The role of robots in the service industry: Factors affecting human-robot interactions

Moriuchi, Emi; Murdy, Samantha

doi:10.1016/j.ijhm.2023.103682

Cited by 15 publications

(1 citation statement)

References 75 publications

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“…The integration of robotics into human-centric environments necessitates a nuanced understanding of HRI to ensure safety, efficiency, and user satisfaction. Recent studies have made significant strides in this domain, addressing cognitive ergonomics in collaborative robotics [ 1 ], the role of robots in service industries [ 2 ], and skill-learning frameworks for manipulation tasks [ 3 ]. Advances in technology, such as AR-assisted deep reinforcement learning [ 4 ], proactive collaboration models [ 5 ], and adaptive neural network control in physical HRI [ 6 ], are enhancing mutual-cognitive capabilities and safety.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Engagement Detection for Human–Robot Interaction in Complex Environments

Lu,

Lo,

Hong

et al. 2024

Sensors

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This study develops a comprehensive robotic system, termed the robot cognitive system, for complex environments, integrating three models: the engagement model, the intention model, and the human–robot interaction (HRI) model. The system aims to enhance the naturalness and comfort of HRI by enabling robots to detect human behaviors, intentions, and emotions accurately. A novel dual-arm-hand mobile robot, Mobi, was designed to demonstrate the system’s efficacy. The engagement model utilizes eye gaze, head pose, and action recognition to determine the suitable moment for interaction initiation, addressing potential eye contact anxiety. The intention model employs sentiment analysis and emotion classification to infer the interactor’s intentions. The HRI model, integrated with Google Dialogflow, facilitates appropriate robot responses based on user feedback. The system’s performance was validated in a retail environment scenario, demonstrating its potential to improve the user experience in HRIs.

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Section: Introductionmentioning

confidence: 99%

Implementation of Engagement Detection for Human–Robot Interaction in Complex Environments

Lu,

Lo,

Hong

et al. 2024

Sensors

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Leaders Inflate Performance Ratings for Employees Who Use Robots to Augment Their Performance

He,

Yam,

Zhao

et al. 2024

Human Resource Management

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With robot usage becoming increasingly prevalent in contemporary workplaces, a key task for supervisors is conducting performance ratings in the context of employee‐robot value co‐creation. In this research, we explore whether, how, and when employees' robot usage affects supervisors' performance ratings. Drawing upon attribution theory, we suggest that supervisors might overestimate the performance of employees who use robots at work. Across a pilot study, two experiments, and one field study, we find that employees' robot usage is positively associated with supervisors' illusory performance transference (i.e., supervisors' belief that robot–associated performance should be attributed to employees who use robots at work). In turn, this transference is positively associated with high performance ratings for the respective employees. Furthermore, the supervisor–perceived experience of robots (i.e., supervisors' perceptions that robots are able to feel emotions and sensations) weakens this indirect effect. We conclude by discussing the theoretical and practical implications of our findings and future directions.

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Understanding smart service failure: The case of smart restaurants

Wong,

Huang,

Lin

2024

International Journal of Hospitality Management

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The role of robots in the service industry: Factors affecting human-robot interactions

Cited by 15 publications

References 75 publications

Implementation of Engagement Detection for Human–Robot Interaction in Complex Environments

Implementation of Engagement Detection for Human–Robot Interaction in Complex Environments

Leaders Inflate Performance Ratings for Employees Who Use Robots to Augment Their Performance

Understanding smart service failure: The case of smart restaurants

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