Applying Appreciative Inquiry Instead of Problem-Solving Techniques to Facilitate Change
David L. Luechauer
That organizations need to change is not news to human resources (HR) professionals, organizational development (OD) practitioners, or managers. However, it may come as a surprise to learn that the very processes by which change initiatives are developed, facilitated, and evaluated needs to be changed. This article presents a postmodern theory of change known as appreciative inquiry (AI) as an alternative to the problem-solving approach that is most commonly applied in organizational change initiatives. The roots and fundamental tenets of each approach are briefly described and juxtaposed. The approaches are operationalized via relevant case examples. The article concludes by discussing alternative uses for appreciative inquiry and some words of caution about potential misuses of AI.
Is a glass containing half its capacity nearly full or nearly empty? This article suggests that many human resource (HR) professionals, organizational development (OD) practitioners, and managers (hereafter referred to as change-agents) see it as half empty because they would adopt a problem-solving approach based in a logical positivist perspective, a perspective based in deterministic cause and effect. Recently, some scholars and practitioners have suggested that problem solving and the logical positivistic orientation inhibit the facilitation of lasting or deep change (Quinn, 1996). They are promoting appreciative inquiry (AI) as an alternative method of facilitating change. AI is based on the assumption that something works or is going well in every “system” we encounter (Bushe, 1995; Hammond, 1999). Therefore, AI encourages change-agents to look for opportunities instead of problems when designing personal, organizational, and societal change initiatives (Bushe, 1995). AI-based interventions offer change-agents a way to help their “clients” identify, replicate, and magnify the structures, systems, or dynamics that are already working instead of focusing on what is broken and how to fix it.
Logical Positivism: The Historical Roots of Change
Logical positivism, also known as logical empiricism, scientific empiricism, and consistent empiricism, is a philosophy of science founded in Vienna during the 1920s by a group of scientists, mathematicians, and philosophers known as the Vienna Circle. The members of this group shared a distaste for metaphysical speculation. They considered the assertions of traditional metaphysics as meaningless because they were impossible to verify in experience. The fundamental tenets of logical positivism gained prominence through the publication of Sir Alfred Ayer’s Language, Truth, and Logic (1936). The core assumption of logical positivism is that a proposition or statement is factually meaningful only if it is verifiable. Thus, logical positivism is aligned with the technique in which most change-agents have been schooled known as the scientific method. Logical positivism assumes that deterministic cause and effect relationships exist. Those relationships can be explored by a neutral, detached, distant, and hands-off examination of the phenomena under study. Logical positivism is focused on both discovery and explanation of an “objective reality” that exists “out there.”
Attention to organizational change emerged from the work of scholars and practitioners in the late 1960s and 1970s who trace their roots to the ideas and premises espoused by the logical positivists. The techniques they explored and recommended are grounded in the scientific method (Bartunek, 1993). The publishing house Addison-Wesley produced a series of popular press books focused on organizational development in 1969 that help ground and frame both OD and organizational change efforts. Beckhard’s (1969) text from that series provides the seminal and most often cited definition of OD as “an effort (1) planned, (2) organization-wide, and (3) managed from the top to (4) increase organization effectiveness and health through (5) planned interventions in the organization’s ‘processes’ using behavioral science knowledge” (p. 9). Hence, OD is a comparatively conservative change strategy and many early OD efforts were simply social psychological experiments adapted to applied settings (Bartunek, 1993). Therefore, historically and still today, many change-agents assume that their purpose is to “solve a problem.” They approach organizational change as an experiment grounded in the premises of logical positivism and apply the techniques of the scientific method or action science to create, implement, and evaluate their efforts (Bartunek, 1993; Bushe, 1995; Cooperrider & Srivastva, 1987).
The Problem with Problem Solving
As a philosophy of science, logical positivism has dominated the academic world, particularly M.B.A. and business related Ph.D. programs, for the past 50 years. Hence, the problem-solving or action science approach is the way most change-agents were trained. In fact, many change-agents gain prominence or develop good reputations by identifying problems and working toward their resolution. Presently we are at a critical juncture in the development and application of change theory and some serious questions need to be raised about our work thus far. Namely, how many problems do change-agents really solve? How much better do organizations function after the change-agents have concluded their experiments? How lasting are the changes they initiated? Limited analysis could suggest that the problem-solving orientation has not generated many creative or lasting solutions (Quinn, 1996). Why hasn’t the problem-solving approach worked?
