Attitude-Based Negotiation Methodology
for the Management of Construction Disputes
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Saied Yousefi1; Keith W. Hipel2; and Tarek Hegazy, M.ASCE3
Abstract: A systematic negotiation methodology for construction disputes is presented to take into consideration the attitudes of
negotiators at two complementary levels of decision making: strategic and tactical. At the strategic level, the proposed methodology
employs the graph model for conflict resolution and helps negotiators find the most beneficial subset of solutions to the conflict. At the
tactical level, the proposed methodology examines the most beneficial strategic decisions using utility functions to provide agreed-upon
tradeoffs with respect to any conflicting issues. A construction case study is used to illustrate the proposed methodology and demonstrate
the importance of incorporating decision makers’ attitudes into negotiation to better identify the most feasible decisions. The proposed
methodology may assist negotiators with the challenges of conventional negotiation through the incorporation of decision makers’
attitudes into a range of analytical tools that will clarify interests, determine equilibrium outcomes, identify tradeoffs, recognize negotiators’ satisfaction, and generate optimum solutions.
DOI: 10.1061/共ASCE兲ME.1943-5479.0000013
CE Database subject headings: Negotiations; Decision making; Conflict; Optimization; Construction industry.
Author keywords: Negotiation methodology; Strategic; Tactical; Attitudes.
Introduction
Construction projects have become increasingly complex and involve many parties who often have conflicting objectives. The
owner, for example, would like a project to be inexpensive and
speedy, while the contractor wants fewer restrictions. According
to the Litigation Trends Survey Findings 共Fulbright and Jaworski
2006兲, construction firms around the world spend close to $31
million annually on litigation fees, second only to the insurance
industry.
In the highly competitive multiparty environment of the construction industry, many disputes can arise for a host of reasons,
such as the complexity and magnitude of the work, lack of coordination among parties, poorly prepared and/or executed contract
documents, inadequate planning, financial issues, and disagreements about on-the-spot site-related problems. Any one of these
factors can derail a project and lead to complicated litigation or
arbitration, increased costs, and breakdown in the relationships
among the parties 共Harmon 2003兲. Resolving construction disputes, therefore, is an essential component of construction administration. Many studies have been carried out regarding
construction dispute resolution methods 共Doug 2006; Cheung
et al. 2002; Rameezdeen and Gunarathna 2003兲.
1
Ph.D. Candidate, Dept. of Systems Design Engineering, Univ. of
Waterloo, ON, Canada N2L 3G1. E-mail: s2yousef@uwaterloo.ca
2
Professor, Dept. of Systems Design Engineering, Univ. of Waterloo,
ON, Canada N2L 3G1. E-mail: kwhipel@uwaterloo.ca
3
Professor, Dept. of Civil and Environmental Engineering, Univ. of
Waterloo, ON, Canada N2L 3G1 共corresponding author兲. E-mail:
tarek@uwaterloo.ca
Note. This manuscript was submitted on June 7, 2008; approved on
November 4, 2009; published online on June 15, 2010. Discussion period
open until December 1, 2010; separate discussions must be submitted for
individual papers. This paper is part of the Journal of Management in
Engineering, Vol. 26, No. 3, July 1, 2010. ©ASCE, ISSN 0742-597X/
2010/3-114–122/$25.00.
The traditional method of resolving construction disputes, particularly for large and complex projects, is litigation 共Pinnell
1999兲. Since litigation involves enormous amounts of time and
cost for all the parties involved, other dispute resolution methods,
called alternative dispute resolution 共ADR兲 tactics, have emerged
共Harmon 2003兲. There are two groups of ADR techniques for
construction: formal 共binding兲 methods and informal 共nonbinding兲 methods. Binding ADR is predominantly arbitration, while
nonbinding ADR methods include minitrials, mediation, thirdparty neutrals, dispute resolution boards, and negotiation 共Trantina 2001兲.
Negotiation is the least hostile approach and is also fast and
least costly. This has been proven by various studies of negotiation, particularly in the domain of family counseling 共e.g., Bellucci and Zeleznikow 1998兲 which involves complex situations
where the attitudes of individuals have a major impact on the
outcome. Negotiation is also a part of daily routine in construction activities among participants who may have different positions, goals, and attitudes. Negotiations among construction
participants can be, and usually is, the most efficient form of
dispute resolution in terms of managing time and costs, confidentiality, and preserving relationships.
