Implications of Negotiation Theory for Research and Development of Negotiation Support Systems

JOBNAME: Kluwer Journals − GR PAGE: 1 SESS: 8 OUTPUT: Mon May 12 08:55:36 1997/data11/kluwer/journals/group/v6n3art3

Group Decision and Negotiation, 6:3 255–274 (1996)© 1996 Kluwer Academic Publishers

Implications of Negotiation Theory for Research andDevelopment of Negotiation Support Systems

CLYDE W. HOLSAPPLEDepartment of Decision Science and Information Systems, College of Business and Economics, University ofKentucky, Lexington, Kentucky 40506-0034

HSIANGCHU LAIDepartment of Information Management, National Sun Yat-sen University, Kaohsiung, Taiwan 80424,Republic of China

ANDREW B. WHINSTONDepartment of Management Science and Information Systems, Graduate School of Business, The Universityof Texas at Austin, Austin, Texas 78712-1175

Abstract

This paper summarizes a parametric theory of negotiation as a basis for shedding light on negotiation supportsystem possibilities. Previously, the theory has been used to analyze prior research accomplishments in the areaof negotiation support systems. Here, we discuss implications of the theory that are relevant for future researchand development of negotiation support systems. The implications are concerned with three topics: a high-levelgeneric characterization of these systems, an identification of their possible support functions, and a taxonomyfor classifying such systems.

Key words: acceptance region, intervenor, issue space, negotiation support functions, negotiation supportsystem, negotiation theory, taxonomy

1. Introduction

Negotiation support system (NSS) research has progressed to the point where it is arecognizable specialty area within the field of business computing systems. NSS is thesubject of conference tracks and special issues of prominent journals (e.g., ManagementScience). It is a major focus of the TIMS-sponsored Group Decision and Negotiationjournal. Yet, the NSS realm is still a formative area, marked by diverse research streamsthat have not proceeded from a common conception of negotiation phenomena.

A great deal of NSS research has focused on how to get an optimal or acceptablesolution (Fang, Hipel, and Kilgour 1989; Kilgour, Fang, and Hipel 1990; Shakun 1990;Sycara 1990b; Kersten et al. 1991; Bui and Strand 1993). Some is concerned with com-puterizing the process of multicriteria decision methods (e.g., Jarke 1986; Bui and Jarke1986). Problem representation has been another focus of NSS research (Kersten et al.1988; Kersten and Mallory 1990; Shakun 1990). This has extended to problem restruc-turing during negotiation processes (Kersten and Michalowski 1991; Shakun

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1991; Sycara 1991). Major socioemotional issues in negotiation activities have beenexplored by NSS researchers (Anson and Jelassi 1990; Faure, Dong, and Shakun 1990;Foroughi and Jelassi 1990). Research involving issues related to negotiation intervenorshas emerged (Jarke, Jelassi, and Shakun 1987; Anson and Jelassi 1990; Sycara 1990a;Lim and Benbasat 1991; Valley, White, and Iacobucci 1992).

An analysis of the NSS literature yields more complete profiles of the kinds of NSStopics that have been investigated and the investigation approaches employed (Holsapple,Lai, and Whinston 1995). These profiles also show where the NSS coverage has beenrelatively light. Given the area’s current stage of development, it is not surprising thatNSS research efforts have been diverse and unevenly distributed among relevant topics.Nor is it surprising that efforts at implementing NSSs have aimed at satisfying supportneeds in specific negotiation situations (as was the case with implementations in the earlydays of the decision support system area). While these represent definite contributions tocertain aspects of NSS design and development, the applicability and extensibility of suchNSS are limited. As yet, little NSS research has been based on a general-purpose, for-malized theory of negotiation phenomena (Holsapple, Lai, and Whinston 1991).

Our purpose here is to identify and explore the implications of such a theory, particu-larly with respect to the development of negotiation support systems. The results of thisexercise have value not only for NSS developers but for NSS researchers as well. Thevalue stems from unifying perspectives for relating what has been done within the NSSarea with stimulative frameworks for thinking about what could or should be done by NSSdevelopers and researchers.

The theory that forms our starting point has been used to analyze a large collection ofrecent works in the NSS area (Holsapple et al. 1995). The analyses found that one-fourthof the theory’s parameters are absent from over 80% of the NSS publications analyzed.While NSS research has collectively recognized all parameters posited by the theory, noindividual work has explicitly addressed all of them. Therefore, we expect that the theo-ry’s implications as delineated here will be useful for most (if not all) active NSS re-searchers interested in directions for extending their own lines of work.

