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O rd e r N u m b e r 9426907

Group decision-making for a complex dynamic task: An examination o f the effects o f individual and group characteristics on decision processes and performance

Gualtieri, James William, Ph.D.

George Mason University, 1994

Copyright 1994 by Gualtieri, Jam es W illiam . A ll rights reserved.

U M I300 N. ZeebRd.Ann Arbor, MI 48106


Group Decision Making for a Complex Dynamic Task: An Examination of the Effects of

Individual and Group Characteristics on Decision Processes and Performance

A dissertation submitted in partial fulfillment of the requirement for the degree of Doctor of Philosophy at George Mason University.

By

James William Gualtieri Master of Science, George Mason University, May 1991

Bachelor of Arts, University of Pittsburgh, May 1989 Bachelor of Science, Carnegie Mellon University, May 1985

Director: Stephen Zaccaro Psychology Department

Summer, 1994 George Mason University

Fairfax, Virginia


GROUP DECISION MAKING FOR A COMPLEX DYNAMIC TASK:AN EXAMINATION OF THE EFFECTS-QF

INDIVIDUAL AND GROUP CHARACTERISTICS ON DECISION PROCESSES AND PERFORMANCE

by

James William Gualtieri A Dissertation Submitted to the

Graduate Faculty of

George Mason University in Partial Fulfillment of

the Degree Requirements for the Degree of

Doctor of Philosophy Industrial/Organizational Psychology

Committee:Dr. Stephen Zaccaro, Director

Dr. Robert Holt, Committee Member

Dr. Douglas Hershey, Committee Member

Dr. Len Adelman, Committee Member

Dr. Jane Flinn, Department Chairperson

Dean of the College of Arts and Sciences

Summer 1994George Mason University Fairfax, Virginia


Copyright, 1994 James William Gualtieri

All Rights Reserved


Acknowledgments

Abigail Adams wrote "learning is not obtained by chance, but must be sought for

with ardor and attended to with diligence." I find this to be especially true when one is

attempting to obtain one's doctorate. I surely would not have gotten this far without the

help of many others who kept me going when my ardor and diligence faded.

First of all I'd like to thank my committee, Steve, Len, Bob and Doug. They

provide the guidance and support that was needed throughout the process. I lost count

some time last year of the number of revisions and rewrites my dissertation went through.

Most of these changes were due to Dr. Zaccaro's input; though I did not enjoy it at the

time, it has made my dissertation a far better product. Dr. Adelman, with all his questions,

forced me to realize that I had not completely thought through the implications of my

research, and because of that, I have a better understanding of "what it all means". No

dissertation would be complete without someone to make sure that the statistics were done

right and interpreted correctly. Dr. Holt was that person and more. His thoughtful

comments helped to tie together some loose ends and point the way toward future research.

Dr. Hershey's comments on the testing of the input-process-output model have shown me

how to better present my model. I'd also like to thank one additional faculty member, Dr.

Gessner, who, although not on my committee, was there at the major meetings, available

for questions, and kept the rest from getting out of hand. His humor and alternative point

of view helped keep everything moving along.

While the faculty members served as the cognitive shoulders on which I stood to

help me see farther, there were a number of other students that were down in the ditches

with me helping to get the work done. First and foremost of these was Cynthia Wassenar.


Her continued effort in data collection and process encoding allowed me to finish in one

year rather than two. I know that some day soon she will be doing her doctoral dissertation

and I hope that she will find someone as good as her to help with that research. Kevin

Payne's willingness to run subjects at night made my life a whole lot easier and allowed me

to get my other school work done. Id also like to thank my colleague, Dave Minionis. He

was always there to help set up the lab and discuss my latest crazy notion on groups. I

know that it was only luck that I finished first, but I'm sure he'll be right behind me.

Lastly, I'd like to thank my family for all of their love and support. My wife

Kathleen, made this all possible. Without her emotional support I would have never made

it. It was her diligence that kept me going during times when I wanted to take a break. But

emotional support was not her only contribution. Her language and editorial skills made it

possible for other humans to read and possibly understand what I had written. I couldn't

have asked for more support. I hope that now that my dissertation is finished we'll be able

to spend more time together. I'll never be able get all the lost moments back, but I hope

that what follows will be worth the wait. I'd also like to thank my parents for instilling in

me the desire to learn and the belief in education as a path to success. I'd especially like to

recognize my mother for seeing so long ago that I'd eventually get my doctorate even when

I did not.

Finally, for those of you who think that some day you might want to get your

Ph.D., I'd like to quote the sagacious Buckeroo Banzi: "No matter where you go, there

you are."


VTable of Contents

Abstract............................................................................................................................ xii

Introduction........................................................................................................................1

Problem F o c u s ............................................................................................... 2

Research Purpose................................................................................................ 5

Literature Review of Group Decision Making...............................................................5

Decision Making M odels.................................................................................... 5

U nitary Sequence Theories........................................................... 7

Nonphasic Theories................................................................................9

C ontingency T heories ....................................................................10

Functional Theories.............................................................................. 16

New Functional M odel........................................................................ 17

Inputs to Decision Making Process.................................................................24

Individual Level V ariab les...................................................................... 25

D ecision S ty le ................................................................................. 25

Social Intelligence.................................................................................28

Com m unication A pprehension.....................................................29

Group Level Variables...................................................................................... 29

Group S ize............................................................................................ 30

Group Centralization............................................................................ 31

Group Cohesion....................................................................................33

H y p o th e ses ...................................................................................................... 35

Individual C haracteristics.............................................................37


Group Characteristics........................................................................... 38

Decision Function Predictions.............................................................42

M eth o d ...........................................................................................................................45

Subjects & D esign .....................................................................................45

Equipment...........................................................................................................45

Manipulations.....................................................................................................46

Individual Characteristics M easures.......................................................48

Com m unication A pprehension.....................................................48

Problem Solving Style..........................................................................48

Social Intelligence.................................................................................49

Domain Specific K now ledge.......................................................49

In terface T est...................................................................................49

Group Processes.................................................................................. 49

General Leader Impressions............................................................... 50

Intelligence............................................................................................ 50

Group Characteristics Measures.......................................................................50

Group Size............................................................................................ 50

Group Cohesion....................................................................................50

Group Structure....................................................................................50

Decision Function Measures............................................................................ 51

Information Acquisition.......................................................................51

Problem Representation.......................................................................51

Evaluation C riteria .........................................................................51

Option Generation.................................................................................52

Option Evaluation.................................................................................52

Option Selection....................................................................................52


Monitoring............................................................................................ 52

Coordination..........................................................................................53

Sufficiency............................................................................................ 53

Perform ance M easures...............................................................................53

P ro ced u re ......................................................................................................... 56

Results..............................................................................................................................59

Descriptive Statistics..........................................................................................59

Hypothesis 1: Effects on Individual Characteristics..................................... 69

