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The purpose of this paper is to investigate dynamics of causal interactions and financial risk contagion among BRICS stock markets under rare events.

Two methods are adopted: The new causal inference technique, namely, the Liang causality analysis based on information flow theory and the dynamic causal index (DCI) are used to measure the financial risk contagion.

The causal relationships among the BRICS stock markets estimated by the Liang causality analysis are significantly stronger in the mid-periods of rare events than in the pre- and post-periods. Moreover, different rare events have heterogeneous effects on the causal relationships. Notably, under rare events, there is almost no significant Liang's causality between the Chinese and other four stock markets, except for a few moments, indicating that the former can provide a relatively safe haven within the BRICS. According to the DCIs, the causal linkages have significantly increased during rare events, implying that their connectivity becomes stronger under extreme conditions.

The obtained results not only provide important implications for investors to reasonably allocate regional financial assets, but also yield some suggestions for policymakers and financial regulators in effective supervision, especially in extreme environments.

This paper uses the Liang causality analysis to construct the causal networks among BRICS stock indices and characterize their causal linkages. Furthermore, the DCI derived from the causal networks is applied to measure the financial risk contagion of the BRICS countries under three rare events.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

The paper aims to elucidate effective strategies for promoting the adoption of green technology innovation within the private sector, thereby enhancing the value of public–private partnership (PPP) projects during the operational phase.

Utilizing prospect theory, the paper considers the government and the public as external driving forces. It establishes a tripartite evolutionary game model composed of government regulators, the private sector and the public. The paper uses numerical simulations to explore the evolutionary stable equilibrium strategies and the determinants influencing each stakeholder.

The paper demonstrates that government intervention and public participation substantially promote green technology innovation within the private sector. Major influencing factors encompass the intensity of pollution taxation, governmental information disclosure and public attention. However, an optimal threshold exists for environmental publicity and innovation subsidies, as excessive levels might inhibit technological innovation. Furthermore, within government intervention strategies, compensating the public for their participation costs is essential to circumvent the public's “free-rider” tendencies and encourage active public collaboration in PPP project innovation.

By constructing a tripartite evolutionary game model, the paper comprehensively examines the roles of government intervention and public participation in promoting green technology innovation within the private sector, offering fresh perspectives and strategies for the operational phase of PPP projects.

The purpose of this paper is to examine the effects of performance management (PM) practices on in-bound open innovation (OI) and out-bound OI. To do this, the authors examine the organizational effectiveness as well as the non-financial and financial performance of Brazilian startups that have had recent OI relationships with larger companies.

Using data collected from 103 Brazilian startups, the hypotheses were tested via partial least squares–structural equation modeling (PLS-SEM). An additional analysis was performed using fuzzy-set qualitative comparative analysis (fsQCA).

The findings show that PM practices orchestrate in-bound OI and out-bound OI; however, only in-bound OI promotes organizational effectiveness in Brazilian startups. Organizational effectiveness results in good non-financial performance, which in turn improves financial performance. PM practices have an indirect effect on financial performance from the serial mediation of in-bound OI, organizational effectiveness and non-financial performance. Moreover, several combinations of conditions lead to high levels of organizational effectiveness, non-financial performance and financial performance.

This study provides new evidence and insights from an emerging market on the antecedents and consequences of startups' OI adoption.

The purpose of this study is to investigate the impact of corporate social responsibility (CSR) on innovation performance and examine the moderating role of social media strategic capability and big data analytics capability. Specifically, the authors explore the effects of both external and internal CSR on innovation performance.

The authors collected data from 221 senior, middle and research and development (R&D) managers of high-tech firms in China, using a questionnaire survey with a six-month interval.

The empirical results show that both external and internal CSR positively influence innovation performance. Furthermore, social media strategic capability has a positive moderating effect on the relationship between CSR and innovation performance, while big data analytics capability moderates the relationship between external CSR and innovation performance.

The data comes from high-tech firms in China, which may limit the generalizability and external validity of the findings. Future studies should replicate this study in other industries and types of organizations.

The study suggests that high-tech firms should engage in both external and internal CSR activities to promote innovation performance. Moreover, leveraging social media strategic capability and big data analytics capability can enhance innovation performance.

This study contributes to the literature on CSR outcomes by empirically exploring the effects of external and internal CSR on innovation performance, thus extending stakeholder theory. Additionally, by revealing the contingency effects of social media strategic capability and big data analytics capability, this study enriching the research on dynamic capabilities theory in the context of digital transformation.

This study attempts to uncover the nonlinear relationship between public procurement and corporate total factor productivity (CTFP), and investigates the mediating roles of R&D investment and rent-seeking cost. Additionally, it conducts a heterogeneity analysis for firms with varying levels of political connections and corporate social responsibility (CSR).

Employing Ordinary Least Squares (OLS) and Olley-Pakes (OP) methods, the authors gauge CTFP and manually identify government customers to quantify public procurement. Leveraging panel data from Chinese listed companies, this study explores the relationship between public procurement and CTFP.

This study unveils a U-shaped relationship between public procurement and CTFP, highlighting R&D investment and rent-seeking costs as potential mechanisms. Furthermore, it identifies heterogeneous effects among companies with varying levels of political connections and CSR on the relationship between public procurement and CTFP, including their mediating effects.