We begin with a brief summary of the negotiation theory. Its details and formalisms arenot presented, as they are not essential for appreciating the implications discussed here.They can be found elsewhere (Lai 1989; Holsapple, Lai, and Whinston, forthcoming). Thediscussion of implications is organized into three main sections: a possible generic struc-ture for NSSs, general NSS functions and their coverage in the NSS literature, and ataxonomy for classifying NSSs, along with examples for the categories. We conclude witha description of further related research needs.

2. The Negotiation Theory

According to the theory, whose implications we shall explore, a negotiation activity can becharacterized in terms of eight parameters:

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N 5 ~I, E, ACCEPT, LOC, S, M, R, A!

where I is the issue to be negotiated, E is the set of entities involved, ACCEPT representsthe entities’ acceptance regions, LOC indicates the entities’ locations, S and M denote theentities’ strategies and movements, respectively, R denotes the rules of negotiation, and Aindicates assistance from an intervenor (Lai 1989). The theory holds that negotiationscannot be understood, in general or in particular, without understanding (e.g., instantiat-ing) these eight parameters. Related to this major postulate, the theory espouses 14additional postulates and specifies 14 definitions. The theory holds that the major aspectsof a negotiation activity are identified by these postulates and definitions. Here, they areinformally summarized.

At issue is the matter of contention, which is the essential core of a negotiation activity.It can be defined by its dimensions. Each dimension’s possible values can be determinedby a range function. Thus, an issue is represented by its issue space (the terms issue andissue spare are used interchangeably), which consists of all coordinates defined by thecross-product of all dimensions’ range sets. An entity is a participant in a negotiationwhose agreement must be attained for a solution to exist. In the theory, an entity could bea person, multiple persons (e.g., a group), a computer, or a human-machine combination.Because a negotiation process is a kind of multiparticipant decision-making process inwhich each entity contributes to the outcome, the constitution of an entity can be regardedas comparable to that of a decision support system: a system that performs some of theknowledge management activities that occur in a decision-making process (Holsapple andWhinston, in press). The theory postulates that each entity has an acceptance function thatdetermines its acceptance region within the issue space. The intersection of all entities’acceptance regions forms an agreement region, which is a basis for the decision reachedby the negotiating entities.

A location is a coordinate in an issue space. At any moment, each entity will prefer alocation within its acceptance region and will occupy it. Each entity has the freedom tomove to a different location. The set of all entities’ locations defines the current state ofa negotiation process. The goal of a negotiation activity is to reach such a solution statein which all entities are at the same location in the agreement region.

Furthermore, the theory postulates that each entity has a strategy function to derive aset of candidate negotiating strategies and a strategy selection function to select a par-ticular strategy from these candidates. For example, coalition formation can be an impor-tant aspect of entities’ strategies. Once entities form a coalition, they will be at samelocation and move together as long as they stay in that coalition. However, entities mayoccupy the same location coincidentally rather than intentionally. To distinguish this froma coalition, the theory defines the latter circumstance as a rendezvous. Some coalitionsmay not be allowable due to restrictions in the rules of negotiation. Some allowablecoalitions may be infeasible at a certain time due to the lack of an agreement region.Given an entity’s strategy and acceptance region, there may be several desirable locations.The entity’s moving function determines which of these locations it will occupy.

Rules of negotiation define what interactions are allowed among entities, what move-ments are permissible within an issue space, and when and how a negotiation ends. They

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therefore play an extremely important role in a negotiation process. The theory assumesthat no entities will violate the rules of negotiation. It identifies five major categories ofrules: those involving time constraints, regulation of coalition formation, communicationpatterns, resolution policies, and intervention by outside agents (Holsapple et al., forth-coming).

An intervenor in this theory is assumed to be an active agent with a purpose of directlyor indirectly helping the entities reach an agreement. An intervenor can play such roles asmediator, arbitrator, rule manipulator, or facilitator (social or technical). Not every nego-tiation will have or even allow an intervenor. However, once an intervenor is drawn intoa negotiation activity, it plays an extremely important role. Like an entity, each intervenorcould be a person, multiple persons acting as a whole, a computer system, or a human-machine combination. Because negotiation is a kind of multiparticipant decision-makingprocess, an intervenor actually functions as a decision support system for the set ofentities (or perhaps some subset of them).

Figure 1 shows basic interdependencies among the parameters. At the very root of anegotiation activity is the notion of an issue. Who are the participating entities is highlyrelated to the nature of the issue. What is the issue and who are the entities can have a verybig influence on what happens during a negotiation process. Given the issue, each par-ticipating entity will establish its objectives. With these objectives, each entity will ascer-tain its acceptance region and pursue a strategy. The decisions with regard to the accep-tance region and strategy may affect each other.

Relative to its current strategy and acceptance region, each entity will choose a specificmovement that will bring it to a new location. Because of the dynamic and uncertainnature of negotiation processes, each entity may have to repeatedly reassess its objectives,

Figure 1. Interdependence among primary parameters of negotiation.



