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The purpose of this paper is to develop and redefine the “classic” roles of shop floor management and quality control circles (QCCs) in Kaizen. In specific, it aims to examine the linkage between shop floor management and QCCs, and test the relationships among shop floor management, QCCs and long-term Kaizen improvement outcomes.

This study employs qualitative method by using a questionnaire to obtain data from 371 respondents in nine Sino-Japanese automotive joint-ventures. The data are analysed with the method of canonical correlation approach.

The study identifies important factors to assist the adoption of shop floor management and QCCs for Kaizen. The analysis on the survey indicates that not all the shop floor management tools could help to identify improvement opportunities. QCCs are effective in addressing large problems and challenging current policies in companies, however, they have low impacts on individual learning.

The data of this study come from nine Sino-Japanese automotive joint ventures. Therefore, the sample selection is limited to these companies. The findings are able to be applied for improving the similar problems which were identified in this study.

The study has the following practical implications, first is small shop floor problems can be identified and solved rapidly and continuously at source by shop floor management. The second one is QCCs, or other similar group-based improvement approaches take long to be fully addressed and implemented. Third, practical solutions can be achieved from small and gradual changes, and they can prevent the results backsliding to the pre-improvement stage. Finally, QCCs are hardly to achieve a better improvement alone. It requires other Kaizen approaches to support.

This study is probably the first to explore and investigate the implementation of the four building block tools of shop floor management in real business practise, and more specific the first to discuss the relationship among shop floor management, QCCs and long-term improvement outcomes based on empirical data from Sino-Japanese automotive joint-ventures.

Ni coating was electroplated on carbon steel substrate to protect carbon steel.

During electroplating, the ultrasonic irradiation (UI) (1 kHz) action was in situ used with different frequency. The influence of UI on the microstructure, mechanical and electrochemical performance of the coating was studied with scanning electron microscopy, X-ray diffraction, microhardness measurement, polarization curves and electrochemical impedance spectroscopy.

The results show that comparing that without UI imposition, UI during electroplating can refine the coating grain and decrease the micro-pores in the coating, resulting in improvement of the coating corrosion and hardness.

The imposition of UI action during electroplating Ni coating can remove intrinsic pores in the coating and compact the coating. The potential bimetallic cell between substrate and plating layer can be insulated to enhance the corrosion resistance of Ni coating. The imposition of UI action during electroplating Ni coating can refine Ni coating grain size and improve the coating haredness.

The purpose of this paper is to investigate attributes that influence Airbnb customer experience by analysing online reviews from users staying in London. It presents a text mining approach to identify a set of broad themes from the textual reviews. It aims to highlight the customers’ changing perception of good quality of accommodations.

This paper analyses 169,666 reviews posted by Airbnb users who stayed in London from 2011 to 2015. Hierarchical clustering algorithms are used to group similar words into clusters based on their co-occurrence. Longitudinal analysis and seasonal analysis are conducted for a more coherent understanding of the Airbnb customer behaviour.

This paper provides empirical insights about how Airbnb users’ mindset of good quality of accommodations changes over a five-year timespan and in different seasons. While there are common attributes considered important throughout the years, exclusive attributes are discovered in particular years and seasons.

This paper is confined to Airbnb experiences in London. Researchers are encouraged to apply the proposed methodology to investigate Airbnb experiences in other cities and detect any change in customer perception of quality stay.

This paper offers implications for the prioritisation of customer concerns to design and improve services offerings and for alignment of services with customer expectations in the sharing economy.

This paper fulfils an identified need to examine the change in customer expectation across the timespan and seasons in the case of Airbnb. It also contributes by illustrating how big data can be used to uncover key attributes that facilitate the engagement with the sharing economy.

This study aims to explore the impact of ethical leadership on employee creative deviance, with job autonomy as a mediator and creative self-efficacy as a moderator between job autonomy and creative deviance.

A survey was developed based on construct measures from the literature. A total of 316 responses were received from employees of information and communication technology companies located in China's Pearl River Delta.

