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Explore how the degree of humanization affects user misconduct, and provide effective misconduct prevention measures for the wide application of artificial intelligence in the future.

Based on the “Uncanny Valley theory”, three experiments were conducted to explore the relationship between the degree of humanization of service machines and user misbehavior, and to analyze the mediating role of cognitive resistance and the moderating role of social class.

There is a U-shaped relationship between the degree of humanization of service machines and user misbehavior; Social class not only regulates the main effect of anthropomorphism on misbehavior, but also regulates the intermediary effect of anthropomorphism on cognitive resistance, thus affecting misbehavior.

The design of the service robot can be from the user’s point of view, combined with the user’s social class, match different user types, and provide the same preferences as the user’s humanoid service robot.

This study is an important reference value for enterprises and governments to provide intelligent services in public places. It can prevent the robot from being vandalized and also provide users with a comfortable human-computer interaction experience, expanding the positive effects of providing smart services by government and enterprises.

This study avoids and reduces users' misbehavior towards intelligent service robots, improves users' satisfaction in using service robots, and avoids service robots being damaged, resulting in waste of government, enterprise and social resources.

From the perspective of product factors to identify the inducing factors of improper behavior, from the perspective of social class of users to analyze the moderating effect of humanization degree and user improper behavior.

The purpose of this paper is to provide the high-pass (HP) negative group delay (NGD) circuit based (RL) network. Synthesis and experimental investigation of HP-NGD circuit are developed.

The research work methodology is organized in three phases. The definition of the HP-NGD ideal specifications is introduced. The synthesis method allowing to determine the RL elements is developed. The validation results are discussed with comparison between the calculated model, simulation and measurement.

This paper shows a validation of the HP-NGD theory with responses confirming NGD optimal frequency, value and attenuation of about (9 kHz, −1.12 µs, −1.64 dB) and (21 kHz, −0.92 µs, −4.81 dB) are measured. The tested circuits have experimented NGD cut-off frequencies around 5 and 11.7 kHz.

The validity of the HP-NGD topology depends on the coil self-inductance resonance. The HP-NGD effect is susceptible to be penalized by the parasitic elements of the self.

The NGD circuit is usefully exploited in the electronic and communication system to reduce the undesired delay effect context. The NGD can be used to compensate the delay in any electronic devices and system.

Applications based on the NGD technology will be helpful in the communication, transportation and security research fields by reducing the delay inherent to any electronic circuit.

The originality of the paper concerns the synthesis formulations of the RL elements in function of the expected HP-NGD optimal frequency, value and attenuation. In addition, an original measurement technique of HP-NGD is also introduced.

This study examines the phenomenon of “knowledge influencers,” individuals who convey expert information to non-expert audiences and attract users to subscribe to their self-created knowledge products. It seeks to address two research questions: (1) What are the antecedents that promote perceived attractiveness of knowledge influencers? and (2) How does perceived attractiveness of knowledge influencers affect users’ willingness to subscribe to knowledge products?

Guided by self-branding theory, which suggests that individuals strategically shape user perceptions and interactions to create an appealing image, the study employed a sequential mixed-methods approach. Qualitative interviews were conducted with knowledge influencers and their subscribers, followed by a quantitative survey of users with knowledge subscription experience to validate the findings.

Results suggested that knowledge influencers could enhance their attractiveness to users by promoting perceived professionalism, perceived familiarity, and perceived connectedness. Perceived attractiveness of knowledge influencers could directly affect users’ willingness to subscribe or indirectly through the role of users’ attachment to knowledge influencers.

By understanding the factors driving users’ subscription intentions, platform operators and influencers can refine their strategies to enhance user attachment and optimize monetization opportunities through personalized interactions and tailored content offerings.

This study contributes to the literature by elucidating the relationship between perceived attractiveness and users’ subscription intentions, offering new insights into the dynamics of online knowledge consumption.

This study explores identity signaling used by an emerging class of knowledge celebrities in China – Knowledge Wanghong – who sell knowledge products on online platforms. Because identity signaling may involve constructing unique online identities and controlling over product-related and seller-related characteristics, the purpose of this study is two-fold: (1) to uncover different online identities of knowledge celebrities; and (2) to examine the extent to which the online identity type is associated with their product-related characteristics, seller-related characteristics and sales performance.

A unique data set was collected from a Chinese leading pay-for-knowledge platform – Zhihu – which featured the online profiles of tens of thousands of knowledge celebrities. Online identity types were derived from their self-edited content using Latent Dirichlet Allocation (LDA) topic modeling. Thereafter, their product-related characteristics, seller-related characteristics and respective sales performance were analyzed across different identity types using analysis of variance (ANOVA) and multiple-group linear regression.

Knowledge celebrities are clustered into four distinctive online identities: Mentor, Broker, Storyteller and Geek. Product-related characteristics, sell-related characteristics and sales performance varied across four different identities. Additionally, the online identity type moderated the relationships among their product-related characteristics, sell-related characteristics and sales performance.

