Decision Making Groups and Teams: An Information Exchange Perspective 2013010768, 9780415843805, 9780203754191

Table of contents :
Cover
Title
Copyright
Contents
List of Figures
Preface
1 Introduction: Information Exchange in Decision-Making Groups and Teams
2 Background and Framework
3 Dual Motive Agents in the Information Exchange of Interactive Groups and Teams
4 Biases in Member Judgments of Gains and Losses in the Information Exchange of Interactive Groups and Teams
5 Part A: Idea Generation in Interactive Teams: Conceptual Model
5 Part B: Idea Generation in Interactive Teams: Empirical Studies
6 Part A: Negative Evaluations as Information and Affect in Interactive Groups and Teams: Dynamic Model
6 Part B: Negative Evaluations as Information and Affect in Interactive Groups and Teams: Empirical Studies
7 Silence Events as Mediators of Idea Generation and Information Exchange in Interactive Teams: Team Structure and What Members Say and Don’t Say
8 Part A: An Interorganizational Decision-Making Team and Its Subteams: Status Differentiation and Information Exchange
8 Part B: Frames and Scripts in the Information Exchange of Interorganizational Teams and Its Subteams
9 Virtual Teams as Decision-Making Units: The Social Mediator of Trust
10 Part A: Information Exchange in Decision Making Teams: Integrative System
10 Part B: Dynamics of Information Exchange in Decision Making Teams: Computational Exercises
11 Technology for Quality-Maximizing Objectives in Decision-Making Teams
12 Summary and Discussion
Notes
Author Index
Content Index

Citation preview

Decision Making Groups and Teams

In recent years, there has been increasing implementation of group and team decision making within organizations, much of it managed electronically, between members of what are “virtual” groups or teams. Recent research into effective team implementation emphasizes trust as an intermediary process, and trust must be a part of any account of team decision making. This book provides an integrated framework that represents the process in decision making by interactive groups and teams. This framework furthers both our understanding of process and our capabilities in implementation, based on an account of group decision making that differentiates the information types contributing to decision quality and relates them to the process in interactive groups and teams. Author Steven Silver emphasizes the social structure that is inherent in the interaction of decision makers as group or team members and the effects on the information they exchange. This book should be of great value to both audiences with academic and practice-oriented interest in decision-making groups and teams. They are very likely to benefit from its rigorous analytical and comprehensive treatment. Bhargav Adhvaryu CEPT University, India Steven Silver is professor and Lucas Fellow at the Lucas Graduate School of Business, California State University. He has been a research associate, visiting fellow, overseas by-fellow, and post-doctoral fellow at universities that include the University of California, Stanford University, Cambridge University, and London School of Economics.

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57 Decision Making Groups and Teams An Information Exchange Perspective Steven D. Silver

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Decision Making Groups and Teams

An Information Exchange Perspective Steven D. Silver

First published 2014 by Routledge 711 Third Avenue, New York, NY 10017 Simultaneously published in the UK by Routledge 2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN Routledge is an imprint of the Taylor & Francis Group, an informa business © 2014 Taylor & Francis The right of Steven D. Silver to be identified as the author of the editorial material has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilized in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging-in-Publication Data Silver, Steven D. Decision making groups and teams : an information exchange   perspective / by Steven D. Silver.    pages cm — (Routledge advances in management and business studies 57)   Includes bibliographical references and index. 1.  Group decision making.  2.  Teams in the workplace.  I.  Title.   HM746.S55 2013   658.4′036—dc23  2013010768 ISBN: 978-0-415-84380-5 (hbk) ISBN: 978-0-203-75419-1 (ebk) Typeset in Sabon by Apex CoVantage, LLC

Contents

List of Figures Preface 1 Introduction: Information Exchange in Decision-Making Groups and Teams

ix xi

1

2 Background and Framework

23

3 Dual Motive Agents in the Information Exchange of Interactive Groups and Teams

44

4 Biases in Member Judgments of Gains and Losses in the Information Exchange of Interactive Groups and Teams

61

5 Part A: Idea Generation in Interactive Teams: Conceptual Model

85

5 Part B: Idea Generation in Interactive Teams: Empirical Studies

97

6 Part A: Negative Evaluations as Information and Affect in Interactive Groups and Teams: Dynamic Model

116

6 Part B: Negative Evaluations as Information and Affect in Interactive Groups and Teams: Empirical Studies

138

7 Silence Events as Mediators of Idea Generation and Information Exchange in Interactive Teams: Team Structure and What Members Say and Don’t Say

147

8 Part A: An Interorganizational Decision-Making Team and Its Subteams: Status Differentiation and Information Exchange

161

viii Contents 8 Part B: Frames and Scripts in the Information Exchange of Interorganizational Teams and Its Subteams

180

9 Virtual Teams as Decision-Making Units: The Social Mediator of Trust

202

10 Part A: Information Exchange in Decision Making Teams: Integrative System

213

10 Part B: Dynamics of Information Exchange in Decision Making Teams: Computational Exercises

226

11 Technology for Quality-Maximizing Objectives in Decision-Making Teams

242

12 Summary and Discussion

255

Notes Author Index Content Index

275 281 291

Figures

4.1 Judgment of Status Loss from a Negative Evaluation as a Function of Actual Status Distance from the Evaluator 4.2 Loss from Negative Evaluation as a Function of Prestige and Status Distance of the Evaluator from Self 5A.1 Dynamics of Idea Generation in an Interactive Team 5A.2 Dynamic Single- and Multisource Ideas as a Function of Group Structure 6A.1 Dynamic Rates of Initiating Negative Evaluations by the Team Leader and Other Team Members 7.1 Conversation Time per Period in Face-to-Face Groups by Experimental Condition 7.2 Sequence of Information Types in Ten Groups 8B.1 Mean Proportions of Team Scripts Initiated by INT and EXT Members of Subteam and Team Meetings across Four Script Domains 10B.1 Total Idea Number (t idea) as a Function of the Status Distribution in a Team 10B.2 Quality in the Team’s Information Input to an Organizational Decision as a Function of Model Parameters 11.1 Hypothetical Time Paths of Quality-Increasing Information Flows in a Linear Control System: Control in N

69 76 92 92 125 154 154

193 230 238 246

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Preface

The fatal misconception behind brainstorming is that there is a particular script we should all follow in group interactions. . . . [W]hen the composition of the group is right—enough people with different perspectives running into one another in unpredictable ways—the group dynamic will take care of itself. All these errant discussions add up. In fact, they may even be the most essential part of the creative process. —Jonah Lehrer Individual commitment to a group effort—that is what makes a team work, a company work, a society work, a civilization work. —Vince Lombardi In individuals, insanity is rare; but in groups, parties, nations and epochs, it is the rule. —Friedrich Nietzsche Never underestimate the power of stupid people in large groups. —George Carlin Well, uhm, would you like to play in the sandbo- . . . er, I mean, want to participate on our group research on sand resistance? —Akatsuki (Matsuri Huno)

As the above indicate, there are more than a few perspectives on the accomplishments of groups and teams. In the face of these perspectives, the pervasiveness of groups and teams in task-directed applications that range from aircraft design to the design of cultural offerings is increasingly evident. Although there are bases to anticipate their functionality in task-directed applications, there is sizable literature documenting their dysfunctions as interactive units. While groups and teams as decision-making and problemsolving units have a long and varied history, it remains difficult to offer a definitive statement on the efficacy of interactive groups and teams as decision-making units. This is likely to be because of the complexity of both

xii Preface cognitive processing in decision making and social interaction in these units. If early studies appear to have given more attention to social interaction than to cognitive processing, more recent studies have directly addressed cognitive processing as it occurs in interactive problem-solving units. In generally assessing the net contributions that can be expected from interactive teams as decision-making units, there clearly remains some disconnect between recent reports that emphasize the efficacy of applications in teams and the history of controlled experimental studies of interactive groups. Experimental studies have long documented productivity loss as it can occur in the idea generation of interactive groups. Many if not most of the studies have consistently concluded that the combined results of members working independently (e.g., as nominal groups) are more productive in idea generation than the same number of members in interactive groups. The basis for this result has been summarized in the dysfunctional effects or process losses that occur when members freely interact. This disconnect between practitioner procedures and empirical results may partly be attributable to limitations in the theoretical bases for the operation of interactive groups and teams in decision making. Effects of what some conclude to be inevitable social structures that arise early in member interaction remain to be directly defined in controlled studies of decision-making teams. As argued, studies in brainstorming groups have not extended their conceptualization to adequately represent the form that social structure takes in interaction in interactive groups and teams. This is in spite of extensive histories in the study of the formation and operation of status hierarchies in interactive task directed groups. Additionally, objectives in decision making are more complex in cognitive processing than maximizing the number of ideas generated in the brainstorming of the group or team. Accordingly, several authors have considered the differential efficacy of interactive units in convergent and divergent thinking tasks. This will be recognized in the account of cognitive processing in decision making that will be offered. Finally, teams commonly remain embedded in organizations from which they are formed, and this introduces more complex dependencies. Clearly, reports from contextualized implementation and controlled studies continue to contribute to defining what is effective and efficient in decision-making teams. Reconciling their differences and extending the inference they offer to more completely account for effects that will be designated as structure in interactive units is important since decision making for certain classes of decisions is increasingly in teams. Why certain methods in the design and management of interactive teams work well and others less well has not been adequately accounted for in the absence of the representation of structure in the units under study. To summarize the above points: (1) There is recently widespread acceptance of teams in both their potential and accomplishment.

Preface  xiii (2) This acceptance may exceed their demonstrated accomplishment as assessed in controlled experimental studies. (3) In contrast, experimental studies are limited in their definition of causal variables and the criterion variables they study. (4) Social structure in decision-making units as a causal variable has generally not been adequately investigated. (5) Technological capabilities and increases in understanding of process can provide a basis to manage interaction toward objectives in decision quality. The objective of the exposition to follow is to propose an organizing framework for decision-making groups and teams that integrates both extensive histories in the study of these units of analysis and recent contextualized applications. The framework considers team interaction as information exchange and gives a form to group or team structure as an organizer of this exchange. Computational and empirical results are used to assess inference from this framework. An emphasis in interpreting results is to give a form to policy for designs that can increase the effectiveness and efficiency of teams as decision-making teams. My own initial introduction to group processes was in the study of controlled conflict in groups and teams and its implications for innovative decision making. Conflict in this contextualization can increase the range of divergent opinions and ideas that are inputs into the problem solutions or decisions that the interactive unit converges to. However, conflict has to be maintained in a range in which it does not undermine participation and cohesiveness of the decision-making unit. An objective in such a conceptualization is to define a vector of input variables that maintains the level of conflict in a range that increases productivity and innovation. As a visiting scholar and research associate at the Laboratory for Social Research, Stanford University, I interacted with Bernard P. Cohen. My own appreciation of effects of social structure in task-directed groups that social theory implies was greatly increased by collaboration with Bernie and other members of the lab. Bernie was among the investigators that conceptualized structural effects in background characteristics of members that combine to define emergent status orders in interactive groups and the expectations that they put in place. His commitment to the development and continuity of theory was always evident to those who were his colleagues and students. Support from a NSF grant allowed Bernie and I to collaborate on applications of our initial conceptualization of information exchange in task-directed, interactive groups in a series of laboratory studies. The early work on information exchange in interactive groups was elaborated upon and experimentally studied in collaboration with Lisa Troyer. Lisa’s research and capabilities will be evident in a number of the studies that are reported here and her more recent work. Working experiences from team participants conveyed to me by students in the Lucas Graduate School California State

xiv Preface University, San Jose, further contextualized the framework in structured information exchange. I greatly appreciate the comments on drafts of the manuscript chapters by Professor Robert Shelly. Appropriately, all remaining errors are my own. I thank Laura Stearns at Routledge for her early interest and continual encouragement of the monograph. I am also grateful to Erlinda Viray for word processing and editing of the text and Mangesh Dhumne and Akshay Jagtap for competent research assistance.

1

Introduction Information Exchange in Decision-Making Groups and Teams

OVERVIEW This chapter introduces an information exchange framework for the study of decision making by groups and teams. While decision making by interactive units has been given recent impetus by technology that supports virtual teams, fundamental issues in the efficacy of interactive groups and teams remain. The differences between inferences from lab-based experimental studies and studies of organizational teams indicate some of the basic issues. Following a review of the results of a range of studies, it is suggested that the social structure of groups and teams is a source of process losses in these decision-making units that has not been adequately recognized or defined in available studies. Extensive background results in the study of group processes in support of this claim are cited. An account of ill-structured decision making as information exchange is then introduced. In this account, it is recognized that information exchange is in part a social process that introduces social risk to group and team members. This risk occurs because the receipt of negative evaluations can be a meaningful source of status loss for members. The magnitude of the loss can be expected to depend on judgments of the status distance between the source and recipient of the evaluation. Subsequent chapters that offer analytical, numerical, and empirical support for the information exchange perspective that is introduced are briefly reviewed. INTRODUCTION The ascendency of decision making and problem solving by interactive groups and teams is increasingly evident in contemporary organizations (Beers, Boshuizen, Kirschner, & Gijselaers, 2006; Cross, Thomas, & Light, 2009; Park & DeShon, 2010; Rowland & Parry, 2009) as are reports of their effectiveness (e.g., Feri, Irlenbusch, & Sutter, 2010). This is most evident in cases where decisions or problems cannot be given algorithmic forms or even welldefined heuristic forms. Following others (e.g., Mintzberg, 1973; Walker & Cox, 2006), this case will be referred to as ill-structured. Putative reasons

2  Decision Making Groups and Teams for the use of interactive groups and teams as units in ill-structured decision making include the diversity of expertise and ideation that can be exchanged between decision makers (Koh, 2008), increases in perceived equity that participation can impart (Deutsch, 2010), and the contribution that participation in decision making can have on implementation of the decision (Tegarden, Sarason, Childers, & Hatfield, 2005). However, the inferences of those who study the effectiveness of interactive teams in organizations (e.g., Malhotra & Majchrzak, 2005) differ from the most common conclusions of those who have studied interactive groups in controlled experiments (e.g., Kerr & Murthy, 2004; Paulus, Putman, Dugosh, Dzindolet, & Coskun, 2011; Pinsonneault, Barki, Gallupe, & Hopper, 1999; Strobe & Diehl, 1994). Reports from both contextualized implementation and controlled studies contribute to our understanding of process and efficacy of interactive groups and teams as decision-making units. Reconciling these differences is consequently important to the continuing use of groups and teams as decision-making units. In the exposition to follow, inferences from both these sources is assessed to further a more comprehensive conceptual account and its testing and contribute to the design of effective decision-making groups and teams. INFORMATION EXCHANGE IN DECISION MAKING The perspective of the exposition is one in which microprocessing in decision making of interactive groups and teams is conceptualized in terms of information exchange. The quality of a group or team decision will be considered in terms of the amount and type of information that is exchanged and its sequencing. While understanding microprocessing is fundamental in assessing interactive decision making, recognizing social processes in agent interaction within groups or teams increase the complexity of accomplishing this. Team members as individual agents clearly have internalized personal objectives. However, when agents become team members, they in some part assume the objectives of the team. At the least, this requires an integration of agent objectives with those of the aggregate. In considering processing in information exchange in interactive groups and teams, structure in the unit is introduced as an organizer of the content and source of information that is exchanged. Group or team structure is defined here in terms of the distribution of member status in the unit. SOCIAL STRUCTURE IN DECISION-MAKING GROUPS AND TEAMS As well recognized, a status organization is generally emergent in interactive groups and teams (Berger, Fisek, Norman, & Zelditch, 1977; Berger, Rosenholtz, & Zelditch, 1980; Berger & Webster, 2006). The basis to expect

Introduction  3 structure to have pervasive effects on the amount, type, and distribution of information inputs across team members is elaborated. A range of background studies indicate the stability and performance consequences of social structure (e.g., Parise & Rollag, 2010; Sperber & Hirschfeld, 2004). Elucidating how the social structure of groups and teams can affect the organization of information exchange and, consequently, the quality of decisions will be a principal objective of this exposition. Appropriate attention will be given to technology enabled methods to manage in social groups and team structure and information exchange toward quality objectives in decision making. In the rest of this chapter, I introduce major contentions of the conceptual framework and provide brief overviews of the chapters that make up the discourse on decision making by groups and teams. In the next chapter, I elaborate on the history and background of fundamental processes in the interactive groups and teams with task-directed objectives. This includes the basis for linking the amount and type of information that members exchange to both decision quality and social structure in decision-making units.

Pervasiveness of Group Processes across Decision-Making Contexts: R&D Teams and Juries The importance of social structure has now been indicated in a diversity of contexts that include juries and R&D teams. Teams in both of these applications are assumed to be most effective when they are putatively objective and egalitarian. There is now considerable evidence of the basis that social structure introduces in both these applications of interactive groups and teams. For example, Cohen and Zhou (1991) examined the effects of team, organizational, and external (societal) status characteristics on interaction patterns of established work teams. Both status characteristics that are external to the team and status characteristics within the team were shown to influence team interaction. When status within the team is controlled, only one of the external characteristics has a direct effect on interaction. However, status within the team was found to be significantly influenced by each of the putatively external characteristics. These results indicate that competence and performance are clearly not the singular bases for team status. As such, they suggest that status processes in teams closely follow those reported in zero history lab groups. That is, beliefs that follow from diffuse status characteristics commonly affect the social order in interactive groups and teams. In the case of juries, Davis and collaborators (Davis, Hulbert, Au, Chen, & Zarnoth, 1997; Kirchler & Davis, 1986) have followed the historical study of Strodtbeck and Mann (1956) and extensively examined group processes in their interaction. (Also see, Rashotte & Smith-Lovin, 1997). Most recently, Salerno and Diamond (2010) have reviewed classic jury decision-making

4  Decision Making Groups and Teams research on jury deliberation that challenges the view that deliberation itself does not have an important effect on verdicts. These authors call attention to cognitive processing during deliberation that might explain the transition between predeliberation predictions and a jury’s ultimate verdict. Diamond, Rose, Murphy, and Meixner (2011) have subsequently reported direct studies of real jury deliberation and awards that show damage anchors in awards to defendants’ applications to follow from cognitive processes. Although not directly cited, there is a basis to expect that the processing of information described in these studies is organized by structure as it is in other contexts. Following informal reports and group studies that have been cited, status organizing process can be expected to be operative in cognitive processing when jury members interact in deliberation. The present exposition defines and gives forms to the microprocessing of information that result from status orders and consequences for decision making in interactive groups and teams that are applicable across a range of contextual objectives. ILL-STRUCTURED DECISION MAKING The early history of task-directed groups (e.g., Bales, 1950) directed attention to problem solving without defining the process or offering a distinction from decision making. The case of ill-structured decision making as defined above has a commonality with general designations of problem solving. Both cases do not typically begin with a set of well-defined alternatives as in discrete choice models of decision making. Additionally, they have a common dependence on the exchange of ideas and their evaluation. An important difference between decision making and problem solving is that the designation of decision making does commonly imply a definition of discrete alternatives and convergence to one of the alternatives. This is typically what teams in organizations are charged with doing and will be the designated process for which objectives are defined here. For firms, the case of ill-structured decisions includes a range of critical decisions that range from the selection among strategic alternatives and the retention of new products (e.g., Schmidt, Montoya-Weiss, & Massen, 2001). This case clearly requires a more comprehensive consideration of the objectives of decision-making units and member contributions to these objectives than do applications in brainstorming traditions. In these traditions, the objective is strictly defined as maximizing the number of ideas generated by the group or team. Decision-making objectives increase the importance of evaluations as an information type. Additionally, the sequential exchange of information can consequently be expected to be more complex in microprocessing than in studies of problem solving. The design of systems that support group decision making (DSSs: e.g., Gallupe & DeSanctis, 1987; Holsapple & Whinston, 2000) correspondingly require a

Introduction  5 commensurate representation of the complexity in microprocessing. In the exposition to follow, this requires dynamic forms for information typologies in the presence of social structure. The proposed account of microprocessing in ill-structured group decision making is used to provide guidance for the design and implementation of GDSSs. QUALITY IN GROUP AND TEAM DECISION MAKING As reviewed, the preponderance of evidence from experimental studies of brainstorming indicates that nominal groups are more productive than interactive groups. While this dialogue and its evidence is not the principal interest in the discourse to follow, it remains relevant to understanding and designing decision-making teams. In agreement with these studies, it is recognized that when design does not effectively manage the challenge of integrating microprocessing of agents as both individuals and group or team members, there is commonly a net loss in the quality of ill-structured decisions from members interaction in comparison to the quality offered by members working independently. Although the formal evidence remains ambiguous at present, it is suggested that effective understanding and management of processing in interactive groups and teams can result in contributions to decision quality that significantly exceed those offered by the same numbers of individuals working independently. The intention of the exposition to follow is to propose an organizing framework for decision-making groups and teams that integrates process that is generated by both the objectives of members as individual agents and the commitment to group objectives. This will be seen as necessarily requiring the representation of the position in the social structure of the decision-making unit that an agent has. The above is clearly a complex agenda that benefits from taking note of extensive histories in the study of interactive groups. In the next sections of this chapter, I first indicate the pervasiveness of the structural effects in interactive groups and teams that have been cited. I then discuss the basis of distinctions between decision-making units as groups and teams and the decision typology that follows. Finally, I review relevant histories in the study of interactive groups as background for the exposition. DESIGNATIONS OF GROUPS AND TEAMS The designations of groups and teams are often used coordinately. I next briefly take note of concordances and divergences in the designations of groups and teams that are relevant to the present exposition. One of the more available discussions of teams has cited differences from groups in that the former

6  Decision Making Groups and Teams evidence of greater commitment and synergy among members, more egalitarian interaction and longer existence (Katzenbach & Smith, 1993). However, disjunctive designations of groups and teams often too narrowly define groups and too quickly dismiss limitations in teams that are likely to correspond to those commonly reported in interactive groups. It is notable that many of the now cited differences between groups and teams were identified in earlier academic discourse on differences between effective and ineffective work groups (e.g., Campion, Medsker, & Higgs, 1993).1 While delineation of group and team distinctions are not exact, the principal interest in this exposition is in what are now discussed as teams. Since the preponderance of background laboratory studies use the designation of interactive groups, reviews of background studies will use this designation. Here, the designation will refer to the specialized work groups addressed in the history of laboratory and earlier organizational studies, (e.g., Guzzo & Dickson, 1996). When claims are general to both groups and teams, both designations will be designated. LABORATORY STUDIES OF TASK-DIRECTED GROUPS Our understanding of the dynamics of interactive teams is clearly beholden to a long history in the study of group processes (see, for example, Berger & Webster, 2006; Guzzo & Dickson, 1996; and Levine & Moreland, 1990). One of the recurrent investigations in early studies of task-directed groups is in comparisons of the productivity of nominal and interactive groups. Findings of these studies have generally indicated the lower productivity of interactive groups in comparison to the same number of individuals working independently in brainstorming tasks (e.g., Dennis & Valacich, 1993; Diehl & Strobe, 1991; Erez & Somech, 1996; Lamm & Trommsdorf, 1973; Latané, Williams, & Harkins, 1979; McGlynn, McGurk, & Effland, 2004; Mullen, Johnson, & Salas, 1990; Pinsonneault et al., 1999; Pinsonneault & Kraemer, 1990; Strobe & Diehl, 1994). As has been noted, whatever the balance of empirical evidence on their relative efficacy has been, groups and teams have been given new enfranchisement as decision-making units in a range of organizations (e.g., Baker, 2002; Gilliam & Oppenheim, 2006; Houghton, Simon, Aquino, & Goldberg, 2000; Malhotra & Majchrzak, 2005; Mello & Ruckes, 2006; Qureshi & Vogel, 2006; Schmidt et al., 2001). This has often been in studies of virtual teams in which most or all communication between members is electronic. One reason for this discordance between inference from lab studies and current organizational studies may be that efficacy in decision making is more complex in cognitive processing than the objective of maximizing the number and quality of ideas that has predominantly been the dependent variable in lab studies. An additional reason may be that the benefits of

Introduction  7 representation and participation by those who would be implementing the decision is not included in lab assessments of the relative efficacy of interactive and nominal groups. A new generation of technology in the management of virtual groups and teams (e.g., Malhotra & Majchrzak, 2005) has further increased acceptance of their efficacy even if the basis for this is not yet well-demonstrated in controlled empirical studies. In contrast to lab studies, reports on the efficacy of this generation of technology-aided teams in organizational decision making are commonly—but not exclusively—in terms of judgments by who have been team members (e.g., Malhotra, Majchrzak, Carman, & Lott, 2001). Experimental studies of technology in the management of interactive teams have more often been directing to testing integration of the technology (Thomas & Bostrom, 2010) than to assessing the efficacy of interactive teams relative to alternatives. Even if interactive groups or teams were not more effective than their nominal or Delphi alternatives, it is likely that the contribution of participation to member satisfaction and to the implementation of decisions will ensure that in many industrial, government, and academic applications, the former will be maintained as decision-making units. While reports of recent industry applications raise questions on the conclusiveness of findings from lab studies on the superiority of nominal groups, there are few, if any, attempts at comprehensive accounts of the basis for the divergence in these findings. I suggest that increased representation of the microprocessing of agents that occurs in interactive groups and teams is an appropriate starting point for increasing our understanding of team decision making in contextual applications. INFORMATION EXCHANGE IN GROUP AND TEAM DECISION MAKING The account of decision making that follows will consider groups and teams as information processors (e.g., Griffith & Neale, 2001; Hinsz, Tindale, & Vollrath, 1997). In support of an account of information exchange processes in ill-structured group or team decision making, I consider a typology in information beyond ideas and propose forms that can more definitively indicate how group and team structure as the distribution of member status mediates the exchange of information. The exchange of negative evaluations is particularly important in this typology because of its significance in status differentiated groups and teams. I also recognize the basis to expect that the amount and type of information that is exchanged in status differentiated groups has a basic dependency on perceptions of equity and trust by group members. Equity here is used to mean that group members judge that the exchange of information closely corresponds to agreed upon objective criteria (e.g., merit). For example,

8  Decision Making Groups and Teams evaluations should be distributed to members on the basis of the goodness of an idea or a fact rather than the status of a member. Both procedural and distributive justice and the newer invocation of interactional justice (Greenberg, 2009) are involved in this application. (On procedural justice see, Blader & Tyler, 2009; Davis, 1980; Davis, Hulbert, Au, Chen, & Zarnoth, 1997; Kirchler & Davis, 1986; Tyler, 2000.) Trust can be defined as confidence in group and team members, especially low-status members, that equity will be maintained in information exchange. Formally, trust can be defined as “state involving confident positive expectations about another’s motives with respect to oneself in situations entailing risk” (Boon & Holmes, 1991, p. 194; also see Kuo & Yu, 2009). What has been designated as trust has been a recurring variable in recent accounts of effective applications of interactive team decision making (e.g., Jarvenpaa & Leidner, 1999; Kanawattanachai & Yoo, 2002; Robert, Denis, & Hung, 2009; Sarker, Ahuja, Sarker, & Kirkeby, 2011; Thomas & Bostrom, 2008) and will be given an explicit form in the system that is introduced. As indicated, increasing the variance in the distribution of member status in the group or team typically results in a downward biasing effect on perceptions of equity and trust that follows these perceptions. GROUP AND TEAM STRUCTURE AS A MODERATOR OF INFORMATION EXCHANGE A contention of the present exposition is that the majority of empirical studies of either brainstorming or decision making by groups and teams may not be definitive since these studies have left the major biasing factor of group structure in the performance of interactive groups uncontrolled. Additionally, while process losses in the productivity in idea generation by interactive groups have been well-established, there is also indication that the difference between interactive and nominal groups on criterion that relate to quality and selection is unclear (e.g., Rietzschela, Nijstada, & Stroebe, 2006). In the application to follow, group and team structure are defined as the social structure that is emergent early in the history of interactive groups. Although “evaluation apprehension” as referenced by Diehl and Strobe (1991) and Strobe and Diehl (1994), and “social influence” as referenced by Pinsonneault et al. (1999) can have pervasive effects on the exchange of information in group decision making and are likely to be related to group structure, these variables, as operationalized, do not adequately give a form to the complex effects that social structure in groups activates. Background studies of microprocessing in interactive groups support an inference that an emergent social structure most often results in a downward biasing of the performance of task-directed interactive groups (e.g., ThomasHunt, Ogden, & Neale, 2003; Wittenbaum, 1998) but would by definition be unrelated to the performance of nominal groups. The underperformance may

Introduction  9 occur because the structural positions of members of status-differentiated groups are often in variables other than the expertise that contributes most to objectives of groups and teams. These variables commonly include the demographics of group members (e.g., Sinaceur, Thomas-Hunt, Neale, O’Neill, & Haag, 2010; Webster & Foschi, 1988). From the above, there is a basis to expect that group structure is commonly an unmeasured source of process losses in interactive groups and teams. However, as I indicate, there are also sources of process gains in interactive groups and teams that benefit from being more explicitly represented. These have been noted in some accounts but not given explicit forms. They include the contributions that groups with heterogeneous memberships to decision quality through ideational diversity and multicriteria evaluations (DiTomaso, 2010; McLead, Lobel, & Cox, 1996; Nemeth & Nemeth-Brown, 2003). As such, while nominal groups may remove or minimize structural effects that are sources of process losses, they are likely to correspondingly remove sources of process gains. GROUP AND TEAM MEMBERS AS BOTH SELF-DIRECTED AGENTS AND CONTRIBUTORS TO OBJECTIVES OF THE AGGREGATE UNIT Members of interactive groups and teams simultaneously act as individual agents and members of an aggregate. As social agents, they can be expected to consider their individual status position in the group or team. As committed members of the unit, they are motivated to adopt and contribute to its objectives. As will be proposed, the objectives of individual members of a decision-making unit include the dual of (1) maximizing a quality function for the decision and (2) maintaining or increasing the member’s own status position in the unit. The distribution of status in the unit is generally a mediator of the integration of these motives in the information that a member initiates. THE EXCHANGE OF NEGATIVE EVALUATIONS As emphasized, negative evaluations have particular importance in the information exchange of ill-structured decision making by interactive groups and teams. Since this information type can filter ideas on quality, the minimally constrained exchange of evaluations is important to quality objectives. Brainstorming traditions that have predominated in experimental studies do not typically give adequate attention to the contribution of evaluations to quality objectives. However, it is also the case that negative evaluations can be used by members to maintain or increase their status in the group. For example, higher status members can maintain their position in the social hierarchy by sanctioning others through negative evaluations.

10  Decision Making Groups and Teams Since receiving a negative evaluation can constitute status loss under certain conditions, this can correspondingly inhibit or distort the functional contribution that the exchange of this information type can make to a quality objective. If it can be assumed that members are motivated to maintain their position in the group’s social structure as well as contribute to the task objectives of the group, it is assumed that they at least intuitively understand that the receipt of negative evaluations can have a social cost. This, in turn, is likely to be integrated into their a priori expectations of receiving a negative evaluation for initiating any information type and their judgment of the expected cost of such an evaluation. As has been demonstrated, when unmanaged groups become more status differentiated, the distribution of evaluations is increasingly distributed according to member status rather than contribution to the group objective (i.e., performance-related criteria; e.g., Ridgeway & Johnson, 1990; Silver, Cohen, & Crutchfield, 1994). One of the consequences of the above is that member judgment of perceived equity in the distribution of negative evaluations to group members will be inversely related to the variance of the status distribution in the group since, ceteris paribus, increases in the variance of this distribution increases the tendency to undersend negative evaluations and oversend positive evaluations to higher status members. Undersend and oversend here refer to a merit-based criterion for the distribution of evaluations. When the distribution is closely matched in number and sequence to the ideas that members initiate rather than their status, their willingness to offer appropriate evaluations will increase. Such a construction of a trust mediator in information exchange is consistent with recent emphasis on this variable in studies of virtual teams (Alanah & Ilze, 2009; Malhotra, Majchrzak, & Rosen, 2007; Rosen, Furst, & Blackburn, 2007). Whether receipt of a negative evaluation is interpreted for its information content or for its cost in status is mediated by the trust that members have in the motives of other members. As elaborated, members typically form a judgment of equity and trust based on their observations of the distribution of negative evaluations in the group. When evaluations are sent to members in proportion to their status in the decision-making unit rather than an objective criterion such as the number and quality of ideas they initiate, this can undermine judgments of equity and trust and the willingness of middle or lower status members to publicly offer socially risky message types such as ideas or negative evaluations. THE MEMBER LOSS FUNCTION In considering the form of the function that members use in assessing the likely consequences of information they initiate for their status in the decisionmaking unit, it is maintained that a negative evaluation generally contributes

Introduction  11 more to a member’s judgment of his or her status loss than a positive evaluation contributes to their judgment of status gain. This is because (1) positive evaluations are far more common than negative evaluations and (2) losses generally are weighted more heavily than equivalent gains (Peeters & Czapinski, 1990; VanDyk, Danner, Nieweg, & Sumter, 2003). It is further argued that this occurs because the loss function that members use in evaluating the consequences of initiating an information type is not linear. Rather, members generally underweight small status differences from themselves and overweight large status differences in their loss functions (e.g., Silver & Troyer, 1998). The form of the proposed social loss function corresponds to the ones that have been typically proposed for applications to monetary gains and losses (e.g., Kahneman & Tversky, 1979; Koszegi & Rabin, 2006; Schmidt, Starmer, & Sugden, 2008; Trepel, Fox, & Poldrack, 2005). If an agent’s assessment of status loss from a behavior is weighted by differences in the status between a sender and target using this loss function, then higher status members have further advantage in status competition. Evidence for this loss function will be introduced in a subsequent chapter. Since as has been noted, structural differences commonly arise from naturally occurring differences in member backgrounds and demographics, including age and gender, that are often minimally related to their taskrelated abilities, the above assumptions can have important consequences for the amount and type of information exchanged in status differentiated groups. This claim is elaborated upon next. EVALUATIONS AND THE PUBLIC EXCHANGE OF REALIZED IDEAS As has been noted, ideas and negative evaluations are information types that can be expected to contribute most to the quality of ill-structured decisions also have the greatest social risk. Moreover, the loss functions that have been suggested introduces disproportionate increases in judgments of social risk by medium- or low-status group members as status differentiation in the decisionmaking unit increases. In information exchange, trust has been defined as confidence that other group members will evaluate information initiated by others on objective criteria such as contribution to the quality of a decision rather than as means of maintaining or increasing their own relative status in the group. It can thereby be conjectured that the willingness of most group members to publicly offer the risky information types of ideas and negative evaluations will depend on a member’s trust. At this time, trust has been reported as a major factor in the performance of virtual teams (e.g., Coppola, Hiltz, & Rotter, 2004; Greenberg, Greenberg, & Antonucci, 2007; Jarvenpaa, Shaw, & Staples, 2004; Morris, Marshall, & Yrainer, 2002; Staples & Webster, 2008; Webster & Wong,

12  Decision Making Groups and Teams 2008). Recent research is increasingly defining the sources and formation of trust in groups and teams (e.g., Kuo & Yu, 2009). In the framework of the present exposition, it has been proposed that members use observations of information exchange in a team, as in the distributions of evaluations sent by higher status members to other team members, in forming judgments on equity and trust. When an observed distribution of evaluations does suggest that a distribution of evaluations on merit is being followed, member judgments of equity increase. This, in turn, can be expected to increase member trust and the amount and type of information that members of medium- and low-status are willing to initiate. Here and in a later chapter, it is further observed that it is at least questionable whether formal statements of rules for distribution of evaluations are adequate to ensure merit-based distributions in heterogeneous groups and teams. DEFINING THE DECISION-MAKING OBJECTIVE IN INFORMATION TYPES As has been previously maintained, decision-making quality in interactive groups and teams depends on information exchange that is often too readily reduced to the information type of ideas. A typology of the information that is exchanged in processing for objectives in ill-structured decisions can be extended to include categories of positive and negative evaluations, data/ facts, and interrogatives. Given the importance of evaluations—especially negative evaluations—to decision quality, a subsequent chapter indicates how this information type relates to ideation and idea initiation in the presence of social structure. As indirectly suggested by others, negative evaluations are a complex information type (e.g., Santuzzi, 2007). While there has been an emphasis on inhibiting effects of negative evaluations in available literatures, their influence on quality objectives through the filtering of ideas and introduced data has been recognized. It is clear that both too few and too many negative evaluations can reduce decision quality. The former can result in inadequate filtering of candidate solutions. The latter can increase member judgments of the social risk in initiating ideas. As has been indicated, the distribution of negative evaluations is also important to decision quality through equity and trust that mediates the amount and type of information exchanged in the group. From the above, the distribution that maximizes decision quality should overcome structural tendencies to undersend negative evaluation to higher status group members and oversend this information type to lower or medium status members, while also ensuring that the number of evaluations is adequate to appropriately evaluate ideas. The forgoing descriptions of the quality and loss functions in the exchange of information types will be used to give a form to an integrated

Introduction  13 system that represents microprocessing in team decision making. These forms will include a representation of equity in the perceptions of group members and its relationship to trust in information exchange. The system that will be proposed and its underlying concepts will also be used to provide the basis for group decision support systems that manage groups and teams toward quality objectives in ill-structured decision making. Having introduced and discussed the bases for the study of information exchange in interactive decision-making groups and teams and the mediation of this exchange by social structure in the unit, I next provide an overview of the chapters to follow.

OVERVIEW OF CHAPTERS

Chapter 2: Background and Framework Background studies of structural variables that relate to the effectiveness and efficiency of interacting groups as decision-making units is reviewed in this chapter. These studies have evolved to increasingly definitive accounts of microprocessing that supports and maintains social structure in interactive groups. The divergence between findings that these studies report and the increase in use of interactive decision-making teams in organizations is also documented. A basis to conceptualize “process gains and losses” in the information exchange of interactive groups that relate to group structure is introduced.

Chapter 3: Dual Motive Agents in the Information Exchange of Interactive Groups and Teams In the third chapter, the motives of team members acting as both individual agents with self-directed objectives and as members committed to the team objective is considered. This chapter recognizes the “mixed-motives” of members of interactive teams and provides a closed form representation of the information exchange that these motives introduce. The form to be offered for the objective function is in terms of the set of information types under study. This form is then used to make an initial statement on the team structure that can be expected to maximize decision quality. This chapter provides a closed form representation that supports initial conjecture on effects of structure on information exchange in interactive teams. Analytical results that relate the number of ideas exchanged by group members to the distribution of status in the group is reported. Empirical studies are reported in support of the inferences. Results reported in the chapter accommodate the complex heuristics that agents use in initiating information in an interactive decision-making unit and show the effects that team structure can have on this exchange.

14  Decision Making Groups and Teams

Chapter 4: Biases in Member Judgments of Gains and Losses in the Information Exchange of Interactive Groups and Teams The fourth chapter considers the distance weighting function that team members use in processing judgments of gains and losses in their information exchanges with other team members. This function is important to quality in team decision making since it indexes the expected status loss or gain that members anticipate from sending an information type, which thereby mediates the exchange of information in the team. The weighting function that team members use is proposed to be one in which they overweight the status of members they perceive to have more status than themselves and underweight the status of members who they perceive to have less status to themselves. The basis for this function in background on agent decision heuristics is reviewed. Using a form for the distance function that represents the above cited property in judging gains and losses, it is demonstrated that team objectives in idea number and quality are maximized when either (1) the status distance in the loss function are strictly proportional to actual status distances and member status differences are equal or (2) members trust that status differences do not lead to biases in the distribution of evaluations. Empirical studies that support the conjecture on the distance function used by team members are reported.

Chapter 5: Idea Generation in Interactive Teams: Conceptual Model and Empirical Studies This chapter addresses ideas as principal information types in ill-structured decision making. Part A of the chapter conceptualizes and gives forms to the exchange of ideas in interactive teams. In the absence of algorithmic or heuristic rules, ideas often predominate in giving direction and form to an optimal decision. Multiple-source or combinational ideas are conceptualized as contributors to process gains in groups and teams are elaborated. Following this, explicit dynamic forms that proceed from the conceptualization and give representation to team structure are proposed. Numerical and empirical evidence in support of these and the effects of experimentally introduced structural forms are then reported in Part B of the chapter.

Chapter 6: Negative Evaluations as Information and Affect in Interactive Groups and Teams: Dynamic Model and Empirical Studies This chapter elaborates on negative evaluations as information types in decision-making teams. Part A of the chapter elaborates a conceptualization of evaluations as an information type. Evaluations have been noted as contributing to decision objectives through their filtering property in the assessment

Introduction  15 of ideas. The inconsistent results of previous conceptual and empirical studies of this information type are summarized. It is observed that this may at least partly reflect differences between the dynamics of evaluations and other information types exchanged in the group. Evaluations are then conceptualized as having both informational and affective content. Evaluations are informational to the extent that they can give factual feedback on quality criteria to the initiators of information. However, evaluations also inherently have affective content in that they directly relate to the self-esteem of those who receive them. Both the informational and affective content of evaluations are shown to influence team structure but in different directions. Analytical implications of candidate forms for the dynamics of evaluations as information and affect are offered. In Part B of the chapter, empirical studies of inferences from the model of evaluations as simultaneously informational and affective are reported. Results indicate how asymptotic levels of exchanged evaluations can depend on structure in the team.

Chapter 7: Silence Events as Mediators of Idea Generation and Information Exchange in Interactive Teams In this chapter, the under recognized importance of silence in interactive teams to objectives in ill-structured decision making is considered. It is conjectured that silence can be periods of incubation that facilitate ideation. A further conjecture is that the willingness of team members to coact in silence is related to member judgments of equity and trust. I suggest that status-equal teams typically interact under conditions of higher trust than status differentiated teams. This conjecture is initially assessed with Markov models of the sequences of periods of silence and information exchange in an experimental study of interactive groups that differ in status distributions. Results that are reported support the conjectures that are offered and show differences between status differentiated and undifferentiated groups in the number and length of the periods of silence and their relationship to idea initiation.

Chapter 8: An Interorganizational Decision-Making Team and Its Subteams: Status Differentiation and Information Exchange and Interorganizational Teams and Its Subteams This chapter reports two field studies of group decision making in formal organizations. These studies were undertaken to provide external validity for the conceptual framework that is introduced. In the studies, predictions from the framework in teams that were engaged in what has been designated as an ill-structured decision is tested. The teams that are studied had completed team-building training that included training to overcome biases introduced by structure in the team. Background information on team members was used to predict the status that they would have in the teams in

16  Decision Making Groups and Teams which they participated. Videotaped records of the team interaction were then coded in terms of the amount and type of information exchanged by team members. In Part A of the chapter, the amount and type of information exchanged in the team is related to their status position in the team. In Part B, information exchange is analyzed at a more abstract and molar level. To do this, normative statements by team members are categorized and coded for a defined typology in scripts of the information exchange. Proportions of statements in the different categories in videotaped interaction will then be related to the status distributions that are constructed from background information in different teams and subteams.

Chapter 9: Virtual Teams as Decision-Making Units Virtual teams, in which members rarely or never interact in face-to-face meetings and interaction is often asynchronous, have been increasingly implemented as problem-solving and decision-making units in complex organizations. Background and results in the study of virtual teams is first reviewed. The emphasis upon trust and its antecedents in judgments of competence and reliability are made evident in available studies of virtual team effectiveness. Consistent with the exposition, a form is given to the formation and maintenance of equity and trust in terms of the information that is exchanged and its distribution across each member.

Chapter 10: Information Exchange in Decision-Making Teams: An Integrated System: Integrative System and Computational Exercises In this chapter, the discourse of preceding chapters is used to propose an integrated system for team information exchange. In Part A of the chapter, conjectures on mixed motives, the distance function in microprocessing, and the dynamics of ideas and evaluations are used to provide a basis for the forms that are introduced in the system. This includes the proposed forms for ideas and evaluations as information types. The integrated definition of perceived equity in the information exchange of a team and its relationship to generated trust, and a quality function in these information types is then reported. In Part B, this system is used to investigate the dynamics that the system implies for quality objectives in a series of simulation studies. A computational model of the integrated system is applied to directly study the dynamics that it implies and examine important relationships in the account of quality in ill-structured decision making by groups and teams as decision-making units. A principal interest is in trade-offs that arise from composition of these units (e.g., heterogeneity in background and disciplinal training of team members) that is related to quality but also to status differentiation.

Introduction  17

Chapter 11: Technology for Quality-Maximizing Objectives in Decision-Making Teams The system introduced in the previous chapter is used to describe how conflicting effects of heterogeneity can be managed in the information exchange of virtual teams in this chapter. Designs for technology-based management of information exchange in team decision making are considered. The computational model is used to define a quality-increasing path of information types in solutions to control problems for quality objectives. The proposed system is then applied to a formulation of the quality objective of the team as a control problem in which the number of negative evaluations and balance in information types initiated are control variables. Operational methods to manage the team for a quality-objective that uses information from the solution of the control problem within a DSS are indicated. Finally, GDSSs that can implement these controls are described.

Chapter 12: Summary and Discussion In a final chapter, the exposition is summarized to discuss its implications for the effectiveness and efficiency of interactive groups as decision-making units and its operational implications for applications in organizations. It is suggested that the present consensus or at least the majority opinions in lab studies that the same number of individuals acting independently are more effective than interacting in a group is premature and not definitive. An important part of this is because of limitations in a conceptual basis for the studies that omit group or team structure and empirical evidence in support of its effects. At the same time, it is recognized that more recent literature on team decision making has made claims on their inherent contribution that remain to be adequately supported by conceptualization and empirical results. The effort to provide a framework that broadens the conceptualization of structure in interactive groups and teams in this exposition is briefly reviewed. The applications of this framework in analytical, computational, and empirical results on ill-structured decision making in interactive groups is then discussed. Procedures that technology makes available to bring what we expect to underlie decision quality closer to its optimum is then reviewed. As suggested, advances in technology to manage interactive teams provides the basis to expect that a more comprehensive account of their processing can provide a basis to operationally define procedures that ensure that the inherent strengths of teams as a decision-making unit exceed their weaknesses. SUMMARY AND DISCUSSION This chapter has introduced an information exchange framework for the study of decision making by groups and teams. In information exchange, the pervasive importance of structure in team decision making across

18  Decision Making Groups and Teams application contexts has been reviewed and the importance that social structure in these units can have to information exchange in this framework has been noted. While interactive decision-making units have been given recent impetus by technology that supports both face-to-face and virtual teams, fundamental issues in the efficacy of interactive groups and teams remain. A review of the differences between inferences from lab-based experimental studies and studies of organizational teams indicates some of the basic issues. A review of results of a range of studies has suggested that the social structure of groups and teams is a source of process losses that has not been adequately recognized in available studies of decision making. Extensive background results in the study of group processes in support of this claim are cited. In the account of ill-structured decision making as information exchange, it is recognized that information exchange is in part a social process that introduces social risk to group and team members. This risk occurs because the receipt of negative evaluations can be a source of status loss. The magnitude of the loss can be expected to depend on the status distance between the source and recipient of the evaluation. Subsequent chapters that offer analytical, numerical, and empirical support for the information exchange perspective that is introduced were briefly reviewed. REFERENCES Alanah, M., & Ilze, Z. (2009). Trust in virtual teams: Solved or still a mystery? SIGMIS Database, 40, 61–83. Bales, R. (1950). Interaction process analysis: A method for the study of small groups. Cambridge, MA: Addison-Wesley. Baker, G. (2002). The effects of synchronous collaborate technologies on decisionmaking: A study of virtual teams. Information Resources Management Journal, 15, 79–93. Beers, P., Boshuizen, H., Kirschner, P., & Gijselaers, W. (2006). Common ground, complex problems and decision making. Group Decision and Negotiation, 15, 529–556. Berger, J., Fisek, M. H., Norman, R., & Zelditch, M. (1977). Status characteristics and social interaction. New York, NY: Elsevier. Berger, J., Rosenholtz, S., & Zelditch, M. (1980). Status organizing processes. Annual Review of Sociology, 6, 479–508. Berger, J., & Webster, M., Jr. (2006). Expectations, status and behavior. In P. Burke (Ed.), Contemporary social psychological theories. Stanford, CA: Stanford University Press. Blader, S., & Tyler, T. (2009). Testing and extending the group engagement model: Linkages between social identity, procedural justice, economic outcomes, and extra role behavior. Journal of Applied Psychology, 94, 445–464. Boon, S., & Holmes, J. (1991). The dynamics of interpersonal trust: Resolving uncertainty in the face of risk. In R. A. Hinde & J. Groebel (Eds.), Cooperation and prosocial behavior (pp. 190–211). Oxford, England: Basil Blackwell. Campion, M., Medsker, G., & Higgs, A. (1993). Relations between work group characteristics and effectiveness: Implications for designing effective work groups. Personnel Psychology, 46, 823–850.

