REFERENCE PRICES AS DETERMINANTS OF BUSINESSTO-BUSINESS PRICE NEGOTIATION OUTCOMES: AN
EMPIRICAL PERSPECTIVE FROM THE CHEMICAL
INDUSTRY
DIRK C. MOOSMAYER
RWTH Aachen University
BJOERN SCHUPPAR
Schuppar Consulting Ltd & Co KG
FLORIAN U. SIEMS
RWTH Aachen University
Price negotiations in supply chain relationships often take place during
annual pricing reviews. This study integrates transaction cost economics
and reference price thinking from consumer behavior to understand better
how a seller’s reservation price, aspiration price, and initial price offering
might influence the ultimate settlement price. We apply ridge regression
to negotiation data from 282 business relationships of a German chemicals supplier with customers in six client industries. Overall, the three
determinants explain 86 percent of the variation in the settlement price. A
seller’s reservation price is substantially less important than the aspiration
price or the initial price offering. Although this outcome can be explained
via a reference price perspective, transaction cost economics theory helps
clarify the industry differences that determine the impact of reservation
prices and initial price offerings on settlement prices.
Keywords: business-to-business marketing; negotiation; buyer/supplier relationships;
ridge regression; transaction cost economics
INTRODUCTION
Price negotiations in business relationships provide
an opportunity to adjust prices to recent changes in
material and process costs, as well as realize loyalty premiums. However, literature that approaches price negotiation from a supply chain management (SCM)
perspective has been scarce and dispersed (Zachariassen
2008). Existing frameworks and models are mainly
rooted in neoclassical thinking and transaction cost
economics (TCE), neglecting a potential psychological
perspective (Carter, Kaufmann and Michel 2007).
Moreover, student experiments in the psychological
negotiation domain (Krause, Terpend and Petersen
2006) require particular attention to internal validity
(Bachrach and Bendoly 2011; Eckerd and Bendoly
2011) and may be limited in their external validity (Ste-
92
vens 2011). Finally, empirical approaches that use mail
surveys in the SCM domain are likely to produce less
innovative insights (Carter and Ellram 2003).
To overcome these shortcomings, we integrate a TCE
perspective with the reference price concept that is
common in consumer behavior research and thus pursue a better understanding of price negotiations in
supply chain relationships. With industrial price negotiation data from the chemical industry, we also overcome some methodological shortcomings of existing
studies. In particular, we seek to understand how
three reference prices — reservation, aspiration and
initial price offering — influence the settlement price
ultimately achieved in a negotiation.
We begin by drawing on a TCE perspective on business relationships, then discuss negotiations in the
Volume 48, Number 1
B2B Price Negotiation Outcomes
supply chain. Thereafter, we explain adaption-level
theory and prospect theory, which are common inputs
to consumer-oriented reference price research, and use
them to model the settlement price of price negotiations in a SCM context. To test our proposed model,
we use price negotiation data obtained from salespeople at a German chemical producer. In discussing the
results and relating them back to TCE and psychological negotiation literature, we derive implications for
researchers and industrial sellers and buyers.
PERSPECTIVES ON PRICE NEGOTIATIONS
IN A SUPPLY CHAIN CONTEXT
A Transaction Cost Perspective on Business
Relationships
A common method to investigate and explain business exchange relationships is TCE (e.g., Williamson
1996, 2008; Joshi and Stump 1999), which reflects
the assumption that production costs are not influenced by the organization of the exchange (Williamson 1996). Accordingly, transaction costs, which result
from preparing, negotiating and realizing a transaction, are the main unit of analysis. A transaction takes
place if its transaction cost is lower than that of any
alternative transaction (van Hoek 2000). In this context, a transaction’s asset specificity, uncertainty and
frequency are relevant cost drivers (Ellram 1991).
Asset specificity refers to the extent to which investments made to support a transaction have more value
for that specific transaction than they would if used
elsewhere (Lonsdale 2001). On the one hand, asset
specificity constitutes the basis of cost advantages that
result from joint action. On the other hand, it can
lead to lock-in and operational and fiscal hostage taking. We reconsider this aspect carefully in the discussion section. Uncertainty results from “[un]anticipated
environmental changes [that] consist of two types:
changes in market factors (such as price) that impact
demand or changes in technology” (McNally and
Griffin 2004, p. 7). With regard to technological
development and market factors, uncertainty provides
the basis for regular renegotiations of specified transactions in a continuous relationship. We account for
it in our basic approach of investigating annual pricing reviews. Finally, frequency describes the extent to
which transactions are repeated on a regular basis,
because “preserving continuity between a particular
buyer–seller pair is the source of added value” in
supply chains (Williamson 2008, p. 5). Transaction
frequency offers a basic rationale for continuous
business relationships and is thus inherent to our
conceptual and empirical perspective.
With increasing asset specificity, uncertainty and frequency, transaction partners likely begin to integrate
their solution developments and implement joint
activities. Williamson (1975, 2008) describes a path,
from a simple market exchange to hybrid contracting
to a hierarchy. If we consider annual price renegotiations in business-to-business (B2B) relationships,
transactions represent hybrid contracting: The products are more specific than simple commodities, yet
the degree of integration is not as strong as it would
be in a hierarchy. Some transactions may be based on
substantial regular basis (Schuppar 2006), but the
partners’ positions are still distinct enough to require
renegotiations on a regular basis.
