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The performance of corporations in sustainable development is not only a concern of investors, but has also captured ever-increasing attention from consumers. However, the evidence on how these good practices would ultimately benefit brands economically remains insufficient. This study tests the causal effect between corporate Environmental, Social, and Governance (ESG) performance, media coverage, and brand value to reveal the underlying mechanisms of how consumers would react to high ESG performance.

This study uses panel data regression analysis with a sample of Chinese A-share non-financial listed companies from 2010 to 2021. ESG performance, brand value, and media coverage are assessed with Huazheng ESG Rating, the rankings from the China’s 500 Most Valuable Brands' list published by the World Brand Lab, and media index compiled by the Chinese Research Data Services Platform (CNRDS) respectively.

This research confirmed that ESG performance positively impacted brand value in terms of profitability, and that media coverage played a role as a megaphone in this relationship. Large-scale corporates, compared to small ones, benefited more from good ESG ratings due to increased media coverage.

The findings provide evidence of the megaphone effect of media coverage on the relationship between firms’ ESG engagements and brand value in the product market, which has extended the knowledge of media’s monitoring role in the financial market. And this megaphone effect is strengthened by firm size in which larger firms have spotlight effect in draw public attention due to higher expectations in terms of social responsibility.

Based on the weak seismic performance and low ductility of coupled shear walls, engineered cementitious composites (ECC) is utilized to strengthen it to solve the deformation problem in tall buildings more effectively and study its mechanical properties more deeply.

The properties of reinforced concrete coupled shear wall (RCCSW) and reinforced ECC coupled shear wall (RECSW) have been studied by numerical simulation, which is in good agreement with the experimental results. The reliability of the finite element model is verified. On this basis, a detailed parameter study is carried out, including the strength and reinforcement ratio of longitudinal rebar, the placement height of ECC in the wall limb and the position of ECC connecting beams. The study indexes include failure mode and the skeleton curve.

The results suggest that the bearing capacity of RECSW is significantly affected by the ratio of longitudinal rebar. When the ratio of longitudinal rebar increases from 0.47% to 3.35%, the bearing capacity of RECSW increases from 250 kN to 303 kN, an increase of 21%. The strength of longitudinal rebar has little influence on the bearing capacity of RECSW. When the strength of the longitudinal rebar increases, the bearing capacity of RECSW increases little. The failure mode of RECSW can be improved by lowering the casting height of the ECC beam in a certain range.

In this paper, ECC is used to strengthen the coupled shear wall, and the accuracy of the finite element model is verified from the failure mode and skeleton curve. On this basis, the casting height of the ECC casting wall limb, the strength and reinforcement ratio of longitudinal rebar and the position of the ECC beam are studied in detail.

This study seeks to explore the impact of “fear of missing out” (FOMO) and “psychological enhancement” (PE) on addiction to social media applications, subsequently influencing users' life satisfaction and continuous usage intention.

This research involved the administration of two sets of questionnaires during distinct periods: December 15 to December 30, 2022 and August 26 to September 2, 2023. The participants were college students from three universities in China, and the data collection utilized the “Questionnaire Star” platform. Only responses deemed valid and consistent were included in the subsequent statistical analysis. A total of 1,108 valid samples were used for the final analysis. Analyses including reliability, validity, path analysis, structural equation modeling, mediation effects and moderation effects were conducted using SPSS and AMOS software.

The study revealed that both FOMO and PE exerted positive influences on users' addiction to social media applications. Furthermore, this addiction was found to have a negative effect on users' life satisfaction while simultaneously contributing positively to their intention to continue using these platforms. The mediating effect of social media application addiction and the moderating impact of self-regulation were also substantiated through the analysis.