Introduction  19 Cohen, B., & Zhou, X. (1991). Status processes in enduring work groups. American Sociological Review, 56, 179–188. Coppola, N., Hiltz, S., & Rotter, N. (2004). Building trust in virtual teams. IEEE Transactions on Professional Communication, 47, 95–104. Cross, R., Thomas, R., & Light, D. (2009). How ‘who you know’ affects what you decide. MIT Sloan Management Review, 50, 35–46. Davis, J. (1980). Group decision and procedural justice. Progress in Social Psychology, 1, 157–229. Davis, J., Hulbert, L., Au, W., Chen, X., & Zarnoth, P. (1997). Effects of group size and procedural influence on consensual judgments of quantity: The examples of damage awards and mock civil juries. Journal of Personality and Social Psychology, 73, 703–718. Dennis, A. R., & Valacich, J. (1993). Computer brainstorms: More heads are better than one. Journal of Applied Psychology, 78, 531–537. Deutsch, M. (2010). Equity, equality, and need: What determines which value will be used as the basis of distributive justice? Journal of Social Issues, 31, 137–149. Diamond, S., Rose, M., Murphy, B., & Meixner, J. (2011). Damage anchors on real juries. Journal of Empirical Legal Studies, 8, 148–178. Diehl, M., & Strobe, W. (1991). Productivity loss in brainstorming groups: Toward the solution of a riddle. Journal of Personality and Social Psychology, 61, 392–403. DiTomaso, N. (2010). A sociocultural framework on diversity requires structure as well as culture and social psychology. Psychological Inquiry, 21, 100–107. Erez, M., & Somech, A. (1996). Is group productivity loss the rule or the exception? Effects of culture and group-based motivation. Academy of Management Journal, 39, 1513–1537. Feri, F., Irlenbusch, B., & Sutter, M. (2010). Efficiency gains from team-based coordination: Large-scale experimental evidence. American Economic Review, 100, 1892–1912. Gallupe, R., & DeSanctis, G. (1987). A foundation for the study of group decision support systems. Management Science, 33, 589–610. Gilliam, C., & Oppenheim, C. (2006). Review article: Reviewing the impact of virtual teams in the information age. Journal of Information Science, 32, 160–175. Greenberg, J. (2009). Promote procedural and interactional justice to enhance individual and organizational outcomes. In E. Locke (Ed.), Blackwell handbook of principles of organizational behavior (pp. 255–271). Chichester, England: Wiley. Greenberg, P., Greenberg, R., & Antonucci, Y. (2007). Creating and sustaining trust in virtual teams. Business Horizons, 50, 325–333. Griffith, T. L., & Neale, M. A. (2001). Information processing in traditional, hybrid, and virtual teams: From nascent knowledge to transactive memory. Research in Organizational Behavior, 23, 379–421. Guzzo, R., & Dickson, M. (1996). Teams in organizations: Recent research on performance and effectiveness. Annual Review of Psychology, 47, 307–338. Hinsz, V., Tindale, R., & Vollrath, D. (1997). The emerging conceptualization of groups as information processors. Psychological Bulletin, 121, 43–64. Holsapple, C., & Whinston, A. (2000). Decision support systems: A knowledgebased approach. Minneapolis, MN: West Publishing. Houghton, S., Simon, M., Aquino, K., & Goldberg, C. (2000). No safety in numbers: Persistence of biases and their effects on team risk perception and team decision making. Group and Organization Management, 23, 325–354. Jarvenpaa, S., & Leidner, D. (1999). Communication and trust in global virtual teams. Organizational Science, 10, 791–815. Jarvenpaa, S., Shaw, T., & Staples, D. (2004). Toward contextualized theories of trust: The role of trust in global virtual teams. Information Systems Research, 15, 250–267.

20  Decision Making Groups and Teams Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263–292. Kanawattanachai, P., & Yoo, Y. (2002). Dynamic nature of trust in virtual teams. Journal of Strategic Information Systems, 11, 187–213. Katzenbach, J. R., & Smith, D. K. (1993). The discipline of teams. Harvard Business Review, 71, 111–119. Kerr, D., & Murthy, U. (2004). Divergent and convergent idea generation in teams: A comparison of computer-mediated and face-to-face communication. Group Decision and Negotiation, 13, 381–399. Kirchler, E., & Davis, J. (1986). The influence of member status differences and task type on group consensus and member position change. Journal of Personality and Social Psychology, 51, 83–91. Koh, W. (2008). Heterogeneous expertise and collective decision-making. Social Choice and Welfare, 30, 457–473. Koszegi, B., & Rabin, M. (2006). A model of reference dependent references. The Quarterly Journal of Economics, 121, 1133–1169. Kuo, F., & Yu, C. (2009). An exploratory study of trust dynamics in work-oriented virtual teams. Journal of Computer-Mediated Communication, 14, 823–854. Lamm, H., & Trommsdorf, G. (1973). Group versus individual performance on tasks requiring ideational proficiency (brainstorming): A review. European Journal of Social Psychology, 3, 361–387. Latané, B., Williams, K., & Harkins, S. (1979). Many hands make light the work: The causes and consequences of social loafing. Journal of Personality and Social Psychology, 37, 822–832. Levine, J., & Moreland, R. (1990). Progress in small group research. Annual Review of Psychology, 41, 585–634. Malhotra, A., & Majchrzak, A. (2005). Virtual workplace technologies. MIT Sloan Management Review, 46, 11–14. Malhotra, A., Majchrzak, A., Carman, R., & Lott, V. (2001). Radical innovation without collocation: A case study at Boeing-Rocketdyne. MIS Quarterly, 25, 229–249. Malhotra, A., Majchrzak, A., & Rosen, B. (2007). Leading virtual teams. Academy of Management Perspectives, 21, 60–70. McGlynn, R., McGurk, D., & Effland, V. (2004). Brainstorming and task performance in groups constrained by evidence. Organizational Behavior and Human Decision Processes, 93, 75–87. Mclead, P., Lobel, S., & Cox, T., Jr. (1996). Ethnic diversity and creativity in small groups. Small Group Research, 21, 248–264. Mello, A., & Ruckes, M. (2006). Team composition. Journal of Business, 79, 1019–1040. Mintzberg, H. (1973). The nature of managerial work. New York, NY: Harper and Row. Morris, S., Marshall, T., & Yrainer, R., Jr. (2002). Impact of user satisfaction and trust on virtual team members. Information Resources Management Journal, 15, 22–30. Mullen, B., Johnson, C., & Salas, E. (1990). Productivity loss in brainstorming groups: A meta-analytic integration. Basic and Applied Social Psychology, 12, 3–23. Nemeth, C., & Nemeth-Brown, B. (2003). Better than individuals? The potential benefits of dissent and diversity in group creativity. In P. Paulus & B. Nijstad (Eds.), Group creativity: Innovation through collaboration (pp. 63–84). Oxford, England: Oxford University Press. Parise, S., & Rollag, K. (2010). Emergent network structure and initial group performance: The moderating role of pre-existing relationships. Journal of Organizational Behavior, 31, 877–897.

Introduction  21 Park, G., & DeShon, R. (2010). A multilevel model of minority opinion expression and team decision-making effectiveness. Journal of Applied Psychology, 95, 824–833. Paulu, P., Putman, V., Dugosh, K., Dzindolet, M., & Coskun, H. (2011). Social and cognitive influence in brainstorming: Predicting production gains and losses. European Review of Social Psychology, 12, 299–325. Peeters, G., & Czapinski, J. (1990). Positive-negative asymmetry in evaluations: The difference between affective and informational negative effects. European Review of Social Psychology, 1, 23–60. Pinsonneault, A., Barki, H., Gallupe, R., & Hopper, N. (1999). Electronic brainstorming: The illusion of productivity. Information Systems Research, 10, 110–133. Pinsonneault, A., & Kraemer, K. (1990). The effects of electronic meetings on group processes and outcomes: An assessment of the empirical research. European Journal of Operational Research, 46, 143–161. Powers M, (2008) “What is the difference between groups and teams?” http://voices. yahoo.com/what-difference-between-teams-groups-2123954.html Qureshi, L., & Vogel, D. (2006). The effects of electronic collaboration in distributed project management. Group Decision and Negotiation, 15, 55–75. Rashotte, L., & Smith-Lovin, L. (1997). Who benefits from being bold: The interactive effects of task cues and status characteristics on influence in mock jury groups. Advances in Group Processes, 14, 235–255. Ridgeway, C., & Johnson, C. (1990). What is the relationship between Socio emotional behavior and status in task groups? American Journal of Sociology, 95, 1189–1212. Rietzschela, E., Nijstada, B., & Stroebe, W. (2006). Productivity is not enough: A comparison of interactive and nominal brainstorming groups on idea generation and selection. Journal of Experimental Social Psychology, 42, 244–251. Robert, L., Denis, A., & Hung, Y. (2009). Individual swift trust and knowledgebased trust in face-to-face and virtual team members. Journal of Management Information Systems, 26, 241–279. Rosen, B., Furst, S., & Blackburn, R. (2007). Overcoming barriers to knowledge sharing in virtual teams. Organizational Dynamics, 36, 259–273. Rowland, P., & Parry, K. (2009). Consensual commitment: A grounded theory of the meso-level influence of organizational design on leadership and decision-making. The Leadership Quarterly, 20, 535–553. Salerno, J. M., & Diamond, S. S. (2010). The promise of a cognitive perspective on jury deliberation. Psychonomic Bulletin & Review, 17, 174–179. Santuzzi, A. (2007). Perceptions and metaperceptions of negative evaluation: Group composition and meta-accuracy in a social relations model. Group Processes and Intergroup Relations, 10, 383–398 Sarker, S., Ahuja, M., Sarker, S., & Kirkeby, S. (2011). The role of communication and trust in global virtual teams: A social network perspective. Journal of Management Information Systems, 28, 273–310. Schmidt, J., Montoya-Weiss, M., & Massen, A. (2001). New product development decision-making effectiveness: Comparing individuals, face-to-face teams and virtual teams. Decision Sciences, 32, 575–601. Schmidt, U., Starmer, C., & Sugden, R. (2008). Third generation prospect theory. Journal of Risk and Uncertainty, 36, 203–223. Silver, S., Cohen, B., & Crutchfield, J. (1994). Status differentiation and information exchange in face-to-face and computer-mediated idea generation. Social Psychology Quarterly, 57, 108–123. Silver, S., & Troyer, L. (1998). Judgments of the magnitude of expected status loss from negative evaluations as a function of the judged status distance of the evaluator. In J. Skovertz & J. Szmatra (Eds.), Advances in group process: The second international conference on theory and research in group processes. Greenwich, CT: JAI Press, 103–132.

22  Decision Making Groups and Teams Sinaceur, M., Thomas-Hunt, M., Neale, M., O’Neill, O., & Haag, C. (2010). Accuracy and perceived expert status in group decisions: When minority members make majority members more accurate privately. Personality and Social Psychology Bulletin, 36, 423–437. Sperber, D., & Hirschfeld, L. (2004). The cognitive foundations of cultural stability and diversity. Trends in Cognitive Sciences, 8, 40–46. Staples, D., & Webster, J. (2008). Exploring the effects of trust, task interdependence and virtualness on knowledge sharing in teams. Information Systems Journal, 18, 617–640. Strobe, W., & Diehl, M. (1994). Why groups are less effective than their members: On productivity loss in idea generating groups. European Review of Social Psychology, 5, 271–304. Strodtbeck, F., & Mann, R. (1956). Sex role differentiation in jury deliberations. Sociometry, 19, 3–11. Tegarden, L., Sarason, Y., Childers, J., & Hatfield, D. (2005). The engagement of employees in the strategy process and firm performance: The role of strategic goals and environment. Journal of Business Strategies, 22, 75–100. Thomas, D., & Bostrom, R. (2008). Building trust and cooperation through technology adaptation in virtual teams: Empirical field evidence. Information Systems Management, 25, 45–56. ———. (2010). Vital signs for virtual teams: An empirically developed trigger model for technology adaptation interventions. MIS Quarterly, 34, 115–142. Thomas-Hunt, M., Ogden, T., & Neale, M. (2003). Who’s really sharing? Effects of social and expert status on knowledge exchange within groups. Management Science, 49, 464–477. Trepel, C., Fox, C., & Poldrack, R. (2005). Prospect theory on brain? Toward a cognitive neuroscience of decision under risk. Cognitive Brain Research, 23, 34–60. Tyler, T. (2000). Cooperation in groups: Procedural justice, social identity, and behavioral engagement. New York, NY: Psychology Press. VanDyk, W., Danner, U., Nieweg, M., & Sumter, S. (2003). Positive-negative asymmetry in evaluation of trivial stimuli. The Journal of Social Psychology, 143, 783–784. Walker, E., & Cox, J. (2006). Addressing ill-structured problems using Goldratt’s thinking processes: A white collar example. Management Decision, 44, 137–155. Webster, J., & Wong, W. (2008). Comparing traditional and virtual group forms: Identity, communication and trust in naturally occurring project teams. International Journal of Human Resource Management, 19, 41–62. Webster, M., & Foschi, M. (1988). Status generalization: New theory and research. Stanford, CA: Stanford University Press. Wittenbaum, G. (1998). Information sampling in decision-making groups: The impact of members’ task-relevant status. Small Group Research, 29, 57–58.

2

Background and Framework

OVERVIEW This chapter reviews contributions of background studies of decision making and problem solving in interactive groups and teams to comprehensive accounts of process in these units. Reasons to frame team decision making as information exchange are reviewed in this chapter, and an overview of an account of information exchange in the presence of team structure is offered. From the perspective of the present exposition, direct representation of the social structure of teams as defined by the distribution of social status in the unit and its effects on information exchange can advance both lab studies and the design of organizational teams. The historical background in the study of status organization and process dynamics in interactive groups is first reviewed. The basis to predict robust and pervasive effects of status organization on performance is then concisely detailed. Finally, effects of group and team structure are integrated into information exchange in these units when they are task-directed, and generalizations from this integration are offered. INTRODUCTION The objectives of this chapter are to provide a review of background studies and the inferences that they support in the study of information exchange in decision-making groups and teams. Whatever the balance of empirical evidence on their relative efficacy may be, teams have been given new attention and enfranchisement as decision-making units (e.g., Houghton, Simon, Aquino, & Goldberg, 2000; Malhotra & Majchrzak, 2005; Mello & Ruckes, 2006; Qureshi & Vogel, 2006; Schmidt, Montoya-Weiss, & Massen, 2001). Although there is now an extensive body of studies on factors that underlie effective teams, the striking increase in the use of groups and teams in decision making that involves significant resource commitments is somewhat surprisingly in that it has occurred with no more than limited supporting empirical demonstration of their relative efficacy in controlled studies (Allen & Hecht, 2004; Paulus & Van der Zee, 2004).

24  Decision Making Groups and Teams The history of studies in group processes provides important background for applications to decision-making teams. As indicated, a part of this background has been in comparisons of interactive and nominal groups. The objective in reviewing this part of the background here is not to simply assess whether groups or teams are likely to be more or less productive than the sum of the same number of individuals working independently. There are many reasons to expect that organizations will continue to enfranchise interactive teams whatever the conclusions of most investigators on the comparison are. Reasons for this enfranchisement include the motivational effects of increased satisfaction of members of interactive teams and benefits of representation in decision making to subsequent implementation. The most important objective of the review is to give direction to design through increased understanding of the bases of “process gains and losses” (Paulus, Putman, Dugosh, Dzindolet, & Coskun, 2011; Steiner, 1972) and methods to manage these in the recurring dynamics of task-directed interactive groups. As will be a premise of this exposition, both the exercise of agency by members and emergent effects at the aggregate level contributes to understanding process losses and gains in groups and teams (e.g., Bandura, 2000). SOCIAL STRUCTURE IN GROUPS AND TEAMS The increased implementation of teams in organizations in the face of limited evidence on their relative efficacy does provide an incentive to extend the conceptualization of decision-making teams to encompass additional structural variables. Group and team structure as referenced here is defined in terms of the distribution of member status in the decision-making unit. As indicated, background studies of group processes suggest the bases for inference on effects that structural variables can have in applications to taskdirected teams. BACKGROUND IN GROUP PROCESSES While the study of interactive teams now has its own distinct literature in assessing task-directed performance, it continues to have a lineage in the study of group processes (e.g., Berger & Webster, 2006; Harrison, Price, Gavin, & Florey, 2002; Ilgen, Hollenbeck, Johnson, & Jundt, 2005; Salomon & Globerson, 2002; Stewart & Barrick, 2000). Studies in traditions of group processes provide indirect evidence that status distributions have consequences for the distributions of information that are inputs to a team’s decision making (e.g., Ahuja, Galletta, & Carley, 2003; Wittenbaum, 2000). Results of background studies in these traditions encourage an elaboration and direct integration of structural effects in processes of group and team decision making. Somewhat surprisingly, given its background in group processes, effects of social

Background and Framework  25 structure have been either omitted or incompletely represented in both the extensive lab studies of brainstorming and the more recent studies of virtual team decision making. Studies of group processes have clearly been more qualified in their evaluations of the efficacy of groups as decision-making units than the studies of interactive teams. In fact, the efficacy of interactive groups relative to nominal groups has been a commonly addressed basis for investigation in background lab studies of task-directed groups. These studies have generally assessed idea generation using brainstorming tasks (e.g., Diehl & Stroebe, 1991; Erez & Somech, 1996; Lamm & Trommsdorf, 1973; Latané, Williams, & Harkins, 1979; Paulus & Brown, 2007; Pinsonneault, Barki, Gallupe, & Hopper, 1999; Pinsonneault & Kraemer, 1990). Although as indicated, there are bases to integrate other information types in objectives of ill-structured decision making, idea-generation as studied in brainstorming clearly retains importance to the quality of decisions that are ill-structured. As such, findings of these studies are briefly reviewed. COMPARATIVE STUDIES OF INTERACTIVE AND NOMINAL GROUPS Pinsonneault et al. (1999) is among the more definitive experimental studies that have comparatively assessed idea generation by interactive and nominal groups. The studies these authors report investigated groups that interact in electronic communication. The authors noted claims that electronic media can reduce or eliminate what has been cited as production blocking in interactive groups and have other advantages over nominal groups in the medium. Production blocking occurs when team members have to hold ideas in memory while others are proposing ideas to the team. Other cited advantages include the possibility of process gains from interactive groups through reducing redundancy in ideas of nominal groups and inspiring others in interactive groups to think in novel directions. In addition to production blocking, the authors also note bases for process losses through evaluation apprehension and free riding. Evaluation apprehension refers to the withholding of ideas or other information types because of anticipated evaluation by other team members. Free riding refers to the loss that can occur when members benefit from the ideas of others without participation. In results that they report, these authors replicated previous findings that nominal groups consistently outperformed interactive groups in idea generation. As they indicate, in their review, the only criterion on which interactive groups have been found to be consistently superior to nominal groups is in reported satisfaction. From their five studies, Pinsonneault et al. (1999) conclude that nominal groups are at least as productive as electronic brainstorming groups. In addition to factors that differentiate interactive and

26  Decision Making Groups and Teams nominal groups that have been previously cited, these authors introduce effects that are labeled as distraction, attentional product blocking, cognitive complexity, striving for originality, and cognitive dispersion as the basis for the underperformance of interactive groups relative to nominal groups in a brainstorming task. In distraction, group members pay exclusive attention to ideas of others and are less efficient in idea generation. Attentional production blocking occurs when synchronous idea generation diverts attention from a member’s own ideas. Cognitive complexity refers to cognitive burdens of reading and processing the ideas of others that can interfere with a member’s own idea generation. Striving for originality can result in undue focus on ideas of others and undermine the internal processes in idea generation. Cognitive dispersion occurs when ideas of others interrupt a member’s internal processing. Paulus and Brown (2007) have more recently reviewed the background in group brainstorming studies that include their own integration of cognitive, social, and motivational factors in semantic networks and associative memory. In their designation, diversity in idea pools and cognitive stimulation exemplify cognitive factors. The cohesiveness that can be observed in interactive groups is a social factor. These authors note that brainstorming productivity can be increased by appropriate motivational factors that include group norms and social facilitation (Christian, Bagozzi, Abrams, & Rosenthal, 2012; Shepherd, Briggs, Reinig, Yen, & Nunamaker, 1995/1996). STRUCTURE IN INTERACTIVE GROUPS Recognizing the rigor in the experimental studies that have been cited, I would again note that neither they nor related studies address the social structure of groups as a source of process losses in the detail that extensive and well-documented background studies in group processes support. While what has been defined as evaluation apprehension may be related to group structure, it is implicitly considered as an internal variable of group members. Although effects of group structure do operate at the level of an agent, group structure itself and the processes it engenders operate at aggregate levels. Since status distributions that are the basis for group structure are present in interactive groups but not in nominal groups, any differences in efficacy between these group forms may be related to its effects. If these effects are confirmable as a basis for the underperformance of interactive groups in certain applications, then a key issue in managing performance differences between nominal and interactive groups may be more in terms of whether there are procedures that can effectively modify structural effects while retaining the facilitating effects of member interaction. There is an imperative to do this since electronic communication media and related advances in technology increase our capabilities in managing

Background and Framework  27 dysfunctional effects of group structure. Correspondingly, other sources of process losses that Pinsonneault et al. (1999) and Paulus and Brown (2007) identify may be within the capabilities that such technology has the capability to manage. These are directly addressed in a subsequent chapter on applications of technology. Finally, even if interactive groups were not more effective than nominal groups as decision-making units, their contribution in representing diversity and satisfaction make it likely that they will be maintained as decision-making units in many instances. This encourages efforts to contribute to their efficacy through the understanding and management of process losses that studies of group structure have identified. An objective of this and subsequent chapters is upon integrating structure into the information exchange of interactive decision-making groups. In pursuing the objectives of this chapter, I begin by reviewing background studies of status organizing processes in interactive groups as a precursor to representing structural effects. These studies provide comprehensive insight into underlying process from which generalizations on structural effects can be drawn for application to decision-making teams. STATUS-ORGANIZING PROCESSES IN TASK-DIRECTED GROUPS AND TEAMS Some distinguishing accomplishments of theorists on status-organizing merit direct recognition since they elucidate empirically observable effects in task-directed interactive groups that are not well-explained in the background literature on problem solving and decision-making teams. A key aspect of status positions in task-directed teams is in the influence that it can yield. Influence in task-directed interactive groups and teams typically takes such forms as differences in time talking or the differential weighting of ideas, facts, and opinions offered by team members of different status positions in decisions. Insight on how differences in status come about begins with the observation that social influence in the team occurs through processes that commonly link a member to perceived competence. In the formation of status beliefs in group members, there is internal inference in individual group members on competence of other members that converges to shared inference in the group. Since it occurs internally in individual group members and is tacit and in the absence of discernible norms, this is unlikely to simply be the result of conformity effects. It is notable that the perception of competence is often contrary to direct observations of competence displays. Rather, it commonly results from shared beliefs that link discernible traits as in social and demographic categories of a team member to expectations of competence (e.g., Berger & Webster, 2006). For example, shared beliefs may generate expectations of gender differences in certain types of tasks without confirmation in criterion-relevant, observed

28  Decision Making Groups and Teams behavior. These beliefs take the form of expectations that are self-fulfilling since other members subsequently give selected members more or less opportunity to display competence (Foschi, 2000, 2009). That such linkages are typical in interactive groups is a primitive of prominent research programs on status-organizing processes in task-directed groups (e.g., Ridgeway, 2006; Ridgeway & Erikson, 2000). Early investigators documented stable interaction differences that quickly emerged within what initially were homogenous individuals cooperating in problem solving (Bales, 1950). These early lab studies were effectively the background for the development of Expectation States Theory (EST; Berger, Cohen, & Zelditch, 1972). A major focus of EST is on the process that underlies the formation, stability, and consequences of initial expectations in interactive groups. In EST, expectations for performance of oneself and other group members arise early in the interaction history of the group from observations of exchanges between group members that are imputed to be task relevant. In the research program that followed, the differentiation of expectations for individual members in the group was shown to result in observable prestige and power orders. Subsequent elaborations of EST provided detailed indication of behavior such as assertiveness that can generate expectations and status in interactive groups even when it is unrelated to actual basic competence (Balkwell, 1991; Berger et al., 1972; Berger, Wagner, & Zelditch, 1985). Even disadvantaged individuals tend to accept the status-related beliefs. This occurs even when they resist any actual penalty to them that the belief would imply (e.g., Rashotte & Webster, 2005; Ridgeway, 1991; Ridgeway, Backor, Li, Tinkler, & Erickson, 2009; Ridgeway & Erickson, 2000). This effect differs from accounts of social identity theory in which members send to maintain their beliefs in the inherent equality or superiority of their in-group even when it tends to be disadvantaging (e.g., Hogg, 2006). Within the EST program, Status Characteristics Theory (SCT) provided a more detailed account of so-called culturally defined beliefs as they relate to performance expectations and behavior of group members. SCT distinguishes diffuse and specific status characteristics. The former include what are most often performance-neutral demographic characteristics such as gender and race. The latter include more specific skills whose effects are not assumed to be as generalizable across tasks as diffuse characteristics are. The distinction is important because it recognizes that although status characteristics are likely to be most salient to a status order when they have obvious task relevance as specific characteristics, minimally relevant or irrelevant diffuse characteristics can be the bases for a status order in the absence of adequately defined specific characteristics. The capability of characteristics that are minimally related or unrelated to competence to be sufficient for the formation of a social hierarchy has been demonstrated in the results of a number of studies. Once formed, diffuse characteristics tend to be stably associated with the status order in relevant contexts. These

Background and Framework  29 status characteristics combine to form aggregated performance expectations that result in behavioral differences in the group interaction. The finding that characteristics are minimally relevant to the group task or objective can become the basis for a status order in the group is not entirely surprising from an evolutionary perspective. Survival in primitive forms can be expected to have depended on group cohesiveness and coordination. Hierarchy is one efficient way to ensure this, as any military organization can attest. It is likely to be the case that when multiple characteristics are available for differentiation, specific characteristics that are most directly relevant to the group objective predominate. In the absence of these, minimally relevant characteristics may be invoked to ensure a stable hierarchy. These are often diffuse. Correspondingly, it may be evolutionarily efficient to discriminate own groups (i.e., ingroups) from competing groups (outgroups) as social identity theory (e.g., Brewer & Caporael, 2006; Hogg, 2006) has detailed. The characteristics to be used for this can be the same that are operative in the formation of within group hierarchies. As noted, there are findings on process in status hierarchization within groups that distinguish it from intergroup processes. The inherent formation of status hierarchies in interactive groups was well recognized in historical exposition, theorists such as Weber (1978 [1922]) and Veblen (1953 [1899]) emphasized material criteria such as wealth and possessions. Both SCT and its historical predecessor in EST have reasonably established that a wide range of identifiable factors that differentiate individuals can be the basis for a status order and the biased judgments it results in. Ridgeway (e.g., Ridgeway, 2006) has extended status characteristic theory to status construction and has indicated how inequality in the possession of exchangeable resources can be the basis for a characteristic having status value in group interaction. A series of experiments this author reports (e.g., Ridgeway, 2006) directly demonstrate some of the operations through which this can come about.1 These experiments support many of the claims of what can be designated as status construction theory. They show that resource (e.g., assigned pay) differences even without other legitimation lead to judged status and competence differences. More important, the results of the studies indicate that on-lookers tend to infer similar status and competence judgments. Webster and Hysom (1998) further extend the basis of generating status value from the possession of exchangeable resources to one of perceived contribution to goal objectives that possibly explains many additional status characteristics. Other studies extend our understanding of how intermediaries that include sentiments are integrated in the formation of expectations and processing of status assignment (e.g., Shelly, 2001). To understand the generality of effects that have been cited by the above authors, it can be noted that when a person who holds a status belief acts by either deferring or assenting in accordance with the belief, persons either not having the belief or being disadvantaged by the belief could resist. However,

30  Decision Making Groups and Teams it has been found that most people learn and agree with the belief (e.g., Troyer & Younts, 1997). This finding adds to our understanding of group structure and the pervasiveness of its effects. It further helps to explain why it tends to have enduring performance effects. Status beliefs clearly diffuse most rapidly when they are socially validated as through consensual acceptance by group members or of a legitimate authority. It does appear that a few nonsupportive observers can interfere with general acceptance of status beliefs (McLeod, Baron, Marti, & Yoon, 1997). Although the conditions and microprocessing that supports this are not well defined in either the conceptual accounts of SCT or related literature, studies of minority influence in task-directed groups (Martin, 2010; Nemeth, 1986; West, 2012) have documented the occurrence. A separate but related literature has recently addressed status organizing in interactive groups and teams. Anderson and Kilduff (2009a, 2009b) consider a shortcoming of what they cite as “functionalist” perspectives to be in their inability to explain why individual differences in dominance and the need for power that are not socially valued or correlated with competence or communal orientation lead to higher status. In the absence of conditioning statements, these authors maintain that individuals compete for status by behaving in ways that communicate high levels of competence, generosity, and commitment to the group. They further note that the effects of these individual differences in motives are unlikely to be simply due to assertiveness by these group members, since prior study has shown that groups often sanction rather than acquiesce to those who try to acquire status by intimidating others or try to claim higher status than the group believes that they deserve.2 Anderson and Kilduff (2009a, 2009b) additionally note cases in which individuals may gain status by enhancing the perception of competence in a group task rather than actual competence. They may engage in acts such as providing more task-relevant information even when it does not make them more competent than others by objective criteria. In these cases, giving the appearance of being more competent through initiative and display of confidence can increase their status. However, they emphasize that in most cases, status is gained through actually enhancing a member’s value to the group in terms of actual competence and display of commitment to the group. While these authors may well be addressing processing by which status can be attained in certain task-directed groups, previous studies suggest the conditioning of such an occurrence. There are clear instances in which the process account they define is not the basis for status attainment. For example, documentable bases for status and leadership in terms of asymmetry in the possession of exchangeable resources or shared goals (e.g., Ridgeway, 2006; Webster & Hysom, 1998) are not dealt with in Anderson and Kilduff (2009). Additionally, these authors mention that it is the group judgment that matters but do not address processing in the formation of such judgments. There is now a substantial literature on group beliefs and how convergence to the beliefs in the group occurs (e.g., Miles & Kivlighan, 2008).

Background and Framework  31 The extensive background on status organizing in groups for applications in team decision making and problem solving provides bases to at least question whether prior agreements or commonly held values on equal opportunities to participate and influence will be sufficient to ensure that this occurs. That such agreements will influence behavior in the group is often a premise of team-building methodology (Dyer, Dyer, & Dyer, 2010; Yeh, Smith, Jennings, & Castro, 2006). Even if prior attempts to define norms and shared values are not sufficient to counter effects of statusorganizing processes, increased understanding of the basic and often covert processes in the formation and operation of social structure can provide a basis for procedures that minimize anticipable dysfunctional effects of structure on team performance. How this can be embodied in an account of decision-making teams as information exchange is discussed in chapters that follow. DECISION-MAKING AS INFORMATION EXCHANGE IN INTERACTIVE TEAMS As background results clearly establish, status hierarchies typically emerge from interactive individuals on the basis of whatever information is available. Since many decision-making teams have limited histories, instances where structure is based on diffuse attributes that are not likely to relate to competence have particular importance in the present application. However, it is shown that even when status hierarchies are based on attributes that are competence related, structure typically has to have dysfunctional effects that are not anticipated in the mainstream studies of problem solving and decision making by teams. What may be most surprising about the results that have been cited is how readily status can be granted when characteristics that relate to the distribution of status are explicitly made irrelevant to members’ contribution to a group. How these effects may operate in decision-making teams can be further elaborated by a more detailed and explicit conceptualization of information exchange in interactive teams. I next turn to the effects that social structure in teams, once formed, can have when integrated into an account of the information exchange in team decision making. In the process of decision making, members of a team exchange information in the form of ideas, evaluations, facts, and other information types. The amount and type of information exchanged in the team is clearly fundamental to the quality of a decision. Team members generally recognize that any information they initiate is likely to elicit a response from other members. Questions from one person are, for example, commonly followed evaluations and facts or possibly ideas from another. Correspondingly, an idea from one team member may commonly be followed by an evaluation of the idea from another team member.

32  Decision Making Groups and Teams STATUS LOSS IN INFORMATION EXCHANGE Sequential information exchanges can be a synergistic process that leads to more innovative and effective outcomes than individuals alone could generate (e.g., Baruah & Paulus, 2009, McGrath, 1984; Steiner, 1972). However, as observed, sequences in the exchange of information can have dysfunctional effects. This is because group members typically have multiple motives in their interaction (e.g., Davis, Laughlin, & Komorita, 1976; Silver, 1995). Although one of these motives generally is commitment to the group’s objectives, a competing motive that commonly underlies the exchange of information is the maintenance of a team member’s status in the group (e.g., Berger, Fisek, Norman, & Zelditch, 1977; Loch, Huberman, & Stout, 2000; Vignoles, Regalia, Manzi, Golledge, & Scabini, 2006). Status loss for a member can arise from the receipt of a negative evaluation. Additionally, as taken up in detail in a subsequent chapter, there are bases to anticipate that status losses and gains by team members of different status are not evaluated proportionally to the status difference between the members. In interactive team decision making as in other contexts, a greater effect of loss aversion in comparison to the valuation of an equivalent gain may be commonly evidenced. This can introduce further biasing effects of increases in the variance of a status distribution. All else equal, the management of a member’s status by medium or lower status team members would then take the form of lower rates of initiating more risky information types such as ideas and opinions and higher rates of initiating information types such as facts, questions, and positive evaluations. The targets of most negative evaluations they initiate are likely to be inversely related to the member’s status.3 From the above, a key point in the framework that is offered is that status biases not only the amount of information a member contributes to the team’s objective, but also the distribution across types of information sent by members of different statuses and targets of the information that these members initiate. Such a bias can move the distribution of initiated information types away from distributions that get closer to quality-optimizing decisions. When the group’s status hierarchy is not based on ability relevant to the decision, the bias can be expected to further reduce the quality of team decision making. As has been suggested here and demonstrated elsewhere (e.g., Berger, Cohen, & Zelditch, 1972; Berger & Fisek, 2006; Bunderson, 2003; Cohen & Zhou, 1991; Fisek, Norman, & Nelson-Kilger, 1992; Ridgeway, 2006; Thye, Willer, & Markovsky, 2006; Webster & Hysom, 1998), this can occur even in the absence of discernible characteristics of group members that are ability-relevant for the advancement of the group or team objective. As has been noted, status hierarchies evolve rapidly (Barchas & Fisek, 1984; Bothner, Stuart, & White, 2004; Loch, Huberman, & Stout, 2000; Roland, 2004; Washington & Zajac, 2005), are enduring, and underlie differences in participation rates and influence over the course of interaction (Fisek & Ofshe, 1970; Ilgen et al., 2005; Stewart & Gosain, 2006).

Background and Framework  33 As a result of these dynamics, members occupying higher status positions in a group can be expected to receive more positive evaluations, fewer negative evaluations, more opportunities for participation (as in floor time), and generally be more influential in group decisions. The differences in influence that follow from positions in status hierarchies have been confirmed in industry studies that complement results of lab studies (e.g., Cohen & Zhou, 1991). As was reported in these studies and is reported in detail in a subsequent chapter, organizational attributes (e.g., occupation, seniority) and personal attributes (e.g., gender, age) can be key determinants of the level of influence that members exercise in industrial teams. INFORMATION TYPES What can be designated as a social risk conceptualization can provide additional insight by recognizing that certain types of information inherently have a greater likelihood of eliciting a negative evaluation than do other information types. In information types, ideas, negative evaluations, and opinions are more likely to return a negative evaluation as a response from other group members than are positive evaluations, facts, or questions. These latter information types can thus be considered to represent less risky information types, while the former represent more risky information types. If members are concerned with maintaining their status in a team, there is commonly an incentive for them to overinitiate low-risk types of information and underinitiate high-risk types of information. If the expected cost of sending a negative evaluation to a group or team member increases nonlinearly with the target member’s status, lower status agents are more likely to be the recipients of negative evaluations, ceteris paribus. If the social cost of an evaluation is a function of the likelihood of its being negatively evaluated, weighted by the status differential between the sender and receiver of the negative evaluation, initiating this information type is especially risky to lower status team members. Since the social risks in information exchange are greater for lower status group members than for higher status group members, it can be expected that lower status group members will not only participate at lower rates than their higher status counterparts, but also be more likely to manage the content of the information (i.e., the distribution of information types) that they initiate to reduce risk. THE EXCHANGE OF NEGATIVE EVALUATIONS The dysfunction effects of negative evaluations have been emphasized in influential accounts of interactive directed-task teams. Negative evaluations are critical information types in decision quality. As noted, their consideration in brainstorming traditions has tended to be too limited in defining and investigating implications of social structure unidimensional in emphasizing

34  Decision Making Groups and Teams dysfunctional effects. In differentiating it from other information types, negative evaluations are recognized to be both informational and affective. They can be informational in their objective content; they can be affective through their subjective interpretation as an evaluation of self by a recipient. These effects can be expected to operate simultaneously. As such, their dynamics reflect this dual processing. Implications of this conceptualization for decision making by teams is examined in a chapter to follow. An explicit form for their possible contribution to decision quality that reflects this duality in the account of idea initiation is offered at aggregate and individual levels. The focus is upon their interaction with other information types and their possible contribution to quality in the case of team decision making. An active process of filtering of idea generation on quality-related criteria includes both positive and negative evaluations. In contrast to negative evaluations, positive evaluations have low expected costs to initiators of information. As such, positive evaluations are relatively frequent in comparison to negative evaluations and generally are much less discriminating and useful than negative evaluations as filters for quality. DIVERSITY IN GROUP MEMBERSHIPS AND INFORMATION EXCHANGE In assembling organizational teams, diversity is often sought since it increases the likelihood that the team will recognize a wider array of possible solutions from which alternatives can be evaluated. The contributions that diverse representation can have to facilitating implementation of a decision and to satisfaction when the perceptions of equity are further reasons for diversity in team memberships. As such, diversity in membership has commonly been sought in organizational teamwork (DiTomas, Post, & Parks-Yancy, 2007; Joshi & Roh, 2009; Stumpf & Freedman, 1979). The recognition of the effects of status distributions in a team becomes increasingly important when diversity in the membership (e.g., variation in the background, experience, and expertise of members) is a policy objective. As reviewed, biases in information exchange that are consistent with the predictions of the foregoing account of team structure and information exchange can be expected to occur even when (1) the differentiating status characteristic is explicitly dissociated from the team task in instructions to members and (2) team members are explicitly instructed to disregard differences among members that arose from the characteristic. Such biases may be particularly costly to team decision making for several reasons. As diversity increases, the variance in status distributions as it defines social structure in the team can correspondingly be expected to increase. If team members show the biases in information exchange that have been described in previous sections, then benefits of diversity in unmanaged, freely interacting teams may be reduced or negated. Additionally, as taken

Background and Framework  35 up in detail, organizational memberships introduce additional attributes to the formation and maintenance of status distributions. Such memberships exemplify exogenous influences that can introduce other biases in the information exchange within a group or team. These biases can operate at both micro and molar levels, as described in the frames that operate in the decision-making unit (Bennett & Segerberg, 2012; Suddaby, Elsbach, Greenwood, Meyer, & Zilber, 2007). VIRTUAL DECISION-MAKING TEAMS Virtual teams are increasing being implemented as decision-making units in complex organizations. Virtual teams can be defined “as interdependent individuals physically separated from one another and relying on information technologies to communicate, collaborate and coordinate to achieve a common objective” (Cramton, 2001; Majchrzak, Rice, Malhotra, & Ba, 2000). The exchange of negative evaluations is likely to have increased importance when information exchange occurs in virtual teams. Accounts of effective virtual teams (Aubert & Kelsey, 2003; Kanawattanachai & Yoo, 2002) introduce trust as an additional mediator of the performance of decision-making teams. In particular, the concept of swift trust (e.g., Meyerson, Weick, & Kramer, 1996; Robert, Dennis, & Hung, 2009) can be particularly important to teams with little or no interaction histories. As in the case of the establishment of status hierarchies, it typically occurs early in interactions. Although not emphasized in recent studies of the formation of trust in virtual teams, equity can be expected to be a precursor of trust in such teams. Equity and trust can be expected to be particularly important to the initiation of the high-risk information types of ideas and negative evaluations. Equity or fairness in information exchange can be described in terms of the correspondence between the distribution of positive and negative evaluations across members who are sources of the ideas that are being evaluated. That is, a member who initiates fewer ideas should receive fewer evaluations. This is counter to a tendency to underevaluate high-status members and overevaluate low-status members, which is frequently observed in status-differentiated groups (Dovidio, Saguy, & Shnabel, 2009; Ellemers, Wilke, & Van Knippenberg, 1993). While the quality of ideas is clearly a mediator of the distribution of negative evaluations across members, the bias in the distribution across members has been documented in the absence of quality differences. If the receipt of a negative evaluation is a source of status loss, members can be expected to be biased toward initiating information that limits or minimizes the receipt of this information type. As noted, the expected consequences of being negatively evaluated for initiating information that contributes to the group objective can be mediated by member trust in other group members to manage the exchange fairly.

36  Decision Making Groups and Teams From the above, operationally defining a closed form for equity and trust that recognizes the extensive background of these variables in interpersonal interaction can further contribute to the systemic representation of interactive decision-making groups and teams. In a subsequent chapter, a form for this is proposed in an integrated system for information exchange in interactive decision-making teams. Finally in this discussion, it should be noted that while status orders once in place tend to be stable, there is evidence in selected applications that they can be modified (Berger & Webster, 2006; Cohen & Lotan, 1995; Markovsky, Smith, & Berger, 1984). Available technology may have capabilities to implement some of these findings in applications to decision-making teams. Design of technology to accomplish this is facilitated by comprehensive conceptual accounts of the process of information exchange in interactive groups. INFERENCE ON INFORMATION EXCHANGE IN STRUCTURED TEAMS The background in group structure and the overview of decision making as information exchange offers a range of inferences in generalizations that relate information exchange to decision quality. The first two generalizations establish the quality criterion in terms of the information types that have been identified. Generalization 1:  The quality of an ill-structured decision is monotonically increasing in the number of ideas exchanged in a team. This is implicitly a premise of brainstorming and supported in a range of studies (e.g., Christensen, Guilford, & Wilson, 1957; Duch, 2006; Maltzman, Belloni, & Fishbein, 1964; Silvia, 2007). Generalization 2:  The quality of an ill-structured decision is a quadratic function of the number of negative evaluations exchanged in a team. While there is general agreement on the contribution of the number of ideas to the quality of an ill-structured decision, the contribution of the number of negative evaluations has not been as well defined. As noted in Cohen and Silver (1989) and Silver, Cohen, and Rainwater (1988) and the present exposition, too few negative evaluations can underevaluate ideas and decision alternatives. Too many negative evaluations can inhibit idea generation. This implies that the quality-maximizing ratio of negative evaluations to ideas and other information types is a quadratic function. The next generalizations follow from the claim on the form of the loss function that team member’s use in initiating information as put forth in Silver and Troyer (1998). This claim maintains that members judge a status loss of a given magnitude as greater than a gain of the same magnitude and

Background and Framework  37 thereby disproportionately overweight expected costs of negative evaluation by higher status team members. Generalization 3:  A member’s judgment of the magnitude of perceived loss from receipt of negative evaluation increases faster than the magnitude of a status distance between the sender and recipient. Generalization 4:  The number of ideas will decrease faster than the variance in the team’s status distribution increases. Generalization 5:  The number of data messages will increase faster than the variance in the status distribution of a team. Generalization 6:  The proportion of idea messages that are sent by the higher status member will increase faster than the variance in the status distribution. Generalization 7:  The proportion of data messages sent by higher status members will decrease faster than the variance in the status distribution. Generalizations 3 to 7 follow from the typology of information that has been introduced, the properties of information types from a social loss perspective, and the loss function that has been postulated. These generalizations relate individual member information initiations to the status distribution. Generalization 3 is a direct consequence of the assumed form of a loss function in social interaction. Generalizations 4 to 7 follow from an assumption that ideas have much higher conditional probability of returning a negative evaluation than do data messages. Positive evaluations have normative restrictions on their credibility; data messages are less restricted. Whereas positive evaluations lose credibility when they are too large in number and nonresponse can be judged to be nonparticipation, data are low risk information types that can be increased with less constraint. The above generalizations address both the number and distribution of information types in status-differentiated groups and teams. Evidence in their support is reported in chapters that follow. SUMMARY AND DISCUSSION This chapter has reviewed the background of microprocessing in team decision making in the presence of social structure that this exposition proceeds from. In contrast to conclusions of an extensive background in lab studies, the more recent literature on team decision making in organizations has presumed or emphasized their effectiveness for decision objectives. These latter studies have generally not been in controlled experiments. Factors that are likely to relate to team effectiveness have not been adequately represented in the body of lab studies or organizational decision-making teams that have been reviewed. As such, neither of these literatures may adequately assess

38  Decision Making Groups and Teams the effectiveness of interactive decision-making groups when decisions are ill-structured. From the perspective of the present exposition, direct representation of the social structure of teams and its effects on information exchange can advance both lab studies and the design of organizational teams. The bases to introduce social structure in the interaction of decision-making teams have been reviewed in several research traditions. Status organizing in these traditions provides a compelling basis for the integration of social structure in decision-making teams that will follow. Reasons to frame team decision making as information exchange were reviewed in this chapter, and an overview of an account of information exchange in the presence of team structure were offered. It was recognized that members interacting in a task-directed team have motivation to both contribute to the team’s objective and to maintain their own status in the team. Status loss can occur from the receipt of negative evaluation. Such an effect also depends on the status distance of the member from the source of the evaluation. However, there is a basis to expect that judgments of this distance may commonly be biased, and this will directly affect judgments of gains and losses from the initiation of information. Members also recognize that different types of information they initiate have different likelihoods of returning a negative evaluation. While such an effect is mediated by the quality of the initiation (i.e., the quality of the idea or critical evaluation), there remains uncertainty of the quality of the initiation to a member. As a consequence, members (particularly those of medium or lower status) are likely to under- or oversend certain message types. This effect can bias the exchange in the team away from one that can be expected to be quality maximizing for an ill-structured decision. Trust has been noted as a well-identified mediator of the effectiveness of virtual teams and mediators of expected gains and loss from information initiation. In the framework to be presented here, trust evolves from equity judgments as well as competence judgments. As noted in several research traditions, competence judgment in groups and teams is commonly not veridical with actual competence in status organizing processes. Equity and trust can reduce expectations of lower or medium status members, that they face observed biases in the sending of negative evaluations. Both microlevel motives and processes that operate at the team level are essential to a comprehensive account that supports application and are taken up in chapters that follow. The chapters give forms to the processing of information in an interactive team and to information types that have been described as most instrumental to quality in ill-structured decision making and examine dynamics that they imply. Basic agent microprocessing in interactive teams is first addressed. Following this, dynamics of key information types that are fundamental to team decision making is considered. Forms for the information types and dynamic processing is then integrated in a system and used to generate designs for quality in ill-structured decisions. Finally,

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Background and Framework  41 Ilgen, D., Hollenbeck, J., Johnson, M., & Jundt, D. (2005). Teams in organizations: From input-process-output models to IMOI models. Annual Review of Psychology, 56, 517–543. Joshi, A., & Roh, H. (2009). The role of context in work team diversity research: A meta-analytic review. Academy of Management Journal, 52, 599–627. Kanawattanachai, P., & Yoo, Y. (2002). Dynamic nature of trust in virtual teams. Journal of Strategic Information Systems, 11, 187–213. Lamm, H., & Trommsdorf, G. (1973). Group versus individual performance on tasks requiring ideational proficiency (brainstorming): A review. European Journal of Social Psychology, 3, 361–387. Latané, B., Williams, K., & Harkins, S. (1979). Many hands make light the work: The causes and consequences of social loafing. Journal of Personality and Social Psychology, 37, 822–832. Loch, C., Huberman, B., & Stout, S. (2000). Status competition and performance in work groups. Journal of Economic Behavior and Organization, 43, 35–55. Magee, J. C., & Galinsky, A. D. (2008). Social hierarchy: The self-reinforcing nature of power and status. The Academy of Management Annals, 2(1), 351–398. Majchrzak, A., Rice, R., Malhotra, A., & Ba, S. (2000). Technology adaption: The case of a computer-supported inter-organisational virtual team. MIS Quarterly, 24, 569–600. Malhotra, A., & Majchrzak, A. (2005). Virtual workspace technologies. MIT Sloan Management Review, 46, 11–14. Maltzman, I., Belloni, M., & Fishbein, M. (1964). Experimental studies of associative variables in originality. Psychological Monographs: General and Applied, 28, 1–21. Markovsky, B., Smith, L., & Berger, J. (1984). Do status interventions persist? American Sociological Review, 49, 373–382. Martin, R. (2010). Minority influence and innovation. East Sussex, England: Psychology Press. McGrath, J. (1984). Groups: Interaction and performance. Englewood Cliffs, NJ: Prentice-Hall. McLeod, P., Baron, R., Marti, W., & Yoon, K. (1997). The eyes have it: Minority influence in face-to-face and computer-mediated group discussion. Journal of Applied Psychology,82, 706–718. Mello, A., & Ruckes, M. (2006). Team composition. Journal of Business, 79, 1019–1040. Meyerson, D., Weick, K., & Kramer, R. (1996). Swift trust and temporary groups. In R. Kramer & T. Tyler (Eds.), Trust in organizations: Frontiers of theory and research (pp. 166–195). Thousand Oaks, CA: Sage. Miles, J., & Kivlighan, D. (2008). Team cognition in group interventions: The relation between coleaders’ shared mental models and group climate. Group Dynamics: Theory, Research, and Practice, 12, 191–209. Nemeth, C. (1986). Differential contributions of majority and minority influence. Psychological Review, 93, 23–32. Paulus, P., & Brown, V. (2007). Toward more creative and innovative group idea generation: A cognitive-social-motivational perspective of brainstorming. Social and Personality Psychology Compass, 1, 248–265. Paulus, P., Putman, V., Dugosh, K., Dzindolet, M., & Coskun, H. (2011). Social and cognitive influence in brainstorming: Predicting production gains and losses. European Review of Social Psychology, 12, 299–325. Paulus, P., & Van der Zee, K. (2004). Should there be a romance between teams and groups? Journal of Occupational and Organizational Psychology, 77, 475–480. Pinsonneault, A., Barki, H., Gallupe, R. B., & Hopper, N. (1999). Electronic brainstorming: The illusion of productivity. Information Systems Research, 10, 110–133.