Problem solving, action science, and the scientific method are all based on the assumptions of logical positivism. A key assumption in logical positivism is that social and psychological reality is something fundamentally stable, enduring, and “out there” (Sussman & Evered, 1978). The stability assumption is no longer tenable in the current business environment. Interventions based on this assumption are likely to have short-run value. Bushe and Pitman (1991) suggest that the problem-solving approach has a number of other unappealing consequences.
First, a problem-solving approach generally requires us to ask what is wrong or what went wrong. The problem-solving approach creates an environment in which “. . . people spend most of their time focusing on what is not working well and they can only do this for so long without becoming demoralized and resigned to being in a problem filled work-place” (Bushe & Pitman, 1991, p. 1). This form of inquiry also reduces the possibility of generating new theories and new images of social reality that might help us transcend current social forms (Bushe, 1995). The frames or assumptions generated by the problem-solving or “what is wrong” approach limit our thinking about what has occurred and why it has occurred (Cooperrider, 1990). It keeps us in the proverbial box. We seek to address such complex issues as maintaining customer satisfaction, low morale, turnover, or decreased quality by asking what is wrong in the existing system rather than questioning the appropriateness or use of the system that produced those results in the first place. Then we ask how can we fix what we have rather than asking how can we accomplish that which we desire to achieve.
A good example of the limits caused by adopting the problem-solving approach is performance evaluations. The first issue is related to framing. To this point, common sense suggests that employees should be evaluated and that the evaluation process should lead to improved performance that is good for the individual and the firm. This frame emanates from a logical positivist and deterministic perspective that although plausible is as yet unverified. Yet, we unquestionably assume that there must be some beneficial causality between performance evaluation and desired personal/organizational outcomes. Then, satisfaction with the performance review process becomes a frequently-discussed issue both in and outside the organization.
Critical issues in the process such as rater consistency, timeliness to completion, and linking the evaluations with key personal and organizational needs are then discussed as “problems” in the popular press, professional associations, and gatherings around the water cooler. This gives rise to the adoption of a problem orientation which causes the evaluation process and subsequent dynamics to be viewed as a very independent, static, and narrow issue rather than as a complex, integrated, and fluid situation. This creates an assumption that the evaluation “problem” can be “solved” with a set of techniques instead of asking deeper questions such as, “Do we have to evaluate employees?” “Is the creation of meaningful evaluation systems possible?” or “What are the benefits of not evaluating employees?”
The problem-solving approach virtually forces us to ask what is wrong (e.g., timeliness) and generate solutions (e.g., create a new form) that stay within current practices and assumptions (e.g., evaluations are good). Ultimately, then, the focus becomes trying to improve a dysfunctional system with incremental changes rather than creating a process that is aligned with critical needs, values, and desires. Unfortunately, those efforts rarely produce much in the way of substantive change in the conduct of the evaluation process, and employees still dread the experience. Looking at other organizational dynamics from this limited perspective is commonplace.
Second, a problem-solving approach to organizational events generates a downward cycle of discussion. To this point, “data collection [a key element of the problem-solving approach] consists of having people discuss, and often display to others, their failings” (Bushe & Pitman, 1991, p. 2). If you tell people they have problems, the most likely response will be blame, denial, defensiveness, anger, or depression. Most people do not like to discuss their problems or to be told that they have a problem. More importantly, they usually resist acknowledging their contribution to the problem. The cause, they almost always will tell you, is “out there somewhere.”
A few years ago I conducted focus groups for a relatively large organization around the issues of low morale, decreased product innovation, and decreased sales. The executives were quick to say that the problem was middle management’s inability to carry out their directives. Middle managers swore the problem was staff and line workers who refused to take ownership or responsibility. The staff and line-workers were convinced that the problem was that the executive committee had no vision and the middle managers had no leadership skills. The problem was everywhere but with the people who were speaking. The sessions concluded with people feeling powerless and they were unwilling and unable to generate ideas on how to improve the situation. The attitude was, “They created this mess, let them fix it!”
Third, as Bushe and Pitman (1991) say, “addressing problems, setting targets and working to accomplish them creates a culture of problem-centered improvement” (p. 2). This mentality frequently leads to a culture best stated by the phrase, “if it ain’t broke, don’t fix it.” Hence, people wait to take action until problems are identified or create pressure on the existing system. The evening news is full of stories about business reacting to situations that could and should have been prevented with a little employee pro-action. Yet, these stories persist because a “wait and see” mentality is at the core of many organizational cultures. This mentality is antithetical to the learning oriented or continuous improvement culture that is necessary for survival in the current business environment.
