innovation in the context of information systems and innovation theory. These two parent theories overlay complementary and sometimes divergent perspectives relating to IT innovation.
Information systems theory covers off on the design, development, implementation and use of IT within organisations and society. A great deal of the research draws on technology diffusion and adoption perspectives. It highlights how organisations realise the value from IT development and describes important techniques associated with the methods of design and implementation of technological and organisational change. A key area of information systems theory relating to IT innovation is the literature concerning IS implementation. This research provides important understandings of factors relating to success or failure of IS implementation, the role of user-designer interaction, the processes associated with implementation and adoption, and political factors that facilitate or impede IS implementation (Kwon & Zmud 1987).
Whilst the theoretical and empirical work concerning IS implementation provides an important contribution to understandings of IT innovation, this research is dominated by diffusion and adoption based perspectives of innovation (Fichman 2004; Ruttan 1996).
97 Fichman (2004) emphasises that IT innovation research needs to move beyond this dominant paradigm to further understand what may be other important dimensions IT innovation. Diffusion of innovation theory (DOI) used in the context of IS implementation has also been the subject of substantial criticism. DOI assumes that technologies are discrete packages that diffuse into a fixed homogenous environment. This has been found to be particularly untrue in the case of large complex information systems where implementation and adoption can be subjected to a range of alterative social interpretations in relation to context (Lyytinen & Damsgaard 2001). DOI also implies that the adoption process follows a rational process of careful analysis and selection in order to maximise the benefits of the proposed adoption (Lyytinen & Damsgaard 2001). A notion particularly at odds with principles of uncertainty and entrepreneurship found within the innovation literature. There are also problems determining a definition for operational adoption and distinguishing between acquisition at the organisational level, and adoption at the end-user or individual level (Bayer & Melone 1989). Issues also remain with the under emphasis of unsuccessful, abandoned or incomplete innovations (Rogers 1995) and under representing the influence of historical choice and path dependence (Arthur 1989; David 1986).
The theoretical and empirical work concerning IS implementation is also somewhat fragmented and contrasting (Agarwal & Lucas 2005; Lucas, Swanson & Zmud 2008), particularly in the context of defining and understanding IT innovation. Previous research has attempted to examine and unify these contrasting perspectives. It has endeavoured to combine macro-level perspectives of innovation theory with the micro-level understandings of IT innovation practice from the IS implementation literature (Kwon & Zmud 1987; Mustonen-Ollila & Lyytinen 2003). Swanson (1994) integrates perspectives from organisational innovation to map different types of IT innovation to organisational assets and capabilities; Lyytinen and Rose (2003) explore IT innovation in the context of disruptive innovation theory (Christensen 1997), and further call for a dynamic theory of IT innovation; and Wang and Ramiller (2009) emphasise the role of acquiring new, or modifying and reinforcing existing IT knowledge, through community interaction.
The theoretical and empirical work relating to IT/IS development and engineering is also an important source of knowledge relating to IT innovation. The knowledge relating to development and project management methodologies within the IT/IS disciplines provide
98 considerable insight into activities and process involved in the successful development and adoption of IT/IS. Some of this knowledge is bound to notions of prescriptive staged/linear development processes, however recent IS/IT development practices have begun to adapt interactive and emergent techniques more commonly associated with innovation theory.
Whilst this body of work remains highly relevant, it potentially overlooks factors associated with technological product and process innovation. There are also number of prevailing issues associated with (1) the limited differentiation of IT innovation from IT development, implementation and evaluation processes; (2) a lack of insight into the different ways in which IT innovation occurs in practice, and the factors important in any determination of IT innovation success and/or sustainability; and (3) a prevalence of assumptions that IT innovation can be meaningfully conceptualised through a linear model of staged activities. Theoretical insights from the innovation literature highlight the pervasiveness of innovation processes and the important role of collaboration amongst customers (users), competitors and suppliers operating within innovation systems. Innovation theory also emphasises the complex nature of innovation, the role of uncertainty and the emergent non-linear nature of technological developments that are themselves historically constrained and temporally situated.
The experience and research knowledge obtained from empirical studies outlined in the Oslo Manual (OECD/Eurostat 2005) have also assisted to consolidate and unify the important theoretical dimensions of innovation.
Whilst innovation theory may appear to have the potential for furthering understandings IT innovation, there is however limited empirical work conducted within this domain, nor is there any research that utilises the consolidated guidance for studying innovation presented in the Oslo Manual (OECD/Eurostat 2005).
Where IT innovation may have been explored within the innovation literature, theory and empirical data is often abstracted a general or macro level. Rosenberg (1982) stressed this issue in the context of technological change and argued that technological change was different between sectors. Arguably innovations with different technologies required different innovation systems. The detailed descriptions of innovation in one industry are unlikely to be
99 relevant to another. Rosenberg (1994) advocates that insights into the process by which technological knowledge grows should involve a detailed examination of the sequences of events and institutions within particular industries or sectors.
In summary the IT/IS implementation literature provides important theoretical and empirical knowledge within information systems theory to support the best explanation about what IT innovation is and what it involves. However it does not capture everything. It has been demonstrated in a few studies that understandings of innovation theory can assist to provide clarity and improve understanding of IT innovation. The potential overlapping (and non- overlapping) relationships between information systems theory and innovation theory in the context of understanding IT innovation are illustrated in Figure 2-26. Descriptions of the overlapping areas of knowledge are also provided in Table 2-14.
