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This research is aimed to examine how environmental pollution affects consumers' preference for self-improvement products.

Through a series of three experimental studies, this research substantiates our hypotheses by employing various manipulations of environmental pollution and examining different types of self-improvement products.

The research demonstrates that environmental pollution enhances consumers' preference for self-improvement products via the mediation of perceived environmental responsibility. And the effect is negatively moderated by social equity sensitivity.

The recurrent incidence of environmental pollution has elicited significant concern among the general public and academic scholars. An overwhelming majority of research examining the impact of pollution on consumer behavior has concentrated on its influence on environmentally friendly and healthy consumption patterns. Nevertheless, the current research proposes that pollution fosters a preference for products associated with self-improvement, mediated by perceived environmental responsibility, with the effects being moderated by social equity sensitivity.

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.

The incorporation of phosphide additives is regarded as a highly effective strategy for enhancing the lubricative qualities of base oils. This study aims to assess the lubrication behavior and efficacy of various phosphide additives in polyethylsiloxane (PES) through the employment of the Schwingum Reibung Verschleiss test methodology, across a temperature range from ambient to 300°C.

PES demonstrated commendable lubrication capabilities within the Si3N4/M50 system, primarily attributable to the Si-O frictional reaction film at the interface. This film undergoes disintegration as the temperature escalates, leading to heightened wear. Moreover, the phosphide additives were found to ameliorate the issues encountered by PES in the Si3N4/M50 system, characterized by numerous boundary lubrication failure instances. A chemical film comprising P-Fe-O was observed to form at the interface; however, at elevated temperatures, disintegration of some phosphide films precipitated lubrication failures, as evidenced by a precipitous rise in the coefficient of friction.

The results show that a phosphide reactive film can be formed and a reduction in wear rate is achieved, which is reduced by 64.7% from 2.98 (for pure PES at 300°C) to 1.05 × 10^–9 μm³/N m (for triphenyl phosphite at 300°C).

The data derived from this investigation offer critical insights for the selection and deployment of phosphide additives within high-temperature lubrication environments pertinent to PES.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0139/

The occurrence of segment cracks caused by load changes in shield tunnels would affect the safety of the tunnel structure. To this end, a three-dimensional fine shield tunnel segment model based on the extended finite element method (XFEM) is established.

The cracking law of shield segment cracks is studied in two forms: overloading and unloading. The relationship between crack length, width and depth and transverse convergence and deformation is analyzed.

The results show that the cracks in shield tunnels mainly occur on the outer side of the arch waist and the inner side of the crown and bottom. Under overloading and unloading conditions, the length, width and depth of cracks increase non-linearly as the transverse convergence deformation increases. Under the same convergent deformation, the deeper the buried depth, the smaller the crack length, width and depth. Meanwhile, under overloading conditions, the influence of buried depth on the width and depth of cracks is more significant. In terms of crack width and depth, unloading conditions are more dangerous than overloading conditions.

The findings have a guiding effect for the management of cracks in shield tunnels during operation.

In the supply chain disruption risk, the issue of supplier evaluation and selection is solved by an extended VIKOR method based on regret theory.

Considering the influence of irrational emotions of decision makers, an evaluation model is designed by the regret theory and VIKOR method, which makes the decision-making process closer to reality.

The paper has some innovations in the evaluation index system and evaluation model construction. The method has good stability under the risk of supply chain interruption.

The mixed evaluation information is used to describe the attributes, and the evaluation index system is constructed by the combined method of the social network analysis method and the literature research method to ensure the accuracy and accuracy of the extracted attributes. The issue of supplier evaluation and selection is solved by an extended VIKOR method based on regret theory.

This study investigates and compares the earnings management strategies of financially distressed and non-distressed banks.

Using a regression analysis, this study examines a sample of banks operating in the MENA region. We focus on real earnings management strategies via commission and fee income (CF) and accrual-based earnings management strategies via loan loss provisions (LLP). A subsample analysis was performed, lagged dependent variables and additional control variables were included as a robustness check.

The findings consistently reveal a more extensive use of real earnings management strategies via CF among distressed banks than among non-distressed ones. Specifically, banks smooth their income via CF under distress conditions. However, LLP-based earnings management strategies are only implemented in healthy banks. These behaviors persist in banks that operate under different monitoring systems and institutional settings.

This study marks its entry into the literature debate on accounting and non-accounting decisions that influence bank financial reporting. It argues that, in the presence of financial difficulties, bank managers define earnings management strategies based on the probability of being detected, rather than looking at their costs.

From a prudential perspective, the findings suggest the need for prudential rules to supervise the reporting of CF income associated with high fees or discount incentives used intentionally by bank managers to convince clients to delay or accelerate payments and, consequently, affect reported earnings.

This study adds to the literature by investigating the effect of bank financial distress on both real and accrual-based earnings management to provide a comprehensive analysis of bank earnings management strategies in the presence of financial difficulties.

Not all apps can be favored on a smartphone. Users are bound to abandon some if they lose value. Analyzing the reasons behind this phenomenon can help develop strategies for avoiding app abandonment, which is the focus of this study.

This study conducted in-depth open interviews with 47 respondents who had abandoned or deleted mobile apps before and used the qualitative research method grounded theory to analyze the interview content, followed by open coding, axial coding and selective coding.

Seven main reasons are summarized after the relationship between every statement and concept is found and constructed, namely functional value, information expectation, emotional value, platform service, user trust, cost, and social influence.

Based on these findings, this study provided suggestions for mobile app developers to help them effectively improve the user experience and retain their customers.

This study highlights the significance of knowledge management in the relationship between organizational ambidexterity and organizational performance within the context of Indonesian SMEs.

The study adopts a quantitative research approach, employing a survey questionnaire to collect data from a sample of SMEs operating in Indonesia. Structural Equation Modeling using Partial Least Square is used to investigate the relationship between variables.

The findings of this study show that in the context of small and medium enterprises, the variables of environmental and technological uncertainty are not driving factors in organizational ambidexterity. In contrast, market uncertainty has a significant effect on organizational ambidexterity. Moreover, it is also found that knowledge management does not support the variables of environmental, market, and technological uncertainty on organizational ambidexterity in small and medium enterprises. The results show that knowledge management plays a significant role in organizational ambidexterity. It also shows that knowledge management could support a significant way between organizational ambidexterity and organizational performance.

The findings of this study give insights for SMEs to attain a sustainable competitive edge in a dynamic business landscape by implementing efficient knowledge management techniques that bolster their ambidextrous capacities.