Manual Analysis and Theme Identification

The nodes at this point are disconnected and need to be merged or grouped into specific categories. For instance, the nodes ‘financial returns’, ‘financial performance’, ‘financial sustainability’ are found to contain similar codes and reflect the same ideas, so they are merged into one node ‘financial performance

and returns’. The researcher also read through each transcript line by line to code ideas that have not been directly captured in the initial coding process. For instance, the quote “researchers will communicate with each other, are the results you got the same as the results I got?” (F6) belongs in the node ‘consistency’, but it was not captured in the initial analysis as it did not contain the word ‘consistency’ or stemmed words of ‘consistency’. The node ‘consistency’ is also found to relate to ‘credibility’, so the ‘consistency’ node is merged into the ‘credibility’ node.

The data was analysed based on thematic analysis, “a method for identifying, analysing and reporting patterns (themes) within data” (Braun & Clarke, 2006, p. 79). Thematic analysis is an alternative to content analysis and the boundaries between these two methods are not clearly defined, the terms are therefore commonly used interchangeably (Vaismoradi, Turunen, & Bondas, 2013). Vaismoradi et al. (2013) explains ‘content analysis’ as a systematic method of coding and categorising large amounts of textual information. This approach is used to determine trends and patterns of words used, including the frequency, relationships, and the structures and discourses of communication. The primary aim of content analysis is to describe the phenomenon in a conceptual form. ‘Thematic analysis’ explores the themes embedded within data and pays greater attention to qualitative characteristics of texts (Braun & Clarke, 2006; Vaismoradi et al., 2013). Thematic analysis is appropriate as the research is embedded in a qualitative methodology. Furthermore, the identification of themes allows a more structured discussion of the research findings and enables more breadth and depth in analysing the interview data.

Although thematic analysis is widely used in qualitative research and is recognised as a flexible and useful research tool (Braun & Clarke, 2006; Vaismoradi et al., 2013), the technique has been criticised as being relatively ambiguous as there is no precise agreement as to what constitutes thematic analysis (Braun & Clarke, 2006). There is no definitive answer to what counts as a ‘theme’. What constitutes as a theme is dependent on the researcher’s judgement (Braun & Clarke, 2006). A theme is not determined by quantifiable measures as it may be derived from ideas that occupy relatively little space in the dataset. Braun and Clarke (2006) explains two main approaches to thematic analysis: inductive and deductive. An ‘inductive’ approach is data-driven. The data collected may bear little resemblance to the research questions; therefore, the researcher codes the data without trying to fit it into the original research assumptions and ideas. This study is based on the latter ‘deductive’ approach. A deductive approach is analyst-driven as the analysis process is based on the researcher’s research interest. For this study, the coding of themes was driven by the research objectives. Braun and Clarke (2006) also explains that themes are identified according to one of two levels: semantic and latent. The researcher identified themes based on the ‘semantic’ level. The data have been organised into descriptive themes, resembling patterns across the dataset. The ‘latent’ level goes beyond the ‘semantic’ level and “examine the underlying [emphasised in original] ideas, assumptions, and conceptualisations – and ideologies – that are theorized as shaping or informing the semantic content” (Braun & Clarke, 2006, p. 84). In order to address the research objectives, the interview results were presented in three overarching themes. These themes as well as other findings are presented in the next chapter.

4.4 Chapter Summary

This chapter explained the research paradigm, research approach and research method adopted by the researcher in conducting the study. The first part of the chapter consists of the research methodology, which introduced the contrasting views of positivism and interpretivism, and also discussed the philosophical assumptions underpinning the research. An interpretivist approach was undertaken as the researcher’s philosophical views align with the interpretivism paradigm and the study focuses on understanding the social world. Qualitative research was considered to be the most appropriate approach as, apart from qualitative research being associated with the selected paradigm, the study is exploratory in nature and seeks to understand an underexplored social context. The second part of the chapter outlined the specific research design used. It involved discussions on research preparation, data collection, and data analysis. In preparation for the research, a literature review was conducted to identify gaps in literature. These gaps motivated the study and formed the research purpose and objectives. Two pilot tests were conducted and research data were then obtained through 16 semi-structured interviews with a mixture of individuals from various investment backgrounds. The interviews were transcribed, subjected to initial data analysis using NVivo, and then manually analysed for a deeper understanding of the data and to identify themes.