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Digital platform work monitored by algorithms is increasingly supplementing or substituting standard employment. Though gig workers are faced with the vulnerable, fragile and precarious digital platform work environment, the reason why gig workers remain highly willing to show good task performance has been so far unexamined. Building upon the reciprocity of the social exchange theory, this study aims to explore the antecedents and boundary condition of facilitating gig workers’ task performance.

First, to minimize common method variance, decline spurious mood effects and ensure data robustness, we conducted a two-wave time-lagged survey and collected 269 survey responses from gig workers on different gig platforms in China (e.g. Meituan, Eleme, Didi, Credamo, Zaihang) at two time nodes. Second, abiding by two stage procedures of the PLS-SEM (partial least square structural equation model) approach, we analyzed a moderated mediation model in the digital platform work context.

Results present that both platform work remuneration and flexibility help gig platforms develop an affective trust relationship with gig workers, thus encouraging them to repay the platform by performing platform tasks well. Algorithmic monitoring shows a “double-edged sword” moderating role since it weakens the indirectly positive relationship between platform work remuneration and task performance via affective trust but enhances the indirectly positive relationship between platform work flexibility and task performance via affective trust.

Understanding the importance of remuneration and flexibility in developing affective trust can help platforms design effective human resource management (HRM) strategies that enhance worker motivation of maintaining high engagement and performance under precarious working conditions. Additionally, optimizing the “double-edged sword” moderating role of algorithmic monitoring makes it more humanized, enhancing the efficiency with these HRM strategies and making both workers and platforms beneficial.

These findings offer an affective trust-based explanation for the mechanism of maintaining high work performance motivation in the nonstandard and precarious employment from the social exchange perspective, while understanding the (de)humanized aspect of algorithmic monitoring by revealing its “double-edged sword” moderating role.

This paper aims to propose a real-time evolutionary multi-objective semi-analytical inverse kinematics (IK) algorithm (EMSA-IK) for solving the multi-objective IK of redundant manipulators.

Within EMSA-IK, the parameterization method is applied to reduce the number of optimization variables of the evolutionary algorithm and calculate semi-analytical solutions that meet high target pose accuracy. The original evolutionary algorithm is improved with the proposed adaptive search sub-space strategy so that the improved evolutionary algorithm can be used to efficiently perform global search within the parametric joint space to obtain the global optimal parametric joint angles that satisfy multi-objective constraints.

Ablation experiments show the effectiveness of the improved strategy used for evolutionary algorithms. Comparative experiments on different manipulators demonstrate the advantages of EMSA-IK in terms of generalizability and balancing multiple objectives, for example, motion continuity, joint limits and obstacle avoidance. Real-world experiments further validate the effectiveness of the proposed algorithm for real-time application.

The semi-analytical IK solution that simultaneously satisfies high target pose accuracy and multi-objective constraints can be obtained in real time. Compared to existing semi-analytical IK algorithms, the proposed algorithm achieves obstacle avoidance for the first time. The proposed algorithm demonstrates superior generalizability, applicable to not only redundant manipulators with revolute joints but also those with prismatic joints.

Blockchains used by e-commerce consortia are a novel form of governance that facilitates coordination and collaboration among the numerous organisations that comprise e-commerce supply chains. Despite the increasing prevalence of consortium blockchain networks for e-commerce, there is a limited understanding of the economic and social dynamics that influence the behaviour of blockchain consortium members. By utilising transaction cost theory and social exchange theory, this research investigates the interplay between blockchain transaction-specific investment (BTSI), trust, adaptive collaboration (ADC) and the overall performance of supply chains in consortium blockchains

A quantitative research approach was employed to collect data from a representative sample of blockchain organisations affiliated with e-commerce consortium blockchains worldwide. Following this, the data obtained from 361 participants were analysed using descriptive and inferential statistics.

The results of our study indicate that BTSI has a substantial impact on trust. Furthermore, trust plays a pivotal role in shaping ADC, and ADC, in turn, acts as a mediator in the relationship between trust and performance outcomes.

