%0 Conference Paper %1 ls_leimeister %A Reinhard, Philipp %A Neis, Nicolas %A Kolb, Lisa %A Wischer, Dennis %A Li, Mahei Manhai %A Winkelmann, Axel %A Teuteberg, Frank %A Lechner, Ulrike %A Leimeister, Jan Marco %B International Conference on Design Science Research in Information Systems and Technology (DESRIST 2024) %C Trollhättan, Sweden %D 2024 %E Mandviwalla, Munir Y. %E Söllner, Matthias %E Tuunanen, Tuure %K AI_Hybrid Generative Intelligence_Customer Service dempub itegpub pub_jml pub_mli pub_pre %T Augmenting Frontline Service Employee Onboarding via Hybrid Intelligence: Examining the Effects of Different Degrees of Human-GenAI Interaction %U http://pubs.wi-kassel.de/wp-content/uploads/2024/04/JML_969.pdf %X High turnover rates within help desks, caused by excessive workloads, make the efficient onboarding of novices a persistent and recurring challenge. Generative artificial intelligence (GenAI) possesses the potential to augment novice frontline service employees (FSE) during their onboarding phase. However, there is a lack of knowledge on how to design the interaction of FSE and GenAI. Thus, following design science research (DSR), we propose a conversational (co-)agent that leverages the capabilities of large language models and the concept of hybrid intelligence to augment novice FSE. To examine the interaction between novices and GenAI given various task difficulties, we derive and instantiate two hybrid intelligence meta-designs - a supporter that provides recommendations and a collaborator that allows for prompting the coagent. The results from an online experiment with 75 laypeople show that novices interact with GenAI more frequently and show a higher engagement, especially in difficult tasks. Overall, we uncover a paradox: Despite an increased interaction and a greater time investment, FSEs experience a lower perceived workload with a GenAI-based collaborator. From that, we derive implications for designing employee-facing co-agents in customer services.

@inproceedings{ls_leimeister, abstract = {High turnover rates within help desks, caused by excessive workloads, make the efficient onboarding of novices a persistent and recurring challenge. Generative artificial intelligence (GenAI) possesses the potential to augment novice frontline service employees (FSE) during their onboarding phase. However, there is a lack of knowledge on how to design the interaction of FSE and GenAI. Thus, following design science research (DSR), we propose a conversational (co-)agent that leverages the capabilities of large language models and the concept of hybrid intelligence to augment novice FSE. To examine the interaction between novices and GenAI given various task difficulties, we derive and instantiate two hybrid intelligence meta-designs - a supporter that provides recommendations and a collaborator that allows for prompting the coagent. The results from an online experiment with 75 laypeople show that novices interact with GenAI more frequently and show a higher engagement, especially in difficult tasks. Overall, we uncover a paradox: Despite an increased interaction and a greater time investment, FSEs experience a lower perceived workload with a GenAI-based collaborator. From that, we derive implications for designing employee-facing co-agents in customer services.}, added-at = {2024-04-25T13:21:45.000+0200}, address = {Trollhättan, Sweden}, author = {Reinhard, Philipp and Neis, Nicolas and Kolb, Lisa and Wischer, Dennis and Li, Mahei Manhai and Winkelmann, Axel and Teuteberg, Frank and Lechner, Ulrike and Leimeister, Jan Marco}, biburl = {https://www.bibsonomy.org/bibtex/231bc7ac955c16d1f0171bfd396896eec/ls_leimeister}, booktitle = {International Conference on Design Science Research in Information Systems and Technology (DESRIST 2024)}, editor = {Mandviwalla, Munir Y. and Söllner, Matthias and Tuunanen, Tuure}, eventdate = {03.06.2024}, eventtitle = {International Conference on Design Science Research in Information Systems and Technology (DESRIST 2024)}, interhash = {99425444360e599a92ea3cc3861e43bf}, intrahash = {31bc7ac955c16d1f0171bfd396896eec}, keywords = {AI_Hybrid Generative Intelligence_Customer Service dempub itegpub pub_jml pub_mli pub_pre}, timestamp = {2024-04-25T13:56:57.000+0200}, title = {Augmenting Frontline Service Employee Onboarding via Hybrid Intelligence: Examining the Effects of Different Degrees of Human-GenAI Interaction}, url = {http://pubs.wi-kassel.de/wp-content/uploads/2024/04/JML_969.pdf}, venue = {Trollhättan, Sweden}, year = 2024 }