42  Decision Making Groups and Teams Pinsonneault, A., & Kraemer, K. L. (1990). The effects of electronic meeting on group-process and outcomes: An assessment of the empirical research. European Journal of Operational Research, 42, 143–161. Qureshi, L., & Vogel, D. (2006). The effects of electronic collaboration in distributed project management. Group Decision and Negotiation, 15, 55–75. Rashotte, L., & Webster, M. (2005). Gender status beliefs. Social Science Research, 34, 618–633. Ridgeway, C. (1991). The social construction of status value: Gender and other nominal characteristics. Social Forces, 70, 367–386. ———. (2006). Status construction theory. In P. J. Burke (Ed.), Contemporary social psychological theory (pp. 301–323). Stanford, CA: Stanford University Press. Ridgeway, C., Backor, K., Li, Y., Tinkler, J., & Erickson, K. (2009). How easily does a social difference become a status distinction? Gender matters. American Sociological Review, 74, 44–62. Ridgeway, C., & Erikson, K. (2000). Creating and spreading status beliefs. American Journal of Sociology, 106, 579–615. Robert, L., Jr., Dennis, A., & Hung, Y. (2009). Individual swift trust and knowledgebased trust in face-to-face and virtual team members. Journal of Management Information Systems, 26, 241–279. Roland, G. (2004). Understanding institutional change: Fast moving and slow moving institutions. Studies in Comprehensive International Development, 38, 109–131. Salomon, G., & Globerson, T. (2002). When teams do not function the way they ought to. International Journal of Educational Research, 13, 89–99. Schmidt, J., Montoya-Weiss, M., & Massen, A. (2001). New product development decision-making effectiveness: Comparing individuals, face-to-face teams and virtual teams. Decision Sciences, 32, 575–601. Scholten, L., Van Knippenberg, D., Nijstad, B., & De Dreu, C. (2006). Motivated information processing and group decision-making: Effects of process accountability on information processing and decision quality. Journal of Experimental Social Psychology, 43, 539–552. Shelly, R. (2001). How performance expectations arise from sentiments. Social Psychology Quarterly, 64, 72–87. Shepherd, M., Briggs, R., Reinig, B., Yen, J., & Nunamaker, J. (1995/1996). Invoking social comparison to improve electronic brainstorming: Beyond anonymity. Journal of Management Information Systems,12, 155 –170. Steiner, I. (1972). Group process and productivity. New York, NY: Academic Press. Silver, S. D. (1995). A dual-motive heuristic for member information initiation in group decision making: Managing risk and commitment. Decision Support Systems, 15, 83–97. Silver, S. D., Cohen, B. P., & Rainwater, J. (1988). Group structure and information exchange in innovative problem-solving. In E. J. Lawler & B. Markovsky (Eds.), Advances in group processes (Vol. 5, pp. 169–194). Stanford, CT: JAI Press. Silver, S. D., & Troyer, L. (1998). Judging the consequences of evaluation by others in status heterogeneous groups: Biases in the microlevel heuristics of group information exchange. In E. J. Lawler, J. Skvoretz, & J. Szmatka (Eds.), Advances in group processes (Vol. 15, pp. 103–132). Greenwich, CT: JAI Press. Silvia, P. (2007). Another look at creativity and intelligence: Exploring higher-order models and probable cofounds. Personality and Individual Differences, 44, 1012–1021. Stewart, G., & Barrick, M. (2000). Team structure and performance: Assessing the mediating role of intrateam process and the moderating role of intrateam process and the moderating role of task type. Academy of Management Journal, 43, 135–148. Stewart, K., & Gosain, S. (2006). The impact of ideology on effectiveness in open source software development teams. MIS Quarterly, 30, 291–314.

Background and Framework  43 Stumpf, S., & Freedman, R. (1979). Designing groups for judgmental decisions. Academy of Management Review, 4, 589–600. Suddaby, R., Elsbach, K., Greenwood, R., Meyer, J., & Zilber, T. (2007). Organizations and their institutional environments. Academy of Management Journal, 53, 1234–1240. Thye, S., Willer, D., & Markovsky, B. (2006). From status to power: New models at the intersection of two theories. Social Forces, 84, 1471–1495. Troyer, L., & Younts, C. (1997). Whose expectations matter? The relative power of first-order and second-order expectations in determining social influence. American Journal of Sociology, 103, 692–732. Veblen, T. (1953 [1899]). The theory of the leisure class. New York, NY: New American Library. Vignoles, V., Regalia, C., Manzi, C., Golledge, J., & Scabini, E. (2006). Beyond self esteem: Influence of multiple motives on identity construction. Journal of Personality and Social Psychology, 90, 308–333. Washington, M., & Zajac, E. (2005). Status evolution and competition: Theory and evidence. Academy of Management Journal, 48, 282–296. Weber, M. (1978 [1922]). Economy and society. Berkeley: University of California Press. Webster, M., & Hysom, S. (1998). Creating status characteristics. American Sociological Review, 63, 351–378. West, M. (2012). Effective teamwork: Practical lessons from organizational research. Chichester, England: Wiley-Blackwell. Wittenbaum, G. (2000). The bias toward discussing shared information: Why are high-status group members immune? Communication Research, 27, 379–401. Yeh, E., Smith, C., Jennings, C., & Castro, N. (2006). Team building: A 3-dimensional teamwork model. Team Performance Management, 12, 192–197.

3

Dual Motive Agents in the Information Exchange of Interactive Groups and Teams

OVERVIEW In this chapter, information exchange in the decision making of interactive teams is examined at the level of individual team members. I recognize the dual or competing motives of members who act as both individuals and team members, and propose a two-state heuristic for decisions on the type and amount of information they initiate. At the first stage, individual members intuit or solve the problem of maintaining their status in the team through information initiations that minimize the probability of receiving negative evaluations weighted by a measure of the sender’s status. In the second stage, the member accepts some increment to this minimum to contribute to the team objective of decision quality. The increment that the member accepts is proportional to his or her status and is the basis for the initiation of ideas and negative evaluations. The explicit forms that are proposed for the solution of the two-stage problem allow the quality-maximizing probability of an idea to be expressed in terms of a members’ status. This result is used to examine a conjecture on status distributions and the probability of idea initiations that maximize the quality of a team decision. Initial evidence from recent studies that supports assumptions of this work is presented. The capability of procedures in computer-mediated information exchange to maintain the exchange of ideas and negative evaluations at close to quality maximizing levels is briefly noted. INTRODUCTION Even when agents are the units of analysis, their memberships in interactive groups have well-recognized the consequences for their microprocessing. As has been extensively cited, group memberships generate normative and referent influence on agents (Haines & Cheney, 2011; Whitworth, Gallupe, & McQueen, 2000). In internal motives, investigators have cited the identity (Iedema, Rhodes, & Scheeres, 2005; Vallaster, 2005) and commitment (Caballer & Gracia, 2005) that these memberships generate. Status

Dual Motive Agents in the Information Exchange  45 maintenance is corresponding of a motive that is fundamental to individual agents in group and team interaction (e.g., Berger, Rosenholtz, & Zelditch, 1980; Bunderson, 2003). Group and team membership also generate member commitment to the objectives of the unit (Hackman, 1992; Kirkman & Rosen, 1999; Kozlowski, Watola, Jensen, Kim, & Botero, 2009). Given these influences, it can be anticipated that the heuristic that agents use as group or team members is likely to depart from the one they use when they act independently. Although the relative weights of self-motives and group motives may vary, the modifications in heuristic that group membership introduce is generally operative across contexts. In this chapter, I give a form to a representation of competing motives in conceptualizing group and team members as dual motive agents. Although the concept that group members act as dual or mixed motive agents has been recognized by a number of investigators (Cory, 2006; Davis, Laughlin, & Kormorita, 1976; Kahneman, Slovic, & Tversky, 1982; Lynne, 2006; Morgan & Tindale, 2002; O’Connor, Gruenfeld, & McGrath, 1993; Tyler, 1994), how this can be decomposed into differentials in the likelihood of initiating information types and given closed form representation has not been addressed. The perspective of decision-making groups and teams as information processors has now been well recognized (e.g., Hinsz, Tindale, & Vollrath, 1997). Consistent with the exposition in previous chapters, the conceptualization of information exchange in interactive teams to follow classifies differentials in the social risk of initiating information types. Social risk is again defined as the conditional probability of receiving a negative evaluation for initiating the information type weighted by a measure of the distance between the sender and recipient of the evaluation. A form will be given to team and individual motives in initiating different information types. This form will then be used to examine effects of team structure on objectives of groups and teams. I begin by conceptualizing the initiation of information by a member who acts as a dual motive agent (i.e., a member who acts both to maintain his or her own status and to contribute to the group objective). I follow Hogarth’s (1987) observation on the use of ordered heuristics to simplify complex decisions. The heuristics are not claimed to be explicit calculations of solutions to the problem of information initiation a member of a decisionmaking group faces. Rather, they are procedures that decision makers follow to approximate such a solution. The intention here will be to formalize candidate heuristics and use the formalization for inference on information exchange in decision-making teams. The following are assumed to be scope conditions in the processing that underlies information initiation by team members. (1) Agents are motivated to gain status and avoid status loss. The motive to avoid status loss is greater than the motive to gain status. (2) Agents occupying positions in a group have some basis to judge their own relative status position.

46  Decision Making Groups and Teams (3) Agents are motivated to contribute to the group objective. (4) Agents expect that the amount and type of information they send will influence the amount and type of information they receive. Thus, agents at least implicitly recognize the causal relationship between information initiation and evaluation by others. (5) Agents simultaneously recognize that contribution to the team objective requires information initiation, and this has social risk.

MINIMIZING STATUS LOSS IN INFORMATION INITIATION As in assumptions in the scope conditions, the ordering in an agent’s heuristic is in minimizing status loss more than maximizing status gain. Here, status loss is considered to occur through the receipt of negative evaluations from other group members. As noted, the magnitude of the loss from a negative evaluation is seen as proportional to the status distance between the source and target of an evaluation. This claim can be given a form by defining the unconditional probabilities of message types that a group member sends to the rest of the group. Attention here is restricted to the information types of ideas and evaluations and a residual category of a blank (i.e., no response). To further tractability, the residual category includes all other information types. As has been argued, the information types of ideas and evaluations contribute most to quality in ill-structured decisions. For a four-person group, the entries of a vector of unconditional probabilities for Member 1 are as follows: P12 = Pr[P(1 → 2)]N12 = Pr[N(1 → 2)] O12 = Pr[P(1 → 2)] P13 = Pr[P(1 → 3)]N13 = Pr[N(1 → 3)] O13 = Pr[O(1 → 3)] P14 = Pr[P(1 → 4)]N14 = Pr[N(1 → 4)] O14 = Pr[O(1 → 4)] P1∑ = Pr[P(1 → ∑)]N1∑ = Pr[N(1 → ∑)] O1∑ = Pr[O(1 → ∑)] I1∑ = Pr[I(1 → ∑)] Where I denotes ideas, N and P are negative and positive evaluations, respectively, and O denotes the case of no response to a message (a blank).1 The symbol, Σ, denotes the entire group as the target of a message. Since ideas are predominantly sent to the group, I limit the initiation of this message type to the single case (1→Σ). To be concise, P12, . . . , I12 is hereafter used to denote Pr[P(1→2)], . . . , Pr[I(1→Σ)].

Dual Motive Agents in the Information Exchange  47

The Minimization Problem Group members acting as agents are seen as first considering the message initiations in the exchange of information that will minimize the statusweighted negative evaluations they receive. The constrained problem of minimizing the sum of status-weighted probability of a negative evaluation for all messages initiated by the i-th group member can be written as:   min ∑  ∑ Pr N ji | Xij σ j Pr Xij + ∑ Pr N ji | Xi ∑ σ j Pr ( Xi ∑ )  j ≠ i  X = P, N,O X = I,O 

(

s.t. ∑

∑

j ≠ 1 X=P, N,O

)

( )

( )

(

Pr Xij + ∑ Pr ( Xi ∑ ) = 1

( )

4

j =1

(3.1) (3.2)

I,O

 Pr Xij , Pr ( Xi ∑ ) ≥ 0, all j ≠ i, for each j ≠ i ∑ Pij ≤ Pp

)



(3.3) (3.4)



Where X = P, N, O, I, and σj is the judged status of j by i and Pp is the upper bound for the credible probability of sending positive evaluations in the interaction period. It is assumed that each member has a maximum probability of sending positive evaluations that varies with his or her relative status in the group. When this bound is exceeded (e.g., when a member sends only positive evaluations), Nji|Pij increases by a large magnitude. The objective in Eq. (3.1) is defined in terms of the sum of the status weighted probabilities of receiving a negative evaluation from messages sent either to individuals or to the group, respectively. The constraints (3.2) and (3.3) are the standard requirements that the probability of all acts fill the probability space and be nonnegative. Constraint (3.4) bounds ΣP at Pp. I further make the intuitively reasonable assumption on the ordering of the conditional probabilities of being negatively evaluated for initiating an information type: Pr(Nji|Nij) > Pr(Nji|Ni∑) > Pr(Nji|Oij) = Pr(Nji|Oi∑) > Pr(Nji|Pij) That is, the conditional probability of receiving a negative evaluation is highest for sending a negative evaluation and lowest for sending a positive evaluation. It is assumed that sending a positive evaluation generally has a lower conditional probability of receiving a negative evaluation than sending an O category message does. This is because a positive evaluation conveys favorable judgments of others and is status increasing to them in public exchanges, whereas an O category message is more neutral. Given the above form of the problem, the solution to the minimization is straightforward. Since P is bounded by Pp and Pr(O) is bounded only at 1,

48  Decision Making Groups and Teams one could send only positive evaluations until Pp is reached and then send O category messages. Since Pr(Nji|Oij) > Pr(Nji|Pij), it further follows that sending a positive evaluation to a higher status person and a blank to a lower status person will increase the objective function (i.e., decrease the status weighted probability of receiving a negative evaluation) less than sending a positive evaluation to a low-status person and a blank to a high-status person. To minimize (3.1), one would consequently send all positive evaluations to the high-status person. Because of the bound Pp in (3.4), the minimization problem reduced to: 4  4  min  ∑ sjPr N ji |Pij Pr Pij + ∑ sjPr N ji |Oij Pr Oij  j = 2 j = 2    4  ∑ Pr( Pij ≤ Pp )  s.t.  j = 2 s.t. Pr Oji ≥ 0 Pr( Pji ) ≥ 0 

(

) ( )

(

) ( )

(3.5)

( )

Since the solution is only in Pr(P) and Pr(O), and ∑j ≠ i Pr(Oij) = 1 – ∑ Pr(Pij) the objective can be written as:

(

) ( )

(

)(

( ))

min ∑ σ jPr N ji |Pij Pr Pij + ∑ σ jPr N ji |Oij 1 − Pr Pij j≠ i

j≠ i

The terms may then be rearranged so that the problem is:

(

)

( ) (

)

(

)

min ∑ σ jPr N ji |Oij − ∑ σ jPr Pij Pr N ji |Oij − Pr N ji |Pij    j≠ i j≠ i



(3.6)

The first term of (3.6) is independent of Pr(Pij). Since Pr(Nji|Xij) is defined, Pr(Pij) is the only undefined variable in (3.6) and is assumed to be a constant. It is further assumed that Pr(Nji|Pij) and Pr(Nji|Oij) are increasing with the status of the j-th member and that σj[Pr(Nji|Oij) – Pr(Nji|Pij)] is positive and increasing in the status of j. Therefore, to minimize (3.6), the sum ∑j ≠ 1σjPr(Pij)i and the second term is maximized. This occurs when j is set equal to the highest status group member and the probability of sending a positive evaluation to this member is set at the maximum level of Pp. By definition, O category messages are sent to everyone else in the group. Thus, the solution to the minimization of status loss at this stage is to send positive evaluations to the highest status member until what we have defined as the bound of credibility for this information type is reached. Then O category messages, including blanks, are sent to everyone including the high-status member.

Dual Motive Agents in the Information Exchange  49 These results indicate that under an objective of minimizing status weighted negative evaluations, (1) no ideas or negative evaluations will be initiated and that (2) the high-status member will overreceive positive evaluations because of his or her position. Moreover, the solution suggests that one will expect to see a high probability of message types in the O category that have low social risk (e.g., data messages), whether or not the information contributes to the group objective in a task. This is a consequence of the upper bound on sending positive evaluations and the comparatively low probability of receiving a negative evaluation for sending O-category messages. The results of (1) zero (or very low) proportions of ideas or negative evaluations, (2) high proportions of positive evaluations sent to high-status members, and (3) high proportions of blanks (low participation) indicated by the solution of this minimization problem are commonly observed in groups with large status distances between members and low commitment to group objectives by medium- and low-status members. Although the heuristic solutions to the minimization problem that have been described are intuitively realized by most group members, and thus available, they are unlikely to be implemented in their strict forms in most cohesive groups because the goals of most group members do not solely lie in their status maintenance (i.e., a minimization of the sum of the status weighted negative evaluations one receives in information exchange). In the section to follow, I elaborate on a representation of the more complex objectives group members face and implication of these objectives for the exchange of information in ill-structured decision making.

Contributing to Group Decision Quality I propose that group members treat their heuristic solutions to the problem of maintaining their status (minimizing their status loss) as constraints on the number, type, and recipients of messages they are willing to initiate, and then consider the objective of maximizing the quality of group decisions in deciding on information initiation. As previously noted, quality is a shared objective in cohesive groups since members have a group as well as an agent identity. Although ideas and negative evaluations are the most important contributors to the quality of group decisions, sending both of these information types have higher expected status costs to individual members than sending data, questions, or positive evaluations. To conceptualize the operation of these dual motives, I begin with the simple assumption that the i-th group member is willing to accept some increment, ɛi, to minimize ∑j ≠ 1 σj Pr(Nji) = ni, min (the minimized status-weighed sum of the probability of receiving negative evaluations from each of m other group members). This increment to the minimized status loss is accepted as contributing to the group goal of decision quality. Additionally, the magnitude of the increment, ɛi, is considered to be an increasing function of the

50  Decision Making Groups and Teams member’s perceived status in the group (i.e., ɛi = ɛ(σi). The second stage problem for the group member in terms of a group quality objective can then be written as: Max Q s.t. ∑

∑

j ≠ i x = P, N, O

)

( )

(

(

)

( )

(

)

Pr N ji | Xij σ jPr Xij + ∑ Pr N ji | Xi ∑ σ j PrXi ∑ ≤ ni, min (1 + ε ) = x = I, O

)

N ji | Xij σ jPr Xij + ∑ Pr N ji | Xi ∑ σ j PrXi ∑ ≤ ni, min (1 + ε ) = ni, max x = I, O

and the conditions (2) and (3) hold for each group member. In the second stage of the problem, Q is the quality of the group’s decision, ni, min is as defined above, and ni, max is an upper bound on the summed (statusweighted) probability of receiving a negative evaluation from other group members that a member judges he or she can attain. In the solution to this problem, ɛ = ɛ (σi) is the increment in ni, min that the member is willing to accept to contribute to the quality objective, and ni, max is the maximum statusweighted probability of receiving negative evaluations for the group that the member is willing to accept after adjusting for his or her contribution to the group quality objective. THE QUALITY MAXIMIZATION PROBLEM: SOLVING THE QUALITY MAXIMIZATION PROBLEM To solve the constrained quality maximization problem, the member-agent requires assumptions on: (1) The contribution of information types of ideas and negative evaluations to quality (i.e., Q = Q[Pr(Ii), Pr(Ni)]). (2) An explicit form for setting the level of ɛ (in place of the general form, ɛi = ɛ (σi)). DECISION QUALITY FROM IDEAS AND EVALUATIONS To develop an explicit form for quality in decisions as it is generated from ideas and negative evaluations, I begin with two assumptions on decision quality that were introduced as generalizations in the previous chapter. Assumption (1.0).  Quality in group idea generation is monotonically increasing in idea number. Assumption (2.0).  Quality in group idea generation is the quadratic function of the number of negative evaluations exchanged.

Dual Motive Agents in the Information Exchange  51 The first of the above assumptions follows from consistent findings that idea uncommonness is monotonically increasing across ideas in the sequence in which they are generated (e.g., Christensen, Guilford, & Wilson, 1957; Yilmaz, Seifert, & Gonzalez, 2010). It suggests that maximizing the number of ideas is one part of a quality-maximizing procedure.2 The second assumption follows from the observation that while negative evaluations contribute to the sorting of ideas on quality, too many negatives can rapidly increase the expected cost of sending ideas (i.e., Pr(Nij|IiΣ) in the group) and inhibit the initiation of ideas (e.g., Girotra, Terweisch, & Ulrich, 2009; Nijstad & Stroebe, 2006). An inference that follows from the above assumption is: Inference (1.0).  Ideas and negative evaluations make the greatest contribution to quality when they occur in an ideal proportion. From this inference, the normative goal of the group is to set the total number of negative evaluations exchanged in a decision at a level that is proportional to the number of ideas. The exact proportion will depend on the status distribution in the group, the type of decision, and the group’s interaction history. Generally, the idea rate is expected to be decreasing over the group’s interaction time, and the rate of negative evaluations is expected to increase in its contribution to quality as the cumulative sorting requirement increases. Thus, the group-maximizing ratio of ideas to negative evaluation (R) is considered to be time-varying and monotonically decreasing. This ratio can be written as:

( )t = R t ∑ Pr ( N ji ) t i≠j

∑ Pr I j ∑ ∑ j

For the present static application, Rt will be approximated by the constant R. A second inference addresses the distribution of negative evaluations across group members: Inference (2.0).  Negative evaluations contribute most to quality when they are distributed to members in proportion to the number of ideas these members have initiated. Negative evaluations contribute to quality by sorting ideas and therefore should be sent to members in proportion to the number of ideas a member initiates. Procedures that result in such an allocation counter the tendency to oversend negative evaluations to lower status members and to undersend them to high-status members.

52  Decision Making Groups and Teams

A Function for Decision Quality From generalizations in Inferences (1.0) and (2.0), a candidate for a member’s quality objective can be written as:   Q i = f Pr ( I i ∑ ) , ∑ Pr ( I i ∑ ) , ∑ Pr N ij , R  − ∑ Me j≠ i j≠ i   j

( )

( ) − Pr ( Ij ∑ ) ∑ Pr ( N ij ) ∑ Pr ( I j ∑ ) j i ≠jj

δ

Pr N ij

(3.7) 

where Qi is the contribution of the i-th member to the quality of the group decision; R is the ideal ratio of negative evaluations to ideas; Pr(IjΣ) is the probability of the j-th member sending an idea to the group; and Me is a scaling constant. δ is the sensitivity of quality to the absolute value of deviations from the optimal distribution of negative evaluations among members and δ ≥ 1. The first term in (3.7) is the general form for quality from ideas and negative evaluations; specific forms for this term are considered below. The second term is the allocation rule for negative evaluations from Inference (2.0). This term penalizes quality for negative evaluations that are not distributed according to the initiation of ideas by group members.

Idea Quality from Negative Evaluations and Ideas Having provided a general form for the group’s objective function, specific functional forms for quality production from ideas and negative evaluations may now be considered. From Assumptions (1.0) and (2.0), a quality production function can be written as the first term of the RHS of (3.7). For each member, decision quality is (1) increasing in ideas and (2) requires negative evaluations to be proportional to ideas:   f Pr ( I i ∑ ) , ∑ Pr ( I i ∑ ) , ∑ Pr N ij , R  = Pr ( I i ∑ ) − α ∑ Pr I j ∑ − R ∑ Pr N ij j≠ i j≠ i j≠ i j≠ i   α ≥ 0, β ≥ 1

( )

( )

( )

β

,

(3.8)



In (3.8), the exponent, β, determines the sensitivity of quality to the absolute value of deviations from the optimal ratio.3 The first term of (3.8) indicates that quality is increasing in the probability of an idea. The second term indicates that quality also depends on setting a probability of sending a negative evaluation that is proportional to other members’ probabilities of sending an idea—incrementing the minimized probability of receiving a negative evaluation to increase decision quality. I next give an explicit form to ɛ the increment to ni, min, (the minimized status-weighted probability of a negative evaluation the i-th group member is willing to receive while participating in the group’s information exchange). This form is written as the concave increasing function, ɛ(σi) = ciσjθ where

Dual Motive Agents in the Information Exchange  53 0 < θ < 1. The form implies that the magnitude of the status-weighted negative evaluations a group member will accept to contribute to group quality increases at a decreasing rate as his or her relative status increases. THE DECISION QUALITY MAXIMIZATION PROBLEM

Forms for quality production and the increment to ni, min, the minimized statusweighted probability of a negative evaluation, have now been proposed. With these forms, I write a more complete form for the group members’ problem of maximizing their contribution to the quality of the group decision, subject to a constraint on the acceptable status-weighted probability of receiving a negative evaluation. ∑ Pr N ij 2 Pr I j ∑ j max Q i = max Pr ( I i ∑ ) − α ∑ Pr I j ∑ − R ∑ Pr N ij − ∑ Mc − j j≠ i ∑ ∑ Pr N ij ∑ Pr I j

(

(

( )

)

( )

max Pr ( I i ∑ ) − α ∑ Pr I j ∑ − R ∑ Pr N ij j≠ i

( )

)

(

2

− ∑ Mc j

( ) Pr ( Ij ∑ ) − ∑ ∑ Pr ( N ij ) ∑ Pr ( I j ∑ ) j j i≠ j

∑

X=P,N,O

j i≠ j

( ) ( )

∑ Pr N ij j

) ( )

(

( )

( )

s.t. ∑ ∑ σjPr N ji |Xij Pr Xij ≤ ni,min 1 + j

( )

c1σiθ

(3.9) 

) , θ ≤ 1, for each i.

( )

Pr Xij = 1,

∑ Pr ( Xi ∑ ) = 1, ∑ Pr Pij ≤ Pp , Pr Xij ≥ 0

X=I,O

Where, ni, min (1 + c1σiθ)= ni, max is the maximum sum of the probabilities of receiving a negative evaluation from all other group members that is acceptable to the member. For other constants, α ≥ 0, β > 1, and c1 ≤ 0. I have set β, δ = 2, for this problem. Eq. (3.9) and the constraints that group members face define the quality maximization problem. In this problem, members seek to maximize their contribution to the quality of group decisions subject to the constraints of the maximum status-weighted probability of a negative evaluation they are willing to receive in contributing to the group quality objective. From this explicit form for the quality function, one can begin to investigate both idea initiation and quality production as a function of the status distribution of group members in limited cases. QUALITY-MAXIMIZING INFORMATION EXCHANGE In this section, the distribution of status in the group that maximizes decision quality is taken up. This is an important consideration since technology can enable the perceived status distribution to be a design variable. I anticipate

(

j

(

54  Decision Making Groups and Teams subsequent conceptual and empirical results by a claim on the status distribution that maximizes the probability of idea initiations in a group that meets the condition of maximizing decision quality. As indicated, these conditions also include initiating a definable proportion of total messages that are in negative evaluations. For tractability, I begin with the case of a dyad. The quality-maximizing probability of an idea in a dyad as it relates to the status distribution can be stated in the following generalization. Inference (3.0).  For the case of dyads, the probability of an idea initiation by members in a quality-maximizing group (Pr(I*)) is maximized when the status of the two group members is equal (i.e., σI = σj = .5). The dyadic case furthers tractability in analysis, since the objective function does not require a term for the distribution of negative evaluations among group members (i.e., the third term of the RHS of Eq. (3.9)). I thus write the constrained problem of maximizing decision quality, with possible actions given by Pr(Ii), Pr(Ni), and Pr(Oi), as follows:

( )

2

max Q = ∑ Pr ( I i ) − α Pr I j − RPr ( N i )  , if j = 1, i = 2, and vice versa.   i = 1, 2

   s.t. n1, i Pr ( Oi ) + n2, i Pr ( N i ) + n3, i Pr ( I i ) = σ jPr N j i = 1, 2 ,  σ jPr( N j ) ≤ ni, max = ni, min (1 + c1 σ iθ ), i = 1, 2;  if j = 1, i = 2 and vice versa, c1 = 1 Pr(Oi) + Pr(Ni) + Pr(Ii) = 1, i = 1, 2 Pr(Oi) + Pr(Ni) + Pr(Ii) ≥ 0 where n1, i = σjPr(Nj|Oi), n2, i = σjPr(Nj|Ni), n3, i = σjPr(Nj|Ii), and ni, max is the maximum number of status-weighted negative evaluations that the i-th group member is willing to accept. To solve this problem and examine the relationship between the qualitymaximizing probability of an idea and the distribution of status in the dyad, I proceed as follows. I first show that maximum quality occurs when a member sends as many ideas and negative evaluations as the constraint allows (i.e., Pr(Ni) is set at Pr(Ni, max) = Ni, max). I then use this result to derive a reduced-form expression for the Pr(Ii) that maximizes decision quality in terms of Pr(Ni) and Pr(Nj). Finally, I use this reduced form expression to examine the distribution of status under which the quality-maximizing probability of an idea initiation occurs.

Dual Motive Agents in the Information Exchange  55 From the constraints, the minimum attainable Pr(Ni) can be written as:

N i,min = N min =

(

(

Pr N j |Oi

)

(

)

)

Pr N j |Oi − Pr N j |N i + 1

The derived Pr(Ni) and Pr(Nj) that maximize Q has relationships in conditional probabilities and constants that are complex and difficult to interpret. As such a numerical exercise was used to show that, for this problem, Qmax, the maximized decision quality in the dyad, occurs when Pr(Ni) = Ni, max. This contention is demonstrated for an interval of parameter values around the best estimate of the key parameters for the objective and its constraints. For the estimate, I use the following conditional probabilities and constraints from an unpublished experimental study of group information exchange. Pr(Nj|Ij) = .09; Pr(NjOj) = .007; Pr(NjNj) = .14; R = 10; initially, α = .10. In the numerical exercises, the quality estimates from Eq. (3.9), with the values of the parameters varied between 0.5 and 2.0 times each of the above parameter estimates were examined. Four evenly spaced points in the 0.5 to 2.0 range of the initial estimates of the above parameters, and four evenly space points in the (0,1) interval for σi and θi were examined. For the total of 47 combinations of parameters examined, quality was found to be maximized when Pr(Nj) is set to equal to Ni, max. The results of this search procedure for a smooth quadratic objective function support the contentions that the quality-maximizing Pr(Ii) occurs when the constraints on Pr(Ni) is set equal to Ni, max. Definition of the quality-maximizing Pr(Ni) now allows us to find an expression for the quality-maximizing Pr(Ii). From the constraints, Pr(Ii) may be expressed in terms of Pr(Nj) as follows: Pr(Ii) = aiPr(Ni) + biPr(Nj) + ci, Where,

ai = bi =

(

)

(

) −Pr ( N j |Oi ) + Pr ( N j |I i ) Pr N j |Oi − Pr N j |N i

(

1

)

( ) −Pr ( N j |Oi ) ci = −Pr ( N j |Oi ) + Pr ( N j |I i ) −Pr N j |Oi + Pr N j |I i

56  Decision Making Groups and Teams If at Qmax, Pr(Ni) = Ni, max then:

( ) (

∑ Pr I*i = ∑ ai N i, max + bi N j, max + c i

(

(

)

)

(

) )

= ∑ ai N i, min 1 + siθ + bi N j, min 1 + siθ + c i

where Pr(I*) is the quality-maximizing Pr(Ii) initiated by the i-th group member. With an explicit form for ∑ Pr(Iθi ), inference (3.0) can be directly investigated:

Demonstration of Inference (3.0) By definition, ∑Pr(Ii*) = Pr(Ii*) + Pr(Ij*), substituting the obtained expressions for Pr(Ii*) and Pr(Ij*) and simplifying: Pr ( N j |Oi ) ( σθi + (1 − σi ) ) ( ) ( ) ∑ Pr ( ) = = −Pr ( N j |Oi ) + Pr ( N j |I i ) −Pr ( N j |Oi ) + Pr ( N j |I i ) I*i

θ

Pr N j |Oi σiθ + σθj

The value of σI at which ∑Pr(Ii) is maximized can be obtained from the derivative of ∑Pr(Ii*) Set: ∂ ∂σi

σθ + (1 − σ )θ  = θσθ−1 − θ (1 + σ )θ−1 = 0 i  i i  i 

Then, σ iθ–1 = (1 – σ i)θ–1   σi = 1 – σ i, σ i = .5 Examining the second derivative for the critical point, σ = .5, ∂2  θ θ σi + (1 − σi )  |σ1 = .5  ∂σ2  i

= θ ( θ − 1) σiθ − 2 + θ ( θ − 1) (1 − σi ) 1 = θ ( θ − 1)   2

θ

θ −1

Pr N ji Iij > Pr N ji Fij ≥ Pr N ji Bij > Pr N ji Pij (4.3)

For the information types under study, negatively evaluating another group or team member is seen as having the greatest probability of resulting in an negative evaluation by that member. In contrast, positively evaluating another member is seen as having the smallest probability of resulting in a negative evaluation by that member. Note that in (4.3), nonresponses or blanks are seen as having a greater conditional probability of generating a negative evaluation than initiating positive evaluations. In such a case, optimal behavior to minimize status loss for lower status group or team members is unlikely to be nonparticipation or blanks. It would, in fact, be to initiate positive evaluations of all information initiations by other members. Since a member can credibly send only a limited number of positive evaluations after which the probability of a negative evaluation for initiating a positive evaluation is likely to substantially increase, optimal strategy for avoiding status loss is likely to also involve blanks and data or facts. This in fact, the profile of information initiations by lower status members in a status-differentiated group that is reported in studies of the next chapter. The dependencies of information exchange on the type of information exchanged and the relative status of group and team members that has been discussed as made explicit in Eqs. (4.1) to (4.3) can be used to further formal arguments on information exchange in status-differentiated decision-making units. Inference on the relationship between status distributions and the proportion of types of information exchanged in the unit, as well as statements on quality in ill-structured decisions by groups and teams is offered from these arguments.

68  Decision Making Groups and Teams FUNCTIONAL FORMS OF JUDGED STATUS DISTANCES As maintained in the foregoing arguments, member judgments of status distances from other members are integral to the expected costs of initiating information, and consequently to the amount and type of information that members exchange. As indicated, the functional form that group or team members use in weighting status distances in unlikely to be linear. A linear function would imply that increasing the variance of a status distribution in a group or team would have no effect on information initiation of its members if it was symmetrical around the mean of the unit.3 This is clearly not supported either intuitively or in data that is presented. The expected status cost of a negative evaluation or expected gain from a positive evaluation to a medium and lower status target clearly differs depending on whether the target of the evaluation uses a linear, convex, or concave weighting of status distance. While many investigators may be intuitively aware that linear representations of status effects in such cases are inadequate, the consequences of nonlinear forms for social risks have not been explicitly addressed in group decision making. The preceding discussion of status weighting leads the following assumption on the functional form that individual judgments on status distances of other group and team members take. Assumption (3.0): A group or team member overweights the expected status loss from a negative evaluation by a higher status member and underweights a negative evaluation from a member of lower status. This assumption suggests that a group or team member judges the expected status loss from a negative evaluation by a member who is z status units higher than his or herself in status to be more than twice the expected loss from a negative evaluation by a member who is z/2 units higher than his or herself. Correspondingly, the assumption indicates that a group member judges the expected status loss from a negative evaluation by a member who is z units lower than his or herself in status as less than twice the expected loss from a negative evaluation by a member who is z/2 units higher than his or herself. A further claim is that a member’s judgment of status losses from an information initiation increase at an increasing rate for the target of a negative evaluation as the source member of the evaluation increases in the distance by which it exceeds the target member in status. Correspondingly, status losses decrease at a decreasing rate for the target of a negative evaluation as the target of the evaluation increases in the distance by which it exceeds the source in status. A value function for the weighting distances of group members that is consistent with Assumption 3.0 is illustrated in Figure 4.1.

Biases in Member Judgments of Gains and Losses  69

Figure 4.1  Judgment of Status Loss from a Negative Evaluation as a Function of Actual Status Distance from the Evaluator

Note: Vij is a fixed difference in status distance (i.e., σi – σj) ^ is judged status loss V ij The convex form of the value function given in Figure 4.1 portrays the effects of status loss from negative evaluation as a function of the status distance of the target from the source of the evaluation. This effect is consistent with a natural concave form for the dependence of the i-th member’s probability of initiating an idea on her or his status. Implications of Assumptions (1.0) to (3.0) for the weighting of positive and negative evaluations are in the section to follow. Inference on how these assumptions on the weighting of status distances between the source and target of an evaluation effects information exchange in the group. As previously assumed, a group member is motivated to minimize the total status-weighted negative evaluations he or she receives. Given the preceding assumptions, the following inference is offered on the initiation of an idea as an information type in a group or team when the initiation by a member is a concave function of his or her status: Inference (1.0): The probability of an idea being initiated in a group or team will be maximized when the members of the unit are equal in status.

70  Decision Making Groups and Teams As the judged distance between a source of information and one or more targets with higher status increases, the probability of an idea being initiated by the source can be expected to decrease. A simple form of this relationship in a dyad can be written as:

(

)

Pr ( Ii ) = k − V σ j − σ i (4.4) Where Pr(Ii) is the probability of the i-th member initiating an idea; V(σj – σi) is the i-th member’s judgment of the status between his or herself and the member when these members are ordered σj > σi, and k is a constant. The first and second derivatives of (4.4) with respect to (σj – σi) make it clear that if V is convex, then Pr(Ii) will be concave.4 A natural concave form for the initiation of information in a group or team can be written as: Pr ( Ii ) = f (σ i ) = c1σ iθ1 , θ ∈ ( 0, 1) ,

Where, Pr(IΣ) is the probability of an idea sent by the i-th member.5 f (σ )) where Pr(I ) is the probability of the exchange initiation Then, Pr(IPr∑( I) )== ∑ f(σ i i of an idea to the group or team. The standard property of concave functions as it follows from Jensen’s Inequality for Sums that is extensively used in information theory (e.g., Cover & Thomas, 2006) can then be used to further inference on the implications of this form of a response function. The theorem for this is stated and proven for convex functions in Karlin and Taylor (1975)—also see Boyd and Vandenberghe (2004) and Land and Huber (2006) for Jensen’s Inequality. The key property that follows from Jensen’s Inequality can be stated as follows: Let ϕ be a concave function whose domain is some interval, M; if x1, . . . xn are n points in M, and σ1, . . . σ1 are positive numbers such that ∑αi = 1 then: n

∑

i

i =1

 n  α iφ ( xi ) ≤ φ  α i xi  (4.5)   i =1  i =1  n

∑

∑

For previous arguments, Pr(Ii) = f(σi) is considered to be a concave function representing the probability of an idea being sent. The domain of f is M = (0,1), with n points defined as σi, . . . , σn, the statuses of members. If, for the case at hand, each member is weighted equally (i.e., αi = –1n for each i, where n is the number of group or team members), it can be shown that the number of messages sent is maximized when all statuses are equal; n n nn nn  1  11  f1∑ σ ≤ c f σ ≤ ff   namely when αi = –1n for all i, that is, cc1 1 ∑c1c f (σ ) ≤ c . This can be shown ≤c11cf1∑ f (∑i )f(i (σi )i1)∑ ∑ n  nn  i =1 i =i1=1 i =1 i=i1=1     as follows:

Biases in Member Judgments of Gains and Losses  71 Pr ( Ii ) =

n

∑

f (σ i ) = n

i =1

n

1

∑ n f (σ ) i

i =1

 n σ  i  ≤ nf  by Jensen n’s Inequality  i =1 n    n 1 n  σ i  since σ i = 1 = nf   n i =1  i =1  

∑

∑

n

=

1 f   since n i =1  

∑

(4.6)

∑

n

∑σ i =1

i

=1 

Therefore, there is no distribution of σi such that the function for the probability of an idea initiation, ∑f (σi), is greater than f(–1n). STATUS DISTRIBUTIONS IN GROUPS AND TEAMS AND THE QUALITY OF ILL-STRUCTURED DECISIONS This section considers how the status distribution in a group or team effects the quality decisions when the decision cannot be made by a heuristic that closely approximates an analytical solution. Quality as it relates to such decisions has been difficult to define in both conceptual and empirical terms. As suggested, quality can be expected to depend on the number of ideas as well as the adequate filtering of ideas. Negative evaluations are generally an important basis for such filtering, and a contributor to decision quality. While it is common to assume that ideas contribute to quality when they are maximized in number, negative evaluations are likely to contribute most when they are kept in a bounded interval. Too high a number of negative evaluations can increase the expected costs of initiating ideas and thereby inhibit their exchange. Correspondingly, too low a number of negative evaluations can result in inadequate filtering of ideas. A high profile example of this is in the phenomenon of groupthink (Janis, 1982, 1987; Janis & Mann, 1977). The condition on the number of ideas follows from the historical finding that idea originality increases monotonically with the number of ideas initiated in a group or team (e.g., Christensen, Guilford, & Wilson, 1957; Yilmaz, Seifert, & Gonzalez, 2010). The condition on negative evaluations provides for adequate filtering of ideas, while not excessively increasing the expected costs of initiating ideas or reducing the cohesiveness of the group. This can be formally stated in the next assumption. Assumption (4.0): Quality in ill-structured decisions is maximized when the number of ideas initiated by group or team members is maximized, and negative evaluations are initiated by group or team members in proportion to the number of ideas that other members offer.

72  Decision Making Groups and Teams Consistent with these conditions, the quality-maximizing solution, Q*, in terms of the total number of ideas and the ideal ratio of negative evaluations to ideas in a dyadic case can be written as: Q* = Ii + Ij – α(Ii – RNj)2 – α(Ij – RNi)2

(47)

where Ik is the number of ideas initiated by the k-th group or team member; Nk is the number of negative evaluations initiated by the k-th group or team member; R is the ideal ratio of negative evaluations to ideas and k = i, j. In previous discussion, it was noted that the probability of initiations of ideas and negative evaluations would be increasing with a group or team member’s status. Since the total number of messages that a member initiates is expected to be increasing with the member’s status, the dependence of the number of ideas and negative evaluations a member can be written as a monotonically increasing function of his or her status. That is, Ii = σ iβ and Ni = σiγ, respectively, where β and γ are nonnegative and nonzero. On this basis, the quality function in relation to status is given the form:

Q * (σ k ) = c1 (σ iβ + σ jβ ) − α ( c1σ iβ − c2 Rσ jγ ) − α ( c1σ jβ − c2 Rσ iγ ) 2

2

(4.8)

To examine how status homogeneity affects solution quality, as in (4.8),   = 0 whether σ i = σ j = 0.5 is a global maximum for Q* (i.e., that Q*ʹ (0.5) = 0  dQ  dβ  d Q  ʺ and Q* (0.5) < 0)  d β < 0  will next be assessed. By the symmetry at σ i = σ j = 0.5, this point is a critical point of Q, when the function is considered in its dependency on σi as in (4.8). Hence, Q*′ (0.5) = 0.Given the form of (4.8) and its dependency on σi, σj, and R, the sign of Q*″ (0.5) is not readily evaluated by analytical methods. As a consequence, numerical methods were used to indicate the functional form of Q(σk) implied by (4.8) and to test whether σ = 0.5 is a global maximum for Q. For this, the constants are set as c1 and α = 1, and (4.8) is examined for a range of values that σi, σj, and R can assume. Given the assumption on the concavity of the I and N functions, σi and σj are bounded by 0 and 1, and a range of parameter values in all but the extremes of parameter ranges are investigated (i.e., σi, σj ∈ (.20 to .85). Results of a previous study are also used to bound the interval of R to 0.10 ≤ R ≤ 0.25.6 A direct search procedure was used to examine the levels of σi at the maximum Q for gradations of 0.05 in the defined interval of σi, σj, and R. For all points inside this interval, the maximum Q* was found to be obtained at σ i = σ j = 0.5. These results demonstrate the important consequences of the weighting heuristics that members use when judging the amount and type of information to initiate. On the basis of the analysis and simulation, an inference on decision quality can be offered that extends the inference on idea number. *

2

*

Biases in Member Judgments of Gains and Losses  73 Inference (2.0): The quality of an ill-structured decision will be maximized when the members of the group or team are equal in status. Assumptions that the foregoing account of ill-structured decision making in groups are premised on clearly require empirical testing. The first and second assumptions are of particular interest because these claims represent the adaptation of insights from behavioral decision making for monetary outcomes to social judgments that underlie information exchange in decision-making groups and teams. In the next section, studies that directly assessed assumptions used to derive our inferences on weighting in a quality function and on the status distribution that maximizes the exchange of ideas as a contributor to quality is reported. EMPIRICAL INVESTIGATION OF THE FRAMEWORK Study 1 employs a vignette methodology to investigate (1) the comparative valuing of negative and positive evaluations asserted in Assumption 2.0 and (2) the differential weighting of negative evaluations from higher and lower status sources represented in Assumption 3.0. Study 2 offers a first test of Inference 1.0, the claim that the probability of idea initiations in a group is greatest when members of the group are equal in status. STUDY 1: THE VALUING OF SOCIAL GAINS AND LOSSES In this study, participants read a vignette that asked them to consider themselves members of a group involved in a decision-making task. The vignette instructed the subjects to judge the status gains and losses from positive and negative evaluations that might hypothetically be initiated by six other putative members of the group portrayed in the vignette. Other group members represented in the vignette were described in terms of their variation on the status attributes of year in school and grade point average (GPA). Respondents:  Nineteen junior and senior undergraduates at a private Western university participated in the study. Participants were each paid for their participation and completed the vignette-based questionnaire independently in sessions with three to five other participants. Procedures and Instruments:  Since preliminary studies indicated a form to contextualize status judgments results to be more meaningful for judgments by participants, respondents were told that in this study they would be asked to make hypothetical judgments about working with others who were described in terms of their year in school and GPA. They were advised that they would

74  Decision Making Groups and Teams begin by reading a brief summary of the other members of this hypothetical group as well as a short description of the hypothetical group’s task. The respondents were then given the instrument, which consisted of a vignette and a set of social judgment scales. In the vignette, six members of a group were described in terms of their year in school and GPA. Three levels of year in school (freshman, junior, first-year graduate student) were crossed with two levels of GPA (2.4 or 3.8 on a 4.0 scale) to define the six members of the group. This information was intended to produce a status hierarchy in the hypothetical group. The respondents were instructed to consider themselves to be members of this group. The vignette next described a decision that the group was responsible for making. The decision was to be on the recommendation for use of a grant that the university had received to design a new computing facility. The vignette also noted that in the course of the task, participants should expect that the ideas of group members would be evaluated. It was noted that a positive evaluation would increase a person’s status in the group, while a negative evaluation would lower a person’s status in the group. Following the vignette, participants completed two sets of social judgment scales. When all respondents in a session had finished reading the vignette, they were instructed to read and complete the first set of scales. This set elicited respondents’ ratings of the status costs and gains of receiving a negative or positive evaluation, respectively, from each member of the group. For each scale, the respondents were instructed to make a vertical stroke on a horizontal line to indicate the cost or benefit of receiving a negative evaluation or positive evaluation from a particular group member (identified in terms of year in school and GPA). The scales were anchored at the minimum with the label “not at all costly” or “not at all beneficial,” and at the maximum with the label “highly costly” or “highly beneficial.” The horizontal line was 80 millimeters in length. Costs and benefits were operationalized in terms of the measured distance from the low end of the line to the point at which the respondent made the vertical mark. When all respondents had completed the first set of scales, they were instructed to read and complete the second set. The second set of social judgments included instructions directing the respondent to rate each of the six group members and themselves on the dimension of prestige. This was defined within the instrument as, “One’s judgment of the relative social standing of another (i.e., how an individual is ranked in relation to the other people that she or he is compared with).” The scale was anchored at the minimum with the label “very low prestige” and at the maximum with the label “very high prestige.” As in the first set of scales, respondents were asked to indicate their judgment of each group member’s (and their own) prestige by placing a vertical mark on a horizontal line. The line for these scales set at 130 millimeters in length to increase the range of this judgment. Prestige was thus operationalized in terms of the measured distance from the low end of the line to the point at which the

Biases in Member Judgments of Gains and Losses  75 respondent made the vertical mark. After all respondents had completed the second set of scales, the study administrator debriefed the study participants. RESULTS

Judgments of the Magnitude of Gains and Losses from Positive and Negative Evaluations Respondents’ judgments of the magnitude of costs and benefits from negative and positive evaluations were analyzed in two (gender of the judge) by two (year in school of the judge: junior versus senior) by two (type of evaluation: positive versus negative) by six (position of the evaluating group Table 4.1  Mean Status Losses and Gains from Evaluations by Grouping Factors (Standard Deviations in Parentheses) Factor Between Subjects Factors Gender:   Female (n = 10)   Male (n = 9) Class:   Junior (n = 10)   Senior (n = 9) Within Subjects Factor Source:   1st Year Grad, 3.8 GPA   Junior, 3.8 GPA   1st Year Grad., 2.4 GPA   Freshman, 3.8 GPA   Junior 2.4 GPA   Freshman, 2.4 GPA   Grand Mean

Loss from Negative Evaluation

Gain from Positive Evaluation

55.38 (12.33) 57.59 (11.42)

44.15 (14.73) 46.46 (14.08)

58.42 (11.00) 54.22 (12.57)

46.60 (14.88) 43.74 (13.86)

72.74  (5.04) 61.26  (7.39) 55.53  (8.30) 50.32 (10.78) 53.74  (8.82) 45.00  (8.45) 56.43 (11.96)

58.79 (11.61) 49.32 (12.78) 48.21 (11.51) 38.05 (13.71) 45.42 (11.94) 31.68  (9.84) 45.25 (14.48)

76  Decision Making Groups and Teams member in the vignette) MANOVAs with repeated measures on the type of evaluation and positions of group members. GPA of the respondent was also included as a covariate in the analysis. Interactions higher than first order were pooled in the error term, since such effects were not predicted. Means for these analyses are provided in Table 4.1. Main effects of gender and year in school of respondent were not significant (gender: F(1,16) < 1.0; year in school: F(1,16) < 1.0). The GPA covariate was also not significant. However, main effects of both type of evaluation the position of the hypothetical source of evaluation (as determined by year in school and GPA) were highly significant (type of evaluation: F(1,169) = 48.4, p < .001; position of evaluator: F(5,169) = 16.5, p < .001). Respondents rated the magnitude of the cost of a negative evaluation as significantly greater than the magnitude of the gain from a positive evaluation. None of the first-order interactions were significant (p > .10). These results are consistent with Assumption 2.0. JUDGMENTS OF THE COST OF A NEGATIVE EVALUATION AS A FUNCTION OF THE STATUS OF THE EVALUATOR As indicated, respondents provided judgments of prestige of each hypothetical group member, as well as for themselves. For each respondent and each assessment of prestige, the difference between the respondent’s rating of himself or herself and the rating of the particular group member was calculated. This provides an operationalization of the respondent’s perceived status distance from each of the hypothetical group members. Each respondent’s judgment of the expected cost of a negative evaluation from each

Loss from Negative Evaluation (z-score)

2.0 1.5 1.0 0.5 0.0

–0.5 –1.0 –1.5

–2.0 –2.0

–1.5

–1.0

–0.5 0.0 0.5 Prestige Distance from Self (z-score)

1.0

1.5

2.0

Figure 4.2  Loss from Negative Evaluation as a Function of Prestige and Status Distance of the Evaluator from Self

Biases in Member Judgments of Gains and Losses  77 hypothetical group member as a function of the judged prestige of the member was then analyzed. The value function in Figure 4.1 predicts that this function should be convex. This requires that the best fit of the function have a positive and significant quadratic component. A nonlinear least squares procedure yielded a coefficient for the quadratic component of β = .106, s.e. = .041 (t(114) = 2.58, one-tailed p < .01). The relation between perceived status distance and expected cost of a negative evaluation is illustrated in Figure 4.2. These results provide support for Assumption 3.0. This assumption asserted that group members overweight the expected status loss from a negative evaluation by members higher in status than themselves, and underweight the losses from members lower in status than themselves. STUDY 2: STATUS DISTRIBUTIONS AND IDEA GENERATION IN INTERACTIVE GROUPS The objective in the second study is a first test of Inference 1.0, (i.e., the probability of idea initiations is greatest when group members are equal in status). Respondents:  Ninety-six freshman and sophomore undergraduates at the University of California campus received credit in a course for participating in this study. Twelve male and twelve female groups of four persons were formed from these participants. Same-sex groups were constructed since a participant’s sex has been found to have significant effects on participation in mixed-sex groups (e.g., Carli & Eagly, 1999; Eskilson & Wiley, 1976; Lockheed, 1985). Participants were randomly assigned to the same-sex groups, and groups were randomly assigned to one of two experimental conditions. Task:  An adaptation of the Winter Survival Exercise (e.g., Johnson & Johnson, 2012) was used as an ill-structured group task. This exercise requires that the group evaluates the usefulness of salvaged items for survival in a hostile environment. In the adaptation of the task for this study, the group is asked to generate as many survival-related uses as possible for each of six salvaged survival items (six-feet of rope, a newspaper, a pistol, a can of shortening, a chocolate bar, and a bottle of whiskey).

