@article{WannerHermHeinrichetal.2022,
  author    = {Wanner, Jonas and Herm, Lukas-Valentin and Heinrich, Kai and Janiesch, Christian},
  title     = {The effect of transparency and trust on intelligent system acceptance: evidence from a user-based study},
  series = {Electronic Markets},
  volume    = {32},
  journal   = {Electronic Markets},
  number    = {4},
  issn      = {1019-6781},
  doi       = {10.1007/s12525-022-00593-5},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-323829},
  pages     = {2079-2102},
  year      = {2022},
  abstract  = {Contemporary decision support systems are increasingly relying on artificial intelligence technology such as machine learning algorithms to form intelligent systems. These systems have human-like decision capacity for selected applications based on a decision rationale which cannot be looked-up conveniently and constitutes a black box. As a consequence, acceptance by end-users remains somewhat hesitant. While lacking transparency has been said to hinder trust and enforce aversion towards these systems, studies that connect user trust to transparency and subsequently acceptance are scarce. In response, our research is concerned with the development of a theoretical model that explains end-user acceptance of intelligent systems. We utilize the unified theory of acceptance and use in information technology as well as explanation theory and related theories on initial trust and user trust in information systems. The proposed model is tested in an industrial maintenance workplace scenario using maintenance experts as participants to represent the user group. Results show that acceptance is performance-driven at first sight. However, transparency plays an important indirect role in regulating trust and the perception of performance.},
  language  = {en}
}
@article{WienrichReitelbachCarolus2021,
  author    = {Wienrich, Carolin and Reitelbach, Clemens and Carolus, Astrid},
  title     = {The Trustworthiness of Voice Assistants in the Context of Healthcare Investigating the Effect of Perceived Expertise on the Trustworthiness of Voice Assistants, Providers, Data Receivers, and Automatic Speech Recognition},
  series = {Frontiers in Computer Science},
  volume    = {3},
  journal   = {Frontiers in Computer Science},
  doi       = {10.3389/fcomp.2021.685250},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-260209},
  year      = {2021},
  abstract  = {As an emerging market for voice assistants (VA), the healthcare sector imposes increasing requirements on the users' trust in the technological system. To encourage patients to reveal sensitive data requires patients to trust in the technological counterpart. In an experimental laboratory study, participants were presented a VA, which was introduced as either a "specialist" or a "generalist" tool for sexual health. In both conditions, the VA asked the exact same health-related questions. Afterwards, participants assessed the trustworthiness of the tool and further source layers (provider, platform provider, automatic speech recognition in general, data receiver) and reported individual characteristics (disposition to trust and disclose sexual information). Results revealed that perceiving the VA as a specialist resulted in higher trustworthiness of the VA and of the provider, the platform provider and automatic speech recognition in general. Furthermore, the provider's trustworthiness affected the perceived trustworthiness of the VA. Presenting both a theoretical line of reasoning and empirical data, the study points out the importance of the users' perspective on the assistant. In sum, this paper argues for further analyses of trustworthiness in voice-based systems and its effects on the usage behavior as well as the impact on responsible design of future technology.},
  language  = {en}
}