationship. Several
authors have investigated subjective value with regard to first offers: Maaravi etal.
(2014) found that people assess their own results as worse if a strong anchor is used
by the counterpart. This also leads to less willingness to negotiate with the counter-
part in the future. Further, there is some evidence that anxious negotiators are less
satisfied after making first offers, even if these lead to superior negotiation outcomes
(Rosette etal. 2014).
Fifth, several moderators and boundary conditions influence the first-offer effect in
negotiation. One of them is gender stereotype confirmation: Kray etal. (2001) have
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Beyond theFirst Offer: Decoding Negotiation Openings and…
shown that women make lower first offers if gender stereotypes are activated. The
authors manipulated the purpose of the negotiations by telling participants that the
negotiation was used to diagnose their negotiation abilities. This led to lower first offers
for female participants while there was no difference between female and male partici-
pants, if no diagnosis was introduced.
Lastly, there are downsides to anchoring in negotiations. Extreme first offers can
increase the chances of an impasse in negotiations (Schweinsberg et al. 2012; Wang
etal. 2008), and this risk of an impasse limits the usefulness of the anchoring strategy,
or at least the possible extremity of the first offer. A potential explanation for this is that
an extreme first offer can signal to the counterpart that there is no zone of agreement.
According to our proposal above, we expect that a second offer would influence
negotiation results in a manner comparable to the first offer. The second offer is not
independent of the first offer. First offers influence counteroffers, together with reserva-
tion prices (Kristensen and Gärling 1997, 2000a). Moreover, first offers and counter-
offers are mutually correlated (Moran and Ritov 2002; Ritov 1996; VanPoucke and
Buelens 2002). This effect has been witnessed as more pronounced for extreme first
offers for which more extreme counteroffers result (Benton etal. 1972).
2.3 The Impact oftheNegotiation Type
Most of the above findings are based on the investigation of distributive negotiations.
A negotiation is distributive if negotiators have almost strictly opposed interests on one
issue (Raiffa 1982) and need to distribute the value amongst them. Distributive negotia-
tion is often described with the analogy of “splitting a pie.”
In addition to distributive negotiations, there are also integrative negotiations. In
integrative negotiations, there are shared interests, and value can be created by dis-
covering and meeting these interests (Mannix etal. 1989). The integrative win-win
potential is often described with the analogy of “expanding the pie.” However, even an
enlarged pie usually has to be distributed. In most negotiations, therefore, negotiators
have an incentive to claim and create value simultaneously. Mannix etal. (1989) speak
of mixed-motive negotiations in this context.
These integrative and mixed motive negotiations have a potential to significantly
change the role of reference points and first offers compared to a distributive negotia-
tion. For example, in an integrative negotiation, multiple first offers are possible. Due
to the novelty of the counteroffer aspect, however, this study—as most of the extant
research—focuses on a single-issue distributive negotiation.”
3 The Proposed Eects oftheCounteroer
Research on first offers is primarily concerned with single first offers or anchors and
their impact on decision-making and outcomes. However, negotiations are at least
dyadic in nature and after the first offer (anchor), a counteroffer is typically made by
the negotiation partner. We propose that both opening offers work together as two
anchors. This is because both convey essential information and form a basis for an
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assessment of the situation (like reservation points and the zone of agreement) and
further negotiating behaviors. Raiffa (1982) referred to the first moves and the fol-
lowing exchange of offers as a “negotiation dance” and, in a sense, first offers define
the “available dance floor”.
In the following paragraphs, we explain our hypotheses regarding the counterof-
fer based on the existing literature and theoretical considerations. We begin with the
effect of the counteroffer on negotiation outcomes.
As described above, the most widely accepted theory at present is the selective
accessibility model (Chapman and Johnson 1999), according to which, any strategy
that provides an additional point of reference or distorts the anchor should help to
de-bias the second negotiator and transform a single point into a range of points.
Following this, a negotiator now has more leeway to selectively assess values, which
will then serve as reference points in the negotiation. Chapman and Johnson (1999)
argued that anything that would make people pay attention to unique features dimin-
ishes the anchoring effect. Mussweiler (2002) found in an experiment that contra-
dicting evidence could reduce the effect of anchoring. Focusing on one’s own goals
can also reduce the impact of an initial offer (Galinsky and Mussweiler 2001). The
counteroffer could therefore draw attention away from the first offer toward the value
of a counteroffer, and provide a first disconfirmation of initial beliefs. Galinsky and
Mussweiler (2001) found that thinking about the opponent’s alternatives and the
reservation price could further reduce the effect of the first offer. The counteroffer
could also serve to facilitate this behavior by introducing a contrary reference point.
Another theory that supports the effect of a counteroffer is the “scale distortion
theory” (Frederick and Mochon 2012). This scale distortion theory was tested by
the authors and shows that an initial stimulus (e.g., asking to estimate the weight
of a dog) distorts the reference scale for any additional estimate (e.g., the weight
of a giraffe), making its value lower than without the initial stimulus. A subsequent
study on scale distortion of anchoring by Bahnik etal. (2019) revealed that two ini-
tial stimuli in opposite directions also had an effect on a subsequent judgment, and
that the second stimulus seemed to have an even greater influence. Even though the
author’s results were not statistically significant at the 5% level, this could provide
clues about the effect of a second anchor. In this line, Schaerer etal. (2016) also
found that several lower alternatives to an agreement had a negative impact on the
first offer and the negotiation outcomes. Thus, a counteroffer could have an aug-
menting (in the case of a counteroffer in line) or contrasting (in the case of a lower
counteroffer) effect in regard to the first offer. The counteroffer is therefore expected
to work in a way that is comparable to that of the first offer, and to influence negotia-
tion outcomes by means of the described mechanisms. Formally:
Hypothesis 1 (H1) The counteroffer is positively associated with the economic
negotiation outcome (agreed settlement price of the dyad).
In addition to the proposed base effect of the counteroffer, we propose that the
size of the counteroffer makes a difference. The literature on negotiations indicates
that extreme anchors do work (Chertkoff and Conley 1967), but that overdoing it
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Beyond theFirst Offer: Decoding Negotiation Openings and…
could result in higher impasse rates (Maaravi etal. 2014; Schweinsberg etal. 2012;
Wang etal. 2008). We expect the same logic for the counteroffer in that a longer dis-
tance from the first offer should have a stronger impact on the process and the out-
come of negotiations. As discussed above, the first offer and the counteroffer create
a range in which agreements are possible. We define this zone as the anchor zone. A
larger anchor zone suggests a more aggressive counteroffer.
The anchor zone differs from the zone of agreement specified by Raiffa (1982) in
that the former is determined by the first offers and the latter by reservation prices.
They are only congruent if the first offers correspond exactly to the reservation
prices, which is more of an exception.
Van Poucke and Buelens (2002) introduced a concept called the “offer zone” and
defined it as the difference between the aspiration price and the initial offer. This offer
zone significantly predicted the negotiation outcomes. Even though the offer zone
comes from two values of one negotiator and not two negotiators, it highlights the
importance of the idea of a zone, as it captures more information than single values.
In addition to the zones mentioned in prior literature, Raiffa (1982) provided evi-
dence for another reference point: the midpoint between first offers. According to
Raiffa, the midpoint is the best predictor of the final contract if it falls within the
zone of agreement. However, the midpoint fails to fully cover the information of
both first offers as different distances yield the same midpoint. For example:
• First offer 13, counteroffer 17
→
midpoint of 15, range of 4
• First offer 5, counteroffer 25
→
midpoint of 15, range of 20
Thus, we used the richer concept of the anchor zone as it captures additional informa-
tion. We expected the anchor zone to predict negotiation outcomes due to the fact that
it sets the “dance floor” for negotiation and potential outcomes. Notwithstanding, the
anchor zone is not ideal when it comes to predicting economic outcomes since the same
anchor zone could lead to different (higher or lower) negotiation outcomes. For example:
• First offer 20, counteroffer 15, anchor zone 5, outcome 17
• First offer 40, counteroffer 35, anchor zone 5, outcome 37.5
We therefore needed to focus on another outcome variable to measure the effect of
the anchor zone on the success of negotiations. We argue that the distance between
the first offer and the final outcome is suitable for gauging this effect and we termed
this variable “outcome distance”. This variable allows us to judge how much the
counteroffer proposer has changed the outcome in a favorable direction. Thus, we
posited the following:
Hypothesis 2 (H2): The size of the anchor zone is positively associated with the dis-
tance between the first offer and the final agreement (outcome distance).