Hypothesis 2: Effects on Group Characteristics........................................... 73

Hypothesis 3: Decision Function Effects on Performance.....................76

Hypothesis 4: Effects of Sufficiency on Performance.................................79

Hypothesis 5: Mediation Role of Process Variables............................. 81

Exploratory A nalysis................................................................................. 94

Summary.......................................................................................................... 104

D isc u ss io n ...................................................................................................................105

Decision Processes...........................................................................................105

Group Resources..............................................................................................113

Group Motivation............................................................................................ 120

Mediation Analysis...........................................................................................121

Performance Over T im e..................................................................................123

Conclusion........................................................................................................124

R efe re n c es .................................................................................................................. 128

Appendix A: Power Analysis.....................................................................................138

Appendix B: Analysis Plan......................................................................................... 141

Appendix C: General Information on SimEarth............................................. 147

Appendix D: Instructions for Using SimEarth......................................................... 151


Appendix E: Manipulation Checks.........................................................................159

Appendix F: Mars Terraforming Submission Form................................................ 161

Vita.................................................................................................................................163


List of Tables

Table Page

1. Summary of Specific Predictions for Individual Traits....................................... 39

2. Summary of Specific Predictions for Group Characteristics.............................. 41

3. Mean and Standard Deviation for Task CohesionManipulation Check...........................................................................................60

4. Mean and Standard Deviation for Group Size Manipulation Check...................60

5. Mean and Standard Deviation for Group StructureManipulation Check...........................................................................................60

6. Mean and Standard Deviation and Standardized Inter-Item Alphafor Individual Characteristics........................................................................... 61

7. Inter and Intra Rater Reliability for Video Tape Coding.......................................62

8. Mean and Standard Deviation at the Individual and Group Levelfor D ecision Functions............................................................................. 63

9a. Correlation Matrix Displaying the Relationships BetweenIndividual Characteristics and Process Functions..........................................64

9b. Correlation Matrix Displaying the Relationships Between Individual Characteristics and Process Functions with Group Effects Partialed Out..............................................................................65

10. Correlation Matrix Displaying the Relationships Among thePerform ance M easures...............................................................................68

11. Mean and Standard Deviation for Performance Measures................................69

12. Mean Decision Function Behaviors by Decision Style......................................70

13. Mean Individual Decision Function Behaviors by Level ofCommunication Apprehension with the Group EffectsPartialed O u t...................................................................................................... 72

14. F Values for the Effect of Manipulation Interactions on theDecision Functions............................................................................................ 76


XTable Page

15a. R2 and Parameter Estimates for the Regression ofProcess M easures............................................................................................. 77

15b. R2 and Parameter Estimates for the Regression ofProcess Measures onto Error Rate and Temporal M easures........................78

16. R2 for the Relationship Between Sufficiency andPerform ance M easures...............................................................................80

17. Non-linear Effects of Sufficiency on Performance M easures...........................82

18. Overall R2 and Beta Weights for the Regression of the InputVariables onto the Decision Functions............................................................ 83

19. R2 and Parameter Estimates for the Regression of Process Measuresonto Perform ance M easures.................................................................... 84

20. Overall R2 and Beta Weights for the Regression of the InputVariables onto the Performance Variables.......................................................86

21. Overall R2 and Beta Weights for the Input and Process Variableson Performance Variables.................................................................................87

22. Correlation Between Domain Specific Knowledge, GeneralIntelligence and the Decision Functions......................................................... 94

23. Overall R2s for Process and Performance Measures Regressedonto Individual Characteristics, Group Characteristics, andIndividual and Group Characteristics Combined......................................... 119


List of Figures

Figure Page

1. Models of Decision Making..................................................................................... 14

2. Group Decision Making Functions..............................................................19

3. General Model of Relationships Between Variables............................................ 36

4. Exploratory Analysis for the Performance Quality Measure Biomass............... 90

5. Exploratory Analysis for the Performance Quality MeasurePercent Methane................................................................................................ 90

6. Performance Speed Measure Total T im e...............................................................91

7. Biomass Level Over Time By Condition...............................................................97

8. Level of Omega Over Time By Condition..............................................................99

9. Average Number of Biomes Over Time By Condition...................................... 101

10. Average Number of Life Forms Over Time By Condition........................102

11. Decision Functions' Sufficiency Level Effect on Biom ass............................. 109

12. Hierarchical Display of Euclidean Distances Between theDecision Functions.......................................................................................... I l l

13. Input-Process-Output M odel...............................................................................123

14. Summary of Significant Links Among the Intput, Process andOutput Variables.............................................................................................. 126


Abstract

GROUP DECISION MAKING FOR A COMPLEX DYNAMIC TASK:AN EXAMINATION OF THE EFFECTS OF INDIVIDUAL AND GROUP CHARACTERISTICS ON DECISION PROCESSES AND PERFORMANCE

James W. Gualtieri, Ph.D.

George Mason University, 1994

Dissertation Director: Dr. Stephen Zaccaro

A new conceptual model of the group decision making process is presented that

identifies important individual traits, group characteristics and decision functions required

for effective group performance on a complex dynamic task. This study expands on

previous research in three ways (1) aspects of the decision making were studied (2) the

effects of motivation on the decision making process were examined and (3) the effect of

group resources in the forms of individual attributes and group characteristics on

performance were studied.

In this study three individual traits (social intelligence, academic intelligence,

problem solving approach, communication apprehension and domain specific knowledge)

were measured, three group characteristics (size: 3 or 7; structure: centralized or

decentralized; and task cohesion: hi or lo) manipulated, and behaviors within eight decision

functions (information acquisition, problem representation, criteria selection, option

generation, option evaluation, option selection, coordination and monitoring) were

recorded.

Four hypotheses were tested in this study (1) the effects of the completion of

decision functions on outcome measures, (2) the importance of the group and individual

characteristics on the decision functions, (3) the mediation effects of the decision process


on the relationship between the inputs and outputs were tested, and (4) the importance of

decision process sufficiency in predicting group performance.

As predicted individuals that used a bottom-up problem solving style displayed

more decision function behaviors than those individuals who used a top-down style. The

results also demonstrated that high levels of communication apprehension leads to fewer

decision function behaviors. Group size and task cohesion were also shown to have a

positive effect on the decision functions. An input-process-output model was supported by

the results as was the usefulness of the decision process sufficiency measure in predicting

group performance. Finally, the importance of a functional approach to the study of group

decision making was demonstrated.


