Los bienes como instrumentos del alma

The data analysis proceeded through three cycles of inductive and deductive reasoning (cf. Hoffman & Ocasio, 2004; Gavetti & Rivkin, 2007). An initial review of the literature on

140

sensemaking led me to expect that rich episodes of sensemaking would follow substantial adverse deviations from the business plan in entrepreneurial businesses. My analysis of sensemaking during these episodes, however, revealed the presence of subgroups in the entrepreneurial ventures I studied, which in turn led me to explore the group faultlines literature which I had not reviewed at the start of this research.

The first round of analysis involved a fine-grained reading of the data, which Strauss & Corbin (1990) refer to as microanalysis. Narratives were then created for each of the three case studies with emphasis on periods when there was an observed adverse deviation from the business plan, as well as the sensemaking processes that followed. These narratives comprised a chronological depiction of events based on data derived from interviews, archival sources, correspondence and meeting minutes.

From these narratives, I built an initial list of themes and codes for data analysis. My interest was around capital and disposition and whether an episode contained open or encapsulated sensemaking. Faultlines were observed from the data and included in Table 5.7, where an assessment of their relative strengths could be made. The data were read multiple times to generate any further coding. The views of different stakeholders were integrated by using interviews, public documents, emails and other sources of relevance. The richness of the data enabled triangulation between these various documents to allow for refinement, critique and further interviewing if it became evident that further clarification and explanation was needed to piece together the chain of events.

In the second round of analysis, I began to theorise on the written longitudinal narratives. I proceeded to code these in an inductive manner, creating a set of largely empirical codes categorised as “open coding” (Strauss & Corbin, 1990). Open coding refers to analysis that deals with the labelling and categorising of phenomena as indicated by the

141

data. Subsequently, data are compared and similar incidents are grouped together under the same conceptual label.

The third round of analysis involved axial coding (Strauss & Corbin, 1990), which is a practice of structuring data into aggregate dimensions (Corley & Gioia, 2004). During this stage of analysis, the emphasis tends towards teasing out theoretical interpretations of the data contained in the empirical results. Table 5.1 details the data coding and structure employed in moving from the first order codes to the aggregate theoretical dimensions. The fourth round of analysis involved temporal sequencing. This integrates all the previous rounds into a temporal explanation of actors’ capital, dispositions and the sensemaking process, and differs from the first round in that the first round merely builds the story from individual data sources, and does not link the data into the theory. Although there can be many interpretations of events (Van Maanan, 1988), the rounds of analysis that were followed helped to avoid fitting the data to illustrate a theory (Wodak, 2004 200). Further, the approach taken constantly interrogated the three theories (faultlines, Bourdieu’s theory of practice and sensemaking) along with the data generated throughout the study (Locke, 1996).

142

First-Order Codes Theoretical

Categories

Aggregate Theoretical Dimensions

Degree to which an actor has residual rights of control through equity ownership, or through the representation

of an owner

Economic capital

Capital Actors’ ownership of other investments

Background / role played by an actor (eg finance,

scientist, commercial) Jurisdictional calls on specialists’ knowledge Cultural capital Evidence of actors’ prior experience of creating economic

wealth

Diversity of relationships spanning different stakeholder

groups Social capital

Awareness and understanding of the interests and

perspectives of other professional groups _{Allocentrism /} egocentrism

Disposition Perception of degree to which they were able to act

independently of the influence of others The degree to which the actor’s focal concern is defined

by their role in the venture _Role orientation The extent to which the actor’s orientation is to the

venture (as opposed to other commitments) Statements about how actors worked through equivocal issues surrounding the sensemaking episode with other

members employed in their role

Form of sensemaking Statements about the influence of those in similar/same

roles in shaping one’s thinking about the sensemaking episode

Encapsulated sensemaking Evidence of actors forming subgroups and holding

discussions behind closed doors Statements about how they worked through issues

around uncertainty with a wide range of actors employed

in different roles _Open sensemaking Statements about the diversity of people, across different

roles, who shaped their thinking about the venture

Table 5.1: Data coding for capital, disposition and sensemaking 5.6 Findings and analysis

My analysis of the episodes revealed a distinct set of common attributes for these groups of actors. I initially use Bourdieu to consider the capital and disposition of each actor, and Ithen consider whether I can group the actors based on such attributes as the degree of homogeneity across the grouping. Finally, I explore why any homogeneous groups based on capital as influenced by disposition may affect the faultline activation and strength and

143

the relationship between sensemaking and faultlines. Tables 5.2 - 5.4 detail the capital ascribed to each actor for both episodes within each company. Since Lim et al. (2013) have already suggested the potential for faultlines between entrepreneurial managers and investor directors; I started arranging the data in this manner and in doing so discovered homogeneity in some of the subgroups. Using the data from Tables 5.2 - 5.4, the following observations can be made in relation to the two homogeneous groups, with the Chair in some cases representing a distinct actor from the entrepreneurial managers and investor directors.

144