Firstly, it is important to note that the research population of this study is limited to college students, which may limit its generalizability and representativeness. Although college students are a group known for their familiarity with and frequent use of smartphones and social media apps, the findings may not fully capture the behaviors of social media app users in other age groups. To enhance the understanding of social media app addiction across different age groups, future studies should consider expanding the research population and conducting multi-group difference analyses. Secondly, while focusing on specific users within a particular region can minimize unexplained variance in model estimation, it may also restrict the broader applicability of the study results. Therefore, future studies should consider testing the research model with diverse groups from different regions and cultural backgrounds. This approach will provide valuable insights into how social media app addiction may vary across various contexts, thereby enriching our understanding of this phenomenon.

Our findings reveal that in the “attention economy” environment shaped by addiction, social media app managers should leverage technology to swiftly and accurately target audiences, attract them to their platforms and cultivate long-term relationships. Encouraging users to develop new beneficial habits through app-specific functions and precise services will foster continuous usage and unlock revenue and marketing opportunities for app companies.

Despite the extensive scholarly discourse on social media application addiction, there is a lack of a well-defined framework delineating how addictive user behaviors can be leveraged in the marketing strategies of social media application platforms. The present study seeks to address these gaps, contributing to a better understanding of the formation mechanisms and knowledge systems related to social media application addiction. By investigating the causes and consequences of such addiction, this research offers valuable insights and recommendations for the innovative development of these apps, given their widespread popularity. Concurrently, the study establishes a theoretical basis for the concept that users can mitigate the negative effects of social media addiction by exercising their own self-regulation.

As the functionalities and features of social media apps converge, their individual uniqueness starts to diminish, intensifying the competition among social media companies. This escalating rivalry places higher demands on these companies. This study aims to aid social media app companies in comprehending and analyzing the diverse psychological needs of users. By enriching their platform features and services, leading users towards addiction and gaining an edge in the “Attention Economy” competition. Understanding and catering to users' needs will be instrumental in thriving within this dynamic and evolving attention economy landscape.

The developed plasto-elastohydrodynamic lubrication (PEHL) model is used to demonstrate the permanent change of macro morphology by critical high local stress at micro asperities in contact, which may further affect the fluid-film characteristics.

Geometric morphology is integrated into the PEHL model to elucidate the fluid-film properties governed by both macro- and micromorphologies.

Results show the model, accounting for combination of elastic and plastic deformations, realistically reveals fluid film distribution affected by the significant pressure highly concentrated within surface micro roughness interaction. The designed macroscopic textured surface mitigates the fluid film rupture phenomenon and prevents accumulated wear degradation from plastic deformation.

The PEHL model takes into account both elastic and plastic deformations and realistically reveals the fluid film distribution affected by large pressures that are highly concentrated in surface micro-roughness interactions. The macro-textured surfaces are designed to mitigate fluid film rupture phenomena and prevent cumulative wear caused by plastic deformation.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0170/

Although many countries are focusing on the management of construction and demolition waste (CDW) resource utilization, the effect of risk aversion of the green innovation-led enterprise on the performance of the CDW resource utilization supply chain is unclear when considering different green innovation contexts (green innovation led by the building materials remanufacturer or by the construction waste recycler). This study aims to investigate how the level of risk aversion of the green innovation-led enterprise affects CDW resource utilization under different green innovation contexts based on contingency theory.

Using Stackelberg game theory, this study establishes a decision model consisting of a building materials remanufacturer, construction waste recycler and CDW production unit and investigates how the level of risk aversion of the green innovation-led enterprise under different green innovation contexts influences the performance level of the supply chain.

The conclusions are as follows. (1) For the green innovation-led enterprise, the risk-averse behaviour is always detrimental to his own profits. (2) For the follower, the profits of the construction waste recycler are negatively correlated with the level of risk aversion of the green innovation-led enterprise in the case of a small green innovation investment coefficient. If the green innovation investment coefficient is high, the opposite result is obtained. (3) When the green innovation investment coefficient is low, the total supply chain profits decrease as the level of risk aversion of the green innovation-led enterprise increases. When the green innovation investment coefficient is high, total supply chain profit shows an inverted U-shaped trend with respect to the degree of risk aversion of the green innovation-led enterprise.