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strategies, acceptance regions, and movements. Given the issue and the involved entities,all of these interdependencies are constrained by the rules of negotiation and perhapsinfluenced by the actions of an intervenor.

Instantiations of some parameters may be null at the outset of a negotiation. Forinstance, there may be no initial assistance from an intervenor. Also, the instantiation ofa particular parameter can change as negotiation unfolds. For example, the value of LOCchanges to reflect the current state of a negotiation process, entity strategies can change,and acceptance regions are not necessarily fixed. Knowing the initial instantiations, finalinstantiations, and intermediate instantiations of the eight parameters allows us to under-stand the structure and dynamics of a particular negotiation.

The foregoing negotiation theory systematically characterizes important constructs andprinciples involved in negotiation. It does so in a way that can be expressed via a formallanguage (Lai 1989; Holsapple et al., forthcoming). This theory is not only more com-prehensive but also more descriptive than the negotiation research in game theory; com-pared to the negotiation research in social behavior science, it formalizes importantfactors in a systematic way (Lai 1989). The validation of its descriptive power has beendemonstrated by its ability to formally characterize several diverse negotiation activitiesand lines of NSS research (Lai 1989; Holsapple et al. 1995). Moreover, this negotiationtheory was developed with the intent of understanding possibilities of computer support,while negotiation research in both game theory and social behavior science does notharbor such an intent. As such, we contend that the negotiation theory is a useful vehiclefor exploring alternative NSS types and issues in a systematic manner. Toward that end,we now consider some of its implications for negotiation support systems.

3. Implications for Negotiation Support Systems

The foregoing theory has important implications for studying and constructing NSSs.What aspects of negotiation activities can a NSS help and in what ways? The theoryprovides a foundation for answering this question. That is, the negotiation theory providesa basis on which NSS researchers and developers can better appreciate NSS possibilities.It allows them to approach alternative types of NSSs in a flexible, practical, and integratedway rather than in an ad hoc way. The delineation advanced here functions as a stimulantfor researchers and a guide for prospective developers. The discussion is pursued fromthree angles. First, a generic structure for NSSs is introduced. Second, we identify generalfunctions that an NSS might be expected to possess in light of the theory developed.Third, a framework for classifying alternative kinds of NSSs is introduced, along withexamples of the application of alternative NSS types.

3.1. A generic characterization of NSSs

The theory views negotiation as a kind of multiparticipant decision-making activity.Through strategies and movements, multiple entities endeavor to achieve a common



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position within the intersection of their acceptance regions inside an issue space. Thiscommon position is the multiparticipant decision. It follows that a NSS is really a specialcase of a decision support system (DSS), where the nature of the decision to be supportedinvolves negotiation. This implies that the generic structure of a DSS (Bonczek, Hol-sapple, and Whinston 1981; Dos Santos and Holsapple 1989) also can be regarded as ageneric characterization of essential NSS components.

As illustrated in Figure 2(a), an NSS has four main subsystems. It has a languagesystem (LS) composed of messages it can accept from whomever uses it and a presenta-tion system (PS) consisting of messages that can be presented to its user(s). An NSS hasa problem processing system (PPS) to process the messages in its LS. The PPS does so bydrawing on contents of the NSS’s knowledge system (KS). The result is an alteration to theKS or the presentation of a PS message.

The constitution of an entity or an intervenor can also be regarded in terms of thegeneric DSS structure (Dos Santos and Holsapple 1989). Therefore, it follows that an NSScan support not only entities or an intervenor but also the negotiation by functioning as anentity or intervenor. When an NSS supports an entity, the relationship between them is asshown in Figure 2(b). The PS of a NSS issues messages that are elements in the LS of theentity. Conversely, the message presented by the entity will become an element in the LSof the NSS.

Given the generic NSS structure indicated in Figure 2(a), what kind of knowledge shouldbe collected in a KS? What kinds of abilities should the PPS have? Potentially, the knowl-edge system consists of values for all important parameters in a negotiation activity asproposed in the negotiation theory. For example, it might include knowledge about beliefs,preferences, and goals of all entities; a map of the issue space and other issue-relatedknowledge; specification of negotiation rules; mechanisms to forecast or recommend anentity’s strategy, movement, and location; and previous similar negotiation cases. This maybe in the form of descriptive knowledge, procedural knowledge, reasoning knowledge,linguistic knowledge, assimilative knowledge, or presentation knowledge (Holsapple andWhinston 1988). The abilities of a PPS must be consistent with KS contents—capable of

Figure 2. Negotiation support systems.



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processing them for negotiation support purposes. Within the scope of the generic NSSstructure, the precise natures of each of the four subsystems in a specific NSS depend onrequirements of the application and developer preferences in the choice of tools and meth-ods for implementation. For example, the requirements for developing an NSS that willcollectively be used to support a group of people are likely to be very different from thoseneeded to support a single negotiator only. Further examples of such variations are de-veloped in the next section’s consideration of support possibilities.

