Stage three: Themes identification

The third stage in the thematic analysis deals with identifying key themes of the study. It included two steps: searching for themes and making decisions on key themes.

Searching for Themes: Themes in thematic analysis generally refer to abstract

constructs or broad categories that conceptually link expressions found in the data (Ryan & Bernard, 2003). Themes are patterns in the data that explain and organise “the possible observations” and “aspects of the phenomenon” (Boyatzis, 1998).

The process of identifying themes in the study was begun with transcribing interviews where the emerging prospective themes were documented in the memo for each interview. The codes and categories that had emerged in previous steps were

reviewed and challenged multiple times against the main research question, “social media potential to support tacit knowledge sharing”, to determine the main themes of the study.

First, codes from different areas were combined to form some tentative overarching themes. Next, keeping the main research question in mind, the transcripts were reviewed again in case there were any further references to the themes or any new themes had emerged from the data. Tacit knowledge sharing theories were also consulted in creating main themes at times. The themes emerged by employing both “data-driven” and “theory-driven” approaches (Braun & Clarke, 2006). No limitations were imposed at this stage in terms of scope, significance, or number of themes or sub-themes.

Making decisions on key themes: After identifying initial themes in previous

steps the process of making decisions on key themes of the study was begun. Following Ryan & Bernard’s (2003) recommendations, several techniques were employed in identifying the major themes of the study:

Similarities and differences: Categorising concepts based on similarities and differences was the main strategy used in the process of identifying the main themes. Subsequent to initial identification of themes, the constant comparative technique (Corbin & Strauss, 2008) was also utilised to compare and contest data at different levels. First, candidate themes and sub-themes were reviewed individually by examining their collated extracts. In other words, all extracts of each theme were separately read and scrutinised to determine whether they really formed a coherent pattern. Next, each theme was reviewed in relation to the entire data set to ascertain how meaningful they were in relation to other themes and how they matched with each other for accurate representation of the phenomenon under study (Braun & Clarke, 2006). The modified themes were also compared to each other using the constant comparative technique to discover similarities, differences, and relationships between them. Finally, the themes were weighted according to their significance, relevance to the research question, and distinctiveness to be included as one of the most important and fine-grained themes of the study.

Theoretical link: Themes can emerge both from the data and the theory (Bazeley, 2009; Ryan & Bernard, 2003). Salient themes were chosen according to their strong link to theories related to tacit knowledge sharing. This ensured that the

data was properly connected to the main questions of the research. This was approached by searching the data for evidence and examples of tacit knowledge, which were already discussed in the literature, and exploring how they are shared in the context of social media. In spite of following an inductive approach in coding, identifying major themes was done by balancing findings from the data and the theory, with broader themes mainly borrowed from the literature in the area of face- to-face tacit knowledge sharing, examined in the data and modified to social media context. This had already been predicted as the purpose of the study was to see how far social media can help in different aspects of face-to-face tacit knowledge sharing. This implies that there might be overlap between the themes of two contexts, face-to- face and social media. However, this means that the more overlap there was between the two, the more potential social media has to facilitate tacit knowledge sharing.

Repetition: Another factor that to some degree was considered in choosing the main themes of the study was to see whether there was sufficient support from the data in relation to the identified themes. Although frequency of occurrence of codes in the data was not viewed as important as the two other factors mentioned above, without sufficient support from the data developing an in-depth discussion about them would not be possible. Therefore, topics with high frequency were initially chosen to be included in the main findings of the study and topics with less frequency remained for further analysis. As a result, codes and categories that were rarely reported as potentials of social media in facilitating tacit knowledge sharing were discarded from the analysis due to lack of data support for developing a meaningful discussion. Final decisions were made by trading off between quantitative counts and qualitative importance.

A combination of the above strategies was used to identify the major themes of the study. The existing codes and themes were remixed, refined, recoded, new ones generated, or irrelevant themes and sub-themes discarded whenever it was required, even during the reporting of the findings of the study. This phase was ended by making a decision on certain representative themes which posed a maximum “internal homogeneity and external heterogeneity” in regard to answering the main research question (Braun & Clarke, 2006).