The Effect of Decision Aids on Work Group Performance

by:

Marla E. Hacker

Dissertation submitted to the Faculty of the Virginia Polytechnic Institute and State

University in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

IN

INDUSTRIAL AND SYSTEMS ENGINEERING

APPROVED:

_________________________

Brian M. Kleiner, Chair

_________________________

Larry D. Alexander

_________________________

Janice F. Cerveny

_________________________

C. Patrick Koelling

_________________________

Robert C. Williges

April 10, 1997

Blacksburg, Virginia

Key Words: CSCW, Decision Making, GDSS, Macroergonomics,

Work Group Performance

The Effect of Decision Aids on Work Group Performance

Marla E. Hacker

ABSTRACT

Organizations increasingly use work groups to perform process improvementtasks. Little research exists about groups assigned complete tasks such as process im-provement which involves completing all group processes, such as: generating, selecting,negotiating, and executing. This research tested the impact of decision aids on workgroup processes and work group performance. Laboratory and field experiments wereperformed.

Decision aids were shown to impact work group processes. Decision aids in-creased the number of ideas considered by the work group, increased the equality of par-ticipation in the work group, decreased the overall level of conversation, and reduced con-sensus during evaluation of sensitive issues. No significant difference was found betweendecision aid types and work group performance. A regression model was identified whichpredicts group performance. Two variables were high predictors of work group perform-ance: the level of conversation occurring in the group and the range between high and lowidea contributors. The range between high and low idea contributors was correlated withthe skill level of participants in the group.

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Dedication

Dedicated to Stephen, Jessica, Mark, my parents, Larry, Rini, Tom and Brian. Thank youfor your love and support throughout my doctorate program.

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ACKNOWLEDGEMENTS

Each of my committee members played an important role in guiding me through the re-search process. A special word of thanks for their easy accessibility, quick document turnaround, and consistent encouragement.

Without students willing to participate in the experiment this dissertation would not havebeen possible. A special word of thanks to all seventy-two VPI students that made timefor this dissertation.

Several individuals from industry made this dissertation possible by permitting workgroups to participate in the experiment. A special word of thanks to: James TorgersenDaniel Hughes, Michelle Meyers; Alan Stephenson, Paul Kudlaty; and Mike Martin.

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Table of Contents

Abstract iiDedication iiiAcknowledgments ivTable of Contents v

CHAPTER ONE: INTRODUCTION AND SCOPE OF THE RESEARCH 11.1 Background 11.2 Problem Statement 31.3 Experimental Design 31.4 Research Questions and Hypotheses 41.5 Delineations 51.6 Research Model 5

CHAPTER TWO: LITERATURE REVIEW 72.1 Sociotechnical Systems 82.2 Social Subsystem 102.3 Work Groups 10

2.3.1 Group Performance 102.3.2 Homogeneity-Heterogeneity Group Composition 13

2.4 Technical Subsystem 152.5 Implementation 15

2.5.1 Implementation Models 162.5.2 Implementation Elements 20

2.6 Small Group Decision Making 232.6.1 Group vs. Individual Decision Making 232.6.2 Decision Making Processes 242.6.3 Decision Making Aids 25

2.7 Group Decision Support Systems 262.7.1 Input Variables 272.7.2 Operating Conditions 292.7.3 Process and Outcome Variables 30

2.8 Summary 31

CHAPTER THREE: METHODOLOGY 323.1 Subjects 323.2 Independent Variables 333.3 Laboratory Facilities and Equipment 33

3.3.1 Control Condition 333.3.2 Formalized Procedures Condition 343.3.3 Facilitated Condition 343.3.4 Facilitated and Technology Condition 34

3.4 Experimental Task 35

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3.5 Pre-experiment Training (Laboratory Work Groups) 353.6 Pilot Study 363.7 Measures of Dependent Variables 36

3.7.1 Task Performance 363.7.2 Equality of Participation (laboratory and field) and Task-focused Communication (laboratory) 363.7.3 Participant Evaluation 373.7.4 Number of Brainstorming Ideas (laboratory) 383.7.5 Idea Selection (field) 383.7.6 Participant Qualitative Data 38

3.8 Experimental Procedure 383.9 Data Analysis 38

3.9.1 Hypotheses Testing 383.9.2 Post Hoc Testing 39

CHAPTER FOUR: RESULTS AND DISCUSSION 404.1 Hypothesis One - Task Performance 404.2 Hypothesis Two - Equality of Participation 42

4.2.1 Number of Comments 444.2.2 Associating Group Characteristics with Communication Level 454.2.3 Linking Group Processes with Group Performance 46

4.3 Hypothesis Three - Participant Evaluation 504.4 Hypothesis Four - Task-focused Communication 534.5 Hypothesis Five - Ideas Generated 534.6 Hypothesis Six - Anonymity and Consensus 554.7 Contribution to the Body of Knowledge 57

CHAPTER FIVE: RESEARCH IMPLICATIONS 635.1 Research Implications 63

5.1.1 Future Research 635.1.2 Differences in Conduction Laboratory and Field Experiments 65

5.2 Practitioner Implications 675.2.1 Insight About Work Group Performance 675.2.2 Work Group Member Perception 705.2.3 Work Group Consensus 70

5.2.4 Human Computer Interaction 71

REFERENCES 72

APPENDIX A GDSS EXPERIMENTS 81

APPENDIX B PRE-EXPERIMENTAL FORMS 85Laboratory and Field Study Schedules 86

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Consent Form 87Laboratory Study Pre-Experiment Questionnaire 89Laboratory Experiment Background Information 91Field Experiment Group Exercise 98

APPENDIX C EXPERIMENTAL PROCEDURES AND DATA COLLECTION INSTRUMENTS 99

Laboratory Experiment Formalized Procedures Instructions 100Laboratory Experiment SYMLOG Data Sheet 107Field Experiment Instructions 108Field Experiment Data Sheets for Tinker Toys 112Field Experiment Data Sheets for Legos 113Field Experiment Data Sheets for Miniquadros 114Field Experiment Work Group Role Descriptions 115

APPENDIX D POST-EXPERIMENT QUESTIONNAIRES 116Laboratory Experiment Post-experiment Questionnaire 117Field Experiment Post-experiment Questionnaire 121

APPENDIX E RAW DATA 123Laboratory Experiment Task Performance 124Laboratory Experiment: Association, Process Knowledge Test Results, andIdeas 126Laboratory Experiment SYMLOG 127Laboratory Experiment Questionnaire 147Field Experiment Task Performance 151Field Experiment Work Group Characteristics 153Field Experiment Frequency of Verbal Comments 154Field Experiment Idea Generation by Participants 162Field Experiment Idea Evaluation 163Field Experiment Role Evaluation Facilitated Condition 164Field Experiment Role Evaluation Facilitated with Technology Condition 165Field Experiment Questionnaire 166Field Experiment Qualitative Comments 181

APPENDIX F ADDITIONAL ANOVA TABLES 183

VITA 193

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List of Tables

Table 2.1 Group Performance Constructs 11Table 2.2 Summary of GDSSs’ Impact on Dependent Variables 31Table 3.1 Presentation Order 35Table 4.1 Summary of Hypothesis One Results 41Table 4.2 Laboratory Task Performance Means by Condition 41Table 4.3 Field Task Performance Means by Condition 41Table 4.4 Laboratory Findings for Equality of Participation 42Table 4.5 ANOVA Summary Table for Dominance Distance as a Function

of Condition 43Table 4.6 ANOVA Summary Table for Dominance Dispersion as a

Function of Condition 43Table 4.7 Equality of Participation Means by Condition 44Table 4.8 ANOVA Summary Table for Number of Comments as a Function

of Condition 44Table 4.9 Correlation Between COMMENTS and Work Group

Characteristics 45Table 4.10 Regression of COMMMETS and IDEAS Predictors 47Table 4.11 Correlation Analysis Between IDEAS and Demographic Data 48Table 4.12 Regression of COMMENTS and ENGR Predictors 48Table 4.13 Summary of Hypothesis Three Results 50Table 4.14 ANOVA Summary Table for Discussion Quality as a Function

of Condition 50Table 4.15 Laboratory Participant Evaluation Means by Condition 51Table 4.16 Field Participant Evaluation Means by Condition 51Table 4.17 Qualitative Comments About Structure 52Table 4.18 Task-focused Communication Means by Condition 53Table 4.19 ANOVA Summary Table for Ideas Brainstormed as a Function

of Condition 53Table 4.20 Number of Ideas Brainstormed Means by Condition 54Table 4.21 Findings for Consensus When Evaluating Nonsensitive Issues 55Table 4.22 ANOVA Summary Table for Role Scoring Variability (range

between vote scores) as a Function of Condition 55Table 4.23 ANOVA Summary Table for Roles Scoring Variability (items

with votes) as a Function of Condition 56Table 4.24 Voting Variability Means by Condition 56Table 4.25 ANOVA Summary Table for Time as a Function of the Control

Condition - Laboratory versus Field 57Table 4.26 ANOVA Summary Table for Time as a Function of the Facilitated

Condition - Laboratory versus Field 57Table 4.27 ANOVA Summary Table for Time as a Function of the Facilitated

and Technology Condition - Laboratory versus Field 57

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Table 4.28 ANOVA Summary Table for Cost as a Function of the Control Condition - Laboratory versus Field 58

Table 4.29 ANOVA Summary Table for Cost as a Function of the Facilitated Condition - Laboratory versus Field 58

Table 4.30 ANOVA Summary Table for Cost as a Function of the Facilitated and Technology Condition - Laboratory versus Field 58

Table 4.31 Means for the Laboratory and Field Experiments 61

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List of Figures

Figure 1.1 Research Model 6Figure 2.1 Chapter Two Organization 7Figure 2.2 Group Performance Model (adapted from McGrath) 12Figure 2.3 A Corporate Implementation Model (adapted from Stonich) 16Figure 2.4 An Operational Implementation Model (adapted from Hrebiniak

and Joyce) 17Figure 2.5 Consolidated Implementation Model 19Figure 2.6 Consolidated Framework for GDSS Research 27Figure 2.7 Scheme of Task Type (Adapted from McGrath, 1984) 28Figure 3.1 Research Model 32Figure 4.1 Dominance Distance Means as a Function of Condition 43Figure 4.2 Number of Comments Means as a Function of Condition 45Figure 4.3 Plot of Residuals for Regression Model (COMMENTS

and IDEAS) 47Figure 4.4 Plot of Residuals for Regression Model (COMMENTS and

ENGR) 48Figure 4.5 Discussion Quality Means as a Function of Condition 51Figure 4.6 Number of Ideas as a Function of Condition 54Figure 5.1 Management’s Influence on Work Group Performance 67

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Chapter One: Introduction and Scope of the Research

“Post-industrial society will be characterized by more and increasing knowledge,more and increasing complexity, and more and increasing turbulence. These, in combina-tion, will pose an organizational environment qualitatively more demanding than those inour experience (Huber, 1984, p.931).” The impact on organizations will be significant.Successful organizations will no longer be able to seek unidimensional strategies of lowcost, high quality or fast cycle times are insufficient. The new environment will demand anorganization perform competitively in all areas (Goldman, Nagel, and Preiss, 1995). In-creasingly, groups are the organizational response to the new environment (Hamel, andPrahalad, 1989; Beer, Eisenstat, and Spector, 1990; Bettenhausen, 1991; Dumaine, 1994;Parker, 1994; Tompkins and Associates, 1995; Hackman, and Wageman, 1996).

Our understanding of how effective groups achieve objectives is limited though.Most studies of small work groups have been done under laboratory conditions and aredifficulty to generalize to the field (Pavitt, 1993). Most research has focused on only oneor two group processes out of the following: generating, choosing, negotiating and exe-cuting. Little empirical evidence is available about how groups function when performingthe complete task (McGrath and Hollingshead, 1994). Also, little is known about howsuccessful groups implement strategy, since scholars have traditionally focused on strate-gic planning vs. tactical planning or implementation (Stonich, 1982; Hrebiniak, and Joyce,1984; Alexander, 1985; Huff, and Reger, 1987; Pavitt, 1993). This study adds to the bodyof knowledge by investigating how decision aids impact group processes and performanceduring tasks similar to implementation. Decision aids are tools or techniques which in-crease the decision making process or increase the quality of the decision. The task in thisresearch is a complete task requiring all group processes to be performed. The specifictask is similar to a process improvement activity which many groups increasingly are beingformed to perform. To test the generalizability of the laboratory results, this study in-cludes a field experiment.

1.1 BackgroundThe theoretical foundation for this research is found in sociotechnical system

(STS) research. Sociotechnical research has shown that jointly optimizing the social andtechnical subsystems of an organization leads to higher productivity and job satisfaction(Cherns, 1987; Hendrick, 1986; Pasmore, 1988; Trist and Hugh, 1993). The social sub-system includes how people form relationships and networks to accomplish work. Thetechnical subsystem includes equipment, rules, procedures, policies, and methods for ac-complishing work. Consistent with STS, small group research and strategic implementa-tion research support the concept of jointly optimizing technical and social components ofan organization. Small group research has demonstrated the need to balance the group’sefforts on both task and social concerns to improve group performance (Bales, 1954; Del-becq, Van de Ven, and Gustafson, 1975; DiSalvo, Nikkel, and Monroe, 1989). Imple-mentation research has demonstrated the need to align organizational and individual ob-jectives for successful strategy execution (Stevens, Beyer, and Trice, 1980; Bourgeois.

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and Brodwin, 1984; Guth, and MacMillan, 1986; Kotter, 1995). And recent disappoint-ments with the new approaches to performance improvement, reengineering, with its in-tense focus on the technical system and deengineering, with its intense focus on the socialsystem, continue to confirm the necessity of balancing the social and technical aspectsperformance improvement for sustainable, optimized results (Kleiner, 1996).

Simultaneously, as organizations increase their use of groups, groups are becomingmore complex. Complexity challenges the ability of the group to perform effectively. Thesocial system of groups is becoming increasingly complex due to:

1. the use of cross-functional members;

2. the ability to use technology to enable members located remotely to participate ongroups;

3. the increasing ethnic and demographic diversity represented in the workforce; and4. the representation of multiple hierarchical levels on any single group.

Complexity of the social system reduces group effectiveness due to increases inprocess losses. “Process losses are those group performance “inefficiencies” (e.g., discus-sion in the group that is apparently extraneous to the group’s assigned task, domineeringmembers, non-participating members, etc.). These are presumably reflected in the extentto which the task performance of a particular group falls short of predictions (McGrath,and Hollingshead, 1994, p. 3).” The potential benefits of multiple perspectives found ongroups are frequently negated by these losses. Increasing group performance requires thatthe source of process losses be addressed (Steiner, 1972). Since process losses are withinthe social subsystem, sociotechnical theory leads us to investigate technical subsystemfactors that, when combined with the increasing complex social subsystem, enable a workgroup to leverage its complexity instead of becoming disabled by it.

Structuring group processes is an increasingly used technical subsystem interven-tion aimed at reducing process losses and improving group performance. Technical sub-system interventions that structure group processes have positively impact group perform-ance (Hollman, and Hendricks, 1972; Delbecq et al., 1975; Pinto, Pinto, and Prescott,1993). Scholars have recently started studying computer aided structured group proc-esses and group decision support systems (GDSS) (DeSanctis, and Gallupe, 1987;McGrath et al., 1994). Although not consistently, computer augmented meetings havebeen shown to alter the way the group interacts, and to reduce process losses. Most ofthe GDSS research has been laboratory experiments or single case studies. Very few labo-ratory studies have been confirmed in the field. And very few case studies generalize be-yond the specific situation. This dissertation involved a laboratory experiment with fieldconfirmation of the laboratory results. The focus of the dissertation was on whether thedecision aids: structured group processes and computer augmentation improve groupperformance.

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The subjects for this experiment are cross-function work groups. In this disserta-tion cross-functional groups are operationalized to indicate different roles and responsibilities (e.g., supervisors, production operators, mechanics, engineers, etc.). There is anincreasing trend in using cross-functional groups because the implementation of many or-ganizational projects require the cooperation and coordination of various functional areas.Cross-functional groups represent a structurally complex design to achieve the coopera-tion and coordination since these groups are temporary and each member maintains his orher functional reporting relationship. However, advantages gained by cross-functionalgroups are often negated by internal group dynamics or process losses which keep thegroup from realizing its objectives (Wagner, Pfeffer, and O'Reilly, 1984; Pfeffer, andO'Reilly, 1987; Tsui, and O'Reilly, 1989). Structuring group processes has been shown toimprove group interaction and task performance (Maier, and Maier, 1957; Lanzetta, andRoby, 1960; Brilhart, and Jochem, 1964; Bayless, 1967; Kepner, and Tregoe, 1968; Hall,and Watson, 1970; Hollman et al., 1972; Delbecq et al., 1975; Pavitt, 1993; Pinto et al.,1993). Because they are more complex than other groups, cross-functional groups havethe most to gain from interventions that improve group interactions.

1.2 Problem StatementImproving work group performance is a relevant issue in organizations today

since:1. The use of work groups to achieve performance objectives is increasing (Bettenhausen, 1991; Bowen, 1995).2. The long-term success of most management intervention strategies requires increasing levels of small group participation by organizational members (Hersey and Blanchad, 1982).3. Research has demonstrated that in multi-phase decision making involving integrated tasks, groups have higher decision quality than individuals working alone (Hollman et al., 1972; Shaw, 1976; Rowe, Boulgarides, and McGrath, 1984; Smith, 1989).4. Most organizations using work groups are dissatisfied with their group’s performance (Tompkins et al., 1995) and individuals within the group are dissatisfied with their group experience (Kepner-Tregoe, 1996).

Identifying interventions that enable work groups to leverage their complexity is impor-tant, relevant research today.

1.3 Experimental DesignThis dissertation included a laboratory experiment and a field experiment. The

laboratory experiment was a between subjects study using students role playing differentfunctions. The field experiment was a within subjects study using natural work groups.The independent variable was decision making aid type. Three types of decision aids werestudied in the laboratory: (1) formal manual procedures, (2) facilitated procedures, and(3) facilitated with technology. Two types of decision aids were studied in the field: (1)facilitated procedures and (2) facilitated with technology.

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1.4 Research Questions and HypothesesThe three research quesitons this dissertation focused on were:

1. What impact do decision aids have on work group performance?2. What impact do decision aids have on work group interactions?3. How do laboratory work groups differ from field work groups?

From the body of knowledge several research hypotheses have been developed for testingin this dissertation.

Hypothesis One: H1: Facilitated groups have higher task performance than groupsworking without facilitation. Facilitation provides structure to group processes. Thefacilitator, which is a person that is not a member of the group guides the group throughthe group’s processes. Most studies support the premise that structuring group processesimproves group interactions and group decisions (Reukert, and Walker, 1987; Pinto et al.,1993). GDSS research has shown that procedures that simplify the handling of informa-tion, organize group processes and let the group deal with internal conflict and consensusbuilding whether computer aided or not, improve group performance (DeSanctis et al.,1987; Pinsonneault, and Kraemer, 1990; McLeod, 1992). Anecdotal case studies supportthe structuring of group processes as a mechanism for increasing group performance(King, 1989; Collins, and Huge, 1993; Bechtell, 1995; Melum, and Collect, 1995). Theempirical evidence from the laboratory and the anecdotal evidence from the field suggestthat structuring group processes is a critical factor impacting group performance.

Most GDSS research has not studied GDSS componets (eg., structure, facilitation,anonymity). The few empirical studies of GDSSs that exist usually evaluate GDSS groupsvs. non-GDSS groups, and find GDSS groups make higher quality decisions (Pinsonneaultet al., 1990; McLeod, 1992; McGrath et al., 1994). A field study conducted by Jarvenpaa,Rao, and Huber, (1988) that did compare groups using GDSS and groups using an elec-tronic blackboard found electronic blackboard groups had higher decision quality thanGDSS groups. This hypothesis was tested in both the laboratory and the field.

Hypothesis Two: H1: Facilitated groups that use technology have the highest equalityof participation. Groups using structured group processes have more equality of partici-pation than groups not using structured group processes (Van Gundy, 1981). Prior re-search has shown that GDSSs increases equality of participation in a work group(McGrath and Hollingshead, 1994; McLeod, 1992; and Pinsonneault and Kraemer, 1990).This hypothesis was tested in the laboratory and the field.

Hypothesis Three: H0: No significant difference is seen in participant evaluationsacross treatments. The empirical literature is mixed on the impact of decision aids such asGDSSs on member satisfaction with the process and the group’s decisions (Pinsonneaultet al., 1990; McLeod, 1992; McGrath et al., 1994). When only the experimental studies

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are considered though, no difference is found in participant evaluations (McGrath, 1994).Almost all experimental studies have been with student work groups. This hypothesistests whether the laboratory findings are consistent with prior research and whether thenatural work groups have similar findings as the student work groups. This hypothesiswas tested in the laboratory and the field.

Hypothesis Four H1: Groups using any type of decision aid have higher task-focusedcommunication than control groups. GDSSs have been shown to increase task-focusedcommunication (McLeod, 1992). Scholars hypothesize that more task-focused communi-cation occurs because of the structured group processes feature of GDSSs. These proc-esses constrain the group from extraneous discussions and limit dysfunctional behaviors.This hypotheses was tested only in the laboratory.

Hypothesis Five H1: Facilitated groups generate more ideas during brainstorming.The nominal group technique has been shown to increase the number of ideas generated ina group (Delbecq, Van de Ven and Gustafson, 1975). This hypothesis states that groupsusing technology to facility the nominal group technique generate more ideas than groupsnot using electronic support. This hypothesis was tested only in the laboratory.

Hypothesis Six: H1: Groups given anonymity demonstrate less consensus in theirevaluations. With anonymity participants feel less worried about how their message willbe received by other members on the work group, so there is less personal and social as-pects in the interaction (McGrath and Hollingshead, 1994). Consensus is more difficult toobtain when individuals have anonymity. The results of a meta-analysis indicated thatconsensus decrease when anonymity is provided through a Group Decision Support Sys-tem (McLeod, 1992). This hypothesis states the findings from this research will be con-sistent with prior research findings. This hypothesis was tested only in the field.

1.5 Delineations1. This research measured work group performance, not organizational performance.The dependent variables are: work group processes, task performance, and participantevaluation.

2. This research did not attempt to manipulate or control the internal composition of thework groups (i.e., ability, experience, personality, gender, race, size, knowledge and skill,leadership, or capability).

1.6 Research ModelThere is convergence between small work group research and group decision sup-

port research that work group performance includes three dimensions: work group inter-action or process variables, task performance variables and participant evaluation vari-ables. Many of the most commonly studied process and performance variables are in-cluded in this research (see Figure 1.1).

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Work Group Performance

Task Performance

decision quality(cost, defect rate, time)

Participant Evaluation

level of agreement

discussion quality

interaction

role evaluation (field) Decision Aid Type

control

formalized procedures(laboratory)

facilitated

facilitated and technology

Work Group Type

Laboratory cross-functional

student teams

Fieldcross-functional

natural work groups

Technical Subsystem

Social Subsystem

Equality of Participation

Task-focused Communication(laboratory)

Number of Ideas Brainstormed(laboratory)

Idea Evaluation (field)

Work Group Processes

FIGURE 1.1 Research Model

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Chapter Two: Literature Review

The focus of this research is how technical system interventions (decision aids) im-pact a complex social system (the work group). This chapter is organized according tosociotechnical systems, with a chapter relating to the social subsystem and several chap-ters relating to the technical subsystem (shown in Figure 2.1).

2 . 1 S o c i o t e c h n i c a l S y s t e m s

2 . 4 T e c h n i c a l S u b s y s t e m2 . 2 S o c i a l S u b s y s t e m

2 . 7 G r o u p D e c i s i o n S u p p o r t S y s t e m s

2 . 7 . 1 I n p u t V a r i a b l e s

2 . 7 . 2 O p e r a t i n g C o n d i t i o n s

2 . 7 . 3 P r o c e s s & O u t c o m e V a r i a b l e s

2 . 8 S u m m a r y

2 . 5 I m p l e m e n t a t i o n

2 . 5 . 1 I m p l e m e n t a t i o n M o d e l s

2 . 5 . 2 I m p l e m e n t a t i o n E l e m e n t s

2 . 3 T e a m s

2 . 3 . 1 T e a m P e r f o r m a n c e

2 . 3 . 2 H e t e r o g e n e i t y - H o m o g e n e i t y G r o u p C o m p o s i t i o n

2 . 6 S m a l l G r o u p D e c i s i o n M a k i n g

2 . 6 . 1 G r o u p v s . I n d i v i d u a l D e c i s i o n M a k i n g

2 . 6 . 2 D e c i s i o n M a k i n g P r o c e s s e s

2 . 6 . 3 D e c i s i o n M a k i n g A i d s

FIGURE 2.1Chapter Two Organization

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2.1 SOCIOTECHNICAL SYSTEMS

This dissertation is concerned with optimizing decision making in work groups.Since work groups are becoming increasingly complex in their social systems, this study isfocused on technical interventions that may enable the group to benefit from its complex-ity versus becoming disabled by its own complexity. Sociotechnical systems (STS) theoryis an established research area that seeks to optimize the social and technical subsystemsof an organization to achieve maximum performance. Macroergonomics is a new area ofstudy with its theoretical basis in STS. Macroergonomics is “a top-down approach tosystem design based on a sociotechnical system perspective (Hendrick, 1986, p. 467).”The central focus of macroergonomics is optimizing work system design (Brown, Hen-drick, Imada and Kleiner, 1997). STS provides the theoretical framework for this disser-tation.

Sociotechnical theory emphasizes the interrelatedness of the functioning of the so-cial and technical subsystems of the organizations (Pasmore, Francis, and Haldeman,1982). “Sociotechnical Systems Theory is an open systems approach that seeks to opti-mize the relationship between the social and technical systems of an organization. Whenthese two systems achieve consonance, the organization is expected to experience higherproductivity and job satisfaction together with lower absenteeism and turnover (Beekun,1989 p877).” The technical subsystem defines the tasks to be accomplished and the socialsubsystem defines how the tasks are to be performed (Hendricks, 1991). The advantagesof sociotechnical design are: (1) increased innovation; (2) better human resource devel-opment; (3) consideration of the environment’s influence; (4) increase cooperation; (5)increased commitment; and (6) better utilization of resources (Pasmore, 1988). The his-tory of sociotechnical theory is briefly summarized below.

In 1939, controlled studies found that greater vitality, creativity, cooperation,commitment to and time spent on the work task was characteristic of democratic struc-tures. The first natural field experiment to find this structure in operation was in the Brit-ish coal industry in 1951, where an alternative form of work structure based around self-managing work groups was producing 25 percent higher output and 40 percent lowercosts than another mine, similar in every respect (conditions, equipment and personnel)except for the work structure. In 1962-69 the Norwegian Industrial Democracy Programproved the practical feasibility of self directed group structures. Consultancy organiza-tions sprang up to provide this expert driven open systems approach which sought tojointly optimize the social and technical systems design of organizations, called Sociotech-nical Systems (Cabana, 1995). Cherns (1987) describes the sociotechnical design princi-ples as:

1. Compatibility: The organizational design process must be compatible with the design’sobjective. Design involves conflict since various functions are represented. Handling theconflict in constructive ways is a feature of compatibility.

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2. Minimal Critical Specification: It’s essential to define what are critical changes to thedesign. Only what is absolutely necessary should be specified. In other words, specifyingwhat must be accomplished is important, but a feature of this principle is leaving the howto the appropriate people.

3. Variance Control: Successful organizational design requires variances be controlled attheir source and not exported across departments, functions, or organizations.

4. Boundary Locations: Boundaries should not be drawn to impede sharing information,knowledge, and learning.

5. Information Flow: Organizational information has three uses: control, record, and ac-tion. Information for action should be provided directly to those who must act.

6. Power and Authority: Employees that need resources to perform their responsibilitiesshould have access to their resources and authority to direct these resources.

7. Multifunctionality: Organizations have two methods of adapting to their environment.Organizations can either hire the expertise required or train existing employees in the newexpertise. Hiring experts creates additional complexity and confusion to the already sen-sitive line-staff balance. Training adds expertise without complicating the line of com-mand. STS advocates the serious consideration of training before hiring expertise.

8. Support Congruence: Organizational design must consider consistent changes in sup-porting systems (e.g., rewards, information systems, financial control, marketing, sales,purchasing, and planning just as it considers production, maintenance and quality control).

9. Transitional State: The transitional state between old and new organizational systemsrequires its own planning and design component.

10. Incompletion: The present period of transition is not between a past and future stablestate but between one period of transition and another.

Sociotechnical research has demonstrated that jointly optimizing the social andtechnical subsystems of an organization leads to the best overall performance results(Pasmore, 1988; Trist, and Hugh, 1993). Increased productivity, decreased costs, im-proved quality and attitudes, and decreased absenteeism, turnover, injuries, and grievanceshave been reported as outcomes of sociotechnical interventions (Pasmore, 1988). Themajority of recent sociotechnical interventions have deviated from the original sociotech-nical theory by focusing mainly on the social subsystem and the creation of groups(Pasmore et al., 1982; Hackman et al., 1996). Intervening solely on the social subsystemis of limited value since technology plays an increasingly integral role in organizations(Pasmore et al., 1982). It could be our disappointing results in implementing groups isdue to not considering both the social and technical subsystems in these interventions.Consequently, it is especially important that any study of work groups consider both thesocial and technical subsystems concurrently.

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2.2 SOCIAL SUBSYSTEM

The social subsystem is defined as the people in the work group and their relation-ship to each other and others within the broader organization (Pasmore, 1988). From so-ciotechnical research we understand social complexity can impact group performance.Complexity is the degree of differentiation and integration (e.g., structural communication,control and coordination mechanisms) the work group experiences (Hendrick, 1991). Thethree major kinds of social differentiation are:

1. Horizontal: degree of separation between units.

2. Vertical: depth of an organization’s hierarchy

3. Spatial Dispersion: degree of geographical dispersion

There is a direct link between the level of differentiation that exists and the level ofintegrating mechanisms required for the effective and efficient functioning of the organiza-tion or group (Hendrick, 1986). A common mechanisms for integrating activities in agroup is formalization. Formalization is the degree to which work is standardized orstructured (Hendrick, 1986).

This dissertation studies how formalizing group processes with structure impactsgroup interaction and group performance.

2.3 WORK GROUPSUsing groups or work groups in organizations is pervasive and increasing

(Bettenhausen, 1991; Bowen, 1995). The most frequently used sociotechnical system in-tervention is establishing and developing groups (Pasmore et al., 1982). The single mostcommonly used Total Quality Management technique is forming short-term problem-solving groups (Hackman et al., 1996). A group is “A distinguishable set of two or morepeople who interact, dynamically, interdependently and adaptively toward a common andvalued goal/objective/mission, who have each been assigned specific roles or functions toperform and who have a limited life-span of membership (Salas, Dickinson, Converse, andTannenbaum, 1992 p4).” This dissertation uses work groups as the unit of analysis.

2.3.1 Group PerformancePerformance and effectiveness are often used interchangeably in discussions about

an organization’s ability to achieve objectives. There are currently two important bodiesof literature which have employed or are employing empirical methods to understandgroup performance: the social psychology literature and the human factors literature. Thesocial psychology literature has historically used the construct of effectiveness and opera-tionally defined it as task performance and member satisfaction. Human factors research-ers have recently started studying group performance and, consistent with the historicalhuman factors literature, the construct being used is performance. The human factors op-erational definition includes task and groupwork components. There really is not muchdifference in the definitions used by the two disciplines.

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The group performance construct has been shaped by the construct of organiza-tional performance. Like group performance, there is no commonly-cited organizationalperformance construct. A theoretical concept for organizational performance has re-mained fairly stable through several decades as shown by the findings of scholars. Thedegree of accomplishment of the organizational objectives indicates the level of perform-ance (Barnard, 1938). A high performing organization produces higher quality outputsand adapts more effectively to the environment and internal problems than others (Mott,1972) .

These ideas about organizational performance include both a task and a social di-mension. Task and social dimensions are also found in the group performance constructs.Most scholars agree group performance has two dimensions: task outcomes and psycho-social outcomes (McGrath, 1964; Hackman, and Morris, 1975; Kolodny, and Kiggundu,1980; Gladstein, 1984; Tannenbaum, Beard and Salas, 1992).

Several leading scholars have developed their own construct of group performanceas shown in Table 2.1. In all but one case, group performance does have the two dimen-sions. The Kolodny and Kiggundu (1980) construct is taken from their sociotechnicalwork. The Swezey, and Salas (1992) construct is presented in human factors. TheMcGrath (1964), Hackman (1983) and Gladstein (1984) constructs are from social psy-chology. Task outcomes refer to the traditional measures such as meeting schedules,achieving performance goals, and remaining within budget restrictions. Psycho-socialoutcomes refer to how groups feel about working with each other, the extent to whichthey feel the time devoted to group activities is worthwhile, and whether they are proud ofthe groups outcomes (Pinto et al., 1993).

