Enhancing the Understanding of Manufacturing SME Innovation Ecosystems : A Design Visualisation Approach

Nthubu, Badziili and Cruickshank, Leon and Richards, Daniel (2021) Enhancing the Understanding of Manufacturing SME Innovation Ecosystems : A Design Visualisation Approach. PhD thesis, Lancaster University.

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Abstract

Small and Medium-sized Enterprises (SMEs) associated with manufacturing often form complex ecosystems that are difficult to understand and manage. This is particularly common in developing economies. Whilst the role of manufacturing SMEs has grown in creating jobs and businesses in most industrialised nations, SMEs in developing economies are lagging. To enhance the understanding of local SME ecosystem complexities, this thesis engages 17 manufacturing SMEs and two incubators in Botswana. The research also explores four makerspaces and eight manufacturing SMEs in the United Kingdom (UK). Participants are engaged through semi-structured interviews and exploratory visualisations to construct rich knowledge on their local innovation ecosystem micro-level structures. Further, the qualitative data is analysed through thematic and visual network analysis techniques. Data from Botswana and the UK contexts provide the opportunity to perform a cross-case discussion between an industrialised and a developing economy. This thesis proposes a framework to enhance the understanding of manufacturing SMEs' innovation ecosystems and contribute to the scarce local SME ecosystem design literature. The ‘Jigsaw ecosystem design framework’ is built through exploratory case study projects in Botswana and the UK contexts. This framework is tested through a series of co-design workshops with 105 participants in Botswana and at a virtual conference. The thesis findings demonstrate that the framework is useful and applicable in enhancing the understanding of local manufacturing SME ecosystems, suggesting a continual learning process of ecosystem structures by all key stakeholders in local ecosystems. The thesis concludes by highlighting the potential for future research focused on developing the Jigsaw framework into a digital application that can capture local ecosystem configurations in real-time. This work may further enhance the continual learning of ecosystem configurations and support decision-making at the micro-levels of the local ecosystem. Further testing of the framework with diverse agents and contexts is proposed to increase its scope.