COOPERATIVE CONTEXT, LEARNING
AND NEGOTIATIONS IN SUPPLY CHAIN
HOLD UP SITUATIONS
EVIDENCE FROM AN INDUSTRY
SIMULATION
Karel Cool and James Henderson
IMD 2007-03
Karel Cool
B.P. Chair in European Competitiveness, Professor, Strategic Management
INSEAD
Fontainebleau
France
e-mail [email protected]
Tel: +33 1 60 72 40 00
and
James Henderson
Professor of Strategy
IMD International
Chemin de Bellerive 23
1001 Lausanne
Switzerland
Email: [email protected]
Tel: +41 21 618 0370
Fax: +41 21 618 0707
Copyright © Karel Cool and James Henderson
February 2007, All Rights Reserved
COOPERATIVE CONTEXT, LEARNING AND NEGOTIATIONS IN SUPPLY
CHAIN HOLD UP SITUATIONS
EVIDENCE FROM AN INDUSTRY SIMULATION
Karel Cool** and James Henderson*
October 2005
Please send correspondence to:
James Henderson
Professor of Strategy
IMD International
Chemin de Bellerive 23
CH 1001 Lausanne
Switzerland
Tel. 41-21-618-0370
Email: [email protected]
or
Karel Cool
B.P. Chair in European Competitiveness, Professor, Strategic Management
INSEAD
Fontainebleau
France
Tel. 33-1-60-72-40-00
e-mail [email protected]
**
*
B.P. Chair in European Competitiveness, Professor, Strategic Management, INSEAD
Associate Professor, Strategic Management, Babson College
2
COOPERATIVE CONTEXT, LEARNING AND NEGOTIATIONS IN SUPPLY CHAIN HOLD
UP SITUATIONS: EVIDENCE FROM AN INDUSTRY SIMULATION
This paper examines the link between the cooperative mechanisms to prevent supply chain holdup and learning in negotiations. The analysis comes from data generated from an experiment
based on the French Champagne industry. Results show that dyads where both parties have prior
negotiation experience could best align context, intent, and negotiation approach with higher
individual and joint outcomes. While dyads with asymmetric negotiation experience achieved
more win-win outcomes than inexperienced dyads, their results still tended to be driven by the
less experienced party.
These results suggest that prior negotiation experience rather than
cooperative context matters more for negotiation outcomes.
Key words: learning, supply chain negotiations, simulation
COOPERATIVE CONTEXT, LEARNING AND NEGOTIATIONS IN SUPPLY CHAIN HOLD
UP SITUATIONS:
EVIDENCE FROM AN INDUSTRY SIMULATION
Hold up, in addition to imitation, substitution and slack, has always been considered a
threat to superior business performance (Ghemawat, 1991). Owners of scarce, non substitutable
complementary products and assets along the supply chain may negate any performance
advantage a focal firm may have in an industry. However, there is a growing view that deeper
supply chain relationships or the movement from arm’s length agreements to long-term
partnerships (see e.g. Dyer, 1997; Fisher, 1997; Lee, Padmanabhan and Whang, 1997) are seen
as a way to solve the hold up problem. Most researchers argue that once parties freely exchange
information (e.g. sharing cost and demand data), coordinate decision-making, invest in mutually
dependent specific assets, commit to a supply chain relationship (because of external
competition), and have a successful history of cooperation, a successful partnership will be
sustained (see e.g. Dyer, 1997, Dyer and Singh, 1998). Through these context factors, selfenforcing safeguards such as trust and reputation are established, which are considered the basis
of achieving higher individual and joint outcomes (Dyer, 1997).
Interestingly strategic management research tends to stop at these context mechanisms to
prevent the hold up problem. Yet, negotiations, or the process by which managers try to settle
what they shall give or take in a transaction between them (Rubin and Brown, 1975), still need to
take place. While solutions may be found through formal game theoretic analysis, empirical
outcomes may be very different because of characteristic, motivational and cognitive differences
of the negotiating parties (see e.g. Thompson, 1990a for a review). Indeed, the generally held
view (and substantiated in practice) is that negotiators take a distributive or fixed pie position.
1
However, negotiation researchers have argued joint outcomes may be improved by negotiation
experience (see e.g. Thompson, 1990b, Neale and Northcraft, 1986, Northcraft and Neale, 1987)
whether accumulated through repeating the task, receiving outcome and information feedback,
understanding the principals of negotiation, observing others negotiating or analogizing from
other situations (see e.g. Nadler et al, 2003). While experienced negotiators may still be affected
by biases (see e.g. Neale and Northcraft, 1987), more experienced negotiators are more likely to
trade off issues and reach higher joint payoffs than inexperienced negotiators. Yet, negotiations
research has typically been conducted devoid of the contextual factors that may have already
been established. Does negotiation experience even matter when safeguards to prevent
opportunism and enhance value creation are already in place? It is within this context that our
paper fits.
This paper has two objectives: one broad and one more specific. More broadly, the paper
seeks to raise the awareness and importance of bargaining and negotiations in the strategic
management literature. Curiously, while the strategic management literature has numerous
papers on bargaining power, contracts, trust and supply chain partnerships, only four papers in
Strategic Management Journal concern bargaining and negotiations processes. Given the
increasing interest in using cooperative game theory in strategic management which results in the
bargaining over surplus or value creation (see e.g. Lippman and Rumelt, 2003, Brandenburger
and Stuart, 1996) caused by resource advantage, there is indeed an opportunity for strategic
management researchers to enhance the negotiations literature by exploring the interplay
between context and negotiation processes.
More specifically, the paper examines the impact of prior negotiation experience in a
more context-rich experiment than those conducted by traditional organization theory and
2
negotiations researchers. We explore our research question using data gathered from a
negotiation simulation experiment based on the French Champagne industry. The industry and
the simulation have several characteristics, which make it interesting as a research setting for
negotiations and strategic management research. First, the negotiating parties own co-specialized
resources. The growers own a unique resource: Champagne grapes and the Champagne
producers, or “houses,” global distribution and their brand names necessary to sell the sparkling
wine. This situation is a classic strategic problem where the parties need to determine how to
create and distribute the resource surplus through bargaining. Secondly, the industry has
developed a number of governance mechanisms to reduce opportunism: information
transparency, multi-issue bargaining, ex-post monitoring and punishment (see Henderson and
Cool, 2005) allowing us to examine negotiation experience within a context that “supports”
cooperation. Thirdly, the payoffs from changing an issue in the agreement among growers and
houses are asymmetric thus making the achievement of integrative solutions worthwhile.
However, the payoffs are uncertain as industry dynamics (e.g. price wars triggered by excess
stocks) may produce outcomes that are different than forecast. This uncertainty constantly forces
growers and houses to evaluate their intent (competitive or cooperative) and negotiation
approach (distributive or integrative). Thus, we believe that the Champagne industry and the
simulation provide an appropriate setting to test several hypotheses about prior negotiation
experience and negotiation effectiveness in the presence of mechanisms that support cooperation.
The next section of the paper provides a review of the literature and derives hypotheses
on how experience may help or impede the establishment of integrative outcomes in context-rich
negotiation situations. The section thereafter describes the Champagne industry, the negotiation
experiment, the variable definitions and procedures to test the hypotheses. The third section
3
discusses the results, followed by a section on the implications for management, limitations and
future research directions.