3.2. General NSS functions

Using the theory, we can derive a host of implications about possible kinds of support thata NSS might provide. Some of these have been implemented in specific NSSs or inves-tigated in NSS research; others have not. A straightforward way to approach this genera-tive analysis is to consider support possibilities with respect to each important parameteridentified by the theory. Resultant support possibilities for each parameter are first sum-marized, then discussed in more detail. The support possibilities identified here are notnecessarily exhaustive but offered in a stimulative spirit to both developers and research-ers of NSSs. Which of these are to be incorporated into a specific NSS is a choice that lieswith the discretion of its developer. In the following summary, we note functions that aNSS could perform in support of a negotiation:

Parameters Support PossibilitiesIssues To determine the dimensions of a negotiation issue.

To determine the value of each dimension to each entity.To determine the range set for each dimension.To formally represent the issue space.To identify the feasible portion of an issue space.To process the representation of an issue space.To update and keep track of changes in an issue space.

Entities To accommodate knowledge about entities.To update knowledge about entities.

Acceptance region To identify the acceptance region of one or more entities.To change an entity’s own acceptance region.To monitor and catalog all entities’ acceptance regions.To compute the agreement region.

Locations To represent a location within the issue space.To portray all possible locations within an acceptance region.To portray all possible locations within the agreement region.To evaluate the relative values of all locations within the accep-

tance region of an entity.To detect whether a location is within the acceptance region.To keep track of all entities’ locations and analyze the changes.



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To measure the dispersion of current or potential locations.To detect the trend of dispersion among locations at each state of

a negotiation process.Strategies To track entities’ previous strategies and analyze their changes.

To forecast entities’ strategies.To design, select, and review strategies.To recognize a coalition state and track it.To analyze the likely effects of possible coalition formations.

Movements To derive all candidate locations for moving based on a selectedstrategy.

To select or recommend a new location from candidate locations.To keep track of all entities’ historical movements and analyze

their changes.To forecast other entities’ movements.

Rules of negotiation To collect and maintain knowledge about rules of negotiation.To prevent entity behavior that would violate the rules.To detect whether prospective behavior would violate the rules.To recognize the existence of an impasse, adjournment, or break-

down.To recognize whether a termination rule is satisfied.

Intervenor To keep entities informed about intervenor actions and vice versa.To recognize the time and situation in which an intervenor is likely

to act.To accommodate knowledge characterizing the nature of a particu-

lar intervenor.

Such support possibilities obviously are related to each other. Rather than attempting tobe exhaustive, we identify them for stimulatory purposes. Of course, a particular NSSneed not have all these traits. The required functions will depend on the role a particularNSS will play and the degree of sophistication needed. We now examine the foregoingpossibilities in more detail.

3.2.1. Issues. The notion of an issue is at the very root of a negotiation activity. So, anNSS is likely to have some means for representing the issue space. For this reason, thedeveloper of a NSS should ask, What is the issue space? How might it be represented?Could a generic general tool be devised to represent and manipulate issue spaces in muchthe same way as a database management tool can represent and manipulate databases? Arepresentation of an issue space should be able to capture all dimensions, each dimen-sion’s range set, and the feasible portion of the issue space. This should help an NSS userachieve and maintain a complete and correct understanding of the characteristics of anissue space. It also forms a basis for the NSS performing various analyses.

Researchers have begun to explore such NSS abilities. For example, the NEGOPLANnegotiation support system represents basic negotiation issues as Prolog-like facts(Matwin et al. 1989). Negotiation issues are described in terms of a goal representation



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tree in which a negotiation goal is split hierarchically into symbols, with facts at thebottommost levels. Koperczak, Kersten, and Szpakowicz (1990) have used this to modelissues for a financial management problem. Similarly, Kersten et al. (1988) and Kerstenand Mallory (1990) use an AND/OR tree to represent negotiation issues. On the otherhand, Bui and Strand (1993) use neural networks and Kilgour et al. (1990) use a graphmodel to represent negotiation issues. Bui’s (1992) individual DSS used to support anegotiator allows that user to define the range of values for all issues.

If an NSS represents an issue space within its KS, then its PPS must be able to processthe representation in some way. For instance, an issue or a dimension can be added or therange of a dimension can be changed. A minimal processing capability would be able toretrieve an issue space for presentation purposes.

The issue space involved in one negotiation can be quite different from the issue spacein another. However, the notion of an issue space, its dimensions, each dimension’s rangeset, and its feasible region are common across different negotiation episodes. Therefore, itmight be expected that a general tool for NSS development could be implemented formanaging issue spaces. Such a tool could be thought of as an issue management system,providing a language for defining specific issue spaces and another for manipulating thesespaces.