In addition to their direct impact on the economic outcome, we propose that con-
cession-making strategies following the opening offers also mediate the effect of the
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second offer on the subjective outcome. The first element in the sequence of conces-
sions is the third offer. We propose that this third offer is influenced by the coun-
teroffer, and that such a change influences the economic outcome. In the case of a
buyer making a second offer, a lower second offer and the signaled pushback should
lead to a lower third offer by the first offer proposer occupying the role of seller and
vice versa in the case of buyers.
Hypothesis 3 (H3): The effect of the counteroffer on the economic outcome is medi-
ated by the third offer.
The second element in the sequence of concessions is the subsequent concession-
making behavior of the negotiators. Overall, we expected that a larger anchor zone
would lead to a higher number of concessions overall as a larger gap needs to be
bridged to reach a deal. The more offers made should then lead to greater adjust-
ments from the first offer to the final deal (outcome distance).
Hypothesis 4a (H4a): The effect of the anchor zone on the distance between the first
offer and the final agreement (outcome distance) is mediated by the number of con-
cessions made in the negotiation process.1
In addition to the number of concessions, the size of the concessions matters.
We expected that the concession size for both negotiators would mediate the effect
of the anchor zone on the final outcome. We first theorized that the counteroffer
proposer would change the concession making behavior. By focusing on informa-
tion that conflicts with the initial anchor, the counteroffer proposer is expected to
make lower average concessions if the anchor zone is large. The first offer would
typically require higher concessions to reach a deal in order to “bridge the gap.” The
counteroffer introduces a point for which lower concessions are required. This is
expected to reduce the average concession size made by the counteroffer proposer.
This altered concession size is in turn expected to mediate the relationship between
anchor zone and negotiation outcomes.
Hypothesis 4b (H4b): The effect of the anchor zone on the distance between the first
offer and the final agreement (outcome distance) is mediated by the average conces-
sion size of the counteroffer proposer.
In addition to the effects on the counteroffer proposer, we also expected an
effect on the first offer proposer. There are two potential explanations for this: On
the one hand, the counteroffer is expected to function as a reference point for the
first offer proposer. This assertion is based on the different effects of self-gener-
ated versus other generated anchors. Comparative studies have demonstrated that
self-generated anchors are more strongly adapted than anchors generated by coun-
terparts (Epley and Gilovich 2001, 2005). Epley et al. argue that proposers know
1 We did not formulate a hypothesis for each role since the offers were made in an alternating way and
thus always differ by 1.
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Beyond theFirst Offer: Decoding Negotiation Openings and…
that a self-generated anchor is highly subjective and potentially too optimistic, and
thus adjust it more. For an externally generated anchor, this adjustment is less pro-
nounced. In our case, the counteroffer is an externally provided anchor that should
thus be more powerful in the mind of the first offer proposer and, in return, we pro-
pose that the first-offer maker would be biased by the counteroffer. On the other
hand, if more aggressive push-back in the form of the counteroffer happens, we
would expect the first offer proposer to also adjust their concession-making strategy
to higher concessions.
Moreover, the findings of Frech et al. (2019) offer a possible explanation. The
authors found that scale granularity leads to smaller adjustment steps from an anchor
due to the fact that their mental scale is more fine-grained. If a large anchor zone
creates a very wide scale for further adjustments, this could lead to higher average
concessions by the first offer proposer, which in turn leads to changed quantitative
outcomes.
Hypothesis 4c (H4c): The effect of the anchor zone on the distance between the first
offer and the final agreement (outcome distance) is mediated by the average conces-
sion size of the first offer proposer.
In addition, we expected that the effect of the counteroffer or the anchor zone
would apply, regardless of whether the participant is a buyer or a seller. Although
there is some evidence that a first offer has a distinct effect on buyers and sellers
(Weingart etal. 1990), we had no reason to assume that the base effect of the coun-
teroffer should differ across roles.
Hypothesis 5 (H5): The effect of the counteroffer on quantitative negotiation out-
comes is role-independent.
Finally, we expected that the counteroffer would influence subjective negotiation
outcomes: Maaravi etal. (2014) found that the use of anchors led to a deterioration
in the perception of the substantial outcome for the counterparts and reduced will-
ingness to negotiate for the future. Extreme offers can also offend negotiators and
lead to an impasse (Schweinsberg etal. 2012). We expected comparable effects for
the counteroffer and the resulting anchor zone governed by two mechanisms: (1) A
dissatisfaction with the first offer leads to a lower counteroffer and/or (2) The anchor
zone size leads to dissatisfaction due to more effort required to reach a deal, as the
initial offers are further away from each other. This increased effort is expected to
reduce satisfaction with the negotiation in general since it is more ‘painful.’
Hypothesis 6 (H6): The size of the anchor zone is negatively associated with the
satisfaction level of both negotiators.
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4 Study 1: Vignette Study
We carried out a vignette study to test the feasibility of the experimental design
and to conduct a test of the first hypotheses. The main goal of the vignette study
was to confirm that a counteroffer alters the behavior of the other (first-offer mak-
ing) negotiator, as this is key to changing negotiation outcomes. In this vignette
study, we asked the participants to make a first offer to either the buyer or the
seller of a car. After the first offer, the participants received an automatically cal-
culated counteroffer; they were asked to react to the counteroffer with another
offer and to assess their satisfaction, as well as to rate their counterpart.
4.1 Method
4.1.1 Participants andDesign
An a priori estimation of the target sample size using G*Power software (Faul
etal. 2009) yielded 103 participants with a target power of .8 at a medium effect
size (
f2
.15), 7 predictors, and .05
𝛼
error probability. We recruited participants
via the Amazon Mechanical Turk marketplace. This recruitment method is also
used by other authors in the field of negotiation (e.g., Ames and Mason 2015) and
has the advantage of providing access to a diverse population. We only recruited
participants from the US to generate a culturally homogeneous sample; 236 par-
ticipants took part and we paid 1 USD as compensation for an expected time
investment of 5 minutes; this was in order to reach 12 USD/hour compensation,
which is above the minimum wage of 7.25 USD/hour. Of the participants, most
were in the age group of 25–34 (41%) and 35–44 (33%); 60 (32%) participants
were female. Most of the participants had more than 10 years of work experience
(66%) and only 2 participants had no work experience. A full table of the demo-
graphic data is in included in “Appendix2”.
We structured the experiment as a 2 (role: buyer, seller)
×
3 (counterof-
fer: extreme, medium, accommodating) between-subjects design. We randomly
assigned participants to one of the experiment cells using the experiment soft-
ware. Due to random assignment, we achieved an almost equal split of partici-
pants per treatment, ranging from 29 to 34 per cell.
4.1.2 Task
We used a slightly adapted version of the car sale case (Ames and Mason 2015,
study 2) in which a participant either had a car for sale or wanted to buy a car
(see the Appendix for case instructions). This task represents a distributive, sin-
gle-issue negotiation. Depending on their role (buyer, seller), the instructions
differed. The participants were free to choose their first offer. According to the
experiment conditions, counteroffers were automatically computed in each con-
dition: extreme, medium, and accommodating. In the extreme condition, − 50%
(in case the participant was the seller) versus
+
50% (in case the participant was
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Beyond theFirst Offer: Decoding Negotiation Openings and…
the buyer) of the initial offer was subtracted or added to the initial offer. In the
medium condition, the factors were − 20% versus
+
20%, and in the accommo-
dating condition, − 5% versus
+
5% respectively. The case instructions can be
found in the “Appendix1”.
4.1.3 Procedure andMaterials
We performed the experiment using an online survey software. Participants searched
for the task on Mechanical Turk and were redirected to the survey software after
accepting the task. After the participants started the experiment and accepted the
information producing informed consent, the instructions for the assigned roles
were displayed. The participants were then asked to provide a target price (which we
defined as the aspiration value) and the first offer they wanted to make to their coun-
terpart. Based on the offer, an automatically calculated counteroffer was proposed
on the next page. The counteroffer was displayed and the participant was asked to
provide a subsequent offer. After answering a satisfaction question (“How satis-
fied are you with the negotiation so far?” on a 7-point Likert scale, which ranged
from “extremely dissatisfied” to “extremely satisfied”), the participants were asked
“What kind of ’overall’ impression did the counterpart make on you?” (rated on a
7-point Likert scale, ranging from “extremely negative” to “extremely positive”). At
the end of the survey, demographic questions were asked. The survey also included
two comprehension/attention checks that were used to identify participants who had
not paid attention to the survey questions. One attention check question asked for
the mileage of the car in the experiment, and the other asked for two boxes to be
checked for a particular question.
