The Handbook of Applied Communication Research, 2 Volume Set 2019052199, 9781119399858, 9781119399872, 9781119399865, 1119399858

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The Handbook of Applied Communication Research

Handbooks in Communication and Media This series aims to provide theoretically ambitious but accessible volumes devoted to the major fields and subfields within communication and media studies. Each volume sets out to ground and orientate the student through a broad range of specially commissioned chapters, while also providing the more experienced scholar and teacher with a convenient and comprehensive overview of the latest trends and critical directions. The Handbook of Children, Media, and Development, edited by Sandra L. Calvert and Barbara J. Wilson The Handbook of Crisis Communication, edited by W. Timothy Coombs and Sherry J. Holladay The Handbook of Internet Studies, edited by Mia Consalvo and Charles Ess The Handbook of Rhetoric and Public Address, edited by Shawn J. Parry‐Giles and J. Michael Hogan The Handbook of Critical Intercultural Communication, edited by Thomas K. Nakayama and Rona Tamiko Halualani The Handbook of Global Communication and Media Ethics, edited by Robert S. Fortner and P. Mark Fackler The Handbook of Communication and Corporate Social Responsibility, edited by Øyvind Ihlen, Jennifer Bartlett, and Steve May The Handbook of Gender, Sex, and Media, edited by Karen Ross The Handbook of Global Health Communication, edited by Rafael Obregon and Silvio Waisbord The Handbook of Global Media Research, edited by Ingrid Volkmer The Handbook of Global Online Journalism, edited by Eugenia Siapera and Andreas Veglis The Handbook of Communication and Corporate Reputation, edited by Craig E. Carroll The Handbook of Media and Mass Communication Theory, edited by Robert S. Fortner and P. Mark Fackler The Handbook of International Advertising Research, edited by Hong Cheng The Handbook of Psychology of Communication Technology, edited by S. Shyam Sundar The Handbook of International Crisis Communication Research, edited by Andreas Schwarz, Matthew W. Seeger, and Claudia Auer The Handbook of Organizational Rhetoric and Communication, edited by Øyvind Ihlen and Robert L. Heath The Handbook of Applied Communication Research, edited by H. Dan O’Hair and Mary John O’Hair

The Handbook of Applied Communication Research Volume 1

Edited by H. Dan O’Hair and Mary John O’Hair

Editorial Assistants Erin B. Hester and Sarah Geegan

This edition first published 2020 © 2020 John Wiley & Sons, Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions. The right of H. Dan O’Hair and Mary John O’Hair to be identified as the authors of the editorial material in this work has been asserted in accordance with law. Registered Office John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA Editorial Office 111 River Street, Hoboken, NJ 07030, USA For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com. Wiley also publishes its books in a variety of electronic formats and by print‐on‐demand. Some content that appears in standard print versions of this book may not be available in other formats. Limit of Liability/Disclaimer of Warranty While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Library of Congress Cataloging-in-Publication Data Names: O’Hair, H. Dan, editor. | O’Hair, Mary John, editor. Title: The handbook of applied communication research / edited by H. Dan   O’Hair, Mary John O’Hair ; editorial assistants, Erin B. Hester, Sarah   Geegan. Description: Hoboken, NY, USA : Wiley-Blackwell, 2020. | Series: Handbooks   in communication and media | Includes bibliographical references and  index. Identifiers: LCCN 2019052199 (print) | LCCN 2019052200 (ebook) | ISBN   9781119399858 (hardback) | ISBN 9781119399872 (adobe pdf) | ISBN   9781119399865 (epub) Subjects: LCSH: Communication–Research–Methodology. Classification: LCC P91.3 .H325 2020 (print) | LCC P91.3 (ebook) | DDC  302.2/0721–dc23 LC record available at https://lccn.loc.gov/2019052199 LC ebook record available at https://lccn.loc.gov/2019052200 Cover Design: Wiley Cover Images: binary hemisphere © Navidim/Getty Images, Profiles of Technology series © agsandrew/Shutterstock Set in 9.5/11.5pt Galliard by SPi Global, Pondicherry, India Printed in the United States of America 10 9 8 7 6 5 4 3 2 1

Contents

List of Contributors

ix

The Promise of Applied Communication Research H. Dan O’Hair, Mary John O’Hair, Erin B. Hester, and Sarah Geegan

1

Part I  Theoretical Perspectives

11

1. Inoculation Theory as a Strategic Tool Bobi Ivanov, Kimberly A. Parker, and Lindsay Dillingham

13

2. Addressing Life Transitions of Aging: Integrating Indigenous and Communication Theory to Develop a Tuakana-Teina/Peer Educator Model John G. Oetzel, Brendan Hokowhitu, Mary Simpson, Sophie Nock, and Rangimahora Reddy

29

3. Connecting Attitudes and Motivating Behavior: Vested Interest Theory Bradley J. Adame

47

4. A Comparative Analysis of Theoretical Propositions Focusing on Apologia Michel M. Haigh

63

5. Applying Social Marketing Strategy to Social Change Campaigns Kimberly A. Parker, Sarah Geegan, and Bobi Ivanov

75

6. Engaged Communication Scholarship: The Challenge to Translate Communication Research into Practice Gary L. Kreps

93

7. The Role of Negative Emotions in Applied Communication Research Elena Bessarabova, John A. Banas, and Daniel R. Bernard

103

Part II  Media, Data, Design, and Technology

123

8. Adventures in “Big Data” Application in Strategic Applied Communication Research, Theory, and Method Yi Grace Ji and Don W. Stacks

125

9. Serious Games as Communicative Tools for Attitudinal and Behavioral Change Jessica Wendorf Muhamad and Soyoon Kim

141

vi Contents 10. Leveraging Social Media for Applied Problems: Case Studies in Mapping Cyberspace to Realspace Brian H. Spitzberg, Ming‐Hsiang Tsou, and Chin‐Te Jung

163

11. Exploring Applied Practices in Entertainment Marketing: How Brands Connect with Today’s Modern Family Laura H. Crosswell and Meghan S. Sanders

183

12. Enhancing Public Resistance to “Fake News”: A Review of the Problem and Strategic Solutions 197 Marcus W. Mayorga, Erin B. Hester, Emily Helsel, Bobi Ivanov, Timothy L. Sellnow, Paul Slovic, William J. Burns, and Dale Frakes 13. Data Visualization for Health and Risk Communication Fan Yang

213

14. Visual Communication as Knowledge Management in Design Thinking Beth S. Rous and John B. Nash

233

Part III  Organizational Communication

249

15. Communication Technology and Organizational Life Keri K. Stephens and Courtney J. Powers

251

16. Understanding Maxcers: Are All Opinions Equal? William A. Donohue, Richard Spreng, and Charles Owen

267

17. The Intersections of Organizations, Health, and Safety: Designing Communication for High Reliability Organizations Tyler R. Harrison, Elizabeth A. Williams, and Ashley R. Reynolds

279

18. Anticipatory Model of Crisis Management and Crisis Communication Center (CCC): The Need to Transfer New Knowledge to Resources Bolanle A. Olaniran and Juliann C. Scholl

297

19. Communication Challenges of Volunteers Michael W. Kramer and Laurie K. Lewis

313

20. Towel Cards Revisited: The Environmental Communication of Green Hotels Finn Frandsen and Winni Johansen

327

Part IV  Risk and Crisis Communication

343

21. Discourse of Renewal: State of the Discipline and a Vision for the Future Andrew S. Pyle, Ryan P. Fuller, and Robert R. Ulmer

345

22. Visual Framing of Conflict and Terrorism in the MENA Region Michael D. Bruce

363

23. Revisiting the Best Practices in Risk and Crisis Communication: A Multi‐case Analysis Shari R. Veil, Kathryn E. Anthony, Timothy L. Sellnow, Nicole Staricek, Laura E. Young, and Pam Cupp 24. Evolving Coverage of Risk in the Mass and Social Media Sharon M. Friedman and Jeannette Sutton

377

397

Contents 25. Terror Management Theory Perspectives on Applied Communication Research Claude H. Miller and Zachary B. Massey 26. The Consequences of Risk Amplification in the Evolution of Warning Messages during Slow‐Moving Crises: Hurricane Irma as a Case Study Deborah D. Sellnow‐Richmond and Timothy L. Sellnow 27. Psychological Reactance and Persuasive Message Design Claude H. Miller, Zachary B. Massey, and Haijing Ma

vii 417

443 457

List of Contributors

Bradley J. Adame, Arizona State University Kathryn E. Anthony, University of Southern Mississippi John A. Banas, University of Oklahoma Daniel R. Bernard, California State University Elena Bessarabova, University of Oklahoma Michael D. Bruce, University of Alabama William J. Burns, Decision Research, California State University San Marcos Laura H. Crosswell, University of Nevada Pam Cupp, University of Kentucky Lindsay Dillingham, Lipscomb University William A. Donohue, Michigan State University Dale Frakes, Portland State University Finn Frandsen, Aarhus University Sharon M. Friedman, Lehigh University Ryan P. Fuller, California State University Sarah Geegan, University of Kentucky Michel M. Haigh, Texas State University Tyler R. Harrison, University of Miami

x

List of Contributors

Emily Helsel, University of Central Florida Erin B. Hester, University of Kentucky Brendan Hokowhitu, University of Waikato Bobi Ivanov, University of Kentucky Yi Grace Ji, Boston University Winni Johansen, Aarhus University Chin‐Te Jung, Esri, Inc. Soyoon Kim, University of Miami Michael W. Kramer, University of Oklahoma Gary L. Kreps, George Mason University Laurie K. Lewis, University of Texas at San Antonio Haijing Ma, University of Oklahoma Zackary B. Massey, Georgia State University Marcus W. Mayorga, University of Oregon Claude H. Miller, University of Oklahoma John B. Nash, University of Kentucky Sophie Nock, University of Waikato John G. Oetzel, University of Waikato H. Dan O’Hair, University of Kentucky Mary John O’Hair, University of Kentucky Bolanle A. Olaniran, Texas Tech University Charles Owen, Michigan State University Kimberly A. Parker, University of Kentucky Courtney J. Powers, University of Texas Andrew S. Pyle, Clemson University Rangimahora Reddy, Rauawaawa Kauma¯tua Charitable Trust



List of Contributors

xi

Ashley R. Reynolds, University of Miami Beth S. Rous, University of Kentucky Meghan S. Sanders, Louisiana State University Juliann C. Scholl, Centers for Disease Control and Prevention/National Institute for Occupational Safety and Health Timothy L. Sellnow, University of Central Florida Deborah D. Sellnow‐Richmond, Southern Illinois University Mary Simpson, University of Waikato Paul Slovic, University of Oregon Brian H. Spitzberg, San Diego State University Richard Spreng, Michigan State University Don W. Stacks, University of Miami Nicole Staricek, University of Kentucky Keri K. Stephens, University of Texas Jeannette Sutton, University of Kentucky Ming‐Hsiang Tsou, San Diego State University Robert R. Ulmer, University of Nevada Shari R. Veil, University of Nebraska Jessica Wendorf Muhamad, Florida State University Elizabeth A. Williams, Colorado State University Fan Yang, University of Alabama Laura E. Young, University of Nebraska

The Promise of Applied Communication Research H. Dan O’Hair, Mary John O’Hair, Erin B. Hester, and Sarah Geegan

Applied communication research (ACR) is a very special endeavor and it brings to bear all of the elements we value in the discovery process, while at the same time, it is focused squarely on addressing real‐world problems. Represented in this two‐volume book are a number of different contexts, methodologies, and theories—we value the rich heterogeneity by which participating scholars have made their contributions. In this introductory chapter, we offer some opinions on the nature of ACR and continue to argue, as we have done previously, that pursuing this type of exploration is a noble enterprise. We speculate on several of the ways that investigators and scholars approach the work of ACR, and we highlight two processes that we feel can enrich and extend the findings of applied research: (a) citizen science and (b) entrepreneurship. We conclude this first chapter by providing an overview of the chapters in Volume 1—chapters that constitute a wonderful assemblage of what is possible in the field of ACR. It is these chapters, and those in Volume 2, that support our claim of promises that only ACR can keep.

Viewpoints In some ways, ACR is basically a convenient term for problem‐based research. Many scholars in communication have interests in issues toward problem‐based research, action research, critical research, and social justice research. Terms frequently associated with applied research include approaches that are socially relevant, “scholarship that can make a difference” (Kreps, Frey, & O’Hair, 1991, p. 71) or research that is driven by “meaningful inquiry” (Plax, 1991, p. 59). O’Hair and Kreps (1990) argue that “applied researchers provide opportunities for the testing of basic theories in applied contexts….” (p. ix). Our primary assumption resides in the argument that basic and applied communication research are, and should be, interdependent. Applied research brings into use theory and methodology in order to understand how communication can solve problems. Basic research leverages applied research to offer practical accountability of the work (O’Hair, Ploeger, & Moore, 2010). It is important to remember Kurt Lewin’s famous statement: There is nothing more practical than a good theory. In a complementary fashion, Kreps et al. (1991) have argued that there is nothing more theoretical than good practice. Theory and practice are mutually informing and recursive practices. Julia Wood (2000, p. 189) appropriately argued that, “applied communication The Handbook of Applied Communication Research: Volume 1, First Edition. Edited by H. Dan O’Hair and Mary John O’Hair. © 2020 John Wiley & Sons, Inc. Published 2020 by John Wiley & Sons, Inc.

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research is not bounded by domain. Its nature cannot be demarcated usefully by context. What defines and distinguishes ACR is its insistence on putting theory and research into the service of practice, and equally, of studying practices to refine theory in order to gain new understandings of how communication functions and how it might function differently, or better.” ACR can provide a real‐world test of the predictive validity of communication theory (O’Hair et al., 2010). To provide additional context for where ACR has been situated in the recent past, we discuss an article by Steimel (2014), who conducted a four‐decade analysis of the topics appearing in the flagship ACR journal in the field, The Journal of Applied Communication Research (JACR). Although organizational and health were consistent themes across the four decades, Steimel did find studies in subsequent decades that were addressing “contemporary communication issues of social concern” (p. 3). And while her focus was on analyses of different decades of published articles in JACR, her final conclusion was more encompassing: Across the decades, JACR research prominently features concepts … that align closely with many of the National Communication Association’s largest interest group divisions. However, the research within those concepts has evolved over the four decades not only to reflect the social issues of relevance at any given time, but also to embrace increasingly diverse and complex communication relationships (for example between individuals and organizations). The future challenges (and opportunities) of applied communication research center around continuing to embrace diverse voices, contexts, and methodologies while foregrounding theory as both a tool and outcome of applied research. (p. 32)

Other social science disciplines have developed robust portfolios of applied research and in very few cases have these disciplines of study withdrawn from the challenge of pressing social and economic issues confronting society. ACR and its very capable scholars can be found investigating some of the most serious conditions and circumstances that confront us. While we would not argue that ACR is preeminent or more important than other social science disciplines’ work in applied contexts, we would be so bold as to offer evidence that the work of our scholars in applied contexts is as meaningful as the other disciplines and certainly as substantial as ever before.

An Eclectic Perspective on ACR What has been learned over the past 50 years, and that which is prominently highlighted by the contributions to this two‐volume set, is that it is inaccurate and even inappropriate to pigeonhole research styles of those pursuing ACR. We could point to a few exemplars of prominent and consistent forms of scholarship, and will do so in the next section, but generally characterizing a researcher’s tendencies is probably a risky gesture. One thing seems certain: ACR can be successfully conducted from a number of different perspectives (Kreps et al., 1991; Wood, 2000). One such perspective comes from peering into the purpose and/or context for study. In some cases, a research team typically does not conduct ACR but finds themselves in a situation where the only fruitful approach is one that is applied in nature (solving a problem). The research team in this regard may return to ACR from time to time (e.g., being asked to play a role on a grant that is wholly applied) but their primary purpose is to conduct basic research. A second type takes the opposite approach where the researcher and/or context are predominantly in the ACR domain. These are scholars who would rarely consider a research project that did not have a practical problem or challenge directly in sight. Those pursuing this applied paradigm see research as a practical endeavor, although this is not to imply that they are always pursuing the same problem. Rather, they may venture into various venues in search of solving different and interesting problems (e.g., sunscreen, water quality, hurricane warnings). Their predilection for and skills in the applied research arena seem compatible with numerous challenges facing people.



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Still another way in which researchers view their role in ACR is that of studying a persistent problem. Cancer control scholars, climate change researchers, those studying various challenges of sexual harassment, and even classroom communication experts find a home in a specific context and enjoy applying communication theory in ever more nuanced ways. A more in situ perspective describes ACR that studies a specific setting such as a particular locale (New York), regions (Appalachian Mountains), countries (Palestine), or even otherworldly settings such the International Space Station. It is these investigators who become experts in the place of study and can bring to bear rich backgrounds for their studies. Still other investigators are skilled theoreticians and/or methodologists who are often sought after to join ACR teams. These scholars find these opportunities worthwhile for a number of reasons, not the least of which is the prospect to work with different people, the chance to apply the knowledge and skills that they have been building for some time, or even the opportunity to watch firsthand how basic theory and methods can be brought to bear in practical settings. Of course, for many, ACR reflects a blended type where problem, place, or space are not necessarily primary pursuits but conducting ACR is employed to sharpen research skills, have fun, work with interesting people, and make a difference in the world.

Applied Communication Research—Practice Collections of studies and programs of research represented in this two‐volume set on ACR take large steps toward improving economic development, changing lives for the better, and protecting people and property from risks and crises. ACR serves as a means that allows other processes to engage where the research can be used in actual practice. In the following sections we will discuss citizen science and entrepreneurship as promising opportunities to move ACR into action stages.

Citizen Science Public Participation in Scientific Research is a term advanced by Bonney et al. (2009) and Shirk et al. (2012) which examines a host of participatory research approaches, including citizen science, participatory action research, crowdsourcing, and community‐based research (Eitzel et al., 2017). According to Hecker and colleagues, “[t]he long tradition of volunteer engagement in science has taken a big leap forward over the past two decades. Varied approaches of public engagement in science, public understanding of science, crowdsourcing, and community science have come together under the umbrella of citizen science. The result is a growing, global, citizen science community devoted to working together to bridge the science‐society‐policy interface” (Hecker et al., 2018). Even the popular press such as the AARP Bulletin and PBS have featured stories on citizen science (Greenberg, 2019). Research projects involving citizen scientists are varied and their numbers seem to be growing. Some examples of recent projects worthy of mention are marine conservation by fishers, dozens of water quality projects, tropical forest crimes, monitoring caterpillars, patients as citizen scientists, and numerous projects supporting public health. Bonney, Cooper, and Ballard were early champions of citizen science and even helped to establish the journal Citizen Science: Theory and Practice. According to them, “… through its many configurations of science‐society partnerships, citizen science holds the potential for developing new ways to collectively solve big problems and to fundamentally change the relationship between science and society” (Bonney, Cooper, & Ballard, 2016, p. 1). Citizen science research projects are appealing because they have shown the potential to collect large data sets of field data less expensively and in a shorter amount of time (Gura, 2013).

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Perhaps one of the largest challenges of citizen science is quality control (Greenberg, 2019; Gura, 2013). Even if the method and procedures are strictly held to the highest scientific standards, the data collected by citizen volunteers can be questioned. That is one reason that the United States Environmental Protection Agency (2019) developed a citizen science quality assurance tool entitled the Handbook for Citizen Science: Quality Assurance and Documentation. Contained within the Handbook are instructions, descriptions, and templates that chart the procedures necessary for producing a project that can stand up to most scientists’ and policy makers’ scrutiny. This is a positive step in reinforcing the significance and value of citizen science. There is so much at stake in promoting this type of applied research, including good data collected in an ecologically valid manner (locally), citizenry involvement in research, and a real chance to influence public policy.

Entrepreneurship A different opportunity to extend the reach of ACR lies in the realm of entrepreneurship. Research‐based entrepreneurship is enjoying a great deal of attention, but it involves a lot of work that many researchers are not used to doing. While imagination, intelligence, and tenacity can transform a great idea into a thriving business or a global enterprise, entrepreneurial success is a function of many factors—such as adequate financing, a good support structure, and of course, favorable timing. However, in the churning world of small business, firms come and go as quickly as the Greek God of opportunity, Kairos, whose ephemeral presence offers a fleeting chance of success to those prepared to grasp it. There are many obstacles thinning the ranks of would‐be entrepreneurs, but self‐imposed unrealized potential—a business that never gets started because the would‐be entrepreneur did not act on his or her idea—is the most insidious. While research confirms what common sense suggests, that the intellectual prowess found at the nation’s universities has tremendous innovation and commercialization potential (Kim & Marschke, 2007), there is also a strong sense that much of this potential goes unrealized. What Thomas Edison famously said decades ago is equally true today, “the value of an idea lies in the using of it.” As many before us have noted, serious concerns have been raised about the ability or willingness of American research universities to push their research findings out into the marketplace. Underlying efforts to effectively advance entrepreneurship and innovation practices is engaging partners in the various forms of communication, whether represented by interpersonal, group, or organization dynamics. Research focusing on communication practices is especially ripe for application in entrepreneurship contexts. In September 2009, President Obama released his national innovation strategy; at the center of this initiative were two closely related goals—sustaining economic growth and creating quality jobs. Intrinsic to this strategy is capitalizing on basic research at US research universities and the ensuing commercialization of research discoveries. Unfortunately, the commercialization of university research is a persistent challenge often referred to as “the valley of death.” By their very nature, university researchers are most talented in seeking answers to questions that are not necessarily practical or suitable for the end user. This “valley” that prevents viable research discoveries from reaching consumers, patients, and businesses costs the US economy billions of dollars in unrealized economic valuation. To address these challenges, the National Science Foundation’s (NSF) Innovation Corps (I‐Corps) has been put into place and is intended to extend the research of researchers and ­scientists into practical, and hopefully profitable, endeavors. NSF‐funded researchers learn to identify valuable opportunities that can develop from university research projects, and at the same time, acquire entrepreneurial skills. NSF created I‐Corps to train researchers and students in innovation and entrepreneurship skills, to encourage collaboration between academia and industry, and to stimulate the translation of fundamental research to the marketplace. NSF seeks to strengthen a national innovation ecosystem that helps foster innovation among faculty and students, p ­ romotes regional coordination and linkages, and develops networks to address



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pressing societal challenges and economic opportunities for the nation. The I‐Corps Program helps to ensure that participants gain an understanding of how to (a) identify and develop promising ideas that can generate value, (b) create and implement tools and resources that boost innovation capacity, and (c) develop innovation practices that can be passed along to others (especially students). Social entrepreneurship is an enterprise that seeks to address social and/or community issues through innovation. Sometimes associated with nonprofit entrepreneurship, social entrepreneurship can lead to financial outcomes such as economic growth and jobs creation. Moreover, social entrepreneurship can instill idealism among students about innovation and entrepreneurship that leads to outcomes beyond financial returns. Social entrepreneurs will play a central role in corresponding to the challenges of the modern world, and we believe their presence will vastly increase the impact research universities have in addressing these problems. Aside from this lofty vision of the social entrepreneurs’ new role, there are practical reasons why embracing social entrepreneurship makes sense for a research university. (Thorpe & Goldstein, 2010, p. 63)

Regardless of purpose or motivation, entrepreneurship is a logical extension of ACR. Its value is multifaceted and can play a key role in extending the shelf life of an ACR project or program of research. In important ways, entrepreneurship lengthens the chain of the discovery process.

Goal of the Book A set of guidelines recently used in a separate volume (O’Hair, 2018) was adapted for the Handbook. Authors were asked to consider a set of strategies for conceptualizing and organizing chapter contents. In general, authors were asked to address the following issues in their chapters. ●● ●● ●● ●● ●●

What is the best available research in applied communication? What communication and media theories are most relevant and applicable in this context? What new ideas do you have to offer in this area (framework, model, theory)? What are specific research directions that should be pursued for this context of ACR? What pragmatic implications can you offer practitioners in this area of applied communication?

Contents of Volume 1 Twenty‐seven chapters constitute the contents of Volume 1. Divided into four sections, the chapters are generally associated with one another around a common theme. Volume 1 includes the follow themes: (Part I) Theoretical Perspectives; (Part II) Media, Data, Design, and Technology; (Part III) Organizational Communication, and (Part IV) Risk and Crisis Communication. Table  I.1 provides a more detailed examination of Volume 1’s content, including descriptions, research approaches, and advances and implications of the chapters. The promise of ACR has never offered more possibilities for positive change in the human condition. The work contained in these volumes is a testament to the identification of problems and challenges, some of which are only emerging on the horizon of scholarly endeavor. The promise of ACR is real.

Preview of Volume 2 Volume 2 is organized in a similar fashion with a tabular format described in the section above. Before that, we offer an opportunity for those involved in the university research process, where the preponderance of research is supported and disseminated—the opportunity is engaged

Table I.1  Volume 1 Chapter Synopses. Theoretical Perspectives

Description

Research approaches

Inoculation (Ivanov, Parker, & Dillingham)

Inoculation messages are effective in protecting, establishing, and changing attitudes—superior to one‐sided across many persuasive contexts

Formative research for message design and First amendment, recruit/retain minority tailoring students in IT field, misinformation, cross‐cultural. Unexplored: driverless cars, space travel

Advances and implications

Indigenous (Oetzel, Hokowhitu, Simpson, Nock, & Reddy)

Indigenous theory, community‐led to address aging/elderly populations that transform discourse from emphasizing dependency, weakness, limitations to independence and self‐determination

Community‐based participatory research (CBPR), build on experience, peer educator and support interventions, culture‐centered approach, narrative, shared culture social support derived from experience

Health interventions, stewards of cultural integrity, cultural sensitivity to combat ethnocentric approaches, empowering community members, build trust and long‐term relationships, benefit to the community is ultimate success

Vested Interest Theory (Adame)

Motivating attitudinally consistent behavior with personal impact or stake

Formative research to design messages, predict risk perceptions, refined scales

Natural hazards, flood risk, earthquakes, wearing seatbelts, concussion risk among college athletes, need for manipulating perceived vestedness

Apologia (Haigh)

Theory of image restoration and situational crisis communication theory in apologia

Qualitative for image repair, experimental for situational crisis communication theory, reputation, strategies, medium

Medium impacts credibility, trust, balance between the two to apply proactive strategies to crisis

Social Marketing (Parker, Geegan, & Ivanov)

Social marketing for sustainable social change (health promotion, environment, safety, and injury prevention)

Audience‐centered approach to designing Systematic process, strategic roadmap design, audience segmentation, tailored to compelling campaigns, converges traditional marketing with practical application audience needs, goals, marketing mix (4 Ps)

Engaged Communication Scholarship (Kreps)

Problem‐based, social issues

Community participative research and intervention programs, interdisciplinary, multimodal

Negative Emotions (Bessarabova, Banas, & Bernard)

Anger, fear, and guilt as distinct behavioral tendencies

Information processing, persuasion, risk Cognitive functioning model, appraisal‐ perception, empathy, receiver characteristics, tendency framework, psychological reactance, extended parallel process model motivating, efficacy, interplay of emotion

Informs public health policy, demystifying complexities, multiple communication channels, longitudinal, disseminating

Media, Data, Design, and Technology

Description

Research approaches

Advances and implications

Big Data (Ji & Stacks)

Predictive messaging strategies, assess textual communication, analyze large data sets in seconds

Artificial intelligence, algorithms, continuous data collection, streaming and storage, machine coding

Understand, predict, solve, interconnectedness with social media, eWOM (electronic word of mouth)

Serious Games (Muhamad & Kim)

Immersive experience, problem solving, incidental learning

Role‐taking, role‐playing, active, experiential, digital gaming interventions, participatory paradigm

Humanize data, autonomy, homophily, transportation, identification, competence, social relatedness, debriefing

Cyberspace (Spitzberg, Tsou, & Jung)

Geographic information science, computational linguistics, public health

Volume, velocity, variety, variability, visualization, veracity, value, machine learning, Twitter, SMART Dashboard

Analyze social media analytics, industry or market analytics, disease surveillance, disease response, boundless

Entertainment Marketing (Crosswell & Sanders)

Direct, individualized, borderless communication, example of Modern Family sitcom, viewer perception of entertainment content and advertising

Social cognitive theory, product placement, parasocial interactions, product–character associations, storylines, content analysis, eye tracking experiment

Visual attention, character favoritism, perceptions of branded content, product integration, interpersonal aspects of entertainment marketing

Fake News (Mayorga, Hester, Helsel, Ivanov, Sellnow, Slovic, Burns, & Frakes)

Inoculation as strategy to counter negative effects of fake news and attitude polarization, intent to deceive, accuracy

Public susceptibility, confirmation bias, algorithm changes, credibility assessment, inoculation can protect (three studies to date)

Climate change, health care, wealth distribution, national security, cynicism, extremism, stop spread of fake news

Visualization (Yang)

Communicating health and science information

Appealing visual presentations to increase Present encoded quantitative data, public understanding, facilitate decision graphical display, icon arrays, message making, behavior change perception, comprehension, interactive data visualization, fear appeals, fuzzy trace theory

Design Thinking (Rous & Nash)

Participatory design, design thinking cycle, solution‐oriented approach, building empathy

Needfinding, brainstorming, prototyping, testing and feedback

Knowledge management, organization initiatives, knowledge visualization, interactivity, creativity, novelty (Continued)

Table I.1  (Continued) Organizational Communication

Description

Research approaches

Advances and implications

Communication Technology (Stephens & Powers)

Managerial technology use, organizational translucency, access vs. purpose, affordances

Big data, data‐handling, human resource information systems, enterprise, visibility

Hiring, cyberslacking and productivity, social connectedness, habitual checking, civility, acceptability, meetings

Brand Identity (Donohue, Spreng, & Owen)

Influencers drive corporate branding

Voice of the consumer, eWOM, social media content analysis, network connectivity, diffusion of innovations

Connectedness, knowledgeable, innovativeness, persuasiveness, precisely identify Maxcers, use for insights about brand coordination

Designing for High Reliability Organizations (Harrison, Williams, & Reynolds)

Consistently operate in uncertain conditions, organizing processes that makes an organization reliably safe

Communication as design works as an experiential intervention, deference to expertise, useful, functional, essential perspective, critical changes

Culture fosters confrontation and negativism, power, identity, resilience, teamwork, collaboration, credibility, social capital, production quality

Crisis Communication Community‐based site as intermediary organization to assist in crises, Centers organizational misdeeds, natural (Olaniran & Scholl) disasters

Anticipatory model, vigilance of technology, planning and prevention, research translation, knowledge‐to‐action (K2A)

Empowerment, communities have control in crises, respond appropriately, protect community, institutionalization

Volunteers (Kramer & Lewis)

Negotiate, navigate, complex roles and boundaries, credibility, professionalism, power

Altruistic, prosocial and self‐serving motivation, self‐determination theory, social exchange theory

Satisfaction, enrichment for community and self, reward/risk, recruiting and retaining and dialectical tensions, reducing unmet expectations, commitment, balance

Towel Cards (Frandsen & Johansen)

Environmental communication in hotel/travel industry to implement sustainable practices

Social influence theory, attribution theory, genre analysis, discourse community, move structure, visual and verbal rhetoric strategies, politeness, social‐psychological mechanisms

Symbolic visual representations, audience reception analysis, encouragement, environmental protection, social responsibility, environmental cooperation, benefits to the hotel

Risk and Crisis Communication

Description

Research approaches

Advances and implications

Discourse of Renewal (Pyle, Fuller, & Ulmer)

Shift from post‐crisis apologia to pre‐crisis planning, prospective vision, alternative to image repair, promotes effective, ethical communication and organization learning, “reservoir of goodwill”

Exclusively qualitative, but new advances with quantitative and scale development for digital media platforms Survey research, social media research, narrative of blame, lingering stakeholders

Vicarious learning, transparent ethical communication, gain trust, rebuilding organizational rhetoric, recovery

Terrorism (Bruce)

Structural features of television/film Diffusion and influence of technology, messages, “videostyle,” content and unrestricted flow of visual content, visual framing to promote interpretation structural analysis methods of event

Visuals elicit emotional impact more than words, tell a story, sensationalism, political cultural implications of visual conflict coverage, provoke empathy, moral/just framing with images

Best practice in environmental Best Practices contamination crises, risk inspires crisis (Veil, Anthony, Sellnow, Staricek, Young, & Cupp) planning, uncertainty, ambiguity

Case study examination

Lessons learned, tailoring messages to provide instruction, acknowledge vulnerable publics, strategic planning, greater concern for recovery of stakeholders over reputation, resilience, goodwill

Media Coverage (Friedman & Sutton)

Evolution of mass media risk coverage, audience attention and participation/ engagement in technologies

Agenda setting, gatekeeping, social amplification of risk framework, social media to collect, curate, and communicate info, geographic reach

Impact of social media, public perceptions. Diluting top‐down, public‐oriented risk conversations, individual differences in risk perception, echo chambers, striking visuals, balanced viewpoints in story

Terror Management (Miller & Massey)

Existential anxiety motivating human Experimental, conscious task of fantasy, behavior, anger, subliminal death primes anxiety‐buffering function of self‐esteem, personal relationships, world‐view validation

Hurricane Warnings (Sellnow‐Richmond & Sellnow)

Communicating hurricane warnings, slow‐moving crises, risk amplification, shifting needs, milling, narrative

Milling, proactive information seeking, confirmation, prediction, social comparison, case study method

Co‐created, building narrative, milling, amplified perception of risk, humor, predicting uncertainty, strain on supply chain, exposure to risk, safety preparatory action, assurance, shareability, user‐generated (mis)information

Psychological Reactance (Miller, Massey, & Ma)

Psychological reactance resistance to influence, motivating

Experimental design health risk contexts, anger, negative cognitions, reflexive, restoration of freedom, message features, inductive format, controlling language, scales of state/trait reactance

Predict frustration, autonomy, self‐ determination, increased attraction, boomerang, source derogation, reactance‐ enhanced inoculation,

Motivation, intimacy, social influence/ validation, favoritism, prejudice, outgroup derogation, stereotypes, conformity, benevolence, prosocial satisfaction, patience, tolerance, flexibility in thinking

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scholarship. We are not naive enough to think that this concept is new, on the contrary it is a battle‐tested notion championed by some of the biggest thinkers of our time. We offer it as a complementary set of ideas that have withstood the test of time and are no less important than when they were considered novel. We find the concepts of engaged scholarship and ACR to be highly complementary processes.

References Bonney, R., Cooper, C., & Ballard, H. (2016). The theory and practice of citizen science: Launching a new journal. Citizen Science: Theory and Practice, 1(1), 1. doi:10.5334/cstp.65 Bonney, R., Cooper, C. B., Dickinson, J., Kelling, S., Phillips, T., Rosenberg, K. V., & Shirk, J. (2009). Citizen science: A developing tool for expanding science knowledge and scientific literacy. Bioscience, 59(11), 977–984. doi:10.1525/bio.2009.59.11.9 Eitzel, M. V., Cappadonna, J. L., Santos‐Lang, C., Duerr, R. E., Virapongse, A., West, S. E., … Jiang, Q. (2017). Citizen science terminology matters: Exploring key terms. Citizen science: Theory and practice, 2(1), 1. doi:10.5334/cstp.96 Greenberg, G. (2019). Citizen science. AARP Bulletin, April, 36–38. Retrieved from https://www.aarp. org/home‐family/personal‐technology/info‐2019/volunteer‐scientists.html Gura, T. (2013). Amateur experts. Nature, 496, 259–261. Retrieved from https://www.nature.com/ naturejobs/2013/130411/pdf/nj7444‐259a.pdf Hecker, S., Bonney, R., Haklay, M., Hölker, F., Hofer, H., Goebel, C., … Bonn, A. (2018). Innovation in citizen science—perspectives on science‐policy advances. Citizen Science: Theory and Practice, 3(1), 4. doi:10.5334/cstp.114 Kim, J., & Marschke, G. (2007). How much U.S. technological innovation begins in universities? Federal Reserve Bank of Cleveland, Economic Commentary. Retrieved from https://www.clevelandfed.org/ en/newsroom‐and‐events/publications/economic‐commentar y/economic‐commentar y‐ archives/2007‐economic‐commentaries/ec‐20070415‐how‐much‐us‐technological‐innovation‐ begins‐in‐universities.aspx Kreps, G. L., Frey, L. R., & O’Hair, D. (1991). Applied communication research: Scholarship that can make a difference. Journal of Applied Communication Research, 19, 71–87. O’Hair, H. D. (Ed.). (2018). Risk and health communication in an evolving media environment. New York, NY: Routledge. O’Hair, H. D., & Kreps, G. L. (Eds.). (1990). Applied communication theory and research. Hillsdale, NJ: LEA. O’Hair, H. D., Ploeger, N., & Moore, S. (2010). Applied communication theory and research. In J. Chesebro (Ed.), From 20th century beginnings to 21st century advances: Developing and evolving from a century of transformation (pp. 89–106). New York, NY: Oxford University Press. Plax, T. G. (1991). Understanding applied communication inquiry: Researcher as organizational consultant. Journal of Applied Communication Research, 19, 55–70. Shirk, J. L., Ballard, H. L., Wilderman, C. C., Phillips, T., Wiggins, A., Jordan, R., … Bonney, R. (2012). Public participation in scientific research: A framework for deliberate design. Ecology and Society, 17(2), 29. doi:10.5751/ES‐04705‐170229 Steimel, S. (2014). Mapping a history of applied communication research: Themes and concepts in the Journal of Applied Communication Research. The Review of Communication, 14, 19–35. Thorpe, H., & Goldstein, B. (2010). Engines of innovation: The entrepreneurial university in the twenty‐first century. Chapel Hill, NC: North Carolina University Press. US Environmental Protection Agency. (2019). Handbook for citizen science: Quality assurance and documentation. Retrieved April 30, 2019 from https://go.usa.gov/xEw43 Wood, J. (2000). Applied communication research: Unbounded and for good reason. Journal of Applied Communication Research, 28, 188–191.

Part I

Theoretical Perspectives

1

Inoculation Theory as a Strategic Tool Bobi Ivanov, Kimberly A. Parker, and Lindsay Dillingham

Introduction Greenwald suggested that theory “is the most creative form of scientific contribution,” and that, in general, the disciplines that are associated with theory‐driven work are often perceived and characterized as basic or pure as opposed to technical or applied, which are terms reserved for disciplines that are more empirically or practically focused (2012, p. 99). The implication of this classification, as Greenwald notes, is to suggest that applied work is of lower status. Yet, there is no reason why theory cannot be the driving force behind sound, applied empirical research. After all, Lewin’s maxim proposes that “there may be nothing as practical as a good theory” (1943, p. 118; as cited in McCain, 2015). However, what exactly constitutes a good theory? Greenwald suggested that a good theory is one that moves beyond the assignment of conceptual labels to laboratory research procedures and into a real‐world application (2012). Indeed, as the proponent of action research, Lewin celebrated the importance of combining theory and practice (1946; McCain, 2015), as theory can guide our applied strategy in practical settings. Thus, theories with real‐world application hold considerable value not only for practitioners, but also for theoreticians interested in testing and pushing the conceptual boundaries of the theory. Theories that can be applied to phenomena in multiple contexts are especially of value, as they may help explain or predict multiple events of significance. As such, good theories may provide practitioners with tools to help guide and shape their strategies. Consequently, the better these theory‐ based strategies perform in single and multiple contexts, the more practically useful the theories become. A good example of a theory with such practical utility is the theory of inoculation. Labeled as the “grandparent theory of resistance to attitude change” (Eagly & Chaiken, 1993, p. 561), over 50 years of research have established inoculation theory as one of the most recognizable theories in the areas of persuasion research, in general, and resistance research, in particular (Compton, 2013; Ivanov, 2017). What has given this theory such prominence is its strategic application in multiple applied contexts, including political communication (see Compton & Ivanov, 2013), commercial communication (e.g., Pfau, 1992), corporate communication (e.g., Dillingham & Ivanov, 2017), public relations (e.g., Burgoon, Pfau, & Birk, 1995), interpersonal communication (Sutton, 2011), cross‐cultural communication (Ivanov, Parker, Miller, & Pfau, 2012), instructional/educational communication (Compton & Pfau, 2008), risk and crisis communication (e.g., Ivanov et al., 2016), health communication, (e.g., Parker, Ivanov, & Compton, 2012), and sports, injury, and exercise The Handbook of Applied Communication Research: Volume 1, First Edition. Edited by H. Dan O’Hair and Mary John O’Hair. © 2020 John Wiley & Sons, Inc. Published 2020 by John Wiley & Sons, Inc.

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(e.g., Dimmock et al., 2016), among other contexts. For this reason, this chapter examines the strategic utility of inoculation theory as applied in various contexts. More specifically, this chapter begins with an introduction of inoculation theory’s conception, logic, theoretical mechanisms, and boundaries. It then reviews the diverse contexts in which the theory has been practically applied and/or tested and proposes additional contexts in which the theory may be applied with success. The chapter concludes with suggestions for, and an example of, how to design inoculation theory‐based messages for practical application.

Overview of Inoculation Theory In a seminal study on persuasion and propaganda, Lumsdaine and Janis (1953) tested the effectiveness of one‐sided (presenting arguments only from one side of the issue) and two‐sided (presenting arguments from both sides of the issue) messages in generating resistance to forthcoming counterattitudinal challenges. Their findings showed two‐sided messages to be more effective, an outcome the authors contributed to the inoculating power of two‐sided messages, which they believed to have given the message recipients “an advanced basis for ignoring or discounting the opposing communication” (1953, p. 318). Intrigued by these findings and this logic, McGuire (1964) proceeded to propose and explain the mechanisms responsible for the inoculation process. According to McGuire (1964), the success of inoculation relies on two key mechanisms, threat and counterarguing. Threat, McGuire (1964) suggested, is the motivating force that initiates the inoculation‐ based process of resistance by delivering a “shock value” (McGuire, 1961, p. 185) to the inoculation recipient, which McGuire defined as the person’s realization of attitudinal vulnerability. Stated differently, the threat presented is intended to inspire the individual to take action in the form of attitudinal defense‐building in order to preserve his or her attitudinal position when rendered to counterattitudinal persuasive attempts. Shocked into action, according to McGuire, the individual would proceed to build and accumulate counterarguments to the forthcoming challenges, thus preparing the inoculated individual to better protect the attitude (belief, intent, opinion, value, behavior, etc.; henceforth referred to only as attitude to avoid repetitiveness) in place. The process of attitudinal inoculation is initiated through the use of inoculation messages (Ivanov, 2012, 2017). These messages incorporate two key components—forewarning and refutational preemption—that unleash the inoculation process. The message forewarning overtly introduces the threat to the message recipient by directly informing the individual of the vulnerability of his or her attitude in place. The refutational preemption component of the message, on the other hand, exposes the individual to a weakened form of a counterattitudinal argument, which the individual then proceeds to vigorously refute. Thus, the refutational preemption serves multiple functions. First, it provides a covert form of attitudinal threat by exposing the individual to potential counterattitudinal arguments he or she may face, thus rendering the threat real. Next, it provides direct refutation of the counterattitudinal arguments, which also arms the individual with specific arguments (content or material) that can be used in the defense of the current attitude. In addition, it affords the individual an example of how to engage in effective attitudinal defense through counterarguing practice. Thus, an inoculation message is designed to elicit threat that acts as a motivational catalyst that inspires the individual to shore up his or her defenses in preparation for forthcoming attitudinal challenges. In this manner, inoculation messages supply the individual with motivation, material (or content to be used in attitudinal defense), and counterarguing practice, all of which contribute to attitudinal resistance. A question of significant practical import is whether the effectiveness of this strategy is limited when inoculated individuals face counterattitudinal challenges that are novel or different, as opposed to the same, from those encountered in the refutational preemption component of the



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inoculation message. McGuire (1964) tested this possibility by challenging inoculated individuals with the same counterattitudinal arguments introduced in the refutational preemption or different ones. The results of his studies showed no significant difference in the ability of inoculation to generate attitudinal resistance regardless of whether the content of the counterattitudinal challenges was encountered previously (i.e., the refutational preemption) or faced for the first time. In their meta‐analysis, Banas and Rains (2010) confirmed these findings, thus extending the practical utility of inoculation‐based strategies, which do not have to account for all potential arguments the individual could possibly face. Thus, inoculation may form an umbrella of protection for all related arguments within an issue domain (Compton & Pfau, 2005). However, could this umbrella extend to related attitudes outside the issue domain umbrella? In a study examining the possibility of cross‐protection, Parker and colleagues (2012) found evidence that inoculation may provide protection to attitudes not just directly targeted with the inoculation message (e.g., condom use), but also related to the inoculated attitudes (e.g., binge drinking). Combined, the above‐presented results suggest that inoculation‐based strategies have the potential to protect vulnerable message‐targeted and related attitudes from familiar or novel persuasive counterattitudinal challenges. In medicine, inoculations are preemptive—or proactive—in nature as they are used to protect healthy individuals from contracting diseases. Following the logic of its biomedical analogy, attitudinal inoculations, therefore, are used to protect healthy attitudes from yielding to persuasive counterattitudinal efforts. As such, inoculation‐based strategies are seemingly limited to the realm of preemption, prevention, or protection as one cannot protect attitudes that are not present. Stated differently, for example, one cannot protect a person’s attitude against smoking, if that attitude is not present in the first place. However, this chapter is less concerned with the theoretical import or consistency of inoculation theory with its biomedical analogy to which it is inevitably tied, and more with the practical utility of inoculation‐based strategies. Therefore, the relevant question of practical significance is whether inoculation messages would be effective not just in protecting attitudes but in establishing and/or changing them as well. Ivanov and colleagues (2017) compared the effectiveness of inoculation‐based (two‐sided) and one‐sided messages in protecting, establishing, or changing the attitudes of individuals. The results showed that in all the cases, inoculation‐based messages were more effective than, or just as effective as, one‐sided messages. From a practical standpoint, inoculation‐based messages provide the basis for a superior strategy irrespective of the original position of the attitude. Thus, instead of having to possibly use multiple message strategies (e.g., inoculation to protect current customers and one‐sided strategy to win over prospective customers) to reach more than one objective, inoculation offers a single message strategy that can accomplish multiple objectives (e.g., protecting current customers and attracting new customers). A few key strengths of using inoculation as a single message strategy include message consistency with different target audiences (e.g. current and prospective customers), reduced market research costs associated with assessment of multiple audiences, and lower costs associated with preparing a single message strategy. Another issue of practical significance is the personal relevance of the topic (issue, product, etc.) to the individual. Pfau and colleagues (2007) suggested that the effectiveness of inoculation is moderated by the individual’s involvement with the topic. They posited a curvilinear relationship where people at the moderate levels of involvement would find inoculation to be most useful and effective. Pfau and colleagues suggested that individuals who are not very involved with the topic at hand would not find defense‐building to be pertinent given the low relevance of the topic. Conversely, individuals on the high end of the involvement spectrum may already be aware of the threats and may have already prepared for the upcoming challenges, thus rendering the effectiveness of inoculation limited. Yet, Banas and Rains (2010) did not find confirmation for this curvilinear relationship in their meta‐analysis, which suggests that inoculation may be quite effective regardless of the individual’s level of involvement with the topic.

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Overall, this overview explicates the effectiveness of inoculation suggesting that inoculation‐ based strategies may have clear advantages over one‐sided message strategies in targeting intended and related attitudes regardless of whether the strategic objective is to protect, establish, or change an attitude that may be of different import to members of the target audience(s). Thus, inoculation‐based strategies are applicable in numerous contexts. The next section provides an overview with examples of some of the contexts in which this strategy has been applied to date. It also introduces additional contexts in which this approach could be successfully applied in the future.

Contextual Application Compton (Compton, 2013; Compton & Pfau, 2005) identified four general contexts in which inoculation‐based strategies have been successfully tested and applied: politics, health, commerce, and public relations. However, the application of this message strategy is not limited to any particular context and, instead, it is applicable in any context in which an individual’s motivation can be elicited to protect, create, or change attitudes (Ivanov, 2017). To that end, this section will organize, review, and discuss the application of inoculation in the aforementioned contexts as well as introduce some additional relevant contexts in which inoculation has been, or could potentially be, applied. The presentation of the results of different studies in specific contexts is intended to be instructive, rather than definitive, as many of the studies are cross‐contextual and, as such, could easily fit in multiple contexts. It is also important to note that the intent of this overview is not to provide an exhaustive account of all contexts, and studies within those contexts, that have received—or potentially could receive—inoculation application, but rather to provide a sample of the possibilities that inoculation‐based strategies could offer.

Civic and Legal Communication Rather than just focus on politics in its contextual overview, this section also includes discussion of the potential efficacy of inoculation in government, policy, and legal communication.

Political communication

Compton and Ivanov (2013) noted that political communication inoculation scholarship has taken a significant applied perspective with practitioners showing interest in protecting pro‐candidate and/or pro‐issue attitudes with inoculation messaging. Early research (Pfau & Burgoon, 1988) illustrated the ability of inoculation to deflect political attack messages from opposing candidates by inoculating the voter base. Follow‐up studies extended the efficacy of inoculation messages to topics such as issue position and candidate perception (for review, see Compton & Ivanov, 2013). Yet, Compton and Ivanov (2013) argued that most of the studies looking at the effect of inoculation on political campaigns have focused on the latter stages of the campaign. The authors proceed to cite Pfau and Burgoon, who suggest that “inoculation should prove more effective early in a political campaign, prior to the saturation of political campaign messages…” (1988, p. 106). In addition, Compton and Ivanov suggested that very little is known on the effect that inoculation messages have in political campaigns beyond the US borders. Both of these areas warrant further exploration.

Public agencies

Ivanov and colleagues (2016) have used inoculation messages to promote confidence in response of public agencies to politically motivated acts of violence. Ivanov et al. (2016) found that, compared with a control group, participants exposed to an inoculation message reported greater



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confidence in government agencies to both prevent and effectively manage the aftermath of a politically motivated act of violence. Participants also reported less fear, greater coping efficacy, and greater confidence in government agencies to effectively respond to national crises in general and not just to those related to politically motivated acts of violence. In addition, more recent scholarship has demonstrated inoculation’s ability to protect attitudes against government conspiracy theory rhetoric (Banas & Miller, 2013).

Policy and government regulation

Past inoculation studies have explored the ability of inoculation messages to protect individuals’ attitudes toward proposed or implemented government policy and regulation. The results of these studies show that inoculation can successfully protect established attitudes on issues, such as the regulation of sale and distribution of hand guns, gambling, violence on television, and legalization of marijuana (see Miller et al., 2013). Niederdeppe, Heley, and Barry (2015) conducted a study regarding the ability of inoculation messages, as compared to a narrative message strategy, to protect pro‐regulation attitudes when faced with persuasive industry messages advocating against regulation. Their study focused on polices designed to prevent obesity, reduce cigarette use, and combat opioid use. The authors’ findings demonstrated that inoculation messages increased overall support for health policy and provided resistance against anti‐regulation industry messages (Niederdeppe et al., 2015).

Legal communication

Ziemke and Brodsky (2015) explored the potential of inoculation messages in trial by jury. More specifically, the authors acknowledged that jurors may show negative bias toward expert testimony by viewing the expert as the “hired gun” for the paying party. Ziemke and Brodsky’s findings demonstrate that inoculation messages may boost jurors’ assessment of the experts’ field of testimony, thus giving the legal expert more courtroom credibility. Beyond boosting the credibility of expert testimony, attorneys could use inoculation in their courtroom opening statements in an attempt to preempt the arguments from the opposing side. In addition, inoculation messages could be used to instruct the jury on how to avoid the temptation of foreclosing on an idea or accepting limited, flawed, or inconsistent arguments. Finally, inoculation could be used to preempt courtroom tactics frequently used by the opposition and their (un)intended effects on the jury, such as entering testimony that should be disregarded by the jury but which is nevertheless heard and impactful.

Health Communication Both practitioners and scholars have noted that health is not only a function of genetic factors but also a function of health‐concerned personal decision making (see Khera, et al., 2016). As a result, 20–40% of the leading causes of death (stroke, cancer, heart disease, chronic lower respiratory diseases, and unintentional injuries) are preventable (Centers for Disease Control and Prevention, 2014). Thus, the applied potential of inoculation theory is prominent in the realm of health communication. The focus on health communication in inoculation scholarship is, in some ways, an extension of its theoretical beginnings. In his early work, McGuire (1964) tested and confirmed the ability of inoculation messages to protect widely regarded positive health attitudes toward dental hygiene, annual physical exams, and disease screenings.

Risky behaviors

Scholars have suggested that the threat associated with engaging in risky, undesirable, or unhealthy behaviors has resulted in “the discovery, testing, implementation, and refinement of preventive strategies … in the health context for the past few decades” (Ivanov, 2012, p. 73).

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As a preventive strategy, inoculation has played an important role in the development of effective strategies to combat the attitudes toward, and the practice of, risky behaviors. Parker and colleagues (2012) tested the efficacy of inoculation messages as a way to protect safe sex (i.e., pro‐condom use) attitudes of college students. The authors suggested that “one of the best strategies for reducing sexual risk among young people is correct and consistent condom use” (p. 224). Research results demonstrated not only that, compared with control messages, inoculation messages generated more positive attitudes toward condom use, but that these messages also had positive cross‐protection effects on attitudes toward binge drinking (Parker et al., 2012). While Parker and colleagues (2012) explored the relationship between inoculation messages and attitudes toward binge drinking as a theoretical question about the ability of inoculation messages to generate attitudinal cross‐protection, or protection of attitudes about a related but separate issue (e.g., unprotected sex and binge drinking), other authors have focused on prevention of binge drinking as a potential direct application of inoculation theory. In the early 1980s, Duryea (1983) suggested that inoculation could be employed in preventative alcohol education. Godbold and Pfau (2000) compared different types of inoculation messages (i.e., normative and informational) and found that inoculation messages emphasizing peer disapproval of alcohol use boosted resistance. More recently Richards and Banas (2015) found that inoculation messages could mitigate reactance to anti‐alcohol messages, or, more specifically, the receiver perception that messages against binge drinking represented a constraint of personal decision‐making freedom. The authors demonstrated that inoculation messages had the potential to reduce “self‐ generated cognitions that might otherwise lead toward negative health behaviors” (p. 451). Similar to scholarly and practitioner interest in drinking prevention, inoculation researchers have also aimed to prevent smoking initiation (Godbold & Pfau, 2000; Pfau, Van Bockern, & Kang, 1992), since nicotine is highly addictive and can generate stronger addictions if smoking is adopted during adolescent years. Several scholars have noted these risks and found encouraging research results when testing the efficacy of inoculation in the cigarette smoking context. Pfau and colleagues (Pfau et al., 1992; Pfau & Van Bockern, 1994) explored the effect of inoculation‐based anti‐smoking messages among middle school students. Their research demonstrated that even up to 19 weeks after inoculation treatment administration, young adolescents reported a resistance to smoking onset and more negative attitudes toward smoking. These findings were pervasive in students who reported low self‐esteem and, as the authors argued, are therefore most at risk for smoking initiation. Furthermore, the authors suggested that smoking prevention messages are most effective in late elementary or early middle school, before teens are faced with the opportunity to smoke (Pfau & Van Bockern, 1994). Banerjee and Greene (2007) also found success when testing an inoculation‐based anti‐smoking intervention. Finally, although young women may be well aware of the risk of cancer associated with indoor tanning bed use, they nevertheless continue to use this service, at times justified by misinformation associated with the benefits of indoor tanning (Kelley, 2017). Using a content analysis, Kelley identified two major areas of misinformation, health and safety, which could be targeted with an effective message design. In her study, Kelley compared the effectiveness of control, inoculation, and one‐sided messages. The results provide support for the superiority of inoculation messages as inoculated individuals, among other benefits, perceived the inoculation messages as more effective and reported lower intentions to engage in indoor tanning.

Vaccination

The rise of anti‐vaccination attitudes presents a current public health issue (see Jolley & Douglas, 2017; Wong & Harrison, 2014). Even though vaccines prevent the risk of contracting preventable diseases (Centers for Disease Control and Prevention, 2019), some parents hesitate or opt to delay or not vaccinate their children (McKee & Bohannon, 2016). A number of reasons could be attributed to the decision to forgo child vaccination, such as cultural beliefs, religious beliefs,



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personal beliefs, safety concerns, or desire for more information from healthcare providers (see McKee & Bohannon, 2016). Jolley and Douglas (2017) recognize conspiracy theories as an important factor that undermines participation in the child vaccination process. The most impactful conspiracy theory related to vaccination suggests that data are falsified to assist the government and pharmaceutical companies to financially benefit from the procedure while hiding the harmful side effects of vaccinations (Jolley & Douglas, 2017). In their study, Jolley and Douglas set out to explore the possibility of inoculating against this popular conspiracy theory by using anti‐conspiracy arguments presented before or after the presentation of the pro‐conspiracy arguments. Their results showed partial success. While the presentation of anti‐conspiracy arguments following the presentation of pro‐conspiracy arguments proved to be ineffective, the presentation of anti‐ conspiracy arguments prior to the presentation of pro‐conspiracy arguments was indeed effective, thus increasing the intent to vaccinate a child. The authors concluded that to effectively inoculate against such conspiracy theories it is important to use preemptive strategies. In a separate study, inoculation scholars (Wong & Harrison, 2014) explored the ability of inoculation messages to protect pro‐HPV (human papillomavirus) vaccination attitudes from anti‐vaccination message challenges. The authors’ findings demonstrated that inoculation messages protected not only specifically against erosion of positive HPV assessments, but also against the erosion of anti‐vaccination attitudes in general. In addition, the results demonstrated the potential of inoculation to impact other relevant outcomes such as perceived vaccine safety and behavioral intentions (Wong & Harrison, 2014).

Physical activity

As detailed in the preceding paragraphs, much inoculation research in the health context has focused on preventing unhealthy choices. These findings are valuable as scholars (e.g., Parker et al., 2012; Pfau et al., 1992) have noted that, particularly in certain health‐related contexts, pro‐health attitudes are likely to come under attack, leading to engagement in unhealthy behaviors. However, Compton and Ivanov (2018) note that inoculation is a strategy well‐poised to encourage proactive healthy behavior rather than just prevent unhealthy behavior. They offer that, compared with preventing unhealthy practices, “Much less attention has been devoted to initiating or continuing healthy practices” (Compton & Ivanov, 2018, p. 73). The authors point to inoculation messages as a way to combat the challenges associated with consistently engaging in physical activity, including discouragement, anxiety, and perceived lack of time. Inoculation‐based messages have also been studied alongside perceptions of physical exercise. While Compton and Ivanov (2018) explored the potential of inoculation messages to promote actual engagement in exercise, other authors have looked at the ability of inoculation to boost the enjoyment of exercise. For example, Dimmock and colleagues (2016) found sustained interest and enjoyment, even amidst a monotonous exercise class, for individuals who had been inoculated against perceiving the class negatively. Similarly, Jackson and colleagues (2015) found that participants reported greater self‐efficacy in their ability to perform a physical task—despite negative instructor comments—when they had been inoculated against their own negative self‐ assessment. Taken together, these findings offer the idea that inoculation can strengthen mental resolve to appreciate the exercise experience. Application in the pro‐exercise context is a promising area, as scholars have noted that one’s value judgment of one’s own exercise behavior is a key factor in exercise persistence (Dimmock et al., 2016).

Additional health‐related topics

While inoculation, as aforementioned, has been successfully applied in numerous health communication contexts related to both behavioral intervention and policy, the “application of the strategy [is] boundless” (Ivanov, 2012, p. 77) when considering the promotion of healthy behavior and/or prevention of unhealthy behavior. For example, Rosenberg (2004) argued that

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inoculation could be implemented in schools to guard against increases in students’ verbal aggression. Matusitz and Breen suggested that inoculation could be used as an effective strategy to reduce recidivism in criminal prison inmates (Matusitz & Breen, 2013). They also argued that this strategy could be useful in discouraging youths from joining gangs (Breen & Matusitz, 2008). Thieneman (2017) posited that inoculation could show success in relapse prevention, a significant threat to sobriety for recovering substance users. Additional areas of future application of inoculation in the health context could also include the promotion and/or protection of attitudes toward health screenings (e.g., mammograms or colonoscopy), as well as breastfeeding and bullying, just to name a few.

Commercial Communication Commercial applications of inoculation theory have covered a wide range of industries and situations. Marketing scholars were quick to suggest the utility of inoculation theory as a tool for marketing tactic development (see Bither, Dolich, & Nell, 1971). Pfau (1992) discovered inoculation to be an effective approach in protecting the images of brands from forthcoming challenges, while Ivanov and colleagues (2009a) showed its efficacy in shielding current customers from repeated competitor attacks, regardless of whether the attacks featured the same or different content. In general, inoculation is well‐positioned to protect images such as those of celebrities and corporations (Ivanov & Parker, 2011). In addition, inoculation messages may be able to favorably impact both pre‐buying choice behaviors (Bechwati & Siegal, 2005) and post‐purchase impressions (Ivanov, Parker, & Compton, 2011). For example, Mikolon, Quaiser, and Wieseke (2015) successfully inoculated customer satisfaction ratings against future organizational service failures.

Tourism and foreign manufacturing

Scholars have also explored the way inoculation messages can protect not only tourist destinations, but the way the consumers perceive the country of origin associated with manufactured products or services. Ivanov and colleagues’ (Ivanov et  al., 2017, Ivanov, Dillingham et  al., 2018) research results demonstrate the potential for tourist destination managers to inoculate potential travelers against negative reviews of the destination on social media. Inoculated participants maintained a more positive attitude toward destination cities even after they were presented with an unfavorable review of the city via e‐word‐of‐mouth communication (Ivanov, Dillingham et al., 2018). Similarly, Ivanov, Pfau, and Parker (2009b) protected positive attitudes toward country‐of‐origin image for manufactured products.

Financial marketing and communication

Inoculation scholars have explored issues of consumer welfare as they relate to financial marketing. Compton and Pfau (2004) used inoculation messages to protect college student attitudes against aggressive credit card marketing efforts. As the authors noted, credit card debt among college students is a grave and ongoing concern. More recently, Dillingham and Ivanov (2017) successfully used inoculation messages to protect the beliefs of stock market investors that they should not liquidate their holdings as a reaction to turbulence. As the authors argued, selling during substantial market downturns could have long‐term repercussions for retirement planning.

Public Relations Aspects of organizational welfare addressed by inoculation include internal message strategies and crisis recovery. Burgoon, Pfau, and Birk (1995) found that issue advocacy inoculation messages produced by organizations could protect general pro‐organization attitudes. Haigh and



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Pfau (2006) used inoculation messages to boost employee organizational identity, commitment, and citizenship amidst negative organizational messaging. In addition, Sims (2016) demonstrated the ability of inoculation to protect value‐in‐diversity attitudes for highly involved individuals. Wan and Pfau (2004) and Wigley and Pfau (2010) found that using inoculation messages before an organization crisis could insulate pro‐organizational attitudes during the post‐crisis phase. While the Wigley and Pfau study was conducted in the United States, Wan and Pfau collected their results in Taiwan, thus attesting to the robust nature of an inoculation message strategy. Public relations represent a fertile ground for inoculation application. Inoculation could be applied to manage organizational risk, handle internal and external crisis, and promote and/or protect products, corporate images, and perceived corporate social responsibility.

Additional Applied Contexts Politics, health, commerce, and public relations represent contexts that have yielded the greatest interest and application of inoculation to date (see Compton, 2013; Compton & Pfau, 2005). Yet, as previously mentioned, inoculation can be strategically applied in any context that provides a need and opportunity to protect, shape, or defend attitudes. Thus, a few additional contextual applications of inoculation‐based strategy are exemplified below. Once again, these are intended to provide samples of what is possible with inoculation, rather than provide an exhaustive list of contextual applications.

Misinformation

Scholars have given recent attention to the ability of inoculation messages to insulate citizens against media misinformation. For example, Cook, Lewandowsky, and Ecker (2017) found that preemptive inoculation messages explaining the flawed arguments contained in forthcoming misinformation campaigns (i.e., reports denying climate change) could “neutralize the effects of misinformation” (p. 1). As argued by van der Linden and colleagues (2017), inoculation messages can protect attitudes against “a range of falsehoods in diverse domains such as climate change, public health, and emerging technologies” (p. 1141). Research results of van der Linden and colleagues (2017) support the idea that inoculation can protect the perception of truthfulness of climate change attitudes in misinformation environments. Roozenbeek and van der Linden (2018) found that an educational game in which participants composed fake news denying the effects of climate change was an effective way to inoculate against forthcoming misinformation.

Cross‐cultural communication

Since the vast majority of inoculation studies have been conducted with American research participants (see Ivanov et al., 2012), scholars have addressed the need to understand whether or not the utility of inoculation messages could persist across cultures. Ivanov and colleagues (2012) found that not only is culture a moderator of inoculation’s success, but that the collectivist orientation that predominates Asian cultures can weaken the effects of inoculation messages as compared with the effects of inoculation messages on members of an individualistic (e.g., American) culture. Although weakened, the effect still persisted, thus extending the utility of this message strategy cross‐culturally. In addition to identifying differences in cultural message processing (Ivanov et al., 2012), Heuett and Westerman (2014) successfully tested inoculation theory as a way to reduce anxiety and negative affect related to intercultural interactions. The fact that inoculation may work successfully across cultures provides possibilities for this strategic approach to be implemented in intercultural settings. For example, Briggs and Harwood (1983) suggest that corporations use millions of dollars repatriating employees who find themselves maladjusted to the demands of the local culture. Thus, they recommend inoculation as a

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potential solution for delivery of a cultural communication capsule that is designed to prepare expatriates for the challenges of living and operating in a foreign culture. However, this possibility should not be limited to corporate expatriates, but instead, could apply to foreign dignitaries, exchange students, or relief workers, just to name a few.

Educational and instructional communication

Compton and Pfau (2008) ventured into educational communication by testing whether or not inoculation could protect the academic integrity viewpoints of students against pro‐plagiarism justifications. While their study yielded disappointing findings, subsequent work has pointed to inoculation’s value in various educational and instructional settings. Westerman, Margolis, and Kowalski‐Trakofler (2011) suggested that inoculation could be employed in training messages about how to deal with crisis. More recently, Jackson and colleagues (2017) used an inoculation‐based strategy as a way to reduce and reframe public speaking anxiety.

Interpersonal communication

Research related to inoculation has even extended into the realm of interpersonal communication. Sutton (2011) used inoculation messages to encourage proper coping with feelings of jealousy in interpersonal relationships. While inoculation did not prevent jealousy itself, inoculated individuals reported an increased likelihood to cope with jealousy in a healthy manner following exposure to jealousy‐provoking stimuli.

Additional applied contexts

Along with the contexts already described, inoculation has been considered in, and/or applied to, even more settings as inoculation scholars have explored the potential of this strategy to counter the negative effects of: gambling addiction among youth (Lemarié & Chebat, 2013); experiencing a losing season by a sports team (Compton, 2016); pressures against breastfeeding (Natoli, 2015); deceptive front‐group stealth campaigns (Pfau et al., 2007); and threat to medical experimentation on animals (Nabi, 2003). Further, inoculation has been implemented, or considered, as a potential strategy to recruit and retain students in the information technology industry, especially as it pertains to women and minority students (Fagnot, 2011). Yet further areas considered include the promotion of emergency preparedness (Pace, 2013), community acceptance of recycled water (Kemp, Randle, Hurlimaan, & Dolnicar, 2012), and protection of corporate social responsibility perceptions (Wagner, Lutz, & Weitz, 2009). Still, many more opportunities remain for applying inoculation‐based strategies. For example, inoculation should be a strategy capable of protecting attitudes toward driverless cars. While some people may favor the idea of not having to drive their own cars, which might be especially a desired outcome during longer trips, news about fatal accidents may erode the favorability of this driverless mode of transportation. Space travel and exploration have captivated the imagination of many, that is, until the high cost of this endeavor challenges the enthusiasm of exploring the unknown space beyond our sight. Ideals such as the prevention of climate change or promotion of sustainable energy and lifestyle frequently receive conceptual support, but fall short empirically as individuals face the realization that these ideals come with a financial (e.g., higher cost for pollution safeguards), personal (e.g., effort of switching to energy efficient lightbulbs), and time (e.g., driving glass bottles to a recycling plant) cost. In any of these cases, and many more, inoculation‐based strategies can play a significant role in the pursuit of desired outcomes. Overall, the above review attests to the robust effect of inoculation and its strategic applicability in various persuasive and resistance contexts. Thus, inoculation represents a potent tool at the disposal of the message strategist. However, the success of inoculation, not unlike any other message strategy, rests on the ability of the strategist to design an effective inoculation message. Via a guided example, the next section provides suggested considerations on how to design effective inoculation messages.



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Inoculation Message Design A key to effective inoculation message design is to start with formative research. The first step in this process is to gain an understanding of the message’s target audience in order for the message to be properly tailored to its recipients. For example, the message reading level (e.g., eighth grade) should not exceed that of the audience members (e.g., Miller et al., 2013). The modality used to transmit the message should also match the processing capabilities and/or preferences of the audience members. For example, using either print or video to transmit the message may be equally appropriate for college students (e.g., Pfau, Holbert, Zubric, Pasha, & Lin, 2000), but video may be preferred as a method of message delivery by middle school students (e.g., Pfau et al., 1992). The next step in the message design process is to prepare the main components of the inoculation message: the explicit forewarning and refutational preemption. The objective of the explicit forewarning is to overtly deliver threat. Stated differently, the forewarning is designed to inform the individual in no uncertain terms that his or her attitude is vulnerable and subject to forthcoming attitudinal pressures, as the following excerpt from a recent inoculation study designed to protect First Amendment rights of college students illustrates. [A] September 2017 study from the Brookings Institute suggests that college‐age Americans are beginning to question—or even publicly denounce—the 1st amendment, suggesting that it should be extensively scaled back, thus aligning with a growing trend on university campuses across the nation…. The conversations taking place on college campuses have had a surprisingly strong effect on perceptions of the 1st amendment among young people leading to increasing number of students advocating in favor of sacrificing their, and your, 1st amendment rights… The results of the Brookings Institute research study show that you may not be as prepared to stand by your beliefs on the 1st amendment in the face of a strong pressure to conform to the “new norm.” Not when such arguments have already successfully disarmed students across the nation, who have subsequently changed their beliefs… Many college‐aged students, just like you, who were convinced that their belief on the 1st amendment was strong and unchangeable, have already begun to modify their positions toward the idea of limiting the scope of the 1st amendment. The above research suggests that you will be next.

The forewarning, while often effective (Compton & Ivanov, 2013), is not required in inoculation message design. As previously discussed, threat can be introduced through the presentation of weakened counterattitudinal arguments nested in the refutational preemption. In fact, it could be argued that introducing a forewarning may be erroneous when the messages are used for persuasive instead of, or in addition to, resistance purposes. More precisely, if the messages are used to shape or change attitudes, an acknowledgment of attitudinal vulnerability for attitudes that are either not in place or are inconsistent with the forewarning may appear nonsensical; thus suggesting an omission of the forewarning when inoculation is not exclusively used as a strategy of resistance. Yet, Ivanov et al. (2017) included a forewarning when the inoculation messages were used for the purpose of attitudinal protection, creation, and change and found the messages to be effective with all of the intended audiences. While an explicit forewarning may not be required in the design of inoculation messages, a refutational preemption is. This message component begins with a presentation of a weakened challenge to the attitude in place, as the following excerpt illustrates: “[The First Amendment] does more to harm us than to protect us. Why do we put up with a society that allows individuals—through free speech—to utter any words they want, even disrespectful or hateful ones?” Subsequently, the counterattitudinal argument is refuted, as exemplified in the following excerpt. [T]he 1st amendment has opened doors to crucial societal progress throughout American history. Freedom of speech provided abolitionist William Lloyd Garrison with the right to publish an

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anti‐slavery newsletter. It allowed women’s suffragist Alice Paul and her followers to protest outside the White House for women’s rights to vote. In the Supreme Court case of Edwards v. South Carolina, the 1st amendment protected 187 African Americans who were arrested for marching through South Carolina in opposition to segregation … a powerful reminder of the importance of the 1st amendment.

The weakened form of the counterattitudinal argument presented above suggests that the First Amendment protections produce more harm—in the form of hateful speech—than good. The inoculation message then proceeds to refute this claim by pointing to the important historical role the First Amendment has played in influencing societal change. The inoculation message, generally, concludes with a call to action. More specifically, it recommends vigilance (e.g., “be wary of frivolous and/or hollow arguments designed to lead you to surrender or limit your inalienable 1st amendment rights”) and defense preparation. However, providing a call to action may be tricky as inoculation messages could inadvertently elicit the process of psychological reactance (Ivanov, 2012), thus countering the intended message effect. Stated differently, should message recipients perceive the persuasive intent of the inoculation message as forceful or limiting their freedoms to make a decision on their own, they may reject its content and recommendations. To prevent this undesired outcome, more recent designs of inoculation messages (e.g., Ivanov et al., 2016) have relied on including a message postscript (see Miller et  al., 2013) intended to restore the freedom of individuals to make their own decisions, as shown in the following excerpt. But, whether you decide to defend your 1st amendment belief against pressures to limit its scope is ultimately your decision; your call. No one can tell you what to believe or how to act. At least now, you have all of the facts to be able to make a better informed decision. The rest is up to you.

While effective, it is also important to recognize that the message used as an exemplar in this review is relatively long (over 1,000 words). At times, the platform or resource constraints may render such a message impractical. By going so far as providing a 160‐character exemplar of a terse message, Compton and Ivanov (2013) have suggested that the length of inoculation messages could be considerably reduced for as long as the main message components responsible for the inoculation process are included. As a final note, the effect of inoculation messages may dissipate with the passage of time (Ivanov, 2017), whether due to message (Stiff & Mongeau, 2003) or motivation (Insko, 1967) decay. To prolong the effect of inoculation, McGuire (1961) introduced the concept of inoculation message boosters, borrowed from the biomedical context. Booster (double defenses or reinforcements) messages are intended to extend the effect of the inoculation message and thus maximize the practical utility of an inoculation‐based strategy. Although the best presentation form and timing for boosters is yet to be empirically tested, evidence shows that using the original inoculation message as a booster presented every 2 weeks can extend the effect of inoculation (Ivanov, Parker, & Dillingham, 2018).

Conclusion This chapter opened with a discussion of what a good theory may represent. Greenwald (2012) asserted that a good theory is one that can be applied with success in practice, thus building on Lewin’s (1946; McCain, 2015) belief that theory and practice should combine to guide strategy. As such, theories that could be applied cross‐contextually may be of significant import to practitioners. As this chapter demonstrates, inoculation can be strategically applied to any situation in which the strategist is motivated to influence attitudes (beliefs, opinions, values,



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behaviors, etc.). The success of inoculation has prompting Ivanov to render “the application of the strategy boundless” (2012, p. 77), thus cementing its relevance in persuasion scholarship and strategic practice.

