Doing ‘strengths-based’ research: Appreciative Inquiry in a probation setting
| Author | Fergus McNeill,Camilla Priede,Gwen Robinson,Joanna Shapland,Stephen Farrall |
| Published date | 01 February 2013 |
| DOI | 10.1177/1748895812445621 |
| Date | 01 February 2013 |
| Subject Matter | Articles |
Criminology & Criminal Justice
13(1) 3 –20
© The Author(s) 2012
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DOI: 10.1177/1748895812445621
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Doing ‘strengths-based’
research: Appreciative Inquiry
in a probation setting
Gwen Robinson, Camilla Priede,
Stephen Farrall and Joanna Shapland
University of Sheffield, UK
Fergus McNeill
University of Glasgow, UK
Abstract
This article considers the application of Appreciative Inquiry (AI) as a research methodology in
the field of probation research. Although AI has previously been used in prisons research it has
not to date been applied to research on probation. In this article we describe why and how AI
was applied in an exploratory study of ‘quality’ in probation practice. The article includes some
reflections from us as researchers and from the participants in our study (staff in three English
Probation Trusts). It is argued not only that AI served our project well (in terms of furnishing us
with a wealth of relevant, good quality data) but also that our choice of methodology rendered
visible aspects of contemporary probation culture which, we believe, would have remained
hidden had we not chosen to explore quality through an ‘appreciative’ lens. It is further argued
that in organizations experiencing challenging times, an appreciative stance has ethical as well
as instrumental advantages. There are, thus, both instrumental and normative rationales for
recommending AI as a suitable approach in probation research.
Keywords
Appreciative Inquiry, methodology, probation, quality, research
Introduction
As a research methodology, Appreciative Inquiry (AI) is still in its infancy, but it has
recently come to the attention of researchers in the criminal justice field via the
Corresponding author:
Gwen Robinson, University of Sheffield, School of Law, Bartolome House, Winter Street, S3 7ND, UK
Email: G.J.Robinson@sheffield.ac.uk
445621CRJ13110.1177/1748895812445621Robinson et al.Criminology & Criminal Justice
2012
Article
4 Criminology & Criminal Justice 13(1)
innovative work of Alison Liebling (2004; Liebling et al., 1999, 2001) in the prisons
context. The last decade has also seen the publication of a number of studies in health
and social care settings which have adopted and described the use of AI (e.g. Carter,
2006), as well as a small number of other studies in the criminal justice arena, both in
the UK and elsewhere (e.g. Cowburn et al., 2010; Fischer et al., 2007). In this article
we discuss the design and conduct of the first research study to deploy the methodol-
ogy of AI in a probation setting. The study in question was funded by the National
Offender Management Service (NOMS) under the aegis of its Offender Engagement
Programme,1 and it set out to explore understandings of ‘quality’ in one-to-one
offender supervision2 from the perspectives of a range of probation staff. In the course
of this research we interviewed over 100 members of probation staff in three Probation
Trusts, by means of both individual and focus group interviews. In this article our
focus is methodological: we do not present the substantive findings of our research in
respect of participants’ perspectives on ‘quality’ in probation practice.3 Rather, we
outline the choices and challenges which confronted us in designing and conducting
the research, and reflect on the experience of research using an ‘appreciative’ approach
from the perspectives of both researchers and participants. We draw in particular upon
data gathered in a part of our research interviews dedicated to ‘debriefing’ partici-
pants, and which included an opportunity for them to reflect on the experience of
taking part in an ‘appreciative’ interview. These data represent a novelty in the AI
literature, where reflective accounts are not uncommon from the researcher’s per-
spective, but where participants’ reactions tend to be reported anecdotally, and rarely
in their own words.
Appreciative Inquiry and Its Applications: A Brief
Overview
Appreciative Inquiry is a methodology which has been applied in a variety of contexts,
but which is best known for its applications in organizational settings. Liebling (2004)
explains that while much of the AI literature describes its use as a ‘mode of transforma-
tion’, designed to effect organizational change (e.g. Cooperrider and Srivastra, 1987;
Elliott, 1999; Ludema et al., 2001), AI can and has also been applied in a more limited
way: that is as a ‘mode of inquiry’, oriented towards understanding organizations and
their practices rather than (necessarily) setting out to change them. It is in this latter sense
that AI bears a closer resemblance to ‘traditional’ conceptions of research activity;
although in its original guise as a mode of transformation, AI has been characterized as a
form of action research (e.g. Ludema et al., 2001). What is distinctive about AI, in either
mode, is the particular focus it adopts when examining and thinking about the social
world – a focus which might be characterized as relentlessly positive. Thus, AI is
described by Liebling (2004: 132, 133) as ‘a focus on best experiences … a way of look-
ing at an organization, which concentrates on strengths, accomplishments, best practices,
and peak moments’. A key feature of AI is that it does not set out with a problem orienta-
tion and then seek to ‘fix’ the problems that are found. As Carter (2006: 50) has explained,
AI ‘focuses on what is good, strong, already working and being achieved … AI researches
aim to find out “what’s right” and help “enhance it”.’
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