TABLE 2.1Group Performance Constructs

Scholar Group Performance ConstructMcGrath (1964) • performance outcomes: quality, speed, errors

• other outcomes: satisfaction, attitude, cohesiveness

Salas, Dickson, Converse, andTannenbaum (1992)

• quality/quantity, time, errors

Kolodny and Kiggundu (1980) • productivity: cost, output, maintenance of skills• satisfaction: pride, cohesiveness

Hackman (1983) • task output acceptability• capability of members to work together in thefuture• satisfaction

Gladstein (1984) • performance• satisfaction

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These group performance constructs are very similar. Only the human factorsmodel does not include member satisfaction as a dimension of group performance(probably because of the engineering paradygm of knowledge being only valid when it isnot dependent on human processes). These models are extensions of the earliest model,presented by McGrath in 1964 (Figure 2.2).

g r o u pi n t e r a c t i o np r o c e s s e s

i n d i v i d u a ll e v e l

f a c t o r s

g r o u p l e v e lf a c t o r s

p e r f o r m a n c eo u t c o m e s

o t h e ro u t c o m e s

( t e a m w o r k )e n v i r o n m e n t a l l e v e l

f a c t o r s

FIGURE 2.2Group Performance Model (adapted from McGrath, 1964)

All the models consider group interaction processes as important factors influenc-ing group performance. Group interaction processes include indicators such as: commu-nication, adaptability, cooperation, acceptance of suggestions or criticisms, giving sugges-tions or criticisms, group spirit and coordination (McIntyre, and Salas, 1995). Membersatisfaction is often the typical measure used in group performance measures. But groupinteraction may be more accurate since most satisfaction indicators are self-reportedmeasures subject to biases.

2.3.1.1 Critical issues in assessing performanceFrom the models, it is seen that most scholars agree group performance includes

both task and social components and there is agreeemnt in how to measure these two di-mensions of performance. The task performance component is frequently measured interms of quality, quantity, time and errors. The social component is most often a self-reported measure of group member satisfaction.

There are at least three issues that should be considered when assessing groupperformance.

1. Organizational and member factors that influence performance should be considered.Groups are members of larger organizations. These organizations control many

critical aspects involving the group, including the following: resources, boundaries, oper-ating procedures, communication patterns, and power and authority distribution

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(Fleishman and Zaccaro, 1992). All of these elements may have a significant impact on agroup’s ability to achieve objectives. Individual group members influence the knowledge,skills, and experiences brought to bear on a task, and the quality of the group interaction(Fleishman and Zaccaro, 1992). Again, these are elements with the potential to impactperformance. Quantifying to what extent organizational and individual factors influencegroup performance is difficult. Research has confirmed that organizational factors andgroup member composition do influence group performance (McGrath, 1964; Hackman etal., 1975; Kolodny et al., 1980; Gladstein, 1984; Goodman, Ravlin, and Argote, 1986;Bettenhausen, 1991; Fleishman et al., 1992). These elements must be considered in re-search designs that use group performance indicators as the dependent variables.

2. Self reported satisfaction measures may involve significant biases.Member satisfaction is typically determined through self-report surveys. This type

of data is abhorred by objective measurement advocates because misrepresentation andmisperception are possible (Muckler, and Seven, 1992). The role of satisfaction is alsoinconclusive in the empirical literature as to its influence on performance (Sink et al.,1989). Maybe because of this, a current direction being lead by human factors researchersis to focus on group work or group interaction processes as a better picture of groupfunctioning. This is not to say group satisfaction should not be included in measures ofgroup performance. Most scholars do include it. It may be best to also include a measureof group interaction to supplement the self-report data.

Measuring group interaction processes or group work skills creates other issuesthough. For example, there are few accepted instruments to measure group work dimen-sions. One well-established instrument is the behavior observation system: SYMLOG- ASystem of Multiple-Level Observation of Groups (Polley, Hare, and Stone, 1988).SYMLOG can measure just a few types of group interactions. Additional groupworkfactors are beginning to be dimensionalized and empirically studied so additional instru-ments will need to be developed to measure them.

3. The type of task may impact group performance.If there is one factor with high consensus by scholars and supported by empirical

research it is that the group’s task influences the group’s performance. The task influ-ences the group’s interactions and its choice of strategies to accomplish the objective.The task also influences the group’s motivation to accomplish the objective (Guzzo,1986).

2.3.2 Homogeneity-Heterogeneity Group CompositionHeterogeneity of group membership is an increasingly popular way to bring to-

gether more skills, experiences, and perspective to a specific problem (Dumaine, 1994;Parker, 1994). Studies have demonstrated that heterogeneous groups are generally moreeffective than homogeneous groups (Hoffman, 1959; Hoffman, and Maier, 1961; Hoff-man, Harburg, and Maier, 1962; Triandes, Hall, and Ewen, 1965). “When group members

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have a variety of opinions, abilities, skills, and perspectives, the probability is increasedthat the group as a whole will possess the characteristics necessary for efficient groupperformance (Shaw, 1976 p232)”. Member heterogeneity enhances group problem-solving because more alternatives become available for consideration and a wider criticalbase is provided (Collins, and Guetzhow, 1964). The potential benefits of multiple per-spectives are frequently negated by internal group dynamics though (Wagner et al., 1984;Bettenhausen, 1991; Pinto et al., 1993; McGrath et al., 1994). Heterogeneous groupsmust overcome greater social demands than homogeneous groups (Kirchmeyer, and Co-hen, 1992). Heterogeneity may have the potential to improve performance but it alsogreatly increases the complexity of the group processes that must occur for the group torealize this potential (Steiner, 1972).

2.3.2.1 A Homogeneity - The Functional GroupA functional group is composed of “People who work together every day: same

office, same machine, same location or same process (Scholtes, 1995 p56).” We knowvery little about how effective natural work groups function (McGrath, 1991; Pavitt,1993; Bowen, 1995). Scholars have studied work groups extensively but typically underlaboratory conditions using simulated groups focused on simple problems (Pavitt, 1993).There are serious limitations in our knowledge of whether small group theories derivedfrom laboratory findings can be replicated with natural work groups in the field (McGrath,1991).

2.3.2.2 A Heterogeneity Group- The Cross-Functional GroupA cross-functional group is: “A group of people with a clear purpose representing

a variety of functions or disciplines in the organization whose combined efforts are neces-sary for achieving the group’s purpose. The group may be permanent or ad hoc and mayinclude vendors and customers as appropriate (Parker, 1994 p39).” The move to cross-functional groups is due to the need to bring a greater diversity of knowledge and skill to-gether at one time (Monczka, and Trent, 1993). And a recognition that many organiza-tional issues involve several functions (Parker, 1994).

The inherent divergent interests and multiple points of view of cross-functionalgroups make reaching agreement on any course of action difficult though (Gersick, andDavis-Sacks, 1990). Few studies have been done to understand the factors associatedwith cross-functional group performance. Several studies, though, have found that struc-turing group processes improves cross-functional group performance (Reukert et al.,1987; Pinto et al., 1993).

Cross-functional groups have become increasingly important for organizations tocoordinate lateral efforts between functions (Reukert et al., 1987; Pinto et al., 1993; Du-maine, 1994; VanAken and Kleiner, 1997). Increasing cross-functional group perform-ance is critical for long-term organizational viability (Zmund, and McLaughlin, 1989).

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2.4 TECHNICAL SUBSYSTEM

The technical subsystem includes more than just technology. It also includes therules, procedures and policies used by organizational members to transform inputs intooutputs (Pasmore, 1988). The most visible impact of the technical subsystem on the or-ganization is productivity since technology is often used to increase production speed andefficiency (Pasmore et al., 1982). Technical subsystem interventions also include less visi-ble changes in group processes, including implementation processes and decision- makingprocesses (with and without computer augmentation). The context of this dissertation isthe implementation process of a specified task. The task the experimental groups will en-gage in involves an implementation task. The independent variable of this study are deci-sion aid types which structure the group’s processes. Both implementation processes anddecision- making processes are technical subsystem interventions impact the social sub-system. In this dissertation it is hypothesized that these technical subsystem interventionswill enable groups of increasing complexity to optimize performance.

2.5 IMPLEMENTATION

Implementation processes provide groups with a technology or a systematic ap-proach to changing desired objectives into actual performance, similar to how equipmentchanges inputs into outputs. Research suggests the ability to remain effective over timedepends on strategic planning and implementation (Hrebiniak et al., 1984). For example,one 10-year study compared profits of organizations with formal planning and implemen-tation mechanisms to organizations that used only informal approaches. The study foundorganizations with formal mechanisms significantly outperformed the organizations em-ploying only informal mechanisms (Karger, 1973).

Strategic management is defined as the set of decisions and actions resulting in theformulation and implementation of strategies or means designed to achieve the objectivesof the organization (Steiss, 1985; David, 1987). Strategic management can be operation-alized through three phases of activity: (1) formulating the strategic plan based on cus-tomer requirements, competitive analysis and long-term vision; (2) deploying the plansthroughout the organization; (3) implementing activities or projects required to achievethe plan (Collins et al., 1993). Deployment can be considered an element the strategic im-plementation phase.

Strategic implementation is concerned with how to put a formulated strategy intoeffect; it is the process of carrying out the organization’s strategy which is typically for-mulated by someone else (Alexander, 1991). Implementation involves translating strategicgoals into annual performance objectives, deploying the objectives throughout the organi-zation, allocating resources, and motivating and aligning employees (David, 1987). Suc-cessful strategic implementation implies change for the organization. Many implementa-tion efforts fail. The reasons for failure can be either a poorly formulated strategy or apoorly executed implementation effort (Stonich, 1982; Slevin, and Pinto, 1987; Alexander,1991).

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Alexander (1991) summarizes the key reasons strategy implemenation efforts oftenfail as: (1) lack of a practical, yet theoretically-sound implementation model; (2) problemsthat occur during implementation (e.g., internal politics, competing priorities, resistance tochange); (3) poor policy formulation; and (4) not considering critical success factors whenimplementing change. Frequently cited critical success factors include: communication,controls, reward systems, goal setting, resource considerations, objectives, an implemen-tation plan, and organizational structure (Quinn, 1980; Stonich, 1982; Hrebiniak et al.,1984; Slevin et al., 1987; Hambrick, and Cannella, 1989; Beer et al., 1990; Alexander,1993; Kotter, 1995). The most frequently cited factors in strategic management modelsare: organizational structure; objectives, control, and human resources considerations, indescending order of inclusion (Alexander, 1993).

2.5.1 Implementation ModelsThe overwhelming focus of strategic management scholars has been on the plan-

ning content vs. the implementation of the plan (Stonich, 1982; Hrebiniak et al., 1984; Al-exander, 1985; Huff et al., 1987; Pavitt, 1993). Scholarly research on strategic imple-mentation effectiveness is sparse. This has created a gap in our level of knowledge aboutboth strategic planning and implementation since “a well conceived strategy is one that isimplementable (Hambrick et al., 1989 p 279).”

A number of scholars have proposed conceptual models for implementing strategy(Stonich, 1982; Hrebiniak et al., 1984; Galbraith, and Kazanjian, 1986). Most strategicmanagement textbooks cover implementation. Typically, the level of analysis chosen bythe author is the strategic business unit (SBU). SBU implementation models tend to bebroad and descriptive as shown in Figure 2.3 versus specific and prescriptive.

O r g a n i z a t i o nS t r u c t u r e

M a n a g e m e n t P r o c e s s e s

C u l t u r eH u m a nR e s o u r c e s

S t r a t e g i c O b j e c t i v e s A c h i e v e d

I m l e m e n t a t i o n

F o r m u l a t i o n

FIGURE 2.3

A Corporate Implementation Model (adapted from Stonich, 1982)

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Hrebiniak and Joyce (1984) provide one of the few models which link operationaland corporate stradegies (see Figure 2.4).

O p e r a t i n gO b j e c t i v e s

OperatingStructure

I n c e n t i v e s a n d

C o n t r o l s

F o r m u l a t i o n P r i m a r y S t r u c t u r e

I m p l e m e n t a t i o n E l e m e n t s

FIGURE 2.4

An Operational Implementation Model (Adapted from Hrebiniak and Joyce, 1984)

To increase implementation effectiveness, Hrebiniak and Joyce (1984) call for in-creased structure of the implementation process. Their thesis is more organizations wouldbe successful in their strategic activities if they continued the formalization process startedin strategic planning through unit level implementation. Although Hrebiniak and Joyce donot provide empirical data to support this assertion, other scholarly efforts have shown thepositive impact of structuring group processes (Maier et al., 1957; Lanzetta et al., 1960;Brilhart et al., 1964; Bayless, 1967; Kepner et al., 1968; Hall et al., 1970; Hollman et al.,1972; Delbecq et al., 1975; Pavitt, 1993; Pinto et al., 1993). Structuring group processesis discussed further in Section 2.6.2.

Three implementation models, Kotter (1995), Beer (1990), and Quinn (1980), aredistinct from the others in the literature. The key difference in these models is the visibleeffort to build commitment to the strategy prior to action. Kotter (1995) identifies thecritical elements for change efforts as: (1) establishing a sense of urgency; (2) creating apowerful coalition; (3) creating a vision; (4) empowering others to act on the vision; (5)planning for and creating short-term wins; (6) consolidating improvements to producemore change; and (7) institutionalizing the new approaches. Beer (1990) identifies thecritical elements for successful change efforts as: (1) mobilizing commitment throughjoint diagnosis of business problems; (2) developing a shared vision of how to organizeand manage competitiveness; (3) fostering consensus for the new vision; competence toenact it and cohesion to move it along; (4) integrating the change to all departments with

18

out pushing it from the top; (5) institutionalizing the change through policies, systems, andstructures; and (6) monitoring and adjusting strategies in response to problems in thechange process.

Quinn (1980) advocates using a series of incremental processes to build strategy.Momentum and commitment are created, along with the strategy implementation throughthe incremental processes. The result is implementation begins naturally. In the Quinnmodel, formulation and implementation are not separate sequential processes but inte-grated processes that happen simultaneously. Several elements of the incremental processare: (1) building awareness of the issue; (2) legitimizing new viewpoints; (3) partial tacti-cal deployment; (4) broadening political support; (5) overcoming opposition; (6) buildingorganizational structure flexibility; and (7) creating pockets of commitment (32).

Consolidating the models found in the literature into a single implementationmodel and connecting it to strategy formulation (Figure 2.5) provides a framework forreviewing the elements of implementation.

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r e w o r k r e q u i r e d

STRATEGIC MANAGEMENT

Establ ish vision,mission, pr inciples

Develop 3-5 yearplan

FORMULATION DEPLOYMENT IMPLEMENTATION

A s s e s s i n g &a d d r e s s i n g

i m p l e m e n t a t i o nb a r r i e r s

Develop annualobject ives and goals

O R

D e p l o y m e n tp r o c e s s

a n n u a l r e v i e w

P L A N

r e v i e wp r o g r e s s

s t a n d a r d i z e

p r o b l e ms o l v i n g

p r o c e s s e s

D O A C TS T U D Y

e x e c u t et h e a c t i o n

p l a n s

I m p l e m e n t a t i o n T e a m P r o c e s s e s

FIGURE 2.5Consolidated Implementation Model

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2.5.2 Implementation ElementsMost implementation models include: deployment processes, problem solving

processes, execution of action plans, and review or feedback mechanisms. Though notincluded in most implementation models, scholars advocate including methods for assess-ing and addressing implementation barriers and methods for standardizing performanceimprovements once achieved. Each of these implementation elements are discussed be-low.

2.5.2.1 Addressing Implementation BarriersMany of the factors that contribute to failed implementation relate to the social

aspects of the organization (Giles, 1991; Kono, 1992). Many problems arise not from thequality of the plan, but from the difficulty in implementing it. After an organizational di-rection has been determined in the strategy formulation phase, many managers leap to im-plementation and are surprised at all that is involved and the level of resistance they en-counter. Research supports the need to address the reasons behind the resistance or lackof commitment (Stevens et al., 1980; Bourgeois et al., 1984; Alexander, 1985; Guth et al.,1986; Giles, 1991; Alexander, 1993; Kotter, 1995). The empirical research is somewhatweak in its evaluation of specific techniques to overcome resistance. Participation, or in-volvement, is often cited as a means to overcome resistance and build commitment to astrategy. The empirical literature does not consistently support even this well-known tac-tic (Bettenhausen, 1991).

Determining which implementation tactic will enhance implementation effective-ness is a critical assessment that should be done as part of the implementation plan. Re-gardless of time pressures, importance, and change type, managers typically use only fourtypes of tactics to implement strategy: intervention, participation, persuasion, and edict(Nutt, 1986). Govindarajan (1988) found no single uniform or standardized strategy isappropriate. Selecting the appropriate implementation tactic requires considering severalfactors such as leader characteristics, culture, and task complexity. Many scholars havefound the fit between the SBU leader and the implementation strategy are important forimplementation success (Gupta, and Govindarajan, 1984; Govindarajan, 1988; Govinda-rajan, 1989). Participation tactics are only appropriate when management is interested inideas from the organization relevant to the change under consideration and the culturesupports participation (Lawrence, 1954). The complexity of the change influences the de-cision on who needs to be included in the design of the implementation plan and, conse-quently, the type of participation required. Also influencing the implementation tactic de-cision is whether the strategy is associated with reengineering or continuous improvement.The point is, the actual implementation tactic (intervention, participation, persuasion, oredict) is an important consideration to be made before deploying the strategy to the or-ganization.

2.5.2.2 Deployment ProcessThe deployment process is the transitional phase between formulation and imple-

mentation. During deployment, strategic objectives are negotiated with the implementers

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and the entire organization. The infrastructure to execute the strategy is installed and thealignment of individual rewards with strategy execution occurs. The positive relationshipbetween specific, challenging goals and individual performance is well documented(Mento, Steel, and Karren, 1987). The ‘strategy and structure’ relationship has been con-firmed repeatedly in research (Alexander, 1991). In a meta-analysis review communica-tion and organizational commitment were found to be correlated (Mathieu, and Zajac,1990). The only issue with mixed empirical support is the linking of individual rewardswith strategy execution. Several studies have linked strategy execution with individualperformance assessment which improves implementation (Guth et al., 1986; Longenecker,and Gioia, 1991). And even though many implementation scholars advocate aligning im-plementation activities with individual performance objectives (Hrebiniak et al., 1984;Hambrick et al., 1989; Zmund et al., 1989; Floyd, and Wooldridge, 1992), the long-termconsequences of this approach are still not understood (Kaplin, and Norton, 1996).

Additionally, research supports that end results or strategic objectives should bedeployed down through the organization, but that the tactics or means be left to the oper-ating unit (Guth et al., 1986; Marcus, 1988). The operating unit being impacted by thestrategy should be allowed to influence the means to achieve the strategy (Hamel et al.,1989; Beer et al., 1990).

2.5.2.3 Group ProcessesImplementation efforts include determining the appropriate means to achieve the

ends, executing actions associated with each mean, and monitoring results. Increasingly,organizations are using work groups to bring greater numbers of skills, experiences, andperspectives to implementation efforts (Hamel et al., 1989; Beer et al., 1990; Betten-hausen, 1991; Dumaine, 1994; Parker, 1994; Tompkins et al., 1995; Hackman et al.,1996).

Implementation is a multi-stage problem requiring problem solving, exectuing,studying or reviewing results and standardizing improvements. As groups complete eachof these components, they are also experiencing group processes as defined by McGrath(1994) of generating, choosing, negotiating, and executing. Most studies support thepremise that structuring group processes improves the quality of decisions (Pavitt, 1993).Support for formalization of activities is also found in the related project management lit-erature (Pinto, and Slevin, 1988). Computer augmented meeting studies have shown pro-cedures that simplify the handling of information and organize the group processes andprocedures that enable the group to deal with internal group dynamics, whether computeraided or not, improve group performance (DeSanctis et al., 1987). Anecdotal case studiessupport the formalization of the implementation methodology as a mechanism for increas-ing group and organizational performance (GOAL/QPC, 1989; Melum et al., 1995).There is also empirical evidence that formalized group problem solving processes help thegroup with its social demands. These demands include conflict resolution,

22

equality of participation, etc. (Reukert et al., 1987; Reukert et al., 1987; GOAL/QPC,1989; Pinto et al., 1993; Melum et al., 1995).

Total Quality Management has established a framework for structuring implemen-tation efforts supported by groups. The plan-do-study-act (PDSA) cycle categorizes thegeneral steps required in an implementation effort.

1. PlanPlanning is pervasive throughout the formulation and implementation process.

Implementation planning is critical in the initial phase of implementation. Studies haveshown groups tend to select an initial work strategy early, with little deliberation, andtypically follow that initial strategy until forced to change because of inadequate task per-formance (Gersick, 1988; Gersick, 1989). Intervening early in a project is likely to haveespecially high impact (McGrath, 1991). Implementation planning activities are a sourceof high leverage for increasing implementation effectiveness. Since implementation plan-ning involves decision making, improving group decision making relative to implementa-tion planning is the leverage area to initially improve implementation efforts.

2. DoImplementation involves the actual execution of programs or projects. Success-

fully executing specific project activities involves planning and controlling functions simi-lar to the overall implementation effort, but related to the specific supporting project ac-tivities. Proven project management concepts and techniques, such as budgeting, sched-uling, and contingency planning, are extensive and well documented in the literature(Archibald, 1976; Lewis, 1995).

3. StudyControl mechanisms involve determining or studying whether activities are

achieving desired results and whether new decisions must be made (Guzzo, 1986). Con-trol mechanisms are one of only two implementation elements appearing in all surveyedimplementation models (Alexander, 1991). Limited research has shown effectiveness inachieving goals is enhanced when control levels are high (McMahon, 1984). Also, Stevenset al.,(1980) determined strategy is better implemented when managers believe the processto implement the strategy will be reviewed. Anecdotal studies support control mecha-nisms that are implementation reviews which provide regular feedback that tests, validates,and modifies the cause and effect relationships assumed in the decision- making process(King, 1989; Collins et al., 1993; Melum et al., 1995; Kaplin et al., 1996).

4. ActInstitutionalization or standardization is the process by which all persons follow

the current standard. After a process has been improved it is critical for management to

23

ensure the means of achieving the improvement are institutionalized (Collins and Huge,1993). Although not included in implementation models, most scholars advocate the con-cept: improvement is inseparable from standardization (Nakamura, 1993); and standardi-zation is inseparable from strategy implementation (Beer et al., 1990; Kotter, 1995; Kaplinand Norton, 1996). Institutionalization, though included in Deming’s well-known PDSAcycle, is almost nonexistent in practice. This permits backsliding in the gains achieved fromthe implementation process (Collins and Huge, 1993). The specific activities associatedwith institutionalizing the improvements are documenting, training, and monitoring results.

2.6 SMALL GROUP DECISION MAKINGDecision making and problem solving are generally linked in the literature. Most

schemes of decision making processes are variations of Dewey’s (1910) reflective thinkingprocess which included the following steps: (1) identifying the nature of the problem; (2)analyzing the type of problem and its basic dimensions; (3) identifying alternative solu-tions; and (4) considering the consequences of alternative solutions and the final choice.Another decision making model is ‘intelligence (problem analysis), design (idea genera-tion) and choice (selection)’ developed by (Simon, 1977). McGrath, (1991) chooses torefer to his problem-solving scheme as modes of functioning. The modes are: the incep-tion and acceptance of a project (goal choice), the solution of technical issues throughproblem solving (mean choice), resolution of conflict (policy choice), and the execution ofperformance requirements (goal attainment). Both of these well known decision makingmodels are similar to Dewey’s original model. Many factors affect the decision-makingprocess (Katz and Kahn, 1978). Factors relevant to this dissertation are: group versusindividual decision making, the choice of the decision making process, and using decision-making aids.

2.6.1 Group versus Individual Decision MakingFor certain tasks, groups have consistently demonstrated higher quality decision

making than individuals for certain tasks (Hollman et al., 1972; Rowe et al., 1984). A 10-year study of decision making concluded groups perform better when tasks are conjunc-tive, multi-phase problems than do individuals working alone (Smith, 1989). “Groups aremore effective than individuals on tasks which require a variety of information, which canbe solved by adding individual contributions and which require a number of steps thatmust be correctly completed in a definite order; individuals are better on tasks that call forcentralized organization of parts (Shaw, 1976 p68).”

Many hypotheses explain the rationale for groups being better than individuals inconjunctive, multi-phase problem solving. For example, scholars hypothesize the sum ofseveral individual’s contributions is greater than a single individual’s contribution(Watson, 1928). Another hypothesis is members in a group can check each other for er-rors. This improves decision quality. Individuals do not have this ability by working inde-pendently (Shaw, 1932). Other scholars hypothesize groups just make available a greateramount of information (Shaw, 1976). These factors and others probably contrib

24

ute to why group performance exceeds individual performance. The degree to which eachcontributes to the decision-making exercise depends on additional factors such as task(Shaw, 1976). Implementation planning is a conjunctive, multi-phase type decision mak-ing task.

2.6.2 Decision Making ProcessesAs the external environment requires faster decisions, the decision-making process

will receive more attention and the actual decisions are likely to be viewed as‘projects’due to the interdependency of system elements (Huber, 1984). Improving thequality of decision making will continue to increase in importance. Most studies confirmthat structuring group decision processes enhances the quality of the decision (Maier etal., 1957; Lanzetta et al., 1960; Brilhart et al., 1964; Bayless, 1967; Kepner et al., 1968;Hall et al., 1970; Hollman et al., 1972; Delbecq et al., 1975; Pavitt, 1993; Pinto et al.,1993). Some studies have found unstructured processes enhance group performance(Hirokawa, 1982). An explanation for the inconsistent findings may be as long as criticaldecision making functions are accomplished, the decision-making process is of little con-sequence (Hirokawa, 1985). A study by Hirokawa (1985) identified the critical functions(which are very similar to Dewey’s reflective thinking steps) as:

1) The group understanding, thoroughly and accurately, the problem;2) The group identifying a realistic and acceptable alternatives;3) The group assessing, thoroughly and accurately the positive consequences associated with each alternative; and4) The group assessing thoroughly and accurately the negative consequences associated with each alternative choice.

Successful accomplishment of these factors resulted in higher decision quality.Results from the study also suggested that satisfaction of the first and last critical functionslisted above may be more important than the others relative to increasing the quality ofdecision (Hirokawa, 1985). The study indicates structuring group processes is importantonly to the extent that the critical factors are successfully accomplished but the specificprocedures to do this are of little consequence. The point is to have a decision makingprocess that includes the critical functions. Since most decision making processes arevariations of Dewey’s (1910) reflective thinking process, most decision making process doinclude the critical functions.

Group performance is impacted by processes losses which result from group orindividual behaviors which reduce group performance (Steiner, 1972). Examples of proc-ess losses which have been empirically studied include: being solution-minded vs. prob-lem-minded, reactive search behavior, focusing effect, and member dominance or inhibi-tion. Groups tend to solve the problem prior to really understanding it (Maier N. andHoffman, L., 1960). Studies have found that the amount of time spent working on theproblem versus solving it is related to the quality of the decision (Rotter, 1969). Groupstend to display reactive search behavior, reacting to the ideas of other group

25

members. Reactive search behavior leads the group to reach quick decisions before criti-cal dimensions of the problem have been considered (Maier et al., 1960). Reactive searchbehavior is characterized by short periods focused on the problem, frequent interruptions,tangential discussion and a high effort in maintaining relationships (Van de Ven, and Del-becq, 1971). Groups tend to pursue a single train of thought for long periods of time wellpast the point of effectiveness (Taylor, 1958). For example, groups tend to keep their ini-tial strategy until forced to change by inadequate task performance (Gersick, 1988; Ger-sick, 1989). Individual inhibitions in groups result in lower quality decisions (Collaros,and Anderson, 1969). The quality of decision making is reduced by dominating memberswho restrict full participation of others in discussion (Lanzetta et al., 1960; Chung, andFerris, 1971).

Another process loss area is conflict. Conflict resolution is a process loss allgroups must address (McGrath, 1991; Thomas, 197 ). Although constructive conflict canlead to higher quality decision making, Van de Ven (1974) found cohesion and interper-sonal relationships in small groups develop around areas of agreement and conflict leads topolarization and personal attacks. A study of conflict resolution methods on task per-formance found only confrontation or problem solving of the area in dispute always led toconstructive use of disagreements and perceived satisfaction by members involved (Burke,1970).

If left to their own devices, groups will often adopt processes exhibiting the char-acteristics described above. This impacts task performance (Hackman, and Kaplan, 1974).To reduce the sources of process loss, decision aids are frequently used by groups.

2.6.3 Decision Making AidsFor the group member, a meeting implies two sets of demands: those which stem

directly from the task of problem solving and those from building and maintaining inter-personal relations with other group members (Bales, 1954; Delbecq et al., 1975; DiSalvoet al., 1989). The more effort required by the group in meeting social needs, the less pro-portionate time and effort remaining for task problem solving and visa versa (Campbell,1952). The two demands are independent; solving one does not necessarily solve theother. Research has identified social issues groups must attend to including: attendanceproblems, distracting behaviors, deceit, egocentric behavior, interruptions, defensive atti-tudes, closed-mindedness, prejudice, and unprofessional behavior. These affect task per-formance (DiSalvo et al., 1989). DiSalvo et al. (1989) suggested two thirds of group in-teractions should deal with social demands and one-third should deal with task demands.Structured decision processes ensure critical functions of decision making are accom-plished and process losses associated with social demands are minimized. Decision aidsare tools or specific techniques to improve decision making that address specific areas ofprocess losses. Techniques exist which enable groups to effectively analyze problems,generate ideas, evaluate and select ideas, and resolve conflict (Van Gundy, 1981). Re-search has demonstrated that decision aids lead to more effective decision making thanintuitive processes (Goodman et al., 1986).

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One popular decision aid is the nominal group technique (NGT). In the NGT,people work in the presence of others but write ideas independently rather than talk aboutthem (Delbecq et al., 1975). The NGT assures that different processes for each phase ofcreativity are used, that participation is balanced, and that mathematical voting techniquesare used to aggregate the group’s judgment (Delbecq et al., 1975). Nominal groups havebeen found to be significantly superior to other work groups in generating relevant infor-mation to a problem (Taylor, 1958). This may be because the NGT process gathers in-formation using systems in which everyone volunteers information as part of the process.This has been shown to lead to better performance than when information is requestedoutside of a standardized process (Lanzetta et al., 1960). Groups using structured proc-esses tend to be more satisfied with their decisions (Nemiroff, Passmore, and Ford, 1976)and more committed to implement the decision (White, Dittriich, and Lang, 1980). Peoplewith a preference for structure were found to work best with structure, while people withlow structure preferences were able to work equally well with high or low structure(Hirokawa, Ice, and Cook, 1988). Providing structure to all groups may be an appropri-ate on-going task since people who desire structure need it to optimize their performance.Those who don’t desire structure are not impacted by it either way.

2.7 GROUP DECISION SUPPORT SYSTEMSRecently, scholars have become interested in using computers to improve group

performance. Group decision support systems (GDSS) are computer-based systems thatcombine communication, computing, and decision support technologies to improve groupdecision making and task performance (DeSanctis et al., 1987). GDSSs aim to increasegroup decision making efficiency and effectiveness by altering the structure of interper-sonal communication by increasing information accessibility and processing, structuringgroup processes, and improving communication between group members (DeSanctis etal., 1987; McGrath et al., 1994; Poole, and Holmes, 1995). GDSSs are technologies thatenable computer supported cooperative work (CSCW) to occur. GDSSs enhance deci-sion making by allowing everyone to input and organize ideas and to choose among theideas in a rational manner (Pollock, and Kanachowski, 1993). GDSSs differ from groupcommunication support systems (GCSS). GCSSs focus on internal work group commu-nications (eg., email, video conferencing, etc.) without the problem solving element(McGrath and Hollingshead, 1994). Similar to models from small group research, aframework for analyzing the impacts of GDSS on group processes and outcomes hasmany factors that affect the flow of work in groups including input variables, operatingconditions, process variables and outcome variables (McGrath et al., 1994). A commonframework in GDSS literature is presented in Figure 2.6 and the elements of the model arediscussed below.

