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Drawing on the stakeholder theory, this study aims to empirically analyse the impact of platform enterprises’ corporate social responsibility (CSR) announcements on corporate stock market value. This study also estimates the moderating effect of stakeholder orientation and responsibility categories of CSR announcements, the platform enterprise type and the degree of CSR disclosure.

The event study method is used to analyse the change in stock market value of 191 CSR announcements from 137 Chinese platform enterprises. In addition, a case analysis is presented for two platform enterprises with the best practices to validate and complement study findings.

CSR announcements improve platform enterprises’ stock market value. Specifically, CSR announcements responding to platform enterprises’ external stakeholders, and CSR announcements with economic responsibility, have obvious positive impacts on stock market value. Furthermore, the maker platform’s CSR announcement has a more positive impact on stock market value than the exchange platform.

To the best of the authors’ knowledge, this study is the first attempt to identify the link between platform enterprises’ CSR announcements and stock market performance by empirical evidence, and it contributes to new knowledge of operating and evaluating platform enterprises’ CSR.

This papers aims to study lattice structures in terms of geometric variables, manufacturing variables and material-based variants and their correlation with compressive behaviour for their application in a methodology for the design and development of personalized elastic therapeutic products.

Lattice samples were designed and manufactured using extrusion-based additive manufacturing technologies. Mechanical tests were carried out on lattice samples for elasticity characterization purposes. The relationships between sample stiffness and key geometric and manufacturing variables were subsequently used in the case study on the design of a pressure cushion model for validation purposes. Differentiated areas were established according to patient’s pressure map to subsequently make a correlation between the patient’s pressure needs and lattice samples stiffness.

A substantial and wide variation in lattice compressive behaviour was found depending on the key study variables. The proposed methodology made it possible to efficiently identify and adjust the pressure of the different areas of the product to adapt them to the elastic needs of the patient. In this sense, the characterization lattice samples turned out to provide an effective and flexible response to the pressure requirements.

This study provides a generalized foundation of lattice structural design and adjustable stiffness in application of pressure cushions, which can be equally applied to other designs with similar purposes. The relevance and contribution of this work lie in the proposed methodology for the design of personalized therapeutic products based on the use of individual lattice structures that function as independent customizable cells.

This paper aims to reduce flight delay propagation, improve flight punctuality rate and ensure aircraft maintenance opportunities by establishing an integrated aircraft scheduling model, aiming at minimizing the total propagated delay and direction operational cost.

In this paper, flight data sets are obtained through automatic dependent detection broadcast. To accurately predict flight delay time, the flight delay prediction eXtreme gradient boosting model adds the data set obtained by random forest advance model learning and predicts the newly generated flight delays. Finally, based on the forecast results, the flight plan can be optimized and adjusted by using the improved column generation algorithm.

It is verified by the actual weekly planned operation data of an airline company, experiments show that the model established in this paper can reduce flight delay propagation by 30% in case tests and each aircraft has the opportunity to be repaired at the base airport.

Optimize the aircraft scheduling plan, cover a wide range of data, not just a single route and airport, supplement the gap in the aircraft scheduling plan based on weather factors to predict flight delays.

In this study, response surface methodology (RSM) is applied to a three-point bending stiffness analysis of low-cost material (PLA) specimens printed using FDM technology to analyze the performance of different internal lattice structures (Octet and IsoTruss principally). The purpose of this study is to extend the definition from a discrete (lattice) model to an analytical one for its use in subsequent design phases, capable of optimizing the type of cell to be used and its defining parameters to find the best stiffness-to-weight ratio.

The representative function of their mechanical behavior is extrapolated through a two-variable polynomial model based on the cell size and the thickness of the beam elements characterizing it. The polynomial is obtained thanks to several tests performed according to the scheme of RSM. An analysis on the estimation errors due to discontinuities in the physical specimens is also conducted. Physical tests applied to the specimens showed some divergences from the virtual (ideal) behavior of the specimens.

The study allowed to validate the RSM models proposed to predict the behavior of the system as the size, thickness and type of cells vary. Changes in stiffness and weight of specimens follow linear and quadratic models, respectively. This generally allows to find optimal design points where the stiffness-to-weight ratio is at its highest.