In previous SCM research, TCE has appeared in various applications with widespread empirical support,
within and across diverse industries (Shelanski and
Klein 1995; Rindfleisch and Heide 1997; Grover and
Malhotra 2003; McNally and Griffin 2004; Halldórsson and Skjøtt-Larsen 2006). For example, by surveying 126 company representatives across different
industries and cross-functionally, McNally and Griffin
(2004) investigate joint action, or the act of contracting solutions instead of making one’s own. Although
their results fail to attain some significance thresholds, they find that asset specificity, behavioral uncertainty and demand uncertainty are associated
negatively with buying and positively with joint
action. Moreover, relationship-oriented versus priceoriented compensation increases support for joint
action.
The basic rationale for moving from sourcing and
selling in a free, unassisted market to hybrid contracting assumes that buyers and suppliers agree on a
broader exchange of information based on credible
commitments. Accordingly, a supplier would charge
lower prices than in a free market (see arrow 1a in
Figure 1), because all measures undertaken to fulfill
demand, such as buying a new machine, have more
time to amortize. In addition, buyers could pay prices
higher than in the free unassisted market (see arrow
1b in Figure 1), because they do not need to provide
safeguards to suppliers and because they avoid future
search costs. These win–win relationships result in a
larger negotiation space, determined by the buyer’s
and seller’s reservation prices (Walton and McKersie
1965; Raiffa 1982; White, Valley, Bazerman, Neale
and Peck 1994) that is called the bargaining zone.
The bargaining zone also describes a transaction’s
margin, which the parties distribute between themselves. In price negotiations, each party aims to
increase its share of this margin (see arrow 2 in
Figure 1).
Negotiations in a B2B Context
The tension between free market competition and
collaborative joint action, inherent to TCE thinking, is
also reflected in the general negotiation literature.
January 2012
93
Journal of Supply Chain Management
FIGURE 1
Reference Prices and Bargaining Zone (Stereotypical)
2a
Seller negotiates for higher prices
1a Joint activity
decreases seller’s
reservation price
Seller’s
Initial Price
Offering
Seller’s
Aspiration
Price
Seller’s
Reservation
Price
$ or percentage
price increase
0
Bargaining Zone
$ or percentage
price increase
0
Buyer’s
Aspiration
Price
Buyer’s
Reservation
Price
2b
Buyer negotiates for lower prices
In negotiations, parties with (some) opposing interests
make a joint business decision, often to purchase a
certain amount of a specifiable product or service at a
specifiable price. Integrative approaches that focus on
joint outcomes thus can be contrasted with distributive approaches that aim to maximize individual outcomes (Lewicki, Saunders and Minton 2000).
Integrative negotiations “bring two or more parties
together to try to accomplish mutually beneficial outcomes, while meeting individual goals that may be at
odds with the other negotiating parties’ goals” (Swaidan 2007, p. 163). In contrast, distributive negotiations are more competitive, involving “a process of
potentially opportunistic interaction by which two or
more parties, with some apparent conflict, seek to do
better through jointly decided action than they could
otherwise” (Lax and Sebenius 1986, p. 11). Ramsay
(2004) empirically finds that most negotiations are
distributive and ignore the potential for improved
business processes that ultimately would lead to more
profit through integrative negotiations. Similarly,
Smeltzer, Manship and Rossetti (2003) find that integrative sourcing strategies rarely lead to integrative
negotiations but rather produce negotiations that
focus on optimizing the share of an outcome’s distribution. In contrast, conventional wisdom often assumes
the strategies to be mutually exclusive (Walton and
McKersie 1965), because integrative negotiations
require information disclosure, whereas distributive
negotiations build on informational differences.
In this context, it appears valuable to separate sourcing from price negotiation. In line with TCE, we assume
that business partners can pursue an integrated sourcing strategy. By defining more efficient sourcing processes, they reduce transaction costs and increase the
bargaining zone. However, unlike Walton and McKersie (1965), we also assume that integrative sourcing
94
Joint activity 1b
increases buyer’s
reservation price
strategies may coexist with distributive price negotiation strategies, within a single business relationship.
In hybrid contracting relationships in particular (Williamson 2008), elements of more distributive market
transactions integrate with features of more integrative
joint actions. On the basis of in-depth interviews with
key accounts, Zachariassen (2008) has suggested a
matrix of sourcing strategies (arm’s-length versus partnership) and negotiation strategies (distributive versus
integrative), in which he describes distributive negotiations in long-term partnerships as “manipulations,”
such that negotiators apply partnership terminology
(e.g. trust, mutual understanding) to obscure their distributive aims.
Beyond these general negotiation strategies, SCMbased negotiation research sheds some light on other,
diverse perspectives. Some studies at the relationship
level investigate power (e.g., McHugh, Humphreys
and McIvor 2003) and trust (e.g., Zaheer, McEvily and
Perrone 1998). Others address the system level of
business negotiations to consider communication
(Karkkainen, Laukkanen, Sarpola and Kemppainen
2007) and price-building mechanisms, such as when
Schoenherr and Mabert (2007) note bundling in bidding processes or Kaufmann and Carter (2004) study
reverse auctions. Yeniyurt, Watson, Carter and Stevens
(2011) add influences on an individual level to their
study of electronic reverse auctions and find that the
supplier’s need for cognition is associated negatively,
whereas the number of prior auction failures and
experience in the current auction (total bids and relative rank of the latest bid) are associated positively
with suppliers’ propensity to continue bidding. Furthermore, Rinehart and Closs (1991) investigate purchasers’ personality as an individual influence. In their
experimental approach, Wilken, Cornelissen, Backhaus
and Schmitz (2010) include 119 student dyads and
Volume 48, Number 1
B2B Price Negotiation Outcomes
41 key account manager dyads to explore the influence of cost information on sales managers’ negotiation behavior and negotiation outcomes: Full cost,
rather than direct cost information increases sellers’
reference prices and leads to increased settlement
prices.
