Institut für Marketing & Management

Permanent URI for this collectionhttps://hohpublica.uni-hohenheim.de/handle/123456789/29

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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; Gimpel, Henner; FIM Research Center for Information Management, Augsburg, Germany; Laubacher, Robert; Center for Collective Intelligence, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA; Meindl, Oliver; FIM Research Center for Information Management, Augsburg, Germany; Wöhl, Moritz; FIM Research Center for Information Management, Augsburg, Germany; Dombetzki, Luca; TUM.ai, Munich, Germany
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.
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.
Sustainability-oriented macro trends and innovation types - Exploring different organization types tackling the global sustainability megatrend
(2021) Gaudig, Anja; Ebersberger, Bernd; Kuckertz, Andreas
The prevailing environmental and social challenges worldwide require comprehensive and sustainability-oriented changes in central areas of society—endeavors that call for more sustainability-oriented innovations. Sustainability can be understood as a megatrend within our society comprising sustainability-oriented macro trends such as Agricultural Innovation, Circular Economy, or Clean Tech. In line with this conceptualization, the current paper analyzes to what extent different types of organizations, such as startups and established companies, have been tackling sustainability-oriented macro trends and how much they have been focusing on sustainability-oriented innovation activities within their organization types. For the study, 758 organizations from the Trendexplorer database were examined through univariate and bivariate analyses. The results underscore that sustainability can be perceived as a key driver of structural change by illustrating that different organization types focus on multiple yet diverse sustainability-oriented macro trends simultaneously while concentrating on a specific type of innovation, whereby all three types of innovations (technological, marketing, and product and service innovations) can be integrated.

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