Bridging Minds and Machines: Human and Computer Collaboration for Creative Problem Solving and Business Innovation

Permanent URI for this collectionhttps://hdl.handle.net/10125/112400

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    Creativity in Human-AI Co-Creation: A Two-Stage Model and the Da Vinci Score
    (2026-01-06) Jiang, Ling; Wagner, Christian
    Standard collaborative systems for creative problem-solving and innovation often focus on process efficiency rather than the content of ideas. Generative artificial intelligence (GenAI) promises a shift: it can actively generate and evaluate ideas, potentially transforming innovation workflows. Integrating concepts from group support systems and collaboration engineering, we propose a two-stage model of human–AI co-creation. In Stage 1 (AI-seeded ideation), the AI rapidly establishes a foundation by generating knowable ideas; in Stage 2 (human-enhanced ideation), humans engage in unpatterned ideation using this AI-generated output, both refining initial ideas and introducing content gains/losses that extend traditional process gains/losses frameworks. We further define the Da Vinci Score as a composite creativity metric that enables differential weighting of criteria, aligning evaluation with business relevance and practical collaboration. Our three-condition hypotheses (AI-only, Human-only, Hybrid), partially supported by prior data, highlight implications for innovation practice, process design, and future research in business contexts.
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    Understanding Human-AI Co-creation in the Workplace: A Study on Organizational Drivers and Innovation Outcomes
    (2026-01-06) Neves, Catarina; Oliveira, Tiago; Neves, Joana; Cruz-Jesus, Frederico; Falcão, Henrique
    As artificial intelligence (AI) tools become increasingly used in organizations, understanding how to achieve real performance gains from their use is a growing priority. While much research has focused on automation, this study shifts attention to human–AI collaboration, examining how organizational factors enable employees to co-create value with AI. Drawing on structural empowerment theory, we propose an holistic model that investigates both antecedents of AI use behavior and human–AI co creation, and the consequent outcomes in terms of innovation and productivity skills. Results reveal that structural empowerment significantly impacts use and co-creation, and that human–AI co-creation not only increases innovation and productivity but also moderates the impact of AI use, highlighting that collaboration quality can compensate for lower usage frequency. These findings extend empowerment theory into AI studies and support socio-technical perspectives by showing the interaction between social and technological elements.
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    Augmentation Innovation Paradox: Rethinking Validation in Generative AI-Driven Open Innovation
    (2026-01-06) Abhari, Kaveh; Safaei Pour, Morteza; Sanatizadeh, Aida
    Open innovation is undergoing a fundamental transformation with the rise of artificial intelligence, particularly through the emergence of augmented innovation, characterized by the collaborative interplay between human creativity and generative AI. This study investigates a central tension in this shift: the ideation–validation paradox. While generative AI (GenAI) significantly accelerates the process of idea generation, it simultaneously introduces new complexities around validating the quality, novelty, and feasibility of those ideas. To address this paradox, we propose an extended ideation framework that identifies five socio-technical mechanisms aimed at making GenAI-driven contributions more systematic, auditable, and responsible. We conclude by outlining a future research agenda to advance our understanding of human–AI collaboration within open innovation ecosystems.
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    Beyond the Screen: Memory-Based Mechanisms and Personal Innovativeness in Voice Assistant Use
    (2026-01-06) Pillet, Jean-Charles; Carillo, Kévin; Vitari, Claudio; Pigni, Federico
    This study investigates how the mental retrieval of IT features influences innovative use behaviors with voice-activated devices (VADs). We conceptualize two memory-based constructs—IT feature recognition and IT feature recall—and examine their complex interplay. We theorize that their influence is moderated by users' personal innovativeness. Using a survey of 319 smart speaker owners, we found that both recognition and recall enhance innovative use, but their effects differ significantly based on an individual's disposition to innovate with IT. For users with high innovativeness, IT feature recognition drives innovation, while for those with low innovativeness, both recall and recognition contribute. These findings suggest that memory-based mechanisms are critical enablers of innovative use, highlighting the need for interfaces that support memory cues to foster broader feature utilization and innovative outcomes in voice-based interfaces.
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