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Inquiring Appreciatively in Albuquerque—Part 2
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By: Kim Hansen
In Part 1 of this article, author Kim Hansen briefly introduced the methodology and underlying rationale of the Appreciative Inquiry (AI) approach. In this second part, she examines its use in evaluation and performance improvement of organizations.
Evaluate: 1.) To ascertain or fix the value or worth of. 2.) To examine and judge carefully; appraise.
Evaluation is used to improve performance in organizations by identifying problems to develop solutions, or to answer critical organizational questions. Evaluation tools are created and chosen to bring awareness to the perceived obstacles, issues, and concerns, which limit the systems optimum performance. As instructional designers we are taught to objectively assess needs and problems to plan our interventions, filling the “gaps” between the skill or knowledge deficit, and the needed training. When we evaluate training we are determining our ability to have accurately identified the problem, and addressed it’s cause. How does this traditional process mesh with the Appreciative Inquiry (AI) approach?
Evaluators may effectively use AI to identify performance issues to understand, duplicate and enhance high functioning areas and encourage this success in other parts of the system. AI adherents use methods of pre-phrasing questions when interviewing stakeholders, to promote positive language, creative thinking and imagery. They then facilitate the design and development of a mutually agreed upon plan of action, evaluating how the plan effects the whole system.
How does the evaluation process impact an organization?
We could view the 9/11 hearings to see various examples of negative reactions
to the inquiry of “what went wrong”. Is this process likely to bring
about positive change, promote communication between departments, and create
a broader awareness, or is it likely to create continued inter agency conflict,
disfunctionality, and further weaken already broken organizations? AI proposes
that a more functional method of examining and evaluating systems would be to
phrase questions to promote a clearer understanding of how to create stronger
interdepartmental information networks. AI would study examples of where these
government agencies are working well together, recognizing good examples of
coordination and sharing of information. In doing so they would ask questions
to give departments an opportunity to articulate what they would envision as
a high quality system, and what their role would be within it. AI assumes that
most people in an organization are aware of its problems, and potential solutions.
Workers want to believe that they add value through their work, and that their
time spent is meaningful. AI‘s methods promote this belief through phrasing
inquiry in ways that articulate what people value, appreciate and so may further
develop within their organizations.
Dr. Hallie Preskill, from the University of New Mexico, and Prism Evaluation Consulting Services (www.evalutionpractice.com) was a guest speaker on evaluation at the New Mexico AI conference. I talked with her recently about the ways she uses AI in evaluation with her clients:
She states that all these approaches emphasize what is already working in the organization being evaluated. To create the future people want, they should consider what has worked well, been effective, satisfying, and useful in the past. Sometimes it is important to give an example of how a typical problem solving approach to an issue has not succeeded in solving the problem, reflecting on how AI might shed new light on the issue, to create new approaches and possibilities. Thus evaluation acknowledges that we might have more to learn from exploring when, how, and why, things have worked well, if that is what we want more of.
Most existing evaluation tools are by definition problem focused; therefore I asked Dr. Preskill how she alters data collection instruments to correspond to the AI methodology. She answered that she designs her interview and survey questions for each evaluation she performs by looking at each unique situation, to see how it would respond to the AI methods. She first asks, “what is the purpose” of my evaluation. Depending on the organization’s needs, she collaboratively designs the evaluation to discover, and build upon the healthiest aspects of that system.
She states in her articles that, “often the language of evaluation is deficit based,” given this traditional approach I wondered how one would rephrase the questions in evaluation. She responded that language is very much at the heart of the AI approach. AI questions are a good model for how one can focus a problem-based question to a more open-ended positive inquiry. For example, if we wanted to understand the ways and extent to which collaboration is working in an organization, typical questions might be:
Using an AI approach, the questions might be rephrased like this:
Lastly I asked Dr. Preskill about her experience using AI in evaluations with high conflict/stressed systems. She finds that AI evaluations work very well in these situations, because people are able to refocus their energies toward strengths, successes, and times they were effective, thus leading them to develop new understandings and appreciations for others. During the interview, process people pair up to talk and begin to question their assumptions about each other, begin to understand where the other is “coming from”, and very often discover something positive about the other. This conversation can lead to a foundation of mutual respect, from which they find common ground to work with one another more effectively. This communication must be maintained through building a culture change, modeling through leadership, using communication models, and a reward system, which recognizes performance improvement
“I had always thought we used language to describe the world – now I was seeing that is not the case. To the contrary, it is through language that we create the world.” —Joseph Jaworski
The use of language is crucial to facilitating the Appreciative Inquiry approach in organizational development and evaluation work. Rather than assuming the traditional position of objective observer, the evaluator uses the language of the inquiry to intentionally lead stakeholders toward mutually developed positive plans of action. Participants who’s habit is to air grievances, and attach blame could find this positive prompting to be irritating, stifling their need to complain, or air past grievances. My own experience in the conference was that this new language could feel forced, and awkward at times, though problems with clichés and jargon is a common hazard in any organizational development work. However, the discipline to frame communication within the boundaries of appreciating success, and opening up future possibilities also felt refreshing and hopeful. Intuitively I think it’s accurate that we create the reality we live in through how we perceive it, and transfer this to others through our conversations. If the communication holds a reminder to care for and inspire the systems around us, and we use these tools in an honest manner to grow, rather than stifle our conversations, then it seems incorporating these tools would benefit most interactions and interventions.
There are a number of case studies on AI and ROI, found in the AI commons site
http://appreciativeinquiry.cwru.edu/
A case study at the local Hunter Douglas Company may be found at this link.
http://ai.cwru.edu/intro/bestcasesDetail.cfm?coid=209
In summary, AI can be an effective evaluation tool for organizations to improve and enhance performance through examining success, rather than problems. While an aspect of evaluation is to objectively gather, analyze and interpret data, through the AI facilitation process it becomes a dynamic method of positively impacting a system to stimulate creative thinking and planning and so enhance performance. As an organizational development tool it complements much visionary thinking as a way to understand and work with our complex human systems. Perhaps in our professional toolbox we need a new broader definition of evaluation:
"Evaluation is a process for enhancing knowledge and decision-making within organizations and communities. It involves answering questions and/or addressing issues through the collection and analysis of information about programs, systems, processes, procedures, products, and services. Evaluation is best implemented as a systematic process that is planned and purposeful, and with a clear intention of using the evaluation findings. Evaluation is a means for understanding what we do and the effects of our actions in the context of the work environment and the society in which we live.” —Hallie Preskill http://www.evaluationpractice.com/evaluation.htm
For further information about AI and evaluation:
Preskill, H. & Coghlan, A. (2003). Evaluation and Appreciative Inquiry.
New Directions for Evaluation, Vol. 100. San Francisco: Jossey-Bass.
http://www.evaluationpractice.com/pubs.htm
For general information about Appreciative Inquiry please see:
http://www.taosinstitute.com
Kim Hansen is an Instructional Designer who develops online programmed instruction, interactive simulations, e-learning and presentation materials for technology and soft skill training. She works with clients to develop and enhance instruction through blending innovative technologies, identifying measurable objectives, and creating well designed assessments and evaluations. Her Masters degree is in Education and Information Learning Technologies, and she consults with clients within corporate, science, manufacturing, healthcare, and higher education environments.
You may see more of her work at Transformative Designs, where she specializes in transforming complex, abstract information, into clear, elegant, instruction.
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