You have been asked to conduct an analysis of your care setting that will result in two potential pathways toward a strategic plan to improve health care quality and safety in your org

Appreciative Inquiry and Evaluation – Getting to What Works

David J. MacCoy First Leadership Limited

Toronto, Ontario

Abstract: Appreciative Inquiry (Ai) is described as the cooperative search for the best in people, their organizations, and the world around them. Th is article describes how Ai has been applied to evaluation in ways that build upon strengths and gener- ate support for improvements. An initial criticism of AI can be that it focuses only on positivity and fosters an unrealistic view of human experience. Contributing to tension with the AI process is a mistaken belief that negative phenomena must be ignored. However, evaluators using AI have found that its appreciative questions, reframing, and generative features set the stage for sound assessment of worth as well as off er potential for powerful solutions.

Keywords: Appreciative Inquiry, defi cits, generativity, positivity, reframing, social constructionism

Résumé : L’approche de l’enquête appréciative (AEA) connue en anglais sous l’appellation “Appreciative Inquiry” se défi nit comme étant la recherche collective de ce qu’il y a de meilleur chez les gens, dans leurs organisations et dans les milieux dans lesquels ils évoluent. Le présent article vise à démontrer comment l’AEA a été utilisée dans des exercices d’évaluation pour mettre en relief les forces déjà en place et créer des conditions propices à l’amélioration. On reproche souvent à l’AEA de ne miser que sur les éléments positifs d’une situation et ainsi de promouvoir une perspective idéalisée et irréaliste de la dynamique humaine. Cette critique refl ète la croyance erronée voulant que l’AEA passe sous silence tous les éléments négatifs d’une situation, croyance qui a pour eff et de susciter encore plus de méfi ance envers l’approche. Toutefois, il a été démontré par des évaluateurs que les façons de recadrer le thème et de formuler des questions telles que proposées par l’AEA contribuent à eff ectuer des évaluations solides et fi ables tout en permettant de générer des solutions pertinentes et effi caces.

Mots clés : l’approche de l’enquête appreciative, défi cits, générativité, positivité, rec- adrage, constructivisme social

Corresponding author: David J. MacCoy, First Leadership Limited, 45 Elm Ridge Drive, Toronto, ON, Canada M6B 1A2; david.maccoy@fi rstleadership.com

© 2014 Canadian Journal of Program Evaluation / La Revue canadienne d’évaluation de programme 29.2 (fall / automne), 104–127 doi: 10.3138/cjpe.29.2.104

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Appreciative Inquiry (AI) is a process of search and discovery designed to fi nd the best in people, their organizations, and the world around them. As an organization development intervention, it is a collaborative, participative approach that involves asking questions to strengthen a system’s capacity to heighten positive potential, generating new ideas and actions. In the AI process, questioning moves from determining what is valued and appreciated to combin- ing strengths and activating people’s creative energy to ignite change (Cooper- rider & Serkerka, 2003).

Th is article discusses what Appreciative Inquiry is, how it has been used in various domains, and how it has been applied to evaluation. It draws upon the conceptual literature including empirical reports of application, results, and the author’s practical experience. It is intended to be of value to evaluators who might consider using AI or some of its components in their work.

Th is article contains four major parts. Th e fi rst part provides a brief history of Appreciative Inquiry, its applications, and an overview of the process. Th e second part describes a diffi cult AI evaluation case application and several tools and strat- egies that were used to make it work. It also includes several brief case examples to illustrate the use of tools in the evaluation approach. Th e third part off ers some thoughts on making the AI process work, based on the author’s experience. Th e fourth part discusses when the AI approach to evaluation may be appropriate, followed by some conclusions.