This research enhances understanding of demand-side policies and provides crucial insights for the government to further improve public procurement policies.

By offering empirical evidence of how public procurement impacts CTFP, this paper enriches the literature on the behavioral repercussions of public procurement and the determinants of CTFP. It also overcomes the “black box” of the mechanism between public procurement and CTFP, based on the government’s dual role as a pathfinder and customer of enterprises. It broadens the application scenarios of institutional theory and principal-agent theory. Additionally, the heterogeneity analysis of firms with varying political connections and CSR extends the frontiers of related research.

This purpose of this paper is to address the research problem of optimizing photovoltaic (PV) panel placement on building facades to maximize solar energy generation.

The study examines the significance of various design configurations and their implications for PV system performance. The research involves analysis of relevant literature and energy simulations. An exemplary case study is conducted in a hot climate zone to quantify the impacts of PV panel placement on energy generation. Various application scenarios are developed, resulting in 28 scenarios for PV on building facades. Energy simulations using Grasshopper Rhino software and Ladybug plugin components are performed.

The paper identifies key factors influencing PV panel placement and energy generation through qualitative analysis. It introduces an appropriateness matrix as a decision-making framework to evaluate placement options. The study identifies design configurations and external features impacting PV location selection and performs a qualitative classification to determine their impact on energy generation.

The results and decision-making framework enable informed choices based on solar radiation levels, shading conditions, and building requirements. Optimizing PV panel placement enhances solar energy harvesting in buildings, benefiting architects and engineers.

The novel contributions of this paper include practical insights and guidance for strategically placing PV panels on building facades.

This study aims to investigate the influence of parent company personnel embedding on the stock price crash risk (SPCR) of listed companies, along with the moderating effect of disparate locations between parent and subsidiary companies and other major shareholders.

This research empirically tests hypotheses based on a sample of listed subsidiaries in China during the period between 2006 and 2021.

Our results demonstrate that personnel embeddedness in the parent company significantly alleviates SPCR in subsidiaries. This effect is even more substantial when the parent and subsidiary companies are in different places. However, other major shareholders in the subsidiary company weaken it. Our additional analysis indicates that, relative to executive embeddedness, director embeddedness exerts a stronger effect on the SPCR of the subsidiary. Mechanism examination reveals that the information asymmetry and the level of internal control (IC) within the subsidiary are significant channels through which the personnel embeddedness from the parent company influences the SPCR of the subsidiary.

This study expands the literature on how personnel arrangements in corporate groups within emerging countries influence SPCR. We have extended the traditional concept of interlocking directorates to corporate groups, thereby broadening the understanding of the governance effects of interlocking directors and executives from a group perspective.

Reusable takeaway food containers (RTFCs) are a newly emerging green packaging choice for the takeaway industry that can effectively reduce campus solid waste but are not yet well accepted. Therefore, this study aims to identify the key factors influencing university students’ intention to choose RTFCs, seeking to enhance RTFC project management practices and contribute to developing a sustainable “green university.”

In total, 316 valid respondents from a Chinese university were surveyed for data collection. A multivariate ordered logistic regression model was used to conduct empirical analysis.

The results of this study underscore the crucial role of perceived value in the relationship between perceived green attributes and students’ intention to choose RTFCs. The positive impacts of perceived green attributes on intention are direct and indirect, through the lens of perceived value. When the value is substantial, it significantly boosts the student’s intention to choose RTFCs. Conversely, the perception of lower hygienic quality or higher returning time cost dampens this intention, with a more pronounced effect than perceived green attributes. Notably, perceived publicity activities have the most significant impact on student’s intention to choose RTFCs.

This study contributes to the understanding of promoting RTFCs, a key strategy for reducing plastic waste on campuses. The findings provide actionable recommendations for the project company and the university, offering practical ways to encourage students to use RTFCs and contribute to plastic waste reduction.

This study analyses the way various components of intellectual capital (IC), namely, human capital (HC), structural capital (SC), relational capital (RC) and innovation capital (INNC), act as mediators in the relationship between managerial ability (MA) and a firm’s ability to achieve growth.

This study employs data envelopment analysis to quantify the MA of 825 Taiwanese listed electronics companies from 2017 to 2022. The proxies of firm growth are return on asset growth, operating income growth and total asset growth. This study then utilises a three-step mediation analysis methodology to examine the relationships between MA, IC and firm growth.

Findings indicate that HC, SC, RC and INNC mediate the link between MA and firm growth. This suggests that competent managers can capitalise on the potential benefits of these investments to achieve firm growth.

Competent managers can utilise different IC investments to grow the financial performance and strength of their businesses. Managers should continually scan, secure opportunities and adjust their investments in knowledge assets in accordance with the dynamic capabilities view. That is, managers, in general, and operations managers, in particular, can implement guidelines that prioritise IC investments in the future to expedite firms’ development.

This study extends the existing frameworks that study investment variables as mediators between MA and firm outcomes. Most particularly, this study adopts four components of IC for measurement. Moreover, firm performance is measured using dynamic growth indicators rather than static measures.