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Community detection is a key factor in analyzing the structural features of complex networks. However, traditional dynamic community detection methods often fail to effectively solve the problems of deep network information loss and computational complexity in hyperbolic space. To address this challenge, a hyperbolic space-based dynamic graph neural network community detection model (HSDCDM) is proposed.

HSDCDM first projects the node features into the hyperbolic space and then utilizes the hyperbolic graph convolution module on the Poincaré and Lorentz models to realize feature fusion and information transfer. In addition, the parallel optimized temporal memory module ensures fast and accurate capture of time domain information over extended periods. Finally, the community clustering module divides the community structure by combining the node characteristics of the space domain and the time domain. To evaluate the performance of HSDCDM, experiments are conducted on both artificial and real datasets.

Experimental results on complex networks demonstrate that HSDCDM significantly enhances the quality of community detection in hierarchical networks. It shows an average improvement of 7.29% in NMI and a 9.07% increase in ARI across datasets compared to traditional methods. For complex networks with non-Euclidean geometric structures, the HSDCDM model incorporating hyperbolic geometry can better handle the discontinuity of the metric space, provides a more compact embedding that preserves the data structure, and offers advantages over methods based on Euclidean geometry methods.

This model aggregates the potential information of nodes in space through manifold-preserving distribution mapping and hyperbolic graph topology modules. Moreover, it optimizes the Simple Recurrent Unit (SRU) on the hyperbolic space Lorentz model to effectively extract time series data in hyperbolic space, thereby enhancing computing efficiency by eliminating the reliance on tangent space.

This paper aims to test the empirical validity of the dynamic trade-off theory in its symmetric and asymmetric versions in explaining the capital structure of a panel of publicly listed US industrial firms over the period from 2013 to 2019. It analyzes the existence of an adjustment of leverage toward its target level and whether the speed of this adjustment is influenced by the debt measure, the model specification or/and the fact that the actual debt ratio is higher or lower than its long-term target level.

This paper uses a quantitative research methodology using panel data analysis under the partial adjustment model and the error correction model using the generalized moment method in first differences and in systems to explore the dynamic nature of firms’ capital structure behavior.

The results show that the effects of the conventional determinants of leverage are globally consistent with the trade-off theory predictions. The dynamic versions confirm that firms exhibit leverage-targeting behavior. Although this speed of adjustment (SOA) depends on the debt and model specifications, it is around 60% on average. The estimated SOA is higher for the market leverage measure compared to the book leverage. The asymmetric adjustment model reveals that firms are more sensitive to reducing leverage than increasing it when they are away from their target; overleveraged firms exhibit approximately 5% faster adjustment than underleveraged firms when book leverage is used.

The originality of this research paper lies in its development and test of an asymmetric model to allow the leverage adjustment speed to vary depending on whether the firm’s debt ratio is above or below its target level and the methodological approach as well as the different model specifications used and the insights generated through the application of rigorous econometric techniques.

This study aims to examine the impact of corporate governance on the speed of adjustment (SOA) of capital structure in a developing market, Pakistan.

The study's sample includes 173 non-financial enterprises that were listed on the Pakistan Stock Exchange (PSX) between 2011 and 2020. The capital structure of the sample companies is determined by the ratio of total debt to total debt plus the market value of equity. Corporate governance is measured by board size, independence, CEO duality, management ownership, blockholders ownership and institutional ownership. A two-step difference GMM model was used to achieve the study's objectives.

Through applying the reduced form model approach, we discovered that corporate governance variables have a considerable negative impact on the speed of targeted leverage adjustment in sample firms. Additionally, to check the robustness of results, the two-stage technique used to examine this corporate governance-SOA relationship. Furthermore, we discovered that smaller enterprises modify their capital structure more than larger firms. Furthermore, corporations prioritize short-term debt adjustment above long-term debt adjustment.

The study's findings provide further information to company managers and investors on the relationship between corporate governance quality and the pace of adjustment towards targeted leverage across Pakistani enterprises. Furthermore, this study adds new information from growing countries such as Pakistan to the existing literature, which can help regulatory authorities and policymakers improve the quality of corporate governance. It is commonly known that improving the quality of corporate governance practices improves the firm's capital structure, which benefits all stakeholders.

