@article{37155,
  abstract     = {{Artificial intelligence (AI) has moved beyond the planning phase in many organisations and it is often accompanied by uncertainties and fears of job loss among employees. It is crucial to manage employees{\textquoteright} attitudes towards the deployment of an AI-based technology effectively and counteract possible resistance behaviour. We present lessons learned from an industry case where we conducted interviews with affected employees. We evaluated our results with managers across industries and found that that the deployment of AI-based technologies does not differ from other IT, but that the change is perceived differently due to misguided expectations. }},
  author       = {{Stieglitz, Stefan and Möllmann (Frick), Nicholas R. J. and Mirbabaie, Milad and Hofeditz, Lennart and Ross, Björn}},
  issn         = {{1477-9064}},
  journal      = {{International Journal of Management Practice}},
  keywords     = {{Artificial Intelligence, Change Management, Resistance, AI-Driven Change, AI Deployment, AI Perception}},
  publisher    = {{Inderscience}},
  title        = {{{Recommendations for Managing AI-Driven Change Processes: When Expectations Meet Reality}}},
  year         = {{2021}},
}

@inproceedings{24280,
  abstract     = {{Challenges in decisions on technical changes are the lack of knowledge about the expected impact and change propagation. Currently, no literature study contains a systematic differentiation and evaluation of existing approaches, which is a prerequisite for practitioners to select a suitable approach. This research aims at defining differentiation criteria as well as generally applicable requirements for evaluation. A four-step approach is used: systematic literature review on approaches for impact analysis of engineering changes (1), categorization and prioritization of approaches based on reoccuring elements (2), derivation of context specific requirements for evaluation (3), and evaluation of approaches (4). The result indicates existing potential of object-oriented modeling approaches.}},
  author       = {{Gräßler, Iris and Wiechel, Dominik}},
  booktitle    = {{DS 111: Proceedings of the 32nd Symposium Design for X}},
  editor       = {{Krause, Dieter and Paetzold, Kristin and Wartzack, Sandro}},
  keywords     = {{Engineering Change Management, Impact Analysis, Engineering  Changes, Model-based Systems Engineering, Product Developmen}},
  location     = {{Tutzing}},
  title        = {{{Systematische Bewertung von Auswirkungsanalysen des Engineering Change Managements}}},
  doi          = {{10.35199/dfx2021.12}},
  year         = {{2021}},
}

@article{24973,
  abstract     = {{Die Frage, wie sich die Weiterentwicklung der Lehre an Hochschulen systematisch 
verankern lässt, erfährt mit dem Auslaufen von Förderprogrammen wie dem QPL 
erneute Aufmerksamkeit. Bislang fehlt es an einer kontextspezifischen Theorie, die 
lehrbezogenen Wandel an Hochschulen analysier- und gestaltbar macht. In jedem 
Fall sind Change-Konzepte aus dem betriebswirtschaftlichen Bereich nur sehr 
beschränkt auf Hochschulen übertragbar. Demgegenüber gibt neuere Forschung 
Hinweise darauf, welche Kernkategorien eine hochschulspezifische Change-
Theorie umfassen könnte. Darauf aufbauend schlägt der Beitrag zwei Konzepte als 
Kernkategorien einer Theorie lehrbezogenen Wandels an Hochschulen vor. }},
  author       = {{Jenert, Tobias}},
  journal      = {{Zeitschrift für Hochschulentwicklung}},
  keywords     = {{educational development, change management, educational innovation}},
  number       = {{4}},
  pages        = {{204--222.}},
  title        = {{{Überlegungen auf dem Weg zu einer Theorie lehrbezogenen Wandels an Hochschulen}}},
  doi          = {{10.3217/zfhe-15-04/12 }},
  volume       = {{15}},
  year         = {{2020}},
}