Action Research, Action Learning, and Appreciative Inquiry: Interventions That Build Individual and Group Capacity for EBOCD

Action Research, Action Learning, and Appreciative Inquiry: Interventions That Build Individual and Group Capacity for EBOCD

Karen E. Watkins (University of Georgia, USA), Victoria J. Marsick (Teachers College, Columbia University, USA) and Ilene Wasserman (ICW Consulting Group, USA)
DOI: 10.4018/978-1-5225-6155-2.ch004

Abstract

This chapter introduces Action Research (AR), Action Learning (AL), and Appreciative Inquiry (AI) as three evidence-based intervention approaches to support learning and change for individuals, groups, and larger systems. The authors show that, despite differences, all three approaches share intellectual roots and participatory learning strategies. Each approach privileges relevance for local knowledge; hence, knowledge is defined in local terms. What is particularly powerful about these approaches is that they have embedded processes, tested over time, that facilitate transformation and that reliably support local knowledge generation along with insight into conditions and system dynamics that improve results. From the perspective of organization development (OD), the authors rely on these approaches as particularly effective because they operate at multiple levels: they change individuals, groups, and systems. These multi-faceted, systemic evidence-based approaches are very powerful OD strategies that simultaneously deepen individual and organizational learning and growth.
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Introduction

This chapter introduces Action Research (AR), Action Learning (AL), and Appreciative Inquiry (AI) as three evidence-based intervention approaches to support learning and change for individuals, groups, and larger systems. Interventions, as used in this chapter, refers to human engagement intended to foster and support change in a system. Multiple stakeholders in the system intentionally gather, interpret, and use evidence to more deeply understand and alter the situation as initially defined; and then monitor results. They use their learning to engage in subsequent cycles of data gathering, interpretation, and further action. Steps taken to learn from past experience and/or research—central to evidence-based work—vary, but are usually taken. Lessons learned may or may not then be recorded in articles, videos, blogs, knowledge management centers, or other communication.

In this chapter, the authors describe the nature of data-based interventions commonly used in AR, AL and AI. They draw on their research, learning and facilitation experiences to illustrate these approaches. Volumes have been written about each approach. Here the authors focus on how each approach uses evidence to guide and stimulate change. Throughout, they compare differences among approaches, and discuss how they contribute to evidence-based initiatives in the field of organizational change and development (OCD).

The authors show that, despite differences, all three approaches share intellectual roots and participatory learning strategies. Each approach privileges relevance for local knowledge; hence, knowledge is defined in local terms. Knowledge is often inter-subjective and perceptual, and focused on change in particular contexts. Torbert (2004) describes this as first-person (intrapersonal) and second person (interpersonal/group) knowing; but such local knowledge can but does not always generalize to what Torbert calls third-person knowledge about causality or effectiveness—which is the Holy Grail of much evidence-based work.

To what, then, does improvement of effectiveness refer when using AR, AL, or AI interventions? The authors use the following definition of evidence-based management (EBMgt) from Briner, Denyer, and Rousseau (2009) to guide their interpretation in this context:

Evidence-based management is about making decisions through the conscientious, explicit, and judicious use of four sources of information: practitioner expertise and judgment, evidence from the local context, a critical evaluation of the best available research evidence, and the perspectives of those people who might be affected by the decision.

In medicine, an early pioneer of evidence-based work, the aim is to enable improvement of interventions themselves; what Torbert (2004) would describe as third-person knowledge that applies in many circumstances. As the authors argue here, AR, AL and AI contribute to third-person knowing in two ways: 1) insight into contextual conditions that influence effectiveness when applying / adapting what has been learned in other settings; 2) knowledge-based, tested processes, tools and strategies that adopters can use to be more successful in improving implementation to get desired results.

As will be argued throughout this chapter, choices made about actions taken via these approaches are guided by a collaboratively developed “theory of change” (The Evaluation Forum, 2003)—hunches or expectations that are more or less explicitly laid out about a logical chain of ‘if-then’ results that lead to desired improvement that are “tested” in action and modified over time. Factors influencing decisions include: unique context, levels of complexity or scale, number of stakeholders and their concerns, as well as background and training of facilitators.

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