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.

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.