Qualitative Data Analysis

All interviews were transcribed into MS Word documents and input into the QSR NVIVO version 10 (NVIVO). NVIVO was incorporated in this research as computer-assisted qualitative data analysis software. Some authors argue that NVIVO improves the rigor of the analysis process and assists the researcher to better manage data and ideas in qualitative studies (Bazeley 2007, p. 3; Gibbs 2002, p. 11; Welsh 2002). Each transcription was input and named considering the sequential number of the participant‟s interview. For example, the third interview conducted was input and named into the NVIVO as transcription 3. Once all transcriptions were input and named into NVIVO, the data analysis of the qualitative data through the coding process started.

O‟Reilly (2009) states that coding involves close exploration of collected data and assigning it codes, which may be names, categories, concepts, theoretical ideas or classes. It also involves thinking about what codes mean in the context of the object under investigation.

Benaquisto (2008) points out that the coding process refers to the steps the researcher takes to identify and systematize the ideas, concepts and categories uncovered in the data identifying features, behaviours or ideas and distinguishing them with labels. The coding process stated by those authors was applied combined to the pattern matching concept stated by Yin (1993), which involves in comparing data with predicted patterns to draw solid conclusions.

The coding process for each transcription started by coding the participant‟s group. Reporters were coded “group 1”, assurers were coded “group 2” and readers were coded “group 3”. For example, the third interview was performed with an Assurer, so the transcription 3 was coded as group 2.

After this first phase where data was initially organised by group on NVIVO, the analysis process started with the researcher reading all transcriptions by group. According to Abu-Azza (2012), reading through all the data allows the researcher to gain a general sense of the information and to reflect on its overall meaning.

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After reading all transcriptions by group the second code was attributed on NVIVO. All questions included on the questionnaire were coded according to the interview questionnaire‟s sections. For example, the question 1 of section B was coded as B1. The process of coding by question and by group started with the researcher selecting sentences in participants‟ answers that represented participants‟ ideas in relation to the question under assessment. This approach reduces the large number of responses identified by different participants to a smaller and more manageable number by minimising similar answers and classifying them under one main answer (Abu-Azza 2012). Once all sentences were selected they were coded by idea and by comment in NVIVO, and they were then exported to an MS Excel worksheet. The use of Excel worksheets allowed the researcher to organise data in an easy-to-read format and to see the degree of agreement/disagreement between the responses provided by participants (Abu-Azza 2012). Table 13 provides an example of this codification:

Table 13 - Example of codification.

Table 13 demonstrates for instance that participant 22 and 23 provided a similar comment regarding their opinion about the current assurance process (question E2). As a result, their comments were categorised as “Assurance process must be standardised” and the group idea was categorised as “Assurance Methodology”. Participant 22 also provided an additional opinion that assurers must have minimum technical skills to provide assurances. This second comment was categorised as “Assurers must have minimum technical skills to provide assurances” and the group idea was categorised as “Assurers‟ Technical Skills”.

Once all participants‟ answers were coded by comment and by idea the descriptive analysis was used to summarise, present and analyse the phenomenon under investigation through frequencies and percentages. Descriptive statistics are the numerical and graphical techniques used to organise, present and analyse data and to identify events that are correlated with the occurrence of some target response (Fisher & Marshall 2009; Kimberly 2010). Table 14 provides an example about how the descriptive analysis was employed to analyze qualitative information.

Group Commnent Group Idea Comment Provided Participant

Assurers Assurance Methodology Assurance process must be standardised 22

Assurers Assurance Methodology Assurance process must be standardised 23

Assurers Assurance Methodology Assurance process must be standardised 4

Assurers Assurance Methodology Assurance process must be standardised 9

Assurers Assurance Methodology Scope of the assurance must be defined by the organizations'

stakeholoders 50

Assurers Assurance Statement format Assurance statements must be standardized 4

Assurers Assurance Statement format Assurance statements must be clearer to readers. 16

Assurers Assurers Independence Assurers must assess just one time the final version of the sustainability

report and do not participate to the sustainability report development 3

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Table 14 – Example about how descriptive analysis was employed.

Table 14 demonstrates how the descriptive analysis was used summarising and presenting through frequencies and percentages all comments and ideas provided by participants. Finally, based upon the results obtained through the coding process and the descriptive analysis results, conclusions were developed. The coding process and the descriptive analysis were performed by question and by group for all open-ended questions used during the interviews (appendices 5, 6, 7 and 8).