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Servitization and research and development (R&D) innovation provide new developmental opportunities for transformation in the construction industry. This study aims to explore the transformative impact of servitization and R&D innovation on the value added of the construction industry, offering new insights into industry transformation and growth.

This study utilizes panel data from Chinese listed construction companies from 2014 to 2022 to empirically investigate the relationship among servitization, R&D innovation and value added in the construction industry. The data analysis is augmented by incorporating text mining techniques to rigorously investigate the interplay among servitization, R&D innovation and the value added within the construction industry.

The research findings indicate that the impact of servitization on value added follows a positive U-shaped relationship, while the influence of R&D innovation on value added exhibits an inverted U-shaped relationship. Additionally, innovation investment plays a negative moderating role in the relationship between servitization and value added.

This study reveals a fresh perspective on how construction companies can leverage servitization as a strategic pathway for transformation and competitive advantage. The research also lays a theoretical groundwork for future innovation investment strategies in the construction industry, emphasizing the need for a balanced approach to innovation investments to maximize value added.

This research is aimed to examine how environmental pollution affects consumers' preference for self-improvement products.

Through a series of three experimental studies, this research substantiates our hypotheses by employing various manipulations of environmental pollution and examining different types of self-improvement products.

The research demonstrates that environmental pollution enhances consumers' preference for self-improvement products via the mediation of perceived environmental responsibility. And the effect is negatively moderated by social equity sensitivity.

The recurrent incidence of environmental pollution has elicited significant concern among the general public and academic scholars. An overwhelming majority of research examining the impact of pollution on consumer behavior has concentrated on its influence on environmentally friendly and healthy consumption patterns. Nevertheless, the current research proposes that pollution fosters a preference for products associated with self-improvement, mediated by perceived environmental responsibility, with the effects being moderated by social equity sensitivity.

The purpose of the study is to investigate and analyze the dynamics of the government-enterprise grain joint storage mechanism, particularly, focusing on profit-driven speculative behaviors exhibited by enterprises within this context. The study aims to understand the various factors influencing the behavior of stakeholders involved in grain storage, including government storage departments, agent storage enterprises and quality inspection agencies.

The study employs a tripartite evolutionary game model to investigate profit-driven behaviors in government-enterprise grain joint storage. It analyzes strategies of government departments, storage enterprises and quality inspection agencies, considering factors like supervision costs and speculative risks. Simulation analysis examines tripartite payoffs, initial probabilities and the impact of digital governance levels to enhance emergency grain storage effectiveness.

The study finds that leveraging digital governance tools in government-enterprise grain joint storage mechanisms can mitigate risks, enhance efficiency and ensure the security of grain storage. It highlights the significant impact of supervision costs, speculative risks and digital supervision levels on stakeholder strategies, offering guidance to improve the effectiveness of emergency grain storage systems.

The originality of this study lies in its integration of digital governance tools into the analysis of the government-enterprise grain joint storage mechanism, addressing profit-driven speculative behaviors. Through a tripartite evolutionary game model, it explores stakeholder strategies, emphasizing the impact of digital supervision levels on outcomes and offering insights crucial for enhancing emergency grain storage effectiveness.

This study aims to investigate the effect of galvanic corrosion on the sulfide stress corrosion cracking (SSCC) of X80/Inconel 625 weld overlay by altering the cathode/anode area ratios, Na₂S₂O₃ concentrations and temperatures.

The effects of galvanic corrosion on X80/Inconel 625 weld overlay SSCC were investigated by immersion test, galvanic corrosion current test, electrochemical measurement, four-point bending experiment, hydrogen permeation experiment and scanning electron microscopy.

The anodic dissolution of the fusion boundary was enhanced as the cathode/anode area ratio increased, which is necessary for the SSCC of the X80/Inconel 625 weld overlay. However, severe galvanic corrosion reduced the SSCC susceptibility. The SSCC susceptibility showed a linear increase with Na₂S₂O₃ concentration in the range of 10⁻⁴ ∼ 10⁻² mol/L. However, further increasing the Na₂S₂O₃ concentration to 10⁻¹ mol/L resulted in the disappearance of SSCC. This is likely because sufficient hydrogen was required for SSCC initiation even under severe anodic dissolution conditions, which was further supported by the reduced SSCC susceptivity at elevated temperatures.