Group Decision Making for a Complex Dynamic Task: An Examination

of the Effects of Individual and Group Characteristics on

Decision Processes and Performance

Groups are an important part of many organizations. They serve a central role in

the development of organizational strategy and problem solving (Mintzberg, Raisinghani &

Theoret, 1976; Poole, 1983a; Milliken & Vollrath, 1991). Group importance to the

organization has been the subject of research in the area of group decision making for over

eighty years, beginning with Dewey (1910). Due to its importance, many diverse academic

fields have attempted to explain the group decision making process (Hirokawa & Johnston,

1989). For example, researchers from the fields of psychology, communication,

management, political science, and economics all have attempted to clarify the decision

process (e.g., Steiner, 1972; Davis, 1973; Janis & Mann, 1977; Simon, 1976). However,

despite the level of research surrounding the group decision making process, there are still

areas in the group decision making literature that are unexplored.

Most of the previous research in group decision making has focused simply on the

generation and selection of alternatives by the group. This research ignores decision

process such as problem identification and alternative implementation (e.g., Davis, Kerr,

Atkin, Holt & Meek, 1975; Kaplan & Miller, 1987). Individual level and group level

factors are frequently ignored or examined separately (Hirokawa & Johnston, 1989;

Milliken & Vollrath, 1991). For example, Hinsz (1990) focused on an individual level

variable, cognitive characteristics of the group members, while ignoring group level

1


2variables like size and structure. Understanding what combination of group and individual

level factors influence the performance level of the group and the decision functions that

need to be completed for the group to reach a decision are critical to improving group

effectiveness.

Problem Focus

Guzzo (1986) explored several deficiencies in group research, including the lack of

field research and the impact of decision support systems for groups. He also offered

multiple directions in which group researchers should proceed to advance the

understanding of the group decision making process. Several of his prescriptions are the

focus of this study.

One of Guzzos points was that all phases of the decision making process should be

studied. While the problem solving literature has traditionally taken triphasic approaches,

problem identification, option generation and selection, and option implementation (e.g.,

Dewey, 1910; Simon, 1960), too often only the option generation and selection phase have

been attended to (Davis et al, 1975; Kaplan & Miller, 1987). More attention should be paid

to the problem identification phase and the implementation phase of decision making for

greater understanding of the process.

Each of these phases of group decision making (e.g., problem identification) is

composed of more specific group functions and tasks. For example, the chief tasks during

the problem identification phase includes gathering information, aggregating it, and using it

to define the problem facing the group. Too often the problem facing the group is given to

its members and they simply are required to generate solutions for the problem (Davis,

1973; Kaplan & Miller, 1987). This is generally not the case in 'real world' settings; the

group must first discover problems from cues in the environment (Moreland & Levine,

1992) before attempting to solve the problem.


3The implementation phase involves the execution of a decision that has been

reached. There are several activities that the group must engage in during this phase. For

example, the group needs to coordinate its activities, pool its resources and monitor its

implementation. Each of the three phases places different demands on the group and

requires that the group respond with the appropriate behaviors. These differing demands

suggest that certain individual traits and group characteristics may be more important for

certain group functions than for others. For example, having a large diverse group may be

help in generating a broad set of alternatives to a problem, but will hinder the coordination

of member's actions (Milliken & Vollrath, 1991).

Guzzo (1986) went on to suggest that the study of the entire decision making

process should lead to better normative theories. These theories can then be tested with

later empirical research. Milliken & Vollrath (1991) supported this argument by suggesting

theoretical reasons for why different group characteristics are important at different stages

in the group decision making process. For example, they suggest that large heterogeneous

groups will produce a greater number of and a more creative set of options than a small

homogeneous group. Hirokawa & Johnston (1989) developed a similar perspective,

suggesting that different system and individual level factors become important as the group

proceeds through the decision making process. They suggest that the cognitive schema of

each individual affects his/her ability to recognize a problem in the environment.

A second area in need of additional research is the influence of motivation in the

group decision making process (Guzzo, 1986). Motivational factors play an important

role in many theories of group performance (Hackman, 1976; Isenberg, 1978; Hirokawa,

1982; Zaccaro, Gualtieri & Minionis, 1993); however, few theories tie it directly to the

group decision making process. Indeed, most theories of group decision making focus on

how communication, information processing, or social factors affect the decision making

process. A notable exception is Adelman, Zirk, Lehner, Moffett & Hall (1986), which


4showed that motivational factors (i.e., type of reward structure) have an important effect on

the decision processes (i.e., group member cooperation). Similarly, high group task

cohesion, manipulated with a group reward structure, has been shown to lead to greater

effort being exerted by members on behalf of the group (Zaccaro et al. 1993). Yet, the

critical links between individual motivation and group decision processes, the type of

causal linkages, and their effect remains unclear.

Individual motivational factors are not the only type of motivational factors that may

influence group decision making. Group cohesion also can provide a motivational

influence. Researchers have recently begun to view cohesion as a multidimensional

construct (Carron, 1982; Tziner, 1982; Zaccaro & Lowe, 1988). Zaccaro & Lowe (1988)

provide evidence for distinguishing between social-based and task-based group cohesion

by showing the differential effect on group performance. They found that while high task

cohesion improved performance, high social cohesion served to hinder performance.

Similarly, Carron (1982) found evidence supporting cohesion as a multidimensional

construct and that the different components differentially influenced group productivity and

behavior. The role of each component of group cohesion in group decision making needs

to be more closely studied to determine its effect.

Guzzo (1986) identified group resource use and requirements as a third area for

further examination. As groups pass through different decisional phases, different

resources become important and are utilized by the group. For example, while high

communication apprehension in a group member may not hurt his/her monitoring abilities,

it will prevent him/her from presenting many options to the group for consideration.

At what point in the decision making process, and for what purpose does the group

seek additional resources? To address this question, current research focuses primarily on

either individual characteristics (e.g., intelligence, problem solving skills, communication

skills) or group characteristics (e.g., size, cohesion, structure) separately (Guzzo, 1986).


5Hirokawa & Johnston (1989) have suggested that a more careful examination of the level

of influence of individual and group characteristics on group decision processes is needed.

Specifically, they suggest that knowing which decision functions these characteristics

affect, and how this in mm influences performance requires additional study .

Research Purpose

These considerations (the study of the entire decision making process, the role of

motivation and cohesion, and resource use and requirements) represent the central concern

of this study. The goal of this study is to address these areas by expanding on and

integrating current theories of group decision making. In seeking to study the entire

decision making process, this study excludes some research (e.g., Davis, 1973; Davis et

al, 1975; Penrod and Hastie, 1979; and Kerr, 1981) which focused on alternative selection,

and includes those studies (e.g., Mintzberg et al, 1976; Janis & Mann, 1977; Poole, 1983;

and Milliken & Vollrath, 1991) which examined all aspects of problem solving. First,

several models of the decision making process from the literature will be presented. These

models will then be integrated into a single model which accounts for deficiencies in each

of the separate models. This new model will be a descriptive model of the functions

necessary for effective group decision making. Research will then be presented that

examines several individual and group level variables proposed to affect the group decision

process. Finally, specific hypotheses based on this model will be presented.