The few negotiation models which have been prepared for the
construction industry 共e.g., Cheung et al. 2004 and Omoto et al.
2002兲 do not consider the psychological mindsets 共i.e., attitudes兲
of the decision makers 共DMs兲 when describing situations. In psychology, attitude is defined as a hypothetical construct that represents an individual’s likes or dislikes for an item. An individual’s
attitude can be positive, negative, or neutral toward an object,
behavior, or event. Unlike personality, attitudes are expected to
change as a function of experience. Breckler and Wiggins 共1992兲
defined attitudes as “mental and neural representations, organized
through experience, exerting a directive or dynamic influence on
behavior.”
The attitudes of participants play an unavoidable role in the
outcome of negotiation. Negotiators with aggressive attitudes
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a construction negotiation methodology is proposed, based on the
graph model for conflict resolution 共GMCR兲 and taking into account the attitudes of negotiators. A construction case study is
also used to illustrate the proposed model and show how negotiators’ attitudes toward one another can change the outcome of the
negotiation. The proposed methodology provides decision support
at two complementary levels of negotiation 共Fig. 1兲: strategic
共i.e., the general terms of agreement兲 and tactical 共i.e., the detailed settlement of any conflicting issues兲. The developments related to these two negotiation levels are discussed in the
following sections.
Strategic Negotiation: A Graph Model Approach
Fig. 1. Proposed negotiation model
usually believe in hard negotiation in which concessions are almost nonexistent. On the other hand, negotiators who possess
cooperative attitudes encourage soft negotiation by taking the risk
of having to concede with respect to various issues in the dispute.
With cooperative attitudes, negotiators share their concerns and
interests positively and try to reach a mutual win-win solution
which would be better than leaving the negotiation table with no
resolution 共Scott and Billing 1990兲. In aggressive negotiation, the
parties often have negative attitudes toward one another and they
consider any concession made as a positive improvement for their
opponents.
Proposed Negotiation Methodology
The primary objective of this research is to investigate how the
attitudes of participants can influence the outcome of construction
negotiations and how the incorporation of attitudes can help negotiators make more reliable decisions. To achieve this objective,
To support negotiation at the strategic level, the GMCR 共Fang et
al. 1993兲 is used. GMCR is a methodology for modeling and
analyzing DMs’ interactions in a conflict in order to find the
stable decision states for each DM, as well as the decision states
that represents potential resolutions 共or equilibria兲 of the conflict.
GMCR has been reported as an appropriate method for providing
an understanding and insight into strategic decisions 共Kilgour
2007兲.
GMCR, which originated from conflict analysis 共Fraser and
Hipel 1984兲 that in turn is based on the metagame theory
共Howard 1971兲, utilizes concepts and definitions from the graph
theory, the set theory, and the game theory. Each DM’s possible
moves from one decision state to another are captured within a
directed graph, in which nodes represent states and arcs indicate
state transitions controlled by the DM. A decision state is a potential outcome of the conflict. The systematic procedure for applying the graph model follows two main stages: modeling and
analysis. At the modeling stage, the problem is structured by determining the DMs, the possible decision states, the DMs’ preferences among the decision states, and the possible moves and
countermoves that a DM can make among the decision states.
Next is to analyze the stability of each decision state from the
viewpoint of each DM individually. Such stability analysis is conducted with respect to a set of solution concepts 共stability concepts兲, as listed in Table 1. The objective is to find which decision
state共s兲 is/are stable for all the DMs with respect to each solution
concept. At the end of the analysis, the decision state that exhibits
the highest stability level represents a potential resolution of the
conflict. The use of the graph model technique in the construction
industry has been explained by Kassab et al. 共2006兲.
Table 1. Solution Concepts for Conflict Resolution 共Kassab et al. 2006兲
Solution concepts
Nash stability 共R兲
General metarationality 共GMR兲
Symmetric metarationality 共SMR兲
Sequential stability 共SEQ兲
Limited move stability 共Lh兲
Nonmyopic stability 共NM兲
Descriptions
Moving to a different state brings no benefit to the DM.
Moving to a more preferred state may trigger the opponent’s countermove with less benefit to
the focal DM, even if the countermove is less preferred for the opponent.