A BRIEF HISTORY OF APPRECIATIVE INQUIRY Initially, Appreciative Inquiry (AI) was constructed as a research method and an organization development intervention. David Cooperrider is credited with the origination of Appreciative Inquiry in the 1980s while he was a doctoral student at Case Western Reserve University (Cooperrider, 1986; Bushe, 2012). His study of physician leadership at the Cleveland Clinic focused on data while the organiza- tion was most eff ective and truly at its best. He has continued to provide thought leadership, though he is quick to dispute his role as “founder,” sharing credit with many others for refi ning AI as an organization change technique.

Social constructionism, which argues for human science as social construc- tion (Gergen, 1982), had a profound impact on Cooperrider’s thinking and is em- bedded in AI philosophy. Social constructionism actually distinguishes AI from what is popularly called positive thinking in that it maintains reality is constructed in the social interactions of people and not in the mind of an individual. AI is a highly relational approach to systemic and structural change that is about asking questions and engaging people in learning about and co-constructing the change they want (Watkins & Mohr, 2001).

In 1993, the Taos Institute was founded by scholars and practitioners, includ- ing Gergen and Cooperrider, as a nonprofi t educational organization dedicated to the development of social constructionist theory and training for organizations, consultants, family therapists, educators, and others.

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In 1998, a newsletter, AI Practitioner: Th e International Journal of Apprecia- tive Inquiry, evolved into a widely read monthly journal for sharing ideas and experiences (Bushe, 2012). Case Western Reserve University has continued to be a focal point for teaching, research, and information sharing about AI. Th rough the Appreciative Inquiry Commons, practitioners and researchers share tools and academic resources focused on the discipline of positive change (http:// appreciativeinquiry.case.edu).

During the 1990s, books, papers, and training courses describing the prin- ciples, methods, and applications of AI began to appear (e.g., Cooperrider & Whitney, 2000; Elliott, 1999; Hammond, 1996; Mohr, Smith, & Watkins, 2000). In addition, many large-scale processes such as Imagine Chicago, the Global Excellence in Management Initiative (GEM), and the United Religions Initiative brought AI to the forefront of organization development and transformation (Watkins & Mohr, 2001).

Th e literature on Appreciative Inquiry continues to grow, with emphasis on practice advances and the various organization development applications of the approach. In addition, the growing literature on Positive Organizational Scholar- ship (Cameron, 2013), Positive Psychology (Seligman & Csikszentmihalyi, 2000), Positivity (Fredrickson, 2009), and projects focused on learning from success such as those reported by Sykes, Rosenfeld, and Weiss (2007) contributes to the work of appreciative inquiry practitioners in all areas of application.

Appreciative Inquiry Applications Appreciative Inquiry has been applied in a wide variety of contexts and settings in the private and public sectors. Th e choice to use the AI approach has oft en resulted from limitations encountered in using problem-focused or defi cit approaches. Th e problem-focused approach was generally very eff ective in solving existing problems and “fi xing” them, but oft en less eff ective in identifying what is going “right” and taking it to the next level.

AI has been applied extensively in change management (Anderson & McKenna, 2006; Cooperrider & Whitney, 2000; Preskill & Catsambas, 2006), stra- tegic planning (Stavros & Hinrichs, 2007), organization design and development (Cooperrider & Srivastva, 1987), team building (Whitney, Trosten-Bloom, Cher- ney, & Fry, 2004), performance review (Shaked, 2010), leadership development (Bushe, 2001), quality assurance (Catsambas, Kelley, Legros, Massoud, & Bouchet, 2002), coaching (Orem, Binkert, & Clancy, 2007), and research (Reed, 2007).

Th e AI Summit, a methodology for whole system positive change, has been used to engage large groups of people, most oft en in the hundreds or even thousands in medical centres, universities, manufacturing, transportation, high- technology companies, service organizations, and the United Nations (Cooper- rider, Whitney, & Stavros, 2003; Cooperrider, Zandee, Godwin, Avital, & Boland, 2013). However, most AI engagements are more modest, involving appreciative interviews, group work, and surveys with smaller numbers of people including boards of directors, management teams, and staff of organizations.

