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How to make accurate action decisions based on visual information is one of the important research directions of industrial robots. The purpose of this paper is to design a highly optimized hand-eye coordination model of the robot to improve the robots’ on-site decision-making ability.

The combination of inverse reinforcement learning (IRL) algorithm and generative adversarial network can effectively reduce the dependence on expert samples and robots can obtain the decision-making performance that the degree of optimization is not lower than or even higher than that of expert samples.

The performance of the proposed model is verified in the simulation environment and real scene. By monitoring the reward distribution of the reward function and the trajectory of the robot, the proposed model is compared with other existing methods. The experimental results show that the proposed model has better decision-making performance in the case of less expert data.

A robot hand-eye cooperation model based on improved IRL is proposed and verified. Empirical investigations on real experiments reveal that overall, the proposed approach tends to improve the real efficiency by more than 10% when compared to alternative hand-eye cooperation methods.

Examines the tenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Aims to realize the high accurate contour control with high‐speed motion of articulated robot manipulator (ARM) with interference.

Proposes a new contour control method by using Gaussian neural network (GNN) to solve the problem of the deterioration of the contour control performance due to the interference between robot links. The construction of the GNN controller and the approximation of the interference are based on the Euler‐Lagrange model of ARM. The actual input/out data about the motion of ARM are used for training the GNN to accurately represent the inverse dynamics of ARM with interference. With the Lyapunov function, the stability and the robustness of the GNN controller are discussed. Through the simulation and experiment, it verified that the precision of the contour control has been improved, and illustrated the good features of the proposed method.

Finds that the actual data about the motion of ARM, which is easily obtained from the working field, can express the real features of ARM, and the GNN controller can improve the precision of the contour control with good features.

The proposed method provides an effective method for realizing high accurate contour control of ARM with interference. It can be extended to the ARMs with more than two links and concerning more factors affecting the precision of the contour control, such as friction or gravity.

Proposes a new GNN controller for realizing high accurate contour control of ARM with interference, which is significant for industry.

Examines the ninth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Despite technological advancements within process improvement (PI) projects, two out of three PI projects failed. This paper aims to identify the critical failure factors (CFFs) linked to PI projects and propose suitable mitigation strategies. It also examines how PI practitioners can leverage unsuccessful projects to stimulate learning from failure, turning them into opportunities for development and growth.

A qualitative approach involving semi-structured interviews with 21 PI professionals was utilized to capture the CFFs and their corresponding mitigation best practices.

The study reveals 14 CFFs spanning from issues at the top-level management, down to challenges at the project level and shortcomings at the individual level. We uncover new evolving factors related to “big data analysis and technology implementations”, “project scope complexity”, “unrealistic goal setting”, “frequent shifts in management priorities”, “unclear problem definition” and “prolonged execution duration”. Moreover, results emphasize the significance of fostering a “culture of learning from failure” and “transparency in failure acknowledgment” to avert failures.

This study is the first to provide real-world solutions and guidelines to proactively mitigate PI failures. It offers insights for PI practitioners and broadens their view on emerging factors to implement preventive strategies, building on learning from failures and those of others.

This study aims to examine the association between the effectiveness of risk committee (RC) and firms’ performance in Malaysian context. It also explores whether political connection has an impact on the relationship.

This study, using a principle components analysis, derives a factor score for RC attributes to proxy the effectiveness of RC. It also uses both accounting and market performance to measure the company performance.

Using a sample of financial firms from 2004 to 2018, this study finds that both accounting and market performance are higher for firms with an effective RC. It also finds that the effectiveness of RC in monitoring and management of risks is more pronounced for politically connected firms (PCFs). In further tests, the paper finds that RC attributes (i.e. RC independence, qualification and gender) are positively and significantly associated with accounting performance, while those of RC existence and overlap are positively and significantly related to market performance. The study also finds that RC size (RC diligence) has a positive (negative) impact on financial firms accounting and market performance. The further analysis also shows that PCFs with a separate as well as larger RCs experience both higher accounting and market performance. This study’s results are robust for concerns of endogeneity.

