GUT-IS: A Data-Driven Approach to Integrating Constructs and Their Relations in Information Systems

Problem
This paper addresses the inconsistency in construct definitions within Information Systems (IS) research, which hampers the cumulative development of knowledge in the field. The authors propose a data-driven approach to integrate structural equation models into a unified framework. This work is presented as a preprint and has not yet undergone peer review, indicating that the findings should be interpreted with caution.

Method
The authors introduce GUT-IS, a methodology that combines task-adapted text embeddings with clustering techniques to generate candidate construct groupings. The core technical contribution lies in the formulation of a loss function that balances two competing objectives: semantic purity (the degree to which constructs accurately represent their intended meanings) and parsimony (the simplicity of the model, measured by the number of clusters). The approach involves the following steps:

1. Text Embeddings: Constructs are represented using embeddings tailored to the specific tasks in IS research.
2. Clustering: A clustering algorithm is applied to the embeddings to identify potential groupings of constructs.
3. Loss Function: The optimal clustering solution is selected based on a loss function that explicitly incorporates the trade-off between semantic purity and parsimony.
4. Empirical Evaluation: The methodology is evaluated on two datasets from the IS domain, although specific details regarding the datasets and their characteristics are not disclosed.

Results
The authors report that their approach successfully identifies construct groupings that enhance the coherence of structural equation models. While specific numerical results and comparisons to baseline methods are not provided in the abstract, the paper claims that the GUT-IS methodology outperforms traditional methods in terms of achieving a better balance between semantic purity and parsimony. The effectiveness of the approach is demonstrated through empirical evaluations, although detailed metrics and effect sizes are not explicitly stated.

Limitations
The authors acknowledge that their approach may be sensitive to the choice of text embeddings and clustering algorithms, which could affect the robustness of the construct groupings. Additionally, the reliance on specific datasets may limit the generalizability of the findings. The paper does not address potential biases in the datasets used or the implications of varying definitions of constructs across different contexts, which could impact the applicability of the proposed methodology.

Why it matters
The GUT-IS methodology has significant implications for the field of Information Systems by providing a systematic approach to resolving inconsistencies in construct definitions. By explicitly modeling the trade-off between semantic purity and parsimony, researchers can better understand how different construct groupings influence the interpretation of structural equation models. This work paves the way for more coherent theoretical frameworks in IS research, potentially leading to improved model accuracy and interpretability. Furthermore, the approach could inspire similar methodologies in other domains where construct integration is critical.

Authors: Maximilian Reinhardt, Jonas Scharfenberger, Burkhardt Funk
Source: arXiv:2605.18567
URL: https://arxiv.org/abs/2605.18567v1