Both ethical leadership and job autonomy have a positive impact on employee creative deviance; job autonomy plays a mediating role between ethical leadership and creative deviance; creative self-efficacy does not have a significant moderating effect on the job autonomy-creative deviance relationship.

Future studies could explore the potential moderating role of both job autonomy and creative-self efficacy in the link between ethical leadership and creative deviance.

This study recommends that organizations should adopt and promote an ethical leadership approach to manage creative deviance at work. Organizations could explore alternative methods of task completion to support the job autonomy for the employees to mitigate the dilemmas associated with creative deviance.

This is one of few studies that examine the impact of ethical leadership on employee's creative deviance, despite the fact that the influence of ethical leadership on the followers has been extensively examined.

The purpose of this paper is to develop a better understanding of how Sino-Japanese joint ventures implemented the three Japanese improvement methods, i.e. Kaizen, Kaikaku and Kaizen Blitz. The specific objectives of this study are to identify the key enablers for the three improvement methods; and to identify the most selected improvement method.

This study employs a fuzzy analytic hierarchy process to pairwise-compare the three improvement methods. Data are collected from 28 industry experts from Sino-Japanese joint ventures. The study then adopts extent analysis approach for pairwise comparisons and extent analysis to obtain synthetic extent values for priority weights.

The results of the study indicate that personnel (humanware) factor enablers are the most important factor for Kaizen, whilst software factor enablers (essential rules, policies and institutional arrangements) weight second and hardware factor enablers (physical, measurable hard facts or resources) weight last. The study also reviews that Kaizen is the most selected improvement method among the three.

The sample of this study is limited to Sino-Japanese ventures in Guangzhou, China. This study only identifies the key improvement enablers based on interviews with shop floor managers and improvement experts.

Practical implications are threefold: the improvement implementations should be based on factors such as regular training, incentives for motivations and shop floor management; improvement methods are transferable and standard operations may only have small effects on collecting improvement ideas; and Kaizen is the appropriate method to support long-term and process-oriented improvements.

This study is the first to specifically pairwise-compare the three Japanese improvement methods and to identify priorities of their key enablers in Sino-Japanese joint ventures.

The purpose of this paper is to examine the mediating role of job characteristics and social exchange in transformational leadership (TFL) and employability relationship.

The sample is composed of 760 participants employed in Yangtze Delta and Pearl River Delta in China. The participants have worked under their line manager for more than one year. In order to better prevent data from possible common method bias, two waves of surveys (in 2014) on a stratified sample, included a mix of industries, such as construction, manufacturing, finance, insurance and communications, were used to investigate the proposed relationship between TFL, job characteristics, social exchange and employability.

The research has empirically tested the relationship between TFL and employability. While previous research has analyzed the relationship between them, the authors have enriched existing literature by exploring the mediating factors and illustrating the importance of indirect effects. Besides the direct effect, the results of this study showed that TFL could also improve employees’ employability through job demands, skill discretion, decision authority, perceived organizational support and team–member exchange, but not leader–member exchange.

The study opens up a debate around the employability of employees as it stands apart from the performance measurement. The authors believe that this new mediating model can provide an insight into complex mechanisms of employability enhancement from the perspective of leader development.

The density peak clustering algorithm (DP) is proposed to identify cluster centers by two parameters, i.e. ρ value (local density) and δ value (the distance between a point and another point with a higher ρ value). According to the center-identifying principle of the DP, the potential cluster centers should have a higher ρ value and a higher δ value than other points. However, this principle may limit the DP from identifying some categories with multi-centers or the centers in lower-density regions. In addition, the improper assignment strategy of the DP could cause a wrong assignment result for the non-center points. This paper aims to address the aforementioned issues and improve the clustering performance of the DP.

First, to identify as many potential cluster centers as possible, the authors construct a point-domain by introducing the pinhole imaging strategy to extend the searching range of the potential cluster centers. Second, they design different novel calculation methods for calculating the domain distance, point-domain density and domain similarity. Third, they adopt domain similarity to achieve the domain merging process and optimize the final clustering results.