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o p e r a t i n g c o n d i t i o n s

p r o c e s sv a r i a b l e s

E x a m p le s :

d e g r e e o fa n o n y m i t y

c h a n n e l s o f c o m m u n i c a t i o n

p r o c e s ss t r u c t u r e

E x a m p le s :

e q u a l i t y o f p a r t i c i p a t i o n

t a s k - f o c u s e dc o m m u n i c a t i o n

d e c i s i o nm a k i n g t i m e

o u t c o m ev a r i a b l e s

i n p u t v a r i a b l e s

E x a m p le s :

T a s kP e r f o r m a n c e :

d e c i s i o n q u a l i t y

M e m b e rS a t i s f a c t i o n :

p r o c e s sg r o u p

o u t c o m e s

d e g r e e o f c o n s e n s u s

t a s kc h a r a c t e r i s t i c s

m e e t i n gc h a r a c t e r i s t i c s

g r o u p &m e m b e r

a t t r i b u t e s

FIGURE 2.6Consolidated Framework for GDSS Research (Hollingshead and McGrath, (1986); De-

Sanctis et al., (1987); McLeod, (1992))

2.7.1 Input VariablesInput variables are contextual factors that have an immediate impact on the group.

Little attention to group or member characteristics is found in the empirical GDSS litera-ture (McGrath and Hollingshead, 1994). A summary of the few studies with relevance tothis dissertation are summarized in this section.

2.7.1.1 Group and Member AttributesStudies have shown prior computer experience will not influence performance on

the new system until the user has acquired a level of experience (Egan, and Gomez, 1985;Egan, 1987). Age has been shown to impact performance when learning a new computertask (Gomez, Egan, and Bowers, 1986; Egan, 1988).

2.7.1.1 Task CharacteristicsA critical factor in evaluating group performance, which is seldom adequately re-

flected in the GDSS literature, is the group’s task (McGrath and Hollingshead, 1994).Task attributes determine information requirements and influence communication practices(Poole, 1978). A group task is characterized by its goals and constraints. Many categori-zation schemes have been developed. McGrath’s (1984) scheme is one of the most widelycited and is shown in Figure 2.7. In McGrath’s scheme, tasks are categorized accordingto what the group must accomplish during its meeting. GDSS research has concentratedon generating ideas and evaluating ideas after identifying a problem (Gallupe and DeSanc-tis, 1988). Even though laboratory and field experiments have given little regard to thetask and whether the technology employed in the experiment would enhance problemsolving, theoretically this interaction is expected to impact process and outcomes variables(McGrath and Hollingshead, 1994).

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3

1

8

GENERATE

executingperformancetasks

resolvingconflictsof viewpoints

resolvingconflicts ofinterest

resolving conflictsof power

solving problemswith correctanswers

deciding issues withoutright answers

generatingplans

generating ideas

7

2

4

5 6

NEGOTIATE

CHOOSE

EXECUTE

FIGURE 2.7Scheme of Task Type (Adapted from McGrath, 1984)

2.7.1.3 Meeting CharacteristicsThe setting or the environment is designed to not only support meetings, but also

to support relationships between individuals and group interactions (Schrage, 1990). Themost critical factors to GDSS settings are group size and proximity (DeSanctis et al.,1987). Larger groups have more difficulty reaching consensus (Hoffman, 1979) and expe-rience less equality of participation (Hare, Borgatta, and Bales, 1967). Group size hasbeen dichotomized in the GDSS literature as relatively small (having 7 or fewer members)or relatively large (above twelve). Member proximity is a factor for setting design.Communication exchange patterns are different between remote versus face-to-facegroups. Dispersed groups, using computer-mediated communication, participate moreequally (McGrath, 1994; McLeod, 1992; Pinsonneault and Kramer, 1990). Research hasfound that groups also reach final decisions furthest from the initial preferences than face-to-face groups (Siegel, Dubrovsky, Kiesler, and McGuire, 1986). Four environmentalsettings based on difference in group size and dispersion of group members are providedby DeSanctis and Gallupe (1987):

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1. Decision rooms (smaller group, face-to-face): The electronic equivalent of the tradi-tional meeting. Each member is able to send electronic messages to each other and thepublic screen.2. Legislative sessions (larger group, face-to-face): Only a facilitator is permitted to sendinformation to the public screen and member-to-member communication is typically lim-ited.3. Local area decision network (smaller group, dispersed members): Project groups, salesgroups and committees benefit from this setting. Meeting formats are flexible to meetmember needs (e.g., asynchronous).4. Computer-mediated conferences (larger group, dispersed members): These are typi-cally not decision making bodies today. Meetings are more often discussions spread overseveral days and do not require simultaneous on-line participation. Another characteristicof the meeting is the meeting process.

Meetings may also be chauffeured, supported or interactive, or any combination(Nunamaker, Dennis, Valacich, Vogel, and George, 1991).• chauffeured meetings - traditional verbal communication predominates, one person en

ters group information to the system and the public screen provides a groupmemory

• supported meetings - both verbal and electronic communication exist, all members can enter comments to the system, and the public screen provides a group memory.

• interactive meetings - electronic communication predominates, all group members can enter data into the system and all comments on the public screen are accessible via workstations.

There is little research that evaluates the type of electronic meeting process withspecific task characteristics (McGrath and Hollingshead, 1994). Most research withGDSS as an independent variable typically use the levels: GDSS and no GDSS versusevaluating the features of GDSS.

2.7.2 Operating ConditionsAspects of meeting processes improve outcomes (process gains) while others im-

pair outcomes (process losses) relative to individuals working by themselves or in othergroups (Hill, 1982). Group performance is contingent on balancing process losses withprocess gains (Connolly, Jessup, and Valacich, 1990). A goal of GDSSs is to reduceprocess losses associated with disorderly activity, member domination, social pressure,and inhabitation of expression (DeSanctis et al., 1987; Watson, DeSanctis, and Poole,1988). GDSSs imporves group interaction processes in three unique ways: by providingan additional channel of communication, by allowing anonymous communication, and byproviding structuring to the group processes (Nunamaker et al., 1991).

One of the unique features of GDSS communication is the additional channel ofcommunication available to all participants, which allows parallel communication ex-changes (Zigurs, Poole, and DeSanctis, 1991). The additional channel enables more equal

30

participation of members. Research has demonstrated that for some tasks, more equalparticipation results in higher quality decision making (Van Gundy,1981; Pavitt, 1993).GDSSs may increase participation by otherwise nonparticipating members (Sproull et al.,1986). The additional communication channel is provided in most local, small groupGDSSs through individual workstations and through a public display screen. The elec-tronic communication channel may also provide the anonymity feature. Workstations canallow members to input to each other or the public screen anonymously.

Nearly all scholars agree the structuring provided by GDSSs has a significant affecton group process and task performance (McLeod, 1992). Research has confirmed workgroups will adopt processes that are suboptimal and sometimes dysfunctional to task ac-complishment (Hackman et al., 1974). GDSSs provide structure to group processesthrough embedding tools for structuring generation of ideas, setting agendas, attainingconsensus, etc., and providing a system of communication (Hollingshead, and McGrath,1986). Structuring the communication process improves organizational decision makingbecause it turns ill-defined problems into semi-structured ones that are relatively easier tosolve (Turoff, and Hiltz, 1982). Rules and procedures provided by GDSS structure im-proves performance along several dimensions such as information sharing (Stasser, Taylor,and Hanna, 1989), task quality (Delbecq et al., 1975), and conflict resolution (Hall et al.,1970).

2.7.3 Process and Outcome VariablesThe majority of GDSS research has been laboratory experiments or case studies

without comparison groups. Very few studies have been field experiments (McGrath andHollingshead, 1994). GDSS evaluation requires the assessment of GDSS impacts to de-pendent variables or group interaction, task performance, and member satisfaction(McGrath and Hollingshead, 1994). Process variables represent group interaction and canbe regarded as either outcome variables or as antecedents of other outcome variables(Hollingshead et al., 1986). The most commonly studied dependent variables in GDSSresearch are equality of participation, degree of focus on the task, degree of consensus,the time to complete the task, satisfaction (with decision, process, or group), and decisionquality (McLeod, 1992). The two most used decision quality measurements are the de-gree of consensus achieved and how close the group’s solution is relative to an expert’ssolution.

Several major reviews of the empirical literature have been published. The resultsof these reviews relative to dependent variables are shown in Table 2.2. As can be seenfrom the table, the findings from these reveiws are inconsistent. The probably causes forvariation in the findings is methodological differences, time frame, and interpretation ofthe data.

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TABLE 2.2Summary of GDSSs’ Impact on Dependent Variables

How GDSSs Impact Common Dependent VariablesMcGrath

and Hollingshead(1994)

McLeod

(1992)

Pinsonneault andKraemer(1990)

dependent variables literature review meta-analysis literature reviewequality of participation increases increases increasesdegree of task-focus mixed increases increasesdecision making time increases increases decreasesdegree of consensus mixed decreases increasesdecision quality mixed increases increasessatisfaction-process mixed mixed increasessatisfaction-outcome mixed not evaluated increasessatisfaction-group mixed not evaluated not evaluated

Summaries of several GDSS experiments that are often cited are shown in Appen-dix A, page 81.

2.8 SUMMARYSociotechnical research has demonstrated that jointly optimizing the social and

technical subsystems leads to higher overall performance. Organizations are changingtheir social subsystem to include using groups to bring more perspectives to bear onproblems. The increasing complexity of groups often negates the benefits of organizing ingroups due the difficulty in gaining consensus and the process losses experienced bygroups.

Consistent with sociotechnical theory, a response to organizing by groups and theincreasing complexity being experienced by groups is modification of the technical sub-system to jointly optimize both the social and technical subsystems. The technical sub-system includes both equipment and procedures. Since groups are often formed toachieve performance objectives, aiding the group in its decision making processes con-cerning tactics to be employed during implementation is relevant today. Structured deci-sion making processes with and without computer augmentation have demonstrated apositive impact on work group performance. However, the majority of the research hasbeen in the form of laboratory tests involving ad hoc groups. Very little research has beenconducted using natural work groups. This dissertation fills a void in the literature bytesting structured decision making processes with and without computer augmentationduring implementation planning first in the laboratory and with confirmation testing in thefield.

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Chapter Three: Methodology

This research studies the impact of decision aids on group performance. Decisionaids may enable work groups to better assess and execute the task required to accomplishobjectives. The research model for this experiment is shown in Figure 3.1. The experi-ment includes a laboratory study and a field study.

W o r k G r o u p P e r f o r m a n c e

T a s k P e r f o r m a n c e

dec is ion qua l i t y(cos t , de fec t ra te , t ime)

Par t i c i pan t Eva lua t i on

leve l o f ag reemen t

d i scuss ion qua l i t y

in te rac t ion

ro le eva lua t ion ( f i e ld ) Dec i s i on A id Type

con t ro l

f o rma l i zed p rocedu res( labora to ry )

fac i l i t a ted

fac i l i t a ted and techno logy

W o r k G r o u p T y p e

Labo ra to ry c ross - func t i ona l

s t uden t t eams

F ie ldc ross - func t i ona l

na tu ra l wo rk g roups

T e c h n i c a l S u b s y s t e m

Soc ia l Subsys tem

Equality of Participation

Task-focused Communication(laboratory)

Number of Ideas Brainstormed(laboratory)

Idea Evaluation (field)

W o r k G r o u p P r o c e s s e s

FIGURE 3.1Research Model

3.1 SubjectsThe subjects for the laboratory study were groups of seniors and graduate students

that had process management training and volunteered for the experiment. The trainingprovided an overview of process management and focused on the nominal group tech-nique. The process management training was received by the students in one of two ways.Students were given training by the researcher during two of the students’ normal classperiods, or the student had the training during other course work or outside classroomtraining. During the class training students learned about process management methodol-ogy and decision making tools (nominal group technique and multi-criteria decision mak-ing). Several classes received this training and then a request for volunteers was an-nounced.

The student groups were composed of three members. The twenty groups formeda between subjects study (each group completed one condition of the experiment). To

33

simulate natural cross functional groups, each member role played one of the followingroles: quality control manager, manufacturing manager, or purchasing manager. Eachstudent was given an objective to achieve corresponding to his or her role. The studentthat role played the best (as evaluated by the researcher) was given an additional $10.

The subjects for the field study were natural work groups, groups which exist forreasons beyond this experiment. Six organizations provided groups each for the fieldstudy. The twelve groups participated in a within subjects study (each group completedeach condition). How well the group members knew each other was determined prior tothe study through a questionnaire.

3.2 Independent VariablesThe independent variable in the experiment is decision aids. In the laboratory

study, four conditions were studied: control, formalized manual procedures, facilitated,and facilitated and technology.

In the field experiment three conditions were studied versus four conditions in thelaboratory. The field experiment required each group to complete all conditions so re-ducing the time required to complete the experiment was important. Since the formalizedprocedures condition was not found to be significant relative to any dependent variables inthe laboratory study and because formalized procedures is the least likely condition foundin the field, the formalized procedures condition was dropped for the field study. Thethree conditions studied in the field were: control, facilitated, and facilitated and technol-ogy.

3.3 Laboratory Facility and EquipmentThe setting for both the laboratory and the field study is a conference room. In the

laboratory study there were four networked computers (three for the participants and onefor the facilitator). An overhead displayed the facilitator’s and group’s output in front ofthe room. An observation room is located at the rear of the room. The observation roomhas a one-way mirror which enabled unobtrusive observation and video recording. Allmeetings were video taped.

In the field study, the conference room used in the study was at the site of thegroup participating. Instead of an overhead, a chart pad was used by the facilitator. Allfield sessions were video taped but the camera was not hidden as in the laboratory experi-ment. The following sections describe additional equipment required by each condition.

3.3.1 Control ConditionThe control groups were given only:• the task’s objective;• cost, quality, and specification information; and• templates for calculating cost, quality, efficiency, productivity and production time; and and effectiveness.

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Control groups were provided decision aids that would normally be available inconventional meetings (e.g. pencils and paper), problem specifications, performance tem-plates. The control groups were allowed to use a free discussion format to complete thetask.

3.3.2 Formalized Procedures ConditionOnly the laboratory study included this condition. In addition to the information

provided to the control groups, the formalized procedures groups were provided with astructured problem guide to aid them in completing the task. The structured problemsolving approach follows process management techniques used to improve work proc-esses. The process management steps the groups used were:

1. Agree on the task objectives2. Identify critical design considerations (using nominal group techniques)3. Build two alternative models4. Select the model to build (using multi-criteria decision making)

3.3.3 Facilitated ConditionThe facilitated groups had all the tools the formalized procedures groups had and a

facilitator to guide the group through the problem solving process and display the group’swork and decisions. The facilitator led the nominal group process. In the laboratory, aLCD panel was used to display the group’s work. In the field, a chart pad was used todisplay the group’s work.

3.3.4 Facilitated and Technology ConditionThe facilitated and technology groups have everything the facilitated groups had

and the additional feature of inputting to the meeting in an anonymous method. In thelaboratory study, Group Decision Support System (GDSS) technology was used to pro-vide the anonymous feature. GDSS consisted of personal workstations which were net-worked and controlled individually by group members. The GDSS software used in thisexperiment was GroupWare software developed by the University of Arizona and nowsold through the Ventana Corporation. GroupWare provides each group member with acomputer terminal as an additional channel of communication, beyond voice, and permitsindividual members to communicate in the meeting anonymously (Jarvenpaa et al., 1988;Ho et al., 1991). GroupWare is similar to SAMM (developed at the University of Minne-sota) which was developed to promote participative, democratic decision making in 3 to16 person groups (Poole et al., 1995) and incorporates the Dewey (1910) reflective-thinking problem solving process (Watson et al., 1988).

In the field study, Option Finder technology was used to provide the anonymousfeature. Option Finder technology consisted of individual keypads that input, through ra-dio frequency, data to a facilitator’s computer.

35

3.4 Experimental TaskFrom McGrath’s (1994) task classification schema, the experimental task repre-

sents generation, choosing, negotiating and executing work group activities, or multi-phase decision making. The task is an adaptation of the “Moon Mobile” exercise whichrequires participants to design, build and evaluate a product built with construction mate-rials. This task was designed to illustrate continuous improvement principles. Continuousimprovement efforts seek sustainable, on-going improvement unlike a reengineering effortwhich seeks a discontinuous or step-change in performance (Kleiner and Hertweck, 1996).In the laboratory, each student work group was asked to design and build two prototypemoon mobiles. The work group was then asked to evaluate the two prototypes and selectthe model best meeting specifications, and operating parameters.

In the field study, each work group built three models (to support the within sub-jects experimental design). To address maturation issues, each model was built using adifferent material (T-tinker toys, L-legos and M-miniquadro) with different cost and qual-ity data associated with each material. Also the different materials were randomly as-signed to the condition. The order of presentation of the material was controlled. Allwork groups started with the control condition. Since the other two conditions includedformalized problem solving, if either of these were the first condition the control could becontaminated. The presentation of conditions 2 and 3 were alternated. A summary ofpresentation order for the materials and conditions is shown in Table 3.1 for the partici-pating field groups.

TABLE 3.1Presentation Order

Order of PresentationK1 T1TinkerToy T2Lego T3MiniquadroF3 T1Lego T3Miniquadro T2TinkerToyV1 T1Miniquadro T2TinkerToy T3LegoV2 T1TinkerToy T3Lego T2MiniquadroS1 T1Lego T2Miniquadro T3TinkerToyS2 T1Miniquadro T3TinkerToy T2LegoF1 T1TinkerToy T2Lego T3MiniquadroF2 T1Lego T3Miniquadro T2TinkerToyG1 T1Miniquadro T2TinkerToy T3LegoG2 T1TinkerToy T3Lego T2MiniquadroB1 T1Lego T2Miniquadro T3TinkerToyB2 T1Miniquadro T3TinkerToy T2Lego

3.5 Pre-Experiment Training (Laboratory Work Groups)Before the laboratory study, student participants attended two fifty minute class

lectures on process management problem solving methodology. The training occurred asa normal part of Fall 1996 course in Management Systems Engineering. Following thetwo classes students were asked to volunteer for the experiment. Sixty students volun-teered.

36

Before beginning the laboratory study, each participant was given a process man-agement knowledge test which focused on the Nominal Group Technique and Multi-criteria Decision Making. The group scores were calculated to determine if prior knowl-edge about process management had an effect on the outcome of the experiment.

3.6 Pilot StudyEach of the conditions was piloted prior to studies in the laboratory or the field.

The primary objective of each pilot was to fine tune the data ranges given to the groupsconcerning cost and quality. After each test the data range was tuned further and testedagain.

3.7 Measures of Dependent VariablesThe effects of conditions were assessed for both work group performance vari-

ables and work group process variables. Group performance variables included both taskperformance and participant evaluations. Task performance measured cost, defect rateand time. Participant evaluation included level of agreement, discussion quality, andgroup interaction. The work group processes variables were: equality of participation,task-focused communication, and number of ideas brainstormed.

3.7.1 Task PerformanceThe first measure of task performance is effectiveness. The moon mobile must

meet two specifications (roll twelve inches and contain a power source). Task perform-ance incorporates a number of important variables-not all of which are positively corre-lated with one another (McGrath, 1994). In this research task performance variables werecost, defect rate, and total task time. In both the laboratory experiment and the field ex-periment, the moon mobiles were rank ordered according to each metric of task perform-ance (e.g., the lowest costing moon mobile will be ranked 1, the second lowest cost willbe ranked 2, etc.). An overall measure of decision quality was made which combined therank order metrics for cost, defect rate, and time for each model.

3.7.2 Equality of Participation (laboratory and field) and Task-focused Communication(laboratory)

Equality of participation and task oriented communication are directly observablegroup interaction variables. The sessions were video taped and the researcher evaluatedthe tapes after the study to determine levels of equality of participation and task-focusedcommunication. To do this, SYMLOG- A System of Multiple-Level Observation ofGroups (Polley et al., 1988) was used which provides a quantitative measure of thesegroup interactions.

The SYMLOG measure of Dominant-Submissive has been defined as a measure ofparticipation (Bales, and Cohen, 1979). McLeod and Liker (1992), used SYMLOG ina GDSS experiment to measure equality of participation. In their experiment, to the de-gree group members were active, talkative, and forceful in their style, observers were

37

trained to rate them as dominant (McLeod, and Liker, 1992). The laboratory study usedthe McLeod and Liker (1992) evaluation of participation. When some individuals domi-nate most of a meeting, while others are withdrawn and submissive, the meeting will beevaluated as low participation equality. Two measures from each group’s scores on theequality of participation were derived: dominance distance and dominance dispersion.Dominance distance is defined as the absolute difference in the dominance scores com-puted between the most and least dominate group members. Dominance dispersion is thestandard deviation among the dominance scores (McLeod et al., 1992). Task-focusedcommunication was measured using SYMLOG’s Task-Socioemotional dimension. Thisdimension assessed the degree to which the group is focused on the task vs. socioemo-tional behaviors such as joking and making light conversation not related to the task(McLeod et al., 1992).

Based upon inconclusive equality of participation findings from the SYMLOG datain the laboratory study, the system of multiple-level observation of groups (SYMLOG)was abandoned for the field study. In the field study, equality of participation was meas-ured as the number of comments spoken by each individual. The number of commentsspoken was normalized by meeting time.

3.7.3 Participant EvaluationMember satisfaction was operationalized to include level of agreement, discussion

quality, and group interaction. Participants were asked to evaluate their agreement withthe outcome, the quality of discussion and their perception of group interactions duringthe study.

Knutson, and Holdridge, (1975) developed a six item questionnaire, The PerceivedConsensus Test, to measure the level of agreement. This questionnaire was given tomembers following the study as part of an overall participant evaluation. To measure per-ceived discussion quality and group interaction, a Likert like survey was developed. Theitems for the survey were taken from an instrument used by Gouran, Brown, and Henry(1978) for measuring behavioral correlates of perceptions of quality in decision-makingdiscussion.

The questionnaire for the field study was modified from what was given laboratoryparticipants. Based upon Cronbach’s Alpha data from the laboratory study, items weredeleted which had reduced reliability or which did not affect reliability. Consequently, ashorter questionnaire was given to the field study participants since they were asked tocomplete the questionnaire three times, once for each condition.

The field groups also evaluated through voting how well group roles were per-formed. The level of consensus the group achieved in evaluating the roles with and with-out anonymity was of interest in this study.

38

3.7.4 Number of Brainstorming Ideas (laboratory)The number of ideas generated by the group was measured during the laboratory

study. The control group was not required to brainstorm ideas. The comparison wasmade between the formalized procedures condition where work groups were asked tobrainstorm ideas and the facilitated treatments where work groups were guided throughbrainstorming ideas. There wasn’t a formalized procedures condition in the field so thismetric was not measured in the field study.

3.7.5 Idea Selection (field)The work groups were asked to brainstorm ideas on how to achieve cost, quality,

time and specifications prior to building in two of the conditions. Ideas were selected asimportant to the design through voting. The level of consensus that the groups achievedin voting was of interest in this study.

3.7.6 Participant Qualitative DataFollowing the field study, groups were asked two questions about their experi-

ences during the experiment:A. In which condition do you believe the group had the best participation? Why?B. In which condition do you believe produced the most competitive moon mobile? Why?

3.8 Experimental ProcedureThe experimental procedures were:

1. Individuals completed consent forms, read background information, and determined their level of association with group members by completing a questionnaire (see Appendix B, page 85).

2. The laboratory groups read through their role play descriptions and were given a short quiz on process management techniques (see Appendix B, page 85).

3. After completing the control condition, the field groups selected individuals to play group roles (builder, timer, designer).

4. Groups designed, built, and evaluated their moon mobiles following the prescribedcondition guidelines (see Appendix C, page 99). Laboratory groups participated inonly one condition. Field groups participated in all three conditions.

5. Individuals completed the post-experiment questionnaire (see Appendix D, page 116).6. Field groups evaluated how well the group roles were performed.7. At the conclusion of all three conditions, field groups were asked the qualitative

questions: which condition do you believe the group had the best participation? and which condition do you believe produced the most competitive model?

3.9 Data Analysis3.9.1 Hypotheses Testing

One-way ANALYSIS OF VARIANCE (ANOVA) was used to test for significantoverall effects of the decision making aids on each of the dependent measures. Fisher pro

39

cedures were used to identify where differences existed following a significant ANOVAresult. All raw data is provided in Appendix E., page 123.

3.9.2 Post Hoc TestingCorrelation and regression analysis were used to identify significant relationships

between process variables and work group characteristics and the dependent variable taskperformance. ANOVA testing was performed to determine whether a difference existedbetween laboratory and field findings for group performance variables.

40

Chapter Four: Results and Discussion

This chapter is organized by hypothesis. For each of the following hypotheses,results, discussion, and post hoc results and discussion are presented. The last section inthis chapter summarizes the contribution this dissertation has made to the body of knowl-edge.

Hypothesis One - H1: Facilitated groups have higher task performance than groupsworking without facilitation.

Hypothesis Two - H1: Facilitated groups using technology have the highest equality ofparticipation.

Hypothesis Three - H0: No significant difference is seen in participant evaluations acrossconditions.

Hypothesis Four - Groups using any type of decision aid have higher task-focused com-munication than control groups.

Hypothesis Five - Facilitated groups generate more ideas during brainstorming.

Hypothesis Six - Groups given anonymity demonstrate less consensus in their evaluations.

4.1 Hypothesis One - Task PerformanceH1: Facilitated groups have higher task performance than groups working

without facilitation. Hypothesis one was tested in both the laboratory and the field. Taskperformance was measured as product cost, product defect rate, and the length of timerequired to complete the task. Additionally, each model was ranked from best to worstfor the three dependent variables. The rank sum for each model was calculated as a meas-ure of decision quality. Hypothesis one was not supported at the 0.05 level of significancefor any task performance dependent variable (see Table 4.1). ANOVA summary tablesare shown in Appendix F, page 183.

41

TABLE 4.1Summary of Hypothesis One Resultstask performance experiment F test statistic p value

cost laboratory F(3,19) = 0.74 0.543defect rate laboratory F(3,19) = 0.35 0.789

time laboratory F(3,19) = 1.22 0.334decision quality laboratory F(3,19) = 2.04 0.149

cost field F(2,36) = 0.08 0.926defect rate field F(2,36) = 0.31 0.739

time field F(2,36) = 1.36 0.271decision quality field F(2,36) = 2.46 0.101

Task performance means for the laboratory experiment are shown in Table 4.2.

TABLE 4.2Laboratory Task Performance Means by Condition

Condition

task performance controlstructuredprocedures facilitated

facilitatedwith

technologycost-$MM 17.4 30.8 31.2 22defect rate-% 1.09 .77 1.1 .81time-minutes 67 73.4 75 77.4decision quality-rank sum 31.3 30.0 41.4 31.4

Task performance means for the field experiment are shown in Table 4.3.

TABLE 4.3Field Task Performance Means by Condition

Condition

task performance control facilitatedfacilitated with

technologycost-$MM 45.33 45.75 48.75defect rate-% 1.71 1.71 2.11time-minutes 18.33 21.33 25.50decision quality-rank sum 50.67 56.63 65.88

Structure as provided through decision aids did not significantly improve taskperformance. Most prior research has concluded that structure improves performance.One possible explanation for why this study did not find a causal relationship between

42

structure is the nature of the task and comparing findings from differing tasks may not beappropriate. Group interaction and performance is greatly affected by the type and diffi-culty of the tasks that the group is performing (McGrath and Hollingshead, 1994). UsingMcGrath’s (1884) task-type scheme, team processes are characterized as involving gen-eration, choosing, negotiating and executing. The task in most research involves only oneof McGrath’s team processes. Few studies have included multiple team processes(McLeod, 1992). Experiments rarely study work groups completing a complete task(Guzzo, 1986). The task in this study was different from most prior research since it re-quired the work groups to generate, choose, negotiate, and execute. Consequently, oneexplanation about the differences found in this experiment and in prior research is the dif-ference in task types between this study and prior studies.

4.2 Hypothesis Two - Equality of ParticipationH1: Facilitated groups using technology have the highest equality of partici-

pation. Hypothesis two was tested in the laboratory and the field. In the laboratory ex-periment, two measures using the SYMLOG observation system were used to evaluateequality of participation. First, dominance distance, was defined as the absolute differencebetween the most and least dominant group members during the experiment. The secondmeasure was dominance dispersion, defined as the standard deviation among the domi-nance scores of the group members. In the laboratory experiment no statistical differencewas found between the conditions at α = 0.05 (see Table 4.4). ANOVA summary tablesare shown in Appendix F, page 183.

TABLE 4.4Laboratory Findings for Equality of Participation

equality of participation F test statistic p valuedominance distance F(3,19) = 0.58 p =0.639

dominance dispersion F(3,19) = 0.659 p =0.659

Prior research has demonstrated structure provided with facilitation or technologyincreases equality of participation among group members. In the laboratory experiment,no difference was found in equality of participation across conditions. There are twopossible explanations for this finding. First, the data provided by the SYMLOG observa-tion method may be too course to identify differences. Second, the inexperience of theresearcher in using the SYMLOG observation method may have decreased the reliabilityof the measurement system. In the field study, SYMLOG observation measurements werereplaced with frequency of comments for equality of participation comparisons. Thenumber of times each person spoke was recorded. Dominance distance was the measurebetween the most and least frequent speaking group members. Dominance dispersion wasthe standard deviation among the frequency of comment scores for all group members.Unlike the findings in the laboratory, the findings from the field experiment were similar toprior research findings. At α = 0.05 the hypothesis was supported for the dominance dis-tance variable, F(2,35) = 9.66, p =0.0001 (see Table 4.5). At a significance level of 0.05the hypothesis was not supported for the dominance dispersion variable (see Table 4.6).

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TABLE 4.5ANOVA Summary Table for Dominance Distance as a Function of Condition - Field

Source DF SS MSF test

statistic p valuecondition 2 11.49 5.745 9.66 0.0001

error 33 19.624 .595Total 35 31.114

TABLE 4.6ANOVA Summary Table for Dominance Dispersion as a Function of Condition - Field

Source DF SS MSF test

statistic p valuecondition 2 560.7 280.3 2.91 0.069

error 33 3182.1 96.4Total 35 3742.8

Fisher’s procedure for multiple comparison testing (with an individual error rate at0.05) indicated the control condition had lower equality of participation than the facilitatedcondition or the facilitated with technology condition (see Figure 4.1). The differencebetween the facilitated condition and the facilitated with technology condition was notsignificant.

0

0.5

1

1.5

2

2.5

control facilitated facilitated w ithtechnology

condition

do

min

ance

dis

tan

ce m

ean

s

A

BB

FIGURE 4.1Dominance Distance Means as a Function of Condition

44

The means for equality of participation indices, dominance distance and dominancedispersion, for the laboratory and the field experiments are shown by condition in Table4.7 (the structured manual condition was tested only in the laboratory).

TABLE 4.7Equality of Participation Means by Condition

Condition

equality of participation control

structuredmanual

procedures facilitated

facilitatedwith

technologylaboratory - SYMLOGdominance distance-range 53.4 42.6 43.0 29.0dominance dispersion-σ 29.0 22.2 24.2 15.8field - comment frequencydominance distance-range 2.32. not measured

in field1.19 1.06

dominance dispersion-σ 5.64 not measuredin field

4.36 2.21

4.2.1 Number of CommentsPost hoc analysis identified a positive correlation between dominance distance, the

measure of equality of participation, and level of comments (COMMENTS) in the group(ρ = 0.75, p =0.005). As dominance distance increased (or equality of participation de-creased), the number of comments made in the group increased. Further analysis revealeda difference in the number of comments spoken in the group versus the condition at α =0.05, F(2,35) = 40.95, p =0.0001 (see Table 4.8).

TABLE 4.8ANOVA Summary Table for Number of Comments as a Function of Condition

Source DF SS MSF test

statistic p valuecondition 2 281.97 140.98 40.95 0.0001

error 33 113.62 3.44Total 35 395.59

Fisher’s multiple comparison procedure indicated the control condition had signifi-cantly more comments than the facilitated condition and the facilitated and technologycondition (see Figure 4.2).

45

4

5

6

7

8

9

10

11

contro l facilitated facilitated

with

technology

condition

nu

mb

er o

f co

mm

ents

/tim

e m

ean

s

A

B

B

FIGURE 4.2Number of Comments Means as a Function of Condition

4.2.2 Associating Group Characteristics with Comment LevelThe finding that there is a correlation between number of comments and equality

of participation is consistent with prior research. What is missing in prior research is howcomments made in a work group are related to work group characteristics and to workgroup performance. Further post-hoc analysis, using Pearson’s product-moment correla-tion procedures were performed to study potential relationships between comment leveland group characteristics.

Perhaps the inclusion of roles in the work group during facilitated conditions re-sulted in a difference in the number of comments spoken. Roles may have reduced com-ments made by members without roles. A correlation test between the number of com-ments made by members without roles and members with roles was not significant at α =0.05 (ρ = 0.31). A correlation test to determine whether the type of role (builder,timer, designer) influenced an individual’s number of comments was not significant at α =0.05 (ρ = 0.24). A correlation test between the size of the team and COMMENTS wasnot significant at α = 0.05 (ρ = 0.31). To determine if COMMMENTS was related toother work group characteristics, additional correlation tests were performed. No signifi-cant results were found at α = 0.05 (see Table 4.9).