Moving to a more preferred state may trigger the opponent’s countermove to harm the DM,even
if the countermove is self-harmful to the opponent. The focal DM has the chance to
counter-respond.
Moving to a more preferred state may trigger the opponent’s countermove to improve the
opponent’s positions where self-harmful countermoves are not considered.
The DM acts optimally within a defined number of action/countermove cycles 共h state
transitions兲.
Same as limited move stability but for infinite state transitions.
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Table 2. Representation of Attitudes 共Inohara et al. 2007兲
DM
i
j
i
j
eii = +
e ji = 0
eij = 0
e jj = +
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Attitude Representation in Strategic Negotiation
This research represents a major expansion of GMCR: combining
attitudes within the paradigm of GMCR furnishes a flexible analytical tool which reflects how the DMs’ attitudes may change the
strategic outcomes of a negotiation. Attitudes are formally defined
by Inohara et al. 共2007兲 and can be represented in a matrix format. Table 2 represents the attitudes between two DMs, i and j,
where each cell entry in the table can take on a value of “+,” “0,”
or “⫺” to represent a positive, neutral, or negative attitude, respectively. As shown in Table 2, the DMs i and j have positive
attitudes 共+兲 toward themselves since eii = + and e jj = + and have
neutral attitudes toward one another 共eij = 0 and e ji = 0兲.
Brownfield Case Study
The brownfield case study in this research represents a common
conflict situation in which the land of a privately owned property
is found to be contaminated, and according to municipal laws, the
property is considered to be a brownfield. Due to the enormous
remediation costs, responsibilities, risks, and uncertainties involved with brownfield problems, conflicts have often risen be-
tween the property owner and the municipality, thus starting a
negotiation process between the owner and the local government
in order to find the most beneficial solution. More information
about brownfield projects can be found in De Sousa 共2000兲.
In this brownfield case study, it is assumed that both DMs have
defined the different options available to them as follows:
1. Owner’s options:
• Accept liability; and
• Sell the property.
2. Government’s options:
• Share costs; and
• Lawsuit.
The two DMs and their options are shown in part a of Fig. 2.
Each option can be either accepted or rejected by each DM, and
since there are four choices in total, the number of decision states
is 24 = 16, however, the total number of feasible states is 12, as
shown and listed on the right in part a of Fig. 2, where each
column represents a state in which Y means “Yes” and N indicates “No.” For convenience, the states are numbered from 0 to
11. Infeasible states 共e.g., the situation where all the four options
in part a of Fig. 2 are “Y”兲 and the process of deleting them are
explained in Fraser and Hipel 共1984兲.
Once feasible decision states are identified, DMs’ preferences
and the possible transitions among them are defined as shown in
Fig. 2. For example, one of the owner’s possible moves is among
States 0, 1, 2, and 3 in which the options of the government are
similar 共i.e., “N” and “N” for its two options兲. Similarly, all transitions are defined for the DMs as shown in Figs. 2共b and c兲.
As an important step in the graph model process, the prefer-
Fig. 2. Integrated feasible states, graph model, and state preference for the DMs
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Fig. 3. Stability analysis tableau for two DMs with rational attitudes
ence of each DM among the decision states are defined, as shown
in part d of Fig. 2, which started from the most preferred states on
the left to the least preferred states on the right. As shown, the
owner prefers most that the government only shares the costs of
redevelopment 共State 4兲 and prefers least to accept liability and
have the case filed in court 共State 9兲. The most preferred state for
the government is State 1, in which the current owner accepts
liability and no other actions are taken, and the least preferred
state is State 4, in which the government shares the costs of the
brownfield redevelopment.
Stability Analysis Considering DMs’ Attitudes
A stability analysis is the systematic evaluation of potential
moves and countermoves by the DMs as they negotiate for more
preferred positions 共Hipel et al. 2007兲. The solution concepts defined in Table 1 are used to carry out stability analysis that takes
into account the DMs’ attitudes using three attitude scenarios as
described in the following subsections.
Case 1: DMs’ Neutral Attitudes toward Each Other
The first situation considers the DMs to have positive attitudes
with respect to themselves and neutral attitudes toward each
other. The tableau in Fig. 3 shows the analysis process. The tableau has three parts: the top part for the analysis of DM1 共owner兲
stability, the middle part for the analysis of DM2 共government兲
stability, and the bottom part for equilibrium analysis. The rows in
each DM section show its list of preferences 共state ranking兲, possible moves from each state to any other state, and stability types.
