Possibly the most fundamental skill that is required in the emerging Knowledge Age is the ability to learn. This capability resides not only with individuals, but also at every other systemic level from dyads, groups and teams, to organisations, institutions and society at large. Indeed, learning is a veritable haystack of complex, interacting and inter-related elements that span the levels of the social system. Thus, research into learning that focuses on one or another level of analysis is necessarily limited in its explanatory capacity. It cannot be said that any element or isolated set of elements enables learning; to take this view is to err on the side of naivete. This paper has emerged out of a long-term research project that aims to understand, from an organisational learning perspective, the process of innovation in small and medium sized manufacturing enterprises. Since its inception, the project has adopted an inductive, multiple case study approach designed to elucidate the extreme complexity of innovation processes. To date, the research has produced a detailed knowledge typology (Simpson et al., 2000; 2001) that identifies the key elements of technological learning. The typology comprises four knowledge categories, namely Identity, Direction, Capability, and Relationship, that interact with each other in a "generative dance" (Cook and Brown, 1999) of new knowledge creation. Although these four knowledge categories are well grounded in our data, the typology nevertheless fails to capture the dynamic nature of the learning process. In fact the majority of published models of organisational learning are similarly static (e.g. Hitt et al., 2000; Lam, 2000; Spender, 1996). Only very few scholars have endeavoured to extend their theorising into the realms of dynamic process (e.g. Crossan et al., 1999; Nonaka and Takeuchi, 1995). Reflecting on this parlous state of affairs, it became apparent to us that one of the primary obstacles to researching dynamics is methodology. The majority of research methods that are available in the social I organisational domain are based on realist assumptions, focussing narrowly on techniques for classification and measurement. But understanding dynamics demands an understanding of time, which by its very nature poses difficulties for this functionalist approach. As Ricoeur explains in his analysis of Augustine's Confessions, time must exist in order to be measured or classified, but "time has no being since the future is not yet, the past is no longer, and the present does not remain" (1984:7). So, to really come to grips with dynamic process, it is necessary to operate within an entirely different ontology, one that permits a relativist perspective on the nature of time and reality. Faced with this ontological challenge, we began to search for research methods that are not constrained by the usual assumptions of functionalism. Further, we sought an approach that accommodates the systemic interdependencies and high level of complexity inherent in innovation and organisational learning. Our search led us to Soft Systems Methodology (Checkland and Scholes, 1999), which appears to hold the potential to address all of these needs. The aim of this paper then, is to explore the efficacy of Soft Systems Methodology (SSM) as a means of inquiry into dynamic learning processes. We begin with a description of SSM, emphasising the assumptions that underpin this approach, and we then proceed to demonstrate the application of SSM techniques to a specific technological learning incident that is drawn from our case studies. Finally, we conclude with an assessment of the insights that these techniques have provided into the dynamics of a technological learning process.
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