A central problem for chatbots in the customer care domain revolves around how people collaborate with the agent to achieve their own situated goals. The majority of the previous research, however, relied on experiments within artificial settings, rather than on observation of real-world interactions. Moreover, such research mostly analyzed users' responses to communication breakdowns, rather than the wider collaboration strategies utilized during a conversation. In this paper, we qualitatively analyzed 12,477 real-world exchanges with a task-based chatbot using a Grounded Theory approach as a rigorous coding method to analyze the data. We identifed two main aspects of collaboration, behavioral and conversational, and for each aspect we highlighted the different strategies that users perform to "work together" with the agent. These strategies may be utilized from the very beginning of the conversation or in response to misunderstandings in the course of ongoing interactions and may show different evolving dynamics.

Collaborating with a Text-Based Chatbot: An Exploration of Real-World Collaboration Strategies Enacted during Human-Chatbot Interactions

Rapp, A
First
;
Boldi, A;Curti, L;Perrucci, A;Simeoni, R
2023-01-01

Abstract

A central problem for chatbots in the customer care domain revolves around how people collaborate with the agent to achieve their own situated goals. The majority of the previous research, however, relied on experiments within artificial settings, rather than on observation of real-world interactions. Moreover, such research mostly analyzed users' responses to communication breakdowns, rather than the wider collaboration strategies utilized during a conversation. In this paper, we qualitatively analyzed 12,477 real-world exchanges with a task-based chatbot using a Grounded Theory approach as a rigorous coding method to analyze the data. We identifed two main aspects of collaboration, behavioral and conversational, and for each aspect we highlighted the different strategies that users perform to "work together" with the agent. These strategies may be utilized from the very beginning of the conversation or in response to misunderstandings in the course of ongoing interactions and may show different evolving dynamics.
2023
2023 CHI Conference on Human Factors in Computing Systems (CHI '23)
Amburgo, Germania
23-28 Aprile 2023
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23)
ASSOC COMPUTING MACHINERY
1
17
9781450394215
Conversational agents; human-machine cooperation; human-AI cooperation; chatbots
Rapp, A; Boldi, A; Curti, L; Perrucci, A; Simeoni, R
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1943091
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