(1) This study is the first to construct a green innovation context led by different enterprises in the CDW resource utilization supply chain, which provides a new perspective on green management and operation. (2) This study is the first to explore the operation mechanism of the CDW resource utilization supply chain based on contingency theory, which provides new evidence from the CDW resource utilization supply chain to prove contingency theory. At the same time, this study examines the interactive effects of the green innovation cost coefficient and the degree of risk aversion of green innovation-led enterprises on the performance of supply chain members, expanding the contingency theory research on contingencies affecting enterprise performance. (3) This study will guide members of the CDW resource utilization supply chain to rationally face risks and achieve optimal supply chain performance.

The purpose of this study is to address the gaps in existing research on the nonlinear characteristics of floating ring bearings, particularly by focusing on second-order nonlinear stiffness and damping coefficients. Traditional analytical models have limitations in terms of accuracy and computational efficiency. This research aims to develop a more efficient and accurate method for analyzing these nonlinear characteristics, which are crucial for optimizing the design and performance of turbocharger floating ring bearings. The study also seeks to explore how variations in clearance ratio and load influence these coefficients.

This study develops a novel approach for analyzing the nonlinear characteristics of floating ring bearings by utilizing second-order nonlinear stiffness and damping coefficients. The proposed method replaces traditional analytical solution models with a nonlinear stiffness and damping coefficient model, enhancing both computational efficiency and accuracy. The model is validated through extensive simulations that account for varying clearance ratios and load conditions. The results are compared with those obtained from conventional methods, demonstrating the effectiveness of the proposed approach in accurately capturing the nonlinear behavior of turbocharger floating ring bearings.

The study finds that the proposed nonlinear stiffness and damping coefficient model significantly enhances computational efficiency while accurately representing the nonlinear characteristics of floating ring bearings. The model not only reduces computation time but also provides a more precise analysis compared to traditional methods. Moreover, the research reveals that the clearance ratio and load conditions of the floating ring bearings have a substantial impact on the nonlinear stiffness and damping coefficients. These findings suggest that the proposed model and method could be highly beneficial for advancing the design and research of floating ring bearings in turbochargers.

With this statement, the authors hereby certify that the manuscript “Investigation on the nonlinear behaviors of floating ring bearings based on nonlinear stiffness and damping coefficient models” submitted to the journal Industrial Lubrication and Tribology is the results of their own effort and ability. They hereby confirm that this manuscript is their original work and has not been published nor has it been submitted simultaneously elsewhere. They further confirm that they have checked the manuscript and have agreed to the submission.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-09-2024-0324/

The proposed model aims to tackle the data quality issues in multivariate time series caused by missing values. It preserves data set integrity by accurately imputing missing data, ensuring reliable analysis outcomes.

The Conv-DMSA model employs a combination of self-attention mechanisms and convolutional networks to handle the complexities of multivariate time series data. The convolutional network is adept at learning features across uneven time intervals through an imputation feature map, while the Diagonal Mask Self-Attention (DMSA) block is specifically designed to capture time dependencies and feature correlations. This dual approach allows the model to effectively address the temporal imbalance, feature correlation and time dependency challenges that are often overlooked in traditional imputation models.

Extensive experiments conducted on two public data sets and a real project data set have demonstrated the adaptability and effectiveness of the Conv-DMSA model for imputing missing data. The model outperforms baseline methods by significantly reducing the Root Mean Square Error (RMSE) metric, showcasing its superior performance. Specifically, Conv-DMSA has been found to reduce RMSE by 37.2% to 63.87% compared to other models, indicating its enhanced accuracy and efficiency in handling missing data in multivariate time series.