The experimental results on analyzing 12 synthetic data sets and 12 real-world data sets show that two-stage density peak clustering based on multi-strategy optimization (TMsDP) outperforms the DP and other state-of-the-art algorithms.

The authors propose a novel DP-based clustering method, i.e. TMsDP, and transform the relationship between points into that between domains to ultimately further optimize the clustering performance of the DP.

The purpose of this paper is to propose a casting process for the production of double-chamber soft fingers, which avoids the problems of air leakage and fracture caused by multistep casting. This proposed method facilitates the simultaneous casting of the inflation chamber and the jamming chamber.

An integrated molding technology based on the lost wax casting method is proposed for the manufacture of double-chamber soft fingers. The solid wax core is assembled with the mold, and then liquid silicone rubber is injected into it. After cooling and solidification, the mold is stripped off and heated in boiling water, so that the solid wax core melts and precipitates, and the integrated soft finger is obtained.

The performance and fatigue tests of the soft fingers produced by the proposed method have been carried out. The results show that the manufacturing method can significantly improve the fatigue resistance and stability of the soft fingers, while also avoiding the problems such as air leakage and cracking.

The improvement of the previous multistep casting method of soft fingers is proposed, and the integrated molding manufacturing method is proposed to avoid the problems caused by secondary bonding.

This study aims to tackle control challenges in soft robots by proposing a visually-guided reinforcement learning approach. Precise tip trajectory tracking is achieved for a soft arm manipulator.

A closed-loop control strategy uses deep learning-powered perception and model-free reinforcement learning. Visual feedback detects the arm’s tip while efficient policy search is conducted via interactive sample collection.

Physical experiments demonstrate a soft arm successfully transporting objects by learning coordinated actuation policies guided by visual observations, without analytical models.

Constraints potentially include simulator gaps and dynamical variations. Future work will focus on enhancing adaptation capabilities.

By eliminating assumptions on precise analytical models or instrumentation requirements, the proposed data-driven framework offers a practical solution for real-world control challenges in soft systems.

This research provides an effective methodology integrating robust machine perception and learning for intelligent autonomous control of soft robots with complex morphologies.

In this paper, the standard Peridynamic Timoshenko beam model accounting for the shear deformation is chosen to describe the thick beam kinematics. Unfortunately, when applied to very thin beam structures, the standard Peridynamics (PD) encounters the shear locking phenomenon, leading to incorrect solutions.

PD differs from classical continuum mechanics and other nonlocal theories that do not involve spatial derivatives of the displacement field. PD is based on the integral equation instead of differential equations to handle discontinuities and other singularities.

The shear locking can be successfully alleviated using the developed selective integration method. In particular, this technique has been implemented in the standard PD, which allows an accurate result for a wide range of slenderness from very thin to thick (10 < L/t < 10³) structures. It can also accelerate the computational time for particular dynamic problems using fewer neighboring integration particles. Several numerical examples are solved to demonstrate the effectiveness of the proposed method for modeling beam structures.

The paper highlights the severe shear locking phenomenon in the Peridynamic Timoshenko beam available in the literature, especially for very thin structures. A new alternative for the alleviation of shear locking in the Peridynamic Timoshenko beam, using selective integration. Hence the developed Peridynamic Timoshenko beam model is effective for thin and thick structures. A new peridynamic formulation for the low-velocity impact beam models is presented and validated.

1. The paper highlights the severe shear locking phenomenon in the Peridynamic Timoshenko beam proposed in the literature, especially for very thin structures.

2. The developed Peridynamic Timoshenko beam model based on selective integration is effective for thin and thick structures.

3. A new peridynamic formulation for the low-velocity impact beam models is presented and validated.

The paper highlights the severe shear locking phenomenon in the Peridynamic Timoshenko beam proposed in the literature, especially for very thin structures.

The developed Peridynamic Timoshenko beam model based on selective integration is effective for thin and thick structures.

A new peridynamic formulation for the low-velocity impact beam models is presented and validated.