References Banas, J. A., & Miller, G. (2013). Inducing resistance to conspiracy theory propaganda: Testing inoculation and metainoculation strategies. Human Communication Research, 39(2), 184–207. doi:10.1111/ hcre.12000 Banas, J. A., & Rains, S. A. (2010). A meta‐analysis of research on inoculation theory. Communication Monographs, 77, 281–311. Banerjee, S. C., & Greene, K. (2007). Antismoking initiatives: Effects of analysis versus production media literacy interventions on smoking‐related attitude, norm, and behavioral intention. Health Communication, 22(1), 37–48. Bechwati, N. N., & Siegal, W. S. (2005). The impact of the prechoice process on product returns. Journal of Marketing Research, 42(3), 358–367. doi:10.1509/jmkr.2005.42.3.358 Bither, S. W., Dolich, I. J., & Nell, E. B. (1971). The application of attitude immunization techniques in marketing. Journal of Marketing Research, 18, 56–61. Breen, G. M., & Matusitz, J. (2008). Preventing youths from joining gangs: How to apply inoculation theory. Journal of Applied Security Research, 4, 109–128. Briggs, N. E., & Harwood, G. (1983). Furthering adjustment: An application of inoculation theory in an intercultural context. ERIC reproduction service No. ED225. Burgoon, M., Pfau, M., & Birk, T. S. (1995). An inoculation theory explanation for the effects of corporate issue/advocacy advertising campaigns. Communication Research, 22, 485–505. Centers for Disease Control and Prevention. (2014, May 1). Up to 40 percent of annual deaths from each of five leading US causes are preventable. Retrieved from https://www.cdc.gov/media/ releases/2014/p0501‐preventable‐deaths.html Centers for Disease Control and Prevention. (2019, August 5). Making the vaccine decision: Addressing common concerns. Retrieved from https://www.cdc.gov/vaccines/parents/why‐vaccinate/vaccine‐decision.html Compton, J. (2013). Inoculation theory. In J. P. Dillard & L. Shen (Eds.), The SAGE handbook of persuasion: Developments in theory and practice (2nd ed., pp. 220–236). Thousand Oaks, CA: Sage. Compton, J. (2016). Inoculation against a losing season: Can inoculation‐informed public relations strategies protect fan loyalty? International Journal of Sports Communication, 9, 1–12. Compton, J., & Ivanov, B. (2013). Vaccinating voters: Surveying political campaign inoculation scholarship. In E. L. Cohen (Ed.), Communication yearbook 37 (pp. 250–283). New York, NY: Routledge (Taylor & Francis). Compton, J., & Ivanov, B. (2018). Persuasion and communication in sport, exercise, and physical activity. In B. Jackson, J. Dimmock, & J. Compton (Eds.), Psychology of sport, exercise, and physical activity (pp. 73–90). New York, NY: Routledge (Taylor & Francis). Compton, J., & Pfau, M. (2004). Use of inoculation to foster resistance to credit card marketing targeting college students. Journal of Applied Communication Research, 32, 343–364. Compton, J., & Pfau, M. (2005). Inoculation theory of resistance to influence at maturity: Recent progress in theory development and application and suggestions for future research. In P. Kalbfleisch (Ed.), Communication yearbook 29 (pp. 97–145). Mahwah, NJ: Lawrence Erlbaum. Compton, J., & Pfau, M. (2008). Inoculating against pro‐plagiarism justifications: Rational and affective strategies. Journal of Applied Communication Research, 36(1), 98–119. Cook, J., Lewandowsky, S., & Ecker, U. (2017). Neutralizing misinformation through inoculation: Exposing misleading argumentation techniques reduces their influence. PLoS One, 12(5), e0175799. doi:10.1371/journal.pone.0175799 Dillingham, L. L., & Ivanov, B. (2017). Inoculation messages as a pre‐emptive financial crisis communication strategy. Journal of Applied Communication Research, 45(3), 274–293. doi:10.1080/00909882. 2017.1320571 Dimmock, J. A., Gagné, M., Proud, L., Howle, T. C., Rebar, A. L., & Jackson, B. (2016). An exercise in resistance: Inoculation messaging as a strategy for protecting motivation during a monotonous and

26

Ivanov, Parker, and Dillingham

controlling exercise class. Journal of Sport & Exercise Psychology, 38(6), 567–578. doi:10.1123/ jsep.2016‐0146 Duryea, E. J. (1983). Utilizing tenants of inoculation theory to develop and evaluate a preventive alcohol education intervention. Journal of School Health, 53, 250–256. Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Orlando, FL: Harcourt Brace Jovanovich. Fagnot, I. J. (2011). The good, the bad, and the persuasive: Enhancing retention of future information professionals through attitude inoculation (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses database. (UMI No. 3454366) Godbold, L. C., & Pfau, M. (2000). Conferring resistance to peer pressure among adolescents. Communication Research, 27, 411–437. Greenwald, A. G. (2012). There is nothing so theoretical as a good method. Perspectives of Psychological Science, 7, 99–108. doi:10.1177/1745691611434210 Haigh, M. M., & Pfau, M. (2006). Bolstering organizational identity, commitment, and citizenship behaviors through the process of inoculation. International Journal of Organizational Analysis, 14(4), 295–316. Heuett, K. B., & Westerman, C. Y. (2014). Reducing negative affect about intercultural interactions through inoculation. Journal of International Communication, 20(1), 42–51. doi:10.1080/1321659 7.2013.872161 Insko, C. A. (1967). Theories of attitude change. New York, NY: Appleton‐Century‐Crofts. Ivanov, B. (2012). Designing inoculation messages for health communication campaigns. In H. Cho (Ed.), Health communication message design: Theory and practice (pp. 73–93). Thousand Oaks, CA: Sage. Ivanov, B. (2017). Inoculation theory applied in health and risk messaging. In R. Parrott (Ed.), The Oxford encyclopedia of health and risk message design and processing. New York City, NY: Oxford University Press. doi:10.1093/acrefore/9780190228613.013.254 Ivanov, B., Burns, W., Sellnow, T., Sayers, E. P., Veil, S., & Mayorga, M. (2016). Using an inoculation message approach to promote public confidence in protective agencies. Journal of Applied Communication Research, 44, 381–398. doi:10.1080/00909882.2016.1225165 Ivanov, B., Dillingham, L. L., Parker, K. A., Rains, S. A., Burchett, M., & Geegan, S. A. (2018). Sustainable attitudes: Protecting tourism with inoculation messages. Annals of Tourism Research, 73, 26–34. Ivanov, B., & Parker, K. A. (2011). Protecting images with inoculation: A look at brand, country, individual, and corporate images. The International Journal of the Image, 1, 1–12. Ivanov, B., Parker, K. A., & Compton, J. (2011). The potential of inoculation in reducing post‐purchase dissonance: Reinforcement of purchase behavior. Central Business Review, 30, 10–16. Ivanov, B., Parker, K. A., & Dillingham, L. L. (2018). Testing the limits of inoculation‐generated resistance. Western Journal of Communication, 82, 648–665. doi:10.1080/10570314.2018.1454600 Ivanov, B., Parker, K. A., Miller, C. H., & Pfau, M. (2012). Culture as a moderator of inoculation success: The effectiveness of a mainstream inoculation message on a subculture population. The Global Studies Journal, 4(3), 1–22. Ivanov, B., Pfau, M., & Parker, K. A. (2009a). Can inoculation withstand multiple attacks? An examination of the effectiveness of the inoculation strategy compared to the supportive and restoration strategies. Communication Research, 36, 655–676. Ivanov, B., Pfau, M., & Parker, K. A. (2009b). The potential of inoculation in protecting the country of origin image. Central Business Review, 28, 9–16. Ivanov, B., Rains, S. A., Geegan, S. A., Vos, S. C., Haarstad, N. D., & Parker, K. A. (2017). Beyond simple inoculation: Examining the persuasive value of inoculation for audiences with initially neutral or opposing attitudes. Western Journal of Communication, 81, 105–126. doi:10.1080/10570314.2016. 1224917 Jackson, B., Compton, J., Thornton, A. L., & Dimmock, J. A. (2017). Re‐thinking anxiety: Using inoculation messages to reduce and reinterpret public speaking fears. PLoS One, 12(1), 1–18. doi:10.1371/ journal.pone.0169972 Jackson, B., Compton, J., Whiddett, R., Anthony, D. R., & Dimmock, J. A. (2015). Preempting performance challenges: The effects of inoculation messaging on attacks to task self‐efficacy. PLoS One, 10(4), e0124886. doi:10.1371/journal.pone.0124886 Jolley, D., & Douglas, K. M. (2017). Prevention is better than cure: Addressing anti‐vaccine conspiracy theories. Journal of Applied Social Psychology, 47, 459–469.



Inoculation Theory as a Strategic Tool

27

Kelley, D. (2017). Countering indoor tanning arguments: An experiment using skin cancer prevention messages (Doctoral dissertation). Retrieved from https://cdr.lib.unc.edu/record/uuid:a010ff39‐16c3‐ 4969‐911e‐5baf3d051863 Kemp, B., Randle, M., Hurlimaan, A., & Dolnicar, S. (2012). Community acceptance of recycled water: Can we inoculate the public against scare campaigns? Journal of Public Affairs, 12, 337–346. Khera, A. V., Edmin, C. A., Drake, I., Natarajan, P., Bick, A. G., Cook, N. R., … Kathiresan, S. (2016). Genetic risk, adherence to a healthy lifestyle, and coronary disease. The New England Journal of Medicine, 375, 2349–2358. doi:10.1056/NEJMoa1605086 Lemarié, L., & Chebat, J.‐C. (2013). Resist or comply: Promoting responsible gambling among youth. Journal of Business Research, 66, 137–140. Lewin, K. (1943). Psychology and the process of group living. Journal of Social Psychology, 17, 113–131. Lewin, K. (1946). Action research and minority problems. Journal of Social Issues, 2, 34–46. doi:10.1111/j.1540‐4560.1946.tb02295.x Lumsdaine, A. A., & Janis, I. L. (1953). Resistance to “counterpropaganda” produced by one‐sided and two‐sided “propaganda” presentations. Public Opinion Quarterly, 17(3), 311–318. Matusitz, J., & Breen, G. M. (2013). Applying inoculation theory to the study of recidivism reduction in criminal prison inmates. Journal of Evidence‐Based Social Work, 10, 455–465. doi:10.1080/1543371 4.2012.760929 McCain, K. W. (2015, November). “Nothing as practical as a good theory.” Does Lewin’s Maxim still have salience in the applied social sciences? Paper presented at the meeting of the Association for Information Science and Technology, St. Louis. McGuire, W. J. (1961). The effectiveness of supportive and refutational defenses in immunizing and restoring beliefs against persuasion. Sociometry, 24(2), 184–197. McGuire, W. J. (1964). Inducing resistance to persuasion: Some contemporary approaches. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 1, pp. 191–229). New York, NY: Academic Press. McKee, C., & Bohannon, K. (2016). Exploring the reasons behind parental refusal of vaccines. The Journals of Pediatric Pharmacology and Therapeutics, 21, 104–109. Mikolon, S., Quaiser, B., & Wieseke, J. (2015). Don’t try harder: Using customer inoculation to build resistance against service failures. Journal of the Academy of Marketing Science, 43(4), 512–527. Miller, C. H., Ivanov, B., Sims, J. D., Compton, J., Harrison, K. J., Parker, K. A., … Parker, J. L. (2013). Boosting the potency of resistance: Combining the motivational forces of inoculation and psychological reactance. Human Communication Research, 39, 127–155. Nabi, R. L. (2003). “Feeling” resistance: Exploring the role of emotionally evocative visuals in inducing inoculation. Media Psychology, 5, 199–223. Natoli, K. M. (2015). Helping mothers defend their decision to breastfeed (Doctoral dissertation). Retrieved from http://stars.library.ucf.edu/cgi/viewcontent.cgi?article=2391&context=etd Niederdeppe, J., Heley, K., & Berry, C. L. (2015). Inoculation and narrative strategies in competitive framing of three heath policy issues. Journal of Communication, 65, 838–862. doi:10.1111/jcom.12162 Pace, K. M. (2013). Using inoculation to promote interpersonal discussion about emergency preparedness (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses database. (UMI No. 3558838) Parker, K. A., Ivanov, B., & Compton, J. (2012). Inoculation’s efficacy with young adults’ risky behaviors: Can inoculation confer cross‐protection over related but untreated issues? Health Communication, 27, 223–233. doi:10.1080/10410236.2011.575541 Pfau, M. (1992). The potential of inoculation in promoting resistance to the effectiveness of comparative advertising messages. Communication Quarterly, 40, 26–44. Pfau, M., & Burgoon, M. (1988). Inoculation in political campaign communication. Human Communication Research, 15, 91–111. Pfau, M., Haigh, M. M., Sims, J., & Wigley, S. (2007). The influence of corporate front‐group stealth campaigns. Communication Research, 34, 73–99. Pfau, M., Holbert, R. L., Zubric, S. J., Pasha, N. H., & Lin, W. (2000). Role and influence of communication modality in the process of resistance to persuasion. Media Psychology, 2, 1–33. Pfau, M., & Van Bockern, S. (1994). The persistence of inoculation in conferring resistance to smoking initiation among adolescents: The second year. Human Communication Research, 20, 413–430.

28

Ivanov, Parker, and Dillingham

Pfau, M., Van Bockern, S., & Kang, J. G. (1992). Use of inoculation to promote resistance to smoking initiation among adolescents. Communication Monographs, 59, 213–230. Richards, A. S., & Banas, J. A. (2015). Inoculating against reactance to persuasive health messages. Health Communication, 30, 451–460. doi:10.1080/10410236.2013.867005 Roozenbeek, J., & van der Linden, S. (2018). The fake news game: Inoculating against the risk of misinformation. Journal of Risk Research. Advanced online publication. doi:10.1080/13669877.2018.144 3491 Rosenberg, S. (2004). Inoculation effect in prevention of increased verbal aggression in schools. Psychological Reports, 95, 1219–1226. Sims, J. (2016). Using inoculation to protect value‐in‐diversity attitudes: An unsuccessful test and a nuanced antidote. Journal of Business Diversity, 16, 23–37. Stiff, J. B., & Mongeau, P. A. (2003). Persuasive communication. New York, NY: The Guilford Press. Sutton, C. A. (2011). Inoculating against jealousy: Attempting to preemptively reduce the jealousy experience and improve jealousy expression (Master’s thesis). Retrieved from https://getd.libs.uga.edu/pdfs/ sutton_chadwick_a_201105_ma.pdf Thieneman, A. (2017). A beacon of hope: Inoculating against relapse (Master’s thesis). University of Kentucky, Lexington, KY. Retrieved from https://doi.org/10.13023/ETD.2017.105 van der Linden, S., Maibach, E., Cook, J., Leiserowitz, A., & Lewandowsky, S. (2017). Inoculating against misinformation. Science, 358(6367), 1141–1142. doi:10.1126/science.aar4533 Wagner, T., Lutz, R. J., & Weitz, B. A. (2009). Corporate hypocrisy: Overcoming the threat of inconsistent corporate social responsibility perceptions. Journal of Marketing, 73, 77–91. Wan, H. H., & Pfau, M. (2004). The relative effectiveness of inoculation, bolstering, and combined approaches in crises communication. Journal of Public Relations Research, 16, 301–328. Westerman, C. K., Margolis, K. A., & Kowalski‐Trakofler, K. M. (2011). Training for safety in emergencies: Inoculating for underground coal mine emergencies. Professional Safety, 56, 42–46. Wigley, S., & Pfau, M. (2010). Arguing with emotion: A closer look at affect and the inoculation process. Communication Research Reports, 27(3), 217–229. Wong, N. C. H., & Harrison, K. J. (2014). Nuances in inoculation: Protecting positive attitudes toward the HPV vaccine & the practice of vaccinating children. Journal of Women’s Health, Issues & Care, 3(6). doi:10.4172/2325‐9795.1000170 Ziemke, M. H., & Brodsky, S. L. (2015). Unloading the hired gun: Inoculation effects in expert witness testimony. International Journal of Law Psychiatry, 41–42, 91–97. doi:10.1016/j.ijlp.2015.08.012

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Addressing Life Transitions of Aging Integrating Indigenous and Communication Theory to Develop a Tuakana‐Teina/Peer Educator Model John G. Oetzel, Brendan Hokowhitu, Mary Simpson, Sophie Nock, and Rangimahora Reddy Much of the developed world is aging, and studies illustrate with advancing age come increases in injuries, chronic illness, and healthcare costs, among other issues (Hayman et al., 2012). With these increased challenges, there is an assumption that the increasing proportion of older people will result in increased burdens on the socioeconomic and health systems (Hayman et al., 2012). Further, as people age, they face significant transition points such as retirement, loss of independence (e.g., loss of driver’s license), loss of independent living (e.g., move to retirement community), loss of a spouse, and changing health conditions (Kendig, Browning, Thomas, & Wells, 2014; Rohr & Lang, 2009). Successfully navigating these transitions depends on managing economic, social, and emotional challenges, as well as social and health services, while often being reliant on family (Fowler, Gasiorek, & Giles, 2015; Oetzel, Simpson, Berryman, Iti, & Reddy, 2015; Wyeth, Derrett, Hokowhitu, & Samaranayaka, 2013). Elders who cannot success­ fully navigate these transition points are exposed to many negative consequences, including social isolation, poor health, and low quality of life (Dyall et  al., 2014; Wham et  al., 2015). Unfortunately, most social and health service organizations do not have the resources to meet needs around transitions. In Aotearoa (New Zealand), the challenges around aging and life transitions are similar to other nations. However, a disproportionate burden of aging falls on Māori (Indigenous people of Aotearoa) communities. Specifically, there are inequities between Māori and non‐Māori around poor aging and health outcomes that in turn affect social, cultural, and economic costs (Blakely, Shilpi, Bridget, Martin, & Martin, 2004; Howden‐Chapman, Blakely, Blaiklock, & Kiro, 2000). As a result of these findings, the primary public discourse about Aotearoa’s aging population is undergirded by a deficit model emphasizing dependency, weakness, and limita­ tions (Blakely et al., 2004). Because of this research and overall approach, the government in Aotearoa issued the Ageing Well National Science Challenge (Ageing Well; www.ageingwellchallenge.co.nz). This strategic approach to investing in science looks to focus more on positive aging and also to reduce some of the inequities faced by Māori. The project we describe here was funded by Ageing Well and led to a strengths‐based approach grounded in Māori tikanga (cultural customs and protocols). While aging is associated with increasing costs and disease rates, there are also many positive aspects to aging. Our project highlights the potential of kaumātua (elders) to use their knowledge and experience to help other kaumātua and provides a potential transformative approach to the public discourse on aging. Māori culture reveres its elderly and kaumātua are central to a Māori The Handbook of Applied Communication Research: Volume 1, First Edition. Edited by H. Dan O’Hair and Mary John O’Hair. © 2020 John Wiley & Sons, Inc. Published 2020 by John Wiley & Sons, Inc.

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way of life. They uphold tikanga on the marae (community meeting/gathering grounds) and in various settings (Durie, 1999; Mead, 2003). As Sir Hirini Moko Mead noted: “The kaumātua and kuia, the elders, are often the guardians of tikanga. They are expected to know… Experience is definitely helpful in knowing what to do (Mead, 2003, p. 14).” Kaumātua have active roles in the community, including mediating disputes and governance (Mead, 2003). Further, they often hold fragmented extended families together through raising grandchildren (Pihama et al., 2014). Kaumātua also desire mana motuhake (autonomy and self‐actualization; Hokowhitu et al., 2010). This is one reason they often resist support and information provided by someone much younger than themselves, as often received through social and health services. However, it also is an opportunity to use mana motuhake to enhance social relations and provide oppor­ tunities through a peer support role. It is this foundation that guides the intervention devel­ oped in this project. The purpose of this chapter is twofold. First, we present the theoretical grounding of a peer educator/support intervention for addressing life transitions in aging for kaumātua. Second, we discuss implications and future directions of blending Indigenous and communication theory for applied communication, particularly around health issues. The first section discusses Indigenous theory from Aotearoa guiding the framework of our intervention. The second sec­ tion introduces communication (and related) theory that guides the process of intervention development. Third, we present the intervention itself to illustrate how the work was informed by theory. Finally, we conclude with implications and future directions.

Indigenous Theory: Kaumātua Mana Motuhake The history of colonization in Aotearoa is a complex one (for further reading, see Walker, 1990) and there is not the space to go into it in any depth here. Like other “settler‐colonial” states such as Australia, Canada, and the United States, Aotearoa was founded on colonist dreams of terra nullius (Latin, nobody’s land); in this case fantasies of a British rural paradise in the South Seas. Significantly, the Māori/Pākehā (settlers usually of European decent) binary was constitutional­ ized within Aotearoa’s founding document, the Treaty of Waitangi, signed in 1840 between representatives of the British Crown and a significant proportion of rangatira (chiefs). Given one of the Treaty’s intents (from the rangatira perspective at least) was to safeguard Māori people, their lands, and culture from the increasing hordes of British settlers hungry for land and resources, and barbaric in their approach to Māori custom, it is ironic that following 1840 the majority (i.e., approximately 95%) of Māori land was sold for a pittance and/or misappropriated (particularly as a result of the “land wars” of the 1860s centered in Taranaki and Waikato [center and western regions of the North Island]) (Kingi, 2008). The “land grab” included illicit judi­ ciary appropriation, such as “the New Zealand Settlements Act 1863, under which land was confiscated by declaring a district and all land within it Crown land” (Kingi, 2008, p. 136). Beyond land, colonial policies of cultural assimilation through state education in particular, and the general impoverishment of the Māori population due to colonization, meant that by the 1970s only pockets of Māori culture survived the Imperial onslaught. Yet, from the 1970s, a Māori‐focused renaissance movement led to the reinvigoration of Māori language, culture, politics, and pride in “being Māori.” An offshoot of the Māori renaissance has been Māori‐led research. Concomitantly, an offshoot of colonization has been a focus on Māori health research that, ostensibly at least, attempted to counter the poor Māori health statistics employed to validate research on Māori communities. Research access to Indigenous communities validated upon pathologizing Indigenous peoples as unhealthy and, consequently, in demand of medical intervention has a long genealogy in colonial history (Smith, 1999). Underpinning the “ethics” of colonialism was “the white man’s burden” to civilize the world and, furthermore, inherent to the civilization project were the merciless



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l­anguages of medicine and morality that pathologized Indigenous peoples as ravaged by dis­ ease and uncleanliness. This pathology has its roots in Social Darwinism, whereby mere contact with the stronger, more evolved European exposes frailties (both physical and moral), leading to degradation and extinction. Indigenous scholars and practitioners have turned to methods and practices of “decoloniza­ tion” leading to the development of decolonial theory, which has become the panacea for the pan‐Indigenous movement. The vanguard of this movement has included the ideas of organic resistance and praxis developed by Māori, particularly the pedagogical advancements of kōhanga, kura, and wānanga (pre‐school, primary/secondary school, higher education), and Indigenous theorization such as kaupapa Māori (Māori framework for conducting research). It should be noted that among the research team in the present project is Professor Linda Tuhiwai Smith, who wrote the influential book Decolonizing Methodologies (1999), which is widely accepted as “a” if not “the” seminal text in the field. The production of decolonial theory in its very nomen­ clature demands an understanding of the philosophies and history of colonization in order to understand the genealogy of colonial power. The underpinning desire for redress has meant that decolonial theory has developed as “re‐scholarship” where alternative knowledges are re‐inserted into text so that Indigenous people can deconstruct occidental history to produce alternative Indigenous ontologies. For instance, Smith (1999) argues, [t]ransforming our colonized views of our own history (as written by the West)… requires us to revisit, site by site, our history under Western eyes. This in turn requires a theory or approach, which helps us to engage with, understand and then act upon history. (p. 34)

Unsurprisingly, Māori health research has become a prominent outcome of decolonial theory because, when done well, it recognizes the impact of colonization on the wellbeing of Māori people and, consequently, tries to provide theory, methods, and research that act upon colonial history. Health research thus is the most prominent of all research fields focused on Māori, and produced by both Māori and non‐Māori scholars. A number of Māori health models, for in­ stance, have sprung up in the past 30 years in particular. Mason Durie’s (1998) “Whare Tapa Wha” (four‐sided house) model reflects a holistic health model including tinana (physical), hinengaro (mental), whānau (relationships), and wairua (spiritual). While Durie’s model is popular and often cited, the reason for this is possibly that the four cornerstones merely reflect Western holistic models of health, and thus simplistic translations of wairua to spirituality, for example, allow for conceptual assimilation. In reality, none of these concepts is translatable to Western frameworks, especially wairua, which is akin to a subatomic global essence that pervades all things, both living and inanimate. The point being that, although Durie’s health configura­ tion begins with Indigenous concepts, its production within the broader medical discourse soon disfigures, disassembles, and reconfigures it to fit a Western medical taxonomy. Whilst Indigenous and community‐led research can legitimize and make central Indigenous epistemologies, it is extremely important that Indigenous research does not become reductive, lest we follow the universalizing footsteps of European modernism. And here we should be mindful of Robert Young’s (2001) critique of the best (and worst) part of postcolonial theory that, “despite its espousal of subaltern resistance, scarcely values subaltern resistance that does not operate according to its own secular terms” (p. 338). Such inattention to the epistemologies of local Indigenous cultures inherently devalues the very concept of Indigeneity because of its localized tithing to place, identity, and language. It is a time, therefore, not to dislocate local knowledges in favor of universalizing discourses, but rather for Indigenous peoples to theorize their existence both locally and through the tacit and ambiguous space of Indigenous and community researcher‐led research. In the context of Māori health research then, increasingly research is conducted by Māori on Māori, but this does not necessarily mean that it is good research. “Good,” here, refers to

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research that does not merely replicate the Western scholarly and research frames from where the researcher or researchers draw their disciplinary knowledge. That is, research that actually listens to and reflects upon the community from whence the research is conducted. Nor can this listening to and reflection mirror the tokenistic “consultation” that these communities are all too familiar with. Research that, for instance, makes systematic community self‐determination and researcher accountability so that the research produced reflects the worldviews of, in this case, Māori kaumātua. The central concept behind mana motuhake within academia at least is the sovereign right of Indigenous and community‐based researchers to theorize and use methods that make sense to the communities they are accountable to. This also involves claiming the epistemic complexity to determine, put on the table, and debate the concepts that will become central to the research itself. As described below, tuakana/teina (older/younger sibling or cousin), although in some senses in the present research merely a practical concept recruited to enable the larger goals of the research to be realized, nonetheless became a concept keenly debated by the kaumātua and community‐led advisory boards. Such debates in essence reflect kaumātua mana motuhake in that the research demonstrated the autonomy of participants to make their views central to the research, in all its manifestations. Mana motuhake foregrounds autonomy and independence to achieve (self‐ and other‐) actu­ alization and collective determination. Thus, kaumātua affirm their autonomy and independence so they can live a long and quality life for self and others (Hokowhitu et al., 2010). Historically, kaumātua received messages from a dominant society that diminishes their full potential as they age (Hokowhitu, 2003). However, for Māori, the elderly are “carriers of culture, anchors for families, models for lifestyle, bridges to the future, guardians of heritage, and role models for younger generations” (Taskforce, 2010, p. 14). The current project is fundamentally about sup­ porting Māori aspirations, including the ability to express values, principles, and epistemologies (Hokowhitu, 2009). This mana motuhake is reflected through a “tuakana‐teina” peer educator model where kaumātua work with other kaumātua in relation to significant life‐transitions. Thus, the research sees the experience and mana (charisma, status, power) of kaumātua as key tools for positive aging and demonstrates values for older people in all settings. The project pro­ vides perspective about how a Māori worldview of aging and interaction about aging have the potential to improve life courses. The research is built from a strengths‐based approach. It also does not assume that the tikanga surrounding Māori elders is the same or practiced by all Māori. Instead, Māori episte­ mology and worldviews are integrated in a way that is accessible to all (e.g., including mate­ rials in both English and Māori languages). In addition, the research imbibes cultural concepts into a peer support model that helps elders build on their experiences and help other elders address significant life‐transitions and hopefully to help transform the predominant public discourse about aging (i.e., as a burden and deficit). Thus, elders are valued and have mana motuhake—that is, independence and self‐determination as their lives evolve, including taking on new and vital roles.

Communication (and Related) Theory Health challenges in Indigenous communities have been addressed by a variety of approaches and yet many of these are problematic as they perpetuate the problems of colonization. A common thread in those approaches that are strongly advocated for, and those that have shown to be effective, is that they were developed through participatory approaches. Participatory approaches are consistent with strengths‐based interventions, the importance of self‐determination, and Indigenous theorizing. These participatory approaches are particu­ larly relevant in working with Indigenous communities where there is historical mistrust of,



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and disenfranchisement from, mainstream organizations due to colonization and inequitable treatment (Wallerstein & Duran, 2010). Beyond simply using a participatory approach, we used two theories that explain how and why participatory approaches work; thus these theories guided the current project in design and implementation. The first is the community‐ based participatory research (CBPR) conceptual model developed in a collaboration by public health, communication, and other social science scholars. While not a communication theory per se, the model emphasizes communication process as a key fulcrum for partnering researchers and Indigenous communities. The second is the culture‐centered approach (CCA) developed by communication scholar Mohan Dutta (2007, 2008).

CBPR Conceptual Model CBPR is based on the equitable partnership of community members and academics in all phases of research, including the choice of the health problem, identifying the research question, research design, data collection, data analysis, and interpretation and dissemination of findings (Israel, Eng, Schulz, & Parker, 2013; Wallerstein, Duran, Oetzel, & Minkler, 2018). CBPR also adopts a number of key principles, including the following: (a) building on the strengths in the community with a strong emphasis on culture and cultural humility; (b) promoting co‐learning and capacity building; (c) focusing both local perspectives and systems and multilevel perspec­ tives to attend to multiple determinants of health; (d) involving a long‐term commitment and focus on sustainability; and (e) a social justice, action‐oriented approach to enhance the com­ munity’s health (Israel et  al., 2018). There is a vast body of literature that demonstrates the benefit of the CBPR approach, and other forms of community‐engaged research, for health gains, empowerment, and capacity building (e.g., Oetzel et al., 2017; O’Mara‐Eves et al., 2015; Wallerstein et  al., 2018). However, until relatively recently, there was a dearth of theoretical work explaining how and why CBPR works. The CBPR conceptual model was developed by public health scholars Nina Wallerstein and Bonnie Duran, along with communication scholar John Oetzel and some of their colleagues. The model was created through a consultative process with an advisory board of community and academic experts in CBPR and has been refined over the past decade based on empirical data from a large study of 300 CBPR projects in the United States (Kastelic, Wallerstein, Duran, & Oetzel, 2018; Wallerstein et al., 2008). Figure 2.1 displays the latest iteration of the model. The model includes four domains: context, partnership processes, intervention and research, and outcomes (Kastelic et al., 2018; Wallerstein et al., 2008). Context references the sociocul­ tural background and includes past history of collaboration and trust, capacity to engage in the research, community readiness to address a health issue, health and social policies, and socio­ economic factors. Partnership processes include individual characteristics of the members (e.g.,  motivations, cultural identities, cultural humility), partnership structures (e.g., formal agreements, control of resources), and relationships. Relationships include a variety of commu­ nication elements such as mutual influence, conflict management, leadership, and participatory decision making. Intervention and research is the work of the partnership and includes both processes and outputs. The process of integrating community knowledge results in culture‐cen­ tered interventions. The process of empowerment results in partnership synergy. The process of community involvement in research results in appropriate research design. Outcomes are the results of the research and include intermediate and long‐term outcomes. Intermediate out­ comes include capacity development of members, policy changes, and research productivity. Long‐term outcomes include community transformation and improved health and health equity. The conceptual model suggests that each domain shapes the subsequent domain (Wallerstein et al., 2008). First, context shapes the nature of the partnership processes. Second, partnership processes influence the nature of the intervention and research. Third, the intervention and research result in intermediate and long‐term outcomes. Each of these elements also has feedback

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Oetzel, Hokowhitu, Simpson, Nock, and Reddy CBPR Conceptual Model Adapted from Wallerstein et al, 2008 & Wallerstein and Duran, 2010, https://cpr.unm.edu/research-projects/cbpr-project/cbpr-model.html

Contexts Social & Structural

Partnership Processes Individual Characteristic

Political & Policy

Relationships

Partnership Structures

Health Issue Importance

Agency

CBOs

Intervention & Research

Outcomes Intermediate

Processes

Outputs

Integrate Community Knowledge

CultureCentered Interventions

Empowering Processes

Partnership Synergy

Community Involved in Research

Appropriate Research Design

Funders Capacity & Readiness

Collaboration Trust & Mistrust

Academic

Community Government

Health Care

• Policy Environment • Sustained Partnership • Empowerment • Equal Power Relations in Research • Cultural Reinforcement • Individual/Agency Capacity • Research Productivity

Long-term • Community Transformation • Social Justice • Health/Health Equity Visual from amoshealth.org 2017

Contexts • Social-Structural: SocialEconomic Status, Place, History, Environment Safety, Institutional-Racism, Culture • Role of Education Research Institutions • Political & Policy: National/ Local Governance/Stewardship Approvals of Research; Policy & Funding Trends • Health Issue: Perceived Severity • Collaboration: Historic Trust/ Mistrust between Partners • Capacity: Community History of Organizing/Academic Capacity /Partnership Capacity

Partnership Processes Partnership Structures: Relationships: • Diversity: Who is involved • Complexity • Formal Agreements • Control of Resources • % Dollars to Community • CBPR Principles • Partnership Values • Bridging Social Capital • Time in Partnership Individual Characteristics: • Motivation to Participate • Cultural Identities/Humility • Personal Beliefs/Values • Spirituality • Reputation of P.I.

• Safety/Respect/Trust • Influence/Voice • Flexibility • Dialogue and Listening/ Mutual Learning • Conflict Management • Leadership • Self & Collective Reflection/ Reflexivity • Resource Management • Participatory DecisionMaking • Task Roles Recognized

Commitment to Collective Empowerment

Intervention & Research • Processes that honor community and cultural knowledge and voice, fit local settings, and use both academic & community language lead to Culture-Centered Interventions • Empowering Co-Learning Processes lead to Partnership Synergy • Community Members Involved in Research Activities leads to Research/Evaluation Design Reflecting Community Priorities • Bidirectional Translation, Implementation & Dissemination

Outcomes

Intermediate System & Capacity • Policy Environment: University & Community Changes • Sustainable Partnerships and Projects • Empowerment–Multi-Level • Shared Power Relations in Research/ Knowledge Democracy • Cultural Reinforcement/Revitalization • Growth in Individual Partner & Agency Capacities • Research Productivity: Research Outcomes, Papers, Grant Applications & Awards

Long-Term Outcomes: Social Justice • Community/Social Transformation: Policies & Conditions • Improved Health/Health Equity

Figure 2.1  Community‐based participatory research (CBPR) conceptual model.

loops on the previous elements. However, the model is meant to illustrate a dynamic process rather than a linear one as partnerships last for many years. Wallerstein, Duran, Oetzel, and their partners conducted a comprehensive study of 300 CBPR projects to test this conceptual model (Lucero et al., 2018; Oetzel, Villegas et al., 2015; Oetzel, Zhou et  al., 2015; Oetzel et  al., 2018; Pearson et  al., 2015). Survey questionnaires of 200 principal investigators and 454 partners of the projects were complemented by seven in‐depth case studies. The findings of their research demonstrate support for the conceptual model. Specifically, Oetzel et  al. (2018) examined a structural equation model of the four domains. They found significant and positive pathways between context and partnership processes. Partnership processes had positive associations with partnership synergy and community involve­ ment in research; both of these had positive associations with intermediate and long‐term out­ comes. Further, there appeared to be two patterns in the model. First, having partnership values consistent with CBPR principles led to strong relationships and that led to synergy. Second, having strong governance led to shared resources and community involvement in the research. Thus, both governance structures and good communication dynamics are important elements for producing positive interventions and outcomes.

Culture‐Centered Approach The CCA was developed to recognize the importance of cultural approaches to health commu­ nication and also the agency of cultural communities that are often the “subject” of health inter­ ventions (Dutta, 2008). These cultural community members are often marginalized and described as subaltern; that is, they are people whose voices have been erased or not fully recognized. The CCA seeks to identify the ways that subaltern members are made invisible or unheard during



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discourses about health issues. Thus, the CCA proposes to change “social structures surrounding health through dialogues with cultural members that create spaces for marginalized cultural voices” (Dutta, 2007, p. 305). The CCA was integrated into the CBPR conceptual model and thus also provides a foundation for the current project. The CCA is composed of three interrelated constructs: structure, agency, and culture (Dutta, 2008; Dutta & Basu, 2011). Structures are those aspects of social organizing that enable and constrain health behavior. They include rules and resources that allow certain behavior (e.g., when doctor services are paid for by insurance or the government). These structures make certain health services available or unavailable in certain contexts. Agency is the capacity of community members to choose and to participate in conversations about policies and practices. Agency is present when community members can actively select structures or have a voice in which structures are created. Culture is the local context in which meaning is created and nego­ tiated. Culture is a way of making sense of the world. It connects to agency and structure as cultural members make choices that reflect their reality (or desired reality) and the local mean­ ings may or may not (often not) be reflected in structures that are created by health systems. There is a substantial amount of empirical and critical evidence supporting the CCA (Dutta, 2018; Dutta & Basu, 2011; Oetzel et al., 2017). From a practical standpoint, three concepts underlie the structure‐culture‐agency nexus: community agency for problems and solutions, reflexivity, and structural transformation and resources. Community agency means that affected community members are included in the con­ versations when health problems are defined and also when solutions are being created (Basu & Dutta, 2009; Dutta, 2007). Key components are listening and participation. Listening to community ideas creates possibilities for new visions and solutions. A key part of listening is transforming the researcher and practitioner stance from expert to co‐constructing participant in the dialogue. Participation emphasizes different perspectives and creating spaces for multiple interpretations of health challenges. Participation enables local knowledge to be brought to the table and thus enhances intervention effectiveness. Reflexivity is a process of questioning unstated positions of power and privilege when inter­ acting with community members (Dutta, 2008). Reflexivity is part of the action, reflection, learning cycle that is critical to empowerment (Freire, 1970). Reflexivity can be individually or collectively based and recognizes the need to understand privilege to ensure that research is co‐constructed with community members. Elements of privilege might include rights for mak­ ing contact with communities, choices about best research methods, agency of participants, and who gets to define problems. Even well‐intentioned researchers can be oblivious to the taken‐for‐granted assumptions about how we participate and the ways that we recognize exper­ tise and silence others who might not have traditional expertise (e.g., a degree). Structural transformation and resources are the means by which structures are changed and resources provided to enable health solutions that are consistent with local ways of knowing (Dutta, 2007). These changes are also important to address social determinants of health such as racism and poverty. The space for community members to interpret the structures and resources for themselves and to participate in the transformation on the basis of co‐constructed meanings is central to determining the effectiveness of a health intervention. Agency is critical for transformation dialogues as it creates a space of ownership and self‐determination so that the changes are enacted and sustainable. Further, uncovering and progressing resources for the transformation occurs through participatory processes that leverage relationships with external stakeholders. In sum, the CBPR conceptual model and the CCA were integrated with Indigenous theo­ rizing of mana motuhake to guide both the research processes and the intervention development. The research processes guided by CBPR and CCA enabled the space for local and cultural knowledge to be at the forefront of the intervention design. Some of our research processes include the use of an Advisory Board composed of members of a Board of Trustees to ensure

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strong governance and cultural guidance. We also developed formal agreements that identified roles and responsibilities and sharing of resources to ensure members of the community organi­ zation were employed by the project and thus there was strong community agency. Our processes include shared decision‐making and communication responsibilities as well and regular meetings to engage in reflexive process. The intervention itself was strongly influenced by Indigenous theorizing and is discussed more directly in the next section.

The Tuakana‐Teina/Peer Educator Program Peer educator, peer support, and peer training are terms used to describe a range of assistance activities provided by non‐professionals to others experiencing a health or social need, and who are of a similar age, health, cultural, ethnic, or other same‐factor group (Dennis, 2003). The peer educator program or intervention in this research study aims to bring a holistic, cultural approach to meeting the social and health support needs of kaumātua and their whānau (extended family relationships) and seeks to address the mana motuhake of kaumātua where kaumātua work with other kaumātua in relation to significant life‐transitions. The program introduces a kaupapa Māori approach to social integration and engagement through the tuakana‐ teina relationship. It is a “for‐kaumātua‐by‐kaumātua” approach and principally recognizes the continuing value and contributions that kaumātua can make to society. Therefore, the overall philosophical approach to the design of the program has a distinctive cultural, epistemological, and metaphysical foundation (Smith, 2012). It implies the validity and legitimacy of the Māori language and culture; and autonomy over cultural wellbeing (Smith, 2003). Tuakana‐teina is a prominent pedagogical tool stemming from a well‐known Māori concept about the relationship between an elder and younger sibling or cousin, often of the same sex (Mead, 2003). The relationship is based on reciprocity and responsibility and can be applied in a variety of peer educator mentor/mentee settings “… in mutually beneficial ways that uplift the mana of both tuakana and teina” (Winitana, 2012, p. 32). The Tuakana Orientation Program (TOP) for this project used the definition of tuakana as an “experienced person” working with a teina who was “in‐experience” (Winitana, 2012, p. 34). In this research, a tuakana is a source of support, shares culture, age, and experience, has specific knowledge that is derived from experience rather than formal training, is open to draw on his/her experience, and is open to orientation training to be an effective tuakana/peer edu­ cator. More specifically, four kinds of support offered by tuakana included affirmational, emo­ tional, informational, and cultural support (Dennis, 2003). In this context, a tuakana‐teina/peer relationship is one where two or more people are connected by a health‐related situation and are willing and able to take part in a conversational relationship involving a range of communication skills and approaches. Overall, the tuakana‐teina relationships are able to enhance social integration, expand teina access to sources of information and access to services, prevent minor illnesses developing into serious health issues, influence positive health practices, and promote positive mental–emotional states and motivation, to name a few. This section describes the cultural values and principles and models of communication within the TOP manual as grounded in Indigenous and communication theory.

Mātāpono Māori (Cultural Values and Principles) The first main process to develop the TOP included consultation with a Board Advisory Group (BAG) and the Expert Advisory Group (EAG). The BAG is composed of 11 kaumātua from the Board of Trustees of the organization (Rauawaawa Kaumātua Charitable Trust–Rauawaawa) where the program was initiated; they provided governance and cultural insights to the research team, which was composed of university researchers and a community researcher from the



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organization. The EAG is comprised of 12 members who are health and social service pro­ viders; about two thirds of the members are Māori. The second main developmental process included searching the literature related to peer education in the areas of Māori aging and health, as well as the international and Indigenous literature more broadly. The proposed TOP was set out in several stages. This was to help explain the rationale for the structure, to provide validity for the approach, and to make available sufficient detail to enable the BAG and EAG to provide feedback, advice, and comments on the proposed program and ways that it may be improved. Both advisory groups approved the proposed program prior to initiation. The actual framework of the program is underpinned by Te Ao Māori (The Māori world), creation stories, and tikanga (cultural customs) guiding concepts, values, and practices. The worlds of, and relationships between, elements of creation stories, Te Korekore (The Nothingness), Te Pō (The Night), and Te Ao Mārama (The World of Light; Marsden, 1992), provided an engaging cultural resource to approach the processes involved with acknowledging the potential of kaumātua, engaging the knowledge and mana of kaumātua, and enlisting kaumātua as tuakana in action. Te Korekore represents the realm of potential; Te Pō, the realm of becoming; Te Ao Mārama the realm of being. In the context of this project, the TOP uses the experience of kaumātua to bring into being ways to meet the potential needs, and achieve the aspirations, of kaumātua in significant life transitions. Te reo Māori (the Māori language) is the heritage language of Aotearoa, and both the BAG and the EAG acknowledged that the TOP needed to be inclusive of kaumātua who were fluent speakers of te reo Māori and those who were not. In addition to te reo Māori and Te Ao Māori as the foundational framework, the integration of the practice of tikanga such as karakia (prayer), mihimihi (greetings), whakawhanaungatanga (forging relationships), and whakataukī (prover­ bial sayings) were embedded throughout the program. The TOP is interwoven with mātāpono Māori (principles and values) that guide the tuakana role and tuakana‐teina relationship. These include: (a) Kotahitanga—collaboration and working together; (b) Mana—power, authority, charisma; (c) Manaakitanga—the ability to care and share knowledge; (d) Rangatiratanga—independence and autonomy; (e) Tautokotanga—providing support and encouragement; (f) Aroha—love, compassion, empathy, and respect; (g) Wairuatanga— spirituality; and (h) Whanaungatanga—forging relationships and communities. The selected mātāpono have been successfully applied within several different models of tuakana‐teina/peer mentoring relationships used in educational, justice, and research contexts (e.g., Hook, Waaka, & Raumati, 2007; Ratima & Grant, 2007; Rawlings & Wilson, 2003), alongside Māori models of support used successfully with kaumātua in health settings (e.g., Levack et al., 2016). In 2017, a pilot of the TOP was trialed with kaumātua from another community organization offering health and social wellbeing services, including for kaumātua. The trial proved to be very useful in consolidating the design, the facilitation, the overall content, and the delivery of the program. During the TOP pilot there was also robust discussion with the kaumātua and staff of the organization, namely about the meaning of some of the key communication models dis­ cussed. Because of these discussions, the research team decided to investigate alternative com­ munication models that would better fit with the different roles, and communication styles, within the tuakana role.

Ngā Huarahi Whitiwhiti Korero (Models of Communication) In order to bind the tuakana‐teina relationship, and following the supplementary investigation into alternative models of communication, the team decided on three Māori cultural concepts as the models of communication: (a) Kōrerorero/Whakawhiti korero—the conversation, talking together, understanding and checking (Elder & Kersten, 2015); (b) Pūrākau—the sharing of stories, experiences, knowledge, and feelings (Lee, 2009); and (c) Āta whakarongo—listening with reflective deliberation (Pohatu, 2004).

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The whakawhiti kōrero approach enables kaumātua to reclaim traditional Māori ways of learning; provides a culturally safe environment for kaumātua; and contributes to the relation­ ship between tuakana and teina. Whakawhiti kōrero allows tuakana to maintain a peer, rather than para‐professional, role. Wairuatanga (embracing connected spirituality) infuses the whakawhiti kōrero and informs the goal of the project to help teina to identify issues associated with transitions in later life, and to express their thoughts and feelings about health, life, and goals. The tuakana use this communication tool to establish a safe and supportive environment where teina feel comfortable to express positive and negative aspects of any given life transition. Narrative is a traditional teaching strategy. “Pūrākau is a traditional form of Māori narrative, contains philosophical thought, epistemological constructs, cultural codes, and worldviews that are fundamental to our identity as Māori” (Lee, 2009, p. 1). In this respect, the use of pūrākau enables tuakana to create a sense of shared cultural experience, knowledge, and feelings. The Māori cultural concept of Āta is a strategy that helps to set boundaries and create safe spaces, it reminds us how to act and take part in respectful relationships (Pohatu, 2004). The elements of Āta have synergies with the mātāpono for this program, such as, respectfulness, reciprocity, planning, reflection, effort, and energy. Kaumātua are encouraged to use the ­ mātāpono to help guide them throughout their communications to check what they have heard, to check their understanding accurately, to offer new information with permission, and to invite teina to korero (talk). The notion of whakarongo is to listen with deliberation and care, to listen with intent, or to listen with the heart. Whakarongo helps to focus the tuakana and teina on listening with intent and “It signals the elements of trust, integrity and respectfulness of what is being shared” (Pohatu, 2004, p. 6). In sum, the final program comprises four wānanga (sessions): Wānanga 1: Whakatūwheratanga: An introduction to the research project, peer education, and the life transitions highlighted by kaumātua; Wānanga 2: Te Korekore: Acknowledging the potential of kaumātua; Wānanga 3: Te Po: Engaging the knowledge and mana of kaumātua in becoming tuakana; Wānanga 4: Te Ao Mārama: Kaumātua as tuakana‐teina/peer educators in action. The principles and models of communication were integrated within each of these sessions with the final session being an opportunity to practice the models of communication. Thus, the development and content of the TOP was strongly grounded in Indigenous and communication theory.

Implications and Future Directions In concluding this chapter, we discuss the implications of integrating Indigenous and commu­ nication theory for applied communication related around health issues. We also identify future directions for our tuakana‐teina/peer educator intervention model, and integrating of commu­ nication and Indigenous theory to develop health interventions.

Implications This section identifies five key implications: (a) the importance of taking self‐determination and strengths‐based approaches in developing health interventions; (b) the value of using an Indigenous lens to enhance applied communication; (c) the power of participatory approaches for implementation effectiveness; (d) the importance of stewardship for research and cultural integrity; and (e) the value of long‐term partnerships. The importance of self‐determination and strengths‐based approaches for developing health interventions is evident in the interconnected­ ness and practice of both concepts. In privileging self‐determination of the community impacted by a proposed intervention, the community itself is positioned at the center of developing that intervention. Also, grounding the research in a strengths‐based approach means that, through self‐determination, a community uses its known strengths within its own worldview, and cultural,



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political, and social structures. Thus together, self‐determination and strengths‐based approaches result in improved and sustainable health outcomes for a community. Each of these is demon­ strated by the tuakana‐teina/peer educator/peer support program. The tuakana‐teina/peer educator intervention was instigated by kaumātua themselves. The program was drawn from an earlier research project which found that kaumātua wanted to know about palliative care and related services before they, or their whānau, needed to use such ser­ vices (Rauawaawa Kaumātua Charitable Trust Research Project Team, 2014). This led to the idea that there may be other areas of later life that kaumātua would like to be prepared for. The Board of Trustees then consulted with kaumātua users of Rauawaawa services about the transi­ tions in later life that were most important to them. The outcome was five transitions decided by kaumātua. In this way, the processes facilitated, and were the result of, kaumātua self‐deter­ mination. Significantly, privileging kaumātua self‐determination avoided the dominant culture’s othering or medicalization of Indigenous peoples’ health status (Smith, 1999), and subsequent subjugation of Indigenous understandings of health and wellbeing. For developing a health intervention, Indigenous self‐determination means enabling or respond­ ing to community‐identified health priorities. Where a community seeks support with determining health priorities, and or developing health interventions, the strength of relationship between the community and researcher is central to success. This is especially the case with relationships bet­ ween Indigenous peoples and non‐Indigenous researchers, where, historically, research by out­ siders has negatively impacted Indigenous peoples (Smith, 1999). Relationships and trust are key to successful research collaboration related to health intervention (Wallerstein & Duran, 2010), and community self‐determination needs to be privileged throughout the collaboration. Second, the value of using an Indigenous lens to enhance applied communication lies not only in the planning, the decision making, and designing the interventions, but also in the contribu­ tion to communication theory and the CBPR conceptual model. In terms of its contribution to communication theory, using an Indigenous lens enables researchers to interrogate concepts such as cultural awareness, cultural sensitivity, and cultural competence, and to consider carefully ways in which culture‐centered (Dutta, 2007, 2008, 2018; Dutta & Basu, 2011) and cultural safety (Ramsden, 1992, 2002; Ramsden & Spoonley, 1993) approaches may guide practice. The concepts of cultural awareness, cultural sensitivity, and cultural competence may be viewed as a continuum of ethnocentric approaches to health communication, health interven­ tions, and health research. Cultural awareness amounts to little more than knowing at an individual level that culture makes a difference (somehow) in communication, or in healthcare delivery for example. Cultural sensitivity and cultural competence concern approaches to community health issues that impose knowledge structures, and adapt health inventions from elsewhere—usually from a dominant culture outside of the given community (Kearns & Dyck, 1996; Resnicow, Braithwaite, Dilorio, & Glanz, 2002). Cultural competence focuses on individual practice, and the ability of health workers to communicate effectively with people of cultures different from their own (Tervalon & Murray‐García, 1998). In contrast CCAs, shift the health intervention lens from outside to inside a given community, and focus on the everyday experiences and knowledge of the members of that community (Dutta, 2007, 2008). Critically, CCAs emphasize the role of communication in co‐creating spaces in which Indigenous voices participate (Dutta, 2007, 2008, 2018). In this respect, privileging Indigenous theorizing of mana motuhake and kaupapa Māori methodology enhanced the approach to researchers and members of Rauawaawa engaging with each other, as well as developing the tuakana‐teina/peer educator intervention. Aotearoa New Zealand has its own version of a CCA, cultural safety or kawa whakaruruhau, which was first used and developed for nursing in 1991 (Ramsden, 1992). Cultural safety was developed by the late Irihapeti Ramsden as a model of practice to shift attention from Māori as disadvantaged users of healthcare services to focus on the healthcare service itself (Ramsden, 1992). The aim was to ensure that Māori users of healthcare services felt culturally safe.

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As with the CCA, Ramden’s cultural safety model required health workers to be reflexive in considering the dimensions of power and privilege within their roles, to review their own prac­ tice and adopt new ones (Ramsden, 1992, 2002; Ramsden & Spoonley, 1993). In the reflexive questioning of their individual roles, nurses were able to examine aspects of their own agency and that of the people they cared for; interrogate the impact of health delivery systems on dif­ ferent communities; and take account of alternative voices within the care journey, by talking less about and “more with ‘them’” (Kearns & Dyck, 1996, p. 374). Thus, in adopting the cultural safety approach in the provision of health care, nurses as agents of the dominant healthcare model were in effect working on the “inside” helping to create change in the system itself. Most importantly, Ramden’s cultural safety approach is Indigenous, that is, born of Indigenous expe­ rience within local conditions and specifically Māori health inequities, and poor responses by the dominant healthcare system to address those health inequities. In this way, Indigenous created and informed cultural safety and CCAs enable new perspec­ tives for health communication theory and research. These approaches help to avoid positioning the local and/or Indigenous as an “exotic case” situated within prevailing Anglo‐Western world­ views and ostensibly value‐neutral and unlocated international domains (Simpson, Richardson, & Zorn, 2009). Instead, local, Indigenous created and informed approaches ensure that Indigenous worldviews, experiences, voices, and practices are prioritized in health communica­ tion, theory, research, and interventions. Third, in terms of our tuakana‐teina/peer educator intervention, the CBPR conceptual model (Wallerstein & Duran, 2010) illustrated Indigenous theory at work through its participatory approach. With its commitment to culture‐centered communication, the model, when used with an Indigenous lens, sharpens prioritizing Indigenous experiences, voices, and practices across and within the four domains of context, partnership, relationships, and intervention and research. In this sharpening process, the value of taking an Indigenous lens within the CBPR conceptual model, and therefore enhancing applied communication, becomes apparent. As an example, the tuakana‐teina/peer educator intervention was contextually positioned within kaupapa Māori. A kaupapa Māori approach normalizes Māori language and culture, and Māori autonomy over cultural wellbeing. This approach resulted in a culturally distinctive, epis­ temologically founded Māori, for‐kaumātua‐by‐kaumātua program (see Smith, 2003, 2012). In working together at each stage of the research, roles and responsibilities of the parties aimed to support and safeguard kaumātua and Māori cultural values. A specific example within the tuakana‐teina/peer educator intervention was the robust re‐interrogation of the “tuakana‐ teina/peer educator” descriptor for the intervention. Kaumātua in the pilot questioned the term, and the Board of Trustees and EAG talked through possible alternatives before again set­ tling on “tuakana‐teina/peer educator” as the appropriate Māori cultural concept to use. The partnership between Rauawaawa and its Māori community researchers, Māori studies, and health communication researchers was established and maintained through the tuakana‐ teina/peer educator intervention. Agreed upon and culturally determined communication forums and processes were foundational to the partnership. Similarly, a kaupapa Māori approach (Smith, 1999, 2012) to the development of the intervention ensured that the community mem­ bers and researchers jointly decided how respect, recognition, and partnership were enacted, and enabled them to define their own space and work on their own terms. The intervention and research domain of the tuakana‐teina/peer educator intervention prior­ itized Māori Indigenous knowledge, tikanga practices, and communication processes, so that the intervention remained culture‐centered. Consistent with a kaupapa Māori approach, this meant that from the outset Māori epistemic knowledge and ways of knowing informed, drove, and benefitted the research processes and outcomes for all parties. Within the tuakana‐teina/ peer educator intervention, this resulted in drawing on kaumātua knowledge, relying on Māori communication protocols (e.g., language, pōwhiri [a welcome ceremony], wānanga), and empowering kaumātua within the intervention.



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The examples offered above demonstrate the power of participatory approaches for implemen­ tation effectiveness. Each is an act of safeguarding Māori cultural concepts, values, and practices. Each is an example of a trusted relationship based in partnership, with those most impacted by the intervention also participating in shared decision making and able to exercise control, authority, and responsibility (see Durie, 1998). In this way, communication plays a crucial role in health interventions so that they are responsive, participatory, culture‐centered, and culturally safe. Fourth, this project demonstrates the importance of stewardship for research and cultural integrity. Research integrity is often thought of as ensuring that research protocols follow Belmont principles; however, recent perspectives suggest the importance of stewardship of participatory research projects more than simply research ethics (Oetzel, Villegas et al., 2015). Stewardship is oversight that ensures that the research project follows cultural protocols, benefits the community, and meets research ethics standards. Stewardship ensures that the project is carried out in such a way that the research methods result in high‐quality data that is aligned with tikanga. In our context, stewardship ensures that the research project follows principles of the Treaty of Waitangi. Interpretations of the Treaty over the years has resulted in the identification of three Ps in working with Māori communities: participation, protection, and partnership (Simpson & Ake, 2010). Participation reflects a collaborative process of development and implementation of the research and was discussed in more detail in the third implication. Protection is about ensuring the research design and process benefits the community and is aligned with culture. In our project, we had a choice about how best to design our evaluation. In similar studies, often a randomly controlled trial is used with intervention and comparison groups. However, the exclusion of people from the intervention would not fit with tikanga. Hence, we developed a staggered design where half of the participants received the intervention at time point 1 and the other half of the participants received the intervention at time point 2. The result was a non‐ exclusive design that still allowed for intervention and comparison groups at the end of time 1. It was our way of marrying Indigenous theorizing and expectations and scientific expectations imposed by many research funders. The final principle is partnership, which ensures that all parties are involved in the direction of the project. Our project reflects a partnership where Rauawaawa led the governance and cultural oversight, while the university team led research design, reporting, and resourcing. Our partnership builds on the strengths of the respective organizations and our previous work. The final implication is the value of long‐term partnerships. Our partnership has been going for 10 years and we have conducted four funded projects together. However, there have also been periods where we had no funding and yet we maintained our relationships and trust. Part of this has occurred through volunteer work for the organization such as student volunteers at the Kaumātua Olympics (games for elders), helping with fundraising, and other ceremonies. When the opportunity arose for this current research funding, it was quite easy to develop research protocols and governance structures. The reviewers of our proposal commented that they could clearly see an established partnership, and this was key to our successful bid. The trust and strength of ties that have developed over the years guide our work, and more than simply being a facilitator to getting the project done, enable us to do high‐quality work that supports kaumātua mana motuhake. For applied communication researchers, the benefit to the community is the ultimate measure of suc­ cess of our research and theorizing. It also speaks to the need of applied researchers to build long‐ term relationships and not simply depart the community once the data has been collected.

Future Directions The next steps in the development of the tuakana‐teina/peer educator intervention involve eval­ uating the tuakana‐teina/peer educator model using qualitative and quantitative data as well as process evaluation. First, we will use quantitative data to determine whether the tuakana‐teina/ peer educator intervention results in changes to the health and wellbeing of kaumātua and their

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mana motuhake. Second, we will analyze responses to open‐ended questions about kaumātua understanding of health services related to life transitions, including general life concerns, best things in life, what would improve their lives, and how they assessed their own mana motuhake. Finally, members of both Board and EAGs and the research team will complete a brief process evaluation questionnaire about their experiences, including factors such as cultural safety, cultural integrity, quality of interaction, and quality of the tuakana‐teina/peer educator intervention training. The outcomes of these combined analyses will provide the community and research team with indicators of the level of success not only of the intervention, but also of the culture‐ centered participatory approach. In terms of examples of integrating communication and Indigenous theory in the development of health interventions, it would seem that there is some way to go. However, a couple of studies highlighting the need for more work and offering direction are emerging. For example, one recent study offers insight into the lack of impact of health interventions involving online health promo­ tion messages for Australian Aboriginal peoples (Kariippanon & Senior, 2018). Using grounded Aboriginal theory which integrates theory and practice, the study demonstrates how the disconnec­ tion between Western biomedical knowledge and Australian Aboriginal knowledge results in the failure of online health promotion messages to have the desired impact. This is in spite of Aboriginal community members using mobile phone technology to support the various “needs of a community separated by vast distances” (p. 35). The authors highlight the role communication technology plays in sustaining this Australian community, and the role of structural barriers to using the tech­ nology and community connectedness in implementing effective health interventions. Another example is a literature review of implementation effectiveness in Indigenous Australian health care (McCalman, Bainbridge, Percival, & Tsey, 2016). The authors found that factors such as the recognition of the value of local Indigenous knowledge and effective partnerships contrib­ uted to implementation success for Indigenous health services. Barriers to implementation and/ or sustaining services included the lack of designated implementation leaders and poor imple­ mentation of communication plans. Significantly, the authors identified the need to explore what is important to Indigenous people’s understandings, principles, and knowledge of healthcare implementation (p. 12). Of particular note were issues related to community control and equity. In sum, there are some initial efforts to integrate Indigenous theorizing and communication theory for applied purposes. Our experience, along with these other studies, speaks to the need for culture‐centered interventions that integrate Indigenous and communication theory. These approaches provide novel interventions to address key health challenges and highlight the importance of participatory research, self‐determination, and strengths‐based perspectives. These elements provide the foundation for mana motuhake and can help address some of the long‐standing mistrust resulting from colonial histories.

References Basu, A., & Dutta, M. J. (2009). Sex workers and HIV/AIDS: Analyzing participatory culture‐centered health communication strategies. Human Communication Research, 35, 86–114. Blakely, T., Shilpi, A., Bridget, R., Martin, T., & Martin, B. (2004). Decades of disparity: Widening ethnic mortality gaps from 1980 to 1999. New Zealand Medical Journal, 117(1199), 1–21. Dennis, C.‐L. (2003). Peer support within a health care context: A concept analysis. International Journal of Nursing Studies, 40, 321–332. Durie, M. H. (1998). Whaiora: Māori health development (2nd ed.). Auckland, New Zealand: Oxford University Press. Durie, M. H. (1999). Kaumatuatanga: Reciprocity: Māori elderly and whānau. New Zealand Journal of Psychology, 28(2), 102–106. Dutta, M. J. (2007). Communicating about culture and health: Theorizing culture‐centered and cultural sensitivity approaches. Communication Theory, 17, 304–328.



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Dutta, M. J. (2008). Communicating health: A culture‐centered approach. Malden, MA: Polity Press. Dutta, M. J. (2018). Culture‐centered approach in addressing health disparities: Communication infra­ structures for subaltern voices. Communication Methods and Measures, online. doi:10.1080/1931245 8.2018.1453057 Dutta, M. J., & Basu, A. (2011). Culture, communication, and health: A guiding framework. In T. L. Thompson, R. Parrott, & J. F. Nussbaum (Eds.), The Routledge handbook of health communication (2nd ed., pp. 320–334). New York, NY: Routledge. Dyall, L., Kepa, M., Teh, R., Mules, R., Moyes, S. A., Wham, C., … Kerse, N. (2014). Cultural and social factors and quality of life of Maori in advanced age. Te puawaitanga o nga tapuwae kia ora tonu—life and living in advanced age: A cohort study in New Zealand (LiLACS NZ). New Zealand Medical Journal, 127(1393), 62–79. Elder, H., & Kersten, P. (2015). Whakawhiti kōrero, a method for the development of a cultural assessment tool, te waka kuaka, in Māori traumatic brain injury. Behavioural Neurology, 2015, 137402. Fowler, C., Gasiorek, J., & Giles, H. (2015). The role of communication in aging well: Introducing the communicative ecology model of successful aging. Communication Monographs, 82, 431–457. Freire, P. (1970). Pedagogy of the oppressed. New York, NY: Herder and Herder. Hayman, K. J., Kerse, N., Dyall, L., Kepa, M., Teh, R., Wham, C., … Jatrana, S. (2012). Life and living in advanced age: A cohort study in New Zealand—Te Puawaitanga o Nga Tapuwae Kia Ora Tonu, LiLACS NZ: Study protocol. BMC Geriatrics, 12, 33. Hokowhitu, B. (2003). Te tāminga o te mātauranga Māori: colonisation in education. In T. Ka’ai, J. Moorfield, & M. Reilly (Eds.), Ki te whaiao: An introduction to Māori society (pp. 190–200). Auckland: Pearson Education New Zealand. Hokowhitu, B. (2009). Indigenous existentialism and the body. Cultural Studies Review, 15, 101–118. Hokowhitu, B., Kermoal, N., Andersen, C., Reilly, M., Rewi, P., & Petersen, A. (2010). Indigenous identity and resistance: Researching the diversity of knowledge. Dunedin: University of Otago Press. Hook, G., Waaka, T., & Raumati, L. P. (2007). Mentoring Māori within a Pākehā framework. MAI Review, 3, Article 1, 1–13. Retrieved from http://ndhadeliver.natlib.govt.nz/delivery/DeliveryManagerServlet? dps_pid=FL1050867 Howden‐Chapman, P., Blakely, T., Blaiklock, A. J., & Kiro, C. (2000). Closing the health gap. New Zealand Medical Journal, 113(1114), 301–302. Israel, B. A., Eng, E., Schulz, A. J., & Parker, E. (2013). Methods in community‐based participatory research for health (2nd ed.). San Francisco, CA: Jossey‐Bass. Israel, B. A., Schulz, A. J., Parker, E. A., Becker, A. B., Allen, A. J., Guzman, J. R., & Lichtenstein, R. (2018). Critical issues in developing and following CBPR principles. In N. Wallerstein, B. Duran, J. G. Oetzel, & M. Minkler (Eds.), Community‐based participatory research for health: Advancing social and health equity (3rd ed., pp. 31–46). San Francisco, CA: Jossey‐Bass. Kariippanon, K., & Senior, K. (2018). Re‐thinking knowledge landscapes in the context of grounded aboriginal theory and online health communication. Croatian Medical Journal, 59, 33–38. Kastelic, S., Wallerstein, N., Duran, B., & Oetzel, J. G. (2018). Socio‐ecologic framework for CBPR: Development and testing of a model. In N. Wallerstein, B. Duran, J. G. Oetzel, & M. Minkler (Eds.), Community‐based participatory research for health: Advancing social and health equity (3rd ed., pp. 77–94). San Francisco, CA: Jossey‐Bass. Kearns, R., & Dyck, I. (1996). Cultural safety, biculturalism and nursing education in Aotearoa/New Zealand. Health and Social Care in the Community, 4, 371–380. Kendig, H., Browning, C., Thomas, S., & Wells, Y. (2014). Health, lifestyle, and gender influences on aging well: An Australian longitudinal analysis to guide health promotion. Frontiers in Public Health, 14, 70. Kingi, T. (2008). Māori landownersip and land management in New Zealand. In Making land work: Volume two, case studies on customary land and development in the Pacific (pp. 129–152). Canberra: Australian Agency for International Development. Lee, J. (2009). Decolonising Māori narratives: Pūrākau as a method. MAI Review, 2, Article 3, 1–12. http://hdl.handle.net/2292/18230 Levack, W. M., Jones, B., Grainger, R., Boland, P., Brown, M., & Ingham, T. R. (2016). Whakawhanaungatanga: The importance of culturally meaningful connections to improve uptake of  pulmonary rehabilitation by Māori with COPD: A qualitative study. International Journal of Chronic Obstructive Pulmonary Disease, 11, 489–501.

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Lucero, J., Wallerstein, N., Duran, B., Alegria, M., Greene‐Moton, E., Israel, B., … White Hat, E. (2018). Development of a mixed methods investigation of process and outcomes of community‐based partic­ ipatory research. Journal of Mixed Methods Research, 12, 55–74. Marsden, M. (1992). God, man and universe: A Māori view. In M. King (Ed.), Te Ao Hurihuri (pp. 117– 137). Auckland: Reed. McCalman, J., Bainbridge, R., Percival, N., & Tsey, K. (2016). The effectiveness of implementation in Indigenous Australian healthcare: An overview of literature reviews. International Journal for Equity in Health, 15, 47. Mead, H. (2003). Tikanga Māori: Living by Māori values. Wellington, New Zealand: Huia. Oetzel, J. G., Scott, N., Hudson, M., Masters‐Awatere, B., Rarere, M., Foote, J., … Ehau, T. (2017). Implementation framework for chronic disease intervention effectiveness in Māori and other Indigenous communities. Globalization and Health, 13, 69. Oetzel, J. G., Simpson, M., Berryman, K., Iti, T., & Reddy, R. (2015). Managing communication tensions and challenges during the end‐of‐life journey: Perspectives of Māori kaumātua and their whānau. Health Communication, 30, 350–360. Oetzel, J. G., Villegas, M., Zenone, H., White Hat, E., Wallerstein, N., & Duran, B. (2015). Enhancing stewardship of community‐engaged research through governance. American Journal of Public Health, 105, 1161–1167. Oetzel, J. G., Wallerstein, N., Duran, B., Villegas, M., Sanchez‐Youngman, S., Nguyen, T., … Alegria, M. (2018). Community‐engaged research for health: A test of the CBPR conceptual model. BioMed Research International, 2018, 7281405. Oetzel, J. G., Zhou, C., Duran, B., Pearson, C., Magarati, M., Lucero, J., … Villegas, M. (2015). Establishing the psychometric properties of constructs in a community‐based participatory research conceptual model. American Journal of Health Promotion, 29, e188–e202. O’Mara‐Eves, A., Brunton, G., Oliver, S., Kavanagh, J., Jamal, F., & Thomas, J. (2015). The effectiveness of community engagement in public health interventions for disadvantaged groups: A meta‐analysis. BMC Public Health, 15, 1352. Pearson, C. R., Duran, B., Oetzel, J., Magarati, M., Lucero, J., Villegas, M., & Wallerstein, N. (2015). Research for improved health: Variability and impact of structural characteristics in federally‐funded community engaged research. Progress in Community Health Partnerships: Research, Education, and Action, 9(1), 17–29. Pihama, L., Reynolds, P., Smith, C., Reid, J., Smith, L., & Tenana, R. (2014). Positioning historical trauma theory within Aotearoa New Zealand. Alter Native, 10, 248–262. Pohatu, T. W. (2004). Āta: Growing respectful relationships. He Pukenga Kōrero, 8(1), 1–8. Ramsden, I. (1992). Kawa Whakaruruhau: Guidelines for nursing and midwifery education. Wellington, New Zealand: Nursing Council of New Zealand. Ramsden, I. (2002). Cultural safety: Implementing the concept—The social force of nursing and mid­ wifery. In P. T. Whaiti, A. McCarthy, & M. Durie (Eds.), Mai I Rangiatea (pp. 113–125). Auckland: Auckland University and Bridget Williams Books. Ramsden, I., & Spoonley, P. (1993). The cultural safety debate in nursing in Aotearoa. New Zealand Annual Review of Education, (3), 161–174. Ratima, M., & Grant, B. (2007). Thinking about difference across and within mentoring. MAI Review, 3, Peer Commentary 1, 1–5. Retrieved from https://www.researchgate.net/profile/Grant_Barbara/ publication/26500233_Peer_Commentary_1_‐_Thinking_about_difference_across_and_within_ mentoring/links/555afb3008aeaaff3bfad951.pdf Rauawaawa Kaumātua Charitable Trust Research Project Team. (2014). Māori health literacy and communication in palliative care: Kaumātua‐led models. Retrieved from https://www.health.govt.nz/ system/files/documents/publications/maori‐health‐literacy‐communication‐in‐palliative‐care‐ kaumatua‐led‐models‐aug14.pdf Rawlings, C., & Wilson, K. (2003). Tuakana‐teina E‐belonging report. Wellington, New Zealand: Ako Aotearoa. Resnicow, K., Braithwaite, R. L., Dilorio, C., & Glanz, K. (2002). Applying theory to culturally diverse and unique populations. In K. Glanz, B. Rimer, & F. Lewis (Eds.), Health behavior and health education: Theory, research, and practice (pp. 485–509). San Francisco, CA: Jossey‐Bass. Rohr, M., & Lang, F. (2009). Aging well together: A mini‐review. Gerontology, 55, 333–343.



Addressing Life Transitions of Aging

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Simpson, M., & Ake, T. (2010). Whitiwhiti korero: Exploring the researchers’ relationship in cross‐cultural research. Journal of Intercultural Communication Research, 39, 185–205. Simpson, M., Richardson, M., & Zorn, T. (2009). Researching elders’ work in Aotearoa New Zealand: A bicultural response to local contexts. Communication Journal of New Zealand, 10(2), 40–57. Smith, G. H. (2003, December). Kaupapa Māori theory: Theorizing Indigenous transformation of education and schooling. Paper presented at the New Zealand Association of Research in Education/Australian Association of Research of Education (NZARE/AARE) Conference, Auckland, New Zealand. Smith, L. T. (1999). Decolonizing methodologies: Research and Indigenous peoples. London: Zed Books. Smith, L. T. (2012). Decolonising methodologies: Research and Indigenous people (2nd ed.). Dunedin: University of Otago Press. Taskforce of whānau‐centred initiatives. (2010). Whanau ora: Report of the taskforce on whanau‐centred initiatives. Wellington, New Zealand: Ministry of Health. Tervalon, M., & Murray‐García, J. (1998). Cultural humility versus cultural competence: A critical distinc­ tion in defining physician training outcomes in multicultural education. Journal of Health Care for the Poor and Underserved, 9, 117–125. Walker, R. (1990). Ka whawhai tonu matou: Struggle without end. Auckland, New Zealand: Penguin. Wallerstein, N., & Duran, B. (2010). Community‐based participatory research contributions to interven­ tion research: The intersection of science and practice to improve health equity. American Journal of Public Health, 100(Suppl. 1), S40–S46. Wallerstein, N., Duran, B., Oetzel, J. G., & Minkler, M. (2018). Community‐based participatory research for health: Advancing social and health equity (3rd ed.). San Francisco, CA: Jossey‐Bass. Wallerstein, N., Oetzel, J. G., Duran, B., Tafoya, G., Belone, L., & Rae, R. (2008). CBPR: What predicts outcomes? In M. Minkler & N. Wallerstein (Eds.), Community based participatory research for health: From process to outcomes (2nd ed., pp. 371–392). San Francisco, CA: Jossey‐Bass. Wham, C., Teh, R., Moyes, S., Dyall, L., Kēpa, M., Hayman, K., & Kerse, N. (2015). Health and social factors associated with nutrition risk: Results from life and living in advanced age: A cohort study in New Zealand (LiLACS NZ). Journal of Nutrition, Health, and Aging, 19, 637–645. Winitana, M. (2012). Remembering the deeds of Māui: What messages are in the tuakana‐teina pedagogy for tertiary educators? MAI Journal, 1(1), 29–36. Wyeth, E., Derrett, S., Hokowhitu, B., & Samaranayaka, A. (2013). Indigenous injury outcomes: Life sat­ isfaction among injured Māori in New Zealand three months after injury. Health and Quality of Life Outcomes, 11, 120–135. Young, R. (2001). Postcolonialism: An historical introduction. Oxford, UK: Blackwell.

3

Connecting Attitudes and Motivating Behavior Vested Interest Theory Bradley J. Adame A significant body of literature, spanning several related social scientific disciplines, is concerned with the practicalities of motivating attitudinally consistent behaviors. For practitioners tasked with motivating individuals to make healthy decisions, such as abstaining from prescription drug use, preparing for natural disasters, reporting potentially concussive head injuries, or encouraging prosocial behaviors (e.g., opting in to organ donation programs), connecting attitudes to relevant outcomes is a vital task where success can be difficult to achieve. Researchers have long understood that campaigns to change behavior must be based on sound communication theory to understand and meet audiences’ informational and involvement needs (Atkin & Freimuth, 2013). In areas of health, prosocial, and risk decisions, a consistent strategy has been to connect relevant attitudes to corresponding behaviors. Vested interest theory (VIT; Crano & Prislin, 1995; Sivacek & Crano, 1982) is a theoretical perspective of the attitude‐behavior relationship that has shown to be effective in application for both understanding audience needs and crafting effective messages to motivate attitudinally relevant behaviors and/or affect behavioral change. A fundamental assumption of modern social science is that attitudes are linked in a measurable fashion to behavior (Glasman & Albarracín, 2006). Since Allport’s (1935) identification of the attitude as a primary cognitive element, it has served as an avenue for researchers to gain insight into the mind, by explaining, predicting, and influencing behavior. The nature of the attitude construct, however, remains somewhat elusive. The seven decades of research attempting to link attitudes with consistent behaviors have shown mixed results for success (Glasman & Albarracín, 2006). Typically, efforts attempting to influence behaviors by manipulating attitudes have employed one or a combination of variables, either specifying conditions under which an attitude will predict behavior, or moderating the influence of an attitude on potential behaviors. Examples of commonly tested variables include attitude importance (Boninger, Krosnick, & Berent, 1995), accessibility (Laroche, Cleveland, & Maravelakis, 2002), duration (Krishnan & Smith, 1998), confidence (Berger, 1992), conviction (Abelson, 1988), emotion (Breckler, 1993), and hedonic relevance (Miller & Averbeck, 2013). The attitude‐behavior link is likely best described as multidimensional, with more than one variable exerting influence on the attitude‐behavior relationship. Sivacek and Crano (1982) argued that researchers, in their efforts to confirm or deny moderators of the attitude‐behavior relationship, had largely ignored hedonic relevance as an explanatory factor. They argued that if the “logical consequence,” of an attitude affects the individual’s

The Handbook of Applied Communication Research: Volume 1, First Edition. Edited by H. Dan O’Hair and Mary John O’Hair. © 2020 John Wiley & Sons, Inc. Published 2020 by John Wiley & Sons, Inc.

