Detailed Data Analysis Procedure

The data analysis process was divided into the following five stages, namely, preliminary processing of data, closed coding, open coding, creation of categories and their properties, and the establishment of relationships between the core concepts. These stages were conducted iteratively through the continuous comparison and analysis of the data. This approach is based on the argument of Miles and Huberman (1994) that the data analysis process consists of four iterative stages, namely, data collection, data reduction, data display, and the drawing of conclusions. Several other studies also describe qualitative analysis as an iterative process that includes collecting data, coding and establishing concepts (e.g. Morse et al., 2002; Seidel, 1998). Categories and relationships are discussed in chapter six – the discussion chapter.

5.4.1 Preliminary Processing of the Data

I personally transcribed each interview, one at a time. Listening to the audio recordings while transcribing the data gave me an opportunity to pay close attention to the data and to understand fragments of it before the coding process began. In addition, it also helped me focus on the way in which the participants had responded to the questions and also to their tone of voice. I made hard copies of the transcripts and read through them several times. This provided an initial sense of the important issues arising from the data. For example, after reading through the hard copies of the first three interviews, it became apparent that awareness and access were key concepts that affected the use and non-use of Smart City services.

Copies of the interview transcripts were then imported into Atlas.ti and the volume of material in each interview was reduced by categorising data into initial themes. The use of electronic coding software helped in the organisation and classification of the data. The initial themes that emerged were, in fact, the four core concepts which had been adapted from Wang's (2014) theoretical framework. These included user needs, effectiveness, value, and alternative sources of information. The selection of the categories was also based on a preliminary sense of what was important. This, resulted in the introduction of an initial level of closed coding analysis and open coding analysis at this preliminary stage.

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5.4.2 Closed Coding

Coding refers to the process of classifying and categorising textual data segments into concepts that can be used to uncover patterns in data (Bhattacherjee, 2012). This process involved selecting fragments of data from the transcripts and assigning these fragments to categories that belonged to a certain code family. This contributed to data reduction. Before starting the coding process, codes were created. The core concepts adapted from Wang's (2014) theoretical framework were used as the starting codes while the properties of each core concept were used as sub-codes for the purposes of a more detailed closed coding analysis. Each transcript was read and coded before the researcher moved on to the next one. Even though at this stage interview material was assessed in relation to the categories identified in Wang’s (2014) theoretical framework, new concepts emerged. For example, it was identified at this stage that awareness, access and trust should be independent core concepts with their own sub-codes.

5.4.3 Open Coding

Open coding is a coding process that aims to identify and uncover concepts that are hidden within textual data (Bhattacherjee, 2012). I coded the interviews exhaustively with the aim of uncovering new concepts that were not represented in the theoretical framework. I was specifically seeking ideas and concepts that had emerged as both relevant and significant because, although they were not represented in Wang's (2014) theoretical framework, they had been presented repeatedly in the interviews.

However, the open coding was not limited to this stage of the data analysis. During the preliminary processing of the data and the closed coding, an element of open coding was introduced into the process. This ensured that I was open to identifying new concepts at every stage of the data analysis. New categories emerged during this stage. Some of these supported the core concepts and sub-categories as proposed by Wang (2014), while others validated the new core concepts: access, awareness and trust.

When new codes emerged, the previously analysed data was then re-analysed in light of the new codes. This led to the data being re-read several times. Multiple codes were sometimes attached to a single data segment. Atlas.ti was used to group the codes according to shared characteristics. The software also helped me identify the number of quotations that were related to a code and to ascertain the amount that had been said in relation to a specific code

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across the participants. This helped me to keep track of the themes which occurred most frequently, the context in which they occurred, and how they were related to each other. At this point, in line with the approach suggested by Seidel (1998), I endeavoured to keep the holistic picture in mind and to reflect on the relationships between the research questions and the recurring themes, I also paid close attention to surprising, unsuspected, hidden and conflicting issues which emerged from the data.

The Atlas.ti software electronic journal feature was used in order to record steps in the analysis process and my reflections on the data. As previously indicated by De Wet and Erasmus (2005), this helped to illuminate, in a logical order, the procedures followed. The journal entries also proved useful during the writing of the analysis chapter of the research report. In some cases I copied and pasted the paragraphs from the Atlas.ti journals to parts of the analysis chapter and, thus, it may be said that some of the writing process began during the coding stages. The Atlas.ti software provides a system of electronic tools for organising, retrieving and verifying data. This facilitates the effective and efficient organisation and procedural analysis of data. The software also enabled me to devote more time to reflecting on the data and the emerging concepts, and to ensuring rigour than on trying to organise the data.