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Rese, Alexandra ; Tränkner, Pauline:
Perceived conversational ability of task-based chatbots – Which conversational elements influence the success of text-based dialogues?
In: International Journal of Information Management.
Bd. 74
(2024)
.
- 102699.
ISSN 0268-4012
DOI: https://doi.org/10.1016/j.ijinfomgt.2023.102699
Abstract
The use of text-based chatbots offering individual support to customers has increased steadily in recent years. However, thus far, research has focused on comparing text-based chatbots with either each other or with humans, whilst the investigation of task-based dialogues has been scarce. This paper aims to identify the characteristics of dialogues – that is, conversational elements – that lead to a successful task-based conversation. For this purpose, the chatbot, KIM, by MAGGI Kochstudio was used. It was designed to help customers find a recipe tailored to their individual needs. In order to investigate which conversational elements contribute to successful communication between the user and the chatbot KIM, a usability study collecting 123 unstructured dialogues and a scenario-based experiment using four dialogues with 627 respondents was conducted. The quantitative analysis demonstrates that task completion is characterized by a higher perception of the chatbot’s conversational ability and user satisfaction. The chatbot should propose correct recipe suggestions following a short dialogue, without the user needing to provide too much input. Based on these findings, we recommended equipping the skillset of task-based chatbots with elements that will complement their assistive qualities – for example, improved use of standard phrases, and reactions to similar domains and non-requests. Gender-specific differences in task completion should be considered.
Weitere Angaben
| Publikationsform: | Artikel in einer Zeitschrift |
|---|---|
| Begutachteter Beitrag: | Ja |
| Keywords: | Text-based chatbots; Task-based chatbots; Conversational ability; Task completion; Conversational elements; Structural conversation analysis; Conversational ability score; User satisfaction |
| Institutionen der Universität: | Fakultäten > Rechts- und Wirtschaftswissenschaftliche Fakultät > Fachgruppe Betriebswirtschaftslehre > Lehrstuhl Betriebswirtschaftslehre XIV - Marketing und Innovation > Lehrstuhl Betriebswirtschaftslehre XIV - Marketing und Innovation - Univ.-Prof. Dr. Daniel Baier |
| Titel an der UBT entstanden: | Ja |
| Themengebiete aus DDC: | 300 Sozialwissenschaften > 330 Wirtschaft |
| Eingestellt am: | 25 Aug 2023 05:07 |
| Letzte Änderung: | 25 Aug 2023 05:07 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/86684 |