| Summary: | This thesis is an exploratory research concerned with the investigation and identification of complex systems and their innovation life patterns. There is evidence in the literature to suggest the existence of complex systems, which differentiate themselves not only by organisational structure, but also by the way, they innovate. Complex systems seem to display a nested hierarchical formation of technological elements and the clustering of those technological elements in a synergistic manner in order to offer an enhanced service. Another distinct element of complex systems is the dependency that some particular elements in the hierarchy seem to display. This dependency of the elements in the nested hierarchy means that changes (innovation) made in one of the elements of the hierarchy might result in changes in other elements or the whole hierarchy. These characteristics not only differentiate complex from simple systems but are also the main reason why complex systems innovate in a different manner from simple systems (classical view of innovation). There is an important gap in the study of innovation in complex systems in the literature. Firstly, if in fact complex systems innovate differently from simple systems there is no evidence of a model that could clearly identify and separate complex from simple systems. Secondly, previous research on complex systems theory and innovation has studied complexity as a whole; however, the dependency between the elements is the crucial factor that hinders complex systems from innovating according to the classical view of innovation. There is no indication in the literature of a model that could clearly identify those distinct elements within the complex systems hierarchy that display the dependency. If there was a model that could identify the risk elements in the systems that carry the dependency, marketing/design managers could develop more efficient innovation strategies without putting at risk the performance of some elements of the systems or the whole product. This research proposes a model that could help to identify the particular elements that display that dependency and the possible effect that it could have in the whole hierarchy. This model is also used as a tool to identify and separate complex systems from simple systems. This research uses cameras in an example study to test the models suggested by this research. Previous research on complexity has been done in an industrial market; however, there is no empirical evidence in the literature of a model that could help the investigation of the evolution of complex systems in a commercial market. Products in a commercial market are subject to heterogeneity of demand, speed of innovation, and sophistication of needs. A model that could map the innovation pattern of commercial complex systems could help marketing and design companies with innovation strategies and decisions. In this research, this model was applied to the camera example and, in fact, cameras gave high indications and displayed clear evidence that could lead to the classification of cameras as complex systems. Cameras display evidence both of dependency between the elements and of a nested hierarchical formation, which are the elements that separate complex from simple systems. Subsequent to the finding of the evidences that support cameras as complex systems, this research investigates the innovation pattern of cameras from 1955 to 2011, and compares this innovation pattern to the classical view of both innovation and complex systems. As indicated in the literature, even though cameras have some elements common to the classical view of innovation at the beginning of the innovation life cycle, they display a rather different pattern closer to that offered by complex systems innovation. By applying this model, this research not only seems to help the classification and distinguishing complex from simple systems but also studies the complex system as a whole, and the identification of the elements that display dependency and could put any innovation activity at risk. This model also offers the possibility of studying innovation and clearly identifying to what extent and in which manner complex systems innovate differently from simple systems.
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