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With respect to severe working conditions such as heavy load and impact, this paper aims to investigate the friction reduction and anti-wear performance of kaolin and molybdenum dialkyl dithiophosphate (MoDDP) composite lubricant additives to improve the lubrication effect of a single additive.

A four-ball friction test was carried out to determine the optimal concentration of kaolin and organic molybdenum additives and the tribological properties of the kaolin/MoDDP composite lubricant additives. A ring block test of composite lubricant additives was designed to investigate its lubrication performance under the severe working conditions of low speed, heavy load and impact.

The results showed that the optimal addition mass fractions of kaolin and MoDDP were 4.0 and 1.5 Wt.%, respectively, when kaolin and MoDDP were used as single lubricant additives. Compared with the single additive, the 4.0 Wt.% kaolin/1.5 Wt.% MoDDP composite lubricant additive showed excellent friction reduction and anti-wear effects under heavy load and impact conditions. Physicochemical analysis of the wear surface revealed that the lamellar kaolin additive and MoDDP had excellent synergistic effects, and the friction process promoted the generation of lubricant films containing a chemically reactive layer of MoS₂, MoO₂, FeS₂ and Fe₂O₃ and a physically adsorbent layer containing SiO₂ and Al₂O₃, which play important roles in anti-wear and friction reduction.

The excellent friction reduction and anti-wear effects of lamellar silicate minerals and the excellent antioxidant properties and good synergistic effects of molybdenum were comprehensively used to develop the composite additives with great lubricating properties.

This paper aims to explore how psychological perceptions and family situations drive women into necessity- or opportunity-based female entrepreneurship (NBFE or OBFE) and the moderating role of gender equality.

This study adopts multilevel logistic regression analysis to examine relationships based on a sample of 6,843 women across eight developing countries drawn from the Global Entrepreneurship Monitor (GEM).

The findings suggest that capability and opportunity perceptions positively affect NBFE and OBFE. Family responsibility burden positively affects NBFE and has a U-shaped relationship with OBFE. Household income negatively affects NBFE but positively affects OBFE. Gender equality weakens the U-shaped relationship between family responsibility burden and OBFE but strengthens the positive relationship between capability perception and NBFE and between opportunity perception and NBFE.

The study highlights the need for targeted policies and support that consider the distinct antecedents and mechanisms of NBFE and OBFE, as well as the importance of promoting gender equality and entrepreneurial education to empower women in their entrepreneurial endeavors. A limitation of this study is the reliance on older data from the GEM, which may not fully capture the current dynamics of developing societies. While the study provides valuable insights, future research should incorporate more recent data to enhance the applicability of the results.

This study deepens the understanding of antecedents of NBFE and OBFE, breaking through the existing literature that neglects the heterogeneity of female entrepreneurship (FE).

This study identifies a methodology to explore the issues of enterprise innovation ecosystem health (EIEH).

First, this study constructs the indicator system of EIEH based on the research objective; second, the dynamic vertical projection method (DVPM) and entropy weight method are proposed to analyze the status and influencing factors of EIEH; finally, the future development of EIEH is analyzed using GM (1,1).

In terms of methodology, the DVPM can effectively analyze EIEH, which can not only analyze the development status and potential of EIEH every year but also analyze the comprehensive state of EIEH for many years. In terms of practice, the value and grade of EIEH in China have been gradually increasing from 2016 to 2020, but the overall development is unbalanced, and five key factors affecting EIEH have been identified. The EIEH in China is predicted to steadily grow from 2021 to 2025.

The analytical method employed in this study can effectively analyze EIEH, which provides a new analytical perspective for the evaluation of EIEH and enriches the research content of the enterprise innovation ecosystem (EIE). By analyzing the results, we can gain a comprehensive understanding of the state of different EIEs, enabling each EIE to design tailored remedial measures to enhance EIEH and achieve sustainable development.

This contribution investigates the numerical solution of Volterra integral equations with auto-convolution of the third kind (AVIE). The numerical method applied in this investigation employs a collocation method based on the moving least squares (MLS) approximation. The MLS approximation is an effective way of approximating an unknown function by taking a disordered dataset. This method is a mesh-free approach since it does not require background interpolation or approximation cells, and is independent of domain geometry. The proposed method reduces the solution of third-kind AVIEs to the solution of systems of algebraic equations. By employing the Gauss–Legendre integration formula, we can estimate all the integrals of these equations. The applicability and validity of this method are demonstrated by numerical experiments, and its efficiency and robustness are proven by comparison with existing methods.

