@incollection{SteuerDankertLeichtScholten2022,
  author    = {Steuer-Dankert, Linda and Leicht-Scholten, Carmen},
  title     = {Perceiving diversity : an explorative approach in a complex research organization.},
  series = {Diversity and discrimination in research organizations},
  booktitle = {Diversity and discrimination in research organizations},
  publisher = {Emerald Publishing Limited},
  address   = {Bingley},
  isbn      = {978-1-80117-959-1 (Print)},
  doi       = {10.1108/978-1-80117-956-020221010},
  pages     = {365 -- 392},
  year      = {2022},
  abstract  = {Diversity management is seen as a decisive factor for ensuring the development of socially responsible innovations (Beacham and Shambaugh, 2011; Sonntag, 2014; L{\´o}pez, 2015; Uebernickel et al., 2015). However, many diversity management approaches fail due to a one-sided consideration of diversity (Thomas and Ely, 2019) and a lacking linkage between the prevailing organizational culture and the perception of diversity in the respective organization. Reflecting the importance of diverse perspectives, research institutions have a special responsibility to actively deal with diversity, as they are publicly funded institutions that drive socially relevant development and educate future generations of developers, leaders and decision-makers. Nevertheless, only a few studies have so far dealt with the influence of the special framework conditions of the science system on diversity management. Focusing on the interdependency of the organizational culture and diversity management especially in a university research environment, this chapter aims in a first step to provide a theoretical perspective on the framework conditions of a complex research organization in Germany in order to understand the system-specific factors influencing diversity management. In a second step, an exploratory cluster analysis is presented, investigating the perception of diversity and possible influencing factors moderating this perception in a scientific organization. Combining both steps, the results show specific mechanisms and structures of the university research environment that have an impact on diversity management and rigidify structural barriers preventing an increase of diversity. The quantitative study also points out that the management level takes on a special role model function in the scientific system and thus has an influence on the perception of diversity. Consequently, when developing diversity management approaches in research organizations, it is necessary to consider the top-down direction of action, the special nature of organizational structures in the university research environment as well as the special role of the professorial level as role model for the scientific staff.},
  language  = {en}
}
@incollection{StriebingMuellerSchraudneretal.2022,
  author    = {Striebing, Clemens and M{\"u}ller, J{\"o}rg and Schraudner, Martina and Gewinner, Irina Valerie and Guerrero Morales, Patricia and Hochfeld, Katharina and Hoffman, Shekinah and Kmec, Julie A. and Nguyen, Huu Minh and Schneider, Jannick and Sheridan, Jennifer and Steuer-Dankert, Linda and Trimble O'Connor, Lindsey and Vandevelde-Rougale, Agn{\`e}s},
  title     = {Promoting diversity and combatting discrimination in research organizations: a practitioner's guide},
  series = {Diversity and discrimination in research organizations},
  booktitle = {Diversity and discrimination in research organizations},
  publisher = {Emerald Publishing Limited},
  address   = {Bingley},
  isbn      = {978-1-80117-959-1 (Print)},
  doi       = {10.1108/978-1-80117-956-020221012},
  pages     = {421 -- 442},
  year      = {2022},
  abstract  = {The essay is addressed to practitioners in research management and from academic leadership. It describes which measures can contribute to creating an inclusive climate for research teams and preventing and effectively dealing with discrimination. The practical recommendations consider the policy and organizational levels, as well as the individual perspective of research managers. Following a series of basic recommendations, six lessons learned are formulated, derived from the contributions to the edited collection on "Diversity and Discrimination in Research Organizations."},
  language  = {en}
}
@article{MuellerSeginWeigandetal.2022,
  author    = {Mueller, Tobias and Segin, Alexander and Weigand, Christoph and Schmitt, Robert H.},
  title     = {Feature selection for measurement models},
  series = {International journal of quality \& reliability management},
  journal   = {International journal of quality \& reliability management},
  number    = {Vol. ahead-of-print, No. ahead-of-print.},
  publisher = {Emerald Group Publishing Limited},
  address   = {Bingley},
  issn      = {0265-671X},
  doi       = {10.1108/IJQRM-07-2021-0245},
  year      = {2022},
  abstract  = {Purpose In the determination of the measurement uncertainty, the GUM procedure requires the building of a measurement model that establishes a functional relationship between the measurand and all influencing quantities. Since the effort of modelling as well as quantifying the measurement uncertainties depend on the number of influencing quantities considered, the aim of this study is to determine relevant influencing quantities and to remove irrelevant ones from the dataset. Design/methodology/approach In this work, it was investigated whether the effort of modelling for the determination of measurement uncertainty can be reduced by the use of feature selection (FS) methods. For this purpose, 9 different FS methods were tested on 16 artificial test datasets, whose properties (number of data points, number of features, complexity, features with low influence and redundant features) were varied via a design of experiments. Findings Based on a success metric, the stability, universality and complexity of the method, two FS methods could be identified that reliably identify relevant and irrelevant influencing quantities for a measurement model. Originality/value For the first time, FS methods were applied to datasets with properties of classical measurement processes. The simulation-based results serve as a basis for further research in the field of FS for measurement models. The identified algorithms will be applied to real measurement processes in the future.},
  language  = {en}
}