By focusing on the costs as determinants of prices,
this study links to existing economic perspectives
(e.g., Nash 1950, 1953; Tversky and Kahneman 1991)
and the TCE perspective in particular. We integrate
the TCE perspective with influences of individual price
perceptions on the individual psychological level (e.g.,
Tversky and Kahneman 1981; Oliver, Balakrishnan
and Barry 1994; Barry and Oliver 1996; Brenner,
Koehler, Liberman and Tversky 1996), which makes
Krause et al.’s (2006) insights into negotiation partners’ reservation prices, aspirational prices and initial
price offerings of particular interest: In an experimental setting with 128 student teams, they find that the
initial price offering has the strongest influence on settlement prices. We elaborate conceptually on this perspective by detailing the reference price concept from
the consumer behavior domain, together with two of
its dominant underlying theories.
Reference Price Research
A reference price is an individual price norm,
applied when the person judges a price in the market
(e.g., Winer 1986, 1988). A reference price may have
a normative and predictive function (e.g., Rajendran
and Tellis 1994; Kalyanaram and Winer 1995). Reference prices can explain consumer brand choice and
reveal how consumer price perceptions may develop
during and after a purchase. They commonly are differentiated by their sources, whether internal (i.e.,
derived from the mind and based on prior experience) or external (i.e., observed in a decision-making
situation) (Mazumdar, Raj and Sinha 2005). Moreover, the reference price concept appears both in B2B
marketing applications (e.g., Wilken et al. 2010) and
beyond, such as in accounting (Mitter and Siems
2008) and human resource management (Siems, Goelzner and Moosmayer in press). Conceptually, reference prices are associated with expectations of
acceptable adaptation levels (Monroe 1973) and aspiration perspectives (Klein and Oglethorpe 1987).
Often applied to the domain of behavioral pricing
(e.g., O’Neill and Lambert 2001), two social psychological theories appear of particular relevance in this
context: adaption-level theory and prospect theory.
The former assumes that judgments of perceived stimuli (e.g., observed prices) are based on a comparison
of the stimuli with an internal adaption level that
reflects recent experiences (Helson 1964). In the consumer domain, the prices last paid for a product, or
internal memory-based references, constitute the dom-
inant adaption levels (Winer 1986; Urbany, Bearden
and Weilbaker 1988; Kalwani, Yim, Rinne and Sugita
1990; Kalwani and Yim 1992; Putler 1992; Greenleaf
1995; Kalyanaram and Winer 1995). For infrequently
purchased products, external references, such as the
prices paid by friends or recently observed during an
active price search, gain more importance. In contrast,
prospect theory (Kahneman and Tversky 1979) models the utility of a transaction as the sum of gains and
losses, compared against a reference point, in which
loss aversion leads to the overweighting of losses compared with gains of equal size. Prospect theory further
assumes decreasing sensitivity, with increasing deviation of observed prices from references. Furthermore,
reference prices and their framing have relevance for
price negotiation outcomes (Bazerman, Magliozzi and
Neale 1985; Nagel and Mills 1989; Wilken et al.
2010). The most important references are the negotiator’s reservation price, the aspiration price and the initial price offering, as we discuss further in the
following section.
MODEL DEVELOPMENT
With our proposed model, we aim to explain the
settlement price reached in price negotiations in continuous B2B relationships. Because the references possessed by a transaction partner usually are inaccessible
to a negotiator, we focus on the seller’s reference
points, without considering the buyer’s references. We
also assume a hybrid contracting context, including a
continuous relationship and somewhat customized
products. We therefore investigate the percentage price
change, compared with the current price. In our study
context, the seller approaches the buyer with a prepared initial price offering during an annual pricing
review.
Influence of Reservation Price on Settlement
Price
The reservation price is the lowest price at which a
seller is willing to sell. It is internal, so buyers and sellers know their own but rarely can access their partner’s.
Some studies identify reservation prices as important
determinants of settlement price (e.g., White et al.
1994; Kristensen and Gaerling 1997a), and they derive
from two main sources: a company’s marginal production cost (Wilken et al. 2010) and its selling or sourcing alternatives (e.g., Wang and Zionts 2008). Such
alternatives include the so-called best alternative to a
negotiated agreement (Fisher and Ury 1981) or the
market price, in the case of less specific products (White
et al. 1994; Blount White, Thomas-Hunt and Neale
1996; Kristensen and Gaerling 1997b).
Though internal to the seller, the reservation price is
strongly influenced by aspects beyond the negotiator’s
January 2012
95
Journal of Supply Chain Management
control, such as the company’s cost base. Therefore,
TCE predicts benefits of hybrid contracting agreements, which implicitly assume that cost changes
allow renegotiations and can be shared. Nash (1950,
1953) also theorizes that negotiators settle somewhere
in between both parties’ reservation prices, according
to the so-called Nash equilibrium. Cost increases and
related changes in reservation prices ceteris paribus
result in a settlement price increase of half the change
in the reservation price. Despite some support for the
Nash equilibrium (e.g., White et al. 1994), the
assumption of equal cost sharing by both negotiating
partners may be overstated (e.g., Neale and Bazerman
1991, 1992). We thus model a positive influence of
the reservation price on the settlement price and
reconsider the degree of its impact.
