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The current study’s goal is to investigate how lean, agile, resilient and sustainable human resource management (LARS HRM) affects green innovation and environmental performance, both directly and indirectly.

Partial least squares-structural equation modeling (PLS-SEM) was used to analyze the data based on a sample of 273 Tunisian businesses in the industrial and service sectors that were certified ISO 9001.

With the exception of AHRM–GPdtI, the results show that the mainstream advanced theory on direct effects was verified. With regard to indirect effects, everything of the literature that was presented was accepted, with the exception of the relationship between AHRM–GPdtI–EP, AHRM–GPssI–EP and RHRM–GPdtI–EP.

This research is distinctive in that it aims to incorporate every LARGS paradigm within the HRM field. By taking green innovation into consideration, it closes the current gaps on the direct and indirect effects of LARS HRM on environmental performance. Our study is unique in that it incorporates large, industry-operating, certified ISO 9001 firms with those in the service sector, with the goal of achieving greater generalization of results.

The purpose of this paper is to address two major challenges faced by robotic fish when operating in underwater environments: insufficient path planning capabilities and difficulties in avoiding dynamic obstacles. To achieve this, a method is proposed that combines the Improved Rapid Randomized Tree Star (IRRT*) with the dynamic window approach (DWA).

The RRT-connect algorithm is used to determine an initial feasible path quickly. The quality of sampling points is then improved by dividing the regions and selecting each region’s probability based on its fitness value. The fitness function and roulette wheel method are introduced for region selection. Subtarget points of the DWA algorithm are extracted from the IRRT* algorithm to achieve real-time dynamic path planning.

In various maps, the iteration count for the IRRT* algorithm decreased by 61%, 35% and 51% respectively, compared to the RRT* algorithm, whereas the iteration time was reduced by 75%, 34% and 57%, respectively. In addition, the IRRT*-DWA algorithm can successfully navigate through multiple dynamic obstacles, and the average time, path length, etc. do not change much when parameters change, and the stability is high.

A novel IRRT*-DWA algorithm is proposed, which, by refining the sampling strategy and updating sub-target points in real time, not only addresses the limitations of existing algorithms in terms of path planning efficiency in complex environments but also enhances their capability to avoid dynamic obstacles. Ultimately, experimental results indicate a high level of similarity between the actual and ideal paths.

Trust is the glue that holds cooperative relationships together and often exists in an asymmetric manner. The purpose of this study is to explore how to mitigate the issue of losses or increased transaction costs caused by opportunistic behavior in a soft environment where trust asymmetry is quite common and difficult to avoid.

This study focuses on examining asymmetric trust between the government and the private sector in public-private partnership (PPP) projects. Drawing upon both project realities and relevant literature, the primary conditional variables influencing asymmetric trust are identified. These variables encompass power perception asymmetry, information asymmetry, interaction behavior, risk perception differences and government-side control. Subsequently, through the use of a survey questionnaire, binary-matched data from both the government and the private sector are collected. The study employs fuzzy-set qualitative comparative analysis (fsQCA) to conduct a configurational analysis, aiming to investigate the causal pathways that trigger asymmetric trust.

No single conditional variable is a necessary condition for the emergence of trust asymmetry. The pathways leading to a high degree of trust asymmetry can be categorized into two types: those dominated by power perception and those involving a combination of multiple factors. Differences in power perception play a crucial role in the occurrence of high trust asymmetry, yet the influence of other conditional variables in triggering trust asymmetry should not be overlooked.

The findings can contribute to advancing the study of trust relationships in the field of Chinese PPP projects. Furthermore, they hold practical value in facilitating the enhancement of trust relationships between the government and the private sector.

The purpose of this study is to explore the impact of asymmetric trust on construction project management performance in China's construction industry. Moreover, the authors explore the mediating role of two types of knowledge sharing (explicit knowledge sharing and tacit knowledge sharing) in explaining the association between asymmetric trust and project management performance.

A theoretical model based on the research hypotheses proposed in this study was developed and a questionnaire survey was conducted with 271 professionals. The data collected was analyzed by the structural equation modeling (SEM) technique.

The results of this study indicate that there is a significant and negative association between asymmetric trust and project management performance. Moreover, two types of knowledge sharing (explicit knowledge sharing and tacit knowledge sharing) have different degrees of impact on improving project management performance. In addition, tacit knowledge sharing is a mediator between asymmetric trust and project management performance.

The data used in this study is from Chinese scenarios, so the research conclusions and application effects based on this are bound to have certain regional limitations. Besides, there are many factors that affect project management performance improving, and the relationships among them are so complex. The theoretical model proposed in this study may not be fully considered. Therefore, follow-up researchers can consider bringing more suitable variables into their researches, so that the theoretical researches can be more in line with the actual project management practice, and the specific mechanism for improving project management performance can be explained more deeply.