LITERATURE REVIEW: HOLD UP FROM STRATEGIC MANAGEMENT AND
NEGOTIATION PERSPECTIVES
Hold up occurs when one party takes advantage of a trading partner to appropriate
surplus generated by their relationship specific investments. This increased dependence has
many times been shown to improve the performance of the advantaged party. Early solutions to
the hold up problem were either to vertically integrate (Williamson, 1985) or to resort to brute
bargaining power based on size and concentration in the industry, tapered integration, signaling
and reputation, etc (Porter, 1980). However, subsequent strategic management research focused
on the relationship and how over time the parties could increase individual and joint outcomes
through building mutual dependence, joint decision making, increasing the communications,
partnering in innovation and production, and having a history of cooperation etc (see e.g. Singh
and Dyer, 1998). Indeed, managing these mechanisms are considered a way to minimize
transaction costs and at the same time maximizing transaction value. Common to all of these
approaches is that the context, whether due to the industry, firm characteristics or governance
mechanisms, would essentially dictate intent (competitive or cooperative). However, a
negotiation approach based on that intent could only be inferred based on performance outcomes
at the firm level. Only two studies to our knowledge have in fact examined the context –
negotiation approach link (see e.g. Arino and Ring, 2004 and Ness and Haugland, 2005) both
from a case study perspective.
4
The essence of negotiation research, on the other hand, is to predict negotiation
approaches and outcomes from primarily individual and group effects such as individual
differences (gender etc.), motivation (aspirations) and cognition (biases) (see Thompson, 1990a
for a review). For example, since negotiation is a complex task in which the parties are faced
with a number of alternative courses of action, negotiators often rely on heuristics to facilitate
information processing and selection of courses of action. Yet, many of the cognitive shortcuts
lead to biases in decision-making (Bazerman, 1994). Naïve negotiators often take a distributive
negotiation position based on framing (risk aversion versus risk preferring) (Kahneman and
Tversky, 1984), incompatibility error (i.e. the tendency to see conflict when none may exist)
(Thompson, 1990a), fixed pie error (the assumption that the other party places the same
importance on issues) (see Thompson, 1990a), irrational escalation to a stated position (Staw,
1981), overconfidence (Bazerman, Magliozzi and Neale, 1985), ignoring the other parties’
perspective (Bazerman and Neale, 1983), dislike of waiting for payoffs (Lowenstein, 1987), and
sharp discounting of the future (Ainslie, 1975; Mannix, 1991).
Yet, the experiments used in testing these effects on negotiation approaches and
outcomes often do not incorporate the context. For example, in many negotiation experiments,
instructions often incorporate “value points” or upfront payoff structures such that the
negotiating parties can calculate Pareto-optimal solutions and their BATNA’s (see e.g. Pruitt,
1981, Raiffa, 1982, Pinkley et al, 1994.) Yet, in reality, negotiators rarely have value points in
advance; rather, they typically need to forecast what these outcomes may be but with differing
expectations, under varying degrees of uncertainty, and often taking into consideration factors
that are outside of the domain of the negotiations. Furthermore, since most negotiation
experiments are often devoid of governance mechanisms, competitive/cooperative intent and
5
negotiation approach have been de-coupled. As a result, some negotiation researchers have been
suggesting that experiments be conducted in noisier environments using background context
indicating directionality rather than value points showing actual payoff outcomes (see e.g. Teich
et al 2000, Grosskopf et al, 2003).
Based on these two non-overlapping streams of research, several cases could result. The
first two cases can be considered straightforward: a context that supports cooperative intent leads
to integrative negotiations and win-win outcomes.
Alternatively, a context that enhances
competitive intent will lead to distributive negotiations and win-lose outcomes. The off-diagonal
cases, however, are more interesting: a cooperative context may lead to cooperative intent but
not necessarily integrative negotiations or win-win outcomes.
Alternatively, a competitive
governance structure may lead to competitive intent but not necessarily distributive negotiations
or win-lose outcomes. In this paper, we consider the cooperative context, negotiation process
link. In particular, we test whether prior negotiation experience and learning aid individual and
joint outcomes within a context that already supports cooperative intent.
Development of Hypotheses
The incidence and effects of learning and experience have certainly received a significant
amount of attention in a variety of strategy research areas: organizational design (e.g. Duncan
and Weiss, 1979), research and development (e.g. Cohen and Levinthal, 1990), new ventures
(e.g. Burgelman, 1988), capacity expansion (Henderson and Cool, 2003), alliances (e.g. Anand
and Khanna, 2000), and acquisitions (e.g. Hayward, 1997). Negotiations are no exception.
Common in these research streams is that decisions are assumed to arise from routines where the
logic of developing and following routines and heuristics is one of satisficing rather than optimal
6
behavior (Cyert and March, 1963; Nelson and Winter, 1982). Learning and experience is said to
be triggered mainly by four sources: from the experience of doing an activity (Yelle, 1979), from
the feedback of outcomes associated with an activity (Cyert and March, 1963), from observing
(Huber, 1991) and from analogy (Gavetti, et al, 2005). In this paper we focus on the first two.
Learning by Doing
Previous negotiations research on ‘learning by doing,’ or learning through repeating a
task falls into two main areas: experiments conducted on those with prior experience (experts)
and studies concerning repeating negotiation transactions.
Prior Experience (Experts): Numerous negotiation studies have examined the effect of
prior negotiation experience (whether as experts or as those who have had negotiation training)
on negotiation tactics and outcomes. Bargainers with prior experience typically have better
mental models or strategic conceptualizations of the negotiation situation (see e.g. Paich and
Sterman, 1993, Neale and Northcraft, 1990) in the same way that PhD physics students can
organize problems according to abstract underlying relationships such as Newton’s second law
(Chi et al, 1981). While early results comparing the performance of experienced versus naïve
dyads were ambiguous (see e.g. Neale and Northcraft, 1986 versus Scholz et al, 1982),
Thompson and her coauthors found that negotiators with prior experience typically had more
abstract mental models of the negotiations (Van Boven and Thompson, 2003), were able to trade
off more issues to reach more integrative outcomes (Thompson, 1990b), and outperformed naïve
counterparts in both integrative and distributive bargaining situations (Murninghan, et al, 1999).
Given the results of extant negotiation research, we expect similar results even in a
cooperative context, ranging along a continuum of prior negotiation experience. For examples,
7
those dyads with no prior negotiation experience may exhibit cooperative intent but may have no
knowledge of negotiation approaches or the underlying trade-offs associated with the issues
negotiated. Furthermore, those dyads with asymmetric negotiation experience should increase
their individual outcomes at the expense of their counterparts but still achieve higher joint
outcomes. Finally, dyads where both parties have prior negotiation experience should achieve
higher individual and joint outcomes than dyads with those with no prior negotiation experience.
These arguments lead to the following hypothesis:
Hypothesis 1. Even within a cooperative negotiation context, more experienced
negotiators will obtain higher individual and joint payoffs than less experienced negotiators.
Experience Through Repeating Negotiation Transactions: Repeated negotiation
transactions creates two opportunities for potentially increased payoffs: cooperative intent and
increased experience in negotiation approach. First, when negotiators are faced with repeated
negotiations with uncertain or unlimited time horizons, intent can change because the parties
face each other again. Indeed, Axelrod (1984) showed how cooperation was indeed more
evolutionary stable and resulted on average in higher payoffs in a repeated prisoner’s dilemma
game. However, an integrative negotiation approach and high individual and joint outcomes is
certainly not guaranteed. Rather, the parties still have to sort out the payoffs from each issue
that they negotiate and then trade them off. Then, as negotiations are repeated, the negotiation
parties have the opportunity to jointly develop more experience to better understand these tradeoffs. Indeed, numerous negotiation studies have shown how individual and joint outcomes
improve over time as they gain experience (see e.g. Ben-Yoav and Pruitt, 1984, Mannix et al,
1995, Neale, Huber and Northcraft, 1987, Neale and Northcraft, 1986).
