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Via dialectical perspective and configurational approach, this paper aims to explore the relationship between female representation and long-term firm performance when combined with environmental conditions.

For necessary condition analysis and time-series qualitative comparative analysis, a sample of 614 listed Chinese manufacturing firms between 2017 and 2020 was obtained.

The inclusion of female executives can aid firms in their long-term performance and resilience. Seven configurations, categorized as chimpanzee type, African elephant type and queen bee type, can prompt long-term firm performance. Chimpanzee-type configuration is the most prevalent path for firms to achieve long-term performance.

Firms could reconsider the role of female executives in achieving long-term success, assist in breaking the invisible “glass ceiling” and “glass cliff,” and refrain from viewing them as mere “tokens.” Policymakers can improve female representation by institutionally guaranteeing women’s opportunities for empowerment, education and promotion.

This study presents evidence for the legitimacy of female representation by demonstrating the intricate causality between female representation and firm performance beyond the controversy between business ethics and coercive policy. This paper also builds upon and extends the literature on female representation and provides alternative ways to improve female representation by combining female executives’ percentages, professionalism and positions.

The current paper's goal is to examine the productivity of the closed banking sector evidenced from Ethiopia. In addition, the inclusion of intangibles on productivity was examined in the current paper.

First, the standard Malmquist Productivity Index (MPI) was employed for 13 commercial banks for both stages. Second, by excluding the state-owned commercial bank, the analysis employed a bootstrapped MPI for the robust and comprehensive conclusion. Furthermore, from 2010 to 2019, the fixed effect Ordinary Least Square (OLS) regression with balanced panel data was used.

The standard MPI in both stages shows that the productivity of Ethiopian commercial banks is declining. The technological shock was the main reason for the loss. The catch-up in both stages scored above unity, mainly due to the pure efficiency change. Besides, when combined with tangible resources, the inclusion of resource-based view (RBV) proxy variables reduces technological shock regress and ultimately improves productivity change. The bootstrapped MPI also reveals that technological shock is the primary source of the productivity decline. However, efficiency change also contributes to the productivity decline based on this estimation.

Future research could examine the more extensive productivity analysis by considering the primary sources of data collections for resource-based variables.

According to the study's results, banking regulatory authorities and bank management, including the shareholders, should continue to invest in cutting-edge technology to improve the productivity of the banking sector.

This is the first comprehensive study of productivity for Ethiopian commercial banks based on the standard MPI, bootstrapped MPI, and OLS by incorporating all resources into the analysis.

Accurately evaluating fluid flow behaviors and determining permeability for deforming porous media is time-consuming and remains challenging. This paper aims to propose a novel machine-learning method for the rapid estimation of permeability of porous media at different deformation stages constrained by hydro-mechanical coupling analysis.

A convolutional neural network (CNN) is proposed in this paper, which is guided by the results of finite element coupling analysis of equilibrium equation for mechanical deformation and Boltzmann equation for fluid dynamics during the hydro-mechanical coupling process [denoted as Finite element lattice Boltzmann model (FELBM) in this paper]. The FELBM ensures the Lattice Boltzmann analysis of coupled fluid flow with an unstructured mesh, which varies with the corresponding nodal displacement resulting from mechanical deformation. It provides reliable label data for permeability estimation at different stages using CNN.

The proposed CNN can rapidly and accurately estimate the permeability of deformable porous media, significantly reducing processing time. The application studies demonstrate high accuracy in predicting the permeability of deformable porous media for both the test and validation sets. The corresponding correlation coefficients (R²) is 0.93 for the validation set, and the R² for the test set A and test set B are 0.93 and 0.94, respectively.

This study proposes an innovative approach with the CNN to rapidly estimate permeability in porous media under dynamic deformations, guided by FELBM coupling analysis. The fast and accurate performance of CNN underscores its promising potential for future applications.

This study aims to explore how brands’ degree of internationalization influences consumers’ attitudes toward brands’ engagement in cross-border philanthropy by taking legitimacy as a mediating mechanism. The authors further investigate the moderating role of cause acuteness in this effect to identify practical strategies for managers.

The hypotheses are tested via laboratory experiments. In brief, Study 1 investigates the relationship between a brand’s degree of internationalization and perceived legitimacy for corporate cross-border philanthropy and the impact of internationalization on consumers’ brand evaluations of such philanthropy. Study 2 addresses the moderating role of cause acuteness.

The authors discover that companies with a high (vs low) degree of internationalization gained more legitimacy, and thus better brand evaluations, upon engaging in corporate cross-border philanthropy. This effect reverses when the causes are related to sudden disasters rather than ongoing tragedies.

This study provides valuable guidance for marketers seeking to leverage cross-border philanthropy to enhance consumers’ brand attitudes. Specifically, brands’ degree of internationalization should be consistent when performing cross-border philanthropy. Otherwise, brands will struggle to gain legitimacy and will earn less favorable consumer evaluations.

This work enriches the literature on corporate social responsibility in the domain of cross-border philanthropy and elucidates consumers’ attitudes toward this type of philanthropy in a corporate context. This study also meaningfully contributes to research on brands’ internationalization and legitimacy.

This paper aims to present a novel approach for computing particle temperatures in simulations coupling computational fluid dynamics (CFD) and discrete element method (DEM) to predict flow and heat transfer in fluidized beds of thermally thick spherical particles.

An improved lumped formulation based on Hermite-type approximations for integrals to relate surface temperature to average temperature and surface heat flux is used to overcome the limitations of classical lumped models. The model is validated through comparisons with analytical solutions for a convectively cooled sphere and experimental data for a fixed particle bed. The coupled CFD-DEM model is then applied to simulate a Geldart D bubbling fluidized bed, comparing the results to those obtained using the classical lumped model.

The validation cases demonstrate that ignoring internal thermal resistance can significantly impact the temperature in cases where the Biot number is greater than 0.1. The results for the fixed bed case clearly demonstrate that the proposed method yields significantly improved outcomes compared to the classical model. The fluidized bed results show that surface temperature can deviate considerably from the average temperature, underscoring the importance of accurately accounting for surface temperature in convective heat transfer predictions and surface processes.

The proposed approach offers a physically more consistent simulation without imposing a significant increase in computational cost. The improved lumped formulation can be easily and inexpensively integrated into a typical DEM solver workflow to predict heat transfer for spherical particles, with important implications for various industrial applications.