In the context of developing economies, the academic literature lacks research that examine the impact of corporate governance on dynamic capital structure decisions. This study intends to fill this gap.

The purpose of this study is to investigate and compare the dynamic leverage policies of Islamic and conventional banks within selected Organization of Islamic Cooperation (OIC) countries. The study specifically focuses on the concepts of leverage procyclicality and prospect theory.

To achieve the research objectives, the study uses data from three distinct periods: Crisis I (2007–2009), Crisis II (2011–2012) and Crisis III (2020). The analysis uses dynamic panel-data regression, using the generalized method of moments (GMM) technique.

The research findings indicate that both Islamic and conventional banks demonstrate leverage procyclicality. Interestingly, Islamic banks exhibit weaker leverage procyclicality during normal conditions but display stronger procyclicality during crises compared to their conventional counterparts. The application of prospect theory reveals that both bank types exhibit risk-taking or risk-averse behavior through leverage under certain financial and market performance measures as the first-level domain of the gain-vs-loss condition. Furthermore, during crises (as the second-level domain of the normal-vs-crisis condition), both Islamic and conventional banks experience heightened leverage. Notably, Islamic banks, owing to their lower risk exposure and greater shock resilience, demonstrate lesser risk-taking behavior through leverage than conventional banks, both during periods of underperformance and worsening conditions amid crises. These findings validate the extension of prospect theory's applicability in a two-level domain perspective. The dynamic nature of leverage policy, being procyclical and adhering to prospect theory, also varies following different crises specifically.

The study's limitations include the unequal crisis periods (Crises I, II and III), leading to an imbalanced examination of their effects, certain financial and market performance metrics that fail to corroborate the expected hypotheses and the limited generalizability of findings beyond the selected OIC countries.

Understanding the intricate dynamics and behavioral aspects of leverage policy for both Islamic and conventional banks, particularly during crisis scenarios, proves crucial for reviewing banking regulations, making informed financial decisions and managing risks effectively.

This study enriches the current knowledge by presenting two key points. First, it highlights the dynamic nature of leverage procyclicality in Islamic banks, showing a change from weaker procyclicality in normal conditions to stronger procyclicality during crises compared to conventional banks. Second, it expands the application of prospect theory by introducing a dual-level domain context. Examining the comparative leverage policies of Islamic and conventional banks during different crises within OIC countries provides novel insights into leverage procyclicality and behavioral responses.

This study constructs an efficiency evaluation framework for assessing the human, structural and relational capital in the semiconductor industry of Taiwan. Furthermore, we analyze whether there are significant differences in efficiency across different levels concerning the industry supply chain (upstream, midstream and downstream), employee service tenure, capital scale and company establishment years.

This study focuses on Taiwanese semiconductor companies, utilizing data sourced from the Taiwan Economic Journal (TEJ) Database for the period spanning 2017 to 2021, encompassing a total of five years. Due to the nondisclosure of intangible asset values by all companies, an effort was made to ensure a comparable baseline by excluding companies with incomplete or missing data. Finally, empirical analysis was conducted on a sample of 64 companies using the dynamic network data envelopment analysis method.

(1) Overall efficiency demonstrates structural capital as the most prominent, followed by relational capital, while human capital shows relatively poorer efficiency. (2) To enhance the efficiency of intellectual capital, priority should be given to improving the efficiency of outputs related to intellectual property rights such as patents. (3) The midstream segment exhibits the best efficiency in both structural and relational capital. (4) Companies with longer employee service tenures exhibit superior efficiency in human capital in the long run. (5) Companies with extended establishment years and larger capital scales demonstrate superior efficiency in both human and structural capital.

Reflecting on past literature, scholars have primarily focused on the relationship between intellectual capital and firm efficiency, often emphasizing the overall efficiency of intellectual capital. However, within organizations, human capital, structural capital, and relational capital are interrelated. This study, for the first time, assesses the efficiency of these three components within an organization. The research addresses the challenges in analyzing the efficiency of intellectual capital and introduces a highly contemporary approach – dynamic network data envelopment analysis (DNDEA). Using the semiconductor industry in Taiwan as a case study, this paper conducts empirical analysis in a captivating and worthy industry. Therefore, the ideas presented in this paper are original.

