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Article
Publication date: 30 August 2020

Xiangyou Shen, Bing Pan, Tao Hu, Kaijun Chen, Lin Qiao and Jinyue Zhu

Online review bias research has predominantly focused on self-selection biases on the user’s side. By collecting online reviews from multiple platforms and examining their biases…

474

Abstract

Purpose

Online review bias research has predominantly focused on self-selection biases on the user’s side. By collecting online reviews from multiple platforms and examining their biases in the unique digital environment of “Chinanet,” this paper aims to shed new light on the multiple sources of biases embedded in online reviews and potential interactions among users, technical platforms and the broader social–cultural norms.

Design/methodology/approach

In the first study, online restaurant reviews were collected from Dianping.com, one of China's largest review platforms. Their distribution and underlying biases were examined via comparisons with offline reviews collected from on-site surveys. In the second study, user and platform ratings were collected from three additional major online review platforms – Koubei, Meituan and Ele.me – and compared for possible indications of biases in platform's review aggregation.

Findings

The results revealed a distinct exponential-curved distribution of Chinese users’ online reviews, suggesting a deviation from previous findings based on Western user data. The lack of online “moaning” on Chinese review platforms points to the social–cultural complexity of Chinese consumer behavior and online environment that goes beyond self-selection at the individual user level. The results also documented a prevalent usage of customized aggregation methods by review service providers in China, implicating an additional layer of biases introduced by technical platforms.

Originality/value

Using an online–offline design and multi-platform data sets, this paper elucidates online review biases among Chinese users, the world's largest and understudied (in terms of review biases) online user group. The results provide insights into the unique social–cultural cyber norm in China's digital environment and bring to light the multilayered nature of online review biases at the intersection of users, platforms and culture.

Details

Journal of Hospitality and Tourism Insights, vol. 4 no. 1
Type: Research Article
ISSN: 2514-9792

Keywords

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Article
Publication date: 3 December 2024

Xiangchang Meng, Shuo Xu, Ming Han, Tiejun Li and Jinyue Liu

To overcome the shortcomings of traditional dynamic parameter identification methods in accuracy and efficiency, this paper aims to propose a dynamic parameter identification…

11

Abstract

Purpose

To overcome the shortcomings of traditional dynamic parameter identification methods in accuracy and efficiency, this paper aims to propose a dynamic parameter identification method based on improved iterative reweighted least squares (IIRLS) algorithm.

Design/methodology/approach

First, Newton–Euler method is used to establish the dynamic model of the robot, which is linearized and reorganized. Then, taking the improved Fourier series as the excitation trajectory, the optimization model with objective function is established and optimized. Then, the manipulator runs the optimized trajectory and collects the running state of the joint. Finally, the iterative process of iterative reweighted least squares (IRLS) algorithm is improved by combining clustering algorithm and matrix inversion operation rules, and the dynamic model of robot is identified by using the processed collected data.

Findings

Experimental results show that, compared with the IRLS algorithm, the root mean square of the proposed IIRLS algorithm is reduced by 4.18% and the identification time is reduced by 94.92% when the sampling point is 1001. This shows that IIRLS algorithm can identify the dynamic model more accurately and efficiently.

Originality/value

It effectively solves the problem of low accuracy and efficiency of parameter identification in robot dynamic model and can be used as an effective method for parameter estimation of robot dynamic model, which is of great significance to the research of control method based on robot model.

Details

Industrial Robot: the international journal of robotics research and application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0143-991X

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Article
Publication date: 24 March 2023

Runling Peng, Jinyue Liu, Wei Wang, Peng Wang, Shijiao Liu, Haonan Zhai, Leyang Dai and Junde Guo

This study aims to investigate the synergistic friction reduction and antiwear effects of lyophilized graphene loading nano-copper (RGO/Cu) as lubricating oil additives, compared…

93

Abstract

Purpose

This study aims to investigate the synergistic friction reduction and antiwear effects of lyophilized graphene loading nano-copper (RGO/Cu) as lubricating oil additives, compared with graphene.

Design/methodology/approach

The friction performance of freeze-drying graphene (RGO) and RGO/Cu particles was investigated at different addition concentrations and under different conditions.

Findings

Graphene plays a synergistic friction reduction and antiwear effect because of its large specific surface area, surface folds and loading capacity on the nanoparticles. The results showed that the average friction coefficients of RGO and RGO/Cu particles were 22.9% and 6.1% lower than that of base oil and RGO oil, respectively. In addition, the widths of wear scars were 62.3% and 55.3% lower than those of RGO/Cu particles, respectively.

Originality/value

The RGO single agent is suitable for medium-load and high-speed conditions, while the RGO/Cu particles can perform better in the conditions of heavy load and high speed.

Details

Industrial Lubrication and Tribology, vol. 75 no. 3
Type: Research Article
ISSN: 0036-8792

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