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The purpose of this paper is to form propositions about the relationship between top management team (TMT) heterogeneity and peer effects in investment decision-making and explore the mediating role of social learning processes.

To investigate the correlations between TMT heterogeneity and investment peer effects, we considered the TMT heterogeneity category, team process and contextual factors. With a sample of 8,467 firm-year observations from Chinese listed companies, we used the mean linear model and instrumental variable method to empirically examine their relationships. To identify the mediating role of social learning processes, we introduced a social learning model to find out the contextual factors influencing corporate social learning demands from three aspects and subsequently used comparative statics analysis to explore the variations in the main effect under these contextual factors.

For task-oriented heterogeneity (e.g. functional background, education and tenure heterogeneity), the opposite effects of information elaboration and social categorization processes make it a nonlinear multiplex correlation with investment peer effects. For relation-oriented heterogeneity (e.g. age and gender heterogeneity), the sole effect of social categorization processes leads to a negative linear correlation. Further, we identify the mediating role of social learning processes. In summary, we established a connection from the TMT heterogeneity, to information elaboration theory or social categorization theory, to social learning processes and ultimately to investment peer effects.

The results of this study provide a comprehensive perspective to predict the decision-making outcomes of team heterogeneity and contribute to heterogeneity research and practice.

This study compares the fatigue performance and biocompatibility of as-built and chemically etched Ti-6Al-4V alloys in TPMS-gyroid and stochastic structures fabricated via Powder Bed Fusion Laser Beam (PBF-LB). This study aims to understand how complex lattice structures and post-manufacturing treatment, particularly chemical etching, affect the mechanical properties, surface morphology, fatigue resistance and biocompatibility of these metamaterials for biomedical applications.

Selective Laser Melting (SLM) technology was used to fabricate TPMS-gyroid and Voronoi stochastic designs with three different relative densities (0.2, 0.3 and 0.4) in Ti-6Al-4V ELI alloy. The as-built samples underwent a chemical etching process to enhance surface quality. Mechanical characterization included static compression and dynamic fatigue testing, complemented by scanning electron microscopy (SEM) for surface and failure analysis. The biocompatibility of the samples was assessed through in-vitro cell viability assays using the Alamar Blue assay and cell proliferation studies.

Chemical etching significantly improves the surface morphology, mechanical properties and fatigue resistance of both TPMS-gyroid and stochastic structures. Gyroid structures demonstrated superior mechanical performance and fatigue resistance compared to stochastic structures, with etching providing more pronounced benefits in these aspects. In-vitro biocompatibility tests showed high cytocompatibility for both as-built and etched samples, with etched samples exhibiting notably improved cell viability. The study also highlights the importance of design and post-processing in optimizing the performance of Ti64 components for biomedical applications.

The comparative analysis between as-built and etched conditions, alongside considering different lattice designs, provides valuable information for developing advanced biomedical implants. The demonstration of enhanced fatigue resistance and biocompatibility through etching adds significant value to the field of additive manufacturing, suggesting new avenues for designing and post-processing implantable devices.

The purpose of this study is to explore the potential of trainable activation functions to enhance the performance of deep neural networks, specifically ResNet architectures, in the task of image classification. By introducing activation functions that adapt during training, the authors aim to determine whether such flexibility can lead to improved learning outcomes and generalization capabilities compared to static activation functions like ReLU. This research seeks to provide insights into how dynamic nonlinearities might influence deep learning models' efficiency and accuracy in handling complex image data sets.

This research integrates three novel trainable activation functions – CosLU, DELU and ReLUN – into various ResNet-n architectures, where “n” denotes the number of convolutional layers. Using CIFAR-10 and CIFAR-100 data sets, the authors conducted a comparative study to assess the impact of these functions on image classification accuracy. The approach included modifying the traditional ResNet models by replacing their static activation functions with the trainable variants, allowing for dynamic adaptation during training. The performance was evaluated based on accuracy metrics and loss profiles across different network depths.

The findings indicate that trainable activation functions, particularly CosLU, can significantly enhance the performance of deep learning models, outperforming the traditional ReLU in deeper network configurations on the CIFAR-10 data set. CosLU showed the highest improvement in accuracy, whereas DELU and ReLUN offered varying levels of performance enhancements. These functions also demonstrated potential in reducing overfitting and improving model generalization across more complex data sets like CIFAR-100, suggesting that the adaptability of activation functions plays a crucial role in the training dynamics of deep neural networks.

This study contributes to the field of deep learning by introducing and evaluating the impact of three novel trainable activation functions within widely used ResNet architectures. Unlike previous works that primarily focused on static activation functions, this research demonstrates that incorporating trainable nonlinearities can lead to significant improvements in model performance and adaptability. The introduction of CosLU, DELU and ReLUN provides a new pathway for enhancing the flexibility and efficiency of neural networks, potentially setting a new standard for future deep learning applications in image classification and beyond.

This research paper aims to optimize the calibration process for an ABB IRB 120 robot, specifically for robotic orbital milling applications, by introducing and validating the use of the observability index and telescopic ballbar for accuracy enhancement.