48 Adame life, attitudes should be maximally consistent with behaviors (p. 210). Hedonic relevance—­ distinct from ego involvement, at least where consonant behavior is concerned—may be a necessary, but not sufficient, condition to motivate behavior. When an important attitude is coupled with the perception of direct, personal consequences, individuals are more likely to act in attitudinally consistent ways (Fazio & Roskos‐Ewoldsen, 1994; Regan & Fazio, 1977). In their initial study, Sivacek and Crano (1982) took advantage of impending legislature that changed the legal drinking age from 18 to 21. They found that students who perceived the greatest personal impact—those who would no longer be able to consume alcohol—were also the most likely to volunteer and dedicate the most amount of time to oppose the referendum. In a replication investigating the perception of undergraduate comprehensive exams, they found that while most students opposed the exams, those who were most likely to have to pass them were the same students who were willing to campaign against them. In other words, students who would actually have to pass the exams to graduate perceived greater personal stake and were more willing to act on that perception. Subsequent research refined the concept of hedonic relevance and noted that several variables influence perceived stake in an attitude‐object (Crano & Prislin, 1995). These impingements, they argue, influence the degree to which individuals perceive their stake in a given context or set of outcomes. In their research, Crano and Prislin argued that stake functioned as a global proxy for hedonic relevance; that was then influenced by salience of relevant information, the certainty or probability that those consequences would manifest, the immediacy of those consequences, and the relative degree of self‐efficacy an individual perceives. In specifying this list, Crano and Prislin noted that it was not exhaustive, but that the VIT framework is designed to integrate additional variables as they demonstrate influence on the attitude‐behavior relationship. A growing body of research demonstrates that VIT is a valuable framework for understanding audience messaging needs and can serve as a foundation for designing effective campaign messages. This chapter begins with an overview of VIT and its components, discusses recent developments in the theory’s structure and application, and concludes with practical recommendations for applying VIT in communication campaigns and messaging interventions.

Vested Interest Theory The theory indicates that six variables (stake, salience, certainty, immediacy, self‐efficacy, and response‐efficacy) function to predict individuals’ relative vestedness in a particular attitude or attitude‐object. Stake is global proxy for vestedness and also describes perceived gain/loss potential for the attitude‐behavior relationship; salience is the relative level of attitudinal accessibility, certainty describes the perceived probability of associated outcomes, immediacy is the anticipated time when those outcomes will manifest, and efficacy delineates the attitude holder’s perceived ability to act (self‐efficacy) and that person’s faith in the available response.

Stake Stake refers to the perceived personal consequences of an attitude‐object in terms of potential gain/loss judgments (Crano & Prislin, 1995; Sivacek & Crano, 1982). Past research has characterized stake as a global proxy for vested interest, and demonstrated that elements of self‐interest influence the perception of the attitude‐object (cf. Petty & Cacioppo, 1986). When perceived stake is high, messages are processed more systematically, generate more issue‐relevant thoughts, and require increased cognitive load, compared to low perceived stake (Cacioppo, Petty, & Chuan, 1984; Petty, Cacioppo, & Goldman, 1981). Other research has characterized stake as a demographic variable where individuals, due to their geographic location or place in society, are



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assumed to have an interest in the attitude object because they are necessarily subject to p ­ otential gain/loss outcomes (Adame & Miller, 2015; Miller, Adame, & Moore, 2013). Likewise, when individuals perceive no personal relevance, they are less likely to report having a stake in the ­attitude‐object, even if their demographic characteristics suggest that they do.

Salience Salient attitudes are those that are cognitively easy to access and articulate. Research shows that salience also moderates attitude‐behavior consistency (Mirels & Dean, 2006; Shaffer, 1975; Shavitt & Fazio, 1991). For an attitude to be salient it must be perceived as personally relevant and cognitively accessible. Because attitude‐objects considered merely important may be more difficult to access and recall than attitudes that are salient, both dimensions are necessary for high salience (Crano, 1997). Sivacek and Crano (1982) argue that salience is typically a function of personal experience. People who have recent and pertinent experience with the attitude‐ object are more likely to report higher levels of salience. Attitudes that are not salient are less likely to exhibit attitude‐behavior consistency (Crano, 1983; Crano & Prislin, 1995).

Certainty Certainty addresses perceptions of the probability of a given outcome related to the attitude‐ object. When the outcomes associated with performing (or not performing) an attitude‐relevant behavior are not certain, the individual is less likely to engage in the particular behavior (Crano & Prislin, 1995). When the opposite is the case, and relevant outcomes are perceived as highly certain, attitudes will be more predictive of relevant behavior (Clarkson, Tormala, & Rucker, 2008). A high degree of attitudinal certainty contributes to higher probabilities of attitude‐ behavior consistency (Crano & Prislin, 1995; Miller et al., 2013). Research strongly supports the function of certainty in attitude‐behavior consistency (Clarkson et al., 2008). Tormala, DeSensi, Clarkson, and Rucker (2009) found increased attitude certainty shares a positive relationship with attitude‐behavior consistency, with one notable exception: when two strong and competing attitudes are present. Strong positive and strong negative a­ssociations about a particular attitude‐object can reduce attitude‐behavior intention when attitudes are made certain for one group of associations over another. This finding has important implications for health and related domains where behaviors such as smoking and diet can inspire s­ imultaneous positive and negative attitudes. A high degree of attitudinal certainty contributes to a greater likelihood of ­attitude‐behavior consistency (Crano, 1997; Crano & Prislin, 1995; Miller et al., 2013).

Immediacy Immediacy refers to the temporal element of an attitude‐object, any relevant behaviors, and its associated outcomes. Typically, attitude‐objects are associated with decisions and an array of potential behaviors. Immediacy, then, represents the time that an individual perceives between the present and when the anticipated outcomes will manifest. When the outcomes are perceived as more immediate—temporally closer—the attitude will be more predictive of the relevant behavior. Likewise, when outcomes are viewed as temporally distant, perceived vestedness will be reduced, thus attenuating the attitude‐behavior link (Crano & Prislin, 1995). Siegel, Alvaro, Lac, Crano, and Dominick (2008) found individuals considering living organ donation perceived a significantly greater impact on themselves and their community relative to attitudes about non‐living organ donation. In this case, the consequences of living organ donation would be immediate, as the living‐donor would be undergoing an immediate operation, whereas a non‐living donor would not donate until after her or his death. Additionally, Soman (2001) found similar dynamics for purchasing behaviors; groups of consumers were asked to anticipate

50 Adame the amount of money they would spend and use a payment method that would either immediately reduce their wealth or allow them to defer payments. Those who would pay immediately reported diminished purchasing intentions.

Self‐Efficacy The last element articulated by VIT is self‐efficacy. Based on Bandura’s (1997) original conception, self‐efficacy is defined as one’s perception of one’s ability to affect change. Self‐efficacy occurs as both a trait and a state variable, where individuals have a criterion level of self‐efficacy that influences their interactions on a global level (i.e., trait‐based), and other criterion levels of self‐efficacy that vary from context to context (i.e., state‐based; Beck & Lund, 1981). Self‐efficacy is conceived of as influencing individuals in three ways: cognitively, affectively, and motivationally. Cognitively, individuals with high self‐efficacy are able to perceive long‐term goals and consequences and remain committed to goals and overcoming challenges. For affective processes, self‐efficacy influences how individuals perceive their ability to cope with negative outcomes and stressful environments. Motivationally, higher levels of self‐efficacy promote goal setting and achievement and mediate levels of effort, perseverance, and resilience in the face of adversity. As Crano and others have observed (Adame & Miller, 2015; De Dominicis et al., 2014; Miller et al., 2013), VIT’s variables function in an additive relationship such that when all five are perceived to be high, vestedness is maximized, and attitudes should reliably predict relevant behaviors. When one or more of these is low or nonexistent, vestedness is attenuated, and the attitude‐behavior relationship is less predictable.

Recent Theoretical Developments Research demonstrates that VIT is efficacious in several contexts, and provides a plausible explanation of when attitudes are predictive of behavior, independent of attitude strength. Scholars continue to develop both the scope of the theory and its capacity for application to solve pressing social problems. Two areas where this research has been especially fruitful include VIT’s component array and its potential boundary conditions. Additionally, applied research continues to expand the ­contexts where VIT‐based insights can answer critical questions regarding perceived risks.

Component Array Much of VIT’s early research relied on either “known group” techniques or third‐person methods where individuals were asked to report their perception that a hypothetical person would act, given a set of situational characteristics (Anker, Feeley, & Kim, 2010; Prislin, 1996). Though this research provided valuable insights and led to the initial specification of VIT’s factor structure, questions of component measurement and naturally occurring levels of vestedness were yet to be answered. Anker and associates (2010) adapted the manipulation checks and created new items to measure perceived vestedness in the context of organ donation decisions. Their study suggests that three factors (i.e., salience, stake, and self‐efficacy) are the primary elements relevant to VIT as a moderator of attitude‐behavior consistency. Interestingly, these same variables emerged in Crano and Prislin’s (1995) initial investigation of their five‐factor model. The second part of Anker’s study sought to test their revised model of VIT and found that positive attitudes and self‐efficacy were the best predictors of intentions to donate. Also, of note, Anker’s study showed that rather than moderating the attitude‐behavior dynamic, self‐efficacy fully mediated the relationship. Another study sought to replicate these findings in the same context while comparing the efficacy of VIT to two competing models, the bystander intervention model (BIM; Latane & Darley,



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1968), and the organ donation model (ODM; Morgan et al., 2011). Quick, Anker, Feeley, and Morgan (2016) reproduced Anker and colleagues’ (2010) investigation and found that the VIT model mediated organ donation attitudes and donation status and accounted for more variance in donor status (R2 = 0.34) than either the BIM (R2 = 0.26) or the ODM (R2 = 0.21). Their structural equation model also confirmed a three‐factor structure for VIT of stake, salience, and self‐efficacy. The findings of Anker et al. (2010) and Quick et al. (2016) dovetail with previous work, in the context of organ donation decisions. Quick et al.’s (2016) study offered a direct comparison of effectiveness between the theory of planned behavior (TPB; Ajzen, 1991) and VIT. The results showed that the VIT variables stake, salience, and immediacy predicted more of the variance in outcomes related to living and non‐living organ donation. Though Siegel and colleagues’ (2008) work did not include a factor analysis, their results suggest a different subset of effective variables. While Anker’s findings did not support the five‐factor structure of VIT, other research found support for the inclusion of stake, salience, immediacy, and self‐efficacy (Thornton & Knox, 2002). Research in natural hazard contexts confirmed another factor structure, and offered support for incorporating an additional variable to the model. Crano and Prislin (1995) specified the original set of five variables thought to influence perceived vestedness. While doing so, they suggested that there may be additional attitudinal dimensions that should be included in the VIT model. In an effort expanding to the context of natural hazards, Miller et al. (2013) argued that response‐efficacy should be added to VIT. The extended parallel process model (EPPM) refers to response‐efficacy as an individual’s perception that a given response will successfully mitigate or remove the threat (Witte, 1992, 1994). In the context of fear appeals, response‐efficacy and self‐efficacy are necessary elements that predict an adaptive response to the message. For instance, an individual may perceive her/himself to have the personal ability to wear a seatbelt while driving (i.e., self‐efficacy), but if s/he does not believe that seatbelts enhance driver safety (i.e., response‐efficacy), that person is less likely to wear a seatbelt while driving—unless, of course, other factors are present, such as the prospect of receiving a traffic ticket. In natural hazard contexts, developing a personal emergency kit can greatly enhance one’s probability for minimizing negative outcomes (Federal Emergency Management Agency [FEMA], 2013). Despite this knowledge, research continues to show that Americans are generally unprepared for natural and man‐made disasters. National survey data indicate that knowing how to prepare and the cost of preparing (i.e., self‐efficacy), as well as the perceived effectiveness of preparation activities (i.e., response‐efficacy), remain significant barriers to engaging in these behaviors (FEMA, 2014). Miller et al. (2013) reasoned that, even if at‐risk (i.e., high stake) individuals had the ability to prepare for natural hazards (e.g., tornados and earthquakes), their beliefs about the efficacy of personal preparedness might explain why they had yet to prepare for these hazards. To test these ideas, Miller et al. (2013) proposed both a modified theoretical structure that includes response‐efficacy, and a new way to measure the elements of perceived vestedness. The preliminary investigation included three populations and two hazard contexts: residents of tornado‐prone areas in Oklahoma were solicited through convenience sampling and random digit telephone techniques. Residents of earthquake‐prone areas of California were solicited through snowball sampling. In all three cases, participants were given the new set of scales that included a measure for response‐efficacy, adapted from the Risk Behavior Diagnosis Scale (Witte, Cameron, McKeon, & Berkowitz, 1996; Witte, Meyer, & Martell, 2001). The results showed the scales to be internally reliable, with alpha indexes ranging from a low of 0.71 to a high of 0.94. Subsequent analysis of the scales confirmed the factor structure of the psychological ­components of VIT—salience, certainty, and immediacy—and found that these factors predict 57% of the variance in perceived susceptibility, or risk, of suffering consequences due to a tornado or earthquake.

52 Adame More recent research tested these particular scales in the context of catastrophic flooding. Adame and Miller (2016) further tested the new scales on two samples (convenience and random digit telephone) in Oklahoma, this time concentrating on perceived risk of catastrophic flooding. Here, the alpha reliabilities ranged from 0.79 to 0.87, again demonstrating good internal consistency, in a new hazard context. Their analysis also reaffirmed VIT’s capacity for predicting variance (60%) in perceived risk, and showed that the revised VIT model, including response‐ efficacy, predicted 19% of the variance in perceived flooding preparedness, indicating that those who reported higher levels of vestedness had already engaged in preparedness behaviors. Note that the relevant variables specified in Adame and Miller’s hazard research present a different factor structure than is evident in the results described by Anker and associates (stake, salience, and self‐efficacy; 2010). The variety of results suggests that different combinations of relevant variables will emerge, depending on the attitudes targeted, the research design, and potentially, other contextual factors. Future research should continue to search for the optimal factor structure and refine techniques to measure VIT sub‐components.

Boundary Conditions In its earliest conception, VIT was thought to be relevant only for individuals for whom personal consequences or outcomes would manifest; vestedness relied on a direct link between the individual and the attitude‐object (Crano, 1983). Johnson, Siegel, and Crano (2012) argued that this conclusion might have been premature. The authors hypothesized that situations might exist in which individuals would be vested because a significant other is highly vested, or through the potential of indirect consequences. For example, parents of a child who plays contact/ collision sports might not be explicitly vested in concussion consequences since they are not the ones at risk. However, they may be vested by virtue of the fact that their child might be injured. Moreover, the indirect ramifications of a child who suffers a mild brain injury would likely require significant involvement from concerned parents as the child recovers and returns to play. In the context of support for legislation that would limit access to depression medication, Johnson and colleagues (2012) confirmed that VIT explains patterns in attitudes toward the legislation. For vested participants, the data showed a positive correlation with attitudes toward the legislation, whereas for those who were non‐vested, the correlation was nonsignificant. This same data was then re‐analyzed to account for potential indirect effects and found a similar pattern of support. Their analysis also revealed that directly and indirectly vested participants were more likely to engage in attitude‐relevant behaviors. To build confidence in their findings, Johnson et al. (2012) then conducted an experiment in a second context: tobacco use. The researchers used a similar paradigm where hypothetical legislation would affect tobacco users’ access to health care. The results support the expansion of vestedness beyond the individual who would be directly affected. Directly vested individuals were found to have the most negative attitudes toward the legislation, with indirectly vested individuals reporting nearly similar attitudes that differed from non‐vested respondents. Regarding interpersonal closeness, they found that among individuals categorized as indirectly vested, interpersonal closeness moderated the effect for engagement in corresponding behaviors.

VIT in Applied Contexts Natural Hazard Preparedness Based on the strength of these studies, researchers then attempted to manipulate perceived vestedness using a campaign‐style message designed to motivate citizen‐level hazard preparedness. Adame and Miller (2015) adapted an existing television‐based campaign message, designed by



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the Oklahoma Office of Homeland Security. The modified message retained the visual images and animation, but substituted a VIT‐grounded script. Specifically, the narrative included explicit textual features designed to either enhance or attenuate perceived certainty, immediacy, self‐efficacy, and response‐efficacy. Salience and stake were not manipulated in the message, but were assumed high due to the content, regardless of the manipulation condition. Here, the VIT scales were used as manipulation checks to determine the effectiveness of the message manipulations. The results showed that the scales were reliable, with alphas ranging from 0.80 to 0.87. Additional details for the version of the VIT scales discussed here can be found in Miller and Adame (2016). Patterns in the data indicated that the scales accounted for manipulations for perceived self‐ efficacy and risk. However, certainty and immediacy returned marginal results and salience and response‐efficacy were nonsignificant. This study also sought to measure behavioral intentions to prepare for a natural hazard. In this regard, the results showed that individuals exposed to a high‐vested message reported higher levels of behavioral intentions to assemble an emergency kit, make a hazard plan, and volunteer in the event of a hazard. This research also showed that the elements of perceived vestedness could be manipulated via strategic messaging.

Flood risk perception

Related research has continued to develop VIT within the natural hazard context by investigating the perceptions of exposure to flood risk among Italian citizens with varying levels of flood risk exposure. In two studies, De Dominicis et al. (2014) found that citizens’ perceived level of flood risk corresponded with their actual level of that risk, such that residents in objectively higher risk areas perceived greater risk, and those in safer areas perceived lower risk. They also report that these same residents were unmoved by their risk perception to prepare for or attempt to mitigate their risk. That is, despite this relatively accurate perception, the citizens reported no differences in preparation or coping behaviors. Their second study found that citizens living in a high‐risk city who were exposed to VIT‐based messages reported enhanced stake, salience, certainty, and immediacy. Surprisingly, the messages did not enhance self‐efficacy. Perhaps this contradiction to other studies can be explained by the high likelihood that residents felt powerless to mitigate a catastrophic flood. These same residents also reported greater intentions to prepare and engage in mitigation and proactive coping behaviors. Together, these studies (Adame & Miller, 2015; De Dominicis et al., 2014) demonstrated that perceptions of vestedness could be manipulated through persuasive campaigns. These findings represent a significant step forward for both VIT’s development and for opportunities to apply its principles to promote attitude and behavior change. Practitioners who wish to connect relevant attitudes to motivate behavior adoption or change now have a theoretically driven instruction set for developing effective messages. Furthermore, the same research yields strategies and tools for measuring target‐audience perceptions prior to message design—an approach that is well known to enhance campaign and message efficacy (Atkin, 2001; Atkin & Freimuth, 2013). The application of VIT to understand and enhance risk perception continues to generate new theoretical development and considerations for the application of VIT to change attitudes and motivate risk‐aversive behaviors.

The Cascadia Subduction Zone

Adame and Miller (2018) used VIT to analyze Oregon and Washington residents’ informational needs and perceptions of their earthquake and tsunami risks. Much of Oregon and Washington are subject to the very high risk of a catastrophic dual threat of a major earthquake and corresponding tsunami, and related research reveals that the areas are vastly unprepared for such a threat (FEMA, 2016a, p. 4). The very real nature of this threat offers a valuable opportunity to both facilitate hazard preparedness and develop theory by applying VIT.

54 Adame The area most subject to this threat is known as the Cascadia Subduction Zone, a 700‐mile long tectonic plate seam. In this zone, Juan de Fuca and Pacific oceanic plates are being forced beneath the westward moving edge of the North American continental plain. In a process known as subduction, the confluence of these tectonic plates generated the volcanic activity that raised the Cascadia Mountain range, resulting in the rugged and beautiful landscape that attracts so many of the area’s current residents (Schelling et  al., 2013). The southernmost end of the Subduction Zone begins along the northernmost coast of California and continues along the Oregon and Washington coasts ending near Vancouver Island in Canada. With a combined population of over 8 million people, within this region are several major cities, including Salem and Portland, Oregon; Olympia, Tacoma, and Seattle, Washington; and Victoria and Vancouver, British Columbia (FEMA, 2016b). The research used a modified version of scales to measure the components of VIT, including response‐efficacy, that were redesigned to include five items for each element, and a standard response set anchored by strongly disagree‐strongly agree. This newer set of subscales demonstrated good to excellent internal consistency with reliability coefficients ranging from 0.86 to 0.96. This analysis also included measures for perceived stake and behavioral intentions that were operationalized on a scale of 0–100. The results of the analysis revealed valuable insights into how the sample perceives their risk and potential responses and coping mechanisms. Analysis of the VIT residents’ responses shows that they are largely non‐vested in the threat. Means for VIT’s individual elements indicate that Subduction Zone residents are somewhat aware of the threat, but perceive the disaster probability to be low, and the consequences to be far in the future. Generally, the residents are unsure of their ability to cope with a potential disaster, but are relatively confident that personal ­preparedness will help mitigate the consequences. The same is true for tsunami risk, where means for salience, certainty, and immediacy top out below 4 on a similar scale, with the mean for self‐efficacy averaging only about a point higher. The means for response‐efficacy were higher than expected, reaching above 5, again indicating that the sampled residents had a relatively high degree of faith in preparedness as key mitigation strategy. Interestingly, these patterns occurred independently of how far the respondents reported living from the coast. In other words, people who lived closer in proximity to the threat were no differently vested than those who lived farther away. Several nuanced analyses of the data revealed that the VIT model was efficacious in predicting perceived susceptibility of hazard consequences for the sampled population. As in past research, not all of VIT’s variables were drivers of the variance in susceptibility. However, the model accounted for 52% (R2adj.) of the variance in earthquake threats and 67% (R2adj.) of the variance for susceptibility to the tsunami threat, with stake, salience, certainty, and self‐efficacy driving the significance of the equation. The results of this study reveal a substantial opportunity for real‐world application of VIT. Armed with the knowledge that VIT’s variables predict this much variance in risk perception, the authors argue that VIT’s principles should be applied both to target citizen‐level hazard preparedness and to influence the region’s leaders and policy makers. Though as yet untested empirically, the case is made by Adame and Miller (2018) that VIT should be applied to garner support for the critical, expensive, and long‐term redesign of the area’s critical infrastructure. Researchers note that many of the zoning and planning decisions were made when the region was thought to be seismically inactive. This discrepancy results in numerous examples where critical infrastructure is located on alluvial land, or in the tsunami inundation zone. Hospitals, schools, highways, and other vulnerable structures and resources are at risk either of being washed away by subsidence and earth liquefaction in the event of an earthquake, or of being inundated and destroyed when the ensuing tsunami washes ashore. Likewise, immediate response and recovery operations in the area are likely to be hindered as many railways, freeways, bridges, and other transportation infrastructure are also either built on unstable land or located in the inundation zone, or a combination of the



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two (Atwater et  al., 2005; Schelling et  al., 2013). Applied over the long term, VIT‐based messages could help influence decisions that facilitate the redesign of much of the region’s infrastructure. Ultimately, VIT could help mitigate the consequences of the earthquake and tsunami, while building the capacity for immediate response, long‐term recovery, and community resilience.

Perceived Concussion Risk Among Collegiate Athletes The most recent application of VIT is using the theory to investigate perceived concussion risk among collegiate athletes participating in contact and collision sports in a Division I, Power‐5 athletic conference. Adame and Corman (2018) used VIT as part of a larger investigation, framed by the social‐ecological approach (Sallis & Owen, 2002), to investigate individual‐level perceived concussion risk in the context of team‐based and cultural‐level influences on c­ oncussion reporting behaviors. The project is ongoing, with two comprehensive goals. The short‐term goal of the project is to apply VIT’s principles to craft an effective educational module that ­motivates athletes to report severe head impacts and suspected concussion symptoms to medical staff and athletic trainers. In the long term, VIT‐based insights will form one part of a larger campaign to change the culture of concussion reporting among collegiate athletes. Concussion risk for collegiate athletes represents a significant health concern, as athletes are at risk for both immediate and life‐long consequences of repeated mild traumatic brain injuries (Daneshvar, Nowinski, McKee, & Cantu, 2011). As yet, there is no reliable biomarker or ­conclusive test to detect a concussion injury; therefore, effective diagnosis and treatment often rely on veridical communication from the athlete (McCrory et  al., 2013). Unfortunately, research shows that collegiate athletes commonly choose to either hide or underreport potential concussive events and symptoms (Meier et al., 2015). Past research investigated this problem using principles derived from the TPB, with limited efficacy (Kerr et al., 2014). The results of this most recent application of VIT again demonstrate the theory’s value in framing perceptions surrounding the acceptance of personal health risk. In a sample of 435 a­thletes, across 12 Division I universities, and six high‐risk sports, Adame and Corman (2018) found that the VIT model predicted 36% (R2adj.) of the variance in perceived risk of concussion consequences. Here, Crano and Prislin’s (1995) five original components all function to explain variance. The researchers note that response‐efficacy was included in the model, but did not emerge as a significant predictor of risk. Across the sample, perceived response‐efficacy was reported as relatively high and invariant. In other words, virtually all of the athletes had faith in their universities’ responses to mitigate their concussion risk. To lend further evidence of VIT’s predictive capacity, researchers showed that VIT explains approximately 14% (R2adj.) of the variance in the number of athletes’ recalled concussions and head impacts. The researchers reasoned that athletes who were more highly vested in concussion risks would be able to recall greater numbers of events that increase the risk of health consequences. This analysis showed that, while response‐efficacy did emerge as a predictor, perceived immediacy did not. Following the same logic, they also reasoned that more highly vested athletes would report higher levels of perceived concussion education; the model predicts approximately 8% (R2adj.) of the variance here, with salience, self‐efficacy, and response‐efficacy emerging as predictive. This finding makes sense, considering the athletes receive mandated education (i.e., salience) that tends to focus on symptom recognition (i.e., self‐efficacy) and organizational responses (i.e., response‐efficacy). An important theoretical insight emerged from this research as well. In the analyses for both perceived risk and recalled head impacts, self‐efficacy, though significant, showed a negative ­relationship with the outcome variables. While past research argued that VIT’s variables share an additive, positive relationship with outcomes, this result appears to be slightly different in risk contexts. Athletes who reported higher perceived risk and larger numbers of head impacts also

56 Adame reported lower levels of self‐efficacy. Logically, those who feel less equipped to exert control in a given risk situation should feel more at risk; that dynamic is captured in the analysis. The finding points to a potential strategy for enhancing perceived risk with the goal of motivating protective behaviors. Formative research revealed that many of the athletes received coaching and training on good sports technique to protect their heads from injury, but neither coaching messages nor concussion education included messages to expressly promote or model reporting behaviors. The importance of good athletic technique notwithstanding, the authors argue that education efforts should enhance the athlete’s efficacy in reporting behavior and recovery protocol adherence. The results of this research support the use of VIT in concussion risk contexts. The results reported here will be applied, as per recommendations from past VIT studies, in the development and testing of an education module. The design will incorporate messages targeted at the nature of the injuries (i.e., salience), the probability of suffering injury‐related consequences (i.e., ­certainty), short‐term risks (i.e., immediacy), and effective personal (i.e., self‐efficacy) and organization‐based responses (i.e., response‐efficacy) athletes can engage to protect themselves. The expectation is that these targeted messages will not only educate athletes, but also motivate them to report potentially concussive events and symptoms.

Future Directions in Theory Development Questions remain about the theoretical structure of VIT. As noted, several areas exist where future researchers can work in both applied and basic research arenas to validate VIT’s structure, develop its measurement tools, and refine message‐design strategies for applying VIT in the solving of pressing social problems where the attitude‐behavior relationship is a key point of intervention. Studies investigating Crano and Prislin’s (1995) original impingements on perceived stake have resulted in several competing perspectives on the critical elements that influence vestedness. Some have argued for a more parsimonious model that includes stake, salience, and self‐efficacy (Anker et al., 2010; Quick et al., 2016), while others have argued that consideration of a broader model is warranted (Miller et al., 2013). Future research should continue to refine VIT and, if possible, validate a consistent model for investigation and application. Closely tied to the issue of VIT’s structure are the methods used for its measurement. As noted above, several different scales currently exist for measuring perceived vestedness and constituent variables. Future research should continue to test and refine these measures, as they are crucial to the use of VIT in real‐world settings where the found group technique is impossible to execute. Research demonstrates that individuals’ perceived vestedness in attitude‐objects exists in the messaging environment and varies according to multiple factors. Measurement of VIT’s variables allows for researchers to assess levels of vestedness in audiences rather than relying on the divining of situations where individuals are likely to be vested. Moreover, the refinement of measurement tools should facilitate the use of confirmatory factor techniques that allow for examination and validation of VIT’s structure. Researchers should also consider contextual influences on vestedness. An individual’s ­vestedness in a particular attitude‐object may be influenced by the variables discussed in this chapter, but the possibility exists that overall vestedness may be influenced by organizational‐ and cultural‐level variables as well. Practically, there may be insight to be gained by treating a composite score of vestedness as an outcome variable in a theoretically relevant analysis. Especially for risk perception contexts, perceived vestedness may be influenced by the messages one hears or the constraints of a given location, organization, or some other environmental influence. Investigating these potential influences should help researchers uncover new and innovative ways to explain variance in the attitude‐behavior relationship.



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Another fruitful area of VIT research could blend insights from related theories. Researchers might investigate message source characteristics including credibility and expertise, perceived similarity, and/or attractiveness. For example, future risk research should investigate the role of message framing and behavioral decision making (Tversky & Kahneman, 1981). Related research has shown that gain‐frame messages can motivate preventative and adaptive health behaviors (Detweiler, Bedell, Salovey, Pronin, & Rothman, 1999; Lee & Aaker, 2004; Rothman, Bartels, Wlaschin, & Salovey, 2006). In contexts where positive or prosocial outcomes are anticipated, gain‐frame messages can be effective, while loss‐frame messages may be more effective in other contexts including behaviors aimed at detecting disease (Abhyankar, O’Connor, & Lawton, 2008; Cho & Boster, 2005; Quick & Bates, 2010). Messages designed to influence perceived vestedness could be adapted to either gain‐ or loss‐frame orientations and crossed in full‐­factorial designs to test for their persuasive efficacy. Finally, theoretically oriented research should seek to refine the ways in which VIT is applied through messaging to influence perceived vestedness. Research in this area represents much of the most recent activity inspired by VIT and evinces perhaps the theory’s greatest advantage: adaptability to diverse modalities. In manipulating perceived vestedness, researchers have typically used either written, scenario‐based messages or video‐based messages. While these methods have proven effective, the present media environment presents a rich set of opportunities for testing VIT in other messaging platforms. Perhaps VIT’s variables could be integrated into a serious game, where winning the game is the result of reporting maximized vestedness. Elements of vestedness embedded in social media or static visual methods for manipulating perceived vestedness may also be effective in changing attitudes and behaviors. Message manipulations may also take the form of single discrete messages designed to ­manipulate one aspect of vestedness at a time. In this case, an entire campaign would be conceived and delivered as an array of distinct messages, each with the goal of enhancing one aspect of vestedness. The accumulation of the effects of single messages that target stake, salience, certainty, immediacy, self‐efficacy, and response‐efficacy, respectively, may result in more desirable levels of attitude and behavior change. The possibility exists that some of the effectiveness of each ­message manipulation is diminished when they are presented together in a single video or written script. Alternatively, the success of this strategy might rely on an expensive and time‐consuming campaign where researchers can be assured that audiences have consumed each of the messages. In either case, the question is an empirical one and warrants further investigation, as this could lead to valuable insights for VIT’s application to mitigate or solve practical problems.

Recommendations for Practitioners A growing body of research continues to argue that VIT is an effective tool for designing both audience analysis strategies and motivational campaigns to connect attitudes with relevant behaviors. Despite the variety of conclusions regarding VIT’s theoretical structure and measurement strategies, virtually all of the VIT research shares the conclusion that highly vested individuals are more likely to act in attitudinally consistent ways. Though the available data ­suggests that VIT’s critical elements may differ between various attitude‐behavior contexts, the body of research suggests a clear path for applying VIT to design messages that connect relevant attitudes with consonant behaviors, motivate attitude and behavioral change, and influence risk perception. Applying VIT is a straightforward endeavor that relies on designing messages in such a way that increases perceived levels of each of the sub‐components that then influence perceived stake, salience, certainty, immediacy, self‐efficacy, and response‐efficacy. Researchers have described several measurement strategies and provide information for practitioners to replicate the method they see fit (cf. Anker et al., 2010; De Dominicis et al., 2014; Miller & Adame, 2016; Quick et  al., 2016). These scales should be used for audience analyses that can gauge current levels of vestedness relative to the attitudes and behavior(s) in question. Though the

58 Adame current research offers several perspectives about VIT’s requisite variables, practitioners would be wise to measure all six components, and then determine which ones to target. As recent research shows, some audiences may not need additional information about response‐efficacy (collegiate athletes), because it is already perceived high. These audiences may, however, be shocked when informed of the immediate consequences that untreated concussions can have on their athletic and academic performance. Likewise, although Cascadia Subduction Zone ­residents may be aware of the twin risks of earthquakes and tsunamis, they may need information that helps them plan and prepare for these threats (i.e., self‐efficacy and response‐efficacy). Once audience needs have been determined, campaign designers create messages that target the variables that need enhanced. Each of the variables should be operationalized through the use of clear and concrete language that emphasizes personal relevance (i.e., stake), pertinent information (i.e., salience), probabilities (i.e., certainty), time (i.e., immediacy), personal ­capability (i.e., self‐efficacy), and response effectiveness (i.e., response‐efficacy). Depending on the media in question, visual elements that underscore the verbal or textual elements should enhance message effects. In instances where message reactance or boomerang effects (Brehm, 1972) are anticipated, message designers may engage individuals who are relationally close to those whose behavior they seek to influence. Johnson et  al.’s (2012) research points to the potential effectiveness of this strategy. Targeting highly vested but indirectly affected individuals could lead to desired attitudinal and behavioral change. For example, messages targeted toward children could influence children to, in turn, prod the adults in their lives to stop smoking, refrain from cell phone use while driving, and engage in other mutually beneficial behaviors. Aside from providing a clear factor structure, VIT also has the capacity for scaffolding and integrating recommendations from other theories as well. Audiences consuming VIT‐based messages that are comprised of clear and concrete language are likely to be processing these points systematically, as per the heuristic‐systematic model (Todorov, Chaiken, & Henderson, 2002). Given that both heuristic and systematic processing can lead to behavior change, m ­ essages emphasizing vestedness could be delivered either by a recognized expert or by an otherwise attractive source. Campaign designers may also wish to promote audiences’ use of decision rules or other heuristics that can enhance personal relevance and increase efficacy. Some contexts (e.g., health) and messaging strategies that use concrete language enhance the potential for inducing psychological reactance in audiences (Dillard & Shen, 2005). Psychological reactance is a negatively valenced emotional state, typically characterized by anger and negative cognition, that arises when an individual perceives threats to behavioral freedoms (Brehm, 1972). Psychological reactance typically results in source derogation, message rejection, and boomerang effects (Dillard & Shen, 2005). In contexts where message reactance may be anticipated and indirect strategies are inappropriate, a restoration of freedom technique may dissipate any anger or perceived threat to freedom (Miller, Lane, Deatrick, Young, & Potts, 2007). This messaging strategy would involve the use of concrete language to communicate vestedness and end the ­message with a short post script to remind audience members of their autonomy and freedom. The available research suggests that VIT offers much promise for practitioners who wish to both analyze and then respond to audience perceptions with the hope of changing or encouraging relevant behavior. While the relevant variables may differ according to context, VIT ­provides a straightforward method for designing effective persuasive messages.

Conclusion Vested interest theory has been and promises to be a valuable approach to measuring relevant attitudes and using them to motivate consonant behavior. Research in experimental and applied contexts demonstrates the efficacy of VIT’s elements—stake, salience, certainty, immediacy, self‐ efficacy, and response‐efficacy—for framing attitude‐behavior relationships in situations where personal relevance and consequences are crucial.



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The value of VIT lies in its simplicity and the ease with which its principles can be converted to effective campaign messaging. Researchers have proposed and validated several techniques for measuring VIT’s variables, making the theory an effective tool for pre‐intervention audience assessment. Correspondingly, studies have shown that VIT is an effective framework for linking relevant attitudes to behavioral outcomes through motivational and educational campaigns. Applied researchers and practitioners interested in creating, modifying, enhancing, or even extinguishing behaviors through effective messaging will find utility in VIT’s adaptability across contexts, and its scalability across communication media. These advantages position VIT as an indispensable tool in the endeavor to create more effective messaging. This chapter explained VIT’s structure, surveyed the available research to provide recommendations for future research, and gave practical recommendations that practitioners can use to apply VIT. The evidence and recommendations provided here will hopefully motivate practitioners and campaigners to tackle practical problems, motivate healthy and prosocial behaviors, and ultimately save lives and conserve resources by developing theoretically grounded and effective communication campaigns.

References Abelson, R. P. (1988). Conviction. American Psychologist, 43(4), 267–275. doi:10.1037/0003‐ 066X.43.4.267 Abhyankar, P., O’Connor, D. B., & Lawton, R. (2008). The role of message framing in promoting MMR vaccination: Evidence of a loss‐frame advantage. Psychology, Health and Medicine, 13(1), 1–16. doi:10.1080/13548500701235732 Adame, B. J., & Corman, S. R. (2018). Vested interests and perceived risk of concussion consequences among power‐5 college athletes. Health Communication, 1–10. doi:10.1080/10410236.2018.1517707 Adame, B. J., & Miller, C. H. (2015). Vested interest, disaster preparedness, and strategic campaign ­message design. Health Communication, 30(3), 271–281. doi:10.1080/10410236.2013.842527 Adame, B. J., & Miller, C. H. (2016). Vested interest: Developing scales for assessing flooding preparedness. Disaster Prevention and Management, 25(3). doi:10.1108/DPM‐08‐2015‐0196 Adame, B. J., & Miller, C. H. (2018). Risk perception and earthquake preparedness motivation: Predicting responses to a Cascadia Subduction Zone catastrophic event. In C. V. Fletcher & J. Lovejoy (Eds.), Natural disasters and risk communication: Implications of the Cascadia Subduction Zone megaquake (pp. 49–86). Lanham, MD: Lexington Books. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211. doi:10.1016/0749‐5978(91)90020‐T Allport, G. W. (1935). Attitudes. In C. Murchinson (Ed.), Handbook of social psychology (pp. 798–844). Worcester, MA: Clark University Press. Anker, A. E., Feeley, T. H., & Kim, H. (2010). Examining the attitude‐behavior relationship in prosocial donation domains. Journal of Applied Social Psychology, 40(6), 1293–1324. doi:10.1111/j.1559‐ 1816.2010.00619.x Atkin, C. K. (2001). Theory and principles of media health campaigns. In R. E. Rice & C. K. Atkin (Eds.), Public communication campaigns (pp. 49–68). Thousand Oaks, CA: Sage. Atkin, C. K., & Freimuth, V. S. (2013). Guidelines for formative evaluation research in campaign design. In R. E. Rice & C. K. Atkin (Eds.), Public communication campaigns (4th ed., pp. 53–68). Thousand Oaks, CA: Sage. Atwater, B. F., Musumi‐Rokkaku, S., Satake, K., Tsuji, Y., Ueda, K., & Yamaguchi, D. (2005). The orphan tsunami of 1700: Japanese clues to a parent earthquake in North America. US Geological Survey Professional Paper. doi:10.1080/00207230600720134 Bandura, A. (1997). Self‐efficacy. Harvard Mental Health Letter, 13(9), 4–6. Beck, K. H., & Lund, A. K. (1981). The effects of health threat seriousness and personal efficacy upon intentions and behavior. Journal of Applied Social Psychology, 11(5), 401–415. doi:10.1111/ j.1559‐1816.1981.tb00832.x Berger, I. E. (1992). The nature of attitude accessibility and attitude confidence: A triangulated experiment. Journal of Consumer Psychology, 1(2), 103–123. doi:10.1016/S1057‐7408(08)80052‐6

60 Adame Boninger, D. S., Krosnick, J. A., & Berent, M. K. (1995). Origins of attitude importance: Self‐interest, social identification, and value relevance. Journal of Personality and Social Psychology, 68, 61–80. Breckler, S. J. (1993). Emotion and attitude change. In M. Lewis & J. Haviland (Eds.), Handbook of emotions. New York, NY: Guilford Press. Brehm, J. W. (1972). Responses to loss of freedom: A theory of psychological reactance. Morristown, NJ: General Learning Press. Cacioppo, J. T., Petty, R. E., & Chuan, F. K. (1984). The efficient assessment of need for cognition. Journal of Personality Assessment, 48(3), 306. doi:10.1207/s15327752jpa4803_13 Cho, H., & Boster, F. J. (2005). Development and validation of value‐, outcome‐, and impression‐relevant involvement scales. Communication Research, 32, 235–264. doi:10.1177/0093650204273764 Clarkson, J. J., Tormala, Z. L., & Rucker, D. D. (2008). A new look at the consequences of attitude certainty: The amplification hypothesis. Journal of Personality and Social Psychology, 95(4), 810–825. doi:10.1037/a0013192 Crano, W. D. (1983). Assumed consensus of attitudes: The effect of vested interest. Personality and Social Psychology Bulletin, 9(4), 597–608. doi:10.1177/0146167283094009 Crano, W. D. (1997). Vested interest and symbolic politics—observations and recommendations: Reply to Sears (1997). Journal of Personality and Social Psychology, 72(3), 497–500. doi:10.1037/ 0022‐3514.72.3.497 Crano, W. D., & Prislin, R. (1995). Components of vested interest and attitude‐behavior consistency. Basic and Applied Social Psychology, 17(1 & 2), 1–21. doi:10.1080/01973533.1995.9646129 Daneshvar, D., Nowinski, C., McKee, A., & Cantu, R. (2011). The epidemiology of sport‐related concussion. Clinics in Sports Medicine, 30(1), 1–17. doi:10.1016/j.csm.2010.08.006 De Dominicis, S., Crano, W. D., Ganucci Cancellieri, U., Mosco, B., Bonnes, M., Hohman, Z., & Bonaiuto, M. (2014). Vested interest and environmental risk communication: Improving willingness to cope with impending disasters. Journal of Applied Social Psychology, 44(5), 364–374. doi:10.1111/ jasp.12229 Detweiler, J. B., Bedell, B. T., Salovey, P., Pronin, E., & Rothman, A. J. (1999). Message framing and sunscreen use: Gain‐framed messages motivate beach‐goers. Health Psychology, 18(2), 189–196. doi:10.1037/0278‐6133.18.2.189 Dillard, J. P., & Shen, L. (2005). On the nature of reactance and its role in persuasive health communication. Communication Monographs, 72(2), 144–168. doi:10.1080/03637750500111815 Fazio, R. H., & Roskos‐Ewoldsen, D. R. (1994). Acting as we feel: When and how attitudes guide behavior. In T. C. Brock & S. Shavitt (Eds.), Psychology of persuasion. Boston, MA: Allyn & Bacon. Federal Emergency Management Agency. (2013). Personal preparedness in America: Findings from the 2012 FEMA national survey. Washington, DC: FEMA. Retrieved from http://www.fema. gov/media‐librar y‐data/662ad7b4a323dcf07b829ce0c5b77ad9/2012+FEMA+National+ Survey+Report.pdf Federal Emergency Management Agency. (2014). Preparedness in America. Washington, DC: FEMA. Retrieved from https://www.fema.gov/media‐library‐data/1409000888026‐1e8abc820153a6c8cd e24ce42c16e857/20140825_Preparedness_in_America_August_2014_Update_508.pdf Federal Emergency Management Agency. (2016a). Cascadia Rising 2016 Exercise: Joint Multi‐State After‐ Action Report (AAR). Washington, DC: FEMA. Retrieved from https://www.fema.gov/media‐ library‐data/1484078710188‐2e6b753f3f9c6037dd22922cde32e3dd/CR16_AAR_508.pdf Federal Emergency Management Agency. (2016b). Cascadia Rising: Cascadia Subduction Zone catastrophic earthquake and tsunami functional exercise 2016. Washington, DC: FEMA. Retrieved from https:// www.idahoares.info/_downloads/exercises/2016/Cascadia_Rising/Cascadia_Rising_ Overview_06012015.pdf Glasman, L. R., & Albarracín, D. (2006). Forming attitudes that predict future behavior: A meta‐analysis of the attitude‐behavior relation. Psychological Bulletin, 132(5), 778–822. doi:10.1037/0033‐2909. 132.5.778 Johnson, I. M., Siegel, J. T., & Crano, W. D. (2012). Expanding the reach of vested interest in predicting attitude‐consistent behavior. Social Influence, 9(1), 20–36. doi:10.1080/15534510.201 2.738243 Kerr, Z. Y., Register‐Mihalik, J. K., Marshall, S. W., Evenson, K. R., Mihalik, J. P., & Guskiewicz, K. M. (2014). Disclosure and non‐disclosure of concussion and concussion symptoms in athletes: Review and application of the socio‐ecological framework. Brain Injury, 28(8), 1009–1021. doi:10.3109/ 02699052.2014.904049



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Krishnan, H. S., & Smith, R. E. (1998). The relative endurance of attitudes, confidence and attitude‐ behavior consistency: The role of information source and delay. Journal of Consumer Psychology, 7(3), 273–298. doi:10.1002/mar.1049 Laroche, M., Cleveland, M., & Maravelakis, I. (2002). Attitude accessibility, certainty and the attitude– behaviour relationship: An empirical study of ad repetition and competitive interference effects. International Journal of Advertising, 21(2), 149–174. doi:10.1080/02650487.2002.11104924 Latane, B., & Darley, J. M. (1968). Group inhibition of bystander intervention in emergencies. Journal of Personality and Social Psychology, 10(3), 215–221. doi:10.1037/h0026570 Lee, A. Y., & Aaker, J. L. (2004). Bringing the frame into focus: The influence of regulatory fit on processing  fluency and persuasion. Journal of Personality and Social Psychology, 86(2), 205–218. doi:10.1037/0022‐3514.86.2.205 McCrory, P., Meeuwisse, W., Aubry, M., Cantu, B., Dvorak, J., Echemendia, R. J., … Tator, C. H. (2013). Consensus statement on concussion in sport: The 4th international conference on concussion in sport, Zurich, November 2012. Journal of Athletic Training, 48(4), 554–575. doi:10.4085/1062‐6050‐48.4.05 Meier, T. B., Brummel, B. J., Singh, R., Nerio, C. J., Polanski, D. W., & Bellgowan, P. S. F. (2015). The underreporting of self‐reported symptoms following sports‐related concussion. Journal of Science and Medicine in Sport, 18(5), 507–511. doi:10.1016/j.jsams.2014.07.008 Miller, C. H., & Adame, B. J. (2016). Scales for measuring the dimensions of vested interest. In D. K. Kim & J. Dearing (Eds.), Health communication measures (pp. 265–278). New York, NY: Peter Lang. Miller, C. H., Adame, B. J., & Moore, S. D. (2013). Vested interest theory and disaster preparedness. Disasters, 37(1), 1–27. doi:10.1111/j.1467‐7717.2012.01290.x Miller, C. H., & Averbeck, J. M. (2013). Hedonic relevance and outcome relevant involvement. Electronic  Journal of Communication, 23(3). Retrieved from http://www.cios.org/www.cios.org/ EJCPUBLIC/023/3/023031.html Miller, C. H., Lane, L. T., Deatrick, L. M., Young, A. M., & Potts, K. A. (2007). Psychological reactance and promotional health messages: The effects of controlling language, lexical concreteness, and the  restoration of freedom. Human Communication Research, 33(2), 219–240. doi:10.1111/ j.1468‐2958.2007.00297.x Mirels, H. L., & Dean, J. B. (2006). Right‐wing authoritarianism, attitude salience, and beliefs about matters of fact. Political Psychology, 27(6), 839–866. doi:10.1111/j.1467‐9221.2006.00540.x Morgan, S. E., Stephenson, M. T., Afifi, W., Harrison, T. R., Long, S. D., & Chewning, L. V. (2011). The University Worksite Organ Donation Project: A comparison of two types of worksite campaigns on  the  willingness to donate. Clinical Transplantation, 25(4), 600–605. doi:10.1111/j.1399‐ 0012.2010.01315.x Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 19, pp. 123–205). New York, NY: Academic. Petty, R. E., Cacioppo, J. T., & Goldman, R. (1981). Personal involvement as a determinant of ­argument‐based persuasion. Journal of Personality and Social Psychology, 41, 847–855. doi:10.1037/ 0022‐3514.41.5.847 Prislin, R. (1996). Attitude stability and attitude strength: One is enough to make it stable. European Journal of Social Psychology, 26(3), 447–477. doi:10.1002/(SICI)1099‐0992(199605)26:33.0.CO;2‐I Quick, B. L., Anker, A. E., Feeley, T. H., & Morgan, S. E. (2016). An examination of three theoretical models to explain the organ donation attitude—registration discrepancy among mature adults. Health Communication, 31(3), 265–274. doi:10.1080/10410236.2014.947468 Quick, B. L., & Bates, B. R. (2010). The use of gain‐ or loss‐frame messages and efficacy appeals to dissuade excessive alcohol consumption among college students: A test of psychological reactance theory. Journal of Health Communication, 15(6), 603–628. doi:10.1080/10810730.2010.499593 Regan, D. T., & Fazio, R. (1977). On the consistency between attitudes and behavior: Look to the method of attitude formation. Journal of Experimental Social Psychology, 13(1), 28–45. doi:10.1016/0022‐10 31(77)90011‐7 Rothman, A. J., Bartels, R. D., Wlaschin, J., & Salovey, P. (2006). The strategic use of gain‐ and loss‐framed messages to promote healthy behavior: How theory can inform practice. Journal of Communication, 56(Suppl.), 202–220. doi:10.1111/j.1460‐2466.2006.00290.x Sallis, J., & Owen, N. (2002). Ecological models of health behavior. In K. Glanz, B. K. Rimer, & K. Viswanath (Eds.), Health behavior and health education theory, research, and practice (4th ed., pp. 462–484). San Francisco, CA: Jossey‐Bass.

62 Adame Schelling, J., Kubler, M., Ash, C., Moore, T., Walsh, T., Kandathil, H., … Nourse, K. L. (2013). Cascadia Subduction Zone earthquakes: A magnitude 9.0 Scenario. Retrieved from http://file.dnr.wa.gov/ publications/ger_ic116_csz_scenario_update.pdf Shaffer, D. R. (1975). Some effects of consonant and dissonant attitudinal advocacy on initial attitude salience and attitude change. Journal of Personality and Social Psychology, 32(1), 160–168. doi:10.1037/ h0076854 Shavitt, S., & Fazio, R. H. (1991). Effects of attribute salience on the consistency between attitudes and  behavior predictions. Personality and Social Psychology Bulletin, 17, 507–516. doi:10.1177/ 0146167291175005 Siegel, J. T., Alvaro, E. M., Lac, A., Crano, W. D., & Dominick, A. (2008). Intentions of becoming a living organ donor among Hispanics: A theory‐based approach exploring differences between living and  nonliving organ donation. Journal of Health Communication, 13(1), 80–99. doi:10.1080/ 10810730701807142 Sivacek, J., & Crano, W. D. (1982). Vested interest as a moderator of attitude‐behavior consistency. Journal of Personality and Social Psychology, 43(2), 210–221. doi:10.1037/0022‐3514.43.2.210 Soman, D. (2001). Effects of payment mechanism on spending behavior: The role of rehearsal and immediacy of payments. Journal of Consumer Research, 27(4), 460–474. doi:10.1086/319621 Thornton, B., & Knox, D. (2002). “Not in my back yard”: The situational and personality determinants of  oppositional behavior. Journal of Applied Social Psychology, 32(12), 2554–2574. doi:10.1111/ j.1559‐1816.2002.tb02756.x Todorov, A., Chaiken, S., & Henderson, M. (2002). The heuristic‐systematic model of social information processing. In J. P. Dillard & M. Pfau (Eds.), The persuasion handbook: Developments in theory and practice (pp. 195–213). Thousand Oaks, CA: Sage. Tormala, Z. L., DeSensi, V. L., Clarkson, J. J., & Rucker, D. D. (2009). Beyond attitude consensus: The social context of persuasion and resistance. Journal of Experimental Social Psychology, 45(1), 149–154. doi:10.1016/j.jesp.2008.07.020 Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453–458. doi:10.2307/1685855 Witte, K. (1992). Putting the fear back into fear appeals: The extended parallel process model. Communication Monographs, 59(4), 329–349. doi:10.1080/03637759209376276 Witte, K. (1994). Fear control and danger control: A test of the extended parallel process model (EPPM). Communication Monographs, 61(2), 113–134. doi:10.1080/03637759409376328 Witte, K., Cameron, K. A., McKeon, J. K., & Berkowitz, J. M. (1996). Predicting risk behaviors: Development and validation of a diagnostic scale. Journal of Health Communication, 1(4), 317–342. doi:10.1080/108107396127988 Witte, K., Meyer, G., & Martell, D. (2001). Effective health risk messages: A step‐by step guide. Thousand Oaks, CA: Sage.

4

A Comparative Analysis of Theoretical Propositions Focusing on Apologia Michel M. Haigh

Bad things happen to good organizations (Marconi, 1992). Many of these bad things are crises. Unpredictable events that threaten the organization’s relationship with key publics; crises can come in all different forms (Barton, 1993). There is not one accepted definition of crisis; however, for the purpose of this chapter, the following definition will be used: a crisis is “the perception of an unpredictable event that threatens important expectancies of stakeholders and can seriously impact an organization’s performance and generate negative outcomes” (Coombs, 2010, p. 19). This chapter explores two dominant theories used in crisis communication research by strategic communication scholars: image repair and situational crisis communication theory (SCCT). Both theories’ history and development are examined. A comparison of SCCT and image repair is provided, along with a review of the literature which provides practitioners guidance on what strategies work best to repair an organization’s image. Crisis communication is an applied body of research. Historically, work about crisis communication appeared in non-academic journals (see Bergman, 1994; Carney & Jorden, 1993; Loewendick, 1993), and early theoretical work discussing risk communication from a risk analysis perspective appeared in academic venues (e.g., Keeney & von Winterfeldt, 1986; Slovic, 1986). An and Cheng (2010) conducted a quantitative content analysis on crisis articles that appeared in The Journal of Public Relations Research and Public Relations Review, the major journals in the area of public relations. A sample of 74 articles was published in the two journals between 1975 and 2006. Findings indicate 14 articles employed SCCT and seven articles employed image restoration theory. A number of other theories were included as well (e.g., apology theory, attribution theory, contingency theory, situational theory, and organizational theory), and not all of the articles in the analysis included a theoretical framework (An & Cheng, 2010). In addition, Avery, Lariscy, Kim, and Hocke (2010) conducted a quantitative content analysis to evaluate longitudinal trends of crisis research over an 18-year period. They reviewed articles from 16 journals, for a total of 66 articles. They focused on reviewing research that employed SCCT and image restoration. Their results indicate Benoit’s theory was employed in 24 articles published from 1997 to 2009. SCCT was employed as a theoretical framework sparingly from 1991 to 2002 (three articles in total), and was applied more often after 2003 (12 articles). These

The Handbook of Applied Communication Research: Volume 1, First Edition. Edited by H. Dan O’Hair and Mary John O’Hair. © 2020 John Wiley & Sons, Inc. Published 2020 by John Wiley & Sons, Inc.

64 Haigh two theories dominate the crisis literature, so it is important to understand their similarities and differences. SCCT is reviewed first, followed by image repair.

Situational Crisis Communication Theory SCCT is composed of three core elements: the crisis situation, the crisis response strategy, and matching the crisis situation with the response strategy. When examining the crisis situation, crises can fall into three clusters: victim cluster, accidental cluster, and preventable cluster. The victim cluster includes crisis types in which the organization is a victim, such as natural disasters, rumor (false information about an organization is being circulated), workplace violence, and product tampering (external agent causes damage; Coombs, 2006). The accidental cluster includes crises that occur when the organization’s actions lead to an unintentional outcome. Crises in this cluster include: challenges, megadamage, technical breakdown accidents, and technical breakdown recalls. Challenges are crises in which stakeholders claim an organization acted inappropriately. Megadamage crises happen when a technical accident occurs, and the organization focuses on the environmental damage. A technical breakdown accident occurs when technology or equipment fails and causes an accident. A technical breakdown recall is an instance in which technology or equipment failure causes a product to be recalled (Coombs, 2006). The final crisis cluster is the preventable cluster. This cluster is defined by crises in which the organization places people at risk, acts inappropriately, or violates a law/regulation. Examples of crises in this cluster include: human breakdown accidents (human error causes the accident), human breakdown recalls (human error causes the product to be recalled), organizational misdeed with no injuries (stakeholders are defrauded without harm), organizational misdeed with injuries (stakeholders are placed at risk and injuries occur), and organizational misdeed management misconduct (management violates regulations; Coombs, 2006). The two most common crisis clusters studied in the literature are accidental and preventable (Kim et al., 2009). This trend also appears when expanding the timeframe, as An and Cheng (2010) did in their research. The most common type of crisis studied includes technical error accidents or equipment failure that caused an industrial accident. This was followed by human error accidents, or illegal behavior within the organization. The most common crisis issue was product recall, environmental issues, or health risks (e.g., food poisoning; An & Cheng, 2010). The second aspect of SCCT is the crisis response strategy. Coombs (2006) states the initial crisis response needs to provide instructing information or information that includes the basics about what happened (crisis basics), information about what stakeholders should do to protect themselves (protection information), and information about what the organization is doing to correct the problem (correction information). The SCCT response strategies are closely tied to Benoit’s theory of image restoration. They are placed on a continuum. As the organization becomes more accommodating and expresses concerns for victims, it is taking more responsibility for the crisis. There are three basic options for crisis response strategies. The organization can state no crisis exists (deny). Alternatively, it can alter the attribution of the crisis event to be less negative (diminish), or it can alter how stakeholders perceive how the organization repairs its image (rebuild; Coombs, 2013). When an organization wants to deny the crisis exists, it attacks the person or group claiming the crisis occurred, denies there was a crisis, or blames a group outside the organization for the crisis (Coombs, 2006, 2013). The diminish options include an organization making excuses to say it was not able to control the events that triggered the crisis, and/or the organization tries to minimize the damage caused. When an organization wants to rebuild, it might use the strategies of compensation (offer money/other gifts to victims) or offer an apology. Supplemental



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strategies fall into the bolstering category, including the reminder strategy (the organization reminds stakeholders of past good deeds); ingratiation (the organization thanks stakeholders for their help during the crisis); and victimage (when managers remind stakeholders that the organization is a victim of the crisis; Coombs, 2013). The strategies can be placed on a continuum (Coombs & Schmidt, 2000): Arranged from defensive to accommodative, the seven categories are as follows: (a) attack the accuser involves aggressively denying claims of a crisis and punishment of the accuser; (b) denial claims there is no crisis or that the organization is uninvolved in the crisis; (c) excuse admits there is a crisis but minimizes organizational responsibility for the crisis; (d) justification admits a crisis exists but downplays its severity; (e) ingratiation tries to create positive impressions of the organization by reminding stakeholders of past good works, associating the organization with positive qualities, or both; (f) corrective action attempts to repair crisis damage, prevent a repeat of the crisis, or both; and (g) full apology and mortification takes responsibility for the crisis. (pp. 165–166)

The final aspect of SCCT involves matching the strategy with the crisis situation. The severity of the crisis and the organization’s responsibility should influence the strategy employed. In a case of low responsibility and low severity, an organization should attack the accuser or deny the event. In a case of low responsibility and high severity, an organization should offer an excuse. For a high-responsibility and low-severity crisis, justification and ingratiation are two strategies that may be employed. When there is high severity and high responsibility, the organization should take corrective action and offer a full apology (Kim, 2002). The repair strategy should be selected based on the organization–public relationship or the transactions and exchanges between an organization and its stakeholders (Broom, Casey, & Ritchey, 2000). If there is not a positive or favorable relationship with stakeholders, the strategies employed will have little effect on attitudes toward the organization, and there will be a negative perception of the crisis. The relationship history will influence how the public perceive the crisis and the restoration strategy used (i.e., attack accuser, denial, excuse, justification, ingratiation, corrective action, and full apology). The initial positive attitudes publics have based on the organization–public relationship will help deflect the negative damage a crisis may have on publics (Kim, 2002).

Image Repair/Image Restoration The other dominant theory studied in the area of crisis communication is the theory of image restoration—renamed image repair—by Benoit (1995a, 2015). The theory was built on the literature in the area of rhetorical studies (e.g., apologia) and sociology (e.g., accounts and excuses). Benoit updated his original theory of image restoration stating the growth of the literature made him revise his theory, “I have started to refer to this theory as ‘image repair’, hoping that the phrase avoids creating the impression that we can or should expect to complete[ly] restore all tarnished images” (p. x). He proposes five major strategies for trying to repair images after a crisis: denial; evasion or reduction of responsibility; reduction of the offensiveness; corrective action; and mortification. The first defense is to deny an undesirable act occurred or that the actor is the one who performed it. One might also shift the blame (another form of denial) to another (Benoit, 1995a, 2015). Another defensive strategy is to evade or reduce responsibility for the act. Benoit (1995a) lists several ways actors or organizations can reduce responsibility. They can use provocation, invoke defeasibility, claim an accident, or state they had good intentions. The actor/organization may claim someone provoked him/her/it to perform the act. Defeasibility is another option, which refers to the actor claiming he/she lacked information or ability. A third way to reduce responsibility is to declare the action was an accident. A final way to reduce responsibility is to claim the

66 Haigh actor had good intentions when carrying out the act. One example of this is when Sears representatives claimed the organization had a policy of preventive maintenance after it was accused of performing unnecessary auto repairs (Bradford & Garrett, 1995). The actor/organization may also try to reduce the offensiveness of the act by using the following strategies: bolstering, minimization, differentiation, transcendence, attacking the accuser, or offering compensation (Benoit, 1995a, 2015). Bolstering “attempts to improve the accused’s reputation in hopes of offsetting or making up for the damage to the image from the undesirable act” (Benoit, 1995a, p. 73). Minimization attempts to reduce the degree of negative feelings toward the act, thereby reducing the negative feelings toward the actor. “Differentiation and transcendence, in their different ways, attempt to reduce the negative affect associated with the act” (Benoit, 1995a, p. 73). The differentiation strategy reframes the actions to be less offensive and mitigates the organization’s responsibility. Alternatively, transcendence refers to the organization reframing its actions in a more positive context to reduce the offensiveness of its actions. Threat to the image is associated with the function of committing the act; therefore, reducing the offensiveness of the act should restore the reputation (Benoit, 1995a). The actor/organization may take a corrective action strategy by fixing the problem and promising they will not repeat the action (Benoit, 1995a). Finally, the actor/organization might employ the mortification strategy, or apologize, express regret, and request forgiveness. These strategies might partially restore the actor’s image (Benoit, 1995a). Much of the research employs reflective case studies to examine the crisis and the organization’s response (e.g., Benoit & Brinson, 1994; Benoit & Lindsey, 1987; Brinson & Benoit, 1996, 1999). This type of research provides “retrospective analysis that can be used to inform future decisions” (Dardis & Haigh, 2009, p. 103). The case studies are useful, but other researchers call for more prescriptive research focusing on predictive value and causal inferences (Coombs & Holladay, 2008; Coombs & Schmidt, 2000). This call has been met with research that has tried to determine the best type of strategy to employ for different types of crises. Research has applied both theories in a variety of contexts to try to determine how to guide strategic communication professionals in creating crises responses.

SCCT and Image Repair Applied Which Strategy Works the Best? Arendt, LaFleche, and Limperopulos (2017) conducted a content analysis of published apologia research. They analyzed 110 articles from 51 peer-reviewed journals from 1986 to 2016. The most common strategies found in their analysis were: denial, reducing the offensiveness, corrective action, and shifting the blame. They also suggest that author(s) of the studies concluded image repair worked. The cases that included successful image repair usually employed corrective action and evasion of responsibility. Corrective action was used successfully with reducing the offensiveness. Some empirical research found no differences among specific image-repair techniques (e.g., Bradford & Garrett, 1995; Coombs & Schmidt, 2000). Coombs and Schmidt (2000) explained the lack of significant differences among strategy types was due to the fact the strategies tested fell toward the accommodative end of the continuum. However, later research found implementing different image-restoration strategies affects individuals’ perceptions of a company in a crisis situation. Specifically, the reduce the offensiveness strategy was stated as being most effective. Dardis and Haigh (2009), Haigh and Dardis (2012), and Haigh and Brubaker (2010) did find differences in strategy types. Their results indicate the reduce the offensiveness strategy— which actually is positioned toward the center of the continuum proposed by Coombs and Schmidt (2000)—might be most effective in enhancing perceptions of the company during a time of crisis. A more accommodative strategy outperformed those on the defensive side.



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Haigh and Brubaker (2010) conducted an experiment to test messages employing different crisis response strategies. One unique thing about this study was that it used an actual organization and not a fictitious one. The study aimed to apply image restoration strategies to a single crisis situation to understand how the strategy an organization employs impacts stakeholders’ perceptions of the organization–public relationship, perceptions of corporate social responsibility (CSR), and credibility. Results indicate the reduce the offensiveness strategy was most effective in protecting stakeholders’ perceptions of relationship and goodwill activities. These findings replicate what Haigh and Dardis (2012) and Dardis and Haigh (2009) had found in their experimental research using a fictitious organization. Furthermore, the reduce the offensiveness strategy is the best strategy to employ during a product recall crisis. The reduce the offensiveness strategy is most effective in protecting the trust and commitment dimensions of the organization–public relationship. Messages with mortification and corrective action were not more effective than messages employing only mortification (Twork & Blaney, 2013). When testing strategies in a consumer product case, messages that clearly employed mortification, corrective action, and compensation worked better than messages that were unclear attempts at mortification and corrective action (Dawar & Pillutla, 2000). Mortification and corrective action are both good strategies for salvaging stakeholders’ perceptions of the organizational reputation (Spence, Lachlan, & Omilion-Hodges, 2016). Mortification and corrective action strategies also led to more positive feelings toward the organization than transcendence or minimization (Cos, Worrell, & Blosenhauser, 2016). Gribas, Disanza, Legge, and Hartmann (2018) conducted an experiment in which participants were exposed to a hypothetical scenario edited to reflect four different types of crisis (malevolence, managerial failure, systematic failure, and natural disaster). Based on their findings, they recommend practitioners use compensation, corrective action, mortification, and good intentions for any type of crisis. They also confirm that denial, shifting the blame, attacking the accuser, differentiation, and provocation are not strategies that should be employed for a crisis situation. They suggest the best image repair strategy should be based on the crisis type. For example, their results indicate transcendence works best for crisis types involving malevolence. Transcendence, stating it was an accident, and minimization work best for a systematic failure crisis; while transcendence, stating it was an accident, minimization, and defeasibility worked best for a natural disaster. Additional research is needed to confirm these results.

Weaknesses of SCCT SCCT presents several weaknesses. For example, SCCT suggests the crisis type can be verified a priori. However, the responsibility of the crisis changes based on an individual’s perception of reality (Benoit, 2015). Organizations can craft messages that alter audiences’ perceptions. More importantly, not everyone has the same perception of a crisis, so there is no single crisis type and no single repair strategy (Benoit, 2015). SCCT also ignores audiences’ beliefs and attitudes. Benoit (2015) believes the crisis response should be based on the audience’s attitudes, values, and beliefs. SCCT states the situation type determines the response without taking into consideration different audiences and their distinct attitudes, values, and beliefs. One major difference between the two theories is that SCCT does not include corrective action as a form of image repair. This strategy has been applied in corporate apology research (e.g., Benoit & Brinson, 1994; Dardis & Haigh, 2009; Haigh & Brubaker, 2010). Furthermore, SCCT does not consider the truth. “In addition to being unethical, using denial when the accused is guilty may well backfire when the truth emerges. The truth, insofar as human beings perceive it, must be considered in all persuasion, including image repair” (Benoit, 2015, p. 40). The major weakness of SCCT is that it is trying to be too prescriptive/deductive. There is a list of response strategies, and trying to match the strategy to the level of crisis is at the crux of

68 Haigh experimental research trying to examine the impact crisis messages have on stakeholders. However, one could argue each crisis is unique. There will never be a prescriptive handbook that states if this type of crisis happens, the organization should use X strategy and the organization will bounce back as if the crisis never happened. The crisis message will be interpreted by stakeholders who have varying attitudes, values, beliefs, and experiences with the organization. The only thing that will really determine if the crisis response worked is if the stakeholder continues to have a relationship with the organization. In the end, research can continue to test different strategies in different crisis situations to be as prescriptive as possible. However, no matter how prescriptive the “playbook” strategic communication researchers develop, the apology rests in the eye of the beholder.

Image Repair Weakness Much of the research applying the theory of image repair consisted of descriptive case studies. The theory has been applied in a number of different types of settings, such as politics (Benoit, 2006; Benoit & Brinson, 1999; Blaney & Benoit, 2001; Compton & Miller, 2011), athletics (Benoit & Hanczor, 1994; Brazeal, 2008; Haigh, 2008; Haigh & Alwine, 2016; Walsh & McAllister-Spooner, 2011), celebrities (Benoit, 1997; Kauffman, 2012), and organizations (e.g., Benoit, 1995b; Blaney, Benoit, & Brazeal, 2002). Most of this research consists of case studies examining specific political figures having to repair their images. For example, Benoit and Brinson (1999) examined Queen Elizabeth’s image repair strategies after the death of Princess Diana. Research has examined President Clinton’s use of image repair following his public relations struggles in the 1990s (see Blaney & Benoit, 2001), President Bush’s use of an April 2004 news conference to rally support amid declining approval ratings and an increasing number of Iraq War casualties (Benoit, 2006), and President Trump’s attempts to repair his image after the notorious Access Hollywood video was released (Benoit, 2017). A variety of specific case studies have examined athletes’ use of image repair, focusing on well-known competitors such as Tiger Woods (Benoit, 2013), Lance Armstrong (Haigh & Alwine, 2016), and Michael Phelps (Walsh & McAllister-Spooner, 2011). More importantly, image repair has also been applied to specific organizations’ crisis management responses. Case studies examine Dow Corning’s use of image repair in the breast implant crisis, the strategies Texaco managers employed when accused of racism, and United Airlines’ response to the backlash of removing a man from a flight (Benoit, 2018; Brinson & Benoit, 1996; Brinson & Benoit, 1999). Dow Corning had to repair its image after the company developed a $2 billion fund to resolve legal claims. The organization released documents that demonstrated its silicone breast implants were safe after a Congressional subcommittee hearing. Dow Corning used denial, minimization, bolstering, and attacked the accuser (Brinson & Benoit, 1996). Another case that examined the image repair strategies of an organization after a crisis employing the case study approach was the Texaco case study. A secret tape of an executive meeting surfaced. One soundbite from the executive discussion stated African Americans are “black jelly beans … glued to the bottom of the jar” (Brinson & Benoit, 1999, p. 483). Brinson and Benoit (1999) conducted a case study analysis to examine the strategies Texaco employed to repair its image. They analyzed news releases, letters to employees, video messages to employees, and a variety of other messages to analyze the strategies Texaco used across crisis messages. Benoit (2018) examined how United Airlines tried to repair its image after it received negative media attention for physically removing a passenger from a flight. The video showed the passenger being dragged through the aisle, screaming and bloodied. He conducted a crisis case analysis of the incident, once again examining the messages that United sent out during and after the crisis situation. All three of these examples (Dow Corning, Texaco, and United) are case studies. The authors examined the specific case and the specific strategies the organizations



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employed. The case study provides a retrospective of what happened when, but there are no prescriptive or experimental conclusions provided. Image repair, compared to SCCT, has been more widely applied as a theoretical framework because of its application in crises not tied to organizations. However, one of the most important differences between the two theoretical frameworks is that a large chunk of the image repair literature consists of qualitative case studies focused on one crisis event. The case studies are centered on Benoit’s (1995a, 2015) typology; however, case studies do not help predict what might happen, which is crucial for strategic communication professionals. SCCT, on the other hand, is a predictive model. That being said, there is a push for more quantitative research applying image repair theory.

Methodology—A Significant Difference between the Theoretical Frameworks One of the biggest differences between research applying image repair and SCCT is the method used. Most of the image repair research features rhetorical or qualitative analyses of the strategies used in a specific case of an individual or organization having to repair an image, whereas most of the SCCT literature is experimental in nature. That is not to say that past image repair research has not tested the strategies experimentally (e.g., Dardis & Haigh, 2009), but it is not as common in the published research. More specifically, in a review of crisis communication research published from 1991 to 2009, the articles adopted a rhetorical analysis (42% of the articles) or an experiment (27% of the articles). Of the 29 articles published during that time employing image repair theory, 24 were rhetorical analyses and five were case studies. Of the 17 articles applying SCCT, 15 were experiments (Avery et al., 2010). Additionally, when reviewing 30 years of published research, case studies were more common than non-case studies. Case studies tended to focus on evaluating the crisis incident, and non-case studies were more likely to examine strategies used to repair the image. Experiments became more common after 1996. Textual/rhetorical analysis was the most common method (An & Cheng, 2010). Past experimental research has tested different crisis response strategy messages (e.g., Arpan & Roskos-Ewoldsen, 2005; Coombs & Holladay, 1996; Dean, 2004), but limited research has examined the medium used to apologize (e.g., Coombs & Holladay, 2009). Past experimental research has employed print stimuli (e.g., Dardis & Haigh, 2009; Haigh & Brubaker, 2010; Haigh & Dardis, 2012). However, print content is processed differently than visual content in the sense that visual content is processed using more of the right brain, which is more holistic and emotional (Barry, 1997). Paivio (1986) posits, “affective reactions would ordinarily occur more quickly to pictures than words because the former have more direct access to affect-mediating images” (p. 79). Visual content is processed quickly and heuristically, sometimes bypassing conscious thought (Zhou, 2005). People process visual information “simultaneously rather than sequentially” (Graber, 1987, p. 76). Thus, visual content is absorbed quickly. Coombs and Holladay (2009) found no meaningful difference between the use of video and print messages trying to repair an organization’s image. They noted a small effect size, where the print condition produced slightly more positive reputation scores than the video condition. However, they note this result should be taken cautiously as it accounted for a small percent of the variance. Haigh and Ngondo (2018) conducted an experiment of a non-student population to test different forms of messages from two organizations the participants would have heard about through media consumed in their daily lives. They tested the written messages against the visual messages, as well as the impact of whether the message appeared on a news channel or a channel the organization owned (e.g., YouTube). Results indicate the written transcript of the Blue Bell Chief Executive Officer’s YouTube apology was the best means used to apologize. Readers of the Blue Bell transcript had significantly more trust in the relationship than viewers of the Chipotle news  story. Similarly, readers of the Blue Bell transcript had more positive perceptions of the organi­zation–public relationship compared to those who viewed the Blue Bell YouTube apology.

70 Haigh The current findings suggest taking out a full-page ad (paid channel) apologizing in The New York Times may be as impactful as recording a video apology and playing it on an organization’s YouTube channel (owned channel), as it does not affect purchase intent or perceptions for CSR. In most cases, print messages work better than video apologies. One thing to note, however, is that this study did not compare strategies, but rather only focused on the medium of the message. After looking at the method used (rhetorical analysis or experiment) and what’s being tested (the strategy or the medium), it is also important to examine the organization being employed. In the image repair cases, authors focus on a specific organization (see the strategies section earlier in this chapter). Experimental research is mixed. For example, Dardis and Haigh (2009) and Haigh and Dardis (2012) used fictitious organizations, whereas Haigh and Brubaker (2010) and Haigh and Ngondo (2018) used real organizations. It’s easier to control for attitude toward an organization when using a fictitious organization; however, one could argue it is hard to measure the impact of the crisis message or medium used to apologize if the participants don’t have any knowledge of or background—a relationship—with an organization. The end goal in strategic communications is to maintain and bolster organization–public relationships and the goodwill organizations have built over time, usually seen through CSR. It is also important to understand how the crisis is going to impact the organization’s bottom line—usually measured through purchase intent. One might say it is hard to measure these types of variables (organization–public relationships, CSR, and purchase intent) when using a fictitious organization because perceptions of these things don’t exist for the participant in the study. Coombs (2016) addresses the use of fictitious versus real crisis scenarios. He states if a real organization is used, the researcher must control for prior reputation, which is why the researcher might decide to use a fictitious one. When the experimental stimuli are based on a real organization with only the name of the organization changed, Coombs (2016) states this scenario “is equivalent to a real crisis scenario” (p. 121).

