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The purpose of this study is to investigate whether negative reports by state-controlled media affect firms’ CSR performance. Negative reports by state-controlled media indicate the signals of deteriorating relationships between firms and the government and then generate greater political pressure on firms, which may force firms to engage in more CSR activities. This study first examines the influence of negative reports by state-controlled media on CSR performance. Then, we further figure out whether the degree of dependence on the government exhibits an impact on the relationship between negative reports by state-controlled media and firms’ CSR performance.

The sample for this study is based on all Chinese A-listed firms from 2010 to 2020. The study employs CSR scores data released by HEXUN to measure firms’ CSR performance. HEXUN is one of the most professional institutions that sell CSR-related products. Following You et al. (2018) and An et al. (2022), the authors identify the nine most popular media consisting of state-controlled media. The ordinary least squares (OLS) method is adopted for regression, and various robustness tests are conducted including using alternative measures, expanding the regression model and instrumental variable method.

The empirical results show a significant positive relationship between negative reports by state-controlled media and firms’ CSR performance. The cross-sectional analyses indicate that the effect of negative reports by state-controlled media on firms’ CSR performance is stronger for firms with mandatory CSR disclosure requirements, firms with political connections and firms with more severe financial constraints. Furthermore, improved CSR performance resulting from negative reports by state-controlled media indeed helps repair firms’ relationship with the government and thus leads them to attain government benefits, such as more government subsidies and lower tax rates.

This study finds that media reports issued by state-controlled media can be treated as signals of the relationships between firms and the government, which generate political pressure to push firms to take CSR as a strategic management tool to repair their relationships with the government. It helps policymakers and investors more comprehensively understand firms’ incentives behind their improved CSR performance and develop more effective policies. This study focuses on firms’ overall CSR performance. We anticipate that future research can extend the analysis of the impact of negative reports by state-controlled media on specific aspects of CSR investment.

This study illustrates the significantly positive effect of negative reports by state-controlled media in promoting CSR performance. It fills the research gap in studying the role of state-controlled media in CSR, especially for emerging markets. Moreover, the study also contributes to the strand of literature on strategic CSR management.

Drawing on the moral foundations theory (MFT), we examine what nonprofit organizations (NPOs) discuss and how NPOs engage in gun-related issues on Twitter. Specifically, we explore latent topics and embedded moral values (i.e. care, fairness, loyalty, authority, and sanctity) in NPOs’ tweets and investigate the effects of the latent topics and moral values on invoking public engagement.

Data were retrieved by the Twint Python and the rtweet R packages. Finally, 5,041 tweets posted by 679 NPOs were analyzed via unsupervised topic modeling and the extended moral foundations dictionary (eMFD). Negative binomial regression analysis was employed for statistical analysis.

NPOs’ engagement in gun-related issues mainly focuses on laws and policies, calling for action and collaborations, and school safety. All five moral foundations are more salient in the cluster of laws and policies. When NPOs discuss the above-mentioned three topics, the public is less likely to like or retweet NPOs’ messages. In contrast, NPOs’ messages with the sanctity foundation are most likely to receive likes and retweets from the public. The fairness foundation interacts with Cluster 3 of school safety on the number of likes.

This study enhances the understanding of gun-related social media discussions by identifying the crucial involvement of NPOs as major stakeholders. In addition, our study enriches the existing literature on NPOs’ social media communication by including moral values and their moral-emotional effects on public engagement. Finally, our study validates the eMFD dictionary and broadens its applicability to gun-related topics.

The purpose of this study proposes a strategy based on vehicle kinematics, dynamics and fusion estimation. The estimation signal of vehicle driving state is crucial for vehicle driving safety and stability control, and the issue of fault-tolerant reconstruction estimation of vehicle driving state under the failure of yaw rate or lateral acceleration sensors is a significant research topic.

A strategy based on vehicle kinematics, dynamics and fusion estimation is proposed. To address the issue of inaccurate calculation of tire forces because of sensor failure, a method combining adaptive sliding mode observer, genetic algorithm and particle swarm optimization algorithm is proposed to accurately calculate tire forces, and the Square Root Cubature Kalman Filter algorithm is used to reconstruct the estimation of vehicle driving state under sensor failure. To improve the accuracy of fault-tolerant reconstruction estimation of vehicle driving state, an error-weighted multi-method fusion estimation strategy for vehicle driving state is proposed. A fast terminal sliding mode control algorithm is proposed to control the stability of the fault-tolerant reconstruction estimation signal of vehicle driving state.