Literature Review of

Group Decision Making

Dfi.cisiQn._Ma king-Moitels

The nature and process by which individuals and groups make decisions has been

studied extensively in recent years (Hackman, 1987; Kaplan & Miller, 1987; Hirokawa &


6Johnston, 1989; Poole & Roth, 1989a). Theorists from many divergent fields have studied

decision making and have offered several models depicting the underlying processes and

their affect on individual and group outcomes. Each of these different models (e.g., Davis,

1973; Hirokawa, 1990) conceptualizes the decision making process based on the theorists

research. For example, the work of Hirokawa (1990) emphasizes the importance of

communication, while Davis (1973) is interested in which alternative the group selects, and

Hackman (1987), in group structure and social interaction. Each model emphasizes

different components of the decision process.

These many studies tend draw differing conclusions about the decision making

process. These results are likely due to the fact that each uses a different perspective to

examine the decision making process and potentially different definitions. For example,

Huber (1980) defines decision making as the activities that occur between problem

identification and alternative choice; and defining problem solving as decision making plus

solution implementation. Some psychologists (e.g., Kerr, 1981; Stasser & Davis, 1981)

have defined decision making as simply the choice of an alternative from a set of

alternatives. Finally, others (e.g., Hirokawa & Johnston, 1989; Poole & Roth, 1989)

view decision making as the complete process from problem recognition to solution

implementation. It is this last conceptualization that will be used in this study. Decision

making and problem solving will be treated as identical concepts within the context of this

study, and the terms used interchangeably.

Despite these differences there appears to be common elements in each of the

models of decision making. Most models of decision making emphasize a triphasic

approach: problem identification, option generation/selection and solution implementation.

Poole & Roth (1989) have recently attempted to categorize the many decision model types.

They have identified three classes of decision making models: unitary sequence phase

theories, nonphasic theories and contingency phase theories.


7Unitary Sequence Theories. Unitary sequence phase theories comprise the largest

group of the decision making models. Unitary sequence phase theories have two primary

characteristics. First, they assume that the group follows some systematic logic that

requires a set of activities to be performed. For example, as noted above, most unitary

sequence models arrange the sequence of decision making as problem identification, option

generation/selection and solution implementation. Although each decision making model

specifies a different set of behaviors (e.g., Dewey, 1910; Bales & Stodtbeck, 1951; and

Simon, 1960), there are many similarities. For example, the orientation behaviors of Bales

& Stodtbeck (1951) and the intelligence gathering behaviors of Simon (1960) both are

attempting to capture the behaviors characteristic during the problem identification phase.

Second, they assume that decision making is best described as a specific series of

stages or phases. This leads to the conclusion that there is one best way to reach a

decision. This unitary series of steps does not allow for the group to move back to

previous phases if the group finds that it has made a mistake.

One final point is that stage theories, in general, assume that the decision making

process can be broken down into periods in which the activity of the group is relatively

homogeneous. For example, if the group is currently in the option generation phase, then

there will be no members engaging in problem identification behaviors.

Dewey (1910) was one of the first theorists to examine the decision making

process. This early unitary phase model consisted of three stages. His model saw the

decision making process as the answering each one of three questions: 1) What is the

problem? 2) What are the alternatives? and 3) Which alternative is best? These questions

are analogous to the first two phases of the aforementioned triphasic model. Problem

identification occurs when the first question is answered. The group has to first recognize

that a potential problem exists before it can proceed through the decision making process

(Moreland & Levine, 1992). The second and third questions are components of the option


8selection phase. In this second phase, potential solutions to the identified problem are

submitted by the groups members. Finally, the potential solutions are evaluated and

compared with one another, as the group attempts to find the best solution. The three

questions specified by Dewey (1910) served as the basis for many subsequent theories.

Bales & Strodtbeck (1951) also adopted a group decision making model, having

three distinct phases in their model of decision making: orientation, evaluation, and

control. Although not identical, Dewey (1910) and Bales & Strodtbeck (1951)

conceptualized the decision making process similarly. Both models consist of an initial

orientation phase in which the group becomes aware of the problem.

Bales & Strodtbeck (1951) included the generation and evaluation of potential

options in the evaluation phase of their model. These two activities are analogous to the

option selection phase in the basic triphasic model. An option is selected and then

implemented by the group during the control phase. The inclusion of an implementation

phase within the model is an improvement over Deweys conceptualization.

Bales & Strodtbeck's (1951) model dominated the study of groups for

approximately ten years (e.g., Bales & Slater, 1955; Morris, 1966). The system's features

(e.g., group perspective; contentless categories; and micro level analysis) were both its

strength and its weakness (McGrath, 1984). Its general stages allowed for its application

to a broad range of situations. Bales' theoretical focus (every act was either task or socially

based) made it difficult to apply the model to some theoretical domains (McGrath, 1984).

A further criticism was the model's reliance on individual behaviors rather than higher level

group action.

The dividing of the decision making process into three phases continued into the

later half of this century. Simon (1960) conceptualized the decision making process as

consisting of three phases: intelligence, design and choice. During the intelligence phase,

the decision maker conducts a search of the environment for information. Options are


9generated and evaluated with respect to a particular problem in the design phase. Finally,

one of the options is selected and implemented by the group during the choice phase. Other

unitary sequence models of group decision making include similar phases.

This view of decision making as a linear progression of stages is still commonly

held (Knutson, 1985). Kowitz and Knutson (1980) sought to support the use of stages by

noting that stages have three characteristics. First, each stage has a distinct theme. This

premise is supported by the work of Bales & Strodtbeck (1951); they found that the relative

distributions of behavior patterns were different for each phase. Second, Kowitz and

Knutson (1980) theorized that each stage contributed unique information to the decision

making process. For example, information from the environment is aggregated only

during the problem identification phase. During the option selection phase, solutions are

generated and selected to solve the problem. The last phase contributes information on the

execution of the decision. Finally, Kowitz and Knutson (1980) noted that each subsequent

phase builds on the contributions of previous stages. Option implementation cannot

proceed until an option has been selected. Option selection cannot occur until after the

problem has been identified.

Nonphasic Theories. Although the use of sequential stages as a descriptive

framework for decision making may be helpful, it tends to oversimplify the decision

making process (Poole, 1983b). This is especially true when it is assumed that the

decision maker passes through each of the stages in a linear fashion, never repeating and

never skipping any of the stages (e.g., Bales & Strodtbeck, 1951).

A second problem of these early models is that they do not contain a monitoring

phase. During a monitoring phase, the decision maker can observe the consequences of the

decision implemented. This helps the decision maker gain experience and knowledge.

This experience then can be used to help the decision maker conduct additional


10

informational searches for either the current decision or for subsequent decision making

situations.

