The research findings and their implications in this study are not without limitations.
Appreciation of the limitations allows us to better understand the boundary of this study and its contributions, and to minimise the ambiguity that may lead to misinterpretation of the research findings and contributions. Two limitations will be discussed, including the generalisability of the research findings and the credibility of the case study research design.
In defence of these limitations, the measures taken to maintain the validity in the research findings and the credibility in the research design will also be discussed.
The first limitation is the generalisability of the research findings, which is relevant because qualitative research studies often risk ambiguity in the interpretation and type of generalisability of research findings and this is particularly relevant where theorybuilding approach are applied (Eisenhardt 1989; Mayring 2007; Meredith 1998; Yin 2003). There is also the inherent risk of generalisability of research findings in an emerging research area (Dedrick and West 2004; Overby et al. 2006), which is relevant because research in OSS adoption is still in its infancy (Agerfalk et al. 2006; Dedrick and West 2003; Fitzgerald and Kenny 2003; Holck et al. 2005; Larsen et al. 2004; Overby et al. 2006). These situations have made it difficult to compare our research findings with that in the existing, albeit limited, literature on OSS adoption. This problem has led us to use diverse factors influencing ICT adoption, through augmentation (in sections 2.2 to 2.5), to develop a literaturebased framework of factors that influence the adoption of OSS by SMEs. The factors from that augmentation were also applied in the comparative literature analysis (Mayring 2007) in sections 6.3 to 65, to evaluate the empirical factors identified in this research study. The limitation owing to issues of generalisability will now be discussed in detail from the perspectives of the sampled cases and the timeliness of the research empirical framework.
Generalisability is also an issue in this research study owing to the scope of the cases sampled (in section 5.2.1). This issue raises the question of bias in the sampled cases and it has three implications for the limitations of the research findings. The first implication is that the study's case organisations are UK SMEs in the IT industry. Although, the research findings reflect our observations and analysis of OSS adoption by the sampled cases, the findings may not be generalisable across the SMEs population, in the context of statistical generalisation (Eisenhardt 1989; Mayring 2007; Meredith 1998; Miles and Huberman 1994; Yin 1994).
Instead, the emergent theory of OSS adoption is applicable to cases of OSS adoption, in the context of theoretic and analytical generalisation (Eisenhardt 1989; Mayring 2007; Meredith 1998; Miles and Huberman 1994).
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The second implication is that various types of OSS were identified from different UK SMEs in this study. However, the different OSS applications observed in each case were generalised as the withincase findings for each sampled case (in Chapter 5). Thus, the research findings (in Chapter 6) represent theoretic and analytical generalisation of OSS adoption rather than the adoption of particular software applications such as desktop or server applications, operating systems and embedded systems. However, the research framework developed in this study represents an analytical theory of OSS adoption: the meanings of the factors observed and the explanations of their influences are embedded in an analytically generalisable theory (the DTPB), which continues to be applicable over time and across different contexts of ICT adoption and behavioural studies. Therefore, the analytical theory of OSS adoption developed in this study is likely to be applicable over time. In particular, the flexibility of the research framework allows it to be adapted to new situations in the context of time and the arena in which OSS adoption takes place.
The third implication is that majority of the study participants were OSS vendors/consultants, who can be expected to have a positive attitude towards the use of OSS. Although the use of a purposeful sampling strategy has allowed us to pursue common views from common participants, the resulting common samples in this study raises the question of possible bias in the views of participants observed. Similarly, the targeting of IT managers or managers/owners in the roles of participants and informants and who are seen in this study as rich sources of information, also raises the question of bias of using one viewpoint in the organisation. This particular bias is relevant because users, IT staff and external parties such as consultants may also influence the adoption of OSS in the organisation. However, we have applied a sample scope of recommended maximum number of cases, allowing us to capture diverse and rich sources of information for this study.
The generalisability of the research findings may also be affected by the timeliness of the emerged theory of OSS adoption by UK SMEs. Generally, time has been discussed as an influential factor in the diffusion of an innovation (Rogers 1995). In the context of this study, there is potential for variations in the relevance and influences of the existing and the emerging OSS characteristics, socioenvironmental factors, and organisational characteristics which influence the adoption of OSS by SMEs: increasing competitiveness of OSS and its popularity may drive its wider acceptance; and increasing participation of large players in the software industry and the better supportive government initiatives are also likely to enhance its diffusion (Rogers 1995). From an organisational perspective, the internal awareness about
K. Mijinyawa
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OSS and its potentials are likely to enhance SME managers'/owners' confidence in trying and using it in their enterprises. Research frameworks that capture relevant issues within these timeline perspectives are likely to vary. Therefore, time is likely to influence the particular set of factors observed to influence the adoption of OSS.
The second research limitation is an issue inherent to the use of maturing qualitative research methodologies (Creswell et al. 2007; Meredith 1998; Priest et al. 2002; Seale and Silverman 1997). The design and implementation of research methodology is important for establishing valid research procedures and credible research results. Also, the maturity of qualitative research methods and procedures has an influence on the credibility of the research design and the validity of the research findings. These issues are relevant for qualitative research studies in the IS field because theories in qualitative research design are still emerging.
Although we acknowledge the issues mentioned above, studies suggest that IS researchers have applied existing qualitative research instruments and procedures in different research areas, including OSS and general ICT adoption (Dedrich and West 2004; Fitzgerald and Kenny 2003; Gilmore et al. 2001; Martin 2005; Overby et al. 2006; Ritchie and Brindley 2005). The use of existing literature on qualitative research methodology, albeit complex and difficult to understand, and hence requiring extra effort to implement, has also enabled us to develop a qualitative case study research methodology for this study. Therefore, this study is also open to the inherent limitations of using maturing qualitative research methodologies.
However, we have applied rigour in developing the structured qualitative research methodology presented in this thesis. For example, we have justified the choice of an interpretivist research paradigm (see section 4.2), the selection of qualitative research methods (see section 4.3), the use of a case study strategy (see section 4.4), and presented the data collection instruments (see section 4.6) and the analysis techniques and procedures (see section 4.8). Further measures of rigour applied to this research design include ensuring highest quality and credibility in research design (see section 4.9); extensive sampling of rich and diverse case data from different sources (see section 5.2); and have maintained a structured case study database (see section 4.9.4), including the documentation of the case transcripts (see section 5.2.2), the theoretical framework for the data analysis (see section 5.3), the processes and results of withincase analysis (see section 5.4 and Appendix C – WithinCase Analysis), the processes and results of crosscase analysis (see section 5.5 and Appendix D – CrossCase Analysis; the frequency analysis of factors in Appendix E – Frequency Analysis of Factors; and the theorisation of factors in Appendix F – Description of Factors).
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