DEPENDENT VARIABLES: NUMBER AND PROPORTION OF MESSAGES The likelihood of idea initiations represented the dependent variable in this study. To measure this, the number of survival ideas group members generated for the six salvaged items was first calculated. The unconditional probability of an idea initiation was then operationalized as the ratio of the

78  Decision Making Groups and Teams number of idea initiations to the total of all types of initiations generated in the group. INDEPENDENT VARIABLE: GROUP STATUS DISTRIBUTION To manipulate the group status distribution, each group member independently completed a Desert Survival Test (e.g., Johnson & Johnson, 2012) prior to the group’s work on the Winter Survival Exercise. After completing the test, members were given fictitious feedback on their own scores as well as the distribution of other scores in the group (though not the scores of specific individuals). Each member of the group was randomly assigned a score. In the status-differentiated condition (SD), the distribution of test scores members received was 2, 4, 5, and 8, where 10 was the maximum possible score. In the status-undifferentiated condition (SU), the score distribution members received was 4, 4, 5, 5. Based on Inference 1.0, it was hypothesized that groups in the SU condition would generate more and higher proportions of ideas than groups in the SD condition. RESULTS As predicted, groups in the SU condition generated significantly more ideas than groups in the SD condition (Means: SU = 1.204, SD = 87.7; F (1,22) = 5.10, p < .05). Proportions of ideas initiated in SU groups were also higher than in SD groups; however, these differences were only marginally significant (Means: SU = .514, SD = .466; F(1,22) = 4.02, p < .07). Neither the effect of sex-type of the group (i.e., whether it was composed of male or female members) nor the interaction of sex-type and condition generated significant differences with respect to number or proportion of ideas initiated in the groups. These results provide support for Inference 1.0. SUMMARY AND DISCUSSION Information exchange is fundamental to group problem solving and decision making. As such, understanding how groups and teams solve problems requires understanding the factors that condition information exchange. This chapter has sought to add to this understanding by formally developing arguments on subjective bias in judgments of social gains and losses that occur in the course of information exchange. These arguments were linked to the social meaning of information types that are exchanged and the reciprocation that occurs in interactive groups. It has been contended that (1) different types of information carry different risks of generating a social loss (through the likelihood of eliciting

Biases in Member Judgments of Gains and Losses  79 a negative evaluation from others), (2) negative evaluations are viewed as more costly to one’s social status than positive evaluations are beneficial to status, (3) members acting as social agents seek to minimize the receipt of status-weighted negative evaluations, (4) the subjective judgment of members of groups and teams imply that the expected loss in status from receiving a negative evaluation increases faster than the status distance from the source of the evaluation, and (5) solution quality in ill-structured decision making is maximized when the number of ideas initiated in the group or team is maximized and when negative evaluations are initiated in close proportion to the number of ideas initiated in the group. Through formal representation of these assertions, inferences were derived on the effects of status heterogeneity on the information exchange of agents in a group or team and the quality of solutions that are generated through information exchange. These arguments are expected to be general for groups and teams whose members are engaged in ill-structured decision making. The empirical studies that were presented support the assumptions and inferences. The first study indicated that participants do view negative evaluations as more costly than positive evaluations are beneficial. This study also supported the assumption that group members view the loss from a negative evaluation as increasing faster than the status distance from the source of the evaluation. The second study examined the effects of status-differentiation on the initiation of ideas. As has been claimed by others, ideas represent an information type of greatest importance in these contexts (De Dreu, Nijstad, & Van Knippenberg, 2008; Edelenbos & Klijn, 2006; Freeley & Steinberg, 2008; Proctor, 2010). Correspondingly, in the context of the chapter’s framework, this type has one of the high likelihoods of eliciting a negative evaluation from others. Using the formal model, it is demonstrated that the perception of social risk is minimized in status-equal groups, and as a consequence, these groups evidence higher rates of idea initiation. The second empirical study supported this inference. Researchers studying social influence have long directed attention to biases arising from status processes (e.g., Berger, Fisek, Norman, & Zelditch, 1977; Ridgeway, 2006). These researchers have particularly focused on the opportunities that higher status group members take and are given in social interaction. The differential influence of higher status group members is commonly seen as both expected and legitimate by other group members (Berger, Ridgeway, Fisek, & Norman, 1998; Ridgeway, 2006). This chapter has directly addressed inherent biases of information that arise in the microprocessing from the distribution of status in task-directed groups. As postulated, decisions of group and team members on information exchange are motivated in part by judgments on the expected status gains and losses. The direction and magnitude of such expected gains and losses, in turn, depends on the type of information and the judgment of the social distance between the member and the high-status member of the unit. As recognized, such judgments are subjective and commonly from objective distance. Results

80  Decision Making Groups and Teams from the Prospect Theory traditions were used to provide insight into the biases in judgments of social distance in this context. In studying the valuing of monetary gains and losses, it was confirmed that under commonly encountered conditions, losses are disvalued more than the equivalent gains are valued. This bias may be related to the differential judgments of social distance from a member to higher and lower status members of the group or team. Members typically judged the social distance of a higher status member as greater than the equivalent distance from a lower status member. As shown in analysis under simplifying conditions, this in turn implies that all else equal, the difference between the expected costs of more risky information types (such as ideas, opinions, and negative evaluations) and the expected costs of less risky information types (such as positive evaluations, facts, and silence) is likely to increase faster than the judged social distance of the member from the high-status member.7 As a consequence of this, domination by higher status members of decision-making units is likely to be concentrated in certain types of information initiations that include ideas and negative evaluations. These are information types that contribute most to quality in ill-structured decisions. Correspondingly, lower status group members are likely to oversend information types that are low risk and include positive evaluations and facts. These patterns in information exchange have been cited as leading to premature acquiescence to a more obvious solution that is often suboptimal or can actually be detrimental to the decision-making unit’s objectives. The losses to quality from such biases are further increased when it is recognized that status differences are often generated on the basis of social characteristics that are at best minimally related to actual competence (e.g., Johnson, Dowd, & Ridgeway, 2006; Pugh & Wahrman, 1983; Yzerbyt, Provost, & Corneille, 2005). As the foregoing discussion suggests, a framework on social risk also highlights policy considerations in the design of decision-making groups and teams. Current approaches to decision making have emphasized the benefits of diversity in dealing with uncertain and turbulent environments (Bhappu, Zellmer-Bruhn, & Anand, 2001; Gruenfeld, Mannix, Williams, & Neale, 1996; Jackson et al., 1991; Pearce & Ravlin, 1987; Watson, Kumar, & Michaelsen, 1993). This recognition often overlooks internal consequences that unmanaged diversity can have. Diversity commonly increases distance in status hierarchies, with attendant effects on perceived status risk for medium and lower status members. Results from studies in Prospect Theory offer an additional policy implication. Research on framing within “heuristics and biases” arguments (e.g., Sullivan & Kida, 1995; Tversky & Kahneman, 1991) suggest that if group or team members change the reference points that they use in the exchange of negative evaluations, then the expected costs of this information type may be reduced. For example, compared with a direct negative evaluation, an evaluation that is framed by its source as a distinction between degrees of positive evaluation rather than positive and negative evaluation may have

Biases in Member Judgments of Gains and Losses  81 less of an effect on member internal judgments of the social costs of initiating risky information. A statement that a decision alternative is “not quite as good as” a different alternative may be less polarizing than a statement that the alternative is “seriously faulted” while accomplishing the equivalent in discriminating quality in alternatives. While common diplomacy or etiquette may suggest this direction, the biases in social judgment that are highlighted here make it more compelling to introduce in group and team norms. It may, in fact, be a procedure that contributes more to decision quality more than the proscribing of evaluation in certain team-building training. Finally, more definitive assessments of status judgments are needed. Since the metric of such judgments is often at issue (e.g., Baker, 1977; Wegener, 1982), it may be that status distributions that follow continuous and interval scaled objective criteria (such as GPA or years of tenure in a position) can be examined with psychophysical methods in laboratory studies. Judgments of social gains and losses in interpersonal exchange are important to a wide range of social processes. As such, systematic attempts to definitively state the rules of these judgments and demonstrate their effects represents a research agenda with far-reaching effects across economic and social contexts. This chapter has further conceptualized how social risk can bias member participation in group and team decision making, and how this, in turn, can influence the quality of solutions and decisions. In integrating research and theory from social, behavioral, and cognitive conceptualizations of decision making, it has been suggested that social judgments by agents in decision-making groups and teams are subject to the types of cognitive biases that are at least similar to those that characterize monetary and social dilemmas. These cognitively biased judgments are fundamental to the amount and type of information that members initiate. At a time when globalization and technology often increase the turbulence that organizations face, investigators such as (Danneels & Sethi, 2011; Driskell & Salas, 1991) note that environments that produce high uncertainty and threat can also rigidify internal status hierarchies and increase the effects of status on group performance. The framework introduced in this chapter gives a form to microlevel processes through which this can occur and the effects it can have. At the least, this suggests directions to manage a group’s internal processes in high-risk environments. As taken up in detail in a subsequent chapter, recent technology in managing interactive groups and teams has capabilities to retain the benefits while reducing function. Applications of this technology arise from and are facilitated by more complete accounts of microprocessing. In the next chapter, dynamics in the exchange of negative evaluations is given a form that extends available conceptualizations of this information type. As indicated in this and previous chapters, this information type is a critical contributor to quality in ill-structured decisions. As indicated, the dynamics of this information type can be differentiated from other information types by their simultaneous informational and affective properties.

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84  Decision Making Groups and Teams Pearce, J. A., & Ravlin, C. E. (1987). The design and activation of self-regulating work groups. Human Relations, 40, 751–782. Proctor, T. (2010). Creative problem solving for managers. New York, NY: Routledge. Pugh, M. P., & Wahrman, R. (1983). Neutralizing sexism in mixed-sex groups: Do women have to be better than men? American Journal of Sociology, 88, 746–762. Ridgeway, C. (2000). The formation of status beliefs: Improving status construction theory. In S. Thye, E. Lawler, M. Macy, & H. Walker (Eds.), Advances in group processes. Stamford, CT: JAI Press. Ridgeway, C. L. (2006). Status construction theory. In P. J. Burke (Ed.), Contemporary social psychological theory (pp. 301–323). Stanford, CA: Stanford University Press. Ridgeway, C., Boyle, E., Kuipers, K., & Robinson, D. (1998). How do status beliefs develop? The role of resources and interactional experience. American Sociological Review, 63, 331–378. Schmidt, U., Starmer, C., & Sugden, R. (2008). Third generation prospect theory. Journal of Risk and Uncertainty, 36, 203–223. Sullivan, K., & Kida, T. (1995). The effect of multiple reference points and prior gains and losses on managers’ risky decision making. Organizational Behavior and Human Decision Processes, 64, 76–83. Trepel, C., Fox, C., & Poldrack, R. (2005). Prospect theory on brain? Toward a cognitive neuroscience of decision under risk. Cognitive Brain Research, 23, 34–60. Tversky, A., & Kahneman, D. (1991). Loss aversion in riskless choice: A referencedependent model. Quarterly Journal of Economics, 106, 1039–1061. ———. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5, 297–323. Wakker, P. P. (2010). Prospect theory: For risk and ambiguity. Cambridge, England: Cambridge University Press. Watson, W. E., Kumar, K., & Michaelsen, L. K. (1993). Cultural diversity’s impact on interaction process and performance: Comparing homogeneous and diverse task groups. Academy of Management Journal, 36, 590–602. Wegener, B. (Ed.). (1982). Social attitudes and psychophysical measurement. Hillsdale, NJ: Erlbaum. Wegener, B. (1987). The illusion of distributive justice. European Sociological Review, 3, 1–13. Yilmaz, S., Seifert, C. M., & Gonzalez, R. (2010). Cognitive heuristics in design: Instructional strategies to increase creativity in idea generation. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 24, 335–355. Yzerbyt, V., Provost, V., & Corneille, O. (2005). Not competent but warm . . . Really? Compensatory stereotypes in the French-speaking world. Group Processes & Intergroup Relations, 8, 291–293.

5A Idea Generation in Interactive Teams Conceptual Model

OVERVIEW In previous chapters, the information type of ideas has been recognized as a predominant contributor to quality in ill-structured decision making. In Part A of this chapter, the effects that the social structure of groups and teams can have on idea generation is elaborated upon and given explicit forms. Single-source ideas are discriminated from the combinational or multiple source ideas that can be generated in interactive groups and teams. Although this has been directly recognized in recent studies, it has not been given explicit forms that support inference on process gains from the dynamics of multisource idea generation in interactive teams. The extent of process gain from combinational ideas is mediated by overlap or redundancy in the idea pools of group or team members. Overlap can generally be decreased by increasing heterogeneity in member backgrounds. However, as noted, process gains from member heterogeneity can be offset by the increases in the variance of the status distribution that typically follow from increasing the variability in member background variables. Analytical and numerical results from the forms for idea generation show both contributions that interactive groups and teams can offer to decision quality through combinational ideas and the mediating effects that social structure can have on this objective. Results of a pilot study support the dynamics of singlesource and multisource idea generation suggested by the conceptualization. INTRODUCTION Most background studies of the productivity of interactive groups and teams have been in brainstorming traditions with the number of ideas as the objective. Quality in ill-structured decision making requires a more complex form for objectives that includes information types that filter the relevance and quality of ideas, most notably negative evaluations. However, since decision quality in this case is expected to be increasing with the number of ideas exchanged, maximizing the information type of ideas

86  Decision Making Groups and Teams is likely to remain a critical objective of teams. The form that is proposed for the dynamics of idea generation in a team represents both process gains from combinational ideas and process loss from effects of social structure. Numerical and empirical studies of the proposed dynamics are reported. BACKGROUND

Idea Generation in Interactive Groups A commonly studied framework conceptualizes the basis of idea generation in associative processes (Brown & Paulus, 2002; Dugosh, Paulus, Roland, & Yang, 2000; Nijstad & Stroebe, 2006; Schumann, 2011). In this framework, decision alternatives can be considered in terms of associations between a set of their constitutive elements. Generally, the feasible relationships between constituent elements of decision alternatives are expected to be hierarchically ordered in terms of their strengths of association (Mednick, 1962; Runco, 1986). Individuals typically generate the strongest (i.e., most common) associations before the uncommon ones become accessible. As such, the original, uncommon, or remote associations that are expected to be a basis for higher quality decisions are generally lower in the hierarchy and less accessible to a decision maker. While it is clear that not every remote association translates into a useful idea or candidate solution, a goal is to make these associations available. Settings can affect the gradient of associations in a hierarchy. For example, stressful events such as personal evaluation are likely to increase the strengths of common associations relative to uncommon ones (Major & O’Brien, 2005) and make the more remote association less accessible. This conceptualization of idea generation by an individual decision maker provides a basis for explicit representation of idea generation in an interactive team. Here it will be assumed that the most common associations take the form of single-source ideas that are initiated by individual team members. As the hierarchical ordering of associations implies, these typically will be initiated early in the interaction. What will be considered as multisource or combinational ideas are typically initiated later in the interaction. SINGLE-SOURCE AND MULTISOURCE IDEA GENERATION IN INTERACTIVE TEAMS The dynamics of idea generation in interactive teams should follow from what we have come to understand about the generative process in individual ideation and how it can be extended to multiperson interaction. One difference between individuals and teams is that in interactive teams, members are more likely to combine associations or ideas generated by other team members as well as using their own to generate new ideas. This is increasingly

Idea Generation in Interactive Teams   87 recognized in recent reviews (Kohn, Paulus, & Choi, 2011; Kohn & Smith, 2010). In such a case, the number and quality of ideas is dependent on the number of nonoverlapping associations that team members bring to the exchange of information. A contribution of heterogeneity in the backgrounds of group and team members is to increase the initial reservoir or pool of distinct associations at time zero of the interaction. However, heterogeneity in member backgrounds can also increase the variance in the team’s status distribution and thereby reduce or eliminate the advantage of heterogeneity. Technology that can contribute to managing such dysfunctional effects of heterogeneity in member backgrounds is considered in a subsequent chapter. From the above points on combinational ideas, a distinction can be made between single-source or internalized ideas and multiple source or combinational ideas in the dynamics of idea generation in an interactive team. While as cited, a number of studies have qualitatively recognized that information exchange between team members can augment idea generation of individual members, explicit forms for single- and multisources have not been offered. The section that follows gives a form for idea generation that integrates single- and multisource ideas and examine the dynamics it implies.

SINGLE-SOURCE IDEAS A general form to represent rates of generating single-source ideas can be written as Eq (5A.1):

(

(

)

J (j1) = K j Aj − α j J1(1) ,… , Jn(1) − J (j1)

)

Where, ˙J j is the j-th member’s rate of giving single-source ideas; J j is the (1) cumulative number of single-source ideas given by the j-th member (J j ≥ 0). Kj is the rate at which the j-th individual both becomes aware of and initiates a single-source idea. Aj is the total number of single-source ideas in the j-th individual’s hierarchy of associations. (Hereafter their initial idea pool.) αj is the overlap (i.e., duplication) between the ideas of the j-th member and all other members in their initial idea pools. In Eq. (5A.1), the rate of initiating single-source ideas by the j-th member depends on the pool of relevant ideas of this member that are not redundant (i.e., do not overlap with those in the idea pool of other members) less the number of ideas that the member has already given. Kj moderates the overall rate of initiating ideas due to the member’s judgment of the social risk of doing so and in turn, will be a function of the distribution of status in a group or team. (1)

(1)

Jk(1) + 1 1 3

88  Decision Making Groups and Teams In the case where an individual is a member of a team, initiation of ideas by a member is also a function of the team’s social structure (i.e., the status distances between a team member and other team members). As indicated in Chapter 3, initiating ideas is typically seen as having a higher likelihood of resulting in a negative evaluation by another member than initiating information types other than negative evaluations themselves. The cost to a member in status loss from a negative evaluation depends on the status of the evaluator. Given defensible assumptions on loss functions of team members, increases in the variance of the status distribution decreases the number of ideas generated in the team. An exemplary σfunction for effects of status distribution (K), cann be written as: Kj =K c=3c (1 + var (σ )), where σj is the status of the jth member and ∑σσii == 11. An expanded K function is offered in a subsequent chapter. The overlap or redundancy in the initial idea pools of team members, αj can be given the form (5A.2) j

j

3

i

α j = 1 ∑ α jk1 Jk(1) + 1 1 2 k1 3

(

)

(

(

(

1

)

α jk1  kn Jk(1) +  + Jk(n) . 1

n

)

α jk1  kn Jk(1) +  + Jk(n) .

jn − 1

∑ α ik1k2 Jk(1) + Jk(2) +  + 1 ∑ 1 2 n k1c  ckk

k1 < k2 k1 , k2 ≠ j

)

∑ α ik1k2 Jk(1) + Jk(2) +  + 1 ∑ 1 2 n k1c  ckk

k1 < k2 k1 , k2 ≠ j

jn − 1

In Eq. (5A.2), α jk1k2 is the fraction shared only by the to k1-th and k2-th group members α jk1kn and is the fraction shared by k1-th k2-th . . . kn-th group members. If the members are perfectly homogeneous in initial pools of ideas, then α jk1 = α jk21 =  = α jk1k2 kn − 2 = 1. If members are perfectly heterogeneous in their initial ideas of pools (i.e., have no overlap), then α jk1 = α jk2 =  = α jk1k2 kn − 2 = 0.

MULTISOURCE IDEAS In an interactive team, members as individuals can generate new ideas from combinations of ideas that have already been initiated. Under certain conditions in the interactive exchange of information by team members, a common idea pool becomes established from ideas that are generated by the members as separate entities. Members can access this pool and combine its constituent ideas with their own ideas to form new ideas. Such combinational ideas can represent an important source of the number and originality of the ideas exchanged in the team and will be suggested to constitute a fundamental basis for process gains that the team as a decision-making unit can offer. Multisource or combinational ideas that a member can generate can be given a form in terms of (1) a unique idea by the j-th member and an idea of

n

Idea Generation in Interactive Teams   89 another team member and (2) two ideas already given by team members other than the j-th member that are combined by the j-th member. The rate of generating these combinatorial or multisource ideas by the j-th member is given a form in Eqs. (5A.3) and (5A.3′) below.

(

  J (j2) = K j  ∑ Jk(1)  η j + K j ∑ Jk(1) k j ≠  

( )

)

2

− c1 ∑ Jk(2) k≠ j

(2)

Where J j is the cumulative number of multisource ideas given by j, Jj is the corresponding rate of giving multisource ideas; ηj is the number of single-source ideas that the j-th member has realized but that have not been initiated (i.e., and Kj is as previously defined). The first term of Eq. (5A.3) is the product of all single-source ideas given by team members other than j and the j-th member’s total unduplicated (1) (1) . ideas (η j = Aj − J(1) j − α j ( J1  Jn ) ) The second term is the number of unique combinatorial ideas given by all individual members of the team from their own ideas. The final terms of Eq. (5A.3) removes all combinatorial ideas already given by any team member. Substituting for ηj from Eq. (5A.1), allows Eq. (5A.3) to be rewritten as Eq (5A.3ʹ):

(

(

 (1) (1) (1) (1)  J(2) j = K j  ∑ Jk  Aj − J j − α j J1 ,... Jn  k≠ j 

)) + K ( ∑

k

Jk(1)

)

2

− c1 ∑ Jk(2) k

Taken together, Eqs. (5A.3) and (5A.3′) represent the dynamics of combinational idea initiation by the j-th member. Eq. (5A.3) represents this as the rate of generating his or her original ideas that are not combined with ideas of other team members. Eq. (5A.3′) represents the corresponding rates of generating ideas that result from combining the j-th members’ own ideas with ideas of other team members. The dynamic paths of single-source and multisource ideas are next investigated to more explicitly consider the properties of these forms. THE DYNAMIC PATH OF SINGLE-SOURCE IDEAS To further tractability, the dynamic path of Eq. (5A.1) is first examined when it is restricted to the case of a dyad. For a dyad, the solution of (5A.1) in terms of the cumulative number can be written as: J1(1) = c11e λ 1t + c12 e λ 2t + c10 and  J2(1) = c21e λ 1t + c22 e λ 2t + c20

90  Decision Making Groups and Teams where, c10 =

A1 − α 21 A2

(1 − α12α 21 )

, c20 =

A2 − α12 A1

(1 − α12α 21 )

K1 + K2 2 + 1 / 2 ( K1 − K2 ) + 4K1K2α12α 21 2 K1 + K2 2 λ2 = − + 1 / 2 ( K1 − K2 ) + 4K1K2α12α 21 2 and c11, c12 and c21, c22 are constants, such that

λ1 = −

c11 =

K1 A1 + λ2 c10 K A + λ2 c20 , c21 = 2 2 λ1 − λ2 λ1 − λ2

c12 =

K1 A1 + λ1c10 K A + λ1c20 , c22 = 2 2 λ2 − λ1 λ2 − λ1

∑ J (A − α ( J k≠ j

(1) k

j

j

(1) (1) j  Jn

))

In the above, α12 is the proportion of Member 2’s initial ideas shared with Member 1. The parameters λ1 and λ2 are the eigenvalues of J(1) for the respective members of the dyad and define the exponential decay or decline rate of ideas. As functions of K1 and K2, these parameters depend on the distribution of status between the members of the dyad. The status of an individual member and the heterogeneity coefficient (α), also influence the decline rate through the constants cij, i, j .= 1, 2. Since the eigenvalues of J(1) are negative if c11 and c21 are negative, J(1) is monotonically declining at a negative exponential rate. Since J(1) is bounded, the solution will decay asymptotically to c10.1 The dynamic equation for the rate of single-source idea evidences the monotonic decline reported in studies of idea generation (see, e.g., Christensen, Guilford, & Wilson, 1957; Olczak & Kaplan, 1969; Runco, 1986). When the individual is a team member, this decline can be increased by the overlap in idea pools between the member and other team members (i.e., the magnitude of αj). When teams increase in the homogeneity of their background variables, redundancy in ideation can be expected to increase and the contribution of a member’s single-source ideas to the team’s cumulative idea pool can correspondingly be expected to decrease. MEMBER HETEROGENEITY For single-source ideas, interactive groups or teams whose members are (1) relatively homogeneous in their associative hierarchies and (2) differentiated in status-related attributes would commonly not outperform nominal groups or teams in the absence of procedures that reduce structural effects. In the series of previously cited laboratory studies that compare nominal to interactive groups, heterogeneity in relevant background is not assessed.

Idea Generation in Interactive Teams   91 Their samples are commonly drawn from relatively homogeneous populations (e.g., undergraduates in the same year or members of similar rank in an organization). As demonstrated in a range of lab studies of social structure in groups, status hierarchies remain emergent in these groups and have performance effects. Thus, many studies that compare nominal and interactive groups may be assessing idea generation in groups that are limited to an approximation to single-source ideas. In this case, its upper bound limit in the number and originality of ideas would be close to that of a nominal group. More recently, there is direct evidence that heterogeneity contributes to the quality in group idea generation (e.g., Kavadias & Sommer, 2009). The possible process gains from the exchange of multisource ideas is considered next. PROCESS GAINS IN INTERACTIVE GROUPS The qualitative properties of Eq. (5A.3′) can be examined for inference on the dynamic path of combinational ideas. Since Aj, the cumulative number of single-source ideas is fixed and Jk(1) is less than Ak, and on the RHS of the A A equation, Eq. (5A.3′) is bounded above by ∑A kAj. It also can be seen from Eq. (5A.3′) that if c1∑ J(2) is initially small relative to J(1)c2∑ J(2), then J (j2) will be k k increasing and then decreasing. Since it is further expected that multisource ideas are initially fewer in number than single-source ideas, in most groups, the form shown in Figure 5A.1 for J(j2) is expected to be typical of this function. The form of Eqs. (5A.1) and (5A.2′) implies that the rate of single-source ideas is monotonically decreasing and the multisource rate is increasing to a maximum and then smoothly decreasing to zero. The decay rates in both single- and multisource ideas is in general exponential and influenced by the K function.2 As indicated, this function is a rate modifier that indexes the extent to which team members both realize and initiate ideas from their initial idea pools. These results suggest the form for the dynamics of single-source and multisource ideas in Figure 5A.1. In this form, the rate of single-source idea generation monotonically decreases as reported in background studies that have been cited. Multisource ideas initially increase as the pool of single-source ideas from which to generate combinational ideas increases. At some subsequent t, both multisource and single-source ideas are decreasing. Figure 5A.2 shows dynamical forms for single-source, multiple-source, and cumulative idea numbers and rates as generated from a numerical solution to Eqs. (5A.1) to (5A.3′) for a fixed set of parameters. Here var (σj) is defined as the variance of the status distribution. The figure also shows the sensitivities of idea rates for levels of the variance in member status when σ∈(0,1), at minimum, maximum, and intermediate levels of an n person team. The effects of σ are through the K parameter defined above that mediates the rate at which realized ideas are initiated. The results show the nonlinear k

k≠ j

j

92  Decision Making Groups and Teams ∑J

j

(i)

∑∑J j j

i

∑J j

j

∑Jj(0) t

Figure 5A.1  Dynamics of Idea Generation in an Interactive Team Note:  ∑jj(1) is single-source ideas, and ∑jj(2) is multisource ideas.

40

J(1): min var(sj) 30

J(1): midpoint var(sj) J(1): max var(sj) J(2): min var(sj) J(2): midpoint var(sj)

20

J(2): max var(sj)

10

0

0

1

2

3

Time

Figure 5A.2  Dynamic Single- and Multisource Ideas as a Function of Group Structure Note:  Var (σj), j = 1, . . . , n is the variance in the distribution of status.

Idea Generation in Interactive Teams   93 decreases in the initiation of ideas as the variance of the status distribution increases. Having given a form to multiperson idea generation and numerically generated the dynamics paths implied by the form, I next report empirical evidence for the dynamics in idea generation in a team implied by the form. Studies using experimental methodology to directly test predictions on the effects of the status distribution are reported in Part B of this chapter. DYNAMICS OF SINGLE-SOURCE AND MULTISOURCE IDEA GENERATION IN AN INTERACTIVE GROUP: EMPIRICAL RESULTS Data from a pilot study of idea generation in face-to-face, interactive groups was used to examine group rates of single-source and multisource idea generation across 30 minutes of interaction time. The foregoing analyses and numerical results indicate monotonically decreasing rates of single-source ideas and rates of multisource ideas that increase to a maximum with time as the groups interacts. Participants. Participants in the studies were first- and second-year undergraduates at a private Western university. Same-sex groups were used in each study since the sex of a group member in mixed-gender groups has been shown to have highly significant effects on participation (Carli, 1989; Eagly & Karau, 1991; Ridgeway & Smith-Lovin, 2006). All participants were randomly assigned to group memberships, and groups were randomly assigned to experimental conditions. Experimental task. A modified version of the Winter Survival Exercise (e.g., Johnson & Johnson, 2012) was used as the idea generation task. In this task, members generated uses for five items salvaged from a plane crash in an isolated area. The data on idea generation comes from the coding of videotaped records of the interaction. In the Winter Survival Exercise, participants rank a set of items on their importance to survival. In the modification, the group task was to generate as many survival related ideas for uses as possible for each of six salvaged survival items (e.g., six feet of rope, a newspaper, and a .45 caliber pistol). Dependent variables. Number and uncommonness of ideas. The idea number score was the sum of uses given to all six items in the Winter Survival Exercise. An uncommonness score for each use given in a group is defined as the number of times the use was given for an item by all groups in all conditions of a study. Lower frequencies on this measure thus indicate more statistically original ideas. Cronbach’s coefficient alphas for the total number (α = .943) and mean uncommonness score across all items (α = .812) in 20 four-person groups indicated that the survival items have acceptable reliability as composite measures. Results. In Figures 5A.1 and 5A.2, combinational ideas, J(2), are approximated by a concave-down function. This is because combinational ideas

94  Decision Making Groups and Teams initially increase as the number of single-source ideas increases and then decrease as the number of unduplicated single-source ideas goes to zero. As predicted, the proportion of total ideas that are multisource ideas in the information exchanged by members of these groups was found to increase across four of the five six minutes of study periods. In contrast, mean single-source ideas show a linear decline across interaction time from a mean of 20 ideas per minute to a mean of less than 8 in the last period. The proportion of multisource ideas in total ideas across these periods was T1 = .097, T2 = .122, T3 = .119, T4 = .158, and T5 = .233. A MANOVA for the ratio of multisource ideas to single-source ideas across the five periods shows the increase in this ratio to be highly significant (p < .01). These differences in the paths of single- and multisource ideas have a clear correspondence to the hypothetical paths in numerical results. They indicate a correspondence of empirical results to the conceptualization of single- and multisource ideas. Moreover, the proportions of multisource ideas in this study are likely to be biased downward in a lab group by the absence of prior interaction histories and training to facilitate idea generation. SUMMARY AND DISCUSSION It is clear that quality in ill-structured decision making occurs through a complex process of information exchange. In Part A of this chapter, the information type of ideas has been recognized as a predominant contributor to quality in ill-structured decision making. The review of background studies of idea generation has noted that the findings of these studies on the lower productivity of interactive groups in comparison to nominal groups did not investigate member heterogeneity in backgrounds that is likely to be related to what has been designated as overlap or redundancy in initial idea pools. The initial points in the discussion of idea generation were that (1) there are defensible bases in multisource or combinational ideas to anticipate process gains from interactive groups and teams that have not been adequately given a form in idea generation by interactive groups and (2) group or team structure as a source of process loss has not been controlled in the experimental assessments of the comparative efficacy of interactive groups. As indicated in the studies that we have reported, such structural effects can be activated even in groups or teams with zero interaction history. Single-source ideas were discriminated from the multisource ideas can be generated in interactive groups and teams. The latter were conceptualized as a potential source of process gains in these units since they allow an active borrowing or usage of other members’ ideas. Although the possible contribution of combinational ideas to group productivity in idea generation has been recognized in the most recent studies (Kohn, Paulus, & Choi, 2011), it has not been given explicit forms. Forms that recognize member heterogeneity and its effects on both single-source

Idea Generation in Interactive Teams   95 and multisource ideas have been proposed. As has been demonstrated analytically and in numerical exercises, the dynamics of these forms predict that the contribution of combinational or multisource ideas increases across time intervals. Results of a lab study of the proportion of total ideas that are multisource over time support this prediction. The extent of process gains from combinational ideas is likely to be mediated by overlap in the idea pools of group or team members. This, in turn, is a function of variables in their demographics and other background variables. Overlap can generally be decreased by increasing heterogeneity in member backgrounds. However, as noted, process gains are mediated by the increases in the variance of status distributions that are typically introduced by increasing the variance in background variables of members. Such increases in the variance of the status distribution can, in turn, undermine process gains or have the net result of process losses. Documenting these effects facilitates the design of technology to manage them and increase contributions of interactive units to ideational productivity and decision quality. In Part B of this chapter, results of empirical studies that test hypotheses from the foregoing account of idea generation in interactive groups and teams is reported. In the chapters to follow, the inquiry is expanded to increase the representation of information exchange in ill-structured decision making. The information type of negative evaluations is addressed, and dynamic forms that combine ideas and evaluations are proposed. REFERENCES Brown, V. R., & Paulus, P. B. (2002). Making group brainstorming more effective: Recommendations from an associative memory perspective. Current Directions in Psychological Science, 11, 208–212. Carli, L. (1989). Gender differences in interaction style and influence. Journal of Personality and Social Psychology, 56, 565–576. Christensen, P. R., Guilford, J. P., & Wilson, R. C. (1957). Relations or creative responses to work time and instructions. Journal of Experimental Psychology, 53, 82–88. Coppola, N. W., Hiltz, S. R., & Rotter, N. G. (2004). Building trust in virtual teams. Professional Communication, IEEE Transactions, 47, 95–104. Dugosh, K. L., Paulus, P. B., Roland, E. J., & Yang, H. (2000). Cognitive stimulation in brainstorming. Journal of Personality and Social Psychology, 79, 722–735. Eagly, A., & Karau, S. (1991). Gender and emergence of leaders: A meta-analysis. Journal of Personality and Social Psychology, 60, 685–710. Greenberg, P. S., Greenberg, R. H., & Antonucci, Y. L. (2007). Creating and sustaining trust in virtual teams. Business Horizons, 50, 325–333. Jarvenpaa, S. L., Shaw, T. R., & Staples, D. S. (2004). Toward contextualized theories of trust: The role of trust in global virtual teams. Information Systems Research, 15, 250–267. Johnson, D. W., & Johnson, F. P. (2012). Joining together: Group theory and group skills (11th ed.). Boston, MA: Allyn and Bacon. Kavadias, S., & Sommer, S. (2009). The effects of problem structure and team diversity on brainstorming effectiveness. Management Science, 55, 1899–1913.

96  Decision Making Groups and Teams Kohn, N., Paulus, P., & Choi, Y. (2011). Building on the ideas of others: An examination of the idea combination process. Journal of Experimental Social Psychology, 47, 554–561. Kohn, N., & Smith, T. (2010). Collaborative fixation: Effects of other’s ideas on brainstorming. Applied Cognitive Psychology, 25, 359–371. Major, B., & O’Brien, L. (2005). The social psychology of stigma. Annual Review of Psychology, 56, 393–421. Mednick, S. (1962). The associative basis of the creative process. Psychological Review, 69, 220–232. Nijstad, B., & Stroebe, W. (2006). How the group affects the mind: A cognitive model of idea generation in groups. Personality and Social Psychology Review, 10, 186–213. Olczak, P., & Kaplan, M. (1969). Originality and rate of response in association as a function of associative gradient. American Journal of Psychology, 82, 157–167. Ridgeway, C., & Smith-Lovin, L. (2006). Gender and interaction. In H. Kaplan (Ed.), Handbook of sociology and social research (Part 3, pp. 247–274). New York, NY: Springer. Runco, M. (1986). Flexibility and originality in children’s divergent thinking. Journal of Psychology, 120, 345–352. Schumann, J. (2011). Supporting initial trust in distributed idea generation and evaluation (Unpublished master’s thesis). University of California, Irvine, CA. Staples, D. S., & Webster, J. (2008). Exploring the effects of trust, task interdependence and virtualness on knowledge sharing in teams. Information Systems Journal, 18, 617–640.

5B Idea Generation in Interactive Teams Empirical Studies

OVERVIEW Results of experimental studies that document inference on idea generation in groups and teams from the conceptualization and numerical studies of Part A are reported in this section. Ideas have been conceptualized as an information type with high social risk; positive evaluations and data messages exemplify lower risk of information types. The first study in this part of the chapter confirms predictions that status differentiation decreases the exchange of ideas and increases the exchange of positive evaluations, questions, and data facts. A second study examines effects of experimenter-inserted negative evaluations on information exchange in status-differentiated groups. Results show that as predicted, experimenter-inserted negative evaluations decreased the number and proportions of ideas exchanged. As also predicted, inserts of negative evaluations are found to increase the number of data and question messages. Results further show that inserts of negative evaluations can have unanticipated effects of increasing idea uncommonness in the group. Hierarchical regressions of uncommonness on the total number of messages in different information categories suggest candidate effects of question types on idea uncommonness. This and other findings begin to indicate the complexity and importance of negative evaluations to the performance of interactive groups and teams. Negative evaluations that are general and from the group of a high-status source may motivate increases in the quality of questions and data messages that increase ideational uncommonness. This is expected to contribute to the quality objective in ill-structured decision making. The results also provide insight into the design of managerial procedures for the objective. INTRODUCTION Part B of this chapter reports results of two laboratory studies that investigate the conceptualization of idea generation and information exchange in interactive groups and teams introduced in Part A of the chapter.1 The

98  Decision Making Groups and Teams studies use a common task, dependent measures, and methodology to experimentally vary social structure in groups. The first study demonstrates effects of experimentally implemented status distributions on idea generation and information exchange. Since negative evaluations are infrequent in zero history groups but are expected to effect idea initiation, the second study examines effects of experimentally inserted negative evaluations on the number and uncommonness of ideas generated in interactive groups.

STUDY 1: EFFECTS OF THE EXPERIMENTALLY INTRODUCED STATUS DISTRIBUTIONS ON IDEA GENERATION AND INFORMATION EXCHANGE

Method Participants. Participants in both studies were first- and second-year undergraduates at a private Western university. Same-sex groups were used in each study since the sex of a group member in mixed-gender groups has been shown to have highly significant effects on participation (Carli, 1989; Eagly & Karau, 1991; Ridgeway & Smith-Lovin, 2006; also see, Booth & Nolen, 2012). All participants were randomly assigned to group memberships, and groups were randomly assigned to experimental conditions. Experimental task. The modified version of the Winter Survival Exercise (e.g., Johnson & Johnson, 2012,) described in the previous section of this chapter was used as the idea generation task. In Study 1, the data on idea generation were obtained from the coding of videotaped records of face-to-face interaction. In Study 2, interaction was computer-mediated. The data in this study were from transcripts of the information exchange in the groups. Dependent Variables: Number and Uncommonness of Ideas The idea number score was the sum of uses given to all six items in the Winter Survival Exercise. An uncommonness score for each use given in a group is defined as the number of times the use was given for an item by all groups in all conditions of a study. Lower frequencies on this measure thus indicate more statistically original ideas. Coefficient alphas for the total number (α = .943) and mean uncommonness score across all items (α = .812) in a 24-person group benchmark indicated that the survival items have acceptable reliability as composite measures. Independent Variable: The Group Status Distribution The variance in an experimentally induced distribution of members on a task-relevant status attribute was the independent variable in these studies. Fictitious scores on an abbreviated version of a Desert Survival Exercise (Johnson & Johnson, 2012) were randomly assigned to members

Idea Generation in Interactive Teams   99 to define the status distribution in each group. This exercise requires individuals to rank 10 items according to their importance to survival in a desert setting. A status hierarchy in one condition and a near-equal status distribution in a second condition were defined from the fictitious scores on the Desert Survival Exercise returned to group members. In a SD (for status-differentiated condition), the distribution of test scores returned to members was: 2, 4, 5, and 8, where 10 was the maximum possible score. In a SU (for status-undifferentiated condition), the score distribution was 4, 4, 5, and 5. Members were informed of their own scores and the distribution of other scores in the group, but not the scores of specific individuals. Instructions then emphasized that good ideas come from all members, and performance is best when all members participate. Although a member’s score on the Desert Survival Exercise was explicitly made unequivalent to ability in idea generation, it remains likely to be activated in the status organization of the group because of its relevance to the group task (Berger, Rosenholtz, & Zelditch, 1980; Ridgeway, 2000). Procedure. In Study 1, one half of the groups were randomly assigned to each of the SD and SU conditions. In Study 2, all groups were statusdifferentiated but only the two middle status individuals were real subjects. The experimenter enacted the role of two other putative group members. One of the group members enacted by the experimenter was imputed to be high status; the other was imputed to be of low status.2 Actual participants were always of middle status (i.e., receiving scores of 5 and 6 in the 2, 5, 6, 9 score distribution). The experimenter sent messages as members with the scores 2 and 9, the high- and low-status members, in all conditions. She initiated all messages according to a script. In the conditions with inserted negative evaluations, the experimenter inserted evaluations were always of the group performance rather than the performance of any individual. Groups in a control condition completed the task with the experimenter as a high- or low-status member but in the absence of inserted negative evaluations. Groups in these conditions were orthogonally crossed with the imputed status of the experimenter as a source of the inserted negative evaluations. In one half of these groups, the experimenter inserted negative evaluations as the low-status member; in the other one half, she inserted evaluations as the high-status member. Dependent Measures In both studies, the dependent variables were (1) the number and proportions of ideas, (2) uncommonness of ideas, and (3) the number and proportions of four other types of information (positive and negative evaluations, data or facts, and questions). All other information types were coded into a residual category. Ideas were defined as proposed uses of survival items that directly or indirectly contribute to the group’s survival (e.g., “We could use the gun to signal rescuers”). Positive and negative evaluations were

100  Decision Making Groups and Teams defined as evaluative judgments of communication content in the group as a whole or as sent by other group members (e.g., “Drinking the whiskey for warmth is a bad idea” and “Member A has not contributed any worthwhile ideas”). Questions were defined as interrogatives that sought information (e.g., “Does anyone know how many bullets there are in a .45 caliber handgun?”). Data or fact messages were defined as declarative statements about the task or a group member (e.g., “The instructions say that the nearest town is 80 miles away” and “I personally have never learned to tie a tourniquet”). The idea number score was the sum of uses given for all items in the Winter Survival Exercise. The uncommonness score for each use was the number of times the use was cited for a given survival item by all groups in both conditions of a study. Lower frequencies on this measure thus indicate more statistically original ideas. The uncommonness score used in analyses was the mean of all items included in the Winter Survival Exercise. In Study 1, Cronbach’s alphas for five items as single measures of number and uncommonness of ideas were .98 and .78, respectively. Coding. Two independent observers coded videotapes from Study 1 and the transcripts from Study 2. Before coding in Study 1, the videotapes were marked at three-minute intervals with a computer-generated tone to allow analyses of the results in consecutive periods. Coders employed a uniform procedure of nine minutes of active coding followed by a twominute rest period. The more experienced coder scored all records of interaction; to assess reliability, the second coder coded a subset of these records. For a sample of 62 three-minute periods, the correlation between the numbers of ideas coded in the three-minute periods by the two coders was .86. Correlations between coders for data or facts, questions, and negative evaluations were .91, .94, and .82, respectively. The lowest r between coders, .69, was for positive evaluations. In Study 2, the first coder completed all transcripts of all groups; the second completed 105 two-minute periods.

Results Table 5B.1 presents total number and mean uncommonness scores by experimental condition in Study 1. SU groups exchanged significantly greater numbers and proportions of ideas but significantly lower numbers and proportions of data or facts and question messages than SD groups. This may be because data or facts and question messages have distinctly lower likelihoods of being evaluated negatively than do other types of messages with the exception of positive evaluations. When group norms require participation and set limits on the number of positive evaluations that can credibly be exchanged, members of status-differentiated groups may commonly substitute data or facts for riskier message types.

Idea Generation in Interactive Teams   101 Table 5B.1  Number and Proportion of Information Types Exchanged in Study 1 Number Information Type

SU

Proportion SD

SU

SD

Ideas

  92.70a

  76.27b

 .669a

 .566b

Positive Evaluations

   7.78

  11.27

  .071

 .089

Negative Evaluations

   0.50

     1.00

  .003

 .007

a

b

Directions

    13.80

    16.71

  .046

 .039

Questions

    4.31a

  11.18b

 .032a

 .083b

Data/Facts

   8.04a

  17.64b

 .060a

 .128b

Other

    14.50

    11.73

  .119

 .088

Total

  133.80

  134.45

—

—

Notes:  Comparisons of differences between conditions were based on Scheffé tests. Means with different superscripts (a,b) differ at the .05 level of significance.

STUDY 2: EFFECTS OF EXPERIMENTER-INSERTED NEGATIVE EVALUATIONS ON IDEA GENERATION AND INFORMATION EXCHANGE Despite the recognizable importance of negative evaluation as a filter of idea quality, there are few well-conceptualized experimental studies that directly relate this information type to the number and uncommonness of ideas in experimental studies. The small number of negative evaluations exchanged by groups in Study 1 may reflect norms or etiquette for evaluating others in groups with no interaction history. In view of the conceptual importance of negative evaluations to information exchange, and the generally small number of overt negative evaluations observed in freely interacting groups, Study 2 used a procedure to experimentally insert negative evaluations into group’s information exchange to test their effects on idea generation.

Negative Evaluations of the Group In this study, effects of increased negative evaluation on the idea generation and information exchange of members of a group were examined with a procedure of experimenter inserted negative evaluations. Evaluations were of the group as an entity rather than specific members. While negative evaluations of the group may be mitigated in their effects upon individuals by the diffusion of responsibility among all members, the direction of their effects on ideas and information exchange is expected to be the same as evaluations of individual members.

102  Decision Making Groups and Teams Generalizations in Ch. 2 provide the basis for hypothesis testing in this study. H1: Groups in which generalized negative evaluations directed to the group as an entity are inserted into the interaction by either a high- or low-status source will generate fewer ideas and smaller percentages of ideas than groups in which no negative evaluations are inserted. H2: Groups in which generalized negative evaluations directed to the group as an entity are imputed to be inserted by a high-status source will generate fewer ideas and smaller percentages of ideas than groups in which these evaluations are inserted by a low-status source. H3: Groups in which generalized negative evaluations are imputed to be inserted by either a high- or low-status source will generate more negative evaluations and higher percentages of negative evaluations than groups in which no negative evaluations are inserted. H4: Groups in which generalized negative evaluations are imputed to be inserted by a high-status source will generate more negative evaluations and higher percentages of negative evaluations than groups in which the negative evaluations are inserted by a lowstatus source. H5: Groups in which generalized negative evaluations were imputed to be inserted from either a high- or low-status source will generate a lower total volume of messages than groups in which no negatives evaluations are inserted. H6: Groups in which generalized negative evaluations are imputed to be inserted from a low-status source will generate a lower total volume of messages than groups in which negative evaluations are inserted by a high-status source. The bases of these hypotheses are discussed below. H1 instantiates the basic claims that negative evaluations are a higher social cost to medium- and low-status group members than does any other information type. H2 follows from the basic assumption that evaluation from high-status sources imply more risk than evaluations from low-status sources. Following other writers on climate or imitative effects of evaluations (e.g., Knippenberg, Kooij-de Bode, & Van Ginkel, 2010), H3 and H4 predicts that negative evaluations will increase the number of the same types of evaluations by the other group members, and that this effect will increase when the evaluators are assumed to be a high-status source. H5 and H6 predict that inserted negative evaluations will decrease the total number of messages initiated other than negative evaluations, and this effect will be greater for a high-status source. This follows from the tendency of members to reduce message initiations other than negative evaluations when they observe an increased number of negative evaluations.