In this context several issues worthy of further research can be identified at the intersection of the research literature:
• Potentially untested knowledge of IT innovation from within innovation theory or from other theoretical domains remains a possibility (‘3+4’ overlaps).
• Many shared and common understandings that seem implicit in the information systems and innovation literature have not been empirically tested in the context of IT innovation (‘1’ overlaps).
• Despite the existence of shared and common theoretical positions, there are still conflicting issues across both domains that require further clarification and understanding. For example there is no clear definition for IT innovation that includes elements of innovation beyond the diffusion and adoption perspective.
• There is no coherent framework or guidance for capturing information about IT innovation that relates directly to the contemporary literature pertaining to innovation.
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Figure 2-26. The parent theory overlay for understandings of IT innovation.
Table 2-14. Descriptions of the overlapping areas of knowledge associated with IT innovation Area of
Knowledge Description
Shared and common understandings of IT innovation from both information systems and innovation theory.
Understandings of IT innovation exclusively from information systems theory. Understandings of IT innovation exclusively from innovation theory.
Understandings of IT innovation outside of information systems and innovation theory e.g. management science.
Current empirically tested knowledge of IT innovation, encompassing IT/IS implementation theory.
Potentially untested knowledge of IT innovation from innovation theory, or from other theoretical domains.
This research proposes that the key issue for understanding IT innovation is that information systems theory should be linked to contemporary innovation theory in order to establish a consolidated view of IT innovation. Linking these theories through the common notions of diffusion and adoption have already been shown to assist understanding IT innovation. Extending this work and incorporating additional dimensions of innovation theory may also assist to consolidate IT innovation research.
101 Such a proposal is also supported in the IS/IT literature. Lucas, Swanson and Zmud (2008, p. 8) argue that ‘that innovation and innovation-induced transformation provide powerful lenses through which to view the IS field’, and suggest that recent innovation theory has the capacity to correct earlier deficiencies in implementation research. Lucas, Swanson and Zmud (2008, p. 8) recommend that information systems theory needed to account for the technological, institutional and historical context of IT/IS implementation and that research should be ‘oriented toward telling rich and complete stories of innovation with information technology’. This view is also supported within the innovation literature that highlights the diversity and pervasiveness of innovation across all sectors of the economy. Rosenberg (1994) suggests that to understand innovation beyond more general concepts inevitably involves drilling down into the domain to examine the common patterns and cases. Lucas, Swanson and Zmud (2008) also wanted research to focus on how IT innovation become involved in the creation of organisational capabilities and competitive advantage.
To this end the high level guidance provided within the Oslo Manual (OECD/Eurostat 2005) may provide a potential launching pad to guide the exploration of IT innovation. As discussed in the previous section, the Oslo Manual (OECD/Eurostat 2005) provides comprehensive consolidation of contemporary innovation theory. It provides specific guidance for innovation data collection that is founded upon the experience and research knowledge obtained from empirical studies relating innovation. It is important to acknowledge that the data collection guidance provided by the Oslo Manual (OECD/Eurostat 2005) is oriented towards the general phenomena of innovation. The guidance is generally not domain or industry specific, but arguably well suited to providing a high level framework for exploring innovation data.
It is possible to reconstruct and summarise the guidance relating to innovation data found in the Oslo Manual (OECD/Eurostat 2005) using a traditional A-B-C antecedents, behaviour and consequences heuristic model (Brancheau & Brown 1993; Skinner 1938), where the antecedents are represented by IT innovation decisions, behaviour is represented by IT innovation activity and consequences are represented by IT innovation outcomes (see Figure 2-27).
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Figure 2-27. Summarised model for innovation data collection adapted from OECD/Eurostat (2005)
The core elements of this model are:
A. The decision to innovate – understanding the reasons, motivations and/or objectives driving innovations;
B. Innovation activity – ‘all scientific, technological, financial and commercial steps which actually, or are intended to, lead to the implementation of innovations’ (OECD/Eurostat 2005). This includes activity associated with research and experimental development, the acquisition of capital goods and services, the acquisition of external knowledge and activities associated with implementation and deployment; and
C. Innovation outcomes – understanding the economic and social outcomes associated with innovation. Asking about the success or failure of innovation activities and possibly measuring the impact of innovation in terms of organisation performance, degree of novelty, breadth of diffusion and the creative effort required to progress innovation (OECD/Eurostat 2005; Smith 2005).
The summarised Oslo Manual based model can be further elaborated to accommodate the general theoretical characteristics and dimensions of innovation described in section 2.4.3, adding the pervasiveness and complexity of innovation; the uncertain and emergent nature of innovation; the role of collaboration within institutional structures and ecosystems; and the lasting implications of historical choices and events.
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Figure 2-28. An elaborated theoretical framework for innovation data collection
The resulting model is a heuristic device to assist the researcher to explore IT innovation practice, confirming what is real, relevant and potentially missing from our understanding of IT innovation. The same heuristic also has application for guiding analysis, acting as a skeleton or organising framework for discussion of IT innovation in the context of innovation theory.