Simulation results show that the proposed fault-tolerant reconstruction estimation algorithm for vehicle driving state can accurately estimate the actual driving state of the vehicle and stably participate in the vehicle stability control system, achieving fault-tolerant reconstruction estimation and control of vehicle driving state under sensor failure.

The problem of vehicle motion state estimation under yaw velocity sensor fault or lateral acceleration sensor fault is solved, and fault tolerance control under sensor state is realized.

Building on upper echelons theory, this study explores the influence of managerial cognition on firm innovation during times of crisis. Specifically, we aim to disentangle the concept of cognitive complexity by examining how CEOs’ cognitive depth and cognitive width differently influence their firms’ innovation outcomes. Additionally, we investigate how organizational slack moderates the impact of these cognitive attributes on innovation, providing a deeper understanding of the conditions under which managerial cognition drives firm adaptability in crises.

This study utilized a sample of 115 listed US firms ranked in the top 200 in terms of market capitalization share in 2020. We measured the key variables by analyzing text and archival data from interviews with CEOs, particularly focusing on their discussions regarding the impacts of the COVID-19 pandemic. Regression analysis was employed to test the hypothesized relationships in the research model.

The results reveal that under the crisis, CEO cognitive depth enhances firm innovation, while CEO cognitive width impedes firm innovation. Moreover, organizational slack weakens the positive relationship between CEO cognitive complexity and innovation.

This study significantly contributes to and extends the established body of research on a leader’s cognition during a crisis. Our study goes beyond traditional views of cognitive complexity by highlighting the distinct impacts of two critical elements: cognitive depth and width, on decision-making processes. This study contributed to the innovative decision-making literature by opening up the black box behind the decision-making process of innovation during uncertainty. This underscores the multifaceted nature of cognitive processes in innovation, highlighting the interplay between cognitive depth, cognitive width and organizational resources in driving firm innovative outcomes during the crisis. We also broaden the temporal scope of empirical research on CEO cognition by gathering data from CEO interviews conducted during the COVID-19 pandemic.

This study reveals that when CEOs have a broader focus and attend to a wide range of information, their ability to quickly utilize firm resources for formulating competitive actions decreases during uncertainty. Consequently, it is crucial for CEOs to acknowledge the limitations of their attentional capacity. The allocation of their attention and information processing capacity has significant implications for their innovative decision-making processes, particularly in navigating through crises.

Our study finds that excessive attention during times of crisis may not necessarily be beneficial to firm innovation. An excessive focus on problems can lead to scattered attention, impairing judgment and decision-making abilities. Moreover, excessive attention to problems may trigger panic and unnecessary stress, further impacting decision quality. High cognitive width can trap teams in short-term thinking and emergency mode, neglecting long-term strategies and opportunities such as innovation investment. Yet, firms with more slack resources can reduce the negative impacts of cognitive depth.

This study proposes a comprehensive cognitive model to understand managers’ decision-making during a crisis. The research posits that different dimensions of CEOs’ managerial cognition have distinct impacts on firm innovation in crisis environments. This study significantly contributes to the study of managerial cognition and innovation literature.

Continuum robots offer unique advantages in various specialized environments, particularly in confined or hard-to-reach spaces. Inverse kinematics and real-time shape estimation constitute crucial aspects of closed-loop control for continuum robots, presenting challenging problems. This paper aims to present an inverse kinematics and shape reconstruction method, which relies solely on the knowledge of base and end positions and orientations.

Based on the constant curvature assumption, continuum robots are regarded as spatial curves composed of circular arcs. Using geometric relationships, the mathematical relationships between the arc chords, points on the bisecting plane and the coordinate axes are established. On this basis, the analytical solution of the inverse kinematics of the continuum robots is derived. Using the positions and orientations of the base and end of the continuum robots, the Levenberg–Marquardt algorithm is used to solve the positions of the cubic Bezier curves, and a new method of spatial shape reconstruction of continuum robots is proposed.

The inverse kinematics and spatial shape reconstruction simulation of the continuum robot are carried out, and the spatial shape measurement experimental platform for the continuum robot is constructed to compare the measured and reconstructed spatial shapes. The results show that the maximum relative error between the actual shape and the reconstructed shape of the continuum robot is 2.08%, which verifies the inverse kinematics and shape reconstruction model. Additionally, when the bending angle of a single bending section of the continuum robot is less than 135°, the shape reconstruction accuracy is higher.