Idea Generation in Interactive Teams   103

Method Participants. Participants were drawn from the same pool of first- and second-year undergraduate students described in the previous study. Students were randomly assigned to 1 of 80 four-person groups. Groups were then randomly assigned to one of the experimental conditions. Same-sex participants were used to eliminate the previously cited influence of gender in interactive groups. Experimental tasks. The experimental tasks for the Winter Survival uses generations, and the Desert Survival pretests were as described in Study 1.

Experimental Procedures Information exchange between participants in this study was computer mediated. In all conditions, the experimental procedures introduced status distributions that formed status-differentiated groups with a fictitious fifth member. The experimenter and the system simulated this member a source of negative evaluations in two of the conditions. Distribution of fictional scores based on the Desert Survival pretest were again used to differentiate the groups on status, following the procedures that were described in the previous study. The exchange of biographical information among subjects provided an additional means for manipulating the status of the fictitious fifth member. In the 10 groups with a high-status source, messages ostensibly from this member indicated that she was 24 years old and a graduate student; since all subjects were college freshmen or sophomores, this information established a high status on the two attributes of age and school class level. In the 10 groups with a low-status source, biographical messages described the fifth member as a 17-year-old high school senior, and therefore lower status on the two demographic attributes. Instructions in all status differentiated conditions emphasize the importance of individual differences in survival information to group performance exercise. The Experimenter as a Fictitious Group Member The experimenter acting as the fictitious group member sent a general negative evaluation of the group’s performance at three predetermined points in the group interaction. These points were defined as the exchange of the third idea for three of the six task items. In addition to sending these evaluations, it was necessary for the experimenter to participate in the exchange of messages in order to maintain the credibility of the fictitious group member. When a group member sent a direct question to this member, the experimenter answered with a yes or no agree or disagree response. In cases where it was necessary, the experimenter invented a brief sentence to keep the interaction believable. Credibility also

104  Decision Making Groups and Teams required that the experimenter acting as the fictitious group member contribute to the group task by sending some idea messages. For each of three planned idea insertions, the experimenter had a list of three ideas from which to choose and made the final choice at the time of the insertion based on contextual guidelines. Idea insertion occurred during consideration of the three items for which negative evaluations were not inserted. These lists of ideas given the experimenter were drawn from a list of moderately common ideas exchanged for the same items in a previous study. Debriefing statements generally indicated that the participants believed that the fictitious member was an actual member of the group and were not suspicious of the inserted messages.

Dependent Measures As in Study 1, the number and proportion of ideas, negative and positive evaluations, data, and questions were coded from transcripts of the interactive groups.

Idea Number and Idea Uncommonness Two measures of idea generation were again used to evaluate the hypotheses of the study. The first was the total number of individual ideas summed across the six survival items used in the study. The second was the mean idea frequency (i.e., the number of times a given idea appears in the total set of ideas generated by all the groups in the experiment for the item). Coefficient alpha for item scores on idea number and uncommonness have been reported in the previous study.

Other Information Types Four other information types were also coded from the messages that participants exchanged: (1) positive evaluations, (2) negative evaluations, (3) questions, and (4) data or facts. Other information types were included in a residual category. The coding procedure provided frequencies of each of these information types in two-minute periods across each group’s interaction.

Procedure Participants arrived at separate locations in waiting areas so that they did not see one another beforehand. A host experimenter met them and escorted them to their workstation rooms. The host then read a short introductory statement and informed them that instructions for the session would appear on their workstation monitors. The instructions included an interactive tutorial on the use of the computer-based communication system. After all participants had completed the tutorial, the system requested each of the participants to read the text of the Desert Survival pretest and rank nine

Idea Generation in Interactive Teams   105 desert survival items according to their survival usefulness. Following a short session in which participants exchanged biographical information, they were given the results of their pretest. During the biographical exchanges, the system stored the actual text of the messages sent by the participants and, instead, sent standardized biographies to all participants to establish differentiation in diffuse status attributes. Two practice rounds in which participants generate two uses for a survival item that was not included in the test phase of the experiment, preceded the free exchange of information. RESULTS

Numbers and Proportions of Message Types Exchange Counts of message types exchanged were used to test study hypotheses. Condition effects of inserted evaluations on the information types studied are summarized in Table 5B.2A.

Number and Proportions of Ideas Exchanged Results indicated that groups in the no inserts (NI) condition gave significantly more ideas than groups in either the conditions with inserts from a low-status (LS) or high-status (HS) source. Differences in the proportion of ideas between conditions were in predicted directions but not statistically significant. Contrary to a hypothesis, there were no significant differences in either the number of ideas or their proportion in total messages between conditions when inserts of group negative evaluations were from imputed low- and high-status fictitious sources.

Idea Uncommonness The mean uncommonness score based on frequencies of all ideas given by groups in each condition were found to be: MNI = 18.28, MLS = 14.69, MHS = 12.52. Lower mean frequencies of ideas on this measure represent Table 5B.2A  Information Type Means for Three Experimental Conditions Number Information Type IDEAS DATA POSITIVE EVALS. NEGATIVE EVALS. QUESTIONS OTHER TOTAL

NI

LS

HS

F(2.27)

 76.0909a  17.2727  12.1818   1.0000  10.9091ab  29.1818 146.6364a

 58.8000b   11.5000     7.5000     0.8000   6.9000a   20.4000 105.9000b

 57.7000b   13.1000     7.8000     1.7000  14.8000b   22.7000 117.8000b

3.5308* 1.8465 1.3870 0.9575 6.3190*** 2.3790 7.220**

106  Decision Making Groups and Teams Table 5B.2B  Proportion Information Type

NI

LS

HS

F(2.27)

IDEAS

  52.4648

  56.5170

 49.4942

0.8658

DATA

  11.5988

  10.8180

 10.8602

0.0677

POSITIVE EVALS.

    8.0801

    6.7868

   6.0907

0.5158

NEGATIVE EVALS.

  0.6384a

  0.7129a

  1.6465b

 2.1717*

QUESTIONS

  7.4524a

  6.5730a

 12.7550b

 6.855**

  19.7655

  18.5923

 19.1335

0.1140

OTHER Notes:

1. Proportions are reported as x102 NI = No Inserts; LS = Inserts from a Low Status source; HS = Inserts from a High Status source 2. Within each row, means with different subscripts differ at the .05 level of significance. For planned comparisons in ideas, positive and negative evaluations, the statistic was a one-tailed ‘t’. For all other comparisons, the statistic was a Tukey B test. * p < .05. ** p < .01.

higher uncommonness scores. The condition differences were significant F(2, 28) = 6.19, p < .01. Tukey B tests on the pairwise comparisons of condition means show that HS groups had significantly greater uncommonness in their ideas than either LS or NI groups (p < .05). The differences in mean uncommonness between the LS and NI groups were not significant. Since researchers have reported a positive relationship between idea number and idea uncommonness (e.g., Christensen, Guilford, & Wilson, 1957; Duch, 2006; Maltzman, Belloni, & Fishbein, 1964; Silvia, 2007), and groups in the NI condition generated significantly more ideas than groups in the LS or HS conditions, the obtained condition differences in idea uncommonness were unexpected. Moreover, when the number of ideas was entered as a covariate in the analysis of the effects of condition on uncommonness, the F statistic for overall condition differences in idea uncommonness increased to F(2, 27) = 23.3 (p < .001), and the proportion of total variance in uncommonness scores accounted for by conditions increased from .31 to .58.

Number and Proportions of Positive and Negative Evaluations The number of negative evaluations in comparison with other information types was low in all conditions. Condition differences in positive and negative evaluations were in predicted direction but attained statistical significance only for the proportion of negative evaluations. The planned pairwise comparisons indicated that the proportion of negative evaluations in groups in the HS condition were significantly greater than the proportion of negatives in groups in both the LS and NI conditions. This reflects the result that

Idea Generation in Interactive Teams   107 the number of negative evaluations was higher in the HS condition while total message volume decreased in comparison with the NI condition. These results partially support H3, but are contrary to H4. Total Number of Messages Condition differences in the total number of messages exchanged were significant. Groups in the NI condition exchanged significantly more messages than groups in either the LS or HS inserted conditions. Differences between groups in the LS and HS conditions were not significant. Number and Proportions of Data or Fact Messages There were no significant condition differences in either the number of proportion of messages in the data or fact category and messages in the residual category. Number and Proportion of Questions Condition differences in the number and proportions of questions exchanged were highly significant. Pairwise comparisons of means with Tukey B tests (α = .05) indicated that groups in the HS condition exchanged significantly more questions that groups in the LS condition and significantly higher proportions of questions than groups in either the LS or NI conditions. Analysis of Question Exchanges To gain insight into the process through which inserted negative evaluations could be related to idea uncommonness, the highly significant condition effects on the exchange of questions were examined in more definitive coding of questions. Categorization and Analyses of Question Exchanges All questions in the information exchanges were coded into subcategories based on a preliminary classification of a random sample of approximately 100 questions that were not identified as to their group source. Table 5B.3 shows the nine categories of the coding. The mean number and proportion of each question type for the coding of all messages that were interrogatives are presented in Table 5B.4. Results of ANOVAs showed significant condition differences in the number of requests for task clarification, more ideas from others, specific information, and ideas phrased as questions. Tukey B tests of pairwise comparisons indicate that groups in the conditions with inserts (LS and HS) exchanged significantly fewer requests for task clarification than groups in the NI condition. Groups in the HS condition exchanged significantly more requests for ideas from others requests for specific information and for evaluation than groups in either the LS or NI condition. Groups also in the LS and HS conditions sent fewer ideas phrased as questions than the NI groups. These differences were significant only for the LS and NI pair wise comparison.

108  Decision Making Groups and Teams Table 5B.3 Categories For Question Exchanges 1   Requests for general information (e.g., “Exactly what is a tourniquet?”). 2 Requests for directions (e.g., “Should we move on to the next item?”). 3 Requests for task clarification (e.g., “Were we supposed to think of ideas for all of the items?”). 4 Requests for general clarification on communications (e.g., “What is the rest of your message?” or “Where’s number 2?”). 5 Requests for more ideas from other group members (e.g., “Do you have any more ideas?” or “Have we run out of ideas for the pistol?”). 6 Requests for specific information on survival procedures or new ideas for specific uses (e.g., “How would you do that?” (use item) or “What would you do with that”?). 7 Requests for evaluation (e.g., “Am I wrong?” or “What do you think of this idea?”). 8 Ideas phrased as questions (e.g., “Could we somehow use the shortening to cook any food we might kill?”). 9 Other (e.g., rhetorical: “I wonder if we’re surviving?” or uninterpretible: “Sure, why not?”).

Table 5B.4A  Question Category Means for Three Experimental Conditions Number Question Category

NI

LS

HS

F(2.27)

5.000

   2.3000

  3.3000

2.1334

  3.0909

   4.4000

  4.8000

1.4059

REQUESTS FOR TASK CLARIFICATION

 1.3636a

      0.4000b

       0.3000b

REQUESTS FOR GENERAL CLARIFICATION

  4.8182

   5.0000

  5.6000

REQUESTS FOR MORE IDEAS FROM OTHERS

 0.1818a

      0.3000a

      1.4000b

  7.1160**

REQUESTS FOR SPECIFIC INFORMATION

 4.0909a

   3.9000ab

      8.1000b

3.7424

REQUESTS FOR EVALUATION

 1.8182a

  0.6000ab

      0.3000b

 3.9946*

IDEAS PHRASED AS QUESTIONS

 5.0000a

      1.8000b

  2.7000ab

 3.6123*

OTHER QUESTION TYPES

  1.0000

  0.2000

  0.6000

2.6093

REQUESTS FOR GENERAL INFORMATON REQUESTS FOR DIRECTION

 5.3967* 0.0907

Idea Generation in Interactive Teams   109 Table 5B.4B  Percent Question Category REQUESTS FOR GENERAL INFORMATON REQUESTS FOR DIRECTION REQUESTS FOR TASK CLARIFICATION REQUESTS FOR GENERAL CLARIFICATION REQUESTS FOR MORE IDEAS FROM OTHERS REQUESTS FOR SPECIFIC INFORMATION REQUESTS FOR EVALUATION IDEAS PHRASED AS QUESTIONS OTHER QUESTION TYPES

NI

LS

HS

F(2.27)

   16.2554

    12.5824

   12.4244

0.3958

   13.0336

    24.8750

   19.7255

3.0701

     9.3894

      2.4702

     0.9978

 3.5071*

   16.0032

   27.2121

   21.6194

1.8502

  0.4491a

   1.6709ab

  4.3522b

 4.8374*

   16.6703

    18.4845

  27.8971

2.5834

     6.4520

      2.7200

     1.3025

3.0962

   18.0757

     9.0420

   9.2586

2.5191

     3.6712

     0.9729

    2.4225

2.1432

Note:  Within each row, means with different subscripts differ at the .05 level of significance according to a Tukey B test. * p < .05. ** p < .01.

The Exchange of Questions and Idea Uncommonness Given the condition differences in idea uncommonness and the number and types of questions exchanged, the relationship between these measures was directly investigated. Reviewing the categorization of question messages, requests for specific information on survival procedures or new uses for specific items and requests for more ideas were hypothesized to have direct effects on idea uncommonness. Requests for specific information can stimulate the exchange of more specialized information and inference. Questions that directly request additional ideas can increase the uncommonness of ideas if more common ideas tend to occur late in idea sequences (e.g., Christensen et al., 1957; Olczak & Kaplan, 1969; Runco, 1986). The above conjectures on the effects of question types were tested in stepwise regressions of idea uncommonness scores on the number of question and data messages in groups with and without inserts of negative evaluations.

110  Decision Making Groups and Teams To increase the sample of groups without inserts, all 10 groups in a previous study with the same communication system and procedures that did not include experimenter-inserted evaluations were used in the comparisons to the groups in the present study. The contribution of the total number of questions and data messages to uncommonness was initially tested. The same procedure was then used in assessing the contribution of the total number of questions in categories of Table 5B.3. The stepwise procedure followed was to first enter numbers of ideas and positive evaluations to remove the direct contribution of these information types to uncommonness. Additional independent variables were then entered in the order of the magnitude of their F-to-enter. The effects of these variables were assessed in terms of the incremental contribution of the entered variables to the explained variation in uncommonness. For the regression of uncommonness on total numbers of data or facts, positive evaluations and the residual (other) category of information, coefficients were found to be zero-order (t < 1.0) in preliminary analyses. These variables were omitted from the final regression. Results are presented in Table 5B.5. Since definitionally, idea uncommonness increases as idea frequency decreases, independent variables that increase uncommonness have negative coefficients. To facilitate interpretation, the signs of the coefficients in this table and other reports of regression results have been reversed. The results show that when effects of ideas are accounted for, the number of questions exchanged is a significant predictor of idea uncommonness in Table 5B.5  Hierarchical Regression of Idea Uncommonness on Number of Ideas and Information Types LS and HS Inserts Step

Variable

β To Enter

F Change

Final β

Se β

1 2 3 4

IDEAS POSEVLS QUESTIONS FACTS

.683*** .224 .422 .174

15.773***  1.691  5.504*  1.236

 .710*** –.002  .376*  .174

.162 .180 .183 .156

Adj. R2 = .578   F(4,15) = 7.504** No inserts Step

Variable

β To Enter

F Change

Final β

Se β

1 2 3 4

IDEAS POSEVLS QUESTIONS FACTS

 .787***  .106  .017 –.348

29.336***   .368   .011  2.565

.906*** .282 .201 –.348

.181 .205 .194 .217

Adj. R2 = .598   F(4,15) = 8.056* POSEVLS = Positive Evaluations * p < .05; ** p < .01; *** p < .001

Idea Generation in Interactive Teams   111 groups with inserts of negative evaluation. For groups with no inserts, a comparable effect of questions was not indicated. Stepwise entry of predictors indicated that the incremental contribution of questions to the adjusted R2 after all other information types were entered was .110 (p < .05) among groups with inserts of negative evaluations and less than .010 (ns) among groups in which there were not inserted negative evaluations of the group. The stepwise regression procedure described above was also followed in testing the effects of the numbers of questions of each type in Table 5B.4 on idea uncommonness. Only those categories showing significant condition differences were entered into the regression. The numbers of ideas and the question categories of requests for task clarification, requests for evaluations, and ideas phrased as questions were entered in the first steps of a regression of uncommonness on categories to assess effects of question categories that are not predicted to effect idea uncommonness. These question categories were not found to yield a significant increase in the adjusted R2 for idea uncommonness and were omitted from the final regression. Question categories of requests for specific information and for more ideas were then entered in subsequent steps. Table 5B.6 presents the results of the regression of the latter two question categories on idea uncommonness. The incremental contribution to explained variation in uncommonness from requests for more ideas and requests for more specific information was .272 (p < .01) in the condition with inserted negative, but .009 (ns) in the conditions with no inserts.3 Table 5B.6  Hierarchical Regression of Idea Uncommonness on Number of Ideas and Question Types LS and HS Inserts Step

Variable

β To Enter

F Change

Final β

Se β

1 2 3

IDEAS QRID QRINF

.683*** .432 .344

15.773*** 9.151** 4.756*

.722*** .237 .344*

.130 .157 .158

Adj. R2 = .683 F(4,15) = 14.637*** No Inserts Step

Variable

β To Enter

F Change

Final β

Se β

1 2 3

IDEAS QRID QRINF

.787*** .049 –.089

29.336*** .107 .287

.753*** .058 –.089

.165 .153 .166

Adj. R2 = .559 F(4,15) = 9.034* †QRID = Requests for ideas, QRINF = Requests for more specific information Note:  Significance tests for beta coefficients are one-tailed * p < .05; ** p < .01; *** p < .001

112  Decision Making Groups and Teams

Categorization and Analyses of Data or Fact Exchanges All messages with data or fact statements were also classified into subtypes. A preliminary classification of approximately 100 randomly selected data or fact messages generated three subcategories of data facts: personal (e.g., “All this food is making me hungry”), informational (e.g., in answer to a question on the usefulness of a tourniquet, “Tying a tourniquet . . . stops the bleeding”), and inferential (e.g., given one member’s idea of using the candy bar wrapper as a reflector and another member’s statement that Minnesota winters are too cold to be sunny, the inference offered by a third member, “I think it can be both cold and sunny, so that we can use the foil as a reflector.”). Data or facts were observed to be less differential and more closely related to context than questions were. Analysis of variance tested condition differences in the data or fact categories. Table 5B.7 reports these results. Although the total number of data or fact messages did not differ between conditions, results of this analysis showed that groups with inserts of negative evaluations (the LS and HS conditions) produced smaller numbers and percentages of personal messages and messages that were informational only, but more messages that included an inferential statement than groups in the condition without inserts (NI). Tukey B tests (α = .05) on the pairwise comparisons of conditions show that condition differences in numbers of informational and inference data messages were significant, but that the differences in numbers of personal messages were not. Differences in proportions of the category messages corresponded to differences in the number of messages in the different categories. Table 5B.7  Data or Fact Category Means for Three Experimental Conditions Number Data/Fact Category

NI

LS

HS

INFERENCE PERSONAL INFORMATIONAL

 4.4545  5.0000 13.1818a

10.7000  2.4000  6.8000ab

11.4000  3.1000  5.7000b

F(2.27)  3.4871*  1.9951  3.8459*

Proportion Data/Fact Category

NI

LS

HS

F(2.27)

INFERENCE PERSONAL INFORMATIONAL

 0.2068a  0.2169  0.5763a

 0.4889b  0.1668  0.3443b

 0.5573b  0.1522  0.2906b

19.8043***  0.3425 14.4251***

Note:  Proportions are reported as ×102 Within each row, means with different subscripts differ at the .05 level of significance according to a Tukey B test. * p < .05; ** p < .01; *** p < .001

Idea Generation in Interactive Teams   113 In a stepwise regression of idea uncommonness on data or fact categories for the 20 groups with inserts of negative evaluations, the change in adjusted R2 from the data or fact categories was increased only when the number of data or fact messages with inference (INFER) was entered (adj. R2 change = .029, ns; final Beta = .481, SE = .296, t(19) = 1.6, ns). The results indicate that the category of data or facts with inference made a positive but nonsignificant contribution to explain variation in idea uncommonness among groups with inserts. In contrast, the contributions of messages in this or any other data or fact category among the groups without inserts was negative. Table 5B.8 summarizes results of empirical studies that have been reported in Parts A and B of this chapter. Table 5B.8  Summary of Study Results Dependent variables Independent variables

Experiment

Idea number

Idea proportion

Idea uncommonness

Effect

Dynamics of single-source and multisource ideas in interactive groups

Time in interaction

Ratio of multisource to singlesource increases over time

n.a.

n.a.

As time in interaction increases, ratio of multi-source to single-source ideas increases

Effects of status differentiation on idea generation

Variance of the experimentally introduced status distribution

SU > SD, p < .05

SU > SD, p < .05

n.a.

Increase in the variance of the status distribution decrease idea number and proportion

Effects of inserted negative evaluations of the group on idea generation

Experimenter inserted negative evaluations of the group

NI > GN

NI > GN

GN > NI p < .05

Group negative evaluations decrease idea number and proportions but increase idea uncommonness

SD = Status Differentiated SU = Equal Status GN = Inserted Negative Evaluations of the Group NI = No Inserted Evaluations

114  Decision Making Groups and Teams SUMMARY AND DISCUSSION Results of experimental studies reported in Part B of the chapter test hypotheses offered in the preceding conceptualization and numerical studies of Part A. In the lab study reported in Part A, the dynamic paths of single- and multisource ideas in laboratory groups were shown to further support the dynamic form given to single- and multisource idea generation and the results of numerical studies. The first study reported in Part B shows effects that status differentiation can have on information exchange on interactive groups and teams. This study showed that status differentiation decreased the exchange of ideas and increased the exchange of positive evaluations, questions, and data or facts. As conceptualized in Chapters 3 and 4, ideas have significantly higher likelihoods of returning a negative evaluation than either questions or data messages do. If group members seek to minimize or reduce status loss that typically results from negative evaluations by a higher status source, they can substitute positive evaluations or data messages for the ideas and negative evaluations they might otherwise initiate. As the variance in the group’s status distribution increases, the magnitude of these effects can be expected to increase. This experiment confirmed these predictions. In Study 2, the effects of experimenter-inserted negative evaluations were shown to reduce the number of ideas generated in groups. The findings on the uncommonness of ideas in this experiment are notable in that the significant increase in uncommonness in groups with experimenter-inserted negative evaluations was obtained with a corresponding significant decrease in the number of ideas between these conditions. Most often mean uncommonness can be expected to increase with the number of ideas. When this effect was tested in further analyses of the questions and data or fact messages across conditions, groups in the condition, where the insertednegative evaluations were from a putative high-status source, were found to have exchanged significantly more and higher proportions of requests for specific information on survival procedures and more general requests for more ideas from other group members than groups in which the inserts were from a putative low-status source or a control condition with no inserts. Hierarchical regressions of uncommonness on the total number of messages in different information categories showed significant effects of total questions in the conditions with inserts of evaluations but no comparable effects in the no inserts conditions. In hierarchical regressions of uncommonness on numbers of questions in the different categories, significant effects of requests for ideas and requests for specific information were obtained for conditions with inserts of evaluations, but not among the no inserts conditions. Groups in both the conditions with high- and low-status sources (HS, LS, respectively), of inserted negative evaluations also differ from groups in the control condition of no inserts (NI) in the categories of data or fact messages sent. HS insert groups sent higher percentages of data or facts in the inference category but lower numbers of data or facts in the information

Idea Generation in Interactive Teams   115 and personal categories than LS or NI groups. Examination of correlations between the question categories and data or fact categories showed that in groups with inserts of negative evaluations, the question categories of requests for specific information and requests for more ideas were most highly related to data in the inferential category. In groups without the inserts, the same question categories were most highly related to data in the personal and informational categories. This and other findings begin to indicate the complexity and importance of effects that negative evaluations can have to the performance of interactive groups and teams. It is clear that quality in ill-structured decision making occurs through a complex process of information exchange in which both idea generation and negative evaluation have particular importance. In this chapter, the dynamics of idea generation have been examined. In the next chapter, dynamics of negative evaluations is addressed. REFERENCES Berger, J., Rosenholtz, S., & Zelditch, M. (1980). Status organizing processes. Annual Review of Sociology, 6, 479–508. Booth, A., & Nolen, P. (2012). Choosing to compete: How different are girls and boys? Journal of Economic Behavior and Organization, 81, 542–555. Carli, L. (1989). Gender differences in interaction style and influence. Journal of Personality and Social Psychology, 56, 565–576. Christensen, P., Guilford, J., & Wilson, R. (1957). Relations or creative responses to work time and instructions. Journal of Experimental Psychology, 53, 82–88. Duch, W. (2006). Computational creativity. International Joint Conference on Neural Networks. Vancouver, Canada. Eagly, A. H., & Karau, S. J. (1991). Gender and the emergence of leaders: A metaanalysis. Journal of Personality and Social Psychology, 60, 685–710. Johnson, D. W., & Johnson, F. P. (2012). Joining together: Group theory and group skills (11th ed.). Boston, MA: Allyn and Bacon. Knippenberg, V., Kooij-de Bode, K., & Van Ginkel, W. (2010). The interactive effects of mood and trait negative affect in group decision making. Organization Science, 21, 731–744. Maltzman, I., Belloni, M., & Fishbein, M. (1964). Experimental studies of associative variables in originality. Psychological Monographs: General and Applied, 28, 1–21. Olczak, P., & Kaplan, M. (1969). Originality and rate of response in association as a function of associative gradient. The American Journal of Psychology, 82, 157–167. Ridgeway, C. (2000). The formation of status beliefs: Improving status construction theory. In S. Thye, E. Lawler, M. Macy, & H. Walker (Eds.), Advances in group processes (pp. 77–102). Stanford, CA: Stanford University Press. Ridgeway, C., & Smith-Lovin, L. (2006). Gender and interaction. In H. Kaplan (Ed.), Handbook of sociology and social research (Part 3, pp. 247–274). New York, NY: Springer. Runco, M. (1986). Flexibility and originality in children’s divergent thinking. The Journal of Psychology, 120, 345–352. Silvia, P. (2007). Another look at creativity and intelligence: Exploring higher-order models and probable cofounds. Personality and Individual Differences, 44, 1012–1021.

6A Negative Evaluations as Information and Affect in Interactive Groups and Teams Dynamic Model

OVERVIEW Part A of this chapter conceptualizes and investigates the exchange of negative evaluations in the information exchange of interactive teams. Investigators have often identified the exchange of negative evaluations as a source of performance decrement in decision-making groups. A long time observation has been that mimetic or imitative effects from the initiation of this information type increase its generation by others. This is considered to be dysfunctional for the exchange of ideational information since increases in negative evaluations commonly reduce idea initiations. An alternative account emphasizes the functional effects that negative evaluations as information can have on group and team decision making through a discrimination of ideas and other information types exchanged in the group on quality-related criteria. Recognizing the duality in this information type, this chapter provides an account of its exchange in interactive teams that recognizes negative evaluations as simultaneously having affective and informational properties. The elaboration in dual processes is used to give a closed-form model of dynamics in the exchange of evaluations. The model is then used for abstract and general statements on processing and analytical inference. INTRODUCTION In the absence of an algorithmic or well-defined heuristic procedure for a decision, both the definition of decision alternatives and the basis for selecting among alternatives is heavily dependent on the exchange of ideas. Much of the extensive literature on ill-structured decision making and problem solving has in fact been in terms of this information type. However, quality in ill-structured decisions requires the filtering and sorting of decision alternatives implied by ideas. This is, in turn, dependent on the exchange of evaluations, particularly negative evaluations. The general importance of negative evaluations as regulators of social exchange and the performance of groups and teams have been well-recognized in several research traditions.

Negative Evaluations as Information  117 In traditions of social control in interactive groups (Jain, Giga, & Cooper, 2011; Meyers, Brashers, & Hanner, 2006; Moldoveanu & Baum, 2011; Mudd, 1968; Schacter, 1951) and dissent and minority influence (e.g., Hinsz, Tindale, & Vollrath, 1997; Moscovici, 1985; Moscovici & Faucheux, 1972; Nemeth, 1979, 1985; Smith, Tindale, & Dugoni, 1996), negative evaluations are commonly seen as being used by some group members to maintain the normative order or restrict deviant members. Other investigators have observed that whatever the objective basis for their exchange, negative evaluations result in socioemotional climates that increase the level of their exchange in the group or team. These investigators maintain that such socioemotional climates reduce the number of ideas generated in a group and thereby undermine common group objectives (Knippenberg, Kooij-de Bode, & Van Ginkel, 2010; Schneider, 1975, 1983). Some of these investigators have proposed procedures to proscribe or limit negative evaluations by members of the group to further objectives in idea generation (e.g., Bartunek & Murningham, 1984; Lloyd, 2011). A range of managerial perspectives have also emphasized procedures to manage dysfunctional effects that the exchange of negative evaluations can have. In particular, procedures in brainstorming and team building (Klein et al., 2009; Yeh, Smith, Jennings, & Castro, 2006) often proscribe the exchange of negative evaluations for most of the interaction procedures. A small group of studies do recognize that under certain conditions, negative evaluations can constitute feedback. This, in turn, can increase the quality of factual information and contribute to performance (e.g., Berkowitz, Levy, & Harvey, 1957; Hwang, Yuan, & Weng, 2011; Kuhnen & Tymula, 2012; Nadler, 1979). I would suggest that the diversity in the above treatments of negative evaluations and the remaining in clarity in their implications for managing interaction in task-oriented groups is a consequence of the inherent complexity in processing that underlies the exchange of this information type. In spite of the importance that negative evaluations generally have for objectives in applications by groups and teams, it will be proposed that available accounts do not adequately represent the process by which this information type is exchanged and its consequences for ill-structured decision making. The discussion in previous chapters has noted that the receipt of negative evaluation is commonly a source of status loss. As such, increases in the exchange of this information type increase a team member’s likelihood of receiving a negative evaluation for sending a negative evaluation and thereby decrease the number of negative evaluations that the member sends. In contrast, accounts emphasizing socioemotional climates in interactive groups, (Knippenberg et al., 2010) imply that increases in the exchange of negative evaluations result in further increases in the exchange of this information type. However, these accounts do not offer a detailed statement of the microprocessing that generates such climate effects. If there are both status-related and climate-related effects of negative evaluations, and these effects have

118  Decision Making Groups and Teams opposite implications for the numbers of negative evaluations exchanged in a team, then what are the conditions under which the respective effects are operative and what are the implications for managing the exchange of this information type in interactive teams? From the perspective of the current discourse, a reason why it is difficult to unambiguously answer these questions is in limitations in the conceptualization of processing in the exchange of negative evaluations in interactive teams. This incompleteness in available accounts of microprocessing in the exchange of negative evaluations is a serious limitation to our understanding of information exchange in interactive decision-making units and the contributions that these units can make to decision-making objectives. I next elaborate on a conceptualization of the exchange of negative evaluations that integrates several theoretical traditions. This conceptualization is used to propose a form for the dynamic exchange of this information type in interactive teams and examine some fundamental sensitivities of the rate at which negative evaluations are exchanged. Empirical tests of inference from the conceptualization are then reported in Part B of this chapter. NEGATIVE EVALUATIONS IN THE INFORMATION EXCHANGE OF INTERACTIVE GROUPS AND TEAMS I begin with a basic contention that orients the study of the exchange of this information type in interactive groups and teams: More than any information type that members of these units exchange, evaluations convey both information and affect. Negative evaluation as information can offer explicit criteria for discriminating between alternatives and thereby contribute to the quality-sorting of ideas and decision alternatives. This informational quality can be considered as cognitive. However, negative evaluations are inherently affective since they relate to recipient self-judgments (Higgens, 1987; Van der Vegt, de Jong, Bunderson, & Molleman, 2010). As used here, affect refers to feeling states (Ilies, De Pater, & Judge, 2007) that are coordinate effects of negative evaluations whatever the factual bases for the evaluations are. I would further suggest that the informational or cognitive and affective sources of initiating negative evaluations generally differ in their generating processes. Whereas rates of initiating negative evaluations as information may be set through the active agency of group or team members, rates of exchanging evaluations as affect are more likely to be generated through a mimetic process in group or team. Informational content is also most often conveyed in verbal or propositional forms whereas affective content is generally associated with nonpropositional forms of representation (Zajonc & Markus, 1984). Given the above distinction between information and affect in the exchange of this information type, I further consider the different underlying processes that underlie the rates at which group or team members initiate negative

Negative Evaluations as Information  119 evaluations. In doing this, particular attention will be given to how group and team structure can mediate these rates. Since process in what are designated to be the exchange of negative evaluations as information has been approximated in previous chapters as active agency and given explicit form in a maximization heuristic, a form for the exchange of evaluations as affect will be emphasized. A rudimentary model of process in the exchange of information as affect is proposed and used for inference on equilibrium rates of exchanging negative evaluations in a group or team. MICROPROCESSING IN THE EXCHANGE OF NEGATIVE EVALUATIONS: EXCHANGE OF NEGATIVE EVALUATIONS AS INFORMATION: AGENCY IN INTERACTIVE  GROUPS AND TEAMS As noted, there is a basis to expect that individuals acting as members of interactive teams have the mixed motives of contributing to the team objective and of maintaining their own status position in the unit. In terms of the latter motive, receiving a negative evaluation as a group or team member can frequently be a source of status loss. Since sending a negative evaluation increases the likelihood of being negatively evaluated more than sending any other information type, members of these units recognize it as having a social cost to the sender. When negative evaluation is factually based and clearly contributes to a quality objective, it may have less of a cost. However, in many, if not most cases, the factual basis of a negative evaluation is at least arguable. In setting initial levels of negative evaluations as information, committed members can be conceptualized as applying informal heuristics to the mixed motive problem they face. In support of team objectives, they frequently send more negative evaluations than the number that they estimate would minimize the number of negative evaluations they will receive in reciprocation. An increase in the number of negative evaluations they are willing to receive in support of the team objective can generally be expected to increase the number of ideas and negative evaluations they are willing to send. This increase can be important since it can increase quality in the team’s decisions. A form has been given to this conceptualization in a previous chapter and its implications have been examined. Historical rates of the exchange of evaluation in a group or team may still matter because they can informally be used to estimate the conditional probability of being negatively evaluated for initiating a message type. However, reports on climate suggest these rates matter more to their exchange as affect (Knippenberg et al., 2010). From the above, the exchange of negative evaluations as information is proposed to primarily depend on an agent’s trade-off between (1) the judged contribution of this type of message initiation to the team’s quality objective and (2) the expected costs of the message initiation to a team member’s status position.

120  Decision Making Groups and Teams THE EXCHANGE OF NEGATIVE EVALUATIONS AS AFFECT: CONTAGION IN INFORMATION EXCHANGE Given the functional basis for the exchange of negative evaluations in support of explicit objectives that has been described, findings on mood and affect suggest a contrasting basis for the exchange of this information type. I propose that rates of negative evaluations as affect evolve through mimetic processes of contagion and adjustment, and that there is leadership by the high-status individual in the stable rates of exchanging negative evaluations that are observed in groups and teams. AFFECT LEVELS AS AN INTERACTIVE ADJUSTMENT PROCESS Affective states in a group or team have been described as reflecting the prevailing feeling, mood, or socioemotional orientation of members (Costarelli, 2009). In these interactive units, such diffuse, affective states clearly influence information exchange and thereby the outcomes in the unit (see, for example, discussions of Hersberger, Murray, & Rioux, 2007; Parayitam & Dooley, 2007; Poole, 1985). While the exchange of negative evaluations as information has been related to heuristic-based processing by a team member, the exchange of negative evaluations as affect is proposed to follow from a mimetic adjustment process by these members. That is, members tend to adjust toward rates being exchanged by other members. This suggests that affective rates of team members tend more to normative rates that become established in a team than to the outcome of individual maximization heuristics. As suggested, there is a basis to relate such normative rates to social structure in the team. Descriptions of group affect levels in qualitative writings on climate (e.g., Knippenberg et al., 2010), in fact, emphasize normative levels of affect and evaluation in the group and contagion among all members in the affect they generate. Affective states, in turn, have important similarities to descriptions of the establishment of mood states (Davies & Turnbull, 2011; Sinclair, Ashkanasy, & Chattopadhyay, 2010; Zillman & Bryant, 1985). Although group and team climate continues to be recognized in a number of qualitative studies (Knippenberg et al., 2010), the link to affective states and its implications has not been explicitly taken up in detail. INFORMATION PROCESSING OF AFFECT A frequent observation on the organization of affective states is in the persistence of the state, once it is established (see, for example, Kim, Payne & Tan, 2006; Nowlis, 1970). Whatever their source, a cluster of affect-related thoughts or events leads to similarly toned thoughts. Once enough affective

Negative Evaluations as Information  121 thoughts are generated, an affective state tends to perpetuate itself through selective retrieval of similarly toned thoughts and the selective invocation of behavior that is consistent with the thoughts (e.g., Bäuml & Samenieh, 2012; Rowe, Hirsh, & Anderson, 2007; Spruyt, Hermans, De Houwer, Vandromme, & Eelen, 2007; Verde, Stone, Hatch, & Schnall, 2010). Examples of such affect-behavior connections have been most extensively discussed in the literature on rates of altruistic behavior (e.g., unsolicited helping) by individuals in different affective mood states (e.g., Fehr & Fischbacher, 2003; Gintis, Bowles, Boyd, & Fehr, 2003; Moore, Underwood, & Rosenhan, 1984; Post, 2005). Such affect-behavior connections can be described in terms of cognitive loops (e.g., Isen, Shalker, Clark, & Karp, 1978) since they often result in selfperpetuated stable rates of initiation of evaluations. Many of these effects are likely to result from affect as an organizer of stored information (Arapakis, 2010; Blaney, 1986). Affectively toned stimuli quickly and intensely retrieve affectively toned memory (see, for example, Biss & Hasher, 2011; Bower, 1981). These connections have sometimes been suggested to occur through priming (e.g., Brown, 1979; Gibson, Dhuse, Hrachovec, & Grimm, 2011; Neely, 1976; Philippot, Schaefer, & Herbette, 2003), where affective states function like a category name or cue to organize cognitive content. That is, affect may be among the small set of pervasive labels individuals use to locate content addressable stored experiences (e.g., Gabora & Ranjan, 2012; Kitto, Bruza, & Gabora, 2012; Wyer & Shull, 1981). For example, Bousfield (1950), Hale and Strickland (1976), Hertel and Matthews (2011), Teasdale and Fogarty (1979), and Teasdale and Russell (1983) report effects of mood induction that result in the selective recall of similarly toned thoughts and thus may be involved in the organization of memory as in a storage bin concept. Feeling states tend to selective recall of similarly toned affective thoughts that keep them close to the top of the bin (i.e., highly salient). Once an adequate number of affectively toned thoughts are activated, the saliency of the state can be maintained by selective recall and replacement of affectladen information that cumulates at the top of the bin. While the above results are consistently reported for positively toned content, selective recall for negatively toned content has not been as consistently replicated. It has been suggested that the differences in the links between positive and negative affective states and recall may arise from efforts by individuals to modify negative affective states, while maintaining and augmenting positive affective states (e.g., Parrott & Sabini, 1990). An alternative perspective to explaining priming effects and the maintenance of affective states is available in terms of network activation concepts of memory (see, for example, Collins & Loftus, 1975; Lerner, Bentin, & Shriki, 2012; Roediger, Balota, & Watson, 2001; Shiffrin & Schneider, 1977). In such conceptualization, information is stored in networks (rather than storage bins) that have a large and permanent collection of nodes

122  Decision Making Groups and Teams (network points of intersection). The nodes become increasingly complex and interrelated through learning. When a concept is processed, activation spreads out along portions of the network from the nodes, the longer a concept is processed (as either by seeing, hearing, or thinking about it), the longer activation is released from the node of the concept at a fixed rate. These accounts suggest that what may make affective information different from other types of information is the pervasive linkages of events or stored memory in terms of their feeling tone (Fuster, 2009; Singer & Salovey, 1988). While these explanations provide insight and evidence on the maintenance of affective states in individuals, this evidence does not directly address processes in groups and teams and is cited here to be suggestive. However, such descriptions of affective states and their stability in individuals do provide a basis to extend the explanation of process in generating and maintaining affective states and climates in the exchange of evaluations observed in groups and teams. SELF-MAINTAINING AFFECTIVE STATES IN TEAMS: THE DYNAMIC ADJUSTMENT PROCESS I suggest that affect in groups is likely to take the form of an interactive, interdependent, and mutually sustaining exchange process in which members adapt their levels of affective exchanges to normative rates at which affect is exchanged in the team. While all members of the team adjust to exogenous influence of all other members, the high-status member or leader can be expected to have particular influence in setting the normative rate (Berkowitz & Macaulay, 1961; Bion, 1961; Pellegrin, 1953; Stein, 1982; Stewart & Johnson, 2009). Since cumulative levels are also important (i.e., rates in a past period set a state or carryover in the next period), the spread of affect through evaluation can be described as an adjustment process in which members’ rates in previous periods are adjusted in setting contemporaneous rates. I next develop these qualitative characterizations in further detail and then consider their implication for information exchange in interactive groups and teams with at least moderate status differences between members. Groups or teams are assumed to begin with zero interaction history, and members initially exchange evaluations at a rate that is a function of their status in the unit. Exchange of evaluations is then influenced by their affective content through a common history that follows. Once the exchange of messages with positive or negative affect reaches a high enough level, cumulative affective states tend to be self-established and maintaining. As noted, the team leader is expected to generally be most influential in initiating the level of evaluations that activate affective states in other team members. This person is generally the high-status person in the team, often for reasons of background variables that are not directly tied to the team’s immediate objectives. As teams become more status differentiated, there is a

Negative Evaluations as Information  123 basis to further expect the highest status member to have higher initial rates of sending negative evaluations. Through high rates of initiating evaluations in an early period of interaction, high-status members can rapidly establish affective states in the team and lead it to converge toward a stable rate in the absence of exogenous disturbances. Thus, what is possibly being observed in many hierarchically ordered groups or teams is that the introduction of high rates of evaluation by a leader frequently establishes and maintains a state that, in turn, has enduring effects on the number of evaluations initiated by other group or team members. STATUS DISTRIBUTIONS IN GROUPS AND TEAMS AND THE INITIAL RATES OF NEGATIVE EVALUATIONS All else equal, as status differentiation increases, there is a basis to expect that the cost of sending negative evaluations decreases for the high-status person relative to the rest of the team. This decrease in the expected cost of a negative evaluation constitutes an incentive to increase the number and proportion of negative evaluations this member initiates. Other team members respond to this through both agency and mimetic processes. Agency effects can be expected to define the number of negative evaluations that members other than the high-status member initiate per unit of time in response to their status distance from the high-status member. This number is likely to be set relative to the normative number or rate that is being observed. If the rate of sending negative evaluations by the high-status member as information is an initial or normative rate toward which other members adjust through mimetic processes, the result would be to increase the number of negative evaluations exchanged in the team. Although both adjustment through both agency and mimetic processes are expected to operate, direct observation and data that others cite in socioemotional climate accounts suggests that mimetic effects commonly predominate in interactive groups and teams. As such, the preceding discussion can be used to propose a dynamic model of the exchange of negative evaluations as affect in interactive decision-making units. The model represents equilibrium rates in initiating negative evaluations that groups and teams attain and their relationship to the relative status of the high-status member. INITIAL RATES OF EXCHANGING NEGATIVE EVALUATIONS AS INFORMATION IN INTERACTIVE GROUPS AND TEAMS In proposing a closed form for the process that has been described, I start with the assumption of a team leader in the rate of negative evaluations exchanged in a time period (NL) and a rate that is more or less comparable across the other team members (NG). I propose that the setting of initial

124  Decision Making Groups and Teams L

G

rates of negative evaluations (N0, N0  ) can be approximated as the solution to the problem of maximizing the quality of a decision given a bound on the status-weighted number of negative evaluations that a member is willing to receive for information initiation. In the solution to this constrained quality maximizing problem, the initial rates of ideas and negative evaluations will be proportional to the status of members as they judge it, relative to other team members. For negative evaluations, G NG 0 = N 0 (σ L , σ 1 , σ 2 , , σ n −1 )

N0L = N0L (σ L , σ 1 , σ 2 , , σ n −1 ) Where, σL > σ1, σ2, . . . , σn–1 are judged relative statuses of the leader and other group members, respectively, and ∑σi = 1. L It can be expected that N0 > NG0 , since the expected status loss from negative evaluations by the leader is higher for members other than the leader in status than it is for the leader’s loss from negative evaluation by other members. THE ADJUSTMENT EQUATIONS FOR TEAM MEMBERS AND THE TEAM LEADER Following upon the discussion of the rate of initiating evaluations as an adjustment process, initial rates are seen as subsequently being adjusted by the affective content of the exchanged information. This adjustment process for the rates of members other than the high status member and the highstatus members’ rates are represented as (6A.1) and (6A.2), respectively:

( − b(N

G L G NG k +1 = N k + a N k − β N k

N Lk +1 = N Lk G

L k

− βNG k

) )

(6A.1) (6A.2)

where: N k is the mean number of negative evaluations sent by all team members other than the high-status member in the k-th period; L Nk i is the number of messages sent by the high-status member (i.e., the leader) in the k-th period; β is a positive constant; and a and b are positive rate constants, with a > b. Since it is expected that the adjustment to ng or nl in any k to be small, (a + b) < < 1. The high-status member and all other team members are considered to adjust their rates toward some proportion of each others’ rates (i.e., adjust out some of the observed difference in their respective rates in each period). The proportion of the others’ rates they adjust toward is a normative rate

Negative Evaluations as Information  125

NL o

G 8

N

L 8

N

NG o K

Figure 6A.1  Dynamic Rates of Initiating Negative Evaluations by the Team Leader and Other Team Members

that is formed through considerations of their relative status. The parameter β is defined as this proportion. At a stable equilibrium, member rates go to L β = N∞ / N∞G. As has been argued, the high-status member can generally be expected to adjust toward a higher rate than the team. Thus, it is assumed that β > 1. Eqs. (6A.1) and (6A.2) are partial adjustment forms that are proposed to represent the behavior of members in sending negative evaluations as affect. In these equations, the team’s mean rate in the previous period is adjusted to some proportion of the leader rate in that period. Similarly, the leader adjusts his or her rate in the previous period toward the observed rate of all other group members including him or herself. The equations thus model smooth transitions in behavior as an adjustment process in affect. All else equal, the adjustment of the team to the leader is expected to be greater than the adjustment of the leader to the other team members. The dynamic behavior that has been described is illustrated in Figure 6A.1. L

1. N 0 and NG0 are initial rates of initiating negative evaluations by the team leader and other team members, respectively. L 2. N∞ and NG ∞ are equilibrium rates of initiating negative evaluations by the team leader and other team members, respectively.

