Emerging Technology in Collaboration and Cocreation
Permanent URI for this collectionhttps://hdl.handle.net/10125/112405
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Item type: Item , Enhancing Framing Effects Through AI Prompt Engineering: The Role of Human–AI Co-Creation and Brand Familiarity(2026-01-06) Holopainen, Jani; Konttinen, Netta; Tuominen, SasuOur study addresses a significant research gap in AI prompt engineering and audience subject familiarity. Our online experiment shows that audiences unfamiliar with the brand are less sensitive to AI-generated marketing content compared to familiar audiences. However, increasing human effort in AI prompt engineering and human-AI co-creation can mitigate these negative effects. These findings extend framing theory to include AI prompt engineering and suggest that human-AI co-creation can reduce negative effects among brand-familiar audiences. Additionally, a human prompter can guide the AI to generate more compelling messaging by highlighting specific factors or dimensions. Our study offers important future research avenues and practical implications for practitioners co-creating content with AI tools. We emphasize the importance of skilled personnel and continuous staff education, training, and self-motivation to gain a competitive advantage in the rapidly evolving AI landscape.Item type: Item , Hierarchical Machine Vision application for Automated Diagnosis of Dental X-Ray Images(2026-01-06) Ha, Jee Hae; Dubach, Richard; Schau, HopeThis study proposes a unified machine vision framework for the automated analysis of dental panoramic X-ray images, addressing a critical intersection between artificial intelligence and healthcare operations. We developed a deep learning pipeline that integrates advanced models to perform tooth detection, segmentation, and disease classification within a single workflow. By converting medical images into a rich source of diagnostic information, this research enhances the clinical utility of existing imaging practices. From a managerial perspective, the integration of AI enables scalable, real-time diagnostics that can reduce clinician workload, support clinical decision-making, and improve resource allocation. Furthermore, the framework has the potential to expand access to dental care through telemedicine applications, particularly in underserved regions. This research lays the foundation for the development of AI-driven diagnostic tools that align with broader goals in healthcare innovation and digital transformation.Item type: Item , Introduction to the Minitrack on Emerging Technology in Collaboration and Cocreation(2026-01-06) Akaka, Melissa; Schau, Hope; Sebesta, John; Vargo, Stephen