Such a tableau helps in the systematic analysis of the moves and
countermoves by the DMs. The arrows moving down from the
state ranking indicate the moves that can put a DM in a more
preferred decision state. For example, the arrow from State 5
共circled in Fig. 3兲 indicates that the owner can move to State 4
which is more preferred to State 5 共according to the owner’s
ranking兲. While it is possible for the owner to move from State 5
to States 4, 6, or 7 共as shown in the middle figure in part b of Fig.
2兲, only State 4 is more beneficial than State 5 while States 6 and
7 have less ranking. Accordingly, only State 4 appears under State
5 in Fig. 3, which is consistent with the owner’s positive attitude
toward itself. Similarly, all the moves from the various decision
states in state ranking to other states that are consistent with the
DMs attitudes in Case 1 are shown in Fig. 3.
Using Fig. 3, the procedure for determining the type of stability for each state is quite straightforward. For example, to check
for Nash stability 共first type in Table 1兲, the analysis starts from
the state ranking 共first row, for the owner兲 and looks at the states
that have no arrows 共i.e., cannot be improved兲. As such, States 4,
2, and 10 for the owner and States 1, 7, 2, and 8 for the government are Nash stable states, which are marked with “Nash” in
Fig. 3. Similarly, other stability types are indicated as shown in
Fig. 3. Let us consider, for example, the stability of State 5 for the
owner. Once the owner moves from State 5 to State 4, the examination of the government’s moves reveals that the government
can respond by moving from 4 to 8 or to 0. However, in the
owner’s state ranking, both 8 and 0 are less preferred than 5, and
thus, the owner is deterred from improving from 5 because it can
lead to states 8 or 0. Consequently, State 5 is sequentially stable
as indicated in Table 1 and, as such, a “SEQ” is written below 5
in the owner’s stability analysis. It should be noted that the states
that do not have any type of stability are unstable states and, as
such, the DMs can move from these states to more preferred
states. Unstable states are indicated by “U” in Fig. 3.
Once all stability types are examined for each state with respect to each DM, equilibrium is investigated as shown at the
bottom part of Fig. 3. As indicated by “E,” States 2, 5, and 7 are
stable for both DMs because they have some type of stability for
both DMs. If a state possesses some type of stability for all DMs,
it is called an equilibrium state, and this state constitutes a possible resolution to the conflict 共Fraser and Hipel 1984兲. All other
states are unstable for at least one of the DMs and, therefore, are
not considered possible resolutions. The end result of the analysis
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Fig. 4. Stability analysis tableau for two DMs with negative attitudes
is that Decision States 2, 5, and 7 are equilibria, or possible strategic solutions for this case study, based on the DMs’ neutral
attitudes toward each other.
Case 2: DMs’ Negative Attitudes toward Each Other
To examine the influence of DMs’ attitudes toward each other, a
situation is considered in which the DMs change their attitudes
from neutral to negative attitudes toward each other. The stability
process is explained as shown in Fig. 4.
In Fig. 4, all beneficial moves are initially indicated underneath the state rankings, however, some of these moves are
crossed out by an “X” 共Fig. 4兲 to unbeneficial moves according to
the DMs’ attitudes in Case 2. State 6 共encircled兲 is considered as
an example. Since the owner has a negative attitude toward the
government, the owner desires to move only to the states that will
not benefit the opponent. From the ordering of the states for the
government in Fig. 4, it can be seen that State 5 will put the
government in a better position, and as such, State 4 is the only
state that the owner moves to in order to improve its own position
and lower the opponent’s position. State 5 is therefore crossed out
in Fig. 4. The same procedure is then carried out for all other
states to determine the moves for each DM.
Once the DMs’ moves are determined, the stability type of
each state is determined according to the DMs’ attitudes. The
stability of State 6, for example, is assessed. From the owner’s
perspective, if he/she moves from 6 to 4, then the government can
move from 4 to 8. Although 8 is less preferred to 6 for the government, State 8 is also less preferred to 6 for the owner and
because the owner has a positive attitude toward itself the owner
is deterred to move from 6 to 4. Thus, State 6 becomes SEQ for
the owner and a “SEQ” is written below State 6. Afterwards, the
stability of State 6 is assessed from the government’s perspective.