Nonphasic theories argue that decision making is far too complex to be explained

by a simple set of stages. For example Seeger (1983) has argued that phases in decision

making do not exist. He suggests, instead, that decision making can best be described as a

continuous flow. Seeger (1983) and Cissna (1984) argue that the flow of the decision

process is far too complex to be explained simply a series of stages. Nonphasic theories

have no discernible phases. Decision behaviors occur dynamically throughout the decision

process with no homogeneous periods.

One approach of the nonphasic models (Scheidell & Crowell, 1964) argues that the

decision making process is cyclic in nature. It has been argued that the decision making

process consists of a series of activities that are repeated until a decision is reached.

Scheidell & Crowell (1964) theorized that these activities are anchoring and adjustment.

Anchoring involve" the process of generating an option or hypothesis. During the

adjustment portion of the cycle, the option or hypothesis is evaluated and clarified. It is

accepted if it passes this further examination on the decision makers selected criteria. If

not, the option or hypothesis is rejected, but serves as a cue for the generation for the next

option or hypothesis. The nonphasic models argue that groups are not always completely

orderly; however, they fail to demonstrate that there are no coherent periods within the

decision making process (e.g., Mann, 1966; Poole, 1981).

The most damaging evidence against the nonphasic theories is the lack of

compelling models (Poole & Roth, 1989a). Despite a series of studies using various

mathematical techniques (e.g., Markov Analysis), no general theories of decision making

have been formulated (e.g., Hawes & Foley, 1976; Ellis, 1979; Hewes, 1986).

Contingency Theories. A third group of theories is the contingency phase theories.

They argue that there is no single sequence for the decision making process. Like


11

nonphasic theories, contingency theories suggest that there are periods of unorganized

group decision behavior. But like the stage theories, contingency theories also recognize

that periods of organized behavior exist during the decision making process.

For example, Eilon (1969) adopted a contingency approach, and argued that the

stages in his model need not be taken in order. Eilon (1969) went on to suggest that it was

not necessary for the group to pass through all of the stages before a decision could be

reached. These two characteristics are what differentiate the contingency from the unitary

sequence theories.

Eilons (1969) model of the decision making process consisted of eight stages: 1)

information input, 2) information analysis, 3) specification of important outcomes, 4)

construction of a model of the situation (i.e., problem identification), 5) generation of

alternatives for solving problem, 6) prediction of consequences for each alternative, 7)

specification of criteria for choosing among the alternatives (option evaluation), and 8)

resolution of the decision (option selection and implementation).

Like earlier models of decision making (Dewey, 1910; Simon, 1960), Eilons

(1969) stages of the decision making process can be classified into three phases: problem

identification, option generation/selection and implementation. The information input

through construction of a model of the situation stages of Eilons (1969) model would be

within the problem identification phase. The next three stages (generation of alternatives

for solving problem, prediction of consequences for each alternative, and specification of

criteria for choosing among the alternatives) would fall within the option

generation/selection phase. The final stage of Eilons (1969) model, resolution of the

decision, seems to subsume both the option generation/selection and the implementation

phases.

This model has two characteristics that differentiate it from earlier models. First, it

is a contingency based theory of decision making. Second, it is far more elaborate than


12

many earlier theories. Where many early models contain only three or four categories

(e.g., Bales & Strodtbeck, 1951; Simon, 1960), Eilon's (1969) model contains eight

distinct phases. This trend towards more elaborate decision making models continues for

later contingency theories.

Mintzberg et al. (1976) developed one of the most complex contingency models

(Poole & Roth, 1989a). Their model contained three superordinant phases and nine sub

phases. They developed their model of group decision making based on data from twenty-

five field studies. Although the pattern of the phases was dependent on group and

situational characteristics, each pattern included many of the same set phases.

The first phase of decision making is identification, according to Mintzberg et al.

(1976). The identification phase is constructed of two components: decision recognition

and diagnosis. During decision recognition, problems and crises are recognized. This

initiates decision making activities. Diagnosis is the seeking of cause and effect

relationships between the problem and other related variables.

The second stage of decision making is the development phase. Solutions to the

identified problem(s) are generated during this phase. Several methods are hypothesized to

obtain these solutions. One method is through active searching. Another method is the use

of preplanned or designed solutions for similar problems.

The final stage of the decision making process is the selection phase. Like the two

previous phases, it has a number of components. Screening is the evaluation of solutions

based on situation requirements. Screening is essentially an elimination by aspects. All

potential solutions that do not meet minimum requirements are dropped from further

consideration.

The next component of the selection phase is the evaluation-choice routine. During

this routine, three modes of evaluation may be used: judgment, bargaining and analysis. A

single option is selected and resources are authorized for its implementation once all


13

potential solutions are evaluated. According to Mintzberg et al. (1976), each of these

phases and their subcomponents has the potential for feeding back earlier into the system.

Several factors were found to affect the pattern of the decision making process.

The first was a simple impasse. Some small problem prevented the decision process from

being a unitary sequence in stages. In two of the cases that Mintzberg et al. (1976)

examined, it was during the implementation phases that the impasse arose.

A second factor that affected the decision process was differences in evaluation

criteria among the group members. Extensive evaluation of alternatives was required by

the group until an option that was acceptable to all group members could be found. When

evaluation criteria are consistent and clear, the option generation and selection phases

proceed smoothly.

The success of a selected alternative is the third factor that affected the decision

making process. If a selected option failed to reached desired goals, additional information

was collected, and generation and evaluation of options performed was done to meet the

groups goals.

A fourth factor was decision complexity. If the decision domain is complex,

greater time has to be spent conducting information acquisition and developing robust

evaluation criteria. This pattern of behavior was amplified when the decision had great

organizational consequences.

The remaining factor that affected the group decision making process was external

to the group, resistance from outside forces to the groups decision. The group was forced

to begin a new decision making process when its selected option was rejected by outside

constituencies and concerns.

Figure 1, summarizes the stages/functions within each of the decision making

models discussed previously. It should be clear from this figure the number of similarities

between the models, as well as their differences.


14

Dewey(1910)

Bales & Strodtbeck

(1957)Simon(1960)

Scheidell & Crowell (1964)

Eilon(1969)

Mintzbergetal

(1976)Hirokawa

(1985)

problemidentification

orientation intelligence informationinput

recognition problemrepresentation

informationanalysis

outcomespecification

diagnosis

problemidentification

optiongeneration

evaluation design anchoring optiongeneration

search & screen

optiongeneration

prediction of consequences

design positiveevaluation

optionselection

choice adjustmentoption

evaluationevaluation & choice

negativeevaluation

control resolution o f decision

authorization

Figure 1: Models of Decision Making

Poole (1983a) and Hirokawa (1983) both have recently supported the proposition

that groups do not follow a uniform pattern of phases. Based on experiments on group

communication during decision making, Poole (1983a) favored a contingency approach to

group decision making. He suggested that the pattern of phases during the group decision

making process is dependent on the task and characteristics of the group. Poole (1983a)

suggested that groups will attempt to follow a unitary sequence of decision making stages,

because it is the simplest path to effectiveness; but situational constraints and group

characteristics often prevent this from occurring.

