Appreciative Inquiry

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Appreciative Inquiry is an approach to organisational change which focuses on strengths rather than on weaknesses - quite different to many approaches to evaluation which focus on deficits and problems.  

"Appreciative Inquiry is about the coevolutionary search for the best in people, their organizations, and the relevant world around them. In its broadest focus, it involves systematic discovery of what gives “life” to a living system when it is most alive, most effective, and most constructively capable in economic, ecological, and human terms. AI involves, in a central way, the art and practice of asking questions that strengthen a system’s capacity to apprehend, anticipate, and heighten positive potential."

(Cooperrider & Whitney 2005, p.3)

Appreciative Inquiry is often presented in terms of a 4 step process around an affirmative topic choice:

1. DISCOVER: What gives life? What is the best?  Appreciating and identifying processes that work well. 

2. DREAM: What might be? What is the world calling for? Envisioning results, and how things might work well in the future.

3. DESIGN: What should be--the ideal? Co-constructing - planning and prioritizing processes that would work well. 

4. DESTINY (or DELIVER): How to empower, learn and adjust/improvise? Sustaining the change

(Source: The 4-D Model was developed by Suresh Srivastva, Ron Fry, and David Cooperrider in 1990 - Appreciative Inquiry Commons - AI Hisory and Timeline. See David Cooperider's website for more information on these stages)

While Appreciative Inquiry has always had an evaluative focus (working out what is working well and seeking to improve performance and conditions), in recent years there have been explicit efforts to embed AI principles and processes in formal evaluation processes:

"Appreciative Inquiry (AI) is a group process that inquires into, identifies and further develops the best of “what is” in organizations in order to create a better future. Often used in the organization development field as an approach to large-scale change, it is a means for addressing issues, challenges, changes and concerns of an organization in ways that builds on the successful, effective and energizing experiences of its members. Underlying AI is a belief that the questions we ask are critical to the world we create." (Preskill & Catsambas 2006 p2)

Resources

Resources suggested by BetterEvaluation members

Appreciative Inquiry Principles: the Anticipatory Principle: blog post by Andy Smith in a series on the principles that underpin AI.

Ray Calabrese's Buckeye Blog; website of Professor Ray Calabrese which includes copies of his journal article on using AI in education and other programmes.

Sources

Preskill H, & Catsambas T T (2006), Reframing Evaluation through Appreciative Inquiry, Thousand Oaks, California. Sage Publications 

Cooperrider, D., & Whitney, D. D. (2005). Appreciative inquiry: A positive revolution in change. Berrett-Koehler Store.

A special thanks to this page's contributors
Contributor
Director , Roberts Evaluation Pty Ltd.
Australia.

Comments

Kate Roberts's picture
Kate Roberts

Appreciative Inquiry is a 4 process which focuses on:
1. DISCOVER: The identification of organizational processes that work well.
2. DREAM: The envisioning of processes that would work well in the future.
3. DESIGN: Planning and prioritizing processes that would work well.
4. DESTINY (or DELIVER): The implementation (execution) of the proposed design

David Cooperrider has long been associated with Appreciative Inquiry.

Patricia Rogers's picture
Patricia Rogers

Thanks, Kate, for these comments. We've updated the page to add this information, and added some more resources which discuss processes and principles of AI.

Anonymous's picture
Joseph Tibyata

Its nice to share from this intellectual and academic work

Anonymous's picture
Vic Taylor

We are interested in using the power of appreciative inquiry to learn how we can reduce food insecurity in our rural community. Given the highly personal nature of the reasons for family hunger we anticipate that the initial interviews will need to be one-on-one. We have been told that there is recording hardware and software that can be used to make common sense of rage interview results. Do you agree that this recorder with software is necessary and, if so, where can we learn more about it?  Thank you

Alice Macfarlan's picture
Alice Macfarlan

Hi Vic,

Thanks for your email. I'm unfamiliar with the hardware/software package that you mention - do you know what this is called? And is it specifically for use with Appreciative Inquiry, or with collecting and analysing qualitative data in general?

I'm not the best person to advise on software if it is specifically tailored to use with AI, but I can talk generally about qualitative analysis tools.

My overall feeling is that whatever tool you use needs to suit you and your team. In terms of hardware, two important components are being able to gather quality audio, and ease in terms of getting the audio off the recording advice - most smart phones have this capability, but some apps are more difficult to export data from, whereas purpose built recorders or cameras will likely have an SD card or similar that makes data transfer easy. I also tend to always record on two devices so I have a backup. I'd also be surprised to find software that couldn't have recordings imported from sources other than their specific hardware - though I could be wrong, transcription packages being one possible exception. I would think that so long as the audio or video was in the right format it would be fine to import, so this would be worth checking before investing in expensive hardware.

For software, there are a lot of options, but what I'd really be thinking about are your needs in terms of amount of data you'll be analysing and what sort of analysis you need to do, as well as your team's capacity - in terms of time and familiarity with various software packages. Often qualitative data analysis software has quite a steep learning curve, and it could be worth allocating some time and possibly budget for training of staff to use whatever package you decide is right for you. For an overview of some specialist tools for qualitative data analysis, see the CAQDAS site at the University of Surrey which compares ten packages including Atlas.Ti, HyperResearch and NVivo. It's also worth noting, that you may not necessarily need to invest time or money in a new software package - in this blog Helen Marshall talks briefly about what members of a Qualitative Interest Group (QIG) use, which ranges from post-it notes to programs like Excel, as well as more specialised software. So again, it's worth thinking about the needs of your team and your project.

Best of luck with this research - I'd be interested in hearing how you go and what you end up using.

Kind regards,

Alice

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