Data Analysis Plan

3.5.1 General Procedure for Content Analysis

In this study, interviews of 25 individuals from five professions have been analyzed.

The selection of more than one profession gave the researcher the opportunity to delineate the variation of elements and circumstances enabling professional learning within different professions.

The interview transcriptions were initially analyzed using the “inductive content analysis” approach, following these outlined steps: data reduction, grouping and conceptualization of data (Patton, 1990; Postareff, 2007; Flick, 2014).

Following the standard protocols of qualitative content analysis using induction to conceptualize generalizations about the interactions between the participants and their organizational learning systems, the principles of abductive reasoning were then applied to test whether the additional contextual details uncovered through the interviews - in conjunction with the inductive generalizations - could uncover any hypothetical causal relationships which would provide additional avenues for exploration and studies in future research. The generalizations uncovered through inductive reasoning, in turn, would not just serve as a jumping-off point from which abductive hypotheses could be conceptualized and tested, but also as a means to uncover innovations or practices which could then be integrated into the field of teaching.

3.5.2 Steps in Analysis

3.5.2.1 Transcription. As assured to the participants in the consent form and questionnaire (outlined in section 3.2.4.3.1), the first step undertaken by the researcher was to create transcriptions of all audio recorded at the time of the interviews. The interviews were typed into text format, and the recordings were destroyed in order to uphold the terms of participation in the afore mentioned form. In case of the interviews where no such recordings existed, the textual information - either written by the researcher during the course of face-to-face interviews, or through digital communication platforms, or provided to the researcher as answered questionnaires - was transcribed into a consistent format so as to regularize the process of coding across all data input.

3.5.2.2 Coding. In this study, coding of the transcripts to recognize and compile thematic categories was performed primarily by the researcher. A primary manual coding of the data was performed by the researcher in order to familiarize themselves with the primary information available at hand. A secondary coding set was, then generated through the use of NVivo qualitative analysis software. This tool provided a computerized, and hence objective, breakdown of the data into thematic sets and sub-sets, thus avoiding the subjectivity of the researcher from influencing the output of this stage of data analysis. By running the primary data through multiple queries on this tool, this tool allowed the researcher to gain a clear understanding of emergent thematic categories through groupings of similar word usages in responses. Where needed, the researcher added contextual primary

data gathered during the interviews as memos to the thematic categorizations done by the tool in order to ensure that the nuanced relationships being developed through the textual breakdown were as insightful as the data allowed.

An additional coder was also employed to code a subset of the total data sample in isolation from the PhD candidate in order to provide another of objective verification of the coding undertaken by the researcher. The secondary coder’s work was then utilized to evaluate Cohen’s Kappa - the value for agreement between raters of data - in order to ascertain the utility and scientific value of the collected information with respect to their integrity and ability to answer the research questions guiding this study.

Table 6. Example of initial coding framework Table 5. An example of an initial coding framework

Interview transcript Initial coding framework

Interviewer: What do you mean by working culture and environment ?

Professional: The company culture is decisive.

That is responsible for the quality of the colleagues and for the motivation. Whether you can identify yourself with the team, the project and the company.

-So (from my last job) this is a big difference because before I knew there was knowledge that I cannot reach anywhere and I needed to ask. And I didn't know what (type of) knowledge and where is not. Here I know that everything is shared, everything is somewhere published so I can just access it… So it's always a very equal (knowledge sharing) position for everyone.

Open culture

Institutional Knowledge sharing

Interviewer: What about the environment?

Professional: I get accepted by the colleagues, which is the key for efficient co-working….

Whether you can identify yourself with the team,

the project and the company Social recognition Relationships

-to share your IT skills is important, that your colleagues accept you and appreciate you as an expert

-The personal qualities of the colleagues and your good relation to them.

Source: Researcher

3.5.2.3 Objective verification for categorization. In order to provide an objective verification of the various categories coded - thematic headers, sub-headers, and constituent points of focus - the interview analysis and categorization was further inspected by both supervisors of the thesis, as well as critical colleagues, and the categorizations were corrected and revised to the extent that it was deemed necessary at the end of the process.

Table 7. Example of final coding framework after reduction of categories in the initial coding framework

Table 6. An example of final coding framework after reduction of categories in the initial coding framework

Final coding framework Initial coding framework

1. Knowledge sharing

Attractors Open culture

Open communication Positive feedback Social recognition Relationships Technical experience Self-confidence

Hindrances Time

Lack of records Power-distance Social features Uncovered knowledge Isolation

Confidentiality Lack of freedom Bureaucracy Source: Researcher

3.5.2.4 Thematic analysis. Once the open coding, secondary coding, and objective verification of categorization was concluded, the researcher then proceeded to compile and analyze the available data to answer the four major queries referenced in section 3.2.1.

Since this particular subject matter does not have an existing body of research which could satisfy the lens of complexity theory, inductive processes were initially used to break down the data into component thematic categories. However, from this point onwards, both inductive and abductive processes were used to analyze the data. The generalized inferences available through inductive processes served to provide an overview into the individual participant's relationship with their organizational learning systems. From there on, abductive processes were used by testing theories through the Mind Map tool of NVivo to test various hypotheses concerning hitherto unexplored non-linear causal relationships as hinted at by the data sets in their minutiae