🤖 AI Summary
This study addresses the challenge of inconsistent construct definitions in information systems research, which impedes cumulative knowledge development. To resolve this issue, the authors propose a novel approach that leverages task-adapted text embeddings and clustering to generate candidate construct groupings. They introduce the first custom loss function explicitly balancing semantic purity against model parsimony, enabling the construction of unified and interpretable structural equation models. The method further supports dynamic analysis of how construct groupings evolve under varying optimization objectives. Empirical validation on two information systems datasets demonstrates the approach’s effectiveness in achieving semantically coherent integration of constructs and their interrelationships.
📝 Abstract
Structural equation modeling is widely used in IS research. However, inconsistent construct definitions impede the cumulative development of knowledge. In this work, we present an approach that aims at the integration of structural equation models into a unified model: We use a combination of task-adapted text embeddings and clustering to produce a candidate set of construct groupings. Subsequently, we select the optimal solution using a loss function that explicitly trades off semantic purity and parsimony in the number of clusters. By making this trade-off explicit, our approach allows to analyze how construct groupings and their relations change as one shifts the priority from purity to parsimony. Empirically, we evaluate and explore the proposed methodology on two datasets from the IS domain.