Yet, the improvement of joint and individual payoffs may still be moderated by prior
negotiation experience. Given a quicker understanding of the issues, those dyads with prior
8
negotiation experience may faster converge on understanding the payoff implications for each
issue than those with little or no prior negotiation experience. Neale and Northcraft (1986) found
dyads with prior negotiation experience had a better joint strategic conceptualization of the
context, and thus reached higher joint outcomes earlier, negating any potential steep learning
effects over repeating the negotiation task. Yet, these results were bounded by the payoff
outcomes provided prior to the negotiations. In more realistic settings where actual payoffs are
not supplied (but have to be calculated), experienced dyads which share similar negotiation
mental models more likely arrive not only at integrative outcomes faster but ones that increase
more steeply over time.
In contrast, a joint understanding of the payoff implications of each issue may be
hindered if parties have unequal prior negotiations experience. A negotiating party with prior
experience may exhibit frustration in having to “educate” the naïve negotiating. Without training
they may not share the same mental models but still may exhibit cooperative intent. This
asymmetry is likely to extend the period necessary to reach the same understanding of the issues
and payoffs than if both parties had prior negotiating experience. However, given their superior
understanding of the situation, and of negotiation tactics, experienced parties may still be able to
capture more value of a potentially increasing pie over successive negotiating rounds. These
arguments are summarized in the following hypothesis:
Hypothesis 2. Within a cooperative negotiation context, more experienced parties will
increase their payoffs individually and jointly more rapidly than less experienced parties.
Learning from Outcome Feedback
Learning from experience may not just come from ‘doing’ but also from the gap between
expected and actual outcomes. Traditional learning theory states that if an outcome is deemed a
failure or if there is a large gap, individuals and organizations will engage in a search for the
9
causes of and solutions to the problem. This knowledge could subsequently be embodied in
existing routines or lead to new routines (Cyert and March, 1963, Hogarth, 1981). Learning,
then, occurs by closing the gap between expectations and outcomes and by incorporating the
acquired experience into the routines.
A priori, one expects learning from outcomes to also take place in supply chain hold up
situations. However, learning from outcomes in negotiations suffers from the difficulty of
determining whether the gap is due to the opportunistic behavior of either or both parties or due
to an incomplete understanding of the drivers of the payoffs. Misperceptions of feedback can
occur due to lack of search for disconfirming evidence (Staw, 1976), incorrect interpretation of
the outcomes (Neale and Northcraft, 1990, Sterman, 1989), or use of the wrong outcome
measures (Northcraft and Earley, 1989, Cummings, Schwab and Rosen, 1971, Einhorn, 1980).
For example, when both negotiating parties “lose” in any given stage of the negotiation, they
may blame each other for acting opportunistically and may get trapped in a downward spiral.
Alternatively, they may try to work together and analyze the causes of Pareto-destroying
outcomes. They may realize that the results were a product of the system in which they are
embedded (e.g. delays in feedback) rather than a result of opportunistic behaviour.
Unsurprisingly, because of this difficulty in interpreting outcomes, previous studies on the topic
have shown insignificant results from outcome feedback on decision making and negotiation
performance (see eg. Balzer et al, 1989, Thompson and DeHarpport, 1994).
However, previous studies were conducted in the absence of prior negotiation experience.
With a better strategic conceptualization, those parties with negotiation experience may be able
to more accurately decipher outcomes from negotiations. For example, because of uncertain,
noisy environments, they too will be subject to negative outcomes. Rather than justifying their
10
position despite this negative outcome, they may try to search for causes. For example, they
would try to control for factors that are outside the bounds of the negotiations but may have
affected the outcomes in some way. As a consequence, those parties with prior negotiation
experience may be better able to switch Pareto-destroying outcomes (e.g. lose-lose, win-lose,
lose-win) to Pareto-improving (win-win) while still potentially claiming more of the total surplus
created. Furthermore, those parties with prior experience may be better able to sustain Paretoimproving outcomes over time, again while claiming more of the total surplus created. While
experienced negotiators are not devoid of their own biases (see e.g. Neale and Northcraft, 1986,
Northcraft and Neale, 1987) they would certainly be in a better position than naïve negotiators
who may misinterpret negative outcomes.
Building on the above arguments, we specify the following hypotheses to evaluate the
incidence and determinants of learning from joint outcomes:
Hypothesis 3a: Within a cooperative negotiation context, more experienced negotiations
will more likely reach win-win outcomes than less experienced negotiators.
Hypothesis 3b: Within a cooperative negotiation context, more experienced negotiators
will more likely change lose-lose outcomes into win-win outcomes than less experienced
negotiators.
Hypothesis 3c: Within a cooperative negotiation context, more experienced negotiators
will more likely change lose-win/win-lose outcomes into win-win outcomes than less
experienced negotiators.
Hypothesis 3d: Within a cooperative negotiation context, more experienced negotiators
will more likely persist with win-win outcomes than less experienced negotiators.
In sum, several hypotheses have been proposed to test whether negotiation experience
affects performance outcomes in cooperative contexts. The first set of hypotheses relates whether
prior negotiation experience leads to better individual and joint outcomes overall and over
several negotiation rounds. The second set of hypotheses test whether prior negotiation
11
experience affects how negotiating parties learn from gaps from intended and actual outcomes.
In the next section, the research setting used to study these hypotheses is described.
RESEARCH DESIGN
Simulation Setting: The Champagne Industry
A negotiation simulation was developed on the French Champagne industry. Historically,
this fragmented industry has been very successful at maintaining cooperation between growers
and houses through self-regulation, the Comité Interprofessionelle du vin de Champagne
(CIVC), explicit development of multiple issues to improve the likelihood of reaching integrative
solutions, information transparency on stocks, sales, financials for both sides and extensive and
on-going formal and informal enforcement of the negotiated agreement (see e.g. Cool and
Henderson, 2005). Traditionally, growers and houses negotiated each year over several issues
and these typically were resolved in the spirit of cooperation.
However, the industry did experience occasional hold up in spite of the safeguards that
fostered cooperation. For example, in 1989, champagne achieved record sales of 249 million
bottles. Yet, in the same year, the collective agreement between growers and producers
collapsed. The grape growers saw an opportunity to capture more of the large retail margins for
themselves by producing their own champagne. Given the limits on the land and the
“appellation” decision (i.e. the number of kilograms of grapes per hectare that may be used for
champagne making), they decided to keep more of the grape harvest for themselves.
Negotiations proceeded with growers and houses on an individual rather than collective basis
with little or no prior negotiation experience on either side. The results were quite astounding.
Spot prices for champagne grapes rose from an average of 30FF (€ 5) /kilo to as high as 60FF (€
12
10) /kilo. At the same time, growers and cooperatives pressed forward with their plans to sell
their own champagne. Subsequently, as the world economy entered a recession at the end of
1990, the industry exhibited a period of significantly lower profitability as their stocks of
champagne bottles began to climb.
The difficulty for the industry was, therefore, how to re-establish cooperation. As one
observer put it, “Industry associations are the only way for agricultural sectors to succeed. The
CIVC is an obvious and envied proof. But success is never definitively achieved. It only
continues so long as it is managed [i.e. negotiated] with a spirit of compromise and fairness.”1 In
other words, despite the supposed mechanisms to ensure cooperation in the Champagne industry,
the final outcomes were seen to be very much dependent on negotiation approach.