One benefit of an NSS’s ability to manage issue space representations is the foundationit provides for an entity’s negotiation efforts. Without a clear appreciation of the issuespace, an entity’s efforts at designing and selecting strategies and movements are ham-pered. Another benefit may come from the NSS functioning as a reminder or monitor ofdimension limits. For multiple users, there is the benefit of entities being able to obtain aconsistent specification of the issue space. Indeed, some NSS research has studied how anegotiation issue can be restructured during a negotiation process (Shakun 1990; 1991;Kersten et al. 1991; Sycara 1991; Matwin, Szapiro, and Haigh 1991).

3.2.2. Entities. Like issues, entities have a very big influence on a negotiation. They areprime movers in the dynamic interactions that make up negotiation activity. Because theoutcome of a negotiation depends on these interactions, an entity must know not onlyitself but also other entities. Within the bounds of negotiation regulations, the more anentity knows about other entities, the better it understands a negotiation situation. Forinstance, cognitive mapping methods focus on the representations of individuals’ posi-tions or viewpoints in ways that can be analyzed by others (Eden 1989). By accommo-dating knowledge about entities within its KS, a negotiation support system can fostersuch understanding. Conceivably, an NSS should have the ability to acquire, use, andpresent knowledge about entities’ acceptance regions, locations, strategies, movements,value systems, objectives, social roles, and so forth.

In this direction, some NSS researchers have studied cognitive difficulties, thought,emotion, preference, and confidence of entities (Faure et al. 1990; Foroughi and Jelassi1990; Jones and Jelassi 1990; Lim and Benbasat 1993; Bui 1994). Gauvin, Lilien, andChatterjee (1990) have examined the impact of information about entity payoff functionson negotiators’ joint and individual performance. Some research has examined how anNSS can forecast an opponent’s behavior or represent the nature of an opponent (Kersten



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et al. 1991). Bui (1992) has proposed an individual DSS that offers its user analytical andstrategic insights about other entities in a negotiation. Bui and Strand (1993) use a neuralnetwork to represent entities’ offers. In a related vein, Watabe, Holsapple, and Whinston(1992) have devised a formal method for representing and processing influence strengths,criteria priorities, and persuasion difficulties of participating entities.

Because some knowledge about an entity is likely to change rapidly, it is important foran NSS to be able to update such knowledge in a timely manner. Retentive update can beimportant, allowing an NSS to keep and use knowledge about entities’ past behaviors.Actually, case-based reasoning that draws on past experiences has been used in NSSresearch (Sycara 1990b; Bui 1992). How training or preparation of an entity affects itsperformance also has been explored (Gauvin et al. 1990; Eliashberg et al. 1992).

3.2.3. Acceptance Regions. How the entities’ acceptance regions intersect will form abasis for reaching an agreement. Therefore, we might expect an NSS to be able to help anentity correctly identify its own acceptance region as well as those of other entities.Moehlman, Lesser, and Buteau (1992) have specified “relaxation of negotiation con-straints” as one step in their iterative problem-solving process. To relax constraints is toenlarge an acceptance region. Matwin et al. (1991) discuss how the aspiration levels ofeach entity form an aspiration grid. An aspiration grid is a way of defining an entity’sacceptance region. Under pressure and facing a complicated negotiation issue, a negoti-ating entity can get lost and even attempt to move to a location not in its acceptanceregion. Clearly, the ability to identify an entity’s acceptance region and ensure that achosen location is within that region are important topics for negotiation support research.Andriole (1993) has pointed out that to “identify the range of acceptable negotiationoutcomes” is one of main tasks in defining a problem or opportunity.

By tracking entities’ acceptance regions, an NSS provides a foundation for forecastingentities’ behavior such as selected strategies and movements. Certainly, this also dependson understanding the issue space and the involved entities. If an NSS is able to keep trackof all entities’ acceptance regions, it should also be able to compute the agreement regionat any moment. A user of this NSS can understand the situation more clearly and com-pletely a basis for contemplating an appropriate strategy. In all, little NSS research hasexplicitly explored acceptance region topics (Holsapple et al. 1995).

3.2.4. Locations. Even though an entity can identify its acceptance region, it may not beeasy to recognize and evaluate all the locations in the region, especially when the numberof dimensions is large. An NSS may be able to help. For instance, it may graphicallyportray all possible locations that exist within an entity’s acceptance region and provide anability to ensure that the selected new location is within the acceptance region. A prereq-uisite for having such abilities requires that an NSS can represent a location. Hipel, Fang,and Kilgour (1990) use a graph model to portray possible locations. The NEGOPLANsystem represents a location by a set of metafacts and can generate a set of best locations(Kersten et al. 1990).