The paper's goal is to examine and illustrate the useful uses of submodeling in finite element modeling for topology optimization and stress analysis. The goal of the study is to demonstrate how submodeling – more especially, a 1D approach – can reliably and effectively produce ideal solutions for challenging structural issues. The paper aims to demonstrate the usefulness of submodeling in obtaining converged solutions for stress analysis and optimized geometry for improved fatigue life by studying a cantilever beam case and using beam formulations. In order to guarantee the precision and dependability of the optimization process, the developed approach will also be validated through experimental testing, such as 3-point bending tests and 3D printing. Using 3D finite element models, the 1D submodeling approach is further validated in the final step, showing a strong correlation with experimental data for deflection calculations.

The authors conducted a literature review to understand the existing research on submodeling and its practical applications in finite element modeling. They selected a cantilever beam case as a test subject to demonstrate stress analysis and topology optimization through submodeling. They developed a 1D submodeling approach to streamline the optimization process and ensure result validity. The authors utilized beam formulations to optimize and validate the outcomes of the submodeling approach. They 3D-printed the optimized models and subjected them to a 3-point bending test to confirm the accuracy of the developed approach. They employed 3D finite element models for submodeling to validate the 1D approach, focusing on specific finite elements for deflection calculations and analyzed the results to demonstrate a strong correlation between the theoretical models and experimental data, showcasing the effectiveness of the submodeling methodology in achieving optimal solutions efficiently and accurately.

The findings of the paper are as follows: 1. The use of submodeling, specifically a 1D submodeling approach, proved to be effective in achieving optimal solutions more efficiently and accurately in finite element modeling. 2. The study conducted on a cantilever beam case demonstrated successful stress analysis and topology optimization through submodeling, resulting in optimized geometry for enhanced fatigue life. 3. Beam formulations were utilized to optimize and validate the outcomes of the submodeling approach, leading to the successful 3D printing and testing of the optimized models through a 3-point bending test. 4. Experimental results confirmed the accuracy and validity of the developed submodeling approach in streamlining the optimization process. 5. The use of 3D finite element models for submodeling further validated the 1D approach, with specific finite elements showing a strong correlation with experimental data in deflection calculations. Overall, the findings highlight the effectiveness of submodeling techniques in achieving optimal solutions and validating results in finite element modeling, stress analysis and optimization processes.

The originality and value of the paper lie in its innovative approach to utilizing submodeling techniques in finite element modeling for structural analysis and optimization. By focusing on the reduction of finite element models and the creation of smaller, more manageable models through submodeling, the paper offers designers a more efficient and accurate way to achieve optimal solutions for complex problems. The study's use of a cantilever beam case to demonstrate stress analysis and topology optimization showcases the practical applications of submodeling in real-world scenarios. The development of a 1D submodeling approach, along with the utilization of beam formulations and 3D printing for experimental validation, adds a novel dimension to the research. Furthermore, the paper's integration of 1D and 3D submodeling techniques for deflection calculations and validation highlights the thoroughness and rigor of the study. The strong correlation between the finite element models and experimental data underscores the reliability and accuracy of the developed approach. Overall, the originality and value of this paper lie in its comprehensive exploration of submodeling techniques, its practical applications in structural analysis and optimization and its successful validation through experimental testing.

In the rapidly evolving landscape of HR, the role of the HR business partner (HRBP) has significantly changed. This paper shares some of the latest research on how it is changing for the worse, and how it now sorely needs to evolve further.

The paper includes reference to two studies, an analysis of 55,000 comments from HR professionals and a self-assessment of 7,100 HR professionals across 96 different HR capabilities and five capability levels. Otherwise, the paper includes insight and expertise from across the Josh Bersin Company.

This paper and report it references highlights the emergence of the role in the 90s, and how it is expectations have changed to the present day: with the rise of digital technology, globalization and changing workforce expectations, HRBPs have had to acquire a broader skill set, focusing not on personnel management but on strategic initiatives, such as people analytics, employee experience and organization design. However, far too many HRBPs have ended up in admin or tactical roles, rather than being used to their full potential with the above. This paper includes actionable guidance for how companies can develop the role to get them back on track, including case study examples from Mastercard and LEGO.

To the best of the authors’ knowledge, this report is unique, novel and the latest research from the Josh Bersin Company.

This study aims to investigate the institutional, macroeconomic and firm-specific determinants of financial leverage in Vietnam and provides new evidence from the dynamic panel fractional estimator.