This research's value is as follows: Firstly, this paper contributes to the trust and relational governance literature by expanding the research perspective of mutual trust to asymmetric trust. Specially, this research designs a measurement scale for asymmetric trust and then reveals the impact mechanism of it on project management performance, which will certainly promote research paradigm change of trust. Secondly, this research is beneficial to knowledge sharing literature in the construction management field by expanding the research scope of knowledge sharing from a cross-organizational perspective.

This study aims to examine the effect of supervisor developmental feedback on employee silence behaviour by developing a moderated mediation model. The model focuses on the mediating role of role breadth self-efficacy and high activated positive affect underpinning the relationship between supervisor developmental feedback and employee silence behaviour, and the moderating role of interdependent self-construal.

The two-wave survey was conducted among 265 employees. Structural equation modelling was conducted to test the mediation and moderation mediation hypotheses.

Results indicated that high activated positive affect mediated the negative relationship between supervisor developmental feedback and employee silence behaviour. The authors also found that interdependent self-construal moderated the relationship between supervisor developmental feedback and role breadth self-efficacy, as well as the indirect effect of supervisor developmental feedback on employee silence behaviour via role breadth self-efficacy.

This empirical study provides preliminary evidence of the mediating role of breadth self-efficacy and high activated positive affect in the negative relationship between supervisor developmental feedback and employee silence behaviour. The moderated mediation results further show that the mediation of role breadth self-efficacy between supervisor developmental feedback is contingent on individual interdependent self-construal, such that the mediation effect is significant among individuals with high interdependent self-construal, but the mediation effect of high activated positive effect is independent of individual interdependent self-construal. The findings further extend boundary conditions (interdependent self-construal) that may constrain the effect of supervisor developmental feedback on role breadth self-efficacy and high activated positive affect. The research makes considerable contributions to the cognitive-affective personality system theory by specifying the cognitive and affective mechanisms between supervisor developmental feedback and employee silence behaviour, as well as the boundary conditions.

The study aims to equip robots with the ability to precisely maintain interaction forces, which is crucial for tasks such as polishing in highly dynamic environments with unknown and varying stiffness and geometry, including those found in airplane wings or thin, soft materials. The purpose of this study is to develop a novel adaptive force-tracking admittance control scheme aimed at achieving a faster response rate with higher tracking accuracy for robot force control.

In the proposed method, the traditional admittance model is improved by introducing a pre-proportional-derivative controller to accelerate parameter convergence. Subsequently, the authors design an adaptive law based on fuzzy logic systems (FLS) to compensate for uncertainties in the unknown environment. Stability conditions are established for the proposed method through Lyapunov analysis, which ensures the force tracking accuracy and the stability of the coupled system consisting of the robot and the interaction environment. Furthermore, the effectiveness and robustness of the proposed control algorithm are demonstrated by simulation and experiment.

A variety of unstructured simulations and experimental scenarios are designed to validate the effectiveness of the proposed algorithm in force control. The outcomes demonstrate that this control strategy excels in providing fast response, precise tracking accuracy and robust performance.

In real-world applications spanning industrial, service and medical fields where accurate force control by robots is essential, the proposed method stands out as both practical and straightforward, delivering consistently satisfactory performance across various scenarios.

This research introduces a novel adaptive force-tracking admittance controller based on FLS and validated through both simulations and experiments. The proposed controller demonstrates exceptional performance in force control within environments characterized by unknown and varying.

Computer vision and deep learning (DL) methods have been investigated for personal protective equipment (PPE) monitoring and detection for construction workers’ safety. However, it is still challenging to implement automated safety monitoring methods in near real time or in a time-efficient manner in real construction practices. Therefore, this study developed a novel solution to enhance the time efficiency to achieve near-real-time safety glove detection and meanwhile preserve data privacy.

The developed method comprises two primary components: (1) transfer learning methods to detect safety gloves and (2) edge computing to improve time efficiency and data privacy. To compare the developed edge computing-based method with the currently widely used cloud computing-based methods, a comprehensive comparative analysis was conducted from both the implementation and theory perspectives, providing insights into the developed approach’s performance.

Three DL models achieved mean average precision (mAP) scores ranging from 74.92% to 84.31% for safety glove detection. The other two methods by combining object detection and classification achieved mAP as 89.91% for hand detection and 100% for glove classification. From both implementation and theory perspectives, the edge computing-based method detected gloves faster than the cloud computing-based method. The edge computing-based method achieved a detection latency of 36%–68% shorter than the cloud computing-based method in the implementation perspective. The findings highlight edge computing’s potential for near-real-time detection with improved data privacy.