Much research has focused on the search for optimal or final locations (Kersten andMallory 1990; Matwin et al. 1991; Bui 1992; Sycara 1990a; Quaddus 1993; Iz 1994;



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Zigurs et al. 1994). The methodologies applied in this work include multiple-criteriadecision methods, simulation, economic models, rule-based formalism, neural networkmechanisms, case-based reasoning, machine learning, graph model analysis, and fuzzylogic.

The location of an entity directly influences a negotiation process and result. An entitycan benefit from keeping track of other entities’ locations and recognizing how theychange over time. Therefore, an NSS might be expected to take care of location trackingand analysis. In a related vein, researchers have attempted to maintain logs to keep recordsof collaborators’ interactions (Carmel, Herniter, and Nunamaker 1993).

Conceivably, an NSS may have a mechanism to measure the distance between anentity’s current location and each candidate location and evaluate the relative valuesamong these locations. It also may have the ability to measure the dispersion of locationsin each state of the negotiation process. An NSS may provide a mechanism to detectwhether the dispersion of locations at each state of a negotiation process is decreasing.This could include the use of an importance weight for each dimension.

3.2.5. Strategies. Because of the very uncertain and dynamic nature of some negotiations,an entity can be prone to review, devise, and select varying strategies over time. Thesealternative strategies can be numerous and complex. One potential function of an NSS isto keep track of all previous strategies of an entity as a basis for review. From the review,an entity may determine to keep the same strategy, shift to another strategy selected fromrecorded strategies, or design a new strategy. To aid in such deliberation, an NSS couldhave some procedural and reasoning knowledge allowing it to recommend or derivecandidate strategies. Strategy selection could be aided by NSS-generated comparisons ofstrategies with respect to an entity’s value system and objectives. Some reported NSSs(e.g., DECISION MAKER, CAP, DECISION CONFERENCING, RUNE) provide strat-egy planning abilities (Jelassi and Foroughi 1989).

The ability to help an entity correctly discern other entities’ strategies is another po-tential NSS function. Such conjectures are dependent on knowledge about other entities’previous strategies and may take into account probabilities of the possible strategies. Thus,it can be beneficial for an NSS to keep track of other entities’ strategies as well as thoseof its user. For example, Negotiation Edge has been developed to help a negotiatoridentify strategies derived from self-assessment of its own personal negotiation style aswell as from assessments of other entities’ negotiation styles (Jelassi and Foroughi 1989).

Coalition formation can be an important aspect of an entity’s strategy in many-entitynegotiations. Conceivably, an NSS could be implemented to recognize coalition states,track their shifts over time, support entity decisions about whether to ally itself with otherentities, and offer advice about the activity of forming and maintaining a coalition. Forexample, an NSS might help an entity recognize the feasible coalitions and then evaluateand compare benefits an entity would enjoy by participating in potential coalitions. Oncean entity makes a decision to attempt to form a particular coalition, the NSS may assistan entity’s efforts at persuading target entities to agree on the coalition formation. Among



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developed NSSs, NEGO allows negotiators to change their strategies and form coalitions(Kersten 1985). Shakun (1990, 1991) also examines the evolving nature of coalitionformation.

3.2.6. Movements. Given a strategy, an entity could move to any of multiple candidatelocations. An NSS could offer support by deriving all such candidate locations, ensuringthat all candidate locations are within an entity’s acceptance region, and helping an entityevaluate the candidate locations. Selection of a particular location is likely to be depen-dent on other entities’ movements. An NSS that provides forecasting mechanisms, allow-ing an entity to analyze other entities’ movements could be beneficial. Keeping track of allentities’ movements and analyzing their changes could be prerequisites for applying suchforecasting mechanism. For example, NEGO stimulates movements based on anticipatedbehaviors of both parties (Kersten 1985). NEGOPLAN allows an entity to investigate theconsequences of its own location and the anticipated location of the other side (Koperczaket al. 1990).

An analysis of NSS research indicates that the movement parameter has received moreattention than any other (Holsapple et al. 1995). Much of this work explores the processof finding a solution to the movement problem by applying a particular theory such asMCDM (Sycara 1990a; Quaddus 1993), simulation (Zigurs et al. 1994), economics mod-els (Hipel et al. 1990; Bui 1992), rule-based formalisms (Kersten and Mallory 1990;Kersten et al. 1991), neural networks (Bui and Strand 1993), case-based reasoning (Sycara1990a; Bui 1992), machine learning (Matwin et al. 1991; Sycara 1993), graph modeling(Hipel et al. 1990; Fang and Hipel 1990, 1993), and fuzzy logic (Iz 1994). Some of thiswork is concerned with how movement and location are affected by other factors (Jonesand Jelassi 1990; Faure et al. 1990; Eliashberg et al. 1992; Lim and Benbasat 1993;Carmel et al. 1993).

