essential-guide-to-qualitative-in-organizational-research

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––––––––––––––––––––––––––––––––––––––––––––––––––––––– COGNITIVE MAPPING–––––––––– 77SurfacingMappingAnalysisInterviewFurtheranalysisResearchaccountFigure 7.3A mapping research processInterviewNotes or transcriptsMapping for data managementMapping for data analysisResearch accountFigure 7.4A mapping research processUSING MAPPING IN QUALITATIVE RESEARCH IN ORGANIZATIONS ––––––––––––––––––––––––––––––––The method presented in this section makes use of the Decision Explorer® software tooperationalize a qualitative approach to organizational research. Here the researcher is nottrying to represent cognition, but rather to manage research data and ideas. The aim of thisresearch project was to understand the effect of ‘slack’ (excess capital, labour, capacity andother types of resources) on innovation in organizations (for example, Nohria and Gulati,1997). The researcher used theoretical sampling techniques (Gummesson, 1991) to identifycompanies that had innovation as a central and crucial goal. Some competed in industries thathave a high rate of change (for example, software) and some represented ultra-high technologysectors (for example, defence). Sampling stopped once theoretical saturation had been reached.In all, six companies were included in the study.Data were collected from each organization through a series of in-depth interviews withkey figures. The interviews were in two parts. The first part was unstructured, allowing themanagers to talk about their immediate concerns related (sometimes loosely) with innovation.The second part was semi-structured, where the interviewer returned to salient parts of thenarrative to explore them in more detail, or asked the managers to reflect on particular issueswhich were prominent in the academic literature, or had been raised in earlier interviews.These interviews were taped, with the permission of the interviewees and then transcribedverbatim.
78 –––––––––– QUALITATIVE METHODS IN ORGANIZATION STUDIES ––––––––––––––––––Mapping for data managementUp to this point, this project was conducted like any other qualitative study. However ratherthan moving straight from the transcripts to a data analysis process, the raw interview data weredeveloped into a series of maps like the one shown in Figure 7.2. The maps that were builtfrom each interview reflected the conversation that took place between the researcher and theinterviewee by depicting the lines of argument. It is important to reiterate that these mapswere not meant strictly to represent the mental model of the interviewee, although the processdoes attempt to retain both the language and the sensemaking of the interviewee.The data is still represented from the interviewee’s point of view at this stage, but mayinclude the voice of the researcher in the conversation that was developed, or issues from theliterature that influenced the formation of the research questions. Equally it may contain thevoices of other people (mentors, dissonant voices, sources of influence) as introduced or‘reported’ by the interviewee. In more ethnographic work, or when using a case studyapproach, it is possible to include observations of company conventions, body language or anyother facets of the conventional research diary. These may be simply woven into the map, orcan be explicitly signalled through the use of different colours or fonts for concepts. What isimportant is that the map can capture the conversation with all of its contradictions,meandering threads, underlying priorities and contextual explanations in a way that linearprose never can. We have moved from a linear narrative of what was said in an interview toan exploration of what was meant by preserving, making explicit and depicting the complexityof an interviewee’s own reasoning.Mapping for data analysisThe maps were then analysed both visually and by using the software’s built-in tools. Whena map is complete, it is often possible to detect distinct clusters of concepts in it (Eden et al.,1992). Sometimes these are obvious through simply looking at the map, but DecisionExplorer® can also perform a ‘cluster analysis’, searching for concepts that are linked togetherin a group but more loosely linked with the rest of the map. Another type of analysis that canhelp to highlight themes in the map is ‘domain analysis’, which searches the map for the mosthighly linked concepts. It counts both arrows leading into and out of each concept and thenranks concepts according to how highly linked they are. Concepts with many links mayrepresent issues that are particularly important to the interviewee or project. They may beconcepts that are very influential in terms of being closely connected to many other issuesdiscussed, be considered ‘pivotal’ in some way, or are simply mentioned by many people.These, and further, analysis techniques are discussed in more detail in Eden and Ackermann(1998).The maps in this study showed, for example that some people returned to particular issuesmany times in the course of their interview, creating densely linked areas in their maps. Otherstold stories, or concentrated on detailing their beliefs about the future, generating linear,hierarchical sections of map. The emerging themes in each map were colour coded and itquickly became obvious which issues were most important to each interviewee and eachorganization. This process also helped to identify the similarities and differences between themaps. These techniques helped to surface a number of themes within the data about the kindsof ‘slack’ that interviewees believed was having an impact on innovation within their
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ESSENTIAL GUIDE TO QUALITATIVE METH
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This book is dedicated to our child
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viii ---------- CONTENTS ----------
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Notes on ContributorsJohn Benington
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AcknowledgementsIt has been 10 year
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2 ---------- QUALITATIVE METHODS IN
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10 ---------- QUALITATIVE METHODS I
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12 ---- Group Methods of Organizati
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13 ---- Participant Observation ---
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15 ---- Critical Research and Analy
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18 ---- Talk-in-interaction/convers
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19 ---- Attributional Coding ------
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20 ---- Grounded Theory in Organiza
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Quote Code in open coding Concept T
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Table 20.2 Examples of open coding,
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Table 20.3Collective stressors and
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21 ---- Using Templates in the Them
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Table 22.1Matrix 1: descriptive sum
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Table 22.1 cont1.1 Analysis by colu
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Table 22.2DebbieAndrewMatrix 2: con
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Table 22.2cont.Allan Petrol scam Wa
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23 ---- Preserving, Sharing and Reu
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25 ---- Ethnography ---------------
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27 ---- Soft Systems Analysis: Refl
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30 ---- The Future Conference -----
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