 Human–AI Interaction: From Individual Experience to Team CollaborationAnna Maria Schätzle Publications of Darmstadt Technical University, Institute for Business Studies (BWL) from Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL) Abstract: Today, digital technologies profoundly shape how individuals experience and perform their work, acquire and share knowledge, and collaborate within teams. The quality of these experiences determines whether new technologies ultimately strengthen or undermine human capabilities. Understanding the human side of digital environments has therefore become essential for introducing new technologies responsibly. This includes addressing individual well-being and psychological readiness, developing the competencies required for effective use, and understanding the collaborative dynamics that emerge in technology-mediated teamwork. Generative Artificial Intelligence (GenAI) has emerged as a transformative innovation within this landscape. Unlike earlier digital technologies, GenAI actively participates in human communication, reasoning, and decision-making through conversational interactions that increasingly shape how people work, learn, and seek guidance in both professional and personal contexts. GenAI can democratize access to knowledge, accelerate learning, and augment creative processes. Yet these opportunities come with risks. GenAI may produce convincing but inaccurate outputs, foster over-reliance, and require users to critically evaluate its responses. Although research has begun to examine isolated aspects of this transformation, significant questions remain unresolved. These range from human preconditions that shape openness toward GenAI, to the competencies and interaction dynamics required for its responsible use, and to the collective dynamics that emerge when GenAI becomes embedded in teamwork and organizational knowledge processes. This dissertation addresses these questions by examining how GenAI becomes integrated into human activity across three interconnected levels. The analysis moves from individual experiences in digital environments, to the development of capabilities for effective human–GenAI interaction, and finally to the collaborative and knowledge-related dynamics that emerge when GenAI becomes embedded in team and organizational processes. Accordingly, the dissertation is structured around three levels: (1) Human Experience in Digital Environments, (2) Human–GenAI Capability Development and Personal Interaction, and (3) Human–GenAI Collaboration in Teams. The first level of analysis focuses on Human Experience in Digital Environments. It examines the human preconditions for GenAI adoption by investigating how people experience their digital environments. One study examines how employees’ psychological capital shapes their attitudes toward digital transformation and their intention to actively support it. The findings underscore that psychological readiness is a critical enabler of technology adoption. A second study develops and tests a model linking well-being in the digital workplace to employee satisfaction, engagement, loyalty, and business performance, establishing that the quality of the digital work environment is a meaningful predictor of organizational outcomes. A third study proposes structured decision criteria for choosing between low-code and high-code development approaches, providing organizations with actionable guidance on designing digital infrastructure. The second level of analysis focuses on Human–GenAI Capability Development and Personal Interaction. It examines how individuals interact with GenAI across diverse contexts and tackles the challenge of equipping them with the competencies needed to do so effectively and responsibly. The first study designs a comprehensive, three-phase training model for GenAI competence, structured into pre-training, main training, and post-training phases that cover foundational knowledge, prompt engineering, role-specific use cases, critical evaluation, and continuous improvement. The model is explicitly designed to be modular and adaptable to real organizational workflows. A second study experimentally compares ChatGPT and Google as information retrieval tools, offering empirical evidence on how the shift from search-based to dialogue-based information access positively affects user experience, satisfaction, and task success. A third study extends the perspective beyond the workplace by exploring how individuals engage with GenAI in the emotionally sensitive context of romantic relationships. The findings reveal both the benefits of accessible, anonymous AI-mediated support and the risks of overtrust and emotional dependency, demonstrating that questions surrounding human–GenAI interaction extend well beyond professional usage scenarios. The third level of analysis focuses on Human–GenAI Collaboration in Teams. It investigates how GenAI reshapes organizational knowledge processes and how humans and GenAI can collaborate effectively in team settings. The first study at this level develops a research agenda for GenAI in organizational knowledge management by systematically identifying risks that arise in the storage, retrieval, and transfer of organizational knowledge, both in general and as a consequence of GenAI integration. The second study examines the cultural preconditions for AI-based knowledge sharing, revealing that organizational structures, leadership practices, and norms around knowledge exchange critically determine whether AI-based tools become embedded in everyday routines or remain underutilized. A third study qualitatively explores how professionals integrate GenAI into team meetings, revealing shifts in participation, task distribution, and trust dynamics. The findings show that GenAI can act as a creative stimulus and a source of shared understanding but may also deprive less experienced team members of learning opportunities that traditionally accompany routine tasks. A fourth study examines GenAI as a booster for team effectiveness through a quantitative survey, finding significant positive effects of GenAI use on both individual preparedness and contribution to teamwork as well as on 14 of 15 measured team effectiveness attributes. Across all three levels, the findings demonstrate that the successful integration of GenAI depends not only on technological capabilities but also on the human conditions under which the technology is introduced, interpreted, and embedded into everyday practices. The dissertation shows that effective human–GenAI collaboration builds on psychological readiness, the development of GenAI capabilities, and the thoughtful integration of GenAI into collaborative processes. In doing so, it highlights GenAI not merely as a technological innovation, but as a socio-technical transformation that reshapes how people learn, interact, and collaborate. By connecting human experience, individual human–GenAI interaction, and collective collaboration within a single framework, the dissertation contributes to a deeper understanding of human–GenAI collaboration and provides both theoretical foundations and practical guidance for realizing GenAI’s potential responsibly and sustainably. Date: 2026-10-26 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:dar:wpaper:162862 Access Statistics for this paper More papers in Publications of Darmstadt Technical University, Institute for Business Studies (BWL) from Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL) Contact information at EDIRC. |
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