In response to the current situation, the group is forced to deviate from this most

desired path to one that takes into account the current constraints. Mintzberg et al. (1976)

illustrate this point with the five factors that disrupt the decision process. For example, in

one of the groups studied, the task was to implement a new form of treatment in a hospital.


15

Political maneuverings during the implementation phase by conservative doctors forced the

decision to be reevaluated. Further blockings by the conservative doctors delayed

implementation until a consensus could be built. Once a supramajority was formed the new

treatment was able to be implemented (Mintzberg et al., 1976).

Poole & Roths (1989b) contingency model posited that task characteristics and

group characteristics can affect the decision making process in three ways. First, they can

influence the sequence (i.e., pattern of stages) of group decision activity. If a task is

simple, a unitary sequence of decision stages is more likely. It also is possible that, for

routine problems, many of the decision stages will be left out of the process. If a task is

complex, the group will be forced to repeat stages. The pattern of stages and the extent to

which the group must "cycle back" to earlier stages will be determined by the complexity of

the decision.

Poole & Roth (1989b) make no specific predictions about how group characteristics

affect the process. They simply suggest that under different levels of group characteristics,

like cohesion, the group will proceed through the decision process differently. For

example, the work of Zaccaro et al. (1993) suggests that greater task cohesion leads to

better coordination. This improved coordination could lead to a decrease in the number of

times that the group goes back through the earlier stages in the decision making process.

This occurs because high cohesive groups pay greater attention to the task and have higher

levels of motivation to complete the task successfully. Second, task characteristics and

group characteristics determine the complexity of the group interaction. The more complex

the problem or poor the group structure, the more likely the group is going to have to

recycle previous stages before reaching a decision (Poole & Roth, 1989b). Finally, these

two factors determine the level of disorganization within the groups interaction. Groups

that lack structure or cohesion are far more likely to move randomly among the decision

process (Poole & Roth, 1989b). Just as complex problems cause more recycling, they


16

also cause less coherent phases in the group decision making process (Poole & Roth,

1989b).

Functional Theories. Hirokawas (1985) study used a functional approach

similar to a contingency type model to study decision process effectiveness. Both

contingency and functional models emphasize that there is no single path through the

decision process. Similarly, both approaches emphasize the possibility of the group

moving through the stages/functions repeatedly, based on situational demands.

The primary difference between contingency and functional theories is how they

view stages/functions. Contingency theories view stages/functions as a series of states

through which the group passes on its way to a decision. The states/functions are viewed

as steps along a path which the group passes on its way to a decision. On the other hand,

functional theories see stages/functions as a set of requirements for the group to meet in

order to arrive at a good decision. The stages/functions are a set of tasks that need to be

completed if the group is to arrive at a quality decision.

Upon examination of group decision processes, Hirokawa (1985) failed to find that

any single uniform pattern of stages predicted either successful or unsuccessful -

performance. Results did show that the extent to which decision making groups completed

four functions had a significant effect on decision quality. Mintzberg et al. (1976)

supported a similar conclusion that it is the completion of various functions that leads to

effective decisions, not the order of the stages or phases.

Hirokawas (1985) four decision functions were: 1) represent the problem

accurately, 2) generate a range of realistic alternatives, 3) assess the positive consequence

of each alternative, and 4) assess the negative consequence of each alternative. These four

functions correspond to those specified in several decision making models (e.g., Janis &

Mann, 1977; Hirokawa, 1982; and Gouran & Hirokawa, 1983).


17

These decision functions also describe some of the requirements for the first two

phases of a triphasic approach. According to Hirokawa (1985), for a group to have an

accurate problem representation they must (a) understand the nature of the problem, (b)

understand the extent and seriousness of the problem, and (c) understand all potential

causes of the problem.

Generation of a range of realistic alternatives by the group is necessary if the group

is to select a viable solution to the problem. The smaller the set of alternatives, the less

likely an acceptable solution is within the set. When attempting to select among the

alternatives, it is important that the group assesses both the positive and negative

consequence of each alternative (Hirokawa, 1985). This is important, because if all

alternatives and not evaluated against a complete set of criteria, then the group could

incorrectly select the wrong alternative.

These three studies (Mintzberg et al., 1976; Hirokawa, 1985; Poole & Roth,

1989a) suggested that no single uniform set of stages lead to effective decision making.

Hirokawa (1985) went on to suggest that there may in fact be many paths that lead to high

or low quality decisions; it is not the pattern of stages that is important, but the existence of

certain key elements that are crucial for effective decision making to take place.

New Functional Model. Based on the research outlined above, a new functional

model of decision making is proposed. A new functional model is needed to incorporate

the strengths of previous models. This new model also attempts to address current

prescriptions on the nature of group decision making (e.g. Guzzo, 1986; Hirokawa &

Johnston, 1989; Milliken & Vollrath, 1991). As such, this new model is not primarily

empirically supported. The portions of this new model that are empirically supported are

noted within the text below; it is, for the most part, based on theoretical prescriptions of

researchers within the field.


18

Two additional points differentiate this model from previous models. First, unlike

many earlier models, this model seeks to describe decision making as a set of functions

rather than a series of stages, like Hirokawa's (1985) model. The current model expands

on Hirokawa's (1985) model by adding information acquisition, criteria selection,

coordination and monitoring functions, and collapses positive and negative evaluation into

a single function. These changes capture all the functions to complete the decision making

process.

While this model's functional approach makes it unique, many of the functions are

similar to the stages of previous models (e.g., Eilon, 1969; Hirokawa, 1985; Mintzberg et

al, 1976 and Wohl, 1981). For example, Wohl's model (1981) has four major categories:

stimulus, hypothesis, option and response. Wohl's stimulus and hypothesis stages are

covered by the problem identification functions, and the option stage by the option

generation/selection functions in the current model. The primary difference between the

two models is that Wohl's model (1981) has planning, organizing and execution stages in

its response phase, while the current model has coordination and monitoring functions in

its implementation superordinant function. Wohl's activities during the response phase are

covered in the option generation/selection functions.

The proposed model would be described by Poole & Roth (1989b) as a

contingency phase theory, though it is functional in its orientation. This is because it does

not argue for a single sequence of stages. Instead, it presents a set of functions that a

decision making process should meet in order to arrive at an effective decision. This also is

in line with the prescriptions of Hirokawa (1985). A second difference between

contingency and functional models is that functional models do not postulate periods of

homogeneous activity as do contingency models.

Second, the current model classifies functions under the three superordinant stages

of a triphasic model. In the currently proposed model, they are labeled: problem


19

representation, option generation and selection, and implementation. Figure 2, below,

outlines the functions contained within each of these stages.