Fortunately, there is hope for making significant contributions and driving lasting change in organizational settings. The hope lies in AI.
AI: A Brief Theoretical Overview
AI is a term coined by David Cooperrider and Shuresh Srivastva (1987). AI is a method of intervention that attempts to help individuals, groups, organizations, and communities create exciting and new generative images for themselves based on an affirmative understanding of their past and present operation (Bushe, 1998). As a postmodern theory of change, AI has roots to the socio-rationist perspective (Barrett, Thomas, & Hocevar, 1995; Gergen, 1990) rather than logical positivism.
The fundamental tenet of socio-rationism is “that there is little about human development or organizational behavior that is ‘preprogrammed’ or stimulus-bound in any direct physical or biological way” (Cooperrider & Srivastva, 1987, p. 132). Social existence is the result of symbolic interactions beyond deterministic forces. The largely symbolic and interactional nature of social existence is counter to and negates the logical positivistic foundation of social science (Cooperrider & Srivastva, 1987). The socio-rationist perspective would suggest that the postindustrial world is “an unfolding drama of human interaction whose potential seems limited or enhanced primarily by our symbolic capacities for constructing meaningful agreements that allow for the committed enactment of collective life” (Cooperrider & Srivastva, 1987, p. 133). In short, reality is a socially-constructed phenomenon and words are the basic building blocks.
AI is grounded in this perspective and makes the assumption that something works or is going well in every person, group, organization, or society (Hammond, 1999; Hammond & Royal, 1999). Therefore, AI interventions are designed to foster a dialogue that allows clients to identify, magnify, replicate, and build upon the structures, systems, processes, or dynamics that are already working or valuable for them (Hammond, 1999; Hammond & Royal, 1999; Bushe, 1998). Thus, “…as we talk to each other, we are constructing the world we see and think about, and as we change the way we talk, we are changing that world” (Bushe, 1998). Hence, AI would suggest that as we change our conversations from problem-based to opportunity-focused we change the entire dynamic or an interaction and our view of the world. This simple initial change produces multiple positive changes at the second and third order.
Bushe (1995) suggests that AI has three main components. First, an appreciative intervention seeks to discover “the best of” by asking organizational members to recall or recount past and present organizational successes. The success stories may be generic or focused on specific areas of interest (e.g., leadership, team dynamics). Second, the appreciative inquiry asks members to “generate insights” or to develop explanations regarding why those successes occurred. Finally, through the process of inquiry itself, the elements that contribute to exemplary performance are “reinforced and amplified.” “The basic process of appreciative inquiry is to begin with a grounded observation of the ‘best of what is’, then through vision and logic collaboratively articulate ‘what might be’ ensuring the consent of those in the system to ‘what should be” and collectively experimenting with ‘what can be’” (Bushe, 1998, p. 41). The hallmark of successful appreciative inquiries seems to be that the person or system has generated at least one new insight into the elements that contribute to peak performance (Bushe, 1995). That insight is the foundation upon which new ideas and practices can be built.
Through a little preparation, change-agents can become well versed in facilitating AI interventions. AI interventionists should possess certain skills. First, they must be willing to adopt a new paradigm for facilitating change. They must believe that words carry the meanings that create reality and that the words we use to explore reality need to be changed. Second, they must help the “client” to search for what is good in the system instead of focusing the client’s attention on what is broken and wrong. Third, change-agents must be willing and able to help clients generate insights for themselves instead of generating them for the clients. Finally, those who seek to use AI should use their ears and mouth in proportion by listening more than talking. Furthermore, they should use their limited talk time to channel positive energy, to pull the group away from problem and complaint-focused comments, and to help the client identify even small processes that are working. M.B.A. students have been able to facilitate AI discussions in their places of business after they completed a few key readings on the topic and spent a few hours discussing and practicing some techniques such as listening, summarizing, reframing, and channeling energy.
This article does not mean to suggest that AI interventions are easy or require little work. AI is a new paradigm that requires new methods. However, there is an elegance about asking people to discuss what works and how to build upon that which works instead of asking them to focus on the myriad of problems they face. To this end, AI interventions tend to promote an upward and positive cycle of discussion.