TABLE 4.9Correlation Between COMMENTS and Work Group Characteristics

averageassociation

associationstandard de-

viationaverageseniority

senioritystandard de-

viationengineeringrepresented

COMMENTS 0.023 -0.327 0.442 -0.470 0.294

46

The correlation testing did not identify any group characteristics that would ex-plain why some groups were more talkative than other groups. To check whether being inan experiment reduced the comment level of some participants several managers were in-terviewed that had actually observed a portion of the experiment. These managers saidthe behaviors seen during the experiment were consistent with behaviors observed in thenormal work environment.

Anecdotal evidence gleaned from the laboratory experiment suggested the numberof comments contributed by an individual may be related to ethnic background. Most ofthe laboratory work groups included minorities. The frequency of comments was not re-corded during the laboratory experiment, but researcher observations suggested the num-ber of comments by Asian students was significantly less than other students. In the fieldvery few minorities were represented in the work groups so this hypothesis could not betested in the field. White women were observed to demonstrate similar behaviors as whitemales. But the laboratory observation contributes to the hypothesis that individualcharacteristics (e.g., personality, ethnic background, and attitudes) may determine thenumber of comments made by participants (i.e., more extroverted individuals make morecomments, and Asians comment less than white males. Anecdotal evidence in this re-search suggests studying individual factors is necessary in determining why comment lev-els vary within and between groups.

4.2.3 Linking Group Processes with Group PerformanceGroup processes are often hypothesized to impact group performance but little

empirical evidence exists to support these hypotheses (McGrath and Hollingshead, 1994).Further analysis was performed to identity group process variables that were predictors ofperformance. Multiple regression analysis was performed on the dependent variable thatrepresented the summation of all task performance components (cost, defect rate, and tasktime), or decision quality. The predictor variables tested were work group processes(number of comments, ideas generated, and equality of participation) and work groupcharacteristics (organizational seniority, level of group association, and representation ofengineering on the work group). Two models with high adjusted coefficients of determi-nation and p =0.0001 were identified. The first model included the two predictors: aver-age number of comments made during the control condition (COMMENTS) and the rangein the number of ideas between work group members during the facilitated conditions(IDEAS) (see Table 4.10). Every work group had participants who contributed zero totwo ideas during the brainstorming phase of the task. The predictor variable, IDEAS, isan indicator of whether there was at least one high idea contributor in the work group(relative to the low level contributor).

47

TABLE 4.10Regression of COMMENTS and IDEAS Predictors

Decision Quality = 284 - 7.24 COMMENTS - 5.48 IDEAS

Predictor CoefficientStandarddeviation t-ratio p value

Constant 284.18 16.49 17.24 0.0001AVEC1COM -7.235 1.465 -4.94 0.0001

RANGIDE -5.481 1.402 -3.91 0.0004

This model had an R2=81.7%, F(2,11) = 25.58, and p =0.0001 (see Appendix F,page 183). The correlation between the predictors, COMMENTS and IDEAS, was notsignificant (ρ=0.23). The regression model is precise, with a C-p value of 2.2, multicol-linearity was not present VIF=1.1, and there was no evidence of a lack of fit (p =0.1). Aplot of the residuals from the regression model appears random (see Figure 4.3).

-15

-10

-5

0

5

10

15

20

25

130 140 150 160 170 180 190 200 210

Predicted Decision Quality

resi

du

al

FIGURE 4.3Plot of Residuals for Regression Model (COMMENTS and IDEAS)

The inclusion of both predictors in the regression model significantly improves themodel. Using only COMMENTS as the predictor variable produced a model with an ad-justed R2 = 55.6, F(1,11) = 14.77, and p =0.005. Using only IDEAS as the predictorvariable produced a model with an adjusted R2 = 39, F(1,11) = 8.02, and p =0.05.

Correlation analysis was done between the predictor variable, IDEAS and demo-graphic data of the work group. The correlation between IDEAS and whether anengineer was a member of the work group was significant at α = 0.05 (see Table 4.11).Five of the work groups had at least one engineer as a participant.

48

TABLE 4.11Correlation Analysis Between IDEAS and Demographic Data

senioritystandard de-

viationaverageseniority

associationstandard de-

viationaverage

associationengineerincluded

IDEAS .165 -.209 .413 -.499 .913

IDEAS was replaced in the regression model with ENGR, a binary variable repre-senting whether an engineer participated in the experiment (see Table 4.12).

TABLE 4.12Regression of COMMENTS and ENGR Predictors

Decision Quality = 259 - 6.72 COMMENTS - 29.5 ENGR

Predictor CoefficientStandarddeviation t-ratio p value

Constant 259.39 14.10 18.39 0.0001COMMENTS 6.722 1.316 -5.11 0.0001ENGINEER -29.542 6.268 -4.71 0.0001

This model had an R2 = 85.8%, F(2,11) = 34.16 and p =0.0001 (see Appendix F,page 183). The correlation between the predictors, COMMENTS and ENGR, was notsignificant (ρ = 0.294). The regression model was precise, with a C-p value of 0.4. Aplot of the residuals from the regression model appears random (see Figure 4.4).

-15

-10

-5

0

5

10

15

20

110 130 150 170 190 210

Predicted Decision Quality

resi

du

al

FIGURE 4.4Plot of Residuals for Regression Model (COMMENTS and ENGR)

49

Linear regression was performed between decision quality and the predictor vari-able ENGR. The model, when only ENGR was used as a predictor, had an adjusted R2 =50.1, F(1,11) = 12.05, and p =0.006.

Post-hoc data analysis identified two models which predict decision quality withhigh reliability. It can be argued the two models are actually the same since there was ahigh correlation between the predictors: IDEAS and ENGR. Both models were includedin this chapter since there are alternate methods of achieving a high IDEAS score. In-cluding engineers in the work group is just one method. The models suggest the impor-tance of having a participant in the natural work group who will contribute a high numberof ideas relative to the low contributors in the work group for higher task performance.This research did not test whether engineers were the ones contributing the higher numberof ideas. The research only found the inclusion of engineers in the work group led to ahigher range in the number of ideas by participants. And a higher range in the number ofideas contributed to higher decision quality.

Another method for increasing the range in the number of ideas, without adding anengineer to the group, is by increasing the technical skill level in the group through proc-ess management training. Process management training provides groups with the ability toanalyze data. The level of training each group had received in process management wasdiscussed with each group’s manager. Some of the groups had little training beyond re-quirements to do their traditional job, some groups had been given process managementtraining, and as mentioned above some teams had an engineer included in the group. Asignificant correlation was found between IDEAS whether there were individuals withprocess management or engineering skills in the group (ρ = 0.78).

Increasing the level of training individuals in the group have had in process man-agement will increase IDEAS. The experimental task required the work group to generateideas, choose, negotiate, and execute. The task was similar to process improvement ef-forts many individuals are asked to perform today. A model was identified for predictingdecision quality. This model is valid only for work groups with characteristics similar tothe experimental work groups. These characteristics include:- work group size between 4 and 13,- average organizational seniority of members between 7 and 20 years,- a medium to high average level of association between members (based upon a survey question completed by participants),- work groups composed of all operators, all supervisors, or a combination of operators, supervisors, and engineers,- little to no diversity in terms in gender or ethnic background, and- operating in production oriented work environments.

50

4.3 Hypothesis Three - Participant EvaluationH0: No significant difference is seen in participant evaluations across condi-

tions. Hypothesis three was tested in both the laboratory and the field. Participantevaluations included three scales: level of agreement, quality of discussion, and the inter-action of group members. The evaluations were formatted as a Likert type scale.Prior research involving GDSS experiments have reported that participants report no dif-ference in perception data across conditions. This research sought to confirm the findingsfrom prior research. Since the researcher was seeking to accept the null hypothesis forparticipant evaluation, a higher alpha was set (α = 0.20).

Cronbach’s Alphas for the scales agreement, quality of discussion, and interactionof group members were between 0.7881 and 0.8741 (see Appendix F, page 183). At the0.20 level of significance a difference was found only in the laboratory for the discussionquality scale, F(3,59) = 2.55, p =0.065 (see Table 4.13and Table 4.14). ANOVA sum-mary tables for participant evaluations are shown in Appendix F, page 183.

TABLE 4.13Summary of Hypothesis Three Results

participantevaluations experiment test statistic p valueagreement laboratory F(3,59) = 1.51. 0.221

discussion quality laboratory F(3,59) = 2.55 0.065group interaction laboratory F(3,59) = 1.11 0.352

agreement field F(2,35) = 0.19 0.831discussion quality field F(2,35) = 0.04 0.960group interaction field F(2,35) = 0.06 0.940

TABLE 4.14ANOVA Summary Table for Discussion Quality as a Function of Condition

Source DF SS MSF test

statistic p valuecondition 3 3.392 1.131 2.55 0.065

error 56 24.827 0.443Total 59 28.219

Multiple comparison testing was performed using Fisher’s procedure (with a .05individual error rate). Fisher’s procedure indicated the facilitated condition had lower dis-cussion quality scores (see Figure 4.5).

51

3.3

3.4

3.5

3.6

3.7

3.8

3.9

4

contro l structured

manual

facilitated facilitated

with

technology

condtion

dis

cuss

ion

qu

alit

y m

ean

s

A

A

A

B

FIGURE 4.5Discussion Quality Means as a Function of Condition

The participant evaluation means by condition for the laboratory and the field ex-periments are shown in Table 4.15 and Table 4.16.

TABLE 4.15Laboratory Participant Evaluation Means by Condition

Condition

participant evaluation controlstructured

manual facilitated

facilitatedwith

technologyagreement 4.14 4.01 3.99 4.00discussion quality 3.73 3.97 3.41 3.92group interaction 4.03 4.02 3.82 3.92

TABLE 4.16Field Participant Evaluation Means by Condition

Condition

participant evaluation control facilitatedfacilitated with

technologyagreement 3.94 4.13 4.01discussion quality 3.86 3.95 3.83group interaction 3.95 4.09 3.98

Laboratory participants scored the facilitated condition lower in discussion qualitythan the other three conditions. The facilitated condition also had the worst decision

52

quality mean. Participants may have perceived their task performance to be lower in thefacilitated condition and scored discussion quality lower.

Participant evaluations in the field experiment supported prior research findingsthat no differences were perceived by participants among the conditions in the areas ofagreement, quality of discussion or work group interaction. Responses from participantswere analyzed both by average and standard deviation scores. It is interesting, that therewas a significant decrease in the overall number of comments during the facilitated condi-tions, and the level of equality of participation increased during the facilitated conditions,but participants did not perceive any difference in level of agreement, discussion quality orgroup interaction. Prior research has noted the same findings. Systematic biases in par-ticipant self reports are usually biased towards the positive end of the scales when evalu-ating their group.

Discussions with the work groups following the experiment provide additionalparticipant insight. Eleven of twelve work groups thought their most competitive designwas completed when structure (roles, facilitated process and group objectives) was im-posed on the work group. Examples of specific comments made by a participant at thediscussion concerning structure are shown in Table 4.17.

TABLE 4.17Qualitative Comments About StructureRoles help keep all bases covered.Roles legitimatize calling peers on issues.Roles increase accountability and increase stress.Group objectives help focus the team on all important factors (cost, quality, and time).

Actual results show five work groups produced their most competitive design oftheir three designs under the control condition which did not have structure (roles, facili-tated process, and group objectives). As discussed in section 5.1, when all designs werecompared against each other, the control condition without structure had the best meansfor task performance. This study suggests even though participants perceivedstructure to benefit their performance, structure (as provided by the decision aids) may notbe a critical factor in work group performance. Instead, post hoc analysis using regressionprocedures suggested that the better predictors of group performance are the number ofcomments in the work group and the range in the number of participant ideas. These twovariables were shown to be good predictors of task performance in production orientedwork groups, when the task includes generation, negotiation, selection, and execution.

Half of the twelve work groups perceived a higher level of teamwork in the controlcondition. From the discussion with the groups after the experiment, it was learned thatthe definition of teamwork varied between groups. Groups thought the control conditionproduced higher teamwork equated teamwork with activity level even if only by a subsetof the total group. Groups that thought a facilitated condition produced higher teamwork

53

equated teamwork with a high level of participation equality. Five of the six work groupswhich selected the control condition as having the higher level of teamwork did not selectthe control condition as producing the best model. These groups believed learning oc-curred across the three conditions, making the last model their most competitive model.An ANOVA performed between decision quality and order presentation did not support alearning factor (F(2,35) = 1.54, p =0.16).

4.4 Hypothesis Four - Task-focused CommunicationH1: Groups using any type of decision aid have higher task-focused communi-

cation than control groups. Hypothesis four was tested only in the laboratory. At a sig-nificance level of 0.05, this hypothesis was not supported, F(3,59) = 2.27, p =0.09(ANOVA summary table shown in Appendix F, page 183). The means for task-socialcommunication are shown by condition in Table 4.18.

TABLE 4.18Task-focused Communication Means by Condition

Condition

controlstructuredprocedures facilitated

facilitatedwith

technologytask focused communica-tion 21.8 22.3 23.9 30.1

Prior research has found task-focused communication to increase with decisionaids. The inconsistent findings of this research with prior research may be due to the typeof data collected for this hypothesis. The SYMLOG observation method was used. Twopossibilities may explain the lack of significant findings. First, the data from this tool maybe too course for identifying differences. Second, the inexperience of the researcher inusing this method may have prevented differences from being identified.

4.5 Hypothesis Five - Ideas GeneratedH1: Facilitated groups generate more ideas during brainstorming. Hypothesis

five was tested only in the laboratory. Control groups were not required to brainstormideas so this hypothesis only tested three conditions. At a significance level of 0.05 thishypothesis was supported, F (2,14) = 7.91, p=0.006 (see Table 4.19).

TABLE 4.19ANOVA Summary Table for Ideas Brainstormed as a Function of Condition

Source DF SS MS F testStatistic

p value

condition 2 36.93 18.47 7.91 0.006error 12 28 2.33Total 14 64.93

54

Multiple comparison testing was performed using Fisher’s procedure (with a .05individual error rate). Fisher’s procedure indicated facilitated groups with and withouttechnology generated more ideas during brainstorming than groups asked to brainstormideas without facilitation (see Figure 4.6). The difference between the number of ideasbrainstormed by groups using technology and not using technology was not significant.

2

3

4

5

6

7

8

contro l manual facilitated fac w/tech

condition

nu

mb

er o

f id

eas

mea

ns

A

B

B

FIGURE 4.6Number of Ideas as a Function of Condition

The means for number of ideas brainstormed are shown by condition in Table 4.20.

TABLE 4.20Number of Ideas Brainstormed Means by Condition

Condition

controlstructured

manual facilitated

facilitatedwith

technologyideas generated-# n/a 4 7.8 6.4

The laboratory experiment supported prior research findings that facilitated condi-tions with and without technology produce more ideas than the structured manual condi-tion, which did not have facilitation. The use of a facilitator in brainstorming ideas andselecting ideas led to more ideas being considered by the work group than in the struc-tured manual (non-facilitated) condition. This finding was not confirmed in the field sincethe field did not include the structured procedure condition because this conditiondropped as not being representative of natural work conditions.

55

4.6 Hypothesis Six - Anonymity and ConsensusH1: Groups given anonymity demonstrate less consensus in their evaluation.

Hypothesis six was tested only in the field. Individuals voted on ideas from their brain-storming list (a nonsensitive issue) and individuals voted on how well roles were per-formed on their team (a sensitive issue). Voting score variability was used as the measureof consensus. Two measures were used to evaluate voting scores on the nonsensitive is-sue, ideas: the difference between the top two votes and the standard deviation of votes.For voting on a nonsensitive issue (i.e., ideas) this hypothesis was not supported at a sig-nificance level of 0.05 (see Table 4.21). ANOVA summary tables are shown in AppendixF, 183. Also, there was no difference in the average number of ideas receiving at least onevote. The mean number of ideas receiving a vote for the facilitated condition, and the fa-cilitated with technology condition was 6.42 and 6.58, respectively.

TABLE 4.21Findings for Consensus When Evaluating Nonsensitive Issues

consensus F test statistic p valuetop two idea difference F(1,23) = 3.82 p =0.064

standard deviation of votes F(1,23) = 1.95 p =0.176

Two measures were used to evaluate consensus on the sensitive issue, role per-formance: the range between vote scores and how many items on the 5-point Likert typescale had any votes. At a significance level of 0.05 this hypothesis was supported for bothvariables (see Table 4.22, and Table 4.23). The average score in the nonanonymous con-dition was 4.23 (on a 5-point Likert type scale) and the average score in the anonymouscondition was 3.75 (on a 5-point Likert type scale). The means were not statistically dif-ferent from each other.

TABLE 4.22ANOVA Summary Table for Role Scoring Variability (range between vote scores) as aFunction of Condition

Source DF SS MSF test

statistic p valuecondition 1 15.125 15.125 18.3 0.0001

error 70 57.861 .827Total 71 9.986

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TABLE 4.23ANOVA Summary Table for Roles Scoring Variability (items with votes) as a Function ofCondition

Source DF SS MSF test

statistic p valuecondition 1 8.681 8.681 14.71 0.0001

error 70 41.306 .590Total 71 49.986

The voting variability means by condition for both the nonsensitive and the sensi-tive issues are shown in Table 4.24.

TABLE 4.24Voting Variability Means by Condition

Condition

voting variability facilitatedfacilitated with

technologyIdea-top two ideas differ-ence

3.17 2.59

Idea-standard deviation ofvotes

2.08 2.19

Role-range between votescores

1.31 2.14

Role-items with votes 2.22 2.83

Anonymity made a difference in the level of consensus for sensitive topics (i.e.,role evaluation) but not non-sensitive topics (i.e., idea evaluation). The range of scoreswas larger when votes were collected anonymously than when votes were collectedopenly. These findings suggest anonymity may be required to collect accurate data aboutconsensus involving sensitive issues (i.e., performance of individuals or groups). Thevoting scores from idea evaluation indicated a tendency towards voting convergence orconsensus by group members but not at a statistically significant level. There was nostatistical difference in the means between conditions for the number of ideas receiving avote. In this study, the work groups had high average levels of association (4.2 on a 5.0scale) and high seniority (an average of 12.6 years). It may be years of service providesconfidence in idea evaluation and makes anonymity unnecessary. And when people knoweach other well, anonymity may be necessary to get accurate feedback.

Further testing is necessary to determine the impact of level of association andseniority on the need for anonymity for both sensitive and non-sensitive topics. Varyingthese factors in future studies would help answer whether work groups with junioremployees or work groups with members having a low level of association require ano-nymity for sensitive or non-sensitive issues.

57

4.7 Contribution to the Body of KnowledgeThe first research question was ‘What is the impact of decision aids on work group

performance?’ This study contributes to the body of knowledge about the impact of deci-sion aids on work group performance by having studied a complete task (a task requiringgenerating, choosing, negotiating, and executing team processes). Decision aids thatprovide groups with structure have been shown in prior research to improve group per-formance, but the task studied was only a generation, selection, or negotiation task.Studies that have evaluated the impact of structure on a complete task involving genera-tion, selection, negotiation, and execution group processes are almost always case studies.This dissertation adds to the body of knowledge by having experimentally studied the im-pact of decision aids on groups completing a task requiring all group processes. Mostgroups in natural work settings complete tasks involving all group processes. Findingsfrom this study contribute to a better understanding of natural work groups. This researchfound that structure did not improve performance for the production-oriented groups do-ing a process improvement task in this study.

The second research question was, ‘What is the impact of decision aids on workgroup processes?’ This dissertation contributes to the body of knowledge by determiningfor production-oriented groups performing a complete task that decision aids: increase thenumber of ideas considered by the group; increase the level of equality of participation;decrease the overall level of conversation in the group; and decrease the level of consensusfor sensitive topics. Very little experimental research exists, especially with natural workgroups completing tasks requiring all group processes. This dissertation studied a littleresearched area and contributes to our understanding of it. Additionally, this study con-tributes to the body of knowledge by linking work group process variables with workgroup performance variables. Work process variables are often hypothesized to impactgroup performance but little empirical evidence exists linking the two (McGrath andHollingshead, 1994). This dissertation identified a model for predicting task performanceusing work group process variables as predictors. It was an unexpected finding that thenumber of comments predicted work group performance better than equality of participa-tion and that the range in the number of ideas by participants predicted work group per-formance better than the overall number of ideas on the work group. The model is gener-alizable to a wide range of production-oriented work groups with features including: workgroup size between 4 and 13; average organizational seniority of members between 7 and20 years; a medium to high average level of association between members: work groupscomprised of all operators, all supervisor, or a combination of operators, supervisors, andengineers; and little to no ethnic or gender diversity.

An additional understanding was gained by learning (through discussion) that natu-ral work groups perceive structure (roles, facilitated process and group objectives) as en-ablers for better performance. This study did not find structure to be a significant factor inwork group performance. And although work groups thought structure was important forperformance, their actual questionnaire results showed that they did not perceive any dif-ference between structured and unstructured conditions in level of agreement,

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discussion quality or group interaction. These findings confirm the need to be cautiouswith self-report or perception data. Anecdotal data, as provided by the participants, sug-gested that structure improved their performance but empirical data did not support thisconclusion.

This study contributed to the body of literature on GDSSs by studying the impactof anonymity on sensitive and nonsensitive issues. Prior GDSS research has found groupshave more difficulty reaching consensus when participant input is anonymous since indi-viduals can be more open with their input. This study contributes to the body of knowl-edge having evaluated two types of situations: a sensitive task and a nonsensitive task.Anonymity made a difference in the level of consensus for only the sensitive issue. Ano-nymity was not a factor when the issue was nonsensitive. A greater range of scores wascollected when participants evaluated group roles with anonymity than when the voteswere collected openly. This study also suggested that work groups will evaluate them-selves lower when voting is anonymous. Since the work groups all had high levels ofassociation and seniority, this finding can only be generalized to similar work groups. Thefindings suggest that for sensitive issues work groups may require anonymity to accuratelyassess the level of consensus on the issue.

The final research question was ‘How do laboratory work groups differ from fieldwork groups?’ The findings from the laboratory work groups were supported by thefindings in the field. Decision aids did not improve task performance in either the labora-tory or the field experiments. And in general, decision aids did not change participantevaluations (except for discussion quality in the laboratory). Post hoc testing was per-formed to determine if a difference existed in results between the laboratory and the fieldfor group performance. The task performance dependent variables of cost, defect rate,and time findings for the laboratory and the field experiments were compared for eachcondition. At α = 0.05, a difference was found between the laboratory and field data fortask time across all conditions (see Table 4.25, 4.26, and Table 4.27). At α = 0.05, nodifference was found between the laboratory and field data for defect rate at any condition(the ANOVA summary tables are shown in Appendix F, page 183). At α = 0.05, the costcomparisons between the laboratory and the field data are mixed. The control conditionand the facilitated and technology condition show a difference but the facilitated conditiondoes not show a difference (see Table 4.28, Table 4.29, and Table 4.30).

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TABLE 4.25ANOVA Summary Table for Time as a Function of the Control Condition - Laboratory

versus Field

Source DF SS MS

F test

statistic p value

condition 1 8359 8359 50.38 0.0001

error 15 2489 166

Total 16 10848

TABLE 4.26ANOVA Summary Table for Time as a Function of the Facilitated Condition - Laboratory

versus Field

Source DF SS MS

F test

statistic p value

condition 1 10260.0 10260.0 129.23 0.0001

error 15 1190.9 79.4

Total 16 11450.9

TABLE 4.27ANOVA Summary Table for Time as a Function of the Facilitated and Technology Con-

dition - Laboratory versus Field

Source DF SS MS

F test

statistic p value

condition 1 9506.9 9506.9 102.14 0.0001

error 15 1396.2 93.1

Total 16 10903.1

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TABLE 4.28ANOVA Summary Table for Cost as a Function of the Control Condition - Laboratory

versus Field

Source DF SS MS

F test

statistic p value

condition 1 2754 2754 5.38 0.035

error 15 7684 512

Total 16 10438

TABLE 4.29ANOVA Summary Table for Cost as a Function of the Facilitated Condition - Laboratory

versus Field

Source DF SS MS

F test

statistic p value

condition 1 747 747 1.47 0.244

error 15 7619 508

Total 16 8366

TABLE 4.30ANOVA Summary Table for Cost as a Function of the Facilitated and Technology Condi-

tion - Laboratory versus Field

Source DF SS MS

F test

statistic p value

condition 1 2526 2526 8.95 0.009

error 15 4234 282

Total 16 6760

The cost, defect rate, and task time means for the laboratory and the field experi-ments, along with the p values are shown in Table 4.31.

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TABLE 4.31Means for the Laboratory and Field Experiments

laboratory field p valuecost - $MM control 17.4 45.33 0.035

facilitated 31.2 45.75 0.244fac and tech 22.0 48.75 0.009

defect rate - % control 1.09 1.71 0.266facilitated 1.10 1.71 0.422fac and tech 0.81 2.11 0.110

task time -min. control 67.0 18.33 0.0001facilitated 75.0 21.08 0.0001fac and tech 77.4 25.5 0.0001

It is not surprising that the findings between the laboratory and the field are differ-ent for time. The design of the experiment changed between the laboratory and the fieldrelative to task time. In the laboratory, the groups were given up to two hours to com-plete the task while in the field only one hour was allowed. Because the field groupscompleted all three test conditions, unlike the laboratory groups, more stringent time limitsfor each test were used in the field in order to hold the total test time to a maximum ofthree hours.

The findings between the laboratory and the field data for cost and defect rate aremixed. There are some significant findings in the cost dependent variable, and no signifi-cant findings in the defect rate dependent variables. Both Bartlett’s and Levene’s Homo-geneity of Variance procedures were performed on the facilitated condition data for thelaboratory and the field. No heterogeneity of variance was indicated. A possible explana-tion differences in findings is that the laboratory experiment used only one material, whilethe field experiment used three materials. Differences between the materials in terms ofcost may have resulted in the significant findings. This explanation would seem morelikely if all three conditions had resulted in significant differences. No significant differ-ence was found between the laboratory and the field for the facilitated condition. The fa-cilitated condition was also the only condition in the laboratory to show a significant dif-ference in discussion quality. Laboratory participants completing the facilitated conditionrated the facilitated condition lower than participants rated the other conditions. This maybe why there is not a difference between the laboratory and field data for the facilitatedcondition, while the other two conditions show a difference.

No significant difference was seen between the laboratory and the field experi-ments for any condition. This result may indicate there was no difference in this depend-ent variable between the student and the employee groups. There was no difference be-tween the two different groups in this task performance area. Some caution should betaken though with these results. Levene’s Homogeneity of Variance procedure indicatedthat at α = 0.05, heterogeneity of variance may be present in the control condition data(Levene’s test statistic = 4.887, p = 0.043). Bartlett’s Homogeneity of Variance proce

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dure indicated that at α = 0.05, heterogeneity of variance may be present in the facilitatedcondition data (Bartlett’s test statistic = 0.005, p = 0.005). No indication of heterogeneityof data was found using Bartlett’s or Levene’s procedures for the facilitated and technol-ogy condition.

Post hoc testing was also performed to determine if a difference existed betweenthe laboratory and the field data for participant evaluations. At α = 0.05, no differencewas found between the laboratory and the field findings for level of agreement, discussionquality, or group interaction (ANOVA summary tables in Appendix F, page 183). Bar-lett’s and Levene’s Homogeneity of Variance procedures were performed on the partici-pant evaluation data. Neither procedure indicated heterogeneity of variance existed be-tween the laboratory and the field data. The students in the laboratory experiment and theemployees in the field experiment showed no difference between each other in their per-ceptions of the experiment. Both groups demonstrated consistent results with prior GDSSresearch which also indicates that participants perceive no difference between experimen-tal conditions.

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Chapter Five: Conclusions

5.1 Research ImplicationsThis section is divided into future research areas and differences in conducting

laboratory and field experiments.

5.1.1 Future ResearchThere are a number of areas in which this research can be extended and enhanced

in future research:1. What causes some work groups to have higher levels of conversation?2. How can the level of conversation be increased in a work group?3. What level of skill optimizes work group performance for process improvement tasks?4. What is the interaction between facilitation and GDSS?5. In work groups with high conversation levels and high skill levels, does the level of structure impact performance?6. Are the findings in this study similar for other types of work groups (i.e., engineering work groups, design teams, management work groups)?7. How does the level of association and level of seniority impact on the need for anonymity for consensus building?8. How does the level of consensus impact performance?9. What is the impact of CSCW on human-computer interface?Each of these areas are briefly discussed below.

1. What causes some groups to have higher conversation levels and others to have lowerconversation levels? This research found conversation level to be a good predictor ofperformance (when combined with the range in member ideas as a predictor variable).Work group characteristics of average organizational seniority or level of association be-tween members were not found to be related to conversation level. Future researchshould investigate potential other independent variables that influence the dependentvariable conversation level. Anecdotal observation suggested individual characteristicsmay be a rich source of information as to the causes of why some people talk more thanothers.

2. How can the level of conversation be increased in a group? In addition to exploringindividual characteristics that impact conversation level, techniques for increasing conver-sation should be studied. Team building exercises are frequently used to improve internalgroup dynamics. Research is lacking on the causal relationships between team buildingexercises and work group processes. Using conversation level as the dependent variable,and team building exercises as the independent variable, a better understanding of the re-lationship between the two could be established. Also, an unanswered question is whetherthe level of conversation can be increased while increasing equality of participation? Thisstudy, along with prior research has found equality of participation to increase but level ofconversation to decrease with structured processes. Further study is necessary to under-stand this relationship better.

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3. The task in this experiment simulated a process management task that natural workgroups are frequently involved in. The range in member ideas about the task was shownto be a good predictor of performance (when combined with level of conversation as apredictor). The level of skill existing in the group was related to the range of ideas. Threeskill levels were easily identified as existing in the groups in this experiment: operatorskills (basic job skills), exposure to process management skills (data analysis skills), andengineering skills (analytical skills). Further study is needed to determine what specificskills are required for increasing performance for process improvement tasks.

4. This research did not study the interaction of facilitation and GDSS. Due to trainingtime constraints the participants had facilitation in the use of the GDSS. Future research isnecessary to determine the interaction between the facilitation and the technology.

5. This research did not find a causal relationship between structure and performance.Structure has been demonstrated in prior research to improve performance. Typically,prior research has only studied how structure improves group processes involving genera-tion or selection. Further study is necessary to determine how structure impacts workgroups completing an entire task as found in process improvement activities. The lack ofevidence in this research may have been due to large variation in the groups relative toskill level and conversation level. Further testing is necessary to determine if structure im-proves performance when groups are more similar in skill level or conversation level.Structure was defined as providing common group objectives, process roles, and processfacilitation concurrently. The conditions with structure included each of these elements.This research did not attempt to evaluate each component individually. Future research isnecessary for understanding the impact each component has on group processes andgroup performance.

6. This research identified a good model for predicting performance for production-oriented work groups engaged in a process improvement tasks. Further research is neces-sary to test whether the model is a good predictor for other types of groups doing differ-ent tasks (e.g., product development groups, marketing groups, engineering groups, or-ganizational design groups, management groups, etc.).

7. This research found that anonymity was needed to determine actual consensus in thegroup when a sensitive issue was evaluated, but not when a nonsensitive issue was evalu-ated. The groups studied had high seniority and a high average level of association. Fur-ther research is necessary to determine the relationship between consensus and anonymitywhen groups have lower levels of seniority and association.

8. This research did not test how the level of consensus impacts on performance. Thisresearch found consensus to be lower with anonymous input. Further research is neces-sary to determine how the level of consensus in the group impacts on group performanceand processes.

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9. This research only touched the surface of studying human-computer interaction. Manyresearch questions exist, such as: how much training is necessary for people to effectivelyuse CSCW, are certain types of people more open to the use of technology enhancedmeetings, what type of tasks lend themselves more readily to technology enhanced meet-ings and what is the interaction between facilitation, computer training and computer us-age during meetings. All of these questions require future research.

5.1.2 Differences in Conducting Laboratory and Field ExperimentsThis dissertation included a laboratory experiment and a field experiment. Three

main areas of difference existed in conducting the experiment in the laboratory and in thefield. In order of magnitude of effect on the study, the areas were: access, control, andequipment.

Access - It was much easier to get student volunteers for a laboratory experiment at thisresearch university than it was to get natural work groups from industry to participate in afield experiment. Several faculty provided access to student classes where I requestedstudent volunteers. It required only one request of students to identify enough volunteersto complete the laboratory experiment. Access to natural work groups in the field wasmore difficult. An existing relationship between the researcher and someone with author-ity to approve the research was required. Even with this relationship, the field sites ex-pected the research to be relevant to their current interests and to learn something withimmediate applicability.

