When I was doing research for my MBA back in the late 1990s (yes, I’m ancient) I came across a peculiar effect that my presence as an interviewer had on participants. With the exception of the Dissertation, none of my MBA assignments required me to do any primary research. But of course I did some. Not very much – just a few questionnaires or a few short interviews to give a bit of colour to the assignment; nothing that could be considered significant or generalisable. Working full time while studying for my MBA and being asked to tailor my assignments to my own work in publishing or to the sector as a whole, I inevitably used friends and colleagues as my sample. Clearly, this is not a great approach to sampling and, as I’ve said, the findings provided a little colour and context rather than robust data. Anyway, aside from my sampling inadequacies, the point is that I knew a lot of the participants and having often discussed their views about many topics pertinent to the industry, I had a fair idea how they would respond.
Imagine my surprise, then, that they suddenly backtracked on all their erstwhile opinions during my interviews. It transpired that they were worried I would go running to their manager and spill the beans about how wasteful and inefficient a particular process was or how employees didn’t feel valued or that the lunch options were too limited. They didn’t trust my promise of confidentiality and anonymity and therefore told untruths. They were quite relaxed about it – I was astonished at how easy it was for them to lie so fluently. Some of the problem was probably a result of poor question design by an inexperienced researcher, but some of it was also because I had approached the various issues as problems that needed investigating and solving.
Once research questions have been problematised in this way, the research can be perceived by participants as looking for someone to blame. If a research project starts with the premise that a particular process is wasteful and inefficient and we need to do something to make it less wasteful and more efficient, might not prospective participants worry that it is their part of the process that is under scrutiny? That the problem is their fault? Sometimes no matter how strong the commitment to anonymity and confidentiality, some participants will not feel comfortable enough to open up completely honestly.
A few years after my MBA I discovered there is an alternative way to approach research questions, such that they are not part of this ‘problematising’ that happens a lot in research. I discovered appreciative inquiry (AI). AI takes the opposite approach – the method starts the research not from the point of problem solving but from a continuous improvement perspective – what’s going well and how can we emulate that elsewhere and use it to improve things? AI celebrates good performance rather than focussing on poor performance and can help motivate people to engage in the research process.
AI was developed by Cooperrider and Srivastva (1987) who felt that the problem-based approach limited the creation of new ideas and of new theory. They suggested that the use of a more positive approach would help to bring about organisational change. The approach consists of four stages (see Figure 1) that may be used in a cycle to bring about change.
Figure 1 The cycle of appreciative Inquiry (based on Shuayb et al., 2009)
The approach may be easily adapted (by using appreciative questioning) for use in action research, focusing on what is good and how that may be amplified, emulated elsewhere and lead to improvement in service, products, communications, etc. The approach uses interviews or focus groups to uncover stories and examples of good practice. The important point is always to start with a question about what is working well or what is good right now.
I’ve seen AI used in two projects I’ve supervised – an MBA dissertation and a PhD thesis – both extremely effectively.
The MBA dissertation was about the selection process for international distributors at a family-run firm. The student (their sales director) felt the process wasn’t as efficient or as effective as it could be and felt uncomfortable researching it, as the staff were all so close-knit and he liked them very much and didn’t want them to feel he was criticising them. So, rather than using the deficit model (there’s something wrong with the way we select business partners), he used AI to interview employees in the UK and at existing international partners about what worked well for them in the partnership experience. Using the AI approach meant that participants did not feel ‘picked on’ for doing something ‘wrong’. Instead they were given the opportunity to talk about what worked well (discovering), what might work even better in the future (dreaming), what the ideal situation would be (designing) and how that could be achieved (delivering). It worked – people really opened up to the student and he was able to recommend some improvements to the process for selecting international partners, which the firm adopted.
The PhD thesis was about key account management and how it had changed and evolved over the years into something based much more on relationships and co-creation of value rather than a transactional sales approach. The student interviewed a large number of key account managers and their buyers. Similar to the MBA student, he didn’t want to approach this as a problem; rather, he wanted to find best practice. The use of AI worked very well and led to a number of important insights (and led to a successful PhD). The researcher, Dr Christian Veasey, noted that the interviews were very comfortable to do, as participants were discussing what was good about their roles and what worked best to keep their client accounts happy, rather than talking about things that went wrong.
It’s an interesting approach that is worth investigating further, if you’ve not heard about it before. I’ve written some more about this in a chapter on using narratives and storytelling in research in a book I co-edited with Dr David Longbottom, Alternative Market Research Methods: Market Sensing. If you’ve used AI in your research or have any comments on it, please do let me know in the reply section below!
Cooperrider, D.L. and Srivastva, S. (1987) ‘Appreciative inquiry in organizational life’. In: Woodman, R. and Pasmore, W. (eds) Research in Organizational Change and Development, Volume 1, Greenwich, CT: JAI Press.
Lawson, A. (2017) ‘Using narrative and storytelling in research’, in: Longbottom, D. and Lawson, A. (eds) Alternative Market Research Methods: Market Sensing, Abingdon: Routledge. (Companion website available here.)
Shuayb, M., Sharp, C., Judkins, M. and Hetherington, M. (2009) Using appreciative inquiry in educational research: possibilities and limitations, Slough: National Foundation for Educational Research.
Veasey, C. (2019) From key account management to strategic partnerships: critical success factors for co-creation of value, unpublished PhD thesis, University of Derby.