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Appreciative inquiry (AI) is a deliberate search for the positive core of an individual or collective system. It rests on a belief that there is something that works in every system. This goodness can be identified and drawn out. AI, then, is an inquiry into what is valued and good about the individual or collective system. It generally employs a four-phase process of discovery, dream, design, and destiny. The discovery phase is focused on identifying what already exists in the system that is good. Once that is identified, it is possible to imagine an even better system, which is the dream phase. Creating an infrastructure to support this ideal system is the work that takes place during the design phase. As the new system comes into being, it must be maintained and sustained in such a way that its affirmative capacity is continuously strengthened; this is the destiny phase. As the system lives its destiny, a new cycle begins through another inquiry into what makes it good.

The name most closely associated with AI is that of David Cooperrider, a professor of organizational behavior at Case Western Reserve University. As a doctoral student, Cooperrider questioned the wisdom of the problem-solving mentality with its focus on diagnosing what is wrong. He proposed a different approach, now referred to as appreciative inquiry.

Appreciative Inquiry Described

Foundational to AI is a social construction philosophy. A working assumption of AI is that systems (even individual ones) are socially constructed. They are constructed by and through the influence of people. Thus, they are open to change. What has been constructed can be reconstructed. Regardless of its history, any system can be altered. AI uses the positive past history of the system to direct its future. Images of the future are grounded in the system's past positive history, making the image ideal yet within the realm of possibility.

As an approach to research, AI has been most closely associated with action research, case study, narrative, portraiture, and evaluation methods. In practice, this approach seeks to conduct research that begins with a stance of appreciation yielding useful and provocative data (generating more curiosity) and that is collaborative (recognizing the line between researcher and researched is a fine one). AI relies on collecting data through conducting interviews, making it well suited to qualitative research methods.

Appreciative Inquiry Questions

A hallmark of AI is the positive orientation of the interview questions. The questions asked determine the information received. The information received is used to form conclusions and recommendations. Thus, it could be said that the study is only as good as the questions asked. With its emphasis on the social construction of (individual or collective) systems, the supposition is that the questions asked will effect change in the system. When an inquiry is conducted with a spirit of appreciation, the valued factors and forces in the system are affirmed and illuminated. These factors and forces can be used to guide the future direction of the system.

In AI, a fair amount of time is devoted to crafting “good” questions—those that use positive language, are posed as an invitation, are phrased in sometimes ambiguous conversational language, and evoke storytelling about peak experiences. There is an assumption that people carry around with them several stories, some of which are positive. Questions are designed to elicit these positive stories. Four primary types of questions are asked: deep story, value, core factors, and future or miracle.

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