at Google ScholarTitle data
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.
Vol. 74
(2024)
.
- 102699.
ISSN 0268-4012
DOI: https://doi.org/10.1016/j.ijinfomgt.2023.102699
Abstract in another language
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.
Further data
| Item Type: | Article in a journal |
|---|---|
| Refereed: | Yes |
| Keywords: | Text-based chatbots; Task-based chatbots; Conversational ability; Task completion; Conversational elements; Structural conversation analysis; Conversational ability score; User satisfaction |
| Institutions of the University: | Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XIV - Marketing and Innovation > Chair Business Administration XIV - Marketing and Innovation - Univ.-Prof. Dr. Daniel Baier |
| Result of work at the UBT: | Yes |
| DDC Subjects: | 300 Social sciences > 330 Economics |
| Date Deposited: | 25 Aug 2023 05:07 |
| Last Modified: | 25 Aug 2023 05:07 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/86684 |