Control - It was much easier to control the experiment in the laboratory than in the field.In the laboratory, almost everything could be controlled relative to the experimental pro-cedures, location, and groups. In the field, I attempted to control these items the sameway as in the laboratory but the level of control was still less than in the laboratory.

In the laboratory, student work groups were more compliant with experimentalprocedures. If the researcher direction was to work individually, they would work indi-vidually. In the field many work groups had to be reminded to work individually (somemore than once). In the laboratory, all students generated ideas when directed by the re-searcher. In the field, several work groups had members which did not generate any ideaswhen directed to do so. In the laboratory, students were asked to role play a function(manufacturing, purchasing, quality control). Anecdotal observation is the students triedto do this but were not very effective at it. In the field, it was not necessary to role play.In the field experiment, participants had different real world roles. Some were engineers,some were supervisors, some were mechanics, some were operators, etc.

The location of the laboratory experiment was constant. Unlike most of the fieldsites, in the laboratory there were no phones that rang, there were no pagers that went off,there wasn’t a site paging system that distracted participants from the task, and no onecame and asked a participant to leave the experiment. These are realities in the field whichare difficult to avoid. The laboratory simulated a quiet, isolated conference room. Proba

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bly in theory what a conference room is designed to be. But in practice, in this study thelaboratory conditions did not represent the field conditions.

Participant control was easier in the laboratory than in the field. In the laboratory,it was easy to limit the participant population to seniors and graduate students taking spe-cific courses. Ad hoc work groups were comprised of three members. In the field, Iasked organizations to provide access to representative production-oriented work groupsthat either were working on improvement projects or would be typical of groups workingon improvement projects in the future. The resulting work groups had varying numbers ofoperators, supervisors, and engineers represented and varied in size from 4 to 13.

No difference was found between laboratory participants and field participants intheir willingness and eagerness to participate in the experiments. In most cases bothgroups approached the experiment with interest and effort. The majority of people triedto do their best. Both subject groups were equally unfamiliar with the research materials.And in general, both subject groups initially found it funny to be doing research requiringthem to “play with toys”, and quickly got on with the task and found the research fun, butnot funny. The competition in the field experiment seemed to help face validity of theexperiment. Groups were interested in being competitive with the other organizationssigned up to do the experiment.

Equipment - It was easier to collect the data in the laboratory. Work group interactionwas a large data collection effort. Video taping each session was necessary. In the labo-ratory this was easier to accomplish. A control room with video equipment was available.In the field, video taping was still required but no control room was available. Conse-quently, the video equipment was set up in the conference room where the participantscould see the camera. This didn’t appear to impact the field participants once they gotinto accomplishing the task. Several managers confirmed the participant behaviors theyportrayed during the experiment were consistent behaviors outside of the experiment.

The type of group decision support equipment (GDSS) used was impacted byconducting the experiment in the field. It was not feasible to set up the networked work-stations used in the laboratory experiment in the field. So the type of GDSS used in thefield was changed to enable transportability. This modified the field experiment somewhatfrom the laboratory experiment. Ideas had been anonymously input during the laboratoryexperiment by participants at workstations. In the field, participants submitted their ideasto the researcher who then put them into a single work station. In the laboratory, howparticipants voted was not collected by the GDSS. In the field, the GDSS did record par-ticipant voting scores which enabled the addition of the anonymous feature of voting to betested.

In summary, the field experiment was more difficult to conduct than the laboratoryexperiment but was a more sophisticated approximation of the real world. The field ex-periment confirmed laboratory findings that structure did not affect performance. The

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field experiment also improves our ability to generalize the findings to natural workgroups. The field experiment involved six organizations. Work group size varied fromfour to thirteen members. The field experiment was conducted in the actual work envi-ronment. The demographics of the field work groups represents real world demographics.

5.2 Practitioner ImplicationsThis section discusses: insight about work group performance, work group mem-

ber perception, and work group consensus.

5.2.1 Insight About Work Group PerformanceIt is well accepted in the literature that work group interactions (or processes) in-

fluence work group performance. Factors within the work group and factors outside ofthe work group influence the group’s interactions. Management attempts to influencework group interactions in an attempt to improve work group performance. Managementactions can include: establishing membership selection criteria, requiring individual andgroup training, and by providing the group with tools and techniques (see Figure 5.1).

This research provides managers with insight about their usage of selection, training andtools to boost work group performance,

individualcharacteristics

groupinteractionprocesses

work group performance

group characteristics

TRAINING

TOOLSSELECTION

FIGURE 5.1Management’s Influence on Work Group Performance

This study tested the impact of decision aids or tools on work group performance.The decision aids were facilitated processes with and without technology. The facilitatedprocess added structure to the task. Groups were facilitated in problem solving, defininggroup objectives, and establishing meeting roles. The facilitated conditions changed workgroup interaction factors often hypothesized as critical to work group performance. Whengroups were facilitated, they considered more ideas and participation was more balanced

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between members. These factors were not good predictors of work group performance inthis study.

Alternative factors proved to be better predictors of work group performance.The level of conversation in the group when structure was absent (as in the control condi-tion) was a good predictor of performance when there was also someone in the groupcontributing a relatively higher level of ideas than lower contributors. A relationship wasfound between the range of ideas contributed by participants and the skills present on theteam. Work groups with at least one engineer represented and a high level of conversa-tion had the highest performance. Work groups with process management trained indi-viduals performed better than work groups that had only received operator training.

These findings support prior sociotechnical research. The level of conversationthat occurs naturally in the work group is a feature of the social subsystem. The technicalskill level represented in the work group is a feature of the technical subsystem. Socio-technical systems theory advocates the joint optimization of the social and technical sub-systems to maximize performance. Consistent with sociotechnical research, the level ofconversation alone was not a good predictor of performance, Alternatively, the range ofideas or level of skill in the work group was also not a good predictor of performance.Only by considering both variables could performance be adequately predicted.

The implication for management is selection may play a more critical role in in-creasing work group performance than perhaps considered previously. Managers oftenassume that getting the ideas out for discussion by balancing participation across groupmembers will increase work group performance. Tools and techniques designed to bal-ance participation are provided groups through facilitators. More equal participation maynot be a useful management objective for improving work group performance. This studysuggests that selecting members for group work that are capable and willing to communi-cate at high levels may be more important than balancing participation levels. Addition-ally, management should ensure that at least one member in the work group has skills be-yond basic operator training. Work groups with individuals having completed processmanagement training or engineering training performed better than work groups havinghad only operator training. The research findings indicate that management can influencework group interactions and ultimately performance though ensuring the work group hasmembers that talk a lot and members having been trained beyond basic operator training(e.g., process management, or engineering).

Selection as a strategy to improve work group performance is often insufficient.Resources are limited and people available for the task may not be frequent speakers orhad training beyond job requirements. Training is used as a method to close the gapbetween desired state and actual state. Team building is often hypothesized in the litera-ture, and carries high face validity by practitioners, as training which increasesteamwork. Supporting empirical evidence is limited. Some people equate teamwork withbalanced participation. This study cautions managers about equating teamwork with bal

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anced participation. Balancing participation to increase the level of teamwork may be atodds with increasing performance.

Work group characteristics were not identified which influence the level of conver-sation. All of the work groups studied had high seniority and a high level of member as-sociation. It may be individual characteristics are more influential to group work thanpreviously thought. In the laboratory, researcher observation, suggests that ethnic back-ground may be an important factor in how often an individual speaks up. It was observedthat individuals of specific ethnic backgrounds spoke up less than other. A cultural factormay explain why some people speak up more than others. Still, why some groups weremore communicative than others goes unanswered until additional research is conducted.Team building seeks to change interactions within the group enabling more individuals tofeel comfortable speaking. If team building can increase an individual’s capability andwillingness to verbally communicate, and there is training time, team building may be agood alternative to a selection strategy. The impact of team building on significantly in-creasing the level of conversation in the group also requires additional study.

In the work groups studied, there was varying ability between the groups andwithin the groups in analyzing data and suggesting ideas to improve cost, defect rate andmeeting specifications. Individuals were asked to write down on a sheet of paper theirideas so willingness to speak up or share ideas in writing was not a factor. The sheets ofpaper were collected. Several groups struggled with completing this step of the task. Thegroups that struggled had little or no process management training.

Within each group, there was always at least one low idea contributor, someoneonly writing down zero to two ideas. The research findings suggest that the range be-tween low idea contributors and high idea contributors is important for predicting per-formance. The higher the range the better the performance. Since all twelve groups had alow idea contributors, the critical factor for predicting performance was whether there wasa high idea contributor in the group. There was a correlation with whether a high ideacontributor was in the group and the skill level existing in the group. In general, groupswith an engineer had the highest performance, groups which had received process man-agement training (and did not have an engineer in the group) had mid-performance, andgroups that had only received job training had the lowest performance. The implication tomanagement is again the role of selection, and the role of training. Management can eitherselect individuals to be in process improvement groups that have had higher skill trainingin process management up to engineering. Or management can train individuals in processmanagement and engineering. Ensuring individuals are on the team with these skills wasshown in this study to be an important predictor of performance.

In summary, management can influence work group performance though selection,training and tools. This study found tools to be a less useful management strategy thanselection or training. Through selection or training management should ensure there areindividuals in the work group that will stimulate a high level of conversation without the

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use of a facilitator. Management should also ensure the work group has a high idea con-tributor. Skill level was highly correlated with having a high idea contributing team mem-ber. Work groups which included an engineer had higher performance. The findings fromthis study should be considered when managers are constructed groups similar to the onesstudied. This study demonstrated that through selection or training, better performance ispredicted when the work group has a high discussion level, and has at least one technicallyskilled individual.

5.2.2 Work Group Member PerceptionThis study suggested that management should supplement self-report, subjective

survey data with more objective data. The work groups in the field did not perceive anydifference in level of agreement, quality of discussion, or group interaction between thenon-facilitated and facilitated conditions, even though there were differences in workprocesses. Equality of participation was higher in the facilitated conditions and the levelof conversation was higher in the non-facilitated conditions. Work groups also perceivedstructure (in the facilitated conditions) as improving performance. Actual performancewas higher only half of the time with structure. The implication to practitioners is thatsupplementing data collection methods based upon perception is necessary to draw accu-rate conclusions. This does not mean that perception data is unimportant. Understandinghow group members feel can be important in the additional insight it provides. Relianceon perception data alone though may provide erroneous insight. Perception data alone isinsufficient. When possible, management should attempt to gather objective data aboutperformance variables which can be used to identify causation or correlation relationships.When this is not possible, which is often the case, managers should be cautious using theperception data without additional supporting evidence of its reliability and validity.

5.2.3 Work Group ConsensusThis study suggested that work groups with high levels of association and organ-

izational seniority may have difficulty reaching consensus on sensitive topics. Workgroups without anonymity scored themselves higher and had less variation in scoring thanwork groups with anonymity. This finding should alert practitioners to difficulties workgroups have with reaching consensus. In an open environment, without anonymity, it mayappear there is more consensus than actually exists. Management should be cautious ininterpreting or accepting work group data on sensitive topics when the data was gatheredfrom individuals without anonymity. It was shown in this study, that by providing tools towork groups which enable individuals to input anonymously, the level of consensus maybe more accurately reflected. In this study, mean scores were lower and variability largerwhen individuals inputted anonymously. The challenge to management is two-fold: get-ting accurate data on how much consensus exists in the group on the issue and then help-ing the work group increase its level of consensus. This research demonstrated how man-agement might use technology to better assess the level of consensus, the first step in in-creasing consensus.

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5.2.4 Human Computer InteractionTwo types of technology were used for the experiments. In the laboratory,

GroupSystems, a CSSW software program by Ventena Corporation, was used. This sys-tem is run from a server to networked computers. The software has several moduleswhich can be used for meeting management. The modules include: brainstorming, votingand ranking using multiple methods and categorizing ideas. With this system, groupmembers were able to input their ideas and vote through their work stations. The tech-nology enabled these activities to remain anonymous throughout the meeting. TheGroupSystems technology has great potential for permitting asynchronous, dispersedmeetings to occur. Unfortunately, this technology was difficult to use during the experi-ment. It was not user friendly to learn. Because of this, the decision was made to use afacilitator during the GDSS condition. The system also froze up during a couple of thetests, requiring one test to be discarded. Future versions of the software are expected toresolve both issues. An ongoing limitation of the technology for experimentation is thedifficulty in using it in the field. The system runs off of a server. Transporting server andworkstation technology to the field is a barrier to using the system outside of the labora-tory.

Because of the transportation barrier, Option Finder technology was used in thefield instead of GroupSystems. With this system, group members were able to input theirvotes anonymously during the evaluation phases of the task. This system resembles atelevision remote control and is very easy to use. No reliability issues were experiencedwith this technology. The limitation with Option Finder is that it is a voting mechanismand doesn’t accept text input (as does GroupSystems). For practitioners the technology isa good mechanism to gain individual, anonymous input on sensitive topics.

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APPENDIX A GDSS EXPERIMENTS

Summaries of several GDSS experiments that are often cited are shown below. The number in the task cell represents task type relative

to McGrath’s well published scheme.

authors title task independent vari-ables

dependent variables

Poole and Holmes,1995

lab

Decision Developmentin Computer-AssistedGroup Decision Mak-ing

allocation4

no structuremanual structureGDSS

decision path satisfactionconsensus changeperceived decision quality

key findings:• GDSS did not result in decision paths matching normative model• GDSS had more orderly paths than manual structure• orderliness of decision paths had no clear relationship to consensus change• no difference in perceived decision quality• groups more closely aligned with normative decision making model outperformed those with more complex decisionpaths and had higher path satisfaction

McLeod and Liker,1992

lab

Electronic MeetingSystems: Evidencefrom a Low StructureEnvironment

moon sur-vival

3

manual structure

low structure

equality of participationdegree of task focusdecision qualitysatisfaction

key findings:• low structure EMS groups retained existing communication patterns, e.g., influential members remained influential• EMS performed better on simple evaluative tasks, less well on complex generative tasks• no difference in satisfaction or participation equality• EMS groups had lower task focus

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(Poole, M.S. and De-Sanctis, G., 1992)

lab

Microlevel Structura-tion in Computer-Supported Group Deci-sion Making

allocation4

no structuremanual structureGDSS

consensus

key findings:• GDSS groups that used system as designed had higher consensus and better outcomes• conflict in high and low consensus groups no difference

(Ho, T.H. et al., 1991)lab

The Effects of GDSSand Elected Leadershipon Small Group Meet-ings

allocation4

no structuremanual structureGDSSelected leadership

consensusequality of influenceinfluence of leader

key findings:• covariant- premeeting consensus significant• groups with high premeeting consensus had low equality of influence• leaders less influential in structured meetings and elected leaders did not increase post meeting consensus• manual groups displayed a significantly higher postmeeting consensus than GDSS attributed to structure increasingconsensus but the anonymous communication channel in GDSS negated its impact

Zigurs, Poole, and De-Sanctis, 1991

lab

A Study of Influence inComputer-MediatedGroup Decision Making

selection ofapplicants

4

manual

computer supported

influential behavior

key findings:• computer support helped to even out attempted influential behavior• no difference in overall amount of influence• more communication related to procedures vs. the goal with GDSS groups

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authors title task independent variables dependent variablesGallupe, DeSanctis,and Dickson, 1988

lab

Computer-Based Sup-port for Group Problem-Finding: An Experi-mental Investigation

problemfinding

4

non-GDSSGDSStask difficulty

decision qualitynumber of alternativesdecision confidencesatisfaction

key findings:• GDSS improved decision quality in both task conditions• confidence and satisfaction lower for GDSS groups• greater depth of analysis of more alternatives in GDSS groups

Jarvenpaa, Rao andHuber, 1988

field

Computer SupportMeetings of GroupsWorking on Unstruc-tured Problems: A FieldExperiment

conceptualdesignproblems

3

manual structureelectronic bulletin boardwork station

participationsatisfactiondecision quality

key findings:• no difference in participation or satisfaction• EBB and workstation meetings resulted in better performance than conventional meetings• EBB had the highest overall quality scores

Watson, DeSanctis,and Poole, 1988

lab

Using a GDSS to Fa-cilitate Group Consen-sus: Some Intended andUnintended Conse-quences

allocation4

no structuremanual structureGDSS

consensusequality of influenceattitudes

key findings:• no difference in degree of consensus between three conditions, or in equality of influence• less positive attitudes for GDSS groups• GDSS overly concerned with procedural matters, manual structural appropriate focus on procedures• premeeting consensus related to postmeeting consensus in manual structured and GDSS but not baseline

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Turoff and Hiltz, 1982

lab

Computer Support forGroup Versus Individ-ual Decisions

arctic con-ditions

3

formal leadercomputer feedbackunstructured computerconferenceface-to-face

consensusdecision quality

key findings:• either formal leadership or computer feedback had a significant effect on ability to reach consensus in computer modelbut canceled each other when used together• for small groups leader was more effective than computer feedback• all groups improved in quality in all conditions

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APPENDIX B PRE-EXPERIMENTAL FORMS

This appendix contains all the forms that participants were given prior to the ex-periment. This appendix contains:

- laboratory and field experiment schedules- consent form- laboratory pre-experiment questionnaire (includes process management quiz)- laboratory experiment background information (includes operational factor data)- field experiment group exercise (includes level of association question)

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Laboratory and Field Study Schedules

Laboratory Study Schedule

Oct-96date experiment condition

3 1 2.14 2 2.24 3 3.14 4 3.26 5 2.36 6 4.17 7 1.17 8 3.37 9 3.48 10 2.48 11 4.29 12 1.2

10 13 2.510 14 3.515 15 1.315 16 4.321 17 4.422 18 1.424 19 1.524 20 4.5

Field Study Schedule

exp order date1997

company

1 21-Jan S12 3-Feb K13 4-Feb K24 10-Feb B15 10-Feb B26 19-Feb G17 19-Feb G28 20-Feb V19 20-Feb V2

10 26-Feb F111 26-Feb F212 26-Feb F3

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Consent Form

Title of Experiment: The Effect of Problem Solving Aids on Team Performance During Operational Implementation Efforts

Research Investigators: Ms. Marla Hacker (student)Dr. Brian M. Kleiner (advisor)

Purpose of this ResearchThe purpose of the research is to investigate how different types of problem solving aidsimpact team performance. Many organizations are moving to team based structures andnot realizing the anticipated benefits. This research studies how problem solving tools,with and without computer augmentation may improve team performance.

ProceduresSubjects will be assigned to teams. Teams will be given a set of requirements for the de-sign of a system. Subject teams will design, construct and evaluate a prototype moon mo-bile. The experiment will take approximately 2 hours. Some teams will be able to usecomputers to accomplish their task.

Risk and BenefitsThere are no risks associated with this research.

Benefits of this ResearchThis research will determine if specific problem solving tools are helpful to teams whenthey are engaged in operational implementation efforts. No promise or guarantee ofbenefits has been made to encourage you to participate.

Confidentiality/AnonymityThis research is concerned with team performance vs. individual performance so data willbe aggregated to the team level. Each student will role play a functional role. The teamswill be video taped to aid the coding of interaction variables. The researcher will view thetape to collect data concerning equality of participation and the amount of task orientedcommunication that occurs. The data will be coded to the functional roles vs. to studentnames. There is no reason to document the functional role played by each student. Thetapes will be maintained by the researcher. No one else will have access to the tapes. Thetapes will be destroyed or taped over within one year of the experiment.

please turn over to back side

88

CompensationTen dollars will be provided to each participant that completes the experiment. Smallnotebooks that contain information about teams, team performance, team assessments,and team tools will be available to participants desiring this information, along with an ex-ecutive summary of the research findings.

Freedom to WithdrawYou are free to withdraw from this study at any time without penalty.

Approval of ResearchThis research project has been approved, as required, by the Institutional Review Board ofResearch Involving Human Subjects at VPI, and by the Department of Industrial Engi-neering.

Subject’s PermissionI have read and understand the Informed Consent and conditions of this project. I havehad all my questions answered. I hereby acknowledge the above and give my voluntaryconsent for participation in this project.

If I participate, I may withdraw at any time without penalty. I agree to abide by the rulesof this project.

__________________________________ __________________________________ Signature Date

Should I have any questions about this research or its conduct I may contact:

Marla E. Hacker 231-4596Investigator

Dr. Brian M. Kleiner 231-4926Faculty Advisor

Dr. Ernie R. Stout 231-9359Chair, IRBResearch Division

89

Laboratory Study Pre-Experiment Questionnaire

Name:___________________________________ Date:____________________

Is English your native language? Yes No

If a student, what is your functional role (on your name tag) during the experiment:

_______________________________

Do you want a summary of the experimental results mailed to you? Yes No

Do you want a notebook of several articles and references on teams mailed to you? Yes No

If yes to either question immediately above please provide your address,

____________________________________________________

____________________________________________________

____________________________________________________

Which comment best describes your relationship with others doing the experiment withyou:

1. I have never seen either of my team members before today.

2. I have seen at least one team member but I don’t really know either of them.

3. I have worked/studied to some extent with either team member before today.

4. I have worked/studied with both of the team members before today.

5. This team has worked/studied together for about ____________years.

Is there an organization you have worked with in the last few years that would benefitfrom process management training and you believe would participate in this research.

Yes NoIf yes, I will contact you. Thank You.CONTINUE ON THE BACK SIDE

90

Process Management Tools Survey

1. Circle all steps below which are typical in conducting the nominal group technique.

Step 1 discuss possible solutions to a problem

2 list individuals’ ideas

3 clarify ideas, categorize and remove duplicates

4 vote to determine priorities

5 discuss voting results to ensure agreement

2. The nominal group technique can (circle all appropriate):

A. increase the leader’s influence on the group’s decisionB. increase team member anonymityC. increase group agreement with the decision

3. Criterion weightings used in multi-criteria decision making are used to (circle allappropriate):

A. eliminate unimportant criteriaB. determine relative importance between criteriaC. rank the alternatives’ degree of meeting the criteria

4. In multi-criteria decision making, if two of five alternatives have close scores that makeit difficult to choose between them, typical courses of action are (circle all appropriate):

A. start all overB. chose both of the alternativesC. re-analyze just the two close scoring alternativesD. discuss the two close scoring alternatives and select one

5. Multi-criteria decision making is especially good to use when (circle all appropriate):A. alternatives are not emotional issuesB. alternatives have clear quantitative differencesC. there are qualitative factors that must be considered

91

Laboratory Experiment Background Information

WELCOME, please get started by:

² Individually reading and signing the consent form.

² Putting on your name tag which has the role you will play today.

² Completing the pre-experiment questionnaire.

² Reading the background material.

92

Experiment- Background

You may ask questions of the researcher at any time today.

DIRECTIONS

Individually read through functional role descriptions, specifications and evaluation sheets.

Functional Role Descriptions

To simulate real-world cross functional teams during the experiment, role playing will benecessary. Each student will have a role assigned to them and an objective they are tryingto achieve. The name plate in front of you is your role. Please read the objectives of allthe roles listed below.

$$$$ There is a $5 bonus for the person that plays their functional role the best and influ-ences the group the most regarding their functional objective (the researcher will throughobservation determine the winner of the bonus). BUT REMEMBER, this is a team proj-ect and the overall evaluation of your product is based on optimizing several design fac-tors.

Quality Control Managerobjective: Ensure that quality, measured by the number of defects, is the most importantfactor in the design of the prototype.

Manufacturing Managerobjective: Ensure that ease of manufacturability, measured by the number of parts re-quired in the proto-type is considered the most important factor in the design of the pro-totype.

Purchasing Managerobjective: Ensure that all aspects of cost (material, operational and manufacturing) areconsidered the most important factors in the design of the prototype.

The people participating with you today represent a team competing with other team towin a NASA contract to design and construct a moon mobile. Time will be called with20 minutes remaining so that you can complete the evaluation sheets and a final question-naire.

93

Interface with your computer tech (facilitator)-

Your tech is here to support your meeting. YOU control the pace of your meeting and

the outcomes. Your tech will advance the slides for you and give you more detailed in-

structions if appropriate.

The instructions on the screen will guide you in completing the experiment.

SPECIFICATIONS

The completed prototype moon mobile must be able to independently (one push and

then all contact should be removed) roll twelve inches (staying together) on the table.

The moon mobile must be solar powered. The moon mobile collects solar energy

through the red triangular or square fins. The moon mobile must generate 400 joules of

energy. This can be done with one triangular fin if it can rotate 360 degrees or one sta-

tionary square fin. Also, the energy generator must be 12 inches off the ground to ade-

quately collect the sun’s rays.

Connections are defined as where two parts are attached. A bearing is not a connection

(but is a part).

Look closely at the cost and quality information, and the two evaluation sheets. You will

identify critical design factors with this information.

94

COST INFORMATION

Material Cost

Number of Parts Cost

15 or fewer $1,000,000

16 $2,000,000

17 $3,000,000

18 $4,000,000

19 $10,000,000

20 $15,000,000

21 $20,000,000

22 $25,000,000

23 $30,000,000

Over 24 $60,000,000

Operating Cost

Height in Inches Cost

49 or higher $1,000,000

47-48 $2,000,000

45-46 $3,000,000

43-44 $10,000,000

41-42 $15,000,000

40 and under $20,000,000

Manufacturing Cost

Number of Connections Cost

15 or fewer $1,000,000

16-17 $2,000,000

18-19 $3,000,000

20-21 $4,000,000

22-23 $10,000,000

24-25 $15,000,000

26 and over $20,000,000

95

QUALITY INFORMATION

material defect rate per 100

green rod 5

red rod 5

blue rod 10

purple rod 15

orange rod 20

red wheel 20

red triangular fin 0

red square fin 15

purple connector 0

yellow connector spool 0

blue spinning connector spool 0

orange end cap 0

blue lid 0

orange washer 0

green bearing 0

96

OPERATIONAL PERFORMANCE EVALUATION SHEET (1 of 2)

Effectiveness

____ 1. The moon mobile must be able to independently roll twelve inches.

2. The moon mobile must produce at least 400 joules of energy.

_____ A. Triangular fin that can rotate 360 degrees and is 12 inches off the ground.

OR

_____ B. Square fin, 12 inches off the ground.

Cost

material cost actual number of parts: cost from cost sheet:

operating cost actual height in inches: cost from cost sheet

manufacturing cost actual number of connections: cost from cost sheet

Total Cost:

Productivity

time subtotal

number of parts:

x 2 hr. per part =

number of connections:

x 5 hr. per connection =

Total Time:

97

OPERATIONAL PERFORMANCE EVALUATION SHEET (2 of 2)

Efficiency

Number of parts: __________

Quality

material defect rate per 100 percentage number of parts Subtotal

green rod 5 .05

red rod 5 .05

blue rod 10 .10

purple rod 15 .15

orange rod 20 .20

red wheel 20 .20

red triangular fin 0 0

red square fin 15 .15

purple connector 0 0

yellow connector spool 0 0

blue spinning connector

spool

0 0

orange end cap 0 0

blue lid 0 0

orange washer 0 0

green bearing 0 0

Total Defects:

98

FIELD EXPERIMENT GROUP EXERCISE

You will work with others today to design and construct three moon mobiles for NASA. Your moon mo-

biles will be evaluated on: meeting specifications, cost, quality, and time (how long it takes you to build

the mobile). You are competing with other teams from within your company and from other companies.

Two teams each from Siemens, Kollmorgen, and Volvo are scheduled to compete. The winning team will

receive a trophy with their winning mobile attached to a beautiful stand to be exhibited proudly at their

location (after being shown to all competitor teams). The exercise is taped to help the facilitator under-

stand how the exercise was executed. Only Marla will view the video tapes.

Evaluation

You will be given different specification information, cost data, and defect rate data for each of the three

moon mobiles you will be designing and constructing. Use this data to build the “best” mobile. How long

it takes you to design and build each of the moon mobiles will be determined. Once all teams, from all

companies, have built their mobiles, each mobile will be rank ordered based upon cost, defect rate, and

time. A penalty of 20 points is given for not meeting a specification. For example, if one of your models

met all specifications, and had the lowest cost, the lowest defect rate and the fastest time (relative to all the

other models built by the other teams) it would be ranked 3 (1 for cost, 1 for defect rate, and 1 for time).

Which comment best describes how well you know the people you are about to do this group exercise

with today?

1. I’ve seen some of them before but I don’t really know any of them.

2. I’ve worked to a limited extent with most of the people.

3. I’ve worked with at least one or two people fairly extensively before today.

4. I’ve worked with several (more than two) people fairly extensively before today.

5. I’ve worked with most of the people fairly extensively before today.

99

APPENDIX C EXPERIMENTAL PROCEDURES AND DATA COLLECTIONINSTRUMENTS

This appendix contains the formalized procedures used in each condition, the datasheets used by field participants to analyze their models, and researcher data sheets usedto analyze the session. This appendix contains;

- laboratory experiment formalized procedures conditions guidelines- laboratory experiment SYMLOG data sheet- field experiment instructions (includes instructions for all conditions)- field experiment data sheets (legos, tinker toys, miniquadros)- field experiment work group roles description

100

Laboratory Experiment Formalized Procedures Instructions

Planning Your Prototype

These instructions will guide you in completing the experiment. One person should be the

team’s meeting recorder and complete all of the templates (to be handed in) for the team.

A. Agree on the Task

Working together discuss the question on the template below and agree to what is written.

Template One

WHAT IS OUR TASK TODAY?

To give you time to complete the evaluation and final questionnaire, time will be calledwith 20 minutes remaining.

101

B. Identify Critical Factors (using the nominal group technique)

Step 1

² Without talking, individually write down on your notepad SPECIFIC critical factors

that should be considered in the design.

-- Use the specifications and evaluation criterion which you’ve read.

Step 2

² Taking turns, tell your meeting recorder ONE of your ideas for critical design factors.

-- Keep taking turns until all ideas are listed.

-- Clear duplicates should not be repeated.

-- There should be no challenging of ideas.

Step 3

² Discuss meanings of any of the factors. Every team member must agree to changes

made to any factor

² Discuss a factor until there is common understanding.

Step 4

² Determine the most important factors for consideration in designing the proto-type,

each team member votes on the top three factors. Record the votes right next to the fac-

tors.

Step 5

² Circle the three factors with the highest total ranking.

² If there are ties, through discussion try to decide the relative importance of factors.

² Discuss whether these are really the most important design factors or whether

something was missed.

² Do what is necessary to reach agreement on the three most important design factors.

102

Template Two

Critical Design Factors

FACTOR VOTE FACTOR VOTE

103

C. Build Two Proto-types

² Working together, and considering the top three critical design factors, BUILD two

different proto-types.

² FINISH THE FIRST PROTO-TYPE IN 20 MINUTES THEN START THE

SECOND PROTO-TYPE.

² Designate one proto-type as A and the other as B.

104

D. Select the Best Proto-type for Evaluation (using multi-criteria decision making)

Step 1 From your prior template, list your three most critical design factors from most

important to least important in the next template, from your prior ranking.

Step 2 Estimate (without calculating) how well each alternative meets each design factor

listed using the rating scale below the next template. Put the rating in the appropriate cell.

Template Three

design alternatives (drawings)

criteria design factors relative

weighting

A B

1.

10 10 x ________ 10 x ________

2.

7 7 x ________ 7 x ________

3.

4 4 x ________ 4 x ________

Total Score (sum of the column):

criterion for evaluating an alternative drawing against specific design factors

0 - does not meet criteria

2 - somewhat meets criteria, good deal of room for improvement

5 - meets criteria well, some room for improvement

10 - meets criteria very well, little or no room for improvement

Step 3 Calculate the weighted rating for each alternative. Do this by multiplying the rela-

tive weighting by the rating for each design factor and summing the column.

The highest total weighted score may be considered the selected proto-type for evaluation.

The group should agree with this conclusion or chose the other alternative. It is up to the

105

group. If no clear cut winner (scores are close), the group should decide which alternative

to select for evaluation.

106

Evaluating Your Proto-type

Complete the two evaluation sheets for your selected proto-type..

If any time during the evaluation, you modify your model please re-check your evaluation

calculations.

DO NOT TAKE APART YOUR PROTO-TYPE SO THAT A PICTURE

CAN BE TAKEN.

WRAP UP

² Complete the post-experiment questionnaire.

² Leave all paperwork.

² Do not discuss the experiment with others.

² Look for proto-type pictures and operational results sometime in November.

Thanks Again!

