After the data collection was completed, the raw data in the form of audio recordings needed to be processed and converted into a textual format. To do so interview tran- scripts were created using the audio transcription software F5 transcript. While the software simplified the transcription process in general, due to its batch-edit functionali- ty it was very useful for the anonymising of the data, especially for the focus group in- terviews where multiple speakers were involved. In total, 24 interviews needed to be processed before data analysis could start. While two of the six expert interviews were conducted and transcribed in English, the remaining interviews were conducted and transcribed in German.
3.4.2 Processing of the Data through Coding
Generally, qualitative data analysis consists of three successive phases, (1) data reduc- tion, (2) data display, and (3) drawing conclusions (Miles & Huberman, 1994). In the data reduction phase empirically collected data is condensed and organised. This means that information being irrelevant for answering the research question were discarded, but retained in case they would become of value during later stages of the data analysis phase. As a starting point for data reduction and as a form of preliminary analysis, con- tact summary forms were created. These short synopses were created during and shortly after the interviews, and were helpful in acquiring initial knowledge about the gathered data (Miles, Huberman, & Saldaña, 2014). Particularly, they helped identifying poten- tial gaps in the data collection, for example whether certain topics or factors required further investigation or clarification.
After preliminary examination was completed, the data was further condensed and analysed through the process of coding. As described by Miles and Huberman (1994), “codes are tags or labels for assigning units of meaning to the descriptive or inferential information compiled during a study” (p. 56). However, the coding of quali- tative data is not a linear process. In fact, codes and overarching code categories can only emerge through a constant comparison of data (Glaser & Strauss, 1967; Saldaña, 2009). Hence, the coding of qualitative data is a cyclical process that occurs in two phases, First Cycle and Second Cycle coding (Saldaña, 2015; Miles et al., 2014).
3.4.2.1 First Cycle Coding
During first coding cycle the data corpus needs to be carefully examined so that “codes [could be] initially assigned to the data chunks” (Miles et al., 2014, p. 73). In order to do so, the selection of an appropriate coding method was required. Particularly, the re- searcher considered Eclectic Coding as the most suitable coding method for this study, since it allows the purposeful combination of different First Cycle coding methods. In a first step, Provisional Coding, a specific form of exploratory coding, was employed. This coding method is particularly “appropriate for qualitative studies that build on (…) previous research and investigations” (Miles et al., 2014, p. 121).
A predetermined code “start list” was established before the data collection strated (Miles & Huberman, 1994; Miles et al., 2014). This list contained a number of codes “from anticipated categories or types of responses/actions” (Saldaña, 2009, p. 121) that were expected to surface in the data, and which had been derived from the literature review and conceptual framework. In order to have a clear understanding of what kind of data needed to be assigned to each code, a coding framework with clear operational definitions was developed, which can be found in Appendix F. This coding
framework also contains the 26 established provisional codes, a number that lies within the range recommended by Miles and Huberman (1994) and Miles et al. (2014).
While a provisional code list can be a good starting point for initial analysis, it must be treated with caution. Specifically, the researcher had to resist the temptation of forcing the collected data into specific codes or code categories (Saldaña, 2009). In or- der to avoid this, the predetermined codes were modified, revised, expanded or removed if necessary, and additional codes were added when new themes emerged from the data. To achieve this Provisional Coding was used in conjunction with another First Cycle coding method, namely Initial Coding. In Initial Coding the qualitative data is disaggre- gated into separate parts, which are closely examined and compared “for similarities and differences” (Strauss & Corbin, 1998, p. 102).
3.4.2.2 Second Cycle Coding
During the preliminary analysis and first code cycle a first exploration of the data as well as a summary of the segments found in the data corpus was established (Saldaña, 2009). In order to conduct further and more complex analysis of the qualitative data, a second code cycle was employed (Miles & Huberman, 1994; Miles et al., 2014). In this second coding stage the codes that had emerged during the first code cycle were reana- lysed, reorganised, refined and condensed in such a way that “a smaller and more select list of broader categories, themes, and/or concepts” (Saldaña, 2009, p. 150) could be developed. To do so, Axial Coding, which is on of the three coding techniques used in Grounded Theory Research, was employed (Strauss & Corbin, 1990, 1998). The pur- pose of Axial Coding is the strategic reassembling or disaggregation (Wick, 2010) of the data that were “split” or “fractured” during the first code cycle (Strauss & Corbin, 1998, p.124). In order to achieve this, relationships between the codes are established by creating categories and subcategories around the central phenomenon under study. This process is guided by a combination of inductive and deductive reasoning (Glaser & Strauss, 1967; Strauss & Corbin, 1990).
By relying on the Straussian coding paradigm, it was possible to contextualise the phenomenon under study and to structure it in a very systematic manner (Strauss & Corbin, 1998; Muller, 2012). In so doing, different types of relationships between the codes, code categories and subcategories could be identified. Specifically, the coding paradigm consists of (1) the phenomenon under study, (2) causal conditions that lead to the occurrence of the phenomenon, (3) contextual conditions that relate to the context and circumstances under which the phenomenon occurs (i.e. the company and its em- ployees), (4) intervening conditions that may “mitigate or otherwise impact causal con- ditions on phenomena” (Strauss &Corbin, 1998, p. 131), (5) actions or interaction strat- egies that can be used to deal with the phenomenon, and (6) consequences of the actions or interaction strategies that are related to the phenomenon (Kelle, 2005).
Next to the different interdependencies, when structuring the data two factors were important, also for further analysis: (1) Groundedness and (2) Density. Grounded- ness refers to the code frequency – that means the number of quotations, to which a cer- tain code is applied. If large numbers of quotations are associated with a certain code, this shows that in the data strong evidence (groundedness) is found for this code (Feely, 2014; Friese, 2014). On the other hand, the number of links that exist among the codes indicate Density. The more links between codes exist, the stronger is the code density. 3.4.3 Theory Development based on Actor-Network Theory
ANT has been applied in this research as a methodological framework that guided the development of a model, by means of which the different factors that influence organi- sational readiness for change regarding the planned adoption and use of Enterprise So- cial Software as well as their relationships can be better described and understood. Due to the large number of factors as well as the complexity of the relationships between them, it was necessary to find a way through which the results could be presented in a clearly-structured and visual manner. To do so, the researcher relied on graphical syntax from design science research (Hevner, March and Park, 2004). As noted by Alexander and Silvis (2014), “expressing actor-network theory (…) graphically requires a distinc- tion to be made between three different roles that actors can step into during the process of translation” (para. 37), namely (1) the actor as a source that is being translated, (2) the actor as a target, for whom something is translated, and (3) the actor as a translator between a source and a target. Figure illustrates this graphical syntax.
Specifically, in the developed model the graphical symbols for the concepts of source, target, translator and main research foci were used to along with the suggested relationships. However, a few additional symbols were created, such as dotted bi- directional arrows to indicate associations, one-directional arrows to represent affilia- tions between concepts, as well as dotted actor symbols to visualise properties.
4
Findings
This section reports the findings from the qualitative data analysis performed for the different interviews that were conducted in order to answer the main research question and its associated sub-questions. At first, the results obtained from structuring the textu- al data following the Straussian Grounded Theory methodology (GTM) are presented for the two research sub-questions SRQ1 and SRQ2. This will be done by closely exam- ining the different codes, code categories and their interdependencies that were ground- ed in the interview and focus group data. Then, the findings of the two sub-questions are consolidated, in order to answer the main research question.