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Information opacity in donation crowdfunding activities has constrained the healthy development of China’s public welfare activities. Addressing the trust crisis and enhancing public engagement warrants further investigation. This study aims to uncover the moderating effect of activity transparency by utilizing data from 1,029 donation crowdfunding projects on the Sina Weibo Public Welfare Social Platform. In this way, we seek to elucidate the impact of donation crowdfunding events on fundraising ability.

This study selects text complexity, number of supporters, creator experience, and social capital as explanatory variables; innovatively selects the number of updates of online crowdfunding activities and total reading volume as moderating variables; selects the number of shares of crowdfunding activities as a mediating variable; and constructs a moderated mediation multiple regression model for fundraising ability.

Our findings indicate that independent variables, such as text complexity, number of supporters, and social capital, can significantly affect the dependent variable, fundraising ability. However, creator experience does not influence fundraising ability. Furthermore, social interaction has a mediating effect, whereas activity transparency has a reverse moderating effect. These results indicate that social interaction can enhance the fundraising ability of donation crowdfunding events. However, with an increase in information transparency, the fundraising ability of social media decreases.

The originality of this research is in clarifying the internal factors affecting fundraising ability through induction, making bold assumptions, and focusing on how social media’s effective interaction and activity transparency will affect public welfare crowdfunding fundraising ability.

The purpose of this paper is to find if and to what extent industrial clusters boost the regional economy in Heilongjiang Province of China and to more clearly define the relationship between the province's economic development and its industrial cluster strategy. The case of green food is the focus of the paper.

The paper is designed to answer a series of research questions regarding the role of the green food industry in Heilongjiang Province as an actual or potential industrial cluster and the extent to which there is export potential within this.

Although a small proportion of output is being exported, there are still some barriers that need to be overcome if the export potential is to be realized.

The research results to date are useful but limited due to certain data issues. However, they are sufficient even at this stage to determine a number of practical implications.

The paper focuses on the development of industrial clusters in Heilongjiang Province with a specific analysis of the development of the “green” food industry. This is done using statistical trends, volume, varieties, and value data to examine the export potential capacity in Heilongjiang Province. These data are brought together for the first time.

This study aims to investigate the role of aluminium (Al) in marine environment and the corrosion mechanism of galvalume coatings by conducting accelerated experiments and data analysis.

Samples were subjected to accelerated corrosion for 136 days via salt spray tests to simulate the natural conditions of marine environment and consequently accelerate the experiments. Subsequently, the samples were examined using various test methods, such as EDS, scanning electron microscopy (SEM), X-ray diffraction (XRD) and electrochemical impedance spectroscopy (EIS), and the obtained data were analysed.

Galvalume coatings comprised interdigitated zinc (Zn)-rich and dendritic Al-rich phases. Corrosion was observed to begin with a Zn-rich phase. The primary components of the corrosion product film were Al₂O₃ and Zn₅(OH)₈Cl₂·H₂O. It was confirmed that the role of Al was to form a dense protective film, thereby successfully blocking the entry of corrosive media and protecting the iron substrate.

This study provides a clearer understanding of the corrosion mechanism and kinetics of galvalume coatings in a simulated marine environment. In addition, the role of Al, which is rarely mentioned in the literature, was investigated.

The purpose of this paper is to develop a novel interval Multi-Objective Optimization by a Ratio Analysis plus the Full Multiplicative Form(MULTIMOORA) with combination weights to evaluate the employment quality of college graduates, where the criteria are expressed by interval numbers and the weights of criteria are completely unknown.

Firstly, considering the subjective uncertainty of the weights of the criteria, the interval best worst method (I-BWM) was present to determine the subjective weights of the criteria. Secondly, by the improved interval number distance measure, an improved interval deviation maximization method (I-MDM) was introduced to detemine the objective weights. In the following, based on the I-BWM and the improved I-MDM, a combination weighting method that takes into account the subjective and objective weights is proposed. Finally, a multi-criteria decision-making method based on the interval MULTIMOORA with combination weights is present to evaluate the employment quality of college graduates, and then a comparative analysis with some of the existing distance measures of interval numberswas conducted to illustrate the flexibility.

According to the data of the Report on Employment Quality of Chinese College Graduats released by Mycos Research Institute in 2016–2020 and 2021–2022, the proposed method was used to evaluate the employment quality of college graduates during the period before and after the COVID-19 epidemic. The results verify that the method is more reasonable because the subjective and objective weights of the criteria can be fully considered. Finally, the feasibility and practicability of the proposed method are further verified by varying parameters.

Present an evaluation method on the employment quality of college graduates based on the Interval MULTIMOORA with combination weights considering the subjective and objective weights. And the proposed method is proved that it can provide a more reasonable evaluation results. At the same time, it is verified that the feasibility and the practicability of the proposed method are affected by varying parameters in the paper.

The purpose of this paper is to develop a probabilistic uncertain linguistic (PUL) TODIM method based on the generalized Choquet integral, with respect to the interdependencies between criteria, for the selection of the best alternate in the context of multiple criteria group decision-making (MCGDM).

Owing to decision makers (DMs) do not always show completely rational and may have the preference of bounded rational behavior, this may affect the result of the MCGDM. At the same time, criteria interaction is a focused issue in MCGDM. Hence, a novel TODIM method based on the generalized Choquet integral selects the best alternate using PUL evaluation, where the generalized Choquet integral is used to calculate the weight of criterion. The generalized PUL distance measure between two probabilistic uncertain linguistic elements (PULEs) is calculated and the perceived dominance degree matrices for each alternate relative to other alternates are obtained. Furthermore, the comprehensive perceived dominance degree of each alternate can be calculated to get the ranking.