Simulation Method And Assumptions
A simulation was developed to capture this industry context. Systems dynamics is a
modeling approach that is very suitable to studying interactions between decision-makers and
their environment (Paich and Sterman, 1993).
System dynamics can model important
dimensions in strategy research including stocks and flows, direct and indirect feedback loops,
and time lags. The process of modeling strategic decisions, however, forces research to make
assumptions and to construct the variables explicitly (Ginsberg, Larsen, Lomi, 1996, Morecroft,
1984, Schoemaker, 1993). System dynamics models are either theory driven or data driven
(Barlas, 1996, Crossland and Smith, 2002). The validity of a black box model, the one used in
this paper, can be determined by comparing outcomes of the simulation with actual outcomes of
13
the system.
Several software tools have been developed to model systems dynamics; we
constructed the Champagne simulation using Ithink, by Isee Systems.
To make the simulation and experiments both realistic and tractable, several assumptions
were made. On the supply side, we simplified the simulation by separating the industry into two
main parties, growers and houses. As the industry had structured negotiations along these lines,
this simplification was easily justified. Second, we considered only two types of champagne,
those of the growers and those of the houses. This broad classification of champagne is the way
the industry collects and distributes its information. Third, both growers and houses competed in
two businesses: grape growing and champagne production. The houses owned approximately
10% of the total grape supply.
Furthermore, approximately 33% of the champagne was
produced by the growers.
Second, demand for champagne was projected to come from domestic and export
markets. In the domestic house champagne market, a log-linear demand function was constructed
based on the price of house champagne, the price of the grower’s champagne, the GDP per capita
in France, and the houses’ previous year’s sales (i.e. reputation effects). Similarly, domestic
demand for growers’ champagne was estimated as a function of its own price, the GDP per
capita in France, and the growers’ previous year’s sales (i.e. reputation effects). Finally, for the
export markets, only the demand for the producers’ champagne was considered since the growers
focus almost exclusively on the domestic market.2 Producer’s export demand was then estimated
1
2
Yves Bonnet, Government Commissioner
The equations were estimated using data over the period 1960 to 1992. The adjusted R-squared and Durbin
Watson statistics for the estimation of the growers’ equation were as follows:
R-Squared
Durbin Watson
Domestic houses:
.96
1.80
Domestic growers:
.98
1.23
Export houses:
.95
1.34
14
as a function of its price, the OECD income per capita, a proxy for sparkling wine substitutes, a
variable measuring time, and the producers’ previous years’ exports.
Simulation Inputs
Growers and houses needed to negotiate each year some but not all of the seven key
decisions: the allowed yield per hectare (appellation), the percentage of the allowed yield
purchased by the houses, the use of a second and third pressing of the grapes, the percentage of
the allowed yield kept as reserve stocks (blocage), the release date of the blocage and the price
of the grapes per kilo. Growers and houses also needed to decide separately the price at which
they would sell their champagne.
Simulation Price Dynamics
The industry closely monitors stocks of champagne bottles because these often trigger
speculative behavior by the growers if they are too low and fuel price wars if they are too high.
To proxy these intra-year dynamics, the simulation included a mid-year price correction. First,
prices of house champagne were estimated as a function of long-term interest rates, their
previous year’s stock to sales ratio, and the price of grapes. Similarly, grower prices of
champagne were estimated as a function of long-term interest rates, their previous year’s stock to
sales ratio, and the price of house champagne.3 Second, when in the middle of a year, the
estimated prices were lower than the prices decided at the beginning of the year there was a
3
The equations were also estimated using data over the period 1960 to 1992. The adjusted R-squared and Durbin
Watson statistics for the estimation of the growers’ equation were as follows:
R-Squared
Durbin Watson
Houses:
.84
2.56
Growers:
.80
1.79
Note: The price of a bottle of champagne was assumed to be the same for domestic and export markets.
15
“correction” or a “price war.” In other words, the simulation would apply the lower estimated
prices resulting in lower profits for all. If the prices at the beginning of the year were lower than
the estimated prices, no mid-year correction was made.
Simulation Outputs
The outputs of the model included monthly demand, stocks in millions of bottles of
champagne, the stock to sales ratio for the growers, producers and industry, an income statement
of the growers and houses, their capital employed and economic value added (EVA), the
estimate of the negotiation payoffs. The model was validated with historical data to ensure it
reproduced the output, capacity and financial outcomes of the industry.4
Design Of The Experiments
The experiments were conducted at an international business school located in Europe
consisting of students taking an Industry and Competitive Analysis MBA elective. We were
careful in trying the recreate the context of the Champagne industry each year. First, the
experiment was conducted in conjunction with a discussion of a case on the Champagne industry
to ensure students had an adequate understanding of the mechanisms that enhanced cooperative
behaviour within the industry including: restricted access to the Champagne name; full
contribution to the Champagne name; multiple issues in negotiations; transparency in the use of
the Champagne; and the establishment of formal and informal enforcements (see e.g. Henderson
and Cool, 2005). Secondly, we did not tell the students the number of negotiation rounds in
advance to prevent any end-game opportunistic behaviour. Thirdly, the start of the negotiations,
1993, was one of the worst periods for the Champagne industry given the very high stocks of
4
The comparison between simulated and actual results is available from the authors upon request.
16
unsold champagne. This situation made the achievement of improved joint outcomes particularly
challenging but not impossible. Finally, we told the students that their grade was a function of
how well their group did, not the negotiation dyad in order to evoke the mixed-motive situation
faced by the industry’s two main players. These four contextual aspects of the negotiation led us
to believe we had successfully recreated the negotiation context of the Champagne industry at
the time.
Teams of five students were designated to represent either the growers or houses. Before
the discussion of the case in class, the groups were paired and provided instructions. The
instructions asked the students within their own groups to prepare for negotiations, negotiate
with the other party on up to seven issues described above, and finally, to decide privately on a
price per bottle for their champagne. The agreements were then entered into a website dedicated
to the simulation, with room to provide comments on their strategies and agreements. After they
discussed the case in class, they were presented a spreadsheet of their results of the negotiations,
which included their monthly demand for champagne bottles, stocks, the stock to sales ratio, an
income statement, capital employed and economic value added (EVA). They were then told to
negotiate outside of class hours over another three rounds over a two-week period. Since the
negotiations were held at different times and in different places, learning from observation of
other groups’ negotiation strategies was mitigated. In total, 240 simulations were run for each of
the 60 industries (15 industries across four courses) over the four years.
Dependent Variables
To test the hypotheses, two dependent variables were constructed: economic value added
and outcome patterns. The growers’, houses’ and industry’s economic value added (EVA) were
used as the dependent variable for the first two hypotheses. This measure was chosen for two
17
reasons. First, EVA (i.e. net income less the cost of invested capital) compared to return on sales
takes into the consideration the amount of capital employed which is affected the negotiators’
decisions (i.e. the cost of inventory). Secondly, after each round of negotiations, the simulation
fed back the measure (in addition to a stock to sales ratio) for the growers, houses and industry as
their performance yardstick.
Outcome patterns. The second set of hypotheses relate to the effect of learning from
negotiation outcomes. We study this by measuring how joint outcomes evolved (i.e. advancing in
a win-win, lose-lose, win-lose or lose-win pattern). Previous negotiation studies have proxied
Pareto outcomes typically as the sum of the outcomes or joint payoffs of the two parties (see e.g.
Thompson, 1990b). However, this measure provides conflicting information in complex
negotiations where value points are not provided in advance. Refer to Figure 1 that illustrates the
problem.