126  Decision Making Groups and Teams L

From the observations on the status dependencies of NG0 and N 0 and Eqs. (6A.1) and (6A.2), it is expected that the equilibrium rate of initiating negative evaluations by the high-status member and the other team members will depend on the initial status distribution. This relationship is examined further below. I begin with the contention that the weighting of status distances between a member and his or her referent members is fundamental to setting rates of all information types including negative evaluations. MEMBER ADJUSTMENT RATES AS A FUNCTION OF STATUS DISTANCES A further contention is members’ weight status distances as convex functions of actual status distances. This has been discussed in Chapter 4 and has a background in heuristics proposed by Kahneman and Tversky (e.g., Kahneman & Tversky, 1979; Kahneman, Slovic, & Tversky, 1982; also see Abdellaoui, Barrons, & Wakker, 2007; Abdellaoui, Bleichrodt, & Paraschiv, 2007) and examined in detail in a previous chapter. The contention is that In all but extreme cases, a group member weights a given status distance from a member of higher status as greater than the equivalent distance from a member of lower status. The above implies that higher status referents are overvalued and lower status referents are undervalued in status judgments. This assumption justifies the claim in Eqs. (6A.1) and (6A.2) that a > b, (i.e., the adjustment of the team toward the high-status member in a given interval is greater than the leader adjustment to the team). It also suggests that the volume of an information type such as a negative evaluation that a member initiates will be a concave increasing function of the member’s status, as in the form σθ, θ < 1. FINITE TIME PATHS TO EQUILIBRIUM RATES OF NEGATIVE EVALUATIONS IN A GROUP OR TEAM Given the background assumption, the finite time path of NG and NL to their equilibrium rates from Eqs. (6A.1) and (6A.2) are next formally examined. It is demonstrated that at the low adjustment rates (a + b < < 1), both functions can be expected to go to their limiting values without cross-overs G in the rates, of NG and NL (i.e., without N k > NLk for any k) and the adjustL G ment paths to be monotonic that is, Nk+1 < NLk, NGk+1 > N k, for all k. These L conditions imply the convergence of both the rates N∞ and NG∞ to equilibrium values. Monotonicity and no cross-overs are demonstrated below.1 For the high-status member, NLk:

L L L N Lk+1 − N Lk < 0 is equivalent to N Lk − βNG k > 0, since from ( 2 ) , N k +1 − N k = −b(N k −

L L L G t to N Lk − βNG k > 0, since from ( 2 ) , N k +1 − N k = −b(N k − β N k )

Negative Evaluations as Information  127 This relation holds for all k > 0. Assuming it holds for some k, then:

(

)

(

)

L L G L G   G N Lk+1 − β NG k+1 = N k − b N k − β N k − β N k + a N k − β N k  

(1 − ( b + βa ) ) ( N Lk − βNGk ) > 0

(6A.3)  L

NLk,

given the initial condition N 0 > So monotonicity is guaranteed for βNG0 provided: 1 – (b + β a) > 0 (1 − b)  (1 − b)    > 1  i.e., i.eβ., >1 1 N − N − (a + b) ( N − N ) −N > 1 − ( a + b )  ( N − N ) 

L G L G L G N Lk+1 − N G k+1 = N k − N k − b N k − β N k − a N k − β N k

Since β > 1, N Lk+1 N Lk+1

L k

G k

G k+1

G k+1

L k

L k

G k

G k

L k

L k

G k

(6A.4)

G k

Since a + b < < 1, 1 – (a + b) > 0. L If, as assumed in a status differentiated team, NL0 > NGk , then Nk > NGk for all k, and there will be no cross-overs in adjustments to equilibrium of NL and L NG. Thus, given the previous assumptions, both Nk and NGk go monotonically to equilibrium values without cross-overs in their respective rates. SENSITIVITY OF THE RATE OF EXCHANGING NEGATIVE EVALUATIONS TO STATUS DIFFERENTIATION Having demonstrated the convergence of rates of negative evaluations in the team to equilibrium rates and the finite time paths of these rates, I next consider the sensitivities of equilibrium rates for exchanging negative evaluations to the variance of the status level attained in the team and to initial rates. The following sensitivities that the forms given to the exchange of evaluations as affect imply will be considered: (1) the sensitivity of the initial rate of sending negative evaluations to the status distribution in the entire team, (2) the sensitivity of the equilibrium rate of sending negative evaluations L in the entire team (i.e., N ∞ + NG∞) to the status distribution, and the L initial rate (i.e., N 0 + NG0 ) to the status distribution,

128  Decision Making Groups and Teams (3) the sensitivity of the equilibrium rate of sending negative evaluations in the entire team to the status difference between the high-status member and other team members relative to the sensitivity of the team’s equilibrium rate to the difference in initial rates between the high-status and other team members. For analytic tractability, in the analysis to follow, a case in which there is a team leader and a set of other members of approximately equivalent status will be considered. In considering the case of a dyad in which members are unequal in status, the super- or subscripts L and G will refer to the high-status member and other lower status members, respectively. The sensitivity in (6A.1) of the equilibrium rates to status differentiation is obtained as follows: G L G NG k − N k +1 = aδk , where δk = N k − β N k

N Lk − N Lk +1 = −bδk G NG ∞ − N 0 = a ∑ δk

N L − N0L = −b ∑ δk G NG ∞ − N0

N ∞L

− N0L where, βNG ∞

=−

(6A.5)

a b

= N L∞



From previous discussion, the initial rates of sending negative evaluations as a function of the member’s status can be written as: θ θ L NG 0 = cσ G and N 0 = cσ L , 0 ≤ θ ≤ 1.

where σθ is a form for the rate of sending evaluation as a function of sender status that keeps σL and σG within (0,1) and ΣσI = 1. For the sensitivity of the initial rate of sending evaluations in the team to status differences: Let ∆ = σL – σG θ

1+ ∆  N0L = c1 σLθ = c   ,  2  θ

θ 1− ∆  NG 0 = c1 (1 − σL ) = c   ,  2 

so

(

d N0L + NG 0 d∆

) = c  θ  1 + ∆ 

θ −1

   2  2 

1+ ∆  since    2  for 0 < ∆ < 1 and 0 < θ < 1

− θ −1

θ 1− ∆  2  2  1− ∆  0,     2  2  θ −1

β+1

1− ∆ 

Where c2c2== b + a c1 provided that a >a >bb  1 + ∆  , which is only a serious   restriction when Δ is close to 1 (i.e., when, σL = 1.0, σG = 0). This implies that the equilibrium rate of sending negative evaluations in the team will be increasing with increases in status differentiation. The sensitivity of the equilibrium rate of negative evaluations to the initial rate in (6A.3) is given by:

(

d N0L − NG 0

and: with: Therefore,

(

d∆

) =c

d N L∞ − NG ∞ d∆

) = (β − 1) dN

d N L∞ − NG ∞ L 0

−

G ∞

d∆

1− ∆  a1 > b1   1+ ∆ 

( d (N

2

 θ  1+ ∆ θ −1 θ  1− ∆ θ −1     + 2 2       2  2 

NG 0

>0

>0

θ −1

) >0 )

The above results indicate the model implies the following assertions hold for the exchange of negative evaluation in the case that has been examined. (1) The initial rate of initiating negative evaluations in the team is decreasing with increases in the difference in status between members, i.e.,

(

d N0L + NG 0

) 0

d ( σL − σG )

(3) The equilibrium rate of initiating negative evaluations is more sensitive to the status difference between the high-status member and other team members than to differences in their initial rates of sending negative evaluations, i.e.,

(

) > d (N ) d (N

d N L∞ + NG ∞

L ∞

+ NG ∞

d ( σL − σG

L 0

NG 0

−

) )

The first of the above results follows from the conceptualization of initial rates of exchanging negative evaluations as a team member’s solution to the problem of the dual objectives of contributing to the team outcomes and maintaining one’s status in the team. As status differentiation (e.g., σL-cσG) increases, the initial mean rate of sending negative evaluations by members other than the high-status member decreases. The decrease in the rate in the rest of the team is greater than the increase for the high-status person because of the concavity of the loss function. The second result follows from the conceptualization of the adjustment process in affective exchange that has been introduced. It is assumed that the exchange of negative evaluations evokes an affective state. This, in turn, results in an increase in the rates of negative evaluations through mimetic effects associated with affect. Since upward adjustment by the team is greater than the downward adjustment by the high-status member, the equilibrium rate will be closer to the initial rate of the high-status member. Thus, the initial rate of the high-status member is the most influential source of the equilibrium rate to which the entire group tends. Since status differentiation increases the initial rate of the high-status member, it will increase the equilibrium rate of the team. The final result demonstrates that the status difference between the highstatus member and other team members is the most influential source of the final rate of negative evaluations. This is important because it shows that the equilibrium rate of negative evaluations in a model that represents the adjustment process in the team is ultimately driven by the status differentiation of the membership. Status differentiation as team structure is, for example, suggested to be more important than the difference in the initial rates between the high-status and other team members. The above results indicate that the proposed model is consistent with inference from the conceptualization of the exchange of negative evaluations in the team that has been offered. Examination of the derived sensitivities that

Negative Evaluations as Information  131 the model implies provides some insight into the equilibrium rate of negative evaluations in the group. Results show that although the initial rate of exchanging negative evaluation will be decreasing with the distance between the higher status member and other team members, the equilibrium rate is increasing with this distance. The first of the above results are consistent with a social risk account, and the second result is consistent with a climate account. These implications are consistent with a suggestion that in unmanaged groups, climate effects tend to predominate (Knippenberg et al., 2010; Schneider 1975, 1983). Having put forth a form for the exchange of negative evaluation in an interactive team that integrates previous results on social risk and affect-generated climate and analytically examined implication of this form in Part A of the chapter, Part B reports empirical studies of the exchange process that has been described. SUMMARY AND DISCUSSION While negative evaluations are principal regulators of the exchange of ideas and most other information types in interactive groups and teams, contrasting accounts of processing in the exchange of negative evaluations imply different dynamics. A number of early studies have observed that socioemotional climates develop from high rates of negative evaluations and are important to the dynamics of this information type in interactive groups and teams. Many of these studies consider socioemotional climates to override all other factors in the information exchange of a team and result in an increasing exchange of negative evaluations and a decreasing exchange of ideas in the team. These studies have emphasized the mimetic or imitative effects of the initiation of negative evaluations on the subsequent initiation of this information type and the consequent low rate of idea initiation. However, they have not addressed the microprocessing that this account of negative evaluations implies as underlying the effects they report. The cascading of this information type in interactive groups that is described in climate accounts can arise from an inherent property of evaluations. Its implications are best understood in a more comprehensive account of microprocessing in the exchange of this information type by members of interactive groups and teams. In this chapter, microprocessing in the initiation of negative evaluations has been considered to be more complex than it is in other information types. This is because the dual processes that underlie the exchange of negative evaluations in an interactive team. First, negative evaluations are processed as information. In this case, the initiation of negative evaluation by a team member primarily depends on the expected social cost of initiating this information type. This cost arises from the receipt of a negative evaluation as a source of status loss. Receiving a negative evaluation has the effect of inhibiting the subsequent sending of negative evaluations and other risky information types

132  Decision Making Groups and Teams through their expected costs to the initiator’s status. Team structure as in the distribution of status is important in this account since increasing asymmetry in status increases expected cost to medium and lower status members and thereby can inhibit exchange of the risky information types. Second, negative evaluations are processed as affect. This results from the direct relationship of evaluations to self-judgments. As has been elaborated, there is a basis to consider processing of negative evaluations as affect to be mimetic and result in effects that are described in accounts of socioemotional climates. Their exchange as affect can be expected to have a contagion effect through which there is a tendency of members to initiate the same information type as they observe other members initiating this information type. In support of this account, I cite the background in information processing studies that emphasizes the tendency of affect-related thoughts to result in an increase in similarly toned thoughts. Affective states tend to perpetuate themselves through selective retrieval of similarly toned thoughts and the selective invocation of behavior that is consistent with the thoughts. Such closed loops (e.g., Clark & Isen, 1982) can result in self-perpetuating stable or increasing rates of initiation in teams. The elaboration in dual processes was used to give a closed-form model of the dynamic exchange of negative evaluations. The forms were then used for abstract and general statements on processing and prescriptive inference. As described, the informational basis of negative evaluations can be expected to decrease the number of negative evaluations through social cost. The affective basis of negative evaluations can be expected to increase the exchange of this information type through what has been labeled mimetic processing. This account proposes that unlike other information types, negative evaluations have simultaneous social cost and mimetic effects. Processing when there is social cost has been reviewed. Negative evaluations are correspondingly affective, even when they are exchanged as information. The model of the exchange of negative evaluations that was introduced based on the foregoing account assumes that team members adjust their communication of affect according to the normative rates of other team members. The leader or high-status member has particular influence in setting normative rates in the team. The closed form representation of the dynamics of negative evaluations framework was used for analytic inference. The integrative model is shown to imply that (1) the initial rate of negative evaluations for group members other than the high-status member will decrease relative to the high-status member and (2) the equilibrium rate of group members relative to the high-status member will increase. I also show that the model implies the equilibrium rate is likely to be more sensitive to status distances between team members than to differences in initial rates. Dynamics of evaluations in the information members exchange in interactive groups and teams have complex sources. Part A of this chapter has sought to provide some insight into the process underlying this complexity and its sensitivity to team structure as the status distribution of the team.

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Negative Evaluations as Information  137 Yeh, E., Smith, C., Jennings, C., & Castro, N. (2006). Team building: A 3-dimensional teamwork model. Team Performance Management, 12, 192–197. Zajonc, R., & Markus, H. (1984). Affect and cognition: The hard interface. In C. E. Izard, J. Kagan, & R. B. Zajonc (Eds.), Emotions, cognition, and behavior (pp. 73–102). Cambridge, England: Cambridge University Press. Zillmann, D., & Bryant, J. (1985). Affect, mood, and emotion as determinants of selective exposure. In D. Zillmann & J. Bryant (Eds.), Selective exposure to communication (pp. 157–190). Hillsdale, NJ: Erlbaum.

6B Negative Evaluations as Information and Affect in Interactive Groups and Teams Empirical Studies

OVERVIEW Experimental tests of hypotheses from the integrative model of negative evaluations as informational and affective are reported in Part B of this chapter. A method of experimenter- inserted evaluations was used in the two studies that are reported. The first study directly demonstrates the mimetic process described in socioemotional climates. This study shows that under conditions of low threat to individual status, group members exchanged more of either positive or negative evaluations that were inserted by the experimenter. Effects were greater for inserted negative evaluations than for positive evaluations. The second study differentiates (1) negative evaluations that a group member directly receives from those that this member observes other members receive and (2) the imputed source of the evaluations as either a high- or low-status group member. Results of this study show that in comparison with a control condition, increases in inserted negative evaluations increase the number of negative evaluations exchanged. There is also an interaction between observed versus personal negative evaluations and status of the source that is consistent with a social cost account. When evaluations were personally received from a high-status member, they resulted in fewer negative evaluations than evaluations that were observed from either high- or low-status sources. INTRODUCTION In Part B of Chapter 6, hypotheses on the effects of experimentally enacted status distributions on the exchange of negative evaluations from the account of this information type as information and affect in Part A are examined in two experiments. A method of experimenter-inserted negative evaluations is used in both experiments. Both experiments demonstrate the mimetic or socioemotional climate effects that negative evaluations as affect can generate. The first experiment directly demonstrates mimetic effects of negative evaluations. The second experiment is a multifactor investigation of negative evaluations as

Negative Evaluations as Information and Affect  139 affect and information and demonstrates effects consistent with both the social cost and mimetic properties of evaluations. STUDY 1: EXPERIMENTALLY DEMONSTRATING MIMETIC EFFECTS OF NEGATIVE EVALUATIONS The methodology in this study examines the levels of evaluation groups tend to have when an experimenter, acting as a participant, inserts evaluations into the computer-mediated interaction. In demonstrating the adjustment process as affect, an attempt is made to keep the expected cost of initiating negative evaluations low. To provide a more sensitive test of the claims on contagion, the experimenter always evaluated ideas that he had previously inserted into the group exchange, rather than the ideas of an actual group member. This was possible, since message sources in this study were anonymous. Participants. A total of 80 female students from a community college in Northern California were randomly assigned to 1 of 20 four-person groups. Same sex participants were used since background studies show gender effects in mixed sex groups (e.g., Carli & Eagly, 1999; Eagly & Karau, 1991; Ridgeway & Smith-Lovin, 2006). The groups were then randomly assigned to one of two conditions. All groups were status differentiated on a specific status attribute related to ability on the group task and interactively completed an idea generation task in a computer-based interaction. Experimental task. The modification of the Winter Survival Exercise (Johnson & Johnson, 2012) described in the previous chapter was used as the experimental task. This exercise is one of a series in which groups must evaluate the usefulness of salvaged items for survival in a hostile environment. Dependent Variable: Number of positive and negative evaluations. The number of positive and negative evaluations initiated by actual group members were the dependent variables. Independent Variable: In this study, the independent variable was the type of evaluation inserted by the experimenter acting as of group member. METHOD In each condition, the experimenter, acting as an additional member of the group, sent ideas, data messages, and evaluations to the other group members according to a script for the study. The experimenter-member inserted the same ideas in each of the group sessions in which he or she participated. When another member proposed an idea that was in the script, a replacement idea was selected from a prepared list of similar ideas. Since communication between members in this experiment was anonymous as to source and target, it was possible for the experimenter-member to evaluate his or her own idea messages to the group.

140  Decision Making Groups and Teams In one condition, the experimenter-member sent five negative evaluations of his or her own ideas. In a second condition, the experimenter-member sent five positive evaluations of his or her own ideas. While any public evaluation in the group is likely to have some effect on expected status costs of message initiations even by members who are only observing the exchange, the above procedures are intended to keep this effect small. Results indicated that even when all evaluations of the experimenterinserted evaluations by group members other than the leader were eliminated from the counts, members who observed more of one type of evaluation exchanged significantly more of the same type of evaluation that they had observed. While this is true for both negative and positive evaluations, the result for negative evaluations was greater in magnitude than it is for positive evaluations. For groups in which negative evaluations were inserted: Mpos. eval. = 6.60, Mneg. eval, = 14.10. For groups in the condition with inserts of positive evaluations: Mpos. eval. = 18.10, Mneg. eval. = 5.07. Differences in numbers of positive and negative evaluations between conditions were highly significant (positive evaluations: t(19) = 2.99, p < .01; negative evaluations: t(19) = 7.41, p < .001). STUDY 2: EFFECTS OF EXPERIMENTER-INSERTED NEGATIVE EVALUATIONS ON THE EXCHANGE OF IDEAS AND NEGATIVE EVALUATIONS IN INTERACTIVE GROUPS

Effects of Observed versus Personal Evaluation and Status of the Evaluator This multifactor experiment contrasts effects of the observation of the negative evaluation of another group member with the direct receipt of negative evaluation. The contrast is examined under different levels of the putative status of the source of the evaluation. A control condition had no message inserts by the experimenter. The dependent variables are the total numbers of ideas and negative evaluations and total messages. The following hypotheses on effects of the experimental conditions are offered. h1: Inserted negative evaluations of a group member will increase the number of negative evaluations that is exchanged in the group in comparison to a control condition with no inserts. h2: Inserted negative evaluations of a group member will decrease the number of ideas and total messages that are exchanged in the group in comparison to a control condition with no inserts. These study hypotheses follow from the conceptualization of negative evaluations as information and affect in Part A of the chapter. The account of negative evaluations as affect predicts that experimenter-inserted negative evaluations will increase the initiation of this information type through

Negative Evaluations as Information and Affect  141 mimetic processes. The account of negative evaluations as information predicts that experimenter-inserted negative evaluations will decrease the number of ideas and total messages that the group exchanges through their effects on a member judgment of the social cost of message initiation. As proposed, group members recognize that initiating the message type of ideas has a greater likelihood of returning a negative evaluation than initiation of message types such as data or facts. Increasing the number of negative evaluations they observe or receive is hypothesized to increase the expectation of being negatively evaluated for initiating an idea and, as a consequence, decrease the number of ideas sent by group members. Two additional hypotheses that are tested address observed versus personal receipt of inserted negative evaluations and the status of the putative source of the inserted evaluations. h3: Middle status group members will initiate fewer ideas and total messages when the putative source of the inserted negative evaluations is a high-status group member then when the putative source is a lowstatus group member. h4: Group members will initiate fewer ideas and negative evaluations when they personally receive a negative evaluation than when they observe a negative evaluation of another group member. Expected loss from an evaluation is greater when the source is a highstatus group member than when the source is a low-status group member. Group members are also more likely to increase their judgment of social risk from message initiation when they personally receive an evaluation then when they observe another group member being evaluated. METHOD Participants. Participants in the study were first- and second-year undergraduates at a private Western university. As indicated in Study 1, same-sex groups were used, since the sex of a group member in mixed-gender groups has been shown to have highly significant effects on participation (e.g., Carli & Eagly, 1999; Eagly & Karau, 1991; Ridgeway & Smith-Lovin, 2006). All participants were randomly assigned to group memberships. Groups were randomly assigned to experimental conditions. Idea generation task. In this study, the idea generation task was again an adaptation of the Winter Survival Exercise (e.g., Johnson & Johnson, 2012) as described in Study 1. Dependent Variables: Number of ideas, negative evaluations, and total messages. The idea number score was the sum of uses given to all six items in the Winter Survival Exercise. An uncommonness score for each use given in a group is defined as the number of times the use was given for an

142  Decision Making Groups and Teams item by all groups in all conditions of a study. Lower frequencies on this measure thus indicate more statistically original ideas. INDEPENDENT VARIABLES The group status distribution. The variance in an experimentally induced distribution of members on a task-relevant status attribute was an independent variable in this study. A status hierarchy was again defined from the fictitious scores on a putative pretest on survival ability that was returned to group members. The distribution of test scores returned to members was: 2, 4, 5, and 8, where 10 was the maximum possible score. Observed versus personal negative evaluation. In two of the five experimental conditions, the experimenter evaluated four of his or her own ideas. In two other experimental conditions, the experimenter evaluated two ideas of each of the middle-status group members. Status of the source of inserted negative evaluations. The status of the source of the inserted negative evaluations was orthogonally crossed with the factor of observed versus personal negative evaluation. In one half of all groups, the experimenter inserted negative evaluations as the highstatus group member. In the other one half, the individual inserted negative evaluations as the low-status group member. Biographical information that members exchanged was consistent with the status distance conveyed by the distribution of scores on the Dessert Survival exercise. The high-status member was reported to be a 22-year-old college senior; the low-status member was reported to be a 17-year-old high school senior. PROCEDURE Only the two middle-status individuals were real participants in this study. The experimenter enacted the role two other putative group members. One of the group members enacted by the experimenter was imputed to be high-status; the other was imputed to be of low-status. The experimenter initiated all messages according to a script.1 All groups were status differentiated. Table 6B.1  Experiment 2 Conditions Inserted Negative Evaluation

Evaluation Source Higher Status

Lower Status

Control

Observed Personal

1 3

2 4

5

Negative Evaluations as Information and Affect  143 The design of the study to test h1 to h4 is shown in Table 6B.1. Each of the cells in the design of Table 6B.1 had 20 actual participants that were randomly assigned to the experimental condition.

RESULTS Results of the study were analyzed in a 2 (observed versus personal receipt of an inserted negative evaluation) by 2 (status of the source of inserted evaluation) ANOVA in which a control condition of no inserted negative evaluations was a covariate. The overall model F(5,44) = 5.482 was statistically significant (p < .001). While inserted negative evaluations decreased the number of ideas and total messages an actual group member initiated, they increased the number of negative evaluations this member initiated in comparison with the control condition. This is consistent with h1 and h2. Within conditions with inserted negative evaluations, a significant effect of observed versus personal negative evaluations on ideas and total messages was indicated. F(1,48) = 4.12, p < .05. This is consistent with h4. The main effect of status of the source on idea number was not statistically significant (F < 1). However, a highly significant interaction of status and personal versus group evaluations was indicated F(1,47) = 7.612 (p < .01). Personal receipt of negative evaluations from a higher status source significantly reduced ideas and total messages in comparison to all other conditions. This partially supports h4. Means for each cell in the design and a comparison of effects at a .05 alpha level with Sidak-corrected t tests are reported in Table 6B.2. Results of these studies are summarized in Table 6B.3.

Table 6B.2  Mean Numbers of Information Types by Experimental Condition1 Observed

Personal

(1) Higher Status Source

(2) Lower Status Source

(3) Higher Status Source

(4) Lower Status Source

(5) Control

Ideas Negative Evaluations

18.600a  4.850a

17.700a  4.950a

13.250c     3.650ab

15.550a  5.100a

24.450b  2.850b

Total Messages

37.350a

39.750ab

29.900c

38.300ab

47.700b

Information Type

In each row, subscripts that differ indicate that the difference in means in pairwise comparisons is statistically significant with Sidak-corrected t-tests (p < .05).

144  Decision Making Groups and Teams Table 6B.3  Summary of Results: Experiments 1 and 2 Dependent Variables Experimenters 1. Demonstrating the socio emotional climates

2. Observed evaluations of others vs personally received evaluations

Independent Variables

Idea Volume

Idea Idea Proportion Uncommonness

N.A. N.A. Diff N, P across insert conditions differ (p < .05) Observed Observed > N.S. N.S. vs personal Personal evaluations. (p < .05) Status of the Neg-In < N.S. Evaluator Control (p c p (9.1)  ∑ Jk ∑ ∑ ∆kl an 2 N ∑ Jk ∑ ∑ ∆kl ap2 Pkl kl k k l k l k 

Where, J is the rate of idea generation Δ is the difference in a status metric between the j-th and k-th team member N is the rate of negative evaluation P is the rate of positive evaluation an 2 , ap2 are scaling parameters cn, cp1 are the relative importance of inequity in the distribution of negative and positive evaluation to overall equity judgments In (9.1), judged inequity is increased when there is an imbalance in the distribution of evaluations relative to ideas for a member; effect of an imbalance of negative evaluations is expected to be greater than an imbalance in positive evaluations. Trust judgment of the j-th member (Tj) as defined in (9.2) exponentially decreases with increase in inequity (Ej) and is in the interval (0,1). Tj = [1 + Ej]

−1

(9.2)



In a time varying form of (Eq.) 9.2, swift trust can be represented in the time weighting of the observation of inequity in information exchange as it contributes to the overall equity judgment. It implies that early judgments of equity or inequity are weighted much more than judgments made late in interaction.

The Rate Mediator of Initiating Ideas Kj Trust can be integrated into the forms for information exchange as a defined rate mediator for the initiation of risky message types. From the idea function in Chapter 5, trust enters the rate of idea generation through the mediator parameter of K. This term defines the rate at which realized ideas are initiated. Single Source

(

(

))

J (j1) = K j Aj − J (j1) − α j Jk(1) … Jn(1) , J (j1) ( 0 ) = 0

Virtual Teams as Decision-Making Units  209 where Aj is the initial idea pool (or associational base) of the j-th member αj is the overlap of ideas in Aj with other team members Multi Source

(

(

  J (j2) = K j  ∑ J (jk1)  Aj − J (j1) − α j J1(1) … Jn(1)  k≠ j 



)) + K   ∑ J j



k≠ j

  −  c1 ∑ Jk(2)   ; J (j2) ( 0 ) = 0   k≠ j  

(1)  jk 

2

The Kj parameter can be given the form

(

− cn ∑ ∆jk N jk

K j = Tj 1 − an

1

− cp ∑ ∆jk Pjk

+ bn

1

), a

n1

>> bn1 , 0 < c p < ch Pr N ji Iij > Pr N ji Fij ≥ Pr N ji Bij > Pr N ji Pij

)

This inequality posits negative evaluation of another group member as having the highest probability of resulting in a negative evaluation by that member and positive evaluation of another member as having the lowest probability of resulting in a negative evaluation by that member. Nonresponses or blanks (B) are seen as having a greater conditional probability of generating a negative evaluation than initiating positive evaluations. In such a case, optimal behavior for lower status team members (i.e., behavior that best maintains the agent’s status position or minimizes status loss given a contribution to the team objective) is unlikely to be nonparticipation. Moreover, since members can credibly send only a limited number of positive evaluations, this increases the likelihood that lower status members initiate higher proportions of information in the form of relatively neutral or low-risk information types such as data or facts than do higher status team members. This expectation was supported in empirical results that were reported in Chapter 5. BIAS IN MICROLEVEL STATUS JUDGMENTS A basic claim of the present framework is that similar to biases in assessing monetary outcomes, (Abdellaoui, Bleichrodt, & Paraschiv, 2007; Kahneman & Tversky, 1979, 2000) judgments of the status of others introduce systematic bias in the valuation of gains and losses. Contrary to expected value formulations in which gains and losses are treated equivalently, team members are generally loss aversive. That is, they will generally pay more to avoid a loss with a given likelihood than for a gain of the same likelihood. In the case of status judgments, this can be interpreted as meaning that they will overweight the possible status loss from a negative evaluation by a higher status team member in comparison with a negative evaluation from a team member who is lower in status by an equivalent distance.1

216  Decision Making Groups and Teams A form that reflects this bias can be written as

( ) b 1 + (σ j − σ k )

∆ jk = 1 + σ j − σ k

a1

2 n +1

1

2 n +1

if σ j > σ k if σ j < σ k

1 < b1 < a1 Where Δjk is the judged distance between the j-th and k-th member. σ j , σ jk are the actual status of these members a and b are parameters, and n is team size. In this form, the status difference is in the interval (0,1), and under- or overweights actual status as described above. Although a linear loss function will evidence the bias toward information types in a group as it arises from status differentiation, the inherent bias in status judgments that are postulated makes the loss function nonlinear.

DYNAMICS OF IDEA INITIATION IN INTERACTIVE TEAMS SINGLE-SOURCE AND MULTIPLE-SOURCE IDEAS IN THE INFORMATION EXCHANGE OF INTERACTIVE TEAMS Ideas generated by team members can be dichotomized into those which are the initial ideas of a single member and those which are combinations of initial ideas that they or other members of the team have already given. As has been suggested, the latter category of ideas can provide interactive teams with an important basis for process gains (i.e., superiority in the number and quality of ideas over an equal number of individuals acting independently of each other).

Single-Source Ideas A basic form to represent rates of generating single-source ideas has been written as Eq (5A.1):

(

(

)

J (j1) = K j Aj − α j Ji(1) ,… , Jn(1) − J (j1)

)

Where J (j1) is the j-th member’s rate of giving single-source ideas; J (j1) is the cumulative number of single-source ideas given by the j-th member ( J (j1) ≥ 0) . Aj is the initial idea pool of the j-th member, and α is the overlap (or redundancy) in initiated ideas;

Information Exchange in Decision Making Teams  217 Kj is the rate at which the j-th individual becomes aware of and initiates a single-source idea. This rate is defined as a function of the distribution of   the member’s status in the team. σj  , where ∑ σ = 1 K = g First approximation to a K function can be written as Kj =j g  (1 + var (σ k ))  , where ∑σ = 1. The current size of the pool of relevant ideas that the j-th member is aware of Jj and is thus a function of this member’s initial idea pool as corrected by the number of ideas already given by this member and the number of ideas either given by all other team members or common ideas in their idea pools. The form of the K function maintains that the initiation of a realized idea is directly related to a member’s relative status and inversely related to variation in status differences in the group. The functional form of the status distance term Kj is likely to have multiple arguments in social factors that inhibit or enhance idea initiation and is considered in further detail in discussion to follow.

Multiple-Source Ideas Multisource ideas are based on combinations of ideas that can be in terms of (1) an idea by the j-th members that has not been given in the team and an idea of another team member and (2) two ideas already given by team members other than the j-th member that are combined by the j-th member. The rate of generating combinatorial or multisource ideas by the j-th member can be written as

(

  J (j2) = K j  ∑ Jk(1)  η j + K j ∑ Jk(1)  k≠ j 

( )

)

2

− c1 ∑ Jk(2) k≠ j

where J˙ (2) j is the cumulative number of multiple-source ideas given by j, J˙ (2) is the corresponding rate of giving multiple-source ideas j ηj is the number of single-source ideas in the j-th member’s idea pool that (1) have not been initiated (i.e., (ηj = Aj – Jj(1) – αj( J(1) 1  … Jn )) and A, J, and Kj are as previously defined. For the three terms on the RHS of the above equation, the first term results from the product of all single-source ideas given by team members other than j and the sum of the j-th member’s unduplicated ideas (either already initiated or in his or her associative hierarchy). The second term is defined as the number of unique combinatorial ideas by all individual members of the group from their own ideas. The final term removes all combinatorial ideas already given by any team member. Substituting for ηj from J (j1), J (j2) can be rewritten as:

(

(

  J (j2) = K j  ∑ Jk(1)  Aj − J (j1) − α j J1(1) ,… Jn(1)   k≠ j

)) + K ( ∑

k

Jk(1)

)

2

− c1 ∑ Jk(2) k

218  Decision Making Groups and Teams ˙(2) Taken together, J˙(1) j and J j represent the dynamic of idea initiation by the j-th individual. Equation J˙(1) j represents this member’s rate of generating his or her original ideas that are not combined with ideas of other team members. Equation J˙(2) j represents the corresponding rates of generating ideas that result from combining the j-th member’s own ideas with ideas of other team members.

Social Mediators of Information Exchange: Equity and Trust in the Rate Mediator The initiation of ideas is more likely to elicit a negative evaluation from other team members and have the consequence of status loss than the initiation of, say, a data message. The extent to which a team member will be willing to risk his or her status to contribute to team objectives depends in part on their trust in other team members to act similarly and manage the exchange equitably (e.g., Alanah & Ilze, 2009; Malhotra, Majchrzak, & Rosen, 2007; Rosen, Furst, & Blackburn, 2007). Equity in information exchange can be described in terms of a distribution of positive and negative evaluations to members that is proportional to the ideas they initiate in information exchange. That is, a member who initiates fewer ideas should receive fewer evaluations. This is counter to the tendency to underevaluate high-status members and overevaluate low-status members that is frequently observed in status-differentiated teams. The generation of trust can be conceptualized in terms of deviations from an equity state where inequity (E) is defined as:  ∑ ∆jk an 2 N ∑ ∆jk ap2 Pjk jk J j J j k Ej = cn − + cp − k , cn >> c p   J ∑ ∑ Jk ∑ ∑ ∆kl an 2 N ∑ ∑ ∆kl ap2 Pkl kl k k

k l

k

k l

Inequity in the distribution of negative evaluations is weighted more heavily than inequity in the distribution of positive evaluation for reasons discussed in Chapter 6. Trust is defined as decreasing exponentially with increases in E. Tj = 1 + Ej 

−1

In this form, TE (0,1) trust is primarily a function of defined inequity. Trust enters the rate of idea generation through the mediator, K. While it is also influenced by the number of evaluations exchanged, an elaborated form can be written as −c ∑ ∆ N −c ∑ ∆ P K j = T  1 − an 1 jk jk + bn 2 jk jk  ; 0 < c1 < c2 a >> b N jk n j n kj n kj n jk n n n n ∆kj ∆kj

(

)

1 1 Pjk = ap J j + bp Pkj − c p Nkj + d p ; Pjk ( 0 ) = d p , c p > ap >> bp ∆kj ∆kj

(

)

 implies that The form for N jk ∂ ∑ N j (0) ∂ var σ j

< 0 and

∂ ∑ N j (∞) ∂ var σ j

>0

The assumption that the initial rate is decreasing in the var σj, and the asymptotic rate is increasing in var σj is consistent with the discussion of the exchange of negative evaluations as information and affect in Chapter 6, Part A.

Dynamics of Member Status In addition to demographic and organizational sources of status (Washington & Zajac, 2005), a member’s status in a team is updated by the information that he or she initiates and receives. Consistent with this claim, member status in an

220  Decision Making Groups and Teams interactive team can be written as depending on an initial set of bn attributes and the evaluations that are received over the course of the team’s interaction. The initial sets of attributes are commonly in demographics of the team member and organizational position. A form for initial status and its time variation through information exchange can be written as follows:

σ j = α s ∑ ∆jk Pjk − β s ∑ ∆jk N jk , σ j (0) = j ≠k

j ≠k

i ∑ b(i) j w i

∑ ∑ bk(i)w i k i

Where σ is the change of a member’s status in a time period b(i), i = 1, n are a set of sociodemographic variables that are relevant to status judgments, w(i), i = 1, n are the weights of these variables in status judgments, βs >> αs and Δ, P, and N are as previously defined. A heterogeneity index in the team can be written as H = ∑ ∑ ∑ b(ji) − bk(i) wi , b(i) ∈ ( 0, j k≠ j i wi, b(i) ∈ (0,1). The heterogeneity index sums the difference between members in the i-th attribute of b as weighted by the importance of this attribute to status judgments.

Integrated System for Team Information Exchange The dynamic forms that have been proposed for information exchange in ill-structured decision making by interactive teams can be integrated into the system summarized in Table 10B.1.

Dynamic Information Exchange in Interactive Groups and Teams: Integrated Systems and Dynamic Idea Functions Single Source

( (

))

) (

(1) (1) , J (j1) (0) = 0 J (1) j = Ka , j Aj 1 − α j − J1 … Jn



(10A.1)

Multisource

(

(

  J (j2) = Ka, j  ∑ J (jk1)  Aj − J (j1) − α j J1(1) … Jn(1)  k≠ j 

(

Aj − J (j1) − α j J1(1) … Jn(1)

))

2   + Kb, j   ∑ J (jk1)  −   k≠ j  

))

2   + Kb, j   ∑ J (jk1)  −   k≠ j   

  T (2)  (2)  c1 ∑ Jk   ; J j (0) = 0  k≠ j 

  T (2)  (2)  c1 ∑ Jk   ; J j (0) = 0  k≠ j 

(10A.2)

Information Exchange in Decision Making Teams  221 Total Idea Number (2) J j = J (1) j + Jj

(10A.3)



The rate mediator, K, in (9.1) and (9.2):

(

∑ ∆jk N jk

K j = Tj 1 − an

1

∑ ∆jk Pjk

+ bn

1

)

(10A.4)

where Δjk is defined by

( ) b 1 + (σ j − σ k )

∆ jk = 1 + σ j − σ k

a1

1

( 2 n + 1)

if σ j > σ k

( 2 n + 1)

if σ j < σ k

1 < b1 < a1 < 2n + 1

Equity and Trust Dynamics Inequity   J ∑ ∆ jk an 2 N jk j k  Ej = cn −   ∑ J ∑∑∆ a N  k k k l kl n 2 kl

   ∑ ∆ jk ap2 Pjk  + c  Jj − k  p  ∑ J ∑ ∑ ∆ a P   k k k l kl p2 kl

  , c >> c p (10A.5)  n  

Trust Tj = 1 + Ej 

−1

(10A.6)



In (10A.6), Tj, Ej∈(0,1)

Dynamic Evaluation Functions Negative Evaluation  = 1 a J − b P + c N + d ; N (0) = 1 d , c >> a >> b (10A.7) N jk n j n kj n kj n jk n n n n ∆jk ∆jk

(

)



Positive Evaluation 1 Pjk = ap J j + bp Pkj − c p Nkj + d p ; Pjk (0) = ∆kj d p ∆kj

(

)

(10A.8) 

222  Decision Making Groups and Teams

Dynamics of Member Status σ j = α s ∑ ∆jk Pjk − β s ∑ ∆jk N jk , σ j (0) = j ≠k

j ≠k

∑ m(ji)w(i) i

∑ ∑ mk(i)w(i)

(10A.9)

k i

where β s >> α s



Member Heterogeneity H = ∑ ∑ ∑ m(ji) − mk(i) wi , m(i) ∈ (0, 1) j k≠ j i



(10A.10)

Dynamic Information Exchange: Variable and Parameter Definitions is the number of single-source ideas that the j-th member initiates in a defined time interval. Jj(2) is the number of multisource rate ideas the j-th member initiates in a defined time interval. Aj is the initial pool of ideas of the j-th member. α j ( J1(1) , , Jn(1) ) is the overlap in the initial pool of ideas between the j-th and k-th members.2 c1 is a rate constant kj is a modifier if the rate at which the j-th member initiates ideas by the rate at which he or she is evaluated relative to other group members Njk is the number of negative evaluations the j-th member receive from the k-th member a time interval P is the number of positive evaluations the j-th member initiates or receives from the k-th member in a time interval Tj is an index of the level of trust in the j-th member, T∈(0,1) Ej is the j-th member’s judgment of equity in the group (E∈(0,1)) σj is the status of the j-th member σ∈(0,1) M, S are time-varying parameters ϕ is a parameter that defines the rate at which judged inequity decreases trust (how does T increase?) ai, bi, ci, di, i = p, n are rate constants w is the weight of the i-th attribute defining member status m is the level of the i-th attribute in the j-th number Jj(1)

Information Exchange in Decision Making Teams  223

Member Heterogeneity and Quality in Ill-Structured Decisions: Investigating Process Gains and Losses Following upon preceding discussion, ill-structured decision making can benefit from the diversity in team membership since it contributes to heterogeneity in the idea pool and thereby can increase process gains. However, diversity in the membership can also be expected to increase the variance in the distribution of member status, and thereby increase process losses that can result from biases in the distribution of evaluation in the team. As has been noted, effects of the status distribution have not been given a form or controlled for in a range of previous studies that report a net effect of process losses in interactive groups and teams. An implication of the discussion in the chapter to follow is that we now have understanding and technology to manage effects of status information on information exchange so as to maintain net process gains.

Member Status and Team Heterogeneity Relationship The above discussion implies that a policy objective would be to increase the variation in H to reduce the overlap in idea pools. This would ordinarily increase variance in status of the team members. It is evident that a key determinant of the dynamics of the system will be the relative effect of the var bk(1) on these two variables. If var bk(1) increases var αj more than it reduces the overlap, then quality will be decreasing. In such cases, one expects var bk(1) to increase var αj which then increases •∑ N jk and the misallocation of negative and positive evaluations, and thereby decreases equity, trust, and quality. This can be given an explicit form.

Linking Overlap in Initial Ideas (αj) to Member Heterogeneity (var σ) The fundamental relationship between heterogeneity in team member attributes (m) and the key variable of overlap in ideas can be written

{

}

{ }

   α jk = ∑ max 0,  min m(ji) mk(i) − max ml(i)   / ∑ mk(1) ≠ or l j k i  i      α jk1k2 = ∑ max 0,  min m(ji) mk(i) mk(i) − max ml(i)   / ∑ mk(i) + mk(i) 1 2 1 1 2 ≠ l j k k i 1 1 2  i      α jk1kn −1 = ∑ max 0,  min m(ji) mk(i)  mk(i) − max ml(i)   / ∑ mk(i) , mk(i) 1 1 n −1 1 n −1 ≠  l j k k i 1 1 n −1  i  

{

}

{

{

}

= ∑ min m(ji) , mk(i) , , mk(i) i

1

(

{ }

n −1

} / ∑ (m i

(i ) k1

{ }

+ mk(i)

n −1

)

)

(

)

224  Decision Making Groups and Teams Since j, k1 . . . kn–1 is all n people

{

}

{ }

  (i ) ((ii)) (i )    α = ∑ max α jk max 0 0,,  min min m m(jji)m mkk − − max max m mll(i)   jk = ∑ ll ≠ ii   ≠ jj or or k k     (i ) ((ii)) ((ii)) (i ) α =∑ ∑ max max 0 0,,  min min m m(jj1i)m mkk1 m mkk2 − − max max m mll(i) α jjkk11kk22 = ≠ l j k k 1 1 2 ii l ≠ j11k11k22  

{

}

{

}

{ }  

  (i ) ((ii)) (i ) (i ) α = ∑ max α jk max 0 0,,  min min m m(jj1i)m mkk1  m mkk(in)−1 − − max max m mll(i) jk11 k knn −−11 = ∑ l j k k ≠  1 1 n −1 ii 1 1 n − 1 l j k k ≠     1 1 n −1

{

(i ) (i ) (i ) = =∑ ∑ min min m m mkk(in)−1 m(jji) ,, m mkk(i1)  ii

1

n −1

}

{ }  

For example, to obtain α12 for Person 1, 2 take m1(i) and m(2i), find the minimum, and then have overlap in m(i). Then subtract the maximum m(i) for persons 3, 4, 5, . . . n. The result must be at least zero. As noted, heterogeneity decreases overlap but increases the bias from status differentiation. SUMMARY AND DISCUSSION In this part of the chapter, the functional form for the dynamics of information types and processes that mediate their exchange given in previous chapters was reviewed and integrated into a system. In the system, Eqs. (10A.1) and (10A.2) define the dynamics of single-source and multisource ideas. The rate mediator, Kj in Eq. (10A.1) and (10A.2), as defined in Eq. (10A.4), is a function of trust in team members and the number of statusweighted positive and negative evaluations a member has received. The trust variable is, in turn, defined by observed inequity in the distribution of evaluations sent to members of the group. Perfect equity, in Eq. (10A.5), is considered to occur when the number of status-weighted evaluations received by a group member is in a defined proportion to the number of ideas that the member has initiated rather than the member’s position in the status hierarchy. As previously discussed, there is typically a structural bias toward oversending negative evaluations to lower status group members. Team members have also been hypothesized to judge the social cost of a negative evaluation weighted by the status of the sender as increasing nonlinearly. Members generally overweight the cost of negative evaluation from a high-status sender and underweight the cost. This bias is represented in the form of the loss function that has been defined in Chapter 4. The Δij function that indexes the judgment by members of the status distance between themselves and other members is given a form in Eqs. (10A.4), (10A.5), and (10A.7) to (10A.9). If unmanaged, this bias can have the effect of under-

Information Exchange in Decision Making Teams  225 mining trust in the team to distribute evaluations according to member contributions to team objectives. Eqs. (10A.7) and (10A.8) define the rates of sending negative and positive evaluations, respectively, by a team member. These rates are considered to be proportional to the number of ideas and evaluations the member has received. Eq. (10A.9) defines member status change as a function of the numbers of status-weighted positive and negative evaluations that the member has received, with the latter being more influential than the former. A straightforward definition of member heterogeneity in the group as a weighted function of demographic variables is given in Eq. (10A.10). Member heterogeneity can be expected to increase process gains through reduced redundancy in initial idea pools. However, it also can increase process losses through effects that status differentiation can have on the allocation of evaluations. This trade-off has been given an explicit form. In Part B of this chapter, computational methods are used to investigate the dynamics implied by the integrated system. Particular attention is given to parameters that mediate the effects of structure on information exchange. As considered in the chapter to follow, the managerial problem in maximizing decision quality is to decouple process gains from process losses such as those that can occur through member heterogeneity. REFERENCES Abdellaoui, M., Bleichrodt, H., & Paraschiv, C. (2007). Loss aversion under prospect theory: A parameter-free measurement. Management Science, 53, 1659–1674. Alanah, M., & Ilze, Z. (2009). Trust in virtual teams: Solved or still a mystery? SIGMIS Database, 40, 61–83. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263–291. Kahneman, D., & Tversky, A. (Eds.). (2000). Choices, values and frames. New York, NY: Cambridge University Press. Malhotra, A., Majchrzak, A., & Rosen, B. (2007). Leading Virtual Teams. The Academy of Management Perspectives, 21(1), 60–70. Rosen, B., Furst, S., & Blackburn, R. (2007). Overcoming barriers to knowledge sharing in virtual teams. Organizational Dynamics, 36(3), 259–273. Silver, S. D., Cohen, B. P., & Crutchfield, J. H. (1994). Status differentiation and information exchange in face-to-face and computer-mediated idea generation. Social Psychology Quarterly, 108–123. Washington, M., & Zajac, E. (2005). Status evolution and competition: Theory and evidence. Academy of Management Journal, 48, 282–296.

10B Dynamics of Information Exchange in Decision Making Teams Computational Exercises

OVERVIEW In Part B of this chapter, dynamics of the integrated system proposed in the first part of the chapter are examined in computational exercises. Sensitivities of the number of ideas generated by the integrated system functions to key parameters of the integrated system are examined first. Recognizing that objectives in team decision making are in measures of quality rather than the numbers of information types, a form for quality is then proposed. Both team inputs from their information exchange and the information input of sponsoring organizations are represented in a production function for quality in team decisions. Computational exercises then examine the sensitivities of this function to system variables and parameters. The computational studies directly demonstrate dynamics implied by the account of quality functions and their inputs that have been described in previous chapters. Results of the studies also indicate candidate policy variables and procedures to manage toward quality and ill-structured decisions. Finally, implications of the results for management of the information exchange in decision making teams are discussed. INTRODUCTION: COMPUTATIONAL EXERCISES The functional forms that have been proposed for a system in the information exchange of decision-making teams offered in Part A of this chapter are next used in computational exercises that examine system dynamics. A discrete time system of Eq. (10A.1) to (10A.9) in a case of an n = 5 person team will be investigated in these exercises. As has been reviewed, background studies have primarily used brainstorming tasks in which idea number is the dependent variable. The first exercise uses this dependent variable. In exercises that follow, a quality function for ill-structured problems and decisions is defined as a dependent variable. The computational exercises first demonstrate that the number of ideas initiated in the team exhibits hypothesized direction and statistical significance in its sensitivities to key parameters of the system. Exercises examining the

Dynamics of Information Exchange in Decision Making Teams  227 claim that the number of ideas is maximized when the status distribution is equal across members is then reported. Finally, the dynamics of information exchange by a task-directed team is more explicitly set in an organizational context. Quality in information exchange in the team is defined as a labor input to the quality of an organizational decision. Since organizations typically provide an information base for decision-making teams, the accumulated stock of information in the organization is considered as a form of a capital factor in a decision quality function. The sensitivities of the defined quality function for an organizational decision to the team’s information processing and the organizational capital input is then examined in computational exercises.

Directional Sensitivities of the Team’s Idea Generation to System Parameters The sensitivity of the number of ideas generated by a team to three key parameters in the system is first estimated. The first parameter is Kj in Eq. (10A.1). This parameter is a rate modifier for the proportion of realized single- and multisource ideas that is actually initiated in information exchange. As has been defined, Kj is trust-dependent and follows from a member’s judgment of perceived equity in the distribution of evaluations in the team. The second parameter, an in Eq. (10A.7) defines a member’s rate of sending negative evaluations to ideas initiated by other members. For most of its range, as the rate of sending negative evaluations to initiated ideas increases, the proportion of realized ideas that are subsequently initiated is expected to decrease. The third parameter, cn in Eq. (10A.5) defines the sensitivity of a team member’s equity judgments to the distribution of evaluations. A receipt of negative evaluations that is not in a defined proportion to the number of ideas the member has initiated is expected to decrease perceived equity. A team member can observe how closely the number of negative evaluations that he or she receives is related to the number of ideas that he or she has initiated rather than to their status position in the team. As the sensitivity to perceived inequity in the distribution of negative evaluations increases, trust and the proportion of realized ideas that are initiated by the member is expected to decrease. The computational program for the exercises in idea initiation compiles individual records of information exchange by each of the five team members as separate entities and sums them across members for all team levels. Three levels of each of the system parameters over a range in which the system is well-behaved are investigated. For each of the cells of the design, a mean of 10 runs in a total of 104 discrete time periods are reported.

Sensitivity Analyses Results for the sensitivity of the mean number of ideas initiated in a team to key model parameters are reported in Table 10B.1.

