Script (upd_mdof_torre.m)

As can already be gleaned from section 3.5.1, my analytical process was thus somewhat circular, moving from surveying all data, to transcribing, to coding and back to surveying all data again, to transcribing again, to coding again. Figure 3.3 provides an overview of the different analytical steps.

3.5.2.1 Initial coding: Stage 1

After the initial transcription of Project 2, I conducted initial intensive rounds of coding (Silverman, 2014) in order to identify a suitable focus for my study, since relational management is quite broad and would have not been a feasible focus in a longitudinal study focused on studying interactions in-depths. To identify this focus I used coding-techniques as “a procedure for organizing the text of the transcripts, and discovering patterns within that

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organizational structure” (Auerbach & Silverstein, 2003, p. 35) that otherwise would not have been visible in the large data set.

In identifying these patterns I used different types of coding including emotion-coding, in which openly communicated emotions are coded and later structured and versus-coding, in which any antagonisms constructed in talk are highlighted, such as ‘quality versus speed’, or ‘the team versus time pressure’ (Saldaña, 2016). These were chosen as they seemed likely to generate relevant insights for relationships, but I also openly coded what seemed to be of interest (i.e. meta comments on relating, mentioning’s of relationships). However, my main focus at this stage was on structural coding (Saldaña, 2016) in order to break down the data and uncover patterns. This would enable me to explore and compare activities across meetings and create indexes of these that would allow me to find and revisit the incidents among the large data set.

I employed structural coding in different ways targeting three levels of analysis. The first one targeted identifying patterns by looking at micro occurrences in the data, this included coding specific utterances according to the speech acts they constituted. The second form of structural coding focused on the mezzo-level. I coded what type of talk was locally achieved and assigned codes included categories such as “brain-storming”, “clarifying the brief”, “joking”, “decision- making”. There are of course overlaps between these two levels, however my aim at this point was not to generate a complex theory but to gain a better understanding of the data collected and what it was the team was actually doing in team meetings. Finally, the third structural coding procedure targeted the macro-level and I tried to identify in the broadest terms what participants were talking about and doing in the team meetings. It turned out that all talk could be grouped within three categories at this level: 1) On-topic talk (talk directly involved with the achievement of the task); 2) Process-talk (talk involving planning future meetings, delegating tasks and agreeing on how to approach the task) and; 3) Off-topic talk (talk that was focused on non-work related topics, often phatic or small talk). This was also an interesting exercise from the perspective of the organisational behaviour literature I had studied, as this includes actually very little information on what teams actually do. While definitions of teams are abundant, teamwork and what it entails is hardly theorised and some of these categories might be surprising to an organisational behaviour researcher. This initial analysis showed that there were relatively clearly bounded interactional activities occurring in the team meeting (though they were of course fuzzy around the edges), and that specific sets of speech acts tended to appear more frequently in some activities than in others.

Interestingly, several talk activities cut across the boundaries of the larger categories, for example, decision making features in on-, off- and process talk, as does humour. So, while there are some clear patterns in the data as outlined, this is not to suggest that we always have clear boundaries and that the data is not also messy.

63 Figure 3.3 Overview of analytical process

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After these initial coding rounds, I started to look through the larger activities I had coded and started to look into the relational work that was being done. While the off-topic talk was in general much more relationally inclined, small and phatic talk have already received considerable research attention. Other talk was more task-focused and team members’ rapport orientation seemed to be aimed more towards maintenance or neglect, and while this certainly would have led to interesting insights, as outlined I was more interested in the relational work done to nurture good relationships. After several rounds of coding it became clear that one talk activity in particular stood out in this regard: Troubles talk. Interestingly troubles talk was not limited to off-topic talk but featured in all three broader categories (See Figures 5.1 & 5.2 for an overview), while it also featured quite strongly in the emotion and versus-coding, I had deemed relevant for relationships.

I thus started to inductively code the troubles talk incidents, especially regarding their structure but also the topics that were featured, and it became clearer and clearer that I was focusing indeed on a distinct type of talk that seemed relevant for relationships. I then went back to the literature to read more about previous research on troubles talk, as I had not come across it as part of interpersonal pragmatics, nor the teamwork literature, and finally decided that this would make an interesting and very relevant focus for this thesis.

