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This work intends to evaluate experimentally the ratcheting behaviour of AM MS300. Furthermore, cyclic plasticity modelling (modified Abdel-Karim and Ohno model) is examined as a means of predicting ratcheting.

Uniaxial stress-controlled cyclic loading histories were utilised to evaluate ratcheting for Maraging Steel 300 (MS300) fabricated via laser powder bed fusion (LPBF) additive manufacturing (AM). Heat-treated and as-built AM and conventionally manufactured (CM) MS300 coupons were tested at room temperature, under constant and incrementally variable stress amplitude and mean stress. Two sets of AM test coupons were used, printed at horizontal and vertical built orientation. The AM material ratcheting was predicted via constitutive modelling and numerical simulation. The Abdel-Karim and Ohno cyclic plasticity model was modified by introducing a memory surface, to improve ratcheting prediction.

The hysteresis stress–strain response and low cycle fatigue (LCF) life were obtained from the different loading histories. Both the AM and CM MS300 exhibited an accumulation of axial strain (ratcheting) for all tests, attributed to the application of non-zero mean stress. The AM MS300 has demonstrated a higher ratcheting accumulation rate than the CM material. The achieved agreement between the numerical results of the new model and the experimental data offers an indication on the suitability and the robustness of this model.

The ratcheting behaviour of the AM MS300 material has been characterised for the first time in the published literature, for a variety of loading histories selected. A modified Abdel-Karim and Ohno plasticity model has been developed to account for the ratcheting performance of this material.

This study explores the antecedent configurations shaping ambidextrous environmental strategy (AES) and the subsequent performance outcomes. The lack of literature from the configurational perspective and inconsistent performance results suggest that this study has significant implications for practitioners, policymakers and the public. Therefore, this study aims to investigate how different antecedent conditions interact to shape AES and subsequent performance outcomes.

To achieve the research aims, the current research utilize research techniques based on technology–organization–environment framework and configurational perspective. This study collects data from 317 Chinese manufacturing enterprises and tests the theoretical framework using fuzzy set qualitative comparative analysis and propensity score matching.

Perceived institutional pressure, green supply chain integration and digital technology adoption form four paths that lead to the existence of AES. There are four sets of replaceable conditions between distinct paths. In addition, except for configuration P3, all other configurations promote environmental, operational and financial performance.

Our results provide new insights for enterprises to shape AES and achieve multiple performances, and new ideas for promoting environmental policies and public environmental awareness.

This study adds literature on AES and confirms multiple drivers, revealing their interaction mechanisms and key antecedent conditions. In addition, this study promotes the performance practice of AES by examining different AES configurations that achieve triple performance and insignificant operational performance.

This article examines the relationships between objective overqualification, volunteering as an extra-work activity and job satisfaction.

The study draws on a vast secondary sample of 20,686 British employees across four waves covering the period 2009–2017. The bivariate ordered probit estimate was used to test the study hypotheses in the bioprobit procedure in STATA.

Our study unravels compelling insights. Overqualified employees experience lower job satisfaction and engage more in volunteering activities. The results emphasised that voluntary work allows the utilisation of skills and fulfils basic psychological needs, leading to enhanced general well-being and higher job satisfaction.

Overqualified employees, by actively engaging in volunteering, not only make valuable contributions to society but also experience positive spillover effects that significantly influence their workplace attitudes and behaviours. This underscores the potential for promoting volunteering as an effective means to mitigate the private and social overqualification.

This study provides valuable insights into the role of overqualification as well as resulting job dissatisfaction, in shaping volunteering decisions. This insight contributes to the overqualification literature and strengthens our understanding of volunteering as an important mechanism in the relationship between overqualification and job satisfaction.

Machinery operating in a sand-dust environment is more susceptible to sand particles. The purpose of this paper is to investigate the impact of sand particle deposition rate, surface hardness and normal load on the tribological performance.

A predictive model to approximate the number of sand particles within the pin-on-disc contact surface is proposed. The efficacy of the model is validated through experimental method, which replicates a sand environment with two distinct particle deposition rates. Dry sliding friction experiments are also conducted using 45 carbon steel and H90 brass pins against GCr15 bearing steel discs.

When at high particle deposition rate [6.89 × 10^–5 g/(s·mm²)], the contact surfaces are separated by particles, resulting in an indirect metal contact. While at low deposition rate [6.08 × 10^–8 g/(s·mm²)], there is an alternating occurrence of direct and indirect metal contacts. In sand environment, the specific wear rate of 45 and H90 decreases by 50% and 33%, respectively, compared to non-sand environment when the applied load is 2.45 N. However, it is only 0.18% for 45 but remains significant at 25% for H90 at load of 9.8 N.

The predictive model and experimental method used in this paper are helpful for understanding the interaction between particles and sliding surfaces, thereby providing a solid foundation for material selection and load optimization of friction pairs influenced by sand-dust environments.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0155/

This study explores the impact mechanism of corporate digital transformation (CDT) on the quality of accounting information.

Samples of A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2007 to 2020 are used as a research sample. The empirical analysis is based on the ordinary least squares regression model, and mediation and moderation effect models were used in further analysis.

This study finds that CDT enhances accounting information quality by alleviating the agency problem. This positive effect is more significant among firms that exhibit less media coverage, have low industry competition and are not subject to cyber-attack.

This study extends the economic consequences of CDT and enriches the literature on the factors that affect accounting information quality. Further, this study's findings guide the government to actively promote CDT, facilitate the digital upgrading of industries and improve accounting information quality and efficiency in capital markets.

Community question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features.

To address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question.

The proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features.

This work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement.

This work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.

In multi-criteria ranking problems, the UTA-like methods can be used to infer the value functions that restore the decision-maker’s (DM’s) indirect preference information. These value functions represent all possible preference systems for the DM. In this paper, we aim to develop a method for determining the complete ranking of alternatives based on all such value functions.

We extend the DM’s inductive preference for value functions in the selection of a representative value function to rankings of alternatives and construct a novel measure referred as the representativeness index to evaluate the performance of rankings relative to the inductive preference. Subsequently, by exploring all value functions that are capable of generating a ranking, two robust representativeness indices are constructed and a simulation algorithm is proposed for calculating the robust representativeness index.

Determining the ranking based on the representative value function can be seen as selecting the ranking with the largest representativeness index. Additionally, we find through a case study that the ranking determined based on robust representativeness indices has good robustness in the sense of inductive preferences.

The inductive preference is a manifestation of the DM’s preference system. This paper proposes a method for measuring the performance of rankings relative to inductive preferences. The performance of a ranking is defined as the performance of all value functions that can produce that ranking relative to the inductive preference. In turn, it is possible to identify the ranking that best matches the DM’s preference system.