The proposed inverse kinematics solution method avoids iterative solving, and the shape reconstruction model does not rely on mechanical models. It has the advantages of being simple to solve, highly accurate and fast in computation, making it suitable for real-time control of continuum robots.

The integration of big data with artificial intelligence in the field of digital health has brought a new dimension to healthcare service delivery. AI technologies that provide value by using big data obtained in the provision of health services are being added to each passing day. There are also some problems related to the use of AI technologies in health service delivery. In this respect, it is aimed to understand the use of digital health, AI and big data technologies in healthcare services and to analyze the developments and trends in the sector.

In this research, 191 studies published between 2016 and 2023 on digital health, AI and its sub-branches and big data were analyzed using VOSviewer and Rstudio Bibliometrix programs for bibliometric analysis. We summarized the type, year, countries, journals and categories of publications; matched the most cited publications and authors; explored scientific collaborative relationships between authors and determined the evolution of research over the years through keyword analysis and factor analysis of publications. The content of the publications is briefly summarized.

The data obtained showed that significant progress has been made in studies on the use of AI technologies and big data in the field of health, but research in the field is still ongoing and has not yet reached saturation.

Although the bibliometric analysis study conducted has comprehensively covered the literature, a single database has been utilized and limited to some keywords in order to reach the most appropriate publications on the subject.

The analysis has addressed important issues regarding the use of developing digital technologies in health services and is thought to form a basis for future researchers.

In today’s world, where significant developments are taking place in the field of health, it is necessary to closely follow the development of digital technologies in the health sector and analyze the current situation in order to guide both stakeholders and those who will work in this field.

Green investment funds are still imperative in clarifying the fundamental components of their relationship to sustainability. This study aims to investigate the impact of different factors, such as green product design and innovation, green product entry barriers and green environmental awareness, on the success of green investment funds projects to finance environmentally friendly products. The research also investigates how green investment funds facilitate these factors to encourage environmentally sustainable business.

This paper used a questionnaire to collect insights from 210 green entrepreneurs in Asia, Africa, Europe and America. The data were then investigated using statistical tools, such as quantitative analysis of green entrepreneur surveys collected from various industries. The relationship between green product design and innovation, barriers to entry, environmental awareness and green entrepreneurship performance was investigated using partial least squares structural equation modelling, with green investment funds as a mediator.

The results indicate that every construct/variable included in the study supported the success of the sustainable business. The observation was made that the development phase tends to diminish the positive relationship between the success of green investment funds and green product codesign strategies. Implementing green product design and innovation improves the success of a green firm. Also, the progress of such companies might be hindered by entry barriers, and corporate performance is improved by environmentalism. This study found the role of green investment funds in promoting product innovation and positive environmental outcomes while reducing barriers to entry.

Given these results, this work provides a theoretical explanation. Also, it gives doable recommendations for more successful green investment funds of environmentally friendly goods. The analysis emphasises the need for green product innovation and investment funds to mitigate entry obstacles. Corporate entities, investors and lawmakers receive pragmatic guidance on sustainable business practices.

This research, unique because of its multidisciplinary methodology and theoretical advances, examines the relationship between business, finance and sustainability. It provides valuable insights for academics, professionals and decision-makers, enhancing the understanding of green investment and entrepreneurship and offering practical global sustainable economic growth strategies. This paper investigates the impact of green investment funds on product innovation, entry obstacles, environmental consciousness and the success of green entrepreneurs. To the best of the author’s knowledge, this study is one of the limited numbers that models these features, enhancing the precision of green project success information.

This paper analyses and benchmarks the performance of the general and health Insurance companies in India, considering their production, capital allocation and investment efficiencies as three distinct stages.

A three stage Data Envelopment Analysis (DEA) methodology has been used with three years of data of the health and general insurance companies.

In addition to production and investment efficiencies, the capital allocation efficiency of an insurance firm significantly impacts its financial performance. The study shows that notwithstanding the efficiency scores in production and investment, general insurance firms with superior capital allocation efficiencies are the ones that have been able to translate their efficiencies into better business performance.

The study provides deeper understanding of the importance of capital allocation decisions and its linkages to production and investment efficiencies to help insurance firms to make better operational and financial decisions. The standalone health insurance players in spite of their reasonably high capital allocation efficiency scores have not been able to translate their efficiencies into superior financial performance.