The numerical method applied in this study uses a collocation method based on moving least squares (MLS) approximation. This method is a mesh-free approach since it requires no background interpolation or approximation cells and is independent of domain geometry. Using the Gauss–Legendre integration formula, we can estimate all the integrals of these equations.

The authors declare that they have no known competing financial interests.

The manuscript has not been copyrighted or published previously and is not under consideration for publication elsewhere.

Drawing on the tenets of institutional theory, the purpose of this study is to examine the impact of Confucianism on technology for social good, while also considering the moderating influence of extrinsic informal institutions (foreign culture) and intrinsic formal institutions (property rights).

This study constructs a comprehensive database comprising 9,759 firm-year observations in China by using a sample of Chinese A-share listed firms from 2016 to 2020. Subsequently, the hypotheses are examined and confirmed, with the validity of the results being upheld even after conducting endogenous and robustness tests.

The findings of this study offer robust and consistent evidence supporting the notion that Confucianism positively affects technology for social good through both incentive effect and normative effect. Moreover, this positive influence is particularly prominent in organizations with limited exposure to foreign culture and in nonstate-owned enterprises.

The findings contribute to the literature by fostering a deep understanding of technology for social good and Confucianism research, and further provide a nuanced picture of the role of foreign culture and property rights in the process of technology for social good in China.

In this digital age, the rapid technological innovation and adoption, with the increasing use of big data analytics, has raised concerns about the ability of small and medium enterprises (SMEs) to sustain the competition and innovation performance (IP). To narrow the research gap, this paper investigates the role of big data analytics capability (BDAC) in moderating the relationship between digital innovation (DI) and SME innovation performance.

This research has been carried forward through a detailed theory and literature analysis. Data were analyzed through confirmatory factor analysis and structural equation models using a two-stage approach in smartPLS-4.

Results highlight that digital service capability (DSC) significantly mediates the relationship between DI and IP. Additionally, value co-creation (VCC) directly affects digital transformation (DT), while DI has a stronger effect on DSC than IP. Furthermore, BDAC significantly moderates the relation between DSC → IP and DT → IP, whereas it has a detrimental effect on the relation between DI and IP. In addition to that, VCC, DSC, DT, DI and BDAC have a direct, significant and positive effect on IP.

This research was motivated by the practical relevance of supporting SMEs in adopting DT and the resource-based view (RBV) and technology acceptance model (TAM). This study shows that all direct and indirect measures significantly affect innovation performance, including BDAC as moderator. These findings refresh the perspective on what DT, DI, VCC, DSC and BDAC can bring to a firm's innovation performance.

This paper has contributed to DT by empirically validating a theoretical argument that suggests the acceptance and adoption of new technology. This paper aims to fill theoretical gaps in understanding BDAC and DT by incorporating the RBV and TAM theories on BDAC and DT.

The rise of the mobile Internet has accumulated much text information in various online financial forums. Such information often contains the emotional attitudes of investors toward financial technology (fintech) platforms, so extracting the sentimental tendency information has great practical value for the development of fintech platforms. Based on the investor sentiment theory, the paper aims to analyze the relevant social media data and test the influence path of online news evaluation on the stock price fluctuation of fintech platforms.

Taking Oriental Fortune as the research object, this paper selects multiple variables such as stock bar popularity, snowball popularity, news popularity and news sentiment scores collected by UQER and combines the sentiment scores of single daily news into a daily sentiment score. Based on the period from November 1, 2019 to March 31, 2020, during the emergence of the coronavirus disease 2019 (COVID-19) pandemic as the background, the authors conduct the Granger causality test based on the vector autoregressive (VAR) model and analyze the relevant evaluation of Oriental Fortune through the empirical model.

The authors' results show that different online evaluations impact the rise and fall of stock prices differently, while news popularity has the most significant impact. Besides, news sentiment scores on share price fluctuation have a relatively substantial influence. These findings indicate that the authoritative news evaluation can strongly guide investors to make relevant investment behavior operations in the information dissemination process, significantly affecting stock prices.