The study uses the telescopic ballbar and an observability index for the calibration of an ABB IRB 120 robot, focusing on robotic orbital milling. Comparative simulation analysis selects the O₃ index. Experimental tests, both static and dynamic, evaluate the proposed calibration approach within the robot’s workspace.

The proposed calibration approach significantly reduces circularity errors, particularly in robotic orbital milling, showcasing effectiveness in both static and dynamic modes at various tool center point speeds.

The study focuses on a specific robot model and application (robotic orbital milling), limiting generalizability. Further research could explore diverse robot models and applications.

The findings offer practical benefits by enhancing the accuracy of robotic systems, particularly in precision tasks like orbital milling, providing a valuable calibration method.

While primarily technological, improved robotic precision can have social implications, potentially influencing fields where robotic applications are crucial, such as manufacturing and automation.

This study’s distinctiveness lies in advancing the accuracy and precision of industrial robots during circular motions, specifically tailored for orbital milling applications. The innovative approach synergistically uses the observability index and telescopic ballbar to achieve these objectives.

To assess South Africa’s (SA’s) citrus export competitiveness in the global market and identify its macroeconomic drivers.

The Normalized Revealed Comparative Advantage (NRCA) index is employed to measure export competitiveness. An ARDL-EC model is then estimated to identify the macroeconomic determinants of SA’s citrus export competitiveness.

SA’s citrus export competitiveness declined before the mid-1990s and rose thereafter. On balance, the country improved from the fourth to the second most competitive citrus exporter. A long-run relationship was established between the NRCA scores and the real exchange rate and real GDP per capita growth rate. The export price exerted a positive short-run influence on citrus export competitiveness. The rise in SA’s citrus export competitiveness since the mid-1990s was mainly driven by the rising citrus export price and real exchange rate depreciation.

Future research could explore the determinants of SA’s export competitiveness using panel gravity models of bilateral trade flows to isolate the impact of macroeconomic variables and trade restricting/enhancing policies of importing countries.

The article employs the NRCA index, which can measure comparative advantage across space and over time. It is the first to econometrically estimate the macroeconomic determinants of citrus export competitiveness in SA. Application of the ARDL-EC framework yields both short- and long-run effects of macroeconomic variables on export competitiveness.

This study aims to explain the market-oriented transformation dilemma of Chinese urban investment and development companies (UIDCs; also known as local government investment and financing companies) and objectively evaluate their transformation efficiency from both static and dynamic perspectives. The results of the research provide methodological bases for improving the transformation efficiency of UIDCs, thus pointing out the direction for the rational planning of their transformation path.

This study takes Chinese UIDCs in market transformation during 2015–2019 as the research object and uses principal component analysis to screen the index system for measuring the efficiency of market transformation. It then uses a three-stage data envelopment analysis model and the Malmquist productivity index to evaluate the market transformation efficiency of these companies during 2015–2019 and comprehensively analyzes the influence of external environmental factors on the market transformation of Chinese UIDCs.

Research results show that the transformation efficiency of Chinese UIDCs is low and slow overall and that large spatial and temporal differences exist. The transformation efficiency of UIDCs located in eastern China is higher than that of UIDCs in central and western China. The higher the external environmental factors of regional GDP, local debt service pressure and credit rating, the more likely they are to cause input redundancy in the transformation process of Chinese UIDCs, which is not conducive to their market-oriented transformation. In addition, the higher the urbanization rate, the more effective it is to improve the efficiency of market-oriented transformation of UIDCs. If the influence of environmental factors is stripped away, both the overall efficiency value and pure technical efficiency value of market-oriented transformation of Chinese UIDCs will increase while the scale efficiency value becomes smaller.

This research measures the transformation efficiency of Chinese UIDCs and comprehensively analyzes the influence of external environmental factors on their market-oriented transformation. The goal is to enrich the study of the market-oriented transformation efficiency evaluation index system of Chinese UIDCs at the theoretical level and provide important reference values for improving the efficiency of market-oriented transformation of Chinese UIDCs at the practical level.

This study addresses a gap in the literature by examining the marketing strategies adopted by wineries in the context of wine tourism. This study aims to identify marketing strategic orientations and highlight their significance in the context of wine tourism.

The research uses a comparative case study approach, focusing on two wine regions, Roussillon (France) and Empordà (Spain). It involves the analysis of 99 active winery websites to identify marketing orientations. Descriptive statistics, cluster analysis and ANOVA tests were used to achieve this.

The study reveals four distinct wine tourism strategic orientations adopted by wineries in these regions. It emphasizes the importance of external ties, varying levels of competitiveness, website performance and geographical differences as key findings. The results show that wineries with a clear diversification strategy benefit from a higher level of competitiveness.

This research contributes to the academic literature by identifying different marketing strategies within wine tourism, highlighting their importance and providing a comprehensive analysis of key areas, thus adding original insights to the existing body of literature.

For over 25 years, the United States Green Building Council (USGBC) has significantly influenced the US sustainable construction through its leadership in energy and environmental design (LEED) certification program. This study aims to delve into how Baton Rouge, Louisiana, fares in green building adoption relative to other US capital cities and regions.

