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.
%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
}