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Appreciative Inquiry and Evaluation Th e AI approach is also used in evaluation, though there was a limited amount written about it until the late 1990s and early 2000s. Early applications of Ap- preciative Inquiry (AI) to evaluation were described in Elliott (1999), Mohr et al. (2000), Odell (2002), and Jacobsgaard (2003). Slightly later, more focused attention on the rationale for using AI in evaluations and application examples are described by Preskill and Coghlan (2003), Webb, Preskill, and Coghlan (2005), Preskill and Catsambas (2006), and Skov Dinesen (2009). Webb et al. were guest editors of the February 2005 edition of Appreciative Inquiry Practitioner that fo- cused on the application of AI in evaluation. Preskill, who served as president of the American Evaluation Association in 2007, has made extensive contributions as teacher, innovator, and thought leader to evaluation practice in general and to the use of AI in evaluation specifi cally.

A powerful example of choosing the AI evaluation process rather than a defi – cit approach is described by Mohr et al. (2000) in their work with a transnational pharmaceutical company that wanted an evaluation of their process management training program for 400 research managers. Th e consultants explained that one option would be a traditional review to determine whether the program had an impact and then focus on bridging any gaps or defi cits. Th ey also explained that another option would use an appreciative approach. Th is would involve searching for and understanding examples of times when participants successfully applied the intended learning. Th en they would fi nd ways to recreate, enhance, and ex- pand those conditions. Th e company chose the latter and successfully enhanced the training program.

Another example of choosing the AI process for an evaluation is found in a book chapter by Catsambas and Webb (2003) that describes the rationale for using the appreciative approach in the evaluation of the International Women’s Media Foundation (IWMF) Africa Program. Th e IWMF senior staff person recognized the need for participation, dialogue, and discovery of best practices. In particular, the AI interview process, based on story-telling as a means for learning the per- ceptions of participants and stakeholders, was seen as an ideal fi t for the African culture with its oral history traditions. Th e evaluation provided an opportunity for identifying controversial issues, taking action on concerns, increasing commit- ment, and clarifying roles and responsibilities. Th e IWMF concluded two years later that the evaluation process and results had been eff ective in addressing lead- ership issues, strengthening the roles and responsibilities of the African advisory committee, and teaching staff how to learn and grow from successes.

AI is certainly not the only collaborative and participatory approach to evalu- ation. In her article in this issue, Stame discusses the diff erences between AI and several other approaches that can support positive thinking and action, includ- ing Most Signifi cant Change, the Success Case Method, Positive Deviance, and Developmental Evaluation. Th e value of participatory, stakeholder, and learning- oriented approaches (Cousins & Earl, 1995) has been an important theme in eval- uation for many years. Th e similarities of Appreciative Inquiry and participatory

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approaches to evaluation have also been noted by others (Preskill & Coghlan, 2003). However, AI is not evaluation per se, though it off ers an approach, a per- spective, and a set of tools for conducting a full evaluation or various phases of an evaluation. Preskill and Catsambas (2006) describe the use of AI in focusing an evaluation, conducting appreciative interviews, developing evaluation systems, and building evaluation capacity. Th ey also point out that AI is not a panacea for the challenges of evaluation.

The Appreciative Inquiry 4-D Cycle As a process, AI is commonly identifi ed with the 4-D cycle (i.e., four Ds: Discover, Dream, Design, and Destiny; Whitney & Trosten-Bloom, 2003) that provides a guide to identify data about the “best of what is” (Figure 1). Th e process employs the four steps to guide participants in exploring appreciative questions at various points in time. Initially, the guiding questions focus on the discovery of the “what gives life?” now and then to dream “what might be?” in the ideal future. Th is is followed by designing or co-creating “what should be?” Th e fi nal step initiates actions on des- tiny, or “what will be?” (Whitney & Trosten-Bloom, 2003). In evaluation initiatives,

Figure 1. AI 4-D Model (Cooperrider, Whitney, & Stavros, 2003)

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the EnCompass Model of AI (Preskill & Catsambas 2006), the 4-I process with slightly diff erent terms (Inquire, Imagine, Innovate, and Implement), are oft en used.