107

Laboratory Experiment SYMLOG Data Sheetnever rarely sometimes often always

U active, dominant, talks a lot 0 6 12 18 24

UP extroverted, outgoing, positive 0 3 6 9 12

UPF a purposeful democratic task leader 0 2 4 6 8

UF an assertive business-like manager 0 3 6 9 12

UNF authoritarian, controlling, disapproving 0 2 4 6 8

UN domineering, tough-minded, powerful 0 3 6 9 12

UNB provocative, egocentric, shows off 0 2 4 6 8

UB jokes around, expressive, dramatic 0 3 6 9 12

UPB entertaining, sociable, smiling, warm 0 2 4 6 8

P friendly, equalitarian 0 6 12 18 24

PF works cooperatively with others 0 3 6 9 12

F analytical, task-oriented, problem-solving 0 6 12 18 24

NF legalistic, has to be right 0 3 6 9 12

N unfriendly, negativistic 0 6 12 18 24

NB irritable, cynical, won’t cooperate 0 3 6 9 12

B shows feelings and emotions 0 6 12 18 24

PB affectionate, likable, fun to be with 0 3 6 9 12

DP looks up to others, appreciative, trustful 0 3 6 9 12

DPF gentle, willing to accept responsibility 0 2 4 6 8

DF obedient, works submissively 0 3 6 9 12

DNF self-punishing, works too hard 0 2 4 6 8

DN depressed, sad, resentful 0 3 6 9 12

DNB alienated, quits, withdraws 0 2 4 6 8

DB afraid to try, doubts own ability 0 3 6 9 12

DPB quietly happy just to be with others 0 2 4 6 8

108

Field Experiment Instructions

On chart pad write names and years of service at the company.

Hand out the Group Exercise Sheet with Team and Participant numbering already

completed.

Condition 1 -- CONTROL

You will build 3 moon mobiles in the next three hours, about one per hour. You will be

given different materials to use for each moon mobile. You will be given different cost,

and defect rate data about the materials. And you will be given specification of design for

each moon mobile. Also, in each case you will be given differing types of help from me.

The first mobile you will construct is made of ________________ material. Here is the

data sheet for the material. Remember, in addition to cost, defect rate and specification,

how long it takes you to build your moon mobile counts in the final evaluation too. The

maximum amount of time you have to complete this model is 60 minutes. Please tell me

when you are finished so I can record your time accurately.

TURN ON VIDEO

BUILD

QUESTIONNAIRE

109

Condition 2 -- FACILITATED

Design: Identify Critical Factors (using the nominal group technique)

Step 1

² HAND OUT IDEA DATA SHEETS

² For this moon mobile I will help you determine the most critical design factors—

factors you think are important considerations in the design. Without talking, individually

write down on your notepad SPECIFIC critical factors that should be considered in the

design based upon the cost, defect rate, and specification data. This time counts too in the

final evaluation.

Step 2

² One at a time, take turns telling me your design factors.

-- Clear duplicates should not be repeated.

-- There should be no challenging of ideas.

Step 3

² Discuss meanings of any unclear factors.

Step 4

² Vote on the top two factors as I ask you (one at a time).

Step 5

² Circle the two or three factors with the highest total ranking.

Step 6 (IF FIRST EXPERIMENTAL CONDITION)

² Decide who should be the primary builder, time keeper, and design keeper.

BUILD

QUESTIONNAIRE

EVALUATE ROLES

110

Condition 3—OPTION FINDER

Design: Identify Critical Factors (using the nominal group technique and option finder)

Step 1

² HAND OUT IDEA DATA SHEETS

² In this condition, I help you determine the most critical design factors—factors you

believe are important considerations when you build your moon mobile. We will be using

some simple technology in the condition also. Without talking, individually write down on

your notepad SPECIFIC critical factors that should be considered in the design based

upon the cost, defect rate, and specification data. Remember time counts too. Your ideas

will be anonymous. Hand me your list of factors and I will input them into the computer

while you are at break.

Step 2

² Input ideas into the computer while everyone is at break

Step 3

² Discuss meanings of any unclear factors.

Step 4

² Using option finder have people vote on the their top two ideas.

Step 5

² Circle the two or three factors with the highest total ranking.

Step 6 (IF FIRST EXPERIMENTAL CONDITION)

² Decide who should be the primary builder, time keeper, and design keeper.

BUILD

QUESTIONNAIRE

EVALUATE ROLES--using option finder

111

Evaluate Roles:

1-not so good, 2-just okay, 3 is acceptable, 4-very well, 5-could not have done better

Post Exercise Administration

Questions to be asked:

1. Which condition do you believe the team members participated the best in and why?

2. Which condition do you believe produced the best moon mobile and why?

Wrap Up

² Thank everyone.

² In late March or early April I hope to announce the winning team

² Do not discuss the experiment with others.

² Pictures of the models.

² Cost sheets for each model.

² All handouts and chart pad paper.

112

FIELD EXPERIMENT DATA SHEET for TINKER TOYS

COST INFORMATION

Material Cost

Number of Parts Cost number

12 or fewer $1,000,000

13-15 $5,000,000

16-17 $20,000,000

18-19 $40,000,000

20 or more $60,000,000

Operating Cost

Height in Inches Cost height

49 or higher $1,000,000

41-48 $5,000,000

17-40 $10,000,000

15-16 $20,000,000

13-14 $40,000,000

12 and under $60,000,000

Total Cost:

QUALITY INFORMATION

material defect rate defects

red rod .01

purple rod .10

orange rod .20

red wheel .20

red triangular fin 0

red square fin .15

purple connector 0

yellow connector spool 0

blue spinning connector spool .10

green bearing 0

Total Defect Rate:

SPECIFICATION REQUIREMENTS

1. The moon mobile must be able to independently roll 12 inches with one push,

without falling over or apart. You get one “official” push.

2. The moon mobile is solar powered. Solar energy is collected through either the

triangular fin if it can rotate 360 degrees or by a stationary square fin. Either fin

must be at least 12 inches off the ground.

`

113

FIELD EXPERIMENT DATA SHEET for LEGOS

COST INFORMATION

Material Cost

Number of Parts Cost number

10 or fewer $1,000,000

11-14 $10,000,000

15-17 $20,000,000

18 or more $40,000,000

Operating Cost

Height in Inches

of the Propeller

Cost height

30 or higher $1,000,000

25-29 $10,000,000

20-24 $20,000,000

11-19 $40,000,000

10 and under $60,000,000

Total Cost:

QUALITY INFORMATION

material defect rate defects

little lego .01

big lego-1 inch .10

big lego- 2 inch .30

green flatbed 0

yellow flatbed 0

wind propeller with red lego 0

2 wheel set .2

Total Defect Rate:

SPECIFICATION REQUIREMENTS

1. The moon mobile must be able to independently roll 12 inches with one push,

without falling over or apart. You get one “official” push.

2. The moon mobile is wind powered. Energy is generated through the yellow

propeller. The propeller must be at least 12 inches off the ground.

114

FIELD EXPERIMENT DATA SHEET for MINIQUADROS

COST INFORMATION

Material Cost

Number of Parts Cost

20 or fewer $1,000,000

19-30 $5,000,000

31-40 $10,000,000

41 and more $30,000,000

Operating Cost

Height in Inches of the Highest Solar

Panel

Cost

under 2 $30,000,000

3-8 $20,000,000

9-11 $10,000,000

12-14 $5,000,000

14 or higher $1,000,000

QUALITY INFORMATION

material defect rate per 100 parts

small red rod 1

medium red rod 10

large red rod 30

wheel 20

black connector 10

red wheel cover 15

rubber band 0

SPECIFICATION REQUIREMENTS

1. The moon mobile must be able to independently roll 12 inches with one push,

without falling over or apart.

2. The moon mobile is solar powered. Solar energy is collected through the yellow

solar panels. Two yellow panels are required for the moon mobile.

115

FIELD EXPERIMENT WORK GROUP ROLE DECRIPTIONS

Roles and Responsibilities

Primary Building Contractor

Begins the building process. Does allow others to also build. Is the person to call time

and begin the official run.

Time Keeper

Time counts too. The Time Keeper reminds the team of the time they have used.

Design Keeper

The team chose important design considerations. The Design Keeper reminds the team of

these important considerations.

116

APPENDIX D POST-EXPERIMENT QUESTIONNAIRES

This appendix contains the post-experiment questionnaires:

- laboratory experiment post-experiment questionnaire- field experiment post-exercise questionnaire

117

Laboratory Experiment Post-experiment Questionnaire

1. Your group reached a good deal of agreement on the moon mobile design.1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

2. There was a warm, easygoing, supportive atmosphere during the entire experiment.1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

3. Your group probably reached its highest level of agreement and more time would nothave helped in gaining more agreement on the mobile design.1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

4. The members in your group discussed the design in an understanding and orderly man-ner throughout the entire experiment.1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

5. Every member in your group made very helpful suggestions about the moon mobileyou were designing.1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

6. None of the participants in your group were more close-minded and opinionated thanopen-minded and non-opinionated.1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

118

7. The overall quality of my group’s discussion about the design was good and supportedmy group’s objectives:1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

8. The moon mobile design discussion, on the whole, was effective and led to the optimalprototype:1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

9. The final moon mobile proto-type was satisfactory and met specs and operational ob-jectives:1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

10. The moon mobile design was competently executed; it could not have been better:1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

11. The issues explored during design were substantial and complex, requiring depth inanalysis:1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

12. The issues being explored during the discussion were understood by all team membersthroughout the experiment:

1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

119

13. The manner in which team members examined design issues was constructive and allthe issues were always evaluated in a supportive manner:1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

14. My group’s ability to design a moon mobile, under the laboratory circumstances, wasimpressive and could not have been any better:1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

15. The behavior of the group was focused fully on the task:1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

16. My team members initiated discussion on relevant issues and items important to thetask:1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

17. The participation in the design discussion was evenly distributed between team mem-bers:1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

18. The ideas expressed by members in the design discussion were thoroughly explored:1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

120

19. We always dealt with design issues in a systematic manner:1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

20. The interpersonal relationships between my team members was good :1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

21. The objectives of all the functional roles which team members played (quality man-ager, manufacturing manager, purchasing manager) during the experiment were veryvisible throughout the experiment:1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

22. I achieved my functional role’s objective:1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

23. The computer system improved our ability to design an optimal proto-type.1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

24. The use of the overhead display improved our ability to design an optimal proto-type.1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

121

Field Experiment Post-exercise Questionnaire

1. There was a warm, easygoing, supportive atmosphere during the entire experiment.1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

2. The members in your group discussed the design in an understanding and orderly man-ner throughout the entire experiment.1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

3. Every member in your group made very helpful suggestions about the moon mobileyou were designing.1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

4. None of the participants in your group were more close-minded and opinionated thanopen-minded and non-opinionated.1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

5. The overall quality of my group’s discussion about the design was good and supportedmy group’s objectives:1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

6. The moon mobile design discussion, on the whole, was effective and led to the optimalprototype:1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

122

7. The moon mobile design was competently executed; it could not have been better:1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

8. My group’s ability to design a moon mobile, under the circumstances, was impressiveand could not have been any better:1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

9. My team members initiated discussion on relevant issues and items important to thetask:1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

10. The participation in the design discussion was evenly distributed between team mem-bers:1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

11. The ideas expressed by members in the design discussion were thoroughly explored:1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

12. The interpersonal relationships between my team members was good :1 2 3 4 5

s t r o n g l ya g r e e

s t r o n g l yd i s a g r e e d i s a g r e e n e u t r a l a g r e e

123

APPENDIX E RAW DATA

This appendix contains all the raw data from the laboratory and field experiments.This appendix contains the following raw data:

- laboratory experiment task performance- laboratory experiment: association, process knowledge test results, and ideas- laboratory experiment SYMLOG- laboratory experiment questionnaire- field experiment task performance- field experiment work group characteristics- field experiment frequency of verbal comments- field experiment idea generation by participants- field experiment idea evaluation- field experiment role evaluation facilitated condition- field experiment role evaluation facilitated with technology condition- field experiment questionnaire- field experiment qualitative comments

124

Laboratory Experiment Task Performance Raw Data

control cost quality time spec totalteam # #parts $parts h $h total$ rank defect rate rank time rank spec penalty rank score

1 19 10 47.50 2 12 4 1.9 19 55 1 y 242 15 1 15.50 20 21 10 1.05 12 75 12 y 343 14 1 14.50 20 21 10 0.25 4 60 2 NO 20 364 18 4 45.00 3 7 2 1.1 14 65 5 y 215 14 1 14.00 20 21 10 1.15 16 80 16.5 y 43

average: 16.00 3.40 27.30 13.00 16.40 7.20 1.09 12.80 67.00 7.30 31.30

std dev 2.35 3.91 17.33 9.59 6.54 3.90 0.58 5.88 10.37 6.70 8.76

manual cost quality time specteam # #parts $parts h $h total$ rank defect rate rank time rank spec penalty

1 15 1 16.00 20 21 10 0.85 9 71 7.5 y 272 16 2 46.00 3 5 1 1.5 17 78 14 y 323 16 2 12.75 20 22 15 0.3 6 71 7.5 y 284 12 1 15.00 20 21 10 0.15 1 74 11 y 225 26 60 12.00 20 80 20 1.05 12 73 10 y 42

average: 17.00 13.20 20.35 16.60 29.80 11.20 0.77 8.80 73.40 10.00 30.00

std dev 5.29 26.17 14.43 7.60 28.94 7.05 0.55 6.05 2.88 2.72 7.36

125

facilitated cost quality timeteam # #parts $parts h $h total$ rank defect rate rank time rank spec

1 17 3 40.00 20 23 16 0.8 8 89 19 y 432 24 60 50.00 1 61 19 1.6 18 80 16 y 53

3 15 1 44.00 10 11 3 0.9 10 62 3 NO 20 364 15 1 12.25 20 21 10 1.1 14 76 13 y 375 20 15 15.00 20 35 18 1.1 14 68 6 y 38

average: 18.20 16.00 32.25 14.20 30.20 13.20 1.10 12.80 75.00 11.40 41.40

std dev 3.83 25.28 17.40 8.56 19.21 6.69 0.31 3.90 10.49 6.73 7.02

facilitated and tech cost quality timeteam # #parts $parts h $h total$ rank defect rate rank time rank spec

1 9 1 14.50 20 21 10 0.7 7 90 20 y 372 12 1 15.50 20 21 10 0.2 2 79 15 y 273 18 4 15.50 20 24 17 0.3 6 82 18 y 414 20 15 49.00 1 16 5 2.6 20 72 9 y 345 13 1 12.00 20 21 10 0.25 4 64 5 y 19

average: 14.40 4.40 21.30 16.20 20.60 10.40 0.81 7.60 77.40 13.40 31.40

std dev 4.51 6.07 15.55 8.50 2.88 4.28 1.02 7.19 9.89 6.27 8.76

126

Laboratory Experiment: Association, Process Knowledge Test Results, and IdeasRaw Data

level of associationcontrol manual facilitated fac&tec

1 1.33 3.33 2.33 2.002 1.67 2.33 1.67 2.003 1.67 1.67 2.67 2.334 1.33 2.67 2.00 2.005 2.33 2.33 1.00 1.00

sum 8.33 12.33 9.67 9.33

average 1.67 2.47 1.93 1.87

std dev 0.41 0.60 0.64 0.50

process management quiz scorecontrol manual facilitated fac&tec

1 20 11 14 162 12 10 10 103 13 16 12 104 16 17 12 155 16 15 7 12

sum 77.00 69.00 55.00 63.00

average 15.40 13.80 11.00 12.60

std dev 3.13 3.11 2.65 2.79

number of ideascontrol manual facilitated fac&tec

1 6 7 52 4 11 73 3 7 84 4 8 55 3 6 7

sum 20.00 39.00 32.00

average 4.00 7.80 6.40

std dev 1.22 1.92 1.34

127

Laboratory Experiment SYMLOG Raw Data

SYMLOG

Analysis

OctoberCond: 1.1 Exp: 7 Date: 7

demo: wm wm wmManf QC Purch

U 12 6 18UP 3 3 6UPF 4 2 6UF 6 0 6UNF 2 0 0UN 3 0 0UNB 0 0 0UB 0 0 3UPB 2 2 6P 12 12 18PF 6 6 9F 18 18 18NF 3 3 3N 0 0 0NB 0 0 0B 6 6 6PB 3 3 3DP 6 9 3DPF 6 6 4DF 3 9 3DNF 0 0 0DN 0 0 0DNB 0 0 0DB 3 3 3DPB 2 6 2D 12 18 6

group 1.1manf qc purch average difference

B 16 20 23 19.67F 48 32 44 24 49 26 47.00 27.33

N 8 3 3 4.67P 44 36 49 46 57 54 50.00 45.33

D 32 51 21 34.67U 32 0 13 -38 45 24 30.00 -4.67

31.26B- social oriented D- dominance U- submissiveF- task oriented

N- not friendly P- friendly

128

Symlog AnalysisOctober

Cond: 1.2 Exp: 12 Date: 9

demo: mm wm mmManf QC Purch

U 0 18 0UP 0 9 0UPF 0 6 0UF 0 9 0UNF 0 2 0UN 0 3 0UNB 0 2 0UB 3 6 3UPB 2 4 2P 12 12 12PF 9 6 6F 18 18 12NF 3 3 3N 0 0 0NB 0 0 0B 6 6 6PB 3 6 6DP 9 6 9DPF 6 4 6DF 6 3 9DNF 0 0 0DN 0 0 0DNB 0 0 0DB 9 0 9DPB 6 2 6D 18 6 18

group 1.2manf qc purch average difference

B 29 26 32 29.00F 42 13 51 25 36 4 43.00 14.00

N 3 10 3 5.33P 47 44 55 45 47 44 49.67 44.33

D 54 21 57 44.00U 5 -49 59 38 5 -52 23.00 -21.00

51.12

129

Symlog AnalysisOctober

Cond: 1.3 Exp: 16 Date: 15

demo: wm wm wfManf QC Purch

U 12 12 6UP 6 3 3UPF 6 4 2UF 6 9 3UNF 0 2 0UN 0 0 0UNB 0 0 0UB 6 0 0UPB 6 2 2P 18 18 18PF 9 9 9F 18 18 18NF 0 3 0N 0 0 0NB 0 0 0B 6 0 6PB 6 3 3DP 6 6 9DPF 6 4 4DF 3 3 6DNF 0 0 0DN 0 0 0DNB 0 0 0DB 3 3 3DPB 4 2 4D 6 6 12

group 1.3manf qc purch average difference

B 31 10 18 19.67F 48 17 52 42 42 24 47.33 27.67

N 0 5 0 1.67P 67 67 51 46 54 54 57.33 55.67

D 28 24 38 30.00U 42 14 32 8 16 -22 30.00 0.00

19.29

130

Symlog AnalysisOctober

Cond: 1.4 Exp: 19 Date: 22

demo: wm mm wmManf QC Purch

U 6 6 24UP 3 3 6UPF 2 2 6UF 3 3 9UNF 0 2 4UN 0 3 6UNB 0 2 0UB 3 3 3UPB 4 4 4P 12 12 12PF 9 9 6F 12 12 12NF 0 0 3N 0 0 0NB 0 0 0B 6 6 0PB 3 6 3DP 9 9 9DPF 6 6 6DF 9 9 3DNF 0 0 0DN 0 0 0DNB 0 0 0DB 6 6 0DPB 4 4 0D 18 18 0

group 1.4manf qc purch average difference

B 26 31 10 22.33F 41 15 43 12 49 39 44.33 22.00

N 0 7 13 6.67P 52 52 55 48 52 39 53.00 46.33

D 52 52 18 40.67U 21 -31 28 -24 62 44 37.00 -3.67

41.43

131

Symlog AnalysisOctober

Cond: 1.5 Exp: 20 Date: 24

demo: wm mf mfManf QC Purch

U 6 6 6UP 3 3 3UPF 4 4 4UF 3 6 6UNF 2 0 2UN 3 0 6UNB 0 2 0UB 6 6 3UPB 6 6 4P 18 18 12PF 9 9 6F 18 12 18NF 0 3 3N 0 0 0NB 0 0 0B 6 6 6PB 6 6 3DP 6 6 3DPF 4 4 4DF 6 6 6DNF 0 0 0DN 0 0 0DNB 0 0 0DB 3 3 3DPB 2 4 4D 6 6 12

group 1.5manf qc purch average difference

B 29 33 23 28.33F 46 17 44 11 49 26 46.33 18.00

N 5 5 11 7.00P 58 53 60 55 43 32 53.67 46.67

D 27 29 32 29.33U 33 6 33 4 34 2 33.33 4.00

2.00

132

Symlog AnalysisOctober

Cond: 2.1 Exp: 1 Date: 3

demo: wm wm wfManf QC Purch

U 12 12 6UP 3 3 3UPF 6 4 4UF 9 9 3UNF 2 0 0UN 3 0 0UNB 2 0 2UB 3 3 6UPB 4 2 4P 18 12 18PF 9 6 9F 18 18 18NF 3 0 0N 0 0 0NB 0 0 0B 6 6 6PB 6 3 3DP 6 3 6DPF 6 4 4DF 3 6 6DNF 0 0 0DN 0 0 0DNB 0 0 0DB 3 3 3DPB 2 2 2D 6 6 12

group 2.1manf qc purch average range

B 26 19 26 23.67F 56 30 47 28 44 18 49.00 25.33

N 10 0 2 4.00P 60 50 39 39 53 51 50.67 46.67

D 26 24 33 27.67U 44 18 33 9 28 -5 35.00 7.33

11.59

133

Symlog AnalysisOctober

Cond: 2.2 Exp: 2 Date: 4

demo: mm mm mfManf QC Purch

U 6 6 12UP 3 3 6UPF 4 4 6UF 6 6 9UNF 0 0 0UN 0 0 0UNB 0 0 0UB 3 3 6UPB 4 4 6P 18 18 18PF 9 9 9F 18 18 18NF 0 0 0N 0 0 0NB 0 0 0B 6 6 12PB 9 6 6DP 9 9 3DPF 6 6 4DF 9 9 0DNF 0 0 0DN 0 0 0DNB 0 0 0DB 6 6 3DPB 4 4 2D 12 12 6

group 2.2manf qc purch average range

B 32 29 35 32.00F 52 20 52 23 46 11 50.00 18.00

N 0 0 0 0.00P 66 66 63 63 60 60 63.00 63.00

D 46 46 18 36.67U 26 -20 26 -20 45 27 32.33 -4.33

27.14

134

Symlog AnalysisOctober

Cond: 2.3 Exp: 5 Date: 6

demo: wm wm mmManf QC Purch

U 18 12 0UP 6 3 0UPF 4 4 0UF 9 6 0UNF 6 4 0UN 6 3 0UNB 2 0 0UB 3 3 3UPB 2 2 2P 12 6 12PF 6 6 6F 18 18 12NF 3 6 0N 0 0 0NB 0 0 0B 6 6 6PB 3 3 3DP 3 6 6DPF 4 4 4DF 3 6 6DNF 0 0 0DN 0 0 0DNB 0 0 0DB 3 3 6DPB 2 2 4D 6 12 18

group 2.3manf qc purch average range

B 21 19 24 21.33F 53 32 54 35 28 4 45.00 23.67

N 17 13 0 10.00P 42 25 36 23 37 37 38.33 28.33

D 21 33 44 32.67U 56 35 37 4 5 -39 32.67 0.00

37.16

135

Symlog AnalysisOctober

Cond: 2.4 Exp: 10 Date: 8

demo: mm mm wfManf QC Purch

U 12 6 12UP 3 3 3UPF 2 2 4UF 3 3 3UNF 2 2 0UN 3 0 0UNB 0 0 0UB 3 0 0UPB 2 2 4P 12 12 12PF 6 6 9F 12 18 18NF 0 3 0N 0 0 0NB 0 0 0B 6 6 0PB 3 3 3DP 6 3 3DPF 4 4 4DF 3 3 3DNF 0 0 0DN 0 0 0DNB 0 0 0DB 3 6 3DPB 2 2 2D 12 12 6

group 2.4manf qc purch average range

B 19 19 12 16.67F 32 13 41 22 41 29 38.00 21.33

N 5 5 0 3.33P 40 35 37 32 44 44 40.33 37.00

D 30 30 21 27.00U 30 0 18 -12 26 5 24.67 -2.33

8.74

136

Symlog AnalysisOctober

Cond: 2.5 Exp: 14 Date: 10

demo: mm wf wfManf QC Purch

U 18 12 6UP 6 3 3UPF 2 6 2UF 9 6 3UNF 2 2 0UN 6 6 0UNB 2 0 0UB 6 0 0UPB 2 2 4P 12 12 12PF 6 6 9F 18 18 18NF 6 6 0N 0 0 0NB 0 0 0B 12 12 6PB 3 3 3DP 3 3 6DPF 4 6 4DF 3 3 6DNF 0 0 0DN 0 0 0DNB 0 0 0DB 3 3 6DPB 2 2 4D 6 6 12

group 2.5manf qc purch average range

B 30 22 23 25.00F 50 20 53 31 42 19 48.33 23.33

N 16 14 0 10.00P 40 24 43 29 47 47 43.33 33.33

D 21 23 38 27.33U 53 32 37 14 18 -20 36.00 8.67

26.41

137

Symlog AnalysisOctober

Cond: 3.1 Exp: 3 Date: 4

demo: mf wf wmManf QC Purch

U 12 12 12UP 3 3 3UPF 2 2 2UF 6 6 6UNF 4 2 2UN 3 0 0UNB 2 0 0UB 3 0 6UPB 4 4 6P 12 12 18PF 9 9 9F 18 18 18NF 6 0 0N 0 0 0NB 0 0 0B 6 0 6PB 6 6 9DP 6 6 6DPF 4 4 4DF 0 0 0DNF 0 0 0DN 0 0 0DNB 0 0 0DB 3 3 3DPB 2 2 2D 0 0 0

group 3.1bmanf qc purch average range

B 26 15 32 24.33F 49 23 41 26 41 9 43.67 19.33

N 15 2 2 6.33P 48 33 48 46 59 57 51.67 45.33

D 15 15 15 15.00U 39 24 29 14 37 22 35.00 20.00

5.29

138

Symlog AnalysisOctober

Cond: 3.2 Exp: 4 Date: 4

demo: wm mm mfManf QC Purch

U 18 0 18UP 3 0 3UPF 2 2 2UF 6 3 6UNF 2 0 2UN 3 0 3UNB 0 0 2UB 3 0 6UPB 4 2 6P 12 6 12PF 6 6 9F 18 12 18NF 3 0 3N 0 0 0NB 0 0 0B 0 6 6PB 6 3 6DP 6 9 6DPF 4 4 4DF 0 0 0DNF 0 0 0DN 0 0 0DNB 0 0 0DB 3 6 3DPB 2 6 2D 0 24 0

group 3.2bmanf qc purch average range

B 18 23 31 24.00F 41 23 27 4 44 13 37.33 13.33

N 8 0 10 6.00P 45 37 38 38 50 40 44.33 38.33

D 15 49 15 26.33U 41 26 7 -42 48 33 32.00 5.67

41.43

139

Symlog AnalysisOctober

Cond: 3.3 Exp: 8 Date: 7

demo: wm wm mmManf QC Purch

U 18 6 18UP 3 0 3UPF 2 2 2UF 9 3 6UNF 4 0 2UN 3 0 0UNB 2 0 2UB 3 0 3UPB 2 2 4P 12 6 12PF 9 6 9F 18 6 18NF 3 0 3N 0 0 0NB 0 0 0B 0 0 0PB 6 3 6DP 6 6 6DPF 4 4 4DF 0 9 0DNF 0 0 0DN 0 0 0DNB 0 0 0DB 0 6 0DPB 0 6 0D 0 18 0

group 3.3bmanf qc purch average range

B 13 17 15 15.00F 49 36 30 13 44 29 41.00 26.00

N 12 0 7 6.33P 44 32 35 35 46 39 41.67 35.33

D 10 49 10 23.00U 46 36 13 -36 40 30 33.00 10.00

39.95

140

Symlog AnalysisOctober

Cond: 3.4 Exp: 9 Date: 7

demo: wm mm mmManf QC Purch

U 12 6 12UP 3 0 3UPF 2 2 2UF 6 3 9UNF 2 0 2UN 0 0 3UNB 0 0 0UB 0 0 0UPB 2 2 4P 12 12 18PF 9 9 9F 18 18 18NF 0 0 3N 0 0 0NB 0 0 0B 0 0 0PB 6 3 6DP 6 9 3DPF 4 4 4DF 3 9 0DNF 0 0 0DN 0 0 0DNB 0 0 0DB 3 0 3DPB 2 6 0D 6 18 0

group 3.4bmanf qc purch average range

B 13 11 13 12.33F 44 31 45 34 47 34 45.33 33.00

N 2 0 8 3.33P 46 44 47 47 49 41 47.33 44.00

D 24 46 10 26.67U 27 3 13 -33 35 25 25.00 -1.67

29.28

141

Symlog AnalysisOctober

Cond: 3.5 Exp: 15 Date: 10

demo: wm wm wmManf QC Purch

U 12 12 12UP 3 3 3UPF 2 2 2UF 6 3 6UNF 0 2 2UN 0 0 3UNB 0 0 0UB 0 3 3UPB 4 4 6P 12 12 12PF 9 9 9F 18 18 18NF 0 3 3N 0 0 0NB 0 0 0B 0 0 6PB 6 3 6DP 6 3 6DPF 4 4 4DF 0 0 0DNF 0 0 0DN 0 0 0DNB 0 0 0DB 0 0 0DPB 0 0 0D 0 0 0

group 3.5bmanf qc purch average range

B 10 10 21 13.67F 39 29 41 31 44 23 41.33 27.67

N 0 5 8 4.33P 46 46 40 35 48 40 44.67 40.33

D 10 7 10 9.00U 27 17 29 22 37 27 31.00 22.00

5.00

142

Symlog AnalysisOctober

Cond: 4.1 Exp: 6 Date: 6

demo: wf wm wmManf QC Purch

U 12 12 12UP 6 6 6UPF 4 4 4UF 9 9 9UNF 4 4 4UN 6 6 6UNB 2 2 2UB 0 3 0UPB 2 12 2P 12 6 6PF 6 18 6F 18 6 18NF 6 0 6N 0 0 0NB 0 6 0B 6 3 0PB 6 6 3DP 6 4 3DPF 4 0 4DF 0 0 0DNF 0 0 0DN 0 0 0DNB 0 0 0DB 3 0 0DPB 2 0 0D 0 0 0

group 4.1bmanf qc purch average range

B 21 32 7 20.00F 51 30 41 9 51 44 47.67 27.67

N 18 18 18 18.00P 48 30 56 38 34 16 46.00 28.00

D 15 4 7 8.67U 45 30 58 54 45 38 49.33 40.67

12.22

143

Symlog AnalysisOctober

Cond: 4.2 Exp: 11 Date: 8

demo: wm wm wfManf QC Purch

U 12 12 12UP 3 3 3UPF 2 2 2UF 6 6 6UNF 0 0 0UN 0 0 0UNB 0 0 0UB 0 0 0UPB 2 2 0P 18 18 12PF 9 9 9F 18 18 18NF 0 0 0N 0 0 0NB 0 0 0B 0 0 0PB 3 3 3DP 6 6 6DPF 4 4 4DF 0 0 0DNF 0 0 0DN 0 0 0DNB 0 0 0DB 0 0 0DPB 0 0 0D 0 0 0

group 4.2bmanf qc purch average range

B 5 5 3 4.33F 39 34 39 34 39 36 39.00 34.67

N 0 0 0 0.00P 47 47 47 47 39 39 44.33 44.33

D 10 10 10 10.00U 25 15 25 15 23 13 24.33 14.33

1.15

144

Symlog AnalysisOctober

Cond: 4.3 Exp: 17 Date: 15

demo: wm wm wmManf QC Purch

U 12 12 12UP 3 3 3UPF 2 2 2UF 3 3 3UNF 0 0 0UN 0 0 0UNB 0 0 0UB 0 3 6UPB 2 2 4P 12 12 18PF 9 9 9F 18 18 18NF 0 0 0N 0 0 0NB 0 0 0B 0 6 0PB 3 3 6DP 6 6 6DPF 4 4 4DF 0 0 0DNF 0 0 0DN 0 0 0DNB 0 0 0DB 0 0 0DPB 0 0 0D 0 0 0

group 4.3bmanf qc purch average range

B 5 14 16 11.67F 36 31 36 22 36 20 36.00 24.33

N 0 0 0 0.00P 41 41 41 41 52 52 44.67 44.67

D 10 10 10 10.00U 22 12 25 15 30 20 25.67 15.67

4.04

145

Symlog AnalysisOctober

Cond: 4.4 Exp: 17 Date: 21

demo: mf mf wmManf QC Purch

U 0 0 18UP 3 0 3UPF 2 2 4UF 3 3 6UNF 0 0 4UN 0 0 3UNB 0 0 0UB 0 0 0UPB 2 2 2P 12 12 12PF 9 9 9F 18 12 18NF 0 0 3N 0 0 0NB 0 0 0B 0 0 0PB 3 3 3DP 9 9 3DPF 6 4 4DF 9 9 0DNF 0 0 0DN 0 0 0DNB 0 0 0DB 3 3 0DPB 6 6 0D 18 18 0

group 4.4bmanf qc purch average range

B 14 14 5 11.00F 47 33 39 25 48 43 44.67 33.67

N 0 0 10 3.33P 52 52 47 47 40 30 46.33 43.00

D 51 49 7 35.67U 10 -41 7 -42 40 33 19.00 -16.67

43.02

146

Symlog AnalysisOctober

Cond: 4.5 Exp: 20 Date: 24

demo: wm wm mmManf QC Purch

U 6 12 12UP 0 3 3UPF 2 2 2UF 3 6 6UNF 0 2 0UN 0 0 0UNB 0 0 0UB 0 0 0UPB 2 2 4P 12 12 18PF 6 9 9F 12 18 18NF 0 0 0N 0 0 0NB 0 0 0B 0 0 0PB 3 3 3DP 9 6 9DPF 4 4 6DF 6 0 6DNF 0 0 0DN 0 0 0DNB 0 0 0DB 3 0 3DPB 4 0 4D 6 0 6

group 4.5bmanf qc purch average range

B 12 5 14 10.33F 33 21 41 36 47 33 40.33 30.00

N 0 2 0 0.67P 42 42 41 39 58 58 47.00 46.33

D 32 10 34 25.33U 13 -19 27 17 27 -7 22.33 -3.00

18.33

147

Laboratory Experiment Questionnaire Raw Data

perceived level of agreement

exp agree1 agree2 agree3 agree4 agree5 agree6exp1.1 5 4 4 2 5 4exp1.1 4 4 3 4 4 4exp1.1 4 4 3 4 4 3exp1.2 5 5 2 4 5 4exp1.2 5 5 3 5 5 4exp1.2 5 5 4 5 5 5exp1.3 5 5 4 5 5 5exp1.3 5 5 4 5 5 5exp1.3 4 4 3 4 5 5exp1.4 4 4 3 4 4 4exp1.4 4 5 4 5 5 4exp1.4 4 4 3 4 4 4exp1.5 5 5 5 5 5 5exp1.5 5 5 4 4 5 5exp1.5 4 5 4 5 5 4exp2.1 5 5 4 4 5 4exp2.1 5 5 2 5 5 4exp2.1 4 4 2 2 5 5exp2.2 4 5 4 5 5 5exp2.2 4 5 4 5 5 4exp2.2 4 5 2 4 4 3exp2.3 5 4 5 4 4 4exp2.3 5 4 4 4 4 4exp2.3 5 5 4 4 4 4exp2.4 4 4 2 2 4 3exp2.4 4 4 4 3 4 4exp2.4 5 4 4 4 4 4exp2.5 4 4 5 2 5 3exp2.5 4 5 1 5 5 4exp2.5 3 1 2 1 2 1exp3.1 4 4 2 3 4 5exp3.1 4 4 3 4 5 5exp3.1 4 3 4 4 4 5exp3.2 2 3 4 2 4 2exp3.2 4 3 4 3 3 3exp3.2 3 4 1 3 2 3exp3.3 4 4 4 4 4 5exp3.3 5 5 3 4 5 5exp3.3 4 4 4 4 4 4exp3.4 5 5 4 4 4 4exp3.4 5 5 5 5 5 5exp3.4 5 4 4 4 5 5exp3.5 5 4 3 4 5 5exp3.5 4 4 5 4 5 4