Future Directions Moving forward, it will be important for strategic communication researchers to continue to examine a number of variables studied in crisis communication using the two dominant theoretical frameworks, SCCT and image repair. It will be important to continue to conduct experimental research applying image repair (e.g., Dardis & Haigh, 2009), rather than relying on rhetorical/textual analyses and case studies as in the past. More experimental research is needed to fully understand how each strategy impacts stakeholders’ perceptions of the organization and their attitudes toward the organization. Experimental image repair research will also aid in comparing findings with experimental findings of SCCT. There has been some research examining how messages translate across different mediums, but this type of research will be more important as organizations rely more and more on channels they own (e.g., social media, website, etc.) rather than on channels for which they have to earn coverage, such as traditional media with gatekeepers. Understanding how the medium impacts the stakeholder is important because strategic communication professionals may need to produce different types of messages for different mediums. The medium may also impact the credibility of the organization, which is the opposite of what the organization wants during a crisis situation.

Framing Besides understanding the impact of the strategy and the medium, the message frame should also be examined. Frames are studied from a strategic communication point of view as well as a more traditional mass communication approach.



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In the strategic communication area, Kim and Rader (2010) found organizations produce communication materials with a dominant corporate ability strategy, a corporate social responsibility strategy, or a hybrid strategy. When an organization promotes corporate social responsibility activities, the corporate social responsibility strategy is employed. The corporate ability strategy is employed when the organization emphasizes the quality of products and services. A hybrid approach is employed when organizations include information about their products and services as well as a discussion of their corporate social responsibility efforts (Kim & Rader, 2010). When examining framing from the broader mass communication approach, the following definition works well. Entman (1993) states, “frames select and call attention to particular aspects of the reality described, which logically means that frames simultaneously direct attention away from other aspects” (p. 54). The cognitive and affective attributes of frames will influence how the public perceives an issue and its consequences. Individuals make their decisions about an issue by what is discussed in the media, even though they lack all of the details (Jasperson, Shah, Watts, Faber, & Fan, 1998). Omitting definitions, problems, evaluations, and recommendations changes the information the public has available to them (Entman, 1993). There are a number of different frames studied, but two of the most common that would apply to crisis communication are episodic and thematic frames. Episodic frames approach stories as case studies, or event-oriented and concrete instances; alternatively, thematic frames place the issue in a larger, more general context and provide background information. The use of an episodic or thematic frame impacts the attribution of responsibility. Episodic stories lead to individuals attributing responsibility to other individuals, and thematic frames lead to societal responsibility (Iyengar, 1994). Mason (2019) conducted an experiment examining thematic and episodic frames during crisis situations to extend SCCT. She found the frame used (episodic or thematic) impacts attitudes toward organizational responsibility. However, she did not measure other stakeholder-organization variables (e.g., brand attitude, organization–public relationships, purchase intent, etc.). There is little experimental research examining the use of episodic/thematic frames on stakeholders outside of crisis situations. For all of these things (strategies, medium, frame) scholars need additional research to gain a complete picture of what stakeholders think when an organization creates and distributes messages in times of crises. Additionally, types of crises, the type of organization, and the market impact of a crisis should also be evaluated. Different types of organizations may not be able to use the same strategy with the same effect because the stakeholders might be different. Research indicates there has been a shift in the apologia research over the past 30 years. Additional research will help address some of the questions left unanswered. Research will need to find a balance between the two theoretical frameworks. Case studies employed in image repair research provide specifics of how organizations respond to crises, and the experimental results found in SCCT provide some idea of the impact a crisis response strategy has on stakeholders. Both provide guidance to strategic communication professionals. Moving forward, researchers need to acknowledge there may never be a prescribed playbook for a crisis that explains the exact response strategy for each type of crisis and the response expected from the stakeholders, and the specific cases examined using image repair may never be replicated or duplicated because the organization changes. If practitioners focus on the organization–public relationships, presenting ethical messages that keep stakeholders informed during a crisis situation, and communicating clear messages, the organization may be able to repair the relationship with its stakeholders.

References An, S., & Cheng, I. (2010). Crisis communication research in public relations journals: Tracking research trends over thirty years. In W. T. Coombs & S. J. Holladay (Eds.), Handbook of crisis communication (pp. 65–90). Malden, MA: Blackwell.

72 Haigh Arendt, C., LaFleche, M., & Limperopulos, M. A. (2017). A qualitative meta-analysis of apologia, image repair, and crisis communication: Implications for theory and practice. Public Relations Review, 43(3), 517–526. doi:10.1016/j.pubrev.2017.03.005 Arpan, L. M., & Roskos-Ewoldsen, D. R. (2005). Stealing thunder: Analysis of the effects of proactive disclosure of crisis information. Public Relations Review, 31(3), 425–433. doi:10.1016/j.pubrev.2005.05.003 Avery, E. J., Lariscy, R. W., Kim, S., & Hocke, T. (2010). A quantitative review of crisis communication research in public relations from 1991 to 2009. Public Relations Review, 36(2), 190–192. doi:10.1016/j.pubrev.2010.01.001 Barry, A. M. S. (1997). Visual intelligence: Perception, image, and manipulation in visual communication. Albany, NY: SUNY Press. Barton, L. (1993). Crisis in organizations: Managing and communicating in the heat of chaos. Cincinnati, OH: College Divisions South-Western. Benoit, W. (2017). Image repair on the Donald Trump “Access Hollywood” video: “Grab them by the p*ssy.” Communication Studies, 68(3), 243–259. doi:10.1080/10510974.2017.1331250 Benoit, W. L. (1995a). Accounts, excuses, and apologies: A theory of image restoration strategies. Albany, NY: SUNY Press. Benoit, W. L. (1995b). Sears’ repair of its auto service image: Image restoration discourse in the corporate sector. Communication Studies, 46(1–2), 89–105. doi:10.1080/10510979509368441 Benoit, W. L. (1997). Hugh Grant’s image restoration discourse: An actor apologizes. Communication Quarterly, 45(3), 251–267. doi:10.1080/01463379709370064 Benoit, W. L. (2006). Image repair in President Bush’s April 2004 news conference. Public Relations Review, 32(2), 137–143. doi:10.1016/j.pubrev.2006.02.025 Benoit, W. L. (2013). Tiger Woods’ image repair: Could he hit one out of the rough. In J. R. Blaney, L. Lippert, & S. J. Simpson (Eds.), Repairing the athlete’s image: Studies in sports image restoration (pp. 89–96). Lanham, MD: Lexington Books. Benoit, W. L. (2015). Accounts, excuses, and apologies: Image repair theory and research. Albany, NY: SUNY Press. Benoit, W. L. (2018). Crisis and image repair at United Airlines: Fly the unfriendly skies. Journal of International Crisis and Risk Communication Research, 1(1), 11–26. doi:10.30658/jicrcr.1.1.2 Benoit, W. L., & Brinson, S. L. (1994). AT&T: “Apologies are not enough.” Communication Quarterly, 42(1), 75–88. doi:10.1080/01463379409369915 Benoit, W. L., & Brinson, S. L. (1999). Queen Elizabeth’s image repair discourse: Insensitive royal or compassionate queen? Public Relations Review, 25(2), 145–156. doi:10.1016/S0363-8111(99)80159-3 Benoit, W. L., & Hanczor, R. S. (1994). The Tonya Harding controversy: An analysis of image restoration strategies. Communication Quarterly, 42(4), 416–433. doi:10.1080/01463379409369947 Benoit, W. L., & Lindsey, J. J. (1987). Argument strategies: Antidote to Tylenol’s poisoned image. Journal of the American Forensic Association, 23, 136–146. doi:10.1080/00028533.1987.11951338 Bergman, E. (1994). Crisis? What crisis? Communication World, 11(4), 9–13. Blaney, J. R., & Benoit, W. L. (2001). The Clinton scandals and the politics of image restoration. Westport, CT: Praeger. Blaney, J. R., Benoit, W. L., & Brazeal, L. M. (2002). Blowout!: Firestone’s image restoration campaign. Public Relations Review, 28(4), 379–392. doi:10.1016/S0363-8111(02)00163-7 Bradford, J. L., & Garrett, D. E. (1995). The effectiveness of corporate communicative responses to accusations of unethical behavior. Journal of Business Ethics, 14(11), 875–892. doi:10.1007/BF00882067 Brazeal, L. M. (2008). The image repair strategies of Terrell Owens. Public Relations Review, 34(2), 145– 150. doi:10.1016/j.pubrev.2008.03.021 Brinson, S. L., & Benoit, W. L. (1996). Dow Corning’s image repair strategies in the breast implant crisis. Communication Quarterly, 44(1), 29–41. doi:10.1080/01463379609369998 Brinson, S. L., & Benoit, W. L. (1999). The tarnished star: Restoring Texaco’s damaged public image. Management Communication Quarterly, 12(4), 483–510. doi:10.1177/0893318999124001 Broom, G., Casey, S., & Ritchey, J. (2000). Toward a concept and theory of organization-public relationships: An update. In J. A. Ledingham & S. D. Bruning (Eds.), Public relations as relationship management: A relational approach to the study and practice of public relations (pp. 3–22). Hillsdale, NJ: Lawrence Erlbaum. Carney, A., & Jorden, A. (1993). Prepare for business-related crisis. Public Relations Journal, 49(8), 34–35.



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Compton, J., & Miller, B. (2011). Image repair in late night comedy: Letterman and the Palin joke controversy. Public Relations Review, 37(4), 415–421. doi:10.1016/j.pubrev.2011.08.002 Coombs, W. T. (2006). The protective powers of crisis response strategies: Managing reputational assets during a crisis. Journal of Promotion Management, 12(3/4), 241–260. doi:10.1300/J057v12n03_13 Coombs, W. T. (2010). Parameters for crisis communication. In W. T. Coombs & S. J. Holladay (Eds.), Handbook of crisis communication (pp. 17–53). Malden, MA: Blackwell. Coombs, W. T. (2013). Situational theory of crisis: Situational crisis communication theory and corporate reputation. In C. Carroll (Ed.), The handbook of communication and corporate reputation (pp. 262– 278). Malden, MA: Wiley. Coombs, W. T. (2016). Reflections on a meta-analysis: Crystalizing thinking about SCCT. Journal of Public Relations Research, 28(2), 120–122. doi:10.1080/1062726X.2016.1167479 Coombs, W. T., & Holladay, S. J. (1996). Communication and attribution in a crisis: An experimental study in crisis communication. Journal of Public Relations Research, 84(4), 279–295. doi:10.1207/ s1532754xjprr0804_04 Coombs, W. T., & Holladay, S. J. (2002). Helping crisis managers protect reputational assets: Initial tests of the situational crisis communication theory. Management Communication Quarterly, 16(2), 165–186. doi:10.1177/089331802237233 Coombs, W. T., & Holladay, S. J. (2008). Comparing apology to equivalent crisis response strategies: Clarifying apology’s role and value in crisis communication. Public Relations Review, 34(3), 252–257. doi:10.1016/j.pubrev.2008.04.001 Coombs, W. T., & Holladay, S. J. (2009). Further explorations of post-crisis communication: Effects of media and response strategies on perceptions and intentions. Public Relations Review, 35(1), 1–6. doi:10.1016/j.pubrev.2008.09.011 Coombs, W. T., & Schmidt, L. (2000). An empirical analysis of image restoration: Texaco’s racism crisis. Journal of Public Relations Research, 12(2), 163–178. doi:10.1207/S1532754XJPRR1202_2 Cos, G., Worrell, T. R., & Blosenhauser, J. D. (2016). An empirical test of image restoration strategies. In J. Blaney (Ed.), Putting image repair to the test: Quantitative applications of image restoration theory (pp. 85–97). Lanham, MD: Lexington Books. Dardis, F., & Haigh, M. M. (2009). Prescribing versus describing: Testing image restoration strategies in a crisis situation. Corporate Communications: An International Journal, 14(1), 101–118. doi:10.1108/ 13563280910931108 Dawar, N., & Pillutla, M. M. (2000). Impact of product-harm crises on brand equity: The moderating  role  of consumer expectations. Journal of Marketing Research, 37, 215–226. doi:10.1509/ jmkr.37.2.215.18729 Dean, D. H. (2004). Consumer reaction to negative publicity: Effects of corporate reputation, response, and responsibility for a crisis event. The Journal of Business Communication, 41(2), 192–211. doi:10.1177/0021943603261748 Entman, R. M. (1993). Framing: Toward clarification of a fractured paradigm. Journal of Communication, 43(4), 51–58. doi:10.1111/j.1460-2466.1993.tb01304.x Graber, D. A. (1987). Television news without pictures? Critical Studies in Mass Communication, 4, 74–87. doi:10.1080/15295038709360115 Gribas, J., Disanza, J., Legge, N., & Hartman, K. L. (2018). Organizational image repair tactics and crisis type: Implications for crisis response strategy effectiveness. Journal of International Crisis and Risk Communication Research, 1(2), 225–252. doi:10.30658/jicrcr.1.2.3 Haigh, M. M. (2008). ‘The cream,’ the ‘clear,’ BALCO and baseball: An analysis of MLB players image. Journal of Sports Media, 3(2), 1–24. doi:10.1353/jsm.0.0020 Haigh, M. M., & Alwine, L. (2016). “I’m sorry” is hard to say for Lance Armstrong: Examining how this impacts public perception. In J. R. Blaney (Ed.), Putting image repair to the test: Quantitative applications of image restoration theory (pp. 99–112). Lanham, MD: Lexington Books. Haigh, M. M., & Brubaker, P. (2010). Examining how image restoration strategy impacts perceptions of corporate social responsibility, organization-public relationships, and source credibility. Corporate Communications: An International Journal, 15(4), 453–468. doi:10.1108/13563281011085538 Haigh, M. M., & Dardis, F. (2012). The impact of apology on organization–public relationships and perceptions of corporate social responsibility. Public Relations Journal, 6(1), 1–16. Retrieved from https://prjournal.instituteforpr.org/wpcontent/uploads/2012HaighDardis.pdf

74 Haigh Haigh, M. M., & Ngondo, P. (2018, March). Bad press? How CEOs drive business forward via apology strategies. Paper presented at the 21st annual International Public Relations Research Conference, Orlando, FL. Iyengar, S. (1994). Is anyone responsible?: How television frames political issues. Chicago, IL: University of Chicago Press. Jasperson, A. E., Shah, D. V., Watts, M., Faber, R. J., & Fan, D. P. (1998). Framing and the public agenda: Media effects on the importance of the federal budget deficit. Political Communication, 15(2), 205–224. doi:10.1080/10584609809342366 Kauffman, J. (2012). Hooray for Hollywood? The 2011 Golden Globes and Ricky Gervais’ image repair strategies. Public Relations Review, 38(1), 46–50. doi:10.1016/j.pubrev.2011.09.003 Keeney, R. L., & von Winterfeldt, D. (1986). Improving risk communication. Risk Analysis: An International Journal, 6(4), 417–424. doi:10.1111/j.1539-6924.1986.tb00954.x Kim, S., Avery, E. J., & Lariscy, R. W. (2009). Are crisis communicators practicing what we preach? An evaluation of crisis response strategy analyzed in public relations research from 1991-2009. Public Relations Review, 35(4), 446–448. doi:10.1016/j.pubrev.2009.08.002 Kim, S., & Rader, S. (2010). What they can do versus how much they care: Assessing corporate communication strategies among Fortune 500 websites. Journal of Communication Management, 14(1), 59–80. doi:10.1108/13632541011017816 Kim, Y. (2002). Understanding crisis management. Seoul: Chaek and Gil. Loewendick, B. A. (1993). Laying your crisis on the table. Training and Development, 15–17. Marconi, J. (1992). Crisis marketing: When bad things happen to good companies. Chicago, IL: Probus. Mason, A. (2019). Media frames and crisis events: Understanding the impact on corporate reputations, responsibility attributions, and negative affect. International Journal of Business Communication, 56(3), 414–431. doi:10.1177/2329488416648951 Paivio, A. (1986). Mental representations: A dual coding approach. New York, NY: Oxford University Press. Slovic, P. (1986). Informing and educating the public about risk. Risk Analysis, 6(4), 403–415. doi:10.1111/j.1539-6924.1986.tb00953.x Spence, P. R., Lachlan, K. A., & Omilion-Hodges, L. M. (2016). Examining the impact of message strategy on organizational image. In J. Blaney (Ed.), Putting image repair to the test: Quantitative applications of image restoration theory (pp. 29–39). Lanham, MD: Lexington Books. Twork, J., & Blaney, J. (2013). Reputation differences between mortification-only and mortification/corrective action strategies following a transgression by a professional athlete. In J. R. Blaney, L. R. Lippert, & J. S. Smith (Eds.), Repairing the athlete’s image: Studies in sports image restoration (pp. 395–399). Lanham, MD: Lexington Books. Walsh, J., & McAllister-Spooner, S. M. (2011). Analysis of the image repair discourse in the Michael Phelps controversy. Public Relations Review, 37(2), 157–162. doi:10.1016/j.pubrev.2011.01.001 Zhou, S. (2005). Effects of arousing visuals and redundancy on cognitive assessment of television news. Journal of Broadcasting & Electronic Media, 49(1), 23–42. doi:10.1207/s15506878jobem4901_3

5

Applying Social Marketing Strategy to Social Change Campaigns Kimberly A. Parker, Sarah Geegan, and Bobi Ivanov

Introduction Social marketing represents a distinct branch of applied research. Combining communication research methods and theories of persuasion with traditional marketing tactics, social marketers strive to influence behaviors through a systematic planning process to deliver positive benefits to a target audience (Lee & Kotler, 2015). As issues such as obesity, drug usage, and crime continue to pervade society, it is necessary to develop unique and creative strategies to combat these issues. Communication and persuasion theories provide a foundation for understanding how attitudes are formed, altered, and made resistant (Dillard & Pfau, 2002; Ivanov et al., 2017). Social marketing, utilizing this theoretical framework, employs traditional marketing tactics to impact attitudes, with the ultimate goal of motivating individual and social behavioral change. As such, social marketing embodies both components of the applied communication research construct—application and research. Social marketers, utilizing both qualitative and quantitative research methods, gain insight into behaviors of interest and, through a traditional marketing framework, develop objectives, strategies, and tactics to influence those behaviors. This powerful tool has demonstrated a capability to bring about social change and create more desirable, sustainable behaviors. This chapter provides an overview and definition of social marketing before reviewing different contextual applications of social marketing strategies. A discussion on designing and implementing social marketing strategy precedes the conclusion of the chapter.

Overview of Social Marketing and Definition Defining the origin, domain, and practice of social marketing has proven to be a challenge devoid of consensus (Andreasen, 2015; Stewart, 2015). While its association with the traditional marketing discipline seems natural, there is an ongoing debate over how social marketing fits in relation to traditional marketing. Some traditional marketing scholars view social marketing as “merely an interesting application area” of marketing (Andreasen, 2015, p. 23). Others (Lusch & Vargo, 2006; Vargo & Lusch, 2004) suggest that nonprofit and social marketing represent the dominant paradigm, with commercial (or traditional) marketing as its subset (Andreasen, 2015). Still others submit the notion that social marketing is a combination of traditional marketing on one hand and social science and social policy on the other (Truss, Marshall, & The Handbook of Applied Communication Research: Volume 1, First Edition. Edited by H. Dan O’Hair and Mary John O’Hair. © 2020 John Wiley & Sons, Inc. Published 2020 by John Wiley & Sons, Inc.

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Blair-Stevens, 2010). Furthermore, with a significant portion of social marketing field applications focused on influencing health-related behaviors, health education and health communication scholars insist that social marketing represents an encroachment on their domains of study (Andreasen, 2015). In truth, social marketing draws on many disciplines (e.g., economics, marketing, psychology, sociology, communication, political science, and law) to inform social marketing theory and practice (Stewart, 2015). Kotler, Roberto, and Lee suggest that “social marketing is the use of marketing principles and techniques to influence a target audience to voluntarily accept, reject, modify, or abandon a behavior for the benefit of individuals, groups or society as a whole” (2002, p. 5). A significant point of departure between social marketing and traditional marketing is the strategic goal underscoring the persuasive campaign effort. The goal of traditional marketing is to influence the purchase of products or services intended for individual or group consumption. As such, the goal is not necessarily to inspire social change. Instead, the goal is generally commercial in nature; one that benefits the individual purchasers, their families or ingroups, and the companies associated with the production and distribution of the product or service. Social marketing, on the other hand, is concerned with motivating social change. While it offers direct benefits to individuals, its overall intent is to affect important societal issues. Social marketers’ primary focus is on the marketing of desired behaviors in the context of social change, rather than an exclusive focus on commercial products or services (Andreasen, 2015). That is not to suggest that, at times, social marketers may not endorse or even promote the purchase of commercial products (e.g., solar panels), but the primary motivation for the endorsement or the promotion is not financial gain, but rather social behavior change or maintenance (e.g., greater reliance on renewable energy sources). Social marketing uses an audience-centered approach to develop strategic executions of communication-based strategies that rely on behavioral theory, persuasion psychology, marketing science (Evans, Silber-Ashley, & Gard, 2007), and creative reinterpretation of the traditional marketing mix elements (i.e., product, price, placement, and promotion). Research on behavioral and attitudinal change, across many contributing disciplines, from communication (e.g., Dillard & Pfau, 2002; Parker, Ivanov, & Compton, 2012), psychology (e.g., Crites, Fabrigar, & Petty, 1994; Eagly & Chaiken, 1993; Millar & Millar, 1990), and marketing (e.g., Bagozzi, Gopinath, & Nyer, 1999; Petty, Cacioppo, & Schumann, 1983), among others, provides a robust theoretical foundation upon which strategies can be designed. Against this multidisciplinary backdrop, what is consistent about social marketing is that the primary intended beneficiary of this strategic approach is society (Lee & Kotler, 2015).

Contextual Application Researchers and practitioners have successfully implemented social marketing strategic campaigns in important contexts spanning public health promotion, the environment, public safety or injury prevention, community involvement and civic engagement, and economic (or financial) wellbeing (Lee & Kotler, 2012). The following case studies demonstrate social marketing’s effectiveness in influencing behavior in these environments.

Health Promotion Opioid abuse

Opioid abuse is considered a modern public health emergency (see Parker, Thieneman, & Ivanov, in press). The United States Centers for Disease Control and Prevention (CDC) reported more than 60,000 deaths from drug overdoses in the United States in 2016, nearly two thirds of which were linked to opioids (Centers for Disease Control and Prevention [CDC], 2018). In response to



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the growing crisis, community partners in New Jersey launched the American Medicine Chest Challenge (AMCC), a social marketing initiative aimed at increasing awareness of the prescription drug abuse epidemic and supporting responsible disposal of expired, unwanted, or unused medi­ cation (Yanovitzky, 2017). The AMCC campaign promoted five key steps for community members and families to follow, which included: (a) taking inventory of medication in the home; (b) locking the cabinet or container where medication is stored; (c) responsibly disposing of expired, unwanted, or unused medications, either at an official drug collection site or at home; (d) consuming medication precisely as directed by one’s doctor; and (e) talking to children about the danger of incorrectly consuming prescription medication. Social marketers formed coalitions of community stakeholders, including corporate, media, government, nonprofit, and law enforcement partners. Together, organizers promoted an annual national drug collection day. The initiative later grew into a nationwide campaign with more than 1,000 community partners and more than 2,000 designated collection sites across the country. Since the campaign launch in 2010, AMCC has collected several tons of prescription medication nationwide. These social marketing efforts targeted the broad community, promoting the five key messages to communities and families across the demographic spectrum. Often, however, social marketing efforts will target narrower, more specific target audiences (e.g., Parker et al., 2018).

HIV/AIDS

In 2004, Canadian public health officials became increasingly concerned about growing rates of HIV infections in men who have sex with other men (Lombardo & Léger, 2007). Target audience research suggested that many homosexual men, when engaging in unprotected anal intercourse, incorrectly assumed their partners shared the same HIV status. Results further demonstrated, because of the awkwardness associated with these conversations, that men used general indicators to infer a partner’s status, rather than directly inquiring. In response, the AIDS Committee of Toronto launched the “Think Again” campaign, a social marketing initiative which encouraged gay men to challenge assumptions related to a partner’s status and which ultimately aimed to reduce new HIV incidence rates. The campaign’s key message called upon audience members to “Think Again: How Do You Know What You Know?” through bilingual public transport, print, bathroom, and billboard advertisements, as well as product giveaways including postcards, magnets, coasters, and condoms. Materials included both text and imagery: photographs of men in sexual positions with language echoing the two men’s thoughts, such as, “He’d tell me if he’s negative,” and “He’d tell me if he’s positive.” A campaign website promoted awareness of the issue and engagement with the cause by presenting data on HIV/AIDS, hosting discussion forums, and showcasing media coverage of the campaign. At the campaign’s conclusion, 48% of respondents stated that the outreach compelled them to alter “something” about their sexual encounters. While these two examples demonstrate focus on preventing negative health outcomes, social marketing efforts are also used to promote healthy lifestyles and encourage positive outcomes. Furthermore, social marketing campaigns are also not limited to impacting behavior among adults. As many behavioral habits develop during adolescence, one could argue that campaigns promoting healthy behaviors among children offer the most societal good.

Physical activity among children

To that end, the CDC, in an effort to promote physically active lifestyles among tweens (children between ages 9–13), conducted the VERB™ Youth Media Campaign from 2002 to 2006 (Asbury, Wong, Price, & Nolin, 2008). With its title embodying action and activity, this social marketing approach encouraged tweens to explore new ways to engage in physical activity, discover what they enjoy, and participate in both organized sports and free-time play. Campaign messages were designed to reduce barriers (time constraints, fear of embarrassment, lack of access to facilities

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suitable for play, aversion toward competition, and the appeal of other, non-physical, free-time activities) and capitalize on motivators associated with physical activity (potential for fun with friends, a sense of adventure, fulfillment of goals, the idea of exploring something new, lack of judgment related to their performance). Campaign organizers developed brand equity by collaborating with children’s media brands, such as Cartoon Network, Disney Channel, and Nickelodeon. These media partners produced and aired VERB public service announcements featuring characters and celebrities popular among the target age group, as added value for paid advertising. In addition, the partners promoted sweepstakes and contests consistent with VERB messaging. The campaign also executed a variety of outreach activities allowing tweens to further engage with the VERB brand. At these events, organizers distributed branded promotional materials, such as bracelets, balls, flying discs, and tattoos. In the second year of implementation, the campaign expanded into schools, providing tweens with physical activity kits. The campaign saw increasingly positive results over time. At its conclusion, 67% of polled tweens “really agreed” that VERB was “fun,” and 56% “really agreed” that VERB was “cool.” These results illustrated the importance of understanding and connecting with a target audience in authentic, compelling ways. To do so, social marketers must understand the needs, desires, and experiences of the population in question. How is the target audience navigating the desired or undesired behaviors, and what influence might impact that experience?

Online gaming

Research has demonstrated that excessive online gaming has numerous negative effects, including poor behavioral and mental health outcomes, such as depression or suicide ideation, and possible addiction (Grant, Potenza, Weinstein, & Gorelick, 2010). As such, Sato, Drennan and Lings (2015) sought to utilize social marketing and investigate triggers for problem recognition for online gaming that led to help-seeking behavior. They solicited male online gamers and developed six classifications of both negative and positive problem recognition triggers for online gamers. These include: self-realization, negative consequences, negative emotions, social influence, competing priorities, and impact on social skills. The classifications were developed with a social marketing mindset of providing insight into stages of change and expanding the understanding of the processes for gamers as they transition between pre-contemplation and contemplation. Of course, the ability to understand stages of decision making and the factors provoking behavior yields societal benefits outside the realm of health. Social marketing has proven to be a successful strategy in promoting pro-environmental behavior as well.

The Environment Littering

The Texas Department of Transportation rolled out an enormously successful environmental social marketing campaign—launching the “Don’t Mess with Texas” slogan—to discourage roadside littering in 1986 (Andreasen, 2002). The effort yielded impressive results; between 1995 and 2001, the amount of trash on Texas roadways decreased by more than 50%. However, the campaign needed a refresher in 2013 when the department discovered more than 400 million pieces of litter on the state’s roadway system. Audience research on attitudes and behaviors associated with littering suggested that millennials (individuals born between 1980 and 2000) represented a key market, with 48% admitting to improperly discarding trash in the past month. Moreover, most millennials were either very young or not yet born at the time of the original campaign. Research further suggested that the target audience was less concerned, generally, about the seriousness of littering, and was most motivated to avoid the practice by the prospect of being discovered and fined.



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The “CANpaign” targeted these barriers and motivators through 300 branded barrels, placed across the state at strategic locations, including tourist attractions, travel information centers, sports arenas, and other high-traffic areas. Barrels were adorned with a variety of slogans designed to resonate with the millennial audience including, “Can you spot me some trash?,” “Fill me up Buttercup,” and “I hate that empty feeling inside.” Additionally, the campaign spearheaded the “Report a Litterer Program,” which capitalized on the key motivation of avoiding fines. The program provided an anonymous avenue to report individuals who littered by providing descriptive information, such as the make, model, and color of the vehicle, a license plate number, and the location where the littering occurred. The Department of Transportation used the state’s motor vehicle registration database to identify the individual reported littering and sent the individual letters reminding them of littering regulations along with branded “Don’t Mess with Texas” litterbags. Organizers launched the campaign with a press conference featuring the red, white, and blue iconic “Don’t Mess with Texas” barrels, a nod to the original campaign. The department also promoted the efforts through bilingual media buys on radio and television, billboards, and social media. Social media engagement with the campaign took off during its first year, with the number of Facebook fans increasing from 15,000 in March 2013 to more than 31,000 in January 2014. The campaign’s focus on generating buy-in and galvanizing community support fueled its ultimate success. Often, social marketers will seek ways to foster a sense of shared commitment among community members. This was the case for a university which sought to encourage students to view safety as a shared responsibility.

Public Safety or Injury Prevention Bystander intervention

Universities across the United States are grappling with how to reduce occurrence rates of sexual violence and stalking on their campuses. As part of this effort, the University of New Hampshire developed the “Know Your Power” social marketing campaign, focused on bystander intervention (Potter & Stapleton, 2011). A bystander is an individual who witnesses an event; thus, the campaign’s core goal involved educating and motivating bystanders to intervene after noticing behavior indicative of sexual violence, relationship violence, or stalking. Organizers developed eight core images, each featuring actors modeling positive bystander behavior before, during, or after recognizing sexual violence, relationship violence, or stalking behavior. Importantly, they also collaborated with members of the target audience. Students provided input during the design phase to ensure materials realistically modeled bystander scenarios and reflected the look and feel of the target audience, i.e., clothing, apartment décor, etc. For example, one image portrays three college-age men at a party; one of the men states that he plans to have sex with an intoxicated woman, and the bystanders respond that he must leave the apartment, rather than pursue these intentions. Vignettes such as these were used on posters throughout campus and inside residence halls, and later fashioned into bookmarks, table tents dispersed in dining facilities, and bus-wraps, and added to desktops of campus computers. Evaluation took place throughout the campaign. Results suggested that students who recognized the images through repeated exposure were more likely to indicate a willingness to get involved in preventing relationship and sexual violence. In addition, these students were more likely to believe that the responsibility of reducing these types of violence spans beyond others (like the police or a counseling center). These two beliefs were stronger among students with longer periods of exposure to campaign messages. One of this campaign’s key strengths was the insight gained from members of the community. Key insight from the target audience regarding challenges, barriers, strengths, and opportunities associated with the desired behavior is crucial for developing social marketing strategies (Parker & Ivanov, 2013; Parker, Ivanov, & Cohen, 2016). This was especially true in a campaign aimed at reducing drinking and driving.

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Drinking and driving

Alcohol-related car crashes are a major cause of injuries and deaths across the United States and the world. Adding insult to injury, vehicular accidents also present massive financial costs to communities. For these reasons, the National Highway Traffic Safety Administration issued a call for innovative intervention proposals aimed at reducing drinking and driving and provided up to $300,000 in funding for selected pilot programs (Rothschild, Mastin, & Miller, 2006). A social marketing campaign titled “Road Crew” was selected and implemented in rural Wisconsin. Importantly, in this community, taverns and bars constitute the center of social life, sponsoring community activities and serving as destinations for adults to engage. In this community, 21–34-year-olds comprised approximately half of all intoxicated drivers involved in fatal, alcohol-related car accidents. In addition, research suggested that this demographic was most resistant to altering their drinking and driving behavior. Additional analysis suggested that, within the established age range, single males with a high school education or less, working blue collar jobs, were the individuals most likely to engage in drunk driving. Building upon this knowledge, researchers conducted in-depth target audience research—including a literature review, focus groups with community members familiar with the target audience (bar owners, police officers, relatives, etc.), and focus groups with target ­audience members themselves—before designing campaign messages. They found that, for the typical target audience member, his car was important for several reasons: it enhanced his identity, allowed him the option of bringing a woman home with him, and provided him with a feeling of control. In addition, they learned that, after excessive drinking, men were driving for a variety of reasons: they wanted to get home, they feared another drunk driver would damage a left-behind vehicle, and they perceived no other avenues for getting home without hassle. In response, three counties partnered to implement the Road Crew project, tailored to their communities. All three projects involved a ride to the tavern, between taverns, and back to the individual’s home for between $15 and $20 round trip, allowing customers to leave vehicles at home. Organizers promoted the service through paid advertising in bars, movie theaters, television, and newspapers, and through promotional materials, such as beer can coolers, beer mats, and T-shirts. Importantly, thanks to the insight gained through focus groups, organizers in two counties purchased second-hand limousines for the project. While plans originally involved using vans for alternative transportation, qualitative data revealed that target audience members would feel embarrassed to be seen using these modes of transportation. Limousines were viewed much more positively. Evaluation efforts revealed that more than 90% of surveyed respondents from the target counties viewed the ride program positively, as the service boasted nearly 20,000 rides in its first year. Most significantly, during that same time period, alcohol-related vehicular crashes decreased by 17.6%. As these numbers demonstrate, social marketing campaigns can truly make a difference in the communities they touch, even saving lives. That goal—to save lives—was the central focus of a campaign targeting coal miners in Appalachia.

Safety awareness for coal miners

Like many industries built upon master–apprentice relationships, underground mining faces serious disruptions related to worker safety and training, as “master” miners—those who in previous years instructed inexperienced new workers—begin to retire in large numbers. The Health Communications specialists at the National Institute for Occupational Safety and Health Spokane Research Laboratory recognized the need to enhance workplace safety training for less experienced miners in hazardous welding and flame-cutting environments (Cullen, Matthews, & Teske, 2008). The laboratory initiated a social marketing campaign to strengthen those efforts. Researchers conducted occupational ethnography in seven mines across the United States. The goal was to deeply understand the occupational culture within the coal mines and the motivational and cultural barriers tied to safety issues. Interestingly, research revealed that miners place value in



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hard-hat stickers, which they trade and collect. These adornments also serve a key purpose: they are made of reflective material which increases the chances miners will be seen by others operating moving equipment. Additionally, the analysis revealed key values across the mining industry: (a) miners prized their ability to do hard work; (b) miners believed that not everyone had what it takes to be a successful miner; and (c) mining is a masculine, male-dominated culture. These values coalesced in the term used to describe the most respected workers among their ranks: “coal hog.” The term reflects the idea that the best miners are “hungry for coal.” Along those same lines, researchers found that miners were much more likely to heed advice from an insider, or a coal hog, on matters related to their industry. Building upon these insights, organizers developed a social marketing campaign which reflected the master–apprentice relationship. Training videos were re-shot, using recognized and respected “coal hogs” as spokespeople and using the miners’ jargon rather than legal or technical terms. Organizers also developed hard-hat stickers and distributed them as incentives for participating in safety training sessions. The design for the stickers reflected the age of the target audience as well. While one design incorporated a dead canary in a cage and a message reading “Don’t End Up Belly Up,” researchers found that younger miners did not understand the reference to using canaries as detectors of dangerous gas levels in mines (a practice not used in decades). A second design featured a muscular, masculine hog with a message reading, “Coal Hogs Work Safe.” The campaign was successful because it incorporated insight which could only be gained by researchers embedding themselves in the community. Messages resonated because they reflected the language, culture, and experiences of the target audience. Cultural competence was also crucial to a social marketing campaign conducted by the US Census Bureau, targeting ethnic and racial minority groups.

Community Involvement and Civic Engagement Census 2000

The Census Bureau launched the “Census in Schools” program leading up to the 2000 census in an effort to increase voluntary participation, a rate which had steadily declined over previous decades (Andreasen, 2002). In particular, the campaign targeted ethnic and racial minority groups, demographic segments for which participation was predicted to drop. Campaign organizers distributed teaching materials and take-home information, focused on the importance of the census, to every school in the United States with the hopes that children would learn more about the purpose of the census and pass along this knowledge to their parents. Key messages focused on the benefits of the census: the notion that the census provides important information that leads to a better understanding of, and more resources for, their communities. Campaign materials were also distributed across print, radio, television, outdoor, and internet channels, in 17 languages. Additionally, in an effort to enhance self-efficacy, more than 100,000 partner organizations joined forces with the campaign to distribute information and promote cooperation. Just as social marketing can target behaviors among individual community members (dubbed, “downstream”), campaigns can also target decision-making bodies that craft policies impacting a local citizenry (“upstream”).

Active Living by Design

For example, based in Seattle, Active Living by Design, utilized social marketing to promote walking in communities and advocate for policies that support a more walkable city (Deehr & Shumann, 2009). Their efforts focused on strength and capacity building, policy change, and cultural change, with an overall goal of getting “more people walking more often” (Deehr & Shumann, 2009, p. 404). Working over 5 years, they were able to advocate for an increase in

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funding for pedestrian walkways. In addition, there was an increase in safe routes to school activity, a pedestrian master plan developed, and a complete streets policy. Overall, they were able to make great strides in influencing local government to promote active lifestyles and walking in hopes of making long-term change for the city of Seattle.

Economic (or Financial) Wellbeing Social marketing campaigns are also used to improve people’s economic (or financial) wellbeing (Lee & Kotler, 2012). According to the website of the Council on Social Work Education (2016), economic wellbeing is defined as: having present and future financial security. Present financial security includes the ability of individuals, families, and communities to consistently meet their basic needs (including food, housing, utilities, health care, transportation, education, child care, clothing, and paid taxes), and have control over their day-to-day finances. It also includes the ability to make economic choices and feel a sense of security, satisfaction, and personal fulfillment with one’s personal finances and employment pursuits. Future financial security includes the ability to absorb financial shocks, meet financial goals, build financial assets, and maintain adequate income throughout the life-span. Economic well-being may be achieved by individuals, families, and communities through public policies that ensure the ability to build financial knowledge and skills, access to safe and affordable financial products and economic resources, and opportunities for generating income and asset-building. It occurs within a context of economic justice within which labor markets provide opportunities for secure full-employment with adequate compensation and benefits for all. (para. 1–2, italics in the original)

Under this contextual umbrella, social marketing strategies tackle important social issues, such as jobs or lack of employment opportunities (e.g., education, training programs, skills building, etc.), hunger or food shortage, family planning, poverty, responsible financial management (e.g., financial literacy, debt management, bankruptcy management), or economic development, as the following example shows.

Youth employment initiative in Maldives

In 2006, the Maldives government, in association with The Promise Foundation and Bluemoss Consultants, launched the “Yes” social marketing initiative to curb the high unemployment rates in the country’s youth population (Lee & Kotler, 2012). Comprising nearly 25% of the workforce, youth (ages 15–24) were reluctant to join the workforce citing lack of interest in the types of skilled jobs available in the private sector (e.g., masonry, electrical wiring, carpentry, boat building, etc.). Further compounding matters were the well-established family safety nets that provided financial cushions for the unemployed youths, thus contributing to the high unemployment rates. Young people were simply not interested in remedial jobs and skills training, as they preferred trendy courses for jobs that carried high prestige (Lee & Kotler, 2012). To counter this issue, a strong branding campaign was undertaken featuring the acronym “Yes,” which stood for Youth Employment Skills. The campaign was designed to create pride in building work-related skills and the importance of becoming a self-reliant productive member of society. It emphasized the impact of dropping out of school and not attaining employment skills for the future of the country’s youth. The call to action was intended to drive traffic to the “Yes” website. The social marketing campaign generated nearly 50,000 website visits, which was significant, if one has in mind that the total youth population in the country was around 100,000. While these distinct examples demonstrate varied contexts, scopes, targets, timelines, and results, they share a common purpose: impacting behavior for societal benefit. In each case, social marketers began their efforts by deeply understanding their target audiences; then, using that knowledge, they developed strategies to resonate with and engage the community for the public good.



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Designing and Implementing a Social Marketing Campaign Strategy A sample overview of the different contexts1 and tactics used in the application of social marketing strategy exemplified in the previous sections may be stimulating for some practitioners, as it illustrates the many different messages, delivery vehicles, research, and strategic approaches that can be undertaken to engage the public and successfully influence behaviors. At the same time, the seemingly idiosyncratic nature of strategic execution in each of the examples used can also be discouraging for practitioners looking for underlying similarities in the above examples. Indeed, practitioners looking for a roadmap on how to effectively create and execute a successful social marketing strategy may find the above examples less informative and somewhat disappointing, as practitioners search for common threads among all of the successful examples covered in this chapter in order to inform their strategy. While differences abound, what unifies the social marketing strategies discussed above is the process ­followed in the research, strategic design, and execution. As such, this section will provide a roadmap of a general process that practitioners could follow when designing social marketing campaign strategies.

Existing Models of Social Marketing Campaign Development While social marketing scholars have devised processes comprising varying numbers of steps, the frameworks share key characteristics. For example, Andreasen (2002), French and Stevens (2010), and Lee and Kotler (2015) propose six-step, eight-step, and 10-step procedures, respectively, through which practitioners can systematically establish objectives; gather and analyze market research; benchmark; develop campaign materials; and implement strategies and tactics. While Lee and Kotler’s (2015) process provides the most comprehensive and detailed roadmap, it is instructive to review the breadth of models and note the consistent, key functions infused throughout each approach. Andreasen’s (2002) model includes the following six steps, merging key processes outlined in more comprehensive models: 1. Establish behavioral change as the central benchmark measure. 2. Conduct consumer research, including pretests of executions prior to implementation. 3. Segment the target market, while considering how to maximize the campaign’s effectiveness and reach. 4. Develop meaningful messages that will motivate and appeal to the audience. 5. Execute strategies that incorporate the entire marketing mix. 6. Monitor the competition. French and Stevens (2010) extended this framework by emphasizing the importance of a ­theoretical foundation and by including additional emphasis on research and consumer insight.

  The grouping of social issues under the umbrella of five social marketing contexts (i.e., health prevention, the environment, safety and injury prevention, community involvement and civic engagement, and economic [or financial] wellbeing) is intended for organizational purposes only as the contexts are not intended to represent mutually exclusive categories. For example, while the social marketing campaign— discussed in this review—unveiled by the Texas Department of Transportation and aimed at curbing littering on the state’s highways was reviewed in relation to the negative impact of the littering behavior on the environment, the campaign could have been also viewed in relation to public safety or injury prevention (e.g., litter on the highway presenting a driving hazard) or public health promotion (e.g., litter deposits making their way into sewer systems or contaminating water sources).

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This eight-step process involves the following steps, many of which align directly with Andreasen’s (2002) process: 1. 2. 3. 4. 5. 6. 7. 8.

Establish behavioral change as the central benchmark measure. Utilize audience research. Establish a theoretical perspective to direct strategy. Aim to include audience insight gathered from research Focus on consistent and meaningful exchanges with consumers. Examine and evaluate potential competitors. Segment the target audience. Employ strategies that integrate all four components of the marketing mix (product, place, price, and promotion).

While these two processes mirror each other in key ways, they nevertheless reflect an important issue in social marketing scholarship—the need for a unified, primary process for conducting this work. These calls reverberate across the globe. For example, the National Social Marketing Centre in the United Kingdom, formed by the Department of Health, established its own model for developing social marketing campaigns: The total process planning model (TPPM; Dooley, Jones, & Desmarais, 2009). This framework involves five steps, and though more succinct than the previously outlined processes in the raw number of steps, the TPPM builds upon these processes by including, as a central feature, the evaluation process. The TPPM’s first three steps mirror the previously discussed processes; it includes (a) scope, a thorough understanding of the issue through a situational analysis, establishment of behavioral goals, and identification of the target audience; (b) develop, a process of segmenting the target audience, testing campaign materials across the marketing mix, identifying barriers to behavioral change, and developing metrics for success; and (c) implement, the actual roll-out of the campaign (Dooley et al., 2009). The fourth and fifth steps distinguish the TPPM from the previously discussed models. The fourth step instructs the social marketer to evaluate. This step involves a focus on measuring outcomes, impact, and effectiveness, in terms of both strategy and cost. While the TPPM includes evaluation as a distinct phase, Dooley et al. (2009) emphasized that monitoring and evaluation also should take place throughout the entire process. The fifth step involves follow up, with a focus on executing key actions identified throughout the evaluation process. These final two steps are critical; evaluation, both throughout the campaign and at its conclusion, provides invaluable information that practitioners can utilize to refine current efforts and improve upon future campaigns. This mirrors best practices in traditional marketing. As Belch and Belch (2009) asserted, in reference to promotional campaigns in the commercial realm, “all marketing managers want to know how well their promotional programs are working. This information is critical to planning for the next period, since program adjustments and/or maintenance are based on evaluation of current strategies” (p. 647). The same is true for professionals executing campaigns focused on behavioral change. To that end, Lee and Kotler’s (2015) 10-step model presents a comprehensive roadmap, reflective of the recurrent, key components described in the previous models. The model further delineates and explicates the core processes that comprise a systematic, methodical approach to social marketing campaign development.

A Comprehensive Roadmap for Social Marketing Campaign Development Lee and Kotler’s (2015) social marketing campaign planning worksheets provide an informative 10-step guide to an effective social marketing campaign strategy. In the first proposed step, mirroring the initial phase outlined in the models presented by both Andreasen (2002) and French and Stevens (2010), Lee and Kotler suggest that practitioners should initially describe (define or



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specify): the social issue of interest (e.g., teen pregnancy), the organization involved in the strategy development and implementation (e.g., youth organization), the background information on the issue (or problem; e.g., number of teen pregnancies); and the strategic campaign purpose (e.g., teen pregnancy prevention) and focus (e.g., afterschool programs). A clear understanding of the issue (or problem) at hand, the organization conducting the campaign, and the campaign’s purpose and focus help properly and unambiguously define what the campaign is attempting to accomplish. This revelation may be intuitive and simple; however, a campaign with anything less than a clear focus and purpose may lead to improper specification of strategic objectives, which will inevitably derail even the best executed strategic campaigns. The second step proposed by Lee and Kotler (2015) suggests that the practitioner should conduct a situation analysis (SWOT: strengths, weaknesses, opportunities, threats) by starting with the assessment of the organization’s strengths (e.g., strong working relationship with the schools), weaknesses (e.g., understaffed), opportunities (e.g., afterschool programs looking for content for students to engage), and threats (e.g., proposed state budget cuts may affect afterschool programs). Proposing at least two to three key bullet points under each SWOT factor should be a solid start (Lee & Kotler, 2015). After, and/or in conjunction with, the SWOT analysis, the practitioner should examine similar efforts and findings from previous or current campaigns implemented in settings relevant to the current issue. Stated differently, is the issue at hand (e.g., teen pregnancy) unique or has a similar one manifested elsewhere? If the former, the next step is to uncover whether strategic campaigns have been conducted on related or similar issues and explore what could be learned from the corresponding findings. If the latter, then a careful examination of the findings and their applicability in the current context should be pursued, an effort that could inform the creation and execution of the current strategic campaign. As a part of this investigative effort, the practitioner should focus on the: target audience(s) (e.g., middle school girls and their parents/guardians), product (e.g., sex education), price (e.g., program fee), place (e.g., afterschool program facility), and promotion (e.g., direct mail postcards sent to families), as featured in the campaign efforts investigated. The more that can be learned from the successes and failures experienced by practitioners in similar campaign efforts, the better informed and prepared the current strategic campaign will have the opportunity to become. The third step in the strategic campaign process, as recognized by Lee and Kotler (2015), involves the proper identification of the target audience(s). This step begins by initially identifying the primary audience(s) targeted by the strategic campaign in terms of size (e.g., 1,500 middle school girls in the community at risk), problem incidence (e.g., 30% of the middle schools girls became pregnant in the past 4 years), severity (e.g., 80% of middle school girls who became pregnant in the past did not finish high school), and composition (e.g., geographic [e.g., inner city], demographics, psychographics, values, lifestyles, behavioral characteristics, stages of behavioral change, etc.; Lee & Kotler, 2015). This step corresponds with the second step in both the Andreasen (2002) and the French and Stevens (2010) models. In addition, within this step it is also important to identify additional audiences who may play a critical influential role in the strategic campaign either as messengers (e.g., teachers, community leaders, etc.) or vehicles of distribution (e.g., afterschool centers; Lee & Kotler, 2015). Finally, Lee and Kotler suggest that the strategist should also identify: the readiness to act on the part of the audience members, the ability to reach the desired audience, and degree to which the identified target audience matches the mission, expertise, and positioning of the organization responsible for the development and execution of the strategy. Misidentifying the appropriate target audience(s), failing to uncover, and learn from, previous similar strategic campaigns, and/or improperly specifying the project’s purpose and focus would inevitably lead to less than successful social marketing campaign strategy. A key to effective strategic campaign design and execution is the timely engagement in rigorous formative research, which provides the foundation for effective social marketing strategy. Formative research should be utilized to help properly define the issue, purpose of the project, and strategic focus. It should also inform the preparation of the situation analysis as well as assist in properly identifying the

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target audience(s). As a result, formative research plays a pivotal role in the design and implementation of a social marketing campaign strategy. The fourth step of the social marketing strategic campaign process, according to Lee and Kotler (2015), involves the setting of strategic objectives and target goals.2 Although the main strategic focus in social marketing is on behavioral objectives (e.g., We want middle school girls to enroll in the afterschool program geared toward sexual education, which should lead to lower incidence of teen pregnancy), knowledge (e.g., We want middle school girls to know how to sign up for the program) and belief (e.g., We want middle school girls to believe that the afterschool program increases their opportunities for a bright future) objectives may also be appropriate and should be defined. The targeted goals, as suggested by Lee and Kotler (2015), should be quantifiable, measurable, and focused on behavioral impact (e.g., We want at least 60% [how many] of middle school girls to enroll in the afterschool program geared toward sexual education by the end of the first year [when] of the program’s introduction, which should lead to a minimum of 5% [how much] drop in teen pregnancy rate in the inner city district [where], compared to the baseline year [when]), although other goals may be pursued as well (e.g., generating awareness or recall; changing beliefs, attitudes, values, etc.). Setting appropriate target goals is essential as the performance of the campaign is assessed against these target goals. Setting the goals unrealistically high risks the success of the campaign before it is unveiled. Setting the goals conservatively low may miss on potential unexplored opportunities. Once the target audience(s) and goals are identified, the fifth step, Lee and Kotler (2015) suggest, is intended to identify the barriers, benefits, motivators, competition, and influential others that can have an impact on the target audience. According to the authors, the strategist should identify the barriers to assimilating the desired behavior that the target audience members face. These barriers could be economical (e.g., the afterschool program’s cost is too high), geographical (e.g., the location of the afterschool program is far for the middle school students and they don’t have transportation), or psychological (e.g., “If my child attends the afterschool program focused on sex education, does that suggest to my neighbors that I am worried that my child is sexually active in middle school?”), to name a few. In addition, strategists should identify the audienceperceived benefits received from performing the desired behavior (e.g., middle school girls learn about an important topic [sex education], do their homework, receive a meal, and socialize with peers while in the afterschool program), as well as the motivators that would elicit compliance with the strategist’s requests (e.g., afterschool program location closer to school or on school grounds). Alternative behaviors (e.g., desire to play with neighborhood friends after school), benefits associated with alternative behaviors (e.g., gets chores done early and out of the way if going directly home after school), and costs associated with the desired behavior (e.g., getting home too late and tired after the program ends) that are in direct competition with the target behavior, should also be identified. Finally, the strategist should identify influential others (e.g., peers, friends, teachers, parents) who could exert—desired or undesired—influence on the target audience member in regard to performing the target behavior. A well-informed understanding of these influential factors should lead to a more effective social marketing campaign strategy. The sixth step in the process is the development of a positioning statement (Lee & Kotler, 2015). The positioning statement should reference the target audience and the desired behavior,   It is important to note that Lee and Kotler (2015) define objectives in more general terms, i.e. as general behavioral desires of the strategist (e.g., We want middle school girls to enroll in the afterschool program geared toward sexual education). In contrast, they define target goals with greater precision (e.g., We want at least 60% [how many] of middle school girls to enroll in the afterschool program geared toward sexual education by the second year [when] of the program’s introduction). Other scholars treat objectives as more specific than goals (e.g., Andreasen, 1995). To remain consistent with the authors whose work is being cited, this chapter will treat target goals as more specific than objectives.

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while showing the benefits of the proposed behavior over the competition (i.e., alternative behaviors, costs associated with the proposed behavior, and benefits of alternative behaviors). An example consistent with Lee and Kotler’s (2015) recommendation follows: We want the parents/guardians and middle school girls to see the participation in the afterschool program focused on sex education as a conveniently located alternative that offers the opportunity to learn about this important topic, finish homework, have fun and socialize with peers from their neighborhoods and beyond outside of a traditional classroom, and grab a healthy meal while building relationships with mentors who are dedicated to the students’ future success.

The exemplified statement clearly identifies the target audiences (i.e., parents and middle school girls) and desired behavior (i.e., participation in the afterschool program focused on sex education). It then proceeds to offer benefits of participation (e.g., sex education, homework help, opportunity to socialize, and a promise of a meal and mentorship3), while at the same time overcoming competing alternatives (e.g., finishing homework in the program saves time from doing it at home; families who have harder time affording meals secure a meal for their child; attendees are able to play with neighborhood friends while in the afterschool program, etc.). The seventh step is focused on developing marketing strategies consistent with the positioning statement (Lee & Kotler, 2015). This step is concerned with utilizing the best strategic mix of the marketing elements: product, price, promotion, and placement, and corresponds with the fifth step in the Andreasen (2002) model and the eighth step in the French and Stevens (2010) model. Lee and Kotler suggest that the strategist should develop a product platform that features the core product, the actual product, and the augmented product. The core product is tied to the major benefit(s) (e.g., sex education) associated with the product (i.e., desired behavior). The actual product refers to additional tangible benefits the audience members would have access to as well (e.g., free meal). The augmented product, on the other hand, refers to additional tangible goods or services that assist with the performance of the desired behavior (e.g., homework help and mentorship provided in the afterschool program). In addition to the specification of the product characteristics, the strategist should also consider the pricing of the product (i.e., desired behavior). Lee and Kotler (2015) define pricing as the fees and monetary and non-monetary incentives and disincentives associated with adopting and performing the desired behavior. The fees refer to the costs associated with adopting the desired behavior (e.g., fee for participating in the afterschool program). Monetary incentives refer to financial benefits for engaging in the behavior (e.g., 20% discount for siblings who participate in the afterschool program). Monetary disincentives refer to any cost of competing behaviors (e.g., high cost of raising a child). Non-monetary incentives, on the other hand, focus on opportunities, positive recognition, and accomplishments (e.g., receiving mentorship geared toward future success), while non-monetary disincentives focus on lack of opportunities and accomplishments, as well as negative recognition (e.g., a news story about a pregnant middle school girl). Deciding on how many and which of the above “prices” to emphasize in the campaign is of significant importance. Making a decision on a proper “placement” (or distribution) strategy is also important. Lee and Kotler (2015) refer to placement as the location(s) and time where the services will be rendered or the product offered (e.g., every weekday immediately after school in a dedicated building on the middle school grounds). More precisely, when and where will the target audience have the opportunity to engage with the product and perform the desired behavior? This   As a note, the unstated significant benefit of participating in the afterschool program on sex education is the lack of opportunity for middle school girls to engage in sexual intercourse while at home between the hours that school ends and parents return from work. However, this benefit is not explicitly stated due to the sensitivity of its nature.

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question is relevant in relation to not only the core product, but also the actual product and the augmented product. In addition, Lee and Kotler suggest that the strategist also needs to specify who else in the distribution channel may need to be targeted that may provide additional services to the target audience (e.g., afterschool mentors, meal providers, homework tutors, etc.). Promotion is another marketing mix element the strategist should consider. Lee and Kotler (2015) suggest that the strategist needs to decide on messages, messengers, creative strategies, and communication channels that will be utilized in the campaign (e.g., Parker et al., 2019). More specifically, the strategist would need to decide what message the campaign will communicate (e.g., importance of sex education) and who should deliver the message (e.g., teachers at the middle school). Further, decisions will need to be made on the creative strategies that will accompany the message (e.g., logo, design, visuals, etc.), as well as the channels that will carry and transmit the message (e.g., school newsletter, direct mail pieces sent to parents, school meetings, etc.). Altogether, the product, price, placement, and promotion strategy should be: appropriate for the target audience, consistent with one another, and in support of the positioning statement. The development of an effective marketing mix strategy begins with a substantive investment in formative research, which should guide the development and execution of the social marketing mix strategy. The eighth step deals with the development of a plan for proper monitoring and evaluation of social marketing campaigns (Lee & Kotler, 2015). This step corresponds with the fourth step in the TPPM (Dooley et al., 2009), and is critically important, as it allows for the assessment of the campaign while in progress (i.e., monitoring) and once finished (i.e., evaluation). When developing a plan for monitoring and evaluating the campaign, several key questions need to be considered according to Lee and Kotler (2015). First, why is the campaign being evaluated and for what purpose? Who is conducting the evaluation and who is the recipient of the results and/or presentation? What is being measured (i.e., variables, outcomes, etc.) and how is it being measured (i.e., procedures, techniques, methods, etc.)? When will the measures be taken and how much will it cost? Careful answers to the above questions would ensure that a meaningful evaluation of the campaign is performed. The last question in the previous paragraph concerned itself with the cost of the campaign evaluation. Cost, in general, is an important consideration in social marketing campaign development. As such, Lee and Kotler (2015) list budgeting and funding as the ninth step in the social marketing campaign development. More to the point, they suggest that the strategist should consider the costs associated with marketing mix strategies (i.e., product, price, placement, and promotion) in addition to those associated with the evaluation of the project. They further suggest that should the costs of the campaign exceed the funds available, the strategists may want to explore alternative sources for funding the campaign. The tenth step, according to Lee and Kotler (2015), deals with the development of an implementation plan for the social marketing campaign strategy. The authors suggest that the strategist should consider: who will be responsible for which implementation tasks; when and where each task will take place; and how much it will cost. Once the implementation plan is developed, the campaign should be ready for execution. Overall, the 10 steps offered by Lee and Kotler (2015) provided point-by-point suggestions on how to develop an effective social marketing campaign strategy. Although the strategies used in different social marketing projects will inevitably differ, the process used to develop the strategies does not have to. This is evident through the procedures and best practices transcending the different models discussed in this chapter. At the same time, Lee and Kotler’s (2015) model merges the various components and strengths of the different models, providing a detailed and comprehensive approach to developing social marketing campaigns. Lee and Kotler’s thorough process provides the strategist with a practical, elegant, and informative approach to designing an effective social marketing campaign strategy.



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Conclusion This chapter, while providing contextual examples of successful social marketing efforts, offers a process-centered overview. In the same way that each traditional, commercial marketing campaign incorporates unique objectives, strategies, and tactics, campaigns focused on social issues present distinctive characteristics. No single campaign can serve as a template for future social marketing efforts; however, the processes underscoring campaign development provide important insight for future endeavors. Lee and Kotler’s (2015) model provides a detailed process for acquiring crucial insight and, then, translating it into effective marketing strategies. A tenet of applied communication research, social marketing offers a systematic process of planning that incorporates traditional marketing elements and principles. Grounded in sound research methods, the approach provides an empirically and theoretically based framework for addressing real social challenges. Importantly, social marketing is perhaps most distinct—and rewarding—because it converges this fundamental research focus with on-the-ground, practical application, driven by traditional marketing best practices. As this chapter has demonstrated, this audience-centered approach may be employed across contexts to motivate individual and social change. As such, this powerful strategy offers the potential to create lasting change on a number of important social issues and provides insight into the mechanisms underscoring persuasion, influence, and social change. As scholars contemplate the public good offered by applied communication research, social marketing certainly provides a promising avenue for progress. Social marketing is a valuable tool in the arsenal of the campaign strategist; it is a compelling example of the positive impact applied communication research can render for society.

References Andreasen, A. R. (1995). Marketing social change. San Francisco, CA: Jossey- Bass. Andreasen, A. R. (2002). Marketing social marketing in the social change marketplace. Journal of Public Policy & Marketing, 21(1), 3–13. Andreasen, A. R. (2015). What is social marketing? In D. W. Stewart (Ed.), The handbook of persuasion and social marketing (pp. 13–26). Santa Barbara, CA: Praeger. Asbury, L. D., Wong, F. L., Price, S. M., & Nolin, M. J. (2008). The VERB™ campaign: Applying a branding strategy in public health. American Journal of Preventive Medicine, 34(6), S183–S187. Bagozzi, R. P., Gopinath, M., & Nyer, P. U. (1999). The role of emotions in marketing. Journal of the Academy of Marketing Science, 27(2), 184–206. Belch, G. E., & Belch, M. A. (2009). Advertising and promotion: An integrated marketing communications perspective. Boston, MA: McGraw-Hill-Irwin. Centers for Disease Control and Prevention. (2018). U.S. drug overdose deaths continue to rise; increase fueled by synthetic opioids. Retrieved from https://www.cdc.gov/media/releases/2018/p0329drug-overdose-deaths.html Council on Social Work Education. (2016). Working definition of economic well-being. Retrieved from https://www.cswe.org/Centers-Initiatives/Initiatives/Clearinghouse-for-Economic-Well-Being/ Working-Definition-of-Economic-Well-Being Crites, S. L., Jr., Fabrigar, L. R., & Petty, R. E. (1994). Measuring the affective and cognitive properties of attitudes: Conceptual and methodological issues. Personality and Social Psychology Bulletin, 20, 619–634. Cullen, E. T., Matthews, L. N., & Teske, T. D. (2008). Use of occupational ethnography and social marketing strategies to develop a safety awareness campaign for coal miners. Social Marketing Quarterly, 14(4), 2–21. Deehr, R. C., & Shumann, A. (2009). Active Seattle: Achieving walkability in diverse neighborhoods. American Journal of Preventative Medicine, 37, 403–411. Dillard, J. P., & Pfau, M. (2002). The persuasion handbook: Developments in theory and practice. Thousand Oaks, CA: Sage.

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Dooley, J. A., Jones, S. C., & Desmarais, K. (2009). Strategic social marketing in Canada: Ten phases to planning and implementing cancer prevention and cancer screening campaigns. Social Marketing Quarterly, 15(3), 33–48. Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. San Diego, CA: Harcourt Brace Jovanovich College Publishers. Evans, W. D., Silber-Ashley, O., & Gard, J. (2007). Social marketing as a strategy to reduce unintended  adolescent pregnancy. The Open Communication Journal, 1(1), 1–8. doi:10.2174/18749 16x00701010001 French, J., & Stevens, C. B. (2010). Key concepts and principles of social marketing. In J. French, C. BlairStevens, D. McVey, & R. Merritt (Eds.), Social marketing and public health: Theory and practice (pp. 29–41). Oxford, UK: Oxford University Press. Grant, J. E., Potenza, M. N., Weinstein, A., & Gorelick, D. A. (2010). Introduction to behavioral addictions. American Journal of Drug and Alcohol Abuse, 36, 233–241. Ivanov, B., Rains, S. A., Parker, K. A., Geegan, S. A., Vos, S. C., & Haarstad, N. D. (2017). Beyond simple inoculation: Examining the persuasive value of inoculation for audiences with initially neutral or opposing attitudes. Western Journal of Communication, 81(1), 105–126. Kotler, P., Roberto, E. L., & Lee, N. R. (2002). Social marketing: Improving the quality of life (2nd ed.). Thousand Oaks, CA: Sage. Lee, N. R., & Kotler, P. (2012). Social marketing: Influencing behaviors for good (4th ed.). Thousand Oaks, CA: Sage. Lee, N. R., & Kotler, P. (2015). Social marketing: Changing behaviors for good (5th ed.). Thousand Oaks, CA: Sage. Lombardo, A. P., & Léger, Y. A. (2007). Thinking about “Think Again” in Canada: Assessing a social marketing HIV/AIDS prevention campaign. Journal of Health Communication, 12(4), 377–397. Lusch, R. F., & Vargo, S. L. (2006). The service-dominant logic of marketing: Dialog, debate, and directions. Armonk, NY: M. E. Sharpe. Millar, M. G., & Millar, K. U. (1990). Attitude change as a function of attitude type and argument type. Journal of Personality and Social Psychology, 59, 217–228. Parker, K. A., & Ivanov, B. (2013). Why not communicate?: Young women’s reflections on their lack of communication with sexual partners regarding sex and contraception. The International Journal of Health, Wellness, and Society, 2(4), 93–109. Parker, K. A., Ivanov, B., & Cohen, E. L. (2016). When politeness is risky: Positive politeness and sexual debut. Iowa Journal of Communication, 48(2), 145–155. Parker, K. A., Ivanov, B., & Compton, J. (2012). Inoculation’s efficacy with young adults’ risky behaviors: Can inoculation confer cross-protection over related but untreated issues? Health Communication, 27(3), 223–233. Parker, K. A., Ivanov, B., Thieneman, A., Wombacher, K., Watterson, T., Burchett, M., & Adams, E. (2018). “I used to be an addict. I’m still an addict. I’m always going to be a recovering addict:” Understanding the challenges of individuals seeking recovery. Journal of Substance Use, 24(2), 147– 149. doi:10.1080/14659891.2018.1523967 Parker, K. A., Thieneman, A., & Ivanov, B. (in press). “If we don’t use today, we’ll stay sober today”: Inoculation-centered intervention strategy for relapse prevention. The International Journal of Health, Wellness, and Society. Petty, R. E., Cacioppo, J. T., & Schumann, D. (1983). Central and peripheral routes to advertising effectiveness: The moderating role of involvement. Journal of Consumer Research, 10(2), 135–146. Potter, S. J., & Stapleton, J. G. (2011). Bringing in the target audience in bystander social marketing materials for communities: Suggestions for practitioners. Violence Against Women, 17(6), 797–812. Rothschild, M. L., Mastin, B., & Miller, T. W. (2006). Reducing alcohol-impaired driving crashes through the use of social marketing. Accident Analysis & Prevention, 38(6), 1218–1230. Sato, R. A., Drennan, J., & Lings, I. (2015). Problem recognition: Integrating help-seeking theory in social marketing. Journal of Social Marketing, 7(1), 2–17. Stewart, D. W. (2015). Introduction. In D. W. Stewart (Ed.), The handbook of persuasion and social marketing (pp. 1–12). Santa Barbara, CA: Praeger.



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Truss, A., Marshall, R., & Blair-Stevens, C. (2010). A history of social marketing. In J. French, C. BlairStevens, D. McVey, & R. Merritt (Eds.), Social marketing and public health: Theory and practice (pp. 19–28). Oxford, UK: Oxford University Press. Vargo, S. L., & Lusch, R. F. (2004). Evolving to a new dominant logic for marketing. Journal of Marketing, 68, 1–17. Yanovitzky, I. (2017). A multiyear assessment of public response to a statewide drug take-back and disposal campaign, 2010 to 2012. Health Education & Behavior, 44(4), 590–597.

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Engaged Communication Scholarship The Challenge to Translate Communication Research into Practice Gary L. Kreps Engaged Communication Inquiry Communication inquiry has developed as an exciting and rapidly growing area of social scientific, critical, and humanistic research examining the powerful influences of human and mediated communication in society (Berger, 2010; Eadie, 2011; Putnam & Dempsey, 2015). Communication research is naturally applied, since it focuses on the ways that communication processes influence understanding and promote cooperation in achieving individual and group goals (Hummert, 2009; O’Hair & Kreps, 1990; Vorderer, 2016). Communication inquiry is also often problem-based, focused on explicating, examining, and addressing important and troubling social issues (Frey & Palmer, 2017; Kreps, 2012). These investigational issues often include examining difficulties in promoting active coordination and collaboration in accomplishing complex tasks; challenges to effectively sharing and disseminating relevant information; strategies for promoting social influence and interpersonal coordination; demands to reduce communication errors and unintended consequences; attempts to meet the information and support needs of members of different groups; and the quest to promote social justice by overcoming serious communication inequities experienced by marginalized populations (Dutta, 2015; Frey, 2009; Kreps, 2012; Pearce, 1996; Steimel, 2014). These are serious issues that demand attention from communication scholars. The applied nature of communication inquiry is firmly grounded in the implicit goal to facilitate improvements in communicative practices for the accomplishment of both individual and collective goals (Kreps, Viswanath, & Harris, 2002). However, there are concerns about the strength of the connection between the communication research being conducted and applications of this research (Kreps, 2012; Kreps et al., 2002; Vorderer, 2016). There appears to be a greater focus by many scholars on the process of conducting and reporting communication research than on actually applying the results of relevant research to address important social issues. Many policy makers and practitioners (e.g., educators, architects, corporate leaders, social advocates, healthcare providers, counselors, parents, legislators, lawyers, jurists) have been slow to recognize and adopt communication research to help them accomplish their complex societal goals. The result is that many complex issues that might benefit from relevant communication research are guided more by good intentions, precedent, and expedience than by strong evidence (Kreps, 2011a). For example, how often is communication research used to guide the development, implementation, and evaluation of educational programs, public policies, and The Handbook of Applied Communication Research: Volume 1, First Edition. Edited by H. Dan O’Hair and Mary John O’Hair. © 2020 John Wiley & Sons, Inc. Published 2020 by John Wiley & Sons, Inc.

94 Kreps business strategies? Even in the area of health communication research, how often does this work guide healthcare delivery strategies for eliciting full diagnostic information, accomplishing informed consent, or promoting adherence with healthcare recommendations? Too often, the answer to these questions is that these programs and practices are not guided at all by communication research. The complexity of achieving desired communication goals, such as influencing entrenched problematic behaviors (e.g., challenges influencing conflict management, gender relations, intercultural relations, family relations, international relations, and even sexual p ­ ractices) and promoting informed decision making, demands strategic guidance from relevant and rigorous communication research. This chapter examines strategies for promoting the application of the best communication research to guide development, implementation, and institutionalization of evidence-based communication programs, policies, and practices.

Health Communication Inquiry as Engaged Communication Scholarship In the highly applied area of health communication inquiry, for example, communication scholars have exerted increased influence over improving public health promotion and practice. A large and developing body of health communication scholarship has begun to powerfully illustrate the centrality of communication processes in achieving important healthcare and health-promotion goals (Kreps, 2019, 2011a; Kreps & Bonaguro, 2009). For example, as early as 1995, Kreps and O’Hair conducted a series of seminal studies illustrating the powerful influences of communication strategies and programs on health knowledge, behaviors, and outcomes. Even earlier research by Greenfield, Kaplan, and Ware (1985) clearly demonstrated the positive influences of increased patient/provider participation in directing healthcare treatment on achieving desired health outcomes. Kreps and Chapelsky Massimilla (2002) reported a number of studies that illustrate the positive effects of communication interventions on cancer-related health outcomes. The 100th anniversary issue of the scholarly journal Health Communication (2010) also provides extended reviews of the major contributions of health communication research to health outcomes. Communication research has been increasingly used to inform the development of public health policies and legislation, including policies to prevent and respond to serious health risks, promote equity in health care, and improve media coverage of important health issues (Atkin & Smith, 2010; Guttman, 2010; Kunkel, 2010; National Cancer Institute, 2008; Noar, Palmgreen, Chabot, Dobransky, & Zimmerman, 2009; Siu, 2010). Yet, there is so much more that can be done by health communication scholars to improve public health and wellness. While health communication scholarship has already made important contributions to improving health care and health promotion, health communication inquiry has the potential to make even more important and wide-ranging contributions to improving public health. Health communication scholarship has come a long way over the past several decades, but there is great potential to go even farther. The emergence of engaged communication research strategies that are increasingly being used in health communication inquiry have also been adopted to guide applications in other important areas of health communication research, such as intercultural, organizational, political, environmental, interpersonal, family, strategic, public, and mediated communication inquiry.

Asking the Right Research Questions To make a positive difference in society, communication scholars must carefully identify and examine the critical issues confronting at-risk and vulnerable populations (people who experience serious health inequities, social discrimination, violence, injustice, and poverty) and then design studies to address these important societal problems. Cox (2007) has described how environmental communication research has evolved as a crisis discipline to address serious environmental threats and crises. We need more communication studies across all areas of the communication discipline that examine the complex communication factors that influence critical social issues. It is not difficult to identify



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major social issues in modern society. These issues are covered regularly by the popular media (radio, television, magazines, and newspapers), reported by independent agencies in major reports and news conferences, and studied by important federal agencies such as the US Departments of State, Commerce, Defense, and Health and Human Services. A sampling of these important social issues includes serious problems related to poverty, violence, and health disparities, such as poor access to nutritious food, unsafe living environments, physical abuse, low quality of healthcare services, and limited access to good educational programs. Communication scholars should be designing studies to examine the communication factors that are related to these important public issues to increase understanding about how communication can be used to address these problems. Current evidence suggests that most, if not all, of these important social issues are directly related to the effectiveness of human communication. For example, serious inequities in access to nutrition, safe living environments, and social justice for poor, at-risk, vulnerable, and minority populations have also been related to the effectiveness of human communication (Broome & Collier, 2012; Carragee & Frey, 2016; Cox, 2007; Frey & Carragee, 2016). Similarly, some of the serious issues that threaten the delivery of high-quality care, including the insidious recurrence of medical errors, lack of consumer adherence with treatment recommendations, and poor levels of active consumer participation in healthcare decision making have all been linked to the effectiveness of health communication (Greenfield et  al., 1985; Kreps & Bonaguro, 2009). Evidence suggests that these healthcare delivery issues are closely related to miscommunication and misinformation, lack of provider–consumer cooperation, and poor health information sharing (DiMatteo & Lepper, 1998; Kreps et al., 2011). Similarly, evidence suggests that disparities in health outcomes are closely related to poor consumer access to relevant health information, lack of consumer understanding about prevention and treatment opportunities, and ineffective communication relationships between healthcare providers and consumers, as well as mistrust and intercultural communication barriers within the modern healthcare system (Eysenbach & Kohnler, 2002; Kreps, 2006). Challenges with achieving health, risk, and social development goals have also been connected to the effectiveness of public communication, education, campaign, and intervention programs designed to influence health behaviors (Dutta-Bergman, 2005; Hornik, 2002; Kreps, 2007, 2011a). These are all critical communication issues that deserve close attention from communication researchers. Ambitious communication studies need to be designed to directly address serious societal communication problems. Such studies should focus on examining the critical communication processes at play in access to relevant information, social influence, interpersonal relations, and coordination among interdependent participants in key social settings (such as within families, organizations, and schools), while also examining the larger societal, institutional, and cultural communication influences on societal processes (Broome & Collier, 2012; Viswanath et al., 2012).

