Interview Data Analysis

The data analysis method used is consistent with that used and described in detail by O’Dwyer in his 2004 article “Qualitative Data Analysis: Illuminating a Process for Transforming a ‘Messy’ but ‘Attractive’ ‘Nuisance’”. Qualitative data analysis has been described as an ‘attractive nuisance’, because of the attractiveness of its richness but the difficulty of finding analytic paths through that richness (Miles, 1979; O’Dwyer, 2004).

The interviews ranged from 30-90 minutes each, and yielded in total approximately 170 pages of transcription. In order to transform this data set into a logical and enlightening narrative O’Dwyer (2004) outlined three distinct but overlapping phases of analysis including data reduction, data display and data interpretation.

Data reduction involves interacting with the various analysis tools used in the data collection stage such as interview notes, transcripts and contextual information in order to identify key themes and patterns, and preparing interview summaries (O’Dwyer, 2004). During this stage the transcripts were carefully read whilst listening to the recordings and making notes. The transcripts were then sent to the interviewees for verification and follow-up questions were asked. The interviewees’ responses to the follow-up questions, together with the field and other notes, were then included in the data coded, and the notes from various stages were colour-coded

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for ease of differentiation. Following a second reading of the transcripts the summaries were written, which included the best story and best quote to come out of each interview.

Data display then involves visually displaying the reduced data through detailed matrices encompassing the key themes and patterns. It is during this stage that ‘open’ code matrices are prepared, those ‘open’ codes collapsed into ‘core’ codes and the ‘open’ code matrices then reformulated according to the ‘core’ codes. Data interpretation involves five steps, including a ‘detailed ‘analysis tools’ review’, a ‘big picture outline’, ‘formulating a thick description’, ‘contextualising the thick description’ and ‘employing the analytical lens’ (O’Dwyer, 2004). The ‘detailed ‘analysis tools’ review’ involves conducting a detailed examination of the matrices, revisiting the transcripts, preparing second interview summaries and comparing them to the first to determine any new insights that may have emerged, updating and reviewing notes and questioning whether the evidence could be organised differently. The ‘big picture outline’ involves creating a ‘big picture’ story outline of the interviews in thematic form, collating the outlying perspectives and using them to challenge the ‘big picture’ story, and reviewing the matrices, summaries, notes, contextual information and transcript quotations. ‘Formulating the thick description’ involves writing an initial ‘thick’ description of the findings using the ‘big picture’ story outline whilst embracing complexity, revisiting the transcripts and other evidence as necessary, selecting illustrative quotes, and searching for alternative explanations and ideas. ‘Contextualising the ‘thick’ description’ requires consideration of the contextual information, whether the richness of the data has been fully exposed, and whether detachment from the data has been avoided. Finally, ‘employing the analytical lens’ involves interpreting the descriptive evidence using the analytical themes and constructing a narrative using an iterative process. In this stage the researcher should avoid selectivity by highlighting preconceptions and contradictions, and be open to creativity in writing (O’Dwyer, 2004).

All coding and sorting was done manually rather than through the use of computer aided qualitative data analysis software (CAQDAS) as it allows for better reflection upon, and recalling the content of the interviews (Anderson-Gough, 2004). As articulated by Seidal (1998: 14) “[t]he answers we look for are not in the codes, but in ourselves and our data”, and this quote was continually reflected upon throughout the coding, which was a systematic and structured, yet also an iterative, intuitive and all-in-all rather enjoyable process. A personal reflection is that

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accountants, due to their particular training and inclinations, may be particularly suited to this type of qualitative research that requires the classification and interrogation of information. The codes were derived from the literature, notes taken both in the field and during analysis, and intuitively from the transcripts themselves as themes and patterns emerged. Miles and Huberman (1994) provide three types of codes used in qualitative data analysis: descriptive codes, interpretive codes and pattern codes. Examples of descriptive codes used in the present study include COM-INT (communicating internally) and COM-EXT (communicating externally). COM-EXT was later expanded to COM-EXT-STK (communicating externally to stakeholders), COM-EXT-INV (communicating externally to investors), and other specific stakeholders. Interpretive codes were drawn from the prior literature and examples include ENLSI (enlightened self-interest) and INSTPRES (institutional pressure). Finally, pattern codes were applied to all coded data to sort them into the steps of the normative model provided in Chapter 3, with the additional classifications of MOT (motivations) and CONTEXT (contextual information).

Following the analysis of the data regarding the internal organisational choices made in the S&ER process, the second stage of the research involved exploring whether those choices result in a difference in their use of disclosure strategies in their external S&ER. This required archival content analysis of secondary data, which is discussed in the following section.