Data Analysis: Grounded Theory Tools and Techniques

Creswell (2007) describes that data analysis in qualitative research consists of preparing and organising the data for analysis, then reducing the data into themes through varied stages of coding, and finally representing the data appropriately in findings and discussions. Miles and Huberman (1994) also support this view and define data analysis as consisting of three concurrent flows of activities which are data reduction, data display, and conclusion drawing and verification. In the proposed research study, the above three activities will be considered carefully and data will be analysed in the context of cross case analysis as the study will comprise of multiple cases.

Considering the case-study methodology adopted for empirical research and data collection, suitability of associated case-study data analysis will be assessed and discussed. Yin (2009) suggests five analytical techniques i.e. pattern matching, explanation building, time-series analysis, logic models, and cross case synthesis for case study analysis. Pattern matching and explanation building assume defining specific variables prior to data collection and comparing empirical pattern with the predicted one based on prior defined variables. As this research aims to uncover various factors, it is not feasible to define specific variable prior to data collection and limit the scope to those elements. Time-series analysis is not applicable as the research problem defined for this study is not to analyse and capture sensitivity of the phenomena over a period of time. The fourth technique logic model is not pertinent as the research objective is not to observe complex chain of events by

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manipulating variables. The fifth technique cross-case synthesis is relevant for this research as it involves analysis of multiple case studies. Cross-case synthesis suggests aggregating findings across a series of individual cases but does not provide any practical guidance on how to systematically analyse data from each case-organisation. Cross-case synthesis lacks clear and accepted set of conventions to code and analyse qualitative data.

In spite of being a strong proponent of case study methodology, Yin (2009) concedes that “the analysis of case study evidence is one of the least developed and most difficult aspects of doing case studies” because the techniques still have not been well defined. Given these shortcomings and complexities with case study method, alternative data analysis methods have been considered for assessment. These alternative methods include quantifying methods such as informal methods, content analysis and non-quantifying methods such as general analytical procedure, data displays, quasi judicial method, cognitive mapping, and grounded theory (Collis and Hussey, 2009). A review of these varied methods in the light of the research problem and associated objectives indicate grounded theory approach as a valuable alternative for qualitative data analysis due to its well defined techniques (Strauss and Corbin, 1998). Charmaz (1995) also notes that techniques of grounded theory are used extensively in organisational studies. The grounded theory techniques such as open coding, axial coding, and selective coding are well defined and very useful in systematic analysis of qualitative data, particularly primary data emanating through interviews in case organisations. A brief description of these analytical coding techniques is presented in the following sub-sections.

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3.6.3.1 Open Coding

This first analytical step in grounded theory i.e. open coding is meant to ‘open’ up the data and expose the thoughts, ideas and meanings contained therein. Open coding is a process of identifying and categorising concepts along with associated properties and dimensions in data. Strauss and Corbin (1998) define the concept as “an abstract representation of an event, object, or action/interaction that a researcher identifies as being significant in the data”. The concepts identified from the data are named with labels which represent them and similar concepts are grouped together into categories. Open coding process encompasses developing these categories further in terms of their properties and dimensions. The categories developed would be useful in addressing the first research question that is factors influencing CKM in organisations.

3.6.3.2 Axial Coding

The purpose of axial coding is to begin the process of reassembling data that were fractured during open coding. Axial coding is the process of relating categories to their subcategories around the axis of categories, and linking categories at the level of properties and dimensions. The properties and dimensions are explored in depth to identify variety of conditions, actions/interactions, and consequences associated with a phenomenon. Axial coding enables researcher/analyst to look for cues in the data that denote how major categories might relate to each other (Strauss and Corbin, 1998). Identifying and explaining the conditions and consequences associated with varied aspects of CKM would assist in addressing second research

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question which is regarding how various organisational elements influence CKM to achieve 360O view of customers. Actions and interactions associated with phenomenon of CKM and 360O view of customer in organisations would address third research question partially in aspects such as interrelation between various organisational elements and factors within them.

3.6.3.3 Selective Coding

Selective coding is a process of integrating and refining categories until the major categories are finally integrated to form a larger theoretical scheme. Selective coding provides techniques to aid integration which include a) writing the storyline, b) moving from description to conceptualisation, c) using diagrams, and d) reviewing and sorting through memos. In addition, selective coding assists in refining the theory which consists of reviewing the scheme for internal consistency and logic, filling in poorly developed categories and trimming excess ones, and validating the scheme (Strauss and Corbin, 1998). Within the context of this research study data from cases would be integrated into core categories representing the main theme of research at this stage. Integration of major categories representing varied aspects of CKM to achieve 360O view of customers would be helpful in addressing third research question through development of an integrative CKM practice framework for organisations.

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