Insert Figure 1 About Here
In many cases, joint payoffs may increase; however, one party may have received greater
than 100% of the share. Refer to point Y in Figure 1. In other words, joint payoffs increase but
one party benefits to the detriment of the other. Thus, to operationalize joint outcomes, we
coded the changes in payoffs from year to year. For growers, for example, if their EVA
decreased from the previous year, we coded it as a zero; if their EVA increased from the
previous year, we coded it as a one. Thus, three categorical variables were constructed: 0
represented those instances where EVA for both players decreased (i.e. lose-lose); 1 represented
a win-lose or lose-win pattern or those instances where there was an increase in grower’s EVA
and a decrease in house’s EVA or vice versa; and 2 represented a win-win pattern (the EVA of
both the houses and growers increased at the same time).
18
Explanatory and Control Variables
The main explanatory variable concerned prior negotiation experience. In addition, four
control variables were also considered because they were thought to influence EVA: the number
of times they negotiated, the number of issues, the growers’ price of champagne, houses’ price of
champagne, and a weighted average of growers’ and houses’ price of champagne.5
Prior negotiation experience. We constructed a prior negotiation experience measure
based on the percentage of students in each team (growers or houses) that had previously taken a
Negotiations Elective during their MBA program. More specifically, we created four categories
of industries: those with low prior negotiation experience (i.e. less than 50% of the members
representing both the houses and growers had taken the Negotiations course); high negotiation
experience for the growers but low negotiation experience for the houses (i.e. more than 50% of
the members representing the growers and less than 50% for the houses); high negotiation
experience for the houses and low negotiation experience for the growers (i.e. more than 50% for
the houses and less than 50% for the growers), and high negotiation experience for the houses
and growers (i.e. greater than 50% for both the growers and houses).
Time. In order to test the effect of experience as the negotiations continue, we developed
a variable measuring the cumulative rounds of negotiations. This cumulative measure is
consistent with measures used in other studies in negotiation experience (see e.g. Thompson,
1990b).
Number of issues negotiated. In the regressions concerning EVA, we included the
cumulative number of issues negotiated across the four rounds. Increasing the number of issues
negotiated opens up the potential for higher joint outcomes as each issue can be traded off with
5
Since champagne demand was found to be inelastic, higher prices tended to result in higher profits.
19
other issues (Thompson, 1990a). However, trading off these issues will impact not only short
term but also long term outcomes (Mannix et al, 1990). Accordingly, we coded 1 for each time
there was a change in any of the seven issues from one year to the next and 0 if there was no
change. We aggregated the number of changes for each period, the maximum being seven (for
the total number of possible issues to be negotiated during each round) and then cumulated these
changes across the number of times negotiated. Since cumulative number of issues negotiated
are highly correlated with time (as they increase over time), we took the standardized values over
each time period.
Champagne prices. A variable measuring the grower and house prices of champagne was
entered into the tests where the payoffs were the dependent variable in order to control for the
private actions (price setting) that the groups were able to take without negotiation. Since the
prices were highly correlated with time (as they, in general, increased over time due to inflation,)
we took the standardized values over each time period for the house, grower and weighted
average price of champagne.
RESULTS
Overall, out of the 240 rounds of negotiations, only 5 (i.e. 1%) did not come to an
agreement (e.g. the houses refused to buy the grapes from the growers.) Thus, the champagne
industry had instituted safeguards to at least ensure that impasse would be minimized.
Furthermore, the comments of the growers and houses both experienced and inexperienced
consistently mentioned how they had to solve industry’s problems collectively. Cooperative
intent was certainly apparent amongst the negotiation dyads. However, cooperative intent did
not translate into similar Pareto improving outcomes. Refer to Table 1 for summary statistics and
the correlation matrix. As one can see, Table 1 shows that there was substantial variation for
20
each issue and substantial variation on outcomes. Apparently, while the dyads exhibited
cooperative intent (derived from the context), negotiation results stated otherwise. Did prior
negotiation experience align cooperative context, intent, and negotiation outcomes?
Insert Table 1 About Here
Prior Negotiation Experience and Learning By Doing
Hypothesis 1 and 2 examines the direct effect of prior negotiation experience on
outcomes. More specifically under cooperative context, more experienced negotiators are
expected to reach higher payoffs individually and jointly (H1) and exhibit higher rates of
learning during the negotiation rounds or would increase their payoffs more quickly than less
experienced negotiators (H2). Refer to Table 2.
Insert Table 2 About Here
Table 2 provides results for three estimations on EVA over the four negotiated rounds:
with the controls variables consisting of the cumulative number of issues negotiated, time and
grower and house price (Model 1), adding prior experience as a direct effect (Model 2) and
adding prior experience as an interaction effect with time (Model 3). The results of these models
are shown for the growers, houses and industry as a whole.
Model 1 relates the cumulative number of issues negotiated and time to EVA for the
growers, houses and the industry. As numerous other experiments have shown, outcomes for
each party increased over time suggesting that regardless of experience the dyads learnt about the
negotiating task. Interestingly, the cumulative number of issue changes only had a positive
impact for the growers and consequently the industry but not for the houses. This may be
because of several reasons. For example, some of the houses, as buyers, may have framed their
negotiations in terms of losses and thus took a more risk-preferring stance by standing firm on a
21
smaller number of issues. Alternatively, since the effect of the issue changes for the houses occur
more over time than for the growers, it may have been more difficult for them to see the results
of their changes into the future. Thus, what they considered a negative outcome for them may
not have been (and vice versa). For example, many houses viewed that instituting the second
press would only aggravate the over supply problem. However, the second press also decreased
the cost of grapes per bottle. Refer to Appendix B for an explanation of the performance effects
over time of changing each issue. Overall, the results suggest that increasing the number of
issues in a negotiation situation may increase the integration potential (as can be seen from the
significant impact on joint outcomes); however, it does not guarantee equal distribution.
Model 2 examines whether prior negotiation experience has an effect on EVA outcomes.
Contrary to previous experiment results where payoff structures were provided in advance, for
the asymmetric dyads, the party with prior negotiation experience did not have significantly
higher individual or joint payoffs than those dyads with no experience at all. While we can only
speculate as to the reasons, perhaps the more experienced growers/houses spent more time
“educating” rather than taking advantage of the less experienced parties. In cooperative contexts,
negotiations outcomes seem to be driven by the party with less negotiation experience.
Yet, those dyads whose parties have previous negotiation experience do seem to matter
even in cooperative contexts. From Model 2 in Table 3, we can see that grower, house and
industry EVA increased by 476, 246 and 592 respectively. While these dyads did not have the
benefit of payoff structures provided in advance, they did have the benefit of sharing the same
mental models having come from the same negotiations course. This mutual understanding
clearly eased their ability to calculate potential increased outcomes. Indeed, even the average
prices of the champagne bottles (a private decision) were significantly higher for those dyads
22
where the parties had prior negotiating experience. Refer to Table 3, which shows the averages
of input decisions for the dyads with differing prior negotiation experience.
Insert Table 3 About Here
As we can see in Model 3 of Table 2 asymmetric negotiation experience does not
increase the rate of learning for the advantaged party. For example, growers’ payoffs were not
significantly higher when the growers had an asymmetric negotiation advantage. Apparently, in
both cases of asymmetric experience, the experienced party was consistently pulled down by the
inexperienced party; they could not claim any more of the surplus. Only dyads where both
parties had prior experience exhibited a higher rate of learning by doing. In these cases, parties
that had negotiation experience were able to transfer their knowledge of bargaining to this novel
negotiation situation fairly rapidly and then increasingly benefit from it over time.
