Objetivo por sesiones

According to Stake (2006), qualitative data analysis enables the flexibility of comparing results among different cases. The interactive nature of the data collection and analysis of the research study enabled the researcher to recognise and visualise important emerging patterns, themes and relationships as data was collected (Saunders et al., 2009). Qualitative data analysis seeks to sieve out the meaningful content of data by attaching derived meanings to phenomena (Flick, 2010). Data processing was started by organising all the data collected into a database; all recording and interview guide notes were labelled properly and filed electronically on the computer and in the cloud for retrieval in case of accidental damage, loss and theft. Interviews

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were subsequently transcribed by the researcher into text; reading the transcript assisted the researcher to familiarise himself with the information in the interview while making meaning of the relating answers and questions.

The first step in analysing data collected in a study is the representation of that data in written format (Saunders et al., 2009:485). All of the data collected was transcribed or documented in MS-Word, using the Microsoft Word package. The data were arranged and similar concepts and keywords were identified and coded according to their implied meanings. According to Richards and Morse (2007, as cited by Saldana, 2009:8), ―…it leads you from the data to the idea and from the idea to all the data pertaining to that idea‖. Saldana (2009) developed a coding manual to assist researchers in understanding the coding process and concepts, and how it evolves into categories and themes (Figure 3.12). Miles and Huberman‘s (1994, as cited by Neumann, 2011:510) description of codes ascribes that ―…codes are tags or labels for assigning units of meaning to the descriptive or inferential information compiled during a study. Codes usually are attached to chunks of varying size-words, phases, sentences or whole paragraphs, connected or unconnected to a specific setting‖.

34Figure 3.12: Streamlined codes-to-theory model for qualitative inquiry

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A qualitative thematic analysis method was used with meaningful, interpretative and descriptive tools to organise and analyse relevant data collected from the excerpts of interview schedules developed to investigate the research problem. Qualitative data can be analysed using a simple thematic coding system by reading through all data extensively, summarising all of the data collected, noting all of the categories that occur in the data, grouping key concepts into themes and identifying key themes according to their appearances in groups. Quinlan (2011) provides support for this method by stating that this method allows the researcher to look at documents, text or speech to see what themes emerged and identify recurring and similar themes.

3.9.1.1 Hermeneutics

Taylor (1976, as cited by Myers, 1997:10) describes hermeneutics in research as follows:

Interpretation, in the sense relevant to hermeneutics, is an attempt to make clear, to make sense of an object of study. This object must, therefore, be a text, or a text- analogue, which in some way is confused, incomplete, cloudy, seemingly contradictory—in one way or another, unclear. The interpretation aims to bring to light an underlying coherence or sense.

Hermeneutics can be considered as an underlying interpretive philosophy approach of analysing specific qualitative data. Zikmund et al. (2010), state that meanings are derived by the connection of patterns from each case to the other, and to established themes and theories related to the research. Hermeneutics involves a deep and detailed reading of texts to derive a deep understanding with richer meanings rooted within the text (Neuman, 2011). The inherent interpretation of meanings and relationships are expressed by coding the key meanings and concepts in the analysis of the research data.

Hermeneutic units are concerned with the meaning of a text from the interview excerpt that can be connected to a key category within the interview excerpts, or one provided by the researcher (Flick, 2010). Hermeneutic units are used in qualitative data analysis software to group phrases of data that have similar meanings and interpretation. After reading through the transcript and excerpts of the interview and all relevant summaries were made and recorded, the summarised data were critically examined for existing similarities, then coded and categorised into identifying codes accordingly. A spreadsheet was used to categorise the summarised data and keywords into parts and similar parts with meanings called categories. This method called memoing was prescribed by Bhattacherjee (2012:115). Each developed category was given an appropriate description, and the process was done iteratively until all relevant phrases and keywords were coded and categorised.

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Thereafter data were successfully categorised conceptually; meaningful relationships emerged which further lead to the identification of patterns and concepts, which subsequently developed into a theme.

This process continues in a cyclical manner until all available relevant data is captured and coded starting from open coding, focused and selective coding, which leads to axial or thematic coding that in return reveals meaningful patterns and relationships. Bhattacherjee (2012:115) posits that the process of thematic analysis is achieved when ―…coding of new data and theory refinement continues until theoretical saturation is reached‖. Figure 3.13 illustrates the stages of coding in thematic analysis to theory.

Figure 0.13: Stages of Coding to achieve theoretical saturation

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35Figure 3.13: Stages of coding in thematic analysis to theory

(Strauss & Corbin, 1998, cited and adapted by Bhattacherjee, 2012)

The result of the findings was used to build an empirical set of guidelines, and an inductive inference was made to complement prior and relevant existing theory. This was done to offer a logical solution to the lack of sufficient evaluation and adoption of new technological innovations

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in SMMEs. Thus the interviews aimed to explore and provide an in-depth knowledge and understanding of evaluation and adoption issues of new technology surrounding the low rate of adoption of new technology by SMMEs in Cape Town.