Influence of Aspiration Price on Settlement Price
The aspiration price represents the best settlement a
buyer or a seller can expect to achieve (Walton and
McKersie 1965; Zetik and Stuhlmacher 2002), or the
seller’s price target, which is an internal reference. But
Kristensen and Gaerling (1997a) propose that a
party’s aspiration price also reflects its estimate of its
partner’s reservation price. A negotiator thus attempts
to achieve the price at which the counterpart is still
willing to contract. Sellers judge buyers’ counteroffers
primarily in comparison with their aspiration price. In
applying prospect theory (Kahneman and Tversky
1979), we predict that any price below the aspiration
price prompts loss perceptions, whereas prices above
that level indicate gains. Recall that losses are systematically overweighed (Skowronski and Carlston 1987;
Baumeister, Bratslavsky, Finkenauer and Vohs 2001).
Therefore, any price below the aspiration price should
cause dissatisfaction and invoke efforts by the seller to
move the price closer to its aspiration price. In contrast, prices above the negotiator’s aspiration price are
gains and do not merit aggressive tactics. Accordingly,
higher aspiration prices should result in higher settlement prices, in line with findings that show that negotiators with higher goals or aspiration prices achieve
higher settlement prices (Pruitt 1981; Huber and Neale 1986, 1987; White and Neale 1994; Lim 1997).
The asymmetric effort exerted to defend this reference
price should lead settlement prices to move toward
the reference price, such that this determinant should
have a particularly strong impact.
Influence of Initial Price Offering on Settlement
Price
The initial price offering constitutes a strategic informational cue, provided by one partner to the other (e.
g. Raiffa 1982; Galinsky and Mussweiler 2001). Initial
price offerings are the first numbers named in a negotiation; they provide the anchor on which further
96
negotiation is oriented and thus constitute a key element of competitive bargaining. Sellers determine
their initial price offering before the negotiation starts
(Krause et al. 2006); for buyers, it is the first price that
requires a response. Research that investigates initial
price offerings as triggers of counteroffers, which constitute the key elements of competitive bargaining processes, involves both anchoring and adjusting of both
parties’ reference prices (Kahneman 1992). As strategic
informational cues, initial price offerings also affect
the counteroffer by giving the negotiation partner
information that he or she can use to reassess an
intended offer (Liebert, Smith, Hill and Keiffer 1968;
Kristensen and Gaerling 1997c). A seller’s initial price
offering thus should be regarded as a strong adaption
level by the buyer, whose counteroffer should respond
to that level. Because buyers, in the interest of keeping
the negotiation going, likely respond with an offer
that might be assimilated rather than contrasted, an
increased initial price offering tends to raise subsequent offers in a negotiation. Thus, research shows a
positive effect of the initial price offering on corresponding counteroffers (e.g., Yukl 1974; Kristensen
and Gaerling 1997c) and ultimately the negotiation
outcome (Pruitt and Carnevale 1993). Some research
even has asserted that the initial price offering is the
best predictor of settlement price (Van Poucke and
Buelens 2002). We include a positive relationship
from initial price offering to settlement price in our
model, such that by integrating considerations of reservation price and aspiration price, we obtain:
PS ¼ b0 þ b1R PR þ b1A PA þ b1I PI þ e;
ð1Þ
where PS is the settlement price, PR, seller’s reservation
price; PA, seller’s aspiration price; PI, seller’s initial
price offering; bjX, importance coefficients for determinant X in model j; and e, error term.
Industry Influences
Industry differences should affect price negotiations.
Henke, Yeniyurt and Zhang (2009) note the relevance
of power, especially in terms of pressure for price concessions in the automotive industry. Van Poucke and
Buelens (2002) also show that competitive scenarios
influence settlement prices. Together with industryspecific cost structures, such as varying dependence on
raw materials, such influences describe the level of
price increase likely to be realized in annual reviews
in any industry. Accordingly, we assume that industry
context affects the settlement price and conceptualize
Di as the range of i different industry dummies.
Furthermore, industries might differ in the relevance
of their reference prices. Low asset specificity causes
price transparency, and dependence on raw materials
may increase the relevance of cost-oriented reasoning
or reservation prices’ impact. Moreover, the relevance
Volume 48, Number 1
B2B Price Negotiation Outcomes
of variable, performance-related compensation varies
by industry (e.g., Ely 1991). Higher variable portions
in remuneration packages should increase negotiators’
desire to obtain a better price and thus the relevance
of aspiration prices. The negotiation outcome also
could be influenced by the reference used to evaluate
a negotiator’s performance (e.g., market index, seller’s
initial price offering). Accordingly, we integrate the
multiplicative interaction terms proposed by Edwards
and Lambert (2007) to develop our next model:
PS ¼ b0 þ b2R PR þ b2A PA þ b2I PI þ b2i Di þ b2Ri PR Di
þ b2Ai PA Di þ b2Ii PI Di þ e;
ð2Þ
where Di = set of I industry dummies, D1 DI.
METHOD
Approach
In line with recommendations to use the most qualified informants to gather information (Kumar, Stern
and Anderson 1993), we invited the sales staff of a
global German chemical firm to record their negotiation data. To reduce the impact of individual differences in negotiation skills and increase motivation to
participate, we provided each pricing manager with a
2-day negotiation training session prior to the data
collection. These sessions covered general skills for
price negotiations, such as the importance of the initial price offering, the need for a clearly defined aspiration price to develop a focused negotiation strategy,
and the formulation of a reservation price to define
limits. Thus, all negotiators were prepared to employ
the three focal concepts. In addition, the training
included updated market facts, arguments to support
price increases and objection handling. Thus, participants accessed comparable levels of sales and negotiation knowledge, to reduce the influences of different
skill levels and career backgrounds.
