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April 7, 2026Technology in Society2 citationsOpen Access

Risk it, if you dare: A quantitative analysis of the effects of perceived risk facets on the adoption of generative artificial intelligence in knowledge work

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NSNiklas SchulteJNJulien NussbaumDKDominik K. Kanbach

Key Points

  • The aim is to understand how perceived risks affect the adoption of generative AI in knowledge work.
  • Utilized structural equation modeling
  • Analyzed data from three samples across different countries (n = 1,450)
  • Integrated perceived risks into the artificial intelligence device use acceptance model
  • Performance risk negatively affects perceived performance and emotions but increases effort
  • Psychological risk raises effort perceptions and negatively impacts emotions
  • Time risk enhances perceived performance while contributing to perceived effort
  • Privacy and social risks have limited effects on perceived performance, effort, and emotions
  • High awareness of privacy risk underscores the need for responsible AI integration

Abstract

The adoption of generative artificial intelligence (GenAI) is influenced by perceived risks, which can impact perceptions of performance, effort, and emotions. Understanding how different facets of risk drive functional and non-functional perceptions and lead to actual use would be crucial to improving its acceptance and adoption in knowledge work. By assessing performance, privacy, psychological, social, and time risk, and incorporating them into the artificial intelligence device use acceptance (AIDUA) model as part of a sequential adoption process, this study highlights the shaping effect of GenAI on risk perceptions and its broader influence on socio-technological interactions. Using structural equation modeling with three data samples from different countries ( n = 1,450), we identify a negative effect of performance risk and a positive effect of time risk on perceived performance. Performance, time, and psychological risk increase effort perceptions, underscoring the multi-faceted dynamics of effort formation. Performance and psychological risk directly and negatively influence emotions, emphasizing the need to consider non-functional antecedents in adoption. In contrast, privacy and social risk lack salience for perceived performance, perceived effort, and emotions. While not statistically significant, the high awareness of privacy risk highlights the relevance of privacy-calculus discussions in the context of responsible GenAI integration within corporations and society. Moreover, the time-risk-performance paradox opens avenues for human-AI collaboration, whereby learning and augmentation may support adoption. Thus, by specifying the types and impacts of risk, this study offers guidance for addressing risk-related socio-technological interactions and calls for risk mitigation endeavors in knowledge work. • Risk facets shape GenAI adoption via perceived performance, effort, and emotions. • Performance risk reduces perceived performance and emotions, yet increases effort. • Psychological risk increases effort perceptions and negatively influences emotions. • Time risk boosts perceived performance, despite adding to perceived effort. • High privacy risk awareness contrasts with its limited effect on GenAI adoption.

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Cite This Study

Schulte et al. (2026) studied this question.

synapsesocial.com/papers/69d49f1cb33cc4c35a227987https://doi.org/10.1016/j.techsoc.2026.103338
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Perceived Generative AI risks and behavioral vulnerabilities: understanding cognitive and emotional pathways2026
  2. 2Enablers and Inhibitors of Generative AI Usage Intentions in Work Environments2024
  3. 3Enhancing knowledge sharing in generative AI integration: the impact of AI self-efficacy and skill threat perceptions2025
  4. 4Digital Adoption of Generative AI Tools: A Multi-Theory Model Linking Cognitive Load, User Perceptions, and System Attributes2026
  5. 5Emotional ambivalence as an interpretive mechanism in human–computer interaction with generative artificial intelligence: evidence from PLS-SEM, regression-based machine learning, and fsQCA2026