Tensions: It Is Not Only About the Positive Although AI is intended to elicit generative conversation that moves toward the highest aspirations and potential in human systems (Johnson, 2013), it can lead to tension for those learning to apply the approach as well as participants when they are introduced to AI. A frequent concern is the possibility that a focus on positive stories and experiences during the initial dialogue or discovery phase will invalidate the negative organizational experiences of participants and may repress potentially important and meaningful conversations that need to take place (Bushe, 2007; Egan & Lancaster, 2005; Miller, Fitzgerald, Murrell, Preston, & Ambekar, 2005; Pratt, 2002).

Clearly, an alternative perspective that “bad is stronger than good” (Baumeister, Bratslavsky, Finkenauer, & Vohs, 2001) is worth considering as we plan evaluation strategy. A common reaction seems to be “If it’s bad, it could harm (or kill) us; if it’s good, that’s nice but so what, there’s no threat.” Th is leaves many clinging to the view that the only important or worthwhile phenomena to look at in their program are those that are dysfunctional and threatening. Neverthe- less, there are also many who are keen to understand why some things work well, “what gives life to systems,” and what can be learned from success. Th is is where the “inquiry” part of AI is so vital. Creative, appreciative questioning can take the inquiry to many dynamic destinations (Adams, Schiller, & Cooperrider, 2004; Whitney & Trosten-Bloom, 2003).

AN UNEASY EARFUL ABOUT POSITIVITY: EVALUATION OF THE TENANT PARTICIPATION SYSTEM It is diffi cult for practitioners to maintain a completely defi cit-free process when problem-identifi cation and problem-solving have been the predominant process- es for virtually everyone in organizational life (Johnson, 2013). Some participants in an AI evaluation of a program, or some other application such as strategic planning, may be fearful that an AI process would not allow them to mention anything that is not positive. Th ey may be frustrated because they cannot ignore facts, thoughts, and feelings that involve failure, errors, pain, and various forms of suff ering that they may feel are off -limits in the process. If they speak of defi cit situations, facilitators could see them as resisters that have to be managed. Work- ing through this is a signifi cant challenge for practitioners of AI, and avoiding the challenge can be a missed opportunity (Bushe, 2007).

Below is an example of an evaluation team struggling with the assumption of a participant who believed she and her colleagues were forbidden to speak about defi cits in the evaluation process.

My colleague and I were engaged to conduct an evaluation of the Tenant Participation System (TPS) in a large, urban social housing corporation (McGuire,

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2006). Aft er preliminary preparation work, we provided an introduction to AI and led an opening exercise with a group of 50 or so tenants to focus and plan the evaluation. Many of these tenants, an equal number of men and women, had been elected Tenant Representatives who asked questions for clarifi cation about AI and voiced support for the approach.

Aft er about 45 minutes, a woman from the group pulled us aside. She was obviously enraged and literally spitting. She loudly yelled that this AI method fo- cusing on the positive would not get to the truth of their situation. To get the full story, she said, “You have to look at all the problems: bedbugs, fi lth, unfair elec- tions of representatives, extreme heat in the summer, extreme cold in the winter, theft , violence, mental illness, crack houses, broken appliances, and unreliable elevators. No one will fi nd anything positive to talk about!” She described the TPS as a sham that could do nothing to address the real problems she had outlined. She added that she didn’t believe we were there to listen to the tenants and were under the control of insensitive bureaucrats in the corporation.

