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The purpose of this paper is to investigate the friction and wear mechanisms in lubricated sliding conditions of additively manufactured SS316L parts. The different viscous oils 5W30, 15W40, 20W50 and SAE140 are used. These investigations provide a theoretical basis for the high performance of printed and postheattreated SS316L.

Tribological tests were carried out on selective laser melting-made SS316L printed specimens and heat-treated specimens. The parameters in 15 min of test duration are 20 N of load, 200 rpm, 8 mm of pin diameter, 25 mm length, 80 mm of track diameter and EN31 counter disc body. This work presented the phenomena of lubrication regimes and their characterization, as identified by the Stribeck curve, and these regimes affect the tribological properties of additively manufactured SS316L under the influence of industrial viscous lubricants. The results are observed using Scanning electron microscope (SEM), X-ray diffraction (XRD) and wear tests.

The observations indicate that additively manufactured SS316L shows a reduced coefficient of friction (COF) and specific wear rate (SWR). This is credited to the utilization of different viscous lubricants.

This exclusive research demonstrates how various viscous lubricants affect the COF and SWR of printed and post-heat-treated SS316L parts. Lambda (λ), lubricant film thickness (h₀), surface roughness and wear mechanisms are studied and reported.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0110/

This study aims to identify and analyse critical success factors (CSFs) for the successful implementation of distributed ledger technology (DLT) in the Nigerian construction industry.

This study adopts a quantitative approach that uses snowball sampling techniques to identify professionals participating in the study. A structured questionnaire was used to collect data virtually, using Google Forms, resulting in 217 valid responses. The collected data were subjected to rigorous statistical analysis (descriptive and inferential) to identify and prioritise the CSFs and evaluate the participants’ awareness and knowledge of DLT.

This study revealed 24 key CSFs that are pivotal in ensuring the effective implementation and adoption of DLT in the Nigerian construction industry. Furthermore, the research highlights a moderate level of awareness, but significantly low knowledge of DLT among industry professionals.

The findings of this study will benefit professionals, practitioners and policymakers in the Nigerian construction industry by providing insights into the potential of DLT to improve construction operations.

This study contributes to the literature by identifying the CSFs for implementing DLT in the construction industry and shedding light on the current level of awareness and knowledge within the Nigerian context. The findings offer valuable insights for policymakers, industry practitioners and researchers, providing a solid foundation for informed decision-making and developing effective strategies to enhance DLT adoption in the construction sector.

This study aims to enhance the assessment of green governance in energy projects along the Belt and Road, reduce the influence of fuzzy judgment, and construct a grey network analysis model from the perspective of Environmental, Social, and Governance (ESG).

The ESG concept is used to establish an evaluation indicator system. The Analytic Network Process (ANP) and the Grey System Theory are applied sequentially to determine the green governance grade of energy projects, exemplified by an evaluation of five projects.

The Karot hydropower project has the best green governance status among the five projects and is of excellent grade. This is followed by the Hongfeng photovoltaic project, the De Aar wind power project, and the Yamal liquefied natural gas project, which are of good grade. The Lamu coal power station project has the worst green governance and is at a medium level.

This study can assist Belt and Road energy projects in identifying their deficiencies and promoting sustainable development by providing a robust framework for green governance evaluation.

The indicator system developed in this study includes social and project governance aspects in addition to environmental performance, reflecting the comprehensive green governance status of projects. The combined use of ANP and grey system theory fully considers the mutual influence relationship between indicators and improves the objectivity of green governance grade judgment.

This paper aims to examine the blockchain introduction and altruistic preference decisions of the supplier in agricultural food supply chains and discuss how the supplier decisions are influenced by blockchain technology and altruistic preference levels.

The author considers a single period two-level supply chain model to describe the supplier’s decisions. The supplier, as the leader of the game, decides whether to introduce blockchain technology and his own level of altruistic preferences. Consumers have environmental awareness and heterogeneity in green trust. Supply chain members determine their own product pricing and green effort level under Stackelberg game.

The results reveal the negative impact of unit verification cost in the technology introduction process on the supply chain. In addition, the supplier can adjust their profits by adjusting their altruistic preferences after introducing blockchain to offset the impact of blockchain through the influence of altruistic preferences as discussed by the author.

This paper investigates how the profits and green efforts of supply chain members are influenced by blockchain technology and altruistic preferences.

Subway systems are highly susceptible to external disturbances from emergencies, triggering a series of consequences such as the paralysis of the internal network transportation functions, causing significant economic and safety losses to cities. Therefore, it is necessary to analyze the factors affecting the resilience of the subway system to reduce the impact of disaster incidents.

Using the interval type-2 fuzzy linguistic term set and the K-medoids clustering algorithm, this paper improves the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to construct a subway resilience factor analysis model for emergencies. Through comparative analysis, this study confirms the superior performance of the proposed approach in enhancing the precision of the DEMATEL method.

The results indicate that the operation and management level of emergency command organizations is the key resilience factors of subway operations in China. Furthermore, based on real case analyses, the corresponding suggestions and measures are put forward to improve the overall operation resilience level of the subway.