The interviews in contrast were mostly coded regarding the positions team members allocated to themselves and others regards to the team, the teamwork, each other and the occurring team interactions and dynamics. Team members positioning was often quite explicit in the interviews and they discussed their relationships and relational conflicts quite openly. I report on some of the insights in Chapter 4.

3.5.2.2 Identifying data set: Stage 2

The second stage of analysis was relatively short compared to the time I spent on stage 1 and 3 as I was mainly concerned with identifying all incidents in the newly transcribed data that constituted troubles talk episodes. For this, I started to gradually build a definition of troubles talk that derived from the literature review I had undertaken, but that also mirrored the incidents in my own data and tried to capture the essence of how they were related. Based on this evolving definition I surveyed all transcripts repeatedly to identify all the incidents of troubles talk present and thus identify a data set for further analysis. I ended up with 107 incidents of troubles talk across 20 meetings transcribed. Five instances, however, are one- liners where a team member seemingly tried to establish a troubles talk sequence, which was not, however, picked up. Appendix 6 provides an overview of these incidents. In order to identify the troubles talk incidents I repeatedly listened and read through the transcripts. As part of this process I also coded larger segments of the transcripts into on-topic, off-topic and process talk to identify larger patterns in the data and to locate the occurrence of troubles talk in the meetings in relation to these categories.

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After identifying the incidents, I have labelled them according to the Project (P), Meeting (M) and Episode in the meeting (E) they occur in. Thus, the second episode of the third meeting of Project 4 is labelled P4_M3_E2. This helped me keep track of the troubles talk sequences across meetings and is also used as a labelling system throughout the thesis. This allows the reader to refer each incident back to the overview provided (Appendix 6) and to broadly position the episode in the wider teamwork (i.e. P4 occurs towards the end and P2 and P3 in the middle stages).

3.5.2.3 Intensive coding of troubles talk segments: Stage 3

I then began another intensive round of coding. For this I erased all previous codes assigned beyond the broad categorisations and started from scratch focusing only on troubles talk and its surrounding talk. I used inductive coding, with the goal to uncover as many facets and aspects of troubles talk possible. I thus used a number of different coding techniques, including thematic coding, emotion coding, versus coding. I coded for speech acts, for initiating utterances, for responses to these initiators (aligning or non-aligning), whether team members tended to fulfil specific roles in the sequences, stylistics devices such as exclamations, swearing laughing, participation in the episodes, positioning practices regarding the trouble but also whether specific attitudes about teamwork or communication were made explicit during any troubles talk episodes. MAXQDA allowed me to continuously replay the incidents so I could maintain a good sense of how the transcripts I was studying had originally sounded. For all these categories I went over and over the different coded segments repeatedly to compare all incidents, determine they did in fact belong to the same class and to continuously develop tight definitions of codes that allowed me to discriminate between segments. From grounded theory, I adopted the emphasis of memo writing as a part of the analytical process (Corbin & Strauss, 2015) and thus I kept memos on the different codes I was developing, but more importantly memos allowed me to look at my data in context. While coding helped me identify patterns across episodes, it seemed important to not only look at the data after it had been thus ‘broken apart’, but to work from the exact opposite perspective simultaneously and to develop narratives and commentary on the troubles talk incidents in more general terms. For this I kept memos with thoughts and analytical comments on the episodes in context. This approach allowed me to analyse the data from different angles. Coding thus helped me to deconstruct troubles talk into its components and to compare these across episodes, while analysing each episode discursively allowed me to better understand the functions, and the complex ways rapport was managed in each episode. Both approaches are thus reflected in my presentation of the data and analysis in Chapter 5. While my coding was inductive, almost inevitably it was nonetheless informed by the in-depth review of the literature I had undertaken before. I thus had certain ideas of what could be interesting to study and with that some coding categories that I expected to surface. Some of these turned out to be completely unhelpful for coding my data (e.g. “giving advice”, which had come out as a major theme in the literature review, but was basically absent in my own

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data), while others were more interesting, such as “ending troubles talk”. Gradually, some specific foci became clearer that seemed particularly interesting, such as the structure of troubles talk, that seemed to share commonalities across the sequences, recurring stylistic features, the way troubles talk was initiated and so forth.

Figure 3.4 gives an example of what these codes look like in MAXQDA, with regards to the codes applied to structure of troubles talk, while Figure 3.5 shows a screenshot of the MAXQDA project page as a whole.

67 Figure 3.5 Screenshot of MAXQDA

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