228  Decision Making Groups and Teams Table 10B.1  Mean Total Ideas at Three Levels of System Parameters1,2 Ka

Ka1 cn1

Ka2

cn2

cn3

cn1

cn2

Ka3 cn3

cn1

cn2

cn3

an1 2290.90 2279.55 2272.36 2318.61 2312.29 2303.20 2360.62 2355.56 2348.41 an2 2210.90 2198.74 2185.48 4448.50 2240.39 2228.90 2313.44 2305.90 2293.22 an3 2142.48 2127.96 2112.40 2191.49 2178.54 2165.10 2270.30 2260.55 2249.49

Table 10B.2  Regression of Idea Number on System Parameters1,2 Unstandardized Coefficients Model

B

SE

(constant) Ka (eq 10A.4)

 101910.75    2370.55

 2408.34   542.47

an (eq 10A.7)

–370233.46

cn (eq 10A.15)

 –48411.63

Standardized Coefficients Beta

t

sig

4.56

47.1  4.4

.000 .000

54511.46

–.709

 6.8

.000

 2830.01

–.123

 1.7

ns

Table 10B.1 shows that, as expected, mean total ideas are decreasing with decreases in Ka, the rate modifier of realized ideas, and in cn, the sensitivity of equity judgments to the distribution of negative evaluations, and increases in an, the rate of sending negative evaluations to ideas. Table 10B.2 reports results of a regression of the total number of ideas generated by the system on study parameters. Signs of coefficients are in directions predicted by the preceding conceptual account. Effects of the Ka and an parameters on idea number are highly significant. Although the effects of cn are a predicted direction, the coefficient for this effect of this parameter is not statistically significant. IDEA NUMBER AS A FUNCTION OF THE LEVEL OF STATUS INEQUALITY IN THE TEAM The discrete-time model is next used to directly show the dynamic relationship of idea number to the status distribution in a team. In this exercise, it is hypothesized that a condition where members of an n-person team are equal (i.e., σ1 = σ2 = σ3 = . . . = σn) for some of i = 1, . . . , n in a team will result in significantly more ideas in a team than an alternative condition in which team members are unequal in status (e.g., σ1> σ2> σ 3> . . . > σ n. In the

Dynamics of Information Exchange in Decision Making Teams  229 Table 10B.3  Mean Single-Source and Multisource Ideas as a Function of the Status Distribution in a Team

Status Equal Status Unequal

J1

J2

Total

44,463 37,984

180,604 135,053

225,067 170,037

framework that has been elaborated, status equality in a team is expected to increase an objective function that is defined as the number of ideas that members initiate. While as documented in a previous chapter, such equality is generally not realized in teams, available technology has the capability of managing information exchange to get closer to an optimizing objective. Three weighted status attributes of a team member are used in the form in Eq. (10A.9) to generate the member’s status in the team. In actual teams, these are commonly generated from a set that includes age, gender, education, and organizational tenure and rank. In this exercise, the coefficients of variation in the status distributions are set as cv = 0 for the status equal team and cv = .44 for the status unequal team. Following the discussion in Chapter 2, status organization is typically tacit in its generation but emergent early in the history of interactive groups and teams. As reported in Chapter 9, there is evidence that such a covert social structure is in place even in a case where there was prior explicit agreement that members would be regarded as social equals in interaction. Table 10B.3 shows condition differences in the mean numbers of singlesource (J1) and multiple-source (J2) ideas until a stopping criterion was reached for each condition on the status distributions. The midpoint in the range of the three parameter levels that were studied are shown in the table. The number of single-source and multisource ideas and the total number of ideas generated in the group (J1, J2, and ∑Ji, respectively) are significantly higher (p < .001) in the status equal team than in status unequal teams. As indicated in Chapter 5, combinational ideas in J2 can be principal source of process gains from interactive teams in comparison with nominal teams. Differences in both J1 and J2 categories of the idea function (p < .001) show the effects of team structure as defined by the distribution of status on the objective of idea number. NOMINAL AND INTERACTIVE TEAM COMPARISONS The cases in the study of team structure are next expanded to introduce the comparison between nominal and interactive teams that continues to be of interest to investigators. As has been indicated, interactive teams introduce the possibility of process gains by a member through combining the ideas of other team members into new ideas. There is correspondingly a possibility of process loss from the biases in information exchange that a

230  Decision Making Groups and Teams status-differentiated team can introduce. In a nominal team, members do not interact, and the number of two-way ideas is limited to the combining of a member’s own ideas. However, a nominal team does not incur process losses from status differentiation in the team. This exercise is intended to indicate process gains that interactive teams can attain as implied by the model. Effects of status differentiation are parametized as small in magnitude since, as has been cited, technology has capabilities to manage effects of status distributions. This exercise contrasts two magnitudes of status differentiation in the team, a status equal team and a nominal team. Definitionally, cv = 0 in the status equal team and the nominal team. For status-differentiated teams, in this exercise cases of cv = .13 and .44 are again used. In the nominal team, a default proportion of .2 of single-source ideas is initially assumed to be the combinational ideas of a member that use the member’s own previously given ideas. This proportion is close to one that has been observed in lab analyses of interacting groups. It represents the possibility that individual members combine their own single-source ideas into multisource ideas in the absence of interaction with other team members. In interactive teams, numbers of combinatorial ideas are generated by the parameterized system for interaction between members. 70 status equal cv=0

60 50

status unequal 1 cv=0.13

t idea

40 status unequal 2 cv=0.44

30 20

10

nominal group

0 0

2000

4000

6000

8000

10000

12000

time period

Figure 10B.1  Total Idea Number (t idea)as a Function of the Status Distribution in a Team

Dynamics of Information Exchange in Decision Making Teams  231 The differences between dynamic paths of a status equal team, two levels of a status-differentiated teams, and a nominal team that were generated with the computational model over the first 103 time periods are shown in Figure 10B.1. Clearly, these results are parameter dependent. As indicated above, parameters in the model are conservatively set. The results to demonstrate differences in a team objective that technology-enabled interactive teams can generate in the presence of status distributions that they generally carry with them. QUALITY IN ILL-STRUCTURED ORGANIZATIONAL DECISIONS The computational results that have been reported in the previous section exemplify how process gains in idea number are likely to be mediated by the status distribution in the team. Although the information type of ideas is well-recognized as a principal input to the quality of ill-structured decisions, quality functions in these applications generally have additional dependencies. First, evaluations are filters of ideas that merit representation in a quality function. As indicated in Chapter 6, the ratio of the number of negative evaluation to other information types that maximizes quality is likely to be nonlinear. Too low a ratio of negative evaluations are likely to under filter ideas. Too high a ratio of negative evaluation are likely to generate numeric effects that underline quality. Second, decision quality as more broadly considered in an organizational context, generally has exogenous dependencies. One of these is information input that organizations generally offer to a team. This input is commonly a baseline for information exchange. In the next section, these observations on decision quality are elaborated upon and an explicit form is given to a quality function for an organizational decision. The dependencies in this function are then examined in computational exercises. DECISION-MAKING TEAMS IN ORGANIZATIONAL SETTINGS As indicated in previous sections, idea generation in an interactive team increases in its importance when decisions are ill-structured and has been extensively studied in empirical assessments of interactive groups and teams. However, the objective in an organization’s ill-structured decision making is clearly in the quality of the decision rather than idea number. While idea generation in the team’s processing of information is likely to remain a key input to quality in an organizational decision, quality in the team’s input can be expected to depend on the processing of multiple information types and conditions on their combination.

232  Decision Making Groups and Teams As noted, the information base that the organization transfers to the team is typically an exogenous input to a team’s decision making. It can be considered as a form of capital in the production of decision quality. Giving a form to the information base as a capital stock introduces parameters in controllable profiles of organizations that can be related to a team’s decision quality. ORGANIZATIONAL INFORMATION CAPITAL Sponsoring organizations typically transfer influence to decision-making teams. Resource dependency (e.g., Mudambi & Pedersen, 2007; Pffefer & Salancik, 1978) is a well discussed basis for this. It is now not uncommon for organizations to participate in socializing teams through team building and similar procedures that are intended to establish norms (e.g., Ajmal & Koskinen, 2008; Salas, Rozell, Mullen, & Driskell, 1999). Mimetic effects through such transfers of culture that may be less obvious have also been cited (e.g., DiMaggio, 1988; DiMaggio & Powell, 1983; Ke & Wei, 2008; Liu et al., 2010). In addition to these bases for organizational dependencies of teams, there can be transfers of influence through shared information bases. Organizations provide both proprietary and nonproprietary information bases as inputs to team decisions. In many cases, this information is the building block of the team decision (Choi, Lee, & Yoo, 2010; Lambert & Peppard, 2013). As such, a form for a quality function in the organizational decision will depend on both the quality of the team’s information processing and relevant organizational stocks of information. The team’s input to quality can be defined as a labor factor that is generated by the quality-related exchange of information. The organization’s input can be defined in terms of its evidenced investment in information and the information capital that this can result in. NEGATIVE EVALUATIONS IN A QUALITY FUNCTION The functional form for decision quality that is introduced recognizes both the filtering function of negative evaluations and the contribution that equity in the distribution of the evaluations over team members can offer. Team members’ willingness to initiate more risky information (i.e., information types that have higher likelihoods of being negatively evaluated) depends on their observations of the distribution of evaluations across team members. When negative evaluations are distributed more in proportion to the number of ideas or evaluations a recipient has offered and less in terms of their status position in the team, this increases their willingness to initiate what has been designated as risky information types.

Dynamics of Information Exchange in Decision Making Teams  233 QUALITY IN AN ORGANIZATIONAL ILL-STRUCTURED DECISION In general, assume Q = f (E, Ql) Where, Q is the quality of an organizational team’s ill-structured decision, E is an organization-generated stock of information that the team uses as an input to the decision, and Ql is the quality of the input of information processing by the team. The production of quality in an embedded team decisions (Q) can be given the form of a production function in which the information input (E) of a sponsoring organization is a capital factor and the quality of information processing by the team (Ql) is a labor factor. Constant returns to scale for input factors are assumed. The labor factor is assumed to account for approximately two thirds of the factor returns in the quality output. For this form, γ 2 ,γ 1 0

234  Decision Making Groups and Teams The labor input of information processing to quality in an ill-structured decision as defined in (10B.12) is increasing in the number of ideas (J) exchanged in the team. The relationship of the number of negative evaluations (N) to quality of the teams’ information input (QL) is approximated as a quadratic and governed by the number of ideas generated in the team by its members. Up until the ratio r of negative evaluations to ideas is reached, quality is increasing in N. When the number of negative evaluations are greater than in the ratio r to ideas, increases in this information type is expected to be quality decreasing. The condition defined as the third term in QL is on the distribution of negative evaluations across team members, which assigns a penalty for distributing negative evaluations to team members that are not in proportion to the number of ideas that the member has generated. This follows from previous discussion of trust as depending on an equitable distribution of negative evaluations.

Organizational Stock of Information as a Capital Input to Decision Quality A term for the informational capital provided by an organization can be given in the form of a perpetual inventory model (e.g., Jones, 2011). EK +1 = (1 − ρe )Ek + ce e* + R 

(10B.13)

where E is the stock of information ρe is the depreciation or obsolescence rate of information e is the resource input to the information stock by the organization R is an augmentation of the stock of information generated in the organization from the use of one member’s information by other members. In Eq. (10B.13), it is assumed that unlike conventional goods, information can simultaneously be used by multiple members of the organization to generate new information. This nonrival property of the use of information is commonly cited in R&D traditions for the study of spillovers in the growth of firms (e.g., Hall, Mairesse, & Mohnen, 2009). In generating stocks of information, it is assumed that organizations can accumulate stocks of both conventional goods and information by allocating their budgets to input goods for the stocks. A form for the allocation is given in (10B.14) and assumes that allocation depends on what can be termed as a revealed preference of an organization for information and the relative prices of information and conventional goods. The revealed preference typically depends on industry, technology, and cultural orientation in the organization’s history. Even in companies within the same or closely related industries, historical differences in the orientation toward

Dynamics of Information Exchange in Decision Making Teams  235 investment in R&D and information resources are notable (Williams & Lee, 2009). P  e* = B 1 − µ (z)  z  , µe + µz = 1  Pe  

(

)

(10B.14)

where B is the organization’s budget, μ(e), μ(z) are the (normalized) revealed preference for information and conventional goods in the organization’s budget Pe, Pz are the prices of information and conventional goods In 10B.14, the quantity of new information increasing in the size of the budget’s relative price, the organizations revealed preference for information and conventional goods and decreasing in the price of information relative to conventional goods.

Computational Studies of Quality in an Organizational Decision In the next section, computational methods are used to examine the dependencies of quality that have been introduced in Eqs. (10B.11) to (10B.14). A network form for information generation in the organization is used in the exercises. The network allows interaction between members of the organization that contribute to its stock of information to be parametized. A small world form for the network (Watts, 1999) allows structure to be more explicitly represented in intergroup or interdepartmental connections. As indicated, connections can be either contiguous or remote.

Definition of the Network Model of Information Capital The network is defined as small world (Watts, 1999) in N = 4002 vertices connected in a two-dimensional regular square lattice with periodic boundary conditions. As small world, it includes neighbor and near-neighbor connections (agents who are contiguous neighbors or one cell away from contiguous neighbors), respectively, and remote connections. Remote connections (agents who were more than a single-cell distance from a referent agent) are represented as influencing an agent but at lower rates than neighbors or near-neighbors. In organizations, distance is assumed not to be geographical but in departments or sections with different internal paradigms.

Rewiring the Connections in a SWN Following the standard rewiring technique of Watts and Strogatz (1998), in each period a randomly selected fraction of the agents in the network (p) will be reconnected so that one of the end points of their connections is moved to

236  Decision Making Groups and Teams a new vertex. In this application, the vertex to which it is moved is randomly selected from a uniform distribution over the whole graph. The parameter of the proportion of connections that have been rewired to randomly selected vertices is included as an independent variable in the computational exercises with the stock of information.

Dynamics in the Computational Model In the basic implementation of the algorithm for variation across network cells, a set of values for the revealed preference parameter {μi} is defined so that i ranges over the network member cells from 1 to N2 and μi represents the current level or state of the ith edge. These are initialized to random levels picked from a uniform distribution over the range (0,1) At each time step, a synchronous update is performed, with each cell being updated according to the rule.

µt(i+)1 = δ

(( )

)

1 ∑ Mij v µt( j) − µt( j) + si (t) + fe ni j∈Ci



(10B.15)

Where δ is the update rate and ni is the total number of edges connected to the i-th vertex. St is a periodic such as business or fashion cycles and fe is a noise term. These terms allow effects on the revealed performance team to be better parameterized in exogenous effects. In the network, v(x) is defined by: 0  v(x) = 0.5  1 

x < 0 .5 x = 0 .5 x > 0 .5

so as to represent the discrete states of a vertex that an agent is assumed to be at by other agents. The parameter, M in Eq. (10B.15), represents normative influence of a meeting of an agent with another agent on the preference parameter. The periodic driving signal, si (t), is given the form si (t) = A sin(ωt) Where, ω is in radiants In the update rule of (10B.15), the noise variable f is Gaussian-distributed with a mean of zero and a standard deviation of D. After each update, a renormalization procedure is applied to ensure that the value of μi remains in its defined interval.

Dynamics of Information Exchange in Decision Making Teams  237

Computational Exercises in Decision Quality In this section, dependencies of the quality of an organizational decision that have been introduced in Eq. (10B.11) to (10B.15) are examined in computational exercises. Estimates of these factors, QL and E, respectively, are investigated over feasible parameter ranges in the defined equations. RESULTS

Organizational Stock of Information For an organization’s stock of information as an input to decision quality, sensitivities to the level of stock to the obsolescence rate, ρe, the borrowing rate, R, and network remoteness, swp, were examined first. Table 10B.4 shows the results of regressing the defined parameters on the organization’s stock of information as an input factor. For this input factor to decision quality, coefficients also have a predicted sign and are statistically significant. As typical in models of stocks, the depreciation or obsolescence rate has the largest effect. Remoteness in the network and the borrowing rate have significant effects in the stock. As indicated, the borrowing rate indexes the nonrival property of information use. Remoteness in the network reduces overlap in the meetings between members and thereby increases the stock, all else equal.

Quality in the Team’s Information Input Numerical methods are first used to examine the sensitivity of the quality in the team’s information input to an organizational decision (Ql) to parameters of the ka and kb, in Eqs. (10A.1) and (10A.2), respectively, an in Eq. (10A.7) and the variance of the status distribution (varsigma). In Eqs. (10A.3) and (10A.4) of the system, the ka and kb the index rate at which Table 10B.4  The Organizational Stock of Information as a Function of Model Parameters1,2,3 Unstandardized Coefficients Model 1

(Constant) R

Standardized Coefficients

B

Std. Error

Beta

t

Sig.

  525.258    42.619

17.463 19.592

 .078

 30.078   2.175

.000 .032

ρe

–1332.740

53.656

–.890

–24.839

.000

Noise(D)

   41.468

26.828

 .055

  1.546

.125

swp

   76.250

10.660

  256

  7.153

.000

238  Decision Making Groups and Teams realized ideas are actually initiated by team members and follow from the definition of equity and trust. The an parameter defines the rate at which initiated ideas are negatively evaluated in Eq. (10A.7). Up until the quality-maximizing ratio of negative evaluations to ideas (r) is attained, increases in the rate an are expected to be quality increasing. A linear parameter range in this range is investigated in the exercise. All else equal, increases in the variance in the status distribution are expected to decrease the quality of the labor input. Results of a regression of model parameters on the quality of the team’s information input are in Table 10B.5. The dependencies of quality in the information input as defined in Eq. (10B.11) to defined ranges of each of the study parameters over 104 runs are shown in Figure 10B.2a to 10B.2d. In this figure, quality is monotonically increasing in the ka and kb parameters and decreasing in varsigma over the range of these parameters under study. Results shown in Table 10.5 indicate that the parameters have predicted signs for the defined parameter ranges and show significant effects of model parameters on the quality of the team’s information input.

120

120

100

100

80

80 kg(5)

60

kg(4)

60

kg(7) 40

kg(9)

20

kg(6) 40

kg(8)

20

0

0 0

2000 4000 6000 8000 10000 12000

0

2000 4000 6000 8000 10000 12000

(a): Ka

(b): Kb

Figure 10B.2a  Quality in the Team’s Information Input to an Organizational Decision as a Function of Model Parameters1,2,3 1

The dependent variable in (a) to (d) is decision quality as defined in Eq. 10B.12.

2

Ka is a rate modifier for the proportion of realized ideas that are initiated as a function of an.

Kb is a rate modifier for the proportion of realized ideas that are initiated as a function of ap. an is the rate of sending negative evaluations to initiated ideas. ap is the rate of sending positive evaluations to initiated ideas. Varσ is the variance in the distribution of status in the team 3

Ranges of parameters investigated are

Ka = (0.5, 0.7, 0.9); kb = (0.4, 0.5, 0.8); varσ = (0, 0.534, 1.128); an = (0.015, 0.03, 0.045)

Dynamics of Information Exchange in Decision Making Teams  239 120

120

100

100

80

80 mrc (5)

60

ad(0.015)

60

mrc (7) 40

mrc (9)

20

ad(0.03) 40

ad(0.045)

20

0

0 0

0

2000 4000 6000 8000 10000 12000

2000 4000 6000 8000 10000 12000

(c): varσ

(d): an

Figure 10B.2b Table 10B.5  Quality of the Team’s Information Input as a Function of Model Parameters Coefficients1 Unstandardized Coefficients Model 1

Standardized Coefficients

B

Std. Error

130.725 2.234

3.540 .353

kb

3.593

.353

.527

10.188

.000

an

260.971

25.958

.520

10.053

.000

–6.280

1.250

–.260

–5.024

.000

(Constant) ka

varσ

Beta

t

Sig

.328

36.932 6.336

.000 .000

SUMMARY AND DISCUSSION This chapter has reviewed microprocessing in the information exchange of a team’s decision making as put forth in previous chapters. In Part B of the chapter, dynamics of the integrated system proposed in Part A were examined in computational exercises. Sensitivities of the idea number function to parameters were indicated in the first exercise. Consistent effects of the status distribution in the team on idea number were then directly demonstrated. Following these demonstrations, a definition of quality in an ill-structured team decision was then elaborated in which it was recognized that quality is dependent on both information processing in the team and an exogenous input of information from its organizational sponsors. Accounts of microprocessing and dynamics of information types of ideas and negative evaluation introduced in previous chapters were integrated into a system in Part A and studied in computational exercises. The sensitivities

240  Decision Making Groups and Teams of ideas as the dependent variable to system parameters were first examined. This information type is a key contributor to the quality of an ill-structured decision and has most often been investigated in previous empirical studies. Quality in a team decision was then addressed. In information processing by a team, quality is conditioned by a balance between ideas and negative evaluations. The level of negative evaluations has to be of a magnitude sufficient to filter ideas on quality criteria but less than the magnitude that will inhibit subsequent idea initiation. A form for a quality-maximizing decision in terms of the information processing by the team that follows from above premises was then offered as a labor input to quality. The dependencies of the information stock from external organizational sources as a capital input to quality were defined in parameters of an organization’s historical rates of investment in information and the organizational network. The sensitivities of the team and organizational inputs to key system parameters were also examined in computational exercises. The results of the computational studies directly demonstrate dynamics implied by the account of quality functions and their inputs that have been described in previous chapters. They indicate candidate policy variables and procedures to manage toward quality in ill-structured decisions. New technology in managing the information exchange of teams and recently reported practical experience with virtual teams provide a basis to expect substantial improvements in the efficacy of interactive teams for relevant objectives. Given this background, more complete definitions of objectives in the quality of ill-structured decision making and the information inputs to this objective are of increased importance. The definition of quality and its generating functions summarized in this section of the chapter parametizes behavioral and social properties that enter into interactive information exchange and quality objectives. In the next chapter, these results are considered as prescriptive for the design of decision support systems in which managerially imposed conditions on interaction in a decision-making team can be maintained. A more explicit objective function in terms of controllable inputs is defined and procedures to manage toward quality optimizing levels of control variables is defined and exemplified in an application. REFERENCES Ajmal, M., & Koskinen, K. (2008). Knowledge transfer in project-based organizations: An organizational culture perspective. Project Management Journal, 39, 7–15. Choi, S. Y., Lee, H., & Yoo, Y. (2010). The impact of information technology and transactive memory systems on knowledge sharing, application, and team performance: A field study. MIS Quarterly, 34, 855–870. DiMaggio, P. (1988). Interest and agency in institutional theory. In L. Zucker (Ed.), Institutional patterns and organizations (pp. 3–22). Cambridge, MA: Ballinger.

Dynamics of Information Exchange in Decision Making Teams  241 DiMaggio, P., & Powell, W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48, 147–160. Hall, B., Mairesse, J., & Mohnen, P. (2009). Measuring the returns to R&D. NBER Working Paper No. 15622. Cambridge, MA. Jones, B. (2011). The human capital stock: A generalized approach. National Bureau of Economic Research Working Paper No. 17487. Ke, W., & Wei, K. (2008). Organizational culture and leadership in ERP implementation. Decision Support Systems, 45, 208–218. Lambert, R., & Peppard, J. (2013). The Information Technology—Organizational Design Relationship Information technology and new organizational forms. In D.R. Galliers & E.D. Leidner (Eds.) Strategic Information Management (4th edition) (pp. 427–459). Oxford: Butterworth-Heinemann. Liu, H., Ke, W., Kee Wei, K., Gu, J., & Chen, H. (2010). The role of institutional pressures and organizational culture in the firm’s intention to adopt internet-enabled supply chain management systems. Journal of Operations Management, 28, 372–384. Mudambi, R., & Pedersen, T. (Eds.). (2007). Agency theory and resource dependency theory: Complementary explanations for subsidiary power in multinational corporations. In T. Pedersen & H. Volberda (Eds.), Bridging IB theories, constructs, and methods across cultures and social sciences. Basingstoke, United Kingdom: Palgave-Macmillan. Pfeffer, J., & Salancik, G. (1978). The external control of organizations: A resource dependency perspective. New York, NY: Harper and Row. Salas, E., Rozell, D., Mullen, B., & Driskell, J. (1999). The effect of team building on performance: An integration. Small Group Research, 30, 309–329. Watts, D. (1999). Small world. Princeton, NJ: Princeton University Press. Watts, D., & Strogatz, S. (1998). Collective dynamics of small-world networks. Nature, 393, 440–442. Williams, C., & Lee, S. H. (2009). Resource allocations, knowledge network characteristics and entrepreneurial orientation of multinational corporations. Research Policy, 38, 1376–1387.

11 Technology for Quality-Maximizing Objectives in Decision-Making Teams

OVERVIEW In this chapter, the framework for team decision making in which the social structure of a team regulates the flow of information between team members is used as a starting point for managing team interaction toward quality objectives. A form is proposed for the time dependent relationships between the flow of ideas and negative evaluations that are hypothesized to maximize decision quality. The quality of ill-structured decisions is assumed to be a monotone increasing function of the number of ideas exchanged. In contrast, the relationship between the number of negatives and decision quality is assumed to be quadratic. While negative evaluations contribute to decision quality by sorting ideas, too large a number of negative information type can decrease the number of ideas generated in the team. A linear system for the dynamics of these information types in a team is then used to formulate optimal information exchange over time as a control problem. The quality-maximizing amount of negative evaluations is defined as a stage-varying control variable. The control problem highlights issues in defining a stage transition in heuristic decision making phases. Finally, the technology of managing information exchange to follow the quality maximizing path of information types is considered. INTRODUCTION An integrated dynamic form to information exchange in interactive decisionmaking teams that represents effects of social structure in the team was given a form in previous chapters. This chapter addresses methods to manage teams toward objectives in decision quality. In the reduced form of the system introduced in the previous chapter, a decision team’s quality maximizing objective is defined as (1) maximize the number of ideas generated by the team, (2) maintain the exchange of negative evaluations in a defined ratio to

Technology for Quality-Maximizing Objectives  243 the number of ideas, and (3) send evaluations to individuals in proportion to the number of ideas they have initiated. One of the conditions that increases the number of ideas exchanged is in heterogeneity in team membership as, for example, in their demographic and background variables. As noted, the diversity in demographic variables that can contribute to team objectives through increased heterogeneity in the idea pool can be dysfunctional for team objectives through effects of status differentiation on biases in the team’s exchange of information. The basis for this has been reviewed in previous chapters. An additional managerial objective would then be to retain contributions of diversity to the initial idea pool while minimizing the biasing effects of status inequality that can arise from diversity. This chapter considers procedures that can be implemented through GDSS technology to attain these objectives in the information exchange of the team. Preceding discussion has suggested that management of the paths of evaluations is likely to be a key control in maintaining quality maximizing information exchange. One tradition in managing the interaction of team members directly implements rules that order or constrain information exchange. For example, nominal group (Boddy, 2012; Lloyd, 2011) and Delphi procedures (Hasson, Keeney, & McKenna, 2000) either have members generate ideas independently or provide rules for the ordering of member opportunities for idea initiations in one phase of the interaction of group members. However, these procedures for removing process losses that can occur from the biased exchange of negative evaluations also limit or remove process gains that result from unconstrained interaction. This chapter considers methods to manage information exchange toward quality maximizing that continuously maintain interaction. The conjecture is that process gains are best served when the type and timing of information exchanged in the team is set as a function of a sequence of interaction events that can be related to decision quality. Systems for computer-mediated management of interaction have the potential to manage information flows according to concept-defined designs. For example, they can allow teams to reduce structurally generated biases on information exchange, by either anonymity in information exchange or the masking of status cues for at least parts of the interactive exchange of information. Available methods also allow editing and inserting of information to control the path of overt evaluations toward conceptualizations of quality maximizing paths. Computer-mediation and face-to-face interaction can also be mixed in a hybrid system when the objectives of the group are served by different amounts of interaction or by anonymity in different phases of decision making. The foregoing discussion of team objectives in decision making and mediating effects of structure on attaining these objectives is next considered from a managerial perspective.

244  Decision Making Groups and Teams MAXIMIZING QUALITY OBJECTIVES AS A CONTROL PROBLEM The form of a dynamic system for information exchange in team decision making toward quality objectives can provide a framework for the use of technology to manage the team toward quality objectives. To facilitate the design of managerial procedures, the form indicates an objective-maximizing path of dynamic variables that are modifiable by a manager or managerial program. Control systems (Green & Limebeer, 2012 [1994]; Lin & Antsaklis, 2009) are useful specifications of a dynamic system in which a path of a variable can be designated to optimize objectives. In an application that follows from the system that has previously been introduced and the defined objective in decision making, negative evaluations (N) and positive evaluations (P) can be considered as control variables in dynamic idea functions (J). INFORMATION FLOWS IN ILL-STRUCTURED DECISION MAKING For managerial objectives, the system introduced in Chapter 10 is given a reduced linearized form in dynamic rates of ideas and evaluations. dJ/dt = – K11J + K12P – K13N(11.1) dP/dt = K21J – K22P – K23N(11.2) dN/dt = K31J – K32P – K33N(11.3) Where, J is the rate of idea exchange in the team; P is the rate of positive evaluation exchange in the team; N is the rate of negative evaluation exchange in the team; Kij is a rate parameter. The signs of the coefficients in Eqs. (11.1) to (11.3) follow empirical findings and previous discussion. In Eq. (11.1), the rate of idea generation decreases as the number of ideas exchanged in the team increases. The rate of idea generation also increases with the rate of positive evaluation exchanged and decreases with the rate of negative evaluation exchanged. Eqs. (11.2) and (11.3) give rates of exchange of positive and negative evaluations in which the rates of idea exchange are recognized as a causal variable of these rates. Thus, the sign of K21 and K31 are positive since the rate of idea generation increases the rates of both types of evaluations. Relationships between the rates of positive and negative evaluations are assumed to be negative. Empirical results in studies that have been reported suggest that they may be of smaller magnitudes than is frequently asserted. Most unclear are effects of the rates of positive and negative evaluation exchange on their own subsequent rates. Here, these effects (i.e., K22, K33)

Technology for Quality-Maximizing Objectives  245 are specified as negative since it is assumed that members generally recognize that too high a negative rate can disintegrate the team and too high a positive rate can detract from evaluative requirements of the task. However, observations of climate effects (Rupp & Paddock, 2010; Schneider, 1975; Schyns & Van Veldhoven, 2010) do suggest the possibility that K22 and K33 are not uniformly negative over the entire range of P and N, respectively, and that a nonlinear form might eventually be a more accurate representation. Such a functional form would also have potential advantage of not restricting the decline in I to be monotone in team exchanges. From previous discussion, certain strong prior hypotheses about the magnitude of K coefficients can be offered. For example, it is expected that K13 >> K12 (the rate of idea exchange is more sensitive to the rate of negative evaluations exchanged than it is to the rate of positive evaluations exchanged); K21 > K12, K21 > K22 (the rate of ideas has a greater influence on the rate of positive evaluation than the rate of positive evaluations has on the rate of ideas; the influence of the idea rate on the rate of positive evaluations is greater than the (decay) influence of the positive evaluation rate on itself); K11 > K12 (the decay rate of ideas on themselves is greater than the increase in the rate of ideas from the rate of positive evaluations). Although Eqs. (11.1) to (11.3) can be solved for equilibrium values of a matrix of K coefficients and tested for stability conditions, much of the previous discussion focuses on outcomes of controlled paths of N, and attention in explicit modeling is given to this control. In the discussion to follow, I and P are considered as partly determined by their interdependencies and initial values and partly determined by N. The level of N is considered to be adjustable to a designated optimal level.

Stage Transitions More recent research has indicated that segmenting the decision process into at least two steps, information gathering and idea generation followed by integration and decision increases the likelihood that all relevant decisions will be put forth and used (e.g., Heninger, Dennis, & Hilmer, 2006; Kerr & Tindale, 2004). The transition from the generation phase to the evaluation phase is a critical juncture in representing a conceptualization of process in information exchange that increases decision quality. As has been suggested, too early a transition to an evaluation phase can lose the contribution of uncommon ideas that occur late in the generation phase. Correspondingly, the absence of a transition can result in a loss in quality from inadequate sorting of ideas on evaluative criteria. In Figure 11.1, the transition from idea generation to idea evaluation is represented at time r. In practice, it is important to allow r, the time of transition, to be adaptively set as a function of the team’s path of information exchange as determined by criteria or judgments that assess ability, task, and properties of the interaction rather than predefined.

246  Decision Making Groups and Teams N

Jo

Po

Pv Nv aJo = Jr Jv’ Jv

No Time (t)

r

V

Figure 11.1  Hypothetical Time Paths of Quality-Increasing Information Flows in a Linear Control System: Control in N Note:  J0, Jr, Jv, are the levels of idea initiation at time 0, r, and v, respectively. Jv, is the level of J at time v as modified by the implementation of a controlled path in N. P0, Pv are the levels of positive evaluations at time 0 and v, respectively. N0, Nv are the levels of negative evaluations at time 0 and v, respectively. r is the time point in interaction of the decision-making unit at which the level of J falls below a criterion level Jr = aJ0.

In Figure 11.1, this transition is assumed to occur when the idea rate falls to Jr, a proportion, a, of Io, the initial rate of idea exchange. At time r, an increase in N serves the functions of evaluating the generated ideas while keeping the subsequent rate of idea generation at a low level so as not to interfere with evaluation and convergence to a decision. The path of J in the interval r to v is denoted by the solid line extension of the J function from Jr to Jv. The dashed line extending from Jr to Jv indicates the path which the J function would have taken if not suppressed by an increase in the N function. Ideally, the idea rate will be maintained at some low level to allow ideas to be redefined or recombined, and a few new ideas to be exchanged until the team signals convergence. Since N has been designated as the control variable in the system, observation on control of its rate becomes important. Such observations will largely focus on technology for intervention in interactive teams over the dynamic paths of the information they exchange. Figure 11.1 represents the dynamic path of the rate of idea exchange (I) and a hypothetical control path for the rate of negative evaluations (N) implied by the foregoing discussion. The team is considered to start with an initial positive rate of idea exchange that depends on variables that are exogenous to the interaction system (e.g., abilities of team members, content domain, and type of decision). As noted, uncommon, original, or quality ideas occur late in the generation of ideas so that the quality of an ill-structured decision can be expected to increase from

Technology for Quality-Maximizing Objectives  247 allowing the rate of idea exchange to have a natural stopping point. This stopping point is expected to have a fundamental dependency on the idea pool of team members. Evidence from individual sequences in idea generation suggests that teams may reach and remain at relatively low levels of the idea rate during which large proportions of the ideas increase in their uncommonness (Beaty & Silvia, 2012; Christensen, Guilford, & Wilson, 1957). Uncommon ideas are expected to be key contributors to decision making that is ill-structured. The rate of the exchange of negative evaluations is an important regulator of the rate of idea exchange. As has been conjectured, teams benefit from some sorting of content on quality criteria even during idea generation. In the present formulation, idealized paths for information types maintain negative evaluations at a low rate of exchange during team interaction as represented in Figure 11.1. Consistent with the conceptualization of a phase organization in illstructured decision making (e.g., Eisenhardt & Zbaracki, 2007; Gigerenzer & Gaissmaier, 2011), quality in ill-structured decisions requires an independent evaluation phase. Essentially, the team must critically evaluate the initial pool of ideas and converge on a decision. Thus, at some late time in interaction, it is expected that an increase in the rate of exchange of negatives can increase decision quality. Negative evaluations are suitably recognized as powerful regulators of behavior; in large numbers, they may have enduring consequences for individual status position and self-judgments. Recognizing this, too large a number of negative evaluations can consequently turn the team away from the task objective and increase the orientation toward maintenance of a member’s own status. As has been detailed, this can have the effect of limiting the numbers of ideas as higher risk information types. As such, the objective for the rate of negative evaluations is to maintain it in a defined range that may vary across phases in the decision. Thus, as directly represented in Figure 11.1, a moderate increase in the rate of N exchange at some phase in the decision sequence is expected to be quality increasing. As also conjectured, the rate of N that will yield the greatest contribution to decision quality will be in some proportion to the rate of ideas and total number of evaluation exchanged.

The Control Problem The control problem that can be specified from the preceding discussion is given in Eq. (11.4). The definition of time interval (0, ∞) in the control problem leaves the upper bound undefined since the intention is to deal with an initial case in which the interaction time is determined endogenously by I and N. Eq. (11.4) defines the system in matrix notation as:  dJ/dt  I    = K   + bN ( J, P, t) dp/dt  P  

(11.4)

248  Decision Making Groups and Teams Where, PI  is the state vector K is a coefficient matrix b is a vector of constants that defines how efficiently the control is transferred to the system N(J, P, t) is the dynamic path of the rate at which negative evaluations are exchanged in the system. The problem can be stated as: find the optimal control N(I, P, t) such that r

Min | w

v

∫ I(t)dt − ∫ 0

r

(11.5)

N (I,P,t)dt 

Where, w > 0 r = min {t > 0 | I (t) = Ir} v = min {t > 0 | I (t) = 0} and N is continuous in t on (0, ∞) and differentiable on (r, ∞). Moreover, let N (t) ≤ A1. dN/dt ≤ A2, Where, A1 is a maximum value that N can assume and A2 is a maximum rate of change in N (i.e., dN/dt). Both A1 and A2 keep the behavior of the system more consistent with plausible values for variables under consideration. If the number of negative evaluations exchanged reaches to too high a level, the group can lose all cohesion and disintegrate. Similarly, changes in the rate at which negative evaluations are exchanged after point r are bounded to avoid abrupt changes that may threaten credibility. Eq. (11.5) is an objective function that minimizes the difference between a scaling factor proportion of the total number of ideas exchanged in the interval t = 0 to t = r, and the total number of negative evaluations exchanged in the interval t = r to t = v. Time r is defined, as above (i.e., the time in team interaction where I falls below Ir (= a I0). The stopping time, v, is defined as occurring when dI/dt = 0. Interaction is conceived as a continuous exchange of ideas and evaluations. In the latter periods of interaction, it is hypothesized that negative evaluations will predominate over ideas in a quality maximizing sequence. Assessment of ideas and convergence on a decision is a primary task in these periods. When the rate of idea generation in the system goes to zero (or some low rate), interaction is effectively terminated. The quality-optimizing dynamic path of N is determined by the stage or phase in decision making. In the interval t = 0 to r, a constant level of the rate N that is proportional to the initial rate of idea exchange is expected and defined from the solution to Eq. (11.4). Here, the system designer should choose a minimum No that will provide a finite r for given initial values of variables and parameters in the system. In the interval r < t ≤ v, N depends on the total number of ideas exchanged in 0 ≤ t ≤ r, the scaling factor w and the levels of J and P. The path of P is also free in this system. This information type has not been emphasized in discussions since it is seen as a less critical

Technology for Quality-Maximizing Objectives  249 contributor to decision quality than J and N. However, it is expected that dP/ dt > 0 at t = 0 since Io will be large relative to N and K21 > K23. As I decreases and P increases, dP/dt is expected to decrease according to Eq. (11.2). When dN/dt increases at t > r, dP/dt is expected to decrease with a rate proportional to N and go to zero. Minimizing Eq. (11.5) is tantamount to maximizing decision quality under study hypotheses, since in the time interval 0 to r, it allows the system to have approximately as many ideas as a free system, and in the interval r to v, it seeks to match the number of negative evaluations to a proportion of the total number of ideas that have been exchanged. While the ratio of negative evaluations to ideas is an initial benchmark, this can be refined by advances in artificial intelligence that allow more complex rules for managing the information exchange (Lu, Zhang, Ruan, & Wu, 2007) The model is an initial attempt to provide a formal statement of the conceptualization and hypotheses that have been offered. Its primary function is to provide additional form for a statement on the design of information flows and interaction conditions that maximize decision quality. Initially, the behavior of the system with different values of a and w can be investigated computationally to give insight into how sensitive systemic outcomes are to levels of these parameters. Additionally, there can be benefits from disaggregating the model into a more micro version with structural differences among individuals and defined mechanisms of aggregation. NEGATIVE EVALUATIONS AS A CONTROL VARIABLE IN INFORMATION EXCHANGE Having proposed a dynamic system for information exchange, I now turn to the technology of controlling N in group interaction. Since it is also recognized that the flow of information in teams with a well-defined status hierarchy depends on perceived risks to the relative status position of members that are the source or target of specific information exchanges, it can further objectives to modify, restrict, or eliminate source or target identifications in information exchange in early idea generation phases of the decision-making process. For example, in the generation phase, the sources of an idea need not be identified. In the evaluation phase, additional information on member abilities and other task relevant characteristics could be added to communications to increase status differentiation and the influence of team members with high task relevant abilities. The source of an N could also be kept public or private depending on judgments of their contributions and their status position. With experience in design behavior of real groups, machine-based interaction can yield information exchanges that get close to real time faceto-face groups in spontaneity and communication of affect. This capability has been suggested in reporting on virtual teams (e.g., Coppola, Hiltz, &

250  Decision Making Groups and Teams Rotter, 2004; Hertel, Geister, & Konradt, 2005; Kasper-Fuehrera & Ashkanasy, 2001). GDSSS FOR MANAGING INFORMATION EXCHANGE Technology in computer-mediated interaction offers alternatives to Delphi and other restrictive methods for controlling the flow of negative evaluations. It allows communication regimes to be managed so that group members communicate in real time, but the exact information exchanged and source-target identifications can be managed for defined objectives. What Desanctis and Gallupe (1987) and others (e.g., Nunamaker & Deokar, 2008; Poole & Zigurs, 2008) refer to as Level III GDSSs have the capability to actively intervene in the information exchange of a team and are most appropriate for the managerial tasks defined in previous sections. Systems at that level allow the management of the number, source, and target of negative evaluations exchanged in the group. As an exemplary system SMART5 (Silver, Troyer, & Marks, 2007) is designed to manage the exchange of negative evaluations through interventions by the administrator. This system has functional capabilities for • Informing: for example, providing feedback to the group on the exchange of information types that is occurring relative to criteria generated by a quality function. • Prescribing: for example, cueing the group on the amounts and types of information (e.g., evaluations) to achieve a closer approximation to a quality objective. • Message Prohibiting: for example, trapping messages to prevent dysfunctional effects of excessive negative evaluation as defined by the quality function. • Message Inserting: for example, inserting requests for negative evaluations to increase the filtering of ideas when necessary. Informing and prescribing messages are strictly informational messages. Prohibiting and inserting messages provide an option for active intervention to give direction to information exchange when information by the administrator does not result in what is judged to be quality maximizing levels of evaluations. SMART5 and other GDSSs allow a control problem for a quality objective to be formulated with enough complexity to guide the amount, typology, and targeting of information exchange. An experimenter-facilitator who manages within the GDSS can reduce dysfunctional effects that commonly arise in interactive groups and directly contribute to quality objectives in interactive groups until AI capabilities (e.g., Filip, 2008; Zamfirescu & Filip, 2010) are

Technology for Quality-Maximizing Objectives  251 sufficiently advanced to explicitly have the system define and manage toward objectives.

Managing Process Gains and Losses I further note that in addition to the capabilities to manage structural effects in interactive groups toward the objectives designated in (11.1) to (11.3), available GDSSs have the capability to manage sources of process gains and losses enumerated by other investigators and reviewed in an earlier chapter. I provide an exemplary consideration of process losses that Pinsonneault, Barki, Gallup, and Hopper (1999) and other investigators have reported in interactive brainstorming groups. These process losses are suggested to be the basis for lower performance of freely interacting groups in comparison with nominal groups. Table 11.1 provides brief qualitative descriptions of these sources and candidate GDSS capabilities to manage them. This table is intended to indicate GDSS capabilities that can address process losses in unmanaged interactive groups and teams other than those that are directly generated by structure in the decision-making unit. As has also been noted, comparison studies in which brainstorming by interactive and nominal groups have not been given a form to adequately recognize group structure as biasing the performance of unmanaged interactive groups. As such, there is reason to expect that the question of whether Table 11.1  Sources of Process Losses and GDSS Management Capabilities Source Distraction

Production blocking

Cognitive complexity

Definition Group members pay undue attention to ideas of others Synchronous idea generation diverts attention from own ideas

Experimenter-Facilitator that maintains focus in information exchange

“Cognitive burdens” of reading and processing of ideas of others

Ideas of other can be stored and read according to a schedule

“Striving for Undue focus on originality” ideas of others Cognitive dispersion

GDSS Capability

Interaction can be modified so that it is not perfectly synchronous; including “incubation” periods of silence

Experimenter-Facilitator maintains “flow” in the interaction

Attention to ideas of Interaction that is not completely synchronous; others that interrupts storing capabilities can allow members to processing “exhaust” their own idea generation and then return to synchronous interaction

252  Decision Making Groups and Teams interactive groups with managed interaction can be more effective than nominal groups has not been definitively addressed in these studies. Procedures to manage interaction in support of quality optimizing objectives benefit from explicit conceptualizations of process as tested in findings of experimental studies. Previous chapters have sought to provide a framework for directed information exchange in decision making in the presence of team structure. In this chapter, the framework is used to contribute to the design of managerial procedures. SUMMARY AND DISCUSSION Previous chapters have offered a framework for ill-structured decision making by interactive teams that is based on the flow of information between team members and the social structure of the team. This framework gives forms to what are considered to be the information types of ideas and negative evaluations that are critical to decision quality and describes microprocessing in the presence of team structure. Additionally, it directly addresses processes in an interactive team that relate to structure. Processing these information types in decision making requires a mix of divergent and convergent cognitive operations (Paletz & Schunn, 2010) as organized in sequenced phases in which these different types of cognitive operations predominate. A form is proposed for the time dependent relationships between the flow of ideas and negative evaluations that are hypothesized to maximize decision quality. The framework recognizes that cognitive operations and social processes are inextricably linked. As such, understanding the outcomes of cognitive processing in interactive groups benefits from defining social exchange. Toward this end, relationships between negative evaluation, idea initiation, and structure in status distributions have been explicitly considered. In this chapter, conjectures on information exchange, social structure, and quality of decisions and an initial formalization of these conjectures in a dynamic linear system has been offered. Decision quality clearly depends on both the number of ideas and the number of negative evaluations. Quality in ill-structured decisions is expected to be an increasing function of the uncommonness of ideas, and uncommonness, in turn, has been found to be a monotone increasing function of the number of ideas exchanged. It was, therefore, conjectured that increasing idea number increases decision quality. In contrast, while negative evaluations also contribute to decision quality by sorting ideas, too many negative evaluations can decrease the number of ideas generated in a team. Thus, the relationship between the number of negatives and decision quality is hypothesized to be quadratic. Next, the time dependency in contributions of information types to decision quality was addressed. Following a conceptualization of decisionmaking phases, it was hypothesized that the contribution of ideas to quality is greatest in the intervals of an initial phase of the interaction while

Technology for Quality-Maximizing Objectives  253 contribution of negatives to quality is greatest in the final interaction phases. Finally, on structure, it was suggested that status differentiation increases the total number of negative evaluations and the domination of the number of initiated ideas by higher status members. This is at least in part because higher status members are likely to perceive being the source of these types of information as having lower expected costs to their status position. The linear system used to formulate hypotheses about optimal information exchanges over time as a control problem provides an initial basis for considering the implementation of the views that have been put forth on outcome optimization. In particular, it highlights issues in defining the transition between heuristic decision-making phases and the outcome optimizing the amount of negative evaluations to be targeted. Finally, the technology of managing information exchange in interactive groups has been considered. Most notably, managing the exchange of negative evaluations has been cited as a relevant control variable and a policy objective. Consistent with previous discussion, it was suggested that adding or removing information about team members as sources or targets of information can alter the status structure in a team and contribute to decision quality. The capability of GDSSs to accomplish this and manage other dysfunctional effects reported by investigators of brainstorming (e.g., Pinsonneault et al., 1999) has also been enumerated. The discourse presented in this and preceding chapters is intended to offer a starting point for proposed designs in managing team interaction in ill-structured decision making toward quality objectives. The rudimentary formal model provided here benefits from explicit parameter estimation. Experimental tests of key hypotheses are also necessary to support the conceptualization. Finally, technology for control procedures that can seamlessly manage information flows and modify structure in computer-mediated systems for information exchange requires calibration and testing. The foregoing exposition does offer a conceptual basis for the design and management of interactive teams that can reduce commonly cited process losses while retaining process gains. REFERENCES Beaty, R., & Silvia, P. (2012). Why do ideas get more creative across time? An executive interpretation of the serial order effect in divergent thinking tasks. Psychology of Aesthetics, Creativity, and the Arts, 6, 309–319. Boddy, C. (2012). The nominal group technique: An aid to brainstorming ideas in research. Qualitative Market Research: An International Journal, 15, 6–18. Christensen, P., Guilford, J., & Wilson, R. (1957). Relations of creative responses to working time and instructions. Journal of Experimental Psychology, 53, 82. Coppola, N., Hiltz, S., & Rotter, N. (2004). Building trust in virtual teams. Professional Communication, 47, 95–104. Desanctis, G., & Gallupe, R. B. (1987). A foundation for the study of group decision support systems. Management science, 33, 589–609.

254  Decision Making Groups and Teams Eisenhardt, K., & Zbaracki, M. (2007). Strategic decision making. Strategic Management Journal, 13, 17–37. Filip, F. (2008). Decision support and control for large-scale complex systems. Annual Reviews in Control, 32, 61–70. Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual Review of Psychology, 62, 451–482. Green, M., & Limebeer, D. (2012[1994]). Linear robust control. Mineola, NY: Dover Publications. Hasson, F., Keeney, S., & McKenna, H. (2000). Research guidelines for the Delphi survey technique. Journal of Advanced Nursing, 32, 1008–1015. Heninger, W., Dennis, A., & Hilmer, K. (2006). Individual cognition and dual task interference in group support systems. Information Systems Research, 17, 1–10. Hertel, G., Geister, S., & Konradt, U. (2005). Managing virtual teams: A review of current empirical research. Human Resource Management Review, 15, 69–95. Kasper-Fuehrera, E., & Ashkanasy, N. (2001). Communicating trustworthiness and building trust in interorganizational virtual organizations. Journal of Management, 27, 235–254. Kerr, N., & Tindale, R. (2004). Group performance and decision making. Annual Review of Psychology, 55, 623–655. Lin, H., & Antsaklis, P. J. (2009). Stability and stabilizability of switched linear systems: A survey of recent results. Automatic Control, IEEE Transactions, 54, 308–322. Lloyd, S. (2011). Applying the nominal group technique to specify the domain of a construct. Qualitative Market Research: An International Journal, 14, 105–121. Lu, J., Zhang, G., Ruan, D., & Wu, F. (2007). Multi-objective group decision making: Methods, software and applications with fuzzy set techniques. London, England: Imperial College Press. Nunamaker, J., & Deokar, A. (2008). GDSS parameters and benefits. Handbook on Decision Support Systems, 1, 391–414. Paletz, S., & Schunn, C. (2010). A social-cognitive framework of multidisciplinary team innovation. Topics in Cognitive Science, 2, 73–95. Pinsonneault, A., Barki, H., Gallupe, R., & Hopper, N. (1999). Electronic brainstorming: The illusion of productivity. Information Systems Research, 10, 110–133. Poole, M., & Zigurs, I. (2008). Detailed narrative of Minnesota GDSS project results. University of Illinois. Rupp, D., & Paddock, E. (2010). From justice events to justice climate: A multi-level temporal model of information aggregation and judgment. Research on Managing Groups and Teams, 13, 245–273. Schneider, B. (1975). Organizational climate: An essay. Personnel Psychology, 28, 447–479. Schyns, B., & Van Veldhoven, M. (2010). Group leadership climate and individual organizational commitment: A multilevel analysis. Journal of Personnel Psychology, 9, 57–68. Silver, S. D., Troyer, L., & Marks, B. (2007). System for the management and analysis of real-time teams (SMART 5) [Computer software]. Zamfirescu, C., & Filip, F. (2010). Swarming models for facilitating collaborative decisions. Journal of Computers, Communication and Control, 5, 125–137.