The Conv-DMSA model introduces a unique combination of convolutional networks and self-attention mechanisms to the field of missing data imputation. Its innovative use of a diagonal mask within the self-attention block allows for a more nuanced understanding of the data’s temporal and relational aspects. This novel approach not only addresses the existing shortcomings of conventional imputation methods but also sets a new standard for handling missing data in complex, multivariate time series data sets. The model’s superior performance and its capacity to adapt to varying levels of missing data make it a significant contribution to the field.

The purpose of this research is to explore the effect of goal type of user-generated content (UGC) on consumers' intention of within-domain and across-domain compensatory consumption in closed social media platforms, the mediating effect of self-threat, self-enhancement and self-protection motivation and the moderating effect of self-efficacy.

Four between-subject experiments were conducted to verify the hypotheses presented in this research. Study 1a had a sample size of 60 respondents and examined the effect of goal type of UGC on compensatory consumption. Study1b replicated the effect by using a sample size of 60 respondents. Study 2 had a sample size of 70 respondents and verified the mediating role of self-threat, self-enhancement motivation and self-protection motivation. Study 3 examined self-efficacy as a critical moderator by using a sample of 255 participants.

Study 1a and Study 1b demonstrate that UGC with an attainment goal (vs maintenance goal) is more easily produced within-domain and across-domain compensatory consumption. Study 2 verifies that the effect of goal type of UGC on within-domain compensatory consumption is serially mediated by self-threat and self-enhancement motivation, and the effect of goal type of UGC on across-domain compensatory consumption is serially mediated by self-threat and self-protection motivation. Study 3 demonstrates that self-efficacy not only moderates the effects of self-threat on within-domain or across-domain compensatory consumption respectively, but also moderates the whole mediating path structure.

This research extends the understanding of UGC and addresses the inconclusive evidence of the impact of goal type of UGC on consumer compensatory behavior in the context of closed social media platforms. The authors identify the moderating role of self-efficacy, which explains why consumers adopt different compensatory strategies.

Currently, various detection technologies for unmanned underwater vehicles are highly susceptible to environmental impacts. Wake detection technologies have gradually gained attention and development. However, the clarity of detection results remains a challenge. This paper aims to present the design of a MEMS three-dimensional vector wake sensor. Compared to similar sensors, the MEMS three-dimensional vector wake sensor offers improved propeller wake measurement capabilities.

A MEMS three-dimensional vector wake sensor inspired by the fish lateral line system is designed. This paper discusses the working principle of the sensor. Finite element simulation is used to determine the optimal dimensions of the sensor’s sensitive chip and packaging structure. In addition, the wake environment is simulated for performance testing.

Flow velocity calibration test results confirm that the MEMS three-dimensional vector wake sensor exhibits high sensitivity, achieving 1727.6 mV/(m/s). Vector capability tests show that the data consistency in the same direction reaches 91.8%. The sensor demonstrates strong vector detection capability.

The MEMS three-dimensional vector wake sensor plays a critical role in the formation control of unmanned underwater vehicle fleets and target detection.

This study focuses on applications for unmanned underwater vehicles. It enhances the detection capabilities of unmanned underwater vehicles. This is of significant importance for future deep-sea target detection.

Amid rising concerns over unhealthy dietary habits and their impact on public health, this study aims to explore the role of avatars in promoting healthier eating and enhancing customer satisfaction in restaurants. By leveraging the theory of planned behavior (TPB) and nudge theory, this research sought to gain insights into consumer behavior and assess how digital innovations can encourage healthier food choices.

This study recruited 672 participants in the USA. Participants viewed a video featuring an avatar that informed them about available healthy food options, simulating a restaurant ordering scenario. Following the video, participants completed comprehensive online surveys. The collected data was analyzed using partial least squares structural equation modeling to assess the effectiveness and implications of the intervention.

The findings revealed that health consciousness, environmental awareness, social norms and perceived behavioral control significantly influence dietary habits. Restaurant health initiatives and avatar persuasiveness were found to encourage healthier food choices, improve customer satisfaction and loyalty and enhance electronic word-of-mouth. The study confirmed that avatars equipped with attributes such as competence, warmth, trustworthiness and credibility can be an effective digital nudge for consumers toward healthier dietary decisions.

