Studying complex causal processes in technological innovation and entrepreneurship with set-theoretic mediation models

Abstract

Researchers often turn to linear mediation models to understand the complex causal processes inherent within innovation and entrepreneurship phenomena. However, these models are not always the most appropriate methods for increasing our understanding of these phenomena. This is because linear models depend on the principle of reductionism – which separates causal processes into their independent components – and overlooks systemwide attributes. To advance research findings that do not adequately address complex causal processes, we advocate using set-theoretic mediation models that offer analytical features better suited for holistically uncovering interdependent and intervening pathways. This method enables investigating complex causal processes associated with the conjunction, equifinality, and asymmetry that can occur with multiple interdependent variables. We provide researchers with practical guidance on constructing and testing set-theoretic mediation models using widely available software while demonstrating these procedures with an illustrative analysis. In doing so, we seek to guide researchers interested in integrating these models into their studies and recommend best practices for implementation. We argue that set-theoretic meditation models can be utilized in various contexts, as they offer new research opportunities for exploring unified necessity and sufficiency relational systems in ways existing methods have yet to address.