Same Facilitator — Different Approaches
My consulting efforts generally revolve around the areas of servant-leadership (see, e.g., Spears, 1997), empowerment (see, e.g., Block, 1987), and developing more enlightened and spiritual organizations (see, e.g., Hawley, 1993). (Servant-leadership, inspired by the work of Robert K. Greenleaf at AT&T, can best be described as teaching managers to serve rather than to expect to be served.) Attempting to achieve those outcomes following a problem-solving approach was straightforward. The first step was to create something, usually a questionnaire, to assess the status quo. Gathering and analyzing the data was the second step. Highlighting areas for concern or problems as measured by the questionnaire or other measure would follow. Finally, recommendations would be generated regarding how to address and improve the areas of concern. Occasionally clients were invited to play various parts in the design, analysis, and recommendation phases of the project. This was an attempt to apply Kurt Lewin’s force field model of identifying, unfreezing, moving, and refreezing the status quo – a very logical positivistic approach to change (Lewin, 1951). The focus was on what caused the problem and how to overcome the barriers to change.
Problem Solving: Failed Efforts and Poor Results
A little reflection suggests that ambivalence was the most promising result or most common reaction to this approach. In those interventions people would listen politely, ask a few requisite questions, promise to do better in the future, and go back to their “real work.” Time would pass and on many occasions a follow-up would reveal that the organization and people involved were scarcely different.
The more common reaction to this approach was significant resistance to my presence, methods, analysis, conclusions, and recommendations. A manager from a commercial lending department at a bank became very antagonistic and sought to debate the merits of an empowerment scale that had been used. The scores for his department were at the bottom of a pretty depressing barrel. At the feedback session, he said the scale was inadequate. He had it on good authority that “his people” were some of the most satisfied and empowered in the bank. I could offer nothing in the way of reliability estimates, validity measures, or experience to sway his opinion that he was “a great manager,” even though the numbers from “his people” suggested otherwise. His antagonism swelled to the point where he left the room uttering “all this touchy-feely stuff had no place in a bank!” Needless to say, he resisted virtually every attempt we made to generate ideas and implement processes to build trust, open up the lines of communication, and increase autonomy.
It did not seem to matter whether or not other people were involved in the process. The reactions to problem-solving interventions were usually: “I don’t agree,” “You don’t understand,” or “We can’t do anything about that.” We would leave such encounters depressed, hopeless, and yearning for a better way. They wanted a better way to work. I wanted a better way to intervene, to facilitate change, to improve the lives of those being served on such assignments. Those objectives were rarely achieved.
AI to the Rescue
An engagement with a group of senior managers at Kenworth of Indianapolis, a large truck dealership, provides an example of how AI-based interventions offer hope for creating meaningful and lasting change. The owner of the company called and asked if I was interested in conducting a day of leadership training for his senior management team. I suggested instead that it might be more appropriate to work with the team on an on-going basis to create a servant-leadership culture. The principles of AI would be used to achieve that objective and the process would take more than a day. We agreed to meet every three weeks for two hours for approximately eight months in their conference room to try this approach.
The process began by having the participants identify the two best “bosses” for whom they had ever worked and to list the attributes and behaviors those bosses exhibited. It took two sessions for the twenty managers to describe their best bosses. They listed such attributes and behaviors as honesty, integrity, listening, “walking the talk,” trusting, and caring. The sessions were enlivened and jovial. They had fun telling their stories. We solicited “conclusions” about their experiences at the end of the second session. Their answers were simple. One said, “I see that it is possible to have and be a ‘great boss’ in the trucking business.” Another said, “This shows me that it is possible to do well by being and doing good!” They left the room laughing and joking about the joys of working for good bosses. Bushe (1995) would likely call this the discovery phase of AI.
The next few sessions were a combination of learning and sharing. The managers read articles that described organizations and people who had successfully adopted and enacted the principles of servant-leadership. An article by TD Industries President Jack Lowe, Jr. (see Spears, 1997) proved to be the most compelling to the group because it chronicled the story of a company in an industry they perceived to be similar to theirs: construction. A key vice-president left that particular session saying, “If construction workers can do this so can truckers.” We then turned our attention to a session entitled, “the proudest of prouds.” The managers were asked to describe things they had done in the past year that were aligned with the principles of servant-leadership as covered in their readings. We heard a story about someone who took the time to pick up trash in the parking lot. Another spoke about personally washing a truck that needed to be prepared for delivery. Another spoke about taking care of a subordinate after surgery. The managers had many stories to tell. Bushe (1995) would likely call this the understanding phase of AI. They emerged from this phase understanding that small and large acts of service were performed each day by virtually every member of the team and that they were at their best when they were serving rather than commanding. One manager wrapped up the meeting by saying, “ I am blessed to work with each of you.”