I. Problem Identification

A. Information Acquisition

B. Problem Representation

n. Option Generation/Selection

A. Evaluation Criteria Selection

B. Option Generation

C. Option Evaluation

D. Option Selection

in. Implementation

A. Coordination

B. Monitoring

Figure 2: Group Decision Making Functions

Like Eilon (1969) and Mintzberg etal. (1976), the problem identification phase has

several subcomponent functions. In both of the models of these groups there is a problem

representation phase. Group members must recognize that a problem exists from

information obtained from the environment. This phase generally starts the decision

making process. This is because the group cannot begin to solve a problem until it

recognizes that a problem exists. The two functional requirements included in the current

model under problem representation are information acquisition and problem identification.

Both of these functions are represented as phases in Eilons (1969) decision making model.

Information acquisition is the collection and processing of data within the problem

space. Its aim is to determine the cause of the problem, as well as factors affected by the


2 0

problem. Information acquisition is an on going process for the group. The group must

continually monitor its environment so that it can react to changes that affect its survival.

Once a problem has been detected by the group, the information search becomes more

focused. Information that relates to the problems, its causes, symptoms and implications

becomes more important to the group, and they begin actively seeking it.

The quantity and quality of the information search and data aggregation determine

the degree to which the cause of the problem can be correctly represented for the problem

identification function. Moreland & Levine (1992) considered these activities to be among

the most important in the decision making process because they "often initiate the process".

Once a problem is detected, the group generally becomes motivated to solve it (Moreland &

Levine, 1992). The quality of the information determines how directed this aroused state

is. The information acquisition function in effect guides the group's decision making

process.

During the problem identification function, the problems underlying causes are

identified and the problems nature is determined. The extent and seriousness of the

problem also is examined (Hirokawa, 1985). The major focus of this function is to

construct a problem identification that accurately reflects the situation and the group's

relationship to the problem. According to Smith (1989) a problem representation is a

mental model of the problem that includes: labels, potential causes, and some general ideas

about what will occur if the problem is not addressed.

The problem identification function is very important to effective group problem

solving. It is possible for individuals to view the same information and to develop very

different representations of the problem (Moreland & Levine, 1992). It is critical that all

group members view the problem similarly, if the group is to work together effectively.

Different problem representations will lead to different problems being solved by individual

group members, which will in turn lead to chaos.


21

In what is the second stage of a triphasic decision model (option generation/

selection) multiple functions also exist. Again, several of these elements are common with

the model proposed by Eilon (1969). As with most models, there is an option generation

and option evaluation component.

A list of possible alternatives to solving the problem must be formed for the option

generation function to be satisfied. As stated above, generation of a range of realistic

alternatives by the group is necessary if the group is to select a viable solution to the

problem. The larger the set of alternatives, the more likely an acceptable solution is within

the set.

This set of options may be arrived at by several methods. Formal processes, like

Delphi technique or nominal group technique, may be used to facilitate the option

generation process. More often, less formal methods are used to generate alternatives.

Options may be suggested based sole on the problem representation. Solutions for

problem causes or symptoms may be suggested as possible solutions. Brainstorming may

occur, which enables individuals play off of one another's ideas. Random thoughts and

eureka solutions are also possible methods of option generation. It is less important how

the group completes this function than it is that it does complete the function.

Hirokawa (1985) differentiates between positive and negative evaluation functions.

Positive evaluation is the offering of supportive arguments for an option based on some set

of criteria. Negative evaluation is the use of statements that would exclude an option based

on some criteria. Based on the work of Adelman, Gualtieri, & Stanford (1992), this does

not seem necessary. In that study, they found that both positive and negative evaluations

were used more often for selected options than non-selected options. They concluded that

the amount of evaluation was more important than the type of evaluation. Therefore, in this

model, only a single evaluation function will be included.


2 2

Once a list of potential solutions is generated, it is evaluated based on some set of

criteria. The selecting of these criteria comprises a new decision function. This function

does not appear in any of the earlier models. It is one of the areas in which the current

model builds on previous ones. To complete this function, a group must determine what

factors are considered to be important in determining if a solution will be successful if

selected. Adelman et al. (1992) have suggested that these factors are related to the problem

representation, as well as to the data obtained during the informational acquisition. The

criteria can be thought of as tests in which options must pass if they are to become

solutions.

This model proposes that these three functions (criteria selection, option generation,

and option evaluation) are required for a quality option to be selected. Most previous

theories (e.g., Dewey, 1910; Simon, 1960; Eilon, 1969; Mintzberg et al., 1976; Janis &

Mann, 1977) viewed the selection of an alternative as the end of the decision making

process. The current model views it as one function in a continuing process.

Within the final stage of a triphasic decision model (implementation) there are again

several functions. The final phase of Mintzberg's et al. (1976) model (implementation)

serves as the starting phase for the proposed model. During the implementation phase, the

alternative that is selected is put into action (Mintzberg et al., 1976). The group begins to

engage in activities that will allow for it to correct the problem facing it, and potentially, its

underlying cause. During the process of implementing the solution, two functions are

required: coordination and monitoring. Both of these decision functions were recognized

by Shiflett et al. (1982) as functional areas of team performance.

The group needs to coordinate their actions in the implementation of an option.

This function requires that two activities be accomplished by the group, (a) response

sequencing and (b) coordination of member position and timing (Fleishman & Zaccaro,

1992). Response sequencing is the ordering of group member actions according to


23

perceived task requirements (Fleishman & Zaccaro, 1992). This sequencing should avoid

conflict between group members and relate each member's actions to that of other group

members. Coordination of member position and timing involves the placement of specific

activities by group members within a temporal frame. Cues are established by the group to

determine when activities should begin and what actions should follow.

Finally, the group should monitor the effect of their decision. Monitoring refers to

the detection of errors by the group with respect to its timing, placement, and coordination

during implementation of a selected option (Fleishman & Zaccaro, 1992). The monitoring

activity allows the group to recognize if the assumptions made earlier in the decision

making process (i.e., diagnosis, criteria selection) were correct. This monitoring activity

also allows the group to activate contingency plans, in the event that the selected option

does not perform as planned.

Gouran (1982) supported a functional approach by stating that it would be a

mistake to contend that the sequence itself rather than the quantities of mind that it

represents is what determines group effectiveness". Therefore, it is reasoned that it is the

completion of functions that accounts for group performance, not the order in which these

functions are completed (Hirokawa, 1985).

The primary thrust of this research is made based on the above model and the

previous research on group decision making. It is predicted that groups that engage in

behaviors from each and every functional area will outperform those groups that engage in

fewer functional areas. Furthermore, within the context of this experiment, all aspects of

the decision process are considered necessary, and the failure of a group to work through

any individual function will damage the decision process and lead to poorer decisions.

Finally, as the quality of the decision process increases, as measured by the degree to

which each function is completed, so will the quality of the groups performance

(Hirokawa & Johnston, 1989).