The research findings of this paper have good inspiration and reference values for investors and financial regulators.

Contemporary changes have occurred in country-level policies and tourists’ intentions in recent years. The role of maintaining a country’s image is trendy in crisis control but has not yet been discussed in domestic tourism research. Extending the Stimulus Organism Response model, this study aims to focus on “trustable WOM creation” in China. In addition, it aimed to discover how behavioral changes encourage domestic tourism intention (DTI).

This study explored the mediating role of DTI and the moderating role of maintenance of country image (MCI) for trustable word of mouth (WOM) creation. Using the snowball sampling technique, a structural equation modeling analysis (Smart PLS-4) was employed to analyze the data of 487 Chinese tourists.

Findings confirm that behavioral changes positively encourage domestic tourism and discourage international tourism, with significant negative moderation by MCI. MCI has an insignificant positive moderating effect between government-media trust and DTI. Furthermore, DTI positively and directly affects the creation of trustable WOM. In addition, it had a 20% mediation effect (VAF%) between behavioral changes and WOM creation, higher than the rejected mediation effect (12%), in the causal relationship between government-media trust and WOM creation.

WOM creation varies from different behavioral changes, but findings suggest that government-media trust and DTI influenced it significantly. Based on the study findings, the government and media can enhance domestic tourism by maintaining the country’s image. These findings both encourage and control the recovery of tourism.

This study provides a theoretical explanation for tourists' behavioral changes during the pandemic. Moreover, it shows that despite avoiding international tourism due to behavioral changes and government-media trust, MCI moderation with the mediation effect of DTI can create trustable WOM. To the best of the authors’ knowledge, this is the first study to theoretically promote tourism through DTI-induced psychology as a mediator and an organism affect prevailing among Chinese tourists.

Focusing on the resource crowding-out effect, this study aims to examine the relationship between an enterprise’s digital transformation and the internalisation of environmental costs.

This paper manually collects environmental cost data and measures corporate digital transformation constructed through a machine learning word vector (Word2Vec) technology approach based on the text information of annual reports (MD&A) for heavily polluting firms.

Corporate digital transformation has a significant inhibitory effect on the internalisation of corporate environmental costs. This is because low-level digital transformation has crowded out cash flows, preventing China’s heavily polluting firms from having the extra capacity needed to internalise environmental costs. This crowding-out effect emerges when companies face problems such as capital shortages, short-term profit pressure and intense market competition. These results have the following important implications.

The research highlights the need for enterprises to align digital transformation and sustainability strategies by strengthening resource endowment and optimising internal resource allocation. This requires effective use of digital technology and a long-term sustainability vision for heavily polluting firms facing environmental policy pressures.

Enterprises should assume more social responsibility and achieve sustainable socioeconomic development. It will also help mitigate the adverse environmental externalities stemming from their operations.

To the best of the authors’ knowledge, this study considers the impact of enterprise digital transformation on the internalisation level of enterprise environmental costs for the first time and uses enterprises’ financial, management, market characteristics and ownership characteristics to analyse the impact mechanism.

Linear projects often involve lengthy construction periods, necessitating dynamic adjustments to the plan. Completely rescheduling remaining activities every time can lead to unnecessary time and cost wastage and significant deviations in resource supply. To address these issues, this paper proposes a dynamic scheduling method designed to effectively manage both time and cost during construction projects.

Determining the rescheduling frequency through a hybrid driving strategy and buffer mechanism, introducing rolling window technology to determine the scope of local rescheduling and constructing a local rescheduling model under the constraints of time and cost deviation with the objective of minimizing the cost. Combined decision-making for construction and rushing modes constrained by multiple construction scenarios. Opposite learning is introduced to optimize the hybrid algorithm solution.

Arithmetic examples and cases confirm the model’s feasibility and applicability. The results indicate that (1) continuous rescheduling throughout project construction is essential and effective and (2) a well-structured buffer mechanism can prevent redundant rescheduling and enhance overall control of cost and schedule deviations.

This study introduces an innovative dynamic scheduling framework for linear engineering, offering a method for effectively controlling schedule deviations during construction. The developed model enhances rescheduling efficiency and introduces a combined quantization strategy to increase the model’s applicability to linear engineering. This model emerges as a promising decision support tool, facilitating the implementation of sustainable construction scheduling practices.