This section presents the case study strategy, which acts as a blueprint that guides the field research processes involved in data collection and analysis, and the reporting of study findings (Gable 1994). Although field research issues such as a plan of the logistics during data collection, including scheduling and budgeting (Lincoln and Guba 1985) were considered prior to the empirical inquiry, such issues were not specifically determined because these issues were subject to circumstances around the researcher and the research participants. However, adequate financial arrangements, timing of appointments, and contact with participants were considered.
Studies of case study design (see, for example, Eisenhardt 1989; Gable 1994; Lincoln and Guba 1985; Yin 1994) suggest there are seven key design issues: (1) the focus of the inquiry;
(2) the fit of the research paradigm to the research focus; (3) the sources of empirical data;
(4) the instruments, plan, and recording modes of data collection including lines of inquiry, and measures for ensuring validity of data; (5) the phases of the inquiry; (6) the plan of data analysis procedures; and (7) the plan of techniques to determine case study quality and rigour. These issues were considered in this study and form the basis for the design of the field research for this case study strategy (see Figure 4.1).
4.3.1 Focus of the Inquiry
The first issue relevant to this multiplecases research design is the focus of this inquiry. As shown in Figure 4.1, the literature research (encompassing Chapters 1, 2, and 3) is the background to the focus of this inquiry, and encompasses three important components
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including the study question, research propositions, and unit of analysis (Yin 1994, 2003).
These components will now be discussed in turn.
The first component of the focus of this inquiry is the research question (see section 1.4), which focuses on exploring factors that influence the adoption of OSS by IT SMEs, and is relevant here because it sets the same focus for this inquiry – to explore factors influencing OSS adoption by SMEs.
The second component is the unit of analysis in this study. We define the unit of analysis in this research study as a factor that influences the adoption of OSS by an IT SME. That unit of analysis is based on the research question (in section 1.4), and consistent with Yin (2003) who suggests that the unit of analysis is an event or entity that is likely to be at the level of the research questions. This unit of analysis is also evident from three different contexts. The first context is the identification and classification of factors in the literature analysis in sections 2.2, 2.3 and 2.4. The second context is the development of a theoretical framework suitable for exploring factors that influence OSS adoption by SMEs and explaining their influence. The third context is the centrality of the theoretical categories of factors in the design of data collection instrument, and data analysis and reporting methods and techniques for the empirical research.
The unit of analysis established in this study is important for two reasons. First, it helps to narrow the data collection within the limits that best meet the research aim and objectives (Yin 2003), and therefore, helps to focus the data collection on factors that influence OSS adoption by SMEs. Second, it serves as a criterion for comparing empirical findings in cross
case analysis, and across longitudinal studies (Yin 2003), and therefore allows us to use empirical factors and their categories as the basis for the analysis of empirical findings.
The third component is the research propositions developed in this study (see section 3.3) and is related to the conceptual model as shown in the literature research part of Figure 4.1.
It follows that as an interpretivist study, the research propositions which were defined in section 3.3, help to focus the inquiry on the unit of analysis. Thus, there were no objectivist propositions or hypotheses defined with the intention of testing, confirming, or refuting such propositions or hypotheses, as is common with positivist studies (Ivankova et al. 2006; Myers 1997; Sale et al. 2002). However, as discussed in section 3.3, exploratory research propositions were developed and form the analytical framework that guides the design of qualitative data collection instrument and analysis techniques and procedures. In this study,
K. Mijinyawa
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the research propositions developed in section 3.3 are exploratory in nature, and are not tested as would have been the case in a positivist research (Fitzgerald and Howcroft 1998;
Hoepfl 1997; Myers 1997 ).
4.3.2 Fit of the Research Paradigm to the Research Focus
The fit of the research paradigm to the research focus is established for this study. The research question, research aim and objectives (both established in section 1.4) are associated with the research focus and allowed us to establish the fit of the research paradigm to the research focus. The research focus was also discussed in terms of the focus of this inquiry, where the research question (in section 1.4) was, again, argued to be an important issue for the focus of this inquiry (see, section 4.3.1). The research question, research aim and objectives were also the key justifications for the choice of the research paradigm (in section 4.2). Therefore, the research question, research aim and objectives are important links between the research paradigm established and the research focus.
4.3.3 Data Sampling
In the case study strategy, case sampling was applied to clarify the domain of this investigation on cases that are relevant to understanding OSS adoption by IT SMEs (Eisenhardt 1989; Mayring 2007; Miles and Huberman 1994; Yin 2003). Thus, logical replication (Coyne 1997; Eisenhardt 1989; Miles and Huberman 1994) was applied in selecting 10 UK SMEs in the IT industry. A case sampling boundary of 10 was set to help manage limited time, means, and the number of cases, which can be between four and 10 cases for a multiplecases study research (Eisenhardt 1989; Miles and Huberman 1994). The 10 SMEs are then case subjects, and help to extend the discovery of factors that influence the OSS adoption.
The selection of the 10 IT SMEs as case subjects follows an application of a sampling frame (Miles and Huberman 1994), including purposeful, theoretical (analytical), opportunistic, phenomenal, deviant case, and maximum variation sampling (Eisenhardt 1989; Meredith 1998; Miles and Huberman 1994; Patton 1990; Sandelowski 1995). For explicitly justifying the selection of sample cases in this study, three sampling strategies were applied.
The first sampling strategy was a purposeful sampling, described as a flexible sampling technique (Coyne 1997; Miles and Huberman 1994; Patton 1990) applied to extend the richness of information for this exploratory study. It was applied as an initial sampling technique, to identify diverse UK IT SMEs willing to participate, as case subjects, in this
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research study. Setting UK SMEs as a boundary for the purposeful sampling provided a general basis arguing for the analytical generalisation (Meredith 1998; Miles and Huberman 1994; Yin 2003), of the emergent factors influencing OSS adoption in this study.
Within the initial purposeful sampling of UK SMEs, further purposeful sampling was applied to select cases that have potential for rich information (Miles and Huberman 1994) and also focused on two issues related to the conceptual framework (Miles and Huberman 1994). The first was an organisational issue and the preference of an SME manager/owner as a potential rich source of information because such a person is a focalpoint for all information and activities (Gelinas and Bigras 2004; Martin 2005; Taylor and Murphy 2004) and therefore able to contribute both as a participant and as an informant. The second issue was a technological issue, concerned with a preference of the OSS server technology platforms because there appears to be a higher adoption of OSS server platforms compared to a low, but rising adoption of OSS desktop platforms (Giera 2004). Therefore, this research is more likely to generate rich information by sampling case organisations that adopt OSS server platforms.
A second sampling strategy applied in this study was a maximum variation sampling (Patton 1990), which helps to justify the selection of a nonOSS adoption case. This sampling allows us to extend the variation of issues such as barriers or constraints to OSS adoption by IT SMEs.
The third sampling strategy applied was as emergent or theoretical (analytical) sampling (Meredith 1998; Miles and Huberman 1994; Patton 1990; Sandelowski 1995). This strategy allow us to pursue new cases based on insights from existing data. This sampling strategy allows us to justify the inclusion of cases where OSS applications are a core part of an embedded systems platform.