Daria Höhener
Title
Dr. oec.
Last Name
Höhener
First name
Daria
Email
daria.hoehener@unisg.ch
ORCID
6 results
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Item type:Publication, A Taxonomy for the Role Definition of Chief Digital Officers in the Era of Digital Transformation(2023-12-22)Type:journal articleVolume:61 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Guidance Facilitating the Acceptance of Generative Artificial Intelligence: Insights from Digital Leaders(2025-01-07)The hype of generative AI (genAI) is omnipresent, and the characteristics of AI – autonomy, learning, and inscrutability – are well-known. Nonetheless, due to the uniqueness of genAI in producing outputs almost indistinguishable from those of humans and the associated uncertainties, companies struggle to address the challenge of human agent acceptance for the successful adoption of genAI. Based on identified challenges, this paper provides a selection of practices focusing on operational formats and organizational approaches to enhance genAI acceptance. The outcome offers practitioners specific examples to drive acceptance by increasing the usage and impact intensity potential of genAI use cases.Type:conference paperJournal:HICSS Conference Proceedings - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Scaling Artificial Intelligence Literacy(2024-12-10)The specific facets of artificial intelligence (AI) and the human-like nature of generative AI present challenges, such as the changing dynamics of agency, that must be addressed. With rapid technological advancements and increasing speed of AI adoption, non-experts need to become AI literate. However, a comprehensive summary of the manifold elements of AI literacy has not yet been elaborated. This work follows the tradition of taxonomy development in information systems research. Thus, an AI literacy taxonomy is developed based on a literature review and supplementary content from AI online courses and validated through interviews with AI leaders. The artifact is organized into knowledge and skills dimensions regarding technical specialties, technology management, business function, and interpersonal aspects. It differentiates between comprehension, use, and specialization constructs, which align with cognitive processes. In total, 21 dimensions and 58 characteristics specify AI literacy, providing a holistic overview of its complex nature.Type:conference paperJournal:ACIS Conference Proceedings - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Driving Factors in the Technology Acceptance of Generative Artificial Intelligence – Insights from an Exploratory Interview Study with Digital Leaders(2024-10-10); The adoption pace of Generative Artificial Intelligence (GAI) is swift, yet the factors specifically affecting GAI acceptance remain underexplored. Building on the long-standing tradition within information systems to elucidate technology acceptance, this paper adopts an exploratory approach given the novelty of GAI. An interview study involving thirteen experienced digital leaders from established companies has been conducted to understand GAI acceptance among human agents. The inquiry has led to developing the GAI Acceptance Model (GAIAM), which highlights trust, hedonic motivation, convenience, efficiency, and effectiveness as antecedent factors. The proposed model demonstrates that performance expectancy and perceived value creation, influenced by context factors, serve as appraisals that ultimately lead to behavioral intention and usage intensity. GAIAM may serve as an initial model for grounding the acceptance of GAI at an individual level of analysis and encourages further research in this area. Additionally, the hypothesized model provides practitioners with guidance on which levers they can proactively manage to increase the intensity of GAI usage among human agents.Type:conference paperJournal:itAIS Conference Proceeedings - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Dynamic Capabilities to Manage Generative Artificial Intelligence in Digital Transformation Efforts(2024-09-16)Incumbent firms face significant challenges due to rapid technological advancements, notably through generative artificial intelligence (genAI). By interviewing experienced digital leaders in a multiple-case study involving five organizations, this study elucidates eleven microfoundations, also referred to as low-level dynamic capabilities (DC). The specific focus centers on sensing, seizing, and transforming within the context of digital transformation (DT) efforts, offering insights into how organizations can navigate and leverage genAI to enhance their DT strategies. The identified microfoundations encompass 1) empowerment and knowledge utilization, 2) innovation ecosystem engagement, 3) organizational learning and openness, 4) interdisciplinary collaboration, 5) learning-driven innovation network, 6) organizational agility, 7) strategic leadership, 8) alignment and governance enhancement, 9) adaptive and informed culture, 10) organisational resilience, and 11) synergy creation. These foundations collectively provide a framework for leveraging genAI effectively from an organizational perspective.Type:conference paperJournal:WI Conference Proceeedings - Some of the metrics are blocked by yourconsent settings
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