TENTATIVA INIDÓNEA

The data analysis process for this study was supported using the NVivo 11 software package. As Braun and Clarke (2009) note, (supported by the researcher’s previous experience) this tool is useful in operationalising the chosen methodology’s transparency, validity, and reliability. This tool was particularly useful when capturing the initial research design, and when integrating and interrogating all project materials (data) in one place (Silver and Lewins, 2014). The software package was available in the university system, and training for using the software was undertaken by the researcher. As was mentioned above, the process of data analysis was guided by Braun and Clarke’s (2006) six guidelines for phases of analysis, as follows:

Familiarisation with data - Given the time limit within which to complete the project, instead of waiting for the fieldwork to be over the researcher started to transcribe interview data in parallel with fieldwork activities. Transcribing data immediately after each interview (when it was possible) gave the researcher the opportunity to become immersed in the data and to improve the quality of the subsequent interviews.

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The ‘Listen N Write’ software for transcribing audio recordings helped ensure transcription quality. However, as the process of transcription proved time-consuming even with this support, half of the interviews were transcribed by the Way With Words professional service company. Once all of the data had been transcribed, for accuracy all the transcripts were checked against the original audio recordings and the interview transcripts made anonymous before there were installed into the Nvivo 11 ‘Big data capabilities KG’ project.

The transcripts were read over several of times in the interests of becoming familiar with the data, and initial ideas were noted down and saved as memos. While reading the transcriptions the researcher was aiming to identify extracts at the semantic, or explicit, level (Braun and Clarke, 2006), as opposed to a latent level that requires more interpretation of meanings. However, the semantic nature of the data analysis meant that the researcher was not aiming to interpret the participant’s experiences, merely to group them thematically according to their semantic meaning, based on prior identified concepts (e.g. Digital environment, Big Data as a new resource; Other resources; Existing capabilities (Marketing, IT); DC). Different analytical notes (memos) were developed based on each interview within each unit of analysis (Global, EMEA). Generated memos were grouped under relevant themes for every concept. Once all transcriptions had been read and organised, a data analysis plan was created for each research question to guide the analysis process.

Generating initial codes - This phase involved organising the data into meaningful groups. Using NVivo 11, codes/nodes were identified manually or by using text search queries. Transcripts were read line-by-line, and extracts or ‘chunks’ of meaningful text were highlighted. The aim of this step was to build up a list of relevant topics that arose from the interviews and that was initially considered important to the subject under investigation. Informants’ terms, phrases, or meanings that were common between the interviewees were grouped (Appendix 8) with the aim being to answer the research inquiries such as:

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• How does the company engage in Big Data initiatives activity? and

• How does this in turn influence Big Data capabilities development?

In order to achieve further insight, coding data were explored between data and preliminarily identified concepts (from the conceptual framework and from the literature), like deductive codes, and other interesting occurrences relevant to the research queries and codes that emerged from the data. Having completed the initial coding of all transcripts, the component elements of each code were considered for consistency or overlap with other codes. This provided the opportunity to begin defining the codes, and to link these together into groups. Some initial codes were abandoned or merged at this stage due to overlap with others. The resultant codes were then compared against the research questions in order to ensure that only the codes that significantly contributed towards the research were pursued.

By this point, a total of 30 deductive codes/nodes had been established, and a further 23 codes/nodes had emerged from the data, which were kept in separated folders (see Appendix 10 for of Nvivo 11 codes).

Searching for Themes - After the initial coding, the next phase involved sorting the different codes into potential themes. Careful consideration was given to identifying the relationship between codes and sub-themes that were developed from the coding groups, linking the data together, and also meaningfully linking back to the research questions. Using the Nvivo 11 mind-map (Appendix, 11), themes were re-arranged according to the semantic content of the codes, and then a deeper exploration of their meaning was explored. A semantic approach is consistent with the critical realism epistemological position in that it limits over-use of subjective interpretation and focuses on identifying common semantic themes within and

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between the transcripts. This study focussed on providing a description of the patterns in semantic information in the dataset, and then summarising, interpreting, and theorising around the broader meaning and implications of the patterns found.

As Braun and Clarke suggested, deciding on themes is a question of prevalence, ‘in terms both

of space within each data item and of prevalence across the entire data set’ (2006, p. 82).

However, although there is a need for there to be a number of extracts for a theme across the data set, a higher prevalence does not necessarily make the theme more important to the research. As there is no set rule for the proportion of data or number of themes, frequency for this research was considered as the number of participants commenting on a specific issue, rather than the frequency of codes related to it. This again involved discarding themes due to insufficient data, or merging two themes into one. Initially, the themes were influenced by the concepts from the study’s conceptual framework, literature, and research questions.

However, it must be acknowledged that the top-down and the bottom-up processes of identifying themes were interactive in some way, because even though the researcher was specifically interested in identifying themes, after reviewing the themes and their subcategories other important themes emerged from the interview data, such as: e.g. Increase awareness and building trust; Shaping organisational culture; Networking and partnership. This was achieved as a result of discovering in the data certain repeating patterns which related to research queries but which were not fully covered in the conceptual framework. It became apparent that some terms were repeatedly brought up during discussions. Instances of newly-emerged themes such as: Developing shared Big Data vision; Shaping the organisational culture, and; Networking and partnership were themselves broken down into categories to capture the richness of the phenomenon.

Defining and Naming Themes - At this point the researcher identified the essence of each theme and determined which aspect of the data each of them represented. Themes were

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continually reviewed until they were judged to be consistent across all participants and, at the individual level, supported by quotes and named appropriately. The identified themes are intended to provide a rich and detailed description of the overall case. Chapter 5 provides detailed information about the final themes and the analysis of research data within them. Producing the report - This phase involved writing up the report about research data to convince the reader about the validity of the analysis. Braun and Clarke’s (2006) suggest that the report should provide sufficient evidence (data extracts) of the themes within the data to demonstrate prevalence of the theme. All the themes reported in the data findings in Chapter 5 are supported with valid examples or extracts which capture the essence of the point that the theme represents. On the other hand, Baxter and Jack (2008) state that due to its complexity, reporting a case study is a difficult task and it is the researcher’s responsibility to choose a comprehensive format to deliver a reflexive report to ensure transparency of the analysis process. Some suggested ways to deliver a quality report are:

✓ Tell the readers a story, by delivering a chronological report about the case

✓ Write a rich description of the case (who were the actors, what was their interaction, perceptions etc.)

✓ Deliver the report by addressing and describing themes within and across the case The next Chapter (5) provides a synthesis of the above suggestions and presents a description of the data interpretation that informed the themes.

4.9 Chapter Summary

This chapter discussed the research design and methodology that was followed to investigate the research problem and to fulfil the objectives of this study as identified in Chapter 2 and Chapter 3. The first part of the chapter provided justification for the choice of overall qualitative case study research strategy, the unit of analysis, the choice of empirical sample and data collection, while the second part of the chapter outlined the methods used for data analysis.

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The next Chapter (5) presents the findings based on a description of the data interpretation that informed the themes.

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