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The Value of Appreciative Inquiry in the Monitoring & Evaluation, Reporting and Learning Space

By March 5, 2020March 6th, 2020No Comments
Appreciative inquiry value

The evaluation space can be a tricky one to navigate, especially considering that making evidence-based judgements about the merit or worth of programmes, what works and what does not work, is an integral part of the evaluation.

Development Works Changemakers (DWC) has been providing Monitoring & Evaluation (M&E) support and capacity development to a non-profit organisation working in the basic education space since 2018. This organisation wanted to expand its M&E system to also incorporate reporting and learning.

We recently introduced Appreciative Inquiry (AI) to assist them to build on the positive core of their existing reporting practice and to track and magnify that into an improving reporting practice in 2020, as part of moving from a traditional monitoring and evaluation (M&E) system, to a monitoring, evaluation reporting and learning (MERL) system.

Understanding Appreciative Inquiry

Appreciative Inquiry is a useful and interesting approach to create positive energy regarding reporting, by focusing on what works. The methodology focuses on what works best, but also identifies areas that need attention, or could be improved.

It can’t be used in every circumstance – but it is a great tool that can be very useful in certain situations. DWC has used AI to activate organisational change processes related to MERL (as in the example provided above);  to supplement Theory of Change (ToC) workshops, and to elicit data from different perspectives during evaluation processes.

What is Appreciative Inquiry?

AI is an action-research methodology that enables organisations to co-construct their desired future, and which focuses on the positive qualities of an organization. These positive qualities are leveraged to enhance the organization. AI is founded on 8 key principles, namely:

  1. Constructionist – Understanding a reality that is socially constructed through language and conversations
  2. Simultaneity – Inquiries create an intervention and initiate change
  3. Poetic – Organizations are an endless source of study and learning which constantly shapes the world as we know it
  4. Anticipatory – Using a hopeful image to inspire action
  5. Positive  – Believing that positive questions lead to positive change
  6. Wholeness – Bringing out the best in people and organizations to stimulate creativity and build collective capacity
  7. Enactment – Starting the process of positive change with self as a living model of the future
  8. Free choice – Believing that free choice liberates power and brings about enhanced results

Source of principles: Sideways Thoughts

Using AI in evaluations

AI was developed as an organizational change methodology but has been adapted to be used in evaluations. In the evaluation community, Appreciative Inquiry (AI) is at best not widely accepted, and is sometimes even frowned upon. However, it does offer a different approach that adds a unique value.

What evaluators have been doing for the past few decades is to focus on the judgment aspect of evaluation. What distinguishes evaluation from other applied social research is that it has to make a judgment on the merit or worth of programmes and projects.

Each case is unique and AI is not suitable for use in all evaluations.  Care must be given to the nature of the task at hand, and what other methodologies are being used in conjunction.

It should also be noted that AI is not an evaluation approach, and does not feature as an evaluation theory. It is merely a tool that can be used for data collection and process facilitation.

When does Appreciative Inquiry work?

As mentioned above, AI can work where energy is required to move processes forward. It could also be used in evaluations. AI works well in a context where a project or programme is not working so well. In such situations, project or programme stakeholders may become defensive when evaluators are appointed, as they anticipate negative judgement. The idea that our questions have the power to shape reality may be a frightening thought, but one worth exploring.

This may impede the openness of stakeholders, which makes it difficult to learn from failures or challenges. AI provides a non-threatening environment in which stakeholders can discuss a project without fear of judgement. By starting off with the identification of what works, a safe environment is provided to also discuss what does not work so well.

Understanding the approach

The underlying philosophy for AI is that what we focus our attention on in the social world will grow and develop. If we focus on the positive, the positive will grow and multiply, but if we focus on the negative, that will thrive instead.

This means that if we follow a problem-centred approach, we get stuck in the misfortune of the problem. The more we try to fix it, the more it grows.

Well, let’s be fair – sometimes problem-solving works, but how many problems did development initiatives (mostly based on a problem or deficit analysis) manage to solve over the past 50 or more years?

There are some conflicting opinions that speculate that you can’t just look at the positives – what about the negatives? In many ways, this concern is valid, and in others, it highlights how AI can be misunderstood.

AI does look at the negatives but in a different way so that it doesn’t dominate the conversation. The negatives/challenges get lifted out but in a more constructive way without pulling the energy down.

Steps in the AI process

In the monitoring and evaluation space, AI could be used as a fully-fledged AI process, or part of it could be used. The AI process is described in terms of the 4-D or the 5-D or 5-I models. These models can also be linked up to a planning process, which consists of some elements of the traditional SWOT planning process. SWOT planning looks at strengths, weaknesses, opportunities and threats. The SOAR process considers strengths and opportunities, and works with that, to develop aspirations, and articulate desired results.

Evidence that supports AI

Through a remarkable body of research, neuroscience has established that we affect people either positively or negatively by the way in which we engage with them and the way they perceive us (also as evaluators).

Prominent neuroscientist Evan Gordon (2000) reminds us that the “avoid danger and maximize reward” principle is an over-arching organizing principle in the brain, and translates in the approach-avoid response.

When our brain tags a stimulus as “good,” we engage in the stimulus (approach), and when our brain tags a stimulus as “bad,” we will disengage from it (avoid). Translated into the evaluation space, this means that if our evaluation processes are perceived as threatening by stakeholders, they may well disengage.

We also know that when people are “seen, heard and loved”, the associated surge in brain chemicals enable them to think better and creatively (connecting behaviour, or approach). Conversely, when people feel that they are criticized, judged and dismissed, their brains literally shut down, as they go into flight mode (avoiding behaviour, or disengagement).

The power of AI

There is a wealth of evidence that shows the power of our words. When athletes use positive imaging and words to tap into their potential to perform at their best, we think it is extraordinary. Why then, do we hesitate to use the same approach to propel our projects and organisations to perform at their best?

Can we as evaluators find a way of using generative questions to tap into what works, so that we can learn from it and amplify it?

The power of questions is aptly described by Browne (2008) who pointed out that every question has a direction, and because of the direction of the question it either carries generative or destructive energy.

AI is interested in generative questions – those that “build a bridge” or “turn on a light”. The rationale for AI is that if we pose provocative questions that discover the positive core of a project or programme, we can multiply and magnify what works.

By doing this tracking and fanning, we focus our energy on what works, and this creates the energy for the programme to grow in that positive direction.

Final Thoughts

Essentially AI promises a lot of potential, especially when used appropriately. When you identify what works and amplify it, great changes can be implemented.

AI is underpinned by a relational and conversational approach to human systems. This approach pays attention to the patterns in the system and the expressive relationship between the elements of the system.

Human systems are living systems, and in these systems patterns of belief; communication; action and reaction; sense-making and emotion; are important – these are the things that “give life” to the system.

At DWC, we specialize in a variety of methodologies and creative approaches. We will adjust and customise each approach depending on each organization’s specific needs, expectations and other contextual factors.  To find out more about how we can help your organization to measure, evaluate, shape and create positive change in a powerful way, contact Lindy Briginshaw (lindy@dwchangemakers.com).

By Fia van Rensburg