The Impact of Innovation and Optimization on Public Sector Performance: Testing the Contribution of Connective, Ambidextrous, and Learning Capabilities

Abstract

This article makes two contributions to the literature: it tests the impact of innovating and optimizing on perceived public performance. Secondly, it examines the contribution of connective, ambidextrous, and learning capacity to both innovation and optimization in public organizations. Building on previous research, the relevant attributes of connective, ambidextrous, and learning capacity are singled out at the individual, organizational, and network level. Based on the literature, we expect these capacities to relate stronger to either optimizing or innovating. We test this multidimensional framework in a survey among the 22 regional water authorities in the Netherlands using SEM. The results show that optimizing and innovation both contribute to performance. However, optimizing shows a stronger relationship. Furthermore, all three capacities are related to innovation and optimization, but in different degrees at different levels. In line with our hypotheses, we found connective capacity to relate more strongly to optimizing, whereas learning capacity relates stronger to innovating and ambidextrous capacity to both. These results indicate that public organizations will benefit from a deliberate evaluation whether public performance is best served with optimization or innovation, and from a focused approach in developing and employing these capacities that enables a balanced approach to innovating and optimizing.

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