Enhancing hospitality experience with service robots: the mediating role of rapport building

Qiu, Hailian; Li, Minglong; Bo-yang, Shu; Bai, Billy

doi:10.1080/19368623.2019.1645073

Cited by 211 publications

(227 citation statements)

References 43 publications

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“…Although it is beyond the scope of our proposed framework to offer a holistic model of all potential determinants of the success of a service robot implementation effort, we identify some essential factors, with clear managerial implications. In particular, recent research suggests that the elements of our framework influence service performance, customer satisfaction, customer-robot and customeremployee rapport building, and employees retention, among other important management indicators (Fan et al, 2019;Prentice et al, 2019;Qiu et al, 2019). By using the definitions and references we have offered to develop our framework, researchers can continue to investigate each element in greater depth, as variables in unique research projects.…”

Section: Discussionmentioning

confidence: 99%

“…Previous robot design studies note that an appearance that evokes greater perceptions of human-likeness also lead to a stronger sense of social presence (Kim, Park, & Sundar, 2013) and social inclusion (Mourey et al, 2017). Customers prefer to spend more time with robots presenting higher levels of anthropomorphism or perceived intelligence (Qiu, Li, Shu, & Bai, 2019). Crucial human-like features include a head (Yu & Ngan, 2019) and facial expressions of emotions or intentions (Takanishi, 2000), especially during face-to-face interactions (Kiesler, Powers, Fussell, & Torrey, 2008).…”

Section: Anthropomorphismmentioning

confidence: 99%

“…Even if the service outcome of both interactions is similar, customers likely behave differently, depending on their perceptions of the interacting agent, robotic or human. Employees-AI collaboration help build customer-employee rapport (Qiu et al, 2019); though, recent evidence in hospitality found that AI harms the productivity of emotional intelligent employees (Prentice et al, 2019). Huang and Rust (2018) suggest that companies thus must offer dual service provision: Some segments of customers will be willing to pay a premium for human interactions and human touch, and others will prefer basic services provided by autonomous systems.…”

Section: Employee Replacement or Collaboration Outcomesmentioning

confidence: 99%

See 2 more Smart Citations

Service robot implementation: a theoretical framework and research agenda

Belanche

Casaló

Flavián

et al. 2019

The Service Industries Journal

378

285

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Service robots and artificial intelligence promise to increase productivity and reduce costs, prompting substantial growth in sales of service robots and research dedicated to understanding their implications. Nevertheless, marketing research on this phenomenon is scarce. To establish some fundamental insights related to this research domain, the current article seeks to complement research on robots' human-likeness with investigations of the factors that service managers must choose for the service robots implemented in their service setting. A three-part framework, comprised of robot design, customer features, and service encounter characteristics, specifies key factors within each category that need to be analyzed together to determine their optimal adaptation to different service components. Definitions and overlapping concepts are clarified, together with previous knowledge on each variable and research gaps that need to be solved. This framework and the final research questions provide a research agenda to guide scholars and help practitioners implement service robots successfully.

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Section: Discussionmentioning

confidence: 99%

Section: Anthropomorphismmentioning

confidence: 99%

Section: Employee Replacement or Collaboration Outcomesmentioning

confidence: 99%

See 1 more Smart Citation

Service robot implementation: a theoretical framework and research agenda

Belanche

Casaló

Flavián

et al. 2019

The Service Industries Journal

378

285

View full text Add to dashboard Cite

show abstract

“…The findings indicate robots were not capable and qualified enough to replace human employees in restaurants, because they cannot act independently and efficiently to address customers' needs and fulfil guest experiences (Choi et al, 2019;Ivanov & Webster, 2018). That is why, some hospitality firms, especially hotels, preferred not to use service robots anymore (Qiu, Li, Shu & Bai, 2019). The most notable example is the Henn na hotel that turned off half of its robots because they reportedly malfunctioned and created more work for the human employees (Shead, 2019).…”