The study leverages statistical and geospatial analyses of data sourced from the USGBC, among other databases. It scrutinizes Baton Rouge’s LEED criteria performance using the mean percent weighted criteria to pinpoint the LEED criteria most readily achieved. Moreover, unique metrics, such as the certified green building per capita (CGBC), were formulated to facilitate a comparative analysis of green building adoption across various regions.

Baton Rouge’s CGBC stands at 0.31% (C+), markedly trailing behind the frontrunner, Santa Fe, New Mexico, leading at 3.89% (A+) and in LEED building per capita too. Despite the notable concentration of certified green buildings (CGBs) within Baton Rouge, the city’s green building development appears to be in its infancy. Innovation and design was identified as the most attainable LEED benchmark in Baton Rouge. Additionally, socioeconomic factors, including education and income per capita, were associated with a mild to moderate positive correlation (0.25 = r = 0.36) with the adoption of green building practices across the capitals, while sociocultural infrastructure exhibited a strong positive correlation (r = 0.99).

This study is beneficial to policymakers, urban planners and developers for sustainable urban development and a reference point for subsequent postoccupancy evaluations of CGBs in Baton Rouge and beyond.

This study pioneers the comprehensive analysis of green building adoption rates and probable influencing factors in capital cities in the contiguous US using distinct metrics.

This study illustrates the economic and financial dynamics of the sector, analysing the evolution of the main ratios of profitability and financial structure of 1,559 Italian real estate companies divided into the three macro-regions: North, Centre and South, in the period 2011–2020. In this way, it is also possible to verify the responsiveness to the 2020 pandemic crisis.

The analysis uses descriptive statistics tools and the ANOVA method of analysis of variance, supplemented by the Tukey–Kramer test, to identify significant differences between the three Italian macro-regions.

The study shows the increase in profitability after the 2008 crisis, despite its reverberation in the years 2012–2013. The financial structure of companies improved almost everywhere. The pandemic had modest effects on performance.

In the future, other indices should be considered to gain a more comprehensive view. This is a quantitative study based on financial statements data that neglects other important economic and social factors.

Public policies could use this study for better interventions to support the sector. In addition, internal management can compare their company's performance with the industry average to identify possible improvements.

The research analyses an economic field that employs a large number of people, especially when considering the construction and real estate services covered by this analysis.

The study contributes to the literature by providing a quantitative analysis of industry dynamics, with comparative information that can be deduced from financial statements over the years.

This study aims to analyze the implications of the recent European Union Regulation 2023/1542 on the circular economy and stakeholder strategies within the electric vehicle lithium-ion battery (EV-LIB) sector. It aims to explain the policy intentions, recommend practical strategies for stakeholders and examine how the new regulation exerts pressure on stakeholders to transition from older directives to more sustainable practices and operational standards, while also highlighting policy gaps.

The research employs a dual-method approach, combining text analysis of EU legislation with semi-structured interviews of industry stakeholders. This methodology allows for a comprehensive understanding of the regulatory impacts by integrating legislative intent with practical, on-the-ground insights from key players in the EV-LIB sector.

Our findings show that the three aggregated dimensions of operational sustainability, R&D and new technologies and collaborative dynamics are the key dynamics underlying the intended outcomes. The findings also highlight the policy’s historical development, the stakeholder categories, the implications for each and practical recommendations in responding to the policy requirements. Additionally, the findings identify policy gaps, such as weak incentives and broad economic operator classifications, with examples from international markets. The regulation creates proactive stakeholders driving innovation and collaboration and reactive ones adapting to changes, where static implicit implications may affect their viability by imposing unequal burdens.

To the best of the authors’ knowledge, this paper is the first to analyze the new EU Regulation 2023/1542, offering novel insights into the strategic responses required by stakeholders to adapt to the regulatory pressures. By focusing on the latest regulatory framework and its practical implications, the study bridges the gap between policy and practice, providing valuable guidance for industry players navigating the evolving regulatory environment.

• (1)

  EU’s policy shift from Directive to Regulation (EU) 2023/1542 has extended implications on the Electric Vehicles battery sector.

• (2)

  Duel qualitative methods of text analysis and semi-structured interviews validated three aggregate dimensions and policy gaps.

• (3)

  R&D with advancing technology, Operational sustainability and safety and Collaboration dynamics are dominating the scene.

• (4)

  Emergence of Proactive vs Reactive stakeholder dynamics.

• (5)

  The broad classification of “economic operators” and insufficiently detailed incentives, hinting at potential competitive imbalances and underexplored roles of end-users in achieving circular economy goals are appearing policy’ gaps.

EU’s policy shift from Directive to Regulation (EU) 2023/1542 has extended implications on the Electric Vehicles battery sector.

Duel qualitative methods of text analysis and semi-structured interviews validated three aggregate dimensions and policy gaps.

R&D with advancing technology, Operational sustainability and safety and Collaboration dynamics are dominating the scene.

Emergence of Proactive vs Reactive stakeholder dynamics.

The broad classification of “economic operators” and insufficiently detailed incentives, hinting at potential competitive imbalances and underexplored roles of end-users in achieving circular economy goals are appearing policy’ gaps.