Sample
The trained personnel conducted 645 negotiations
and provided complete data about 284 of them,
resulting in a 44 percent response rate. We excluded
two observations with negative gross margins, such
that our final sample consisted of 282 responses. We
chose the chemical industry for its economic relevance
and its market, because chemical firms sell to clients
in a broad range of industries. Our sample thus
includes buyers from the metalwork (MET; n = 74),
automotive (AUT; n = 78), packaging (PAC; n = 35),
maintenance, repair and operating (MRO; n = 40),
ecological sanitation (ESA; n = 15), and industrial
assembly (INA; n = 40) industries. The research team
had limited access to the negotiators during the process; we speculate that their high workload and the
pressure of the negotiation situation can explain most
missing items. As suggested by Little and Rubin
(1987), we compared complete with incomplete
observations; the three independent price variables
were uncorrelated with the completeness of the observations (coded 0 or 1). That is, missing values do not
lead to any substantial bias.
Data
Negotiations usually followed a four-step approach:
(1) a personal phone call to indicate an upcoming
official pricing announcement, (2) an official initial
price offering in writing, (3) personal negotiation and
settlement, and (4) confirmation of the reached agreement in writing. For each relationship, we collected
the sellers’ aspiration and reservation prices prior to
step 1. The initial price offering was recorded after it
was communicated in step 2, and we collected the settlement price from the suppliers after they had sent
out a confirmation letter to their buyers in step 4.
These four measures all refer to relative price
increases, compared with existing pricing schemes,
and we treat them as decimal figures for our analysis.
For example, an initial price offering asking for an
increase of 7.5 percent appears as 0.075 in our analysis. We also collected the annual sales volume per customer (in 1,000,000€) and gross margin per customer
(percentage) as control measures. We detail the
measures in Table 1.
Analyses
The revealed correlations indicate strong relationships among the three independent price measures.
When we apply ordinary least squares regression, we
find strong collinearity among these independent variables as well. When we add interaction effects in
Model 2, the variance inflation factor scores and condition indices exceed 100. This multicollinearity
implies that the effects cannot be attributed to a specific determinant, standard errors increase, and the
analyses are more likely to produce insignificant
results. We consider four potential solutions (e.g.,
Hair, Black, Babin, Anderson and Tatham 2005;
Greene 2011): (1) obtain more data, which is difficult
and might not address the issue, because any effect
can become significant if n is high enough; (2) drop a
variable that causes collinearity, such as aspiration
price, in which case our model would no longer represent developed theory; (3) use a reduced set of principal components constructed from the original
variables, which reduces dimensions and thus is not
appropriate for our model; or (4) apply ridge regression (Hoerl 1962; Hoerl and Kennard 1970), a special
case of Bayesian regression that belongs to the wider
set of generalized linear models (e.g., Hair et al.
2005).
January 2012
97
98
Volume 48, Number 1
0.061
(0.028)
0.048
(0.026)
0.061
(0.031)
0.080
(0.030)
0.465
(0.635)
0.200
(0.141)
0.038
(0.021)
0.034
(0.026)
0.041
(0.029)
0.050
(0.036)
0.514
(0.692)
0.638
(0.157)
0.217
(0.470)
0.523
(0.223)
0.111
(0.128)
0.081
(0.095)
0.059
(0.070)
0.079
(0.091)
0.102
(0.105)
0.472
(0.214)
0.084
(0.104)
0.072
(0.106)
0.034
(0.017)
0.065
(0.084)
0.262
(0.336)
0.295
(0.135)
0.099
(0.071)
0.074
(0.066)
0.057
(0.065)
0.080
(0.078)
0.397
(0.857)
0.378
(0.155)
0.113
(0.105)
0.091
(0.094)
0.068
(0.084)
0.086
(0.082)
0.337
(0.631)
0.426
(0.222)
0.096
(0.097)
0.074
(0.080)
0.055
(0.064)
0.072
(0.075)
0.888***
0.916***
0.112
0.972***
1
(3)
0.110
1
(4)
1
(5)
(6)
0.379*** 0.392*** 0.400*** 0.406*** 0.010 1
0.110
0.917***
0.923***
0.129*
1
(2)
0.868***
1
ESA
INA
MET
ALL
(1)
(n = 15) (n = 40) (n = 74) (n = 282)
Correlations
In 1,000,000€; all other figures are percentages, in decimal terms.
MRO, maintenance, repair & operating; PAC, packaging; AUT, automotive; ESA, ecological sanitation; INA, industrial assembly; MET, metalwork; ALL, overall sample.
*
p < 0.05.
***
p < 0.001.
a
(1)
Settlement
price
(2)
Reservation
price
(3)
Aspiration
price
(4)
Initial
price
offering
(5)
Annual
salesa
(6)
Gross
margin
MRO
PAC
AUT
(n = 40) (n = 35) (n = 78)
Mean Values (Standard Deviations)
Variable Means, Standard Deviations and Correlations
TABLE 1
Journal of Supply Chain Management
B2B Price Negotiation Outcomes
Ridge regression acknowledges that small changes in
data may produce large changes in effect sizes in the
case of multicollinearity, so data can be changed by
adding a constant k to the regression matrix. By
increasing k incrementally from 0 to 1 (based on normalized variables), the estimates initially vary substantially but then become more stable. This stability
decreases the standard errors, though at the expense
of systematic bias. Several approaches can help determine the optimal ridge regression parameter k (e.g.
Hoerl, Kennard and Baldwin 1975; Lawless and Wang
1976). For our analyses, we used the generalized cross
validation (GCV) parameter (Golub, Heath and Wahba 1979), which is appropriate for solving the issue
of high standard errors. We then used the R statistics
package (version 2.13.0; The R Foundation for Statistical Computing, Vienna) to determine the GCV value
and estimated standard errors with a bootstrapping
procedure, using 1,000 resamples. In Table 2, we list
the estimates and their standard errors using a GCV
parameter k of 0.0766 for Model 1 and 0.0218 in
Model 2.