This study underlines these economic and social dynamics in the evolving context of consortium blockchain networks, offering insights into their significance within a technology-driven environment.

Indian railways (IR) is one of the largest railway networks in the world. As a part of its strategic development initiative, demand forecasting can be one of the indispensable activities, as it may provide basic inputs for planning and control of various activities such as coach production, planning new trains, coach augmentation and quota redistribution. The purpose of this study is to suggest an approach to demand forecasting for IR management.

A case study is carried out, wherein several models i.e. automated autoregressive integrated moving average (auto-ARIMA), trigonometric regressors (TBATS), Holt–Winters additive model, Holt–Winters multiplicative model, simple exponential smoothing and simple moving average methods have been tested. As per requirements of IR management, the adopted research methodology is predominantly discursive, and the passenger reservation patterns over a five-year period covering a most representative train service for the past five years have been employed. The relative error matrix and the Akaike information criterion have been used to compare the performance of various models. The Diebold–Mariano test was conducted to examine the accuracy of models.

The coach production strategy has been proposed on the most suitable auto-ARIMA model. Around 6,000 railway coaches per year have been produced in the past 3 years by IR. As per the coach production plan for the year 2023–2024, a tentative 6551 coaches of various types have been planned for production. The insights gained from this paper may facilitate need-based coach manufacturing and optimum utilization of the inventory.

This study contributes to the literature on rail ticket demand forecasting and adds value to the process of rolling stock management. The proposed model can be a comprehensive decision-making tool to plan for new train services and assess the rolling stock production requirement on any railway system. The analysis may help in making demand predictions for the busy season, and the management can make important decisions about the pricing of services.

This paper analyzes the dynamics of agency costs in family firms through a systematic literature review, focusing on the interplay of governance mechanisms, institutional contexts and socioemotional wealth.

A systematic literature review of 91 articles published between 2010 and 2024 was made. The data was sourced from the Web of Science and Scopus databases using a search strategy emphasizing agency theory, family enterprises and emerging economies. Quantitative analysis identified key themes, influential authors and emerging trends, while qualitative synthesis provided deeper insights into governance practices and agency dynamics.

The study highlights the dual nature of family ownership as both a stabilizing force and a source of agency conflicts. While concentrated ownership aligns family and firm interests, it can lead to principal–principal conflicts, such as earnings management and minority shareholder expropriation, particularly in weak institutional contexts. Governance mechanisms, including board independence, external directors and professional management, are critical for mitigating agency costs but are often constrained by socioemotional wealth considerations.

The findings underscore the pivotal role of institutional environments in shaping governance practices and the performance of family firms. The study offers actionable insights for family business leaders, policymakers and practitioners.

This paper aims to estimate the performance of protective clothing used to resist puncture (anti-stab property).

Seven single-layer (one layer) samples were investigated in this research. The first three samples were already used for the purpose of (anti-stab property), manufactured from Du-Pont product (commercial samples). The rest of the samples were locally designed and manufactured for the same purpose. These seven samples have then been examined after been added in conjunction with WL Kevlar XP (S 104) witness multilayers (eight layers) panel to create which are called multilayer samples.

The results of the statistical analysis for one-way ANOVA illustrated significant effect for single layer samples for all properties. While for multi-layer samples, the results showed a significant difference for all variables except displacement. The Tukey post hoc test observed a significant effect for some samples; also, other samples show a non-significant effect between samples.

It was observed that the locally manufactured samples serve the purpose as (anti-stab samples) compared with the commercial samples. The radar chart shows that for single-layer sample, the fifth sample fulfill the highest radar chart area, whereas for multi-layer samples, the sixth sample achieved the highest radar chart area.

There are various uncertain and nonlinear problems in hydraulic legged robot systems, including parameter uncertainty, unmodeled dynamics and external disturbances. This study aims to eliminate uncertainties and improve the foot trajectory tracking control performance of hydraulic legged robots, a high-performance foot trajectory tracking control method based on fixed-time disturbance observers for hydraulic legged robots is proposed.

