Figure 4.1 shows the data analysis stage of field research, an important stage which processes data gathered and outputs the findings. The data analysis techniques discussed here are systematic procedures for analysing empirical data. The systematic procedures are important because they enhance credibility and dependability of the analysis process and the findings (Eisenhardt 1989; Miles and Huberman 1994; Yin 1994, 2003).
The analysis strategy applied in this study provides a guide for exploring meaning and understanding from the field data, logically presenting the empirical findings, and reporting K. Mijinyawa
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conclusions drawn from these findings. This analysis strategy is based on three concurrent flows of data analysis activities to achieve analytical generalisation (Benbasat et al. 1987;
Miles and Huberman 1994; Yin, 1994). The data analysis activities are data reduction, data display, and conclusions drawing and verification (Miles and Huberman 1994; Yin 2003), and are discussed in turn below.
4.5.1 Data Reduction
Data reduction refers to the process of exploring meaning and understanding from qualitative data sources, and includes various iterative processes: data examination, sharpening, rearranging, focusing, categorisation, recombination, and selection (Baskerville 1999; Corbin and Strauss 1990; Hyde 2000; Miles and Huberman 1994; Myers 1999; Roberts and Wilson 2002; Tellis 1997; Thomas 2006; Yin 1994, 2003). These processes are applied in developing a data analysis procedure as shown in Figure 4.3, and consisting of five stages: initial setup, data interpretation, categorisation, analytical generalisation, and factor definition. The initial three stages form the withincase analysis processes. The fourth stage forms the crosscase analysis processes. The fifth stage is a definition of emerged factors. These five stages are now described in detail.
The first stage of data reduction in Figure 4.3 is initial setup and prepares the withincase analysis processes by specifying the case study objective and a definition of initial theoretical categories. Declaring the case study objective helps to focus the data analysis towards identifying units of analysis. The definitions of initial theoretical categories (see Appendix A.4 – Definition of Theoretical Constructs) provides a guide for interpreting units of analysis in the next stage.
The second stage in Figure 4.3 is data interpretation and focuses on identifying evidence within the data, using two processes. The first process is a close reading of a case transcript, involving reading and reflecting on the data and providing an indepth understanding of the data as a standalone entity, allowing the researcher to become familiar with its complexity and contexts (Eisenhardt 1989). The second process is an identification of relevant text segments, focusing on deriving themes by identifying units of analysis in particular text segments. This process is guided by the researcher's knowledge and intimate familiarity (Bryman 2001; Roberts and Wilson 2002) with the unit of analysis (see, for example, sections 2.2, 2.3 and 2.4), and the understanding of the theoretical concepts (in section 3.3).
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Figure 4.3 Data Analysis Procedure
Figure 4.3 shows that a credibility check is performed when relevant text segments are
Case study objectives definition of initial and theoretical categories
Identification of relevant text segments Close reading of transcript
Within-case codes Placement of text
segments into existing or newly defined categories
Identification of common codes across cases Interpretation
of within-case results
Interpretation of cross-case
results Initial Setup
Credibility checks in data interpretation
Dependability checks in code categorisation Data InterpretationCategorisationAnalytical generalisation
Empirical factors influencing OSS adoption
Cross-case codes
Factor definitiom
Consistency checks in definitions
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The third stage of data reduction in Figure 4.3 is categorisation. This focuses on using codes and categories for categorising segments of data identified in the previous stage. The codes and categories help to systematically organise themes that are identified. A theme is coded by linking a unique code to its related segment of data. Thus, each code is a unique identifier for a particular segment of data that represents a unit of analysis.