148

exp3.5 4 4 5 3 4 4exp4.1 2 1 5 2 1 1exp4.1 2 2 4 4 4 2exp4.1 2 4 1 4 5 3exp4.2 5 5 4 4 4 4exp4.2 5 4 4 5 5 5exp4.2 4 4 3 4 5 4exp4.3 4 4 4 4 5 4exp4.3 5 5 5 5 5 4exp4.3 4 5 4 4 5 5exp4.4 5 5 4 5 5 5exp4.4 5 5 4 4 4 5exp4.4 5 4 4 3 5 5exp4.5 3 4 4 5 4 4exp4.5 4 5 4 4 4 4exp4.5 5 4 5 3 2 2

perceived quality of disscus-sionexp qual7 qual8 qual9 qual10 qual11 qual12 qual13 qual14exp1.1 4 4 4 4 3 3 2 2exp1.1 4 4 5 4 3 4 4 4exp1.1 2 3 4 3 4 3 4 3exp1.2 4 4 5 4 4 4 5 4exp1.2 5 4 4 2 4 5 5 4exp1.2 5 5 5 4 4 4 5 4exp1.3 4 4 2 3 4 5 4 4exp1.3 4 4 4 2 2 4 5 2exp1.3 4 3 2 3 2 5 4 4exp1.4 3 4 4 2 4 4 4 3exp1.4 4 3 5 3 3 5 5 4exp1.4 3 4 4 3 3 4 3 2exp1.5 5 5 5 4 3 4 5 4exp1.5 4 4 4 3 3 4 5 4exp1.5 5 4 5 4 4 5 5 4exp2.1 5 4 3 2 4 4 5 3exp2.1 4 5 5 4 4 4 4 4exp2.1 4 4 5 4 5 5 3 4exp2.2 5 4 4 3 4 5 5 4exp2.2 5 5 4 5 4 5 5 5exp2.2 4 4 4 2 3 4 4 2exp2.3 4 5 5 4 4 4 4 4exp2.3 5 5 5 4 3 4 4 4exp2.3 5 5 4 4 2 4 4 3exp2.4 4 4 5 3 3 4 4 4exp2.4 4 4 4 2 4 4 4 2exp2.4 5 4 5 4 4 5 4 4exp2.5 4 4 4 2 3 4 4 1exp2.5 5 5 5 4 1 5 5 5exp2.5 2 2 4 1 3 2 1 1exp3.1 4 3 5 2 3 2 4 2exp3.1 4 4 4 2 4 2 3 2

149

exp3.1 4 4 4 3 4 4 3 4exp3.2 3 2 4 2 2 3 2 2exp3.2 4 2 4 2 3 4 3 2exp3.2 3 2 1 1 4 4 3 1exp3.3 4 1 1 3 4 4 4 5exp3.3 4 4 2 2 3 4 4 2exp3.3 4 1 1 1 4 5 4 1exp3.4 4 3 4 3 2 4 4 3exp3.4 5 3 5 5 3 3 5 5exp3.4 5 5 4 5 3 5 5 5exp3.5 4 4 5 3 4 5 4 5exp3.5 4 3 4 2 3 5 4 2exp3.5 4 5 5 5 4 3 4 5exp4.1 4 3 4 3 4 2 3 2exp4.1 3 4 5 4 4 2 2 5exp4.1 5 4 4 4 4 4 5 4exp4.2 4 4 5 4 5 5 4 4exp4.2 5 5 5 4 2 5 5 4exp4.2 5 5 5 5 2 5 5 4exp4.3 5 4 5 4 2 5 4 4exp4.3 5 5 5 4 4 4 4 4exp4.3 4 3 5 4 3 4 4 4exp4.4 5 4 4 4 2 4 5 4exp4.4 4 5 5 5 4 3 4 4exp4.4 4 4 5 4 3 4 4 4exp4.5 4 4 4 3 2 3 4 2exp4.5 4 3 4 2 2 4 4 3exp4.5 3 2 4 3 5 4 4 2perceived team of interaction

exp inter15 inter16 inter17 inter18 inter19 inter20exp1.1 5 4 4 4 4 4exp1.1 4 4 4 4 3 4exp1.1 4 4 4 3 4 4exp1.2 5 5 4 4 4 4exp1.2 5 4 4 5 4 5exp1.2 4 5 4 4 3 5exp1.3 4 4 4 4 4 4exp1.3 5 5 5 4 4 5exp1.3 4 5 4 4 3 4exp1.4 4 4 3 4 4 4exp1.4 4 5 3 4 3 5exp1.4 4 4 2 3 3 4exp1.5 5 5 5 5 5 5exp1.5 5 5 5 4 4 5exp1.5 5 5 5 4 4 5exp2.1 4 4 5 5 3 5exp2.1 4 4 4 4 4 5exp2.1 4 4 5 4 2 4exp2.2 5 5 5 5 4 5

150

exp2.2 5 5 4 4 3 5exp2.2 4 4 4 4 3 4exp2.3 5 4 4 4 3 4exp2.3 5 4 3 4 5 5exp2.3 5 4 2 4 4 4exp2.4 4 4 4 4 3 4exp2.4 4 4 2 2 2 4exp2.4 4 5 5 4 4 5exp2.5 5 5 3 4 4 4exp2.5 5 5 5 5 2 3exp2.5 2 2 2 2 2 1exp3.1 5 5 4 4 2 4exp3.1 5 5 4 2 3 4exp3.1 3 4 4 5 3 4exp3.2 4 3 2 2 2 3exp3.2 4 3 4 4 2 4exp3.2 2 5 4 1 1 2exp3.3 4 4 4 4 4 4exp3.3 4 4 4 4 4 4exp3.3 4 4 4 5 4 5exp3.4 4 4 4 4 2 4exp3.4 5 5 3 5 3 5exp3.4 4 4 5 5 5 5exp3.5 4 4 4 4 4 4exp3.5 4 4 4 3 4 5exp3.5 4 4 5 4 3 4exp4.1 4 2 2 3 3 3exp4.1 5 5 4 2 2 2exp4.1 5 5 4 3 3 4exp4.2 5 5 5 4 4 4exp4.2 5 5 5 5 2 5exp4.2 4 4 4 3 3 4exp4.3 5 5 4 5 4 5exp4.3 5 5 5 5 2 4exp4.3 5 5 5 5 4 5exp4.4 4 4 5 4 3 4exp4.4 5 4 3 5 4 4exp4.4 4 4 4 4 4 4exp4.5 3 2 1 4 4 4exp4.5 4 4 4 4 3 4exp4.5 5 4 3 2 2 3

151

Field Experiment Task Performance Raw Datacontrol cost quality time spec total

team # #parts $parts h $h total$ rank defect rate rank time rank spec penalty rank scoreS1 10 1 12 40 41 19 0.24 4 16 11 y 34K1 15 5 21 10 15 3.5 1.04 15.5 29 29.5 y 49B1 23 40 12 40 80 33 3.07 30 4 1 y 64B2 20 30 5 30 60 24.5 2.60 25 6 3.5 y 53S2 15 30 28 1 31 10 2.24 23 48 36 y 69G1 39 30 27 1 31 10 3.14 31 20 16.5 y 58G2 13 5 17 10 15 3.5 1.55 18 18 13.5 y 35V1 20 30 19 10 40 15.5 3.24 32 23 20.5 y 68V2 17 20 14 40 60 24.5 0.94 13 10 6 y 44F1 12 1 17 10 11 1 0.23 3 35 32 y 36F2 19 40 12 40 80 33 0.56 7 6 3.5 y 44F3 19 40 12 40 80 33 1.72 21 5 2.00 y 56

average: 18.50 22.67 16.21 22.67 45.33 17.54 1.71 18.54 18.33 14.58 50.67facilitated cost quality time spec total

team # #parts $parts h $h total$ rank defect rate rank time rank spec penalty rank scoreS1 36 30 29 1 31 10 3.60 33 23 20.5 y 64K1 51 40 19 40 80 33 0.87 12 25 24 y 69B1 34 30 12 30 60 24.5 4.80 35 23 20.5 y 80B2 28 40 19 40 80 33 2.80 27 19 15 NO 20 95S2 6 1 12 40 41 19 0.41 6 27 27 y 52G1 21 60 21 10 70 28.5 0.07 2 24 23 y 54G2 17 30 2 30 60 24.5 2.72 26 11 7 y 58V1 15 5 17 10 15 3.5 0.33 5 37 33 y 42V2 16 30 29 1 31 10 3.06 29 12 8.5 y 48F1 80 40 30 1 41 19 0.95 14 27 27 y 60F2 14 5 18 10 15 3.5 0.05 1 18 13.5 NO 20 38F3 15 5.00 15 20.00 25 7 0.82 10 7 5 22

average: 27.75 26.33 18.50 19.42 45.75 17.96 1.71 16.67 21.08 18.67 56.63

152

facilitated & tech cost quality time spec totalteam # #parts $parts h $h total$ rank defect rate rank time rank spec penalty rank score

S1 39 60 53 1 61 27 0.84 11 33 31 y 69K1 41 30 24 5 35 13.5 2.58 24 27 27 y 65B1 27 60 20 10 70 28.5 1.61 19 12 8.5 y 56B2 22 60 16 20 80 33 1.63 20 26 25 y 78S2 12 1 16 20 21 6 0.62 8 42 35 y 49G1 68 40 26 10 50 22 1.04 15.5 15 10 NO 20 68G2 45 40 31 1 41 19 4.6 34 29 29.5 y 83V1 53 40 30 1 41 19 5.5 36 41 34 y 89V2 18 40 12 40 80 33 1.8 22 23 20.5 y 76F1 24 30 28 1 31 10 1.26 17 21 18 y 45F2 7 5 2 30 35 13.5 0.8 9 20 16.5 y 39F3 19 30 20.75 10 40 15.5 3.04 28 17 12 NO 20 76

average: 31.25 36.33 23.15 12.42 48.75 20.00 2.11 20.29 25.50 22.25 65.88

153

Field Experiment Work Group Characteristics Raw Data

team associationS1 K1 B1 B2 S2 G1 G2 V1 V2 F1 F2 F35 5 5 5 5 2 4 5 4 3 5 54 5 5 5 5 5 4 5 5 4 5 43 5 5 5 4 5 4 5 4 2 5 33 5 5 2 5 3 5 5 4 4 5 45 5 5 3 4 4 3 5 32 2 3 5 2 5 54 5 5 5 55 5 13 5 4

4254

seniority at companyS1 K1 B1 B2 S2 G1 G2 V1 V2 F1 F2 F36 4 17 2.5 5 18 2.5 15 21 1.5 5 10

7.5 17 21 14 7 7 4 20 20 2 25 118 23 7 3 5 19 18 13 21 5 24 14

23 9 1 1 19 11 6 16 21 6 23 253 2 28 25.5 19 17 13.5 19 255 2.5 0.5 8 14 20 4.58 3 0.5 25 135 1.5 69 24 6

15217

0.5

154

Field Experiment Frequency of Verbal Comments Raw Data

control condition facilitatedcondition

participant #comments ave/min range ave/n std dev participant

S1.1 27 1.69 S1.1S1.2 9 0.56 S1.2S1.3 16 1.00 S1.3S1.4 53 3.31 S1.4S1.5 27 1.69 S1.5S1.6 1 0.06 S1.6S1.7 40 2.50 S1.7S1.8 8 0.50 3.25 20.11 17.62 S1.8K1.1 13 0.45 K1.1K1.2 17 0.59 K1.2K1.3 86 2.97 K1.3K1.4 70 2.41 K1.4K1.5 95 3.28 2.83 56.20 38.69 K1.5B1.1 7 1.75 B1.1B1.2 3 0.75 B1.2B1.3 9 2.25 B1.3B1.4 9 2.25 B1.4B1.5 8 2.00 1.50 7.20 2.49 B1.5B2.1 6 1.00 B2.1B2.2 7 1.17 B2.2B2.3 7 1.17 B2.3B2.4 0 0.00 B2.4B2.5 0 0.00 B2.5B2.6 11 1.83 B2.6B2.7 11 1.83 B2.7B2.8 0 0.00 B2.8B2.9 0 0.00 1.83 4.67 4.74 B2.9

155

S2.1 94 1.96 S2.1S2.2 24 0.50 S2.2S2.3 56 1.17 S2.3S2.4 46 0.96 S2.4S2.5 30 0.63 S2.5S2.6 33 0.69 S2.6S2.7 20 0.42 S2.7S2.8 2 0.04 S2.8S2.9 29 0.60 S2.9

S2.10 48 1.00 S2.10S2.11 8 0.17 S2.11S2.12 9 0.19 S2.12S2.13 2 0.04 1.90 30.85 25.79 S2.13G1.1 43 2.15 G1.1G1.2 35 1.75 G1.2G1.3 40 2 G1.3G1.4 37 1.85 G1.4G1.5 41 2.05 G1.5G1.6 43 2.15 0.40 39.83 3.25 G1.6G2.1 66 3.67 G2.1G2.2 60 3.33 G2.2G2.3 12 0.67 G2.3G2.4 63 3.50 3.00 50.25 25.62 G2.4V1.1 53 2.30 V1.1V1.2 64 2.78 V1.2V1.3 63 2.74 V1.3V1.4 80 3.48 1.17 65.00 20.94 V1.4V2.1 41 4.1 V2.1V2.2 32 3.2 V2.2V2.3 25 2.5 V2.3V2.4 22 2.2 1.90 30.00 8.45 V2.4F1.1 127 3.63 F1.1F1.2 56 1.60 F1.2F1.3 80 2.29 F1.3

156

F1.4 26 0.74 F1.4F1.5 46 1.31 F1.5F1.6 124 3.54 2.89 76.50 41.76 F1.6F2.1 18 3.00 F2.1F2.2 7 1.17 F2.2F2.3 35 5.83 F2.3F2.4 10 1.67 F2.4F2.6 6 1.00 F2.5F2.7 22 3.67 4.83 16.33 11.11 F2.6F3.1 10 2.00 F2.7F3.2 15 3.00 F3.1F3.3 9 1.80 F3.2F3.4 2 0.40 F3.3F3.6 10 2.00 F3.4F3.7 1 0.20 2.33 9.40 5.34 F3.5

F3.6facilitated conditionparticipant #comments roles ave/min range ave/n std dev

S1.1 16S1.2 7S1.3 16S1.4 32 designS1.5 26 builderS1.6 7 timeS1.7 19S1.8 12 5.87 1.09 0.00 8.76K1.1 5K1.2 25 timeK1.3 30K1.4 40 builderK1.5 43 design 5.72 1.52 0.00 15.08B1.1 16 builderB1.2 22 designerB1.3 42

157

B1.4 38 timerB1.5 38 6.78 1.13 0.00 11.45B2.1 6B2.2 19 timerB2.3 19B2.4 4B2.5 4B2.6 18 designerB2.7 11 builderB2.8 2B2.9 2 4.47 0.89 0.00 7.42S2.1 19S2.2 16S2.3 6S2.4 17 builderS2.5 8S2.6 11S2.7 9 designerS2.8 16S2.9 3

S2.10 9 timerS2.11 4S2.12 0S2.13 0 4.37 0.7 0.00 6.45G1.1 11 timerG1.2 28 designerG1.3 36 builderG1.4 25G1.5 28G1.6 17 6.04 1.04 0.00 8.89G2.1 32 timerG2.2 23 designerG2.3 5G2.4 22 builder 7.45 2.45 0.00 11.27

158

V1.1 37V1.2 77 builderV1.3 44 timerV1.4 76 designer 6.32 1.08 0.00 20.98V2.1 22 designerV2.2 15 timerV2.3 12 builderV2.4 13 5.17 0.83 0.00 4.51F1.1 50F1.2 14 builderF1.3 23F1.4 9F1.5 22 timerF1.6 47 designer 6.11 1.52 0.00 17.10F2.1 10F2.2 12 timerF2.3 12 designerF2.4 6F2.5 8F2.6 5 builderF2.7 15 3.78 0.56 0.00 3.59F3.1 10F3.2 13F3.3 11 builderF3.4 3F3.5 8 timerF3.6 3F3.7 6 designer 7.71 1.43 0.00 3.90

facilitated and technology conditionparticipant #comments roles ave/min range ave/n std dev

S1.1 20S1.2 16S1.3 28 time

159

S1.4 53 designS1.5 34 builderS1.7 38S1.8 9S1.9 9 6.27 1.33 0.00 15.38K1.1 6K1.2 28 timeK1.3 34K1.4 33 builderK1.5 34 design 5.00 1.04 0.00 12.00B1.1 8 builderB1.2 4 designerB1.3 9B1.4 23 timerB1.5 18 5.17 1.58 0.00 7.83B2.1 3B2.2 3 timerB2.3 19B2.4 0B2.5 0B2.6 14 designerB2.7 9 builderB2.8 0B2.9 0 3.00 1.19 0.00 7.07S2.1 18S2.2 19S2.3 7S2.4 16 builderS2.5 0S2.6 6S2.7 11 designerS2.8 9S2.9 6

S2.10 2 timer

160

S2.11 3S2.12 1S2.13 2 2.38 0.45 0.00 6.54G1.1 2 timerG1.2 12 designerG1.3 10 builderG1.4 13G1.5 7G1.6 4 3.20 0.6 0.00 4.43G2.1 12 timerG2.2 15 designerG2.3 4G2.4 9 builder 1.38 0.38 0.00 4.69V1.1 30V1.2 86 builderV1.3 70 timerV1.4 92 designer 6.78 1.51 0.00 27.92V2.1 37 designerV2.2 26 timerV2.3 35 builderV2.4 33 5.70 0.48 0.00 4.79F1.1 39F1.2 21 builderF1.3 29F1.4 5F1.5 9 timerF1.6 18 designer 5.76 1.62 0.00 12.59F2.1 22F2.2 2 timerF2.3 28 designerF2.4 2F2.6 7 builderF2.7 5 3.30 1.30 0.00 11.17F3.1 12

161

F3.2 24F3.3 14 builderF3.4 9F3.5 2 timerF3.6 5F3.7 7 designer 4.29 1.29 0.00 7.23

162

Field Experiment Idea Generation by Participants Raw Data

facilitated conditionS1 K1 B1 B2 S2 G1 G2 V1 V2 F1 F2 F31 2 1 2 3 3 1 4 3 3 5 52 2 3 1 3 2 1 5 1 3 6 40 0 1 0 4 1 4 0 1 3 42 0 3 0 2 1 3 2 0 4 44 0 1 0 4 4 2 34 2 2 3 4 22 2 3 13 0

0

facilitated and technology conditionS1 K1 B1 B2 S2 G1 G2 V1 V2 F1 F2 F34 0 2 0 4 2 1 3 3 2 3 23 3 3 2 3 2 4 3 2 1 3 20 1 2 0 2 3 1 3 2 3 6 42 0 3 1 1 4 2 2 2 0 4 24 0 2 1 3 2 4 1 34 4 4 4 5 2 13 1 5 04 0 0

0 0633

shadowed indicates first condition receive

163

Field Experiment Idea Evaluation Raw Data

how the votes were distributed against different ideasitem S1 K1 B1 B2 S2 G1 G2 V1 V2 F1 F2 F3

1 1 5 3 1 3 0 1 6 0 0 0 42 3 0 4 2 5 0 7 0 3 1 0 73 7 0 0 2 6 1 0 0 0 5 7 14 4 0 5 8 0 0 0 5 0 1 05 1 2 0 1 0 4 2 2 0 26 0 3 4 11 1 0 4 0 07 3 0 1 0 3 08 0 6 0

21

how the votes were distributed against different ideasitem S1 K1 B1 B2 S2 G1 G2 V1 V2 F1 F2 F3

1 2 6 2 1 6 1 3 3 1 4 4 02 2 0 1 2 5 0 5 1 2 1 0 43 4 2 1 8 9 1 0 0 4 0 0 34 1 1 5 5 0 0 2 5 5 0 45 2 1 1 1 1 4 1 3 1 06 4 0 1 0 0 0 0 37 1 0 1 0 38 5 0 4

2 1 0

164

Field Experiment Role Evaluation Facilitated Condition

how the votes were distributed for BuilderS1 K1 B1 B2 S2 G1 G2 V1 V2 F1 F2 F3

not so welljust okay 1 1

acceptable 2 3 1 1 1 6very well 9 3 4 6 1 2 2 3 3 6 5

could not better 2 1 1 12 1 1 1

how the votes were distributed scale for TimerS1 K1 B1 B2 S2 G1 G2 V1 V2 F1 F2 F3

not so well 1just okay 1

acceptable 3 1 3 1 1 2 1very well 6 2 5 6 1 2 3 3 3 5 2

could not better 3 2 13 2 3 4

how the votes were distributed scale for DesignerS1 K1 B1 B2 S2 G1 G2 V1 V2 F1 F2 F3

not so well 1 1just okay 1

acceptable 5 2 4 1 1 6very well 4 2 5 6 5 2 2 4 3 2 4

could not better 3 8 2 3 2

165

Field Experiment Role Evaluation Facilitated with Technology Condition

how the votes were distributed scale for BuilderS1 K1 B1 B2 S2 G1 G2 V1 V2 F1 F2 F3

not so welljust okay 1 1 3 1

acceptable 2 1 1 4 1 1 1 1 1 3very well 5 4 2 3 6 1 2 3 2 4 1 2

could not better 1 3 5 6 1 1 1 1 1

how the votes were distributed scale for TimerS1 K1 B1 B2 S2 G1 G2 V1 V2 F1 F2 F3

not so well 1 2just okay 3 2

acceptable 1 2 2 2 3 1 1 2very well 4 2 3 6 1 3 2 2 1 1

could not better 2 3 2 1 10 1 2 5 1

how the votes were distributed scale for DesignerS1 K1 B1 B2 S2 G1 G2 V1 V2 F1 F2 F3

not so well 1 1just okay 1 2 1 1 2

acceptable 1 1 5 3 2 2 1 3very well 4 2 4 5 5 1 2 1 1 3 1 1

could not better 2 3 1 3 2 2 3 2 1

166

Field Experiment Questionnaire Raw Data

perceived level of agreementcontrol condition

Q1 Q2 Q3 Q4exp agree1 agree2 agree3 agree4S1.1 5 5 5 5S1.2 5 3 2 3S1.3 3 3 3 3S1.4 3 4 5 4S1.5 4 4 4 4S1.6 5 5 4 3S1.7 4 4 4 4S1.8 5 4 5 5K1.1 5 5 5 5K1.2 4 4 4 4K1.3 5 5 5 5K1.4 4 3 5 4K1.5 5 4 4 5B1.1 4 4 4 3B1.2 5 4 4 5B1.3 5 5 5 4B1.4 4 4 4 3B1.5 4 4 4 4B2.1 3 3 4 3B2.2 3 3 4 4B2.3 4 4 4 4B2.4 4 3 5 3B2.5 3 5 4 5B2.6 4 4 3 5B2.7 5 5 4 5B2.8 5 4 4 4B2.9 5 5 4 4S2.1 4 3 4 4S2.2 4 4 3 4S2.3 5 4 4 4S2.4 4 4 4 4S2.5 5 5 5 4S2.6 4 4 3 4S2.7 5 5 4 5S2.8 4 4 4 4S2.9 4 5 4 5

S2.10 5 5 4 3S2.11 5 4 4 4S2.12 4 4 3 4S2.13 4 4 4 4G1.1 5 5 5 5G1.2 4 4 5 3G1.3 4 4 5 3G1.4 4 4 4 3

167

G1.5 5 5 5 3G1.6 4 4 5 3G2.1 4 4 4 4G2.2 5 4 5 5G2.3 4 5 5 5G2.4 4 4 4 2V1.1 4 4 4 4V1.2 4 4 5 5V1.3 4 4 5 4V1.4 5 4 5 5V2.1 4 4 4 3V2.2 4 4 4 4V2.3 5 5 5 5V2.4 4 4 4 4F1.1 5 5 5 4F1.2 5 4 4 5F1.3 5 4 4 4F1.4 5 4 4 3F1.5 4 4 4 4F1.6 4 4 4 4F2.1 2 2 4 1F2.2 5 5 5 5F2.3 4 2 3 4F2.4 5 5 3 4F2.6 5 5 5 5F2.7 2 2 2 3F3.1 4 2 4 2F3.2 4 2 3 2F3.3 4 4 3 4F3.4 3 3 2 2F3.6 5 5 3 3F3.7 3 2 2 3

facilitated conditionQ1 Q2 Q3 Q4

exp agree1 agree2 agree3 agree4S1.1 4 4 5 4S1.2 4 4 4 4S1.3 3 3 3 3S1.4 3 4 4 4S1.5 4 4 4 4S1.6 5 4 4 3S1.7 4 4 4 4S1.8 4 4 4 4S1.9 5 5 3 5K1.1 5 5 5 5K1.2 4 4 4 4K1.3 4 4 5 4K1.4 4 5 4 4K1.5 5 4 4 4B1.1 4 5 4 4B1.2 5 5 5 5

168

B1.3 5 5 4 4B1.4 5 4 5 4B1.5 4 5 4 4B2.1 4 4 4 4B2.2 4 4 4 4B2.3 4 4 4 4B2.4 2 4 2 4B2.5 5 5 5 4B2.6 5 5 4 4B2.7 5 5 5 5B2.8 4 4 4 4B2.9 4 3 3 5S2.1 5 4 4 4S2.2 5 5 5 5S2.3 5 5 5 4S2.4 4 4 4 4S2.5 5 5 5 5S2.6 4 4 3 4S2.7 5 5 5 5S2.8 4 4 4 4S2.9 5 5 4 4

S2.10 5 5 5 1S2.11 5 4 4 4S2.12 4 4 5 4S2.13 5 5 5 5G1.1 4 4 3 4G1.2 4 4 4 3G1.3 4 4 4 3G1.4 4 4 4 4G1.5 5 4 4 3G1.6 4 3 4 3G2.1 4 4 4 4G2.2 4 4 4 4G2.3 5 5 5 5G2.4 4 4 4 4V1.1 4 4 4 4V1.2 4 5 5 5V1.3 5 4 4 4V1.4 5 5 5 4V2.1 4 3 4 3V2.2 4 4 4 4V2.3 5 5 5 5V2.4 4 4 4 4F1.1 5 4 4 4F1.2 5 4 4 5F1.3 4 4 4 4F1.4 5 5 3 4F1.5 3 4 4 3F1.6 4 4 4 4F2.1 4 4 4 4F2.2 5 5 5 5

169

F2.3 4 4 3 4F2.4 4 5 3 4F2.5 4 4 4 4F2.6 4 2 2 5F2.7 4 3 2 2F3.1 4 4 2 2F3.2 4 4 4 4F3.3 4 4 4 4F3.4 4 3 4 3F3.5 4 3 4 4F3.6 5 4 4 5F3.7 4 4 3 3

facilitated and technology conditionQ1 Q2 Q3 Q4

exp agree1 agree2 agree3 agree4S1.1 5 5 5 5S1.2 4 4 4 4S1.3 3 3 3 3S1.4 3 3 3 2S1.5 5 5 5 5S1.7 3 3 2 4S1.8 4 4 4 5S1.9 5 5 5 5K1.1 5 5 5 5K1.2 4 4 3 4K1.3 5 5 5 4K1.4 3 4 5 5K1.5 5 5 5 5B1.1 5 5 4 4B1.2 5 5 5 5B1.3 5 5 5 5B1.4 5 5 4 4B1.5 5 5 5 5B2.1 4 4 3 3B2.2 4 4 3 4B2.3 4 4 4 4B2.4 4 4 2 4B2.5 5 5 4 4B2.6 5 5 3 4B2.7 5 5 5 5B2.8 4 4 4 4B2.9 4 4 3 4S2.1 5 4 4 4S2.2 5 5 5 5S2.3 5 4 4 4S2.4 4 4 3 4S2.5 4 4 4 4S2.6 3 4 3 4S2.7 5 5 5 5S2.8 4 4 4 4S2.9 4 5 4 4

170

S2.10 5 5 5 1S2.11 5 4 4 4S2.12 4 4 4 4S2.13 4 4 4 3G1.1 4 4 4 4G1.2 4 3 2 4G1.3 3 3 3 3G1.4 4 4 3 3G1.5 5 3 3 3G1.6 4 4 4 3G2.1 4 4 4 4G2.2 5 4 4 4G2.3 5 5 5 5G2.4 4 4 4 4V1.1 4 4 4 4V1.2 4 5 5 5V1.3 5 4 5 4V1.4 5 4 5 3V2.1 2 2 3 3V2.2 3 3 3 3V2.3 5 3 5 5V2.4 4 4 4 4F1.1 3 4 4 4F1.2 5 4 5 5F1.3 4 4 4 4F1.4 4 4 3 4F1.5 4 3 4 2F1.6 4 4 4 4F2.1 1 2 2 2F2.2 5 4 5 5F2.3 2 2 2 2F2.4 4 4 3 3F2.6 5 5 4 5F2.7 4 2 2 2F3.1 4 4 4 4F3.2 4 4 4 4F3.3 4 4 4 4F3.4 4 3 3 4F3.5 4 4 3 3F3.6 5 5 5 5F3.7 4 4 3 3

perceived quality of discussioncontrol condition

Q5 Q6 Q7 Q8exp quality1 quality2 quality3 quality4S1.1 5 5 4 5S1.2 3 3 3 3S1.3 4 3 3 3S1.4 4 5 4 4S1.5 4 4 3 3