While the existing literature at best has only considered production and investment decisions as the two stages, the present study has added another stage relating to the allocation of financial resources of the insurance firms. The paper is also distinct in terms of its analysis of linkages between efficiency scores of the three different stages with key financial performance measures.

The COVID-19 outbreak has disrupted the habits of customers as well as their shopping behavior. This study aims to critically examine the associated benefits and challenges of online shopping from the perspective of customers in the COVID-19 pandemic.

A systematic review of the relevant literature published between 2020 and 2022 was conducted via performing comprehensive search query in leading scholarly databases “Scopus and Web of Science” with the restriction of their predefined subject category of “Business.” Overall, 30 research studies were selected for the review and a significant number of studies were published in 2021 (n = 15).

The research findings revealed that customers are motivated to shop online because of perceived benefits such as time-saving, convenience, 24/7 accessibility, interactive services without physical boundaries, trust, website attractiveness and cost-saving. However, challenging factors such as financial scams, privacy concerns, poor quality of products and services, fake promotions and reduced social interaction have hindered the growth of online shopping. The recommendations regarding designing marketing strategies, secured transaction, multiple payment options, trust building, protection of privacy, promotion via social media, effective mechanism to secure and timely delivery of product are helpful to improve the service quality of online shopping.

The outcomes of this research are valuable to online retailers and policymakers, as it highlights how the benefits can enhance customers’ shopping intentions and minimize the impact of associated challenges. This study also recommends the redesigning of user-friendly interfaces of online shopping websites and ensures their privacy, security and performance on a regular basis.

Effectively predicting research and development project portfolio benefit (R&D PPB) could assist organizations in monitoring the execution of research and development project portfolio (R&D PP). However, due to the uncertainty and complexity of R&D PPB, current research remains lacking a valid R&D PPB prediction tool. Therefore, an R&D PPB prediction model is proposed via a backpropagation neural network (BPNN).

The R&D PPB prediction model is constructed via a refined immune genetic algorithm coupling backpropagation neural network (RIGA-BPNN). Firstly, considering the characteristics of R&D PP, benefit evaluation criteria are identified. Secondly, the benefit criteria values are derived as input variables to the model via trapezoidal fuzzy numbers, and then the R&D PPB value is determined as the output variable through the CRITIC method. Thirdly, a refined immune genetic algorithm (RIGA) is designed to optimize BPNN by enhancing polyfitness, crossover and mutation probabilities. Lastly, the R&D PPB prediction model is constructed via the RIGA-BPNN, followed by training and testing.

The accuracy of the R&D PPB prediction model stands at 99.26%. In addition, the comparative experiment results indicate that the proposed model surpasses BPNN and the immune genetic algorithm coupling backpropagation neural network (IGA-BPNN) in both convergence speed and accuracy, showcasing superior performance in R&D PPB prediction. This study enriches the R&D PPB predicting methodology by providing managers with an effective benefits management tool.

The research implications of this study encompass three aspects. First, this study provides a profound insight into R&D PPB prediction and enriches the research in PP fields. Secondly, during the construction of the R&D PPB prediction model, the utilization of the composite system synergy model for quantifying synergy contributes to a comprehensive understanding of intricate interactions among benefits. Lastly, in this research, a RIGA is proposed for optimizing the BPNN to efficiently predict R&D PPB.

This study carries threefold implications for the practice of R&D PPM. To begin with, the approach proposed serves as an effective tool for managers to predict R&D PPB. Then, the model excels in efficiency and flexibility. Furthermore, the proposed model could be used to tackle additional challenges in R&D PPM, such as gauging the potential risk level of R&D PP.

Effective predicting of R&D PPB enables organizations to allocate their limited resources more strategically, ensuring optimal use of capital, manpower and time. By accurately predicting benefit, an organization can prioritize high-potential initiatives, thereby improving innovation efficiency and reducing the risk of failed investments. This approach not only strengthens market competitiveness but also positions organizations to adapt more effectively to changing market conditions, fostering long-term growth and sustainability in a competitive business environment.

Incorporating the characteristics of R&D PP and quantifying the synergy between benefits, this study facilitates a more insightful R&D PPB prediction. Additionally, improvements to the polyfitness, crossover and mutation probabilities of IGA are made, and the aforementioned RIGA is applied to optimize the BPNN. It significantly enhances the prediction accuracy and convergence speed of the neural network, improving the effectiveness of the R&D PPB prediction model.