Evaluation of the innovation capability of cluster-based firms: a Graph-Theoretic Approach

Abstract

Innovation is a distinctive element for competitiveness and economic development. Literature indicates that business networks such as industrial clusters enhance regional economies and are a propitious environment to innovate, since it enables competition and cooperation. In this sense, this dissertation aims to develop a framework to evaluate the innovation capability (IC) of cluster-based firms. The proposed framework is based on Graph-Theoretic Approach (GTA), a multicriteria method, and is able to quantify qualitative aspects, generating an innovation capability index (ICI) for each firm. It measures the influence of factors and sub-factors (mapped in the literature through a systematic review) on the IC of firms in the context of clusters. Five IC factors were identified: network collaboration, knowledge creation and transfer, technology development, market influence, and proximity. To determine the interdependencies among these factors and their sub-factors, the Delphi method was used. Then, to test the proposed framework, multiple case studies were developed in an Information and Communications Technology (ICT) and Creative Economy (CE) cluster located in Recife, Brazil. The identification of factors and sub-factors allows a greater understanding on the innovation capability of cluster-based firms, besides contributing to strategic decision-making regarding the organizational characteristics and capabilities (what to improve, where to invest, etc.). The framework can be considered generic and can be applied in firms based on clusters of diverse industries.