Section: [C111]mentioning

confidence: 99%

Understanding the Robotic Restaurant Experience: A Multiple Case Study

Seyitoğlu¹,

Иванов²

2020

Preprint

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The purpose of this study is to investigate the robotic restaurant experience of travellers around the world and understand the components of robotic restaurant experience. Following this aim, travellers who had experienced a robotic restaurant were purposefully selected as a sample group for the study. Since the robotic restaurants are limited around the world, multiple case study method has been chosen to gather richer data. A user-generated content technique which is a form of qualitative case study method has been benefited. The data of this study were gathered between 18-29 November 2019 from travellers’ reviews (n=587) describing experiences they had between February 2013-November 2019. The results reveal a model of components of robotic restaurant experience that include seven main themes: attraction for kids, robotic system, service quality, memorable experience, ambience related attributes, food related attributes (economic value and gastronomic aspects), and deficiencies (in robotic system, in service quality, in ambience related attributes and in food related attributes). This paper is first to investigate the robotic restaurant experience of travellers around the world. Moreover, it contributes to the research on restaurant experience and offers a model of components of the robotic restaurant experience.

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“…Nowadays, many companies are interested in implementing automated systems supporting customer service [2,[29][30][31]. The use of a humanoid robot can be a natural extension of methodologies of attracting customers attention.…”

Section: System Prototype Evaluationmentioning

confidence: 99%

Human Interaction Smart Subsystem—Extending Speech-Based Human-Robot Interaction Systems with an Implementation of External Smart Sensors

Podpora

Gardecki

Beniak

et al. 2020

Sensors

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This paper presents a more detailed concept of Human-Robot Interaction systems architecture. One of the main differences between the proposed architecture and other ones is the methodology of information acquisition regarding the robot's interlocutor. In order to obtain as much information as possible before the actual interaction took place, a custom Internet-of-Things-based sensor subsystems connected to Smart Infrastructure was designed and implemented, in order to support the interlocutor identification and acquisition of initial interaction parameters. The Artificial Intelligence interaction framework of the developed robotic system (including humanoid Pepper with its sensors and actuators, additional local, remote and cloud computing services) is being extended with the use of custom external subsystems for additional knowledge acquisition: device-based human identification, visual identification and audio-based interlocutor localization subsystems. These subsystems were deeply introduced and evaluated in this paper, presenting the benefits of integrating them into the robotic interaction system. In this paper a more detailed analysis of one of the external subsystems-Bluetooth Human Identification Smart Subsystem-was also included. The idea, use case, and a prototype, integration of elements of Smart Infrastructure systems and the prototype implementation were performed in a small front office of the Weegree company as a decent test-bed application area.The days, when simply having a robot was impressive, are gone. The technology should be more than just presenting content. It should be truly interactive. The authors of this work evaluated methods for the purpose of measurement and influencing the Human-Robot interaction User Experience, (see Reference [1]), concluding that the robot's interaction is perceived as more natural if the robot is able to personalize its communication content towards a particular interlocutor. It became clear, that the system should be able to get as much information as possible during and before the conversation takes place [2]. One of the key aspects in a healthy conversation is emotional state recognition. All the improvements of the robots presented in this paper are aiming for natural, real integration with human customers with the implementation of Artificial Intelligence [3,4].The authors of this work have indicated extending the robot's capabilities by adding additional external wireless sensors in Reference [2], however without Smart Infrastructure. Implementation of a remote PID (Passive Infrared Detector) sensor in order to get a signal "someone is coming" seemed to be enough at that stage.In this paper an extended approach is being presented, introducing Human Identification Smart Subsystems (HISS)-a set of external sensors, which enable us to obtain much more valuable information, resulting from the communication with the Smart Infrastructure [5] or databases of the customer. The idea presented in this paper has been successfully verified and implemented and the con...

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Enhancing hospitality experience with service robots: the mediating role of rapport building

Cited by 211 publications

References 43 publications

Service robot implementation: a theoretical framework and research agenda

Service robot implementation: a theoretical framework and research agenda

Understanding the Robotic Restaurant Experience: A Multiple Case Study

Human Interaction Smart Subsystem—Extending Speech-Based Human-Robot Interaction Systems with an Implementation of External Smart Sensors

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