Once the data is collected, these results need to be analyzed. Analyzing qualitative data can be done in several ways. However qualitative data processing is as much art as science (Babbie, 2010). The reason behind this is that there are several methods how qualitative data can be analyzed; by coding the data, writing memos or mapping concepts graphically. However these methods are no real manuals, but starting points for finding order in qualitative data.
For this research is chosen to code qualitative data, which is the key process in the analysis of qualitative research. Coding is classifying or categorizing individual pieces of data. By doing this, information can be retrieved on specific topics. For example in this research what employees think of the principle of affordable loss. Furthermore you can by these means discover patterns in data and create theoretical understanding (Babbie, 2010). This process of coding within this research is done manually, although there is software available to help this process. However due to the non- experience with this software and the advice by Saldaña (2012) to code on hard-copy printouts at first, this process is done manually.
“Glaser & Strauss (1967) allow for the possibility of coding data for the purpose of testing hypotheses that have been generated by prior theory.” (Babbie, 2010 p.401). This research holds quite the same purpose. Especially concerning the involved problems, applying the broad effectual logic at large corporations and bureaucracies and the applications of organizational aspects. The codes are hereby suggested as variables.
Besides this form of coding, open coding will be used to identify other aspects. The main aspects hereby are the principles and whether the entire model is already in use, should be used and could be used at Rabobank as an example of a large corporation. This open coding is done by seeking to identify the key concepts of all answers and information given. From open coding, axial coding can be used to identify the core concepts. This involves a regrouping of the data, whereby the researcher looks for more analytic concepts. Finally selective coding is used to identify the central codes within the study. These should hereby arise from several axial codes, not just for example from one quotation (Babbie, 2010).
To give an indication of how this concept of coding is used, the coding of an aspect of top management support, positive mentality towards failure, will be highlighted. A part of the coding tree used is displayed in figure 3, with a focus on this topic of positive mentality towards failure.
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Figure 3: Coding tree of Positive mentality towards failure
Within this part of the research the interest lies in whether employees find this aspect relevant to apply corporate effectuation and whether this aspect is desirable. All passages related to this topic need to be coded as “positive mentality towards failure”. Although some sentences might be clearly related to this mentality, also concepts such as “afraid of making mistakes” are related to this code. In this way coding involves more than finding related words or sentences. Because of this fact, before the real process of coding the data starts, the keywords, relevant to the specific code, should be described. In this example these keywords are; (mentality of) failure(s), making mistakes, (afraid to) fail and (culture of) fear. When this scheme of coding is developed, all data needs to be coded according to the description of the codes. The next step involves collecting all data of the code “positive mentality towards failure” and presenting this information in the research. For this example the following sentences are coded as “positive mentality towards failure”. The keywords of this organizational aspect are boldfaced.
Failing is still seen as a mistake. The reputation of the bank is hereby of great importance. It’s hereby good and of importance to implement this support, however, these mistakes should be made on a small scale.
The culture of making mistakes should be implemented better at the organization. This should be an obligation. By these means one will improve and learn.
Making mistakes is a must, you will fail, learn and head on with your business
This mentality towards failures is going downwards, while this is necessary for the process Employees will not be punished for mistakes, but are also not motivated to release new balloons and develop new businesses. This is not sportive, but it’s also difficult to implement.
34 Banks are not that good in failures, based on reputation and safety. Because of this fact the risks should be low. However this should be the case.
Employees are too afraid of making mistakes, but you have to learn from these. These employees are just making too few mistakes. You should show them that mistakes are important to make and to learn from these.
Employees are too afraid to fail, risks need to be described and discussed whether these should be taken. More mistakes need to happen, however, the fear is still measurable. The mentality of making mistakes is decreasing, there use to be more space for doing this.
Making mistakes is a must for innovation.
Within innovation this mentality of failure is present. Other employees should be shown that this mentality works and space should be provided to make mistakes.
Furthermore an analysis will take place to draw certain conclusions from the data. For this example the results are given at paragraph 4.4.2.
Concerning open coding, other relevant and frequent pieces of data will also be coded. Because certain concepts, which were stated quite frequently, might not be a based on the theory, but might provide a very important understanding.
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