In summary, contrary to our first three hypotheses, and contrary to previous experimental
results, only those dyads where both parties had previous negotiation experience reached higher
negotiation payoffs and exhibited higher rates of learning over successive negotiation rounds. In
other words, only when both parties have prior negotiation experience are the context, intent,
negotiation approach and outcomes fully aligned. However, when one of the parties lacks
negotiation experience, then the context may result in cooperative intent but that intent may not
translate into a true integrative negotiation approach where both parties win. Or the adage “you
are only as smart as your dumbest competitor” seems also to very much apply to these
negotiation situations even in cooperative contexts.
Learning from Outcome Feedback
So far, we have examined the effect of negotiation experience on negotiation outcomes
within a cooperative context. We argued above that negotiators might also learn from observed
23
outcomes. More specifically, we hypothesized that dyads without experience that observe that
their EVAs are moving in a lose-lose/win-lose/lose-win pattern may have more difficulty
crafting integrative solutions than those with experience. As we proposed in our hypotheses,
given their ability to better understand the context, we expect that more experienced negotiation
dyads will more likely reach more win-win outcomes (H3a), to switch lose-lose, win-lose and
lose-win situations into win-win outcomes (H3b and H3c) and persist with win-win outcomes
(H3d) than less experienced dyads.
Insert Table 4 About Here
In order to test these learning-from-outcome hypotheses, we developed crossclassification tables with the three categories of joint outcomes on the x-axis: lose-lose, winlose/lose-win, and win-win, and the three categories of experience on the y-axis: inexperienced,
asymmetric and both experienced. Table 4a tabulates whether experienced negotiators reach
more win-win outcomes than inexperienced negotiators. Table 4b illustrates whether experience
dyads more often switch lose-lose to win-win outcomes than inexperienced dyads. Table 4c
shows whether experienced dyads change lose-win/win-lose to win-win outcomes more often
than inexperienced negotiators and finally Table 4d demonstrates whether experienced
negotiators persist with win-win outcomes. Each table shows the percentage frequency of each
pattern of joint outcomes for each of the three categories of negotiation experience. Furthermore,
at the bottom of each table we include a χ2 statistic and Fischer Exact measure to test the
independence of the rows and columns.6
From Table 4a, we can see that out of the 240 rounds, 18% of the outcomes were loselose, 55% lose-win/win-lose and 27% win-win. Thus, in the majority of the cases, one party
6
We included the Fisher’s Exact test due to the small cell counts in some of the tables (see e.g. Agresti, 1984).
24
progressed at the expense of the other (whether intended or not). When we examine Table 4a for
the presence of association, we find that the rows and columns are independent; the chi-squared
statistic is 10.77, which is significant at the 5% level. We see that all negotiation dyads are
equally likely to portray win-lose/lose-win outcomes suggesting the difficulty in reaching winwin patterns in uncertain negotiation situations. However, despite the low frequency of
achievement, the odds or likelihood of achieving win-win outcomes instead of lose-lose
outcomes is about 4.2 times (35*27.42/15.32*15) higher for those dyads with prior negotiation
experience over inexperienced dyads. Furthermore the odds of obtaining a win-win instead of
win-lose/lose-win is approximately 1.6 times (50*27.42/57.26*15) times higher for experienced
versus inexperienced dyads.
Similar results were found for asymmetric experience versus
inexperience (5.3 times for win-win over lose-lose and 2.08 times for win-win over win-lose).7
In summary, dyads with any previous negotiation experience, symmetric or asymmetric, were
ultimately more successful than inexperienced dyads in achieving Pareto-improving or win-win
outcomes, more clearly aligning cooperative context with intent and negotiation outcomes.
However, as the earlier results suggest, dyads with asymmetric prior negotiation experience still
suffered from lower individual and joint EVA.
As shown in Table 4b, when the previous joint outcomes were lose-lose, there was no
significant association between experience and subsequent outcome patterns. Apparently, those
groups that were inexperienced were as likely to switch from lose-lose to win-win, suggesting
that regardless of prior experience both types of parties tried to learn from the outcomes to
improve their situation.
7
Clearly, within cooperative negotiation contexts, regardless of
We also categorized lose-lose, win-lose/lose-win and win-win based on whether EVA was positive or negative
rather than on the change in EVA. Unsurprisingly, the win-win results favored the experienced dyads. See
Appendix C for details.
25
experience, learning or searching for causes for the negative outcomes supersedes getting
trapped into systematic lose-lose patterns or downward spirals.
However, as shown in Table 4c, when the previous joint outcomes were win-lose or losewin, we can see significant differences; the chi-squared statistic (9.04) is significant at the 10%
level and the Fisher’s exact test at the 5% level. In this case, experienced groups are about 3.5
(66.67*39.13/56.52*13.33) times more likely than inexperienced dyads to switch a winlose/lose-win outcome to a win-win pattern than to maintain a win-lose or lose-win pattern. The
asymmetric dyads appeared to have achieved proportionally more win-win outcomes than the
experienced dyads albeit with lower individual and joint EVAs.
Table 4d shows the cross tabulations of experience and outcome patterns when the
previous outcome pattern was win-win. In this case, there is a presence of association between
the variables; the chi-squared statistic is 8.40 and significant at the 5% level; the Fisher Exact
test is significant at the 10% level. First, more experienced parties are approximately 2.64 times
more likely than inexperienced dyads to persist with a win-win pattern than reverting to a loselose pattern. They are also 1.5 times more likely than inexperienced parties to persist in moving
lock-step than switching over to a win/lose, lose/win pattern.8 In other words, while the
probability of persisting with a win-win pattern for experienced parties is still small, compared to
inexperienced parties, it is very high. Furthermore, achieving a win-win pattern for
inexperienced parties seems to stem more likely from luck than from skill given that no win-win
outcome pattern persisted.
In summary, learning from outcome feedback when payoffs are uncertain is not easy
generally supporting previous findings on the topic (see e.g. Thompson and DeHarpport, 1994).
26
Overall, despite the previous outcomes, the win-lose/lose-win pattern was the most prevalent
outcome obtained. While the win-lose/lose-win patterns were not significantly different amongst
the dyads with varying experience, we did find evidence of performance improvement from
outcome feedback. Whether experienced or not, all groups were more likely to change lose-lose
patterns to win-win/win-lose/lose-win patterns suggesting that within a cooperative context
learning to collectively solve the problem overrides the start of a downward spiral. Yet, prior
negotiation experience still does matter within cooperative contexts, despite the overall
ineffectiveness of outcome feedback. More experienced parties (symmetric or asymmetric) were
more likely than inexperienced negotiation dyads to switch win-lose/lose-win to win-win
outcomes and also persist with win-win payoffs.
DISCUSSION AND CONCLUSIONS
One of the critical research streams in strategy concerns how firms deal with threats to
the sustainability of superior performance. Hold up is indeed one of those threats. Owners of
scarce, non substitutable complementary products and assets along the supply chain can always
negotiate away any performance advantage a focal firm may have in an industry. While strategic
management research has focused primarily on cooperative context such as free exchange of
information (e.g. sharing cost and demand data), coordinated decision-making, investments in
specific assets and commitments in the supply chain relationship to increase joint outcomes and
individual profits, the relationship still fundamentally relies on the negotiations of the two
parties. Surprisingly, negotiations studies tend to focus on individual and group influencers
while ignoring the context factors of interest in strategic management. Hence, opportunities to
8
In cases where the frequency is zero in one of the cells, it is still possible to calculate an odds ratio that has a
smaller bias and mean squared error: (n11+0.5)(n22+0.5)/(n12+0.5)(n21+0.5). Using this formula, we
27
better understand the effect of context on negotiation approaches and outcomes clearly exist.