Limited studies aim to establish the relationship between the galvanic corrosion and SSCC of welded joints through altering the cathode/anode area ratios, Na₂S₂O₃ concentrations and temperatures. This work will pave the way for understanding the effect of galvanic corrosion on the SSCC of dissimilar weld joints.

Damage of engineering structures is a nonlinear evolutionary process that spans across both material and structural levels, from mesoscale to macroscale. This paper aims to provide a comprehensive review of damage analysis methods at both the material and structural levels.

This study provides an overview of multiscale damage analysis of engineering structures, including its definition and significance. Current status of damage analysis at both material and structural levels is investigated, by reviewing damage models and prediction methods from single-scale to multiscale perspectives. The discussion of prediction methods includes both model-based simulation approaches and data-driven techniques, emphasizing their roles and applications. Finally, summarize the main findings and discuss potential future research directions in this field.

In the material level, damage research primarily focuses on the degradation of material properties at the macroscale using continuum damage mechanics (CDM). In contrast, at the mesoscale, damage research involves analyzing material behavior in the meso-structural domain, focusing on defects like microcracks and void growth. In structural-level damage analysis, the macroscale is typically divided into component and structural scales. The component scale examines damage progression in individual structural elements, such as beams and columns, often using detailed finite element or mesoscale models. The structural scale evaluates the global behavior of the entire structure, typically using simplified models like beam or shell elements.

To achieve realistic simulations, it is essential to include as many mesoscale details as possible. However, this results in significant computational demands. To balance accuracy and efficiency, multiscale methods are employed. These methods are categorized into hierarchical approaches, where different scales are processed sequentially, and concurrent approaches, where multiple scales are solved simultaneously to capture complex interactions across scales.

It is a complex and dynamic process to provide high-quality rural infrastructure. However, there lacks a holistic performance evaluation method for rural infrastructure provision that reflects changing rural social needs and takes a village as a whole. This study aims to develop a holistic and dynamic performance evaluation model for rural infrastructure in Mainland China.

This study established an evaluation index system by combining the lifecycle theory and the economy, efficiency, effectiveness and equity (4E) theory. This study developed an evaluation model by using the analytic network process (ANP) and matter-element analysis theory (MEAT). The model was validated by two representative villages in Mainland China.

The developed model can reflect dynamic social needs and effectively evaluate the overall infrastructure provision performance of a village. The weight of indicators reflects the changes in Mainland China’s contemporary rural social needs, with particular emphasis on the impact and output performance. The evaluation result shows that the overall performance of the representative villages was excellent but had a tendency toward good. Although the output performance was excellent, different input, process and impact performances resulted in different downgrade trends.

This study provides a theoretical basis for disaggregating the complex issue of the performance of rural infrastructure provision. The results can be used by relevant authorities to make a holistic and dynamic evaluation of the performance of rural infrastructure provision and timely revise planning and management policies.

The purpose of this study is to explore the impact of online reviews on answer adoption in virtual Q&A communities, with an eye toward extending knowledge exchange and community management.

Online reviews contain rich cognitive and emotional information about community members regarding the provided answers. As feedback information on answers, it is crucial to explore how online reviews affect answer adoption. Based on signaling theory, a research model reflecting the influence of online reviews on answer adoption is established and empirically examined by using secondary data with 69,597 Q&A data and user data collected from Zhihu. Meanwhile, the moderating effects of the informational and emotional consistency of reviews and answers are examined.

The negative binomial regression results show that both answer-related signals (informational support and emotional support) and answerers-related signals (answerers’ reputations and expertise) positively impact answer adoption. The informational consistency of reviews and answers negatively moderates the relationships among information support, emotional support and answer adoption but positively moderates the effect of answerers’ expertise on answer adoption. Furthermore, the emotional consistency of reviews and answers positively moderates the effect of information support and answerers’ reputations on answer adoption.

Although previous studies have investigated the impacts of answer content, answer source credibility and personal characteristics of knowledge seekers on answer adoption in virtual Q&A communities, few have examined the impact of online reviews on answer adoption. This study explores the impacts of informational and emotional feedback in online reviews on answer adoption from a signaling theory perspective. The results not only provide unique ideas for community managers to optimize community design and operation but also inspire community users to provide or utilize knowledge, thereby reducing knowledge search costs and improving knowledge exchange efficiency.