Rigorous Communication Inquiry To effectively address serious societal problems, communication scholars must take their work seriously and go the extra mile to translate communication research into practice (Kreps, 2011a; Kreps et al., 2002). Taking communication scholarship seriously means not only asking important communication research questions, but also conducting relevant, rigorous, and far-reaching studies that generate valid, reliable, and generalizable data that can effectively inform communication practices (Kreps, 2001, 2011b). Serious communication researchers take great care to meticulously design studies and carefully operationalize research variables to accurately measure key communication concepts, processes, and outcomes with both precision and depth. This often includes designing new and innovative measures and measurement tools. It also often means using multiple research methods and measurement tools, including triangulating qualitative and quantitative measures, to generate robust and revealing data (Kreps, 2008, 2011b). Serious communication scholars work to actively translate and transform raw health communication research findings into

96 Kreps practical and usable health care/promotion interventions and policies. They carefully test the efficacy of interventions by monitoring the outcomes (both positive and negative) of communication programs within representative social systems with at-risk populations. To really make a difference, communication scholarship must provide important insights into best practices for addressing important social issues (Putnam & Dempsey, 2015). Research must chronicle what works well and what is causing problems in modern life. The quality of the research that communication scholars conduct is directly related to the potential of this research to inform relevant policies and practices. Care must be taken to rigorously design and conduct communication studies to generate the most accurate, valid, and revealing data to demystify the many complexities of communication in modern life, including the use of multiple media ­channels and negotiating unique cultural contexts (Broome & Collier, 2012; Kunkel, 2010). New models and theories should be developed, tested, and refined to help describe and predict the intricate influences of communication within the health system. Innovative methods should be employed to study the complex communication processes that enable the effective delivery of care and the promotion of health (Broome & Collier, 2012; Viswanath & Kreuter, 2007). While a plethora of cross-sectional, one-point-in-time, communication studies have been conducted, there is a tremendous need for studies that collect data over time to avoid myopia and reflect the emergent nature of social practices. It is also imperative for communication scholars to study the most relevant research populations involved in complex social issues to collect relevant (ecologically valid), meaningful, and usable data (Kreps, 2001). In the past, too many communication studies depended on data gathered from convenient samples that often did not reflect very well the actual experiences of those confronting the societal problems to which the studies purported to generalize. If we do not study the specific populations we want to help, we will not generate data that will result in useful interventions, practices, and policies. Kreps et al. (2002) state this quite clearly: While it is reasonable to and even legitimate to use small or convenient sample-based studies, the applied nature of health communication, and other applied areas of focus, forces us to confront the reality of the field. This reality raises many questions. First are population issues. Who are we studying? Do the samples of humans we use have the background, knowledge, and orientation to really answer the big questions we are asking? Do the samples we use provide representative data? (pp. 371–372)

To gather data that will inform societal policies and practices, we need to study members of specific populations who have in-depth experiences and insights to guide evidence-based interventions. Many studies have also used unrealistic conditions and artificial questions that do not fully represent the complexities of social situations. The “law of the hammer” (the tendency to use familiar research instruments inappropriately) can encourage scholars to use popular and easyto-administer research tools that may not accurately measure the key issues under investigation in health communication studies. These research practices pose serious threats to the ecological validity of communication studies (Kreps, 2001). The term “ecological validity” refers to how well research describes what actually happens in real-life circumstances. “To the extent that research procedures reflect what people do in the contexts in which their behavior normally occurs, confidence in the generalizability of findings to other people and situations is increased” (Frey, Botan, & Kreps, 2000, p. 133). It is imperative that communication researchers design and conduct studies that provide valid, reliable, and generalizable data for guiding solutions to the most pressing problems in modern society.

Establishing Relevant Research Partnerships A major strategy for translating communication research into practice depends on developing meaningful interdisciplinary, interprofessional, and community-based partnerships between



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scholars, relevant community members, practitioners, consumers, administrators, government agency representatives, support organization members, and public policy makers (Dutta, 2015; Neuhauser, 2001; Neuhauser & Kreps, 2011; Neuhauser, Kreps, & Syme, 2013). These collaborative partnerships are instrumental in helping communication researchers effectively design, implement, and institutionalize the best evidence-based communication interventions within society. It is clear that although communication scholars have important expertise concerning the process of communication, they certainly do not have many of the answers needed about how different complex social systems work, how members of different communities behave, and how to influence institutional and public policies. Establishing collaborations with key research partners can help provide needed expertise and answers for addressing these important translational issues effectively. A good first step for developing meaningful communication research partnerships is to establish research collaborations with other scholars from related disciplines, such as public health, education, public policy, computer science, and political science, environmental ­science, and other relevant professional fields. For example, Kreps and Maibach (2008) make a strong case for the synergistic opportunities that can derive from collaborations between health communication and public health scholars, citing complementary, yet distinct, areas of expertise, theoretical grounding, methodological orientation, and intervention strategies. Communication research already builds upon research and theory from other disciplines, such as social psychology (i.e., relationship development and social influence), organizational behavior (i.e., leadership and decision making), sociology and anthropology (i.e., socialization processes, social norms), public health (i.e., health beliefs and health campaigns), and computer science (i.e., digital information systems). Major federal funding agencies have begun requesting grant applications from transdisciplinary research teams for conducting large federally funded research programs (Kreps, 2011a; Kreps et al., 2002). These funding agencies recognize the unique contributions, benefits, and insights that transdisciplinary research cooperation can provide. Community-based collaborations are also critically important for supporting the translation of communication research into practice (Dutta, 2015). It is time for communication scholars to move out of their academic ivory towers and develop meaningful collaborations with relevant community partners from government agencies, healthcare delivery systems, nonprofit associations, social service agencies, advocacy organizations, consumer groups, at-risk populations, and even corporations. It is only through these community-based collaborations that we can effectively translate compelling research findings into products, programs, policies, and practices that will be adopted within social systems. Community partners have the embedded social system expertise that scholars desperately need to collaboratively introduce new communication programs, policies, and practices into important social systems and help to refine these programs so they will work effectively. Community participative research and intervention programs have shown great potential to facilitate applications of research results into societal practices (Dutta, 2015; Minkler, 2000; Minkler & Wallerstein, 2002). Community partners can help communication scholars learn the best inside strategies for gathering meaningful data from respondents, for interpreting research results within the framework of cultural contexts, for designing usable and effective communication interventions, for testing these interventions in action within relevant social settings, and for implementing and sustaining these interventions within social systems (Neuhauser, 2001; Neuhauser & Kreps, 2011; Neuhauser et al., 2013). Actively engaging community partners in the applied research process can impart a strong sense of ownership in the research and intervention processes among these community partners, and this can have major influences on minimizing potential community resistance to accepting the interventions and encouraging cooperation in the implementation and institutionalization of new and refined communication programs, tools, and policies (Kreps, 2007).

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Developing and Sustaining Effective Communication Intervention Programs It is imperative that communication scholars not only conduct socially relevant communication research but also make concerted efforts to use their research findings to guide the development of evidence-based communication intervention programs to help address significant social issues. The goals for engaged communication research must go well beyond just asking relevant questions, designing and conducting rigorous research, and reporting the research in scholarly venues; the goals must also involve translating research into programs and policies that can make a difference for those individuals who are confronting serious societal problems. Exemplary communication intervention programs can include evidence-based policies and practices for monitoring and identifying social inequities (e.g., systems for documenting and reporting community concerns and problems), disseminating relevant information to key audiences (e.g., the use of strategically designed edutainment programs and smart, interactive tailored digital information programs that provide usable strategies for avoiding and responding to environmental risks), and strategic communication campaigns (e.g., media campaigns that encourage parents to get their children vaccinated, school-based programs to educate children about the dangers of drug abuse, and comprehensive multimedia education programs to help new parents care for their children). Not only can communication scholars provide relevant data for guiding the development of these engaged communication programs, they can also gather formative evaluation data for refining these programs and summative evaluation data for assessing program impact and value (Abbatangelo-Gray et al., 2007; Kreps et al., 2002; Maibach et al., 1994). Too often communication interventions that are developed and tested as parts of research programs only last as long as they are needed for the studies being conducted. This happens because researchers often do not have the time, resources, inclination, or institutional influence to sustain successful interventions over time. Institutionalization of effective interventions is not strongly encouraged by many academic and research organizations, where recognition and rewards are typically provided to scholars for acquiring research funding and publishing their results more often than for their implementation and maintenance of relevant communication tools, programs, and policies. Efforts need to be directed toward sustaining the best problemsolving communication interventions over time to make long-term improvements in addressing serious societal issues. To accomplish this, collaborative partnerships with affected community members, practitioners, administrators, government agency representatives, consumers, and caregivers can help provide researchers with information, and others are needed to implement, institutionalize, and sustain evidence-based communication interventions. These system participants are motivated to rectify the problems they face, and often have access to unencumbered resources for supporting these interventions, while also possessing unique insights into the operation of social systems that can be used to successfully implement communication interventions. Communication scholars need to recognize the benefits of such collaborations for sustaining interventions and for leveraging relevant community partnerships to improve modern life.

Disseminating Communication Knowledge It is important to develop new and effective strategies for disseminating relevant communication knowledge to participants in social systems who can use such information for making relevant decisions, promoting collaborations, and solving difficult problems they face. Unfortunately, many communication scholars have not done very well communicating with key publics. Most communication research is reported rather narrowly to other communication scholars at academic conferences and in scholarly journals. These dissemination channels have helped spur rapid growth of the communication field, expanded the development of new communication educational programs, and encouraged students and faculty to conduct communication research; however, they have not often made great inroads into rectifying serious social problems.



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New strategies for disseminating relevant communication research findings and implications for public policy and practice need to be utilized. A first step for broader dissemination of relevant communication research findings is to expand publication and presentation of communication research in scholarly outlets outside of the communication discipline, including at relevant conferences, public meetings, and in important journals from related disciplines (e.g., public policy, public health, psychology, sociology, engineering, computer science, organizational behavior, and the health professions). Research findings can also be presented at interdisciplinary conferences and published in interdisciplinary journals. These presentations and publications can help spur interdisciplinary collaborations, and many of these scholarly outlets have greater exposure to the popular media than most communication conferences and journals. However, scholarly conferences and journals may be unfamiliar venues for those without advanced scientific training, and are not likely to reach many at-risk consumers, business leaders, healthcare administrators, government officials, or other policy makers. Efforts need to be taken to identify appropriate communication channels for easily reaching and influencing broader audiences of consumers, caregivers, administrators, government officials, and policy makers. For example, popular magazines, websites, blogs, radio and television programs, newspapers, and special audience presentations can have greater public reach than typical scholarly outlets. Moreover, communication research must be translated out of academic jargon and into language and images that are familiar and meaningful to targeted audiences (Kreps & Goldin, 2009). Communication scholars must learn how to become public scholars and develop needed communication skills to reach and influence diverse audiences, including communicating effectively with vulnerable and at-risk populations. Participation in public events, media interviews, briefings for administrators and government representatives, public presentations, public forums, training programs for practitioners and consumers, and publication of popular articles in different online and print outlets can go a long way to broaden the dissemination of relevant communication knowledge. Interactive dissemination programs can encourage the exchange of questions and answers about communication issues that can clarify the meanings and implications of communication research. Some fruitful interactive channels for dissemination of communication research findings and applications include participation in support groups (both online and in-person groups), training programs, and websites that allow information exchange. For example, the Health Resources Services Administration, a major federal government agency that supports healthcare services for a very large audience of underserved and vulnerable consumers, commissioned the development of a mandatory online health communication training program (the unified health communication course) based on the latest health communication theory and research for the thousands of healthcare providers that they fund, as well as for other healthcare providers who can obtain needed continuing education credits by taking the online course (Health Resources Services Administration, 2019). So far more than 10,000 healthcare professionals have taken this course to learn how to communicate effectively with a culturally diverse population of consumers.

Potential Influences of Engaged Communication Research Increasingly, communication scholars are conducting important research for addressing serious and complicated social issues. As communication scholarship has grown in institutional credibility (within academia, government agencies, and social systems) in recent years, we have seen expanded outreach opportunities for conducting important communication research and interventions, for building exciting new communication collaborations and partnerships, and for influencing public policies and practices (Berger, 2010; Frey & Palmer, 2017). Communication scholars should eagerly seek and leverage these new opportunities for applying communication research to enhance public policies and practices.

100 Kreps With help from external funding, communication scholars can focus on conducting serious, large-scale communication studies that can provide relevant and compelling research results (Kreps et al., 2002). They can garner the resources necessary to mount ambitious, robust, and rigorous longitudinal, multi-methodological field studies with large real-world populations. They can develop new and improved research methods for conducting communication research and innovative theoretical frameworks for guiding communication inquiry. They can vigorously disseminate the findings of communication research to scholarly audiences, public policy makers, social system administrators, practitioners, and consumers, as well as to media representatives. They can also develop and implement new programs, practices, and interventions based upon strong communication research findings, and work with community partners to institutionalize the best programs to be sustained over time. The field of communication is rapidly moving toward a sophisticated, multidimensional agenda for applied research that has the potential to inform enlightened communication practices. There is a powerful need to carefully evaluate the use of a broad and evolving range of communication strategies, media, and technologies to address important communication issues in modern life. Such inquiry can provide important information about the development of cooperative relationships between interdependent participants in social systems; encourage the use of sensitive and appropriate communication; empower those who are vulnerable to minimize risks and garner needed support; and enable informed decisions to be made to achieve desired outcomes. Communication scholars need to go the extra mile to ask important research questions; gather rigorous and insightful data; disseminate relevant findings broadly; build meaningful community research and intervention partnerships; and develop, implement, and sustain important evidence-based communication programs, tools, policies, practices, and interventions to enhance modern life.

References Abbatangelo-Gray, J., Kennedy, M. G., Cole, G. E., Baur, C., Bernhardt, J., Cho, H., … Storey, D. (2007). Guidance for evaluating mass communication health initiatives: Summary of an expert panel discussion sponsored by the Centers for Disease Control and Prevention. Evaluation and the Health Professions, 30(3), 229–253. Atkin, C. K., & Smith, S. W. (2010). Improving communication practices to reduce breast cancer environmental risks. Health Communication, 25, 587–588. Berger, C. R. (2010). Making a differential difference. Communication Monographs, 77(4), 444–451. Broome, B., & Collier, M. J. (2012). Culture, communication and peacebuilding: A reflexive multidimensional contextual framework. Journal of International and Intercultural Communication, 5, 245–269. Carragee, K. M., & Frey, L. R. (2016). Communication activism research: Engaged communication scholarship for social justice. International Journal of Communication, 10, 3975–3999. Cox, R. (2007). Nature’s “crisis disciplines”: Does environmental communication have an ethical duty. Environmental Communication, 1(1), 5–20. DiMatteo, M. R., & Lepper, H. S. (1998). Promoting adherence to courses of treatment: Mutual collaboration in the physician-patient relationship. In L. D. Jackson & B. K. Duffy (Eds.), Health communication research: A guide to developments and directions (pp. 75–86). Westport, CT: Greenwood Press. Dutta, M. J. (2015). Decolonizing communication for social change: A culture-centered approach. Communication Theory, 25(2), 123–143. Dutta-Bergman, M. J. (2005). Theory and practice in health communication campaigns: A critical interrogation. Health Communication, 18, 103–122. Eadie, W. F. (2011). Stories we tell: Fragmentation and convergence in communication disciplinary history. The Review of Communication, 11(3), 161–176. Eysenbach, G., & Kohnler, C. (2002). How do consumers search for and appraise health information on the world wide web? Qualitative study using focus groups, usability tests, and in-depth interviews. British Medical Journal, 324, 573–577.



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Frey, L. R. (2009). What a difference more difference-making communication scholarship might make: Making a difference from and through communication research. Journal of Applied Communication Research, 37(2), 205–214. Frey, L. R., Botan, C. H., & Kreps, G. L. (2000). Investigating communication: An introduction to research methods (2nd ed.). Boston, MA: Allyn & Bacon. Frey, L. R., & Carragee, K. M. (2016). Seizing the social justice opportunity: Communication activism research at a politically critical juncture—epilogue. International Journal of Communication, 10, 7. Frey, L. R., & Palmer, D. L. (2017). Communication activism pedagogy and research: Communication education scholarship to promote social justice. Communication Education, 66(3), 362–367. Greenfield, S., Kaplan, S., & Ware, J., Jr. (1985). Expanding patient involvement in care: Effects on patient outcomes. Annals of Internal Medicine, 102, 520–528. Guttman, N. (2010). Using communication research to advance the goals of the National Health Insurance law in Israel. Health Communication, 5, 613–614. Health Communication. (2010). 100th Anniversary Issue. 25(6–7). Retrieved from https://www.tandfonline. com/toc/hhth20/25/6-7?nav=tocList Health Resources and Services Administration. (2019). HRSA Health Literacy. Retrieved from https:// www.hrsa.gov/about/organization/bureaus/ohe/health-literacy/index.html Hornik, R. C. (2002). Public health communication: Evidence for behavior change. Mahwah, NJ: Lawrence Erlbaum. Hummert, M. L. (2009). Not just preaching to the choir: Communication scholarship does make a difference. Journal of Applied Communication Research, 37(2), 215–224. Kreps, G. L. (2001). Consumer/provider communication research: A personal plea to address issues of ecological validity, relational development, message diversity, and situational constraints. Journal of Health Psychology, 6(5), 597–601. Kreps, G. L. (2006). Communication and racial inequities in health care. American Behavioral Scientist, 49(6), 760–774. Kreps, G. L. (2007). Health communication at the population level—principles, methods and results. In L.  Epstein (Ed.), Culturally appropriate health care by culturally competent health professionals: International workshop report (pp. 112–120). Caesarea, Israel: The Israel National Institute for Health Policy and Health Services Research. Kreps, G. L. (2008). Qualitative inquiry and the future of health communication research. Qualitative Research Reports in Communication, 9(1), 2–12. Kreps, G. L. (2011a). Translating health communication research into practice: The influence of health communication scholarship on health policy, practice, and outcomes. In T. Thompson, R. Parrott, & J. Nussbaum (Eds.), The handbook of health communication (2nd ed., pp. 595–608). New York, NY: Routledge. Kreps, G. L. (2011b). Methodological diversity and integration in health communication inquiry. Patient Education and Counseling, 82, 285–291. Kreps, G. L. (2012). Translating health communication research into practice: The importance of implementing and sustaining evidence-based health communication interventions. Atlantic Communication Journal, 20, 5–15. Kreps, G. L. (2019). Translational health research. In D. Anderson (Ed.), Leadership in drug and alcohol abuse prevention: Insights from long-term advocates (pp. 235–238). New York, NY: Routledge. Kreps, G. L., & Bonaguro, E. (2009). Health communication as applied communication inquiry. In L. Frey & K. Cissna (Eds.), The handbook of applied communication research (pp. 970–993). Hillsdale, NJ: Lawrence Erlbaum. Kreps, G. L., & Chapelsky Massimilla, D. (2002). Cancer communications research and health outcomes: Review and challenge. Communication Studies, 53(4), 318–336. Kreps, G. L., & Goldin, R. (2009). Why you should vaccinate your child against H1N1. STATS. Kreps, G. L., & Maibach, E. W. (2008). Transdisciplinary science: The nexus between communication and public health. Journal of Communication, 58(4), 732–748. Kreps, G. L., & O’Hair, D. (Eds.). (1995). Communication and health outcomes. Cresskill, NJ: Hampton Press. Kreps, G. L., Villagran, M. M., Zhao, X., McHorney, C., Ledford, C., & Weathers, M. (2011). Applying consumer psychology to develop and validate motivational message interventions for improving prescription drug adherence with consumers confronting chronic diseases: A multimethodological field study. In R. Batra, P. Anand, & V. Strecher (Eds.), Consumer psychology and health communication (pp. 233–250). Armonk, NY: M.E. Sharpe, Inc.

102 Kreps Kreps, G. L., Viswanath, K., & Harris, L. M. (2002). Advancing communication as a science: Opportunities from the federal sector. Journal of Applied Communication Research, 30(4), 369–381. Kunkel, D. (2010). Media research contributes to the battle against childhood obesity. Health Communication, 25, 595–596. Maibach, E., Parrott, R. L., Long, D. M., & Salmon, C. T. (1994). Competencies for the health communication specialist of the 21st century. American Behavioral Scientist, 38(2), 351–360. Minkler, M. (2000). Using participatory action research to build healthy communities. Public Health Reports, 115(2–3), 191–197. Minkler, M., & Wallerstein, N. (Eds.). (2002). Community-based participatory research for health. Indianapolis, IN: Jossey-Bass. National Cancer Institute (2008). The role of the media in promoting and reducing tobacco use. Tobacco Control Monograph No. 19. Bethesda, MD: US Department of Health and Human Services, National Institutes of Health, National Cancer Institute. NIH Pub. No. 07-6242 Neuhauser, L. (2001). Participatory design for better interactive health communication: A statewide model in the USA. The Electronic Journal of Communication, 11(3 & 4). Retrieved from http://www.cios. org/EJCPUBLIC/011/3/01134.HTML Neuhauser, L., & Kreps, G. L. (2011). Participatory design and artificial intelligence: Strategies to improve health communication for diverse audiences. In N. Green, S. Rubinelli, & D. Scott (Eds.), Artificial intelligence and health communication (pp. 49–52). Cambridge, MA: American Association of Artificial Intelligence Press. Neuhauser, L., Kreps, G. L., & Syme, S. L. (2013). Community participatory design of health communication interventions. In D. K. Kim, A. Singhal, & G. L. Kreps (Eds.), Health communication: Strategies for developing global health programs (pp. 227–243). New York, NY: Peter Lang. Noar, S. M., Palmgreen, P., Chabot, M., Dobransky, N., & Zimmerman, R. S. (2009). A 10-year systematic review of HIV/AIDS mass communication campaigns: Have we made progress? Journal of Health Communication, 14(1), 15–42. O’Hair, D., & Kreps, G. L. (Eds.). (1990). Applied communication theory and research. Hillsdale, NJ: Lawrence Erlbaum. Pearce, W. B. (1996). On putting social justice in the discipline of communication and putting enriched concepts of communication in social justice research and practice. Journal of Applied Communication Research, 26(2), 272–278. Putnam, L. L., & Dempsey, S. E. (2015). The five faces of engaged scholarship: Implications for feminist research. Women & Language, 38(1), 11–21. Siu, W. (2010). Fear appeals and public service advertising: Applications to influenza in Hong Kong. Health Communication, 25, 580. Steimel, S. (2014). Mapping a history of applied communication research: Themes and concepts in the journal of applied communication research. Review of Communication, 14(1), 19–35. Viswanath, K., & Kreuter, M. W. (2007). Health disparities, communication inequalities, and eHealth. American Journal of Preventive Medicine, 32(5), S131–S133. Viswanath, K., Nagler, R. H., Bigman-Galimore, C. A., McCauley, M. P., Jung, M., & Ramanadhan, S. (2012). The communications revolution and health inequalities in the 21st century: Implications for cancer control. Cancer Epidemiology and Prevention Biomarkers, 21(10), 1701–1708. Vorderer, P. (2016). Communication and the good life: Why and how our discipline should make a difference. Journal of Communication, 66(1), 1–12.

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The Role of Negative Emotions in Applied Communication Research Elena Bessarabova, John A. Banas, and Daniel R. Bernard

Emotions permeate communication and decision making in a variety of applied persuasion contexts, including risk, health, and commercial advertising. Discrete emotions have been shown to improve the quality of decisions and communication (Ferrer, Klein, Lerner, Reyna, & Keltner, 2015), but they also can undermine persuasive efforts (Bessarabova, Turner, Fink, & Blustein, 2015). Capitalizing on the motivational properties of emotions makes sense, since in the realm of health, for instance, many underlying causes of mortality and morbidity affecting people’s quality of life (e.g., heart disease stemming from sedentary lifestyle or diabetes as a result of uncontrolled carbohydrate intake) can be avoided with simple behavioral changes (e.g., physical activity or healthier diet; Fisher et al., 2002; Ford, Zhao, Tsai, & Li, 2011). Because discrete emotions can produce dramatically different persuasive outcomes, resulting in divergent influences on risk perceptions (Lerner & Keltner, 2000) and compliance with message recommendations (Nabi, 1999, 2002a), understanding the nuances of emotion inductions and pragmatic implications of current persuasion and decision‐making research becomes increasingly important. In this chapter, we focus on the effects of anger, fear, and guilt. These emotions are prevalent in applied communication research and are ubiquitous in practice. Messages inducing fear are common in public‐health campaigns and medical decision making (Ferrer et al., 2015). Anger is on the rise in political persuasion, with some analyses finding that anger was employed in about half of recent campaign ads, and in 11% of those ads, anger was the predominant emotion (Brader, 2006). Guilt appeals account for 6% of magazine advertising (Huhmann & Brotherton, 1997) and are one of the frequently reported tools of interpersonal influence (Bybee, 1998). Despite the fact that anger, fear, and guilt are all considered negative emotions, employing these emotions motivates distinct behavioral tendencies (Lazarus, 1991). Thus, understanding their unique effects as well as contexts and message features that help facilitate adaptive changes in attitudes, behaviors, and decision making should be informative for practitioners and scholars of applied communication.

Fundamentals of Emotion Defined “as internal mental states representing evaluative, valenced reactions to events, agents, or objects” (Nabi, 2015, p. 114; see also Ortony, Clore, & Collins, 1988), emotions are characterized by differences in physiological arousal, motor expression, subjective experience, and The Handbook of Applied Communication Research: Volume 1, First Edition. Edited by H. Dan O’Hair and Mary John O’Hair. © 2020 John Wiley & Sons, Inc. Published 2020 by John Wiley & Sons, Inc.

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action tendencies that vary in intensity but are typically relatively short in duration (Nabi, 2002a; Scherer, 1984). In experimental research, scholars distinguish between integral and incidental emotions. Integral emotions—defined as emotion inductions relevant to the judgment being made (Lerner, Li, Valdesolo, & Kassam, 2015), as when fear is being elicited in a fear appeal advocating breast cancer screening—have been shown to produce positive consequences for health‐related outcomes and prevention intentions (Ruiter, Kessels, Peters, & Kok, 2014). Integral inductions of fear, for instance, have been found to motivate self‐protective behaviors (Peters, Ruiter, & Kok, 2013) resulting in adaptive effects in response to a health concern. Incidental emotions—defined as emotion inductions that are unrelated to the persuasive message topic or a decision being made (Lerner et al., 2015), as when sadness is being induced by a video about the death of a pet in a study of economic decisions—have equally been shown to influence those subsequent, albeit unrelated, decisions (Ferrer et al., 2015; see also Loewenstein & Lerner, 2003). In psychology and particularly in studies employing the appraisal‐tendency framework (ATF; Lerner & Keltner, 2000), researchers mostly employ incidental‐emotion inductions, with a few exceptions (Lerner, Gonzalez, Small, & Fischhoff, 2003). In communication research, the inductions tend to be mixed, with some researchers examining incidental affect (e.g., Mitchell, Brown, Morris‐ Villagran, & Villagran, 2001) and others focusing on the effects of discrete integral emotions (e.g., Bessarabova et  al., 2015; Nabi, 2002b). Importantly, however, as Ferrer et  al. (2015) noted, “the pattern of judgment and decision‐making arising from an emotion will be similar regardless of whether it is integral or incidental” (p. 104).

Theories of Affect Contemporary theorizing about the effects of discrete emotions on message processing and decision making is grounded in appraisal theories (Frijda, 1986; Lazarus, 1991), suggesting that all discrete emotions have core relational themes, a system of mental representations comprising positive (e.g., advantages and benefits) and negative (e.g., costs and harms) characteristics associated with a given cause or target of an emotion (Ferrer et al., 2015). Activation of a particular relational theme will produce an emotional response consistent with that theme. For instance, a non‐specific danger or threat will result in anxiety, and a demeaning offense to one’s self or important others will generate anger (Smith & Lazarus, 1990). Furthermore, emotion‐specific core relational themes will motivate different actions on the basis of the theme appraisal (Frijda, 1986; Lazarus, 1991; Roseman, Wiest, & Swartz, 1994; Scherer, 1999). Other foundational ideas for contemporary research on emotions pertain to empirically derived appraisal dimensions—a categorization system that allows classifying cognitive tendencies of each emotion along six dimensions (Smith & Ellsworth, 1985): pleasantness or the extent to which an arousal might be pleasant or unpleasant or, in valence terms, positive or negative; certainty or the extent to which an emotion can be attributed to a predictable/ known cause; self‐other responsibility/control or the extent to which events causing an emotion were controlled by the person or someone else; situational control or the extent to which events causing an emotion were controlled by some impersonal circumstances; attentional activity or the extent to which a stimulus eliciting emotion generates motivation to pay attention; and anticipated effort or the amount of effort required to address an emotion‐ inducing situation. Although many other theories of affect exist, and the predictions of some are only applicable to a specific emotion (e.g., Witte’s, 1992, extended parallel process model, EPPM, only concerns fear), two overarching theoretical perspectives that provide frameworks to examine multiple emotions are the cognitive functional model (CFM; Nabi, 1999) and the ATF (Lerner & Keltner, 2000), which we review below.



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Cognitive Functional Model Building on functional emotion theories (Frijda, 1986; Izard, 1977; Lazarus, 1991) as well as dual‐process models of persuasion (Chaiken, 1987; Petty & Cacioppo, 1986), the CFM (Nabi, 1999, 2002b) synthesized core elements from previous research to explain the effects of discrete emotions on message processing and persuasion (Nabi, 1999). According to the CFM, when a core relational theme of an emotion is recognized in a message, it may elicit an emotional response consistent with its core relational theme. Subsequently, the message will motivate attentional focus according to the attentional tendencies of a given emotion and trigger information processing consistent with the processing tendencies of that emotion (Nabi, 2002b). For example, focusing on the effects of negative emotions, the CFM predicts, “based on the type of emotion experienced, motivated attention sets a baseline attention level that will either impede (for avoidance emotions, like fear) or facilitate (for approach emotions, like anger) subsequent information processing” (Nabi, 2002b, p. 206). The model also predicts that messages containing emotion‐inducing material will result in goal‐directed behaviors consistent with action tendencies of a given emotion.

Appraisal‐Tendency Framework Developed at approximately the same timeframe as the CFM, the ATF (Lerner & Keltner, 2000, 2001; Lerner & Tiedens, 2006) is grounded in the same foundational ideas as the CFM. In the ATF, discrete emotions elicit cognitive and motivational appraisals—appraisal tendencies—associated with the individual or situation that gave rise to an emotion. Subsequently, these appraisal tendencies help shape behavioral response—action tendencies—resulting in specific judgments, decisions, and actions (Lerner et al., 2015). Appraisal tendencies do not only help generate the response to the emotion‐triggering person or event but also have been shown to determine both the depth and the content of an individual’s cognitive activity (Ferrer et al., 2015). Taken together, the CFM and the ATF both provide useful theoretical frameworks for understanding the effects of emotions on persuasion and decision making, with the CFM being applied more in communication and the ATF being more prevalent in psychology. Both rely on the ideas of appraisal themes, action tendencies, and differences in systematic versus heuristic information processing as the basis for their predictions. The ATF mostly considers the effects of incidental emotions, and the CFM is more likely to be applied to integral affect (although both incidental‐ and integral‐emotion inductions have been used in studies employing the CFM). The CFM is also more concerned with mediated or message effects of emotions. Although the two theories mostly agree with regard to the outcomes that arise for any given emotion, the theories produce some divergent predictions: Regarding the emotions discussed in this chapter, the CFM, unlike the ATF, is more likely to view anger as having adaptive outcomes when it comes to message processing (Nabi, 2002b; cf. Lerner & Keltner, 2001).

Anger Anger is a negative emotion that occurs when experiencing obstacles to goal achievement (Lazarus, 1991), facing social injustice (Turner, 2013), or undergoing degradation (Nabi, 1999). In ATF terms, anger produces appraisals of certainty, control, low levels of pleasantness, and others’ responsibility, resulting in a perception that a negative event is “predictably caused by, and under the control of, other individuals” (Lerner et al., 2015, p. 807). Messages that employ anger to produce attitude and behavior change—anger appeals—intentionally capitalize on these appraisals of anger to focus audiences’ attention on a negative event caused by

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another person or entity (e.g., an organization) in hopes of motivating message‐consistent action (Turner, 2013). Anger arousal can range in intensity from feelings of irritation to rage (Allcorn, 1994; Rubin, 1986), producing vastly different outcomes based on its intensity. The effects of moderate‐ intensity anger can be more constructive relative to high levels of anger (Greer & Morris, 1975; Thomas, 1993) and can be reasonably incorporated into daily decisions (Loewenstein, 1996), facilitating the ability to solve problems (Averill, 1982). Intense levels of anger, on the contrary, may result in perceptions of a lack of control and cause people to act “against their own self‐ interest” (Loewenstein & Lerner, 2003, p. 627). Intense anger undermines the ability to process information (Nabi, 1999) and has been linked to aggression (Fein, 1993; Guerrero, 1994) as well as impulsive and self‐destructive behavior (Loewenstein, 1996). Some scholars have argued that anger intensity depends on the type of anger induction being used, with integral anger inducing more intense levels of emotion and incidental anger inductions producing more moderate levels of intensity that have less conscious and stable effects over time (Gino & Schweitzer, 2008; Wiltermuth & Tiedens, 2011). Because “integral emotions are generated from the decision context itself,” they “are more likely than incidental emotions to be infused into the decision process” (Gino & Schweitzer, 2008, p. 1165). Although most research focuses on the effects of anger relative to some other emotion (e.g., fear or happiness; Lerner & Keltner, 2001), some theories attempt to account for differences in types of anger inductions as well as its intensity (Turner, 2007).

Theorizing about Anger The effects of anger have been studied from the theoretical lenses of the ATF (Lerner & Keltner, 2000; see Ferrer et al., 2015; Lerner et al., 2015, for reviews) and the CFM (Nabi, 1999, 2002b, 2015). Anger is also part of more the contemporary view of the psychological reactance theory (TPR; Brehm, 1966; see also Miller, Massey, & Ma, Chapter 27 of this Handbook). The theory of psychological reactance predicts that limiting people’s ability to choose creates an aversive motivational state—reactance—driving people to restore attitudinal or behavioral freedoms. Threats to autonomy adversely affect message effectiveness resulting in message rejection and an increased probability of engaging in risks counter to the message, also known as a boomerang effect (Bessarabova, Fink, & Turner, 2013). Anger plays an important role in reactance: Recent research demonstrates that reactance can be operationalized as a combination of anger and negative cognitions (Dillard & Shen, 2005; Rains, 2013). Another theoretical perspective, the anger activism model (AAM; Turner, 2007), attempts to specify circumstances wherein integral anger appeals are likely to produce adaptive versus maladaptive outcomes on cognitions and behavioral intentions. Per the AAM, anger appeals should only be employed with audiences that hold attitudinal positions consistent with message advocacy, and counterattitudinal attempts—as when trying to anger people who do not believe in climate change into taking action to mitigate it—will fail. In addition to the initial agreement with the advocated position, the AAM argues that anger intensity and efficacy beliefs should be part of the equation because the motivational potential of anger appeals can be maximized when high levels of anger directed at the problem are induced in conjunction with some course of action being offered to solve the problem, correct the wrongdoing, or avenge the injustice. The AAM proposes an interaction between anger intensity and efficacy: “When efficacy beliefs are strong, anger has a linear effect on attitudes, cognitions, and intentions,” whereas at low efficacy, the relationship between anger and these outcomes is curvilinear (Turner, 2013, p. 29). These ideas represent an interesting future direction in anger research. Unlike the ATF (Lerner & Keltner, 2001), which focuses on the effects of anger relative to some other emotion, the AAM posits that different outcomes may be elicited if anger intensity is accounted for. Similar to



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the CFM (Nabi, 1999), the AAM recognizes the importance of efficacy beliefs and acknowledges that the effects of anger can be utilitarian, but the unique contribution of the AAM is in its attempt to theorize about the specific functional form of the relationships between anger intensity and efficacy perceptions on cognitive outcomes and behavioral intentions. Overall, the effects of anger are nuanced. We attempt to summarize the complexities of the effects of anger by discussing both the outcomes that may impede and those that facilitate persuasion in health and risk communication and decision making.

Pragmatic Implications from Anger Research Anger and information processing

Anger has been shown to increase reliance on heuristics (Bodenhausen, Sheppard, & Kramer, 1994), which prompted some scholars to infer a lack of systematic processing or inattention to detail as a result of anger (see Ferrer et  al., 2015). Indeed, employing an incidental anger induction by asking participants to recall an angry, sad, or happy event, Bodenhausen et  al. (1994), for example, found that angry participants were more likely to use heuristic cues of source credibility to guide their judgments about raising the legal driving age. Subsequent research on the effects of anger, however, demonstrates that although angry people do rely on heuristic cues, their attention to heuristics might not be because of limited elaboration and, instead, may be part of an information‐processing strategy, wherein heuristics are evaluated on the basis of their relevance and utility for the judgment being made (Moons & Mackie, 2007). Consistent with this idea, Moons and Mackie’s (2007) findings show that, relative to neutral‐ state inductions, evoking anger facilitated information processing and, although participants did rely on source‐cue information, they were more likely to do so only when the cue itself was valid and relevant to judgment. When a source cue was irrelevant to judgment, participants relied on message features to guide their decisions and were able to accurately distinguish between strong and weak arguments in the persuasive message. Their ability to differentiate between strong and weak arguments is indicative of systematic processing. Nabi (2002b), similarly, reports increased depth of processing as a result of anger. These findings indicate that relative to neutral states (Moons & Mackie, 2007), anger appears to provide a boost to elaborative message processing and could be meaningfully incorporated into message design. Furthermore, it seems reasonable that, because cognitive appraisals of anger are associated with assigning blame, such appraisals might increase attention to source cues. Thus, messages employing anger would benefit from featuring easily identifiable and credible source cues to help facilitate information processing. Despite the benefits associated with employing anger reviewed above, there are circumstances wherein anger inductions might impede message elaboration, thereby facilitating biased processing. Research on political communication demonstrates that anger associated with politics (induced through an essay‐writing task) increased propensity to process information along partisan lines, resulting in an increased likelihood of endorsing own‐party political misperceptions relative to the neutral‐state controls (Bessarabova & Meirick, 2017; Weeks, 2015). For issues that are closely tied to one’s identity and are part of an important set of beliefs (like partisanship), anger motivates defensive processing and desire to confirm prior attitudinal positions (Weeks, 2015). As a result, anger may guide people to process prevention messages with an eye on whether the recommended behavior fits their worldview.

Anger, optimism, and susceptibility to risk

Inductions of incidental anger have been shown to increase optimism about future events, relative to incidental fear inductions (Lerner & Keltner, 2001), a finding that is often discussed in terms of optimism bias, a maladaptive consequence of anger (Lerner & Keltner, 2001).

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Similarly, anger has been shown to reduce perceptions of susceptibility to risk (Nan, 2017). Despite the fact that optimism bias can motivate people to engage in unnecessary risks, feeling optimistic about one’s ability to deal with anger‐inducing issues can be instrumental to goal achievement by helping to bolster efficacy beliefs, thereby making people try harder to resolve a problem. Using anger might also be beneficial in circumstances when making a risky choice increases the probability of a reward relative to choosing a safer option (e.g., deciding to opt for a risky medical treatment when chances of survival using safer approaches are not promising; Ferrer et al., 2015).

Anger and attributions

Anger results in increased stereotyped attributions relative to fear (Tiedens & Linton, 2001), sadness, and neutral‐mood inductions (Bodenhausen et al., 1994). In health decision making and patient–doctor interactions, accounting for interpersonal attributions is particularly important. When anger triggers stereotyping, it might, for instance, foster a perception that a female surgeon should not be trusted to conduct a surgery despite the fact that she is a leading expert in her field. Overall, because anger activates appraisals of injustice and offense, it negatively affects trust (Dunn & Schweitzer, 2005) and may compromise patients’ receptiveness to their doctors (Ferrer et al., 2015). Because the appraisal of offense results in attributions of accountability (Lerner, Goldberg, & Tetlock, 1998) and blame (Lerner & Keltner, 2000, 2001; Smith & Ellsworth, 1985), integral anger activates the schema associated with retribution, resulting in activation of punitive solutions (Kühne & Schemer, 2013; Nabi, 2003) and preference for punitive information seeking (Nabi, 2003). Thus, angry individuals might focus more on blaming their doctor or the healthcare system as opposed to focusing on treatment options or carefully following doctors’ recommendations.

Anger and persuasion

The effects of anger on persuasion are nuanced. On the one hand, anger has been shown to increase systematic processing and facilitate persuasion (Moons & Mackie, 2007). On the other hand, reactance research implicates anger (albeit indirectly) in the reduction of message effectiveness (see Rains, 2013). These findings point to the importance of careful planning in message design: Anger can be utilitarian when guided by a message toward resolving an anger‐inducing issue, but anger will undermine persuasion if message features trigger reactance.

Future Directions in Applied Research on Anger Considering future research directions that should be pursued in anger and applied communication research, a few ideas merit discussion. First, given that prevention recommendations by definition involve goal interruption increasing the likelihood of eliciting anger, research pursuing anger mitigation techniques would be fruitful. Because anger is an approach emotion (Izard, 1977), characterized by desire to engage with the anger‐triggering situation and to tackle obstacles (Frijda, Kuipers, & ter Schure, 1989), research attempting to garner anger’s motivational power would also be of great practical importance. Finally, understanding how other message features interact with the anger component of persuasive communication is another potential direction for future research. For instance, since research demonstrates that emotions affect how people perceive loss‐ vs. gain‐frame messages (DeSteno, Petty, Rucker, Wegener, & Braverman, 2004; Wegener, Petty, & Klein 1994), and that angry individuals appear to find gain frames more persuasive (Gerend & Maner, 2011), systematically evaluating the influence of various message features should be given greater consideration.



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Fear Fear is a negatively valenced emotion accompanied by a high level of arousal that is perceived to be both significant and personally relevant (Easterling & Leventhal, 1989; Ortony & Turner, 1990; Witte, 1992; Witte, 1998). Fear motivates appraisals of negative events as being unpredictable, outside of one’s control, and resulting in an uncertain future (Lerner & Keltner, 2000). The core appraisal theme of fear includes perceptions of being threatened or in danger, mobilizing behaviors to modify a harmful or threatening environment to avoid risk (Lazarus, 1991). Fear appeals capitalize on the motivational tendencies of fear to scare people into compliance by describing the adverse consequences individuals could suffer if they ignore message recommendations (Witte, 1992).

Theorizing about Fear Fear and fear‐appeals research has a longstanding history that laid the groundwork for more contemporary theorizing about the effects of fear. Early fear‐appeals research focused on drive models (Hovland, Janis, & Kelly, 1953; Janis, 1967) to explain the role fear plays in motivation and persuasion. Hovland et al. (1953) focused on the physiological manifestation of fear along with its motivational properties. Regarding the effect of fear on persuasion, Janis (1967), in his fear‐as‐acquired‐drive model, argued that the relationship between fear and persuasion has an inverted‐U function, wherein fear arousal results in greater persuasion only at moderate intensity levels because, at low levels of fear, the arousal is insufficient to create motivational drive, and at high levels, the effects of fear become maladaptive. Focusing on cognitive processing, Leventhal (1970) introduced the parallel response model that bifurcates the response to threatening messages into fear control and danger control (discussed in more detail further below). Although Leventhal’s model lacked theoretical precision, and drive models did not receive empirical support (Witte, 1992), the ideas from early research provided important theoretical foundations for subsequent theorizing on fear. In today’s research on fear appeals, the two most prevalent theoretical perspectives—protection motivation theory (PMT, Rogers, 1975) and EPPM (Witte, 1992)—have generated a substantial amount of research resulting in several meta‐analyses to test their predictions (Ruiter et al., 2014). Since the EPPM builds on the ideas of PMT, we start with PMT. Drawing on Leventhal’s (1970) ideas of danger control, the PMT focuses on message features and cognitive processes of fear appeals that help bolster their motivational potential (Rogers, 1975, 1983). The PMT argues that protection motivation operates as an intermediary between fear and behavioral responses and is likely to be activated when a fear appeal includes a threat component and coping strategies. Perceived threat involves assessing both the severity of and the susceptibility to the danger presented in the persuasive message. Importantly, the message must evoke danger severe enough to focus attention on the consequences, and the audience must feel that the threat can, and will, affect them in a meaningful way. Perceived efficacy involves the assessments of whether the message recommendations will be effective at eliminating the threat (response efficacy) and whether the individual has the capacity to perform the behaviors endorsed in the message (self‐efficacy). The PMT predicts when threat is low or coping strategies are inadequate, a message will be ineffective by failing either to elicit fear or to offer a reasonable mechanism to deal with it. Conversely, if a message arouses enough threat to create fear, it may motivate behavior change but only if audiences feel they have the ability to cope (Rogers, 1975, 1983). Synthesizing the ideas from previous research on fear, the EPPM (Witte, 1992) provides a useful framework for understanding adaptive and maladaptive consequences of fear. Similar to the parallel response model (Leventhal, 1970), the EPPM conceptualizes two distinct responses to fear appeals—fear control and danger control—and specifies conditions when each response may

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take place. The effects of fear control are maladaptive: Aimed at coping with fear rather than mitigating threat, fear control results in attempts to reduce fear through denial, defensive avoidance, or reactance (Witte, Meyer, & Martell, 2001) that can motivate people to act contrary to the message. Conversely, the model predicts that danger control leads to adaptive behaviors to alleviate threat rather than simply addressing feelings of fear. As a result, individuals may become motivated to adopt message recommendations or change their behavior to reduce the threat. In the EPPM, the extent to which an individual engages in fear versus danger control processes depends on the appraisals of threat and efficacy perceptions, resulting in three possible outcomes: (a) when threat is low, the model predicts no change in attitudes or behaviors because in the absence of threat and subsequent fear, individuals will not be motivated to attend to or process the message; (b) when threat is high and efficacy is low, the model predicts fear control, focusing on eliminating the fear without attempting to constructively deal with the threat because people either do not believe the recommendations will work or do not see themselves being capable of enacting the recommended behaviors; and (c) when both threat and efficacy are high, individuals will engage in danger control and will be motivated to alleviate threat by adopting the recommended behaviors (Roberto, Goodall, & Witte, 2009). The predictions of the EPPM as well as the PMT were tested in multiple meta‐analytic studies (De Hoog, Stroebe, & De Wit, 2007; Earl & Albarracín, 2007; Floyd, Prentice‐Dunn, & Rogers, 2000; Milne, Sheeran, & Orbell, 2000; Witte & Allen, 2000), yielding results in support of both theories. When maladaptive outcomes of fear were part of the meta‐analytic investigation (Peters et al., 2013), support for both fear‐control and danger‐control processes was found. The most recent meta‐analysis of fear appeals (Tannenbaum et al., 2015) is yet another confirmation of the effectiveness of fear‐based messages at facilitating adaptive changes in attitudes and behaviors. However, in contrast to the EPPM predictions, Tannenbaum and colleagues’ (2015) results indicate that there are virtually no circumstances under which fear appeals fail or foster a maladaptive change. Testing interaction effects, the meta‐analysis produced results consistent with previous research, suggesting that fear’s effectiveness is maximized when messages emphasize susceptibility and severity along with potential measures to prevent threat (i.e., contain efficacy information) that advocate for a one‐time (not repeated) activity to mitigate threat. Finally, fear appeals also appeared to be more effective in samples that included more females than males. These meta‐analytic findings offer important implications for fear‐appeal scholars and practitioners. In addition, we discuss several other pragmatic implications related to the success of fear‐appeal messages, including potential mitigation strategies and research findings on fear and risk perception, along with research focusing on a particular type of fear, existential anxiety.

Pragmatic Implications from Fear Research Fear and persuasion

Meta‐analytic evidence (Tannenbaum et al., 2015; see also Ruiter et al., 2014) indicates that fear appeals can be an effective strategy to marshal emotion in persuasive messages, and that message components highlighting the severity of and susceptibility to the threat along with a response and self‐efficacy component appear to be key to message effectiveness. Despite the overall effectiveness of fear appeals (Tannenbaum et  al., 2015), considering mitigation strategies to avoid defensive processing and to maximize the motivational potential of fear remains a fruitful avenue in applied research. One such approach considers the effects of self‐affirmation, a mitigation strategy designed to affirm an individual’s identity through responding to questions about a cherished value or writing an essay about a desirable quality; this strategy may help to promote balanced and open‐minded appraisals of threatening health messages (Harris & Napper, 2005;



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Sherman & Cohen, 2002; for a review, see Harris & Epton, 2009). For example, self‐affirmation helped mitigate defensive processing induced by graphic warning labels among smokers (Harris, Mayle, Mabbott, & Napper, 2007) and was likewise effective in a study ­promoting a reduction in alcohol consumption among females (Harris & Napper, 2005). Together, these findings indicate that self‐affirmation is a viable mitigation strategy promoting adaptive outcomes in fear‐inducing messages (Ruiter et al., 2014).

Fear and risk perceptions

Fear is associated with feelings of uncertainty and appraisal themes of being threatened, both of which are likely to result in risk‐averse behaviors and decisions (Ferrer et al., 2015; see also Rivers, Reyna, & Mills, 2008). Appraisals of uncertainty along with the perception that the situation is outside of one’s control (Lerner & Keltner, 2000) also lead to pessimistic assessments of future events (Lerner & Keltner, 2001). For instance, measuring risk perceptions in the aftermath of 9/11, Lerner et al. (2003) found that priming integral fear (by asking participants to write essays detailing what makes them fearful about 9/11) resulted in greater perceived probability of another terrorist attack happening in the United States. The same pattern has emerged regarding risk estimates for which the emotion induction was incidental: The probability of contracting flu, for example, was also judged as being greater as a result of fear (relative to anger).

Fear vs. existential anxiety

Although health communication from agencies like the Centers for Disease Control and Prevention frequently evokes death and dying, research employing the terror management health model (Goldenberg & Arndt, 2008) suggests that death awareness can be maladaptive for risk prevention. Reminders of death can motivate behaviors that help enhance symbolic aspects of the self, such as tanning to enhance one’s attractiveness (Routledge, Arndt, & Goldenberg, 2004) or smoking to fit into one’s social circle (Wong, Nisbett, & Harvell, 2017) regardless of the risks associated with them. Using death‐awareness inductions paired with a health message advocating the prevention of sexually transmitted diseases (STDs), Bessarabova and Massey (2019) found that “after being made death aware, then warned about the deadly nature of STDs, participants rejected safe‐sex advocacy and instead expressed intentions to engage in risky sexual behaviors” (e.g., intention to engage in unprotected sex) “despite negative ramifications to their own health as forewarned by the prevention message” (p. 11). The explanation for these effects is grounded in evolutionary psychology and terror management theory (TMT; Solomon, Greenberg, & Pyszczynski, 1991), stating that death reminders induce existential anxiety, motivating people to buffer uncomfortable death thoughts by seeking ways to symbolically transcend death. Per TMT, one method of symbolic death transcendence is through interpersonal relationships because they “satiate an innate need for connection and belongingness, serve as a source of self‐esteem, and present opportunities to mate, procreate, and in doing, pass one’s genes into the future” (Bessarabova & Massey, 2019, p. 3; see also Mikulincer, Florian, & Hirschberger, 2004). In TMT, procreation is a form of death transcendence since having genetic offspring helps ensure parental genes survive beyond their own death (Dechesne et al., 2003). The symbolic defenses against death anxiety stand in stark contrast with prevention message recommendation referencing potentially deadly consequences of STDs while simultaneously advocating methods to alleviate potential health threat by advocating activities precluding procreation (i.e., promoting safe sex practices such as abstinence or condom use). Thus, Bessarabova and Massey’s findings indicate that referencing death in STD prevention messages may lead to the effects directly opposite to those intended by risk communicators. Overall, as the authors noted, “in messages inducing existential threat, symbolic terror management effects should be considered, and the extent to which these effects might be maladaptive for risk p ­ revention should be assessed” (p. 18).

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Future Directions in Applied Research on Fear A new and promising direction in emotion research in general involves attempts at sequencing emotions (see Nabi, 2015, for a discussion). In fear‐appeal research specifically, emotion sequencing has been applied for the purpose of understanding the role of hope in fear‐inducing messages. Nabi and Myrick (2018), for instance, demonstrate that capturing the effects of hope in fear‐based cancer‐prevention messages helped account for the unique variance in body‐protective behaviors. Applying these ideas to environmental messages that experimentally manipulated fear along with gain vs. loss framing, Nabi, Gustafson, and Jensen (2018) found, when the efficacy component was framed in terms of gains, the fear‐appeal message resulted in an increase in hope relative to efficacy being framed in terms of losses. Furthermore, “messages that evoked fear and then hope had the strongest positive influence on advocacy behavior compared with the message structure that lacked emotional flow” (Nabi et al., 2018, p. 460). Another research direction that focused on efficacy information in risk‐prevention messages suggested examining the role of collective efficacy in fostering adaptive changes (Roberto et al., 2009). Defined as a shared perception of ability within a group to enact a particular task or reach an identified goal (Bandura, 1997), collective efficacy perceptions have been shown to be positively associated with intentions to enact prosocial change (Smith, Ferrara, & Witte, 2007). For instance, examining the role of stigma and collective efficacy beliefs in how the larger community feels about adoption of orphans whose parents died of AIDS in Namibia, Smith et al. (2007) found that collective efficacy (not individual efficacy beliefs) were positively associated with perceptions that people within a community would be willing to adopt an orphan. Collective efficacy has emerged as a factor for those who perceived high personal threat (i.e., high severity and susceptibility to HIV/AIDS) and for those who felt their threat of HIV/AIDS was low. For those with low‐threat perceptions, collective efficacy (along with perceived group stigma) appeared to serve as a motivator even in the absence of a threat (which is traditionally thought of as a motivating factor within EPPM) and was positively associated with perceived group willingness to help the community by adopting orphans. Smith et al.’s results suggest that even in the absence of individual efficacy beliefs the influence of collective efficacy can be powerful. Future research should then explore the relationships when the efficacy component presented in the message is discrepant from collective efficacy beliefs. It is likely that when message recommendations are at odds with collective efficacy perceptions, the latter may override the perceptions of individual self‐efficacy, reducing the likelihood of message acceptance and behavioral change. Overall, accounting for the effects of collective efficacy in risk and health communication presents an interesting future direction in fear‐appeals research (see Roberto et al., 2009, for a discussion). Similar to Nabi’s ideas on emotion flow, some research has focused on understanding the dynamics associated with fear appeals (Dillard, Li, Meczkowski, Yang, & Shen, 2017; Shen & Coles, 2015; Shen & Dillard, 2014), using a within‐participants approach to studying the effects of fear. Rather than focusing on the intensity of fear inductions, Shen and Coles (2015), for instance, argued that the oscillations in fear arousal throughout the exposure to the persuasive message offer a more nuanced understanding of how fear appeals work. Accounting for fear‐ appeal dynamics helped reveal an inverted‐U function of the relationship between fear and persuasion (Dillard et  al., 2017) that was part of Janis’ (1967) drive model and the original conceptualization of EPPM (Witte, 1992), in contrast to the linear effect found using a between‐participants approach (Tannenbaum et al., 2015). Future examinations of within‐participants effects of fear along with studying it in sequence with other discrete emotions present interesting directions for future research.



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Guilt Guilt is a negative emotional state caused by recognizing that one has failed (or anticipates one might fail) to meet some personal standard of conduct (Miceli, 1992; O’Keefe, 2000). Situations associated with feelings of guilt include obvious examples like lying, cheating, or stealing but also include more subtle ones like failure to perform duties (e.g., not taking out the garbage as promised) or not treating others with kindness (Keltner & Buswell, 1996; Tangney, 1992). As the examples above demonstrate, guilt can result from action or inaction, as long as one is aware of the incongruity between one’s conduct and one’s personal standards. When people feel guilty, they wish they could undo what they did wrong and desire to make up for their actions (Roseman et  al., 1994; Tangney, Miller, Flicker, & Barlow, 1996). Because people feel responsible and want to make amends when they feel guilty, using communication to make a person feel guilt can serve as a powerful method for influencing attitudes, intentions, and behaviors. Like all emotions, guilt is associated with distinct cognitive appraisals. Through the lens of appraisal theories, guilt is viewed as an unpleasant emotional state, characterized by a moderate level of certainty as to what event was antecedent to the state (Smith & Ellsworth, 1985). Guilt results in attributions of self‐blame and leads to a perception that attempts to alleviate guilt would likely require substantial effort (Smith & Ellsworth, 1985).

Guilt Appeals Guilt appeals are persuasive messages utilizing guilt as a motivational catalyst. These appeals typically contain two elements: First, there is information intended to elicit feelings of guilt (or anticipated guilt) in the message target, and second, the message contains a recommended behavior or perspective, which ostensibly will ameliorate the feelings of guilt (O’Keefe, 2000). An example of a guilt appeal might be a commercial advertisement targeting working parents that attempts to make them feel guilty about not spending enough time with their children and then suggests a surprisingly affordable trip to a theme park that would presumably alleviate that guilt. Researchers usually test the effectiveness of guilt appeals by comparing messages of varying levels of emotional intensity (O’Keefe, 2000). Typical comparisons test low‐intensity (weak) against high‐intensity (strong) guilt appeals, but some research adds comparisons of guilt appeals to other emotional appeals, like shame (Boudewyns, Turner, & Paquin, 2013). Research on guilt appeals tends to focus on integral rather than incidental emotion inductions.

Approaches to Examining Guilt Research on using guilt to influence others typically follows one of three frameworks: interpersonal relationships, transgressions and compliance, and guilt appeals in persuasive messages (O’Keefe, 2000). The interpersonal relationship research typically involves retrospectively surveying people about typical occurrences of guilt in everyday life. Using guilt as an interpersonal influence technique appears to be quite common; one study found that 93% of recalled incidents of someone trying to make the participant feel guilty involved close interpersonal relationships (Baumeister, Stillwell, & Heatherton, 1994). Furthermore, by far the most common reported reason for arousing guilt in others is persuasion (Vangelisti, Daly, & Rudnick, 1991). The transgressions and compliance framework uses experimental methods to compare whether participants who commit transgressions (and it is assumed will feel guilty) will be more likely to engage in compliance with a request (compared to those who have not committed transgressions). For example, Boster et al. (1999) manipulated a situation in which a college‐student participant waiting to take part in research was in a room with a confederate who was ostensibly taking a makeup exam. The confederate began to cheat on the exam and

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was caught by the experimenter, who expressed disappointment in the participant for allowing the unethical behavior to occur. Subsequently, the participant was asked to comply with a prosocial request. The guilt appeal framework occurs frequently in persuasion, advertising, and health communication studies where a campaign message aims to make viewers feel guilty or anticipate feeling guilty, and then a recommendation is offered that presumably will alleviate the guilt. For example, Bessarabova et al. (2015) manipulated messages to evoke low, medium, or high levels of integral guilt that attempted to influence adolescents to take their studies more seriously. The transgressions and compliance research framework empirically demonstrates that induced guilt increases compliance with strangers’ requests. Meta‐analytic results from O’Keefe (2000) show a positive association between interpersonal transgressions and compliance, r  =  0.28. Conversely, for research from the guilt appeals framework, O’Keefe’s meta‐analysis reported a negative relationship between guilt and persuasion, r = −0.26. Scholars explain this discrepancy between research frameworks by the positive association between guilt and anger, which is counterproductive to attitude change, particularly if the message source is the target of the anger (Nabi, 2002a; O’Keefe, 2002). Similarly, scholars have found guilt to be positively associated with psychological reactance, which has been shown to decrease attitude change (Bessarabova et al., 2015; Quick, Kam, Morgan, Montero, & Smith, 2015). The widely variant results from meta‐analyses of different research paradigms of guilt and persuasion research, as well as the tendency of guilt to increase anger and psychological reactance, indicate the presence of important moderators to consider when deciding whether to utilize guilt in efforts to influence others. These factors and other practical considerations are discussed below.

Pragmatic Implications from Guilt Research Guilt and persuasive campaigns

Perhaps the most important applied lesson regarding guilt is that caution must be used when attempting to use guilt to persuade. High‐intensity guilt appeals frequently fail due to invoking other negative emotions along with guilt. Commercial advertising researchers have found that high‐intensity guilt appeals can lead to negative affective reactions like anger and resentment (Coulter, Cotte, & Moore, 1999; Coulter & Pinto, 1995; Pinto & Priest, 1991). Similar results have been reported among scholars who have studied guilt‐based interpersonal influence (Baumeister et al., 1994; Rubin & Shaffer, 1987). A 2012 study by Turner and Underhill on guilt appeals motivating emergency‐preparedness behaviors reported that high‐intensity guilt appeals caused the most anger toward the message source. Appeals that are seen as manipulative will trigger reactions counterproductive to influence. High‐intensity guilt appeals lead to greater psychological reactance (Bessarabova et al., 2015; Quick et al., 2015). Interestingly, moderate guilt appeals, frequently cited as the solution to the dilemma of high‐intensity appeals leading to anger (Pinto & Priest, 1991), do not necessarily perform better than high‐intensity appeals (O’Keefe, 2000). Although the research findings can be complex, there are several pragmatic implications for practitioners of applied communication concerning the use of guilt appeals. Inducing guilt or anticipated guilt in others can be effective but only when used in the proper circumstances. Interpersonal connection is one way to enhance the effectiveness of guilt as an influence tactic. Research findings suggest that guilt is most effective in interpersonal contexts with strong relational connections (Baumeister et al., 1994). One way to make guilt appeals more effective is to incorporate interpersonal elements into persuasive messages: for example, health communication messages urging receivers to protect loved ones from preventable diseases through ­vaccination. Indeed, a recent meta‐analysis of such health communication studies by Xu and Guo (2018) revealed a strong positive effect of guilt on attitude change.



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Guilt vs. shame

Scholars and practitioners interested in utilizing guilt to persuade need to carefully distinguish between guilt and shame (Boudewyns et al., 2013; O’Keefe, 2000, 2002). Although guilt and shame are sometimes used interchangeably, guilt and shame motivate different reactions or action tendencies (Lindsay‐Hartz, 1984). Feelings of guilt motivate attempts at reparation whereas feelings of shame motivate attempts to hide or withdraw. For this reason, appeals that induce shame instead of guilt or shame mixed with guilt should be ineffective. In a health communication study examining the effects of shame and guilt appeals, Boudewyns et al. (2013) found that “when guilt is fused with shame, it can have negative consequences such as scapegoating and anger. Guilt‐free shame (i.e., ‘pure shame’) was correlated with both anger and perceived manipulative intent, whereas shame‐free guilt (i.e., ‘pure guilt’) was not” (p. 818). Boudewyns et al. (2013) were able to provide evidence that one message feature, focusing on the individual instead of the behavior, produces more shame than guilt and should be avoided when utilizing guilt appeals. Their data provided empirical support for Tangney and Dearing’s (2002) guidelines for parenting guilt appeals: (a) focus on the behavior rather than the person; (b) accentuate the consequences for other people; (c) assist in developing reparative actions; and (d) avoid mocking humor.

Guilt and message features

As evidenced by the research on guilt and shame, message features can enhance or inhibit the effectiveness of guilt appeals. Niederdeppe et  al. (2008) identified another crucial message characteristic that using the word “you” may moderate the effectiveness of guilt appeals. Overemphasis on the individual through using “you” could elicit counterproductive negative emotions like anger. Niederdeppe et al. (2008) contended, “the statement ‘last night, you let a child go hungry again’ produces far more guilt and anger than the statement ‘last night, a child went to bed hungry again’” (p. 502). Perhaps such an overt use of the second‐person pronoun leads to perceptions of manipulation, which, as discussed above, undermines persuasion. Another message element that can trigger anticipated guilt without anger is to frame the persuasive intent as prosocial (O’Keefe & Figgé, 1999). Appeals to guilt for charities or for products with ethical attributes are more successful than using guilt purely for profit (Chang, 2011, 2012; Peloza, White, & Shang, 2013).

Guilt and receiver characteristics

In addition to message features, there are receiver characteristics that need to be considered for guilt appeals to succeed. Bessarabova et al. (2015) demonstrated that adolescents are particularly reactance‐prone when exposed to high‐intensity guilt appeals. Adolescents are keenly aware of manipulation attempts and are at a stage of development where perceived freedoms are especially important. Similarly, highly involved people are also suboptimal targets for guilt messaging, as they already care about the issue and are likely already active. Using guilt with highly involved audiences “may offer a redundant warning and further cause a boomerang effect” (Chang, 2012, p. 761).

Guilt and empathy

Scholars have revealed a positive association between guilt and empathy (Basil, Ridgeway, & Basil, 2006, 2008; Tangney, 1991). Basil et al. repeatedly found that empathy mitigated feelings of anger and reactance. Similarly, research by Shen (2010) has demonstrated that message‐ induced empathy effectively inhibits psychological reactance. Practitioners would be wise to pair guilt appeals with empathy appeals to maximize effectiveness as well as to prevent a guilt appeal arousing shame (Boudewyns et al., 2013).

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Future Directions in Applied Research on Guilt This review highlights future research directions that may be fruitful for scholars of applied communication research to pursue. First, research on guilt appeals in applied communication contexts needs more theorizing regarding guilt and/or increased utilizing of other theoretical models to explain and predict the appropriate circumstances for using guilt appeals. Unlike anger and fear, there is no dedicated theory of guilt and influence. Given the prevalence of guilt appeals in advertising (Peloza et al., 2013) and marketing (Chang, 2012), as well as the sustained research interest in guilt appeals, one obvious future research direction would be to develop a theory of guilt and influence in line with theories like the EPPM (Witte, 1992), PMT (Rogers, 1975, 1983), or AAM (Turner, 2007). Barring the development of a new theory of guilt appeals, future research should continue to incorporate other theories to identify and explain the conditions that inhibit or facilitate the success of guilt appeals. For example, scholars integrated research on guilt appeals with the theory of psychological reactance to help explain why guilt appeals can backfire (Bessarabova et al., 2015). Similarly, researchers have incorporated prospect theory into the study of guilt, finding that messages using loss frames arouse more guilt (Lindsey, 2005) and subsequently, more reactance (Quick et al., 2015). Second, guilt‐appeal research may benefit from future examinations of how guilt functions in conjunction with other emotions. For example, recent research on shame‐free guilt appeals yielded impressive theoretical and practical insights about how to effectively utilize guilt to persuade (Boudewyns et al., 2013). Similarly, studies on how guilt can lead to anger and psychological reactance (Bessarabova et al., 2015) and how to induce empathy to make appeals effective (Shen, 2010) demonstrate the benefits of examining guilt in conjunction with other emotions.

Conclusion Emotions can be powerful motivators of human behavior, and as such, emotional appeals have been a subject of longstanding interest for applied communication scholars. In this chapter, we focused on three distinct emotional appeals: anger, fear, and guilt. All three types of appeals can generally be categorized as negative emotions. Negative emotional appeals can motivate change, but they can also backfire when used improperly. Although this chapter covers the theoretical underpinnings of the different emotional appeals in substantial detail along with several pragmatic implications, we offer some concluding thoughts about how to utilize negative emotional appeals, maximizing their effectiveness. One key conclusion from the research presented in this chapter is the importance of efficacy. Inducing anger, fear, or guilt through emotional appeals can motivate action, but additional communication elements are necessary to channel negative emotions into adaptive action. The relationship between negative emotions, perceptions of efficacy, and message outcomes is perhaps best articulated in fear‐appeals research. As specified in the EPPM, people who are afraid can deal with their fear through adaptive means by addressing the threatening situation (i.e., danger control) or they can engage in maladaptive processes whereby they marshal resources to deal with their negative emotions while ignoring the threat (i.e., fear control). The factor that determines which process is utilized is that of perceptions of efficacy to address the threat. For example, a public campaign using a fear appeal about STDs may elicit fear about contracting an STD after the vivid accounts and statistics about rising rates of infections have been reported. Given this state of fear, participants need to know what they can do to alleviate the threat. Providing information about the effectiveness of condoms (response‐efficacy) as well as their availability, low cost, and ease of use (self‐efficacy) should help message recipients engage in adaptive danger‐control processes. Efficacy is also crucial for anger, and to a lesser degree, guilt appeals. Efficacy in anger appeals needs to demonstrate how anger can be effectively harnessed



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into action that addresses the cause of emotional distress, and efficacy in guilt appeals should guide receivers toward reparative action. Taken together, a crucial conclusion for those hoping to use negative emotional appeals in applied communication is to induce negative emotions and then provide an efficacious means for alleviating distress through message recommendations. Another conclusion from research is the importance of considering the interplay of different emotions when applying negative emotional appeals. The interplay of different emotions may inhibit or facilitate applied communication effectiveness. One possible way campaigns may undermine their success is by inadvertently inducing other emotions in addition to the focal emotion of an appeal. For example, when using guilt appeals, the message may target the individual instead of the behavior, which could lead to shame instead of guilt. Although guilt and shame are sometimes used interchangeably, they motivate different action tendencies. Feelings of guilt motivate attempts at reparation whereas feelings of shame motivate attempts to hide or withdraw. For this reason, appeals that induce shame instead of guilt or shame mixed with guilt should be ineffective. Another way applied communication has to account for the interplay of emotions is manipulative intent. Regardless of what emotion the appeal is designed to induce, if practitioners are seen as attempting to manipulate receivers, it will likely lead to anger directed at the message source, which will lead to message failure. Emotional interplay need not always be counterproductive. Researchers have found the interplay of negative emotional appeals may benefit from also activating positive emotions. For example, scholars have found that message‐induced empathy effectively inhibits anger toward the guilt inducer, which prevents psychological reactance. Further, pairing guilt appeals with empathy appeals can prevent a guilt appeal from arousing shame. In the domain of fear‐appeal research, scholars have focused on sequencing emotions to understand the role of hope in fear‐ inducing messages. In all of this research, moving from negative emotions, as participants understand the problem to be addressed, toward constructive action is facilitated through applied communication.

References Allcorn, S. (1994). Anger in the workplace: Understanding the causes of aggression and violence. Westport, CT: Greenwood. Averill, J. R. (1982). Anger and aggression: An essay on emotion. New York, NY: Springer‐Verlag. Bandura, A. (1997). Self‐efficacy: The exercise of control. New York, NY: Freeman. Basil, D. Z., Ridgeway, N. M., & Basil, M. D. (2006). Guilt appeals: The mediating effect of responsibility. Psychology & Marketing, 23, 1035–1054. Basil, D. Z., Ridgeway, N. M., & Basil, M. D. (2008). Guilt and giving: A process model of empathy and efficacy. Psychology & Marketing, 25, 1–23. Baumeister, R. F., Stillwell, A. M., & Heatherton, T. F. (1994). Guilt: An interpersonal approach. Psychological Bulletin, 115, 243–267. Bessarabova, E., Fink, E. L., & Turner, M. (2013). Reactance, restoration, and cognitive structures: Comparative statics. Human Communication Research, 39, 339–364. doi:10.1111/hcre.12007 Bessarabova, E., & Massey, Z. B. (2019). Testing terror management health model and integrating its predictions with the theory of psychological reactance. Communication Monographs. doi:10.1080/0363 7751.2019.1626992 Bessarabova, E., & Meirick, P. (2017, May). The effects of anger, efficacy, and political knowledge on misperceptions and intentions to participate in the political process. Paper presented at the International Communication Association Convention, San Diego, CA. Bessarabova, E., Turner, M. M., Fink, E. L., & Blustein, N. B. (2015). Extending the theory of reactance to guilt appeals: “You ain’t guiltin’ me into nothin’.” Zeitschrift für Psychologie, 223, 215–224. doi:10.1027/2151‐2604/a000223 Bodenhausen, G. V., Sheppard, L. A., & Kramer, G. P. (1994). Negative affect and social judgment: The differential impact of anger and sadness. European Journal of Social Psychology, 24, 45–62.

