@techreport{KrauseFischer2021,
  type      = {Working Paper},
  author    = {Krause, Theresa and Fischer, Doris},
  title     = {Data as the new driver for growth? European and Chinese perspectives on the new factor of production},
  doi       = {10.25972/OPUS-22979},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-229794},
  pages     = {7},
  year      = {2021},
  abstract  = {Amidst an emerging international systemic competition between China and the Western world, China's sustained high economic growth rates, technological innovations and successful control of the corona pandemic have raised doubts over the West's systemic capabilities. In this context, data resources and regimes play an increasing role. This research note looks at data as present and future driver of innovation and economic growth in more detail. It compares the Chinese and the European perspective on data as well as their respective (planned) policy measures in order to draw tentative conclusions about their different approaches' implications.},
  subject      = {China},
  language  = {en}
}
@techreport{FischerSchaper2021,
  type      = {Working Paper},
  author    = {Fischer, Doris and Schaper, Anna-Katharina},
  title     = {Does Gender Matter for the Entrepreneurship Fairy Tale? An Analysis of Chinese Unicorn Start-ups},
  series = {CBE Research Notes},
  journal   = {CBE Research Notes},
  issn      = {2747-8661},
  doi       = {10.25972/OPUS-24441},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-244415},
  pages     = {9},
  year      = {2021},
  abstract  = {Start-up ecosystems around the world have created a large number of successful and innovative unicorn companies in recent years. Our research note focuses on the case of China and offers a global comparative perspective on the current status of Chinese unicorn start-ups and their founding structure. We identify a predominantly male unicorn founding structure and illustrate a worrying decline of female entrepreneurship in China.},
  language  = {en}
}
@phdthesis{Brzoska2020,
  author    = {Brzoska, Jan},
  title     = {Market forecasting in China: An Artificial Neural Network approach to optimize the accuracy of sales forecasts in the Chinese automotive market},
  doi       = {10.25972/OPUS-20315},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-203155},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2020},
  abstract  = {Sales forecasts are an essential determinant of operational planning in entrepreneurial organizations. However, in China, as in other emerging markets, monthly sales forecasts are particularly challenging for multinational automotive enterprises and suppliers. A chief reason for this is that conventional approaches to sales forecasting often fail to capture the underlying market dynamics. To that end, this dissertation investigates the application of Artificial Neural Networks with an implemented backpropagation algorithm as a more "unconventional" sales forecasting method. A key element of statistical modelling is the selection of superior leading indicators. These indicators were collected as part of the researcher's expert interviews with multinational enterprises and state associations in China. The economic plausibility of all specified indicators is critically explored in qualitative-quantitative pre-selection procedures. The overall objective of the present study was to improve the accuracy of monthly sales forecasts in the Chinese automotive market. This objective was achieved by showing that the forecasting error could be lowered to a new benchmark of less than 10\% in an out-of-sample forecasting application.},
  subject      = {China},
  language  = {en}
}