This study uses a panel dataset of 859 Vietnamese firms from 2008 to 2022 and employs three estimators: Feasible Generalized Least Squares (FGLS), System Generalized Method of Moments (SysGMM) and Dynamic Panel Fractional (DPF), with DPF being particularly suitable for handling fractional dependent variables and the dynamic nature of financial leverage.

The results confirm the dynamic nature of the financial leverage model, with firm-specific factors, institutional factors and macroeconomic factors playing significant roles in shaping firms' financing decisions. The DPF estimator highlights the positive impact of stock market development on leverage. This study contributes to the literature by providing new evidence on the determinants of leverage in Vietnam, using the DPF estimator for more accurate estimation and revealing the significant impact of the size of the banking sector, the size of the stock market, the stock market development index, the financial development index and the corruption perception index on leverage.

This study contributes to the literature by providing new evidence on the dynamic nature of the financial leverage model and the impact of institutional, macroeconomic and firm-specific factors on financial leverage in the context of Vietnam. The use of the DPF estimator allows for a more accurate and reliable estimation of the determinants of leverage, considering the fractional nature of the dependent variable and the persistence of capital structure decisions over time.

With the emergence of the digital era, the role of digital leaders in developing digital capabilities and driving their firms towards digital transformation has gained significant attention. Digital dynamic capabilities involve continuous engagement of leaders in sensing, seizing, and transforming activities needed to digitally transform their firms. However, little attention is given toward the role of digital leadership in developing digital dynamic capabilities. We seek to develop an understanding of the role of digital leadership in building digital dynamic capabilities for successful digital transformation.

We conducted a systematic literature review and looked at relevant articles using Google Scholar, ScienceDirect, and Scopus databases with key search items being “digital leadership”, “dynamic capabilities”, “digital dynamic capabilities,”. We used AND, OR operators in between the key terms to search for the relevant articles.

Our conceptual framework and propositions demonstrate the digital leader's role in building three core dynamic capabilities: digital sensing (technological trends, digital scouting, digital vision, future interpretation, and digital strategies), digital seizing (organizational agility and digital portfolio), and transforming (redesigning internal structures and ecosystem partnerships) for successful digital transformation.

This study pioneers an integrated framework that elucidates the role of digital leadership in fostering digital dynamic capabilities essential for successful digital transformation. While previous research has examined digital leadership and transformation in separate silos, our work bridges this gap by defining and dissecting three core capabilities—digital sensing, digital seizing, and transforming. By doing so, we offer both academic and practical communities a nuanced understanding of how digital leadership shapes dynamic capabilities. The study serves as a foundational roadmap for future research and offers actionable insights for organizations striving to navigate the complex landscape of digital transformation.

Several studies were made on paired site and soil–structure interaction (SSI) effects, but most of them were site specific. This paper aims to investigate the impact of SSI effects in conjunction with local soil condition effects on the seismic response of typical multistory low- to mid-rise–reinforced concrete (RC) buildings resting on Algerian regulatory design sites through a global explicit transfer function (TF).

A preliminary quantification of SSI effects associated with site effects is carried out through a frequency-domain solution based on the concept of rock-to-soil surface displacement TF performed for each design site category. It results from the combination of the TFs of structure, foundation and soil and reflects how seismic waves are amplified due to changes in the geological contrast between the rock and overlying soil deposits. As well, response modification factors, denoting displacement ratios of the building responses within the flexible and site-structure conditions with respect to the fixed-base one, are carried out.

In the context of Algerian seismic regulation, the study provides a clear vision of how and when site or SSI effects are expected to be influential, as opposed to the fixed-base hypothesis still retained by the current regulation. This helps engineers to be aware of the extent of the expected seismic damage.

The research applies to low- to mid-rise RC buildings within the Algerian seismic regulation, but it may also be expanded to other examples that fall under other seismic regulations.

The response modification ratio is a quantitative approach to assessing response fluctuations. It draws attention to how the roof level drift varies depending on the condition. These results can be used as numerical parameters in structural seismic design when the structure is comparable because they provide useful information about how the two phenomena interact with the structure.

The study goes beyond particular situations dealing with site specific and offers effective indicators and quantitative evaluation of combined site and SSI effects according to the current national seismic provisions, where no indication about site or SSI effects exists.