RESULTS
In Model 1, all three references contribute significantly to explaining the settlement price, with an
adjusted R2 value of 0.86. Aspiration price with a
ridge regression coefficient of 0.40 and initial price
offering (0.36) appear to be comparably strong predictors of settlement price; reservation price (0.17)
has a significant but substantially weaker impact. If
we add industry considerations using the dummy variables, the general structure of the results receives further support from Model 2: Aspiration price (0.42)
and initial price offering (0.43) are comparably strong
and reservation price (0.19) has a substantially weaker
impact on the settlement price. The impacts of the
two control variables, annual sales volume and gross
margin, are insignificant in both models, in support
of the internal validity of the results. Moreover, in a
reduced Model 1r without controls, the changes in the
estimates and R2 are <0.01.
Adding industry dummies and the interaction effects
between industry variables and reference prices leads to
an only marginal increase in the adjusted R2 (0.03),
though it produced significant results with regard to the
automotive, industrial assembly and metalwork industries. For example, in the automotive industry, reservation price has a weaker impact and initial price offering
has a stronger impact, but the opposite situation
emerges for the metalwork industry. The direct industry
effect also becomes significantly positive. For industrial
assembly, this direct effect and its interaction with the
reservation price are significantly negative; all other
effects are insignificant, though.
DISCUSSION
We started with two goals: to integrate TCE and the
reference price concept from consumer behavior into
a price negotiation context and to overcome the limitations of the dominant experimental and mail survey
approaches to negotiation research. With regard to the
impact of each reference price on the settlement price,
we show that a seller’s aspiration price and the initial
price offering have comparably strong impacts,
whereas the reservation price has only limited impact.
These findings add insights to existing research: If we
were to assume rational negotiators and attempt to
explain negotiation outcomes in a supply chain using
only underlying costs (Williamson 1996), we would
neglect some dominant individual psychological influences. Furthermore, experimental psychological
approaches have ignored the negotiation context. The
initial price offering is relevant, but prior studies have
overestimated its importance as a negotiation anchor
(e.g., Van Poucke and Buelens 2002), at the expense
of the buyer’s aspirations.
Our use of prospect theory to conceptualize the
impact of aspiration price helps clarify the particular
importance of this price: Any settlement below the
aspiration price gets weighted very strongly, leading
sellers to defend their aspiration price more vigorously. In contrast, gains take less weight, and sellers
make less effort to defend their position when buyers
make offers above their aspiration price. Moreover,
because sellers’ reservation and aspiration prices are
opaque to buyers, buyers who aim to discern a seller’s
reservation price may prompt strong defense of the
aspiration price and thus assume they have found the
reservation price. Accordingly, psychological prospect
theory helps explain the specific importance of sellers’
subjective aspirations. The value of reference prices for
explaining a negotiation settlement also receives support from the importance of the initial price offering,
which is an important subjective adaption level for a
negotiation.
With regard to reservation prices, we show that their
direct impact on settlement price is limited. Regression weights below 0.2 indicate that actual B2B cost
splits rarely follow Nash’s (1950) equal share proposal. Nevertheless, cost increases should affect personal aspirations and initial price offerings, so
reservation prices’ impact might be mediated. In this
sense, our finding that cost changes hardly affect
negotiation results, if they are not converted into aspiration prices or initial price offerings, is critical.
Finally, though it adds little to the explanation of settlement prices, traditional economic thinking can
enhance our understanding of B2B price negotiations:
TCE explains industry differences in the importance of
the three reference prices.
January 2012
99
Journal of Supply Chain Management
TABLE 2
Regression Models
Model 1
Model 2
GCV Ridge Factor k = 0.0766
GCV Ridge Factor k = 0.0218
R2 = 0.86; Ra2 = 0.86
R2 = 0.90; Ra2 = 0.89
B
Constant
Annual sales
Gross margin
Reservation price (RP)
Aspiration price (AP)
Initial price offering (IO)
Industry influences1
MRO
PAC
AUT
INA
MET
Interaction effects1
MRO9RP
PAC9RP
AUT9RP
INA9RP
MET9RP
MRO9AP
PAC9AP
AUT9AP
INA9AP
MET9AP
MRO9IO
PAC9IO
AUT9IO
INA9IO
MET9IO
4.5591014
0.025
0.005
0.172
0.397
0.364
SE
Sig.
B
SE
Sig.
0.022
0.022
0.024
0.044
0.054
0.051
NS
NS
NS
5.3391016
0.039
0.018
0.187
0.424
0.430
0.019
0.020
0.028
0.062
0.061
0.058
NS
NS
NS
0.036
0.023
0.101
0.127
0.255
0.081
0.074
0.055
0.048
0.052
NS
NS
NS
0.007
0.009
0.203
0.081
0.263
0.027
0.063
0.083
0.061
0.099
0.093
0.047
0.229
0.146
0.352
0.050
0.035
0.062
0.037
0.055
0.080
0.083
0.084
0.079
0.083
0.070
0.072
0.069
0.076
0.072
NS
NS
***
***
***
**
***
***
**
***
**
*
***
NS
NS
NS
NS
NS
NS
NS
**
NS
***
1
Dummy coded, with ecological sanitation as the reference industry.
Ra2, adjusted R2; MRO, maintenance, repair & operating; PAC, packaging; AUT, automotive; INA,
industrial assembly; MET, metalwork. NS, not significant. All significances based on one-paired t-tests.
***
p < 0.001.
**
p < 0.01.
*
p < 0.05.