171

S1.6 4 4 4 4S1.7 4 4 4 3S1.8 4 5 4 4K1.1 5 5 5 5K1.2 4 4 5 5K1.3 5 5 4 4K1.4 5 5 4 3K1.5 4 4 4 4B1.1 4 4 4 4B1.2 4 5 3 4B1.3 5 4 3 5B1.4 4 4 3 3B1.5 4 3 2 2B2.1 3 4 5 5B2.2 4 5 4 2B2.3 4 4 2 2B2.4 3 4 5 4B2.5 5 5 4 5B2.6 5 5 5 4B2.7 5 5 5 4B2.8 4 4 4 4B2.9 5 5 4 4S2.1 4 4 3 4S2.2 4 4 3 3S2.3 4 4 4 4S2.4 4 4 3 4S2.5 5 5 3 5S2.6 4 4 4 3S2.7 5 5 5 5S2.8 4 4 2 4S2.9 5 5 5 4

S2.10 5 5 5 4S2.11 4 4 3 3S2.12 4 4 4 4S2.13 4 4 5 5G1.1 5 4 3 3G1.2 4 4 4 4G1.3 4 4 4 4G1.4 4 4 3 4G1.5 5 3 2 4G1.6 4 4 4 4G2.1 4 4 4 4G2.2 5 5 2 2G2.3 5 5 4 4G2.4 4 4 4 3V1.1 4 4 3 3V1.2 4 3 2 2V1.3 4 4 2 2V1.4 5 4 3 4V2.1 4 4 3 3V2.2 4 4 4 4

172

V2.3 5 5 3 5V2.4 4 4 3 3F1.1 5 4 3 5F1.2 5 4 4 4F1.3 4 4F1.4 4 4 4 4F1.5 4 5 4 5F1.6 4 4 3 3F2.1 4 1 1 1F2.2 5 5 5 5F2.3 3 3 3 4F2.4 4 4 2 2F2.6 4 5 4 4F2.7 4 4 2 3F3.1 3 2 2 2F3.2 3 2 2 2F3.3 4 4 3 4F3.4 4 4 4 4F3.6 5 5 4 4F3.7 3 2 1 1

facilitated conditionQ5 Q6 Q7 Q8

exp quality1 quality2 quality3 quality4S1.1 4 4 2 3S1.2 4 4 3 4S1.3 3 3 3 3S1.4 4 4 4 4S1.5 4 4 3 4S1.6 4 3 3 3S1.7 4 3 3 3S1.8 4 5 4 4S1.9 3 5 5 4K1.1 5 5 5 5K1.2 4 4 3 3K1.3 5 5 5 4K1.4 4 4 4 5K1.5 4 4 4 4B1.1 4 4 5 5B1.2 5 5 4 5B1.3 5 4 5 4B1.4 5 4 4 4B1.5 4 4 5 5B2.1 5 4 5 4B2.2 4 4 2 3B2.3 4 4 2 2B2.4 4 4 2 2B2.5 5 4 3 5B2.6 5 5 4 4B2.7 5 4 5 4B2.8 4 4 3 3

173

B2.9 4 3 4 4S2.1 5 4 3 3S2.2 5 5 5 5S2.3 5 5 5 5S2.4 4 4 4 4S2.5 5 5 4 4S2.6 4 4 3 4S2.7 5 5 5 4S2.8 4 4 2 4S2.9 5 4 5 4

S2.10 5 5 5 5S2.11 4 4 4 4S2.12 4 4 4 4S2.13 5 5 5 5G1.1 4 4 3 3G1.2 4 5 4 5G1.3 4 4 4 4G1.4 4 4 3 3G1.5 4 3 2 3G1.6 4 4 3 3G2.1 4 4 4 4G2.2 4 4 4 4G2.3 5 5 5 5G2.4 4 4 4 4V1.1 4 4 3 3V1.2 4 5 4 5V1.3 5 5 3 4V1.4 5 5 3 5V2.1 4 3 2 4V2.2 4 3 2 3V2.3 5 5 3 2V2.4 4 4 3 4F1.1 4 5 5 5F1.2 5 5 4 4F1.3 4 4 2 2F1.4 5 5 4 4F1.5 4 4 3 4F1.6 4 4 3 3F2.1 4 5 4 4F2.2 5 5 5 5F2.3 4 4 5 5F2.4 4 4 4 4F2.5 4 4 3 4F2.6 5 3 2 2F2.7 4 4 4 4F3.1 3 2 1 2F3.2 4 4 3 4F3.3 4 4 4 4F3.4 4 3 2 3F3.5 4 3 3 2F3.6 4 3 3 4

174

F3.7 4 4 2 3

facilitated and technology conditionQ5 Q6 Q7 Q8

exp quality1 quality2 quality3 quality4S1.1 5 5 3 4S1.2 4 4 3 3S1.3 3 3 3 3S1.4 3 3 2 2S1.5 5 5 4 4S1.7 3 3 2 3S1.8 4 4 5 4S1.9 5 4 4 4K1.1 5 5 4 4K1.2 4 3 4 4K1.3 5 5 4 4K1.4 4 5 4 4K1.5 5 4 5 5B1.1 4 5 4 4B1.2 5 5 3 5B1.3 5 4 5 5B1.4 4 4 4 4B1.5 5 5 5 5B2.1 4 4 5 5B2.2 4 4 4 4B2.3 4 4 2 2B2.4 4 4 4 4B2.5 4 4 4 5B2.6 5 5 5 5B2.7 5 5 4 4B2.8 4 4 4 4B2.9 4 3 4 4S2.1 5 4 4 4S2.2 5 5 4 4S2.3 4 4 5 5S2.4 4 4 4 4S2.5 5 5 5 4S2.6 4 4 3 3S2.7 5 5 5 5S2.8 4 4 2 4S2.9 4 4 4 4

S2.10 5 5 5 5S2.11 4 4 4 4S2.12 4 4 4 5S2.13 4 4 3 3G1.1 4 3 2 3G1.2 3 4 4 4G1.3 3 3 4 4G1.4 3 3 2 2G1.5 4 3 3 3G1.6 3 4 2 2

175

G2.1 4 4 4 4G2.2 4 4 4 4G2.3 5 5 5 4G2.4 4 4 4 4V1.1 4 4 2 2V1.2 4 4 3 3V1.3 5 4 3 4V1.4 3 5 5 5V2.1 3 2 1 2V2.2 3 3 2 3V2.3 5 4 3 5V2.4 3 3 3 3F1.1 5 4 4 5F1.2 5 4 4 4F1.3 4 4 4 4F1.4 4 4 4 4F1.5 4 4 4 4F1.6 4 4 3 3F2.1 4 4 2 4F2.2 5 5 4 5F2.3 2 4 2 2F2.4 4 4 3 4F2.6 5 5 2 5F2.7 2 4 2 2F3.1 3 3 2 3F3.2 4 4 3 3F3.3 4 4 4 4F3.4 3 3 2 2F3.5 4 3 2 2F3.6 5 3 3 3F3.7 4 3 2 2

perceived interactioncontrol condition

Q9 Q10 Q11 Q12exp inter1 inter2 inter3 inter4S1.1 5 5 5 5S1.2 4 3 2 4S1.3 4 3 4 3S1.4 4 3 4 3.5S1.5 4 4 4 4S1.6 4 3 3 5S1.7 4 4 3 4S1.8 5 4 5 4K1.1 5 5 5 5K1.2 5 4 5 5K1.3 4 4 5 5K1.4 3 4 4 4K1.5 4 5 4 4B1.1 4 4 3 4B1.2 5 5 5 5

176

B1.3 5 4 5 5B1.4 4 4 4 4B1.5 4 4 4 4B2.1 4 4 4 5B2.2 4 4 4 4B2.3 4 3 2 4B2.4 4 4 2 4B2.5 5 4 4 5B2.6 4 3 4 4B2.7 4 3 4 4B2.8 4 4 4 4B2.9 4 4 4 4S2.1 4 4 3 4S2.2 4 3 4 4S2.3 4 4 4 4S2.4 3 4 4 4S2.5 5 5 4 5S2.6 4 3 4 4S2.7 5 5 5 5S2.8 4 4 4 4S2.9 4 4 5 4

S2.10 5 5 5 5S2.11 4 4 4 4S2.12 4 4 4 4S2.13 4 5 5 5G1.1 4 4 4 4G1.2 4 4 4 4G1.3 4 4 4 4G1.4 4 4 4 4G1.5 5 4 4 5G1.6 4 4 4 4G2.1 4 4 4 4G2.2 4 4 4 4G2.3 4 4 4 4G2.4 4 4 4 4V1.1 4 3 2 4V1.2 4 3 4 3V1.3 5 5 4 4V1.4 4 4 3 5V2.1 4 4 3 4V2.2 4 4 4 4V2.3 5 5 5 5V2.4 4 4 3 4F1.1 5 5 5 4F1.2 5 3 5 5F1.4 5 4 4 4F1.5 4 3 4 4F1.6 4 4 3 4F2.1 4 2 1 2F2.2 5 4 5 5F2.3 2 2 1 3

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F2.4 4 3 2 4F2.6 4 5 4 5F2.7 2 4 4 4F3.1 3 2 3 2F3.2 2 1 2 3F3.3 4 2 3 4F3.4 3 3 4 3F3.6 5 4 5 5F3.7 2 2 1 3

facilitated conditionQ9 Q10 Q11 Q12

exp inter1 inter2 inter3 inter4S1.1 4 4 3 4S1.2 4 4 3 4S1.3 3 3 3 3S1.4 3 3 3 3.5S1.5 4 4 4 4S1.6 4 3 3 5S1.7 4 4 4 4S1.8 4 4 5 4S1.9 4 3 5 4K1.1 5 5 5 5K1.2 4 4 4 4K1.3 4 5 5 5K1.4 4 5 5 4K1.5 5 5 5 5B1.1 5 4 4 4B1.2 5 4 5 5B1.3 5 5 4 5B1.4 5 4 5 4B1.5 4 4 4 4B2.1 4 4 5 5B2.2 4 4 4 4B2.3 4 3 3 4B2.4 4 4 2 4B2.5 5 5 4 5B2.6 5 5 4 4B2.7 4 4 4 4B2.8 4 4 4 4B2.9 4 4 4 3S2.1 4 4 3 4S2.2 5 5 5 5S2.3 5 5 5 5S2.4 4 4 4 4S2.5 5 4 4 4S2.6 4 3 4 4S2.7 5 4 5 5S2.8 4 2 4 4S2.9 5 4 4 4

S2.10 5 5 5 5S2.11 4 4 4 4

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S2.12 4 5 4 4S2.13 4 4 4 5G1.1 4 3 4 4G1.2 4 4 4 4G1.3 4 4 4 4G1.4 4 4 3 4G1.5 4 4 4 5G1.6 4 4 4 5G2.1 4 4 4 4G2.2 4 4 4 4G2.3 5 5 5 5G2.4 4 4 4 4V1.1 4 3 4 4V1.2 4 5 5 5V1.3 5 4 4 5V1.4 5 5 5 5V2.1 3 4 3 4V2.2 4 4 4 4V2.3 5 5 5 5V2.4 4 4 4 4F1.1 5 4 5 4F1.2 5 4 4 5F1.3 4 2 4 4F1.4 5 4 4 5F1.5 4 4 3 4F1.6 4 4 3 4F2.1 4 4 5 5F2.2 5 5 5 5F2.3 4 3 2 4F2.4 4 4 4 4F2.5 4 4 4 4F2.6 3 2 4 2F2.7 4 4 2 4F3.1 4 2 3 4F3.2 4 4 4 4F3.3 4 4 4 4F3.4 3 4 3 3F3.5 4 3 3 4F3.6 4 3 4 5F3.7 3 3 2 4

facilitated and technology conditionQ9 Q10 Q11 Q12

exp inter1 inter2 inter3 inter4S1.1 5 5 4 4S1.2 4 4 4 4S1.3 3 3 3 3S1.4 2.5 2 2 2.5S1.5 4 4 4 4S1.7 4 2 2 3S1.8 4 4 4 4S1.9 5 4 4 5

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K1.1 5 5 5 5K1.2 4 5 5 4K1.3 5 4 4 5K1.4 5 4 5 4K1.5 5 5 4 5B1.1 4 5 5 4B1.2 5 5 5 5B1.3 4 5 5 5B1.4 5 4 5 4B1.5 5 5 5 5B2.1 4 4 4 5B2.2 4 4 4 4B2.3 4 3 3 4B2.4 4 2 2 2B2.5 4 4 4 5B2.6 5 5 5 5B2.7 4 5 5 5B2.8 4 4 4 4B2.9 4 4 4 4S2.1 4 3 3 4S2.2 4 4 5 5S2.3 5 4 4 4S2.4 4 4 4 4S2.5 4 4 5 5S2.6 4 3 4 4S2.7 5 5 5 5S2.8 4 4 4 4S2.9 5 4 5 5

S2.10 5 1 5 5S2.11 4 4 4 4S2.12 4 3 4 4S2.13 4 3 4 4G1.1 3 3 3 4G1.2 4 4 4 4G1.3 4 4 4 4G1.4 4 3 3 3G1.5 4 4 4 4G1.6 3 3 3 4G2.1 4 4 4 4G2.2 4 4 4 4G2.3 5 5 5 5G2.4 4 4 4 4V1.1 4 3 3 4V1.2 4 4 4 4V1.3 5 5 4 5V1.4 4 4 3 5V2.1 4 4 3 4V2.2 3 3 3 3V2.3 5 5 5 5V2.4 4 4 4 4F1.1 5 5 4 4

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F1.2 5 4 4 5F1.3 4 4 4 4F1.4 4 3 4 4F1.5 4 4 3 4F1.6 4 4 4 4F2.1 4 4 2 4F2.2 5 4 5 5F2.3 2 2 2 2F2.4 4 3 3 4F2.6 4 5 4 3F2.7 2 2 2 4F3.1 3 3 2 4F3.2 4 4 4 4F3.3 4 4 4 4F3.4 3 2 3 4F3.5 4 2 3 4F3.6 5 4 4 5F3.7 3 2 3 4

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Field Experiment Qualitative Comments Raw Data

S1C1 more fun, best product, we just worked together without constraintsdesign considerations hurts creativity, too much to considerroles reduce participationmost fun in 23 yrsjust going for it produced the best results

K1didn't know what was going on during C1 by C3 caught on so best producthaving roles helpedC2 and C3 better idea of objectivesparticipation highest on C3 because of issue with keeping yellow panel in placebest product was C3

B1most competitive C2, participative C1talked about liking looks of model best, long term marketability, durability (factors not included as important)roles helped and planning helped

B2performance C3roles were helpfultalked about liking looks of model best, long term marketability, durability (factors not included as important)C3 more fun when they worked together and had creativity to build (not C1)

S2size of group was a factor, more ideas but tougher to deal with all the ideasbest product was c3best participation was last condition (c3) since working as a team got better each timeroles helped

G1thought C1 was best teamwork since all people contributedthought C2 was most competitive though since considered all factorsC3 did not meet spec, too fast (and time wasn't a priority)roles caused people to have more accountability and stress and reduced the level ofactivity in building but performance was better but not as much fun

G2lego C3 bestC1 most fundidn't like rolesdidn't like the experiment, not real, rather be screwing off, bad attitudes

V1most competitive was C2 balanced approachmost challenging and most fun was C1best teamwork c1

V2C2 best, competitiveC1 most fun, highest team work

F1C1 most fun, creativeC3 last model, best teamwork, most efficient model (considering all parameters), experience working togetherroles helped balance focus, having obj helped esp on time

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C2 most competitive also most participationstructure helpedroles legitimize calling peers on issuesroles help all the bases get covered, difficult to focus on more than one thing

F3C3 most competitiveC2 most participationc2 & c3 did not meet specroles and structure helped focus the teamonly team not into doing the experiment

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APPENDIX F ADDITIONAL ANOVA TABLES

ANOVA Summary Table for Cost as a Function of Condition - Laboratory

Source DF SS MS F test

statistic

p value

condition 3 692 231 .74 .546

error 16 5017 314

Total 19 5709

ANOVA Summary Table for Defect Rate as a Function of Condition - Laboratory

Source DF SS MS F test

statistic

p value

condition 3 .469 .156 .35 .789

error 16 7.132 .446

Total 19 7.601

ANOVA Summary Table for Time as a Function of Condition - Laboratory

Source DF SS MS F test

statistic

p value

condition 3 296.8 98.9 1.22 .334

error 16 1294.4 80.9

Total 19 1591.2

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ANOVA Summary Table for Decision Quality as a Function of Condition - Laboratory

Source DF SS MS F test

statistic

p value

condition 3 401.8 133.9 2.04 .149

error 16 1050.4 65.7

Total 19 1452.2

ANOVA Summary Table: Cost as a Function of Condition- Field

Source DF SS MS F test

statistic

p value

condition 2 83 42 .08 .926

error 33 17871 542

Total 36 17955

ANOVA Summary Table: Defect Rate as a Function of Condition - Field

Source DF SS MS F test

statistic

p value

condition 2 1.28 .64 .31 .739

error 33 69.09 2.09

Total 36 70.36

ANOVA Summary Table: Total Time as a Function of Condition - Field

Source DF SS MS F test

statistic

p value

condition 2 314 157 1.36 .271

error 33 3815 116

Total 36 4128

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ANOVA Summary Table: Decision Quality as a Function of Condition - Field

Source DF SS MS F test

statistic

p value

condition 2 1217 608 2.46 .101

error 33 8158 247

Total 36 9375

ANOVA Summary Table for Dominance Difference as a Function of Condition -

Laboratory

Source DF SS MS F test

statistic

p value

condition 3 1502 501 .58 .639

error 16 13904 869

Total 19 15406

ANOVA Summary Table for Dominance Dispersion as a Function of Condition - Labo-

ratory

Source DF SS MS F test

statistic

p value

condition 3 453 151 .54 .659

error 16 4442 278

Total 19 4895

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ANOVA Summary Table for COMMMENTS and IDEAS Regression Model

Source DF SS MS F test

statistic

p value

Regression 2 6883.8 3441.9 25.58 .0001

error 9 1211.1 134.6

Total 11 8094.9

DF SEQ SSSource

COMMENTS 1 4826.8

IDEAS 1 2057.0

ANOVA Summary Table for Decision Quality (COMMENTS and ENGR) Regression

Model

Source DF SS MS F test

statistic

p value

Regression 2 7152.7 3576.3 34.16 0.0001

error 9 942.3 104.7

Total 11 8094.9

DF SEQ SSSource

COMMENTS 1 4826.8

ENGR 1 2325.8

ANOVA Summary Table for Agreement as a Function of Condition - Laboratory

Source DF SS MS F test

statistic

p value

condition 3 2.466 .822 1.51 .221

error 56 30.421 .543

Total 59 32.887

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ANOVA Summary Table for Interaction as a Function of Condition - Laboratory

Source DF SS MS F test

statistic

p value

condition 3 1.175 .392 1.11 .352

error 56 19.741 .353

Total 59 20.916

ANOVA Summary Table for Agreement as a Function of Condition - Field

Source DF SS MS F test

statistic

p value

condition 2 .051 .025 .19 .831

error 33 4.491 .136

Total 35 4.542

ANOVA Summary Table for Discussion Quality as a Function of Condition - Field

Source DF SS MS F test

statistic

p value

condition 2 .018 .009 .04 .960

error 33 7.365 .223

Total 35 7.383

ANOVA Summary Table for Interaction as a Function of Condition - Field

Source DF SS MS F test

statistic

p value

condition 2 .028 .014 .06 .940

error 33 7.513 .228

Total 35 7.541

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ANOVA Summary Table for Task-focused Communication as a Function of Condition

Source DF SS MS F test

statistic

p value

condition 3 1019 340 2.27 .09

error 56 8366 149

Total 59 9385

ANOVA Summary Table for the Difference in Top Two Ideas as a Function of Condition

Source DF SS MS F test

statistic

p value

condition 1 7.04 7.04 3.82 .064

error 22 40.58 1.84

Total 23 47.62

ANOVA Summary Table for the Standard Deviation of Votes as a Function of Condition

Source DF SS MS F test

statistic

p value

condition 1 .992 .992 1.95 .176

error 22 11.187 .508

Total 23 12.179

ANOVA Summary Table for Defect Rate as a Function of the Control Condition for

Laboratory versus Field

Source DF SS MS F test

statistic

p value

condition 1 1.38 1.38 1.34 0.266

error 15 15.42 1.03

Total 16 16.79

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ANOVA Summary Table for Defect Rate as a Function of the Facilitated Condition for

Laboratory versus Field

Source DF SS MS F test

statistic

p value

condition 1 1.3 1.30 0.68 0.422

error 15 28.65 1.91

Total 16 29.94

ANOVA Summary Table for Defect Rate as a Function of the Facilitated and Technology

Condition for Laboratory versus Field

Source DF SS MS F test

statistic

p value

condition 1 5.96 5.96 2.89 0.11

error 15 30.93 2.06

Total 16 36.90

ANOVA Summary Table for Agreement as a Function of the Control Condition for Labo-

ratory versus Field

Source DF SS MS F test

statistic

p value

condition 1 0.018 0.018 0.13 0.728

error 15 2.156 0.144

Total 16 2.174

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ANOVA Summary Table for Agreement as a Function of the Facilitated Condition for

Laboratory versus Field

Source DF SS MS F test

statistic

p value

condition 1 0.056 0.056 0.38 0.548

error 15 2.216 0.148

Total 16 2.272

ANOVA Summary Table for Agreement as a Function of the Facilitated and Technology

Condition for Laboratory versus Field

Source DF SS MS F test

statistic

p value

condition 1 0.002 0.002 0.01 0.940

error 15 4.653 0.310

Total 16 4.654

ANOVA Summary Table for Discussion Quality as a Function of the Control Condition

for Laboratory versus Field

Source DF SS MS F test

statistic

p value

condition 1 0.028 0.028 0.2 0.667

error 15 2.122 0.141

Total 16 2.15

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ANOVA Summary Table for Discussion Quality as a Function of the Facilitated Condition

for Laboratory versus Field

Source DF SS MS F test

statistic

p value

condition 1 0.966 0.966 4.32 0.055

error 15 3.355 0.224

Total 16 4.321

ANOVA Summary Table for Discussion Quality as a Function of the Facilitated and

Technology Condition for Laboratory versus Field

Source DF SS MS F test

statistic

p value

condition 1 0.216 0.216 1.0 0.334

error 15 3.249 0.216

Total 16 3.466

ANOVA Summary Table for Group Interaction as a Function of the Control Condition for

Laboratory versus Field

Source DF SS MS F test

statistic

p value

condition 1 0.168 0.168 1.28 0.276

error 15 1.965 0.131

Total 16 2.133

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ANOVA Summary Table for Group Interaction as a Function of the Facilitated Condition

for Laboratory versus Field

Source DF SS MS F test

statistic

p value

condition 1 0.322 0.322 2.09 0.169

error 15 2.312 0.154

Total 16 2.634

ANOVA Summary Table for Group Interaction as a Function of the Facilitated and Tech-

nology Condition for Laboratory versus Field

Source DF SS MS F test

statistic

p value

condition 1 0.025 0.025 0.12 0.737

error 15 3.162 0.211

Total 16 3.187

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Vita

MARLA E. HACKER

Virginia Polytechnic Institute and State University567 Whittemore Hall

Industrial and Systems EngineeringBlacksburg, VA 24060

(540) 231-4596 (office) (540) 951-7699 (home)mhacker@vt.edu

TEACHING AND RESEARCH INTERESTSTeaching Interests: industrial operations; strategic planning and implementation systems, experimentaldesign and statistics; human factors/macroergonomics, group decision support systems, total quality engi-neering; process management and process control, and installing autonomous and process oriented workteams.

Research Interests: The design, implementation, and measurement of organizational improvement inter-ventions (quality engineering, groupware, reengineering, virtual teaming, management delayering, etc.)on performance.

EDUCATION Ph.D. Industrial Engineering, Virginia Polytechnic Institute and State University (5/97) The title of the dissertation is "The Impact of Decision Aids on Work Group Performance.”

M.S., Industrial Engineering, University of Missouri-Columbia, 1990 M.B.A., Rockhurst Jesuit College, Kansas City, Missouri, 1990 B.S., Industrial Engineering, Oregon State University, 1980

UNIVERSITY EXPERIENCEDepartment of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University

Principal Investigator, United States Postal Service Leading a project to improve performance and the largest USPS distribution centers. Focus of the project is measurement and the deployment of objectives and goals to the shop floor.

Director, Socio-Technical Alliance for Industrial Research (STAIR)-www.ise.vt.edu/ise/tour/stair.html Co-founded a university and industrial alliance for technology transfer and research in: strategic planning and implementation systems, process management, total quality management and organizational design, including the effective implementation of management and technician teams. STAIR also supports local strategic planning sessions with groupware technology enabling large stakeholder groups to effectively contribute to the planning process. Led: establishing client relationships; delivering workshops for corporate managers; training sessions for shop floor employees; and establishing on-going relationships with the industrial affiliates. Organizations participating in STAIR include: Volvo-GM, Lear Corporation, Siemens, Kollmorgen, Eastman Chemicals, Grayson-White, Burlington Industries, Dupont, Hercules, and Hershey.

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Senior Research Associate and Contract Manager, U.S. Department of Energy Environmental Projects Virginia Tech representative on two DOE contracts: 1). working with DOE field sites on technology information exchange practices; and 2). supporting DOE’s mission in increasing public awareness of its activities.

Co-Instructor, ISE 5015 "The Management of Change, Innovation and Performance in Organizational Systems" a graduate level course broadcast across Virginia (Fall 1995) Course received the second highest instructor rating for a distance course in the college of engineering’s history and the highest rating in the department. Topics in the course included: strategic planning and deployment systems, total quality management, delayering management, reward systems, benchmarking, high performance work teams, and leadership.

Lecturer, Management Systems Engineering (August 1994-May 1997) Lecturer on: industrial operations, strategic planning, hoshin kanri, agile manufacturing, statistical process control, process management, organizational performance management, and team performance. Courses include:

ISE 4004 A Theory of OrganizationISE 4015 Management Systems Engineering Theory, Applications, and Design IISE 4016 Management Systems Engineering Theory, Applications, and Design IIISE 4304 Global Issues in Industrial ManagementISE 5015 The Management of Change, Innovation and Performance in Organizational SystemsISE 5016 Performance Measurement of Organizational Systems

Research Associate, Center for Organizational Performance Improvement (August 1994- June 1996) Provided corporate and university employees with training in process management, strategic planning and deployment systems, total quality, statistical process control, and leadership. Led clients through strategic planning and deployment for performance improvement. Clients included distribution centers, finance departments, personnel services, continuing education and information service departments.

Focus Group Leader, Office of Minority Engineering Programs (Academic Year 1995-1996) Led focus group discussions to identify critical factors impacting the ability of first year engineering women to be successful. Led career planning classes for minority engineering students.

Other University ExperienceAccreditation Board for Engineering and Technology (1990-1994)Reviewed Industrial Engineering programs against established national standards of performance. Unableto accept offer to advance to ABET team leader position when returned to graduate school.

CORPORATE EXPERIENCEThe Procter & Gamble Company (June 1980-August 1994)Plant Manager- Paper Plant, Mehoopany, PA (January 1992-August 1994) Responsible for all aspects of the operation at one of the largest P&G production facilities (900 technicians and 100 managers) from raw materials through distribution. Responsible for a $375 Million budget. Site increased its capability to deliver product upgrades to every six months from every two years.

Led a divisional effort across 11 plants to redesign the structure and role of management. The system wide effort included delayering levels of management, streamlining work processes and increasing the capability of the technician work force. Led the prototype implementation efforts. Learnings were gained and shared across the company. A new level of the self-directed team concept was pilot tested, creating the foundation for an advancement in self-directed work systems.

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Operations Manager- Paper Plant, Mehoopany, PA (September 1990-December 1991) Deployed the "Hoshin Planning Process" to created a plant strategy with measures and action plans linking corporate objectives down to individual work plans.

Operations Manager- Soap Plant, Kansas City, KS (September 1987-August 1990) Member of the multi-functional Category Team setting direction for the brand, and reporting to the General Manager. Created a manufacturing strategy to support overall objectives. Created a new product and manufacturing process, for a segment of the market not represented by P&G.

Manufacturing Management Assignments-Beverage Plant, Kansas City, MO (July 1980-August 1987) First and second level assignments in processing, packaging and warehousing, gaining skills in statistical process control, employee training, just in time production systems, and performance management.

PUBLICATIONS WITH PRESENTATIONSHacker, M.E. and Kleiner, B.M. (1997). Creating Win-Win Research Partnerships Between Academiaand Industry. In Proceedings of the Portland International Conference on Management of Engineeringand Technology (to be published). Portland, OR: Engineering Management Press.

VanAken, E.M, and Hacker, M.E. (1997). “Building Effective Cross-Functional Teams”. In Proceedingsof the International Industrial Engineering Conference, (to be published). Norcross, GA: Industrial Engi-neering & Management Press.

Hacker, M.E. (1997). A Case Study in Management Work System Re-Design Via a Delayering Ap-proach. In Proceedings of the ASQC’s 51st Annual Quality Congress (to be published). Milwaukee, WI:ASQC.

Hacker, M.E. and Kleiner, B.M. (1996). Identifying Critical Factors Impacting Work Group Perform-ance. In Proceedings of the International Engineering Management Conference (pp. 196-200). Piscata-way, NJ: Institute of Electrical and Electronics Engineers, Inc.

Hacker, M.E. and Kleiner, B.M. (1996). Understanding Effective Natural Work Group Performancethrough a Sociotechnical Approach. In Proceedings of the Human Factors in Organizational Design andManagement-V (pp. 471-476). Amsterdam, The Netherlands: North-Holland.

Hacker, M.E. and Baker, K.L. (1996). A Sociotechnical Approach To Improving Breakthrough Im-provement Efforts. In Proceedings of the Human Factors in Organizational Design and Management-V(pp. 477-482). Amsterdam, The Netherlands: North-Holland.

Hacker, M.E. and Kleiner, B. M. (1996). Policy Deployment the Missing Link in Operationalizing theTotal Quality Management Philosophy. In Proceedings of the ASQC’s 50th Annual Quality Congress(pp. 226-231). Milwaukee, WI: ASQC.

Hacker, M.E. (1993). Characteristics of Production Line Managers That Deliver Benchmark Results. InProceedings of the International Industrial Engineering Conference (pp. 162-163). Norcross, GA: Indus-trial Engineering & Management Press.

Hacker, M.E. (1992). Improve Results Faster With Benchmarking. Proceedings of the International In-dustrial Engineering Conference (pp. 121-124). Norcross, GA: Industrial Engineering & ManagementPress.

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Hacker, M.E. (1991). Changing the Maintenance Culture in a Traditional Manufacturing Environment.Proceedings of the International Industrial Engineering Conference (pp. 241-245). Norcross, GA: Indus-trial Engineering & Management Press.

PRESENTATIONS"Research: A Missing Link in Successful TQ Implementation", presented at the International IndustrialEngineering Research Conference, 1996.

“Interviewing Skills for Engineers”, presented at the International Industrial Engineering Conference,1995.“Characteristics of Successful Mangers”, Pennsylvania IIE Regional Student Chapter Meeting, 1992.

“A Manufacturing Reliability Case Study”, Kansas City IIE Chapter Meeting, 1990.

“Characteristics of Successful Managers”, Oregon IIE Regional Student Chapter Meeting, 1990.

“Using the Quality of Process Review to Improve Manufacturing Performance”, IIE Kansas City ChapterAnnual Spring Conference, Program Director and Speaker, 1989.

PROFESSIONAL SERVICE AND AFFILIATIONS Institute of Industrial Engineer’s 1997 Vice President at Large Reviewer, Total Quality publications for Institute of Industrial Engineers Reviewer, 1996 Special Edition of Computers and Industrial Engineering Member, Alpha Pi Mu Member, American Society for Quality Control Member, Academy of Management Member, IEEE- Engineering Management Member, Institute of Industrial Engineers Leadership Board Member, University of Scranton, 1992-1994 Graduate Studies Advisory Board Member, Wilkes University, 1993-1994

PROFESSIONAL DEVELOPMENT (workshops attended) Tops, Middles, Bottoms (2 days) Employee/Employer Relations (3 days) High Performance Work Systems (10 days) Hoshin Kanri Training (6 days) Implementing Total Quality (9 days) Leadership and Mastery (visioning) (6 days) Leveraging Diversity (5 days) Mid-Management Leadership Training (3 days) Manufacturing Process Management (4 days) Negotiating Skills (3 days) Seven Habits of Highly Effective People (3 days) Statistical Process Control (18 days)