Fakultät Wirtschafts- und Sozialwissenschaften

Permanent URI for this communityhttps://hohpublica.uni-hohenheim.de/handle/123456789/22

Die Fakultät vereint Forschung und moderne Lehre nach internationalen Standards. Das Hohenheimer Modell verzahnt dabei betriebs- und volkswirtschaftliche, sozial- und rechtswissenschaftliche Aspekte.

Homepage: https://wiso.uni-hohenheim.de/

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Advancing content synthesis in macro-task crowdsourcing facilitation leveraging natural language processing
(2024) Gimpel, Henner; Laubacher, Robert; Meindl, Oliver; Wöhl, Moritz; Dombetzki, Luca
Macro-task crowdsourcing presents a promising approach to address wicked problems like climate change by leveraging the collective efforts of a diverse crowd. Such macro-task crowdsourcing requires facilitation. However, in the facilitation process, traditionally aggregating and synthesizing text contributions from the crowd is labor-intensive, demanding expertise and time from facilitators. Recent advancements in large language models (LLMs) have demonstrated human-level performance in natural language processing. This paper proposes an abstract design for an information system, developed through four iterations of a prototype, to support the synthesis process of contributions using LLM-based natural language processing. The prototype demonstrated promising results, enhancing efficiency and effectiveness in synthesis activities for macro-task crowdsourcing facilitation. By streamlining the synthesis process, the proposed system significantly reduces the effort to synthesize content, allowing for stronger integration of synthesized content into the discussions to reach consensus, ideally leading to more meaningful outcomes.
A generalized representation of Faà Di Bruno'S formula using multivariate and matrix‐valued Bell polynomials
(2025) Evers, Michael P.; Kontny, Markus
We provide a generalization of Faà di Bruno’s formula to represent the 𝑛-th total derivative of the multivariate and vector-valued composite 𝑓 ∘𝑔. To this end, we make use of properties of the Kronecker product and the 𝑛-th derivative of the left-composite 𝑓 , which allow the use of a multivariate and matrix-valued form of partial Bell polynomials to represent the generalized Faà di Bruno’s formula. We further show that standard recurrence relations that hold for the univariate partial Bell polynomial also hold for the multivariate partial Bell polynomial under a simple transformation. We apply this generalization of Faà di Bruno’s formula to the computation of multivariate moments of the normal distribution.
Idea evaluation for solutions to specialized problems: leveraging the potential of crowds and Large Language Models
(2025) Gimpel, Henner; Laubacher, Robert; Probost, Fabian; Schäfer, Ricarda; Schoch, Manfred
Complex problems such as climate change pose severe challenges to societies worldwide. To overcome these challenges, digital innovation contests have emerged as a promising tool for idea generation. However, assessing idea quality in innovation contests is becoming increasingly problematic in domains where specialized knowledge is needed. Traditionally, expert juries are responsible for idea evaluation in such contests. However, experts are a substantial bottleneck as they are often scarce and expensive. To assess whether expert juries could be replaced, we consider two approaches. We leverage crowdsourcing and a Large Language Model (LLM) to evaluate ideas, two approaches that are similar in terms of the aggregation of collective knowledge and could therefore be close to expert knowledge. We compare expert jury evaluations from innovation contests on climate change with crowdsourced and LLM’s evaluations and assess performance differences. Results indicate that crowds and LLMs have the ability to evaluate ideas in the complex problem domain while contest specialization—the degree to which a contest relates to a knowledge-intensive domain rather than a broad field of interest—is an inhibitor of crowd evaluation performance but does not influence the evaluation performance of LLMs. Our contribution lies with demonstrating that crowds and LLMs (as opposed to traditional expert juries) are suitable for idea evaluation and allows innovation contest operators to integrate the knowledge of crowds and LLMs to reduce the resource bottleneck of expert juries.
Predictor preselection for mixed‐frequency dynamic factor models: a simulation study with an empirical application to GDP nowcasting
(2025) Franjic, Domenic; Schweikert, Karsten
We investigate the performance of dynamic factor model nowcasting with preselected predictors in a mixed‐frequency setting. The predictors are selected via the elastic net as it is common in the targeted predictor literature. A simulation study and an application to empirical data are used to evaluate different strategies for variable selection, the influence of tuning parameters, and to determine the optimal way to handle mixed‐frequency data. We propose a novel cross‐validation approach that connects the preselection and nowcasting step. In general, we find that preselecting provides more accurate nowcasts compared with the benchmark dynamic factor model using all variables. Our newly proposed cross‐validation method outperforms the other specifications in most cases.

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