Multi-granularity service composition in industrial cloud robotics

Wang, Fei; Zhang, Lin; Laili, Yuanjun

doi:10.1016/j.rcim.2022.102414

Cited by 10 publications

(3 citation statements)

References 32 publications

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“…Third, previous studies have focused on industrial HRIs (Wang et al , 2022), with less emphasis in frontline settings (Belanche et al , 2021). Even within frontline service robot research, there is heavy emphasis on panel surveys (Chi et al , 2023; Huang et al , 2023) and online experiments (Kao and Huang, 2023; Lu et al , 2019).…”

Section: Discussionmentioning

confidence: 99%

Service robot acceptance in museums: an empirical study using the service robot acceptance model (sRAM)

Wong,

Wong

2024

JSM

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Purpose This study aims to apply the service robot acceptance model (sRAM) to examine how attitude toward human–robot interaction (HRI) and engagement influence consumer acceptance of service robots in a frontline setting. Design/methodology/approach Data was collected from 255 visitors who interacted with a robotic tour guide at a city museum. The data was analyzed using smart PLS 4.0. Findings The findings show the positive effects of subjective norms, appearance, perceived trust and positive emotion on both attitude toward HRI and engagement. In addition, social capability impacted attitude toward HRI, whereas perceived usefulness affected engagement. Practical implications To deliver engaging museum experiences that bring about positive word-of-mouth and intention to visit, managers need to incorporate the sRAM dimensions in the design and deployment of service robots. Originality/value This research uses field data to empirically validate the sRAM in the context of service robot acceptance. It introduces engagement as a novel mediating variable, enriching current understanding of human-like qualities in HRIs.

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Section: Discussionmentioning

confidence: 99%

Service robot acceptance in museums: an empirical study using the service robot acceptance model (sRAM)

Wong,

Wong

2024

JSM

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“…With the development of the Internet and cloud computing, increasingly more services are provided on the Internet [1][2][3][4][5]. As a method of utilizing services, service composition has been widely used in different fields [6][7][8]. It combines atomic services in different areas to meet the needs of users with multiple functions and different QoSs.…”

Section: Introductionmentioning

confidence: 99%

“…It combines atomic services in different areas to meet the needs of users with multiple functions and different QoSs. The service composition approach has been widely used in different industries, such as cloud manufacturing [7,9,10] and metro services [11].…”

Section: Introductionmentioning

confidence: 99%

Service composition considering energy consumption of users and transferring files in a multicloud environment

Zhu

2023

J Cloud Comp

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In the internet and cloud environment, service composition is always used to enhance the function and processing ability of clouds. Those clouds work together for a user and provide different functions. A service request may involve multiple clouds. The past work focuses on the method of service composition and ignores the energy composition when files are transferred between clouds, including the energy consumption for transferring files (sending files from the user to the cloud and receiving files from the cloud to the user) of the user. The paper models the service composition in a multicloud environment. Based on those models, we use the GA (genetic algorithm) algorithm (GA-C) to solve the service composition problem with multiple targets in a multicloud environment. Simulation results show that the GA-C can: (1) reduce the average number of involved clouds and the energy consumption between clouds, and (2) reduce the energy consumption of the user and the failure rate of service composition.

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A universal large-scale many-objective optimization framework based on cultural learning

Wang

Zhang

et al. 2023

Applied Soft Computing

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Multi-granularity service composition in industrial cloud robotics

Cited by 10 publications

References 32 publications

Service robot acceptance in museums: an empirical study using the service robot acceptance model (sRAM)

Service robot acceptance in museums: an empirical study using the service robot acceptance model (sRAM)

Service composition considering energy consumption of users and transferring files in a multicloud environment

A universal large-scale many-objective optimization framework based on cultural learning

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