Yet, cooperative context may only lead to cooperative intent but not necessarily integrative
negotiation approaches and outcomes. Even in the presence of a cooperative context, negotiation
performance may still be quite variable due to prior negotiation experience. This paper examined
two aspects of prior negotiation experience: learning by repeating the task and learning from
outcome feedback.
This study builds upon previous research in the area of learning and negotiations in
several ways. First, contrary to other studies in negotiations, which most frequently set up their
experiments with predetermined outcome payoffs, this study was based on a more realistic
context rich negotiation simulation where outcome payoffs were not known in advance. The
context was the French champagne industry which has over time established a number of
mechanisms to ensure cooperative intent including self-regulation, information transparency,
multi-issue negotiations, and enforcement of contracts. A teaching case was developed to reflect
this context and was then used as the starting point of a negotiations experiment. Student groups
representing the players in the industry were asked to negotiate over a number of issues the
industry focuses on each year. The payoffs from the decisions made by the different negotiating
parties – the growers and houses -- had to be calculated in advance, but were subject to delays,
and uncertainties. These outcomes could result in significant misperceptions of feedback
jeopardizing any form of learning or any benefits from previous experience. Hence, we believe
that the context used in this negotiation experiment provide a more conservative test of prior
experience and learning in negotiations.
Secondly, within a cooperative context, we found that dyads with prior negotiation
experience had an advantage over those with less or no experience. In other words, when both
calculated the odds ratios of 10 and 1.5 (see e.g. Agresti, 1984).
28
parties were experienced in negotiations, the cooperative context, cooperative intent, integrative
negotiation processes and outcomes were aligned. While they did not necessarily negotiate more
issues than inexperienced parties, they were still better able to trade them off resulting in higher
individual and joint outcomes. They also tended to learn more quickly about the task and context
than inexperienced negotiators, leading to higher individual and joint outcomes over time. In
addition, they were more likely than inexperienced negotiators to achieve a win-win outcome
pattern, which included switching win-lose or lose-win to win-win patterns or persisting with
win-win patterns. However, switching from win-lose or lose-win and persisting with win-win
outcomes was certainly not guaranteed, even for more experienced negotiators, reflecting the
inherent difficulties in negotiation situations with asymmetric outcomes. Yet, overall, the
grower, house and joint outcomes were significantly higher for parties with more negotiation
experience.
Thirdly, contrary to previous negotiations research, we found no evidence that the
individual or joint outcomes were higher when one party had a negotiation experience advantage.
Rather, the negotiations tended to be driven by the less experienced party. Interestingly, despite
the lower overall payoffs, these dyads were still much more likely than inexperienced negotiators
to achieve a win-win outcome pattern, which included switching win-lose or lose-win to win-win
patterns or persisting with win-win patterns. The experienced group may have spent more time
educating the other party rather than taking advantage of their lack of negotiation experience.
Certainly this explanation would be consistent within a cooperative context.
Fourthly, parties with no negotiation experience still improved their outcomes over
several negotiation rounds. However, these outcomes were more likely to proceed in a winlose/lose-win than in a win-win pattern. Furthermore, even if they did reach a win-win outcome,
29
it appeared more likely because of luck rather than skill as many inexperienced dyads reverted
back to a lose-lose or win-lose situation in the subsequent rounds.
These results have significant implications for strategic management. Clearly creating a
cooperative context is not enough in achieving integrative win-win outcomes. The parties may
exhibit the “willingness to work together” for the good of the industry, but ultimately the
division of the total value is split through the negotiation process. Our findings confirm that prior
negotiation experience is likely more important than the learning from the negotiation itself and
the contextual mechanisms that were previously established. While all negotiators (experienced
or not) may exhibit cooperative intent within a cooperative context, it is only the dyads with
prior negotiation experience that have a better strategic conceptualization of the situation. This
advanced knowledge allows them to not only perform better sooner but also better as the
negotiations proceed through many rounds. In other words, experienced negotiators are also able
to learn more quickly than their less experienced counterparts.
Several limitations of the study can be highlighted. First, simulations are always subject
to some constraints, such as researcher bias and the definition of the environment in finite,
tractable terms. As a result, there may be some element of the students trying to understand the
mechanics of the simulation rather than the negotiation itself. Secondly, other group factors such
as cultural diversity or within group negotiation dynamics were not taken into consideration,
which could have had an effect on their collective ability to follow integrative negotiations.
Thirdly, negotiation experience came from students that had taken a negotiations course, not
expert negotiators with “on the job” training. It would be interesting to examine in more detail
the relative importance of on the job and theoretical experience. Finally, while we organized the
negotiations over a two-week period to limit the rapid spreading of the better solutions across the
30
groups, we could not be certain that there was not some learning by observation. Despite these
limitations, our objective is to call for more papers in better understanding the link between
context in hold up situations and negotiation approaches.
31
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36
Figure 1
An Example of Outcome Patterns
Player A’ s Payoffs
Z
A3
A1
X
Y
A2
B1
B3
B2
Player B’s Payoffs
37
Table 1
Summary Statistics and Correlation Matrix on Decision Inputs and Variables
Number of Observations = 240
1
2
3
4
5
6
7
8
9
10
11
Mean
St. Dev.
Negotiated Inputs
Appellation (kilos per ha.)
Percent Purchased by Houses
Second Press
Third Press
Percent of Yield into Blocage
Release from Blocage (in mths)
Grape Price
7863.99
77.00
0.41
0.15
5.18
11.16
31.39
2428.35
24.41
0.49
0.36
14.68
16.44
11.29
House's EVA
Grower's EVA
Industry EVA
No Experience
Growers Exp. Houses Inexp.
Growers Inexp. House Exp.
Both Experience
Time
Cumulative Number of Issues
House Price
Grower Price
-248.17
795.99
575.39
16.7%
13.3%
18.3%
51.7%
2.5
8.56
87.28
66.96
1232.30
2154.15
2791.11
1.12
3.52
33.00
24.79
1
1.00
0.33 *
0.70 *
-0.10
-0.03
-0.15 *
0.21 *
0.48 *
0.11
0.53 *
0.41 *
2
1.00
0.86
-0.08
-0.02
-0.06
0.12
0.22
0.16
0.41
0.54
3
*
*
*
*
*
1.00
-0.09
-0.03
-0.11
0.18
0.40
0.18
0.55
0.60
4
*
*
*
*
*
1.00
-0.18
-0.21
-0.46
0.00
0.15
-0.16
0.00
*
*
*
*
*
5
6
7
8
9
10
11
1.00
-0.19 *
-0.41 *
0.00
-0.02
0.01
-0.03
1.00
-0.49 *
0.00
-0.08
-0.09
-0.14 *
1.00
0.00
-0.04
0.19 *
0.13
1.00
0.00
0.00
0.00
1.00
0.14 *
0.12
1.00
0.59 *
1.00
* p < .05
38
Table 2
Regression Results: Growers, Houses and Industry EVA
Variable
Constant
Cumulative Changes
Growers Exp. Houses Inexp
Grower Inexp. Houses Exp.
Growers Exp. Houses Exp.
Time
Growers Exp. Houses Inexp
Growers Inexp. Houses Exp
Growers Exp. Houses Exp.