4.1.4 Variables
The dependent variables of this experiment were: the third offer, the satisfaction of
the participant, and the impression of the (simulated) counterpart.2 The independent
variables were: the participant’s first offer, the programmed counteroffer (
+∕−
5%,
+∕−
20%, and
+∕−
50%, coded as a dummy variable with 5% as the baseline value),
and the participant’s role (either the buyer or seller, coded as a dummy variable). We
also obtained a measurement of aspiration. The demographic variables were: age,
ethnicity, gender, highest education level, occupational status, and work experience.
4.2 Results
Of the 236 participants, we had to remove 46 due to failed attention checks or incon-
sistent or illogical answers or offers; thus, we ended up with 190 valid data points.
We analyzed the data using R version 4.1.0 (R Core Team 2020) with the standard
2 We carried out a first run of the experiment, resulting in 19 valid responses, to test the experimental
setup. After this first run, we recruited the remaining participants. For these participants, we added the
item of satisfaction. The impact of group membership was not significant; we thus included both samples
in the models.
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408
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package, as well as the olsrr package (Hebbali 2017) and the stargazer package (Hla-
vac 2015).
Table1 summarizes the descriptive results for each experimental condition. The
first offers of the participants did not differ significantly in the experimental condi-
tions. Also, the third offers did not significantly differ. The satisfaction and impres-
sion of the counterparts differed significantly across the experimental conditions and
decreased by increasing the aggressiveness of the counteroffer.
We tested our hypotheses using a linear model per outcome variable. As
described in Hypothesis 3, we expected the counteroffer to influence the third offer.
This hypothesis has been confirmed using a regression analysis (Table2, Column
1). In the model, we included the first offer and the counteroffer conditions. Further-
more, we included an interaction term of the experimental conditions and the role,
since we expected the effects for the buyer and seller to point in different directions.
We also included an interaction term for the first offer and role to account for differ-
ences in the impact of the first offer. The model emerged as significant (F(7,182) =
54.04, p < .001), with an
R2
of .68, and the counteroffer was a significant predictor
of the third offer with b = 281.51, t(182) = 2.21, p = .03 for the
+∕−
20% counter-
offer and with b = 582.99, t(182) = 4.75, p < .001 for the
+∕−
50% condition. A
post-hoc power analysis yielded a power of 100%. A larger distance of the counter-
offer from the first offer led to a greater adjustment of the third offer, as observed
via the higher coefficient for the
+∕−
50% dummy. With the help of the interaction
term using the “role” variable, we found that sellers made lower adjustments than
Table 1 Means and standard deviations per treatment group study 1
Means with different superscripted letters are significantly different at p < .05. Group differences were
tested with a Kruskal–Wallis test due to the non-normality of the data. A Wilcox test was carried out as
post-hoc test. An exception is the Satisfaction variable for buyers. This was tested with a one-way analy-
sis of variance (ANOVA) as the data were normally distributed
Buyers Test statistics
+
5%
+
20%
+
50%
M SD M SD M SD
First offer 6448.3
a
656.4 6190.0
a
648.5 6263.2
a
613.4 H(2) = 2.20, p = .33
Third offer 6626.7
a
733.7 6595.0
a
640.1 6985.3
a
1252.2 H(2) = 0.34, p = .84
Satisfaction 5.3
a
1.3 4.3
b
1.4 2.6
c
1.4 F(2, 94) = 52.11, p < .001
Impression 5.2
a
1.5 4.1
b
1.5 2.6
c
1.2 H(2) = 33.86, p < .001
Sellers Test statistics
− 5% − 20% − 50%
M SD M SD M SD
First offer 7218.2
a
511.9 7325.0
a
542.1 7268.8
a
644.9 H(2) = 0.85, p = .65
Third offer 7009.8
a
458.3 6929.7
a
555.2 6687.5
a
975.3 H(2) = 0.90, p = .64
Satisfaction 5.6
a
1.3 3.9
b
1.6 2.1
c
1.2 H(2) = 50.34, p < .001
Impression 5.3
a
1.2 3.7
b
1.7 2.1
c
1.6 H(2) = 37.45, p < .001
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Beyond theFirst Offer: Decoding Negotiation Openings and…
buyers (and also in the other direction, which we expected). Thus, Hypothesis 5 was
rejected. In addition, we noted that the first offer was a significant predictor of the
third offer. The demographic variables did not reveal any significant correlations and
were therefore excluded from the model.
Further, we expected the counteroffer to influence satisfaction with outcomes
(Hypothesis 6) and performed a regression analysis (Table2, Column 2) to validate
this hypothesis. In the model, we included the first offer and the counteroffer dummy
variables. The resulting model was significant (F(3,186) = 59.77, p < .001,
R2
of
.49). A post-hoc power analysis also confirmed the high power (100%) of this test.
As shown in Table2, Column 2, satisfaction decreased with more extreme counter-
offers with b = − 1.35, t(186) = − 5.62, p < .001 for the
+∕−
20% dummy and with
b= − 3.12, t(186) = − 13.19, p < .001 for the
+∕−
50% dummy respectively. These
Table 2 Linear regression model study 1
∗
p<.1;
∗∗
p<.05;
∗∗∗
p<.01
Dependent variable
Third offer Satisfaction Impression
(1) (2) (3)
First offer 1.21
∗∗∗
− 0.00
∗∗∗
− 0.00
∗∗∗
(0.08) (0.00) (0.00)
Role = seller 1243.55
(822.55)
Counteroffer
+∕−
20% 281.51
∗∗
− 1.35
∗∗∗
− 1.40
∗∗∗
(127.18) (0.24) (0.27)
Counteroffer
+∕−
50% 582.99
∗∗∗
− 3.12
∗∗∗
− 2.93
∗∗∗
(122.70) (0.24) (0.26)
First offer*role = seller − 0.25
∗∗
(0.12)
Role = seller * counteroffer
+∕−
20% − 464.64
∗∗∗
(174.81)
Role = seller * counteroffer
+∕−
50% − 954.06
∗∗∗
(171.39)
Constant − 1193.68
∗∗
7.71
∗∗∗
7.97
∗∗∗
(520.89) (0.88) (0.97)
Observations 190 190 171
R
2
0.68 0.49 0.44
Adjusted R
2
0.66 0.48 0.43
Residual std. error 481.93 1.35 1.40
(df = 182) (df = 186) (df = 167)
F statistic 54.04
∗∗∗
2.34
∗∗∗
42.80
∗∗∗
(df = 7; 182) (df = 3; 186) (df = 3; 167)
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410
W.E.Lipp et al.
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results confirm Hypothesis 6. In addition, we found that regardless of the role, the
size of the first offer had a small but negative effect for predicting satisfaction.
Finally, we investigated the role of the counteroffer in the evaluation of the coun-
terpart. We estimated the regression model (Table2, Column 3) according to Model
2; it was also significant (F(3,167) = 42.80, p < .001,
R2
of .44) and with high
power (100%) according to a post-hoc test. There were fewer participants, as we
only added the impression item after the first run of the experiment.3 Our regression
analysis verified our expectation that a more aggressive counteroffer would lead to a
lower rating of the counterpart, with effects of b = -1.40, t(167) = − 5.27, p < .001
for the
+∕−
20% dummy and with b = − 2.93, t(167) = − 11.11, p < .001 for the
+∕−
50% dummy.
For models 2 and 3, the demographic variables did not have any significant pre-
dictive quality and were thus excluded from the model.