Although the literature provides numerous references to studies characterizing and parameterizing lattice structures, the industrial/practical applications concerning lattice structures are often still detached from theoretical research and limited to achieving functioning models rather than optimal ones. The approach here described is also aimed at overcoming this limitation. The software used for the design is nTop. Subsequent three-point bending tests have validated the reliability of the model derived from the method’s application.

Due to the rapid growth of digital economy, improving employees’ creativity is becoming essential to optimizing the development of organizations. This study investigates how enterprise social media can enhance employee creativity and develops an integrated model based on communication visibility and social capital theories.

A two-stage questionnaire was conducted on full-time employees with enterprise social media experience. The first round of this study distributed 1,048 questionnaires and collected 639 valid sample data. A month later, the second survey was sent to the first valid respondents, with 421 valid sample data collected within a week.

Results show that visibility has a positive influence on employee creativity, in which expertise recognition and network recognition play a mediating role. The findings also indicate that bridging social capital positively moderates the effect of visibility on expertise recognition, and bonding social capital positively moderates the effect of visibility on network recognition.

This study contributes to a better understanding of the benefits of enterprise social media by uncovering the mechanism and theoretical boundary of the effect of visibility on employee creativity.

Finite element (FE) analysis can be used for both design and verification of components. In the case of 3D-printed materials, a proper characterization of properties, accounting for anisotropy and raster angles, can help develop efficient material models. This study aims to use compression tests to characterize short carbon-reinforced PA12 made by fused filament fabrication (FFF) and to model its behaviour by the FE method.

In this work, the authors focus on compression tests, using post-processed specimens to overcome external defects introduced by the FFF process. The material’s elastoplastic mechanical behaviour is modelled by an elastic stiffness matrix, Hill’s anisotropic yield criterion and Voce’s isotropic hardening law, considering the stacking sequence of raster angles. A FE analysis is conducted to reproduce the material’s compressive behaviour through the LS-DYNA software.

The proposed model can capture stress values at different deformation levels and peculiar aspects of deformed shapes until the onset of damage mechanisms. Deformation and damage mechanisms are strictly correlated to orientation and raster angle.

The paper aims to contribute to the understanding of 3D-printed material’s behaviour through compression tests on bulk 3D-printed material. The methodology proposed, enriched with an anisotropic damage criterion, could be effectively used for design and verification purposes in the field of 3D-printed components through FE analysis.

The purpose of this study is to improve communication success through barrier identification and analysis so that the identified barriers can help project teams establish effective information-exchange strategies.

The recent publications on construction communication about time management are reviewed. Then, the semi-structured interviews are performed with both questionnaires and audio recordings (n1 = 18). Next, the collected data are analyzed using both statistical measures on the questionnaire survey and qualitative coding analysis on the text transcripts from an audio recording. Particularly, the identified barriers are substantiated using a scientometrics approach based on the published articles (2011–2020, n2 = 52,915) for purposeful information-sharing solutions in construction time management. Furthermore, the intervention strategies from the top 10 most-cited articles are analyzed and validated by comparisons with the results from construction surveys and relevant studies.

Based on the discussed communication difficulties, five main barriers were identified during time-cost risk management: probability and statistical concepts, availability of data from external resources, details of team member experiences, graphics (and graphical presentation skills), and spatial and temporal (a.k.a. 4D) simulation skills. For the improvement of communication skills and presentation quality regarding probability and statistical concepts, project teams should emphasize context awareness, case studies and group discussions. Details of communication techniques can be adjusted based on the backgrounds, experiences and expectations of team members.

The dataset n1 has both size and duration limits because of the availability of the invited industry professionals. The dataset n2 considers the literature from 2011 to 2020. Any before-the-date and unpublished studies are not included in the study.

A thorough comprehension of communication barriers can help project teams develop speaking, writing and analytical thinking skills that will enable the teams to better deliver ideas, thoughts and meanings. Additionally, the established discussion on barrier-removal strategies may enhance time management effectiveness, reduce project delays, avoid confusion and misunderstanding and save rework costs.

This research calls for the awareness of communication barriers in construction project execution and team collaboration. The identified barriers and the established solutions enrich the approaches of construction companies to share information with communities and society.

This is the first identification model for communication barriers in the time management of the construction industry to the authors' knowledge. The influencing factors and the countermeasures of communication difficulties highlighted by the research were not examined systematically and holistically in previous studies. The findings provide a new approach to facilitate the development of powerful communication strategies and to improve project execution.