As emerging-phenomenon-based research, this study extends related literature by using the notion of identity signaling to analyze a peculiar group of online celebrities who are setting an important trend in the pay-for-knowledge model in China.

This study investigates whether Chinese local governments’ environmental attention can mitigate corporate “greenwashing”, focusing on the extent of environmental content in annual government work reports as indicative of government environmental attention. This study aims to determine whether enterprises respond to changes in local governmental attention by improving the quality of their environmental information disclosures.

Data from China’s A-share listed companies spanning 2013–2021 were sourced from the CSMAR database and company annual reports. Environmental attention data were manually gathered from local government work reports published on official local government websites by using text analysis methods. These datasets were analyzed empirically to assess the impact of local governments’ environmental attention on corporate greenwashing behavior.

Results show that increased governmental environmental attention significantly reduces corporate greenwashing behavior by alleviating corporate financing constraints, enhancing independent engagement in environmental initiatives and bolstering stakeholder oversight. Moreover, heterogeneity analysis indicates that the influence of government environmental concerns is pronounced in non-state-owned enterprises, firms with subpar audit quality and those exhibiting myopic management tendencies.

This study enriches the existing literature on the government–business nexus. It also introduces methodological innovations by employing a lexical analysis of environmental themes in local government work reports instead of using typical event study approaches. Furthermore, it uses a mediating effect model to identify the mechanisms through which government environmental attention influences corporate greenwashing, namely, government subsidies, corporate environmental initiatives and external stakeholder oversight.

The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

The purpose of this paper is to study numerically the rheological properties of fiber suspensions flowing through turbulent pipe flows.

The work presented in this paper is derived the fluctuating equation for fiber orientation distribution function (FODF) in turbulent flows and solved using the method of characteristics. The FODF is predicted numerically. The numerical results of root-mean-square velocities generated by kinetic simulation sweeping model and are compared with the experimental data.

The fiber orientation distribution becomes wider with increasing Re. The components of the fourth-order orientation tensor increase with the increase of Re, and also increase along the radial direction and reach the maximum at the center line. The first normal stress difference is much less than the shear stress. For different Re the shear stress increases rapidly in the region far from the pipe center, and reaches its maximums at center, while the first normal stress difference decreases rapidly in the region far from the pipe center, and reaches its minimum at center finally.

By solving numerically the equation in a turbulent pipe flow with Reynolds number ranging from 2,500 to 1,000, the authors obtain the mean FODF which is in agreement with the experimental one qualitatively. Then the shear stress and first normal stress difference of suspensions are calculated based on the mean FODF.

The former residences of historical figures are typical landscape elements of historic districts, which are characterised by the styles of these residences and spiritual historical figures cultures. The purpose of this paper is to determine how the former residences respond once the historical figures living there have passed.

The history of human culture and progression of urban construction – which are submerged in societal transformation – is recorded for old Beijing city. Narrative space theory is used and methods such as a content analysis, map overlay and the Geographic Information System are employed to analyse the selected 300 former residences of historical figures in old Beijing city.

The results are as follows: the political setting played a key role in the evolution process, three political narrative areas in the inner city and one cultural narrative area in the outer city form the narrative spatial structure of the former residences of historical figures, “government construction” and “resident construction” are the main reasons for the loss and destruction of narrative spaces and ordinary life is an important channel for showcasing the history of former residences. The narrative spaces of these residences carry double histories, namely, the development of human history and of city construction.

Different from former studies that focus on the preservation of the single historical building, this study explores the integral logic of historic buildings in the whole city through narrative space theory to get a combination of culture and space.

Antagonistic relationship among the participants of construction projects has significantly improved, and further improving cooperation quality are committed. In this context, expanding new ways to improve cooperation quality has become a new topic in cooperation research. This study is dedicated to exploring the mechanism of cross-organizational private relationships on cooperative behaviors, which is rarely addressed in current research on construction projects, and provides reference for the rational use of cross-organizational private relationships.

Based on analysis of studies related to relational governance theory, institutional theory and project complexity, this study constructs the theoretical model. This study uses survey data from 395 construction professionals in China to test the theoretical model by using structural equation modeling (SEM) and explains the direct and indirect mechanism of cross-organizational private relationships on cooperation behavior.

(1) Cross-organizational private relationships have direct and indirect facilitating effect on cooperation behavior. (2) Relational norms as mediating variables contribute to the expansion of the positive effects of cross-organizational private relationships on cooperation behavior. (3) Institutional environment and project complexity have the moderating effect between cross-organizational private relationships and cooperation behavior.

This research investigates the impact mechanisms and boundary conditions of cross-organizational private relationships at the micro level on the cooperative behaviors in construction projects and conducts empirical research. It is a topic that has not been adequately researched in the field of project management. The research results expand the scope of research on relational governance and deepen the research on the antecedents of relational norms. It provides the base for the proposed contingency theory of relational governance.