To develop a code for a theme by categorising it, the meaning in a segment of data representing the theme is compared to definitions of theoretical concepts in the casestudy database. A match between the two will establish a theme, which is then coded in the next stage. A theme can also be identified by patternmatching (Gable 1994; Mayring 2000; Tellis 1997; Yin 1994, 2003) between a segment of data and a previously coded theme. If a match is not established in the two approaches above, this will suggest the emergence of a new category of themes. In such a situation, the segment of data is coded and a new category is also created. This approach is consistent with an inductive analysis process (Hoepfl 1997;
Mayring 2000; Thomas 2006), and is particularly important for developing categories based on new themes that emerge from the data. Codes are also created for theoretical concepts in the initial setup stage, and these are used for categorising themes that match with the definition of a theoretical concept. Codes for the theoretical constructs have the same name as their theoretical constructs. Codes are also created for the new themes and their categories.
The names for new categories are based on the first theme placed into the category.
Figure 4.3 shows that a dependability check is performed after codes are placed in relevant categories. In this context, dependability is a technique for ensuring research quality and rigour, and will be discussed in greater detail in section 4.6.3. Therefore, this check enhances quality and therefore confidence in the categories created and used for sorting the codes developed. The dependability check is performed by crosschecking that a code is placed in its correct category and that duplication of a category is avoided.
The third stage is categorisation, which is the last stage of withincase analysis, and allows us to organise text segments, codes and categories according to the cases. An iteration through the previous stages and processes extracts themes and develops related codes from all of the case study dataset. The tabulation of segments of text linked to themes, codes and categories enables quick search through a withincase analysis data set. Thus, all codes, categories and related segments of text are organised in flat files in the case study database, an approach that is particularly important for the next stage of analysis which focuses on identifying analytical generalisation (Benbasat et al. 1987; Yin 1994, 2003) across all cases.
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The fourth stage of data reduction in Figure 4.3 is the analytical generalisation, which identifies common codes across cases, leading to a logical replication of the codes. Identifying analytical generalisation is important because it leads to the formation of strong evidence, from the triangulation of multiple sources of data (Guion 2002; Mayring 2007; Meredith 1998; Patton 1999), to support the definition of factors in subsequent stages of the data analysis. To achieve analytical generalisation, common codes and categories across all cases are identified and their frequency is noted in a crosscase frequency table. From that cross
case frequency, codes with high logical replication are selected since these show analytical generalisably of the theme represented by the codes. Furthermore, the selected codes provide strong evidence that can lead to definition of factors in the next stage of analysis.
The fifth stage in Figure 4.3 is the definition of factors, and develops theoretical definitions of the identified factors, which form components of this research's empirical theory of OSS adoption by SMEs. A definition for a factor is developed by creating a theoretical description of the theme represented by the codes, using the explanation of the theoretical category associated with the code. Thus, the theoretical categories provide a strong generalisable description in the definition, and the meanings from the text segments associated with the code provide the context for the emerged definition. The definition is enriched, repeatedly, using the data from similar codes and also checked for consistency with the definition of theoretical category associated with the factor. The emergence of the definition of factors ends the data reduction process.
4.5.2 Data Display
The second data analysis activity in this study is data display, which is an organised and compressed assembly of empirical evidence that leads to conclusion drawing (Miles and Huberman 1994). Data displays in this study provide a collective view of the empirical data, and this is important in order to conduct crosscase analysis that identifies trends across the multiple cases evidence.
Studies suggest that qualitative research studies use tables, extended text, matrices, graphs, charts, and networks (Eisenhardt 1989; Miles and Huberman 1994; Yin 1994) for data display. However, for this study, tabular displays were mainly used for the withincase and crosscase analysis, and matrix frameworks were also used for organising field data, factor codes, and category codes in the crosscase analysis.
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4.5.3 Conclusions Drawing
This study uses an analytic strategy (Tellis 1997), which includes data reduction and data displays as discussed in the sections 4.5.12, and leads to conclusions drawing. The analytical strategy also relies on the theoretical framework (Yin 1994) developed in section 3.3, which provide a structure for the reporting of the research findings and conclusions drawing in this study. Thus, the conclusions drawing presents the factors identified from the crosscase analysis, using the theoretical framework to explain the factors and their influence on OSS adoption in this study. The empirical factors are displayed within the theoretical framework and ultimately represented in a diagram as the empirical model of OSS adoption by IT SMEs.