The Coding Process

Strauss and Corbin (1998) propose three coding phases: open, axial, and selective. The researcher will now briefly introduce their principles in the next three sub-sections. Further explanations will be provided in chapters four, five and six.

3.6.1. Open Coding

During this initial analysis phase, the researcher examines the text (e.g. the transcript of an interview or the minutes of a meeting) for salient themes potentially explaining the phenomenon under study. Those initial themes are called open codes. Open coding is the process of breaking down, comparing, conceptualizing, and categorizing data (Boudreau and Robey, 2005).The researcher may come up with tens or even hundreds of open codes, also called in vivo codes, as they are derived directly from the language and terminology used by participants (Gasson, 2004). The researcher then gradually categorizes these open codes into fewer, more meaningful and conceptual categories as the collection and analysis of data jointly progress. These categories are labeled using terms that are more abstract (theoretical) than the terms used by the interviewees (in vivo code). For example, in the first case study the category “management’s involvement” was created in order to regroup the following open codes: CRM product champion, communication of CRM benefits, support, control and pressure from direct supervisor, and selection of the right staff profile. Categories form the theoretical bones of the analysis, later enriched by their properties (or features) and dimensions (possible values of the properties). For example, one of the properties of the category entitled “management’s

involvement” was labeled “supporting their teams in their daily use of the CRM system,” whose dimensions were: never, occasionally and regularly. Each category carries multiple properties, and each property has several dimensions. After identifying the main categories, the last step of this initial analysis phase is to look for patterns between the categories (i.e. commonality, association, causality). Examples will be provided in the next chapters.

These open codes were all related to the research questions listed in the first chapter, and more specifically to the following five themes: post-adoption usage phases, user behaviours, user transition (across phases), factors influencing usage, and evolution of those factors. The researcher found 83, 110 and 51 open codes for respectively the first, second and third case studies. Chapter four will detail open codes.

3.6.2. Axial Coding

The purpose of axial coding is to begin the process of reassembling data that were fractured during the initial phase of open coding. Axial coding allows data to be recombined in a structured manner in order to identify the causes of the phenomenon, the context in which they appear, and the actions to solve the phenomenon and reach the objective. Strauss and Corbin (1998) argue that by asking questions such as “Who, when, why, how, with what results and consequences,” the researcher can relate structure to process and then start fitting the parts of the jigsaw puzzle together. Figure 1 illustrates the axial coding process designed by Strauss and Corbin (1998) for a category called “bad customer data quality.” It shows the six building blocks of their model and their interrelationships: causal condition (A), phenomenon (B), context (C), intervening conditions (D), action/interactional strategies (E) and consequences (F). Some brief explanations about Figure 1 follow. The issue to be studied is the usage of CRM systems (B. Phenomenon) and one of the causes influencing this phenomenon is the bad quality of customer data stored in those systems (A. Causal condition). The number of causes is intentionally limited to only one (bad data quality) for illustrative purposes, but most of the factors influencing usage and identified as key categories should be listed under the “causal conditions“ heading. The presence of silos, the loss of data during migration, the absence of data quality guidelines, and the individual storage of data by

each department (C. context) have been found as specific conditions under which the phenomenon occurs. The improvement of CRM system usage by implementing specific measures such as issuing a company-wide CRM strategy, providing data quality guidelines, enforcing the guidelines and calculating key performance indicators (KPIs) to track progress, or naming a data quality process owner (E. Action / interactional strategies) can be facilitated or constrained by intervening conditions (D. intervening conditions), such as top management leadership and vision as well as staff CRM maturity. Finally, the consequences (F. consequences) of potential solutions (e.g. users accessing the CRM system because they now trust customer data) are the outcomes of action/interactional strategies. Chapter four will detail all axial categories and their relationships.

Figure 1. Illustration of Axial Coding (Case 2) B. PHENOMENON Low CRM system usage A. CAUSAL CONDITION

Bad customer data quality

C. CONTEXT Presence of silos

Loss of data during migration Absence of data quality guidelines at company level

Customer data stored by each department

D. INTERVENING CONDITIONS Top management leadership and vision

Staff CRM maturity

E. ACTION / INTERACTIONAL STRATEGIES Issue a companywide CRM strategy Provide data quality guidelines

Enforce the guidelines and calculate KPIs Name a data quality process owner

F. CONSEQUENCES

Users access the CRM system because they now trust customer data

3.6.3. Selective Coding

The final stage in the process of theory development is the construction of a core category. The researcher starts this stage when he notices that he cannot find any new categories, properties and dimensions or relationships in the data he collected and analyzed from the case studies (theoretical saturation). The core category must offer an explanation of the CRM system usage behaviour under study. Goulding (2002) specifies the criteria that a core category must meet: it must be central and account for a large proportion of behaviour, it must be based on reoccurrence of the data, a core category takes longer to saturate than other categories / concepts, it must relate meaningfully to other categories, the theoretical analysis should be based on the core category, it should have clear implications for the development of formal theory, and it should be highly variable and modifiable.

This aspect – making it all come together – is probably the most difficult part of the analysis. Open and axial coding are somehow “mechanical” parts. Now is the time when “data become theory” (Strauss and Corbin, 1998, p.144). Selective coding is the process of integrating and refining axial categories so that the analyst ends up with “core” categories ultimately explaining the phenomenon under study and becoming the basis for GT. Selective coding starts only when the researcher is sure that he has found the core variable(s) accounting for most of the variation in a pattern of behaviour. Chapter five highlights the main categories found in each case study, while chapter six aims at finding a core category valid across all cases and potentially explaining overall CRM system usage.