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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.

With the continuous increase in Web services, efficient identification of Web services that meet developers’ needs and understanding their relationships remains a challenge. Previous research has improved recommendation effectiveness by using correlations between Web services through graph neural networks (GNNs), while it has not fully leveraged service descriptions, limiting the depth and diversity of learning. To this end, a Web services recommendation method called LLMSARec, based on Large Language Model and semantic alignment, is proposed. This study aims to extract potential semantic information from services and learn deeper relationships between services.

This method consists of two core modules: profile generation and maximizing mutual information. The profile generation module uses LLM to analyze the descriptions of services, infer and construct service profiles. Concurrently, it uses LLM as text encoders to encode inferred service profiles for enhanced service representation learning. The maximizing mutual information model aims to align the semantic features of the services text inferred by LLM with structural semantic features of the services captured by GNNs, thus achieving a more comprehensive representation of services. The aligned representation serves as an input for the model to identify services with superior matching accuracy, thereby enhancing the service recommendation capability.

Experimental comparisons and analyses were conducted on the Programmable Web platform data set, and the results demonstrated that the effectiveness of Web service recommendations can be significantly improved by using LLMSARec.

In this study, the authors propose a Web service recommendation approach based on Large Language Model and semantic alignment. By extracting latent semantic information from services and effectively aligning semantic features with structural features, new representations can be generated to significantly enhance recommendation accuracy.

This paper aims to develop a novel method and apply it to solve multiple definite integrals. The proposed method is constructed based on multiple sets of correlation extreme learning machines (MCELM).

The authors present a novel method for solving multiple definite integrals. By using an extreme learning machine (ELM) to learn the integrand function, the primitive function is analytically derived based on the functional expression of the trained ELM and expressed by another ELM, while the correlations between the two ELMs are established. Solutions of multiple definite integrals can be realized by applying this process repeatedly.

To verify the validity and effectiveness of the proposed method, various examples are selected and its numerical solutions are obtained by using the proposed method. The proposed method has high computational accuracy and efficiency, and the superiority is illustrated by comparing with some other existing methods.

MCELM method is proposed for solving multiple definite integrals. The method can be applied for solving multiple definite integrals appearing in applications, the strong applicability of the method in engineering problems is demonstrated in structural system reliability analysis of a cantilever beam.

With the dramatic transformation of China’s industrial landscape, since the late 1990s, adaptive reuse of industrial heritage for cultural purposes has become a widely occurring phenomenon in major Chinese cities. The existing literature mainly focusses on specific cases, yet sees heritage conservation similarly at both national and regional scale and rarely identifies the main factors behind the production of China’s industrial-heritage reuse. The purpose of this paper is to examine the differences in heritage reuse outcomes among three Chinese mega-cities and explore the driving factors influencing the differences.

This paper compares selected industrial-heritage cultural precincts in Beijing, Shanghai and Chongqing, and explores the local intervening factors influencing differences in their reuse patterns, including the history of industrial development, the availability of the nineteenth and/or twentieth century industrial buildings, the existence of cultural capital and the prevalence of supportive regional government policy.

The industrial-heritage reuse in the three cities is highly regional. In Beijing, the adaptation of industrial heritage has resulted from the activities of large-scale artist communities and the local government’s promotion of the city’s cultural influence; while in Shanghai, successful and more commercially oriented “sea culture” artists, private developers in creative industries and the “creative industry cluster” policy make important contributions. Chongqing in contrast, is still at the early stage of heritage conservation, as demonstrated by its adaptive reuse outcomes. Considering its less-developed local cultural economy, Chongqing needs to adopt a broader range of development strategies.

The paper contributes to knowledge by revealing that the production of industrial-heritage cultural precincts in Chinese mega-cities is influenced by regional level factors, including the types of industrial heritage, the spontaneous participation of artist communities and the encouragement of cultural policy.

Talent development has become an important policy given prominence by the Macao government since Fernando Chui became its Chief Executive. The purpose of this paper is to review human resources and talent development policies in Macao and discuss the difficulties and challenges that they pose.

Using Macao as a case study the paper indicates the difficulties and challenges experienced by emerging markets in fostering talent development for the sustainability of their local economies.

This paper finds that, as an emerging market, Macao has to solve and overcome its institutional deficiencies (namely, by the establishment of both a professional registration regime and the means to gather metadata to enable predictions of the talents needed for the local economy and their potential scale) so as to please stakeholders. Only then can it truly introduce talent development schemes and programs.

There have been very few studies on Macao’s talent development initiative. This paper tries to fill this gap in the existing understanding of talent development.