12 Summary and Discussion

OVERVIEW This chapter reviews the conceptual foundation of microprocessing in structured teams that has been offered and the empirical regularities that have been reported in support of the conceptualization. The basis for studying ill-structured decision making as information exchange is first reviewed. The intended contributions of each chapter and the inferences they offered for the design of decision-making teams are then summarized. Integration of the chapters in a dynamic system and results of numerical studies with this system are then reported. Finally, directions for subsequent inquiry on interactive groups and teams as decision-making units are considered. Following the chapter on interorganizational teams, the first of these is in studying network models of teams in organizations. Issues in the designation of attachment rules for agents in the network are reviewed, and a basis for attachment rules between agents that represents social structure is exemplified. The integration of technology in GDSSs is then given direction. INTRODUCTION In preceding chapters, the account of microprocessing in decision making has considered teams as decision-making units in a case where the decision is ill-structured. In this case, there is no applicable algorithmic solution or heuristic procedure that is likely to result in an optimal decision. An objective of the account has been to review historical background and conceptualize process in interactive decision-making teams as a basis to better assess their capabilities as decision-making units. Microprocessing in an interactive team was conceptualized as information exchange in which team structure, defined as the status distribution of the membership, was then given a form in the microprocessing of information. As reviewed in Chapter 1, relevant background studies have emphasized the comparison between groups and the same number of individuals acting independently as a member of a nominal group. The majority of formal

256  Decision Making Groups and Teams studies have concluded that groups are less effective as decision-making units than the same number of individuals acting independently. These studies are almost entirely in brainstorming procedures with idea number as the dependent variable. Quality in ill-structured decisions clearly depends on a combination of information types. Following a review of available studies with this inference, it was also suggested that these studies do not represent process in interactive groups well enough to definitively address the contextual comparisons they make between nominal and interactive groups. In particular, it was observed that the preponderance of studies that have comparatively investigated interactive and nominal groups as decisionmaking units do not represent what has been designated to be group structure as in the distribution of status in the group as a source of process losses. If uncontrolled effects of group structure are established as a basis of process loss in interactive groups and teams, then inference that omits structural variables in comparative studies is incomplete. The discourse offered here has elaborated on the microprocessing through which group structure can be a principal source of process loss. Evaluation apprehension in members is among the processes that have been cited as a basis for process losses in brainstorming studies of interactive groups. However, group structure has its dysfunctional effects through a different process than the one described as evaluation apprehension. Evaluation apprehension as described is an individual trait that is situationally aroused. Group and team structure as defined operates at both individual and aggregate levels. The chapters that followed continuously elaborated on microprocessing in the presence of status distributions. Understanding the processing that structure results in can increase our capabilities in addressing dysfunctional effects in interactive groups and teams. It was correspondingly suggested that authors who begin from teambuilding perspectives generally do not adequately conceptualize process losses in interactive groups and the sources of these losses. These authors provide little or no empirical assessment of their claims on process gains that teams as interactive units can have for decision making. Additionally, they often assume that expressed commitment to normative rules in teams adequately assures actual behavior. A number of studies, including the ones presented in Chapter 8, suggest this to be a tenuous assumption. Because of the limitations in conceptualization of process and empirical assessment, these studies may be underestimating the process losses that are inherent in unmanaged interactive teams and overestimating the effectiveness of procedures they designate as quality increasing. A framework of information exchange in which the social structure of teams is an explicit mediator was introduced as the basis for formal representations of the process gains and losses in interactive decision-making teams. A review of background studies of social structure in interactive

Summary and Discussion  257 groups contributed to our understanding of how structure is established in teams and its demonstrable effects. This background provides an important basis for an account of microprocessing in information exchange that integrates agent motives and team objectives. In the chapters that followed, agents were conceptualized as simultaneously being individual social entities and members of a task-directed team. For the former, they act to maintain or advance their status in the team. For the latter, they act to contribute to team objectives. In a framework of information exchange, status loss is a function of the negative evaluations they receive weighted by judgments of their status distance from the evaluator. The judgments of status distance from other members that they use in weighting the expected cost of negative evaluation by another team member is biased. They typically underweight their status distance from members who are close to them in status and overweight the status distance from members who are distant from them. They further recognize that initiating different information type have different likelihoods of returning a negative evaluation. After giving a form to inherent biases in the judgment of social distance by members of different statuses in the team, key information types of ideas and negative evaluations were given explicit forms in ill-structured decision making. An integrated system based on the account of information processing in structured teams was used in analysis of quality optimization in decision making. Having given an overview of the exposition, I next review the contributions that the chapters have sought to make. Directions for subsequent inquiry on process and development of technology to manage interactive teams is then considered. BACKGROUND AND FRAMEWORK FOR INFORMATION EXCHANGE IN DECISION-MAKING TEAMS In Chapter 2, effects of status differentiation as a structural factor that is emergent in all groups and teams were elaborated upon. Extensive background studies on microprocessing in the formation of status hierarchies and their effects on interaction between group members were first reviewed. The emphasis was upon structure in groups and teams as defined by the distribution of social status in a decision-making unit. The historical background in the study of status organization and process dynamics in interactive groups indicated bases to anticipate robust and pervasive effects of status distributions on information exchange in these units. Effects of group or team structure were then directly integrated into the information exchange of decision-making units, and generalizations from this integration were offered.

258  Decision Making Groups and Teams TEAM MEMBERS AS MIXED MOTIVE AGENTS The objective of Chapter 3 was to recognize the mixed-motives of members of interactive teams, provide a closed form representation of the information exchange that these motives result in, and indicate the inference it supports. In the mixed motive case, individuals have motives to both maintain or increase their status in the team and to contribute to team objectives. The chapter addressed microprocessing in terms of these mixed or dual motives. A proposed form for information exchange in structured teams was used to support an initial statement on the distribution of status in the teams that can be expected to maximize decision quality. This account allowed a form for an objective function of teams in terms of a set of information types. Analytical results provided a basis to expect that the number of ideas exchanged in the group or team will be maximized when the status of members was equal or at least perceived by the members to be approximately equal. While interactive groups and teams generally form structures in which they are not status equal and show processing that maintains inequality, the possibility of technology and procedures that can at least move the group closer to perceived status equality was noted. Results further indicated directions to extend the representations of the effects of group and team structure on information exchange to accommodate the complex heuristics that agents use in initiating information in interactive decision-making units. TEAM STRUCTURE AND BIASES IN HEURISTICS OF INFORMATION EXCHANGE Chapter 4 considered the function that group and team members use for weighting the status distance between themselves and other members of the unit in their heuristics. This function is important to information exchange in the decision-making unit since it indexes the expected status loss or gain that members anticipate from sending a message type. It was proposed that the weighting function used by team members is one in which they overweight the status of members they perceive to have more status than themselves and underweight the status of members who they perceive to have less status than themselves. This bias in weighting has some correspondence to the one described for monetary gains and losses in Prospect Theory and can account for the asymmetry in the generation of information types commonly observed in interactive decision-making units. Implications of the proposed function for what has been described as process losses and gains in task directed groups and teams were indicated. In support of this account, empirical studies of the conjecture on the proposed distance function were then reported.

Summary and Discussion  259 CONCEPTUALIZING THE BASES OF PROCESS GAINS IN INTERACTIVE TEAMS: DYNAMIC FORMS FOR THE EXCHANGE OF IDEATIONAL INFORMATION Chapter 5 was presented in two parts. Part A addressed ideas as an information type in ill-structured decision making. Dynamics of idea generation in the presence of team structure was conceptualized and given explicit forms. In the absence of algorithmic or heuristic procedures, the exchange of ideational information typically provides a basis to define and assess decision alternatives. This is among the reasons that the preponderance of background studies have used brainstorming procedures. Distinctions between single-source and multiple-source ideas in the information exchange of interactive decision-making units were elaborated. In the latter, team members combine their ideas with the ideas of other team members to constitute a new idea. The possibility of multiple-source ideas (i.e., ideas that combined multiple single-source ideas) was designated as a contributor to process gains in interactive teams. Dynamic forms that followed the conceptualization of the information type in single-source and multisource ideas gave explicit representation to social structure in the team. In conceptualizing their contribution to a quality objective in team decision making, the objective in idea generation was to maximize the number that is exchanged in the decision-making unit. This is partly because more uncommon or statistically original ideas occur later in the serial order of sequences in idea generation. An empirical assessment in this part of the chapter supported the conjecture that multisource ideas (i.e., ideas that are combinations of ideas that other members have previously generated) increase over time in interaction and can be an important basis for process gains in interactive groups and teams. Part B of Chapter 5 reported results of two laboratory studies that investigated the conceptualization of idea generation and information exchange in interactive groups introduced in the first part of the chapter. The studies used a common task and dependent measure in methodology to experimentally vary group structure. The first study investigated effects of experimentally defined status distributions on idea generation and information exchange. The second study examined effects of experimentally inserted negative evaluations on the number and originality of ideas generated in the group. Results of this study directly showed that increasing status differentiation as group structure significantly decreased the number and quality of ideas exchanged in the group. Results of the second study further indicated some of the conditions under which negative evaluations can increase quality in problem solving and decision making. Experimentally inserted negative evaluations of the group as an entity were found to increase the uncommonness of ideas exchanged in the group.

260  Decision Making Groups and Teams DYNAMICS OF NEGATIVE EVALUATIONS IN THE INFORMATION EXCHANGE OF INTERACTIVE TEAMS Chapter 6 was also presented in two parts and considered negative evaluations as information types in decision-making groups and teams. In Part A of this chapter, the inconsistent results of previous conceptual and empirical studies of this information type was summarized. It was observed that this may at least partly reflect inherent differences in how negative evaluations and other information types typically exchanged in decision-making groups and teams are processed. In elaborating on this observation, negative evaluations were conceptualized as having both informational and affective content. Evaluations have informational content since they inform on the quality of ideas. Evaluations have affective content since they directly relate to the self-esteem of those who receive them. The simultaneous properties of this information type as information and affect were represented in a dynamic form for negative evaluations. Analytical results suggested that initial rates directly depend on effects of group or team structure. Increasing status differentiation in the group or team can be expected to decrease this rate since the distance function previously introduced implies that middle and lower status members will decrease their rates more than higher status members increase their rates. However, results of cited studies on socioemotional climate suggest that asymptotic rates commonly depend more on mimetic effects. In contrast with effects on initial rates, imitative or climate effects imply an increase in the number of negative evaluations as status differentiation increases. Results with the proposed function for this information type provide interpretable parameters that mediate whether what have been designated as information and affect predominates in the exchange of negative evaluations. Under simple assumptions that rates of initiating evaluations are fixed for individual members as functions of their status in the decision-making unit, dynamics imply that status differentiation can be expected to have the net effect of increasing the asymptotic rates. Part B of this chapter reported empirical studies of inferences from the conceptualization of evaluations as simultaneously informational and affective. As noted, it was predicted that when evaluations were perceived as informational, they decreased the number of ideas exchanged in a statusdifferentiated group. In contrast, when evaluations were perceived as affective, they increased the number of ideas and evaluations exchanged by the group. The first study demonstrated that inserted positive or negative evaluations can increase the number of the same information types initiated in the group. Results of the second study showed that inserted negative evaluations increased negative evaluation in comparison with a control group.

Summary and Discussion  261 Results of Study 1 and this result support the account of imitative or climate effects of evaluations. Results of this study further showed that negative evaluation decreased when (1) the source was imputed to be high status and (2) the evaluations were of specific ideas rather than general performance of the group. Both of these factors increase the expected social cost of initiating negative evaluations to middle and lower status members. This effect was conceptualized as the informational property that negative evaluations can have. Taken together, results of these studies supported the proposed account of the exchange of negative evaluations in interactive groups and teams. SILENCE INTERVALS IN THE INFORMATION EXCHANGE OF INTERACTIVE TEAMS Chapter 7 considered the importance that intervals of silence in interactive groups and teams can have to objectives in ill-structured decision making. It was conjectured in this chapter that silence intervals in task-directed groups and teams were often periods of incubation that result in the formation and subsequent exchange of additional ideas, an increased number of which are likely to be multisource ideas. However, coacting in silence has risk for individual team members since as conceptualized in Chapter 3, silence can be perceived as social loafing or free-riding and thereby can increase the likelihood of being negatively evaluated. A lower risk strategy in such a case is to initiate neutral data and facts. The willingness to coact in silence given its social risk to many if not most team members was related to trust that they will not be evaluated as social loafers or free-riders for doing so. As indicated, this can expected to be more typical in status-equal groups and teams. Results with available data allowed this conjecture to be assessed with Markov models of the sequences of periods of silence in the interactive exchange of information. The results confirmed the conjecture on silence. Results showed that status-equal or undifferentiated groups spent larger proportions of their interaction time in silence than did status-differentiated groups. They further showed a close relationship of an idea to an interval of silence and predicted differences in the likelihood of an idea following an interval of silence between status-differentiated and undifferentiated groups. INFORMATION EXCHANGE IN AN ORGANIZATIONAL DECISION-MAKING TEAM Chapter 8 reported field studies of team decision making in formal organizations. In these studies, predictions from the framework on information exchange in interactive teams that engaged in what has been designated as an

262  Decision Making Groups and Teams ill-structured decision were tested in an active team. In Part A of this chapter, videotaped records of team interaction were coded in terms of the amount and type of information exchanged by team members. Measures of the status of a team member as indicated from background sociodemographic variables were used to test predictions on the type and amount of information that they would initiate. Differences in both the amount and type of information exchanged in the team were consistent with predictions on effects of status differentiation. These differences were evidenced in spite of team-building training and a rule that prescribed negative evaluation in the interaction. The differences also cross-cut areas of expertise of higher status members. That is, they persisted even when a higher status member addressed content that he or she did not have expertise in. In Part B of the chapter, information exchange was conceptualized in terms of scripts in the exchanges of teams and subteams. The definition of scripts followed invocations of social theorists. Equalitarian scripts that fit the idealized script agreed upon in the design of the organizational team and team-building training were differentiated from other types of scripts, including a hierarchical script. The same team members were found to have generated fewer equalitarian scripts that were supposed to be the team’s agreed upon mandate in more status-differentiated team meetings than in more status-equal subteam meetings. Results of these studies supplement the lab studies that have been reported and provide external validity for the conceptual framework in team structure and information exchange. VIRTUAL TEAMS AS DECISION-MAKING UNITS Chapter 9 addressed teams in which interaction is entirely through electronic media and is commonly asynchronous. Studies of virtual teams have emphasized trust as a mediator of team performance. In a previous chapter, the willingness of a group to coact in silence was empirically related to their levels of initiated ideas. It was suggested that trust is likely to be a mediator of coaction in silence. The demonstrated relationship of coaction in silence and idea initiation indicates the importance of intervals of silence in information exchange. A basis to represent the formation and maintenance of a trust in a framework of team information exchange was indicated. Trust was framed in terms of equity judgments of a team member from their observations of the distributions of evaluations. When the distribution of evaluations is more closely related to the distribution of idea initiations than to the status distribution, trust is expected to be increased. These conjectures were given explicit form in a definition of trust as it is maintained in information exchange of a decision-making team.

Summary and Discussion  263 AN INTEGRATED SYSTEM FOR INFORMATION EXCHANGE IN A DECISION-MAKING TEAM In this chapter, the accounts of information exchange in decision making by structured decision-making teams proposed in previous chapters was integrated into a system and the dynamics of the system were studied in numerical exercises. In Part A of the chapter, the integrated system for the dynamics of information exchange was related to group and team objectives in interactive decision making. Conjecture in earlier chapters on mixed motives and the distance function in microprocessing was represented in the form that was proposed for the system. To further the integration, the concept of perceived equity and trust in a team as introduced in Chapter 9 was used to functionally link ideas, evaluations, and the initiation of other information types. The exchange of information types was then related to a form for status updates. In Part B of this chapter, a computational model of the integrated system was introduced to directly study the dynamics it implies and examine important relationships in the account of quality in ill-structured decision making by a team. One of the principal interests was in the trade-off in team composition (e.g., heterogeneity in background and disciplinal training of team members) that is related to quality but also to status differentiation. The diversity in composition of members that typically follows from demographic variables, disciplinal training, and rank in formal organizations can be quality increasing by increasing heterogeneity (i.e., reducing overlap) in the aggregated idea pool of members. However, diversity can also be quality decreasing by increasing status differentiation and the effects that it can have on information exchange in the team The computational model of the system was used to demonstrate the effects that have been described on idea generation as a dependent variable. It was then extended to investigate a functional form for a quality objective in ill-structured decisions. Hypothesized sensitivities of this function to interpretable inputs in the system were demonstrated. TECHNOLOGY TO MANAGE INFORMATION EXCHANGE IN DECISION-MAKING TEAMS Chapter 11 reviewed available technology for the management of information exchange following inference from the integrated system proposed in Chapter 10. The integrated system was used to propose a design for GDSSs that manage information exchange in a team. In this application, the quality objective of the team was used to define a control problem in which the number of negative evaluations initiated is a control variable. Unlike the information type of ideas, the number and content of negative evaluations can be managerially controlled. In contrast with the objective

264  Decision Making Groups and Teams of maximizing the initiation of ideas, too many or too few negative evaluations can be expected to reduce decision quality. Too few evaluations can inadequately filter ideas on relevance and criteria for quality. Too many negative evaluations can result in cascades of this information type through the mimetic effects that its exchange can have in interactive teams. When they attain a certain level, negative evaluations will then reduce decision quality by inhibiting the initiation of ideas more than they increase quality through the critical filtering of initiated ideas. The integrated system was used to describe how conflicting effects of status heterogeneity on the initiation of ideas and negative evaluations can be managed by using GDSS for information exchange. The computational exercises of the previous chapter showed that the system is well-behaved and can evidence the relationships of heterogeneity to quality that have been described. This chapter indicated how managing the path of negative evaluations as through the solution to a control problem can be quality increasing. Finally, the chapter described how procedures in a GDSS can manage the negative evaluations exchanged in the team and the design of a system that accomplishes this. DIRECTIONS FOR FURTHER STUDY Having reviewed the chapters of the discourse, directions for subsequent study is next considered. I begin by suggesting that the study of teams can be extended to more completely represent the networks in which they are embodied.

Network Models The chapter on interorganizational teams has indicated the broad-based and enduring connections between teams and the organizations that initiate and support them. The influence of founding organizations on the amount and type of information exchanged in the team and subteams was documented in the chapter. These results and observations of other investigators (Ilgen, Hollenbeck, Johnson, & Jundt, 2005; Mathieu, Heffner, Goodwin, Salas, & Cannon-Bowers, 2000) suggest that network models can usefully represent the influence of organizations on teams. Network models offer a capability to directly represent structural relationships between individual members and the interdependencies between members of teams and organizations that often are not recognized.

Single Period and Dynamic Networks There are applications in both networks that are sequentially repeated, single period networks and dynamic networks (Newman, Barabasi, & Watts, 2011). Small world networks (e.g., Centola, 2010; Watts, 1999; Watts &

Summary and Discussion  265 Strogatz, 1998; Wilson, Boe, Sala, Puttaswamy, & Zhao, 2009) are in the former case since they do not typically have a dynamic mechanism. These networks emphasize structure through communication and influence linkages between agents in the network. In contrast with designs in cellular automata (Wolfram, 1983; Yang & Yang, 2010), small world networks are typically not limited to nearest neighbor rules for linkages and have been used to demonstrate the importance that agents who are remote or distant from other agents in a network can have to the distribution of resource and influence in the network. Dynamic networks (e.g., Barabási, 2009) are time varying typically through the attachment rules of new entrants (i.e., the rules that the entrant uses to link to existing agents in the network). A common finding in the study of dynamic networks that the likelihood of a new entrant attaching to a network agent is a function of the number of links (or attachments) that the network agent already has. Dynamic network models that accommodate new entrants are of particular interest in the study of the external linkages of newly formed teams. This case often introduces entrants from outside the organization’s existing network. At present, most explicit forms for dynamic networks have been empirically generated. These empirically generated models are what some have categorized as descriptive rather than explanatory (e.g., Dalerum, 2012). I propose that behavioral models of the microprocessing in interactive teams that has been understudied can provide a basis for explanatory models of dynamic network forms. I begin by discussing attachment rules that are the basis for dynamic networks.

Attachment Rules It is commonly maintained that studies of dynamic networks should begin with specification of agent behavior that is, be bottom-up (e.g., Epstein & Axtell, 1996; Tesfatsion, 2002). What has been designated as preferential attachment in dynamic networks has been shown to generate network topologies that are close to those naturally observed in a range of disciplinal contexts (e.g., Barabási & Bonabeau, 2003). In the networks that preferential attachment generates, the probability of a new entrant (node) connecting to an existing node is proportional to the number of links that the existing node has (i.e., its degree). In the Barabási-Albert model (e.g., BA: Barabási, Albert, & Jeong, 1999), the degree distribution in a dynamic network is generated by Pr(k) = kg, where k is the network degree. When g = 3 this form fits the WWW, and what is a surprising number of networks across a range of disciplinal applications and sizes—hence its designation a scale-free in this parameter range (Barabási & Bonabeau, 2003). Self-reproduction is sometimes cited as the basis that motivates nodes that have the highest degree of connectivity (i.e., are hubs) in BA networks to

266  Decision Making Groups and Teams maintain their attractiveness (e.g., Epstein & Axtell, 1996; Lima, Hadzibeganovic, & Stauffer, 2009). In economic and social contexts, hubs typically have disproportionate access to resources, and new entrants to the network are likely to be aware of this. If new entrants are advantage seeking, it then can be assumed that they are motivated to attach to network entities that are hubs. However, this is an unelaborated, post-hoc explanation for a fitting of an attachment rule to a corresponding empirical regularity. Moreover, recent studies have shown the existence of subdomains of the www that evidence considerable differences from the BA representation. Pennock, Flake, Lawrence, Glover, and Giles (2002) report that while the connectivity distribution across the entire web may be close to one generated by BA preferential attachment, within specific subsets of the domain that include scientist and newspaper homepages, a combination of preferential attachment and uniform attachment rule generates the best fitting function. In their definition, ∏(ki ) = α

ki 1 + (1 − α ) , 2mt m0 + t

Where, m0 + t = total number of vertices. 2mt = total connectivity at time t. = probability of preferential attachment. = probability of uniform attachment. Although Pennock et al. (2002) do not consider the behavioral basis of connectivity within the subdomains of the www for which they report results; their results do suggest that the generality given to BA preferential attachment benefits from a more contextualized definition. Even recognizing its empirical correspondences, preferential attachment as an agent-based generating function remains a descriptive rule based on empirical regularity that is imputed to agents in the absence of an explicit and well-defined behavioral basis. This is most notably a limitation in cases in which there is assumed to be an active exercise of agency. The foregoing observations do not preclude empirical regularities giving direction to the behavioral foundations of attachment rules. These observations do suggest that attachment rules in cases of active agency benefit from better elaborated behavioral bases. For example, what is the underlying heuristic of scientists or newspaper new entrants to a network that generates the combinatorial of uniform and preferential attachment? How does this heuristic map into differences between scientist and newspaper entities and other subdomains in the weighting of uniform and preferential attachment? While in these instances, microlevel rules are not well-defined, generation of behavioral bases for attachment rules can be described from agent microprocessing in other contexts. One of these is the information exchange between members of decision-making teams and, where appropriate, the organizational members with whom they interact.

Summary and Discussion  267 In an application to networks in professional occupations within an organization, motivation for attaching to entities in a network may include status as well as information gains or losses. When discriminating between entities who are network members, a new entrant may anticipate being evaluated by members of the network and assume positive evaluations to be sources of status gain and negative evaluations to be sources of status loss. INFORMATION EXCHANGE IN ORGANIZATIONAL NETWORKS Organizational networks themselves are generally hierarchical for functional reasons with member status defined by position in the hierarchy. As well described in groups and teams (Kozlowski & Bell, 2003; Magee & Galinsky, 2008; Shelly & Troyer, 2001), a hierarchical organization can be expected to tacitly emerge in designated organizational teams even when equality in interaction is mandated since members have status markers through their organizational linkages. As has been elaborated, the presence of a hierarchy even in the face of initial mandate for equality in interaction provides a basis for members of the subnetwork to discriminate among the other members as to who they communicate with and the content of the communication. In such a case, a general rule for the exchange of information under mandated equality would initially be close to uniform under putative equality and then a form of preferential attachment when a hierarchy emerges. This would follow the Pennock et al. (2002) results. Additionally, if it is assumed that the degree of a current network member maps into their perceived status in the network, then there is a basis to further expect that they would evaluate status gains and losses in a way that reflects the biases shown in other cases of subjective judgments of gains and losses. This can embed the connectivity degree of a network member in gain and loss functions that new entrants use for attachments and can modify the effects of the member’s degree in the network on attachment (or its approximation in communication links). Communication links as used here is analogous to the way that citations are used in the study of scientific collaborations and publications (Newman, 2001).

Attachment Rules in Information Exchange Following upon the above, the attachment rule may then depend on (1) the type of information that the agent expects to send (e.g., ideational information, opinions, positive, or negative evaluations), (2) the agents judgment of the conditional probability of being positively or negatively evaluated for an information type or sequence, (3) the agents judgment of the status distance between his or herself and other network agents, and (4) the gain or loss function that the agent is using. To give a form to this, let be the set of information types, ideas, negative and positive evaluations, and all other

268  Decision Making Groups and Teams (i) types, respectively, and Pr(Nkj|mjk ) and Pr(Pkj|mjk(i)) be the conditional probabilities of the j-th agent receiving negative or positive evaluations from the k-th agent for sending the i-th information type. Let d be the degree of a node and ∆ jk = dk − d j be the degree (or status) distance between the j-th       (i)  (i)  f ( ∆ ))  ∑(Pr ∆ ))  ∑(Pr N ( ) (|m f (( ∆ N m )) and g(∆ f (∆ N m )) be the gain ) mN ( N ) ( m∑ and k-th agents, and f(∆f (jk P(Prkj) (|m ∑(Pr  (Pr jk  jk  (Pr jk    kj and loss functions the j-th agent anticipates in information exchange with the k-th agent in the network. For new entrants, ∆jk is generally positive (i.e., network members generally have higher status than new entrants). In the functional forms for loss and gain that investigators beginning with Kahneman and Tversky (1979) have applied in a range of both monetary and nonmonetary contexts (e.g., Camerer, 1995), (1) the subjective valuing of a gain or aversion to a loss varies nonlinearly with the absolute magnitude of the gain or loss and (2) losses are disvalued more than equivalent gains are valued. As commonly written (e.g., Hastie & Dawes, 2001), the gain and loss functions for new entrants that follow from these fundamental propensities can be given the form of f(x) = , x > 0 for gains and, β g ( x ) = −λ ( − x ) , x < 0 for losses where x can be an increment in a status marker such as the degree of a network member and f(x) and g(x) are agents subjective valuing of this increment for gains and losses, respectively. In a range of applications, the parameters of these functions have been approximated as λ = 2.25 and β = .88. The loss function could then be written as n

kj jk

jk

i =1

 L = −λ  − ∆ jk  

n (i ) jk i =1

 n  (Pr Nkj m(jki)   i =1

( )∑

(

kj

)

n

(i ) jk

 )   

jk kj

jk

i =1

n (i ) jk i =1

kj

(i ) jk

β

and the gain function could be written as

( ) ∑ (Pr ( Pkj m(jki) )))β n

G = ∆ jk (

i =1

The attachment (or communication) rule would depend on degree d as recovered from the difference between the gain and loss functions. The conditional probabilities receiving a positive or negative evaluation for initiating the m-th message type have been estimated in smaller groups but could correspondingly be estimated in a network. The dependence on degree in the network would not be straightforward since if the agent anticipates sending a relatively large proportion of information types that have relatively high conditional probabilities of returning negative evaluations, then preference may be for attachment to an entity with a low-degree of connectivity. Alternatively, the function may be a truncated form in which an above mean field degree of connectivity is preferred to influentially introduce an idea or evaluation to the network but not the risk in exchange with the highest degree agents.

Summary and Discussion  269 The predominant attachment rule by new entrants could be predicted by such factors as the steepness of the status hierarchy, some independent measure of risk preference and the mix of information types that is expected to be exchanged. Although cross-department committees and teams may appear to be a specialized case in organizations, they may not be more specialized in this context than are the subdomains that have been studied in the www. The point of this example is to exemplify the observation that attachment rules are context dependent and can be generated by closed forms for behavioral processes. The study of attachment rules in dynamic networks has advanced in empirics much faster than it has in behavioral bases. Early empirical results on subdomains of domains in which preferential attachment has a good fit begin to indicate the frequency of more complex attachment rules. There are a range of conceptual bases for generating process when entities are economic, and social agents. These merit being given more explicit forms and empirically studied. The attachment rule from more refined microprocessing is likely to be less elegant than BA preferential attachment. However, introducing rules that have behavioral bases and evaluating their implications analytically and computationally can further the understanding of their complexity as well as evaluating the contributions they offer.

GDSSs for Interactive Teams A concomitant objective should be a more seamless integration in GDSS methodology to manage interaction toward optimality in decision making while minimally interfering with natural interaction in the team. Current attempts at GDSS are now advanced in their capabilities (Banuls & Salmeron, 2011; Gray, Johansen, Nunamaker, Rodman, & Wagner, 2011; Salmeron, 2012; Shi, 2011), but in my view, we have not yet adequately detailed procedures to fully address even the microprocessing we can currently define. The discussion of Level 3 GDSSs such as SMART 5 begins to recognize stage differentiation in processing that occurs in team decision making. It introduces time-varying applications in its definitions of procedures to manage the group. These remain to be refined so as to minimally interfere with natural processing. Advances in artificial intelligence (AI: e.g., Shi, 2011) increase the capabilities in accomplishing this. Examples of this direction are in the use of AI in the adjustment of a control variable to the classification of the stage of decision making that the team is in. For example, fuzzy systems (e.g., Salmeron, 2012) can accommodate multicriteria bases for the classification of decision stages. In stage-varying procedures, the number of ideas and evaluations freely initiated in the team could be calibrated to denote stages in decision making. A decrease in the idea rate and in the ratio of positive evaluations to ideas is likely to indicate a transition to convergence stage in decision making. This, in turn, can be the basis for a change in managerial control variables that have been described in Chapter 11.

270  Decision Making Groups and Teams Virtual teams. A final direction follows from the observation that decisionmaking teams are increasingly virtual (Gibson & Gibbs, 2006; Lipnack & Stamps, 1997) and involve multiple organizations. If, as observed, the emergence of structure in teams is endemic to task-directed aggregates, then how it occurs when teams are virtual present particular challenges. Issues here are in how the bases of structure and process in its generation differs in virtual and face-to-face interactive teams and interorganizational designs to manage the underlying processes for team objectives. A starting point may be in the now extensive work on the formation of trust in virtual teams (Jarvenpaa & Leidner, 1999; Jarvenpaa, Shaw, & Staples, 2004; Kramer, 2006). As reviewed, this may depend on judged competence of others, but it also is likely to depend upon observations of the correspondence between recipients of evaluations and sources of ideas and other information types. Trust is increased when evaluations are observed to not be distributed according to the status of team members (i.e., when there is observed to be a high correspondence between the sources of ideas and the targets of positive or negative evaluations). There are a number of reasons to refine and support the current interest and application in decision making by teams, especially the case in which the team is virtual. It has been my view that applications have gotten too far in front of conceptualization and controlled empirical assessment. Available inference also remains too dependent upon what are essentially self-reports of team participants. I have sought to elaborate on process in the case of the decision-making team and the managerial procedures that even our limited account suggest to manage processing to the optimality in decision quality in both face-to-face and virtual teams. The increasing importance of team decision making by teams makes assessing predictions of the framework in experimental study and the refinement of procedures for managing interaction in decision-making units a priority. CONCLUSIONS Interactive teams can offer important contributions to the quality of decisionmaking. This is especially true in cases where decisions lack formal heuristics for optimizing quality (i.e., are ill-structured). In a number of past considerations, interactive teams have been treated as the necessary decision-making units because of dependencies in authority, expertise, or resource control on multiple individuals. I suggest that while the above factors can offer cogent reasons to consider teams rather than individuals as decision-making units in some cases, groups and teams have inherent strengths in decision-making functions that are independent of these factors. The foregoing exposition has suggested that the microprocessing through which interactive teams can contribute to objectives in ill-structured decision making has not been adequately represented in available dialogue and

Summary and Discussion  271 experimental studies of brainstorming tasks. As has been observed, while quality in ill-structured decision making is likely to increase with idea number, it can be expected to have more complex dependencies on other information types and processes that mediate information exchange. Additionally, most available studies also omit effects of social structure that can be expected to activate processes mediating the exchange of information in interactive teams. The limited recognition of net productive contributions of team decision making partly arises from the failure to recognize the concurrent social processes inherent in any team or to effectively manage such social processes, so that they contribute rather than detract from the cognitive operations of members. As is often enough cited, individuals are inherently embedded in social structures that they carry with them in one form or another to all interactive contexts. The contention of the foregoing account is that a more complete understanding of social processes generated by group or team structure, as they occur is a range of task contexts, can allow us to more completely obtain positive contributions that interactive teams can offer in decision making. The framework of the preceding exposition has sought to broaden the conceptualization of microprocessing in team decision making as information exchange and the social structure of the team as a mediator of the exchange. This framework has been used for analytical, computational, and empirical results on ill-structured decision making in interactive teams. Procedures that available technology make available to bring quality closer to its optimum have also been reviewed. It is hoped that the exposition contributes to establishing a basis to expect that technology-augmented managed teams can get closer to appropriate definitions of optimal decision quality than can a number of individuals working independently. This implies that interactive teams can be designed and managed so that process gains are substantially greater than process losses. This claim extends to virtual teams even though the mandates for managing interaction in this case are likely to be different. The increasing complexity of both organizational forms and decisions are likely to have increased challenges in obtaining the contributions that effective interactive teams can offer. Increasing our understanding of the causal relationships in the processing of interactive teams remains a priority because of its potential for substantial contributions to objectives in structured applications. This can provide necessary fundamentals for the use of available technology in developing conceptually driven, integrating systems that are likely to overcome principal limitations of teams as decision-making units for ill-structured decisions. REFERENCES Banuls, V. A., & Salmeron, J. L. (2011). Scope and design issues in foresight support systems. International Journal of Foresight and Innovation Policy, 7(4), 338–351. Barabási, A. (2009). Scale-free networks: A decade and beyond. Science, 325, 412–413.

272  Decision Making Groups and Teams Barabási, A. L., Albert, R., & Jeong, H. (1999). Mean-field theory for scale-free random networks. Physica A: Statistical Mechanics and its Applications, 272, 173–187. Barabási, A. L., & Bonabeau, E. (2003). Scale-free networks. Scientific American. Camerer, C. (1995). Individual decision making. In J. H. Kagel & A. E. Roth (Eds.), Handbook of experimental economics (pp. 587–703). Princeton, NJ: Princeton University Press. Centola, D. (2010). The spread of behavior in an online social network experiment. Science, 329, 1194–1197. Dalerum, F. (2012). Descriptive versus explanatory hypotheses in evolutionary research. Ethology, Ecology and Evolution, 24, 97–103. Epstein, J. M., & Axtell, R. L. (1996). Growing artificial societies: Social science from the bottom up. Cambridge MA: MIT Press. Gibson, C., & Gibbs, J. (2006). Unpacking the concept of virtuality: The effects of geographic dispersion, electronic dependence, dynamic structure, and national diversity on team innovation. Administrative Science Quarterly, 51, 451–495. Gray, P., Johansen, B., Nunamaker, J., Rodman, J., & Wagner, G. (2011). GDSS: Past, present, and future. In D. Schuff, D. Paradice, F. Burstein, D. J. Power, & R. Sharda (Eds.), Decision support (pp. 1–24). New York, NY: Springer. Hastie, R., & Dawes, R. (2001). Rational choice in an uncertain world. Thousand Oaks, CA: Sage. Ilgen, D., Hollenbeck, J., Johnson, M., & Jundt, D. (2005). Teams in organizations: From input-process-output models to IMOI models. Annual Review of Psychology, 56, 517–543. Jarvenpaa, S., & Leidner, D. (1999). Communication and trust in global virtual teams. Organization Science, 10, 791–815. Jarvenpaa, S., Shaw, T., & Staples, D. (2004). Toward contextualized theories of trust: The role of trust in global virtual teams. Information Systems Research, 15, 250–267. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica: Journal of the Econometric Society, 47, 263–291. Kozlowski, S. W. J., & Bell, B. S. (2003). Work groups and teams in organizations. In W. C. Borman, D. R. Ilgen, & R. J. Klimoski (Eds.), Handbook of psychology (Vol. 12): Industrial and Organizational Psychology (pp. 333–375). New York, NY: Wiley-Blackwell. Kramer, R. (2006). Organizational trust. Oxford, England: Oxford University Press. Lima, F. W., Hadzibeganovic, T., & Stauffer, D. (2009). Evolution of ethnocentrism on undirected and directed Barabási–Albert networks. Physica A: Statistical Mechanics and Its Applications, 388, 4999–5004. Lipnack, J., & Stamps, J. (1997). Virtual teams: Reaching across space, time, and organizations with technology. New York, NY: Wiley. Magee, J., & Galinsky, A. (2008). Social hierarchy: The self-reinforcing nature of power and status. Academy of Management Annals, 2, 351–398. Mathieu, J., Heffner, T., Goodwin, G., Salas, E., & Cannon-Bowers, J. (2000). The influence of shared mental models on team process and performance. Journal of Applied Psychology, 85, 273–283. Newman, M. E. J. (2001). Scientific collaboration networks: II. Shortest paths, weighted networks, and centrality. Physical Review, E 64, 016132. Newman, M., Barabasi, A., & Watts, D. (2011). The structure and dynamics of networks. Princeton, NJ: Princeton University Press. Pennock, D. M., Flake, G. W., Lawrence, S., Glover, E. J., & Giles, C. L. (2002). Winners don’t take all: Characterizing the competition for links on the web. Proceedings of the National Academy of Sciences, 99, 5207–5211.

Summary and Discussion  273 Salmeron, J. (2012). Fuzzy cognitive maps for artificial emotions forecasting. Applied Soft Computing, 12, 3704–3710. Shelly, R., & Troyer, L. (2001). Emergence and completion of structure in initially undefined and partially defined groups. Social Psychology Quarterly, 64, 318–332. Shi, Z. (2011). Series on intelligence science (Vol. 1): Advanced artificial intelligence. Singapore: World Scientific Publishing. Tesfatsion, L. (2002). Economic agents and markets as emergent phenomena. Proceedings of the National Academy of Sciences of the United States of America, 99, 7191–7192. Watts, D. (1999). Small worlds: The dynamics of networks between order and randomness. Princeton, NJ: Princeton University Press. Watts, D., & Strogatz, S. (1998). Collective dynamics of small-world networks. Nature, 393, 440. Wilson, C., Boe, B., Sala, A., Puttaswamy, K., & Zhao, B. (2009). User interactions in social networks and their implications. In Proceedings of the 4th ACM European Conference on Computer Systems (pp. 205–218). Wolfram, S. (1983). Statistical mechanics of cellular automata. Review of Modern Physics, 55, 601–644. Yang, X., & Yang, Y. (2010). Cellular automata networks. In A. Adamatzky, L. Bull, & D. Costello (Eds.), Unconventional computing (pp. 289–302). Frome, United Kingdom: Luniver Press.

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Notes

NOTE TO CHAPTER 1

1.  Additionally, there are a number of qualifying statements by experienced practitioners that receive less attention. For example, “There is a lot of discussion on whether or not teams are a fad. I do think the emphasis on teams is a fad because I have witnessed it come and go several times. I remember when working alone was the thing to do, and I remember when it was all about team work. I personally think it should be a balance of approaches. Working alone is a great way to work on particular skills and working together on a team is a great way to learn from others.” (Powers, 2008)

NOTES TO CHAPTER 2 1. In these experiments, members of a dyad that were presumed to be equal in competence were given different pay rates. They were further informed that their selection of one of two preferred notable expressionist paintings put them in a distinct taste category. The interaction of the dyad was then recorded and coded. The person who was assigned lower pay and was informed that she was in a different taste category reliably deferred to the other member of the dyad and rated that person as more competent. This experiment was repeated with just the pay difference, and the results were confirmed (Troyer & Younts, 1997). A subsequent experiment showed that bystanders (observers of the interaction) learn the competence distinction that follows from the assigned pay and taste category differences as defined in third order effects. 2. Correll and Ridgeway (2006) and Magee and Galinsky (2008) show conditions under which assertiveness in early interaction history does lead to status orders that tend to be accepted by all group members. Under such conditions, those with traits that motivate them to be assertive may attain higher status. The conditions that distinguish cases where the group sanctions or accepts assertive behavior as a status indicator remain to be defined. 3. There is direct indication that whatever the basis for status differentiation is, higher status members are less likely to receive negative evaluations for their ideas and other contributions to the group in Gest, Rulison, Davidson, and Welsh (2008) and Scholten, Van Knippenberg, Nijstad, and De Dreu (2006).

276 Notes NOTES TO CHAPTER 3

1. Here, as in actual group interaction, the absence of an initiation is considered to be communication. Since both initiations and blanks are considered to be communications, all members can be assumed to be communicating at equal rates. As a consequence, the probabilities can be regarded as absolute quantities. Communication levels are expressed in terms of probabilities to facilitate expressing conditional statements that are integral to arguments that follow. 2. An alterative to Assumption (1.0) would be to make the conditional probability of a negative evaluation proportional to the quality of an idea. Such a representation could enable quality differences to be directly represented in the model, instead of through the number of ideas. In order to maintain tractability, this representation has not been included in the present formalization. 3. While equations (3.7) and (3.8) have been proposed as candidate form for quality production that represent the key properties of the process, the alternative of a Cobb-Douglas form is noted. Considering quality in the generation of group ideas as a production process in which ideas and negatives are inputs, the standard form can be written as: Q(I , N) = I jα∑1 Nijα 2 where R 1 β; α 2 = β > 0 and α1 + α 2 = β. 1+ R 1+ R A limitation of a Cobb-Douglas production function in this application is in its property of elasticities or relative sensitivities of output (e.g., quality) to input (ideas and negative evaluations) that are constant across all levels of the input variables, here I and N. At high absolute levels of factor inputs, negative evaluations may, for example, decrease in their contribution to quality. α1 =

NOTES TO CHAPTER 4 1. Oversend here means to send more than their expertise would ordinarily be expected to merit. 2. Differences in the total number of messages individuals send as a function of their statuses are not as easily defined as sometimes commonly assumed. Nonresponses or blanks often have a nonzero probability of being negatively evaluated (i.e., silences can be costly in certain groups). If this is true, filling time with positive evaluations, facts, and questions, can be more optimal strategy for minimizing status loss. In such a case, lower status members could actually send more messages in certain information categories than higher status members. This effect could be for quality reducing. Thus the proportion of information types that comprise total messages may be as important as the numbers. 3. For example, a linear weighting would lead to a claim that if a group or team moves from a status distribution that is indexed as 4, 4, 5, 6, 6, to one which is indexed as 2, 2, 5, 8, 8, then the perceived cost of initiating an idea or negative evaluation to the member with the status score of 5 would stay the same, since the distribution remains symmetric about this mean. As we observe, such a claim is counterintuitive and not supported in empirical results. 4. Another possibility with respect to (4.4) is that the relationship of judged status distance in an idea exchange is nonlinear, as in: Pr ( Ii ) = k − V m, where, m is a parameter.

Notes  277 Since Figure 1 implies ∂ Pr ( I ) ∂σ

= −mV m−1

∂ 2 Pr ( I ) ∂σ i2

∂ 2V >0 ∂σ 2 ∂V and ∂σ 2

 ∂ 2V  ∂V  − mV m−1  2 = −m ( m − 1)V m−2    ∂σ   ∂σ

  < 0 

iff m ≥ 1 So Pr(Ii) will be a concave function of (σj – σi) if m = 1. The case m < 1 is indeterminate. This case implies that the change in the probability of an idea initiation for a unit change in the status distance between the source of the idea and source of a potential evaluator is greater when the status differences are small than when they are large. Although over most ranges of status differences, this case would seem counterintuitive, a definitive statement requires empirical tests. 5. Concavity in production functions for a diversity of social and physical outputs is also a common assumption. The importance of assumptions on concavity in rates of information exchange has also been demonstrated in information theory (e.g., Cover & Thomas, 1991) where emphasis is on noise in signals and the amount of factual information that can be extracted from a message. 6. In this study, the relationship between (1) idea uncommoness as a measure of idea quality and (2) the ratio of negative evaluations to ideas was empirically examined in 19 problem-solving groups. Nine of the ten groups with the highest mean uncommoness scores were in the interval 0.10 = R = 0.25. Mean uncommoness for this group was .18, while the mean for the groups outside this interval was .04. 7. For example, define the loss function of the j-th member for initiating an idea and fact (LI, LF, respectively) to the i-th higher status members as: L(j) I = [f(σi, σj)Pr(Nj | Ii)], L(j) F = [f(σi, σj)Pr(Nj | Fi)], where σk is a member’s status, σi > σj, N is a negative evaluation, and F is a fact message. Since: Pr(Nj | Ii) > Pr(Nj | Fi), (σ σ ) (σ σ ) ; the cost of an idea increases faster than the cost of a fact initiation as the distance of the source of the initiation from a higher status member increases. This result holds under a linear form for judged status differences. If the form is nonlinear, as has previously been described, the difference between the expected costs of an idea and fact initiation increase even faster as the judged status difference increases. ∂L(Ij )

∂ f 

i,

j

 

>

∂L(Fj)

∂ f 

i,

j

 

NOTES TO CHAPTER 5A

1. A form for the solution in the n-person case of single-source ideas can be written as: J (1) = c λ 1t + c λ 2t +  c e λ nt + Y * n

11

Where, Yn*

λ 1t

12

1n

λ 2t

n

λ nt

= e c1(t) + e c2 (t) +  e (t) 0 ≤ cj 2. Recent interest in virtual teams provides insight into constituent factors in this function. Most notable among these is trust (e.g., Coppola, Hiltz, & Rotter, 2004; Greenberg, Greenberg, & Antonucci, 2007; Jarvenpaa, Shaw, & Staples, 2004; Staples & Webster, 2008). An elaborated form for the K function from these studies is taken up in detail in a subsequent chapter. The total

278 Notes number of ideas that a team generates also depend on the paths of positive and negative evaluations as well as the status distribution and heterogeneity in associative hierarchies of members. An expanded system for idea generation to be offered includes these variables as explicit arguments of the K function.

NOTES TO CHAPTER 5B

1. Study 1 and 2 were done jointly with Bernard P. Cohen and supported by NSF Grant IS 841794. 2. In addition to the inserts of negative evaluations, the experimenter inserted a prefixed number of ideas from a standard list for the fictitious group members according to a constant schedule across all groups. This procedure was followed to maintain credibility of the fictitious group members as actual group members. 3. Correlations of question categories of requests for more specific information (QRINF) and requests for money ideas (QRID) with data or fact categories were also examined. For groups with negative inserts, the data or fact category most related to the question categories was INFER (QRINF × INFER = .358, ns; QRID × INFER = .519, p < .025). Among groups without negative evaluation inserts, requests for specific information appear to be related most strongly to personal and information messages (QRINF × PERS = .676, p < .001; QRINF × INFO = .734, p < .01; QRINF × INFER = .399, ns; QRID × PERS = .082, ns; QRID × INFO = .061, ns; QRID × INFER = .082, ns).

NOTE TO CHAPTER 6A 1. It is assumed that members other than the high-status individual adjusts toward a normative rate set by the high-status member. As in Figure 6A.1, this rate is generally below the actual rate of the high-status member, since members do not expect to send evaluations or ideas at the leader’s rate.

NOTE TO CHAPTER 6B

1. In addition to the inserts of negative evaluations, the experimenter inserted a prefixed number of ideas from a standard list for the fictitious group members according to a constant schedule across all groups. This procedure was followed to maintain credibility of the fictitious group members as actual group members.

NOTE TO CHAPTER 8A 1. The studies reported in this chapter were completed with Lisa Troyer and Bernard P. Cohen.

NOTES TO CHAPTER 10A 1. If this were not true, increasing the variance but not the mean of status differences would have no effect on the number and proportion of information types that are exchanged in the group. This is not consistent with results of

Notes  279



empirical studies that show the significant effects of increasing the variance in the distribution of status with a constant mean on information exchange (e.g., Silver, Cohen, & Crutchfield, 1994). These results are contrary to expected value forms in which linear distances are assumed. 2. The form for the overlap can be written as:

(

)

(

)

α j J1(1) ,  Jn(1) = 1 2 ∑ α jk1 Jk(11) + 1 3 ∑ α jk1k2 Jk(11) + Jk(12) +  + 1 n

k1 ≠ j

∑

k1 < k2