3.2.7. Rules of Negotiation Anson and Jelassi (1990) indicate that rules should beestablished at the beginning of a negotiation process. All aspects of a negotiation areconfined by the rules of negotiation. A possible NSS function is to help entities keep therules of negotiation in mind and ensure that their actions do not violate those rules. Thiscould extend to an NSS that monitors other entities’ behaviors with respect to the rules.For example, a NSS may be able to remind an entity about time limits and give warningsas expiration approaches (Jones and Jelassi 1989). Other examples include the NSS abilityto check whether a desired coalition formation violates regulations on coalition formation;to aid in recognizing the existence of impasses, adjournments, or breakdowns; to detectwhen a termination rule is satisfied; or aid in recognizing situations where an intervenormight or should come into the negotiation.

A preliminary condition for possessing such abilities is to formally express rules ofnegotiation in the KS of an NSS. The NEGO rule editor is an example of a step in thisdirection (Kersten 1985). The extent to which this preliminary condition is satisfied maydetermine or characterize the intelligence of a NSS. Furthermore, an NSS may be flexibleenough to accommodate various rules across different negotiation situations. For example,depending on the specific situation, time constraint rules could invoke a deadline, the



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number of elapsed periods, or the duration of response intervals. Finally, the rules ofnegotiation may be a basis for designing different types of NSSs, as negotiation activitiesmay be classified into different types based on distinctions among sets of negotiationrules.

3.2.8. Intervenor. Once an intervenor is drawn into a negotiation activity, it can play anextremely important role. An intervenor may try to influence the negotiation process or itsoutcome. An entity that can keep informed about the intervenor as well as the otherentities may have a better understanding of the negotiation situation. Therefore, it mightbe desirable for an NSS to provide knowledge about the intervenor as well as otherentities. This may include knowledge about when the intervenor might or should beinvolved, what kind of role the involved intervenor will play, the constitution of theinvolved intervenor, and the influence wielded by the intervenor (Valley et al. 1992).

On the other hand, desired NSS abilities can be considered from an intervenor’s view-point. First of all, there is an ability to provide an intervenor with “complete knowledge”of the state of a negotiation. The more complete knowledge an intervenor has, the betteris its ability to assist the entities in reaching an agreement. If an NSS serves as anintervenor, it is essential for that NSS to be able to communicate with every entity. Inaddition, the ability to give suggestions, execute termination rules, or provide groupdecision techniques or final arbitration might be desired. Jones and Jelassi (1990) have leta computer suggest a solution during a negotiation process and examined how it affectsthe final agreement and the time needed to reach agreement.

3.3. A Classification Framework for NSSs

No individual NSS has all functions implied by the theory. Those functions that aredesirable depend on the targeted role for the particular NSS and the degree of sophisti-cation required. Figure 3 provides a framework for classifying alternative kinds of NSSroles in terms of the theory. This categorization of NSSs has two attributes. One attributeis concerned with the kind of entity set for which an NSS is used. The negotiating entitiesmay constitute a group or an organization (Holsapple 1991). In a group negotiation, entityauthority levels are comparable, there is little in the way of complex or formal division of

Figure 3. NSS classification framework.



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labor among entities and few restrictions on the pattern of communication among entities.In an organization negotiation, these traits are reversed. Entities have distinct degrees ofauthority, are subject to considerable restriction in communication patterns, and fill arelatively complex set of formal roles. Of course, there can be hybrid entity sets betweenthese extremes. To date, the bulk of NSS literature seems to have been concerned withgroup negotiation. However, organization negotiation has been recognized and studied.For instance, Watabe et al. (1992) have examined formal support of negotiation andconsensus building in Japanese organizations.

The classification framework’s other attribute is concerned with the nature of an NSS’sparticipation in the negotiation. An NSS can support a negotiation by either assisting aparticipant or serving as a participant. In the former case, it assists an entity, subset of thewhole entity set (which may or may not be a coalition), the whole entity set, or anintervenor. In the latter case, an NSS serves as an entity or intervenor. Figure 4 illustratesthe NSS typology in graphical terms. Each part of Figure 4 corresponds to a column inFigure 3.

Relative to NSSs that support organization negotiation, NSSs supporting a group ne-gotiation presumably could be simpler and more uniform. In the organization case, anNSS’s KS may need to hold knowledge about the organization’s structure (i.e., its au-thority, communication, and role structures). Negotiating entities are likely to have dif-ferent levels of access to a shared organizational NSS, reflecting their different authorities,communication restrictions, and roles. NSS support of a coalition may be worthwhile onlywhen the coalition is strong in terms of its stability. More generally, participation in aNSS-aided subset is likely based on the costs of NSS usage.