This paper aims to examine the social engagement of individuals who have recently migrated to cities in India. The objective is to assess the capacity of these migrants to foster social connections in their newfound urban settings, aiming to advocate for urban governance reforms that facilitate inclusive planning for the holistic well-being of all residents, including migrants.

Using quantitative data from the first phase of the longitudinal aging study in India (LASI-2020), this study introduces the term “social engagement” to characterize the interactions of migrants. Incorporating 18 relevant variables, the research uses the Chi-square test to explore correlations between these factors and the social engagement of recent migrants in cities. In addition, multivariate logistic regression is used to identify traits that empower migrants to achieve significant levels of social engagement.

This study reveals that a substantial proportion of recent migrants (over 70%) experience social isolation in their new urban environments. Factors such as increasing age, female gender and lower educational attainment significantly contribute to this isolation. Moreover, wealth, religious affiliations and previous urban experiences play pivotal roles in shaping social engagement among urban migrants. Interestingly, age itself does not directly impact migrants’ social engagement; instead, the intersection of factors such as education, wealth and prior urban experiences assumes a critical role.

Recognizing that social engagement is an integral part of everyday life in contemporary urban societies, it is imperative for urban governance in India to establish structures and systems that facilitate social engagement for the elderly, including migrants. Urban administrators and planners must integrate these dimensions into planning and administrative processes to foster inclusivity for all elderly populations, including migrants, within urban areas.

Addressing a noticeable gap in existing literature, this paper initiates a discourse on the social engagement of recent migrants residing in Indian cities, with specific focus on the intricate interplay of aging, gender, migration status, wealth and education.

In response to the complex external structure of high-precision aviation plugs, which makes it difficult to search outside the hole and adjust inside the hole during automated assembly. This paper aims to propose an assembly framework that combines multi-agent search and variable parameter compliant control to solve this problem.

First, a multi-agent search strategy (MAS) based on Gaussian Mixture Model and Deep Q-Network was proposed to optimize displacement direction and actions, thereby improving search speed and success rate. Then, a variable parameter admittance control method (RL-VPA) based on dual delay depth deterministic policy gradient (TD3) was proposed, which dynamically optimized the internal parameters of the admittance controller and adopted state space discretization to improve convergence speed and assembly efficiency.

Compared to spiral search and single-agent search, the average search success rate has improved by approximately 10% and 6.6%. Compared to fixed admittance control and other RL-based methods, the average assembly success rate has increased by approximately 38.6%, 22% and 8.6%. Compared with the training results of the model without state discretization, it was found that state discretization helps the model converge quickly. To verify the generalization ability of the assembly framework, experiments were conducted on three different pin counts of aviation plugs, the assembly success rate reached 86.7%, all of which showed good assembly results. Finally, combining state space discretization to reduce the impact of environmental noise, improve training effectiveness and convergence speed.

MAS has been proposed to optimize displacement direction and action, improving search speed and success rate. RL-VPA is designed to dynamically optimize the internal parameters of the admittance controller, enhancing the robustness and generalization ability of the model. Additionally, state space discretization is combined to improve training effectiveness and convergence speed.

This study aims to seek and identify key enablers influencing the integration of deconstruction and carbon finance to improve sustainable and resilient construction in the Industry 5.0 era and then examines how these enablers relate to one another and rank, classify and prioritize them appropriately.

This study adopts the Integrated Decision-Making Trial and Evaluation Laboratory-Interpretive Structural Modeling-Matrice d’impacts Croises Multiplication Appliquee a un Classment (DEMATEL-ISM-MICMAC) methodology.

The key enablers are the circular economy approach with closed-loop material flow, prioritizing material recovery and reuse, and structuring carbon credits for affordability.

The construction industry can advance toward environmentally friendly, sustainable building techniques and help achieve the larger objectives of carbon neutrality and environmental preservation by adopting the study’s guiding principles.

This study implies switching to circular rather than linear economic methods. It is still unrealistic to achieve zero carbon emissions, which is why carbon finance is important because it allows companies to trade carbon credits and encourages negative carbon activities and to structure and trade carbon credits affordably so that businesses of all sizes can participate. To extend the lives of current materials and reduce waste, it is imperative to maximize their reuse and minimize their disposal.

The novelty of this research lies in introducing carbon trade finance concepts to the construction sector, particularly focusing on the deconstruction phase, to enhance sustainability and resilience in construction practices in the Industry 5.0 era.