Our time was concluded by taking two sessions to explore the questions “How can we serve more, and who else do we need to serve?” Ideas flowed, action plans were developed, and many of the managers volunteered to be held accountable for the results. This process allowed us to amplify and reinforce what was to occur at Kenworth.
Almost two years into the process Kenworth is still not a perfect organization. Nonetheless, the difference between the Kenworth AI-based intervention and most of my previous interventions can be summed up in two words — willingness and ability. We “appreciated” what was good about management and Kenworth. As a result, the managers were willing and knew they were able to do even more to improve their operations. Our sessions were not always joyous, but for the most part AI fueled a liberating and spirited dialogue at Kenworth rather than fueling a toxic, blame-oriented, and depressive conversation. Enthusiasm replaced helplessness as they appreciated and built upon that which was already being done well. AI helped the managers to ask what is good and how can we build upon it rather than what is wrong and how can we fix it. They were much more excited about building a better organization than fixing a broken organization. As a result, participation was high, new ideas surfaced, and many others became involved in the process.
AI: Other Uses and Some Words of Caution
Bushe (1998) suggests that there are four situations when a change-agent may wish to apply AI: (1) team-building retreats where the focus is to increase effective relationships, (2) sessions focusing on current issues the group faces, (3) interventions for groups stuck in undisclosed resentment, and (4) resolution of group paradoxes. Hammond & Royal’s (1999) book Lessons From the Field: Applying AI chronicles many other times and places where AI-based interventions have proven useful (e.g., improving community relationships). My own work with AI suggests that it can be used to: (1) generate process or work-flow improvements, (2) assist in family or relational development, (3) facilitate strategic planning initiatives, or (4) identify growth opportunities for “at-risk” employees. Finally, it does not seem untenable to assume that AI can be applied to individuals and interpersonal relationships even though most of the literature focuses upon AI as a group or system intervention.
Bushe (1998) suggests that AI may run the risk of becoming the flavor of the month for change-agents. Therefore, a couple of cautionary statements seem in order about the potential misuses of AI. First, AI becomes corrupted if any intervention that looks at positive aspects of social existence is called AI. In this regard, AI is more than simply asking a few questions or distributing a questionnaire designed to measure what the organization does well. Second, indiscriminant focusing on “appreciation” without any theoretical underpinning may cause short-run benefits that quickly lose momentum or fade once a challenge to the system arises. Hence, short-term zeal and optimism cannot be mistaken for meaningful dialogue and sincere change. Third, organizations and social systems are rife with injustices, structural flaws, and unexpressed resentments. Therefore, promoting appreciation should not be undertaken until those feelings or dynamics have been expressed, exposed, and forgiven. To falsely appreciate would seem to be both impractical and insincere. A new book on change seems to emerge each week in the popular press.
Change-agents need to be aware that the methods espoused and the techniques recommended are useful only to the extent the recipe is followed. Omitting key steps, shortening time allocations, or cutting corners on resource allocations have deleterious impacts on those methods, and the same is true for AI. Hence, this is not a method to try simply because it seems expedient, fashionable, or fun. AI is a new paradigm. To be successful change-agents should prepare themselves for an AI intervention by familiarizing themselves with the readings in the reference list and by being willing to change some of their most deeply-held assumptions about the nature of people, the nature of organizations, and the nature of change.
Reality stems from that upon which we are focused (Gergen, 1990). AI allows change-agents to help their clients focus on the positive activities in which they are already engaged rather than the negatives in which they are embedded. This optimistic focus creates a new reality, a reality that says greatness is within us and we can use that greatness to accomplish even greater things. As a result, organizational events are approached from a more exciting, more fun, and more positive base. People want to work on issues and to work together because they know they have done so in the past.
When you see a half-filled glass you have a choice. Either way, the glass is holding half its capacity. AI lets us say, “Hey, we have got a glass and some water – what can we do with them” instead of complaining that we do not have enough to drink. An increasing literature and experience base suggests that AI is a useful and perhaps necessary technique to foster the type of transformation HR professionals, OD practitioners, and managers have long sought to attain.
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David L. Luechauer, Ph.D. is an associate professor and the leadership area coordinator at Butler University. He is also a management consultant for organizations around the world. Currently his teaching, writing, and consulting efforts are geared toward personal and organizational transformation with a focus on spirituality and servant leadership. firstname.lastname@example.org.