# Resilience Analysis Grid-Rasch Rating Scale Model for Measuring Organizational Resilience Potential
## 1 Introduction
A notable drawback of utilizing the AHP in combination with the RAG is the need for comprehensive data gathering and examination, particularly concerning the required pairwise comparison matrices, which can be time-consuming and intricate to complete (an n-sized pairwise matrix necessitates n(n - 1)/2 comparisons, e.g., the standard questionnaire provided by [PERSON] consisting of 39 items requires 741 pairwise comparisons). Additionally, as the AHP is typically based on expert judgement, there is the potential for bias if participants' experiences or organizational roles exert a significant influence on their responses. Indeed, the RAG-AHP approach requires substantial resources and has limited scalability. The RAG-Rasch method offers a streamlined approach to measuring organizational resilience by providing objective, comparable results across multiple organizations. Its advantages include scalability, reduced complexity, and the potential for integration with real-time monitoring tools to enhance adaptability and decision making. Furthermore, there is a more profound issue associated with the RAG method: since its beginning, the RAG was intended to assess the resilience potentials providing a snapshot of the current state of an organization. Therefore, in principle, it is only possible to compare different snapshots of the same organization (i.e., longitudinally), not different organizations at the same time, as there is no benchmark value to which to refer [30; 31]. Implicitly, the standard RAG cannot be used to compare different organizations cross-sectionally. On the contrary, in this paper, we precisely state that the concurrent assessment of several organizations' resilience potentials provides an inherent measurement scale. By means of the Rasch rating scale model (RSM), it is now possible to compare several different organizations at once on a unified scale, without the need to resort to a fixed benchmark. This present research is the counterpart of a parallel study that is also being published in the same Special Issue of _Applied Sciences_. It proposes a general method for the design of specific usage scenarios and the subsequent generation of synthetic data of appropriate parameters and characteristics [34]. This _generative-artificial-intelligence-based workbench for testing new methodologies_ has the ability to overcome the difficulties associated with collecting real organizational data, particularly those related to privacy, scalability, and cost constraints. The utilization of synthetic data facilitates the emulation of diverse scenarios, enabling researchers to methodically evaluate hypotheses and refine methodologies within controlled environments. This approach is congruent with contemporary endeavors aimed at enhancing research flexibility and efficiency. The decision to restrict the sample to a limited number of organizations, consistent with standard RAG use cases, aligns with this objective. The two articles collectively portray an overly extensive and detailed research project that would have been oversimplified if consolidated into a single manuscript. They complement each other, with each article reinforcing the other's findings and justifications. While synthetic data can serve as a preliminary tool, future validation with real organizational data is necessary to ensure the practical applicability and robustness of the developed model. This validation phase will be the subject of future follow-up studies. This article is structured to provide a clear and systematic presentation of the research conducted. Section 2 delineates the materials and methods employed in this study, offering a detailed account of the RAG and the Rasch model, as well as of their combination. In Section 3, the results are presented. Finally, Section 4 provides a comprehensive discussion of the implications of these findings and concludes with a summary of this study's potential avenues for future research.