%0 Conference Paper %1 ls_leimeister %A Janson, Andreas %A Schmitt, Anuschka %A Bevilacqua, Tatjana %B Annual Meeting of the Academy of Management (AOM) %C Boston, Massachusetts, USA %D 2023 %K Agentic_Information Conversational_AI_Ecosystems Systems Trust Voice_Assistants dempub itegpub pub_aja u3bpub %T Disentangling Trust in Voice Assistants - A Configurational View on Conversational AI Ecosystems %X Voice assistants’ (VAs) increasingly nuanced and natural communication via artificial intelligence (AI) opens up new opportunities for the experience of users, providing task assistance and automation possibilities, and also offer an easy interface to digital services and ecosystems. However, VAs and according ecosystems face various problems, such as low adoption and satisfaction rates as well as other negative reactions from users. Companies, therefore, need to consider what contributes to user satisfaction of VAs and related conversational AI ecosystems. Key for conversational AI ecosystems is the consideration of trust due to their agentic and pervasive nature. Nonetheless, due to the complexity of conversational AI ecosystems and different trust sources involved, we argue that we need a more detailed understanding about trust. Thus, we propose a configurational view on conversational AI ecosystems that allows us to disentangle the complex and interrelated factors that contribute to trust in VAs. We examine with a configurational approach and a survey study, how different trust sources contribute to the outcomes of conversational AI ecosystems, i.e., in our case user satisfaction. The results of our study show four distinct patterns of trust source configurations. Vice versa, we show how trust sources contribute to the absence of the outcome, i.e., user satisfaction. The derived implications provide a configurative theoretical understanding for the role of trust sources for user satisfaction that provides practitioners useful guidance for more trustworthy conversational AI ecosystems.

@inproceedings{ls_leimeister, abstract = {Voice assistants’ (VAs) increasingly nuanced and natural communication via artificial intelligence (AI) opens up new opportunities for the experience of users, providing task assistance and automation possibilities, and also offer an easy interface to digital services and ecosystems. However, VAs and according ecosystems face various problems, such as low adoption and satisfaction rates as well as other negative reactions from users. Companies, therefore, need to consider what contributes to user satisfaction of VAs and related conversational AI ecosystems. Key for conversational AI ecosystems is the consideration of trust due to their agentic and pervasive nature. Nonetheless, due to the complexity of conversational AI ecosystems and different trust sources involved, we argue that we need a more detailed understanding about trust. Thus, we propose a configurational view on conversational AI ecosystems that allows us to disentangle the complex and interrelated factors that contribute to trust in VAs. We examine with a configurational approach and a survey study, how different trust sources contribute to the outcomes of conversational AI ecosystems, i.e., in our case user satisfaction. The results of our study show four distinct patterns of trust source configurations. Vice versa, we show how trust sources contribute to the absence of the outcome, i.e., user satisfaction. The derived implications provide a configurative theoretical understanding for the role of trust sources for user satisfaction that provides practitioners useful guidance for more trustworthy conversational AI ecosystems.}, added-at = {2023-04-08T10:44:36.000+0200}, address = {Boston, Massachusetts, USA}, author = {Janson, Andreas and Schmitt, Anuschka and Bevilacqua, Tatjana}, biburl = {https://www.bibsonomy.org/bibtex/24bd91535490f3d27f12656b482a122dc/ls_leimeister}, booktitle = {Annual Meeting of the Academy of Management (AOM)}, eventdate = {04-08 Aug 2023}, eventtitle = {Annual Meeting of the Academy of Management (AOM)}, interhash = {6db1a4960bc19131af899d3f37c42106}, intrahash = {4bd91535490f3d27f12656b482a122dc}, keywords = {Agentic_Information Conversational_AI_Ecosystems Systems Trust Voice_Assistants dempub itegpub pub_aja u3bpub}, language = {English}, timestamp = {2023-08-09T11:50:24.000+0200}, title = {Disentangling Trust in Voice Assistants - A Configurational View on Conversational AI Ecosystems}, venue = {Boston, Massachusetts, USA}, year = 2023 }