4.3 Discussion
We designed Study 1 to gain initial insight into the mechanics of the counteroffer
in negotiations, and to understand whether the counteroffer influences negotiation
behaviors and subjective evaluation. The above results indicate that the counter-
offer has an impact on the following negotiation behaviors and assessment of the
situation: The more extreme a counteroffer, the higher the adjustment of the third
offer (sellers adjust down, buyers adjust up). In addition, an increasing extremity of
the counteroffer leads to a reduction of the satisfaction and impression of the coun-
terpart. However, a counteroffer has a substantially weaker effect on the third offer
compared to the first offer (as first offers were in the range of 6190 and 7325, with
a coefficient of 1.21, this led to an impact of 7489–8863 on the third offer, with the
coefficients of the counteroffer being 282 and 583, respectively). Hence, the first
offer still exerts the strongest effect on the third offer. Therefore, the stronger the
counteroffer, the more adjustments by the counterpart that can be facilitated. How-
ever, this had the disadvantage of a poorer relationship and a reduced level of satis-
faction. This provides support for hypotheses 3 and 6.
In addition, Hypothesis 5 was rejected based on the above results. The coefficient
for sellers was smaller compared to the buyers, so it seems that the sellers defended
their first offer more strongly. This does tie in to the results of Weingart etal. (1990),
who showed that a seller’s first offer is a stronger prediction of the outcome than a
buyer’s first offer.
In sum, Study 1 confirmed that a counteroffer can potentially influence nego-
tiation behaviors, outcomes, and satisfaction. In addition, it verified the soundness
of our research design. Therefore, we decided to conduct a laboratory study that
included more process and outcome variables in a person-to-person negotiation
experiment.
3 As described above, we added the impression variable after the first run. We tested if the group mem-
bership (first run or second run) made a significant difference, but no such influence was confirmed; thus,
we used the full sample for the first two models.
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Beyond theFirst Offer: Decoding Negotiation Openings and…
5 Study 2: Laboratory Study
In Study 2, we expanded the setting of the experiment to include a full negotiation
with two participants negotiating with each other. We did so to test the remaining
hypotheses and to replicate the findings of Study 1. We performed a controlled labo-
ratory experiment in which two negotiators (a buyer and a seller) exchanged offers
in a distributive sequential bargaining situation. This was to last until a deal was
reached or time ran out.
5.1 Method
5.1.1 Participants andDesign
Before the experiment, we conducted an a priori analysis of the target sample size
using G*Power software (Faul etal. 2009). We used an effect size estimate
f2
= .15,
an
𝛼
error probability of .05, and a .8 power with 7 predictors. Our analysis yielded a
target of 103 for the sample size (given the dyadic interaction, this means 103 dyads
or 206 participants). Given the possibility of dropouts due to data quality or a non-
finalized experiment, we aimed to recruit 350 participants; 363 (thereof 200 female
and 153 male) participants from the participant pool of the behavioral economics
laboratory of a large university took part in the experiment. Most participants were
in the age range of 18–24 years (244, 69%), while the minority were in the ranges
of 25–35 (110, 30%), and 35–44 (4, 1%). All of their demographic details can be
found in the “Appendix5”. The participants were compensated with a fixed amount
of EUR 3, although it was announced that it was possible for them to earn up to
EUR 1 as a bonus, depending on their performance during the negotiations. We used
this bonus structure to create an incentive for good negotiating performance by pro-
viding a financial incentive.
We randomly assigned the participants to one experimental treatment in our 2
(role: buyer, seller)
×
3 (first offer size: accommodating, medium, aggressive)
design.
5.1.2 Task
For this experiment, we used an adapted version of the “Pharmaceutical Plant”
negotiation simulation (Galinsky and Mussweiler 2001). This simulation is also a
single-issue distributive negotiation as the price is the only negotiation item and
one party can only gain, if the other party concedes. We chose this simulation as
it featured a negotiation issue that is uncommon for the majority of people, which
means that they would not have reference values (which could have confounded the
experiment) in mind. If we used an iPhone for example, the participants could have
developed an idea about the price of an iPhone. This was most likely not the case
for a chemical plant. Thus, no existing information or anchor points were available
for the negotiators. In the simulation, a buyer and a seller of a pharmaceutical plant
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412
W.E.Lipp et al.
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negotiate the selling price of the plant. Both negotiators have some public infor-
mation (the previous buying price [EUR 15m], an appraisal value [EUR 19m], the
price of a comparable factory [EUR 27m], as well as the average reduction in price
in the real estate market [− 5%]). The instructions included private Best Alternative
to Negotiated Agreement (BATNA) information for the buyer (the construction of a
new plant would cost EUR 25m) and the seller (stripping down the plant and selling
its components parts would produce a revenue of EUR 17m). This led to a Zone of
Possible Agreement (ZOPA) between EUR 17m and EUR 25m. Detailed instruc-
tions can be found in the “Appendix3”.
We adapted the original simulation in two ways: First, we adjusted the case to
be parallel for the buyer and the seller. This means that all values had an equal
distance from the midpoint (EUR 21m) between the buyer and the seller. We did
so to eliminate any influence of non-linearity. This also required adding a second
appraisal value at EUR 23m, as the original case only included one appraisal value
close to the BATNA of the buyer at EUR 19m. Second, we defined the manipu-
lation of the first offer. We defined three values of first offers as manipulation: an
accommodating offer, which was below/above the BATNA and in the ZOPA (buyer:
EUR 19m; seller: EUR 23m); a medium offer, which was below/above the BATNA
with the same distance (buyer: EUR 15m; seller: EUR 27m); and an extreme offer
further below/above the BATNA (buyer: EUR 11m; seller: EUR 31m). We decided
to manipulate the first offer to control for this element and to reduce potential col-
linearity issues between the first and second offers. We structured the distances of
the first offer from the midpoint (EUR 21m) to be comparable to Study 1 (Study
2: 9%, 29%, and 52% vs. Study 1: 5%, 30%, and 50%). Details can be found in the
“Appendix4”.
5.1.3 Procedure andMaterials
We carried out the experiment online at the behavioral economics laboratory of
a large university. We invited participants using the ORSEE software application
(Greiner 2015). Participants registered for a session that started with a 5-min brief-
ing in which the experiment was introduced. The participants then opened the start-
ing link of the experiment in their browser. We programmed the experiment itself
with oTree (Chen etal. 2016). The players started with the case instructions and then
proceeded to the negotiation page, which featured a number entry box and a send
button for making offers. Further, the negotiation page contained a summary of the
role and reference points, as well as an offer history. Making offers was only possi-
ble in an alternating manner, so that the other player had to wait for the counterpart’s
offer to make a new offer. Only one player had the option of making the first offer.
There was a strict time limit of 10 min; however, the number of offers was unlim-
ited. After reaching the time limit, the negotiation ended and participants were for-
warded to the next page. Participants also had the option of ending the negotiation
by offering 999, which needed to be confirmed by the other party. After the negotia-
tion, participants answered the subjective value inventory (SVI) (Curhan etal. 2006)
and the demographic questions. We removed four items from the subjective value
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Beyond theFirst Offer: Decoding Negotiation Openings and…
inventory (items 9, 12, 14, and 15), as we deemed them irrelevant for a negotiation
limited to a mere exchange of offers and no other interactions.
5.1.4 Variables
Due to the dyadic structure of this study, the unit of analysis is the dyad. We took the
viewpoint of the counteroffer proposer to analyze the data. For example, SVI_own
refers to the counteroffer proposer, and SVI_other to the first offer proposer.
The variables that we manipulated include the size of the first offer, which we
dummy-coded as aggressive, medium, and accommodating. A direct use of the vari-
able as a continuous variable was not possible since the experiment featured three
levels of first offers as per the experimental conditions. Also, we dummy-coded the
role of the first offer maker as Buyer/Seller.
As dependent variables, we measured the contract value in million EUR and the
subjective value (SVI) on a scale of 1 to 5. Further, we computed the variable out-
come distance as the difference between the first offer and the final agreement. This
variable thus measures how far the agreement is from the first anchor, or how far it
is adjusted in favor of the counteroffer proposer.
As independent variables, we defined the second offer of the dyad as the counter-
offer and then calculated the anchor zone as the absolute difference between the first
offer and the counteroffer.
The mediating variables consisted of the third offer of the dyad (or the reaction
to the counteroffer), the negotiators’ average concession size, and the offer count of
the dyad. The offer count variable was not used at the player level, as the offer count
is essentially the same for both negotiators since offers were made in an alternating
way.