The findings of this study resolve the ongoing debates surrounding political connection by suggesting financial firms not to have politically connected board members as doing so may deteriorate their performance. This study’s results are also useful for investors, regulators and policymakers.

To the best of the authors’ knowledge, this study, for the first time, introduces on the interaction term between the effectiveness of RCs and political connection to empirically explore how an effective RC may reduce the potential risk of political ties. As such, this study adds to the literature and sheds light on an aspect of risk (i.e. risk stems from establishing close link with the government) that is growing in importance.

This paper aims to investigate the following issues: the mechanisms through which different types of top management team’s social capital influence the innovation quality of high-tech firms, and the moderating effect of organizational knowledge utilization on the relationship between top management team’s social capital and innovation quality in high-tech firms.

This study categorizes top management team’s social capital into political, business and academic dimensions, investigating their impact on innovation quality in high-tech firms. Furthermore, a research model is developed with organizational knowledge utilization as the moderating variable. Data from Chinese high-tech firms between 2010 and 2019 are collected as samples for analysis.

The innovation quality of high-tech firms shows an inverted U-shaped trend as the top management team’s political capital and business capital increase. The top management team’s academic capital has a significantly positive correlation with the innovation quality of high-tech firms. Moreover, organizational knowledge utilization plays a significant moderating role in the relationship between the top management team’s social capital and innovation quality in high-tech firms.

This study explores the relationship among different dimensions of top management team’s social capital, innovation quality and organizational knowledge utilization. It holds significant theoretical value in enriching and refining the interactions between top management team’s social capital, knowledge management theory and innovation management theory. In addition, it offers important practical implications for firms to rationally approach top management team’s social capital, emphasize top management team configuration management and establish a comprehensive and efficient organizational knowledge utilization mechanism.

The purpose of this paper is to examine the effect of flow experience on users’ social commerce intention.

Based on the 287 valid responses collected from a survey, structural equation modeling was used to examine the research model.

The results indicated that social interaction, which includes human–computer interaction and human–human interaction, has a significant effect on the flow experience, which in turn affects social purchase and social sharing intention.

The results imply that companies need to facilitate social interaction to improve users’ experience and promote their social commerce intention.

Although prior research has examined social commerce user behaviour from multiple perspectives such as trust, perceived value and technological perceptions, it has focused on the effect of cognitive beliefs and neglected the effect of affective beliefs such as flow experience. This research tries to fill the gap.

The widespread integration of artificial intelligence (AI)-enabled personalization has sparked a need for a deeper understanding of its transformative potential. To address this, this study aims to investigate the mental models held by consumers from diverse cultures regarding the impact and role of AI-enabled personalization in their lives (i.e. individual well-being) and in society (i.e. societal well-being).

This paper uses the theories-in-use approach, collecting qualitative data via the critical incident technique. This data encompasses 487 narratives from 176 consumers in two culturally distinct countries, Belgium and Pakistan. Additionally, it includes insights from a focus group of six experts in the field.

This research reveals that consumers view AI-enabled personalization as a dual-edged sword: it may both extend and restrict the self and also contribute to an affluent society as well as an ailing society. The particular aspects of the extended/restricted self and the affluent/ailing society that emerge differ across respondents from different cultural contexts.

This cross-cultural research contributes to the personalization and well-being literature by providing detailed insight into the transformative potential of AI-enabled personalization while also having important managerial and policy implications.

The purpose of this paper is to draw on the elaboration likelihood model to examine location-based services (LBS) users’ privacy concern.

Based on the 266 valid responses collected from a survey, structural equation modeling was employed to examine the research model.

The results indicated that privacy concern receives a dual influence from both central cues and peripheral cues. Central cues include privacy policy and information quality, whereas peripheral cues include reputation and privacy seals. Privacy control moderates the effects of privacy policy and privacy seals on privacy concern.

The results imply that service providers need to consider both central and peripheral cues in order to mitigate users’ privacy concern associated with using LBS.

Although previous research has found the effect of privacy concern on user adoption of LBS, it has seldom examined the influence processes of external factors on privacy concern. This research tries to fill the gap.