We were shocked and a bit frightened at the depth of her anger. We explained that we were engaged to look at the role and scope of the TPS, but not specifi cally at all the problems of the tenants living in social housing. Acknowledging the distressing situation and her anger, I added that we would engage as many tenants as possible, start with what is working well in the TPS, and listen for what people really wanted in the future. We pointed out that, even as we would focus initially on what was working well, problems would be identifi ed in the conversations and hopefully the process would help to generate useful ideas for making the overall system better. Th is defence of the AI approach and explanation of what we would do further infuriated her. In our haste to explain the approach, we had failed her test miserably. We had mistakenly left her with the impression that any negative talk was still forbidden!

A few minutes later, she started again in a high-pitched, rapid-fi re voice, “Th is evaluation won’t lead to any changes. You won’t even know what needs to change! Nothing works in the TPS. Th ey do nothing to make things better, and you aren’t even going to let us talk about it. What a waste of time!” With that she stormed off out of the room, leaving other participants and our team perplexed. How could we take into account her input and not let it be the focus of the engagement?

We were puzzled because the critic had been part of the introduction to the approach and initial briefi ngs. She had been quiet then reacted strongly, emotion- ally, and with conviction against the idea of evaluating what might be “working” in the system that she had determined was hopeless and horrible. Still, we realized she may have spoken for many of her peers, even if it was based on misunder- standing the purpose, the process, and the possibilities. To her, the focus of the evaluation had to be on “bad,” and no “good” could be acknowledged. We were seen as stifl ing open dialogue and leaving no room for debate about the extent of “badness” and what needed to be corrected. In her view, our approach was a sell- out to the housing authority and all that didn’t work. Upon refl ection, we recog- nized that we were fearful of the “shadow” and needed to learn how to manage it.

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Despite being extreme, the passion of her opposition made us stop, think, and regroup. It was common knowledge that, despite all eff orts, some of the housing conditions were terrible and life in this situation was intolerable for tenants and staff . We knew that, despite some hope, there was a lot of dissatisfaction with the TPS and many were resigned to continuation of what didn’t work. However, it was our task to evaluate this system in a way that engaged a signifi cant number of people aff ected by it. It wasn’t just that we didn’t have the right words or sell- ing proposition to convince her. Even our best persuasion skills and reframing strategies were not going to have any impact with this critic. She had likely given up on the possibility of change long before our encounter. We had not specifi cally disallowed talk about problems, but she couldn’t see any merit in trying to identify value by looking at what works or what was positive. Further, she believed our motives were suspect. We agreed that we would have to address her issues in some way to make the evaluation process useful.

With the best intentions, some practitioners have presented the AI process as having no room for negativity. A common concern has been that delving into negativity tends to lead to more negativity and hopelessness. Some practitioners have erroneously led participants to believe that defi cit issues are undiscussable in AI initiatives. However, push-back on a positive-only focus in Appreciative Inquiry has been an area of tension that practitioner-scholars have described as a struggle with the dark side or “shadow” (Bushe, 2010, 2012; Fitzgerald & Oliver, 2006; Fitzgerald, Oliver, & Hoxsey, 2010; Hoxsey, 2012; Johnson, 2013; Kolodziejski, 2004).

Cooperrider (2012) suggests that it is necessary to recognize and reverse a common 80/20 defi cit bias that pervades our culture and most organizations. Th e AI strategy is to ensure that defi cits do not monopolize the process and therefore to reverse the bias to 80/20 positivity. Taking this into account in the TPS evaluation, AI was used to engage hundreds of stakeholders, focus the evalu- ation, develop key questions, reframe issues, generate potential solutions, and put the evaluation plan together. Interviews, surveys, and public meetings were conducted using appreciative questions. And yes, defi cit areas, problems, and ob- stacles of the past and present were identifi ed leading to co-created future vision and design of what they believed needed to change in the TPS.