It can be seen from Fig. 4 that by moving to State 2, the government will not only benefit itself 共positive attitude兲 but also State 2
is less preferred to the owner 共negative attitude兲. As such, the
government has an incentive to move from State 6 to State 2, thus
rendering State 6 as unstable indicated by “U” below State 6 in
government stability in Fig. 4. Similarly, stability analysis is performed for all other states to define the type of stability and consequently to obtain equilibrium states. As a final result of this
attitude scenario, States 2, 5, 7, 8, and 10 共indicated by “E” at the
bottom of Fig. 4兲 are equilibria. It is noted that in Case 1, different
equilibria 共2, 5, and 7兲 were obtained. Thus, the change in the
DMs’ attitudes causes a corresponding change in the resulting
equilibria.
As a third attitude case, the stability analysis is carried out for
a scenario in which both the owner and the government have
positive attitudes not only toward themselves but also toward
each other. A summary of the resulting equilibria in the three
cases is shown in Fig. 5.
Discussion of Attitude-Based Strategic Negotiation
In the brownfield case study, the results of the three attitude scenarios in Fig. 5 indicate the following:
1. When the DMs change their attitudes from neutral to nega-
Fig. 5. Equilibria for the three attitude cases
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gotiation support is proposed in the next section to complement
strategic negotiation and help in the settlement of any conflicting/
pending issues involved in the selected strategic solution.
Tactical Negotiation: Utility Analysis
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Fig. 6. Utility function for bid price
tive, the set of equilibria changes toward more hostile solutions 共e.g., States 8 and 10兲.
2. On the contrary, better possible solutions 共e.g., States 1 and
4兲 in the set of equilibria are obtained in case the DMs have
positive attitudes toward each other.
3. The increase in the number of resolutions in a conflict may
help the involved DMs choose a better possible solution from
the resulting equilibria.
4. The positive attitudes of the DMs toward themselves and
each other not only mitigate the degree of hostility involved
in Outcomes 8 and 10 but also shift the range of equilibria in
the DMs’ state ranking from right 共less preferred states兲 to
the left 共more preferred states兲. As such, the positive attitudes
of the DMs help shift the set of equilibria in the state ranking
to the more preferred options for both DMs.
5. One important observation is that outcomes 2 and 5 are the
only common equilibria obtained from the stability analysis
based on three different DM attitudes. Outcomes 2 and 5 are
considered as the most stable outcomes of the negotiation for
both DMs because no matter what type of attitudes 共neutral,
negative, or positive兲 the DMs have toward each other, these
outcomes provide equilibrium solutions.
Based on the case study results, the owner and the government
examine all equilibrium states and select one as their strategic
solution. It is assumed in this case study that both DMs agree on
State 2 which involves the owner selling the property. This strategic solution, however, does not involve determining an agreeable sale price which is an important detail issue that requires the
DMs to continue their negotiation. Therefore, a tactical-level ne-
To support detailed tactical negotiation for any specific issue, utility analysis is proposed in this section as a suitable approach to
maximize the benefits to all DMs. A utility function represents
and describes, for each DM, his/her level of satisfaction 共0 to 1兲
of a certain criterion 共Keeney and Raiffa 1976兲. For example, an
owner may assign utility values of 1.0, 0.9, 0.5, and 0.0 if the
contractor’s bid price 共criterion兲 is $2.6, $2.7, $2.8, and $3.0 million, respectively. In this case, the utility function that represents
the “bid price” criterion can be as shown in Fig. 6. In cases of
decisions that involve multiple criteria, then the alternative that
maximizes the expected utility, considering the criteria weights, is
selected 共Kilgour 2007兲. In the case of multiple DMs, such as
negotiations, it is logical that the best decision is the one that
maximizes the joint expected utility value of all DMs which represents a cooperative environment.