This study implemented and evaluated DL-based safety monitoring methods on different computing infrastructures to investigate their time efficiency. This study contributes to existing knowledge by demonstrating how edge computing can be used with DL models (without sacrificing their performance) to improve PPE-glove monitoring in a time-efficient manner as well as maintain data privacy.

The objective of this study is to investigate the impact of the COVID-19 pandemic on the consumer payment behavior in Italy by correlating financial literacy with digital payment awareness, examining media anxiety and financial security, and including a gender analysis.

Consumers’ attitudes toward cashless payments were investigated using an online survey conducted from November 2021 to February 2022 on a sample of 836 Italian citizens by considering the behavioral characteristics and aspects of financial literacy. Structural equation modeling (SEM) was used to test the hypotheses and to determine whether the model was invariant by gender.

The analysis showed that the fear of contracting COVID-19 and the level of financial literacy had a direct influence on the payment behavior of Italians, which was completely different in its weighting. Fear due to the spread of news regarding the pandemic in the media indirectly influenced consumers’ noncash attitude. The preliminary results of the gender multigroup analysis showed that cashless payment was the same in the male and female subpopulations.

This research is noteworthy because of its interconnected examination. It examined the effects of the COVID-19 pandemic on people’s payment choices, assessed their knowledge, and considered the influence of media-induced anxiety. By combining these factors, the study offered an analysis from a gender perspective, providing understanding of how financial behaviors were shaped during the pandemic.

This study aims to examine the tail connectedness between the Chinese and Association of Southeast Asian Nations (ASEAN) stock markets. More specifically, the authors measure the return spillovers at three quantile levels: median (t = 0.5), lower extreme (t = 0.05) and upper extreme (t = 0.95). The connectedness at extreme upper and lower quantiles provides insightful information to investors regarding tail risk propagation, which ultimately suggests that investors adjust their portfolios according to the extreme bullish and bearish market conditions.

The authors employ the quantile connectedness approach of Ando et al. (2022) to examine the quantile transmission mechanism among the ASEAN and Chinese stock markets.

The results show significant evidence of a higher level of connectedness between Chinese and ASEAN stock markets at extreme upper and lower quantiles compared to the median quantiles, which suggests the use of a quantile-based connectedness approach instead of an average-measure-based one. Furthermore, the time-varying connectedness analysis shows that the total spillovers reach the highest peaks during the global financial crisis, the Chinese stock market crash and the COVID-19 pandemic at the upper, lower and median quantiles. Finally, the static and dynamic pairwise spillovers between the Chinese and ASEAN markets vary over quantiles as well.

This study is the first attempt to examine quantile vector autoregression (VAR)-based return spillovers between China and ASEAN stock markets during different market statuses. Besides, the COVID-19 has intensified the uncertainty in Asian countries, mainly China and ASEAN economies.

Foreign construction subsidiaries play an important role in the global construction market. How to establish and maintain long-term sustainable performance has attracted increased attention, but only a few studies have considered this issue. The purpose of this study is to explore the relationship between autonomy and the sustainable performance of subsidiaries and to provide support for their management control modes.

From an institutional logics perspective, empirical research using a questionnaire survey was conducted following the methodological framework of this study. Relevant data were collected from 106 experienced managers of foreign construction subsidiaries, and the hypotheses were tested through a regression model.

The results show that foreign construction subsidiaries have a high degree of operational autonomy, which tends to strengthen their embeddedness in the host country and improve their sustainable performance. However, the role of strategic autonomy is not found to be significant. The moderation results show that the positive impact between operational autonomy and external network embeddedness is strengthened by institutional distance. Institutional distance has no significant moderating impact on the relationship between strategic autonomy and external network embeddedness, respectively.

Geographical limitations may exist as the survey is focused on the Chinese construction foreign subsidiaries. However, based on an institutional logics perspective, this study discusses the management control mode of foreign subsidiaries, which enriches the antecedents of sustainable performance and can provide an in-depth explanation of the effects of the organizational strategies of multinational construction enterprises.

This study provides beneficial information for the sustainable performance of foreign construction subsidiaries. It will provide detailed guidance to managers located in different institutional environments on optimally promoting the sustainable development of subsidiaries.

This study identifies autonomy as an important antecedent, making it one of the first studies investigating autonomy on the sustainable performance of foreign construction subsidiaries. The findings of this study can contribute to the construction subsidiaries' sustainable performance literature and provide novel, comprehensive knowledge for academia and practice.