118

Bessarabova, Banas, and Bernard

Boster, F. J., Mitchell, M. M., Lapinski, M. K., Cooper, H., Orrego, V. O., & Reinke, R. (1999). The impact of guilt and type of compliance‐gaining message on compliance. Communication Monographs, 66, 167–177. Boudewyns, V., Turner, M. M., & Paquin, R. S. (2013). Shame‐free guilt appeals: Testing the emotional and cognitive effects of shame and guilt appeals. Psychology & Marketing, 30, 811–825. Brader, T. (2006). Campaigning for hearts and minds: How emotional appeals in political ads work. Chicago, IL: The University of Chicago Press. Brehm, J. W. (1966). A theory of psychological reactance. New York, NY: Academic Press. Bybee, J. (1998). Guilt and children. San Diego, CA: Academic Press. Chaiken, S. (1987). The heuristic model of persuasion. In M. P. Zanna, J. M. Olson, & C. P. Herman (Eds.), Social influence: The Ontario symposium (Vol. 5, pp. 3–39). Hillsdale, NJ: Laurence Erlbaum. Chang, C. T. (2011). Guilt appeals in cause‐related marketing: The subversive roles of product type and  donation magnitude. International Journal of Advertising, 30, 587–616. doi:10.2501/ IJA‐30‐4‐587‐616 Chang, C. T. (2012). Are guilt appeals a panacea in green advertising? The right formula of issue proximity and environmental consciousness. International Journal of Advertising, 31, 741–771. doi:10.2501/ IJA‐31‐4‐741‐771 Coulter, R. H., Cotte, J., & Moore, M. L. (1999). Believe it or not: Persuasion, manipulation, and credibility of guilt appeals. Advances in Consumer Research, 26, 288–294. Coulter, R. H., & Pinto, M. B. (1995). Guilt appeals in advertising: What are their effects? Journal of Applied Psychology, 80, 697–705. De Hoog, N., Stroebe, W., & De Wit, J. B. F. (2007). The impact of vulnerability to and severity of a health risk on processing and acceptance of fear‐arousing communications: A meta‐analysis. Review of General Psychology, 11, 258–285. doi:10.1037/1089‐2680.11.3.258 Dechesne, M., Pyszczynski, T., Arndt, J., Ransom, S., Sheldon, K. M., Van Knippenberg, A., & Janssen, J. (2003). Literal and symbolic immortality: The effect of evidence of literal immortality on self‐esteem striving in response to mortality salience. Journal of Personality and Social Psychology, 84, 722–737. doi:10.1037/0022‐3514.84.4.722 DeSteno, D., Petty, R. E., Rucker, D. D., Wegener, D. T., & Braverman, J. (2004). Discrete emotions and persuasion: The role of emotion‐induced expectancies. Journal of Personality and Social Psychology, 86, 43–56. doi:10.1037/0022‐3514.86.1.43 Dillard, J. P., Li, R., Meczkowski, E., Yang, C., & Shen, L. (2017). Fear responses to threat appeals: Functional form, methodological considerations, and correspondence between static and dynamic data. Communication Research, 44, 997–1018. doi:10.1177/0093650216631097 Dillard, J. P., & Shen, L. (2005). On the nature of reactance and its role in persuasive health communication. Communication Monographs, 72, 144–168. doi:10.1080/03637750500111815 Dunn, J., & Schweitzer, M. (2005). Feeling and believing: The influence of emotion on trust. Journal of Personality and Social Psychology, 6, 736–748. Earl, A., & Albarracín, D. (2007). Nature, decay, and spiraling of the effects of fear‐inducing arguments and HIV counseling and testing: A meta‐analysis of the short‐ and long‐term outcomes of HIV‐prevention interventions. Health Psychology, 26, 496–506. Easterling, D. V., & Leventhal, H. (1989). Contribution of concrete cognition to emotion: Neutral symptoms as elicitors of worry about cancer. Journal of Applied Psychology, 74, 787–796. doi:10.1037/ 0021‐9010.74.5.787 Fein, M. L. (1993). I. A. M.: A common sense guide to coping with anger. Westport, CT: Praeger. Ferrer, R., Klein, W., Lerner, J. S., Reyna, V. F., & Keltner, D. (2015). Emotions and health decision‐making: Extending the appraisal tendency framework to improve health and healthcare. In C. Roberto & I. Kawachi (Eds.), Behavioral economics and public health (pp. 101–131). Cambridge, MA: Harvard University Press. Fisher, E. B., Walker, E. A., Bostrom, A., Fischhoff, B., Haire‐Joshu, D., & Johnson, S. B. (2002). Behavioral science research in the prevention of diabetes: Status and opportunities. Diabetes Care, 25, 599–606. doi:10.2337/diacare.25.3.599 Floyd, D. L., Prentice‐Dunn, S., & Rogers, R. W. (2000). A meta‐analysis of research on protection motivation theory. Journal of Applied Social Psychology, 30, 407–429. doi:10.1111/j.1559‐1816.2000. tb02323.x



The Role of Negative Emotions in Applied Communication Research

119

Ford, E. S., Zhao, G., Tsai, J., & Li, C. (2011). Low‐risk lifestyle behaviors and all‐cause mortality: Findings from the national health and nutrition examination survey III mortality study. American Journal of Public Health, 101, 1922–1929. doi:10.2105//AJPH.2011.300167 Frijda, N. H. (1986). The emotions. Cambridge, UK: Cambridge University Press. Frijda, N. H., Kuipers, P., & ter Schure, E. (1989). Relations among emotion, appraisal, and emotional action readiness. Journal of Personality and Social Psychology, 57(2), 212–228. doi:10.1037/ 0022‐3514.57.2.212 Gerend, M. A., & Maner, J. K. (2011). Fear, anger, fruits, and veggies: Interactive effects of emotion and message framing on health behaviors. Health Psychology, 30, 420–423. Gino, F., & Schweitzer, M. E. (2008). Blinded by anger or feeling the love: How emotions influence advice taking. Journal of Applied Psychology, 93, 1165–1173. Goldenberg, J. L., & Arndt, J. (2008). The implications of death for health: A terror management health model for behavioral health promotion. Psychological Review, 115, 1032–1053. doi:10.1037/ a0013326 Greer, S., & Morris, T. (1975). Psychological attributes of women who develop breast cancer: A controlled study. Journal of Psychosomatic Research, 19, 147–153. doi:10.1016/0022‐3999(75)90062‐8 Guerrero, L. K. (1994). “I’m so mad I could scream”: The effects of anger expression on relational satisfaction and communication competence. Southern Communication Journal, 59, 125–141. ­ doi:10.1080/10417949409372931 Harris, P. R., & Epton, T. (2009). The impact of self‐affirmation on health cognition, health behavior and other health‐related responses: A narrative review. Social and Personality Psychology Compass, 3, 962–978. Harris, P. R., Mayle, K., Mabbott, L., & Napper, L. (2007). Self‐affirmation reduces smokers’ defensiveness to graphic on‐pack cigarette warning labels. Health Psychology, 26, 437–446. Harris, P. R., & Napper, L. (2005). Self‐affirmation and the biased processing of threatening health‐risk information. Personality and Social Psychology Bulletin, 51, 1250–1263. Hovland, C., Janis, I., & Kelly, H. (1953). Communication and persuasion. New Haven, CT: Yale University Press. Huhmann, B. A., & Brotherton, T. P. (1997). A content analysis of guilt appeals in popular magazine advertisements. The Journal of Advertising, 26, 35–45. doi:10.1080/00913367.1997.10673521 Izard, C. E. (1977). Human emotions. New York, NY: Plenum Press. Janis, I. L. (1967). Effects of fear arousal on attitude change: Recent developments in theory and ­experimental research. Advances in Experimental Social Psychology, 3, 166–224. doi:10.1016/ S0065‐2601(08)60344‐5 Keltner, D., & Buswell, B. N. (1996). Evidence for the distinctness of embarrassment, shame, and guilt: A study of recalled antecedents and facial expressions of emotion. Cognition and Emotion, 10, 155–172. doi:10.1080/026999396380312 Kühne, R., & Schemer, C. (2013). The emotional effects of news frames on information processing and opinion formation. Communication Research, 42, 387–407. doi:10.1177/0093650213514599 Lazarus, R. S. (1991). Progress on a cognitive‐motivational‐relational theory of emotion. American Psychologist, 46, 819–834. doi:10.1037/0003‐066X.46.8.819 Lerner, J. S., Goldberg, J. H., & Tetlock, P. E. (1998). Sober second thought: The effects of accountability, anger, and authoritarianism on attributions of responsibility. Personality and Social Psychology Bulletin, 24, 563–574. Lerner, J. S., Gonzalez, R. M., Small, D. A., & Fischhoff, B. (2003). Effects of fear and anger on perceived risks of terrorism: A national field experiment. Psychological Science, 14(2), 144–150. doi:10.1111/ 1467‐9280.01433 Lerner, J. S., & Keltner, D. (2000). Beyond valence: Toward a model of emotion‐specific influences on judgment and choice. Cognition and Emotion, 14, 473–493. Lerner, J. S., & Keltner, D. (2001). Fear, anger, and risk. Journal of Personality and Social Psychology, 81, 146–159. doi:10.1037//O022‐3514.81.1.146 Lerner, J. S., Li, Y., Valdesolo, P., & Kassam, K. S. (2015). Emotion and decision making. Annual Review of Psychology, 66, 799–823. doi:10.1146/annurev‐psych‐010213‐115043 Lerner, J. S., & Tiedens, L. Z. (2006). Portrait of the angry decision maker: How appraisal tendencies shape anger’s influence on cognition. Journal Behavioral Decision Making, 19, 115–137. doi:10.1002/ bdm.515

120

Bessarabova, Banas, and Bernard

Leventhal, H. (1970). Findings and theory in the study of fear communications. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 5, pp. 119–186). New York, NY: Academic Press. Lindsay‐Hartz, J. (1984). Contrasting experiences of shame and guilt. American Behavioral Scientist, 27, 689–704. Lindsey, L. L. M. (2005). Anticipated guilt as behavioral motivation: An examination of appeals to help unknown others through bone marrow donation. Human Communication Research, 31, 453–481. Loewenstein, G. F. (1996). Out of control: Visceral influences on behavior. Organizational Behavior and Human Decision Processes, 65, 272–292. doi:10.1006/obhd.1996.0028 Loewenstein, G. F., & Lerner, J. S. (2003). The role of affect in decision making. In R. Davidson, K. Scherer, & H. Goldsmith (Eds.), Handbook of affective science (pp. 619–642). New York, NY: Oxford University Press. Miceli, M. (1992). How to make someone feel guilty: Strategies of guilt inducement and their goals. Journal for the Theory of Social Behavior, 22, 81–104. doi:10.1111/j.1468‐5914.1992.tb00211.x Mikulincer, M., Florian, V., & Hirschberger, G. (2004). The terror of death and the quest for love: An existential perspective on close relationships. In J. Greenberg, S. L. Koole, & T. Pyszczynski (Eds.), Handbook of experimental existential psychology (pp. 287–304). New York, NY: Guilford Press. Milne, S., Sheeran, P., & Orbell, S. (2000). Prediction and intervention in health‐related behavior: A meta‐ analytic review of protection motivation theory. Journal of Applied Social Psychology, 30, 106–143. Mitchell, M. M., Brown, K. M., Morris‐Villagran, M., & Villagran, P. D. (2001). The effects of anger, sadness and happiness on persuasive message processing: A test of the negative state relief model. Communication Monographs, 68, 347–359. doi:10.1080/03637750128070 Moons, W. G., & Mackie, D. M. (2007). Thinking straight while seeing red: The influence of anger on information processing. Personality and Social Psychology Bulletin, 33, 706–720. Nabi, R. L. (1999). A cognitive‐functional model for the effects of discrete negative emotions on information processing, attitude change, and recall. Communication Theory, 9, 292–320. doi:10.1111/ j.1468‐2885.1999.tb00172.x Nabi, R. L. (2002a). Discrete emotions and persuasion. In J. Dillard & M. Pfau (Eds.), The persuasion handbook: Developments in theory and practice (pp. 289–308). Thousand Oaks, CA: Sage. Nabi, R. L. (2002b). Anger, fear, uncertainty, and attitudes: A test of the cognitive‐functional model. Communication Monographs, 69, 204–216. doi:10.1080/03637750216541 Nabi, R. L. (2003). The framing effects of emotion: Can discrete emotions influence information recall and policy preference? Communication Research, 30, 224–247. doi:10.1177/0093650202250881 Nabi, R. L. (2015). Emotional flow in persuasive health messages. Health Communication, 30, 114–124. doi:10.1080/10410236.2014.974129 Nabi, R. L., Gustafson, A., & Jensen, R. (2018). Framing climate change: Exploring the role of emotion in generating advocacy behavior. Science Communication, 40, 442–468. doi:10.1177/1075547018776019 Nabi, R. L., & Myrick, J. G. (2018). Uplifting fear appeals: Considering the role of hope in fear‐based persuasive messages. Health Communication, 9, 1–12. doi:10.1080/10410236.2017.1422847 Nan, X. (2017). Influence of incidental discrete emotions on health risk perception and persuasion. Health Communication, 32, 721–729. doi:10.1080/10410236.2016.1168004 Niederdeppe, J., Bu, Q. L., Borah, P., Kindig, D. A., & Robert, S. A. (2008). Message design strategies to raise public awareness of social determinants of health and population health disparities. The Milbank Quarterly, 86, 481–513. O’Keefe, D. J. (2000). Guilt and social influence. In M. E. Roloff (Ed.), Communication yearbook 23 (pp. 67–101). Thousand Oaks, CA: Sage. O’Keefe, D. J. (2002). Guilt as a mechanism of persuasion. In J. P. Dillard & M. Pfau (Eds.), The persuasion handbook: Developments in theory and practice (pp. 329–344). Thousand Oaks, CA: Sage. O’Keefe, D. J., & Figgé, M. (1999). Guilt and expected guilt in the door‐in‐the‐face technique. Communication Monographs, 66, 312–324. doi:10.1080/03637759909376482 Ortony, A., Clore, G. L., & Collins, A. (1988). The cognitive structure of emotions. New York, NY: Cambridge University Press. Ortony, A., & Turner, T. J. (1990). What’s basic about basic emotions? Psychology Review, 97, 315–331. doi:10.1037/0033‐295X.97.3.315 Peloza, J., White, K., & Shang, J. (2013). Good and guilt‐free: The role of self‐accountability in influencing preferences for products with ethical attributes. Journal of Marketing, 77, 104–119.



The Role of Negative Emotions in Applied Communication Research

121

Peters, G. Y., Ruiter, R. A. C., & Kok, G. (2013). Threatening communication: A critical re‐analysis and a revised meta‐analytic test of fear appeal theory. Health Psychology Review, 7, S8–S31. doi:10.1080/17 437199.2012.703527 Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 19, pp. 123–205). New York, NY: Academic Press. Pinto, M. B., & Priest, S. (1991). Guilt appeals in advertising: An exploratory study. Psychological Reports, 69, 375–385. Quick, B. L., Kam, J. A., Morgan, S. E., Montero, C. A., & Smith, R. A. (2015). Prospect theory, discrete emotions, and freedom threats: An extension of psychological reactance theory. Journal of Communication, 65, 40–61. doi:10.1111/jcom.12134 Rains, S. A. (2013). The nature of psychological reactance revisited: A meta‐analytic review. Human Communication Research, 39, 47–73. doi:10.1111/j.1468‐2958.2012.01443.x Rivers, S. E., Reyna, V. F., & Mills, B. A. (2008). Risk taking under the influence: A fuzzy‐trace theory of emotion in adolescence. Developmental Review, 28, 107–144. Roberto, A. J., Goodall, C. E., & Witte, K. (2009). Raising the alarm and calming fears: Perceived threat and efficacy during risk and crisis. In R. L. Heath & H. D. O’Hair (Eds.), Handbook of risk and crisis communication (pp. 285–301). New York, NY: Routledge. Rogers, R. W. (1975). A protection motivation theory of fear appeals and attitude change. Journal of Psychology, 91, 93–114. doi:10.1080/00223980.1975.9915803 Rogers, R. W. (1983). Cognitive and physiological processes in fear appeals and attitude change: A revised theory of protection motivation. In J. T. Cacioppo & R. E. Petty (Eds.), Social psychophysiology: A sourcebook (pp. 153–176). New York, NY: Guilford Press. Roseman, I. J., Wiest, C., & Swartz, T. S. (1994). Phenomenology, behaviors, and goals differentiate ­discrete emotions. Journal of Personality and Social Psychology, 67, 206–221. doi:10.1037/ 0022‐3514.67.2.206 Routledge, C., Arndt, J., & Goldenberg, J. L. (2004). A time to tan: Proximal and distal effects of mortality salience on sun exposure intentions. Personality and Social Psychology Bulletin, 30, 1347–1358. doi:10.1177/0146167204264056 Rubin, A. M., & Shaffer, W. F. (1987). Some interpersonal effects of imposing guilt versus eliciting altruism. Counseling and Values, 31, 190–193. doi:10.1002/j.2161‐007X.1986.tb00492.x Rubin, J. (1986). The emotion of anger: Some conceptual and theoretical issues. Professional Psychology: Research and Practice, 17, 115–124. doi:10.1037//0735‐7028.17.2.115 Ruiter, R. A., Kessels, L. T., Peters, G. Y., & Kok, G. (2014). Sixty years of fear appeal research: Current state of the evidence. International Journal of Psychology, 49, 63–70. doi:10.1002/ijop.12042 Scherer, K. R. (1984). On the nature and function of emotion: A component process approach. In K. R. Scherer & P. Ekman (Eds.), Approaches to emotion (pp. 293–318). Hillsdale, NJ: Lawrence Erlbaum. Scherer, K. R. (1999). On the sequential nature of appraisal processes: Indirect evidence from a recognition task. Cognition and Emotion, 13, 763–793. doi:10.1080/026999399379078 Shen, L. (2010). Mitigating psychological reactance: The role of message‐induced empathy in persuasion. Human Communication Research, 36, 397–422. doi:10.1111/j.1468‐2958.2010.01381.x Shen, L., & Coles, V. B. (2015). Fear and psychological reactance. Zeitschrift für Psychologie, 223, 225–235. doi:10.1027/2151‐2604/a000224 Shen, L., & Dillard, J. P. (2014). Threat, fear, and persuasion: Review and critique of questions about functional form. Review of Communication Research, 2, 94–11. doi:10.12840/issn.2255‐4165. 2014.02.01.004 Sherman, D. K., & Cohen, G. L. (2002). Accepting threatening information: Self‐affirmation and the reduction of defensive biases. Current Direction in Psychological Science, 11, 119–123. Smith, C. A., & Ellsworth, P. C. (1985). Patterns of cognitive appraisal in emotion. Journal of Personality and Social Psychology, 48, 813–838. doi:10.1037/0022‐3514.48.4.813 Smith, C. A., & Lazarus, R. S. (1990). Emotion and adaptation. In L. A. Pervin (Ed.), Handbook of personality: Theory and research (pp. 609–637). New York, NY: Guilford Press. Smith, R. A., Ferrara, M., & Witte, K. (2007). Social sides of health risks: Stigma and collective efficacy. Health Communication, 21(1), 55–64. doi:10.1080/10410230701283389

122

Bessarabova, Banas, and Bernard

Solomon, S., Greenberg, J., & Pyszczynski, T. (1991). A terror management theory of social behavior: The psychological functions of self‐esteem and cultural worldviews. Advances in Experimental Social Psychology, 24, 93–159. doi:10.1016/S0065‐2601(08)60328‐7 Tangney, J. P. (1991). Moral affect: The good, the bad, and the ugly. Journal of Personality and Social Psychology, 61, 598–607. doi:10.1037/0022‐3514.61.4.598 Tangney, J. P. (1992). Situational determinants of shame and guilt in young adulthood. Personality and Social Psychology Bulletin, 18, 199–205. doi:10.1177/0146167292182011 Tangney, J. P., & Dearing, R. L. (2002). Shame and guilt. New York, NY: Guilford Press. Tangney, J. P., Miller, R. S., Flicker, L., & Barlow, D. H. (1996). Are shame, guilt, and embarrassment ­distinct emotions? Journal of Personality and Social Psychology, 70, 1256–1259. doi:10.1037/ 0022‐3514.70.6.1256 Tannenbaum, M. B., Hepler, J., Zimmerman, R. S., Saul, L., Jacobs, S., Wilson, K., & Albarracín, D. (2015). Appealing to fear: A meta‐analysis of fear appeal effectiveness and theories. Psychological Bulletin, 141, 1178–1204. doi:10.1037/a0039729 Thomas, S. P. (1993). Anger and its manifestations in women. In S. P. Thomas (Ed.), Women and anger: Focus on women (Vol. 15, pp. 40–67). New York, NY: Springer. Tiedens, L. Z., & Linton, S. (2001). Judgment under emotional certainty and uncertainty: The effects of specific emotions on information processing. Journal of Personality and Social Psychology, 81, 973–988. Turner, M. M. (2007). Using emotion in risk communication: The anger activism model. Public Relations Review, 33, 114–119. doi:10.1016/j.pubrev.2006.11.013 Turner, M. M. (2013). Emotional branding: What, when and why. In W. D. Evans (Ed.), The psychology of branding (pp. 15–37). Hauppauge, NY: Nova Science. Turner, M. M., & Underhill, J. C. (2012). Motivating emergency preparedness behaviors: The differential effects of guilt appeals and actually anticipating guilty feelings. Communication Quarterly, 60, 545– 559. doi:10.1080/01463373.2012.705780 Vangelisti, A. L., Daly, J. A., & Rudnick, J. R. (1991). Making people feel guilty in conversations. Human Communication Research, 18, 3–39. doi:10.1111/j.1468‐2958.1991.tb00527.x Weeks, B. E. (2015). Emotions, partisanship, and misperceptions: How anger and anxiety moderate the effect of partisan bias on susceptibility to political misinformation. Journal of Communication, 65, 699–719. doi:10.1111/jcom.12164 Wegener, D. T., Petty, R. E., & Klein, D. J. (1994). Effects of mood on high elaboration attitude change: The mediating role of likelihood judgments. European Journal of Social Psychology, 24, 25–43. Wiltermuth, S. S., & Tiedens, L. Z. (2011). Incidental anger and the desire to evaluate. Organizational Behavior and Human Decision Processes, 116, 55–65. Witte, K. (1992). Putting the fear back into fear appeals: The extended parallel process model. Communication Monographs, 59, 329–349. doi:10.1080/03637759209376276 Witte, K. (1998). Fear as motivator, fear as inhibitor: Using EPPM to explain fear appeal successes and failures. In P. A. Anderson & L. K. Guerrero (Eds.), The handbook of communication and emotion (pp. 423–450). New York, NY: Academic Press. Witte, K., & Allen, M. (2000). A meta‐analysis of fear appeals: Implications for effective public health campaigns. Health Education & Behavior, 27, 591–615. Witte, K., Meyer, G., & Martell, D. (2001). Putting it all together: The extended parallel process model. In Effective health risk messages: A step‐by‐step guide (pp. 23–31). Thousand Oaks, CA: Sage. doi:10.4135/9781452233239.n3 Wong, N. C., Nisbett, G. S., & Harvell, L. A. (2017). Smoking is so ew!: College smokers’ reactions to health‐ versus social‐focused antismoking threat messages. Health Communication, 32, 451–460. doi:10.11114/smc.v1i2.216 Xu, Z., & Guo, H. (2018). A meta‐analysis of the effectiveness of guilt on health‐related attitudes and intentions. Health Communication, 33, 519–525. doi:10.1080/10410236.2017.1278633

Part II

Media, Data, Design, and Technology

8

Adventures in “Big Data” Application in Strategic Applied Communication Research, Theory, and Method* Yi Grace Ji and Don W. Stacks

Introduction This chapter deals with applied communication research. Applied communication research was defined by Mark Hickson (1973) in the initial article of the then newly created Journal of Applied Communications Research that stressed how communication research should be applied to real-world situations. As Frey and Musselwhite (2019) note, “most [applied communication research] ACR seeks solutions to (communication) problems that people experience” (p. 347). Applied communication research does not replace theoretical or rhetorical approaches, but instead adds our understanding of sociological and psychological theories, concepts, and constructs to the theoretical framework of communication (see Stacks, Hickson, & Hill, 1975). What makes applied research important is that it goes beyond simple experiments or case study approaches and triangulates qualitative and quantitative approaches to data acquisition, evaluation, and interpretation (Hickson, 2003). Applied communication research has also tried to integrate qualitative and quantitative data to better understand expected normative behaviors and the outcomes of strategic communication as applied in practice (see Botan, 2019). This chapter, in turn, focuses on what qualitative and quantitative data are and how they are employed in applied communication strategic research. This chapter divides data classes or sets into two major groups: structured data sets, whereby the data are set as fixed variables across cases, and unstructured data sets, whereby the variables are allowed to interact as data are collected and arrayed via artificial intelligence (AI) algorithms to create new variables (Stacks, 2017).1 Further, we can distinguish four different levels of data that can be either qualitative or quantitative in terms of size: small, large, big, and “Big,” much like Miller and Nicholson (1976) defined communication inquiry with three levels in terms of size or breadth of knowledge: psychological, the most precise and aimed at understanding at an interpersonal/intrapersonal level; sociological, less precise and aimed at understanding at a social *Authorship is listed alphabetically as both authors contributed equally to the chapter.   An algorithm is a formula of sorts or computer subprogram that is used to help capture data appropriate to the problem or research question. It may be used to teach the computer in a computer learning situation or to create new variables that meet the conditions the IT architect puts in it, much like a statistical formula that sets the limits for a variable to be included or excluded from analyses.

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The Handbook of Applied Communication Research: Volume 1, First Edition. Edited by H. Dan O’Hair and Mary John O’Hair. © 2020 John Wiley & Sons, Inc. Published 2020 by John Wiley & Sons, Inc.

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group level; and cultural, even less precise but providing a wide depth of information. In the same way, we can see the first three data set sizes as very precise (small data) and used to theoretically test assumptions and hypotheses, normative (large data sets) and used to produce expected or normative communication outcomes that may extend small data research, and huge (big) data sets that provide as many variables as can be defined and as many cases as any given data warehouse can offer (a set of different data sets combined in one location). The key to these three data sets is found in the way the data set is structured or arrayed. That is, the variables are identified, operationally defined, and restricted to those definitions. Further, the number of cases in the data set is fixed when analyses are conducted via algorithms written to call specific data to answer specific problems or questions. Today, big data has been confused with “Big Data.” While big data represents a humongous amount of data variables, they are constrained to only examining those variables. We operationally define this data set as human-generated big data (HDBD). As we will see later in this chapter, “Big Data” goes beyond this simple structure and allows the variables and data to interact to find new variables that might be seen as “out of the box” outcomes, but presents new ways to interpret, evaluate, and apply that data to new questions. We operationally define this data set as computer-generated big data (CGBD). As applied today, much has been made of health communication and disease prediction using big data—unending amounts of information inputted into the data set—and, using today’s evermore powerful computing possibilities, bringing in more precise findings that can be used to further answer the problem. In the case of health, for instance, we gain a better understanding of multiple potential outcomes based on a holistic view of the data. Current big data research takes advantage of the three “v’s” of computing: volume, variety, and velocity, which we will discuss in more detail, along with four other factors that impact the ability to use HDBD and CGBD. CGBD, however, should be studied in terms of its unstructured composition—that through AI and advanced computer learning models, the data can add to the same data set new variables created by the computer’s ability to learn from the data and find new variables that correlate with the expected outcomes. In the pages that follow, we will examine the potential for both big data and “Big Data” in applied communication research as it can be and as it has been used in various communication functions or areas. We will further provide an application in corporate communication and follow this with a prototype of a CGBD case study at the end. It should be noted at this point in time, however, that unless the data has been operationally defined and theoretically linked to some outcome through some theory (or strategy as the professional would view it), HDBD and CGBD often end up with the researcher looking for not just one needle in the proverbial haystack, but hundreds or thousands of needles in as many haystacks. While small and large data sets are ideal in establishing relationships among variables, up to and including experimental studies establishing causation, the outcomes should be viewed as part of a larger concept. In the business world, this has become identified with “business intelligence” (BI). In BI the goal is to better understand what impacts business outcomes as related to very specific management, marketing, organizational, or other functions. For the applied communication researcher, the same is true, except we are focusing on how the practice of communication intelligence (CI) changes communication behavior—whether this be through interpersonal communication networks, small group productivity, or large and small organizations, and whether the communication outcomes are expected to inform or persuade. Finally, as with any research endeavor that collects, analyzes, evaluates, and interprets data collected on human behaviors and attitudes, ethical concerns will and do arise. This chapter focuses on the research applications of “Big Data.” It is understood that there are ethical problems associated with the collection, confidentiality, and storage of data collected by communication researchers. While this is an important area of concern, space limitations inhibit a full discussion of the concerns. A similar discussion related to research and data collection in general can be found in Bowen and Stacks (2013).



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To date, HDBD and CGBD applied studies in communication have yet to find their way into our body of knowledge. Those that have been published are typically in book form, such as Li and Stacks’ (2015) massive HDBD study of public relations social media influence on corporate return on investment (ROI). Other research using HDBD is following a thematic path in areas such as applied corporate communication (e.g., Flynn, Iwata, & Marks, 2018) and fundraising (e.g., Ji, 2017). Outside the communication discipline, HDBD have been published in corporate communication (e.g., Coca-Cola), medical diagnostics (e.g., cancer), and other areas in the social and physical sciences (e.g., Marr, 2016). However, the use of HDBD and CGBD, as defined here, has not reached a critical mass in either theory or data reporting. Why then has communication research not taken advantage of HDBD and CGBD? First, it is expensive to collect, both in terms of time spent collecting and cleaning the data. Second, it requires extremely large data storage mechanisms (i.e., data warehouses). Third, data collection itself requires specialized knowledge of computer programming, such as Python, R, and Javascript to name a few, something in which not many communication researchers are proficient. Fourth, extremely fast computers are required to analyze and interpret the data, often requiring access to “super” computers. Furthermore, HDBD and CGBD require a theoretical understanding of the phenomena under study to understand when “outliers” are actually new and significant variables that need to be understood in both descriptive and causal application. HDBD and CGBD simply exacerbate these problems because the algorithms employed by the computer as a form of AI are not well understood in either theoretical or strategic terms. And, finally, another problem with both is a lack of testing the underlying assumptions of the data. Specifically, in HDBD and CGBD situations where data is continuously streamed into the data warehouses, there is no testing for univariate and multivariate normality. With these caveats in place, we will now explore what HDBD and CGBD are and how they may be used in applied communication research. Along the way we will provide simple potential and actual cases where one or the other or both have been used to understand, predict, and solve communication problems in the real world.

What Are Data? All research employs some form of data to observe, describe, explain, and predict human behavior. According to Bowen and Stacks (2013), data can be viewed in two ways. First, they can be viewed as a part of research methodology—“the observations or measurements taken when evaluating a … [communication] campaign or program” (p. 8). Second, they can be viewed from a statistical approach—presented as the frequencies, means, and percentages used to assess a communication campaign or program (Stacks, 2017). In the language of data, an individual observation is a datum; while a set of observations are data. The following introduces the concept and measurement of data. Typically, data are defined as belonging to two classes based on the type of content each represents: qualitative (i.e., “humanistic”) and quantitative (i.e., “positivistic”). Basically, qualitative communication content is language-based; it takes the symbols and signals employed in communication and examines the content of the communication as themes or objects. Conversely, quantitative communication content can be broken into measurable units—continuous or categorical. Each of them can be further divided into two other subgroups: interval and ratio; nominal and ordinal. Continuous data are observations found on a numerical continuum that can range from negative to positive place holders. These data may be observations of age in years, dollars spent, and temperature (in degrees Fahrenheit, Centigrade, or Kelvin—e.g., +32 °F  =  0 °C  =  273 °K). Temperature is ratio data; it has an absolute zero point and can be expressed as above and below some point—freezing for instance (32 °F, 0 °C, and 0°K). Data such as age is typically defined as interval data, with the units being equivalent distances apart (age in years as compared to age

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from conception to date). Categorical data are observations that can be put into groups based on some defined metric, such as gender (e.g., male/female or 0/1) or socioeconomic status (e.g., low/middle/high or 1/2/3). Normally, an applied communication researcher would use the kind of data that are best suited to answer the questions posed or problem being investigated. This approach provides the applied researcher a way to understand both the norms associated with a particular population through quantitative data that are sampled from the larger population with some precision. At times, however, the meaning behind the communication is of interest—what type of statements or words are used within a particular frame of investigation, such as thematic approaches to understand gender roles in an organization, or social media mentions through Facebook, Twitter, or other social media platforms—likes, tweets, retweets, shares, and so forth. Moreover, it is significant to understand that the way data are initially defined determines the statistical analysis procedures at the later stage of a given research project. Although we continue to use the terms categorical and continuous when describing how data are analyzed, statistical theory redefines them according to how they are distributed (Stacks, 2017). In statistics-speak, continuous data are referred to as parametric data. Because parametric data are at least interval in nature, they have a tendency to group together, to have a common mean from which they individually vary. Parametric data fall under the hypothesized normal curve. The distribution of parametric data can be described or reported in two ways—central tendency (i.e., mean, median and mode) and dispersion (i.e., range, variance, and standard deviation). When statisticians refer to categorical data, they use the term nonparametric. Counts, frequency, and percentage are used to describe categorical data. Traditionally, qualitative data has been content-analyzed, whereby units of analysis (that which we are looking for) are coded numerically into categories (e.g., cat/dog  =  1/2) that are mutually exclusive. Purely themebased content analysis does not normally quantify the communication itself but analyzes it from some theoretical position or theory. The ability of today’s computers to read and analyze actual communications and parse them down to numeric variables has allowed us to extend how we collect, analyze, interpret, and evaluate problems in broader and more applied ways. In applied communication research, the distinction of how to collect and report categorical and continuous data becomes even more important when inferential statistics are needed for hypotheses testing or when the campaign cannot afford anything less than minimal error.

Data Sets Regardless of the type of data collected, that data must be represented somehow and somewhere—in data sets. A data set is a grouping or array of data based on variables and observations made on those variables. In qualitative analyses we find sets of themes or categories into which statements or words can be placed based on some categorical logic—essentially quantifying the qualitative data. Further, researchers can take continuous data and reduce it to categorical data, but we cannot reverse the process to redefine categorical data to continuous data. This is not a problem when we know how the data are initially defined and what we expect them to mean. This said, all data can be reduced to quantitative numbers. These numbers, however, have no real meaning in and of themselves—they must be defined to be understood. Further, as Stacks (2017) has noted, many applied communication researchers understand that “data have no meaning except from the theory from whence it comes” (p. 84). This becomes increasingly important as we move from small to large to big (HDBD) to “Big” (CGBD) data sets, and ­particularly when those data sets are defined as CGBD. The focus of this chapter is on HDBD and CGBD. Before we turn to those types of data sets we need to differentiate how small and large data sets are employed in applied communication research and how they led to the formation of HDBD and CGBD.



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Small Data As noted earlier, data sets can be defined by the number of variables and cases contained in that data set as an array, usually as a spreadsheet (Stacks, 2017). Small data sets are typically used when testing hypotheses based on a given theory. A small data set today contains many more variables and definitions than the same data set would have 30 years ago. Why? The advent of the personal computer. Personal computers allowed researchers to take their data and analyze it via basic statistical programs (e.g., SPSS, SAS). As the personal computer enhanced central processing units (CPUs), this allowed for fast, complex computations. Likewise, as increased data storage allowed for more cases and variables to be stored, the range of data and observations increased. Today, a small data set may contain 100 or more variables and several thousand lines of data cases. Small data sets are structured by the variables defined and the cases obtained; the data collected are then analyzed, evaluated, and interpreted.

Large Data Large data sets differ in that the number of variables is much larger (often in the hundreds) and as many cases can be obtained as possible; the variables and observations are still structured—they do not change unless the researcher operationally redefines them through specific database or statistical commands. Large data is often used to set and analyze an audience’s normative data.

HDBD and CGBD Although extremely large, continuous data set research has recently attracted a lot of attention, these data have been around since the invention of the credit card. In those huge data sets, there exist thousands of observations and sometimes even more variables. Those data and variables are collected often in real-time through technology devices and packaged in data warehouses. In addition, while HDBD data sets are structured, CGBD data sets have variables that are loosely defined; therefore, they allow researchers to create new and meaningful variables with additional data management procedures such computing and merging with other data sets. Typically, while most big data sets are used for segmentation and priority research, more and more are used to predict communication and business outcomes. Table 8.1 demonstrates the differences between the various data types. Regardless of data set type, there are several elements comprising each, which help us to determine what kind of data set we are working with and how it operates. First, all data are arrayed or structured. Data arrays are defined by columns and rows. Data columns represent the variables being measured and may represent nominal, ordinal, interval, or ratio data—or even the actual communication messages themselves. Data rows represent individual cases or observations. In most data sets, the data are structured and must conform to whatever rules the researcher has placed on obtaining and evaluating the data and variable metrics. Structured data sets are under the control of the researcher in terms of using certain variables in their evaluations and also in creating new variables from other variables; hence, they are human-generated data sets. The second type of data set is unstructured. While the data set still contains arrays of columns and rows, the unstructured data set is allowed to create its own variables based on how the data are relating to each other as new data is entered into the data set; hence, they are partially humangenerated, but then computer-generated data sets. Second, data are stored in physical locations. For small and large data sets, the storage is in one place and typically contains the data collected and preserved at the end of data collection. For big HDBD and CGBD sets, however, the data can become so large they are difficult to work with. In HDBD and CGBD data sets, the data are stored in a data warehouse, or larger locations with multiple sets of data that have been defined by the researcher in such a way as to be able to draw

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Table 8.1  Data sets. Small

Large

big data (HDBD)

Big Data (CGBD)

Type

Structured

Structured

Semi-structured

Unstructured

Array

Fixed Variables x cases

Fixed Variables x cases

Variables fixed Cases open

Variables open Cases open

Sizea

Restricted Limited variables Cases finite 1,000 500,000

Unrestricted Infinite variables Infinite cases >500,000

Storage

Local

Local/warehouse

Multiple warehouses

Multiple warehouses

Data Acquisition

Human

Human

Machine

Machine/AI

Methodology

Experimental

Survey

Continuous survey

Continuous survey

Content

Quantitative and qualitative—usually reduced to numbers

Quantitative and qualitative—usually reduced to numbers

Quantitative and qualitative—numbers and actual qualitative data

Quantitative and qualitative—numbers and actual qualitative data

Purpose

Test theory Prediction/ causation

Test theory Establish norms Describe

Answer questions Establish norms Describe

Answer questions Establish new variables/norms Describe Predict

  Source: Stacks (2017, p. 87).

a

from them as needed. Think of how a program such as Microsoft Office operates. It consists of multiple subprograms, each with specific tasks. You may have an Excel spreadsheet with data in it, an Access database with retrieval rules, and, of course, Word with which to prepare your report. You can call different Microsoft Office programs to create your product (a report, for instance) where you can insert spreadsheet data and incorporate the statistical formula employed or algorithms created (frequencies, means, correlations, and so forth), add results from an Access database, and include graphics to illustrate your findings. The data warehouse is a set of related data sets that can be pulled together for whatever problem is being investigated. In some instances, multiple data warehouses may be used so that other research can access them separately. Third, data sets differ in terms of data generation, volume, variety, and velocity (Laney, 2001). Generation deals with how the data are gathered. In small and large data, the data are almost always generated by humans, with the exception of physiological data, which are generated by computer. In HDBD and CGBD, data are generated by humans, but also by the computer itself through different data sets (think of grocery sales where each item purchased is added to a data warehouse without human intervention). Volume refers to the amount of data being inputted to data sets and warehouses. McAfee, Brynjolfsson, Davenport, Patil, and Barton (2012) note that “[A]bout 2.5 exabytes of data are created each day, and the number is doubling every 40 months or so” (p. 4). They went on to note that “Walmart collect[s] more than 2.5 petabytes of data every hour from its customer transitions. A petabyte is one quadrillion bytes” of data (p. 4). Velocity deals with the speed at which the data are collected or created. In small and large data sets, data are first collected then placed in spreadsheet arrays that can be used for analysis and evaluation. HDBD and



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CGBD, however, collect data sets in real-time; therefore, they are constantly changing. This is especially true in CGBD sets where the data sets are often unstructured. Variety refers to the types of data collected. Most small and large data sets employ some sort of quantified transformation of non-numeric data (i.e., the data are content-analyzed and coded via a tested category system). HDBD and CGBD, however, can collect and analyze any type of data, whether that data be Global Positioning System (GPS) coordinates, entire messages, or images (Stacks, 2017). The advantages of HDBD and CGBD, however, yield several problems (van Rijmenam, 2014): veracity, variability, visualization, and value. First, veracity deals with how much trust we can place in the data. Are the data clean? Are the data “normal,” and how were the variables tested for univariate and multivariate normality? As Stacks (2017) noted, “Computers calculate, humans make decisions. As computers move further and farther into interpreting data through AI programs that start to infer rather than report data, all sorts of problems may arise, from bad ‘dictionaries’ to biased coding in the first place” (p. 90). Variability deals with standardized definitions used to create data sets. Bowen & Stacks (2013) offer standardized definitions for most communication variables and data employed in creating a data set. In HDBD and CGBD data sets, where the computer generates its own variables, standardization may become a problem as the computer is allowed to establish its own rules within the rules under which it is operating. Because HDBD and CGBD data sets have so many possible connections, visualization of those relationships is often a problem. No longer can simple charts or graphs reflect the data as analyzed. Therefore, many big data and Big Data visualizations are done through network analysis, such as Pajec (DeNooy, Mrvar, & Batagelj, 2018) or UCINET (Borgatti, Everett, & Freeman, 2002), along with other traditional modes of analysis (see Maheshwari, 2014–2016). Finally, value deals with what we get from the data. Generally, we look for answers to questions or problems. With big data, and Big Data in particular, we have so much data that, as Michaelson (cited in Stacks 2017, p.87) has noted, “you have a better chance of finding a needle in a haystack without some understanding of what the results will be in your analysis.” Any HDBD and CGBD can be a big hammer pounding out analyses that have no real association to the problem or question being asked (e.g., Stacks, 2017). Theory is always a prerequisite for research, especially where CGBD goes beyond the box in finding relationships between variables, creating new variables, and testing them against whatever norms or problems the researcher is interested in. In the field of applied communication, more and more researchers have begun to embrace the power of HDBD and CGBD to test and develop theories and models advancing the best practices of communication. In addition to the cases mentioned above in health research and business communication, oftentimes digital media channels provide tremendous opportunities for the researcher to collect either structured or unstructured data in real time. Although the specific features of social media interfaces may vary, overall five commonalties are widely acknowledged: participation, openness, conversation, communities, and connectedness. It was mentioned earlier that the appearance of CGBD is closely associated with the development and application of new technology. It is clear that the prevalent use of social media by both individuals and organizations has yielded a paradigm shift in applied communication research. The interconnectedness provided by social media has made an impact on public decision making it more profound and salient. Publics such as patients, employees, customers, and regulators now have direct lines for their electronic word of mouth (eWOM) communication. Additionally, they are also influenced by opinions from sources other than the companies or producers themselves. Given the advantages to continuous data collection in HDBD and CGBD research, we now turn to a quick explanation of how the data can be collected for storage in data warehouses.

HDBD and CGBD Data Collection It is noteworthy that the theory and overall procedure of data collection in HDBD and CGBD research is not much different from traditional quantitative research methods. Similarly, it starts

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with forming research questions and/or hypotheses, conceptualizing focal variables, defining the population and sample frame, and then selecting a representative sample using the most appropriate strategy. Due to the features of the three “v’s,” an HDBD or CGBD study oftentimes involves computer assistance during the actual data collection stage.

Problems Associated with HDBD and CGBD Data Sets Given that HDBD and CGBD data sets are drastically different from small and large data sets, researchers and professionals need to proceed with great caution when conducting research and evaluation based on insights generated from HDBD and CGBD. In the next section, the three most commonly disregarded yet controversial issues in HDBD and CGBD research in the context of social media communication are covered.

Assumption of Normality In any statistics class we are taught that testing for data normality has to be one of the first steps of any inferential statistical analysis (e.g., t-test, ANOVA [analysis of variance], regression). In other words, these statistical procedures assume that the distribution of a focal variable will follow a bell-curve distribution. By definition, inferential statistical analysis is the procedure in which we employ sample features to estimate population parameters. Given that we assume parametric statistics (e.g., t, f, and z) follow a normal distribution at the population level, we need to assess the sample’s distribution before making any estimation based on sample characteristics. If such an assumption is violated (i.e., sample parameter does not follow normal distribution), parametric tests will not be applicable to the sample unless certain procedures are taken for adjustment. If normality still cannot be achieved after adjustment, other nonparametric tests should be considered. The same is true for multivariate dependent variable research, where several outcome variables are predicted by several independent variables, often using Mahalanobis distance (Mertler & Vannatta Reinhart, 2016) and Mardia’s measure of skewness and kurtosis (Cain, Zhang, & Yuan, 2017). In applied communication social media research, public engagement is often conceptualized through behavioral indicators such as liking, sharing, and commenting on Facebook, or favoriting, retweeting, and commenting on Twitter. They are theorized as online behavioral outcomes or key performance indicators (KPIs) reflecting how audiences, users, and publics react to communication messages and features (Ji, Li, North, & Liu, 2017). Saxton and Waters (2014) pointed out that small or large data sets collected by traditional approaches, such as surveys or laboratory experiments, fail to fully capture the publics’ natural reactions to organizational communication on social media. They advocated an alternative approach to assess public engagement behaviors, which is based upon naturally occurring behavioral data. Using HDBD and CGBD approaches, collecting behavioral data from social network application programming interfaces (API) in real-time naturalistic settings allows the researcher to obtain quantitative observations in the most unobtrusive manner. In addition, insights drawn from extremely large sets of continuously collected real behavioral data can also help increase the external validity of results yielded from traditional research methods, thus further advancing theoretical understandings. Nevertheless, some researchers assume these behavior outcomes are continuous data following normal distributions. However, due to the features of social media, they are actually count variables that do not meet the assumption of normality. Failing to correctly identify data type causes errors in choosing the most appropriate statistical analyses, which can ultimately lead to false interpretations of data insights. For example, a recent study of how top social chief executive officers use Facebook posts to generate public engagement reported that the engagement indicators displayed larger variances



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than standard deviations and significant positive skewness and kurtosis (Men, Tsai, Chen, & Ji, 2018). To address this issue and meet the normality assumption of multiple regression, log transformations were performed on those variables prior to fitting multiple linear regressions. However, there are times that procedures, such as log transformation, cannot adjust highly nonnormally distributed variables. In such a situation, nonparametric tests that do not need to meet the normality assumption should be run. In another Facebook-related study, researchers found that public engagement variables were discrete and positively skewed with many zeros (Ji, Chen, Tao, & Li, 2019). Moreover, after log transformation, the outcome variables still violated the normality assumption. Therefore, the authors utilized negative binomial regression analysis (Cameron & Trivedi, 2013; Long & Freese, 2006). Negative binomial regression tolerates a skewed and discrete distribution and is free from the restriction of predicted values to nonnegative values. Second, social media data collected in this research obtained count variance of the dependent variables to be larger than the mean. This over-dispersion was adjusted by applying negative binomial regression. In addition, given that zero values were obtained in a greatly higher frequency in the dependent variables, negative binomial modeling with a zero-inflation approach could also be obtained.

Reliability and Validity in Machine Coding Compared to traditional data collection techniques, with great help from machines or computers, researchers are able to collect huge volumes and diverse types of data in a much shorter time span. While much of the data might still be qualitative in origin, it often needs to be further transformed to be ready for quantitative analysis. As discussed earlier, traditionally, qualitative data are coded numerically into categories that must meet the standards of being exhaustive and mutually exclusive. These categorical systems are created on the basis of theoretical positions and themes. In the HDBD and CGBD era, the process of creating measurement and making sense of data, regardless of its volume and variety, is similar. The only difference is that human coders are substituted by computer programs to increase efficiency and meet the feasibility of an empirical investigation. In the academic literature and professional practice of applied communication, computer-assisted content, textual, and even visual analysis are no longer strangers. Researchers have adopted a number of programs, ranging from user-friendly sentiment analysis software (e.g., Linguistic Inquiry and Word Count [LIWC], SentiStrength, and built-in sentiment analysis functions in social monitoring tools) to natural language processing packages using programming languages (e.g., Hadoop, R, and Python), to measure variables of interest. Yet, whichever tool is adopted, the most critical feature of quantitative research methodology is the ability to state the reliability and validity of such measurement employed. Reported reliability and validity is a standard that ensures comparability across research and can be used to evaluate measurement quality (Michaelson & Stacks, 2017; Stacks, 2017). Unfortunately, many researchers fail to demonstrate the efficacy of their measurement tool and proceed with computer-assisted content analysis. To demonstrate that a computer program has a high quality, simply citing that this program has an acceptable reliability rate from previous literature is not sufficient. The researcher needs first to evaluate its validity. The Dictionary of Public Relations Measurement and Research defines validity as whether a measure is actually measuring what one defined it to measure (Bowen & Stacks, 2013, p. 26). Validity is closely related to the question of definition. In other words, how we operationalize a variable impacts the measure’s validity. There are four types of validity, each a little more rigorous in turn: face validity, content validity, construct validity, and criterion validity. Second, the reliability of such a program or package needs to be tested using subsets of the data collected for the current project (for more, see: Lacy, Riffe, & Lovejoy, 2015). To be more specific, machine learning or deep learning approaches should be adopted to follow four basic steps: (a) have multiple trained human coders to code a reasonably large random sample of

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the data; (b) assess intercoder reliability to prove that mutual agreement has been achieved among human coders of the variables defined; (c) compare human coding results with machine coding results; and then (d) report accuracy. Only when a high agreement has been reached can this given program be used for coding the full data set. However, if not, human coding results will be used to train the machine. After that, steps 1–4 need to be repeated until satisfactory results have been achieved.

Overpowering Issue: Significant vs. Effect Size Last but not least, another issue needing attention from researchers is overpowering, given that one of the distinct features of big data research is associated with sample size. In statistical testing, the possibility of rejecting the null hypothesis, assuming that the research hypothesis is true, is closely related to sample size. Simply stated, the larger the sample size, the more power researchers have in obtaining statistically significant results. Many times, people link sample size directly with power. With the infinite power from a huge sample size, it seems that nothing is insignificant: any two variables can be correlated, and subtle group difference can cause disparities in the outcome. However, a statistically significant result is never the ultimate conclusion that researchers should seek to draw. For example, if hypothetically in medical research it was found that drugs tested to reduce the level of blood sugar have the ability to lower it by 0.5–1.25%, and if a new drug is being considered for development, which is expected to be more affordable and carry a more competitive effect (say 2% reduction of blood sugar level), then, during the investigation, researchers should focus more on the effect size rather than whether a significant statistical result is obtained. Depending on the statistical power, results could reveal a statistically significant blood sugar reduction with a large sample size, but data might imply only an effect of 0.3% reduction. Such significant difference might not be observable with a small group of participants. In this case, this drug should not be passed down to the manufacturing stage due to its weak effect, even though a statistical significance was detected. A similar circumstance is also applicable to the field of applied communication research. The application of big data or computational approaches is not necessarily superior to small-scale data or traditional research methods. Without caution, it could be misused. Due to the effect of power, it is much easier to find significant results with a 1 million sample size compared to 100. When answering the “so what” question of a study, researchers should focus more on interpreting effect size in the natural setting, rather than the p-value. However, at the same time, researchers should not underestimate the impacts of a study only because it obtained a small effect size. For example, a recent study investigating not-for-profit organizations’ (NPO) emotion-based content strategies on Facebook found significant differences between emotion-carrying vs. non-emotion-carrying messages in generating awareness (likes), word of mouth (shares), and direct feedbacks (comments; Li, Ji, Tao, & Chen, 2018). Although some of the effect sizes from a series of hypotheses testing were below 0.04 (small to moderate effect) they provide essential practical implications for professionals: When creating online posts for NPOs, a modification mixing factual-based content with emotional appeals is rather simple. However, this single adjustment may effortlessly lead to almost a 4% boost in public engagement. To summarize, whatever effect size is revealed, big or small, significant or not, researchers should not impulsively jump to a quick conclusion.

HDBD and CGBD Case Studies There have been many studies employing HDBD cases. Two excellent sources are Maheswhari (2014–2016) and Marr (2016). Marr, in particular, offers 45 cases in which big data was used by companies from Amazon to Walmart; however, only one actually uses AI for the creation of



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new variables through natural language generation algorithms. There have been few cases of truly CGBD data. Probably the best known is IBM’s Watson learning and writing a song and Watson’s Jeopardy competition. However, neither of these instances is truly CGBD as the data sets were structured—visual and verbal cues in the Jeopardy case could not be applied due to limitations in computer learning at that time, and Watson actually did not write the song but provided deep insight into culture, emotion, and other variables that led to a co-creation by Alex Da Kid and Watson called “Not easy.” At least one other study has examined how natural language was used (Marr, 2016). For the purposes of this chapter, we offer a large data case and how it could be made an HDBD case and then suggest how one would conduct a CGBD study.

Predicting Social Media Strategies on Public Engagement An example of a large data applied communication study that used computer-assisted data collection and evaluative algorithms is one conducted by Ji, Chen, Tao, and Li (2019), who looked at how social media post functional features and message emotional strategies impacted public engagement with Standard & Poor 500 companies on Facebook.

Problem

How do the functional traits and emotional aspect of corporate messages jointly influence public engagement on social media? To answer this question a study was conducted that took, but did not actually use, an HDBD approach. What follows summarizes that study and then considers how it could be expanded to an HDBD approach.

Method

This study used computational approaches to examine naturally occurring data from 2015 Standard & Poor (S&P) 500 companies’ Facebook pages. Specifically, data mining and computer-assisted sentiment analysis were employed. The final data set used for modeling was merged from multiple sources.

Data collection

First, 106 S&P 500 companies’ complete Facebook posting history in 2015 was retrieved with Python through Facebook’s Graph application program interface and saved in a relational database.2 This included textual information of posts, each post’s functional interactivity and vividness features (e.g., post type, link, hashtag, and mention), and the number of likes, shares, and user comments for each post.3 Second, to control for firm-level effects, a set of covariate variables that indicated companies’ Facebook popularity and financial performance (i.e., the number of page likes and companies’ total annual revenue) were also collected and modeled as covariates in analyses. The final sample consisted of 33,379 posts. The financial variables (publicly available data) were collected from Compustat, a financial database.

Operationalizing key variables

To operationalize functional traits, two sub-constructs were measured: interactivity and vividness. Both were assessed based on the features provided by Facebook API. For example, interactivity   317 companies were identified with active Facebook accounts in 2015. The 317 companies spread across 10 sectors as defined by the Global Industry Classification Standard. A proportionate stratified sampling method was then utilized by randomly selecting one third of the companies within each sector, resulting in 106 companies as the final sample. 3   Variables regarding functional interactivity features (i.e., text, link, hashtag, mention, photo, and video) and engagement outcomes (i.e., number of likes, shares, and comments) were not human coded but directly captured and measured by the Facebook API. 2

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was categorized into five levels indicating different multiple interactive features, including hashtags, mentions, interactive external links, and posts/events/music/offers shared by a company in its Facebook post. Following the same approach, vividness was classified as four levels according to the specific post type indicated in the API. Emotion strategy was conceptualized in three sub-dimensions: emotion presence, emotion valence, and emotion strength. Company post content was analyzed via SentiStrength, which uses an algorithm to match words and phrases with its sentiment lexicon and mines opinions from the texts (Thelwall, Buckley, Paltoglou, Cai, & Kappas, 2010). Reliability was tested first between two human coders (Scott’s pi = 0.92) and then between human and machine coding (Scott’s pi = 0.66). Landis and Koch (1977) contended that for more conservative indices, the reliability coefficient of 0.61–0.80 indicates substantial strength of agreement. Therefore, the results were satisfactory. Finally, the Facebook data, financial data, and content analysis databases’ results were matched and merged for the purpose of modeling. DB Browser for SQLite 3.7.0, Python, and R were used for data collection, management, and analyses, respectively.

Results

To answer research questions, several negative binomial regression models were constructed for two reasons. First, dependent outcomes were counted variables with discrete distribution. Second, their count variances were larger than the means; thus, over-dispersion needed to be adjusted. In brief, results indicated negative relationships between functional interactivity and public engagement outcomes. Emotional traits overall led to stronger public engagement. Twoway interactions were also detected between functional and emotional features.

Transforming to BDHG

To move this from a large data to an HDBD study the data collection would be open-ended and data would be collected in real-time mode. At various stages of the study, the data would be tested for assumptions of normality and possibly transformed to meet those assumptions. The analyses would not differ from those reported in the large data study and could be extended by predictive modeling over time, assessing such factors as social media risk analysis, strategy, and so forth.

Creating a Case CGBD Study There are essentially no cases in which CGBD research has been applied to communication or other research. The reason, as discussed earlier, is simple: creating new variables through creative AI use is still beyond our ability. Two cases using HDBD have been done. One examined how the company Narrative Science used natural language to “tell stories” (Marr, 2016, case 21). The other involved a case describing how the British Broadcasting Company (BBC) used HDBD to improve programming depth, breadth, and presentation (Marr, 2016, case 22). Davenport and Ronanki (2018) examined AI use for the “real world” and concluded that while a majority of future business may be made by computers (up to 80% in some cases), humans will still be involved in those decisions (20% or more), depending of course on just what problems they are attempting to solve or what questions are to be answered. While it is clear that any research—applied or theoretical—requires human decision making, CGBD would minimize that to a continued review of data outcomes as they apply to the algorithmic models being used. The difference between current and future use in applied communication research would be that the computer, through machine learning, would be freed to create new variables and, based on design, may then create (or select from a library of messages) messages to effect behavior change. How then would a CGBD study be conducted? The following is a hypothetical, step-by-step case utilizing a healthcare problem dealing with immunization (see Table 8.2).



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Table 8.2  Creating a CGBD case.  1. Identify the problem or question  2. Operationally define independent/dependent variables  3. Identify the data sources  4. Data collection  5. Create data warehouses  6. Test variables for univariate and multivariate normality a.  Quantitative b.  Qualitative  7. Write algorithms for machine learning  8. Write algorithms for model testing  9. Evaluate results  10. Predictive analytics a.  Suggest message strategies b.  Create message c.  Test in population via sampling d.  Refine model  11. Run the model as necessary and update

1.  Identify the problem or question. In this case the problem is that people are not getting immunizations, thus increasing the risk factor for disease to re-establish itself. 2.  Operationally define the variables. The independent variables would include social media posts regarding immunization, disease rates by locations and populations, media coverage of disease trends among children, and so forth. Dependent variables would include immunization attitudes, resistance to immunization messages, and so forth. 3.  Identify the data sources. The data sources would include online media coverage by national and international newspapers (e.g., the New York Times, The Daily Telegraph, China Daily, The Japan Times), social media platforms (e.g., Facebook, Twitter, YouTube, LinkedIn, Weibo), governmental health databases (e.g., HealthData.gov, TRANSFoRM, EHR4CR, Sentinal), and so forth. 4.  Data collection. Python, R, WebCrawler data collection based on algorithms created for data capture of quantitative and qualitative variables. For coding examples see Klassen and Russell (2019). 5.  Create data warehouses. Establish warehouses for different sets of variables—relational databased, disease databased, media coverage databased. 6.  Test variables for univariate and multivariate normality. Check for variable linearity, error distribution with Shapiro–Wilk test, Mahalanobis distance, and Mardia’s measure of skewness and kurtosis for quantitative variables. Check for units of analysis and category validity; coding reliability via Scott’s pi. 7.  Create algorithms for machine learning and AI variable creation. Write algorithms, run the data, examine AI-created variables; test them for normality and validity via correlational analysis and factor analysis. 8.  Create models from algorithms and run. In this case, first with backward stepwise R2 regression to create the models, test for multicollinearity, test for relationships via structural equation modeling (SEM) or mediation analysis. Run geo-fencing data against models.4   Geo-fencing is a technique using GPS, RFID (Radio Frequency Identification), mobile phone, Wi-Fi and other data to create virtual boundaries around populations of interest. The data can then be geo-mapped to demonstrate population size and population changes in variables of interest (http://www.cio.com/ article/2383123/mobile/geofencing-explained.html).

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9.  Evaluate results. Evaluate the relationships obtained, trends for immunization acceptance, locations where risk assessment indicates additional communication required. 10.  Interpretation of results. Based on results, suggest message strategies, create and simulate message reception through AI-based relational algorithms, run pretests on geo-located sampling. Refine based on results. Run AI algorithms and go through steps 1–9 as long as the study continues.

Addressing Applied Communication CGBD Theoretically, the next step in applied data analytics is to take HDBD to its next level—that of computer-generated variables identified by algorithms written by humans, but providing the computer to learn from them and create new data insights. Three things are important here. First, any results from CGBD data must have a theoretical basis for its use. That is, the human element of using CGBD data is a necessary condition to interpretation and evaluation of AI-generated variables. This is prerequisite to any data mining application of big data. Second, data normality and reliability must be continually tested, especially with “new” variables that may come from a number of different levels of data (i.e., qualitative as well as quantitative), and its reliability assessed post hoc by trained observers of that data as actually measuring what the computer thinks it is measuring. And third, CGBD must be treated as specific machine learning applications to a problem or question.

Review The cases presented demonstrate how HDBD can be used to continually update selected variables over time and produce correlated attitudinal, financial, and behavioral results. The move to AI adds a second (or third or fourth) dimension to the data array by sifting through continuously collected data and creating (and modifying over data collection) variables that correlated highly with both existing stated variables and new variables created by the machine learning programs introduced to the data array. The use of HDBD and CGBD research, coupled with predictive analytics and messaging strategies, will provide applied communication researchers the tools to test theory in real-time applications. For example, as this chapter is being written, a new study group of academics, public relations professionals, research firms, and data scientists is examining how HDBD and CGBD can be used to identify potential corporate crisis situations through the use of bots and data mining for environmental variables employed in scanning for threats. Once identified, warehouses of message strategies aimed at bolstering held stakeholder attitudes and beliefs, creating defensive messages aimed at resisting counterattitudinal messages, or overcoming resistance to persuasive messages based on communication theory, would be employed to select the most appropriate message strategies for stakeholders. Continued environmental scanning, combined with analysis of success/failure, would be identified and algorithms written to establish machine learning, ultimately creating AI variables unique to new situations, but still under human review. The use of HGBD and CGBD is only now focusing on applied communication problems associated with a variety of communication outcomes. As communication theory-based research adds to our body of knowledge, combined academic and professional use of both data sets will advance toward enhanced engagement strategies for affected stakeholders and migrate into the world of AI.



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References Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet for Windows: Software for social network analysis. Harvard, MA: Analytic Technologies. Botan, C. (2019). Handbook of strategic communication. Hoboken, NJ: Wiley Blackwell. Bowen, S. A., & Stacks, D. W. (2013). Toward the establishment of ethical standardization in public relations research, measurement and evaluation. Public Relations Journal, 7, 1–27. Cain, M. K., Zhang, Z., & Yuan, K. H. (2017). Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation. Behavioral Research Methods, 49, 1716–1735. Cameron, A. C., & Trivedi, P. K. (2013). Regression analysis of count data. New York, NY: Cambridge Press. Davenport, T. H., & Ronanki, R. (2018, January–February). Artificial intelligence for the real world. Harvard Business Review. Retrieved from http://www.hbr.org/2018/01/artificial-intelligence-for-the-real-world DeNooy, W., Mrvar, A., & Batagelj, V. (2018). Exploratory social network analysis with Pajek: Revised and expanded edition for updated software (3rd ed.). Cambridge, UK: Cambridge University Press. Flynn, T., Iwata, J., & Marks, A. (2018). Building an enterprise digital engagement system. In R. Bolton, D. W. Stacks, & E. Mizrachi (Eds.), The new era of the CCO: The essential role of communication in a volatile world (pp. 123–138). New York, NY: Business Expert Press. Frey, L. R., & Musselwhite, J. (2019). Applied communication. In D. W. Stacks, M. B. Salwen, & K. C. Eichhorn (Eds.), An integrated approach to communication research and theory (3rd ed., pp. 347–358). New York & London: Routledge/Taylor & Francis. Hickson, M. L. (1973). Applied communications research: A beginning point for social relevance. Journal of Applied Communications Research, 1, 1–4. Hickson, M. L. (2003). Qualitative methodology. In J. E. Hocking, D. W. Stacks, & S. T. McDermott (Eds.), Communication research (3rd ed., pp. 194–215). Boston, MA: Allyn & Bacon. Ji, Y. (2017). Is social media worth the investment? Seeking relationship between social-mediated stakeholder engagement and nonprofit public donation (Unpublished doctoral dissertation). University of Miami. Ji, Y. G., Chen, Z. F., Tao, W., & Li, Z. C. (2019). Functional and emotional traits of corporate social media message strategies: Behavioral insights from S&P 500 Facebook data. Public Relations Review, 45(1), 88–103. Ji, Y. G., Li, C., North, M., & Liu, J. (2017). Staking reputation on stakeholders: How does stakeholders’ Facebook engagement help or ruin a company’s reputation? Public Relations Review, 43(1), 201–210. Klassen, M., & Russell, M. A. (2019). Mining the social web. Sebastopol, CA: O’Reilly Media. Lacy, S., Watson, B. R., Riffe, D., & Lovejoy, J. (2015). Issues and best practices in content analysis. Journalism & Mass Communication Quarterly, 92(4), 791–811. Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174. Laney, D. (2001, February 6). 3-D data management: Controlling data volume, velocity, and variety. META Group Research Note. Retrieved from https://blogs.gartner.com/doug-laney/files/2012/01/ ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf Li, C., & Stacks, D. W. (2015). Measuring the impact of social media on business profit & success: A fortune 500 perspective. New York, NY: Peter Lang. Li, Z., Ji, Y. G., Tao, W., & Chen, Z. (2018, May). Engaging your feelings: Emotion contagion and public engagement on nonprofit organizations’ Facebook sites. Paper presented at 68th Annual International Communication Association Conference, Prague, Czech Republic. Long, J. S., & Freese, J. (2006). Regression models for categorical dependent variables using Stata (2nd ed.). College Station, TX: Stata Press. Maheshwari, A. K. (2014–2016). Data analytics made accessible. Fairfield, IA: Amazon Media. Marr, B. (2016). Big data in practice: How 45 successful companies used big data analytics to deliver extraordinary results. Chichester, UK: Wiley. McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–68. Men, L. R., Tsai, W. H. S., Chen, Z. F., & Ji, Y. G. (2018). Social presence and digital dialogic communication: Engagement lessons from top social CEOs. Journal of Public Relations Research, 30, 83–99.

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Mertler, C. A., & Vannatta Reinhart, R. A. (2016). Advanced and multivariate statistical methods: Practical analysis and interpretation (6th ed.). New York, NY: Routledge. Michaelson, D., & Stacks, D. W. (2017). A professional and practitioner’s guide to public relations research, measurement, and evaluation. New York, NY: Business Expert Press. Miller, G. R., & Nicholson, H. (1976). Communication inquiry: A perspective on a process. Reading, MA: Addison-Wesley. Saxton, G. D., & Waters, R. D. (2014). What do stakeholders like on Facebook? Examining public reactions to nonprofit organizations’ informational, promotional, and community-building messages. Journal of Public Relations Research, 26, 280–299. Stacks, D. W. (2017). Primer of public relations research (3rd ed.). New York, NY: Guilford Press. Stacks, D. W., Hickson, M. L., & Hill, S. R. (1975). An introduction to communication theory. Fort Worth, TX: Holt, Rinehart, and Winston. Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61, 2544– 2558. doi:10.1002/asi.21416 Van Rijmenam, M. (2014). Think bigger: Developing a successful big data strategy for your business. New York, NY: AMACOM Books.

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Serious Games as Communicative Tools for Attitudinal and Behavioral Change Jessica Wendorf Muhamad and Soyoon Kim

Introduction Over the past few years the field of serious games, or social impact games, has grown substantially. Today, there are various organizations (e.g., Games for Change, Play2Prevent) whose mission is to facilitate the creation and distribution of games as critical tools in health, educational, and social change efforts. Recent games such as Peacemaker, Darfur Is Dying, and Re-Mission have demonstrated that by providing participants an immersive experience, a deeper understanding of complex social and health issues is possible. Mitgutsch and Alvarado (2012) argue that this understanding extends beyond gameplay sessions and serves to influence an individual’s attitudes and behaviors in real-life contexts. Specifically, the impact of serious games extends beyond knowledge acquisition or execution and rehearsal of skills to incorporate elaborative rehearsal, problem solving, and/or informal and incidental learning (Ritterfeld, Cody, & Vorderer, 2009). According to Glesne (2011) this is possible because serious games are able to make “… the strange familiar” (p. 219) through the presentation of plausible fiction in which their held schemas or beliefs offer very little in terms of guiding their behavior (Wendorf Muhamad, 2016). This, according to Wideman et al. (2007), is a result of presenting an avenue for individuals to access environments rich in new understandings, along with accompanying attitudes and emotions, that might otherwise be inaccessible and are a direct result of creating “lived experiences.”

Games as Communicative Tools for Intervention and Change Serious games function by leveraging entertainment (hedonic processing) characteristics with cognitive and emotional engagement for specific outcomes. Through focusing on persuasion, formation and change of attitudes, increased/enhanced awareness, and overt behavioral changes (Peng, Lee, & Heeter, 2010), serious games serve as activators of information processing. Recent growth in empirical research supports the use of serious games as an effective means of delivering prosocial messages. In an effort to advance the understanding of message processing within game-based interventions, serious games have been explored as a persuasive technology (PT). PTs are interactive systems that foster attitudinal and/or behavioral change among individuals (e.g., Fogg, 2003; Khalil & Abdallah, 2013). A study found that when combined with social motivation, PT yielded promising findings, with significant changes in behavior post exposure The Handbook of Applied Communication Research: Volume 1, First Edition. Edited by H. Dan O’Hair and Mary John O’Hair. © 2020 John Wiley & Sons, Inc. Published 2020 by John Wiley & Sons, Inc.

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noted (Fialho et  al., 2009). Thus, serious games hold the potential to serve as disruptors of ­systems—providing a point of entry that garners less resistance from participants due to unobtrusive persuasive and prosocial subtext. Specifically, this is achieved through the enhancement of self-monitoring, problem recognition and problem solving, decision making, collaboration, and negotiation that is possible in games. Self-monitoring involves the reflective experience an individual engages in throughout a gameplay session. More precisely, it is the self-observation and self-control based on contextual cues to respond in socially desirable ways (Snyder, 1974). Problem recognition, problem solving, and decision making are cognitive processes that are activated through the presentation of situations likely to elicit elaboration (Gee, 2007; McGrath, 1984). Additionally, these demonstrate to individuals how their actions might affect others (Aldrich, 2003). In analog serious games with the requirement of multiple players and due to proximity to others during gameplay, sessions foster socialization, and these in turn breed collaborative efforts. Serious games also hold the potential to humanize data through development of characters and scenarios that embody statistics. Risk factors for sexually transmitted infections (STIs) may be simulated through game mechanisms, such as having a deck of cards individuals have to select from at random stacked to mirror the probability of an infection. Additionally, positive deviance—atypical yet successful behaviors by individuals—might be demonstrated through characters that challenge social and individual norms (Marsh, Schroeder, Dearden, Sternin, & Sternin, 2004) while others’ characters may provide examples of prosocial attitudes and behaviors (Sabido, 2004) to be mirrored. This makes serious games particularly useful in scenarios in which individuals might have increased reactance, fear, or biases.

Serious Digital Games for Health While sharing basic characteristics with traditional games, games played on digital devices are further characterized by their enhanced interactive features using computer technology, such as online communication with other players, responsive game narrative, simulation, and/or feedback on game choices made (Baldwin & Dandeneau, 2009; Baranowski et  al., 2016; Lowood, 2006). According to the Pew Research Center, about half of American adults (49%) have played a digital game on a desktop computer, television, dedicated game console, or handheld device like a smartphone (Duggan, 2015). A recent report from the Entertainment Software Association (ESA, 2015) also demonstrated that 59% of Americans play video games, and more than half of US households (51%) own dedicated game consoles. These intrinsically engaging features, combined with a wide range of available digital platforms that have emerged since the mid- and late nineties, have accelerated the popularity of digital games as important, modern entertainment tools (Vorderer, Bryant, Pieper, & Weber, 2006). A serious digital game—also referred to as digital game-based learning (All, Castellar, & Van Looy, 2016; Prensky, 2001, 2003)—is a specific type of game designed primarily for learning and training purposes within a wide variety of serious contexts, including education, public health, communication, and military settings (Baranowski, Buday, Thompson, & Baranowski, 2008; Ritterfeld et  al., 2009). In particular, findings that demonstrate the positive effects of playing digital games on cognitive, affective, and behavioral learning outcomes have largely contributed to the increased attention to serious digital games as innovative approaches to learning, training, communication, and intervention for serious goals (Knutz, Ammentorp, & Kofoed, 2015; Lee & Peng, 2006; Papastergiou, 2009; Ritterfeld et al., 2009; Sawyer & Smith, 2008). Among the various applications of serious digital games, serious games for health focus on promoting psychological, behavioral, and clinical health by integrating educational goals with entertaining elements of gameplay (Baranowski, 2014; Baranowski et al., 2016). Because of the entertaining nature of games and the prevalence of digital devices, serious health games that use computer-based entertainment technology to teach, train, or change behavior are now popular



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in a wide range of health contexts, from preventing drug and alcohol abuse (Klisch, Miller, Beier, & Wang, 2012; Klisch, Miller, Wang, & Epstein, 2012) and imparting education on diabetes (Baranowski et al., 2008; Brown et al., 1997; Kumar, Wentzell, Mikkelsen, Pentland, & Laffel, 2004) to promoting a healthy diet (Peng, 2009), safe sex (Lightfoot, Comulada, & Stover, 2007; Roberto, Zimmerman, Carlyle, & Abner, 2007), and physical activities (Maloney et al., 2008; Warburton et al., 2007).

Mechanisms Underlying the Effects of Serious Games Considered a modality within entertainment education (EE), a communication strategy of utilizing messages that both entertain and educate while promoting attitudinal and behavioral change (Singhal, Cody, Rogers, & Sabido, 2004), serious games present an effective way of communicating complex information through the use of persuasive narrative and pictorial or graphical information embedded within an engaging environment. The guiding principal posits that by providing messages that are twofold—educational messages housed within entertainment frameworks—EE interventions are better able to increase audience knowledge, thereby shifting attitudes to have more favorable valence and leading to shifts in social norms, and eventually to attitude and behavioral change (Singhal et al., 2004). This is possible through the presentation of multiple dilemmas that simulate elaboration (Singhal et al., 2004). Although many serious games are informed by EE strategies, due to its lack of theoretical principals, EE alone is not sufficient in explicating the impact of serious games. Since early conceptions of EE, there have been numerous advancements and alternative persuasive models presented that aid the understanding of how experiential interactions function. Below, key principles and constructs of extended elaboration likelihood and entertainment overcoming resistance models are outlined.

Extended Elaboration Likelihood Model (EELM) The extended elaboration likelihood model (EELM) is an audience-centered model that extends the understanding of when and why observed behaviors are enacted, through highlighting the importance of reception and processing of persuasive messages presented through entertainment platforms. According to social cognitive theory (Bandura, 1986), learning is sustained when accompanied by observation (i.e., watching someone enact a behavior would lead to behavior change in the observer); however, not all observed behaviors are adopted. Studies found that this is due to motivation—or lack thereof—and that exposure alone does not suffice. Building from this, EELM examines which factors might affect motivation by eliciting resistance, and thus impact adoption of the observed attitude or behavior (Moyer-Gusé, 2008; Slater & Rouner, 2002). EELM advances the notion that resistance is a mediating force, negatively impacting entertainment education outcome variables (Slater & Rouner, 2002). The EELM (Slater & Rouner, 2002) proposes that, in presence of persuasive narrative, involvement increases (an individual is transported to story, an individual is less critical of content) and counterargument or message resistance decreases (Shrum, 2004). Notably, this is possible due to specific constructs –identification, homophily, and transportation—which will be described in more detail later.

Entertainment Overcoming Resistance Model (EORM) The entertainment overcoming resistance model (EORM) provides a framework by which to understand the causes of message resistance and its effect on the adoption of prosocial attitudes and behaviors (Moyer-Gusé, 2008). According to EORM, entertainment education media encounters less resistance and therefore can influence attitudes at a greater rate than traditional messages (Moyer-Gusé, 2008). Message resistance is defined as a psychological phenomenon in

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which individuals reject persuasive messages based on perceived threat and/or other factors (Buller, Borland, & Burgoon, 1998). Brehm (1966)) argues that message resistance stems from an individual’s need to feel unobstructed (free) in their choices, and that obtrusive undertones in a message lead individuals to feel stifled thus causing a boomerang effect, known as reactance. According to Moyer-Gusé (2008), one way to avoid psychological reactance is through the development of environments of increased absorption that present balanced complex situations. The overarching goal of EORM is narrative enjoyment through the movement away from threat. Following, mechanisms by which serious games are able to facilitate frameworks less susceptible to resistance are discussed.

Serious Game Mechanisms While serious games share many constructs and processes with media constructed with EE, EELM, and EORM paradigms, these processes are potentially enacted differently. Serious games facilitate deeper levels and more personalized processes of understanding. According to Abt (1970), serious games serve to highlight “matters of great importance, raising questions not easily solved, and having important possible consequences.” (p. 10). Whereas traditional entertainment education media (e.g., telenovela) invites audiences to imagine and/or pretend they are someone else while retaining their own identity, serious games beckon abandonment of the self (“if I were them” vs. “I am them”). Peng et al. (2010) describe three ways in which this occurs in games: (a) cognitively participants engage in the process of character; (b) emotionally they form healthy attachment through increased empathy; and (c) motivationally they internalize shared goals. Thus, the process of engaging in experiential learning, actively constructing educational outcomes (e.g., constructivism), on-the-spot problem solving, and other features make serious games qualitatively different than traditional entertainment educational approaches. Studies (e.g., Visch, Vegt, Anderiesen, & van der Kooij, 2013) have found that during gameplay, individuals hold the same motivational needs as in real life: (a) autonomy; (b) competence; and (c) social relatedness. These needs mirror some of the basics features of serious games that enable achievement of attitudinal and behavioral change. Following, five major features of serious games (serious game mechanisms) as noted by Charsky (2010) will be discussed. First, games invite players to compete. Competition serves by targeting individuals’ desire to win. Second, games contain goals and/or direct players to an achievement. Games’ goals cannot be simplified to win/lose, but instead should be considered opportunities to minimize doubts of real-world appropriateness of actions in fictional worlds to encourage deeper levels of transportation and identification, leading to greater effectiveness of persuasive content. Third, games contain rules. This serves not only to facilitate gameplay, but also symbolically as it simulates real-world power and control dynamics, as well as social behavioral codes. Next, games provide or limit a player’s choice. Options, decisions, and motivations enable identification with characters, but also serve to reinforce systems and structural inequalities that might be related to embedded educational and/or sociocultural issues. Last, games provide a context/setting in which players are able to enact learning and potentially experiment with alternate choices without high risk/consequences. Within a gameplay session, these mechanisms are activated through attitudes and behaviors executed by a key character, narrative content, and facilitator guidance when applicable. For this reason, it is important to examine the relationship between an individual and each of these constructs. Additionally, for serious games to facilitate persuasive processes they must: (a) create experiences structured around specific goals that are useful for future problem solving; (b) encourage interpretation from participants so that learning and future problem solving is internalized; and (c) provide participants with immediate feedback during the experience (Gee, 2007). Through these formal features, games provide an interactive method of engagement with persuasive messaging that might otherwise be inaccessible (Carcioppolo, Wendorf, & Tran, 2015).



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Role-taking and role-playing

Serious games allow for individuals to actively participate in role-taking, that is, the adoption of a character’s point of view through the development of empathic feelings (Peng et al., 2010). According to Mead (1934), role-taking differs from role-playing—another popular mechanism of experiential interactions—in that it is a purely cognitive process or mental activity that relies on elaboration. Whereas role-playing is occupying a position that is part of an individual’s identity and that appropriately meets social role expectations (e.g., a woman with children playing the role of mother by caring for their child), role-taking requires an individual to temporarily put themselves in the place of the other (Coutu, 1951). During the role-taking process, individuals are able to rehearse what they believe are the attitudes, beliefs, values, and behaviors of the other—from the other’s perceptual field (Coutu, 1951). This experience provides valuable insight that encourages the suspension of held ideologies or schemas that might elicit certain attitudes and behaviors (Wendorf Muhamad, 2016). During role-taking experiences, individuals actively adjust to meet expectations of the assigned or selected role (game character) in a particular setting/context. To accomplish this task, an individual must see themselves as similar to the character in values or goals, a more concerted engagement with a character that extends beyond liking a character (Peng et al., 2010). It is important to note that role-taking is also distinctly different than wishful identification, as the latter requires individuals to imagine they are like a character, whereas role-taking invites individuals to act as if they were the character. Facilitator-led serious games greatly enable this through gameplay session narration (i.e., facilitator calling an individual by the character’s name) and/or embedded cues (i.e., end of turn reminders by nonplaying characters that remind individuals of game goals). Role-playing a character in a game is not a vicarious experience, but an enacted one. This helps achieve experiential absorption, being fully engrossed in a narrative storyline and achieving game flow. Flow is important in serious games because it is a product of deep immersion and/or absorption in the storyline and character as well as the motivation to remain involved; more simply, it can be thought of as the moment when the right balance is struck between identification with character, transportation into narrative, and awareness of the self.

