Ravalier PhD Theis.pdf - Anglia Ruskin Research Online

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2. Why did it go well Further elaboration on the information from the first question. Again creates positivity
and allows the researcher to begin to understand the mechanisms by which these positive areas can be
transferred to other areas which are not working as well.
3. What didn’t go so well A potentially negative question again framed positively in the overall context of the
logs. This allows the researcher to identify the areas within which potential improvement to employees’
working lives can be made.
4. How could it be improved When combined with the results from the department-wide survey as well as log
questions 1 and 2 an idea as to the most feasible changes is formed, thus creating the lens through which
future organisational adjustments can be made.
The decision to use these four questions as the basis of log responses was drawn from varying literature
sources. The first question, in which individuals are asked to describe certain areas of the workplace that had
worked well for them in the preceding working day, is based on the theoretical underpinning of Appreciative
Inquiry. Indeed, investigating 'what's working well' is the essence of the first step in the AI cycle. The second
question allows further investigation of this first question - a common criticism of daily diary/log approaches is the
potential for gaining limited and superficial answers from participants (Willig, 2008). Therefore having participants
further elaborate on answering from question one can overcome this limitation. Answering of the third question, a
potential negative question framed in an overall positive context, may appear to contravene the positivity principle
of AI. However, Newman and Fitzgerald (2001) claim that focussing solely on the positive is unlikely to culminate
in effective and feasible change outcomes. Indeed Reed and Turner (2005) further argue that addressing
organisational problems are part of the AI picture, but these problems should not be the central focus on any AI