Data Analysis

While there are many ways of analysing qualitative data, it is important to choose a method that is consistent with the philosophical and methodological assumptions made during the research design (Easterby-Smith et al, 2008). Without this underpinning connection, the analysis process will not return information which can be used to address the research questions.

Likely outputs from the research methods selected for this study are detailed recordings and transcripts from interviews, completed surveys and notes developed from observations of the various organisational contexts. Broadly speaking there are two different ways to analyse transcripts and other written documentation; content analysis and grounded analysis (Easterby-Smith et al, 2008). The fundamental differences between these two methods are captured in table 3.4.

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Content Analysis Grounded Analysis

Searching for content (prior hypotheses)

Understanding of context and time

Causally linked variables Holistic associations

Objective Subjective Faithful to views of respondents

More deductive More inductive

Aims for clarity and unity Preserves ambiguity and contradiction

Table 3.4: Content Analysis vs. Grounded Analysis

Source: Easterby-Smith et al (2008) Page 173.

Permission to reproduce this diagram has been granted by Sage Publishing (USA) Limited

Given the nature of discussions up to this point of the chapter it is clear that the grounded approach to analysis is potentially more applicable to this study. This study is using an inductive approach with previous discussions noting the importance of holistic associations between the identified factors. In addition to this, truth within this study may vary from place to place and from time to time therefore an approach to analysis which remains faithful to the views of individual respondents is likely to be appropriate.

A further broad technique which is likely to be applicable here is narrative analysis. Stories arguably provide access to and appreciation for context (Tsoukas and Hatch, 1997) and this will be vital in order to develop rich, contextually appropriate case studies from which broader comparisons can be made. Narrative methods can be applied across a range of data (Easterby- Smith et al, 2008) and can assist in the interpretation of organisational ‘stories’.

Although surveys have been designed for use within this study it is important to state that quantitative analysis of the results will likely be limited. While this

194 study is interested in relative rates of dispersion (Bryman and Bell, 2007), complex analytical techniques are unlikely to add to the general picture emerging from each organisation. The reasoning behind this statement is multi- faceted. Firstly, as the study is concentrating on SME environments the small size of the overall sample from each setting could be a potential issue. This study will not be able to claim representativeness, but it will look for either convergence or divergence of views. In other words, do the same issues arise in different contexts, leading towards one final understanding, or is it the case that very different factors affect idea generation across different contexts. It is also important to state that different individuals may interpret questions in different ways; this is because every individual has their own interpretation of creative idea generation (Johnson, 2010; Penaluna et al, 2010). Because of these issues, survey data will be analysed qualitatively, with the aim being to spot trends in narratives and words that individuals use.

Following on from the points made so far in this section it is important to highlight the computer software which will be used to analyse data produced by this study. There are a variety of packages available including SPSS and NVivo. As SPSS is generally useful within quantitative research it will not be used during this study, NVivo being the logical choice. This software will be used to store and assess written transcripts from individual interviews, observation notes and copies of the qualitative survey. Microsoft Excel will be used to analyse patterns from any ranking questions within the qualitative survey. It has been decided to use this software purely because the overall sample size is likely to be relatively small. Connected to the choice of computer

195 software, literature notes that coding is likely to be a crucial issue within this study (Creswell, 2007); it is to this that attention must now turn.

Grounded theory analysis is essentially based on three forms of coding; open, axial and selective (Strauss and Corbin, 1998). Open coding is the first step in the analysis process and is relatively indiscriminate. It seeks to derive general objects of note from individual transcripts or pieces of data. Axial coding seeks to take the information created through open coding and put it together in new ways. The aim within axial coding is to make connections between categories and sub-categories, understanding the ‘core phenomenon’ which can then be taken back to the raw data and other categories created around it. Finally, selective coding assists in the systematic formation of categories, validating any perceived relationships and encouraging a broader story to emerge that describes the interrelationships between the factors or variables (Creswell, 2007). NVivo will be used throughout this coding process, enabling the construction of various codes and the running of searches and queries within the data set.

The ‘data analysis spiral’ (seen in figure 3.4) (Creswell, 2007) is thought to capture the key stages of qualitative data analysis and describes the iterative nature of the coding process which this study will use.

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Figure 3.4: Data Analysis Spiral

Source: Creswell (2007) Page 151.

Permission to reproduce this diagram has been granted by Sage Publishing (USA) Limited

Qualitative analysis is an iterative process and this study will begin the analysis process as soon as data collection begins. The research plan (section 3.8) identified that exploratory surveys will initially be used to capture a broad view of each environment and interview questions may need to be tailored in each setting in order to investigate key issues. It is recognised that analysis of qualitative data is not straightforward (Bryman and Bell, 2007) and that constant comparison between collected information and the wider literature is vital if this study is to arrive at a useful contribution to the field. The practicalities of data analysis will be discussed in further detail during sections 4.0 and 4.1. A final key issue which must be considered here is data security. In keeping with the requirements of the Data Protection Act 1998 (Bryman and Bell, 2007, p143) this study must ensure that data is;

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 Processed fairly and lawfully

 Obtained only for one or more specified and lawful purpose(s) and not processed further in any manner incompatible with that purpose or those purposes

 Not kept for longer than is necessary

 Not excessive in relation to the purpose or purposes for which they are processed

In addition to the points in the list above appropriate measures such as password protection, encryption and secure storage areas must be used where appropriate to prevent unauthorised access of the collected data (Bryman and Bell, 2007). This study will adhere to the requirements of the Data Protection Act 1998 and will ensure that all practical measures to ensure data security are taken.