Nice surprise, more present than a machine

Kaipainen, Kirsikka; Ahtinen, Aino; Hiltunen, Aleksi

doi:10.1145/3275116.3275137

Cited by 24 publications

(2 citation statements)

References 28 publications

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“…The difference in customers' attribution of responsibility for a service outcome to the firm (as proposed in H2) is greater in case of service failure than in case of service success. Kaipainen et al (2018) find that customer responses to a robot are typically a mix of excitement (e.g., people experience delight, wonder, and curiosity) and disappointment (e.g., people experience a low level of control and limited robot abilities). In other words, customers realize that in the current stage of technological development, the outcome of service encounters with frontline robots is not yet predictable or stable.…”

Section: H4bmentioning

confidence: 99%

Robots or frontline employees? Exploring customers’ attributions of responsibility and stability after service failure or success

Belanche

Casaló

Flavián

et al. 2020

JOSM

209

185

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PurposeService robots are taking over the organizational frontline. Despite a recent surge in studies on this topic, extant works are predominantly conceptual in nature. The purpose of this paper is to provide valuable empirical insights by building on the attribution theory.Design/methodology/approachTwo vignette-based experimental studies were employed. Data were collected from US respondents who were randomly assigned to scenarios focusing on a hotel’s reception service and restaurant’s waiter service.FindingsResults indicate that respondents make stronger attributions of responsibility for the service performance toward humans than toward robots, especially when a service failure occurs. Customers thus attribute responsibility to the firm rather than the frontline robot. Interestingly, the perceived stability of the performance is greater when the service is conducted by a robot than by an employee. This implies that customers expect employees to shape up after a poor service encounter but expect little improvement in robots’ performance over time.Practical implicationsRobots are perceived to be more representative of a firm than employees. To avoid harmful customer attributions, service providers should clearly communicate to customers that frontline robots pack sophisticated analytical, rather than simple mechanical, artificial intelligence technology that explicitly learns from service failures.Originality/valueCustomer responses to frontline robots have remained largely unexplored. This paper is the first to explore the attributions that customers make when they experience robots in the frontline.

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Section: H4bmentioning

confidence: 99%

Robots or frontline employees? Exploring customers’ attributions of responsibility and stability after service failure or success

Belanche

Casaló

Flavián

et al. 2020

JOSM

209

185

View full text Add to dashboard Cite

show abstract

“…Interactions in natural settings (e.g., public spaces) are too complex to be simulated in controlled laboratory settings, so to understand natural HRI it is important to conduct field studies. Many HRI systems have been tested in public spaces such as hospitals [55], train stations [31], service points [36,38], airports [35], shopping malls [65], hotels [19], and museums [66], among others. Although robots investigated in these works are sometimes surrounded by a group of people in a public place, these robots typically interact with only one person at a specific time (i.e., one-to-one interaction).…”

Section: One-to-one Hri In Natural Settings Without Learningmentioning

confidence: 99%

Learning to Engage with Interactive Systems

Meng

Lin

Francey

et al. 2020

J. Hum.-Robot Interact.

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Physical agents that can autonomously generate engaging, lifelike behavior will lead to more responsive and user-friendly robots and other autonomous systems. Although many advances have been made for one-toone interactions in well-controlled settings, physical agents should be capable of interacting with humans in natural settings, including group interaction. To generate engaging behaviors, the autonomous system must first be able to estimate its human partners' engagement level. In this article, we propose an approach for estimating engagement during group interaction by simultaneously taking into account active and passive interaction, and use the measure as the reward signal within a reinforcement learning framework to learn engaging interactive behaviors. The proposed approach is implemented in an interactive sculptural system in a museum setting. We compare the learning system to a baseline using pre-scripted interactive behaviors. Analysis based on sensory data and survey data shows that adaptable behaviors within an expert-designed action space can achieve higher engagement and likeability.

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