5.2 Results
From the total of 363 participants, we removed 81 due to an impasse or not reaching
an agreement due to the time limit. We removed another 76 as the first offer was not
made according to the instructions. This led to a final sample of 206 participants or
103 dyads.
The data on the impasse is reported in the “Appendix6” as per the suggestions of
Schweinsberg etal. (2022) on how to report impasse data. The frequencies indicate
a higher impasse rate for the aggressive opening offer condition, but a statistical test
was not meaningful due to the low number of impasses.
We analyzed the data using R version 4.1.0 (R Core Team 2020) and the standard
package, as well as the olsrr package (Hebbali 2017) and the stargazer package (Hla-
vac 2015).
Table3 outlines the means and standard deviations of the main variables meas-
ured. Because the data are dyadic in nature, one data point summarizes a dyad. We
carried out the analysis from the standpoint of the party making the counteroffer
(either the buyer or the seller).
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414
W.E.Lipp et al.
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The statistics in Table3 provide insight with respect to previous findings, even
though these have not been formulated as hypotheses: The different levels of first
offers did not result in different counteroffers. This is in contrast to the findings
of some authors who reported a correlation (Moran and Ritov 2002; Ritov 1996;
VanPoucke and Buelens 2002). On the other hand, the outcomes differed signifi-
cantly between the accommodating and the medium/aggressive conditions, and thus
replicated the effect of the first offer. However, there was no significant difference
between the aggressive and the medium offers.
In the following paragraphs, we proceed to explain how we tested our hypoth-
eses: To test Hypothesis 1, we computed a multiple linear regression predicting
the contract value of the dyad; this was based on the counteroffer, the first offer,
Table 3 Means and standard deviations per treatment group study 2
Means with different superscripted letters are significantly different at p < .05. We tested group differ-
ences with a Kruskal–Wallis test due to the non-normality of the data. We carried out a Wilcox test as a
post-hoc test. Exceptions include the counteroffer and anchor zone variables, which we tested with a one-
way analysis of variance (ANOVA) as the data were normally distributed
Buyers making first offer Test statistic
Aggressive Medium Accommodating
n = 16 n = 20 n = 18
Counteroffer 20.8
a
3.8 20.7
a
5.6 19.8
a
2.5 F(1, 52) = 0.41, p = .52
Anchor zone 10.2
a
3.8 7.2
b
4.3 3.4
c
2.8 F(2, 51) = 16.01, p < .001
Concession average self .5
a
.3 .4
a
.3 .2
b
.2 H(2) = 8.31, p = .02
Concession average other .7
a
.6 .6
a
.9 .3
b
.3 H(2) = 7.65, p = .02
Offer count 13.7
a
14.3 9.9
a
8.4 10.7
a
15.1 H(2) = 1.15, p = .56
Outcome 25.3
a
2.6 24.4
a
3.1 21.4
b
1.2 H(2) = 22.93, p < .001
Outcome distance 5.7
a
2.6 3.5
b
2.1 1.8
c
1.5 H(2) = 19.56, p < .001
SVI self 3.7
a
1.0 3.7
a
.5 4.0
a
.6 H(2) = 2.23, p = .33
SVI other 3.6
a
.7 3.9
a
.8 3.7
a
.6 H(2) = 1.23, p = .54
Sellers Test statistic
Agressive Medium Accomodating
n = 17 n = 22 n = 17
Counteroffer 25.9
a
3.0 25.3
a
1.8 25.8
a
2.5 F(1, 50) = 0.09, p = .76
Anchor zone 14.2
a
2.9 10.0
b
2.0 7.0
c
2.8 F(2, 49) = 36.91, p < .001
Concession average self .6
a
.5 .6
a
.4 .3
a
.2 H(2) = 5.90, p = .05
Concession average other .9
a
1.0 1.1
a
1.6 .3
b
.2 H(2) = 9.90, p = .01
Offer count 15.9
a
12.0 12.3
a
12.7 14.1
a
8.3 H(2) = 2.38, p = .30
Outcome 20.3
a
4.1 21.6
a
2.3 22.5
b
1.6 H(2) = 7.92, p = .02
Outcome distance 8.6
a
3.9 6.2
b
2.4 3.8
c
1.9 H(2) = 18.22, p < .001
SVI self 3.6
a
.7 3.8
a
.5 3.8
a
.5 H(2) = 4.56, p = .10
SVI other 3.8
a
.6 3.7
a
.7 3.9
a
.6 H(2) = 0.70, p = .70
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and the player role. The results can be found in Table4. We included the first offer
as a dummy variable to assess the moderating effect of the first offer. The model
obtained was significant (F(7,98) = 20.99, p <.01), with an
R2
of .60. A post hoc
test for the power achieved yielded high power of this test at 100%. The counteroffer
was a significant predictor of the contract value, b = .76, t(98) = 6.79, p < .01. The
effect of the counteroffer was moderated by the size of the first offer. In the case of
the extreme first offer, the counteroffer effect was reduced by − .41, and in the case
of the medium first offer, the effect was reduced by − .31. Finally, the counteroffers
of the sellers had a greater impact as per the interaction term. We checked the poten-
tial co-linearity of the first offer and the counteroffer using the variance inflation
factor. We did not observe any problematic co-linearity. Hypothesis 1 is therefore
confirmed.
In addition to our hypothesis test, we were also able to replicate Raiffa’s midpoint
prediction (Raiffa 1982) with a separate linear model4 (see the “Appendix7”).
Furthermore, Hypothesis 1 could be formulated as a mediation hypothesis.
We did not develop a mediation hypothesis since we expected the counteroffer
to have an independent impact. However, in a mediation model, we would then
expect the counteroffer to mediate the relationship between the first offer and the
contract. In order to investigate this relationship, we estimated a mediation model
using the Hayes Process Macro for R (Hayes, 2017) with 5000 bootstrap draws
and a 95% confidence interval. This methodology has been used by other authors
in the field of negotiations to investigate mediation effects (Geiger and Hüffmeier
2020). The mediation model can be found in the “Appendix 8”. The mediation
model confirmed the findings of the above linear model and of the descriptive
statistics. In addition, it shows that there was a full mediation for the seller’s role.
Table 4 Linear regression
model agreement
∗∗∗p
<
.01
;
∗∗p
<
.05
;
∗p
<
.1
Contract value
Counteroffer 0.76
∗∗∗
(0.11)
Aggressive first offer 10.05
∗∗∗
(2.89)
Medium first offer 7.94
∗∗∗
(2.68)
Role = Seller −11.63
∗∗∗
(3.48)
Counteroffer*aggressive first offer −0.41
∗∗∗
(0.12)
Counteroffer*medium first offer −0.31
∗∗∗
(0.12)
Counteroffer*role = Seller 0.26
∗
(0.14)
Constant 7.10
∗∗∗
(2.42)
Observations 106
R2 0.60
Adjusted R2 0.57
Residual std. error 1.97 (df = 98)
F Statistic 20.99
∗∗∗
(df = 7; 98)
4 Due to collinearity issues, we were not able to include the midpoint as a variable in the existing model.
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However, for the buyer’s role, there was no mediation since the first offer did not
predict the counteroffer. This is contrary to previous findings (Moran and Ritov
2002; Ritov 1996; Van Poucke and Buelens 2002). We suspect that our design,
with a limited number of first offers and many reference points, could be the rea-
son for this observation.
Hypothesis 2 posited that the distance between the first offer and the counterof-
fer—the anchor zone—would predict how far the final agreement would be from the
first offer. To test this hypothesis, we computed a multiple linear regression predict-
ing the variable of “outcome distance” from the absolute value of the anchor zone
and the role of the player. We added an interaction of the counteroffer and the role
to examine role differences. The results of the model can be seen in Table5. The
model was significant (F(3,102) = 80.426, p <.01), with an R² of .70. Also, this test
is powered at 100%. The anchor zone was a significant predictor of the outcome
distance with b = .45, t(102) = 8.41, p
<.01
. Hence, Hypothesis 2 was confirmed.
In the following, we explain how we tested the mediation hypotheses H3 and H4.
We tested each hypothesis with a separate mediation model.