24

Inputs to the Decision Making Process

Just as it is important to understand the process by which groups reach decisions, it

also important to understand what individual and group factors affect each of the

aforementioned decision functions (Hirokawa & Johnston, 1989). Hackman & Morris

(1975) have proposed that to understand group performance, research must examine three

components: inputs, process and outcomes. Specifically, Hackman & Morris (1975)

proposed that inputs lead to process, which leads to outcomes. This research will measure

all three components.

As discussed above, the groups decision making functions will serve as specific

measures of group process. Inputs will be examined at both the individual and group level.

Based on previous research and the prescriptions of Hirokawa & Johnston (1989) and

Milliken & Vollrath (1991), several individual and group level variables have been chosen

in order to determine their effects on the decision making process.

McGrath (1984) specified three major classes of inputs to the group. The first of

these was the properties of the individual group members. It is the abilities of individual

group members that set the limits on what the group can achieve. These properties affect

not only group performance, but also group process (McGrath, 1984). A second major

input to the group is the properties of the group (McGrath, 1984). Just as an individuals

attributes are important in determining group process and performance, so are the

characteristics of the group. Group characteristics determine the structure of the interaction

among the group's members. The final class of inputs to the group is situational variables

(McGrath, 1984). The environment can affect how individuals behave. Under certain

conditions some behaviors are appropriate while others are excluded.

Within the context of this experiment two of the classes will be manipulated or

measured and the third held constant. Several individual traits will be measured for all

participants in this study. Three group level characteristics will be manipulated and


25

measured as part of this experiment. Finally, situational variables will be held constant

during the study to eliminate differential effects on the group. A brief review of the

importance of each of the input variables is given below.

Individual Level Variables

Steiner (1972) stated that group member resources, in the form of knowledge skills

and abilities, determine the maximum level of performance of a group. Similarly,

Hackman & Morris (1975) suggested that member resources determined the quality of the

interaction among the group's members. Three individual level variables have been

selected in order to determine their effects on the decision process. Decision style

determines how an individual attempts to solve a problem. Social intelligence governs an

individuals behavioral flexibility and ability to perceive social cues. Finally, an

individuals level of communication apprehension determine to what extent he/she will

participate in the group.

Decision Style. Simon (1976) proposed that there was a distinction between well-

defined and ill-defined problem solving tasks. This was one of the early attempts to

classify the cognitive processes involved in human problem solving. Other researchers set

forth simple classification schemes (Greeno, 1978; Card, Moran, & Newell, 1983).

Recently, Enkawa & Salvendy (1989) developed an empirical model to explain the

cognitive processes of human problem solving. Using multidimensional scaling

techniques, Enkawa & Salvendy (1989) found three dimensions of human problem solving

and learning. Two of the dimensions were related to the reasoning process.

The first dimension, labeled top-down/bottom-up, examined a persons attempts to

understand a task. A problem solver whose preferred mode is top-down tends to use

his/her intuition or insight. This mode of reasoning allows for understanding through the

comprehension of general principles and background knowledge. Those problem solvers


26

who prefer a bottom-up approach tend to be more inductive in their reasoning.

Understanding is arrived at through the careful construction of bits of information.

Problem solvers using this mode are more likely to generate and test options sequentially

rather than simultaneously.

The second dimension of interest was labeled conscious/subconscious reasoning by

Enkawa & Salvendy (1989). At one end of spectrum are problem solvers who require

conscious thought about the problem even if it is familiar to them to solve it. Individuals

make inferences about the information available and engage in some reasoning process,

such as abduction. Abduction requires conscious reasoning to work through a problem,

even if it has been completed before. Problem solvers who are near the subconscious end

of this dimension are more likely to arrive at the answer to a problem without being able to

articulate how they arrived. Individuals who prefer this mode of reasoning are more likely

to do well at eureka type problems (Enkawa & Salvendy, 1989). These individuals are

more likely to make factually unsupported predictions when confronted with a problem.

These predictions may be correct, but the reasoning that lead to them is due to automatic

processing, rather than careful consideration of the facts (Enkawa & Salvendy, 1989).

This second dimension differs from the first in that it taps the knowledge base of the

problem solver; the first dimension looks at how information is processed.

Using these two dimensions, four types of problem solvers can be identified: 1)

top-down and conscious; 2) top-down and subconscious; 3) bottom-up and conscious; and

4) bottom up and subconscious). Each of these problem solving types is best suited for

certain kinds of problems. Enkawa & Salvendy (1989) state that individuals do not always

use the same decision style, but they do have a style that is most preferred. When there is a

match between the preferred decision style the problem type, the individuals will be more

accurate and confident in their solutions.


27

For example, if a problem is complex and dynamic task, individuals who use a

bottom-up and conscious decision style will perform best. This decision style is best suited

for a reasoning oriented approach to problem solving. In complex tasks, the problem focus

is neither obvious nor clear. A great deal of information needs to be processed before the

decision maker can gain an accurate picture of the problem (Simon, 1976). Individuals

who attempt to use general principles or make unsubstantiated predictions will find

themselves with an inaccurate picture of the problem. Furthermore, they will generate

alternatives that will not solve the actual problem (Simon, 1976).

According to Enkawa & Salvendy (1989) the following decision styles and problem

types are the best matches. For problems that are well defined and require little data,

utilizing a top-down conscious approach, should be used because it arrives at the solution

faster. A bottom-up subconscious approach is most effective when there is a great amount

of information and processes are poorly understood. Top-down subconscious reasoning is

used most effectively when an individual possesses a great deal of content specific

information about the problem domain (e.g., recognition primed decision making in fire

fighters, Klein, Orasanu, Calderwood & Zsambok, 1993).

The affect of differing decision styles within a group are not explored by Enkawa &

Salvendy (1989), but two scenarios seem likely. First, it is possible that individual

members will adjust their decision style to that of the leader of the group. This will allow

that individual to become part of the in-group of the leader (Sumner, 1906). By shifting

their decision style the group members are able to communicate effectively with the leader,

but will not be able to easily solve the problem. These differences will be especially acute

when the leader's decision style does not match that of the problem before the group. A

second possibility is that individuals whose favored decision style matches that required by

the problem will be more likely to participate in the group's decision process than those that


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do not match. The decision style of these group members will be reflected primarily in the

amount of information that is collected.

Social Intelligence. Thorndike (1920) defined social intelligence as the ability to

understand others and to act wisely in human relations. Social intelligence includes the

ability to perceive social cues from others and to respond in the correct manner. The

components of social intelligence have been examined recendy (Zaccaro, Gilbert, Thor &

Mumford, 1991). Two components were identified. The first was social perceptiveness.

Social perceptiveness was defined as the ability to be aware and sensitive to the needs,

goals, and demands of others (Gilbert, 1992). This capacity allows an individual to attend

to information from the environment and to interpret the s

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