@article{FredrichBounckenTiberius2022,
  author    = {Fredrich, Viktor and Bouncken, Ricarda B. and Tiberius, Victor},
  title     = {Dyadic business model convergence or divergence in alliances?},
  series = {Journal of business research},
  volume    = {153},
  journal   = {Journal of business research},
  publisher = {Elsevier},
  address   = {New York},
  issn      = {0148-2963},
  doi       = {10.1016/j.jbusres.2022.08.046},
  pages     = {300 -- 308},
  year      = {2022},
  abstract  = {In this study, we contribute to the scholarly conversation on firm-level business model changes following a neoconfigurational approach. By exploring configurations of business model changes over time, we add the direction of business model changes-namely business model convergence or divergence-as a vital avenue to the business model innovation literature. We identify necessary business model convergence and divergence recipes in a sample of N = 217 strategic dyadic alliances. Firstly, technological proximity emerges as a single precondition to both converging and diverging business models. Secondly, business models between competitors either converge through complementarities or tend not to change relative to each other. Thirdly, equity participation enables business model divergence through co-specialization. We conclude with a discussion of business model trajectories and future research directions.},
  language  = {en}
}
@article{BauerSommerGaedke2013,
  author    = {Bauer, Barbara and Sommer, Ulrich and Gaedke, Ursula},
  title     = {High predictability of spring phytoplankton biomass in mesocosms at the species, functional group and community level},
  series = {Freshwater biology},
  volume    = {58},
  journal   = {Freshwater biology},
  number    = {3},
  publisher = {Wiley-Blackwell},
  address   = {Hoboken},
  issn      = {0046-5070},
  doi       = {10.1111/j.1365-2427.2012.02780.x},
  pages     = {588 -- 596},
  year      = {2013},
  abstract  = {1. Models aim to predict phytoplankton dynamics based on observed initial conditions and a set of equations and parameters. However, our knowledge about initial conditions in nature is never perfect. Thus, if phytoplankton dynamics are sensitive to small variations in initial conditions, they are difficult to predict. 2. We used time-series data from indoor mesocosm experiments with natural phyto- and zooplankton communities to quantify the extent to which small initial differences in the species, functional group and community biomass in parallel treatments were amplified or buffered over time. We compared the differences in dynamics between replicates and among all mesocosms of 1year. 3. Temperature-sensitive grazing during the exponential growth phase of phytoplankton caused divergence. In contrast, negative density dependence caused convergence. 4. Mean differences in biomass between replicates were similar for all hierarchical levels. This indicates that differences in their initial conditions were amplified to the same extent. Even though large differences in biomass occasionally occurred between replicates for a short time, dynamics returned to the same path at all hierarchical levels. This suggests that internal feedback mechanisms make the spring development of phytoplankton highly predictable.},
  language  = {en}
}