The capacity to innovate is commonly regarded as a key response mechanism, a
critical organisational competence for success, even survival, for organisations
operating in turbulent conditions. Understanding how innovation works, therefore,
continues to be a significant agenda item for many researchers. Innovation, however, is
generally recognised to be a complex and multi-dimensional phenomenon.
Classificatory approaches have been used to provide conceptual frameworks for
descriptive purposes and to help better understand innovation. Further, by the facility
of pattern recognition, classificatory approaches also attempt to elevate theorising from
the specific and contextual to something more abstract and generalisable. Over the last
50 years researchers have sought to explain variance in innovation activities and
processes, adoption and diffusion patterns and, performance outcomes in terms of
these different ‘types’ of innovation.
Three generic approaches to the classification of innovations can be found in the
literature (innovation newness, area of focus and attributes). In this research, several
limitations of these approaches are identified: narrow specification, inconsistent
application across studies and, indistinct and permeable boundaries between
categories. One consequence is that opportunities for cumulative and comparative
research are hampered.
The assumption underpinning this research is that, given artefact multidimensionality,
it is not unreasonable to assume that we might expect to see the diversity of attributes
being patterned into distinct configurations. In a mixed-method study, comprising of
three empirical phases, the innovation classification problem is addressed through the
design, testing and application of a multi-dimensional framework of innovation,
predicated on perceived attributes. Phase I is characterised by an iterative process, in
which data from four case studies of successful innovation in the UK National Health
Service are synthesised with those drawn from an extensive thematic interrogation of
the literature, in order to develop the framework.
The second phase is concerned with identifying whether or not innovations configure
into discrete, identifiable types based on the multidimensional conceptualisation of
innovation artefact, construed in terms of innovation attributes. The framework is
operationalised in the form of a 56-item survey instrument, administered to a sample
consisting of 310 different innovations. 196 returns were analysed using methods
developed in biological systematics. From this analysis, a taxonomy consisting of three
discrete types (type 1, type 2 and type 3 innovations) emerges. The taxonomy provides
the basis for additional theoretical development. In phase III of the research, the utility of the taxonomy is explored in a qualitative investigation of the processes
underpinning the development of exemplar cases of each of the three innovation types.
This research presents an integrative approach to the study of innovation based on the
attributes of the innovation itself, rather than its effects. Where the challenge is to
manage multiple discrete data combinations along a number of dimensions, the
configurational approach is especially relevant and can provide a richer understanding
and description of the phenomenon of interest. Whilst none of the dimensions that
comprise the proposed framework are new in themselves, what is original is the
attempt to deal with them simultaneously in order that innovations may be classified
according to differences in the way in which their attributes configure. This more
sensitive classification of the artefact permits a clearer exploration of relationship
issues between the innovation, its processes and outcomes.
| Identifer | oai:union.ndltd.org:CRANFIELD1/oai:dspace.lib.cranfield.ac.uk:1826/124 |
| Date | 09 1900 |
| Creators | Adams, Richard |
| Contributors | Tranfield, David, Denyer, David |
| Publisher | Cranfield University, School of Management |
| Source Sets | CRANFIELD1 |
| Language | en_UK |
| Detected Language | English |
| Type | Thesis or dissertation, Doctoral, PhD |
| Format | 1883 bytes, 1790948 bytes, text/plain, application/pdf |
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