@article{HohmannPinartTischeretal.2014,
  author    = {Hohmann, Cynthia and Pinart, Mariona and Tischer, Christina and Gehring, Ulrike and Heinrich, Joachim and Kull, Inger and Mel{\´e}n, Eric and Smit, Henriette A. and Torrent, Maties and Wijga, Alet H. and Wickman, Magnus and Bachert, Claus and L{\o}drup Carlsen, Karin C. and Carlsen, Kai-H{\aa}kon and Bindslev-Jensen, Carsten and Eller, Esben and Esplugues, Ana and Fantini, Maria Pia and Annesi-Maesano, Isabella and Momas, Isabelle and Porta, Daniela and Vassilaki, Maria and Waiblinger, Dagmar and Sunyer, Jordi and Ant{\´o}, Josep M. and Bousquet, Jean and Keil, Thomas},
  title     = {The Development of the MeDALL Core Questionnaires for a Harmonized Follow-Up Assessment of Eleven European Birth Cohorts on Asthma and Allergies},
  series = {International Archives of Allergy and Immunology},
  volume    = {163},
  journal   = {International Archives of Allergy and Immunology},
  number    = {3},
  organization    = {The MeDALL Study Group},
  issn      = {1018-2438},
  doi       = {10.1159/000357732},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-196594},
  pages     = {215-224},
  year      = {2014},
  abstract  = {Background: Numerous birth cohorts have been initiated in the world over the past 30 years using heterogeneous methods to assess the incidence, course and risk factors of asthma and allergies. The aim of the present work is to provide the stepwise proceedings of the development and current version of the harmonized MeDALL-Core Questionnaire (MeDALL-CQ) used prospectively in 11 European birth cohorts. Methods: The harmonization of questions was accomplished in 4 steps: (i) collection of variables from 14 birth cohorts, (ii) consensus on questionnaire items, (iii) translation and back-translation of the harmonized English MeDALL-CQ into 8 other languages and (iv) implementation of the harmonized follow-up. Results: Three harmonized MeDALL-CQs (2 for parents of children aged 4-9 and 14-18, 1 for adolescents aged 14-18) were developed and used for a harmonized follow-up assessment of 11 European birth cohorts on asthma and allergies with over 13,000 children. Conclusions: The harmonized MeDALL follow-up produced more comparable data across different cohorts and countries in Europe and will offer the possibility to verify results of former cohort analyses. Thus, MeDALL can become the starting point to stringently plan, conduct and support future common asthma and allergy research initiatives in Europe.},
  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}
}
@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}
}