For Hypothesis 3, we expected the counteroffer to influence the third offer,
which would then lead to a change in contract terms. The results of the mediation
model can be seen in Fig.1. The model indicates that the third offer mediated the
relationship between the counteroffer and the contract terms, as the confidence
Table 5 Linear regression
model outcome distance
∗∗∗p
<
.01
;
∗∗p
<
.05
;
∗p
<
.1
Outcome distance
Anchor zone 0.45
∗∗∗
(0.05)
Role = seller −1.80
∗∗∗
(0.83)
Anchor zone * role = seller 0.28
∗∗∗
(0.09)
Constant 0.54 (0.43)
Observations 106
R2 0.70
Adjusted R2 0.69
Residual std. error 1.75 (df = 102)
F statistic 80.43
∗∗∗
(df = 3; 102)
Fig. 1 Mediation test third offer
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Beyond theFirst Offer: Decoding Negotiation Openings and…
interval of the indirect path did not contain zero (CI 95% [− .2636; − .0699]).
Therefore, Hypothesis 3 was confirmed.
In Hypothesis 4a, we expected the offer count to mediate the relationship
between the anchor zone and the outcome distance. Figure2 displays the out-
comes of the mediation analysis. The results show that the anchor zone increased
the number of offers and that the offer count mediated the relationship. The indi-
rect effect was also significant at the 95% level (CI 95% [− .1071; − .0172]).We
therefore confirmed Hypothesis 4a.
In Hypothesis 4b, we expected a larger anchor zone to lead to smaller average
concessions for the counteroffer proposer, which in turn would lead to an agree-
ment in favor of the counteroffer proposer. The results of the mediation model can
be found in Fig.3. Even though paths a and b were significant, the indirect effect
was not significant as zero falls into the confidence interval (CI 95% [− .709;
Fig. 2 Mediation test offer count
Fig. 3 Mediation test own concession average
Fig. 4 Mediation test other concession average
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.0050]). We therefore rejected Hypothesis 4b. However, the significant c path rep-
licated the effect of the linear regression for Hypothesis 2.
According to Hypothesis 4c, the concession-making behavior of the first offer
proposer would mediate the relationship between the anchor zone and the outcome
distance. Figure4 presents the results of this mediation analysis. The indirect effect
was significant at the 95% level (CI 95% [.0135; .1103]). These findings confirmed
Hypothesis 4c. The effect size might seem small, but since the anchor zone is meas-
ured in millions of EUR, the effect is substantial. As above, the significant c path
replicated the regression outcomes in the above analysis.
Hypothesis 5 was concerned with negotiators’ roles. We expected that the effect
of the counteroffer would be role-independent. The regression results (Tables4,5)
reveal a difference across roles and thus, Hypothesis 5 was rejected. The interaction
terms indicate that the counteroffer had a stronger effect if the seller made it.
The last hypothesis, Hypothesis 6, was concerned with the subjective value for
both parties, which we captured using the SVI. We performed a multiple regression
analysis including the anchor zone, the player’s role, and the negotiation outcome
(see Table6).
Model 1 predicts the subjective value of the counteroffer proposer and has been
found to be significant (F(4,101) = 6.00, p <.01), with an R² of .19. A post-hoc test
for the achieved power yielded a high power of this test at 99%. The anchor zone
was a significant predictor of the contract value, b = − .05, t(101) = − 3.25, p <.01.
In addition, the outcome significantly predicted the subjective value, depending on
the role (higher outcome and lower satisfaction for the buyer and vice versa).
Model 2 predicts the subjective value of the other player (the first offer proposer).
The outcomes were similar but differed in terms of the coefficients. The model was
significant (F(4,101) = 6.27, p <.01, R² = .20) and had a power of 99% as per the
post-hoc test carried out with G*Power. Also in this model, the anchor zone pre-
dicted the subjective value with b = − .04, t(101) = − 2.45, p <.05).
Table 6 Linear regression
model SVI
∗∗∗p
<
.01
;
∗∗p
<
.05
;
∗p
<
.1
SVI_own (1) SVI_other (2)
Anchor zone − 0.05
∗∗∗
(0.02) − 0.04
∗∗
(0.01)
Player = seller − 3.04
∗∗∗
(1.04) 3.99
∗∗∗
(0.96)
Outcome (score) − 0.11
∗∗∗
(0.03) 0.061
∗∗
(0.03)
Player = seller * outcome 0.14
∗∗∗
(0.05) − 0.17
∗∗∗
(0.04)
Constant 6.66
∗∗∗
(0.75) 2.55
∗∗∗
(0.69)
Observations 106 106
R2 0.19 0.20
Adjusted R2 0.16 0.17
Residual std. error (df = 101) 0.66 0.61
F statistic (df = 4; 101) 6.00
∗∗∗
6.27
∗∗∗
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A larger anchor zone thus predicts a lower subjective value, and the analysis pro-
vides support for Hypothesis 6 for both negotiators. In addition, we found that the
negotiation outcome significantly influenced the subjective value of the negotiation
(depending on the role, as per the interaction term).
5.3 Discussion
The goal of Study 2 was threefold: We sought to understand the role of opening
offers in predicting negotiation outcomes, to grasp the impact of the anchor zone,
and to reveal the underlying mechanisms. The strength of Study 2 lies in the highly
controlled experimental design in which possible confounding variables were
reduced by the offer-only set up of the study.
Our results are in line with previous research on the anchoring effect in nego-
tiations and replicated the effect of the first offer on negotiation outcomes. How-
ever, there was no significant difference in the moderate and extreme first offers (see
Table3). Our data suggest that there is a decreasing utility of extreme first offers and
that “overdoing” it does not add additional value. We were unable to replicate the
effect of the first offer on the counteroffer, which has been found in several studies
(Benton etal. 1972; Kristensen and Gärling 2000a; Moran and Ritov 2002). This
may have been caused by the different nature of the negotiation cases: The studies
that observed the effect on the counteroffer did not provide any BATNA informa-
tion. Our case, in turn, featured a BATNA and other reference points, so that these
points would likely be used to formulate the counteroffer.
Our first hypothesis—the impact of the counteroffer on negotiation outcomes—
was confirmed. The counteroffer is an additional predictor of the economic outcome,
although the magnitude of the effect is lower than that of the first offer. In addition,
the power of the counteroffer decreases with more aggressive first offers.
We also devised the concept of the anchor zone (i.e., the distance between the
first offer and the counteroffer). We confirmed that a larger anchor zone leads to
a greater adjustment of the final outcome towards the counteroffer (Hypothesis
2). This finding is novel, as we both conceptualized the anchor zone and proved
its validity empirically. This confirms the intuitive assumption that “pushing harder
against the first offer” works. Further, it seems that for sellers, the effect is greater
than for buyers.
We also investigated some mechanisms that contribute to the effect. In our first
mediation analysis, we showed that the third offer mediates the relationship between
the counteroffer and the final outcome. The counteroffer led to a reduction of the
third offer. As the third offer positively predicted outcomes with a lower coefficient,
the indirect effect of the counteroffer on the outcome was negative. This replicates
the findings of Study 1 and extends them toward the results of the process. These
findings indicate that the counteroffer does influence the negotiation process and
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therefore opens the black box of “how” opening offers influence the negotiation
process.
Furthermore, our mediation analyses revealed which factors mediate the rela-
tionship between the anchor zone and the outcome distance. First, the offer count
emerged as a mediator of the outcome distance. A larger anchor zone led to more
offers which in turn increased the outcome distance. Thus, a “larger gap” leads to
more “negotiation work” but does indeed pay off. The average size of the conces-
sions plays a role, but the effect is different for the negotiators. For the counterof-
fer proposer, we expected that the counteroffer proposers would de-bias themselves
with the counteroffer and thus change any concessions they made in the direction
of a better outcome. It seems, however, that this effect only works for the first offer
proposer: The average concession size of the first offer proposer mediated the rela-
tionship between the anchor zone and the outcome distance. This is remarkable as
the anchor zone can alter the behavior of the first offer proposer. We suspect that a
modified evaluation of the “dance floor” led to this effect.
Finally, we found that the larger the anchor zone, the lower the satisfaction with
the negotiation process. This finding is intuitive, as a large anchor zone leads to a
more intense negotiation process and more offers are needed. This is in line with the
first-offer literature in which extreme first offers have negative effects on subjective
outcomes (Maaravi etal. 2014; Moran and Ritov 2002; Schweinsberg etal. 2012).
