@article{25806,
  author       = {{Lübbecke, Silvia and Schnedler, Wendelin}},
  issn         = {{1058-6407}},
  journal      = {{Journal of Economics & Management Strategy}},
  pages        = {{420--438}},
  title        = {{{Don't patronize me! An experiment on preferences for authorship}}},
  doi          = {{10.1111/jems.12347}},
  year         = {{2020}},
}

@article{31807,
  abstract     = {{Drawing upon recent advances in machine learning and natural language processing, we introduce new tools that automatically ingest, parse, disambiguate, and build an updated database using U.S. patent data. The tools identify unique inventor, assignee, and location entities mentioned on each granted U.S. patent from 1976 to 2016. We describe data flow, algorithms, user interfaces, descriptive statistics, and a novelty measure based on the first appearance of a word in the patent corpus. We illustrate an automated coinventor network mapping tool and visualize trends in patenting over the last 40 years.}},
  author       = {{Balsmeier, Benjamin and Assaf, Mohamad and Chesebro, Tyler and Fierro, Gabe and Johnson, Kevin and Johnson, Scott and Li, Guan‐Cheng and Lück, Sonja and O'Reagan, Doug and Yeh, Bill and Zang, Guangzheng and Fleming, Lee}},
  issn         = {{1058-6407}},
  journal      = {{Journal of Economics & Management Strategy}},
  keywords     = {{Management of Technology and Innovation, Strategy and Management, Economics and Econometrics, General Business, Management and Accounting, General Medicine}},
  number       = {{3}},
  pages        = {{535--553}},
  publisher    = {{Wiley}},
  title        = {{{Machine learning and natural language processing on the patent corpus: Data, tools, and new measures}}},
  doi          = {{10.1111/jems.12259}},
  volume       = {{27}},
  year         = {{2018}},
}