Causal Mapping

Cognisant of my insider role I wished to ensure that the themes emerging from the data were not overly influenced by my insider perspective. As a means of sense checking the original themes emerging from the thematic analysis a second level of analysis was employed described as Causal mapping.

Causal mapping is a formal technique where specific thinking about a problem or issue is modelled using directed graphs. Generally, there are two main types of causal mapping techniques, idiographic and comparative. Idiographic causal mapping collects and describes the causal ideas of a single person or collectivity, such as a CEO or a group of managers, and presents them using a single

composite cause map (Cosette, 2002; Eden & Ackermann, 1998). This

resembles techniques like concept (idea/mind) mapping, typically used for pragmatic and/or personal heuristic purposes. In this study, this form of causal mapping was used because of its ability to respond to the demands of idiographic data.

Comparative cause mapping (CCM) extends the general causal map platform to

research tasks, which require eliciting several individuals' causal ideas and the comparison and aggregation of their causal beliefs/knowledge patterns. When studying any cognition-related construct such as attitudes, values, or mental models, the self-evident problem is that such a construct or the contents of a person's cognitions, like causal knowledge/beliefs, cannot be observed nor elicited directly and independently of that person. Correspondingly, causal maps (or equivalent tables or matrices) do not exist as distinct entities and cannot be acquired as such. In all cases, they must be constructed (by researcher and/or appropriate software) from respondents' causal statements (A → B, B → C), which are either embedded and located in their communications such as interviews and transcripts, specifically administered texts/essays or questionnaires, or acquired by some structured method from the respondents as discussed below.

3.7.1 Process of Causal Mapping

In the majority of study cases, causal mapping is concerned with individual and social cognition, more specifically social actors' knowledge and beliefs, their formation, attributes and impacts in social contexts such as organisations or cultures. Usually, causal maps refer to graphic network representations and consist of nodes and arrows. Causal map nodes depict concepts (people, phenomena, their features) of the area of investigation, in this case CoP tacit knowledge sharing, the arrows indicating the concepts' interlinked causal relationships, usually as perceived by the researcher or research participants. Accordingly, causal mapping was considered as well suited in this study for not just structuring, coding and making sense of the rich idiographic data concerned with the explorations of social practice, but also as a sense checking exercise.

3.7.2 Process of Conducting Causal Mapping in the Study

The process began by re-listening to the recordings of the interviews, with a copy of the raw data in spreadsheet format, which had been constructed from a list of potential impacts on tacit knowledge sharing in CoPs. The importance of this ‘first pass’ was to ascertain in almost real time those activities or characteristics having an impact on tacit knowledge sharing. Bryson et al. (2004) described this visible thinking achieved through causal mapping as a way of helping to understand challenging situations.

Therefore in adopting this approach causal mapping was employed as a useful way to visualise and consider the thinking of the participants and their responses to the questions asked. This was followed by the creation of a draft map with an initial list of ‘causes’ whilst concurrently checking the raw data for evidence to underpin the initial map. What followed was an iterative process of linking raw data to the causes and a series of underpinning statements formed into linked lists for the map. Using the raw data in this way allowed the map to be continually updated whenever a new or previously unrecognised causal effect became obvious. This supported my ability to ‘think out loud’ and to look closely at the

71 relationships formed between causality, that which appeared to be impacting the activity, and the relationship to activities such as behaviours or sharing types supported from the rich idiographic data from the transcripts and notes. This was followed by listening again to the recordings but now specifically listening to the statements and the tone and emotion in which they were delivered to understand the depth of feeling and implications of what the participants were saying (Pyrko

et al., 2016).

What formed by continually returning to the raw data and regrouping it was the identification of a potential causal effect. Referred to as cognitive mapping because of its relationship to personal thinking which may only be able to hold onto several concepts at any given time. However by cross- referencing the map to the statements of the participants and then evolving multiple versions of the map it was possible to surface, from considerable complexity, the key emergent themes from the underlying data. This supported the key themes emerging from the previous qualitative thematic analysis. The technique identified and made conscious the decisions as to what elements were relevant to the study.

The outcome of this exercise served several functions in relation to the trustworthiness of the analysis process. Using causal mapping 9 of the original 10 identified in the first stage of analysis were confirmed in this process with the exception of the two themes known as ‘relationships/personalisation’ and ‘core values’’. The initial causal mapping therefore identified 9 key themes,

1. Cop type

2. Purpose & Boundaries 3. People/Experience 4. Benefit

5. Social

6. Relationships/Personalisation 7. Behaviours

8. Role of the leader 9. Good CoP bad CoP

In cross-referencing two data analysis techniques to surface and confirm the themes, the study benefited from the cross referencing of the original thematic analysis with the causal mapping to confirm the findings. In applying these techniques, it was possible to consider personal biases which may have had the potential to impact the study. The iterative analyses of the data that provided the data sets and the maps was found to be a way of considering the implications and the evidence as opposed to subconsciously discounting what the evidence was highlighting.

Figure 3: Causal Map- Indicating the complexity of the p o s i t i v e and negative influences on tacit knowledge sharing

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