6 General Discussion
6.1 Contributions
The goal of this study was threefold. We aimed to (1) explore the role of opening
offers (the first offer and the counteroffer in reaction to the first offer) in predict-
ing negotiation outcomes; (2) introduce and scrutinize the role of the anchor zone
in predicting negotiation outcomes; and (3) investigate the effects of the opening
offers and the resulting anchor zone on the negotiation process. In the two stud-
ies, we found significant results that contribute to the literature on anchoring and
negotiation.
We found that the counteroffer has a significant influence on negotiation out-
comes. This is a new finding as most of the literature has focused on a single refer-
ence point, while the counteroffer has mostly been neglected in negotiation research.
This brings us back to the chess analogy: Both opening offers define the negotia-
tion situation and thus determine the subsequent game and its results. We argue that
a comprehensive analysis of the anchoring process and its impact on the negotia-
tion process needs to include both parties’ initial offers. Our results show that even
though the counteroffer might be influenced by the first offer, it significantly influ-
ences the rest of the negotiation process and its outcomes. This interplay between
the initial offers calls for a considerable modification of the standard interpretation
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of the anchoring effect in negotiations as compared to individual decision-making
scenarios. Due to the fact that they are at least dyadic in nature, negotiation is essen-
tially a joint decision-making process in which the anchoring process is clearly
bilateral.
These results also add to the findings of de-biasing the anchoring effect (Galinsky
and Mussweiler 2001) and suggest how to reduce the impact of a first offer, if we
cannot make it ourselves. This study also provides initial empirical evidence in an
area in which—despite existing advice on how to make a counteroffer (e.g., Gunia
2017a, b)— empirical substantiation has been lacking.
Further, we introduced and confirmed the relevance of the concept of the anchor
zone. The anchor zone captures an additional element of the negotiation opening:
the extremity of the counteroffer. This underlines the fact even more that we need to
move from a single reference point used as an anchor to an entire range that deter-
mines subsequent negotiation behaviors. This is highly relevant, because different
reactions (counteroffers) to the same first offer might yield completely different
results, which the concept of the anchor zone helps us to better understand these
dependencies.
We also contribute to the literature on the subject of negotiation openings by
uncovering the mechanisms of how counteroffers affect negotiation behaviors, as
well as economic and subjective outcomes. Most studies are concerned with an ini-
tial activity (e.g., a first offer) and the related results. The intermediate processes
and knowledge of how the opening phase of the negotiation affects outcomes are
important contributions made by this paper. Due to our highly controlled setting, we
focused on just a few aspects of the negotiation process, although the complex nego-
tiation process paves the way for a major field of inquiry.
Finally, we extend our findings beyond the economic outcomes by showing how
subjective value is impacted negatively by the anchor zone. The tradeoff between
optimizing negotiation outcomes and subjective value has been discussed in the
negotiation literature Benton etal. (1972) and Schweinsberg etal. (2012). We have
broadened this conversation by adding the anchor zone as another variable influenc-
ing the subjective values experienced by negotiators.
6.2 Limitations andFuture Research Topics
Due to the pioneering nature of our research on counteroffers and their effects, our
work has some limitations and leaves aside more unanswered questions for future
research. We start with reviewing the limitations and then examine future research
avenues.
At first, the studies were limited to only an exchange of offers. On the one hand,
it is necessary to isolate effects but on the other, this forced us to exclude many
variables that are common in real-life negotiations. This includes visual and verbal
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communication that might have a significant impact on the negotiation process,
which we could not observe.
Second, our experiments focused on distributive negotiations. As mentioned
above, the effect of first offers and counteroffers becomes more complex in multi-
issue, integrative negotiations. There could be multiple first offers and counteroffers
and they potentially determine the total outcome. However, this limitation is also
valid for the general field of first offers and anchoring. A recent review of first offers
in negotiations Lipp et al. (2022) did only surface one study investigating multi-
issue, integrative negotiations, namely the study by O’Connor (1997). O’Connor
(1997) found that first offers primarily predicted outcomes in compatible negotiation
items but not in integrative items. The authors did not include distributive items.
In addition, the narrative of the simulations could have influenced behaviors and
results. We limited the manipulations to observe the most natural behavior of the
negotiators, but the reference values of the cases could have already made a differ-
ence. Especially if anchoring is investigated, every value could have its own anchor-
ing effect. Hence, we call for replication of the effects in different settings, to sub-
stantiate the findings and to achieve robust results.
Furthermore, this study relied on simulations, which were “not existentially ’real’
for the participants” (McGrath 1981, p. 185). This caused concerns with extrapo-
lation to the general population (McGrath 1981). Even though most negotiation
research is based on simulations, future studies on opening offers could emphasize
more field research to determine if the above-described effects are robust across
contexts.
Finally, the nature of the participants might induce unwanted effects. In Study
1, we obtained a more experienced sample than in Study 2. Even though we repli-
cated some of the results in both studies, a replication of Study 2 with a professional
sample will add robustness. However, the underlying effects (anchoring) should be
relevant to all negotiators; we would thus not expect any differences due to age or
experience.
As suggested before, due to the novelty of this topic, our research poses a series
of intriguing questions. The most important avenues for future research are summa-
rized in the following:
A first step should be to replicate our findings in other cases, information condi-
tions, and richer communication channels (F2F, video, chat). The anchoring effect
is very robust (Gunia etal. 2013) and we would expect there to be a similar level of
robustness for the counteroffer effect. In particular, the asymmetry of information
seems to be significant, as it is one of the most influential factors in the classical first
offer paradigm.
In addition, it would be interesting to systematically investigate the underlying
processes and mechanisms that lead to behavioral changes; in particular, percep-
tions of reservation prices and their change over time based on different negotiation
openings seems to be a promising area for research. Although we did uncover some
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moderators of the negotiation process, we lack an understanding of the cognitive
processes explaining why negotiators alter their behaviors.
A more practical avenue for further research involves the values of counteroffers
and the associated first offers. Which combinations are the most efficient ones, and
what counteroffer should one make given a particular first offer? An identification of
a “sweet spot” would certainly be of interest to many negotiation practitioners. The
anchor zone should ideally be large enough to lead to a disconfirmation of the oth-
er’s perception of the reservation price. If this happens, the other will likely adjust
the negotiation strategy. This adjustment will likely be dependent on the judgment
accuracy, confidence, and bias before and during the negotiation.
The potential “sweet spot” is also related to the issue of the impasse. Aggres-
sive first offers and counteroffers could increase the impasse rate. They could even
do so unwanted by negotiators because a counteroffer generated the impression that
there is no bargaining zone. A systematic investigation of negotiation openings and
impasses seems necessary to better understand the connection between them.
Finally, researchers could investigate most of the findings regarding anchoring
in negotiations, including those in the context of counteroffers. One example of
this might consist of the arguments used in the counteroffer. Maaravi etal. (2011)
showed, for example, that arguments in a first offer are capable of having detrimen-
tal effects. It is possible that the situation is completely different for counteroffers
and that arguments might help, as they de-bias the negotiation partner. Other first
offer effects could be investigated in the same manner. Of great interest for our point
of view would be the narrative of the situation, namely, how the counteroffer is pre-
sented (e.g., together with information, dismissively), and the availability of infor-
mation in that situation.
6.3 Implications forPractice
As the findings are novel and not yet based on a track record of replication, any
practical advice we can formulate is at best tentative. However, our results have
some important implications. Firstly, our results strengthen the recommendation
that a first offer should be made in such a way that its effect is stronger than that
of the corresponding counteroffer. Although this is clearly true for this scenario, it
might not apply to other information scenarios. Further, our findings suggest that the
effect of a first offer can be mitigated by proposing a tough counteroffer. This, to our
knowledge, is the first empirical proof of a suggestion commonly made in the man-
agement literature. Besides effectiveness, a counteroffer has certain disadvantages
when compared to an aggressive first offer: It necessitates the creation of a larger
overall number of offers and causes the negotiation to be subjectively perceived as
having less value (lower subjective value). It might even lead to the counterpart hav-
ing a worse impression of the counteroffer proposer. Negotiators should certainly
be aware of this particular disadvantage. Finally, knowledge of counteroffer strategy
use could provide helpful advice for the first offer maker: A third offer should not be
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modified following a counteroffer that is more extreme, while concession-making
should not be shifted away from whatever tactics were initially planned.
