Theorising plays a foundational role in Information Systems (IS) research. While the field has made important advances through theory borrowing, via adaptation and instantiation, as well as through contextualisation of established frameworks and models, comparatively little attention has been devoted to the elaboration of existing theories through structured, logic-driven approaches. This commentary problematises that imbalance and advances the concept of deductive theory elaboration as a valuable, yet underutilised, form of theorising in behavioural IS research. We define deductive theory elaboration as a process that extends existing theories by introducing conceptual modifications to their constructs, relationships, or boundary conditions prior to empirical testing. We distinguish this approach from related forms of theorising and propose a four-step framework supported by a repertoire of elaboration patterns for both variance and process theories. We also offer practical reporting guidelines to promote transparency and rigour in elaboration-based contributions. Our aim is to encourage more systematic elaboration efforts to enhance the precision, generalisability, and cumulative potential of IS theories, an optimistic vision of how behavioural IS research can evolve to meet the conceptual challenges of a rapidly transforming digital landscape.

We present a probability-based metric for semantic consistency using a set of uncertain rules. As opposed to existing metrics for semantic consistency , our metric allows to consider rules that are expected to be fulfilled with specific probabilities. The resulting metric values represent the probability that the assessed dataset is free of internal contradictions with regard to the uncertain rules and thus have a clear interpretation. The theoretical basis for determining the metric values are statistical tests and the concept of the p -value, allowing the interpretation of the metric value as a probability. We demonstrate the practical applicability and effectiveness of the metric in a real-world setting by analyzing a customer dataset of an insurance company. Here, the metric was applied to identify semantic consistency problems in the data and to support decision-making, for instance, when offering individual products to customers.