One key step in utility analysis is to determine a suitable utility
function. The form of a utility function depends on the characteristics of the DMs who have a specific attitude with respect to their
preferences for consequences 共Kirkwood 1997; Eliashberg and
Winkler 1978兲. Utility function has been used in many research
efforts 共e.g., Du and Chen 2007, Peña-Mora and Wang 1998,
Mumpower 1991, Zeleznikow et al. 2007, and Darling and Mumpower 1990兲 to develop negotiation supports. However, these research efforts have not considered the psychological aspects of
negotiations such as the DMs’ attitudes. The proposed tactical
negotiation approach in this section considers the attitudes of
DMs when using utility function. Moreover, the proposed tactical
negotiation approach complements the strategic negotiation developed in the previous section, thus forming a comprehensive
negotiation strategy.
Among suggested utility functions 共e.g., Zuhair et al. 1992,
Peña-Mora and Wang 1998, and Hanoch and Levy 1970兲, reformatted polynomial utility functions are used in this research be-
Fig. 7. DMs’ utility functions for the “price” issue
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willing to compromise more than the government. On the other
hand, the government prefers to be cooperative but has no great
interest in buying the property. As such, a utility function form
with n = 2 is a good representation of the government who is
willing to concede less than the owner. Accordingly, the interactive negotiation between the DMs can be modeled. By integrating
the utility function of all DMs an integrated utility function for
the negotiating issue is obtained, as shown in Fig. 8. The integrated utility function is simply the summation of the utility values associated with the two individual functions. Point H, for
example, on the total utility function is obtained by algebraically
calculating the length of line DH= DE+ DF. The same approach is
applied to other points on the integrated utility function.
Step 3: Selecting the Best Decision Value
Fig. 8. Tactical negotiation results
cause they are more flexible for assigning risk attitudes to DMs
共Zuhair et al. 1992兲. The reformatted polynomial utility function
form is f共x兲 = anxn + an−1xn−1 + . . . +a1x + a0; where “x ” = input variable; “n ” = power of function; and “a ” = real number coefficient.
Using this utility form, the tactical negotiation for the case study
follows the steps discussed in the following subsections.
Step 1: DMs’ Utility Functions
In this case study, the DMs’ negotiating issue at the detailed level
is the offered initial price of the owner’s property. According to
the negotiating issue, the DMs start their tactical negotiation by
selecting a proper utility function. The general form of utility
function for the seller 共owner兲 and the buyer 共government兲 is
shown in Fig. 7共a兲. These shapes reflect the seller’s highest preference to get the highest price and, at the same time, the buyer’s
highest preference to pay the lowest price. Generalizing these
functions, various function forms can be generated for the owner
and the government, as shown in Figs. 7共b and c兲, respectively.
Because the government’s position on the price of the property is
in contrast to the owner’s position, the government’s utility function is a mirror image of the owner’s utility function 共Peña-Mora
and Wang 1998兲.
Step 2: An Integrated Utility Function for Each Issue
In this case study, it is assumed that the owner prefers to have a
cooperative attitude and a desire to sell the property and avoid all
risks involved with the brownfield property. As such, a utility
function with n = 7 is a good representation of the owner who is
The maximum point on the integrated utility function represents
the maximum utility value and as such, the maximum level of
satisfaction for DMs 共Kilgour 2006; Darling and Mumpower
1990兲. Accordingly, the maximum point on the integrated utility
function is Point B with the maximum combined utility value. As
shown in Fig. 8, Point B represents the settlement point 共SP兲 or
the point of agreement because both DMs have reached the highest degree of satisfaction for their cooperative effort 共Kilgour
2006兲. The SP is used to obtain the percentage of price 共indicated
on the horizontal axis兲 that each DM should pay. In Fig. 8, Point
N indicates 30% of the price is paid by the government and 70%
共100⫺30%兲 of the price is reduced by the owner. If the initial
price of the property is $200,000, for example, the government
should pay $60,000 共0.3⫻ $200, 000兲 and the owner should reduce the initial price to $140,000 共0.7⫻ $200, 000兲. As it can be
seen, the proposed negotiation methodology at the tactical level
helps the DMs determine the exact amount of concession that
each DM should make to reach mutual agreement on each issue.
Considering Attitudes in Tactical Negotiation
It is important to assess what DMs think about each other during
negotiation at the tactical level. A DM’s attitude toward risk taking determines the shape of his or her utility function. Knowing
that a DM is risk avers can substantially restrict the shape of a
utility function 共Kirkwood 1997兲.