When a NSS supports multiple entities, it may provide support for each entity indi-vidually or for all supported entities as a whole. The computer-supported portion in theexperiment implemented by Jones and Jelassi (1989) is an example of the latter case. Onthe other hand, if an NSS supports the entities individually, then it may provide custom-ized support for each entity. Differences among entities is a major focus of an intervenor’seffort. An intervenor’s NSS may be devised for tracking and analyzing such differences.Among reported NSSs, the Conflict Analysis Program (CAP) can help an arbitrator findan agreement that is rational for all negotiators (Fraser and Hipel 1981). An NSS devisedto support an intervenor conceivably also could support entities in some respects.

The taxonomy suggest that an NSS can serve as a surrogate for an entity. For instance,it may serve as an entity to help prevent an overly risky or cautious solution. Research hasindicated that risky shift and cautious shift can arise in group behavior. If an NSS servesas an intervenor, it may implement various techniques for consensus seeking. When anNSS functions as an entity or intervenor, its linguistic knowledge and presentation knowl-edge are very important. This is because such knowledge is crucial for communicatingwith other participants. Developing NSSs that serve as entities or intervenors will likelydepend heavily on research concerned with intelligent agents (Minsky and Riecken 1994).

Given a group negotiation or organization negotiation, multiple NSS types could beoperable within a particular negotiation. Some conceivable examples are shown in Figure5. In case (a), an NSS is supporting three entities individually, while one of these three



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Figure 4. Basic topology of NSSs.



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entities also has its own personal NSS. In case (b), a coalition is supported by an NSS, butanother NSS supports the entire entity set. In the case (c), an NSS supports all entities asa whole and another NSS serves as an intervenor in the negotiation. As for case (d), threeentities form a coalition that is supported by an NSS, while each of the other two entitieshas its own personal NSS. In case (e), one of the NSSs is a surrogate for a human and isaided by another NSS. Finally, in case (f), an NSS supports all the entities as a whole as

Figure 5. NSS examples.


Kluwer Journal@ats-ss10/data11/kluwer/journals/group/v6n3art3 COMPOSED: 02/24/97 12:07 am. PG.POS. 16 SESSION: 4


well as an intervenor. Use of MEDIATOR (Jarke et al. 1987) is an example of case (f), inwhich an NSS supports a human mediator as well as all negotiators. There are otherexamples of case (f) (Anson and Jelassi 1990; Carmel et al. 1993).

The foregoing examples were suggested for either a group negotiation or organizationnegotiation. However, negotiation might happen in organizational groups or group orga-nizations. Nothing in the theory precludes the possibility of extending these examples tonegotiations in organizations of groups or groups of organizations. Furthermore, it is alsoconceivable that a single NSS could encompass the functions of multiple types of NSS.For instance, an NSS may support all the entities as a whole as well as individually.

To date, negotiation support systems have been discussed, designed, or developed toassist an individual entity (Kersten and Michalowski 1991; Matwin et al. 1991; Bui 1992)or intervenor (Anson and Jelassi 1990; Lim and Benbasat 1991; Shakun 1991; Carmel etal. 1993; Quaddus 1993), to support a whole entity set (Lim and Benbasat 1991; Carmelet al. 1993; Iz 1994) or a coalition, and even to serve as an intervenor (Jones and Jelassi1990; Sycara 1991). Clearly, some categories of the taxonomy have yet to receive exten-sive attention.

4. Conclusion

A generic theory of negotiation geared toward NSS research can be beneficial in orga-nizing our understanding of what has been accomplished by NSS researchers (Holsappleet al. 1995). This paper has taken such a theory as a starting point and proceeded touncover some of its implications. We contend such implications are valuable by-productsof the theory, serving to guide and stimulate future efforts of NSS researchers and de-velopers.

The implications introduced here involve three major topics. First, there is a genericcharacterization of essential elements in a negotiation support systems. This serves as ahigh-level framework for considering the design and study of NSSs. Second, at a morespecific level, there is the identification of potential NSS functions in terms of supportpossibilities linked to the theory’s parameters. Third, there is the introduction of a tax-onomy for classifying NSSs. The implications developed for each of these topics canserve as starting points for further NSS research.

One of these research directions that is particularly interesting to us lies in the area ofdesigning general-purpose tools for NSS construction. Such a tool would consist of a PPSgeared toward one (or more) of the 20 categories identified in Figure 3. Its processingcapabilities would include applicable functions identified each of the eight parameters. ItsLS, PS, and KS representation structures would be consistent with these PPS abilities. TheKS contents (including linguistic and presentation knowledge that help define a LS andPS) would be what distinguishes one NSS built with this tool from others built with thesame tool. This would include knowledge about which of the potential PPS behaviors isto be in force for a particular negotiation support application.



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Implications of Negotiation Theory for Research and Development of Negotiation Support Systems

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