Th e consulting team realized that careful introduction of the AI process is vital to help those who may be unsure of the appropriateness of the approach. It was also necessary to reinforce the commitment of those who thought AI made sense for them. Tenants wanted the evaluation to bring about change and, from this encounter, it was clear that some could even be off ended by the notion that strengths or positives could be used to bring about change. It was necessary to fi nd ways to assure them that their complaints and problems would not be ignored while focusing on “what we want more of ” (Watkins & Mohr, 2001). It was critical to develop appreciative questions and use them to conduct positive interviews and an AI-oriented survey. With these data it was possible to work with subgroups to tell stories and generate new ideas and solutions. In addition, it was necessary to

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accept talk about defi cits and help participants to reframe them toward what they desired in the TPS. To further assist, time was invested in individually coaching TPS representatives and staff of the housing corporation on how to best use the appreciative approach and tools.

Refl ecting further on this experience, though our AI critic did not remain involved, the consulting team was able to help other participants who benefi ted from learning how to reframe and who saw merit in the approach. (Key AI tools such as generativity, appreciative questions, and reframing are described and discussed in the section below.)

In the end, we, with the help of tenants, were able to make recommendations that were valuable to the housing authority and, most importantly, to tenant rep- resentatives. A few examples of recommendations were providing equipment and access to the Internet for tenant representatives; the provision of training to help representatives and housing staff to carry out their roles; agreement to mediation processes to address confl ict between staff and tenants; and establishing a moni- toring system to regularly measure progress toward the objectives of the TPS. Like most evaluations, it did not always go smoothly, though it provided more useful recommendations and outcomes than previous reviews. It demanded a thoughtful combination of evaluation discipline and application of AI processes that tested our team considerably. In many circumstances negatives did seem stronger than positives, and there is still more to be learned about combining evaluation dis- cipline and Appreciative Inquiry to manage this eff ectively. Notwithstanding his commitment to an extreme strength-based approach, Cooperrider’s (2012) advice to AI practitioners to use the 80/20 positivity ratio was helpful in engaging those who cannot ignore negative phenomena in their experience.

The Generativity of Appreciative Inquiry One of the major benefi ts of appreciative inquiry when used in an evaluation process (as well as in other applications) is its capacity to generate new under- standings of problems and even new approaches to instigating enhanced system performance (Bushe, 2007, 2013). For many new to the approach, techniques such as the 4-D model (Watkins & Mohr, 2001) described above or the 4-I cycle (Preskill & Catsambas, 2006) are oft en mistakenly thought of as Appreciative Inquiry. However, it is the collaborative inquiry into the “life-giving forces” or strengths of a system combined with imagining a desired future and co-creating solutions to get there that is closest to the essence of AI.

Two process tools or strategies that contribute to the generative nature of AI and that may be particularly pertinent when used as part of an evaluation approach are appreciative questioning and reframing, which are described below with examples. Appreciative Questions

Aft er defi ning the focus of an inquiry, the starting point of AI is asking—in interviews, surveys, and group work—powerful, positive questions that seek to

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defi ne the “positive core” of a system. Th en, rather than identifying and solving problems, AI concentrates, through co-construction, on imagining and design- ing the future (Avital, Boland, & Cooperrider, 2008; Bright & Cameron, 2009; Cooperrider & Avital, 2004; Th atchenkery, Cooperrider, & Avital, 2010). As both a highly participatory, inquiry-based process and a philosophy (Martinetz, 2002), AI is grounded in the belief that the intervention into any human system will move the system in the direction of the fi rst questions that are asked. Th us, in an evaluation using an appreciative framework, the fi rst questions asked would oft en focus on stories of best practices, positive moments, greatest learnings, success- ful processes, and generative partnerships. Th is enables the system to look for its successes and create images of a future built on those positive experiences from the past (Watkins & Mohr, 2001).

Several principles for preparing interview questions that are used in AI ap- plications including evaluation were developed by Cooperrider et al. (2003). AI questions are craft ed to evoke positive images that lead to positive actions. Ques- tions begin with a positive preface and plant the seed of what is to be studied, whether in a change management process, strategic planning, evaluation, or any of the other AI applications. Th ere are two parts to each question:

• Th e fi rst part must be designed

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