In the proposed tactical negotiation, the attitudes of DMs are
reflected by n, the power of the reformatted polynomial utility
function. As shown in Fig. 7, the shape of utility function changes
with the value of n. When 0 ⬍ n ⬍ 1, then the polynomial function
has a negative convex shape for the government and positive
convex shape for the owner. When 1 ⬍ n ⱕ 10, then the polyno-
Table 3. Results of the Sensitivity of the DMs’ Attitudes
DM 1 共owner兲
n
0⬍n⬍1
0⬍n⬍1
0⬍n⬍1
1 ⬍ n ⱕ 10
1 ⬍ n ⱕ 10
1
DM 2 共government兲
Attitude
n
Attitude
Result
Utility
共%兲
Noncooperative
Noncooperative
Noncooperative
Cooperative
Cooperative
Neutral
0⬍n⬍1
1 ⬍ n ⱕ 10
1
1 ⬍ n ⱕ 10
1
1
Noncooperative
Cooperative
Neutral
Cooperative
Neutral
Neutral
Very negative
Negative/positive
Negative
Very positive
Positive
Neutral
13ⱕ utilityⱕ 93
40ⱕ utilityⱕ 166
31ⱕ utilityⱕ 96
150ⱕ utilityⱕ 200
125ⱕ utilityⱕ 169
utility= 100
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J. Manage. Eng., 2010, 26(3): 114-122
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mial function has a negative concave shape for the government
and positive concave shape for the owner. The function shape is
linear when n = 1. Concavity in utility functions 共i.e., 1 ⬍ n ⱕ 10兲
reflects the risk-taking 共more cooperative兲 attitude of DMs, while
convexity 共0 ⬍ n ⬍ 1兲 reflects a risk-avoiding attitude 共Keeney and
Raiffa 1976兲. When the DMs cooperatively negotiate at the tactical level, they know that they may take the risk of conceding to
come up with a mutual agreement. In the present case study, both
the owner and the government have 共1 ⬍ n ⱕ 10兲 for their utility
function and as such, both utility functions have concave shape.
Therefore, once the range of n values is appropriately selected,
the exact n values are determined based on clear understanding/
observation of the DMs’ behavior. As such, a value of n = 7 is a
good representation of the owner who is willing to compromise
more than the government. On the other hand, the government
prefers to be cooperative but has no great interest in buying the
property. As such, a value of n = 2 is a good representation of the
government. Such mutual positive attitudes can help the DMs
reach a “win-win” solution for their joint effort using the proposed attitude-incorporated utility function. It should be noted
that once a proper n range is selected, the different in the shape of
the utility function between n = 2 and n = 7 is not large 共Fig. 7兲. In
future research, a sensitivity analysis will be incorporated to assess the impact of changes in n on final resolution.
Table 3 summarizes the results of various scenarios in this case
study, under three different n ranges for the DMs’ utility functions: 0 ⬍ n ⬍ 1, 1 ⬍ n ⱕ 10, and n = 1. If low n value 共e.g., 0 ⬍ n
⬍ 1兲 is used in the utility function, it means that the shape of
utility function is convex and the DMs have negative attitudes
toward each other and as such, the range of the utility is low
共Table 3兲. On the other hand, if high n value 共e.g., 1 ⬍ n ⱕ 10兲 is
used in the utility function, it means that the shape of utility
function is concave and the DMs have positive attitudes toward
each other and as such, the range of utility is increased as shown
in Table 3.
Conclusions
This research contributes significantly to the provision of managerial tools that have the potential benefit of supporting construction negotiations by integrating the strategic and tactical
perspectives as well as the decision makers’ attitudes as an important psychological aspect of negotiation. The proposed
attitude-based negotiation introduces a new systems engineering
methodology to help managers tackle various real-world controversies, particularly in the construction industry. The research is
expected to help improve negotiation methodologies for construction disputes and save enormous time and cost due to the following benefits:
1. The proposed negotiation methodology can be conveniently
implemented as a negotiation decision support system. Such
a computer-based system has great advantages with respect
to speed, practicality, and flexibility.
2. Attitude-based negotiation systems can be highly useful for
practitioners who must deal with the costly and complex disputes in construction projects.
3. Practical negotiation support systems can help minimize
emotional costs of the parties involved in construction disputes, and maximize work performance in construction.
4. The research helps to foster a positive environment in which
construction professionals can negotiate.
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