%0 Conference Paper %1 ls_leimeister %A Reinhard, Philipp %A Li, Mahei Manhai %A Peters, Christoph %A Leimeister, Jan Marco %B European Conference on Information Systems (ECIS) %C Paphos, Cyprus %D 2024 %K Chatbot IS_delegation customer_service dempub generative_AI itegpub pub_cpe pub_jml pub_mli pub_pre self-determination_theory %T LET EMPLOYEES TRAIN THEIR OWN CHATBOTS: DESIGN OF GENERATIVE AI-ENABLED DELEGATION SYSTEMS %U http://pubs.wi-kassel.de/wp-content/uploads/2024/04/JML_970.pdf %X While chatbots can be implemented with very little effort, scaling and maintaining chatbots remains a challenge. This is crucial in knowledge-intensive customer service like IT support, where domain knowledge must stay current with the evolving IT landscape. Following design science research, we derive design principles for a generative AI (GPT4) enabled textual training data creation and curation system (T²C²) as part of a new class of systems – bot delegation systems. For the design of T²C², chatbot and domain expert viewpoints are integrated. We evaluate two instances of T²C², each with distinct degrees of human-ai delegation where employees act both as creators and curators of training data. The paper’s theoretical contribution is two-fold: (1) we present a novel kernel theory that represents the material characteristics of bot delegation systems by contextualizing the IS delegation framework to the self-determination theory; (2) the design and evaluation of T²C² as the built-and-evaluated artifact.

@inproceedings{ls_leimeister, abstract = {While chatbots can be implemented with very little effort, scaling and maintaining chatbots remains a challenge. This is crucial in knowledge-intensive customer service like IT support, where domain knowledge must stay current with the evolving IT landscape. Following design science research, we derive design principles for a generative AI (GPT4) enabled textual training data creation and curation system (T²C²) as part of a new class of systems – bot delegation systems. For the design of T²C², chatbot and domain expert viewpoints are integrated. We evaluate two instances of T²C², each with distinct degrees of human-ai delegation where employees act both as creators and curators of training data. The paper’s theoretical contribution is two-fold: (1) we present a novel kernel theory that represents the material characteristics of bot delegation systems by contextualizing the IS delegation framework to the self-determination theory; (2) the design and evaluation of T²C² as the built-and-evaluated artifact.}, added-at = {2024-04-25T15:12:38.000+0200}, address = {Paphos, Cyprus}, author = {Reinhard, Philipp and Li, Mahei Manhai and Peters, Christoph and Leimeister, Jan Marco}, biburl = {https://www.bibsonomy.org/bibtex/247aa09c8883443727fa55c41d5f42cef/ls_leimeister}, booktitle = {European Conference on Information Systems (ECIS)}, eventdate = {17.06.2024}, eventtitle = {European Conference on Information Systems (ECIS)}, interhash = {3efbf6bdd3654045ae3f1a6a48a7dd2b}, intrahash = {47aa09c8883443727fa55c41d5f42cef}, keywords = {Chatbot IS_delegation customer_service dempub generative_AI itegpub pub_cpe pub_jml pub_mli pub_pre self-determination_theory}, timestamp = {2024-04-25T15:18:58.000+0200}, title = {LET EMPLOYEES TRAIN THEIR OWN CHATBOTS: DESIGN OF GENERATIVE AI-ENABLED DELEGATION SYSTEMS}, url = {http://pubs.wi-kassel.de/wp-content/uploads/2024/04/JML_970.pdf}, venue = {Paphos, Cyprus}, year = 2024 }