In particular, the interaction effects reveal notable
differences in the importance of reservation price and
initial price offering between industries. The reservation price has above-average importance for metalwork, an industry with limited asset specificity. As its
product complexity is limited, negotiation outcomes
are easy to compare against raw material price indexes
and commodity stock prices. Negotiators then aim to
beat the index, which represents their best alternative
and thus a reservation price. The high price volatility
100
of these raw materials, such as ores, and dynamic
prices also means that price agreements in this industry usually resemble a cost plus approach. Cost, a key
determinant of the reservation price, accordingly has a
strong impact on negotiation outcomes.
In contrast, in the automotive and industrial assembly industries, reservation price exerts a below-average
impact, whereas the impact of the initial price offering
is stronger. The products sold in these industries are
usually bespoke chemical formulations (chemical
Volume 48, Number 1
B2B Price Negotiation Outcomes
specialties), with high asset specificity and no clear
price indexes. It is thus difficult for buyers to review
prices, considering the sheer volume of chemical components. Thus, buyers shoot to “beat the supplier”
rather than the index, and negotiation success is determined according to the supplier’s initial price offering.
Initial price offerings thus constitute strong and
important influences on further negotiation, and by
presenting initial price offerings beyond their aspiration prices, sellers establish wider negotiation spaces.
Prior literature has warned of the risk of lock-in
effects for companies that contract for highly customized products and services (e.g., Lonsdale 2001; Narasimhan, Nair, Griffith, Arlbjørn and Bendoly 2009).
Industries with higher asset specificity, for which we
observe a lower relevance of reservation prices, should
be particularly prone to such effects; our results provide some support for this view. Industries with high
asset specificity and low reservation price impacts on
settlement prices (e.g., automotive, general assembly)
feature negative direct industry effects, an indication
of strong buyer power. We observe the opposite effect
in the metalwork industry.
In our further effort to overcome the limitations of
experimental and mail survey approaches, we find
that 86 percent of the variation in settlement prices in
continuous B2B relationships in the chemical industry
can be explained by the three price references we
study. This remarkably high explanatory power substantially exceeds that found in prior experimental
approaches; for example, Krause et al. (2006) achieve
an R2 value of 0.26 using the aspiration and reservation prices of sellers and buyers. We suggest two
potential explanations for our model’s strong power.
First, industrial sellers tend to have a very good sense
of what to expect and thus effectively adjust their reference prices. Second, sellers who initiate the negotiations may have clearer goals and a stronger drive to
reach them. We also note that we analyzed relative
price increases and find no reason to anticipate that
they depend on the history of absolute prices paid.
Overall, these results suggest that existing research
that has applied experimental designs has also underestimated the predictive power of sellers’ reference
prices. The negotiators in our study all had extensive
experience of more than 10 years with their company,
and they all acted in real industry contexts that are
responsive to differences in dependence on raw materials, power and cost transparency. In contrast, experimental respondents, and students in particular, may
be unfamiliar with the negotiation context and have
unrealistically large variance in their price perceptions.
This point has been elaborated by Stevens (2011),
who notes that student samples are appropriate for
establishing internal validity with regard to universalistic issues but are inappropriate for establishing
external validity when context-centered issues are the
focus. As Min, LaTour and Jones (1995) suggest, product experience and involvement increase the predictive
power of negotiators’ references. By using industrial
negotiation data and considering industry differences,
our approach thus increases accuracy, though at the
expense of some generalizability (see also Weick
1979; Ketchen and Hult 2011). The increased external
validity of our industrial negotiation data also is
accompanied by reduced control in the data collection
and limited opportunities to integrate control variables more comprehensively. Finally, a transaction’s
size could grant one negotiation party a particular
form of power; annual sales had no significant discernible impact, but our data do not permit us to
make a conclusive judgment about these potential
effects.
Overall, we might still ask how TCE contributes to
the model, particularly because the model elements
that we derived from TCE do not substantially contribute to the model’s R2 value. Yet, we maintain that
TCE plays a crucial role for improving our understanding of negotiation processes. TCE helps set the
initial context for the investigated negotiations. Frequent exchanges are the basis for the relationship in
which the considered negotiations take place. Uncertainty then provides a basis for annual price renegotiations, according to changes in the uncertain aspects
such as raw material prices. Thus, TCE provides a
valid context for describing the observed relationships
as hybrid contracting and defining a bargaining zone
within which to settle negotiations. Finding a settlement price within the bargaining zone remains a gap
for which TCE cannot provide a full explanation
though: Personal aspirations and psychological adoption levels established by sellers’ initial price offerings
are important but neglected by TCE. Yet, once the
aspirations and adoption levels have been identified
as relevant drivers, TCE reasoning can help explain
how these personal references influence the settlement
price. For example, high asset specificity increases the
importance of the relationship and thus the importance of the initial price offering as a reference that is
internal to the relationship in which the negotiation
takes place.
Industrial Implications
For selling companies, these results imply the need
to actively manage aspiration prices applied by their
sales personnel. Companies might provide clear goals
for a specific relationship or per salesperson. Moreover, sales personnel should be trained to increase the
individual aspiration prices that they believe they can
implement. Prior research has shown that companies
can influence negotiation outcomes by offering different cost information to sales personnel (Wilken et al.
January 2012
101
Journal of Supply Chain Management
2010). Similarly, a company might influence negotiations by making the connection between its financial
goals and eventual settlement prices more transparent
to its own salespeople, to stimulate their aspirations.
