TIMEA

This project aims to convert the Multi-Level Analysis (MEA) method—newly developed at Hof University of Applied Sciences—into a web-based format. MEA makes complex task interdependencies—one of the major barriers to technology transfer—transparent and allows for a precise comparison of innovative solutions with conventional ones (including cost-benefit analysis), thereby significantly enhancing the transfer into practice. A web-based version enables the use of MEA without requiring intensive support from researchers at Hof.

Studies conducted by the Institute for Sustainable Water Systems (inwa) at Hof University of Applied Sciences—focusing on success factors for digitalization in the water sector, climate change adaptation in the Upper Franconia region, and "sponge city" development—consistently show that key stakeholders (e.g., municipalities, planning engineers, architects, and authorities) increasingly feel overwhelmed by the complexity of the tasks involved. Consequently, individual stakeholder groups tend to focus solely on their specific duties, neglect the necessary analysis of interconnected challenges, and limit their choice of solutions to established standards, thereby failing to facilitate innovation transfer. To counteract this tendency, researchers at Hof developed the Multi-Level Analysis (MLA) method, designed to capture, define, and evaluate various spheres of impact—particularly during the initial stages of complex (infrastructure) projects. The MLA is based on a spatial perspective of complex tasks, categorizing them into micro, meso, macro, and meta levels. For each level, a set of identical task-related main criteria and corresponding specific sub-criteria—derived from both research and the specific project—are assigned, interlinked, and evaluated through a multi-stage process. This process is applied to both the general task requirements and the proposed solutions.

Depending on the project, hundreds of individual criteria and their interdependencies must be considered. Due to this vast amount of data, the current Excel-based version of the MLA can only be used effectively with the support of the Hof researchers, which significantly limits its widespread adoption. Without this research support, there is a risk that users may develop a distorted, subjective perception—and thus a skewed interpretation—of the data. As a result, important details, dependencies, and trends may be overlooked due to the users' limited mental and temporal capacity and perceptual scope, leaving the benefits of suitable innovative solutions untapped. To prevent errors in assessment arising from individual interpretations of terms by MEA users, a "TextAnalyzer Pro" is to be developed—an innovative, intelligent module for text processing and analysis powered by OpenAI technology. This module offers useful features for identifying keywords within the text and subsequently generates targeted process description attributes and fields (individual criteria) in a practical table format for the MEA web system.

This enables MEA users to navigate the MEA process without the need for expert guidance. While the web-based version allows for a broader scope of criteria, this method of selection also reduces processing time; the time saved can then be used to explore a wider range of potential criteria combinations in order to identify the optimal innovation solution.