Potential application of the PUL-TODIM method is demonstrated through an evaluation example with sensitivity and comparative analysis.

As per author's concern, there are no TODIM methods with probabilistic uncertain linguistic sets (PULTSs) to solve MCGDM problems under uncertainty. Compared with the result of existing methods, the final judgment value of alternates using the extended TODIM methodology is highly corroborated, which proves its potential in solving MCGDM problems under qualitative and quantitative environments.

Although the impact of environmental, social and governance (ESG) on firms' innovation has attracted attention, the existing research findings diverge. The authors believe that failure to consider both innovation input and output is an important reason for the divergence of conclusions in the extant literature when discussing the impact of ESG and firm innovation. Thus, based on signaling theory, this study aims to reconcile these divergent findings by examining the impact of ESG performance on firms' innovation efficiency.

To seek empirical evidence to support the authors’ theoretical view, the authors conduct an empirical test based on the Tobit model using 8 years of data from Chinese listed companies.

Although ESG performance effectively improves firms' innovation efficiency, the institutional-level signaling environment (including state-owned firms and regional market development) weakens the positive effect of ESG performance on firms' innovation efficiency. Further tests suggest that financing constraints partially mediate the relationship between ESG performance and firms' innovation efficiency.

By systematically revealing whether, how and under what circumstances ESG performance improves firms' innovation advantages, this study bridges the gap in the existing literature and highlights important implications to suggest how firms can better capture the value associated with ESG.

With the rapid development of Internet technology, cybersecurity threats such as security loopholes, data leaks, network fraud, and ransomware have become increasingly prominent, and organized and purposeful cyberattacks have increased, posing more challenges to cybersecurity protection. Therefore, reliable network risk assessment methods and effective network security protection schemes are urgently needed.

Based on the dynamic behavior patterns of attackers and defenders, a Bayesian network attack graph is constructed, and a multitarget risk dynamic assessment model is proposed based on network availability, network utilization impact and vulnerability attack possibility. Then, the self-organizing multiobjective evolutionary algorithm based on grey wolf optimization is proposed. And the authors use this algorithm to solve the multiobjective risk assessment model, and a variety of different attack strategies are obtained.

The experimental results demonstrate that the method yields 29 distinct attack strategies, and then attacker's preferences can be obtained according to these attack strategies. Furthermore, the method efficiently addresses the security assessment problem involving multiple decision variables, thereby providing constructive guidance for the construction of security network, security reinforcement and active defense.

A method for network risk assessment methods is given. And this study proposed a multiobjective risk dynamic assessment model based on network availability, network utilization impact and the possibility of vulnerability attacks. The example demonstrates the effectiveness of the method in addressing network security risks.

This study investigates the economic outcomes of the strategic deviation (SD), the fundamental and crucial question in institutional theory and strategic management. Previous studies have yielded contradictory findings. This study reconciles conflicting results by distinguishing the effects of the SD on financial and market performance, examining the mechanism of financing constraints and the boundary condition of institutional investor heterogeneity.

This research collected data from Chinese A-shares listed manufacturing firms from 2009 to 2021 from the CSMAR and Wind databases. This study conducted empirical tests using OLS models with Stata 15.

Empirical results demonstrate that the SD has different impacts on different dimensions of performance. The SD negatively impacts financial performance while positively impacts market performance. Financing constraints mediate the main effects. Moreover, transactional institutional investors positively moderate the negative effect of the SD on financial performance, whereas stable institutional investors negatively moderate the positive effect of the SD on market performance.

By systematically revealing how the SD has different effects on financial and market performance, this study reconciles the debate on the SD between institutional theorists and strategy scholars. This research makes contributions to the research stream by providing reasonable explanations for conflicting conclusions. Furthermore, by introducing the overlooked perspective of financing constraints, this research identifies crucial mediating mechanisms and highlights the double-edged effect of financing constraints, enriching our understanding of financing constraints. Finally, this study investigates the moderating effects of institutional investor heterogeneity, thereby making valuable contributions to the comprehension of boundary conditions.

Twin support vector machine (TSVM) is an effective machine learning technique. However, the TSVM model does not consider the influence of different data samples on the optimal hyperplane, which results in its sensitivity to noise. To solve this problem, this study proposes a twin support vector machine model based on fuzzy systems (FSTSVM).

This study designs an effective fuzzy membership assignment strategy based on fuzzy systems. It describes the relationship between the three inputs and the fuzzy membership of the sample by defining fuzzy inference rules and then exports the fuzzy membership of the sample. Combining this strategy with TSVM, the FSTSVM is proposed. Moreover, to speed up the model training, this study employs a coordinate descent strategy with shrinking by active set. To evaluate the performance of FSTSVM, this study conducts experiments designed on artificial data sets and UCI data sets.

The experimental results affirm the effectiveness of FSTSVM in addressing binary classification problems with noise, demonstrating its superior robustness and generalization performance compared to existing learning models. This can be attributed to the proposed fuzzy membership assignment strategy based on fuzzy systems, which effectively mitigates the adverse effects of noise.

This study designs a fuzzy membership assignment strategy based on fuzzy systems that effectively reduces the negative impact caused by noise and then proposes the noise-robust FSTSVM model. Moreover, the model employs a coordinate descent strategy with shrinking by active set to accelerate the training speed of the model.