This paper identifies four emergency scenarios and 15 resilience factors affecting subway operations through literature review and expert consultation. The improved fuzzy DEMATEL method is applied to explore the levels of influence and causal mechanisms among the resilience factors of the subway system under the four emergency scenarios.

This study aims to examine the effects of product market competition (PMC) on analysts’ earnings forecast attributes, particularly forecast accuracy and dispersion. The authors also investigate whether investor protection moderates the relationship between PMC and forecast attributes.

The sample covers 49,578 firm-year observations from 38 countries. This study uses an ordinary least squares regression, a Heckman two-stage regression and an instrumental two-stage least squares regression.

This study finds that PMC is associated with higher forecast accuracy and lower dispersion. The results also show that investor protection enhances the effect of PMC on forecast accuracy and dispersion. These findings imply that countries with strong investor protection have a better information environment, as exhibited by the stronger relationship between PMC and analysts’ forecast properties.

The findings highlight the importance of strong governance mechanisms in both the country and industry environments. Policymakers, including government agencies and financial regulators, can leverage these insights to formulate regulations that promote competition, ensure investor protection and facilitate informed investment decisions.

This study advances our understanding of how PMC affects analysts’ earnings forecast attributes. In addition, it pioneers evidence of the moderating role of investor protection in the relationship between PMC and forecast attributes.

This research aims to empirically investigate the impacts of product and international diversification strategies on firm-level inventory performance.

This study empirically examines the associations between product and international diversification strategies and inventory performance based on a sample of 64,124 observations across 7,367 US publicly traded firms between 1989 and 2019 from the COMPUSTAT Segment, Fundamental Annual and Fundamental Quarterly data files. We employ both linear and nonlinear regression models to perform our empirical analysis.

This research provides strong evidence that there exists a U-shaped relationship between unrelated product diversification and inventory level and a partially inverted U-shaped relationship between international diversification and inventory level. We also find a positive impact of related product diversification on inventory level, but there is no significant curvilinear relationship between related product diversification and inventory level.

Our research findings offer important insights into top management’s strategic planning for diversification strategies and operations manager’s inventory control policies to achieve the strategic fit between corporate diversification and inventory management.

Product and international diversification strategies not only play an essential role in the firm’s competitive advantage, but also have a significant influence on operations manager’s inventory decision. This research is among the first to systematically investigate how top management’s related product, unrelated product and international diversification strategies may have complex nonlinear impacts on inventory performance.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

This study aims to examine the effect of audit firm tenure (AFT) on corporate tax avoidance (CTA) and the moderating effect of the COVID-19 pandemic.

The sample comprises 41,074 firm-year observations from 32 countries from 2015 to 2020, for which data are collected from various sources: financial data from the Refinitiv database, country corporate tax rates from the Tax Foundation, and other country-level data from the World Bank database. The authors use the book tax difference to measure CTA and multiple proxies for AFT.

This study finds that a longer AFT is associated with higher CTA, confirming the notion that long AFT impairs auditor independence. The findings remain robust when considering various AFT proxies, incorporating Hofstede’s cultural factors, using weighted least-squares estimation and addressing endogeneity through propensity score matching. This study also finds a non-linear relationship between extended client and auditor relationships and CTA, supporting the mandatory audit firm rotation regulation and increasing investors’ caution regarding the consequences of extended client–auditor relationships on firm behaviour.

This study offers new evidence on the effect of the COVID-19 pandemic on the link between AFT and CTA and documents a non-linear relationship between AFT, which has not been addressed in prior studies.

The findings of this study have several significant practical implications. First, governments and policymakers gain insights into the consequences of extended auditor–client relationships, hence calling for a review of auditing and taxation regulations. Second, the findings provide important insights into the issue of auditor independence, especially during long engagements and crises such as COVID-19. Finally, investors and tax authorities should be more cautious about the risks of aggressive tax avoidance during crisis periods.

To the best of the authors’ knowledge, this is the first study to use a global data set to investigate the effect of AFT on CTA during the COVID-19 pandemic.

This study aims to explore the relationship between the level of busyness of Chief Executive Officers (CEOs) and investment efficiency in the context of emerging markets.

The sample includes firms listed on the Indonesia Stock Exchange from 2010 to 2018 using ordinary least square estimation.

The findings suggest that companies led by busy CEOs tend to exhibit lower investment efficiency, thus providing support for the hypothesis that as CEOs’ commitments increase, their ability to concentrate on the company diminishes. Furthermore, our analysis reveals that companies with busy CEOs tend to demonstrate a greater tendency to over-invest, potentially in response to market pressures to showcase strong performance. A more in-depth examination of the data shows that the negative impact of busy CEOs on investment efficiency is especially noticeable in firms lacking risk and management committees (RMC).

These findings have substantial practical implications for the structuring and composition of corporate boards. They highlight the significance of conducting comprehensive assessments to gain insights into the external commitments of incoming CEOs.

This study underscores the importance of establishing RMC.