Identification

A similar concept to role-taking is character identification, or the intentional placing of oneself temporarily in the position of a key character to gain greater understanding (Flavell, Botkin, Fry, Wright, & Jarvis, 1968; Kelley, Osborne, & Hendrick, 1975). More precisely, identification involves the immersion of the player into the character’s environment, in this way allowing for the player to cognitively and emotionally understand the character. For this reason, certain emotive responses (i.e., empathic response) are susceptible to change and provide further motivation for an individual to align goals with the character (Cohen, 2001). Identification is significantly different to imitation, as it entails a mental process of engagement from a position of suspended ideology (Tal-Or & Cohen, 2010). There are four unique dimensions to identification: (a)  shared feeling (empathy); (b) shared cognitions (alignment of cognitive thought with character); (c) shared goals (motivation to engage as character); and (d) absorption (transportation to story). Each dimension allows for a deepening relationship with a key character, permitting individuals to enact responses in the way they believe their character would (their assigned or chosen role), and not how they would typically respond (Cohen, 2001). The process of distancing oneself from held schemas allows for a more nuanced understanding of the lived experiences of the other. Moreover, reflective understanding of the other serves to reduce resistance barriers (e.g., fear), thereby placing individuals in positions of increased susceptibility to prosocial attitudes and behaviors embedded in persuasive narrative content (Slater & Rouner, 2002). Through formulating responses, choices, attitudes, and behaviors from the position of a key character, identification offers individuals an opportunity to consider a dissonant perspective. As a note of caution, Tal-Or and Cohen (2010) found that audience members had a lower

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acceptance of highly stigmatized characters, whereas playing marginally stigmatized characters had less reactive outcomes, leading the authors to suggest that role-taking is hindered when stigma is too high, thus affecting identification.

Homophily

Homophily, or perceived similarity, is an individual’s belief that he/she possesses shared cognition, emotions, and/or goals with a key character. Centrally, the individual believes he/she could exist in the contextual history as a key character. Homophily differs from identification in that it does not require a vicarious experience (i.e., living the emotion of the other) but instead bridges relationships through shared attributes (i.e., liking/disliking similar things).

Transportation

Whereas identification and homophily focus on an individual’s engagement with a character, transportation involves engagement with narrative content. Transportation has been generally defined as complete absorption by an individual whereby the person is able to lose themselves in the storyline (Green & Brock, 2000)—no longer focused on elaborative efforts or processing narrative content, but fully immersed in the experience (Gerrig, 1993); however, within the literature there is discrepancy on how this construct is defined. In serious games, transportation serves to elicit moments of “suspension of disbelief” (Gilbert, 1991) in which stories—fiction or not—come to life for participants. In the presence of transportation, resistance is reduced, and individuals are more vulnerable to persuasive attempts. Counterargument, as a form of resistance, is incongruous to transportation because an individual cannot be fully absorbed and, at the same time, produce counterarguments to media being presented (Moyer-Gusé, 2008). In serious games, absorption enhances cognitive efforts to understand and live the position of the other (a key character), thereby reducing barriers (i.e., message resistance, counterargument, perceived invulnerability). The previously detailed constructs (identification, homophily, and transportation) are similar in that, together, they can measure a larger construct such as engagement or involvement, but it must be stated that they have distinct characteristics. Although proposed models, such as EORM, list all three as possible mediators of resistance, there are too few studies that examine whether they all do so in the same way or to the same degree. Although the difference between identification and perceived similarity was discussed above, it is important to note that a relationship among these concepts has been explored. Tal-Or and Cohen (2010) found that when there was positive character information presented to an audience member prior to their introduction, greater identification was reported. In this way, prior exposure to a positive character is a precursor to identification. This highlights the difference, but, at the same time, demonstrates their inevitable interaction. Also, it is important to note that transportation and identification are imaginative components of narrative persuasion, whereas perceived similarity is not. Similarity is also not as important as engagement—whether one believe oneself to be similar becomes irrelevant once an individual is absorbed in the narrative.

Spaces of Mitigated Risk Perhaps one of the most distinct advantages of serious games is their ability to create spaces in which ideologies can be temporarily abandoned and alternate ideas can be examined (Swain, 2007). Given that games seek to find balance, it is important to consider how character identity might impact this process. Green, Strange, and Brock (2003) found that through simulating a fictional world with real-world issues, games provide a frame of reference that does not compete with existing ideologies. Experiential understanding, or embodiment, within serious games occurs through key elements previously discussed (i.e., identification with character, perceived



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similarity to character or scenarios presented during gameplay, narrative transportation), as well as an experiential understanding of real-life issues within an environment of mitigated consequences. Gameplay sessions are considered spaces of mitigated risk because although they may actively deconstruct social norms—including widely held beliefs and values—audience members are presented with alternate ways of being that, although transgressing the established status quo, offer significant advantages (Wendorf Muhamad, 2016). In serious games, implications of failure are moderated, thus allowing individuals to act differently than they would normally (i.e., taking greater risks, exploring alternate choices) and explore new ways of being with limited repercussions (Stokes, Seggerman, & Rejeski, 2006). The process of experiencing, interacting with game scenarios and other players, and enacting social worlds allows for the psychosocial meaning-making process to unfold (Wideman et al., 2007).

Debriefing Dialogue Serious games use debriefing as a means to expand learning (Lederman, 1992), to facilitate reflection, and as a tool for later re-identification with the self and internalization of new information. Through debriefing, participants are encouraged to explore cognitions and emotions about the issue presented and begin the process of personalization. Although foundational models such as social cognitive theory (Bandura, 1977) accounted for peer influence (observational learning), more recently, models have focused more on individual-level processes. Tabletop, facilitated games provide an ideal environment for conversation and dialogue due to the fact that they simulate complex issues in a social setting, foster physical proximity of players, have mediated dialogue mediated by a trained facilitator, and provide psychosocial refuge (identity conflict) through mandated role-taking.

Effects of Digital Games on Persuasion and Intervention Outcomes Modern learning theories suggest that learning is most effective when it is active, experiential, contextual, and provides immediate feedback (Boyle, Connolly, & Hainey, 2011; Knutz, Ammentorp, & Kofoed, 2015). A serious digital game inherently holds such characteristics; the sense of fun and enjoyment triggered by the interactive nature of serious digital health games is expected to increase people’s motivation to engage with the game elements in which the health educational contents are embedded (Fuchslocher, Niesenhaus, & Krämer, 2011; Street & Rimal, 2013; Vorderer, Klimmt, & Ritterfeld, 2004). Empirical evidence supports the argument. Metaanalysis or systematic review studies (Bailey et al., 2012; DeShazo, Harris, & Pratt, 2010; Noar, Black, & Pierce, 2009; Portnoy, Scott-Sheldon, Johnson, & Carey, 2008; Primack et al., 2012; Rodriguez, Teesson, & Newton, 2014) showed small to moderate effects of playing digital games on improving a wide range of health-related outcomes, including increased motivation and attention (Baranowski et al., 2008; Lieberman, 2006), health knowledge (Klisch, Miller, Beier, & Wang, 2012; Starn & Paperny, 1990), self-efficacy (Brown et al., 1997), health skills (Kato, Cole, Bradlyn, & Pollock, 2008; Peng et al., 2010; Schinke, Schwinn, & Ozanian, 2005), attitudes (Marsch, Bickel, & Grabinski, 2007), and behaviors (Lightfoot et al., 2007). However, individual findings that demonstrated nonexistent or detrimental effects of playing interactive digital games on desired outcomes should not be overlooked (e.g., DeSmet, Shegog, Van Ryckeghem, Crombez, & De Bourdeaudhuij, 2015; Huss et al., 2003; Panic, Cauberghe, & De Pelsmacker, 2014; Pellouchoud, Smith, McEvoy, & Gevins, 1999; Vorderer, Knobloch, & Schramm, 2001). For example, in a study that examined the effects of threat-appeal health messages using different intervention medium types, Panic et al. (2014) found that an interactive game was less effective in leading to children’s choice of health snacks, compared to traditional media. Similarly, a more recent study that examined the effects of a serious digital game developed to discourage indoor tanning among young adults showed no evidence supporting

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the expected merit of using a digital game over other intervention methods in reducing protanning attitudes and intentions (Kim & Ewing, 2010). One of the explanations for the negative impacts of serious digital games is that processing (e.g., paying attention to) interactive features of a digital game may demand a substantial amount of cognitive resources, leaving little bandwidth to process educational information embedded in serious digital games (Panic et al., 2014; Warnick, Xenos, Endres, & Gastil, 2005; Yuji, 1996). It is usually assumed that interactivity and game narrative are essential features that shape the persuasive potential of digital health games as promising intervention tools (Gilliam, Jagoda, Heathcock, & Sutherland, 2014; Kharrazi, Lu, Gharghabi, & Coleman, 2012). However, a number of empirical studies produced conflicting findings regarding the effects of interactivity (Liu & Shrum, 2002; Yang & Shen, 2018). Scholars pointed out that the multidimensional nature of interactivity and varied operationalization and use of measurement instruments might have largely contributed to the mixed findings (Bucy, 2004; Walther, Gay, & Hancock, 2005). To better understand mechanisms underlying the effect of serious digital games, the diverse definitions of interactivity and conflicting findings regarding its impacts are discussed in the following section.

Interactivity and serious digital games

Although interactivity has been defined in various ways across different research domains, scholars have generally agreed that interactivity is a multidimensional concept with varying degrees of presence (Ha & James, 1998; Heeter, 1989; Klimmt, Hartmann, & Frey, 2007; McMillan, 2002; Weber, Behr, & DeMartino, 2014). Broadly, interactivity has been studied in two different ways: one as an objective attribute of communication circumstances, messages, or medium types (e.g., Carey, 1989; Coyle & Thorson, 2001; Liu & Shrum, 2009; Sundar, 2007) and the other as a property of the user’s subjective experience with a particular medium (e.g., Bucy & Tao, 2007; Leiner & Quiring, 2008; McMillan & Hwang, 2002; Wu, 2005; Sohn & Lee, 2005). From the former point of view, a communication modality that has more interactivity features, such as a website or digital game with more hyperlinks and feedback buttons, is considered more interactive than a modality with fewer or a lack of such technology-oriented features, such as a text-based brochure. Conversely, the latter conceptualization of interactivity, also known as perceived interactivity, is determined based on users’ subjective perceptions of interactive features on a particular communication media (Bucy & Tao, 2007; Sohn & Lee, 2005). A number of scholars have emphasized the importance of distinguishing the two different approaches in examining the effects of interactivity, mainly due to the subjective and unpredictable nature of perceived interactivity dependent on individuals’ ability to recognize, understand, and use interactive features (Bucy & Tao, 2007; Sundar, 2004) and the potentially differential impacts of the two types of interactivity on persuasion outcomes (Thorson & Rodgers, 2006; Yang & Shen, 2018). Even when interactivity was narrowly defined with the former, message- or medium-oriented, definition, its multidimensional nature led researchers to focus on different aspects of the concept. For example, some scholars defined interactivity based on the message-exchange characteristic, such as the capacity of a medium that facilitates message exchange between the interface and its users (e.g., degree of responsiveness of a message; Sundar, Kalyanaraman, & Brown, 2003; Sundar & Kim, 2005), while others focused more on the presence, absence, or number of certain technological features that are considered inherently interactive, such as sliders, zooming tools, hyperlinks, video players, or buttons, in operationalizing interactivity (Song & Zinkhan, 2008; Trammell, Williams, Postelnicu, & Landreville, 2006). Another important dimension that has been used to characterize interactivity is the extent to which a user has control over various aspects of given contents—i.e., agency or control (Bezjian-Avery, Calder, & Iacobucci, 1998)—such as managing the pace, sequence, and objects of the content or tailoring messages (Wojdynski, 2011).



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When applying these definitions to digital game contexts, interactivity in digital games is defined as a player’s opportunity to initiate actions and receive evaluative feedback about their performance (Ritterfeld, Shen, Wang, Nocera, & Wong, 2009) or a game’s capacity to allow the players to have control over various game elements, such as adjusting game settings (e.g., visual effects, display resolution, and sound volume), customizing game characters, and personalizing game goals and sequences (Calleja, 2007; Qin, Patrick Rau, & Salvendy, 2009; Raney, Smith, & Baker, 2006; Weber et al., 2014). According to these property-based definitions, a lower level of digital game interactivity can be characterized by players’ commands of simple movements of the game character (e.g., jumping, running) through controllers (e.g., a mouse or game handler), while a more advanced type of interactivity can be characterized by the extent to which players can influence and/or interact with the game environment and story plot (narrative) in the course of gameplay (Fuchslocher et al., 2011; Klimmt, 2009; Weber et al., 2014). Simulation health games that use virtualization technologies to create an immersive game environment, in which the players can safely obtain health knowledge and skills by actively participating in realitybased decision-making situations, can be examples of digital games with a high level of interactivity (Apperley, 2006; Sitzmann, 2011). Defining perceived interactivity in the digital game context might be even more challenging because of the wide variation in the conceptualization and operationalization of perceived interactivity in the literature. For example, based on the synthesis of the literature on interactivity, McMillan and Hwang (2002) suggested that users’ perceptions of the direction of communication (e.g., two-way communication), control (e.g., easy navigation or diverse choices that enable users to actively participate in communication), and time (e.g., real-time, synchronous communication or the speeds at which messages can be delivered) constitute their perceived website interactivity. By developing measures for three different dimensions of perceive interactivity— including real-time conversation, no delay, and engaging—and testing their validity, McMillan and Hwang (2002) demonstrated the multidimensional structure of perceived reality and the possibility that each of the dimensions can overlap. In another seminal work, Wu (2006) identified three dimensions of perceived interactivity, including the following: (a) “perceived control over (a) the site navigation, (b) the pace or rhythm of the interaction, and (c) the content being accessed” (p. 91), which refers to the extent to which a user regards that he or she can exert active control over the interface to create a nonlinear experience of navigating through the content, (b) “perceived responsiveness from (a) the site-owner, (b) the navigation cues and signs, (c) the real persons online” (p. 91), defined as the extent to which a user considers an interface as responsive to his or her earlier input, and (c) “perceived personalization of the site (a) as if it were a person, (b) as if it wants to know the site visitor, and (c) as if it understands the site visitor” (p. 91), which indicate the extent to which a user deems such interactive process reflects his or her own interests in a meaningful way. A series of studies conducted to test the reliability and validity of this suggested structure confirmed the three-dimensional nature of perceived interactivity (Wu, 2006).

Considerations for the Design and Evaluations of Serious Games Research and Theory-Based Design Through Multidisciplinary Collaboration A number of game researchers recommended that the design of a serious game as an intervention tool should be guided by a solid theoretical framework (Baranowski et al., 2008, 2016; Bul et al., 2015). A typical health intervention planning process begins with identifying the significance of a health issue. Based on the assessment of important aspects of the target behavior and the target population, campaign planners can set up an intervention goal with a behavioral objective (Glanz, Rimer, & Viswanath, 2008). The initial research stage provides guidance for

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the development of specific communication objectives and strategies—including the development of the key content and features of a health intervention message and appropriate communication platforms (Schiavo, 2013). An important part of this process involves identifying intermediate persuasive outcomes that can directly or indirectly lead to the intended behavioral change, such as increasing awareness and knowledge about a health issue or making changes in existing health beliefs and attitudes. Theory plays a critical role in this process. The key constructs of various theoretical models can provide a framework for identifying critical determinants of an intended behavioral change, and thus can help game designers select the intermediate processing goals as well as final behavioral goals of a communication intervention strategy (Lwin et al., 2016; Noar, 2006; Peng, 2009). These advantages of using theory in health communication intervention are directly applicable to the development of a serious digital game as an important health intervention tool. For example, a serious game (called Plan-It Commander) designed to teach and promote time management, planning, and prosocial skills for children with attention deficit hyperactivity disorder (ADHD) between 8 and 12 years of age (Bul et  al., 2015) was developed based on psychological principles from influential theories, including the social cognitive theory (Bandura, 1999, 2001). As previously mentioned, one of the key assumptions of social cognitive theory is that human behavior is the product of the dynamic interplay of personal, behavioral, and environmental influences (Bandura, 2001; McAlister, Perry, & Parcel, 2008). In particular, according to the theory, vicarious capacity for observational learning enables people to expand their knowledge and skills rapidly through information conveyed by exposure to interpersonal or media displays of them (Bandura, 2001, 2004). Based on this idea, the game environment was developed to support mastery of target behaviors by providing models for target behaviors (e.g., a virtual mentor who demonstrated polite behaviors in social interactions) and emotional encouragement and positive feedback for intended behavior change (Bul et  al., 2015). In another study, conducted to develop and evaluate a serious digital game (called RightWay Café) as a medium to promote a healthy diet for young adults, behavioral prediction theories, such as the health beliefs model (Rosenstock, 1974; Rosenstock, Strecher, & Becker, 1988) and the theory of reasoned action (Ajzen & Fishbein, 1980), provided a theoretical framework to guide the design of the health messages (content) embedded in the game (Peng, 2009). One important challenge relevant to the integration of theoretical constructs to the design process of a serious digital game is that traditional game designers may not be familiar with factors that influence a target audience’s attention, motivation, and behavior related to a health issue. Similarly, even when health researchers are intrigued by the new medium’s persuasive potential, they are not necessarily familiar with the technical elements and design process of digital games. Among the many suggestions made to circumvent this situation, the use of a collaborative game development process has been emphasized by a number of researchers (Baranowski et  al., 2008, 2016; Bul et  al., 2015; Kato, 2012). This collaborative process allows health researchers and creative game designers to identify critical determinants of a targeted health behavior based on appropriate theory. Furthermore, it allows them to transform the selected theoretical constructs into interactive and engaging game design elements to maximize the game’s effectiveness (Lwin et al., 2016; Noar, 2006). Although crucial game work has been conducted following this suggestion (e.g., Baranowski, 2014; De Jans, Van Geit, Cauberghe, Hudders, & De Veirman, 2017; Kim & Ewing, 2010; Peng, 2009; Spook et al., 2015), more research is needed to offer detailed guidance for the important collaborative process.

Addressing Heterogeneity in Game Design and Evaluation Research In a previous section of this chapter, the multidimensional nature of interactivity has been discussed as one of the factors that might have contributed to the conflicting findings regarding the



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effects of serious digital games. In addition, scholars pointed out various types of heterogeneity in approaches to the design and evaluation of serious digital games as reasons underlying such mixed findings (Baranowski et al., 2016; Kato, 2010). For example, the samples of digital health games examined in the literature included not only games that are designed specifically for health education purposes from the beginning (e.g., It’s Your Game tested in Tortolero et al., 2010), but also commercial games that were developed mainly for entertainment purposes but later repurposed to be examined within the boundary of serious games (e.g., Dance Dance Revolution or Wii Sport tested in Baranowski et al., 2012). The effectiveness of a serious digital game for health should be determined based on whether the game evaluation result meets the health intervention goals (i.e., educational objectives) developed at the planning and design stage of the game. However, the latter approach could lead to unfortunate situations in which the original goals developed based on a theoretical or design framework do not exist or are unavailable at the time of the evaluation. Another important game-evaluation practice that may hinder generalized claims about the effectiveness of digital health games is the differential use of a control group (All, Castellar, & Van Looy, 2016; Baranowski et al., 2016). In some studies, for example, the effectiveness of a serious digital game was claimed against a control group receiving no intervention (e.g., Brown et al., 1997; Peng, 2009) or against a group in which some other intervention modality was implemented (e.g., Panic et al., 2014; Sward, Richardson, Kendrick, & Maloney, 2008). While valuable, the former approach can only provide limited insights based on the comparison between people who are exposed to certain educational content through a serious game and those who are not exposed to any learning content at all (All et al., 2016). To strengthen the reliability and validity of existing findings, more evaluation research that examines a serious game’s effectiveness over other educational activities as well as a control group is needed, particularly with a game originally designed for an educational purpose. Last, it is also important to consider the heterogeneity in the characteristics of a target audience. Serious digital games have been regarded as an advantageous medium to reach young generations who are generally characterized as active gamers who have grown up with the internet and mobile technology (Lieberman, 2006; Peng, 2009). Importantly, in health communication research, the same group of people has been considered an at-risk population, with members who often show poor compliance with conventional communication intervention approaches, such as mass-mediated antidrug messages (Hornik, 2003; Spook et  al., 2015). Traditional intervention messages that utilize a dictating and didactic tone can be perceived as a threat to these young people’s self-esteem and autonomy, often resulting in nonexistent or boomerang effects (Burgoon, Alvaro, Grandpre, & Voulodakis, 2002; Cho & Salmon, 2007). The growing attention to serious digital games as an alternative to traditional intervention tools is partly based on the assumption that digital games can provide a preferred platform through which young people can obtain health knowledge and skills without developing psychological reactance (Vorderer et al., 2006). However, this expectation may overlook the existence of variance in the demographic and psychological characteristics among the seemingly homogeneous group of young individuals. For example, a recent study conducted to develop and test a serious digital game (called Dreamy) designed to discourage indoor-tanning intention among young adults found that playing the game led to a stronger intention to use indoor tanning, particularly among the subgroups of young adults who were male and who had a previous experience with indoor tanning. These types of heterogeneity discussed in this section are just a few examples of factors that can influence the effects of serious digital games for health interventions. Careful consideration of these potential issues and identification of other conditions that might moderate the effects of serious digital games can help future game researchers and designers develop more effective serious digital games and more accurately evaluate the failure and success of the valuable new communication platforms.

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Participatory Paradigm and Transdisciplinary Design Teams In accordance with the shift in the development of applied communication strategies, interventions are increasingly viewed through a dialogic lens whereby individuals are no longer passive recipients of information once designed; instead they are instrumental throughout all stages. Projects constructed within a participatory research paradigm are developed with communities, as opposed to for them (Peterson et al., 2010; Wendorf Muhamad, 2016). A central tenet in this approach is the inclusion of local community members as main actors in the process. Whereas action research is intended to preserve the status quo, participatory action research (PAR) seeks to involve stakeholders throughout the entire process, thereby contributing to social capital and empowerment, and lowering the probability of unintended exclusion (Chantana & Surlchai, 1985). Furthermore, the utilization of a participatory-based approach ensures that game design processes consider the local concerns and cultural norms, as well as the validation of autochthonous knowledge. Participation from target populations in the game design process helps account for key considerations, such as how the individual will interact with the media artifact, which in turn may ensure ease of uptake as it minimizes learning curves associated with technology, but also allows for greater sustainability as materials are easily sourced and produced. Generally, game design is considered an imitative process (Costikyan, 2013), with game designer borrowing evidence-based mechanics. However, in the face of cultural-specific phenomena, game systems cannot be borrowed but instead must be created from the ground up. A pictorial-based serious game, for example, presents itself as an effective means for remote and vulnerable populations with low-to-limited literacy. The efficacy of the game is at risk without shared meaning of signs and symbols. Therefore, inclusion of the target population in the design of visual elements within the game helps mitigate potential intercultural lapses. Beyond inclusion of community members, it is also important to note the breadth of skills and diversity on research and design teams. Research suggests (e.g., Khaled & Ingram, 2012) that games are greatly improved by employing a transdisciplinary team of game designers, researchers, subject matter experts, and local experts. A multifaceted approach ensures a well-designed interactive experience along with accurate depiction of the central issue. This is ensured by integration of all team members from the onset of game design to best inform design choices. As a means of constructing a model by which multidisciplinary teams can engage in the development and implementation of serious games for complex societal issues, team members engaging in: ethnographic observation and documentation; check-ins with community members and partnering organizations; team debriefings; and documentation of both fieldwork and design-related feedback and concerns from the community have been found to be effective (Wendorf Muhamad & Harrison, 2018). Although numerous studies (e.g., Choi & Pak, 2006; Stokols, Hall, Taylor, & Moser, 2008) have examined the efficacy of teamwork, and more specifically among transdisciplinary teams, what follows presents a way in which a team develops as a result of continuous reaffirmation to project goals. Throughout the development of various serious games (e.g., Por Nuestras Calles, Playing it Safe!), four major process themes for conducting participatory research and design across disciplines have emerged as salient: (a) equipoise, (b) collaborative engagement, (c) shared efficacy, and (d) dialogic spaces.

Equipoise

Equipoise refers directly to the tension, flow, conflict, and resolution cycle present among team members, as well as with community members and partnering organizations. Efficacious performance has often been attributed to flow among and within team members. Moreover, this flow has generally been conceived as a positive affective or mood state. However, there is growing evidence to support tension and conflict as alternate channels of effective engagement among teams. Although frequently framed negatively, among communication scholars conflict has long been understood as normative to social processes that do not need to be eliminated or



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resolved (e.g., Putnam & Poole, 1987). Instead, highly charged emotional states (e.g., conflict, tension) may assist members in understanding their interdependence and thus bridge misunderstandings (Harrison & Wendorf Muhamad, 2018).

Collective engagement

Collaborative engagement manifests in two distinct ways: (a) as a reaction to tension in the deliberative cycles generated (equipoise) and (b) as a result of breakthroughs in group dynamics, intervention design, and project mobilization. Specifically, collaborative engagement can be considered the purposeful bringing together of multiple stakeholders in an effort to process intra- and inter- group dynamics. The research and game development processes require extensive expertise in multiple areas (e.g., community-based methodology, game design), which customarily do not have a shared language. As each specialist comes into the project, team members should constantly negotiate ways to assert their knowledge and field while keeping in mind the shared goal. As previously mentioned, transdisciplinary teams often work toward collaboration through the elimination of conflict and restoration of functional flow. Functional flow, or perceived order and movement toward a shared goal, however, holds the potential to undermine true engagement as it negates the influence of power, hierarchy, and competing interests (e.g., Waddock, 1989). At times, due to the nature of the intervention, the context, and the topical area’s psychological and emotional impact, teams are required to move beyond superficial functional flow to full engagement.

Shared efficacy

Shared efficacy, although a desired equilibrium throughout the process, at times manifests post serious game development. At its core, shared efficacy is the desire from all team members to feel that they accurately represent their expertise (e.g., field and skillset), the needs of the community members, and the design principles of the game. It extends beyond the physical action of bringing stakeholders together and transcends the singular focus to that of wellbeing for all. Essential to puissant interventions, teams must have clearly articulated outcomes/deliverables and a shared understanding of how to achieve them. They must have shared objectives that are detailed, nuanced, and time-bound to ground team members throughout the cycle and guide actions toward the best interest of the project and not individual needs. Shared efficacy, however, extends not only to have team members advocate for the project, but to encourage them to trust that the other members have the same intention and that they are acting in the best interest of all—team, community members, and research. Shared efficacy also encompasses an understanding that each team member is acting as an expert in their field, and thus their intentions are to accurately represent their expertise within a transdisciplinary group. While there may be moments of uncertainty in regard to the motivation and intention of a line of questioning or suggestions from a team member, shared efficacy is the trust in the person’s skillset, intention, motivation, and ability to successfully complete the task at hand, if supported by the other members. Shared efficacy is enhanced through the willingness to have open conversations that bring everyone together and onto the same page.

Dialogic spaces

Last, teams may evidence the need for dialogic spaces as a means of processing uncertainty and reaffirming goals. Dialogic spaces have a more intentional root than mere conversations among members and require a structured approach. Whereas conversations among members may organically appear, dialogic spaces must be built into the project just like other critical components (e.g., data collection, design). Commonly, teams are directed away from conflict and redirected toward outcome tasks. When conflict arises, strategies such as mediation and collaboration building are often used to mediate the potential negative effects. Frequently, these occur

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as a response to conflict—reactive as opposed to proactive. Communication efforts not focused on resolving conflict tend to center around rapid project communication (e.g., status updates). Productivity-based communication is usually standard and built into team projects; however, this is not the only form of communication necessary for purposeful engagement among members. Dialogic spaces position individuals, just as games, in safe spaces in which they may openly process difficulty and potentially threatening situations. Through embracing tension as a means of engagement, dialogic engagement beckons individuals to come together for the achievement of a common goal.

Conclusion There is little doubt of the benefits, beyond entertainment value, that serious games provide. Perhaps one of the most distinct advantages of serious games is their ability to create spaces in which ideologies can be temporarily abandoned and alternate ideas can be examined safely, such as those discussed earlier in this chapter. However, it is important to consider the role of serious games in applying communication strategically as opposed to viewing it as some kind of panacea. BaFá BaFá, a diversity training tool, allows individuals to explore cultural differences and similarities with the aim of reaching shared understandings (Simulation Training Systems, 2006). According to Shirts (2009), individuals react to culture from both a cognitive and an affective frame. Key to the BaFá BaFá experience is the immersion into another’s culture. A recent study of BaFá BaFá (Wendorf Muhamad & Yang, 2016) found that although the experiential interaction had higher scores of constructs such as motivation to communication with the other when compared to the standard of care (information intercultural handout), there was no significant difference in other variables such as intercultural sensitivity—a crucial element of intercultural communication and of the game. This provides instrumental information on how environments that encourage execution and application of knowledge and skills, such as serious games, can enhance effectiveness of attitudinal or behavioral change sought. Furthermore, it demonstrates that, although a valuable strategy, serious games do not serve as a replacement for all types of persuasive media, and that in fact interventions should be enhanced not by changing all text- or graphic-based messages to games, but through games’ additive effects when paired with other communication channels. The efficacy of serious games depends not only on properly employing game mechanisms and conditions necessary for entertainment education-based interventions, but also in acknowledgement of the limitation of games. Fundamentally, serious games represent an outcome of the social processes in which dissimilar attitudes, beliefs, and systemic inequalities such as disproportionate power compete for the attention and action. Specifically, games are able to create an environment in which manifest (sought) and latent functions of social action processes, as described by Merton (1957), emerge and are negotiated. Serious games that aim to provide new, never-experienced situations must be carefully constructed to weigh unintended consequences of any inclusion/exclusion of information and/or visual elements. Understanding when and how certain game mechanisms should be employed is also critical to a game’s efficacy as a communicative tool. For example, to simulate structural inequalities, a game might not allow players to choose certain outcomes (i.e., pulling a card from a “risk” or “consequence” deck that limits player); however, this is hugely different than not allowing women to play the game in a society that oppresses women. Although oppression might be tangible in the particular society, it is not an element that needs to be simulated to experience social codes—or at least there are other simulated, reality-based scenarios that might offer less reactance. Thus, the unintended consequences of the use of serious games, particularly those that address complex social issues, should be carefully considered as the inclusion or omission of information may inadvertently recreate oppressive systems, lack cultural  competence, or create a boomerang effect on desired attitude and behavior change.



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Critical game design should not be limited only to narrative content, but should also account for visual elements and mechanisms such as multiplayers versus single player. Given the persistent emergence of new and complex social issues, the need for usable and applied knowledge is critical. While serious games will not obviate all the issues faced today, features of games present opportunities to explore and challenge the individual, society, and built environment constraints that often slow progress and change. Through reconstructing sociopolitical environments and highlighting the interactions of power and force, serious games function as a translation tool, making phenomena accessible to a lay audience, and providing individuals an avenue to transition from this is how I think/act to this this is how I could think/act. Within games, meaningful action is met by directly witnessing results and consequences of an individual’s action/inaction. This is perhaps one of the greatest opportunities serious games present— the power of agency and the immediacy of learning. Serious games are effective tools for demonstrating the intricacy and interconnectedness of social phenomena. As with other media-based interventions, however, it is important to consider the way in which games work. If framed within a cognitivism framework, games function as learning tools. Cognitive-based games provide an opportunity for rehearsal, eliciting the integration of new information with prior knowledge to solve complex scenarios. Constructivist games, however, function not through the presenting of problem-solving-centered scenarios, but instead through inviting players to explain what they are encountering, thereby reinforcing learning (Protopsaltis, 2001). Similarly, experiential learning theory favors this learning by doing model. Experientially based games create environments in which players are able to: (a) see behaviors and attitudes manifested, (b) hear others, and (c) engage in problem solving. Situated games present a particular benefit in that they are more like real life than an imagined possibility. In this way, situated games mirror the lives of players, and information is more easily transferred from the game character to the individual post gameplay sessions. Likewise, simulations, or simulation-based games, center on the role-playing perspective. This type of serious game is often used for training purposes and has succinct goals. Sociocultural games, on the other hand, are not focused on practicing behaviors but instead on facilitating spaces of reflection and transformative dialogue. Last, certain games are considered to be “full-cycle learning,” that is, games that engage players by utilizing most of the learning theories discussed above. This type of game is concerned with having individuals experience exposure to new understanding, execute use of this new knowledge (i.e., problem solving, elaboration), and then engage in direct feedback (BinSubaih, Maddock, & Romano, 2009). This process can occur once per gameplay session or multiple times throughout a game. Future studies on both game development and evaluative efforts should consider ways in which to expand existing theoretical persuasive and learning models. Theoretically driven games paired with participatory approaches to research will allow for the development of more robust models of understanding.

References Abt, C. C. (1970). Serious games: The art and science of games that simulate life. New York, NY: Viking. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice Hall. Aldrich, C. (2003). Simulations and the future of learning: An innovative (and perhaps revolutionary) approach to e-learning. San Francisco, CA: Pfeiffer. All, A., Castellar, E. P. N., & Van Looy, J. (2016). Assessing the effectiveness of digital game-based learning: Best practices. Computers & Education, 92, 90–103. Apperley, T. H. (2006). Genre and game studies: Toward a critical approach to video game genres. Simulation & Gaming, 37(1), 6–23. doi:10.1177/1046878105282278 Bailey, J. V., Murray, E., Rait, G., Mercer, C. H., Morris, R. W., Peacock, R., … Nazareth, I. (2012). Computer-based interventions for sexual health promotion: Systematic review and meta-analyses. International Journal of STD & AIDS, 23(6), 408–413.

156

Wendorf Muhamad and Kim

Baldwin, M. W., & Dandeneau, S. D. (2009). Putting social psychology into serious games. Social and Personality Psychology Compass, 3(4), 547–565. Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice Hall. Bandura, A. (1986). Social foundation of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall. Bandura, A. (1999). Social cognitive theory: An agentic perspective. Asian Journal of Social Psychology, 2(1), 21–41. Bandura, A. (2001). Social cognitive theory of mass communication. Media Psychology, 3(3), 265–299. Bandura, A. (2004). Health promotion by social cognitive means. Health Education & Behavior, 31, 143–164. Baranowski, T. (2014). Description for articles introducing a new game for health. Games for Health Journal, 3(2), 55–56. Baranowski, T., Abdelsamad, D., Baranowski, J., O’Connor, T. M., Thompson, D., Barnett, A., … Chen, T. A. (2012). Impact of an active video game on healthy children’s physical activity. Pediatrics, 129(3), 636–642. Baranowski, T., Blumberg, F., Buday, R., DeSmet, A., Fiellin, L. E., Green, C. S., … Young, K. (2016). Games for health for children—current status and needed research. Games for Health Journal, 5(1), 1–12. Baranowski, T., Buday, R., Thompson, D. I., & Baranowski, J. (2008). Playing for real: Video games and stories for health-related behavior change. American Journal of Preventive Medicine, 34(1), 74–82. Bezjian-Avery, A., Calder, B., & Iacobucci, D. (1998). New media interactive advertising vs. traditional advertising. Journal of Advertising Research, 38, 23–32. BinSubaih, A., Maddock, S., & Romano, D. (2009). Developing a serious game for police training. In R. E. Ferdig (Ed.), Handbook of research on effective electronic gaming in education (pp. 451–477). Hershey, PA: IGI Global. Boyle, E., Connolly, T. M., & Hainey, T. (2011). The role of psychology in understanding the impact of computer games. Entertainment Computing, 2(2), 69–74. Brehm, J. W. (1966). A theory of psychological reactance. Oxford, UK: Academic. Brown, S. J., Lieberman, D. A., Germeny, B. A., Fan, Y. C., Wilson, D. M., & Pasta, D. J. (1997). Educational video game for juvenile diabetes: Results of a controlled trial. Medical Informatics, 22(1), 77–89. Bucy, E. P. (2004). Interactivity in society: Locating an elusive concept. The Information Society, 20(5), 373–383. Bucy, E. P., & Tao, C. C. (2007). The mediated moderation model of interactivity. Media Psychology, 9(3), 647–672. Bul, K. C., Franken, I. H., Van der Oord, S., Kato, P. M., Danckaerts, M., Vreeke, L. J., … Maras, A. (2015). Development and user satisfaction of “Plan-It Commander,” a serious game for children with ADHD. Games for Health Journal, 4(6), 502–512. Buller, D. B., Borland, R., & Burgoon, M. (1998). Impact of behavioral intention on effectiveness of message features: Evidence from the family sun safety project. Human Communication Research, 24(3), 433–453. Burgoon, M., Alvaro, E., Grandpre, J., & Voulodakis, M. (2002). Revisiting the theory of psychological reactance: Communicating threats to attitudinal freedom. In J. P. Dillard & M. Pfau (Eds.), The persuasion handbook: Developments in theory and practice (pp. 213–232). Thousand Oaks, CA: Sage. Calleja, G. (2007). Digital game involvement: A conceptual model. Games and Culture, 2(3), 236–260. Carcioppolo, N., Wendorf, J., & Tran, L. (2015). Serious games, health, and organizing. In T. R. Harrison & E. Williams (Eds.), Organizations, communication, and health. London: Routledge. Carey, J. (1989). Interactive media. In E. Barnouw (Ed. in Chief). International encyclopedia of communications (pp. 328–330). Oxford: Oxford University Press. Chantana, P., & Surlchai, W. G. (1985). Participatory research and rural development in Thailand. In Farmer’s Assistance Board (Ed.), Participatory Research: Response to Asian People’s Struggle for Social Transformation. Manila, Philippines: Farmer’s Assistance Board. Charsky, D. (2010). From edutainment to serious games: A change in the use of game characteristics. Games and Culture, 5(2), 177–198. Cho, H., & Salmon, C. T. (2007). Unintended effects of health communication campaigns. The Journal of Communication, 57(2), 293–317. Choi, B. C., & Pak, A. W. (2006). Multidisciplinarity, interdisciplinarity and transdisciplinarity in health research, services, education and policy: Definitions, objectives, and evidence of effectiveness. Clinical and Investigative Medicine, 29(6), 351.



Serious Games as Communicative Tools for Attitudinal and Behavioral Change

157

Cohen, J. (2001). Defining identification: A theoretical look at the identification of audiences with media characters. Mass Communication and Society, 4, 245–264. Costikyan, G. (2013). Uncertainty in games. Cambridge, MA: MIT Press. Coutu, W. (1951). Role-playing vs. role-taking: An appeal for clarification. American Sociological Review, 16, 180–187. Coyle, J. R., & Thorson, E. (2001). The effects of progressive levels of interactivity and vividness in web marketing sites. Journal of Advertising, 30(3), 65–77. De Jans, S., Van Geit, K., Cauberghe, V., Hudders, L., & De Veirman, M. (2017). Using games to raise awareness: How to co-design serious mini-games? Computers & Education, 110, 77–87. DeShazo, J., Harris, L., & Pratt, W. (2010). Effective intervention or child’s play? A review of video games for diabetes education. Diabetes Technology & Therapeutics, 12(10), 815–822. DeSmet, A., Shegog, R., Van Ryckeghem, D., Crombez, G., & De Bourdeaudhuij, I. (2015). A systematic review and meta-analysis of interventions for sexual health promotion involving serious digital games. Games for Health Journal, 4(2), 78–90. Duggan, M. (2015, December 15). Gaming and gamers. Pew Research Center. Retrieved from http:// www.pewinternet.org/2015/12/15/gaming-and-gamers Entertainment Software Association. (2015). ESA 2015 essential facts about the computer and video game industry. Retrieved from https://sociologyofvideogames.com/2015/04/25/esa-2015-essential-factsabout-the-computer-video-game-industry/ Fialho, A. T., van den Heuvel, H., Shahab, Q., Liu, Q., Li, L., Saini, P., … Markopoulos, P. (2009, April). ActiveShare: Sharing challenges to increase physical activities. In CHI 2009 Extended Abstracts on Human Factors in Computing Systems (pp. 4159–4164). Washington, DC: ACM. Flavell, J. H., Botkin, P. T., Fry, C. L., Wright, J. W., & Jarvis, P. E. (1968). The development of role-taking and communication skills in children. New York, NY: Wiley. Fogg, B. J. (2003). Persuasive technology. Amsterdam, the Netherlands: Morgan Kaufmann. Fuchslocher, A., Niesenhaus, J., & Krämer, N. (2011). Serious games for health: An empirical study of the game “balance” for teenagers with diabetes mellitus. Entertainment Computing, 2(2), 97–101. doi:10.1016/j.entcom.2010.12.001 Gee, J. P. (2007). Social linguistics and literacies. London: Routledge. Gerrig, R. J. (1993). Experiencing narrative worlds: On the psychological activities of reading. New Haven, CT: Yale University Press. Gilbert, D. T. (1991). How mental systems believe. American Psychologist, 46(2), 107–119. Gilliam, M., Jagoda, P., Heathcock, S., & Sutherland, A. (2014). Infection four: Development of a youthinformed sexual health card game. American Journal of Sexuality Education, 9(4), 485–498. doi:10.1 080/15546128.2014.976301 Glanz, K., Rimer, B. K., & Viswanath, K. (2008). Health behavior and health education: Theory, research, and practice (4th ed.). San Francisco, CA: Jossey-Bass. Glesne, C. (2011). Becoming qualitative researchers: An introduction (4th ed.). Boston, MA: Pearson Education. Green, M. C., & Brock, T. C. (2000). The role of transportation in the persuasiveness of public narratives. Journal of Personality and Social Psychology, 79, 701–721. Green, M. C., Strange, J. J., & Brock, T. C. (2003). Narrative impact: Social and cognitive foundations. Hove, UK: Psychology Press. Ha, L., & James, E. L. (1998). Interactivity reexamined: A baseline analysis of early business web sites. Journal of Broadcasting & Electronic Media, 42(4), 457–474. Harrison, T., & Wendorf Muhamad, J. (2018). Engagement in conflict research and practice. In K. Johnston & M. Taylor (Eds.), Handbook of communication engagement (pp. 187–204). Hoboken, NJ: Wiley. Heeter, C. (1989). Implications of new interactive technologies for conceptualizing communication. In J. Salvaggio & J. Bryant (Eds.), Media use in the information age: Emerging patterns of adoption and consumer use (pp. 217–235). Mahwah, NJ: Lawrence Erlbaum. Hornik, R., MacLean, D., & Cadell, D. Prado, A., Barmada, C., Jacobsohn, L. … Steele, D. (2003). Evaluation of the national youth anti-drug media campaign: 2003 report of findings. Retrieved from https://archives.drugabuse.gov/sites/default/files/1203report_1.pdf Huss, K., Winkelstein, M., Nanda, J., Naumann, P. L., Sloand, E. D., & Huss, R. W. (2003). Computer game for inner-city children does not improve asthma outcomes. Journal of Pediatric Health Care, 17(2), 72–78.

158

Wendorf Muhamad and Kim

Kato, P. M. (2010). Video games in health care: Closing the gap. Review of General Psychology, 14(2), 113–121. Kato, P. M. (2012). Evaluating efficacy and validating games for health. Games for Health Journal: Research, Development, and Clinical Applications, 1(1), 74–76. Kato, P. M., Cole, S. W., Bradlyn, A. S., & Pollock, B. H. (2008). A video game improves behavioral outcomes in adolescents and young adults with cancer: A randomized trial. Pediatrics, 122(2), e305–e317. Kelley, R. L., Osborne, W. J., & Hendrick, D. (1975). Role-taking and role-playing in human communication. Human Communications Research, 1, 62–74. Khaled, R., & Ingram, G. (2012). Tales from the front lines of a large-scale serious game project. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 69–78). doi:10.1145/2207676.2207688 Khalil, A., & Abdallah, S. (2013). Harnessing social dynamics through persuasive technology to promote healthier lifestyle. Computers in Human Behavior, 29(6), 2674–2681. Kharrazi, H., Lu, A. S., Gharghabi, F., & Coleman, W. (2012). A scoping review of health game research: Past, present, and future. Games for Health Journal, 1(2), 153–164. Kim, S., & Ewing, C. (2010, October). A pilot study on the design and evaluation of a serious digital game (Dreamy) to prevent indoor tanning among young adults. Poster presented at the 2016 annual convention of Meaningful Play, East Lansing, MI. Klimmt, C. (2009). Serious games and social change: Why they (should) work. In U. Ritterfeld, M. Cody, & P. Vorderer (Eds.), Serious Games (pp. 270–292). New York, NY: Routledge. Klimmt, C., Hartmann, T., & Frey, A. (2007). Effectance and control as determinants of video game enjoyment. Cyberpsychology & Behavior, 10(6), 845–848. Klisch, Y., Miller, L. M., Beier, M. E., & Wang, S. (2012). Teaching the biological consequences of alcohol abuse through an online game: Impacts among secondary students. CBE Life Sciences Education, 11(1), 94–102. Klisch, Y., Miller, L. M., Wang, S., & Epstein, J. (2012). The impact of a science education game on students’ learning and perception of inhalants as body pollutants. Journal of Science Education and Technology, 21(2), 295–303. Knutz, E., Ammentorp, J., & Kofoed, P. E. (2015). Why health care needs design research: Broadening the perspective on communication in pediatric care through play. Health Communication, 30(10), 1032–1045. Kumar, V. S., Wentzell, K. J., Mikkelsen, T., Pentland, A., & Laffel, L. M. (2004). The daily (daily automated intensive log for youth) trial: A wireless, portable system to improve adherence and glycemic control in youth with diabetes. Diabetes Technology & Therapeutics, 6(4), 445–453. Lederman, L. C. (1992). Debriefing: Toward a systematic assessment of theory and practice. Simulation & Gaming, 23(2), 145–160. Lee, K. M., & Peng, W. (2006). A brief biography of computer game studies. In P. Vorderer & J. Bryant (Eds.), Playing computer games: Motives, responses, and consequences (pp. 325–345). Mahwah, NJ: Lawrence Erlbaum. Leiner, D. J., & Quiring, O. (2008). What interactivity means to the user: Essential insights into and a scale for perceived interactivity. Journal of Computer-Mediated Communication, 14(1), 127–155. doi:10.1111/j.1083-6101.2008.01434.x Lieberman, D. A. (2006). What can we learn from playing interactive games? In P. Vorderer & J. Bryant (Eds.), Playing computer games: Motives, responses, and consequences (pp. 379–398). Mahwah, NJ: Lawrence Erlbaum. Lightfoot, M., Comulada, W. S., & Stover, G. (2007). Computerized HIV preventive intervention for adolescents: Indications of efficacy. American Journal of Public Health, 97(6), 1027–1030. doi:10.2105/ AJPH.2005.072652 Liu, Y., & Shrum, L. J. (2002). What is interactivity and is it always such a good thing? Implications of definition, person, and situation for the influence of interactivity on advertising effectiveness. Journal of Advertising, 31(4), 53–64. Liu, Y., & Shrum, L. J. (2009). A dual-process model of interactivity effects. Journal of Advertising, 38(2), 53–68. doi:10.2753/JOA0091-3367380204 Lowood, H. (2006). A brief biography of computer games. In P. Vorderer & J. Bryant (Eds.), Playing computer games: Motives, responses, and consequences (pp. 25–42). Mahwah, NJ: Lawrence Erlbaum. Lwin, M. O., Ho, S. S., Younbo, J., Leng, T. Y., Wardoyo, R. J., & Jung, K. H. (2016). Effects of exergaming and message framing in school environments on physical activity attitudes and intentions of children and adolescents. Journal of Health Communication, 21(9), 969–978.



Serious Games as Communicative Tools for Attitudinal and Behavioral Change

159

Maloney, A. E., Bethea, T. C., Kelsey, K. S., Marks, J. T., Paez, S., Rosenberg, A. M., … Sikich, L. (2008). A pilot of a video game (ddr) to promote physical activity and decrease sedentary screen time. Obesity, 16(9), 2074–2080. doi:10.1038/oby.2008.295 Marsch, L. A., Bickel, W. K., & Grabinski, M. J. (2007). Application of interactive, computer technology to adolescent substance abuse prevention and treatment. Adolescent Medicine: State of the Art Reviews, 18(2), 342–56, xii. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/18605650 Marsh, D. R., Schroeder, D. G., Dearden, K. A., Sternin, J., & Sternin, M. (2004). The power of positive deviance. BMJ, 329(7475), 1177–1179. McAlister, A. L., Perry, C. L., & Parcel, G. S. (2008). How individuals, environments, and health behaviors interact. In K. Glanz, B. K. Rimer, & K. Viswanath (Eds.), Health behavior and health education: Theory, research, and practice (pp. 169–188). San Francisco, CA: Jossey-Bass. McGrath, J. E. (1984). Groups: Interaction and performance (Vol. 14). Englewood Cliffs, NJ: Prentice Hall. McMillan, S. J. (2002). A four-part model of cyber-interactivity: Some cyber-places are more interactive than others. New Media & Society, 4(2), 271–291. McMillan, S. J., & Hwang, J. S. (2002). Measures of perceived interactivity: An exploration of the role of direction of communication, user control, and time in shaping perceptions of interactivity. Journal of Advertising, 31(3), 29–42. Mead, G. H. (1934). Mind, self, and society. Chicago, IL: University of Chicago Press. Merton, R. K. (1957). Priorities in scientific discovery: A chapter in the sociology of science. American Sociological Review, 22(6), 635–659. Mitgutsch, K., & Alvarado, N. (2012). Purposeful by design?: A serious game design assessment framework. Proceedings of the International Conference on the Foundations of Digital Games (pp. 121–128). doi:10.1145/2282338.2282364 Moyer-Gusé, E. (2008). Toward a theory of entertainment persuasion: Explaining the persuasive effects of entertainment-education messages. Communication Theory, 18, 407–425. Noar, S. M. (2006). A 10-year retrospective of research in health mass media campaigns: Where do we go from here? Journal of Health Communication, 11(1), 21–42. Noar, S. M., Black, H. G., & Pierce, L. B. (2009). Efficacy of computer technology-based HIV prevention interventions: A meta-analysis. AIDS, 23(1), 107–115. Panic, K., Cauberghe, V., & De Pelsmacker, P. (2014). Promoting dental hygiene to children: Comparing traditional and interactive media following threat appeals. Journal of Health Communication, 19(5), 561–576. doi:10.1080/10810730.2013.821551 Papastergiou, M. (2009). Digital game-based learning in high school computer science education: Impact on educational effectiveness and student motivation. Computers & Education, 52(1), 1–12. doi:10.1016/j.compedu.2008.06.004 Pellouchoud, E., Smith, M. E., McEvoy, L., & Gevins, A. (1999). Mental effort-related EEG modulation during video-game play: Comparison between juvenile subjects with epilepsy and normal control subjects. Epilepsia, 40(s4), 38–43. Peng, W. (2009). Design and evaluation of a computer game to promote a healthy diet for young adults. Health Communication, 24, 115–127. Peng, W., Lee, M., & Heeter, C. (2010). The effects of a serious game on role-taking and willingness to help. Journal of Communication, 60, 723–742. Peterson, N. D., Broad, K., Orlove, B., Roncoli, C., Taddei, R., & Velez, M. A. (2010). Participatory processes and climate forecast use: Socio-cultural context, discussion, and consensus. Climate and Development, 2(1), 14–29. Portnoy, D. B., Scott-Sheldon, L. A. J., Johnson, B. T., & Carey, M. P. (2008). Computer-delivered interventions for health promotion and behavioral risk reduction: A meta-analysis of 75 randomized controlled trials, 1988–2007. Preventive Medicine, 47(1), 3–16. Prensky, M. (2001). Digital game-based learning. New York, NY: McGraw-Hill. Prensky, M. (2003). Digital game-based learning. Computers in Entertainment, 1(1), 1–4. Primack, B. A., Carroll, M. V., McNamara, M., Klem, M. L., King, B., Rich, M., … Nayak, S. (2012). Role of video games in improving health-related outcomes: A systematic review. American Journal of Preventive Medicine, 42(6), 630–638. Protopsaltis, A. (2001) Learning theories and serious games. Serious Games Institute. Retrieved from http://www.meducator.net/dissemination.activities/files/163.pdf Putnam, L. L., & Poole, M. S. (1987). Conflict and negotiation. Thousand Oaks, CA: Sage.

160

Wendorf Muhamad and Kim

Qin, H., Patrick Rau, P. L., & Salvendy, G. (2009). Measuring player immersion in the computer game narrative. International Journal of Human–Computer Interaction, 25(2), 107–133. Raney, A. A., Smith, J. K., & Baker, K. (2006). Adolescents and the appeal of video games. In P. Vorderer & J. Bryant (Eds.), Playing video games: Motives, responses, and consequences (pp. 165–179). Mahwah, NJ: Lawrence Erlbaum. Ritterfeld, U., Cody, M., & Vorderer, P. (2009). Serious games: Mechanisms and effects. New York, NY: Routledge. Ritterfeld, U., Shen, C., Wang, H., Nocera, L., & Wong, W. L. (2009). Multimodality and interactivity: Connecting properties of serious games with educational outcomes. Cyberpsychology & Behavior, 12(6), 691–697. Roberto, A. J., Zimmerman, R. S., Carlyle, K. E., & Abner, E. L. (2007). A computer-based approach to preventing pregnancy, STD, and HIV in rural adolescents. Journal of Health Communication, 12(1), 53–76. doi:10.1080/10810730601096622 Rodriguez, D. M., Teesson, M., & Newton, N. C. (2014). A systematic review of computerised serious educational games about alcohol and other drugs for adolescents. Drug and Alcohol Review, 33(2), 129–135. doi:10.1111/dar.12102 Rosenstock, I. M. (1974). The health belief model and preventive health behavior. Health Education Monographs, 2(4), 354–386. Rosenstock, I. M., Strecher, V. J., & Becker, M. H. (1988). Social learning theory and the health belief model. Health Education Quarterly, 15(2), 175–183. Sabido, M. (2004). The origins of entertainment-education. In A. Singhal, M. J. Cody, E. M. Rogers, & M. Sabido (Eds.), Entertainment-education and social change: History, research, and practice (pp. 61– 74). Mahwah, NJ: Lawrence Erlbaum. Sawyer, B., & Smith, P. (2008). Serious games taxonomy. Retrieved from https://thedigitalentertainment alliance.files.wordpress.com/2011/08/serious-games-taxonomy.pdf Schiavo, R. (2013). Health communication: From theory to practice. Hoboken, NJ: Wiley. Schinke, S. P., Schwinn, T. M., & Ozanian, A. J. (2005). Alcohol abuse prevention among high-risk youth: Computer-based intervention. Journal of Prevention & Intervention in the Community, 29(1–2), 117– 130. doi:10.1300/J005v29n01_08 Shirts, G. (2009). History of BaFá BaFá—a cross cultural/diversity/inclusion simulation. Retrieved from https://youthandaction.wor dpr ess.com/2013/08/30/histor y-of-bafa-bafa-a-cr ossculturaldiversityinclusion-simulation/ Shrum, L. J. (2004). The cognitive processes underlying cultivation effects are a function of whether the judgments are on-line or memory-based. Communications, 29(3), 327–344. Simulation Training Systems. (2006). BaFá BaFá. Retrieved from http://stsintl.com/schools-charities/ bafa.html Singhal, A., Cody, M. J., Rogers, E. M., & Sabido, M. (2004). Entertainment-education and social change: History, research, and practice. Mahwah, NJ: Lawrence Erlbaum. Sitzmann, T. (2011). A meta-analytic examination of the instructional effectiveness of computer-based simulation games. Personnel Psychology, 64(2), 489–528. Slater, M. D., & Rouner, D. (2002). Entertainment education and elaboration likelihood: Understanding the processing of narrative persuasion. Communication Theory, 12, 173–191. Snyder, M. (1974). Self-monitoring of expressive behavior. Journal of Personality and Social Psychology, 30(4), 526. Sohn, D., & Lee, B. (2005). Dimensions of interactivity: Differential effects of social and psychological factors. Journal of Computer Mediated Communication, 10(3), 6. Song, J. H., & Zinkhan, G. M. (2008). Determinants of perceived web site interactivity. Journal of Marketing, 72(2), 99–113. Spook, J. E., Paulussen, T., Paulissen, R., Visschedijk, G., Kok, G., & van Empelen, P. (2015). Design rationale behind the serious self-regulation game intervention “balance it”: Overweight prevention among secondary vocational education students in the Netherlands. Games for Health Journal, 4(5), 387–400. Starn, J., & Paperny, D. M. (1990). Computer games to enhance adolescent sex education. MCN. The American Journal of Maternal Child Nursing, 15(4), 250–253. Stokes, B., Seggerman, S., & Rejeski, D. (2006). For a better world: Digital games and the social change sector. Whitepaper funded by the MacArthur Foundation. Retrieved from http://www.gamesforchange. org/g4cwp/wp-content/uploads/2011/06/g4cwhitepaper.pdf



Serious Games as Communicative Tools for Attitudinal and Behavioral Change

161

Stokols, D., Hall, K. L., Taylor, B. K., & Moser, R. P. (2008). The science of team science: Overview of the field and introduction to the supplement. American Journal of Preventive Medicine, 35(2), S77–S89. Street, R. L., & Rimal, R. N. (2013). Health promotion and interactive technology: A conceptual foundation. In R. L. Street, W. R. Gold, & T. R. Manning (Eds.), Health promotion and interactive technology: Theoretical applications and future directions (pp. 1–18). Mahwah, NJ: Lawrence Erlbaum. Sundar, S. S. (2004). Theorizing interactivity’s effects. Information Society, 20, 385–389. Sundar, S. S. (2007). Social psychology of interactivity in human-website interaction. In A. Joinson, K. McKenna, T. Postmes, & U. D. Reips (Eds.), The Oxford handbook of internet psychology (pp. 89–104). Oxford, UK: Oxford University Press. Sundar, S. S., Kalyanaraman, S., & Brown, J. (2003). Explicating web site interactivity: Impression formation effects in political campaign sites. Communication Research, 30(1), 30–59. Sundar, S. S., & Kim, J. (2005). Interactivity and persuasion: Influencing attitudes with information and involvement. Journal of Interactive Advertising, 5(2), 5–18. Swain, C. (2007). Designing games to effect social change. Online proceedings of the Digital Games Research Association Conference (pp. 805–809). Retrieved from https://pdfs.semanticscholar.org/15f1/7d1 79a7272cb5a0ec0db524d7596731b7535.pdf Sward, K. A., Richardson, S., Kendrick, J., & Maloney, C. (2008). Use of a web-based game to teach pediatric content to medical students. Ambulatory Pediatrics, 8(6), 354–359. Tal-Or, N., & Cohen, J. (2010). Understanding audience involvement: Conceptualizing and manipulating identification and transportation. Poetics, 38, 402–418. Thorson, K. S., & Rodgers, S. (2006). Relationships between blogs as eWOM and interactivity, perceived interactivity, and parasocial interaction. Journal of Interactive Advertising, 6, 5–44. Tortolero, S. R., Markham, C. M., Peskin, M. F., Shegog, R., Addy, R. C., Escobar-Chaves, S. L., & Baumler, E. R. (2010). It’s your game: Keep it real: Delaying sexual behavior with an effective middle school program. Journal of Adolescent Health, 46(2), 169–179. Trammell, K. D., Williams, A. P., Postelnicu, M., & Landreville, K. D. (2006). Evolution of online campaigning: Increasing interactivity in candidate web sites and blogs through text and technical features. Mass Communication and Society, 9(1), 21–44. Visch, V. T., Vegt, N. J. H., Anderiesen, H., & Van der Kooij, K. (2013, April). Persuasive game design: A model and its definitions. Paper presented at CHI 2013, Paris, France. Retrieved from http:// gamification-research.org/wp-content/uploads/2013/03/Visch_etal.pdf Vorderer, P., Bryant, J., Pieper, K., & Weber, R. (2006). Playing video games as entertainment. In P. Vorderer & J. Bryant (Eds.), Playing computer games: Motives, responses, and consequences (pp. 1–7). Mahwah, NJ: Lawrence Erlbaum. Vorderer, P., Klimmt, C., & Ritterfeld, U. (2004). Enjoyment: At the heart of media entertainment. Communication Theory, 14(4), 388–408. Vorderer, P., Knobloch, S., & Schramm, H. (2001). Does entertainment suffer from interactivity? The impact of watching an interactive TV movie on viewers’ experience of entertainment. Media Psychology, 3(4), 343–363. Waddock, S. A. (1989). Understanding social partnerships: An evolutionary model of partnership organizations. Administration & Society, 21(1), 78–100. Walther, J. B., Gay, G., & Hancock, J. T. (2005). How do communication and technology researchers study the internet? Journal of Communication, 55, 632–657. Warburton, D. E. R., Bredin, S. S. D., Horita, L. T. L., Zbogar, D., Scott, J. M., Esch, B. T. A., & Rhodes, R. E. (2007). The health benefits of interactive video game exercise. Applied Physiology, Nutrition, and Metabolism, 32(4), 655–663. doi:10.1139/H07-038 Warnick, B., Xenos, M., Endres, D., & Gastil, J. (2005). Effects of campaign-to-user and text-based interactivity in political candidate campaign web sites. Journal of Computer-Mediated Communication, 10(3). doi:10.1111/j.1083-6101.2005.tb00253.x Weber, R., Behr, K. M., & DeMartino, C. (2014). Measuring interactivity in video games. Communication Methods and Measures, 8, 79–115. doi:10.1080/19312458.2013.873778 Wendorf Muhamad, J. (2016). Beyond passive entertainment: Evaluating the role of active entertainmenteducation in the prevention of commercial sexual exploitation of children (Unpublished doctoral dissertation). University of Miami.

162

Wendorf Muhamad and Kim

Wendorf Muhamad, J. & Harrison, T. R. (2018, April). The role of identification and social impact games as persuasive instruments for attitudinal change among vulnerable populations. Paper presented at the 14th Annual Kentucky Conference on Health, Lexington, KY. Wendorf Muhamad, J. & Yang, F. (2016, November). Cross-cultural learning in gameplay: BaFá BaFá and the exploration of intercultural sensitivity. Paper presented at the 104th Annual National Communication Association Conference. Salta Lale City, Utah. Wideman, H. H., Owston, R. D., Brown, C., Kushniruk, A., Ho, F., & Pitts, K. C. (2007). Unpacking the potential of educational gaming: A new tool for gaming research. Simulation & Gaming, 38, 10–30. Wojdynski, B. W. (2011). Interactivity as a super-variable: Dimensions and implications for theory. Web Journal of Mass Communication Research, 30. Retrieved from http://wjmcr.info/2019/07/17/ interactivity-as-super-variable-defintions-dimensions-mediators-and-implications-for-theory/ Wu, G. (2005). The mediating role of perceived interactivity in the effect of actual interactivity on attitude toward the website. Journal of Interactive advertising, 5(2), 29–39. Wu, G. (2006). Conceptualizing and measuring the perceived interactivity of websites. Journal of Current Issues and Research in Advertising, 28, 87–104. Yang, F., & Shen, F. (2018). Effects of web interactivity: A meta-analysis. Communication Research, 45(5), 636–658. Yuji, H. (1996). Computer games and information-processing skills. Perceptual and Motor Skills, 83, 643–647.

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Leveraging Social Media for Applied Problems Case Studies in Mapping Cyberspace to Realspace Brian H. Spitzberg, Ming‐Hsiang Tsou, and Chin‐Te Jung In 2010, in a 36‐minute span, 1 trillion dollars of US stock market value evaporated, in part because high‐speed trading that was once interpersonally negotiated had largely been ceded to pre‐programmed algorithms (van Lier, 2016). The intensity of the flu season increasingly can be predicted as well as or better than hospital‐based diagnoses, based on how millions of Twitter messages mention words like “cough,” “sneeze,” and “sore throat” (Aslam et al., 2014; Issa, Tsou, Nara, & Spitzberg, 2017; Kim, Feng, Wang, Spitzberg, & Tsou, 2017; Nagel et al., 2013; Tsou, Aslam, & Nagel, 2016). The spread of civil unrest and revolution, such as the Arab Spring, may be predictable from, and even partly caused by, the spread of information across millions of social media messages and websites (Spitzberg, Tsou, An, Gupta, & Gawron, 2013). The outcome of political elections also may hinge on, and be predictable by, such information diffusion in cyberspace (Clark, Spitzberg, & Tsou, 2018). The likelihood of public policy passage and reform may increasingly be understood through monitoring of social media (Martinez, Spitzberg, Tsou, Issa, & Peddecord, 2017; Sharag‐ Eldin, Ye, & Spitzberg, 2018; Ye, Sharag‐Eldin, & Spitzberg, 2018). As social media messages buzz in proximity to disasters such as wild fires, disaster management and first responder reactions may be increasingly informed by signal detection of information in such messages (Shi et al., 2018; Tsou et al., 2017; Wang & Ye, 2018, 2019). Any organization will be able to achieve near real‐time comprehension of public relations crises, brand recognition, and marketing success by engaging in routine surveillance of public social media data. These are just a few of the many ways in which “big data” will be changing our lives on an everyday basis. This chapter seeks to examine the potential for big data in the form of social media to inform applied communication contexts, primarily through the lens of a particular program of research. The purpose is to illustrate the pragmatic and theoretical potential that awaits focused pursuit of big data and social media analytics. This objective will be elaborated first through a consideration of the nature of social media and big data, followed by an explication of our eight‐year project to conceptualize and implement an applied architecture of social media surveillance in the exploration of various social p ­ roblems. Finally, some implications of such multidisciplinary and multi‐methodological applications are conjectured.

The Handbook of Applied Communication Research: Volume 1, First Edition. Edited by H. Dan O’Hair and Mary John O’Hair. © 2020 John Wiley & Sons, Inc. Published 2020 by John Wiley & Sons, Inc.

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Social Media and Big Data “Big data” is a term casually bandied about yet relatively seldom carefully conceptualized. According to the Oxford English Dictionary, the term “data” refers to an item of information, or datum, and in plural, data, or a set of data. Data usually refers to numerical forms of information, but may also consist of any language, most typically collected for the purpose of scientific or professional analysis. If “data” are relatively easy to define, the qualifier “big” presents more of a moving target. In traditional social scientific research, a sample of 10,000 typically would be considered enormous, but in the context of social media and big data, samples often reach into the millions. Big data are often characterized by several alliterative V‐terms: volume, velocity, variety, variability, visualization, veracity, and value. Volume is the most obviously analogous to the concept of “big.” There are several ways of quantifying volume. The computer code approach involves bits and bytes. A bit is a single binary digit comprised of a 1 or a 0. Every symbol in a language has an 8‐digit binary code of 1s and 0s, which comprises a byte. A kilobyte is 1,024 bytes, a megabyte is 1,024 kilobytes, and so on up the ladder of gigabytes, terabytes, petabytes, exabytes, zettabytes, yottabytes, xenottabytes, and so on. By one estimate, in 2007 the world was capable of storing 2.9 × 1020 bytes, and of carrying out 6.4 × 1018 instructions on computers, and bidirectional telecommunications and globally stored information was growing at over 25% per year between 1986 and 2007 (Hilbert & López, 2011). These data are increasing across various media channels, and the potential to create such data seems virtually limitless (Hilbert & López, 2012a, 2012b; Hilbert, López, & Vásquez, 2010). The human appetite for information and communication is being outpaced by our capacity for creating and storing such information. This capacity was enabled by a growth of general purpose computing of 58% per year, and an increase of bidirectional telecommunications devices of 28% per year, between 1986 and 2007, compared to a modest 6% annual growth in broadcasting channels (Hilbert & López, 2011). This massive increase in information capacity and communicability of information has been greatly facilitated by the developments of the World Wide Web and the internet. Approximately 40% of the world’s population has access to the internet, growing approximately 10‐fold over the past decade and a half (Internet Live Stats, 2018), and it is anticipated that by 2021 40% of the world’s population will own a smartphone (Statista, 2018). Indications are that by 2025, the entire datasphere of information will expand to a trillion gigabytes, or a 10‐fold increase from 2016 (Reinsel, Gantz, & Rydning, 2017). By January of 2016, there were over 300 million tweets per day. Indeed, by one estimate, “Every day, we create 2.5 quintillion bytes of data. To put that into perspective, 90 percent of the data in the world today has been created in the last two years alone—and with new devices, sensors and technologies emerging, the data growth rate will likely accelerate even more” (IBM, 2016, p. 3). Velocity refers to the speed with which information can be stored, processed, analyzed, transmitted, and diffused. Analogous to trends in volume, broadband speeds are expected to double between 2018 and 2021 (Cisco, 2018). Variety refers to the degree to which big data are structured in preconceived categories, formats, functions, or uses. For example, underneath the text of most tweets are their metadata, which include information about the geographic location of the account owner, the time the tweet was generated, and the number of followers of the tweet author. In contrast, the actual texts of the tweets are relatively unstructured except in regard to number of characters. As communicative messages, analysis of such tweets requires new structures, such as coding systems or software programs, to extract meaningful categories and interpretations from collections of tweets. Variability refers to the evolving nature of big data. Almost every format and forum of big data is in a dynamic process of evolving software, uses, users, contexts, and applications. The arms race for utilizing such data requires constant vigilance regarding these changes. As a simple example of this expansion, searching across over 50



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academic search engines, and restricting to scholarly peer‐reviewed journals, the search term “big data” retrieved 46 entries between 1974 and 2010, but 24,393 from 1974 to 2019, as of this writing. Language and texts themselves evolve. The words used to evaluate political candidates in one election cycle may be radically different from the kinds of data that indicate sentiment for the candidates in a subsequent election. Thus, even the language that comprises many social media forums is constantly evolving. The word “bomb,” for example, evolved from a potentially threatening noun reference to an explosive device, to an adjective describing something as “sick” (i.e., awesome, excellent). Interpreting such evolving data requires parallel evolution of analytic tools and expertise. Visualization refers to the ability to transform the 1s and 0s of big data into analog pictographic or model form. Geographers, for example, can track all the social media in a city, and trace the geospatial trends and movements of people through both physical three‐dimensional space and sequential time (Tsou, 2013, 2018). The human mind is often able to understand a two‐ or three‐dimensional graphic representation of data faster and more comprehensively than it can tables of numbers or statistical results. Visualization is an important aspect of big data since it can transform complicated data into actionable knowledge and insights for decision‐making processes. Veracity refers to the accuracy or validity of the data (Young, 2014). For example, big data analysts who use tweets to predict trends often need to account for “bots,” which are automated programs that generate hundreds or thousands of tweets engineered to look like tweets from a person rather than from a software program (Martinez, Hughes, Walsh‐Buhi, & Tsou, 2018). The IP, or internet protocol, address of many website owners is nothing more than a server address, which can be anywhere. The desire for anonymity, and the ability to spoof and hack many authorship features of new media communications, means that much of what transpires as communication is rife with error, deception, and manipulation. Controlling for artifactual noise in, and verifying the accuracy of, big data is one of the great challenges facing analysts, and is made more challenging by the volume, velocity, variety, and variability mentioned above. If issues of volume, velocity, variety, variability, and visualization can be managed, then big data begin to have value. Value refers to the individual, organizational, institutional, or societal utility of big data. It is about what big data can do for us that more traditional forms of data generally could not. In this regard, we are only beginning to scratch the surface of what big data may be able to do, and big data is clearly in its most nascent stages of conceptualization and application. One window into the potential of big data is the development of the surveillance dashboard paradigm, which is explicated and illustrated next.

Surveillance Dashboards for Data Analytics With the dynamic and real‐time features of big data, data scientists can build various types of surveillance dashboards for big data analytics. Different from traditional data analysis approaches (using static reports, surveys, and basic statistics), data analytics utilize customizable, real‐time, and actionable surveillance dashboards created by data scientists or data analysts. The term “surveillance” is often associated with issues of privacy, espionage, and law enforcement. In the health context, in contrast, it refers to a systematic monitoring of some data streams or signals of an actual or potential public health problem. It is in this sense that big data surveillance refers to the development of tools for the monitoring of signals contained in big data streams to provide near real‐time insight into the dynamics of a particular process underlying the data. There are many approaches to big data surveillance. One of the earliest sets of studies involved in‐house mechanisms for big data analysis. For example, early on, studies examined the role of information queries to Google to consider their signal potential for disease outbreaks (Carneiro

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& Mylonakis, 2009; Ellery et al., 2008; Kandula, Hsu, & Shaman, 2017; Martin, 2017; Pollett et al., 2017; Sidana et al., 2018; Strauss, Castro, Reintjes, & Torres, 2017). Research continues to investigate the diagnostic value of such query trends regarding topics as diverse as societal uncertainty (Tran & Castelnuovo, 2017), racial attitudes and climate (Harris & Yelowitz, 2018), and social perceptions (Reyes, Majluf, & Ibáñez, 2018). Another approach is illustrated by HealthMap, which selectively aggregates online information from over a dozen sources such as Google News, expert‐curated sites, and third‐party sources such as the World Health Organization (Brownstein et  al., 2017; Brownstein, Freifeld, Reis, & Mandl, 2008; Dodson et  al., 2016; Freifeld, Mandl, Reis, & Brownstein, 2008), and continues to be investigated for its surveillance value (e.g., Hossain & Househ, 2016; see also: Pei, Yu, Tian, & Donnelley, 2017). As valuable as these surveillance systems tend to be, they are limited by two macro‐level issues. First, they are mostly concerned with point‐based data rather than relationship‐based data. That is, for example, they aggregate an individual’s search of an information source, or a given study of case reports of an illness. In contrast, social media and communications data such as cell phone traffic data inherently introduce a relational dimension to the data—every message is from someone to someone, and both the point‐based data and the relationship‐based data are part of the record. Second, as dynamic as search engines are, they tend to be one or more levels removed from the subsequent behavior of primary interest. Searching for medications for symptoms is considerably different from actual consumer response to searching for such information. In contrast, social media and cell phone call data are communication responses—they are the actual messages or message links produced by users of the systems. Therefore, the remainder of this chapter will primarily focus on social media surveillance systems. One of the more ambitious projects is indicated by Shaw and colleagues (Yu & Shaw, 2008). To fully appreciate the informational potential of such systems, it is necessary to trace a bit of history in the discipline of geography. Hägerstrand (1966, 1967) introduced time geography as a way of understanding diffusion and mobility patterns. His conceptualization allowed a three‐dimensional visualization of behavioral space–time (see Figure  10.1). Shaw, Yu, and Bombom (2008) demonstrate the viability of visualizing generalized space–time paths (GSTP) from migration data in the United States. Chen et al. (2011) extended such methods to diary data providing space–time paths for Beijing, illustrating the potential for visualizing activity paths through space and time at scale (see Figure 10.2). While the approach originated with visualizing individual point‐based data (Chen et  al., 2011; Shaw et  al., 2008; Shaw & Yu, 2009), it has been generalized to more link‐based data such as call (e.g., Chen et al., 2018; Chen et  al., 2018; Yang, Shaw, et  al., 2016; Yin, Shaw, & Yu, 2011) and social media space-time path Tracking data points

Time

Tracking data point

Tracking data point

Figure 10.1  Space–time system in time geography.

Tracking data point

space



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Figure 10.2  Individual and collective space–time paths of migration history in the United States. Source: adapted from Shaw et al. (2008).

(Tu et al., 2017). The potential for such methods to model at scale dynamic human behavior in space and time is extraordinary. When extended and adapted to social media, research indicates that it can map highly localized human activity patterns. For example, Andrienko et al. (2013) analyzed over 300,000 tweets of over 13,000 users in Seattle, and used content analysis and term‐usage cluster analysis, allowing them to identify “folksonomies” of interconnected concepts, meanings, and messages in Seattle. This allowed the identification of geospatially identified activity locations such as “sports,” “love,” “music,” and “public events,” as well as “coffee‐“and “tea‐related” tweets. It also identified key transportation clusters, ranging from transit corridors to transit hubs. Similar applications to Twitter reveal its potential sensitivity to human activity clusters. Serfass and Sherman (2015) analyzed over 20 million tweets to identify stable daily and weekly cycles and temporal activity patterns reflecting changes in sociality‐, negativity‐, positivity‐, mating‐ (affection), and duty‐ (work) related behavior (Serfass & Sherman, 2015). Several other ventures into social media surveillance systems are being explored (e.g., Cao et  al., 2016; Lin, Lazer, & Cao, 2013; Mathioudakis & Koudas, 2010; Zubiaga, Spina, Martínez, & Fresno, 2015).

