Classifying Smart Personal Assistants: An Empirical Cluster Analysis
| dc.date.accessioned | 2020-11-02T14:46:56Z | |
| dc.date.available | 2020-11-02T14:46:56Z | |
| dc.date.issued | 2019 | |
| dc.identifier | doi:10.17170/kobra-202010302037 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/11915 | |
| dc.language.iso | eng | eng |
| dc.publisher | University of Hawaii at Manoa, Hamilton Library, ScholarSpace | |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.subject | decision analytics | eng |
| dc.subject | mobile services | eng |
| dc.subject | service science | eng |
| dc.subject | smart personal assistants | eng |
| dc.subject | intelligent agents | eng |
| dc.subject | classification | eng |
| dc.subject | cluster analysis | eng |
| dc.subject | literature review | eng |
| dc.subject.ddc | 004 | |
| dc.title | Classifying Smart Personal Assistants: An Empirical Cluster Analysis | eng |
| dc.type | Konferenzveröffentlichung | |
| dcterms.abstract | The digital age has yielded systems that increasingly reduce the complexity of our everyday lives. As such, smart personal assistants such as Amazon’s Alexa or Apple’s Siri combine the comfort of intuitive natural language interaction with the utility of personalized and situation-dependent information and service provision. However, research on SPAs is becoming increasingly complex and opaque. To reduce complexity, this paper introduces a classification system for SPAs. Based on a systematic literature review, a cluster analysis reveals five SPA archetypes: Adaptive Voice (Vision) Assistants, Chatbot Assistants, Embodied Virtual Assistants, Passive Pervasive Assistants, and Natural Conversation Assistants. | eng |
| dcterms.accessRights | open access | |
| dcterms.creator | Knote, Robin | |
| dcterms.creator | Janson, Andreas | |
| dcterms.creator | Söllner, Matthias | |
| dcterms.creator | Leimeister, Jan Marco | |
| dc.publisher.place | Honolulu, Hawaii | |
| dc.relation.doi | doi:10.24251/HICSS.2019.245 | |
| dc.subject.swd | Autonomer Agent | ger |
| dc.subject.swd | Klassifikation | ger |
| dc.subject.swd | Cluster-Analyse | ger |
| dc.type.version | publishedVersion | |
| dcterms.event.date | 2019-01 | |
| dcterms.event.place | Wailea, Hawaii | |
| dcterms.source.collection | Proceedings of the 52rd Annual Hawaii International Conference on System Sciences | eng |
| dcterms.source.editor | Hawaii International Conference on System Sciences 2019 | |
| dcterms.source.identifier | ISBN 978-0-9981331-2-6 | |
| dcterms.source.pageinfo | 2024-2033 | |
| kup.iskup | false | |
| dcterms.event | Hawaii International Conference on System Sciences 2019 |
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