Grower Price
House Price
Number of Observations
R-Squared
F-Test
Grower’s EVA
Model 2
Model 3
-623.28*
-245.38
-1.88
-0.99
223.49*
218.55*
2.07
2.05
337.78
0.90
473.82
1.43
476.13†
1.76
416.55**
416.55**
250.83*
3.74
3.76
2.00
185.76
1.17
174.53
1.47
210.88*
1.97
967.73**
989.95**
972.94**
5.57
5.65
5.62
291.16
245.01
239.08
1.53
1.27
1.25
240
240
240
35.56
36.18
36.43
15.97**
9.53**
9.80**
Model 1
-245.38
-0.98
194.87†
1.85
House’s EVA
Model 2
Model 3
-1681.62** -1572.80**
-11.01
-13.52
44.71
41.70
0.91
0.89
22.43
0.14
-114.93
-0.53
245.61*
1.97
529.85**
529.85**
458.17**
9.64
9.71
6.85
31.51
0.47
-21.61
-0.23
138.27*
2.44
186.34**
177.21**
173.32**
2.86
2.57
2.57
539.81**
517.14**
500.47**
6.18
5.82
5.78
240
240
240
52.66
54.05
54.97
39.77**
27.20**
27.77**
Model 1
-1572.80**
-13.20
39.29
0.78
Industry EVA
Model 2
Model 3
-2326.79** -1939.62**
-6.73
-7.67
280.35*
279.47*
2.55
2.61
233.87
0.68
273.99
0.75
591.78*
2.21
1006.01**
1006.01**
770.79**
8.65
8.70
6.65
202.01
1.51
162.01
1.16
345.64**
3.37
1164.84**
1173.64**
1157.98**
6.42
6.23
6.34
831.59**
775.86**
741.62**
5.34
4.91
4.70
240
240
240
59.10
59.73
60.60
33.60**
20.20**
20.95**
Model 1
-1939.62**
-7.53
253.32*
2.31
†
p < .1
* p < .05
** p < .01
39
Table 3
Mean Quantity of Each Issues For Each Category of Prior Negotiation Experience
Category
Both Inexperienced
Growers Experienced
Houses Inexperienced
Houses Experienced
Growers Inexperienced
Both Experienced
Appelation
Percent
Purchased
Second
Press
Third
Press
Blocage
Grape
Price
Grower
Price
House
Price
4.85
Release
from
Blocage
1.02
7617
77
.3
.1
27.3
59.1
74.3
8634
63
.3
.09
3.60
.72
30.1
62.6
82.9
8070
8389
78
74
.3
.5
.02
.2
5.40
8.0
1.02
1.21
28.0
38.8
58.5
67.5
79.1
89.9
40
Table 4
Table of Pattern of Joint Outcomes and Previous Negotiation Experience Based on Change in EVA
a) Overall (Hypothesis 3a)
Experience/Pattern
Inexperienced
Lose-lose
35.00%
Win-lose/Lose Win
50.00%
Win-Win
15.00%
Asymmetric Experience
14.47%
52.63%
32.89%
Both Experienced
15.32%
57.26%
27.42%
Average
18.33%
Chi (4) = 10.77* (significant at 5% level)
54.58%
27.08%
b) When Previous Joint Outcomes are Lose-Lose (n= 50) (Hypothesis 3b: Switching)
Experience/Pattern
Inexperienced
Lose-lose
18.18
Win-lose/Lose-win
45.45
Win-win
36.36
Asymmetric Experience
6.67
46.67
66.67
Experienced
0.00
66.67
33.33
Average
6.00
Chi (4) = 5.55 (not significant)
Fisher's Exact Test = 0.24 (not significant)
56.00
38.00
c) When Previous Joint Outcomes are Win-Lose or Lose-Win (n=84), (Hypothesis 3c: Switching)
Experience/Pattern
Inexperienced
Lose-lose
20.00
Win-lose/Lose-win
66.67
Win-win
13.33
Asymmetric
13.04
34.78
52.17
Experienced
4.35
56.52
39.13
52.38
38.10
9.52
Average
Chi (2) = 9.04* (significant at 10% level)
Fisher's Exact Test = 0.037** (significant at the 5% level)
d) When Previous Joint Outcomes are Win-Win (n=46), (Hypothesis 3d: Persistence)
Experience/Pattern
Inexperienced
Lose-lose
75.00
Win-lose/Lose-win
25.00
Win-win
0.00
Asymmetric
10.53
63.16
26.32
Experienced
26.09
43.48
30.43
50.00
26.09
Average
23.91
Chi (4) = 8.40* (significant at 5% level)
Fisher's Exact = 0.09* (significant at 10% level)
41
Appendix A
Sensitivity Analysis of Champagne Simulation, Percentage Increase or Decrease in EVA Over Time
Increase
No Changes
Appelation
No Second Press
Both Second and Third Press
Percent Purchased
Blocage
Grape Price
Grower Price
House Price
10%
10%
10%
10%
10%
10%
1993
Houses
Growers
-7.6
-65.5
-3.0
28.1
0.0
0.0
0.0
0.0
-2.8
18.1
0.0
0.0
2.5
34.5
0.0
9.7
15.2
-0.6
1994
Houses
Growers
-22.1
2.4
-5.9
-2.5
-6.4
0.3
2.9
-0.3
-6.3
3.8
0.8
-0.9
-10.8
-1.0
0.0
-5.2
-12.5
-0.3
1995
Houses
Growers
12.3
-29.9
-3.7
-2.0
-9.1
-0.3
4.4
0.1
-4.6
3.6
0.6
-1.2
-11.5
-1.1
0.0
-2.4
-6.5
0.3
Joint
Effect
11.0
-15.5
7.2
11.8
-0.5
12.5
2.2
-4.4
42
Appendix B
Table of Pattern of Joint Outcomes and Previous Negotiation Experience Based on EVA
a) Overall
Experience/Pattern
Inexperienced
Lose-lose
17.50%
Win-lose/Lose Win
80.00%
Win-Win
2.50%
Asymmetric Experience
31.58%
50.00%
18.42%
Both Experienced
20.16%
53.23%
26.61%
Average
23.33%
56.67%
20.00%
Lose-lose
40.00
Win-lose/Lose-win
60.00
Win-win
0.00
Asymmetric Experience
35.00
35.00
30.00
Experienced
26.09
52.17
21.74
31.25
Average
Chi (4) = 2.94 (not significant)
Fisher's Exact Test = 0.62 (not significant)
45.83
22.92
b) When Previous Joint Outcomes are Lose-Lose (n= 50) EVA
Experience/Pattern
Inexperienced
c) When Previous Joint Outcomes are Win-Lose or Lose-Win (n=84), Test for Switching EVA
Experience/Pattern
Inexperienced
Lose-lose
12.00
Win-lose/Lose-win
84.00
Win-win
4.00
Asymmetric
31.03
55.17
13.79
Experienced
4.00
49.06
26.42
58.88
17.76
Average
23.36
Chi (4) = 10.92** (significant at 5% level)
Fisher's Exact = 0.03* (significant at 5% level)
d) When Previous Joint Outcomes are Win-Win (n=46), Test for Persistence EVA
Experience/Pattern
Inexperienced
Lose-lose
0.00
Win-lose/Lose-win
0.00
Win-win
0.00
Asymmetric
0.00
50.00
50.00
Experienced
0.00
17.65
82.35
0.00
Average
Chi (1) = 2.82* (significant at 10% level)
Fisher's Exact = 0.12 (not significant)
28.00
72.00
43