Background A multidisciplinary team of scholars in 2010 received a grant from the National Science Foundation (NSF), and an extension of many of its objectives and team members was funded in 2014. The core team members represented geography with an emphasis on geographic information sciences, linguistics with emphasis on computational linguistics, and communication with an emphasis on communication theory. Along the way, various content experts have also been important investigators, including in the areas of public affairs, political science, and public health. The two grants represented close to $2.5 million in funding collectively, and supported over eight years of ongoing research and development. The multidisciplinary nature of the project was facilitated by the NSF call for proposals, but also reflected an increasing campus emphasis on multidisciplinary approaches to substantive

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scientific concerns. This emphasis was reinforced by the campus decision to fund tenure‐track positions for “Centers of Excellence,” which were competitively selected from campus proposals, and required the identification of scholarly and applied issues that warranted multidisciplinary efforts to address. The project team, tasked primarily to identify ways of “mapping cyberspace to realspace” proposed and received funding for four tenure track positions across three departments to support the study of “Human Dynamics in the Mobile Age” (http://humandynamics. sdsu.edu). A Center was subsequently established to provide an administrative structure for this collective, and a graduate Master’s degree in “Big Data Analytics” was proposed and successfully adopted and certified at the state level (https://big.sdsu.edu). The interdisciplinarity of the project team, the Center, and the degree all reflect years of face‐time, virtual time, and project‐ based tasks that have sustained the momentum of the research and applications discussed below.

The SMART Dashboard After several preliminary ventures into surveillance of internet content, the project team shifted substantially to the study of social media in general, and Twitter in particular. As social media became a cyber‐based lingua franca, and as Twitter became publicly available for research purposes, and given its dynamic, relational, and communicatively rich nature, the team decided that there was significant potential for addressing important applied questions and issues through these media. The Social media analytics and research testbed (SMART) is a web‐based user interface for integrating social media (i.e., Twitter) through an application programming interface (API). The system integrates geographic information systems (GIS) with machine learning to assist researchers and professionals in surveillance and hypothesis‐testing of human dynamic processes reflected in and through social media (see: http://vision.sdsu.edu/ec2/smart2). The basic system design elements of the platform are displayed in Figures  10.3 and 10.4. A set of search terms are established by the user that are anticipated to reflect signals of interest in social media (Twitter Search API). The system allows the designation of a number of cities in the United States in which the social media contents are collected based on those search terms (Geo‐targeted search parameters). These data are filtered, which in the case of Twitter means separating tweets from

Twitter Search API

Geo-Targeting Search

Filter

Machine Learning

Statistical Analysis

Spatial Analysis

Visualization (SMART Dashboard)

Figure 10.3  The Social Media Analytics and Research Testbed (SMART) system design.



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Server Side

Software RESTful API

Node.js Express

Analysis Engine GeoNames

Data filtering Geo-Locating

Census data

Normalization

Scikit-learn

Machine Learning

Twitter API

Twitter Search Engine

MongoDB

Database

Client Side Bootstrap

Interactive Dashboard Trend Graph

Trend Map

Top City

Top Hashtag

Top Retweet

Top Mention

Top Keyword

Top Media

Top URL

D3.js Dygraph.js Leaflet.js Twitter Widget JS

Figure 10.4  The server‐ and client‐side technological frameworks of the SMART Dashboard.

retweets and removing tweets that are unlikely to be real‐world cases of the monitoring event or topic. Machine learning involves a linear support vector machine (SVM) classification process in which Twitter messages are transformed to numerical values using a term frequency–inverse document frequency (TF–IDF) model (An, et  al., 2015). Typically, along with expert manual inspection and intercoder analysis, preliminary tweet identification would be identified for the recall (or sensitivity, i.e., percentage of actual positive cases classified as positives relative to the total number of positive cases), precision (i.e., the percentage of correctly identified cases among those retrieved), and F1 (harmonic average of the precision and recall values) values. As indicated in Table  10.1, the statistical analyses provided to the client side include summary descriptive indices of several modal counts, such as the top 10 Uniform Resource Locators URLs, images, hashtags, and so forth. Finally, the spatial analysis component of SMART uses the latitude/longitude metadata from tweets that provide them, and aggregates them at varying levels of map scale. For the vast majority of tweets, which are not geotagged, SMART uses the user profile to approximate geo‐location and aggregate with the geotagged tweets. These data points can then

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Table 10.1  Descriptions of  analytics in  Social Media Analytics and  Research Testbed (SMART) Dashboard. Module

Description

Trend

Total number of daily, weekly, and monthly tweets on slider line graph

Top URL

Top 10 most referred to webpages

Top retweet

Top 10 most popular retweets

Top media

Top 10 pictures, videos, or images included in tweets

Top keyword

Most frequent words used in tweets

Top hashtag

Top 10 hashtags used in tweets

Top mention

Top 10 Twitter users mentioned in tweets

Tweets in cities

Web map of tweeting rates in different cities

Figure 10.5  The web‐based user interface of the SMART Dashboard 2.0 (The example for Shooting in Las Vegas on October 1, 2018).

be displayed in city‐level trends maps and with graduated color fields to indicate tweeting intensity within cities. The city‐level tweet counts are normalized by city population, and display graduated cartographic symbols for reference. These data can also be combined with external software to layer with various existing static and dynamic data sources such as the American Community Survey (https://www.census.gov/programs‐surveys/acs), 500 Cities project (https://www.cdc.gov/500cities), or Centers for Disease Control and Prevention (CDC) health data (https://www.cdc.gov/nchs/nvss/deaths.htm), just to name a few. The client features of the SMART Dashboard also provide a variety of user‐friendly interface options. For example, the user can select tweet numbers at the day, week, or month scale. The user can see line graph trends on a slider scale that allows scoping in or out at various time scales. Different cities can be selected for surveillance, and different words or word combinations can be included for search. On most of the visualizations, such as the line graph, points allow a mouse‐click rollover that displays the actual tweet contents from that time. A word cloud function displays the most frequently occurring words in the tweets at different time scales. Figure 10.5 illustrates a dynamic web‐based user interface of SMART Dashboard 2.0 with the case of the 2017 Las Vegas shooting on October 1.



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SMART Case Studies Disease Surveillance Some of the earliest social scientific studies seeking applied contexts for social media cropped up in the area of disease and public health surveillance. Although there are many forms of big data surveillance in public health (e.g., electronic health records), the SMART Dashboard approach pursued the implications of social media in disease surveillance. The first case study used an early (Visualizing Information Space in Ontological Networks—VISION) version of the dashboard procedures to collect over 150,000 tweets containing word lemma or phrases referring to flu or flu‐like illnesses (e.g., flu, influenza, pertussis, whooping cough) in 11 major US cities (Nagel et al., 2013). In this early study, the user profile or GPS‐enabled information was used for geo‐location. The gold standard criterion was the CDC’s Morbidity and Mortality Weekly Report regarding influenza‐like illnesses (ILI), which are collected through a reporting procedure by individual city or county health departments. Correlations varied significantly by various keywords and types of tweets (e.g., with or without URLs, including or not including retweets), but the ecological correlations between the count of tweets mentioning just the keyword “flu” and the ILI counts revealed coefficients ranging between 0.23 (New York) and 0.75 (Fort Worth and Seattle). The average correlation across all 11 cities was 0.536. Results were similar for the keyword “influenza” (average r  =  0.470). While far from providing perfect indexing of the ground‐truth diagnosis of flu‐like illnesses, this research indicated considerable promise for the potential of disease surveillance. Such surveillance would provide a variety of advantages. First, once the system is established, it provides information in almost real time, whereas the CDC system takes over two weeks to assemble the data. Second, it is flexible in the sense that the surveillance terms can be modified on the fly as epidemics may reflect new vocabularies or labels (e.g., “swine flu”). Third, it can suggest the dynamic directions of the ­epidemic—for example, does an epidemic start on the east coast or west coast? Finally, because it does not require laborious human reporting in clinical settings, once developed the system can be very inexpensive to maintain. In an attempt to refine these procedures, Aslam et al. (2014) collected over 150,000 tweets across the 11 cities, and subjected them to filtering by type of tweet and machine learning classification to determine which tweets were valid indicators of the communicator’s illness or reference to an illness case. Again, the correlations with ILI varied by type of tweet and across cities, but validated tweets correlated on average at 0.614 across 10 cities. Furthermore, across six of the cities for which emergency room ILI rates were available, the validated tweets correlated on average at 0.788. Importantly, this study used tweets from a less severe flu season than the previous study, yet still demonstrated the feasibility of using social media as a proxy indicator of flu‐like illnesses (see Figure 10.6). Since these preliminary studies, further case studies have been undertaken for flu (Issa et al., 2017; Tsou et al., 2014), and disease outbreaks such as the Ebola (Tsou et al., 2014) and the Middle East respiratory syndrome MERS outbreak in South Korea (Kim, Feng, et al., 2017). Tsou et al. (2014) briefly report data collected by the SMART Dashboard across 30 cities for flu, indicating again that it tends to track ILI trends with fair to excellent correspondence. Kim, Feng, et al. (2017) demonstrated that despite indigenous social media (i.e., KakaoStory), tweets collected in South Korea could provide significant insights into the MERS outbreak in 2015. Among other findings, a temporal examination of the most frequent co‐occurring terms suggested four phases of the outbreak. While “patient,” “infection,” and “hospital” were prominent in the early phases of the outbreak, other terms indicated that a number of attributional processes were at work: “government,” “quarantine,” “suspected,” “China,” and “President.” Several geographic markers also showed up in different temporal phases (e.g., Seoul, Wonsoon Park, Guenhye Park, Sewol‐ho). In addition, potential conspiracy theories or rumor‐trends are suggested (e.g., “Anthrax,” “National Intelligence Service,” “Samsung”). The temporal trends also

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(a)

1st 01–04–15 Filtered Tweets: 672

53rd (12–28–14)

48th (11–23–14)

5th 02–01–15

10th 03–08–15

15th 04–12–15

(b)

48

49

50

51

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53

1

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Figure 10.6  Comparing the SMART filtered flu tweeting trend (weekly) with CDC FluView Influenza Positive Tests Reported to CDC, National Summary. Source: Yang, Tsou, et  al. (2016); http://gis.cdc. gov/grasp/fluview/fluportaldashboard.html.

display potentially meaningful social dynamics of the disease. For example, the term “hospital” was sixth ranked in the early phase, but became the highest‐frequency term by the later phases. In contrast, “quarantine” appeared in the earlier phases, but not the latest phases as the disease management response ran its course. The geospatial analyses indicated that many people in a given city tweeted about MERS‐related issues in other cities. Such information may be informative of quarantine, mobility patterns, and intervention resource distribution concerns. Finally, Issa et al. (2017) examined a case study of flu in the United States by comparing and contrasting geotagged and non‐geotagged tweets in several cities. In general, geotagged tweets demonstrated greater relevance to the illness and its local relevance. Simple filtering of tweets by excluding retweets and tweets with URLs demonstrated significant alignment of non‐geotagged tweets with the geotagged tweets. When overlaying land use data onto the geotagged tweets, it was clear that almost half of all flu‐related tweets originated in residential areas, suggesting “that most people tweet from home as they get sick” (p. 228).

Disaster Management A version of the SMART Dashboard, a real‐time geo‐viewer, was prototyped by Tsou et al. (2017). The initial beta version was developed for San Diego Wildfire, and later the official version was released with the fully rendered mapping function. Refining the system continues in collaboration with the Office of Emergency Services in San Diego. Essentially, the Twitter streaming API is tapped on an ongoing basis for terms related to wildfires, and processed into a dashboard designed with user‐centered design concepts (Tsou, 2011). While the publicly available Twitter API tends to provide approximately 1–3% of the total tweets within a region, test results indicate that up to 40–50%, and perhaps much higher proportions, of relevant geotagged tweets in a given region can be retrieved. The geotagged tweets are processed into



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a map with optional map marker functions for color and marker size. The privacy of individual tweeters is protected by a filter that randomly places the tweet author within a 100‐m radius of the actual exact location. The cluster map function provides a generalized view for clusters of tweets and their most popular words, as well as a zoom in‐or‐out feature that allows a quick overview of the social media conversations. Such terms may, for example, inform the monitor of terms that would be relevant for naming a wildfire, and for subsequent reference for coordinating management. The system also provides user‐modifiable heat‐map functions for identifying the most intense conversations. The system also can overlay with the National Oceanic and Atmospheric Administration (NOAA), National Weather Service (NWS) Weather Alert, and the local Office of Emergency Services (OES) mapping services so as to triangulate emergency response. The keywords can be modified on the fly as well, which can assist as wildfires merge and get renamed or involve new unique relevant signals (e.g., animal rescue). The current geo‐viewer also provides both a priori color‐coded text‐tagging (i.e., safe, info, medical help, warning, and danger) and user‐labeling options to assist with machine‐learning training for refining the accuracy of the search and mapping functions. The GeoViewer was also beta‐tested for its relevance to earthquakes (Tsou et al., 2017) in reference to the 2015 Nepal earthquake. Immediately following its announcement, search terms were entered that were expected to be relevant, and map access was shared with relevant officers in the United Nations and affiliated international organizations. Even though approximately 80% of the tweets retrieved were in English and 20% in Nepali, Google translate did not offer automatic translation for Nepali. Despite this, the most trending hashtags were identified and various tweets signaled actionable responses (e.g., “plz help us we are stuck in Nepal Kathmandu. Vinod lila 9851035###. 10:15 pm, 25 April 2015 [geotagged location]”; “This shelter need help http://t.co####, 03:16am, 26 April 2015 [geotagged location]”). The trending keywords suggest potentially relevant intervention (e.g., need drinking water), and the location bounding‐box feature allows clients to focus on the conversations populating a particular geospatial area (see Figure 10.7).

Figure 10.7  The SMART Dashboard for monitoring Hurricane Harvey in Houston in August 2017.

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Industry/Market Analytics There are many industries and markets in which social commentary is likely to be diagnostic of activity. A particularly salient example is the entertainment industry. Movies, for example, directly seek the “buzz” of viral social media. For this reason, social media represent both a window into the vitality of a given movie and a mechanism through which the industry seeks to initiate and maintain such buzz. That is, social media are a medium for both reactive surveillance (i.e., how well is the movie doing?) and proactive marketing (i.e., how well is the marketing working?). In two studies using the SMART Dashboard, Sanguinet, Spitzberg, and Tsou (2017) and Issa et al. (2017) examined the potential for social media analysis in tracking the popularity of movies. Tweets and retweets with the official hashtags of each of five movies were tracked from one week prior to four weeks post debut, using daily revenues (box office) as a dependent variable. Correlations ranged from nonsignificant to substantial (i.e., r > 0.98). Although most correlations were positive, tweet counts were occasionally negatively related to box office receipts, which implied negative sentiment toward the movie, which may have also been pushing rather than just reflecting poor audience reception. Issa et al. (2017) used a GeoSearch API with the Twitter REST API to collect tweets in four major US cities (San Diego, Los Angeles, Denver, New York) regarding the specific movie Ted in 2012. Over 100,000 geotagged and non‐geotagged tweets were collected on the keyword “Ted.” After filtering for some noise in the tweets (retweets and tweets with URLs), time‐series analyses demonstrated a strong correlation between geotagged and non‐geotagged tweets. Further, the geotagged tweets revealed a strong overlap with movie theater and commercial/service geographic areas of the cities, validating the correspondence between a domain of cyberspace and on‐the‐ground human activity. This research suggests that routine surveillance of specific terms affiliated with a movie correspond to movie‐watching activities. As a further test of this conjecture, Sanguinet et al. (2017) randomly selected four movies prior to, during, and after a specified release date. The specific hashtags associated with each movie were used as the collection key terms. The trend analyses were lagged in correlation with their respective box office receipts. Across several hundred thousand tweets, gross original (non‐retweeted) tweets revealed correlations ranging from 0.58 to 0.98 with box office receipts of these movies. The correlation strength tended to be moderated by the success of the movie, indicating that the larger budget movies were probably marketed better and more extensively in ways that linked social media activity with box office performance.

Conclusions These three cases studies only scratch the surface of the types of applications that the big data of social media are enabling. Other project offshoots of our research team’s activities include investigations of: drug abuse and misuse (Tsou, Jung, & Allen, 2015), the Ebola outbreak (Tsou, Jung, & Allen, 2015; Yang, Shaw, et al., 2016), the MERS outbreak (Kim, Feng, et al., 2017), and public attitudes toward policy initiatives (e.g., marijuana legalization: Yang, Shaw et  al., 2016; vaccine exemption reform: Martinez et al., 2017; fracking restriction: Sharag‐Eldin et al., 2018; death penalty reform: Ye et al., 2018), as well as studies of more theoretical concerns such as the degree to which society has experienced a true “death of distance” and the first law of geography (e.g., Han, Tsou, & Clarke, 2018; Wang et al., 2018; Yang et al., 2019), which proposes that “everything is related to everything else, but near things are more related than distant things” (Tobler, 1970, p. 236). The future of big data for the communication discipline will hinge on the success of developing interdisciplinary collaborations. While the communication discipline is making inroads in conceptualizing the roles of social media in social life (e.g., Hall, 2018; O’Sullivan & Carr, 2018; Walther & Valkenburg, 2017), health (e.g., Kreps, 2017), and political life (e.g., Shah



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et al., 2017; Tsou, et al., 2013), the discipline still has only begun to scratch the surface of the multi‐methodological tools required for plumbing the depths of such data. Geography is deeply engaged with the dimensions of social life that intersect with time, place, and space (Adams, 2011, 2017; Adams & Jansson, 2012; Tsou, 2015). Linguistics is also deeply engaged with the structure of language use that constitutes and signals such social construction processes. The communication discipline is deeply engaged with the messages that socially construct, comprise, and attribute meaning to these dimensions. However, methodologically, the communication discipline will need to incorporate a new vocabulary of software tools such as Node XL, Python, Arc‐GIS, MongoDB, and the Twitter API. Statistics in social media are both point‐based (i.e., individual source) and link‐based (i.e., urls, co‐citation, and follower networks), which requires social network analytics, which are less inferential and more descriptive than the field typically uses. Language analytic tools designed for topic and community detection, machine learning, and algorithm development become essential when sifting through millions rather than hundreds of data points. The amounts of data over time are massive compared to ordinary data collection approaches, and call for hardware systems capable of managing petabytes rather than gigabytes. The world is awash in big data in the form of social media, data that are both intentionally and incidentally generated (Brooker, Barnett, & Cribbin, 2016). Collectively, the disciplines of communication, linguistics, and geography are well‐situated to identify what such data reveal about human behavior in time, space, and place. Given the expansiveness of such pursuits, spanning multiple disciplines, multiple methodological paradigms, and manifold potential applications, the need for integrating theory becomes paramount to sensemaking in the realm of big data. This is particularly challenging, given the transitional flux that rapidly evolving technologies, softwares, infrastructures, and entrepreneurial initiatives continue to change the landscape of human communication at such a rapid pace (Cappella, 2017; Carr & Hayes, 2015; Flanagin, 2017; Hall, 2018; Kent, 2015; Walther & Valkenburg, 2017). One attempt to provide such an integrative framework is the multilevel model of meme diffusion (M3D; Spitzberg, 2014). Reflecting the nature of the data and topics, M3D’s origins are diverse, extracting aspects from frame theory, narrative theory, agenda‐setting theory, cultivation theory, diffusion of innovations theory, and evolutionary theory, as well as a variety of integrative models of social media and communication influence and diffusion. The model is predicated on the meme, which is Dawkins’ (1976) neologism for an analog to the gene—a unit or mechanism through which cultural (rather than genetic) information is transferred from one person to another. Not all messages are memes because not all messages are replicable across individuals. However, like genes, all digital messages are by their nature variable and replicable. M3D proposes a set of variables at multiple levels of hierarchy that are likely to predict the successful (i.e., adaptive) diffusion of memes. These variables are conceptualized at the meme level, the source level, the social network level, the societal or cultural level, and the geotechnical level. The span across these micro‐level to macro‐level concepts illustrates the necessity of interdisciplinary approaches to a comprehensive understanding of digital communications and social behavior. The advent of social media as a source of data (Bik & Goldstein, 2013; Ledbetter, 2014; Walther & Valkenburg, 2017) and big data in general (Gandomi & Haider, 2015; Guo, Vargo, Pan, Ding, & Ishwar, 2016; Kent, 2015; Kuiler, 2014; Lin, 2015; Pence, 2014; Tsou, 2013, 2018) portend monumental challenges to and opportunities for advancing the social science of social behavior. Many other theoretical trajectories are beginning to emerge in the communication field. Special issues of the Atlantic Journal of Communication (Kent, 2015) and Human Communication Research (Walther & Valkenburg, 2017) devoted to social media implications for the discipline portend the recognition of the paradigm shift for which the discipline is poised. The advent of new journals (e.g., Big Data & Society, Journal of Information & Politics) illustrates the paths that big data and social media are beginning to forge. The integration of time, place, and space into a broader, richer understanding of communication behavior (and vice

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versa), will continue to bridge the microscopic features of everyday human interaction with the macroscopic patterns and activities of society writ large. Some of these tributaries are already beginning to form. Big data and social media are evolving rapidly, so the predictable horizon is shrinking. However, a few exemplars illustrate the potential that these sciences hold for understanding and managing human behavior. In the field of physical health, social media surveillance is demonstrating the ability to link sentiments with heart disease (Eichstaedt et al., 2015), physical activity (Nguyen, Kath, et  al., 2016; Nguyen, Li, et  al., 2016), and other health outcomes (Ford, Jebb, Tay, & Diener, 2018; Nguyen et al., 2017) on a county‐level basis, although there are still significant methodological issues to resolve in such research (Gibbons et al., 2019). Social media demonstrate potential for detecting population‐level use and abuse of drugs (e.g., Hébert et  al., 2018; Kalyanam, Katsuki, Lanckriet, & Mackey, 2017; Katsuki, Mackey, & Cuomo, 2015; Kim, Marsch, Hancock, & Das, 2017; Martinez et al., 2018). In the field of mental health, depression (Guntuku, Yaden, Kern, Ungar, & Eichstaedt, 2017; Hswen, Hawkins, Brownstein, & Naslund, 2018), suicide (Du et  al., 2018), and a variety of other mental health indicators (Calvo, Milne, Hussain, & Christensen, 2017; Gruebner et al., 2017; Hswen et  al., 2018; McLaughlin, 2017) are being explored for the potential of routine identification, surveillance, and potential intervention. In the field of human relationships, social network site usage has been related to state‐level divorce rates (Valenzuela, Halpern, & Katz, 2014). Social media display potential for predicting, warning, and responding expeditiously to natural disasters (Chen, Zhou, Sellis, & Li, 2018; Eckert et al., 2018; Hagen, Keller, Neely, DePaula, & Robert‐Cooperman, 2018; Lai & Tang, 2018; Resch, Usländer, & Havas, 2018; Wu & Cui, 2018) and disease outbreaks (Allen, Tsou, Aslam, Nagel, & Gawron, 2016; Bernard et al., 2018; Tang, Bie, Park, & Zhi, 2018; Vijaykumar, Nowak, Himelboim, & Jin, 2018). Social media have even demonstrated both trailing and predictive connections to the stock market and performance of particular industries and firms (Bartov, Faurel, & Mohanram, 2018; Oliveira, Cortez, & Areal, 2017), suggesting its potential for macroeconomic policy. In general, policy best follows the best evidence, and big data are not yet the best evidence, but with sufficient scientific attention, they certainly provide important potential for policy and intervention—compared to traditional data collection methods, social media and many other big data applications tend to be more real‐time, increasingly more representative of the entire population than most samples, and generally inexpensive to collect. Their potential for better theory, research, and interventions seem boundless.

References Adams, P. C. (2011). A taxonomy for communication geography. Progress in Human Geography, 35(1), 37–57. doi:10.1177/0309132510368451 Adams, P. C. (2017). Geographies of media and communication I. Progress in Human Geography, 41(3), 365–374. doi:10.1177/0309132516628254 Adams, P. C., & Jansson, A. (2012). Communication geography: A bridge between disciplines. Communication Theory, 22(3), 299–318. doi:10.1111/j.1468‐2885.2012.01406.x Allen, C., Tsou, M., Aslam, A., Nagel, A., & Gawron, J. (2016). Applying GIS and machine learning methods to Twitter data for multiscale surveillance of influenza. PLoS One, 11(7), 1–10. doi:10.1371/ journal.pone.0157734 An, L., Tsou, M., Crook, S. S., Chun, Y., Spitzberg, B., Gawron, J. M., & Gupta, D. K. (2015). Space–time analysis: Concepts, quantitative methods, and future directions. Annals of the Association of American Geographers, 105(5), 891–914. doi:10.1080/00045608.2015.1064510 Andrienko, G., Andrienko, N., Bosch, H., Ertl, T., Fuchs, G., Jankowski, P., & Thom, D. (2013). Thematic patterns in georeferenced tweets through space‐time visual analytics. Computing in Science and Engineering, 15(3), 72–82. doi:10.1109/MCSE.2013.70



Leveraging Social Media for Applied Problems

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Aslam, A. A., Tsou, M.‐H., Spitzberg, B. H., An, L., Gawron, J. M., Gupta, D. K., … Lindsay, S. (2014). The reliability of tweets as a supplementary method of seasonal influenza surveillance. Journal of Medical Internet Research, 16(10), e250. doi:10.2196/jmir.3532 Bartov, E., Faurel, L., & Mohanram, P. S. (2018). Can Twitter help predict firm‐level earnings and stock returns? Accounting Review, 93(3), 25–57. doi:10.2308/accr‐51865 Bernard, R., Bowsher, G., Milner, C., Boyle, P., Patel, P., & Sullivan, R. (2018). Intelligence and global health: Assessing the role of open source and social media intelligence analysis in infectious disease outbreaks. Journal of Public Health, 26(5), 509–514. doi:10.1007/s10389‐018‐0899‐3 Bik, H. M., & Goldstein, M. C. (2013). An introduction to social media for scientists. PLoS Biology, 11(4). doi:10.1371/journal.pbio.1001535 Brooker, P., Barnett, J., & Cribbin, T. (2016). Doing social media analytics. Big Data & Society, 3(2), 1–12. doi:10.1177/2053951716658060 Brownstein, J. S., Freifeld, C. C., Reis, B. Y., & Mandl, K. D. (2008). Surveillance sans frontiers: Internet‐ based emerging infectious disease intelligence and the HealthMap project. PLoS Medicine, 5(7), e151. Brownstein, J. S., Shuyu, C., Marathe, A., Marathe, M. V., Nguyen, A. T., Paolotti, D., … Qian, Z. (2017). Combining participatory influenza surveillance with modeling and forecasting: Three alternative approaches. Journal of Medical Internet Research, 19(11), 64. doi:10.2196/publichealth.7344 Calvo, R. A., Milne, D. N., Hussain, M. S., & Christensen, H. (2017). Natural language processing in mental health applications using non‐clinical texts. Natural Language Engineering, 23(5), 649–685. doi:10.1017/S1351324916000383 Cao, N., Shi, C., Lin, S., Lu, J., Lin, Y., & Lin, C. (2016). TargetVue: Visual analysis of anomalous user behaviors in online communication systems. IEEE Transactions on Visualization & Computer Graphics, 22(1), 280–289. Cappella, J. N. (2017). Vectors into the future of mass and interpersonal communication research: Big data, social media, and computational social science. Human Communication Research, 43(4), 545–558. doi:10.1111/hcre.12114 Carneiro, H. A., & Mylonakis, E. (2009). Google Trends: A web‐based tool for real‐time surveillance of disease outbreaks. Clinical Infectious Diseases, 49(10), 1557–1564. doi:10.1086/630200 Carr, C. T., & Hayes, R. B. (2015). Social media: Defining, developing, and divining. Atlantic Journal of Communication, 23(1), 46–65. doi:10.1080/15456870.2015.972282 Chen, B. Y., Wang, Y., Wang, D., Li, Q., Lam, W. K., & Shaw, S. (2018). Understanding the impacts of human mobility on accessibility using massive mobile phone tracking data. Annals of the American Association of Geographers, 108(4), 1115–1133. doi:10.1080/24694452.2017.1411244 Chen, J., Pei, T., Shaw, S., Lu, F., Li, M., Cheng, S., … Zhang, H. (2018). Fine‐grained prediction of urban population using mobile phone location data. International Journal of Geographical Information Science, 32(9), 1770–1786. doi:10.1080/13658816.2018.1460753 Chen, J., Shaw, S.‐L., Yu, H., Lu, F., Chai, Y., & Jia, Q. (2011). Exploratory data analysis of activity diary data: A space‐time GIS approach. Journal of Transport Geography, 19, 394–404. doi:10.1016/j. jtrangeo.2010.11.002 Chen, X., Zhou, X., Sellis, T., & Li, X. (2018). Social event detection with retweeting behavior correlation. Expert Systems with Applications, 114, 516–523. doi:10.1016/j.eswa.2018.08.022 Cisco. (2018, August 13). The zettabyte era: Trends and analysis. Retrieved from https://webobjects.cdw. com/webobjects/media/pdf/Solutions/Networking/White‐Paper‐Cisco‐The‐Zettabyte‐Era‐ Trends‐and‐Analysis.pdf Clark, J. C., Spitzberg, B. H., & Tsou, M.‐H. (2018, February). The digipolitical animal: Investigating the memetic diffusion of political messages on Twitter. Paper presented at the Western States Communication Association Conference, Santa Clara, CA. Dawkins, R. (1976). The selfish gene. New York, NY: Oxford University Press. Dodson, S., Klassen, K. M., McDonald, K., Millard, T., Osborne, R. H., Battersby, M. W., … Elliott, J. H. (2016). HealthMap: A cluster randomized trial of interactive health plans and self‐management support to prevent coronary heart disease in people with HIV. BMC Infectious Diseases, 16, 1–11. doi:10.1186/s12879‐016‐1422‐5 Du, J., Zhang, Y., Luo, J., Jia, Y., Wei, Q., Tao, C., & Xu, H. (2018). Extracting psychiatric stressors for suicide from social media using deep learning. BMC Medical Informatics & Decision Making, 18(2), 43. doi:10.1186/s12911‐018‐0632‐8

178

Spitzberg, Tsou, and Jung

Eckert, S., Sopory, P., Day, A., Wilkins, L., Padgett, D., Novak, J., … Gamhewage, G. (2018). Health‐ related disaster communication and social media: Mixed‐method systematic review. Health Communication, 33(12), 1389–1400. doi:10.1080/10410236.2017.1351278 Eichstaedt, J. C., Schwartz, H. A., Kern, M. L., Park, G., Labarthe, D. R., Merchant, R. M., … Seligman, M. P. (2015). Psychological language on Twitter predicts county‐level heart disease mortality. Psychological Science, 26(2), 159–169. doi:10.1177/0956797614557867 Ellery, P. J., Vaughn, W., Ellery, J., Bott, J., Ritchey, K., & Byers, L. (2008). Understanding internet health search patterns: An early exploration into the usefulness of Google Trends. Journal of Communication in Healthcare, 1(4), 441–456. Flanagin, A. J. (2017). Online social influence and the convergence of mass and interpersonal communication. Human Communication Research, 43(4), 450–463. doi:10.1111/hcre.12116 Ford, M. T., Jebb, A. T., Tay, L., & Diener, E. (2018). Internet searches for affect‐related terms: An indicator of subjective well‐being and predictor of health outcomes across US states and metro areas. Applied Psychology: Health & Well‐Being, 10(1), 3–29. doi:10.1111/aphw.12123 Freifeld, C. C., Mandl, K. D., Reis, B. Y., & Brownstein, J. S. (2008). HealthMap: Global infectious disease monitoring through automated classification and visualization of internet media reports. Journal of the American Medical Informatics Association, 15(2), 150–157. Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144. doi:10.1016/j.ijinfomgt.2014.10.007 Gibbons, J., Malouf, R., Spitzberg, B. H., Martinez, L. M., Appleyard, B., Thompson, C., … Tsou, M.‐H. (2019). Twitter‐based measures of neighborhood sentiment as predictors of residential population health. PLoS One, 14(7), e‐219550. doi:10.1371/journal.pone.0219550 Gruebner, O., Sykora, M., Lowe, S. R., Shankardass, K., Galea, S., & Subramanian, S. V. (2017). Big data opportunities for social behavioral and mental health research. Social Science & Medicine, 189, 167– 169. doi:10.1016/j.socscimed.2017.07.018 Guntuku, S. C., Yaden, D. B., Kern, M. L., Ungar, L. H., & Eichstaedt, J. C. (2017). Detecting depression and mental illness on social media: An integrative review. Current Opinion in Behavioral Sciences, 18, 43–49. doi:10.1016/j.cobeha.2017.07.005 Guo, L. g., Vargo, C. J., Pan, Z., Ding, W., & Ishwar, P. (2016). Big social data analytics in journalism and mass communication. Journalism & Mass Communication Quarterly, 93(2), 332–359. Hagen, L., Keller, T., Neely, S., DePaula, N., & Robert‐Cooperman, C. (2018). Crisis communications in the age of social media. Social Science Computer Review, 36(5), 523–541. doi:10.1177/0894439317721985 Hägerstrand, T. (1966). Aspects of the spatial structure of social communication and the diffusion of information. Papers in Regional Science, 16(1), 27–42. Hägerstrand, T. (1967). Innovation diffusion as a spatial process. Chicago, IL: University of Chicago. Hall, J. A. (2018). When is social media use social interaction? Defining mediated social interaction. New Media & Society, 20(1), 162–179. doi:10.1177/1461444816660782 Han, S. Y., Tsou, M.‐H., & Clarke, K. C. (2018). Revisiting the death of geography in the era of big data: The friction of distance in cyberspace and real space. International Journal of Digital Earth, 11(5), 451–469. doi:10.1080/17538947.2017.1330366 Harris, T. F., & Yelowitz, A. (2018). Racial climate and homeownership. Journal of Housing Economics, 40, 41–72. doi:10.1016/j.jhe.2017.12.003 Hébert, E. T., Vandewater, E. A., Businelle, M. S., Harrell, M. B., Kelder, S. H., & Perry, C. L. (2018). Real time assessment of young adults’ attitudes toward tobacco messages. Tobacco Regulatory Science, 4(1), 644–655. doi:10.18001/TRS.4.1.10 Hilbert, M., & López, P. (2011). The world’s technological capacity to store, communicate, and compute information. Science, 332(6025), 60–65. doi:10.1126/science.1200970 Hilbert, M., & López, P. (2012a). How to measure the world’s technological capacity to communicate, store, and compute information part I: Results and scope. International Journal Of Communication, 6, 956–979. Hilbert, M., & López, P. (2012b). How to measure the world’s technological capacity to communicate, store, and compute information part II: Measurement unit and conclusions. International Journal Of Communication, 6, 936–955. Hilbert, M., López, P., & Vásquez, C. (2010). Information societies or “ICT equipment societies?” Measuring the digital information‐processing capacity of a society in bits and bytes. Information Society, 26(3), 157–178. doi:10.1080/01972241003712199



Leveraging Social Media for Applied Problems

179

Hossain, N., & Househ, M. (2016). Using HealthMap to analyze Middle East respiratory syndrome (MERS) data. Studies In Health Technology & Informatics, 226, 213–216. doi:10.3233/978‐1‐61499 ‐664‐4‐213 Hswen, Y., Hawkins, J. B., Brownstein, J. S., & Naslund, J. A. (2018). Online communication about depression and anxiety among Twitter users with schizophrenia: Preliminary findings to inform a digital phenotype using social media. Psychiatric Quarterly, 89(3), 569–580. doi:10.1007/s11126‐017‐9559‐y IBM. (2016). 10 key marketing trends for 2017. Retrieved 2018 from http://comsense.consulting/wp‐ content/uploads/2017/03/10_Key_Marketing_Trends_for_2017_and_Ideas_for_Exceeding_ Customer_Expectations.pdf Internet Live Stats. (2018, August 18). Retrieved from http://www.internetlivestats.com/ Issa, E., Tsou, M.‐H., Nara, A., & Spitzberg, B. H. (2017). Spatio‐temporal characteristics of Twitter data with geotagged and non‐geotagged content: Two case studies with topic of flu and Ted (movie). Annals of GIS, 23(219), 235–217. doi:10.1080/19475683.2017.1343257 Kalyanam, J., Katsuki, T., Lanckriet, G. R. G., & Mackey, T. K. (2017). Exploring trends of nonmedical use of prescription drugs and polydrug abuse in the twittersphere using unsupervised machine learning. Addictive Behaviors, 65, 289–295. doi:10.1016/j.addbeh.2016.08.019 Kandula, S., Hsu, D., & Shaman, J. (2017). Subregional nowcasts of seasonal influenza using search trends. Journal of Medical Internet Research, 19(11), e370. doi:10.2196/jmir.7486 Katsuki, T., Mackey, T. K., & Cuomo, R. (2015). Establishing a link between prescription drug abuse and illicit online pharmacies: Analysis of Twitter data. Journal of Medical Internet Research, 17(12), 1–12. doi:10.2196/jmir.5144 Kent, M. L. (2015). Social media circa 2035: Directions in social media theory. Atlantic Journal of Communication, 23(1), 1–4. doi:10.1080/15456870.2015.972407 Kim, I.‐H., Feng, C.‐C., Wang, Y.‐C., Spitzberg, B. H., & Tsou, M.‐H. (2017). Exploratory spatiotemporal analysis in risk communication during the MERS outbreak in South Korea. The Professional Geographer, 69(4), 629–643. doi:10.1080/00330124.2017.1288577 Kim, S. J., Marsch, L. A., Hancock, J. T., & Das, A. K. (2017). Scaling up research on drug abuse and addiction through social media big data. Journal of Medical Internet Research, 19(10), e353. doi:10.2196/jmir.6426 Kreps, G. L. (2017). Online information and communication systems to enhance health outcomes through communication convergence. Human Communication Research, 43(4), 518–530. doi:10.1111/ hcre.12117 Kuiler, E. W. (2014). From big data to knowledge: An ontological approach to big data analytics. Review of Policy Research, 31(4), 311–318. doi:10.1111/ropr.12077 Lai, C.‐H., & Tang, T. (2018). From information behaviors to disaster preparedness: Navigating individuals’ general and disaster curation in US, China, and Australia. Computers in Human Behavior, 88, 37–46. doi:10.1016/j.chb.2018.06.023 Ledbetter, A. M. (2014). The past and future of technology in interpersonal communication theory and research. Communication Studies, 65(4), 456–459. doi:10.1080/10510974.2014.927298 Lin, J. (2015). On building better mousetraps and understanding the human condition: Reflections on big data in the social sciences. Annals of the American Academy of Political and Social Science, 659(1), 33–47. doi:10.1177/0002716215569174 Lin, Y.‐R., Lazer, D., & Cao, N. (2013). Watching how ideas spread over social media. Leonardo, 46(3), 277. Martin, L. J. (2017). A look back: Investigating Google Flu Trends during the influenza A(H1N1)pdm09 pandemic in Canada, 2009‐2010. Epidemiology & Infection, 145(3), 420–423. doi:10.1017/ S0950268816002636 Martinez, L. S., Hughes, S., Walsh‐Buhi, E. R., & Tsou, M.‐H. (2018). “Okay, we get it. You vape”: An analysis of geocoded content, context, and sentiment regarding E‐cigarettes on Twitter. Journal of Health Communication, 23(6), 550–562. doi:10.1080/10810730.2018.1493057 Martinez, L. S., Spitzberg, B. H., Tsou, M.‐H., Issa, E., & Peddecord, M. (2017, May). Vax Populi: The social [media] (de)construction of public health policy. Paper presented to the International Communication Association Conference, San Diego, CA. Mathioudakis, M. & Koudas, N. (2010). TwitterMonitor: Trend detection over the Twitter stream. Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2010. Retrieved from http://snap.stanford.edu/class/cs224w‐readings/mathioudakis10twitter.pdf

180

Spitzberg, Tsou, and Jung

McLaughlin, C. L. (2017). Improving research methods for the study of geography and mental health: Utilization of social networking data and the ESRI GeoEvent processor. School Psychology International, 38(4), 398–407. doi:10.1177/0143034317714617 Nagel, A. C., Tsou, M.‐H., Spitzberg, B. H., An, L., Gawron, J. M., Gupta, D. K., … Sawyer, M. H. (2013). The complex relationship of real‐space events and messages in cyberspace: A case study of influenza and pertussis using tweets. The Journal of International Medical Research, 15(10), e237. doi:10.2196/jmir.2705 Nguyen, Q. C., Kath, S., Meng, H., Li, D., Smith, K. R., VanDerslice, J. A., … Li, F. (2016). Leveraging geotagged Twitter data to examine neighborhood happiness, diet, and physical activity. Applied Geography, 73, 77–88. doi:10.1016/j.apgeog.2016.06.003 Nguyen, Q. C., Li, D., Meng, H., Kath, S., Nsoesie, E., Li, F., & Wen, M. (2016). Building a national neighborhood dataset from geotagged Twitter data for indicators of happiness, diet, and physical activity. JMIR Public Health and Surveillance, 2(2), e158. Nguyen, Q. C., McCullough, M., Meng, H., Paul, D., Li, D., Kath, S., … Li, F. (2017). Geotagged US tweets as predictors of county‐level health outcomes, 2015–2016. American Journal of Public Health, 107(11), 1776–1782. doi:10.2105/AJPH.2017.303993 Oliveira, N., Cortez, P., & Areal, N. (2017). The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices. Expert Systems with Applications, 73, 125–144. doi:10.1016/j.eswa.2016.12.036 O’Sullivan, P. B., & Carr, C. T. (2018). Masspersonal communication: A model bridging the mass‐interpersonal divide. New Media & Society, 20(3), 1161–1180. doi:10.1177/1461444816686104 Pei, J., Yu, G., Tian, X., & Donnelley, M. R. (2017). A new method for early detection of mass concern about public health issues. Journal of Risk Research, 20(4), 516–532. doi:10.1080/13669877.2015. 1100655 Pence, H. E. (2014). What is big data and why is it important? Journal of Educational Technology Systems, 43(2), 159–171. Pollett, S., Boscardin, W. J., Azziz‐Baumgartner, E., Tinoco, Y. O., Soto, G., Romero, C., … Rutherford, G. W. (2017). Evaluating Google Flu Trends in Latin America: Important lessons for the next phase of digital disease detection. Clinical Infectious Diseases, 64(1), 34–41. doi:10.1093/cid/ciw657 Reinsel, D., Gantz, J., & Rydning, J. (2017, April). Data age 2025: The evolution of data to life‐critical (IDC White Paper). Retrieved from https://assets.ey.com/content/dam/ey‐sites/ey‐com/en_gl/ topics/workforce/Seagate‐WP‐DataAge2025‐March‐2017.pdf Resch, B., Usländer, F., & Havas, C. (2018). Combining machine‐learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment. Cartography & Geographic Information Science, 45(4), 362–376. doi:10.1080/15230406.2017.1356242 Reyes, T., Majluf, N., & Ibáñez, R. (2018). Using internet search data to measure changes in social perceptions: A methodology and an application. Social Science, 99(2), 829–845. doi:10.1111/ssqu.12449 Sanguinet, M., Spitzberg, B. H., & Tsou, M.‐H. (2017, May). Hashtags, tweets and movie receipts: Social media analytics in predicting box office hits. Paper presented at the National Communication Association Conference, Dallas, TX. Serfass, D. G., & Sherman, R. A. (2015). Situations in 140 characters: Assessing real‐world situations on Twitter. PLoS One, 10(11), e0143051. doi:10.1371/journal.pone.0143051 Shah, D. V., McLeod, D. M., Rojas, H., Cho, J., Wagner, M. W., & Friedland, L. A. (2017). Revising the communication mediation model for a new political communication ecology. Human Communication Research, 43(4), 491–504. doi:10.1111/hcre.12115 Sharag‐Eldin, A., Ye, X., & Spitzberg, B. H. (2018). Multilevel model of meme diffusion of fracking through Twitter. Chinese Sociological Dialogue, 3(1), 17–43. doi:10.1177/2397200917752646 Shaw, S.‐L., & Yu, H. (2009). A GIS‐based time‐geographic approach of studying individual activities and interactions in a hybrid physical–virtual space. Journal of Transport Geography, 17(2), 141–149. doi:10.1016/j.jtrangeo.2008.11.012 Shaw, S.‐L., Yu, H., & Bombom, L. S. (2008). A space‐time GIS approach to exploring large individual‐based spatiotemporal datasets. Transactions in GIS, 12(4), 425–441. doi:10.1111/j.1467‐9671.2008.01114.x Shi, X., Xue, B., Tsou, M., Spitzberg, B., Gawron, J. M., Corliss, H., … Ye, X. (2018). Detecting events from the social media through exemplar‐enhanced supervised learning. International Journal of Digital Earth, 12(9), 1083–1097. doi:10.1080/17538947.2018.1502369



Leveraging Social Media for Applied Problems

181

Sidana, S., Amer‐Yahia, S., Clausel, M., Rebai, M., Mai, S. T., & Amini, M. (2018). Health monitoring on social media over time. IEEE Transactions on Knowledge & Data Engineering, 30(8), 1467–1480. Spitzberg, B. H. (2014). Toward a model of meme diffusion (M3D). Communication Theory, 24, 311–339. doi:10.1111/comt.12042 Spitzberg, B. H., Tsou, M.‐H., An, L., Gupta, D. K., & Gawron, J. M. (2013). The map is not which territory?: Speculating on the geo‐spatial diffusion of ideas in the Arab Spring of 2011. Studies in Media and Communication, 1(1), 101–115. doi:10.11114/smc.v1i1.64 Statista. (2018, August 18). Smartphones industry: Statistics & facts. Retrieved from https://www.statista. com/topics/840/smartphones Strauss, R. A., Castro, J. S., Reintjes, R., & Torres, J. R. (2017). Google dengue trends: An indicator of epidemic behavior. The Venezuelan Case. International Journal of Medical Informatics, 104, 26–30. doi:10.1016/j.ijmedinf.2017.05.003 Tang, L., Bie, B., Park, S.‐E., & Zhi, D. (2018). Social media and outbreaks of emerging infectious diseases: A systematic review of literature. American Journal of Infection Control, 46(9), 962–972. doi:10.1016/j.ajic.2018.02.010 Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46 (Suppl.: Proceedings. International Geographical Union. Commission on Quantitative Methods), 234–240. http://www.jstor.org/stable/143141 Tran, T. D., & Castelnuovo, E. (2017). Google it up! A Google trends‐based uncertainty index for the United States and Australia. Economics Letters, 161, 149–153. doi:10.1016/j.econlet.2017.09.032 Tsou, M. H. (2015). Research challenges and opportunities in mapping social media and big data. Cartography and Geographic Information Science, 42(Suppl. 1), 70–74. doi:10.1080/15230406.201 5.1059251 Tsou, M.‐H. (2011). Revisiting web cartography in the United States: The rise of user‐centered design. Cartography and Geographic Information Science, 38(3), 249–256. doi:10.1559/15230406382250 Tsou, M.‐H. (2013). Visualization of social media: Seeing a mirage or a message? Cartography and Geographic Information Science, 40(2), 55–60. doi:10.1080/15230406.2013.776754 Tsou, M.‐H. (2018). The future development of GISystems, GIScience, and GIServices. In B. Huang (Ed.), Comprehensive geographic systems (Vol. 1, pp. 1–4). Oxford, UK: Elsevier. Tsou, M.‐H., Aslam, A., & Nagel, A. (2016). Applying GIS and machine learning methods to Twitter data for multiscale surveillance of influenza. PLoS One, 11(7), 1–10. doi:10.1371/journal.pone.0157734 Tsou, M. H., Jung, C. T., Allen, C., Yang, J. A., Gawron, J. M., Spitzberg, B. H., & Han, S. (2015). Social media analytics and research test‐bed (SMART Dashboard). In Proceedings of the 2015 International Conference on Social Media & Society (Article No. 2). doi:10.1145/2789187.2789196 Tsou, M.‐H., Jung, C.‐T., Allen, C., Yang, J.‐A., Han, S. Y., Spitzberg, B. H., & Dozier, J. (2017). Building a real‐time geo‐targeted event observation (GEO) viewer for disaster management and situation awareness. In M. P. Peterson (Ed.), Advances in cartography and GIScience: Selections from the International Cartographic Conference 2017 (pp. 85–98). New York, NY: Springer. Tsou, M.‐H., Kim, I., Wandersee, S., Lusher, D., An, L., Spitzberg, B., … Han, S. Y. (2014). Mapping ideas from cyberspace to realspace: Visualizing the spatial context of keywords from web page search results. International Journal of Digital Earth, 7(4), 316–335. doi:10.1080/17538947.20 13.781240 Tsou, M.‐H., Yang, J.‐A., Lusher, D., Han, S. H., Spitzberg, B. H., Gawron, J. M., … An, L. (2013). Mapping social activities and concepts with social media (Twitter) and web search engines (Yahoo and Bing): A case study in 2012 US presidential election. Cartography and Geographic Information Science, 40(4), 337–348. doi:10.1080/15230406.2013.799738 Tu, W., Cao, J., Yue, Y., Shaw, S.‐L., Zhou, M., Wang, Z., … Li, Q. (2017). Coupling mobile phone and social media data: A new approach to understanding urban functions and diurnal patterns. International Journal of Geographical Information Science, 31(12), 2331–2358. doi:10.1080/1365 8816.2017.1356464 Valenzuela, S., Halpern, D., & Katz, J. E. (2014). Social network sites, marriage well‐being and divorce: Survey and state‐level evidence from the United States. Computers in Human Behavior, 36, 94–101. doi:10.1016/j.chb.2014.03.034 van Lier, B. (2016). From high frequency trading to self‐organizing moral machines. International Journal of Technoethics, 7(1), 34–50. doi:10.4018/IJT.2016010103

182

Spitzberg, Tsou, and Jung

Vijaykumar, S., Nowak, G., Himelboim, I., & Jin, Y. (2018). Managing social media rumors and misinformation during outbreaks. American Journal of Infection Control, 46(7), 850. doi:10.1016/j. ajic.2018.03.014 Walther, J. B., & Valkenburg, P. M. (2017). Merging mass and interpersonal communication via interactive communication technology: A symposium. Human Communication Research, 43(4), 415–423. Wang, Z., & Ye, X. (2018). Social media analytics for natural disaster management. International Journal of Geographical Information Science, 32(1), 49–72. doi:10.1080/13658816.2017.1367003 Wang, Z., & Ye, X. (2019). Space, time, and situational awareness in natural hazards: A case study of Hurricane Sandy with social media data. Cartography and Geographic Information Science, 46(4), 334–346. doi:10.1080/15230406.2018.1483740 Wang, Z., Ye, X., Lee, J., Chang, X., Liu, H., & Li, Q. (2018). A spatial econometric modeling of online social interactions using microblogs. Computers, Environment and Urban Systems, 70, 53–58. Wu, D., & Cui, Y. (2018). Disaster early warning and damage assessment analysis using social media data and geo‐location information. Decision Support Systems, 111, 48–59. doi:10.1016/j.dss.2018.04.005 Yang, C., Xiao, M., Ding, X., Tian, W., Zhai, Y., Chen, J., … Ye, X. (2019). Exploring human mobility patterns using geo‐tagged social media data at the group level. Journal of Spatial Science, 64(2), 221–238. Yang, J.‐A. J., Tsou, M.‐H., Jung, C.‐T., Allen, C., Spitzberg, B. H., Gawron, J. M., & Han, S.‐Y. (2016). Social Media Analytics and Research Testbed (SMART): Exploring spatiotemporal patterns of human dynamics with geo‐targeted social media messages. Big Data & Society, 3(1), 1–19. doi:10.1177/2053951716652914 Yang, X., Shaw, S.‐L., Ziliang, Z., Ling, Y., Feng, L., Jie, C., … Qingquan, L. (2016). Another tale of two cities: Understanding human activity space using actively tracked cellphone location data. Annals of the American Association of Geographers, 106(2), 489–502. doi:10.1080/00045608.2015.1120147 Ye, X., Sharag‐Eldin, A., & Spitzberg, B. H. (2018). Analyzing public opinions on death penalty abolishment. Chinese Sociological Dialogue., 3(1), 53–75. doi:10.1177/2397200918761665 Yin, L., Shaw, S., & Yu, H. (2011). Potential effects of ICT on face‐to‐face meeting opportunities: A GIS‐ based time‐geographic approach. Journal of Transport Geography, 19(3), 422–433. doi:10.1016/j. jtrangeo.2010.09.007 Young, J. M. (2014, May). An epidemiology of big data (Doctoral dissertation). Syracuse University SURFACE. Paper 105. Retrieved from http://surface.syr.edu/etd Yu, H., & Shaw, S.‐L. (2008). Exploring potential human activities in physical and virtual spaces: A spatio‐ temporal GIS approach. International Journal of Geographical Information Science, 22(4), 409–430. doi:10.1080/13658810701427569 Zubiaga, A., Spina, D., Martínez, R., & Fresno, V. (2015). Real‐time classification of Twitter trends. Journal of the Association for Information Science & Technology, 66(3), 462–473.

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Exploring Applied Practices in Entertainment Marketing How Brands Connect with Today’s Modern Family Laura H. Crosswell and Meghan S. Sanders The media industry is ever changing. Digital platforms, touch‐screen interfaces, and networked individualism seem to be leading a communication transformation, one that is simultaneously influencing brand marketing and product promotion. Indeed, digital media investments surpassed television advertising spending for the first time in 2016 and consumer reports show modern marketing techniques are increasingly pulling profit share from traditional ad sales (Poggi, 2017; Sullivan, 2018). While today’s technology creates new means for direct, individualized, and borderless communication, commercial overload concurrently creates an audience that is now armed with the power to control messages consumed. The increase in use of streaming services, such as Netflix, has further forced marketers to look beyond the 30‐second commercial break. Ubiquitous promotional messaging is also altering audience perceptions of credibility and authenticity, thus making it harder to achieve successful communication outcomes. Consequently, media buyers are finding alternate ways, such as social endorsement, brand persona creation, and sponsored content, to reach and form deeper relationships with their consumer base. Product placements, in particular, have demonstrated significant growth in the United States, with industry ventures nearly tripling since 2012 (when the total was $4.75 billion; Statista, 2015). Marketers, aiming to capitalize on the halo effect of beloved media personalities, increasingly expend large budgets to place products into the hands of popular television, movie, and video game characters. Notably, the sales projection for product integration was expected to reach an all‐time high in 2019, with estimations at $11.44 billion (Statista, 2015). As entertainment marketing continues to grow and evolve as a means for message delivery, it is important for both scholars and practitioners to re‐evaluate current approaches to the research and implementation of this marketing strategy. This chapter addresses various aspects of product placement and entertainment marketing as it relates to today’s consumer advertising. Throughout our discussion, we identify brands that have capitalized on parasocial interactions (PSI) and product‐character associations (PCAs) to more effectively connect with target audiences. In a study on ABC’s Modern Family, we specifically focus on the utility of parasocial relationships (PSRs) in effective promotional content. The following cases illustrate the power of a parasocial fan base in the current consumer market and propose mechanisms through which applied research can examine embedded marketing in entertainment narratives for media planning strategies.

The Handbook of Applied Communication Research: Volume 1, First Edition. Edited by H. Dan O’Hair and Mary John O’Hair. © 2020 John Wiley & Sons, Inc. Published 2020 by John Wiley & Sons, Inc.

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Product Placements: An Overview Although many often think of Reese’s Pieces’ prominent branding in E.T. the Extraterrestrial (1982) as the earliest example of product placement, Hollywood and Madison Avenue began working together in earnest back in the 1930s, placing products such as Coca‐Cola, Buick, Bell telephones, and De Beers diamonds into movies (Elliot, 2005). Today, the practice is widely accepted among most media audiences (Gupta & Gould, 1997), including Baby Boomers (Schmoll, Hafer, Hilt, & Reilly, 2006), brand conscious teens (Nelson & McLeod, 2005), and international audiences (Gould, Gupta, & Grabner‐Krauter, 2000; La Pastina, 2001; McKechnie & Zhou, 2003). Accordingly, show producers and creative directors are now moving toward more firmly rooting products into storylines, placing a stronger emphasis on branded content, while also focusing on other elements that make for a good entertainment narrative. As entertainment marketing entered a ninth consecutive year of double‐digit sales growth (CISION PRWeb, 2018), products increasingly play a leading role in characters’ lives and overall storylines (as opposed to peripheral placement in media content). For example, in Paramount’s Transformers (2007), super‐robots transformed themselves into various General Motors vehicles, including a Chevrolet Camaro, Cadillac Escalade, and a Hummer. Not only were these vehicles visible, but they were important characters in the film and recognized as featured heroes, responsible for helping and protecting the primary human characters. Some placement strategies have also demonstrated a trend toward satirical orientation (e.g., Talladega Nights), with content producers relating to audiences through mutual recognition of a branded‐infused industry. The tongue‐in‐cheek approach even served as a meta‐narrative for Morgan Spurlock’s documentary The Greatest Movie Ever Sold. Television shows follow a similar pattern, embedding products within strategically developed, and oftentimes commercial influenced, storylines. In January 2018, for example, consumer goods firm Procter & Gamble purchased an episode of the American Broadcasting Company (ABC) sitcom Black‐ish, a popular television show chronicling the lives of an African American family. The episode’s storyline revolved around the father, Dre Johnson, who works as an advertising executive developing a campaign that focuses on the Procter & Gamble film The Talk. The award‐winning campaign features African American parents talking to their children about racism. Numerous scholars have examined the placement of products in movies and television (Balasubramanian, Karrh, & Patwardham, 2006; Ferraro & Avery, 2000; Gupta, Balasubramanian, & Klassen, 2000; Karrh, Frith, & Callison, 2001; Russell, 2002) and in gaming (Nelson, Yaros, & Keum, 2006; Winkler & Buckner, 2006). Published work in this area demonstrates varying takes on promotional effectiveness. On one hand, studies show the product placement strategy is effective in producing positive emotional responses toward advertised brands (Jin & Villegas, 2007), creating linkages in viewers’ minds between the media content and featured products (Russell, 1998), and outperforming traditional advertising in terms of recall (Gupta & Kenneth, 1998). However, other researchers point out that product placements do not always perform as intended. For example, Gupta and Gould (2007) found traditional commercials outperformed product placements in game shows. Matthes, Schemer, and Wirth (2007) further demonstrated that placement awareness can reduce promotional effectiveness. While viewer recognition minimized impact in this study, the researchers found that those unable to recall specific placements tended to express more favorable reactions to brands featured in the programming. Viewer perception of entertainment content is also key in mitigating effects. Percy (2006) predicted that product placements would be less effective when there is not a “cult association with the context or actors” (p. 113). Indeed, Russell and Stern (2006) found that audience perceptions of a placed product or brand mimic the thoughts of the character providing them, if that character is a favored one. This particular influence is based on viewers’ PSRs with those characters. PSRs are a type of virtual‐social experience, in which media consumers perceive a  mediated persona as a “familiar other” (Rubin & McHugh, 1987). Though traditionally a



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one‐sided relationship, the connection between viewer and character reflects a conceptual model of friendship and is therefore thought to satisfy the need for social affiliation (Cohen & Metzger, 1998). Like interpersonal relationship development, a viewer’s perceived similarity to media characters often initiates the “friendship,” and continued interactions can, over time, shape how viewers come to think of themselves. Scholars indicate, “nowadays, social relationships develop toward virtual forms … and mediated interactions contribute to the structuration of identity” (Annese, 2004, p. 372). Whereas PSRs describe enduring, long‐term bonds with media characters, a PSI is an intimate ­connection/ exchange that occurs episodically. Tian and Hoffner (2010) note that PSIs can also influence viewers’ attitudes and behaviors, as character identification promotes message involvement, “which in turn increases the elaboration of messages and their potential persuasive effects” (p. 260). Therefore, because viewer identification with media characters promotes PSIs, and PSRs promote content engagement, the calculated strengthening of these bonds can produce profitable outcomes for brands featured with these characters. Indeed, Russell and Stern (2006) indicate PSRs are very important to the success of product placements, finding them to be a stronger predictor of attitude toward the product than attitude toward the character. According to their model, a threefold influence exists, involving (a) the relationship between the character and the product within the program, (b) the relationship between the viewer and the character, and (c) other influences that affect viewers’ attitude toward a product. In other words, it is PSRs, coupled with characters’ attitudes toward a product, that drive attraction to the featured content. The scholars further note that product evaluations were influenced by character attachment levels. Specifically, if the character had a positive attitude toward the product, so did the viewer, regardless of relationship strength. However, if the character had a negative encounter with a product, attachment levels needed to be strong for the viewer to think negatively of the product. CrockPot experienced this firsthand in 2018 when National Broadcasting Company (NBC) featured the slow cooker in season two of its hit drama series This Is Us. In its much‐anticipated post‐Super Bowl episode, it was revealed that a malfunctioning CrockPot caused the house fire that led to the demise of family patriarch and beloved character Jack Pearson. CrockPot quickly found itself in the midst of a mini‐crisis as viewers took to social media to air their grievances, assigning blame and expressing distrust of the CrockPot slow cooker. A product’s visual prominence can also drive placement effectiveness. For instance, viewers recognize products that play a significant role in a storyline or contribute to character development at a much higher rate than those placed in the background (Russell, 1998, 2002). These results are in line with the landscape model, which suggests viewers pay most attention to information that is central to a scene or the story’s progression, as well as specific character interactions with featured products (Yang & Roskos‐Ewoldsen, 2007). Yang and Roskos‐Ewoldsen (2007) refer to this as story connection placements. Much time has passed since scholars first identified PSIs as a “common occurrence among television viewers” (Wilson, 2008, p. 6). Since then, media buyers and advertisers have used these viewer‐character connections to their advantage, locking in consumer attention through viewer identification with television characters (Hackley, Tiwsakul, & Preuss, 2008; Kang, 2008). As our media environment continues to evolve, it is important to understand how broadcast capabilities of the twenty‐first century revolutionize marketing approaches and redefine consumer behavior. Accordingly, this chapter explores relevant cases of parasocial placement strategies, highlighting the value of applied research in this area. The following work addresses specific aspects of entertainment marketing through two separate but related studies. We begin with a systematic content analysis of featured product placements in ABC’s hit sitcom series Modern Family, specifically focusing on Episode 19, Season 1 (which heavily featured Apple’s yet‐to‐be‐released, first‐generation iPad). We then discuss a follow‐up investigation in which we look at character favoritism as it relates to parasocial connections and visual attention.

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Study 1: “Game Changer” In September 2009, 12.61 million viewers tuned in to the debut of Modern Family (Nielsen, 2010). The show continued to build a loyal fan base throughout its first season, averaging 8.823 million household viewers per episode. It was the highest‐rated new comedy of the broadcast season that year, and media critics dubbed the series “a sitcom for the ages” (Slezak, 2010). Indeed, Modern Family continues to see success nearly a decade later, boasting 22 Primetime Emmy awards, one Golden Globe, and an audience base of approximately 5.8 million viewers as it enters its eleventh and final season of production (Academy of Television Arts & Sciences, 2018). Early in its premiere airing, Modern Family gained traction as an attractive marketing platform. Advertising rates for a 30‐second spot climbed to $130,388, and product air time was neither easy to attain or easy to afford (Stanely, 2010). Steven Levitan (an executive creator/producer of the show) indicated, “to make it into Modern Family, a product’s appearance has to be relevant to not only the plot, but the characters” (Steinberg, 2012, para. 11). Indeed, the sitcom featured very few sponsored products in its first season, with one notable exception: the storyline of episode 19 (“Game Changer”) heavily played on the anticipated release of Apple’s first‐ generation iPad. Apple had long established a strong brand presence in entertainment prior to Modern Family’s network debut. In 2010 alone, Apple products appeared in 30% of the top US films, and the company earned Brandchannel’s “2010 Award for Overall Product Placement” (Sauer, 2011). Thus, it came as no surprise when Modern Family integrated Apple’s iPad into an episode storyline. The show garnered an attractive demographic for companies trying to reach the younger end of the elusive 18–49 demographic, and Modern Family, in particular, attracted heavy viewership among Apple’s specific consumer base. Three days prior to product launch in a very meta‐like fashion, Modern Family featured the iPad in an episode almost entirely shaped around its anticipated release. Despite promotional appearances in over 40 different types of product‐character interactions, Modern Family co‐­ creator Christopher Lloyd insisted, “there was no product placement” in the episode, as the integrated content “was all story‐driven” (Sandler, 2013, p. 254). Responding to critical reviews of what was perceived as heavy integration of paid placement, Lloyd said (in 2010, via the Hollywood Reporter): In fact, there was no product placement. This was widely assumed, and everybody was wrong. We wanted to do a show about Phil [a main character in the show] getting very excited about a new product and it seemed the perfect one to use, since it was debuting [April 1]. We approached Apple about getting their cooperation (using the product, for example, and they are notoriously secretive about their products prior to their being launched) and they agreed and gave us a few other small concessions. But there were no stipulations as with normal product placement, i.e. we give you X dollars and you have to feature our product such‐and‐such a way and say such‐and‐such nice things about it. We are not angels—we have made those agreements with other companies. But that was not the deal with Apple. (Brown, 2010, para. 1)

In this case, Apple entered a strategic partnership, supplying free iPads (which lowered production costs) in exchange for brand exposure in ABC’s high‐profile series. The business arrangement not only raised consumer hype immediately prior to Apple’s product release (with little cost and tremendous gain1), but ABC’s integration also created opportunities to capitalize on character‐product interactions and similarities between sitcom characters and early adopters.

  Joyce Julius & Associates estimated a $650,000 value in show exposure alone, and an additional $250,000 from print and internet coverage featuring the extensive integration (Stanely, 2010).

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According to Bandura (2001), “a vast amount of information about human values, styles of thinking, and behavior patterns is gained from the extensive modeling in the symbolic environment of the mass media” (p. 271). Indeed, research suggests viewers who identify with specific media characters are more likely to adopt behaviors from the characters’ modeled experiences. This implies that strategic PCAs can promote product interest among specific target markets. Apple, for example, concentrates on the “early adopter”—appealing to those eager to own the newest technology before anyone else. Phil Dunphy, one of the show’s main characters, offers a representation of this consumer profile: a White, educated, upper‐class, middle‐aged male who even goes as far as to self‐identify as an “early adopter” in the episode under study. Consumer reports reinforce demographic and psychographic compatibility between the character and target market, indicating males account for 65% of iPad’s consumer base, and noting a “relative overrepresentation among the 30–54 age range (peaking in the 35–44 age group)” (Slivka, 2010, para. 1). Nielsen (2010) also identified early iPad users as a more affluent demographic, showing 51% held a bachelor’s degree or higher and 25% earned an income of $100,000 or more. Social cognitive theory (SCT) speaks to the psychosocial impact of media content and commercial messaging. The theory is largely recognized as the foundational framework from which to examine symbolic modeling, and it effectively outlines how strategic message design can motivate positive consumer responses (Bandura, 1986; Kim, 2014). Accordingly, SCT provides an appropriate lens through which to more closely examine modern product placement strategies. As noted earlier, we conducted a content analysis to determine if relevant instances of product‐character interactions were present, as well as the forms in which they may have appeared. Despite Christopher Lloyd’s claim that Apple played no role in shaping the episode’s narrative, our findings speak to the practical application of entertainment marketing and demographic targeting strategies. Specifically, we identified each iPad placement/reference based on the a priori codes identified in Figure 11.1. We also used character demographic indicators (e.g., character age, gender, race, occupation, education, marital status, and socioeconomic status) to identify themes in PCAs. Scholars have previously demonstrated that verbal reinforcement of placed products, and implicit endorsement through character product use, promotes the highest levels of recall (Stoertz, 1987). Scenes that feature product interactions among main characters (Soloman & Basil, 1994) and prominent placement in a show’s plot (Gupta & Lord, 1998) also influence promotional effectiveness. Indeed, our research showed that verbalized product references (n = 22) occurred twice as often as physical interactions (n = 11), with characters explicitly identifying the iPad brand name in 80% of verbalized placements. Claire and Phil (two of the show’s main characters, who also represented the target customer) interacted with the product most often, accounting for 68% of both physical and verbal product‐character interactions. Notably, Phil was most often associated with the product through both self‐references (n = 7) and those of others (n  =  9). Character‐product interactions/associations were predominantly positive (n  =  28), with negative placements/referrals accounting for less than 1% of iPad’s featured integration (n = 3); the remaining occurrences were neutral (n = 11). More recent research demonstrates product placements perform best when they are mentioned verbally, embedded multiple times, fully visible, shown longer on screen, and involve direct character interactions with the product (Nielsen, 2011). Again, our content analysis of Episode 19 reflected industry application of scholarly findings, documenting a relevant case of strongly integrated product promotion and strategic entertainment marketing. Apple’s iPad had significant placement in the episode, surfacing throughout more than 40 featured character interactions. Of the 42 references made about the product, 38% of them were direct references, compared to 9.5% of the references being indirect. The majority of the references, 30.10%, were general in nature, while 21.4% were a character connecting the product either to themselves or to another

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Crosswell and Sanders 1. Interaction Type (physical/verbal): a. Physical: Any non-verbal interaction between character and product/company b. Verbal: Character verbally identifies product/company 2. Degree of Integration (direct/indirect/explicit/implicit): a. Explicit Verbal: direct mention of product/company (e.g., “I thought I was getting an iPad”) b. Explicit physical : a scene or incident in which a character has physical contact with the product in way important to the storyline (e.g., character cradles the iPad in both hands, clutching the product to his chest) c. Indirect/lmplied Verbal: product not directly identified, but clearly implied through context of verbal interaction (e.g., “Did you get it?;” or reference to “Steve Jobs”) d. Indirect Physical: an incident in which a character is seen with the product or product-adjacent item/person, but not actually touch it (e.g., Phil blows out candles flickering on the iPad; Claire standing in front of Apple employees) 3. Context of Product-Character Association (self/others/general): a. Self: when a character speaks about themselves in relation to the product (e.g., “I’m just waiting in line to get an iPad”) b. Others: When the character speaks about others in relation to the product (e.g., “We’ve got to find your dad one of those iPad thingies”) c. Generalized: When the character speaks about the product. but in relation to a specific character (e.g., “What is so great about that doohickey anyhow?”) 4. Valence of Product Placement (positive/negative/neutral): a. Positive : an instance in which the character r speaks favorably about the product (e.g., “It’s a movie theater, a library and music store all rolled into one awesome pad.”) b. Negative: an instance in which a character speaks negatively about or questions the value of the product (e.g., “What is so great about that doohickey anyhow?”) c. Neutral: an instance in which a character references the product but provides no evaluative statement about it (e.g., “ I’m just waiting in line to get an iPad”)

Figure 11.1  A priori codes for content analysis.

character. A little over half (52.3%) of the interactions with the iPad were verbal; only 26% were considered to fall within the physical interaction category. Regarding references to the product, 47.6% were implicit references to the iPad. Finally, the vast majority of references to the iPad (66.67%) were positive in nature, while another 26.19% were neutral. In sum, “Game Changer” favorably featured the iPad in several different story arcs throughout the episode, with character interactions demonstrating emotionally engaged and positively framed on‐screen product use. Most notably, Phil, the self‐proclaimed early adopter and character that best represents Apple’s target consumer, interacted with the product much more frequently than any other cast member (aside from Claire). The relationship between character demographics and product interactions speaks to an underlying promotional strategy. Apple’s partnership with ABC created opportune conditions for effective brand integration and memorable PCAs. As this case demonstrates, Apple and ABC quite literally put the featured product into the hands of the character most representative of the target market. While this content analysis helped identify relevant patterns in character‐product interactions, it cannot speak to brand intentions or placement effectiveness. Therefore, more advanced research methods are needed to examine the promotional utility of product placement and brand integration in entertainment narratives. Given that implicit human responses offer more reliable



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and objective measures of consumer attention, autonomic visual connections may further inform practices in entertainment marketing and basic premises in scholarly research. Accordingly, the next study used eye tracking technology to explore the innate dynamics of viewer‐character relationships and PCAs. Though Study 2 exists independently from the research presented here (as it does not focus on a particular episode or product), the following work does put forth a new investigative approach for PSR/PSI research, and together, findings from both cases speak to the subconscious potential of PCAs.

Study 2: Modern Family and Character Relatability Scholars indicate perceived similarities between viewer and media personalities foster PSRs. In addition, conversational techniques and styles of cinematography can promote PSRs (Auter, 1992; Horton & Wohl, 1956; Meyrowitz, 1982; Turner, 1993). Modern Family encourages parasocial connections and continued media friendships through a diverse cast of relatable characters and storylines that more accurately represent evolving interpersonal dynamics of the twenty‐first century. The show uses direct address through confessional/interview format, which can further drive viewer involvement and parasocial intimacy (Cummins & Cui, 2014). Accordingly, this study looks at the degree to which both viewer relatability and character favoritism are related to PSIs with specific characters in Modern Family. Eye tracking indicators are also used as a proxy to examine parasocial connectedness. Eye tracking research and viewer fixation data offer real‐time insight into the perceptual and cognitive processes that influence knowledge acquisition, attitude formation, and consumer behavior. For market research, “eye tracking can provide insight into at least one aspect of the internal consumer model: how the consumer disperses visual attention over different forms of advertising” (Duchowski, 2007, p. 262). Thus, we combined eye tracking observations with pre‐ and posttest questionnaires to assess viewer perceptions of Modern Family characters and the degree to which character favoritism influences visual attention and PSIs. Our mixed‐method research compares micro‐level, physiological data with participant self‐reports, and in doing so, offers a more robust account of audience attention, viewer‐character relationships, and underlying dynamics of PSIs.

Study Procedures

Participants in this study viewed a randomized set of five Modern Family cast photos (copyrighted by the ABC network), as well as the character bios on the show’s official website (http:// abc.go.com/shows/modern‐family/bios). We monitored eye movement patterns/focal attention using the Tobii T‐120 eye tracking system. The tracking hardware is embedded in a computer monitor, allowing for discrete and unobtrusive data collection. To avoid expectations and leading, we did not give participants a specific viewing task, nor directly indicate we were tracking viewing behaviors. We simply asked them to view a (randomized) series of images, briefly interact with the ABC website, and then complete a posttest questionnaire. An adapted version of Rubin, Perse, and Powell’s (1985) original PSI scale facilitated measures of viewer‐character relationships. Among other items, our posttest questionnaire surveyed participants’ perceived similarity with and social attraction to each character, as well as the extent to which they found themselves adapting aspects of their personal identity to be more like that of their favorite character. We also considered participant demographics and psychographics as we assessed self‐reported measures of viewer involvement, character knowledge, content exposure, and character favoritism. We compared fixation density and dwell time with survey responses to better assess relationships between visual attention, character evaluation, and parasocial constructions. To prepare

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for fixation analysis, we drew an oval area of interest (AOI) around each character face in the stimulus images. Each individual face, whether it appeared in the cast photo or as a headshot, was selected as an area of interest. Although there are several ways to express fixation data, “the number of fixations and the cumulative dwell time of fixations recorded in each AOI have been reported as the most useful” (Hallowell & Lansing, 2004, p. 23). Therefore, we examined the frequency of fixations (fixation count) and the amount of time spent fixated on particular characters (fixation duration), presupposing that cast members amassing lengthier fixations are more significant to viewers’ cognition (Wedel & Pieters, 2000). For this study, we defined a fixation as directed gaze within an area of 35 pixels, with a minimum dwell time of 250 milliseconds (ms).

Results

Most participants were female (66%), heterosexual (84%), White (75%), and born in 1990 (making the average participant age 20 years old). Many grew up in a nuclear family household (63%); however, some also reported an extended model (13%), single‐parent model (13%), and composite model (3%). Six percent preferred not to answer. The majority of participants indicated familiarity with the ABC sitcom, with 19% having at one time watched the show and 56% reporting moderate to heavy viewing. Though the remaining 19% reported never having watched an episode of Modern Family, data collected from non‐viewers still contributed to our findings. Pearson’s correlations showed a positive relationship between viewing exposure and perceptions that the storylines of Modern Family present an accurate model of family dynamics (r = 0.60, p