For individual sales representatives, our results suggest
that expecting more might result in more. Thus, salespeople should undertake solid preparation to develop
clear aspirations. Moreover, awareness of industry
characteristics could help them understand which reference prices to manipulate to achieve a higher settlement price. Especially for sales to the automotive,
packaging and industrial assembly industries, higher
initial price offerings promise an effective means to
achieve higher prices.
For buyers, the results indicate that they often leave
money on the table by reacting to sellers’ aspiration
prices instead of sensing and responding to their reservation prices. Buyers could realize better results if
they developed their own clear, well-founded aspirations. When both parties enter the negotiation equally
well prepared, they should have clear subjective aspirations that they defend. The dominance of such subjective influences in B2B negotiations, together with
the Nash equilibrium, should lead to an equilibrium
point at the mean of the buyer’s and seller’s aspiration
prices, instead of their reservation prices.
Negotiators’ aspirations can be reinforced by their
higher variable pay (Ely 1991). For buying organizations, our research suggests that negotiation outcomes
improve if the buyer representative’s performance is
evaluated against an objective measure, such as a market index external to the negotiation, rather than a
measure related to the negotiation process, such as
the seller’s initial price offering. However, we also recommend considering related influences on sourcing
strategies, because price-oriented compensation can
negatively influence relationship behavior (McNally
and Griffin 2004).
Research Implications
Existing research in SCM has neglected individual
perceptions and biases (Carter et al. 2007). Yet, we
offer evidence that individual elements, such as a
negotiator’s aspirations, substantially affect negotiation outcomes in the supply chain. By empirically
integrating a behavioral perspective and the more traditional TCE perspective, we also suggest potential
routes to understand such aspirations and their interactions with economic perspectives. For example, a
negotiator’s prior experience, whether with negotiation in general, with the specific company, or with the
specific counterpart, should have a significant effect.
This claim receives support from Yeniyurt et al.
(2011) who find that experience in a specific electronic reverse auction influences the suppliers’ further
bidding behavior. Perceptions of the negotiation as
102
integrative or distributive (e.g., Krause et al. 2006), as
well as of trust and power in the relationship, also
may be relevant. Beyond the individual level, further
research should investigate more explicitly how cost
structure, dependence on raw materials, and the competitive environment of a company (Van Poucke and
Buelens 2002) or industry (Henke et al. 2009) determine settlement prices. Yeniyurt et al. (2011) control
for cost structure in their study and find only an insignificant impact, consistent with the limited relevance
of the reservation price in our study. Nevertheless, our
study offers some indication of a moderating role for
cost structures; a dual-industry study, comparing high
with low asset specificity contexts, might reveal more
insight. Another type of dyadic approach that analyzes
seller and buyer data related to a specific negotiation
could add further value in this context — it would
clarify buyer influences while also revealing new
insight into the negotiation process.
Investigating the influence of individual and social
and psychological characteristics on negotiation
results is also worth exploring. Research might consider negotiators’ age and gender (Min et al. 1995;
Miles 2010), disciplinary backgrounds (e.g., business
versus engineering degree), and company rank. The
influence of negotiators’ ambition is relevant (e.g.,
Buelens and Van Poucke 2004; Agndal 2007); selfefficacy perceptions might further indicate how aspiration prices are derived and transform into higher
settlement prices. These aspects appear closely related
to performance-related pay and the question of
whether a market index or the initial offer serves as
the performance reference. Thus, investigating these
aspects using industry data would offer relevant
insights.
CONCLUSION
We have argued that TCE is reflected in sellers’ reservation prices, which reflect the raw material prices
and alternative sales opportunities. Existing research
has successfully applied TCE to explain when and
why companies buy from another firm versus produce
the necessary solution themselves. Yet, TCE also
neglects individual psychological influences on the
distribution of a transaction’s bargaining zone, a topic
prominent in psychological theories of price perception that appear comprehensively in consumer pricing
research. Our results suggest that adding psychological
considerations to traditional neoclassical thinking
increase understanding of negotiation processes in the
supply chain. Moreover, transaction cost thinking contributes to help explain industry differences in the relevance of various determinants and thus may provide
a better understanding of when particular individual
biases are most likely to occur.
Volume 48, Number 1
B2B Price Negotiation Outcomes
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Business Marketing of RWTH Aachen University in
Aachen, Germany. He has worked in the banking and
consulting industries across Europe and Africa with
Commerzbank, PricewaterhouseCoopers, The Boston
Consulting Group and UBS. In his research Dirk
investigates the impacts of individual values and
biases on the economic and social performance of
business. His two major areas of interest are pricing
and price perceptions as well as values in management.
Bjoern Schuppar (Dr. rer. pol., University of Mannheim) is the founder and managing partner of
Schuppar Consulting Ltd. &. Co. KG in Duesseldorf,
Germany. He has more than 10 years’ experience as a
business consultant, and founded his own company
in 2004. Dr. Schuppar has extensive experience in the
chemical, construction, healthcare, retail and consumer goods industries, and provides consulting services
in the areas of pricing, negotiation, sales management
and purchasing. His clients include Bosch, Lanxess,
Loctite and Siemens among others in the U.S., South
America, Europe and South East Asia. Dr. Schuppar's
publications include the book Price Management.
Florian U. Siems (Dr. rer. Pol., University of Basel)
is a junior professor and leads the Research Group for
Management and Business-to-Business Marketing at
RWTH Aachen University in Aachen, Germany. His
research focuses on marketing strategy, relationship
marketing, customer satisfaction and pricing management. Prior to his current position, Dr. Siems was
professor of marketing, and headed the marketing
department, at the Salzburg University of Applied Sciences in Salzburg, Austria.
Dirk C. Moosmayer (Ph.D.) is a Researcher at the
Research Group for Management and Business-to-
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