First, the robot leg mechanical system model and hydraulic system model of the hydraulic legged robot are established. Subsequently, two fixed-time disturbance observers are designed to address the unmatched lumped uncertainty and match lumped uncertainty in the system. Finally, the lumped uncertainties are compensated in the controller design, and the designed motion controller also achieves fixed-time stability.

Through simulation and experiments, it can be found that the proposed tracking control method based on fixed-time observers has better tracking control performance. The effectiveness and superiority of the proposed method have been verified.

Both the disturbance observers and the controller achieve fixed-time stability, effectively improving the performance of foot trajectory tracking control for hydraulic legged robots.

Cold-formed steel (CFS) sections are a popular choice for constructing medium and low-rise structures that are engineered to support relatively light loads. An important characteristic of CFS sections is that they are produced without the use of heat during manufacturing. Consequently, it becomes essential to gain a comprehensive understanding in the behavior of CFS sections when exposed to fire or elevated temperatures.

In this study, sections of 1.5 m length and 2 mm thickness were taken and analyzed to find its flexural behavior after heating them for 60 and 90 min. There were two modes of cooling phase which was considered to reach ambient temperature, i.e. air or water respectively. Performance of each sections (C, C with inclined flanges, sigma and Zed) were examined and evaluated at different conditions. Effects of different profiles and lips in the profiles on flexural behavior of CFS sections were investigated fully analytically.

The variation in stiffness among the sections with different lipped profiles was noted between 20.36 and 33.26%, for 60 min water cooling case. For the sections with unlipped profiles, it was between 23.56 and 28.60%. Influence of lip and section profile on reduction in stiffness is marginal. The average reduction in load capacity of sections for 60 min specimens cooled by water was found to be 43.42%. An increase in deflection is observed for the sections in the range of 25–37.23% for 60 min case. This is the critical temperature responsible for reduction in yield strength of material as it substantially increases the material safety margin to be considered for the design. Sections with Zed profile have shown better performance among other types, in terms of its load carrying capacity.

This paper deals with the flexural behavior of Galvanized (GI) based CFS unsymmetric sections at elevated temperature and cooled down to ambient temperature with air or water.

Robotic arms play a crucial role in various industrial operations, such as sorting, assembly, handling and spraying. However, traditional robotic arm control algorithms often struggle to adapt when faced with the challenge of dynamic obstacles. This paper aims to propose a dynamic obstacle avoidance method based on reinforcement learning to address real-time processing of dynamic obstacles.

This paper introduces an innovative method that introduces a feature extraction network that integrates gating mechanisms on the basis of traditional reinforcement learning algorithms. Additionally, an adaptive dynamic reward mechanism is designed to optimize the obstacle avoidance strategy.

Validation through the CoppeliaSim simulation environment and on-site testing has demonstrated the method's capability to effectively evade randomly moving obstacles, with a significant improvement in the convergence speed compared to traditional algorithms.

The proposed dynamic obstacle avoidance method based on Reinforcement Learning not only accomplishes the task of dynamic obstacle avoidance efficiently but also offers a distinct advantage in terms of convergence speed. This approach provides a novel solution to the obstacle avoidance methods for robotic arms.

This study aims to understand fiction readers’ perspectives on the strengths and concerns of incorporating emojis into information systems for fiction. To solicit readers’ feedback, the authors adopted Cho et al.’s (2023) model of three families of fiction mood categories as the theoretical framework. Based on this framework, prototypes of interface designs that implemented textual mood descriptors and/or emojis were developed.

Eighteen adult fiction readers at a US public university were recruited for online interviews. The participants shared their insights into the prototypes and their fiction search and review experiences.

Most participants preferred designs that support both mood terms and emojis. The findings highlighted the potential of emojis to improve metadata inclusivity and serve diverse users’ needs. Technical challenges and accessibility issues for blind or visually impaired users were noted as limitations of emoji implementation.

Based on established theoretical frameworks and emoji mappings for mood categories, this study advances the progress of implementing emojis into information systems for fiction. The findings will inform user-centered interface designs that support the description, search and review of fiction.