Hey Robot, Tell It to Me Straight: How Different Service Strategies Affect Human and Robot Service Outcomes

Naito, M.; Rea, Daniel J.; Kanda, Takayuki

doi:10.1007/s12369-023-01013-0

Cited by 4 publications

(4 citation statements)

References 55 publications

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“…Weiler et al (2022) found that a specific quantitative linguistic form plays a moderating role in mitigating the impact of chatbot failure on perceived performance and discontinuation. Using data-driven communication styles can increase customer satisfaction by making them feel more informed and more natural when used by a chatbot, not a human (Naito et al, 2023). However, compared to quantitative communication, Weiler et al (2022) found that qualitative communication moderates the effect of trusting beliefs on discontinuation intentions.…”

Section: Source(s): Figure Created By Authormentioning

confidence: 99%

“…In the purchase stage, Go and Sundar (2019) investigated the perception of chatbot humanness when choosing a digital camera for purchase, highlighting the satisfaction derived from message interactivity. Furthermore, studies such as Naito et al (2023) focused on customers' informed feelings when chatbots employed data-driven communication during the process of buying new outfits, while Whang et al (2022) explored the impact of personalized pronouns in chatbot messages during lipstick purchases. The unique nature of customer service interactions in each stage is emphasized by Song et al (2022), who discovered that the credibility perception of chatbots was lower at the time of purchase, whereas trust in personalized recommendations was higher during the pre-purchase stage.…”

Section: Contextual Factors 441 Interaction Typementioning

confidence: 99%

“…, 2023). This is particularly important as duplicating human-to-human interaction may not produce the best results for AI in customer service (Naito et al. , 2023).…”

Section: Introductionmentioning

confidence: 99%

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Personal touch in digital customer service: a conceptual framework of relational personalization for conversational AI

Blümel,

Zaki,

Bohné

2023

JSTP

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PurposeCustomer service conversations are becoming increasingly digital and automated, leaving service encounters impersonal. The purpose of this paper is to identify how customer service agents and conversational artificial intelligence (AI) applications can provide a personal touch and improve the customer experience in customer service. The authors offer a conceptual framework delineating how text-based customer service communication should be designed to increase relational personalization.Design/methodology/approachThis paper presents a systematic literature review on conversation styles of conversational AI and integrates the extant research to inform the development of the proposed conceptual framework. Using social information processing theory as a theoretical lens, the authors extend the concept of relational personalization for text-based customer service communication.FindingsThe conceptual framework identifies conversation styles, whose degree of expression needs to be personalized to provide a personal touch and improve the customer experience in service. The personalization of these conversation styles depends on available psychological and individual customer knowledge, contextual factors such as the interaction and service type, as well as the freedom of communication the conversational AI or customer service agent has.Originality/valueThe article is the first to conduct a systematic literature review on conversation styles of conversational AI in customer service and to conceptualize critical elements of text-based customer service communication required to provide a personal touch with conversational AI. Furthermore, the authors provide managerial implications to advance customer service conversations with three types of conversational AI applications used in collaboration with customer service agents, namely conversational analytics, conversational coaching and chatbots.

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Section: Source(s): Figure Created By Authormentioning

confidence: 99%

Section: Contextual Factors 441 Interaction Typementioning

confidence: 99%

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Personal touch in digital customer service: a conceptual framework of relational personalization for conversational AI

Blümel,

Zaki,

Bohné

2023

JSTP

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“…On the one hand, studies focusing on users' perception of polite machines found mostly positive effects of politeness use by agents, for example, increasing the likability of a robot [24], the trust towards non-humanoid robots [12] and the persuasiveness of a virtual agents [11]. On the other hand, studies focusing on the expectations regarding an agents' use of politeness found that users might prefer that robots use more direct language [20] and that indirect or vague language can lead to a bad user experience [7]. A possible explanation could be that concepts such as face are not trivially transferable to HRI [6].…”

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confidence: 99%

Modeling Politeness in Human-Robot Interaction

Lumer

2024

Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction

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Politeness is a linguistic phenomenon central to human communication. The many influences on the choice and interpretation of politeness in language make the phenomenon very complex to model and to account for in Human-Robot Interaction. I therefore question whether and how the implementation of such a social linguistic phenomenon in a robot is desirable. To find answers I conducted studies on politeness in Human-Robot Interaction from different perspectives: users' expectations, users' politeness towards a robot and users' perception of politeness use by a robot. In future research, based on and informed by my results, I aim to computationally model two politeness strategies and implement them in a Furhat robot. CCS CONCEPTS• Human-centered computing → HCI theory, concepts and models; User studies; Empirical studies in HCI .

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Customer service through AI-Powered human-robot relationships: Where are we now? The case of Henn na cafe, Japan

Reis

2024

Technology in Society

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Hey Robot, Tell It to Me Straight: How Different Service Strategies Affect Human and Robot Service Outcomes

Cited by 4 publications

References 55 publications

Personal touch in digital customer service: a conceptual framework of relational personalization for conversational AI

Personal touch in digital customer service: a conceptual framework of relational personalization for conversational AI

Modeling Politeness in Human-Robot Interaction

Customer service through AI-Powered human-robot relationships: Where are we now? The case of Henn na cafe, Japan

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