Appendix1: Role Instructions Study 1
We adopted this case from Ames and Mason (2015).
Buyer instructions
Imagine that you are shopping for a used car. You’ve thought carefully about
your options and have concluded that your ideal car would be a Volkswagen
Passat that is perhaps six to eight years old. You’ve done some research and
concluded that this kind of car, in good shape with low mileage, typically sells
for $6500–$7500. Several cars of this type are typically being sold in your area
at any given time. You recently read an ad for a 2015 Volkswagen Passat. Every-
thing looked promising: low mileage (about 50,000), in good shape, nice color.
You meet with the seller and take the car for a test drive. Everything looks good
about it and you’d like to get this car if possible. You’d also like to pay the least
you possibly can for it. If the price is not attractive, you would consider looking
elsewhere. After the test drive, you talk with the seller. The ad for the car didn’t
say anything about price, but you have done a little homework, as noted earlier.
You are now preparing your offer to the seller.
Seller instructions
Imagine that you are selling your used car. It is a Volkswagen Passat that is seven
years old and has about 50,000 of mileage. You’ve done some research and con-
cluded that this kind of car, in good shape with low mileage, typically sells for
$6500–$7500. Several cars of this type are typically being sold in your area at any
given time and you decided to put an ad online. One potential buyer showed up for a
test drive and seems to be interested in the car. You would like to sell the car to the
interested buyer but you also want to get the best price possible. If the price is not
attractive, you will consider other potential buyers. As your ad didn’t say anything
about the prices, the buyer is now asking you for an offer for the car.
Appendix2: Demographic Data Study 1
See Table7.
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Table 7 Demographic data Study 1
Demographic categories Number before
removal
Number after
removal
% of total
Age
18–24 6 5 3
25–34 100 78 41
35–44 73 63 33
45–54 30 27 14
55–64 18 15 8
65–74 2 2 1
Ethnicity
Asian/Pacific Islander 15 15 8
Black or African American 21 13 7
Hispanic or Latino 11 9 5
Native American or American Indian 4 1 0
Other 2 2 1
White 176 150 79
Gender
Male 155 130 68
Female 74 60 32
Education
Associates or technical degree 21 21 11
Bachelor degree 121 97 51
Graduate or professional degree 28 21 11
High school diploma or GED 18 17 9
Some college, but no degree 39 32 17
Some high school or less 2 2 1
Occupation
Homemaker 4 4 2
Student 1 1 1
Employed for wages 165 138 73
Military 1 1 1
Other 1 1 1
Out of work and looking for work 9 8 4
Out of work but not currently looking for work 4 2 1
Retired 3 3 2
Self-employed 41 32 17
Work experience
1–5 years 42 20 11
5–10 years 49 40 21
Below 1 year 6 3 2
More than 10 years 129 125 66
None 3 2 1
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Appendix3: Case Instructions Study 2
Buyer Instructions5
You are the Chief Financial Officer (CFO) of Biosphere, a pharmaceutical com-
pany, and your company needs a new factory to produce a highly specialized raw
material for production. None of your existing factories can produce the raw mate-
rial, and you cannot convert the existing factories either.
Comptech recently announced the sale of a factory for this very raw material.
Comptech bought the factory three years ago for € 15 million. This price was below
market value, as the then seller was threatened with bankruptcy. Two years ago the
factory was valued at € 19 million. Another appraisal a year ago came to a valuation
of € 23 million. The property market has declined − 5% since the purchase, but gen-
eral trends may not be relevant to these highly specialized factories. A similar fac-
tory, albeit a newer one, was sold for € 26 million a few months ago. Alternatively,
your company could build a new factory itself. This factory would cost € 25 million
and would take a year to complete (including approval by the drug agency).
You have been tasked with negotiating the purchase price of the factory with the
Comptech CFO. The negotiation will take place via a computer system in which
offers can be exchanged without further communication. <You have agreed with
your colleagues that you will make the first offer. In your discussion, you agreed to...
1. ...start with a very low price. The price you agreed on is 11 million EUR
2. ...start with a low price. The price you agreed on is 15 million EUR
3. ...start with a fair offer. The price you agreed on is 19 million EUR
As you agreed on the price with your colleagues, you will start with this price as
first offer.>
Seller Instructions6
You are the Chief Financial Officer (CFO) of Comptech, a pharmaceutical com-
pany. Your company has discontinued a line of products. Since you cannot use the
factory for the product line for anything else, your company wants to sell the factory
and has asked you to sell it. Recently, Biosphere has expressed an interest in the
factory.
Your company bought the factory three years ago for € 15 million. This price was
below market value, as the then seller was threatened with bankruptcy. Two years
ago the factory was valued at € 19 million. Another appraisal a year ago came to
a valuation of € 23 million. The property market has declined − 5% since the pur-
chase, but general trends may not be relevant to these highly specialized factories.
A comparable factory, albeit a newer one, was sold for € 26 million nine months
6 Text within
<>
only visible, if role was instructed to make first offer
5 Text within
<>
only visible, if role was instructed to make first offer
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Beyond theFirst Offer: Decoding Negotiation Openings and…
ago. Alternatively, your company could shut down the factory and sell the individual
parts. That would probably generate revenues of € 17 million.
You have been tasked with negotiating the purchase price of the factory with the
Biosphere CFO. The negotiation will take place via a computer system in which
offers can be exchanged without further communication. <You have agreed with
your colleagues that you will make the first offer. In your discussion, you agreed to...
1. ...start with a very high price. The price you agreed on is 31 million EUR
2. ...start with a high price. The price you agreed on is 27 million EUR
3. ...start with a fair offer. The price you agreed on is 23 million EUR
As you agreed on the price with your colleagues, you will start with this price as
first offer.>
Appendix4: Overview Case Mechanics Study 2
See Fig.5.
Appendix5: Demographic Data Study 2
See Table8.
Fig. 5 Overview case mechanics study 2
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Appendix6: Impasse Frequencies Study 2
See Table9.
Table 8 Demographic data Study 2
Demographic categories Number before
removal
Number after removal % of total
Age
18–24 244 140 69
25–34 110 61 30
35–44 4 3 1
Gender
Male 153 84 41
Female 200 117 57
Non-binary 4 3 2
Prefer not to say 3 2 1
Education
Pre-university school degree 166 101 49
Bachelor degree 131 70 34
Master degree 61 34 17
Post-master degree 2 1 1
Occupation
Student 322 182 88
Employed for wages 31 19 9
Looking for work 4 3 2
Other 3 2 1
Work experience
None 139 81 39
Below 1 year 109 58 28
1–5 years 99 57 28
5–10 years 10 7 3
More than 10 years 3 3 2
Table 9 Overview of impasse rates
Impasse data only for non-removed datasets (e.g., no wrong first offer)
Treatment
Aggressive Medium Accommodating
Buyer made counterofffer 0/16 (0%) 2/22 (9%) 0/18 (0%)
Seller made counteroffer 7/24 (29%) 0/22 (0%) 2/19 (10%)
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Beyond theFirst Offer: Decoding Negotiation Openings and…
Appendix7: Linear Regression Model Midpoint
See Table10.
Appendix8: Mediation Model First Oer
→
Counteroer
→
Outcome
See Fig.6.
Acknowledgements We have no known conflicts of interest to disclose. All data has been made pub-
licly available at the OSF Repository and can be accessed at https:// osf. io/ 7erwc/? view_ only= 81a89 da1f5
4e435 7a5c7 623b2 8c069 54. This study was not preregistered.
Funding Open Access funding enabled and organized by Projekt DEAL.
Declaration
Conflict of interest We have no known conflicts of interest to disclose. All data has been made publicly
available at the OSF Repository and can be accessed at https:// osf. io/ 7erwc/? view_ only= 81a89 da1f5
4e435 7a5c7 623b2 8c069 54. This study was not preregistered.
Table 10 Linear regression
model midpoint
∗∗∗p
<
.01
;
∗∗p
<
.05
;
∗p
<
.10
Contract value
Midpoint 0.84
∗∗∗
(0.06)
Constant 3.93
∗∗
(1.43)
Observations 105
R2 0.63
Adjusted R2 0.62
Residual std. error (df = 104) 1.85
F statistic (df = 1; 104) 174.6
∗∗∗
Fig. 6 Mediation test outcome
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