Philipp Gordetzki
Last Name
Gordetzki
First name
Philipp
Email
philipp.gordetzki@unisg.ch
10 results
Now showing 1 - 10 of 10
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Agency Configurations in Generative AI Ideation: How Textual and Visual Idea Concretizations Shape Idea Creativity and Ideator Effort(2026-06-19); ; ;Clegg, Melanie; Ideators increasingly turn to generative artificial intelligence (GenAI) to improve the creativity of their ideas and reduce the cognitive effort required to refine them. This collaboration is based on finegrained configurations of human-AI agency that allow for partial automation and augmentation of the creative-ideation process. In this study, we examine how representational differences in AI-generated inputs into human ideation in the form of textual and visual concretizations influence the creativity of the jointly produced ideas and the effort ideators must expend. These AI-generated concretizations transform initial raw ideas into more mature representations that ideators can inspect, interpret, and evaluate. In an online experiment, we found that AI-generated textual concretizations improved idea creativity by 18% relative to AI-generated visual concretizations but required 30% greater effort from ideators. This effect was most pronounced for more mature ideas (i.e., specific and actionable). For very immature ideas, visual concretizations enhanced idea creativity without increased effort. We explain these differences through different configurations of human-AI agency in ideation. Textual concretizations correspond to augmentation: GenAI produces coherent textual concretizations that ideators must interpret and complete with their imagination. The material agency of GenAI is matched by human agency, increasing idea creativity but requiring greater effort. As ideas mature, richer textual concretizations provide greater substance for creative elaboration, boosting both idea creativity and ideator effort. In contrast, the use of visual concretizations follows an automation logic. GenAI exerts material agency by autonomously specifying all required details for a visual concretization. While this can boost creativity for very immature ideas by offering stimulating details for creative exploration, these details become constraining as ideas mature. This constrains human agency and ideators’ abilities to integrate novel elements into ideation. As its main contribution, our paper shows that representational differences in AI-generated concretizations shape idea creativity and ideator effort by producing distinct configurations of human-AI agency.Type:journal articleJournal:Information Systems ResearchVolume:Forthcoming - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Refining Ideas with Generative AI: How Text-and Image-Based Scenarios Influence Idea Refinement(2024); ; ; ; In the innovation process, generative AI can be used to refine ideas with text-based scenarios and image-based visualization. Our findings reveal that text-based scenarios versus image-based visualization scenarios enhance creative performance because they increase levels of mental outcome simulations.Type:conference paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Generative AI in Idea Development: The Role of Numeric and Visual Feedback(2023-12-11); ; Human creativity is a crucial factor in developing innovative ideas. Many ideas are being generated, but only a few receive feedback, as creating feedback is a costly and timeconsuming effort in innovation. While feedback promises higher idea quality, previous work requires human experts with domain expertise. Generative AI could provide automated feedback and is expected to transform creative work. This short paper presents an experimental series in which we let humans collaborate with generative AI to develop ideas. Based on dual-coding and media synchronicity theory, we conceptualize numerical and visual feedback to overcome cognitive barriers. We manipulate feedback modalities and timing to personalize the interaction. Our contributions provide evidence on when and why specific co-creative arrangements between humans and generative AI are favorable.Type:conference paperJournal:Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies ICIS 2023Volume:14 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Towards Predicting Supplier Resilience: A Tree-Based Model Approach(2022-01); ; ;Hofmann, ErikWith looming uncertainties and disruptions in today’s global supply chains, such as lockdown measures to contain COVID-19, supply chain resilience has gained considerable attention recently. While decision-makers in procurement have emphasized the importance of traditional risk assessment, its shortcomings can be complemented by resilience. However, while most resilience studies are too qualitative in nature and abstract to inform supplier decisions, many quantitative resilience studies frequently rely on complex and impractical operations research models fed with simulated supplier data. Thus there is the need for an integrative, intermediate way for the practical and automated prediction of resilience with real-world data. We therefore propose a random forest-based supervised learning method to predict supplier resilience, outperforming the current human benchmark evaluation by 139 percent. The model is trained on both internal ERP data and publicly available secondary data to help assess suppliers in a pre-screening step, before deciding which supplier to select for a specific product. The results of this study are to be integrated into a software tool developed for measuring and tracking the total cost of supply chain resilience from the perspective of purchasing decisions.Type:conference paperJournal:Proceedings of the Hawaii International Conference on System Sciences - Some of the metrics are blocked by yourconsent settings
Item type:Publication, LLM-Augmentation for Idea Evaluation: Developing a Reference Model for Evaluation Pipelines(Springer Nature Switzerland, 2025-05-27); ;Samir Chatterjee ;Vom Brocke JanAnderson, RicardoAutomated approaches to idea evaluation increasingly leverage generative artificial intelligence to support decision-makers. However, contextualizing evaluations within specific domains remains challenging, particularly at varying levels of large language model (LLM) augmentation. Existing research employs embeddings to derive semantic insights, yet these representations often lack domain-specific contextualization. Recent advancements, such as chat-based LLMs, present new opportunities to incorporate context through prompting. To address these challenges, we propose a structured evaluation pipeline that integrates embeddings with feature engineering to enhance the contextualization of chat-based LLM evaluations. Using a real-world innovation challenge, we instantiate this pipeline and assess its predictive performance across different levels of augmentation. Our findings reveal that incorporating contextual information improves predictive accuracy but depends on fine-grained idea quality dimensions. By codifying our approach into a reference model, we provide a transferable framework that generalizes across various evaluation contexts employing LLMs.Type:book sectionJournal:Lecture Notes in Computer ScienceVolume:15703 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Enhancing Collaborative Ideation with Generative AI: Choosing Between Text and Image Artefacts(2024); ; ; ; Type:conference contribution - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Beyond Automation: The Transformative Power of AI to Shape Consumer-Firm Relationship(2024-09-26) ;Hajighasemi, Mohammadhesam ;Amir Sepehri ;Cait Lamberton ;Steph TullyChiara LongoniType:conference contribution - Some of the metrics are blocked by yourconsent settings
Item type:Publication, - Some of the metrics are blocked by yourconsent settings
Item type:Publication,