This study’s originality lies in its integration of TPB and nudge theory, bridging the gap between individual psychological factors and external cues. This comprehensive framework provides valuable insights for restaurant managers, artificial intelligence developers and policymakers, offering practical strategies to promote healthier eating and enhance customer experiences through digital innovation.

鉴于公众对不健康饮食习惯及其对公共健康影响的日益关注, 本研究旨在探索虚拟形象在促进更健康饮食和提升餐厅顾客满意度中的作用。通过利用计划行为理论（TPB）和劝导理论（Nudge Theory）, 本研究试图深入了解消费者行为, 并评估数字创新如何鼓励更健康的食品选择。

本研究在美国招募了672名参与者。参与者观看了一个展示虚拟形象的视频, 该虚拟形象向他们介绍了可供选择的健康食品选项, 模拟了餐厅点餐场景。观看视频后, 参与者完成了全面的在线调查。收集的数据通过偏最小二乘结构方程模型（PLS-SEM）分析, 以评估干预的有效性和影响。

研究发现, 健康意识、环境意识、社会规范和感知行为控制显著影响饮食习惯。餐厅健康倡议和虚拟形象的说服力被发现能够鼓励更健康的食品选择, 提高顾客满意度和忠诚度, 并增强电子口碑。研究证实, 具备能力、温暖、可信度和信誉等属性的虚拟形象可以成为有效的数字劝导工具, 引导消费者作出更健康的饮食决策。

本研究的原创性在于它将计划行为理论和劝导理论结合起来, 填补了个体心理因素和外部线索之间的差距。这一综合框架为餐厅经理、人工智能开发者和政策制定者提供了宝贵的洞见, 提供了推动更健康饮食和通过数字创新增强顾客体验的实用策略。

En medio de la creciente preocupación por los hábitos alimentarios poco saludables y su impacto en la salud pública, este estudio pretende explorar el papel de los avatares en la promoción de una alimentación más saludable y la mejora de la satisfacción del cliente en los restaurantes. Aprovechando la Teoría del Comportamiento Planificado (TPB) y la Teoría del Impulso, esta investigación trata de profundizar en el comportamiento del consumidor y evalúa cómo las innovaciones digitales pueden fomentar la elección de alimentos más saludables.

El estudio reclutó a 672 participantes en Estados Unidos. Los participantes visionaron un vídeo en el que un avatar les informaba sobre las opciones de comida saludable disponible, simulando una situación en la que solicitaban comida en un restaurante. Después del vídeo, los participantes rellenaron unas exhaustivas encuestas en línea. Los datos recogidos se analizaron mediante un modelo de ecuaciones estructurales por mínimos cuadrados parciales para evaluar la eficacia y las implicaciones de la intervención.

Los resultados revelaron que la conciencia saludable, la conciencia medioambiental, las normas sociales y el control conductual percibido influyen significativamente en los hábitos alimentarios. Se comprobó que las iniciativas relacionadas con la salud en los restaurantes y la capacidad de persuasión de los avatares fomentan la elección de alimentos más sanos, mejoran la satisfacción y la fidelidad de los clientes y potencian el boca a boca electrónico. El estudio confirmó que los avatares dotados de atributos como competencia, calidez, fiabilidad y credibilidad pueden ser un estímulo digital eficaz para que los consumidores tomen decisiones dietéticas más saludables.

La originalidad de este estudio radica en la integración de la TPB y la Teoría de los Estímulos, que tiende un puente entre los factores psicológicos individuales y las señales externas. Este marco integral aporta valiosas ideas a los gestores de restaurantes, desarrolladores de IA y responsables políticos, ofreciendo estrategias prácticas para promover una alimentación más sana y mejorar las experiencias de los clientes a través de la innovación digital.