@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{JanieschZschechHeinrich2021,
  author    = {Janiesch, Christian and Zschech, Patrick and Heinrich, Kai},
  title     = {Machine learning and deep learning},
  series = {Electronic Markets},
  volume    = {31},
  journal   = {Electronic Markets},
  number    = {3},
  issn      = {1422-8890},
  doi       = {10.1007/s12525-021-00475-2},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-270155},
  pages     = {685-695},
  year      = {2021},
  abstract  = {Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular, we provide a conceptual distinction between relevant terms and concepts, explain the process of automated analytical model building through machine learning and deep learning, and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business. These naturally go beyond technological aspects and highlight issues in human-machine interaction and artificial intelligence servitization.},
  language  = {en}
}