@incollection{ThimGronauHaaseetal.2023,
  author    = {Thim, Christof and Gronau, Norbert and Haase, Jennifer and Grum, Marcus and Sch{\"u}ffler, Arnulf and Roling, Wiebke and Kluge, Annette},
  title     = {Modeling change in business processes},
  series = {Business modeling and software design},
  booktitle = {Business modeling and software design},
  editor    = {Shishkov, Boris},
  publisher = {Springer Nature},
  address   = {Cham},
  isbn      = {978-3-031-36756-4},
  doi       = {10.1007/978-3-031-36757-1_1},
  pages     = {3 -- 17},
  year      = {2023},
  abstract  = {Business processes are regularly modified either to capture requirements from the organization's environment or due to internal optimization and restructuring. Implementing the changes into the individual work routines is aided by change management tools. These tools aim at the acceptance of the process by and empowerment of the process executor. They cover a wide range of general factors and seldom accurately address the changes in task execution and sequence. Furthermore, change is only framed as a learning activity, while most obstacles to change arise from the inability to unlearn or forget behavioural patterns one is acquainted with. Therefore, this paper aims to develop and demonstrate a notation to capture changes in business processes and identify elements that are likely to present obstacles during change. It connects existing research from changes in work routines and psychological insights from unlearning and intentional forgetting to the BPM domain. The results contribute to more transparency in business process models regarding knowledge changes. They provide better means to understand the dynamics and barriers of change processes.},
  language  = {en}
}
@article{SchuefflerThimHaaseetal.2019,
  author    = {Sch{\"u}ffler, Arnulf and Thim, Christof and Haase, Jennifer and Gronau, Norbert and Kluge, Annette},
  title     = {Information processing in work environment 4.0 and the beneficial impact of intentional forgetting on change management},
  series = {Zeitschrift f{\"u}r Arbeits- und Organisationspsychologie : german journal of work and organizational psychology},
  volume    = {64},
  journal   = {Zeitschrift f{\"u}r Arbeits- und Organisationspsychologie : german journal of work and organizational psychology},
  number    = {1},
  publisher = {Hogrefe},
  address   = {G{\"o}ttingen},
  issn      = {0932-4089},
  doi       = {10.1026/0932-4089/a000307},
  pages     = {17 -- 29},
  year      = {2019},
  abstract  = {Industry 4.0, based on increasingly progressive digitalization, is a global phenomenon that affects every part of our work. The Internet of Things (IoT) is pushing the process of automation, culminating in the total autonomy of cyber-physical systems. This process is accompanied by a massive amount of data, information, and new dimensions of flexibility. As the amount of available data increases, their specific timeliness decreases. Mastering Industry 4.0 requires humans to master the new dimensions of information and to adapt to relevant ongoing changes. Intentional forgetting can make a difference in this context, as it discards nonprevailing information and actions in favor of prevailing ones. Intentional forgetting is the basis of any adaptation to change, as it ensures that nonprevailing memory items are not retrieved while prevailing ones are retained. This study presents a novel experimental approach that was introduced in a learning factory (the Research and Application Center Industry 4.0) to investigate intentional forgetting as it applies to production routines. In the first experiment (N = 18), in which the participants collectively performed 3046 routine related actions (t1 = 1402, t2 = 1644), the results showed that highly proceduralized actions were more difficult to forget than actions that were less well-learned. Additionally, we found that the quality of cues that trigger the execution of routine actions had no effect on the extent of intentional forgetting.},
  language  = {en}
}