This study examines how top global brands changed their corporate social responsibility (CSR) communication on social media during a victim crisis, and how their CSR communication on social media influenced consumer sentiment.

Using 18,502 firms’ Facebook posts and their most relevant consumer comments from pre-pandemic and during-pandemic timeframes, this study integrates machine learning techniques (BERTopic) with human-based qualitative analysis to analyze CSR posts. It also measures the polarity and magnitude of consumer sentiment with Google Natural Language AI. We tested seven hypotheses using Hierarchical Linear Modeling (HLM).

The machine learning-based topic modeling analysis showed that firms increased CSR communications intensity on social media and they more intentionally chose different CSR communication strategies for different topics on social media during the victim crisis. The hypothesis testing results show proactive, accommodative and interactive strategies have a significant impact on consumer sentiment polarity and magnitude, and these effects are moderated by the level of interactivity and industry type.

(1) This study takes a dynamic view to examine the firms’ CSR communication on social media during a victim crisis. It used machine learning-based text analytics and found many interesting results on how firms changed their CSR communication topics and strategies on social media during the crisis. (2) It measures both consumer sentiment polarity and sentiment magnitude to conduct sentiment analysis. The results indicate that the CSR communication strategies have different impacts on the two sentiment components. (3) It integrates machine learning techniques with human-based qualitative analysis. It shows how researchers can gain the benefits of both approaches.

The purpose of this research was to conduct a comprehensive bibliometric analysis to explore supply chain resilience and operations management practices in the construction industry, with a particular focus on the transition from Industry 4.0 to Industry 5.0. The study addressed a significant gap in the literature regarding the impact of these advanced technologies on the construction sector’s ability to anticipate, respond to and recover from disruptions.

The methodology employed a bibliometric analysis using the Scopus database to identify key trends, influential publications and emerging research areas using keywords such as “supply chain”, “operations management”, “Industry 4.0”, “Industry 5.0” and “construction”. This approach allowed for a quantitative evaluation of existing literature, offering insights into the intellectual structure of the field.

The findings revealed that while Industry 4.0 technologies, such as IoT and AI, have enhanced the construction industry’s supply chain visibility and efficiency, the shift towards the Industry 5.0 paradigm introduces a human-centric approach that further strengthens resilience through collaboration and sustainability.

The study’s practical implications suggest to both industry and academia that embracing Industry 5.0 principles could significantly enhance the construction industry’s resilience, enabling it to withstand disruptions better and maintain project quality, timelines, and budgets in an increasingly complex global environment.

This research examines the shift from Industry 4.0 to Industry 5.0 within construction supply chains, offering a novel perspective on integrating these technologies.

The purpose of this paper is to introduce four new organic dyes based on naphthalimide for dye-sensitized solar cells (DSSCs).

Four new dyes based on naphthalimide with substitutions of amine and acetylamine in position C4 were designed in conjugation with substituted carbazole as donor–acceptor (D-A) architecture. The absorption and emission characteristics of the prepared dyes were evaluated in H₂O, DMF and their mixture (DMF:H₂O = 1:1). The feasibility of electron transfer in the DSSCs structure and energy levels were evaluated using electrochemical and density functional theory, which confirm the use of dyes in the DSSCs structure. The DSSCs were prepared using an individual strategy and their optical properties were investigated under the light of AM 1.5.

The prepared dyes exhibit orange color with strong emission at λem = 530–570 nm due to charge transfer with a positive solvatochromic effect. The efficiency of DSSCs based on Dye1-4 1 is: 3.69%, 3.71%, 4.69% and 4.76%. Therefore, the power efficiency increases by about 29 % in the presence of acetylamine group.

The design of new structures of organic dyes should be accompanied by the development of optical and electrical properties. In other words, in addition to the continuous production of electrons, efficient dyes must also be resistant to light to increase the life of the device.

Organic dyes play a key role in the production of electrons in the DSSCs structure. The engineering of these structures and the introduction of widely used but low cost types can play an important role in the development of clean energy production.

The application of organic dyes based on naphthalimide was evaluated in the DSSCs structure and its photovoltaic properties were investigated.