Evaluating Appreciative Inquiry as an Organizational Transformation Tool: An Assessment from Nepal

Human Organization, Winter 2008 by Messerschmidt, Don

A key point in understanding the popularity of Appreciative Inquiry in development is the disdain of many of its practitioners with the common preoccupation with "problems" around which both planning and evaluation often focus. On the one hand, they say, a singular focus on problem solving is limiting and tends to de-energize and cast a dispiriting effect on organization staff, administrators, and other stakeholders. They see problem solving approach as limiting and negative, as it typically begins with identifying key problems/concerns/ issues, then analyzing their causes, designing solutions, and developing action plans. In other words, "The basic assumption of problem solving seems to be that 'organizing-is-aproblem-to-be-solved' ..." (Afful 2001 :7). Though both seek "solution[s] to be embraced" (Ryan et al. 1999), problem solvers typically ask: What are we doing wrong? and How can it be repaired? while AI practitioners typically sidestep (or ignore) problems and ask: What have we done well? and What more can be done? See Table 1 . Put another way, the AI approach seeks to "flip" problems into their "positive opposites" by focusing attention on the exceptions to the problems, then search for and build upon the "root causes" of those exceptional successes.

The existence of "problems," development "breakdowns," and "failure" is not denied in AI but is interpreted and handled differently. As one practitioner told us during the studies: "We turn 'breakdowns' into 'breakthroughs.'" AI practitioners point out that the entrenched problem solving approach to development is based almost entirely on the notion that systems under consideration are in trouble: "broken, not working, not functioning, not having, not existing, not living, not happening"...; i.e., as "injured system[s]" in need of fixing (Tamang 2002:48). Typical problem solvers, they say, perceive reality as fragmented pieces, thus as "failure," to which development agents step in with "solutions" (Körten 1990:143). The problem is that looking only for what is wrong within a system tends to create dependencies and perpetuate inferiority relationships, they point out. (The dichotomy, of course, is too simplistic.)

By contrast, AI practitioners elicit "success" stories and experiences, personal and group narratives often around the opposite or exceptions to the problem; i.e., narratives that energize and provide positive focus and feedback, and which do not deprecate. They seek indigenous knowledge and traditional responses to societal and institutional needs from which to encourage participation and create empowerment. (We agree with these goals but find them one-sided.)

To determine the positive, to discover success, and to encourage local solutions to pressing institutions issues, AI practitioners have developed several innovative strategies and tools. The most popular is the highly structured and formulaic "4-D Cycle." It consists of four steps: "Discovery," "Dream," "Design," and "Destiny" (or "Delivery") (Bushe and Khamisa 2005; Cooperrider and Srivastva 1987; Whitney and Cooperrider 2000). See Figure 1.